Global Health Action - World Health Organization

Global Health Action Supplement 2, 2010 CONTENTS Forewords INDEPTH WHO-SAGE study Osman Sankoh

2

The INDEPTH WHO-SAGE collaboration
coming of age Ties Boerma

3

Guest Editorial The INDEPTH WHO-SAGE multicentre study on ageing, health, and well-being among people aged 50 years and over in eight countries in Africa and Asia Richard Suzman

5

Participating Sites - List of Staff

8

Ageing and adult health status in eight lower-income countries: the INDEPTH WHO-SAGE collaboration Paul Kowal, Kathleen Kahn, Nawi Ng, Nirmala Naidoo, Salim Abdullah, Ayaga Bawah, Fred Binka, Nguyen T.K. Chuc, Cornelius Debpuur, Alex Ezeh, F. Xavier Go´mez-Olive´, Mohammad Hakimi, Siddhivinayak Hirve, Abraham Hodgson, Sanjay Juvekar, Catherine Kyobutungi, Jane Menken, Hoang Van Minh, Mathew A. Mwanyangala, Abdur Razzaque, Osman Sankoh, P. Kim Streatfield, Stig Wall, Siswanto Wilopo, Peter Byass, Somnath Chatterji and Stephen M. Tollman

11

Assessing health and well-being among older people in rural South Africa F. Xavier Go´mez-Olive´, Margaret Thorogood, Benjamin D. Clark, Kathleen Kahn and Stephen M. Tollman

23

Health status and quality of life among older adults in rural Tanzania Mathew A. Mwanyangala, Charles Mayombana, Honorathy Urassa, Jensen Charles, Chrizostom Mahutanga, Salim Abdullah and Rose Nathan

36

The health and well-being of older people in Nairobi’s slums Catherine Kyobutungi, Thaddaeus Egondi and Alex Ezeh

45

Self-reported health and functional limitations among older people in the Kassena-Nankana District, Ghana Cornelius Debpuur, Paul Welaga, George Wak and Abraham Hodgson

54

Patterns of health status and quality of life among older people in rural Viet Nam Hoang Van Minh, Peter Byass, Nguyen Thi Kim Chuc and Stig Wall

64

Socio-demographic differentials of adult health indicators in Matlab, Bangladesh: self-rated health, health state, quality of life and disability level Abdur Razzaque, Lutfun Nahar, Masuma Akter Khanam and Peter Kim Streatfield

70

Health and quality of life among older rural people in Purworejo District, Indonesia Nawi Ng, Mohammad Hakimi, Peter Byass, Siswanto Wilopo and Stig Wall

78

Social gradients in self-reported health and well-being among adults aged 50 and over in Pune District, India Siddhivinayak Hirve, Sanjay Juvekar, Pallavi Lele and Dhiraj Agarwal

88

Health inequalities among older men and women in Africa and Asia: evidence from eight Health and Demographic Surveillance System sites in the INDEPTH WHO-SAGE study Nawi Ng, Paul Kowal, Kathleen Kahn, Nirmala Naidoo, Salim Abdullah, Ayaga Bawah, Fred Binka, Nguyen T.K. Chuc, Cornelius Debpuur, Thaddeus Egondi, F. Xavier Go´mez-Olive´, Mohammad Hakimi, Siddhivinayak Hirve, Abraham Hodgson, Sanjay Juvekar, Catherine Kyobutungi, Hoang Van Minh, Mathew A. Mwanyangala, Rose Nathan, Abdur Razzaque, Osman Sankoh, P. Kim Streatfield, Margaret Thorogood, Stig Wall, Siswanto Wilopo, Peter Byass, Stephen M. Tollman and Somnath Chatterji

96

In addition to the mentorship and editing provided by the Supplement Editors, each paper has been subjected to regular peer review.

FOREWORD æ INDEPTH WHO-SAGE Supplement

INDEPTH WHO-SAGE study y foreword to the first INDEPTH supplement published in GHA, which comprised a series of papers by the INDEPTH NCD Surveillance in Asia Working Group, stated that the work demonstrated ‘the increasing ability of the INDEPTH Network to harness the collective potential in Health and Demographic Surveillance Systems in low- and middle-income countries to provide a better, empirical understanding of health issues of populations under continuous evaluation.’ In that foreword I also noted, ‘with that collaborative research, we have seen some of the objectives of INDEPTH being achieved: we have strengthened the capability of several of our young scientists to conduct and analyse longitudinal health and demographic studies; and some of them have become first authors of scientific papers for the first time’ (1). The current supplement, by the INDEPTH Adult Health and Ageing Working Group, is a compilation of a series of excellent site-specific and cross-site papers, which has reinforced the opinions I expressed previously. I feel privileged to be writing these forewords at a time when these studies are being completed and their results are being disseminated in scientific publications. The INDEPTH WHO-SAGE collaboration started several years ago during the tenure of office of my predecessor, Professor Fred Binka. It was he who provided the initial support to the Adult Health and Ageing group, enabling it to engage with WHO in this partnership. I therefore wish to share with him the credit for this success. I am delighted to have taken part in two key analysis workshops graciously hosted by the Umea˚ Centre for Global Health Research, Umea˚ University, Sweden in 2008, and by the Harvard Centre for Population and Development Studies, Cambridge, MA, USA in 2010. I am also well aware of those previously hosted by the University of Witwatersrand’s School of Public Health as well as the WHO, more recently in June 2010. I saw INDEPTH scientists presenting their work and taking part in rigorous data analysis, and witnessed exemplary collaboration demonstrated by our partners in Umea˚ and Boston. They contributed expertise and resources to strengthen the capacities of our scientists to take leading roles in this work. While in Umea˚ and Boston, I saw our colleagues there demonstrating expertise in data analysis and in how to interrogate and make sense out of data that had been collected thousands of miles away. That experience made me feel that there was a great need for INDEPTH to

M

establish a training centre for health and demographic surveillance systems so that many more scientists from low- and middle-income countries could be trained in complex longitudinal data analysis techniques. On behalf of the INDEPTH Board and myself, I wish to thank the World Health Organization who have been exemplary partners in this collaboration, and also the key funder, National Institute on Aging and National Institutes of Health (NIA, NIH). I wish to highlight the pivotal role played by Dr. Richard Suzman (NIA, NIH) who ‘was always there’ as a funder and competent scientist during this collaboration and is the Senior Editor of this Supplement. I also want to acknowledge the Health and Population Division, School of Public Health, University of the Witwatersrand, South Africa, for its ongoing role as satellite secretariat of the INDEPTH Adult Health and Ageing Working Group. This multi-site and multi-country INDEPTH project has succeeded because of the commitment and scientific leadership of Professor Stephen Tollman, the leader of the INDEPTH Adult Health and Ageing Working Group. Furthermore, I wish to appreciate the advice provided by the INDEPTH Advisory Committee through its member Professor Stig Wall at Umea˚ University. Through resources provided for core institutional support to INDEPTH by the Wellcome Trust, Sida/ GLOBFORSK, Rockefeller Foundation, Gates Foundation and Hewlett Foundation, we were able to contribute financially to the Adult Health and Ageing Working Group for the successful completion of this work. I was happy to learn of WHO’s success in securing further resources from NIA, NIH for a Phase II of these INDEPTH WHO-SAGE studies and, in this regard, look forward to our continuing collaboration. The dataset generated by these studies is being made freely available and INDEPTH will encourage wider use of the data. Congratulations! Osman Sankoh Executive Director, INDEPTH Network

Reference 1. Sankoh O. Foreword. Global Health Action Supplement 1, 2009. DOI: 10.3402/gha.v2i0.2085

Global Health Action 2010. # 2010 Osman Sankoh. This is an Open Access article distributed under the terms of the Creative Commons AttributionNoncommercial 3.0 Unported License (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. Citation: Global Health Action Supplement 2, 2010. DOI: 10.3402/gha.v3i0.5441

2

FOREWORD æ INDEPTH WHO-SAGE Supplement

The INDEPTH WHO-SAGE collaboration
coming of age t is no surprise that there is a lack of evidence on the health of older populations in low- and middleincome countries. Much current attention is focused on the Millennium Development Goals, prioritising maternal and child health and leading infectious diseases. The epidemiological transition is relatively recent and health researchers and policy-makers are still grappling with the new data demands. And even in high-income countries, which face increasingly large older populations and predominance of chronic diseases, there are major evidence gaps. The set of papers in this Supplement represent a significant step towards better evidence on the health of older populations. The papers are based on studies in four African and four Asian countries as part of a collaboration between two multi-country networks. The first network is the well-established International Network for the Demographic Evaluation of Populations and Their Health (INDEPTH) in developing countries. It is an international platform of sentinel demographic sites that provides health and demographic data and research to enable developing countries to set health priorities and policies based on longitudinal evidence and includes more than 30 sites, mostly in Africa and Asia. It has an outstanding record of collecting vital statistics and has been a vehicle for the generation of information on a wide range of health topics. The second network is the World Health Organization (WHO) Study on Global AGEing and Adult Health (SAGE). SAGE is a multi-country study that addresses health and health-related outcomes and their determinants in populations around the world with a focus on low- and middle-income countries. The emphasis is on common methodological approaches to ensure cross-population comparability. SAGE country studies aim for a longitudinal cohort design with the inclusion of populations 50 years and over along with a comparative cohort of persons aged 18
49 years. The first round has recently been completed in China, Ghana, India, Mexico, Russia and South Africa. The SAGE and INDEPTH networks have initiated a collaboration to study adult health and ageing in low- and middle-income settings. This collaboration offers several unique features which will allow both the generation of unique evidence and detailed methodological work to validate self-reported morbidity and survey mortality

I

data. INDEPTH sites have relatively large populations under surveillance with regular monitoring of vital events, which allows the inclusion of a standard short module to examine health and health-related outcomes in regular surveillance rounds. In addition, innovative strategies can be developed to link survey and surveillance data to inform larger national estimates as well as developing and testing strategies for robust small area estimates. In three of the eight countries with sites
Ghana, India and South Africa
reported in this volume of Global Health Action (GHA), national SAGE studies are ongoing. The collaboration will also draw upon the expertise within INDEPTH sites to improve methods in data collection in older populations in low- and middleincome countries. This includes improved recording of age, development of verbal autopsy tools to assess the cause of death in the ageing population, the measurement of health and health-related outcomes for ageing care providers caring for HIV/AIDS orphans, and the caregiving burden and its association with health. Other routinely collected demographic data such as migration and its relationship to health outcomes will also be essential. Furthermore, some sites have data from other studies on changing patterns in risk factors and can relate that to the health status of older adults. This Supplement to GHA brings together the first set of papers from this collaboration. This set of papers focuses on describing the current situation among older people and identifies a number of consistent patterns. For instance, the health of women among older adults is worse than that of men; living alone jeopardises health and wellbeing; and being poor is bad for health. There are, however, important differences within and between sites as well. For example, older adults in Vadu, India, who are not in a partnership are not as badly off as in other study sites, probably because of support from extended and adjoined families; older adults with the poorest health in Purworejo, Indonesia, are clustered in the semi-urban belt of the district; and patterns of the older adult population structure are changing as exemplified by the predominance of older men in Agincourt, South Africa and of older women in the slums of Nairobi, Kenya. The results also reveal close relationships between declining health, increasing disability and worsening of quality of life in the ageing population.

Global Health Action 2010. # 2010 Ties Boerma. This is an Open Access article distributed under the terms of the Creative Commons AttributionNoncommercial 3.0 Unported License (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. Citation: Global Health Action Supplement 2, 2010. DOI: 10.3402/gha.v3i0.5442

3

Ties Boerma

These first results herald the coming of more substantive analyses of the complex relationship between non-fatal health status and subsequent mortality and the factors that influence that relationship within and across SAGE
INDEPTH sites. This unique collaboration between WHO
SAGE and the INDEPTH Network will lead to ongoing efforts to follow these populations over time, to look at longitudinal changes in the key outcomes of interest and their predictors.

4

This kind of evidence will be increasingly essential to shape policies and programmes for the health of older populations in low- and middle-income countries. Ties Boerma, Director Health Statistics and Informatics World Health Organization Geneva, Switzerland

Citation: Global Health Action Supplement 2, 2010. DOI: 10.3402/gha.v3i0.5442

GUEST EDITORIAL æ INDEPTH WHO-SAGE Supplement

The INDEPTH WHO-SAGE multicentre study on ageing, health and well-being among people aged 50 years and over in eight countries in Africa and Asia his supplement to Global Health Action presents the first results from the INDEPTH WHO-SAGE multicentre study, comprising background information (1), site-specific results (2
9) and an overall multicentre analysis (10). Reporting on one of the first cross-national studies of ageing in Africa and Asia, this supplement might be termed historic, especially when coupled with the demographic circumstances of population ageing, and the simultaneous public release of the microdata from the eight sites. According to a UN projection, the world is only a few years away from a historic watershed
when for the first time in human history those aged 65 and over will outnumber those under age 5 (11). Awareness of population ageing and its consequences is by now quite widespread in European policy circles; but the issue is only just reaching the radar screens of most low-income nations. What steps should low-resource countries take (and when), in advance of the demographic, epidemiologic, and economic transitions associated with population ageing? Industrialised nations experienced population ageing after they became wealthy; most low-resource countries will have to cope with this transition prior to becoming wealthy. Minimal attention has been given to the dynamics of health and their economic consequences in developing countries, which are now among the fastest ageing nations. To date, the attention of global institutions has been riveted almost solely on children rather than the needed dual focus on both groups of societies’ dependents: children and older people. Unfortunately, no manual exists to guide the preparations of nations at different levels of development or stages of the demographic ageing transition, and governments have to navigate without adequate maps or GPS systems. While the demographic changes occur over a timeline measured in decades, the development of new institutions and systems, including sound pension and insurance systems, need to be set up decades in advance of any transition. The long-term costs of public sector pensions in Africa are already giving rise to expressions of anxiety in some financial circles. The results from the

T

standardised data for the four African and four Asian country sites presented in this Supplement represent a significant advance on previously available information for charting the evolution of the demographic and epidemiological transitions in low-income countries. Two decades ago, there was a distressing paucity of demographic, economic, and health data on adult health and ageing for low-resource countries (12). Most of the available data were cross-sectional. However, longitudinal studies, most especially ones that combine health and economic status data within the same study, are needed to understand many of the dynamics of ageing. To remedy the abysmal lack of information on older populations in low-income countries, the U.S. National Institute on Ageing (NIA), a component of the National Institutes of Health (NIH), commissioned a series of reports on ageing in developing countries from the U.S. Bureau of the Census (13, 14), and the U.S. National Academy of Sciences (15). Although as recently as 1990 almost all industrialised societies also suffered from a lack of adequate data (especially longitudinal), significant progress has since been made in establishing nationally representative longitudinal studies, such as the Health and Retirement Study USA (HRS), the English Longitudinal Study on Ageing (ELSA), and the Survey of Health, Ageing and Retirement in Europe (SHARE). These surveys, with their data on health and economic status, cognitive functioning, and biological assessment are transforming several areas of social and behavioural science (16). Over the past several years, NIA has encouraged efforts to develop nationally comparable representative studies in low-resource countries. We are now seeing successes in developing comparable and coordinated national surveys in countries such as Mexico (MHAS), China (CHARLS), and in earlier stages, India (LASI). Additionally, the NIA, in concert with WHO, seized the opportunity to develop a network of low-cost adult health and ageing-related surveys that piggy-backed on the World Health Survey. The network, known as the Study on Global AGEing and Adult Health (SAGE), has

Global Health Action 2010. # 2010 Richard Suzman. This is an Open Access article distributed under the terms of the Creative Commons AttributionNoncommercial 3.0 Unported License (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. Citation: Global Health Action Supplement 2, 2010. DOI: 10.3402/gha.v3i0.5480

5

Guest Editorial

fielded studies in Ghana, South Africa, India, China, Mexico, and Russia. INDEPTH WHO-SAGE resulted from an opportunity to field a standardised set of surveys of adult health and ageing in eight INDEPTH health and demographic surveillance system (HDSS) sites, with the survey content drawn heavily from the SAGE, SHARE, and HRS surveys. While there have been a number of cross-national surveys focusing on ageing in Asia, this is the first involving sub-Saharan Africa. From the beginning, the INDEPTH network of sociodemographic surveillance sites offered significant potential for understanding health and demographic processes within low-income countries, most especially within rural areas. The addition of new survey data on adult health and ageing to the data portfolio of the INDEPTH sites significantly enhances the value of the surveillance sites themselves, and adds value to the survey data through linkage to the rich local epi-demographic history and context created by the INDEPTH sites. The new survey data also substantially enhance the capacity of the Network to evaluate or assess the impact of policy interventions, such as the establishment or major modification of pension or health systems. Further, the ability to compare the results of three of the INDEPTH WHOSAGE sites [South Africa (2), India (9), and Ghana (5)] with the nationally representative SAGE surveys for those countries will provide the opportunity to assess the generalisability of INDEPTH WHO-SAGE smallarea results for these three countries. In 1996, the Global Burden of Disease project made the remarkable projection that within a few decades, noncommunicable disease would outpace infectious diseases as a cause of morbidity and mortality in all regions of the globe (17). Although the projected epidemiological transition was largely a function of population ageing, the implications of these projections were largely ignored. INDEPTH WHO-SAGE will become an important observatory of the epidemiological transition in lowincome countries. The introductory article in this supplement (1) clearly shows that at baseline, the four INDEPTH WHO-SAGE Asian countries (Viet Nam, Bangladesh, Indonesia, and India) have moved further toward the relative predominance of non-communicable disease than the African countries (South Africa, Tanzania, Kenya, and Ghana). Based on the experience of industrialised nations, the projected increase in degenerative non-communicable diseases that tracks increases in adult life expectancy will be accompanied by an increasing loss of physical and cognitive functioning and growing levels of disability. The increase in disability will result in reduced capacity for work among older workers, loss of autonomy, and the need for substantial care in old age, which is enormously costly in terms of both economics and well-being. During the 1980s in the United States, the prevalent view in epidemiological and

6

ageing circles was that while modern medicine could delay death, it could not prevent or delay the onset of degenerative diseases, which could not be treated effectively. Most believed that increases in old age longevity would lead to a pandemic of disability, with disabled life expectancy increasing substantially. However, an important finding was that in the United States, between 1982 and 2001, disability among those aged 65 and over declined by 25%, demonstrating the substantial plasticity of individual ageing (18). More recently, concern has been rising that the epidemic of obesity will lead to substantially increased disability, offsetting the gains. As life expectancy increases in these middle and low-income countries, no one knows yet whether disabled life expectancy will outpace healthy life expectancy, or whether there will be any compression of morbidity and disability, especially if onset starts later in life. The collection of data on the same individuals in later waves of INDEPTH WHO-SAGE will allow researchers to investigate a whole set of questions not amenable to analysis within the current cross-sectional data. Longitudinal data are needed to tackle a variety of questions posed by the authors of this supplement. Answering questions such as how chronic disease-related disability evolves, how long individuals with specific diseases survive, whether self-reported health predicts survival better than the health score, or how living arrangements and widowhood affect health and well-being, require panel data. Longitudinal data are also needed, for example, to identify the mechanisms by which old age pensions can improve the health and general welfare of grandchildren if part of the pension is distributed to those grandchildren. Similarly, in the absence of a randomised trial, longitudinal data would be essential to assess the impact of pensions on the health of pensioners
do old age pensions that end when the pensioner dies improve the health and well-being of the pensioner? If so, is it by means of increasing pensioners’ ability to purchase food and health care, or is it because they feel more needed by their family, or do their families take better care of them to keep the pension income flowing? It is therefore important that the current samples are followed up regularly and that every effort is made to track individuals during the interim periods
a strength of health and demographic surveillance
in order to ensure a high response rate for these follow-ups. The decision to release the microdata simultaneously with this supplement, via the Global Health Action Web site (http://www.globalhealthaction.net), is a noteworthy milestone for INDEPTH and will be a great boon for research on adult health and ageing in the respective countries. Cross-national research in both developing and developed countries has been seriously hampered by slow release of microdata, sometimes more than a decade after collection, and sometimes not ever as in the case of the Citation: Global Health Action Supplement 2, 2010. DOI: 10.3402/gha.v3i0.5480

Guest Editorial

first WHO cross-national survey on ageing conducted around 1979
1980. The tension between speedy data release and the desire of the data collectors to hold onto the data until they have had a chance to fully mine those data laboriously collected from the study they had designed, is perhaps greatest today in low-income countries of Africa and Asia. However, in order to justify the very considerable expense of cross-national longitudinal studies, costs of the data need to be amortised over as many secondary data projects as possible, and the research products must also become useful to policy makers as soon as possible. Science requires replication, and the lack of data sharing can slow down research and the production of policy-relevant results. It has been the experience of studies such as HRS, ELSA, and SHARE that such longitudinal studies catalyse new fields of social and behavioural science and coalesce whole groups of researchers around the studies’ data, forming new scientific communities. In this case, every effort should be made to get these data as rapidly as possible to pre- and postdoctoral students and junior faculty of at least the eight countries involved in the study. At the same time appropriate efforts must be made to maintain the ethics of data confidentiality, ensuring that respondent anonymity is not breached, especially since these studies were all conducted in specific and known geographic areas, which makes the protection of anonymity more challenging. The agreement by the INDEPTH WHO-SAGE principal investigators to conduct the study with the understanding that the data would be speedily released is highly commendable, and one can predict that the dividends to the study will perhaps be greater than the INDEPTH team imagines. Commendations and acknowledgements are due to several institutions and groups, including the INDEPTH leadership, WHO staff, faculty at Umea˚ and Harvard who facilitated important data analysis workshops for INDEPTH WHO-SAGE, and the many peer reviewers involved in this supplement. Richard Suzman Division of Behavioral and Social Research National Institute on Aging National Institutes of Health Bethesda, MD, USA All views expressed in this editorial are entirely those of the author, and do not necessarily reflect those of NIA or NIH.

2.

3.

4.

5.

6.

7.

8.

9.

10.

11.

12. 13.

14.

15.

16. 17. 18.

References 1. Kowal P, Kahn K, Ng N, Naidoo N, Abdullah S, Bawah A, et al. Ageing and adult health status in eight low-income Citation: Global Health Action Supplement 2, 2010. DOI: 10.3402/gha.v3i0.5480

countries: the INDEPTH WHO-SAGE collaboration. Global Health Action Supplement 2, 2010. DOI: 10.3402/gha.v3i0. 5302. Go´mez-Olive´ FX, Thorogood M, Clark BD, Kahn K, Tollman SM. Assessing health and well-being among older people in rural South Africa. Global Health Action Supplement 2, 2010. DOI: 10.3402/gha.v3i0.2126. Mwanyangala MA, Mayombana C, Urassa H, Charles J, Mahutanga C, Abdullah S, et al. Health status and quality of life among older adults in rural Tanzania. Global Health Action Supplement 2, 2010. DOI: 10.3402/gha.v3i0.2142. Kyobutungi C, Egondi T, Ezeh A. The health and well-being of older people in Nairobi’s slums. Global Health Action Supplement 2, 2010. DOI: 10.3402/gha.v3i0.2138. Debpuur C, Welaga P, Wak G, Hodgson A. Self-reported health and functional limitations among older people in the KassenaNankana District, Ghana. Global Health Action Supplement 2, 2010. DOI: 10.3402/gha.v3i0.2151. Minh HV, Byass P, Chuc NTK, Wall S. Patterns of health status and quality of life among older people in rural Viet Nam. Global Health Action Supplement 2, 2010. DOI: 10.3402/ gha.v3i0.2124. Razzaque A, Nahar L, Khanam MA, Streatfield PK. Sociodemographic differentials of adult health indicators in Matlab, Bangladesh: self-rated health, health state, quality of life and disability level. Global Health Action Supplement 2, 2010. DOI: 10.3402/gha.v3i0.4618. Ng N, Hakimi M, Byass P, Wilopo S, Wall S. Health and quality of life among older rural people in Purworejo District, Indonesia. Global Health Action Supplement 2, 2010. DOI: 10.3402/gha.v3i0.2125. Hirve S, Juvekar S, Lele P, Agarwal D. Social gradients in selfreported health and well-being among adults aged 50 and over in Pune District, India. Global Health Action Supplement 2, 2010. DOI: 10.3402/gha.v3i0.2128. Ng N, Kowal P, Kahn K, Naidoo N, Abdullah S, Bawah A, et al. Health inequalities among older men and women in Africa and Asia: evidence from eight Health and Demographic Surveillance System sites in the INDEPTH WHO-SAGE study. Global Health Action Supplement 2, 2010. DOI: 10.3402/ gha.v3i0.5420. Kinsella K, He W US. Census Bureau, International Population Reports, P95/09-1, An aging world. Washington, DC: U.S. Government Printing Office; 2009 pp. 7
8. Kinsella K, Suzman R. Developing dimensions of population aging in developing countries. Am J Human Bio 1992; 4: 3
8. Kinsella, K. Aging in the third world. CIR staff paper, No. 35, Feb 1988. ix, 80 pp. U.S. Bureau of the Census, Center for International Research, Africa and Latin America Branch: Washington, DC. Kinsella K, He W US. Census Bureau, International Population Reports, P95/09-1, An aging world. Washington, DC: U.S. Government Printing Office; 2009. Cohen B, Menken J. Aging in sub-Saharan Africa: recommendations for furthering research. Washington, DC: National Academies Press; 2006. Suzman R. Epilogue: Research on population aging at NIA: retrospect and prospect. Popul Dev Rev 2004; 40: 239
64. Lopez AD, Murray CJL. The global burden of disease, 1990
2020. Nat Med 1998; 4: 1241
3. Cutler DM, Wise DA, editors. National Bureau of Economic Research. Health at older ages: the causes and consequences of declining disability among the elderly. Chicago, IL: University of Chicago Press; 2009.

7

Participating Sites æ INDEPTH WHO-SAGE Supplement

The INDEPTH WHO-SAGE multicentre study was only possible because of the hard work of many staff at each participating site, as well as the authors of papers in this Supplement: Agincourt, South Africa Hector Dhlamini Victoria Dlamini Regan Gumede Simon Khosa Glory Khoza Thoko Khensani Machavi Muziwakhe Solly Maluka Olga Mambane Nash Manzini Sinah Manzini Merriam Perseverence Maritze Lawrence Pedney Mashale Ishmael Mashigo Ishamel Ntanga Moses Mathabela Phanuel Mathebula Council Mbetse Warren Mdluli Gordon Mkhabe Obed Mokoena Linneth Mthetho Violet Ndlovu Simon Delly Ndzimande Sizzy Ngobeni Vusi Ngwenyama David Morris Sibuyi Busisiwe Sibuyi Morris Mdawu Sibuyi Promise Sibuyi Ellah Sihlangu Ellah Bernard Silaule Phamela Nombulelo Tibane Nomsa Ubisi Ifakara, Tanzania Novatus Chagodola Deogratias Chamanga Yassin Chikoko Timoth Chogo Panga Husein Godwin John Lukresia Kadungula Luitfrid Kaduvaga Tukae Kapati Gonzaga Kasanga Sophia Kayera Godfrey Kidege

John Killian Athuman Kipembe Celsius Kipinga Charles Kuwonga Amoses Kyovecho Mary Lazaro Nassoro Likumi Silivanus Lisoadinge Zuhura Lungombe Emanuel Luvanda Jacob Lyanga Sauda Magubikira Stephen Magwaja Athuman Makanganya Albert Masalu Isaya Mashinga Shabani Matengana Madunda Mkalimoto Ally Mpangile Raphael Msabana Edimund Msalabule Mshamu Mshamu Bernadi Mwambale Elisha Mwandikile Bonaventura Mwarabu Simbani Mwikola Abdala Mwinshehe Joseph Mwonja Honesta Mzyangizyangi Mwanaid Ngagonja Calstus Ngalanga Msafiri Ngalisoni Jonson Ngenga Mwadawa Ngumbi Joseph Njavike Hadija Nyanga Amina Salumu Joyce Shayo Athumani Utwakumwambu Nairobi, Kenya Mohammed Ali Callen Bwari Wekesah Murunga Frederick Abduba Salesa Galgalo Anthony Chomba Gathuita Antony Kagiri Gichohi

Jane Wahake Gitonga James Hotendo David Ireri David Otieno Juma Gedion Kennedy Juma Maureen Kadogo Deborah Kagai Adan Kalicha Phanuel M. Kasuni Joel Kasyoka George Kidiga Catherine Kimatu Joshua Musila Kivonge Esther Nyambura Macharia Mary Marubu Catherine Mbalu Kennedy Mose Momanyi Geoffrey Ndungu Mondia David Karuga Muhika Wanjiru Murigi Stanley Murithi Samuel Mutuma Hawa Hassan Mwangangi Damaris R Mwangi Grace Mumbua Mwania Booker Ndayhaya Henry Ndungu Deborah Nganga Moses Mwithiga Ngugi Esther Wanjiru Njeri Jedidah Njeri Melchizedek Nyakundi Thomas Ondieki Nyandika Peter Nyongesa Audrey Achieng Ocholla George Ochieng Oduor Clement Oduor David Ouma Ojuka Mildred Adhiambo Onyango Peter Onyango Evaline Achieng Otteng Meshack Odede Owino Benson Mbithi Peter Jacqueline Ratemo Sarah Nabalayo Simiyu Ruth Waithera Wairimu Peter Agutu Waka

Global Health Action 2010. # 2010 List of participating staff This is an Open Access article distributed under the terms of the Creative Commons AttributionNoncommercial 3.0 Unported License (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. Citation: Global Health Action Supplement 2, 2010. DOI: 10.3402/gha.v3i0.5493

8

List of participating staff

Moses Wanyama Philip Kibet Wendot Abdikadir Adan Yarrow Abdikadir Adan Yarrow Navrongo, Ghana Diana Abagale Catherine Abakis Irene Abase Aboyinga Abokiya Gana Abongbe Anthony Achana Bawotua Kwoyire Adda Clare Addah Sixtus Addah Martin Adiga Emefa Adiku Ophelia Adjei Charles Adongo Immaculate Adongo Beatrice Afobiku Akwia Agangba Charles Agangzua Evelyn Agomah Timothy Ajubala Felicia Akanlassi Emmanuel Akantosige James Akayasi Roger Akobga Isaac Akumah Jacob Anabia Robert Kwotera Ane Christopher Aniwe Rufina Anoah Scholastica A-Oho Raphael Apana Mathew Apatinga Vida Apayire Freda Apee Thompson Apempale Peter Asobayire Gilbert Asuliwono Rita Asumboya Justina Asumboya Martina Atenka Ajentio Atulugu Joana Awineboya Francis Awineboya Tamgomse Ayaam Peter Ayangba Denisia Ayibello Akua Ayirewora Raymond Azagisiya Jesse Jackson Azambugi

Michael Banseh Emma Chiratogo Afia Damwura Everest Dery Atinganne Dominic Theresa Fumjegeba Yeji Godwin Francis Gweliwo Mohammed T Ibn-Salia Memuna Issaka Fatima Issaka Dauda Ahmed Jadeed Martin Kambonga Joana Kampoe Edmond Kanyomse Joseph Katasuma Mac Kolley Felix Kondayire Fati Kumangchira Ferreol B. Lagejua Richard Latinga Jerry Atua Lucas Christina Luguchura Rose Mary Luguyimang Rita Luguzuri Ziblim Mahama Luuse Matholomew John Memang Ayangba A. Mensah William Minyila Ismail M Mohammed Abangba Moses Anastasia Musah Maxwell Naab Vitus Nabengye Andrews Opoku Rose Parese Lucy Pelabia Boniface Pwadurah Habibatu Salifu Andriana Sumboh Felicity Titigeyire Patience Tito Francis Yeji Yahaya Zulhaq Filabavi, Viet Nam Dang Thi Minh Anh Nguyen Thi Be Nguyen Thi Ngoc Bich Le Thi Thanh Binh Quach Thi Thanh Binh Phung Thi Chien Nguyen Thi Dau

Citation: Global Health Action Supplement 2, 2010. DOI: 10.3402/gha.v3i0.5493

Phung Thi Dinh Tran Thanh Do Phuong Thuy Duyen Nguyen Thi Ngoc Ha Dinh Cong Ha Phung Thi Hai Do Thi Thanh Hien Nguyen Phuong Hoa Nguyen Thi Mai Huan Hoang Thi Hue Chu Phi Hung Nguyen Quoc Hung Do Manh Hung Bui Thi Huong Nguyen Thi Huong Nguyen Thi Thanh Huong Ngo Thi Huyen Nguyen Thanh Huyen Nghiem Thi Hy Nguyen Van Lam Ngo Thi Lien Phan Thi Thanh Lieu Giang Thi Tuyet Loan Truong Hoang Long Nguyen Thi Luyen Nguyen Thi Ly Nguyen Thi Nguyet Minh Phung Thi Minh Nguyen Binh Minh Phung Thi My Nguyen Thi Duy Na Phan Thi Nang Phung Thi Nga Nguyen Thi Minh Nham Tran Thi Nhan Nguyen Thi Nhung Dinh Thuy Nhung Phuong Thi Nhung Tran Thi Kim Oanh Doan Thi Hoang Oanh Phung Thi Thu Phuong Tran Thi Mai Phuong Dao Dinh Sang Nguyen Thi Sinh Nguyen Thi Thanh Tam Nguyen Thi Tam Tran Thi Tha Bui Thi Thanh Thao Phung Thi Thanh Thao Nguyen Thi Thu Dang Thi Hong Thuy Nguyen Thi Thuy Nguyen Thi Thuyet Phung Thi Tinh

9

List of participating staff

Tran Khanh Toan Phung Thi Toan Nguyen Thanh Tu Nguyen Lan Viet Khuat Thi Xuan Matlab, Bangladesh Ali Ahmed Halena Akhter Abul Kalam Azad Jiabonessa Begum Nazma Khanam Bahadur Mia Shirajum Munira Samira Akhter Sultana Purworejo, Indonesia Abdul Wahab Agung Nugroho Ami Rumhartinah Ardiyanti Arif Bambang Sukma Widadi Budi Hartiningsih Devie Caroline Didi Yudha Prastika Didik Fery Kristianto Djaswadi Dasuki Dwi Lestari Priastuti Dwi Rosmalawati Eka Yuli Astuti Eko Setianto Eni E. Erry Ariyanti Fahruddin Fatma Yunita Feri Budiarto Hafsah Tahir Haryanto Hendras Bintar Hendro Budi Irfan Cahyadi Ita Saraswati

10

Joko S. Juana Linda Kartini Khotib Subhan Kusen Lasmi Ledjar Lidya Hastuti Lilik Dewanti Mintorowati Muhtadi Murtiyah Nur Wicaksono Nurtiyah Pitoyo Purnawati Puspita Handayani Ratih Widayanti Ratna Retno Handayani Robert Arian Datusanantyo Rosyid Budiman Rustiningsih Ruwayda Sendy Siti Aminah Siwi Rahmawati Sri Purnaningsih Sri Suryani Sugeng Sugeng Sugun Suharyani Sujarwo Sukarman Sukirman Sumarta Supriyo Pratomo Sutaryo Teguh Imam Teguh Rohaji Tetra Tetra Lintang

Titik Rahayu Tri Atmi Tri Wahyu Tri Wantoro Utari Marlinawati Wahyu Fatmawati Warsiyah Winarti Wisnu Yekti Utami Yudha Prastika Yunardi Yusmiyati Yusuf Vadu, India Kalpana Agale Jyoti Bhosure Bharat Choudhari Nilam Fadtare Shilpa Fulaware Prashant Gaikwad Vijay Gaikwad Tejashri Ghawte Trupti Joshi Deepak Mandekar Anita Masalkar Sayaji Pingale Ratan Potdar Somnath Sambhudas Dinesh Shinde Secretariat Raymond Akparibo Sixtus Apaliyah Zubeida Bagus Sadiya Ooni Dereshni Ramnarain Jackie Roseleur Titus Tei ˚ stro¨m Birgitta A

Citation: Global Health Action Supplement 2, 2010. DOI: 10.3402/gha.v3i0.5493

INDEPTH WHO-SAGE Supplement æ

Ageing and adult health status in eight lower-income countries: the INDEPTH WHO-SAGE collaboration Paul Kowal1,2*, Kathleen Kahn3,4,5#, Nawi Ng4,5,6#, Nirmala Naidoo1, Salim Abdullah5,7, Ayaga Bawah5, Fred Binka5, Nguyen T.K. Chuc5,8, Cornelius Debpuur5,9, Alex Ezeh5,10, F. Xavier Go´mez-Olive´3,5, Mohammad Hakimi5,6, Siddhivinayak Hirve5,11, Abraham Hodgson5,9, Sanjay Juvekar5,11, Catherine Kyobutungi5,10, Jane Menken12,13, Hoang Van Minh5,8, Mathew A. Mwanyangala5,7, Abdur Razzaque5,13, Osman Sankoh5, P. Kim Streatfield5,13, Stig Wall4#, Siswanto Wilopo5,6, Peter Byass4#, Somnath Chatterji1 and Stephen M. Tollman3,4,5# 1 Multi-Country Studies Unit, World Health Organization, Geneva, Switzerland; 2University of Newcastle Research Centre on Gender, Health and Ageing, Newcastle, NSW, Australia; 3MRC/Wits Rural Public Health and Health Transitions Research Unit (Agincourt), School of Public Health, Faculty of Health Sciences, University of the Witwatersrand, Johannesburg, South Africa; 4Centre for Global Health Research, Epidemiology and Global Health, University of Umea˚, Umea˚, Sweden; 5 INDEPTH Network, Accra, Ghana; 6Purworejo HDSS, Faculty of Medicine, Gadjah Mada University, Yogyakarta, Indonesia; 7Ifakara Health Institute, Ifakara, Morogoro, Tanzania; 8FilaBavi HDSS, Faculty of Public Health, Hanoi Medical University, Hanoi, Viet Nam; 9Navrongo HDSS, Navrongo, Ghana; 10African Population & Health Research Center, Nairobi, Kenya; 11Vadu Rural Health Programme, KEM Hospital Research Centre, Pune, India; 12University of Colorado, Boulder, CO, USA; 13Matlab HDSS, ICDDR,B, Dhaka, Bangladesh

Background: Globally, ageing impacts all countries, with a majority of older persons residing in lower- and middle-income countries now and into the future. An understanding of the health and well-being of these ageing populations is important for policy and planning; however, research on ageing and adult health that informs policy predominantly comes from higher-income countries. A collaboration between the WHO Study on global AGEing and adult health (SAGE) and International Network for the Demographic Evaluation of Populations and Their Health in developing countries (INDEPTH), with support from the US National Institute on Aging (NIA) and the Swedish Council for Working Life and Social Research (FAS), has resulted in valuable health, disability and well-being information through a first wave of data collection in 2006
2007 from field sites in South Africa, Tanzania, Kenya, Ghana, Viet Nam, Bangladesh, Indonesia and India. Objective: To provide an overview of the demographic and health characteristics of participating countries, describe the research collaboration and introduce the first dataset and outputs. Methods: Data from two SAGE survey modules implemented in eight Health and Demographic Surveillance Systems (HDSS) were merged with core HDSS data to produce a summary dataset for the site-specific and cross-site analyses described in this supplement. Each participating HDSS site used standardised training materials and survey instruments. Face-to-face interviews were conducted. Ethical clearance was obtained from WHO and the local ethical authority for each participating HDSS site. Results: People aged 50 years and over in the eight participating countries represent over 15% of the current global older population, and is projected to reach 23% by 2030. The Asian HDSS sites have a larger

#

Supplement Editor, Kathleen Kahn, Editor, Nawi Ng, Chief Editor, Stig Wall, Deputy Editor, Peter Byass, Supplement Editor, Stephen M. Tollman, have not participated in the review and decision process for this paper.

Global Health Action 2010. # 2010 Paul Kowal et al. This is an Open Access article distributed under the terms of the Creative Commons Attribution- 11 Noncommercial 3.0 Unported License (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. Citation: Global Health Action Supplement 2, 2010. DOI: 10.3402/gha.v3i0.5302

Paul Kowal et al.

proportion of burden of disease from non-communicable diseases and injuries relative to their African counterparts. A pooled sample of over 46,000 persons aged 50 and over from these eight HDSS sites was produced. The SAGE modules resulted in self-reported health, health status, functioning (from the WHO Disability Assessment Scale (WHODAS-II)) and well-being (from the WHO Quality of Life instrument (WHOQoL) variables). The HDSS databases contributed age, sex, marital status, education, socio-economic status and household size variables. Conclusion: The INDEPTH WHO
SAGE collaboration demonstrates the value and future possibilities for this type of research in informing policy and planning for a number of countries. This INDEPTH WHO
SAGE dataset will be placed in the public domain together with this open-access supplement and will be available through the GHA website (www.globalhealthaction.net) and other repositories. An improved dataset is being developed containing supplementary HDSS variables and vignette-adjusted health variables. This living collaboration is now preparing for a next wave of data collection. Keywords: ageing; survey methods; public health; burden of disease; demographic transition; disability; well-being; health status; INDEPTH WHO-SAGE

Access the supplementary material to this article: INDEPTH WHO-SAGE questionnaire (including variants of vignettes), a data dictionary and a password-protected dataset (see Supplementary files under Reading Tools online). To obtain a password for the dataset, please send a request with ‘SAGE data’ as its subject, detailing how you propose to use the data, to [email protected]

Received: 18 May 2010; Revised: 6 July 2010; Accepted: 8 July 2010; Published: 27 September 2010

he ageing of populations is often considered as a global public health success, but results in many ensuing challenges, particularly in lower- and middle-income countries where societies did not grow wealth before growing old, as in higher-income countries. Societal ageing will affect economic and health systems in all nations, including the ability of states and societies to both maintain contributions from and also provide resources for older population groups. But will population ageing affect lower- and higher– income countries in similar ways? The projected macroeconomic and health impacts from longer life expectancies have only recently become clearer for higher-income nations (1
5); but few non-Organization for Economic Cooperation and Development (OECD) countries have the data to determine if extended longevity coincides with healthier lives until older ages (that is, a compression of morbidity). Unlike wealthier countries, the existing formal social protection systems in most lower-income countries cover only a small proportion of the older population (6); however, if we believe in demographic dividends, lower-income countries will have a long lead period to collect data which can be used to inform economic and health systems (7). Burden of disease shifts from maternal/child health and acute communicable diseases to chronic infectious and noncommunicable diseases in lower-income countries will challenge health systems without the data necessary to inform policy and planning (8
11). Interest in the measurement and comparability of adult health, the ageing process and well-being at

T

12

older ages across countries has been increasing in recent years. The potential benefits of cross-national studies of ageing that enable us to understand the nature of demographic and epidemiological transitions have been widely recognised (12, 13). The US Health and Retirement Study (HRS) and other notable surveys, such as the English Longitudinal Study on Ageing (ELSA), have provided the necessary evidence base to begin to address the needs and contributions of older persons in higher-income countries. However, the majority of older persons now and into the future will reside in lower-income countries where the evidence base is very limited. The HRS and ELSA studies, and more recently the World Health Organization’s (WHO) multi-country Study on global AGEing and adult health (SAGE), have also been used as the basis for harmonisation with other national studies and many cross-national comparisons. Longitudinal ageing studies are critical to develop the evidence base to better understand ageing processes and adult health dynamics, especially in countries with limited mortality data due to poorly functioning or low coverage of vital registration systems. They have particular advantages in their ability to examine multiple exposures, determinants and outcomes, and to measure relationships over time: all essential aspects for understanding ageing across different contexts. However, while critical to research, policy and planning, longitudinal studies are resource and time intensive. The extent to which lower-income countries have begun to generate and use critical evidence for an Citation: Global Health Action Supplement 2, 2010. DOI: 10.3402/gha.v3i0.5302

Ageing and adult health status in eight lower-income countries

effective health response has been slow and suboptimal in many countries (14). This lack of evidence is particularly prominent in low- and middle-income countries, partly because the demographic transitions have been relatively recent and also because political will and financial support have not been sufficient. Combining standardised survey modules with existing surveillance infrastructures, especially systems collecting vital registration details, offers a unique opportunity to reduce research costs and efficiently collect needed data in low- and middle-income countries. If populations in any country are to age well, an improved understanding of ageing processes, of resilience factors for well-being, and of the determinants of health status (HS) across countries are needed. This knowledge will in turn inform health care and social protection policies and planning. Results from a collaboration between the WHO-SAGE survey platform and the International Network for the Demographic Evaluation of Populations and Their Health in developing countries (INDEPTH), involving Health and Demographic Surveillance Sites (HDSS) in eight countries (four African and four Asian) will provide HS, disability and well-being results for ageing and adult health in South Africa, Tanzania, Kenya, Ghana, Viet Nam, Bangladesh, Indonesia and India. Data collection included methods to improve cross-country comparability, thereby providing a basis for comparisons with data from higher-income countries, such as the US Health and Retirement Study and the ELSA. This article describes the background to the INDEPTH WHO-SAGE collaboration and introduces the methods used to generate the first wave of results
which includes site-specific analyses and cross-site comparisons.

Background The WHO’s Multi-Country Studies unit, with the support of the US National Institute on Aging’s Behavioral and Social Research Program (NIA BSR), has implemented multi-country ageing and adult health studies to fill data gaps in lower-income countries and has worked to improve cross-national comparability with available data. WHO’s SAGE conducts nationally representative household health surveys in six countries, with direct links to an additional 14 countries through various collaborations. SAGE is guided by an international expert Advisory Committee and coordinated from WHO’s Multi-Country Studies unit. In addition, comparisons with ageing research in higher-income countries, such as the US HRS, English ELSA and the panEuropean Survey of Health, Ageing and Retirement in Europe (SHARE) are ongoing. WHO’s collaboration with INDEPTH has generated data from HDSS sites in eight countries (Africa: Agincourt, South Africa; Ifakara, Tanzania; Nairobi, Kenya; Citation: Global Health Action Supplement 2, 2010. DOI: 10.3402/gha.v3i0.5302

Navrongo, Ghana; Asia: Filabavi, Viet Nam; Matlab, Bangladesh; Purworejo, Indonesia and Vadu, India) and provides another valuable data collection platform for cross-national comparisons of ageing. The NIA BSR was instrumental in bringing the two groups together from the outset and has provided technical guidance throughout in combining survey and surveillance data collection efforts to fill needed data gaps on ageing and adult health. WHO SAGE, the INDEPTH Adult Health and Ageing Working Group, the NIA and the eight participating INDEPTH HDSS sites have developed a collaboration built on these survey and surveillance data collection platforms. This included health and well-being survey data collected within or parallel to HDSS household (HH) census update rounds and linked sociodemographic household data. While this initial dataset is cross-sectional, there are plans to include longitudinal HDSS data and further waves of data collection using an adapted summary version of the SAGE instrument in the HDSS sites. This will significantly enhance the value of the collaboration and resulting datasets by tracking changes over time in the same population samples and relating them to health determinants, predictors and outcomes, such as mortality in older adults. An introduction to the countries, HDSS sites and research methods follows.

Setting the stage Country characteristics The ongoing demographic shift provides concrete evidence that most countries will be faced with an increasingly old or ageing population
the challenge is for national and international health communities to use available data to best prepare for these changes. At present, 62% of older persons reside in less developed countries and this is projected to increase to almost 80% by 2050 (15). Table 1 includes the estimated and projected total populations and proportions of older adults for the world and participating INDEPTH countries in 2009 and 2030. The World Bank income category is also included for each country, with a mix of five low- and three middleincome countries (16). In 2009, over 281 million people aged 50 years and over resided in the eight nations included in this collaboration, which constitutes 20% of the global population in that age group (15). Similarly, 18% of the global population aged 60 and over lives in these eight countries. These proportions will increase to 23% and 21%, respectively, by 2030. Over the same time period, the percentage of the population aged 0
14 years in these countries will drop from 29.9 to 28.5% and five of the eight countries will have a larger proportion of persons aged 60 and over than under 15 years by 2050 (the four Asian countries and South Africa). Overall, the

13

Paul Kowal et al.

Table 1. Population totals and proportions of older adults for the world and by INDEPTH country, in 2009 and projected to 2030 2009

Country World Sub-Saharan Africa South Africa Tanzania Kenya Ghana Asia Viet Nam Bangladesh Indonesia India Pooled INDEPTH country (8) totals

Country income categorya

UMI Low Low Low Low Low LMI LMI

Total, Nb 6,829 843 50 44 40 24 4,121 88 162 230 1,198 1,836

2030

50, N (%) 1,379 110 8 4 3 2 785 15 20 40 187 281

(20.2) (10.9) (15.0) (9.5) (8.8) (11.2) (19.1) (17.2) (12.9) (17.4) (15.6) (15.3)

60, N (%) 737 54 4 2 2 1 400 6 10 20 89 135

(10.8) (5.3) (7.1) (4.8) (4.1) (5.7) (9.7) (8.6) (6.0) (8.8) (7.4) (7.3)

Total, N 8,309 1,308 55 75 63 35 4,917 105 203 271 1,485 2,293

50, N (%)

60, N (%)

2,283 157 10 8 7 5 1,398 32 46 79 343 531

1,370 78 6 4 3 3 821 19 23 43 185 286

(27.5) (12.0) (19.1) (10.6) (11.5) (15.3) (28.4) (30.6) (22.9) (28.9) (23.1) (23.2)

(16.5) (5.9) (11.1) (5.3) (5.5) (7.7) (16.7) (18.2) (11.3) (16.0) (12.4) (12.5)

a

World Bank country income category: Low, low income; LMI, lower-middle income; UMI, upper-middle income. N in millions (,000,000). Sources: UN Population Division (15) and World Bank (16). b

percentage increase in population aged 60 will grow more in the African than Asian countries. With ageing populations and increasing life expectancies, countries will inevitably see changing population disease burdens. Burdens of disease, risk factors and patterns of injury are changing through a complex combination of evolving social, demographic, health, political and economic processes. Diseases thought to be the domain of higher-income countries are now significant causes of morbidity and mortality in a number of lower- and middle-income countries (17
19). The most recent Global Burden of Disease (GBD) 2004 update includes distributions of mortality and morbidity by three major groupings: (Group I) communicable diseases, maternal health and nutrition; (Group II) non-communicable diseases; and (Group III) violence and injuries. The 2004 update incorporates revisions and new data working from the initial 1990 GBD (20). The 1990 GBD results estimated 44% of total burden was Group I, 41% for Group II and 15% for Group III worldwide (21). These data show that even in 1990, NCDs were a significant contributor to mortality rates. Fig. 1 shows the distributions of fatal disease burden by geographic grouping and country for 2004. Preliminary results indicate a substantial increase in the proportion of deaths due to non-communicable diseases from 59% in 2002 to 69% in 2030 (19). All the participating Asian HDSS sites had higher NCD rates than the 1990 estimates
and Indonesia had a much higher Group III burden. Countries that are at an earlier phase of the demographic transition typically have a higher mortality burden from Group I conditions, and this is more clearly the case for the African countries participating in the INDEPTH WHO
SAGE

14

collaboration (Fig. 1). South Africa’s burden profile is exceptional here because as an upper-middle income country, a lower communicable disease burden is expected; however, the massive HIV/AIDS burden clearly shifts the burden distribution. Similarly, despite being a lowerincome country, Viet Nam has a comparatively lower communicable disease burden. Shifting to morbidity, the top three contributors to morbidity burdens in middle-income countries in 2004 were unipolar depressive disorders, ischaemic heart disease and cerebrovascular disease (20). The top three for lower-income countries were lower respiratory infections, diarrhoeal diseases and HIV/AIDS. Fig. 2 illustrates the burden of non-fatal health outcomes by major grouping 100% 90% 80% 70% 60% 50%

Violence/injury

40%

Non-communicable

Communicable

30% 20% 10% 0%

Africa

Asia

Fig. 1. Mortality profiles (age-standardised death rates) by major Burden of Disease grouping and country, 2004 (WHO 2008). Citation: Global Health Action Supplement 2, 2010. DOI: 10.3402/gha.v3i0.5302

Ageing and adult health status in eight lower-income countries

and INDEPTH HDSS site country for 2004, indicating those conditions which lead to longer years of life lived in a state of less than full health (non-fatal health outcomes or disability). The figure illustrates the mixture of disease burden in the participating low- and middle-income countries, with Group I burden featuring more prominently in African countries and Group II in Asian countries. Still, a majority of the main chronic conditions predominate in older age groups in both regions (19). From currently available data, the overall contribution of disability from non-communicable diseases is projected to grow substantially and ageing will be one of the major drivers of the burden (22).

HDSS characteristics INDEPTH (http://www.indepth-network.org) is a network of 37 sites in 19 countries in Africa, Asia, Central America and Oceania based on health and socio-demographic surveillance within defined areas. The network brings together virtually all of the world’s HDSSs located in low- and middle-income settings, and currently covers over 2 million individuals. Regular household census updates at each HDSS site allow for continuous, household-level monitoring of all vital events (births, deaths and migrations) in the defined population. INDEPTH provides an exceptional resource with which to characterise the health, demographic and social dynamics of some of the world’s most vulnerable populations. The INDEPTH Adult Health and Ageing Working Group has established INDEPTH’s capability to contribute critical insights into the adult health, ageing and disease transitions evolving in Africa and Asia, and to use this understanding to inform policy and evaluate interventions of potentially high impact. 100% 90% 80% 70% 60% 50%

Violence/injury

40%

Non-communicable Communicable

30% 20% 10% 0%

Africa

Asia

Fig. 2. Morbidity profiles (age-standardised DALYs) by major Burden of Disease grouping and country, 2004 (WHO 2008). Citation: Global Health Action Supplement 2, 2010. DOI: 10.3402/gha.v3i0.5302

SAGE characteristics The SAGE project (http://www.who.int/healthinfo/ systems/sage) has become a leading multi-country study on ageing and adult health in lower- and middle-income countries. Launched in 2003 as part of the WHO’s World Health Survey (WHS), SAGE has implemented nationally representative population surveys in six core countries: China, Ghana, India, Mexico, the Russian Federation and South Africa. The specific aims of SAGE are to: . Obtain reliable, valid and comparable data on levels of health on a range of key domains for older adult populations. . Examine patterns and dynamics of age-related changes in health using longitudinal follow-up of survey respondents as they age, and to investigate socio-economic consequences of these health changes. . Supplement and cross-validate self-reported measures of health and the anchoring vignette approach to improve comparability of self-reported measures, through measured performance tests for selected health domains. . Collect data on health examinations and biomarkers to improve reliability of data on morbidity, risk factors and monitor effect of interventions. The baseline data collection for SAGE (Wave 0) was conducted as part of the 2002/2003 WHS with SAGE Wave 1 data collected between 2007 and 2010. Biennial longitudinal follow-up is planned with Wave 2 in 2011 and Wave 3 in 2013. SAGE provides data on the levels and differences in health and well-being across low- and middle-income countries, and methodologies that improve health measurement and cross-national comparability. SAGE covers a broad range of topics, with a focus on health, disability, risk factors, stress, happiness, social networks, economic well-being, care-giving, health care utilisation and health systems responsiveness. Furthermore, a host of biomarker data was collected, including anthropometrics, physical performance tests and dried blood spots. Another objective for SAGE is to develop working relationships and linkages to other data collection platforms, including surveys and surveillance sites, to better understand changing health over the life course, compression of morbidity and perceptions of health, quality of life and economic well-being within and across countries. SAGE has a history of collaborating with other ageing research, like the US HRS; ELSA; SHARE; China Health, Ageing, Retirement Longitudinal Study; Longitudinal Ageing Study in India; and, now with INDEPTH HDSS sites. The collaboration with INDEPTH extends the possibilities of longitudinal household-based research through the combination of survey

15

Paul Kowal et al.

and surveillance methods and provides opportunities to apply new methodological techniques to cross-country ageing research.

The collaboration The collaboration between WHO-SAGE and INDEPTH has pursued four main goals: (1) to develop and implement a survey instrument that can be incorporated into a regular census update round placing minimal additional demands on existing research infrastructure; (2) to implement the full SAGE survey in parallel to a summary short survey round, but with separate infrastructure and resources; (3) to determine key areas where INDEPTH HDSS sites could be used as methodological laboratories to pilot new methods and test hypotheses
so as to exploit the complementary strengths of both survey and surveillance data; and (4) to derive more integrated analytical plans to assess ageing and adult health at national and sub-national levels. For this article, we address goals 1 and 4 above using a summary version of the full SAGE instrument which was implemented in eight INDEPTH HDSS sites. This part of the collaboration had two primary aims. The first was to use survey and surveillance data to describe the situation of ageing and adult health within and across participating HDSS sites. This included the adaptation and implementation of standardised SAGE survey modules on health and wellbeing in INDEPTH HDSS sites. The HDSS sites identified overlapping content in their respective surveillance data and the SAGE survey instruments. HDSS sites then worked to enhance the comparability of the socio-demographic data collected at each site to be included in a cross-site dataset (for example, comparing socio-economic status indicators and mapping education levels to an international standard). The second aim was to determine the feasibility of collecting longitudinal data through combining the two types of data collection efforts as a means to establish ageing and adult health trends in a range of countries. A first step was to develop a survey instrument adapted from the full SAGE questionnaire that could be inserted into a regular census round without significant disruption to the infrastructure and process. The belief was that the potential increase in efficiency from adding modules to the regular data collection rounds, coupled with new analytical techniques, could provide data on changing health and well-being at a reduced cost whilst retaining the strengths of both surveillance and survey data. These data would then be used to inform the design of interventions addressing vital aspects of older adult health and functioning and, importantly, have the potential to be monitored more frequently within the HDSS sites than with the national-level surveys.

16

Methods The initial step was to develop a health and well-being module that could be nested within a typical census update round in an INDEPTH HDSS site. This meant that the interview needed to be approximately 15
20 min in duration with minimal impact on interviewers and respondents. A meeting between WHO and INDEPTH at the University of the Witwatersrand, South Africa in 2006 was used to examine psychometric properties of the health and quality of life sections of the SAGE survey instrument based on results from the 2005 SAGE pilot study (n1,500) conducted in Ghana, India and Tanzania, to determine priorities, to outline the scope of the working relationship and to invite interested HDSS sites to participate. During the meeting, the survey instruments and results from the SAGE pilot were reviewed with commentary from each INDEPTH HDSS site. The group then worked together to create a shortened summary version of the full SAGE questionnaire (the INDEPTH WHO
SAGE instrument, available as a supplementary file to this article, including variants of vignettes) which consisted of questions on HS and vignettes, functioning and subjective well-being. This summary questionnaire was subsequently piloted in each HDSS site in 2006/2007 before implementing the full data collection. Pilot results and interview debriefings were used to refine and finalise the standardised questionnaire to be used across all HDSS sites. This version was then translated and back-translated in local languages using translation protocols from both the WHS and INDEPTH HDSS sites. Standard interview protocols, training curricula (including a DVD with video clips of example interviews) and quality assurance procedures were used across all HDSS sites. Training sessions with experienced interviewers were conducted for survey teams at each HDSS site. These training sessions lasted an average of 4.5 days. The interview teams had the added advantage of longstanding relationships within the surveillance sites. Face-to-face interviews with participants aged 50 and over were conducted in the course of the regularly scheduled census in three HDSS sites. Separate survey activities were used in five HDSS sites, where in one site it was part of a broader ageing survey (Nairobi). Feedback from the survey teams indicated that it took about three weeks to become maximally efficient at interviews and data collection. Across all the sites, the mean interview time, excluding vignettes, was 20 minutes towards the end of the survey process. This was about 14 minutes less than the average time at the beginning of the interview process. The vignettes took an average of 13 minutes of interview time, again, the time decreasing from an average of 19 minutes at the beginning of the process. Citation: Global Health Action Supplement 2, 2010. DOI: 10.3402/gha.v3i0.5302

Ageing and adult health status in eight lower-income countries

Vignette methodology Cross-national comparative data analysis enhances understanding of HS differences, ageing dynamics and cultural differences, but also facilitates the evaluation of the performance of health, social and economic systems, and policies to address ageing and health. Typically, the measurement of HS relies on self-reported responses in surveys and the self-response data take the form of ordered categorical (ordinal) responses. Eight domains of health were used, which account for up to 80% of the variance in HS (23). As part of the WHO cross-country health survey approach, anchoring vignettes have been used to position self-reported responses onto a common scale comparable across individuals. An anchoring vignette is a description of a concrete level on a given health domain that respondents are asked to evaluate with the same questions and response scales applied to self-assessments on that domain. A concrete example of the HS questions and vignettes for one health domain, mobility, follows: Female respondent X is asked two questions about her own level of mobility, Q1 Overall in the last 30 days, how much difficulty did you have with moving around?

‘Was it none, mild, moderate, severe, extreme or cannot do this?’

Q2 In the last 30 days, how much difficulty did you have in vigorous activities?

‘Was it none, mild, moderate, severe, extreme or cannot do this?’

Next the respondent is asked to respond to questions about the vignettes. Vignettes are brief stories that describe a certain fixed level of health, with five vignettes covering a range of mobility levels. The respondent is instructed to put herself in the shoes of the person described in the vignettes and answer the same question as if she were that person: [Someshni] has a lot of swelling in her legs due to her health condition. She has to make an effort to walk around her home as her legs feel heavy. Q3 How much difficulty did ‘Was it none, mild, moderate, [Someshni] have with moving severe or extreme or cannot around? do this?’ Q4 How much difficulty did [Someshni] have in vigorous activities?

‘Was it none, mild, moderate, severe or extreme or cannot do this?’

By mapping responses to various questions on the same health domain to a common comparable scale, anchoring vignettes may provide a bridge between data collected across cultures or population sub-groups [further detailed information about anchoring vignettes and statistical models is available elsewhere (24
27)]. Citation: Global Health Action Supplement 2, 2010. DOI: 10.3402/gha.v3i0.5302

Ethical clearance was obtained from research review boards local to each participating HDSS site (several of which are linked to universities), plus from the WHO Ethical Review Committee as part of SAGE. Informed consent was obtained from each respondent prior to interview. Sample: Six HDSS sites collected data from the entire population aged 50 in their HDSS. Sampling in the two remaining HDSS sites (Navrongo, Ghana and Matlab, Bangladesh) was based on random selection of persons aged 50 and over within the HDSS site. For comparison purposes, a smaller sample of younger adults (aged 18
49, n5,794) was interviewed in five HDSS sites using similar methods. Questionnaire: The abbreviated survey instrument consisted of two modules adapted from the full SAGE questionnaire: the HS and associated vignette questions plus Activities of Daily Living (ADL)-type questions (following the WHO Disability Assessment Scale version II (WHODAS-II) model), and questions on subjective well-being as measured by the 8-item version of the WHO Quality of Life (WHOQoL) instrument (28). Some HDSS sites chose to add additional modules and/or questions, but the primary goal was a standardised questionnaire that could be applied in all HDSS sites embedded within existing HDSS census rounds. Additional data targeted for inclusion into the final dataset, and deriving directly from the HDSS, included socio-demographic characteristics, such as age, sex, education, marital status, socio-economic status and household information, such as the number of household members.

Dataset Following site-level data entry and cleaning, and after a data-sharing agreement was reached between the participating INDEPTH HDSS sites and with WHO, data were forwarded to a central location (Umea˚, Sweden) for cleaning and imputation of missing data. Regular correspondence between HDSS sites improved the efficiency of the data checking and cleaning process. A working meeting held in 2008 at Umea˚ University, Sweden, was used to harmonise data across the sites, finalise the dataset and agree on initial outputs. A first dataset was generated and included: . Comprehensive HH information including roster of all members (by age, sex, marital status, education, location (urban or rural), HH head) and socioeconomic status. . For each respondent: age and date of birth, sex, marital status and education. . From the adapted SAGE modules: overall general selfrated health, HS from eight domains plus related vignette information, functioning assessment from the

17

Paul Kowal et al.

12-item WHODAS and subjective quality of life results from the 8-item WHOQoL. . Plans to archive the data at WHO, INDEPTH and the University of Michigan’s National Archive of Computerized Data on Aging (NACDA) to maximise opportunities to share data and provide multiple access portals. The four main outcome variables derived from this data and reported in the site-specific and cross-site articles in this issue are self-rated general health (SRH), overall HS, disability levels (WHODAS) and subjective quality of life (WHOQoL).

Overall general self-reported health (SRH) Two overall general health questions were asked, each with 5-point Likert-type response scales. The first is a question asked very often in surveys: ‘In general, how would you rate your health today? Would you say, very good (1), good (2), moderate (3), bad (4) or very bad (5)?’; and the other was a question related to general difficulties in day-to-day tasks: ‘Overall in the last 30 days, how much difficulty did you have with work or household activities? Was it, none (1), mild (2), moderate (3), severe (4) or extreme/cannot do (5)?’ These types of global measures of self-rated health are commonly used in health surveys and as measures of population health. At the individual level, the global self-rated health question is a good predictor of many health and healthrelated outcomes (29, 30). However, the true meaning of responses to a single question for a multi-dimensional construct and the reliability of this measure over time has been questioned (31, 32). Health status (HS) Health scores were calculated based on self-reported health in eight health domains covering affect, cognition, interpersonal activities and relationships, mobility, pain, self-care, sleep/energy, and vision. Each domain included at least two questions. Asking more than one question about difficulties in a given domain provides more robust assessments of individual health levels and reduces measurement error for any single self-reported item. Item response theory (IRT) was used to score the responses to the self-reported health questions using a partial credit model which served to generate a composite HS score (33, 34). An item calibration was obtained for each item. In order to determine how well each item contributed to common global health measurement, chisquare fit statistics were calculated. The calibration for each of the health items was taken into account and the raw scores were transformed through Rasch modelling into a continuous cardinal scale where a score of 0 represents worst health and a maximum score of 100 represents best health.

18

Functional status (WHODAS) Self-reported functioning was assessed through the standardised 12-item WHO Disability Assessment Scale, Version 2 (WHODAS) (35). It is a well-tested instrument, with published psychometric properties and a good predictor of global disability (36
38). The WHODAS is compatible with the International Classification of Functioning, Disability and Health (ICF) and contains many of the most commonly asked ADL and Instrumental Activities of Daily Living (IADL) questions. The WHODAS instrument also provides an assessment of severity of disability (39). Results from the 12-items were summed to get an overall WHODAS score, which was then transformed to a 0
100 scale, with 0 as best functioning (no disability) and 100 maximum disability. Subjective well-being and quality of life (WHOQoL) An 8-item version of the World Health Organization Quality of Life instrument (WHOQoL) was used to assess perceived well-being (28). This is a cross-culturally valid instrument for comprehensively assessing overall subjective well-being, yet is also very brief. Knowing that health and quality of life are strongly associated yet distinct concepts, WHOQoL will help describe the relationship in older persons across countries and over time. Results from the 8-items were summed to get an overall WHOQoL score which was then transformed to a 0
100 scale, similar to the health score.

Implementation results Eight INDEPTH HDSS sites collected data using the summary questionnaire (see Table 2). Sample sizes ranged from almost 2,100 to over 12,000, with a total combined sample of over 46,000 persons aged 50 and over. Additionally, a random sample of persons aged 18
49 was included in five HDSS sites
as a comparison population
but these were not included in the initial dataset or analyses. The survey took an average of 4.7 months to complete with a range of 3
8 months. Five sites implemented the survey as a stand-alone effort, with the three remaining HDSS sites (Navrongo, Ifakara and Agincourt) implementing the survey as part of a scheduled census update. Two of these three HDSS sites finished on schedule, with the one site requiring additional time and staff to complete the census and survey.

Discussion Platform for research on adult health and ageing In light of the projected demographic and epidemiologic transitions associated with an ageing world, a WHO and INDEPTH collaboration has demonstrated the capacity to generate data across African and Asian settings to better understand health outcomes and their determiCitation: Global Health Action Supplement 2, 2010. DOI: 10.3402/gha.v3i0.5302

Ageing and adult health status in eight lower-income countries

Table 2. Selected features of participating HDSS sites: INDEPTH WHO-SAGE study, 2006
2007 Approximate HDSS site populations

Study population

Country

Year started

Periodicity of census updates

Total population

Total 50 years and over

Africa Agincourta Ifakaraa Nairobia Navrongoa

South Africa Tanzania Kenya Ghana

1992 1996 2000 1993

Annually Every 4 months Every 4 months Every 4 months

70,000 84,000 69,000 144,000

8,400 9,400 2,700 22,900

6,500 5,000 2,700 5,000

4,085 5,131 2,072 4,584

Asia Filabavib Matlaba Purworejob Vadua

Viet Nam Bangladesh Indonesia India

1999 1966 1990 2003

Every 3 months Every 2 months Annually Every 6 months Totals

50,000 212,000 53,000 68,000 750,000

8,500 33,800 14,200 8,000 107,900

8,500 5,000 14,200 8,000 54,900

8,535 4,037 12,395 5,430 46,269

HDSS site

Anticipated study Final study population, all population ages 50 years and over

a

Support from the US National Institute on Aging. Support from Swedish Council for Working Life and Social Research.

b

nants in older adult populations. The initial results from the collaboration between WHO-SAGE and INDEPTH HDSS sites are a milestone for longitudinal research on ageing and adult health and provide an exceptional platform for multi-site and multi-country, longitudinal research on ageing and adult health in lower-income countries in Africa and Asia. The data collection platform has the potential to substantially enhance the applications of findings from both survey (SAGE) and surveillance-based (INDEPTH) data collection. The very nature of the HDSS sites, with geographic boundaries defining their populations, along with established infrastructure and human resources, present a number of opportunities for methodological development and hypothesis testing prior to scaling to a national-level survey. A number of topics could be explored, such as the relationships between morbidity, well-being, social networks and mortality, because of the documentation levels and frequency of contact. Similarly, surveillance sites benefit from enhanced generalisability of results, expansion of objectives and comparability to other survey data, to name a few. Additionally, the methodological and practical strengths of each are accentuated, resulting in improved financial efficiencies for conducting longitudinal ageing research. The collaboration will also support data harmonisation, data management and analytic capacity development, cross-validation and calibration of measures, contextualisation of the detailed information from HDSS within broader national patterns and trends, joint efforts to disseminate results and consideration of their policy implications. The analysis of levels, trends and differentials in leading health problems globally is needed to identify Citation: Global Health Action Supplement 2, 2010. DOI: 10.3402/gha.v3i0.5302

persistent and emerging health challenges for older populations, and to monitor and evaluate health and social programmes to determine what works, assess how specific programmes are performing and inform decisions regarding programme design and implementation.

Limitations and difficulties As with any longitudinal study, problems were experienced with locating respondents to be included
especially men, many of whom may be migrant labourers. Interviewers found difficulty in questioning the oldest old, even after training and increased awareness about the potential issues with interviewing this population segment. In addition, difficulties were experienced with explaining the vignettes, some of which included scenarios possibly foreign to rural settings. As part of the analysis of results, response patterns to the vignette questions would clearly indicate if, in the end, a respondent did not understand the vignettes.

Feasibility of longitudinal monitoring of adult health and ageing Although we aimed to assess the feasibility of incorporating the INDEPTH WHO-SAGE short questionnaire into routine HDSS activities, only three of the eight sites attempted this, with the other five sites conducting the survey as a separate field activity. Of the three HDSS sites integrating the survey, one found need for additional time and staff. Interviewers needed time to gain experience interviewing older respondents and to develop strategies for high-quality interviews: the average duration of interviews, excluding vignettes, decreased on average by 14 minutes from about 34 minutes at the beginning of

19

Paul Kowal et al.

the interviews to 20 minutes towards the end. The average length ended up at about 28 minutes. In general, sites found value in targeting the age group of 50 and over, focusing on health rather than routine HDSS questions, linking INDEPTH WHO-SAGE data with existing HDSS variables and subsequent health outcomes. Any further data collection efforts will seek to shorten the questionnaire further; incorporate the survey modules into the routine census round; provide more training to implement the vignettes; and interview the entire population under surveillance rather than using a sample, where possible.

Future plans and possibilities The next steps in the INDEPTH WHO-SAGE collaboration include further work on improving the existing dataset, incorporating additional existing HDSS variables and future rounds of data collection. Work will be undertaken to further harmonise HDSS variables, across INDEPTH HDSS sites, for example, re-examining the education data and wealth quintiles from each site. This will help to improve comparability across HDSS sites and countries, and with the nationally representative full SAGE studies implemented in three of the countries (South Africa, Ghana and India). Additional HDSS variables have already been identified and will be added to the current summary dataset to produce an enhanced dataset. Planned additions include longitudinal HDSS data such as in- and out-migration, births, deaths, additional respondent characteristics (mother tongue, ethnicity, religious denomination) or changes in respondent and household characteristics over time (education, marital status, walls, floors, water, sanitation, fuel use for cooking, food security), and relevant data about health (non-communicable disease risk factors for example) and household composition (members). We will also include historical HDSS data to cover at least SAGE baseline years (back through 2002). Three HDSS sites (Agincourt, Navrongo and Vadu) collected data using both the summary and full versions of the SAGE questionnaire. Examination of data from respondents who completed both the short and full survey will be undertaken and then compared with the nationally representative SAGE survey in their respective countries. These steps will allow examination of subnational variation in health levels, as well as variation in the relationships between physical and mental functioning and other socio-demographic factors. The performance of the SAGE health module and vignettes among older adults in the surveillance sites can also be compared to the performance in the community SAGE samples from these countries. It will provide opportunities to compare and correlate findings from African and Asian countries participating in SAGE with INDEPTH sites in the same
as well as contrasting
national settings.

20

Further exploration of results using small area analyses and optimising the combination of survey and surveillance data are needed. Finally, another wave of data collection is planned, for which funding was recently secured. Further hypothesis testing can be undertaken to take advantage of the unique panel data that the ongoing surveillance systems provide. For example, differences in functioning at older ages given different socio-economic and health transition environments may be explored in cross-site comparisons. The contrast, for instance, between the leading health problems in Navrongo, Ghana, which remain dominated by many persistent ‘pre-transition’ challenges (infectious diseases, nutritional disorders, maternal and perinatal conditions) and the emerging epidemics of non-communicable diseases in Agincourt, South Africa, provide a detailed epidemiologic backdrop for analysis of variation in levels on core health domains (40). Other hypotheses that could be examined relate to functioning of older adults in the context of evolving childcare contributions (for example, due to AIDS mortality of household members), levels of family and household support, and associated economic activity. Health issues of mortality, the compression of morbidity and social networks will also be pursued. The ability to connect comparable data on different dimensions of functioning to rich databases on individual and household variables has the potential to support important analyses for a wide range of questions concerning shifting determinants of health in older adults in settings undergoing dramatic sociodemographic changes.

Archiving and sharing Appropriate metadata and the summary SAGE dataset with selected HDSS variables included will be made publicly available to researchers in concert with the publication of this supplement (see Supplementary files under Reading Tools online). The dataset will also be archived in the University of Michigan’s National Archive of Computerized Data on Aging (NACDA).

Conclusion This collaboration provides both the practical tools and infrastructure for collecting critical evidence needed by researchers and policy-makers. Health, disability, living conditions and social support are concerns for ageing populations throughout the world. Considering the dearth of health and well-being data for older people in most lower- and middle-income countries (13, 41, 42), this collaboration directly addresses this data gap now and into the future. WHO and INDEPTH will work to improve availability and use of reliable, valid and comparable health information at the country and global levels, developing and improving tools and methods for collecting this information, and providing norms, stanCitation: Global Health Action Supplement 2, 2010. DOI: 10.3402/gha.v3i0.5302

Ageing and adult health status in eight lower-income countries

dards and technical guidance for data collection, research, analysis and synthesis of knowledge. The articles that follow in this supplement illustrate the value and quality of the data collected as part of this collaboration.

11.

12.

Acknowledgements WHO Multi-Country Studies unit contributed the SAGE survey instruments, supporting materials and technical support. The Umea˚ Centre for Global Health Research provided technical support and advice to the INDEPTH HDSS sites and hosted an analytic and writing workshop in 2008. The Health and Population Division, School of Public Health, University of the Witwatersrand, provides co-leadership for this initiative and serves as a satellite secretariat for the INDEPTH Adult Health and Ageing Working Group.

Conflict of interest and funding Financial support for six HDSS sites (four African sites plus Matlab and Vadu) was provided by the US National Institute on Aging through an interagency agreement with the World Health Organization, and for two HDSS sites (FilaBavi and Purworejo) from the Swedish Council for Working Life and Social Research (FAS) through Umea˚ University.

References 1. Cairncross F. Economics: age, health and wealth. Nature 2007; 448: 875
6. 2. Lee R, Mason A. Population aging, wealth, and economic growth: demographic dividends and public policy. Background Paper for the World Economic and Social Survey. New York: United Nations, Department of Economic and Social Affairs; 2007. 3. Deaton A. Policy implications of the gradient of health and wealth. Health Aff 2002; 21: 13
30. 4. England RS. The macroeconomic impact of global aging. A new era of economic frailty? Washington, DC: CSIS Press; 2002. 5. World Health Organization. Macroeconomics and health: investing in health for economic development. Report of the Commission on Macroeconomics and Health. Geneva: World Health Organization; 2001. 6. International Social Security Association. Social Security Programs throughout the World. Geneva: ISSA; 2009. Available from: http://www.issa.int/aiss/Resources/ISSA-Publications/ Social-Security-Programs-Throughout-the-World6 [cited 10 May 2010]. 7. Bloom DE, Canning D, Sevilla J. The demographic dividend. A new perspective on the economic consequences of population change. Santa Monica, CA: RAND; 2002. 8. Frenk J. The global health system: strengthening national health systems as the next step for global progress. PLoS Med 2010; 7: e1000089. Doi: 10.1371/journal.pmed.10000898. 9. Coovadia H, Jewkes R, Barron P, Sanders D, McIntyre D. The health and health system of South Africa: historical roots of current public health challenges. Lancet 2009. Doi: 10.1016/ S0140-6736(09)60951-X. 10. Tollman SM, Kahn K, Sartorius B, Collinson MA, Clark SJ, Garenne ML. Implications of mortality transition for primary health care in rural South Africa: a population-based surveilCitation: Global Health Action Supplement 2, 2010. DOI: 10.3402/gha.v3i0.5302

13.

14. 15.

16.

17.

18.

19.

20.

21.

22.

23.

24.

25.

26.

27.

28.

lance study. Lancet 2008; 372: 893
901. Doi: 10.1016/S01406736(08)61399-9. Lafortune G, Balestat G. Trends in severe disability among elderly people: assessing the evidence in 12 OECD countries and the future implications. OECD Health Working Papers No. 26. 2007. DELSA/HEA/WD/HWP (2007)2. Banks J, Marmot M, Oldfield Z, Smith JP. Disease and disadvantage in the United States and in England. JAMA 2006; 295: 2037
45. National Research Council. Preparing for an aging world: the case for cross-national research. Panel on a research agenda and new data for an aging world. Washington, DC: National Academy Press; 2001. Ferreira M. Research on ageing in Africa: what do we have, not have and should we have? Generations Rev 2005; 15: 32
5. United Nations Department of Economic and Social Affairs, Population Division. World Population Prospects: the 2008 Revision. New York: United Nations; 2009. World Bank. Country classification; 2009. Available from: http://data.worldbank.org/about/country-classifications [cited 10 May 2010]. Ebrahim S, Smeeth L. Non-communicable diseases in low and middle-income countries: a priority or a distraction? Int J Epidemiol 2005; 34: 961
6. Salomon JA, Murray CJL. The epidemiologic transition revisited: compositional models for causes of death by age and sex. Popul Dev Rev 2002; 28: 205
28. Mathers C, Loncar D. Projections of global mortality and burden of disease from 2002 to 2030. PLoS Med 2006; 3: 2011
30, e442. Doi: 10.1371/journal.pmed.0030442. Mathers C, Fat DM, Boerma JT. The global burden of disease: 2004 update. Geneva: World Health Organization; 2008. Available from: http://www.who.int/healthinfo/global_burden_disease /GBD_report_2004update_full.pdf [cited 10 May 2010]. Murray CJL, Lopez AD. Global mortality, disability, and the contribution of risk factors: global Burden of Disease Study. Lancet 1997; 349: 1436
42. Chatterji S, Kowal P, Mathers C, Naidoo N, Smith J, Suzman R. The health of aging populations in China and India. Health Aff 2008; 27: 1052
63. Ustun TB, Chatterji S, Mechbal A, Murray CJL, WHS Collaborating Groups. The World Health Surveys. In: Murray CJL, Evans D, eds. Health systems performance assessment: debates, methods and empiricism. Geneva: World Health Organization; 2003. King G, Murray CJL, Salomon JA, Tandon A. Enhancing the validity and cross-population comparability of measurement in survey research. Am Political Sci Rev 2004; 98: 567
83. Murray CJL, Ozaltin E, Tandon A, Salomon JA, Sadana R, Chatterji S. Empirical evaluation of the anchoring vignette approach in health surveys. In: Murray CJL, Evans D, eds. Health systems performance assessment: debates, methods and empiricism. Geneva: World Health Organization; 2003. Salomon JA, Tandon A, Murray CJL. Comparability of self rated health: cross-sectional multi-country survey study using anchoring vignettes. BMJ 2004; 328: 258
61. Tandon A, Murray CJL, Salomon JA, King G. Statistical models for enhancing cross-population comparability In: Murray CJL, Evans D, eds. Health systems performance assessment: debates, methods and empiricism. Geneva: World Health Organization; 2003. Schmidt S, Mu¨hlan H, Power M. The EUROHIS-QOL 8-item index: psychometric results of a cross-cultural field study. Eur J Public Health 2006; 16: 420
8.

21

Paul Kowal et al.

29. Bardage C, Saskia M, Pluijm F, Pedersen NL, Deeg DJH, Jylha¨ M, et al. Self-rated health among older adults: a cross-national comparison. Eur J Ageing 2005; 2: 149
58. 30. Frankenberg E, Jones NR. Self-rated health and mortality: does the relationship extend to a low income setting? J Health Soc Behav 2004; 45: 441
52. 31. Salomon JA, Nordhagen S, Oza S, Murray CJL. Are Americans feeling less healthy? The puzzle of trends in self-rated health. Am J Epidemiol 2009; 170: 343
51. 32. Sadana R, Mathers CD, Lopez AD, Murray CJL, Iburg KM. Comparative analysis of more than 50 household surveys of health status. In: Murray CJL, Salomon JA, Mathers CD, Lopez AD, eds. Summary measures of population health: concepts, ethics, measurement and applications. Geneva: World Health Organization; 2002. 33. Wilson M, Allen DD, Li JC. Improving measurement in health education and health behavior research using item response modelling: introducing item response modelling. Health Educ Res 2006; 21: i4
18. 34. Andrich D. Controversy and the Rasch model: a characteristic of incompatible paradigms? Med Care 2004; 42: 1
16. 35. World Health Organization. Measuring health and disability: a manual for the World Health Organization Disability Assessment Schedule (WHODAS 2.0). Geneva: World Health Organization; 2009. 36. Sousa RM, Dewey ME, Acosta D, Jotheeswaran AT, CastroCosta E, Ferri CP, et al. Measuring disability across cultures
the psychometric properties of the WHODAS II in older people from seven low- and middle-income countries. The 10/66 Dementia Research Group population-based Surrey. Int J Methods Psych Res 2010; 19: 1
17.

22

37. Luciano JV, Ayuso-Mateos JE, Ferna´ndez A, Serrano-Blanco A, Roca M, Haro JM. Psychometric properties of the twelve item World Health Organization Disability Assessment Schedule II (WHO-DAS II) in Spanish primary care patients with a first major depressive episode. J Aff Disord 2010; 121: 52
8. 38. O’Donovan M-A, Doyle A. Measure of Activity and Participation (MAP), World Health Organization’s Disability Assessment Schedule (WHODAS II); 2007. Available from: http:// www.hrb.ie/uploads/tx_hrbpublications/mapbulletin.pdf [cited 3 August 2010]. 39. World Health Organization. International classification of functioning, disability and health (ICF); 2001. Available from: http://www.who.int/classifications/icf/en/ [cited 10 May 2010]. 40. WHO SAGE survey programme; 2010. Available from: http://www.who.int/healthinfo/systems/sage [cited 10 May 2010]. 41. INDEPTH Network, iSHARE; 2010. Available from: http:// www.indepth-network.org [cited 10 May 2010]. 42. National Research Council. Aging in sub-Saharan Africa: recommendations for furthering research. Panel on policy research and data needs to meet the challenge of aging in Africa. In: Cohen B, Menken J, eds. Committee on population, division of behavioral and social sciences and education. Washington, DC: The National Academies Press; 2006. *Paul Kowal Multi-Country Studies Unit World Health Organization 20 Avenue Appia CH-1211 Geneva, Switzerland Email: [email protected]

Citation: Global Health Action Supplement 2, 2010. DOI: 10.3402/gha.v3i0.5302

INDEPTH WHO-SAGE Supplement æ

Assessing health and well-being among older people in rural South Africa F. Xavier Go´mez-Olive´1,2*, Margaret Thorogood1,3, Benjamin D. Clark1,4, Kathleen Kahn1,2,5# and Stephen M. Tollman1,2,5# 1

MRC/Wits Rural Public Health and Health Transitions Research Unit (Agincourt), School of Public Health, Faculty of Health Sciences, University of the Witwatersrand, Johannesburg, South Africa; 2 INDEPTH Network Accra, Ghana; 3Warwick Medical School, University of Warwick, Coventry, UK; 4 Centre for Population Studies, London School of Hygiene and Tropical Medicine, London, UK; 5 Umea˚ Centre for Global Health Research, Epidemiology and Global Health, Umea˚ University, Umea˚, Sweden

Background: The population in developing countries is ageing, which is likely to increase the burden of noncommunicable diseases and disability. Objective: To describe factors associated with self-reported health, disability and quality of life (QoL) of older people in the rural northeast of South Africa. Design: Cross-sectional survey of 6,206 individuals aged 50 and over. We used multivariate analysis to examine relationships between demographic variables and measures of self-reported health (Health Status), functional ability (WHODASi) and quality of life (WHOQoL). Results: About 4,085 of 6,206 people eligible (65.8%) completed the interview. Women (Odds Ratio (OR)  1.30, 95% CI 1.09, 1.55), older age (OR 2.59, 95% CI 1.97, 3.40), lower education (OR 1.62, 95% CI 1.31, 2.00), single status (OR1.18, 95% CI 1.01, 1.37) and not working at present (OR 1.29, 95% CI 1.06, 1.59) were associated with a low health status. Women were also more likely to report a higher level of disability (OR1.38, 95% CI 1.14, 1.66), as were older people (OR 2.92, 95% CI 2.25, 3.78), those with no education (OR1.57, 95% CI 1.26, 1.97), with single status (OR 1.25, 95% CI 1.06, 1.46) and not working at present (OR1.33, 95% CI 1.06, 1.66). Older age (OR 1.35, 95% CI 1.06, 1.74), no education (OR 1.39, 95% CI 1.11, 1.73), single status (OR 1.28, 95% CI 1.10, 1.49), a low household asset score (OR 1.52, 95% CI 1.19, 1.94) and not working at present (OR 1.32; 95% CI 1.07, 1.64) were all associated with lower quality of life. Conclusions: This study presents the first population-based data from South Africa on health status, functional ability and quality of life among older people. Health and social services will need to be restructured to provide effective care for older people living in rural South Africa with impaired functionality and other health problems. Keywords: adult health; ageing; self-reported health; disability; quality of life; South Africa; rural; INDEPTH WHO-SAGE

Access the supplementary material to this article: INDEPTH WHO-SAGE questionnaire (including variants of vignettes), a data dictionary and a password-protected dataset (see Supplementary files under Reading Tools online). To obtain a password for the dataset, please send a request with ‘SAGE data’ as its subject, detailing how you propose to use the data, to [email protected]

Received: 3 November 2009; Revised: 10 June 2010; Accepted: 8 July 2010; Published: 27 September 2010

#

Supplement Editor, Kathleen Kahn, Supplement Editor, Stephen M. Tollman, have not participated in the review and decision process for this paper.

he world’s population is ageing and projections show that this increase will continue (1, 2). The percentage of the world’s population aged 65 and over is projected to increase steeply in coming years

T

Global Health Action 2010. # 2010 F. Xavier Go´mez-Olive´ et al. This is an Open Access article distributed under the terms of the Creative Commons AttributionNoncommercial 3.0 Unported License (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in Citation: Global Health Action Supplement 2, 2010. DOI: 10.3402/gha.v3i0.2126 any medium, provided the original work is properly cited.

23

F. Xavier Go´mez-Olive´ et al.

(1
3). The growth in the world population aged 50 and over is expected to increase from 21% in 2011 to 34% in 2050. This increase will affect not only developed countries but also developing countries (1). In particular, in developing countries demographers have predicted an increase of 140% between 2006 and 2030 (4), from 35 to more than 69 million (3). The health effects of this global demographic change are, as yet, not fully known but estimations predict that the change in age structure in coming years will bring an increase in mortality due to non-communicable diseases, changing the pattern of the most common causes of death in the different regions of the world and the world as a whole (2). In 2005 it was estimated that a total of 37 million chronic disease deaths occurred worldwide, and more than three-quarters (77%) were in people aged above 60 (5, 6). Many of these deaths were preventable and a call has already been made for active interventions to decrease this death rate by 2015 (5). For most of the developing world, and particularly for sub-Saharan Africa, this epidemic of non-communicable diseases is appearing at a time when countries are also experiencing a crippling HIV epidemic. The recent availability of highly active anti-retroviral therapy (HAART) means that, for those people with access to treatment, AIDS is becoming a chronic disease requiring long-term clinical management (7, 8). The high HIV prevalence and recent access to HAART, together with an ageing population and the emerging epidemic of non-communicable diseases, will put immense pressure on already weak health services as well as on society as a whole, with important changes in household structure (9) and in the roles and responsibilities of older people (10). In South Africa, the proportion of the population aged 50 and over has slightly increased from 14.8% in 2006 (11) to 15% in 2009 (12) and is predicted to be 19% in 2030 (1). This research is based in the Agincourt subdistrict of rural northeast South Africa, where the proportion 50 years and over in the study population

was 9.9% in 1992, 10.7% in 2000 and 11.7% in 2007 (Fig. 1). In this area there are high labour migration rates of around 60% in adult males 35
50 years old (13) and high HIV-related mortality in young adults (14, 15). Despite a falling life expectancy at birth (14), we have seen an increase in the older population. Information from annually updated health and socio-demographic surveillance has shown an increase of 15% in noncommunicable diseases during the past 10 years, while the number of chronic conditions overall requiring longterm care has increased 2.6-fold (16). This may increase the existing high burden on health services depending on the proportion of older people seeking health care. In addition, this may increase the demand for social support for these individuals in their communities. Changes in the social structure and roles and responsibilities of older people, particularly women, have already occurred (10). In this new reality, older women face additional responsibilities such as nursing their sick children and taking care of their grandchildren (17). Older people have also become the main bread winners through their social pension, which is sometimes the family’s only source of income (18). In 2006, any South African citizen (women 60 years or older and men 65 years or older) living in South Africa could apply for the government monthly pension (the Old Age Grant). This grant also depends on the person’s income, taking into account the total amount in the family if the person is married (19, 20). For all the above reasons, the health and well-being of older adults in rural South Africa has become a crucial issue which may impact the well-being of the entire population. However, the impact of the changing age structure and the growth in chronic disease and disability is poorly understood. We have therefore set out to address this gap. In this article, we describe the findings of a population survey of people aged 50 and over which included information on their self-reported health, levels of disability and overall quality of life (QoL), which is the first time that such findings have been reported.

Methods

Fig. 1. Trend in proportion of population 50 years and older in Agincourt sub-district, South Africa, 1992
2007.

24

Study setting The study site covers an area of 402 km2 of semi-arid scrub land. It is situated in the rural northeast of South Africa in the Bushbuckridge sub-district of Ehlanseni District, Mpumalanga Province. In the 2006 census, there was a population of 71,587 people living in 21 villages and 11,734 households. Individuals aged 50 and over constituted 12% of the population. The MRC/WITS Rural Public Health and Health Transitions Research Unit (Agincourt Unit) has been monitoring causes of death, births and migration in a population of around 70,000 people since 1992 (21). Each Citation: Global Health Action Supplement 2, 2010. DOI: 10.3402/gha.v3i0.2126

Cross-sectional survey of older people in rural South Africa

individual and household has a unique identifying number. The information is updated annually by trained fieldworkers through a household census. Each year, additional modules focusing on specific research and policy issues (for example, food security, household assets, health care utilisation, labour participation and temporary migration) are included. A verbal autopsy, to determine probable cause of death, is conducted on every death. Although there has been substantial development in the area since democratic elections in 1994, and a standpipe providing clean water and an electricity supply to households is available in all villages, the infrastructure remains poor. There is a high unemployment rate with 36% of the total adult population unemployed and looking for work (29% of men and 46% of women
unpublished data, 2004). As is common in rural South Africa and the region, reflecting the structure of the regional economy, labour migration is high, especially in men aged 35
50 years old of whom 60% live outside the study area for more than 6 months per year (13). There are six clinics and one health centre within the study area; these are served by three hospitals situated 25 and 45 km away (22). The public health service staff are heavily over-committed, staff training is limited, and chronic disease management programmes are not yet fully developed. Improvement of primary health care services is a priority for the Province (16).

Sample Using the 2005 Agincourt census update, all 6,206 individuals aged 50 and over and living permanently in the study area were highlighted on the 2006 household roster used by field workers to update census information. In this manner, field workers knew which individuals should be invited to complete the additional questionnaire described in the next section. If an individual was not available for interview at the first visit, the field worker made up to two further visits to attempt to complete the interview. Before the 2006 census update, a similar but more extensive questionnaire was conducted in a sample of 575 individuals 50 years old or more. Those individuals were excluded from this study. Data collection Field workers employed in the annual census update were trained to administer the questionnaire. We used a questionnaire adapted from the World Health Organization (WHO) Study on Global AGEing and Adult Health (23) (the SAGE study). It included questions on selfreported health, functionality (mobility, self-care, pain and discomfort, cognition, interpersonal activities, sleep/ energy, affect, vision and general health conditions) and well-being, as well as the eight questions which form the WHO Quality of Life (WHOQoL) measure. Additional demographic data were extracted from the Agincourt

HDSS database: data routinely collected every year were extracted from the 2006 census, while Household Asset Score and Employment Status data were extracted from the most recent available data (2005 and 2004, respectively). Local staff translated the questionnaires forward and backward into Shangaan, the local language. The final version of the questionnaire included amendments following a pilot conducted in several households before the start of data collection. During the 4 months of field work, three stages of quality control were implemented: (1) field workers crosschecked each others’ forms on a weekly basis; (2) field supervisors carried out daily supervision and weekly quality control checks; and (3) two full-time workers checked the completeness and quality of all census questionnaires including the SAGE questionnaires prior to data entry. Any identified errors were referred back to the field worker who revisited the respondent to correct the data.

Variables We considered factors that could be associated with levels of QoL and disability in our population including: age, education, marital status, household assets, nationality, employment status and household conditions. We calculated age at interview from the recorded date of birth and reported age in four age groups: 50
59 years, 60
69, 70
79 and 80. Education was categorised according to the WHOrecommended levels of education: no formal education; less than six years of formal education; and six years or more of formal education. This information was obtained from the census database, which is updated every 5 years using a full questionnaire on education status (last updated in 2006). Since many unions are traditional rather than civic and polygamy is practised by some people, we categorised marital status into two groups: (1) currently married or living as married; and (2) single, including anyone without a current partner (i.e. those who had never married or were separated, divorced or widowed). To evaluate the potential role of socio-economic status in our analyses, we used a household asset score. This score was developed using principal component factor analysis and 34 variables derived from the 2005 census questionnaire
including information collected about the type and size of dwelling, access to water and electricity, appliances and livestock owned and transport available. During and following the civil war in Mozambique, the Agincourt area received many refugees; hence we recorded a variable ‘nationality of origin’ (South African/ Mozambican). The Mozambican group are separately identified in the census data and it has been previously observed that this group differs from the host South

Citation: Global Health Action Supplement 2, 2010. DOI: 10.3402/gha.v3i0.2126

25

F. Xavier Go´mez-Olive´ et al.

Table 1. Domains and scales Health status

WHODASi

Domains Mobility Self-care Pain and discomfort Cognition Interpersonal activities Sleep/energy Affect Vision

Interpersonal activities Difficulties in daily living: “ Standing “ Walking “ Household duties “ Learning “ Concentrating “ Self-care

Scale

0 (low ability) to 100 (high ability)

0 (poor health) to 100 (good health)

African population in measures such as education, household assets and child mortality (24). Many Mozambicans have now taken South African nationality which allows them to work legally and receive state pensions. Employment status (currently working or not) is based on Agincourt 2004 census data, when it was most recently collected. The majority of those not working were not looking for work, but had retired in the sense they had concluded their working career. In order to examine whether health and well-being were affected by the age structure of the household, we created a dichotomous variable for those living in households with younger members and those living in households with no one under the age of 50, using data from the 2006 census.

Health Status, Disability and Quality of Life (QoL) scores These three measures progress from what may be seen as a more basic health status assessment (Health Status) through to more complex functioning of the person (WHODAS) and then the person’s satisfaction with their life (WHOQoL). WHODAS is a scale designed to measure disability (with a high score indicating a severe lack of physical functioning). Thus, for consistency between the scores used in this study, an inverted score designated WHODASi has been used, with the consequence that all three scores are based on a 0
100 scale, and in all cases a high score indicates a good outcome. Table 1 shows the domains used to calculate the variables and their scales. Health Status is a composite score which includes functionality and QoL domains. Health Status generally refers to physical and occupational functions, psychological states, social interaction and somatic sensations (25). This general health score was derived using item response theory (IRT) parameter estimates in Winsteps, a Rasch measurement software package (http://www.winsteps. com). IRT uses Maximum Likelihood Estimation, which combines the pattern of responses as well as the characteristics of each specific item for the multiple health

26

WHOQoL Enough energy for daily life Enough money to meet needs Satisfaction with: “ Your health “ Yourself “ Ability to perform daily activities “ Personal relationships “ Condition of your living place Rate your overall quality of life 0 (low quality of life) to 100 (high quality of life)

Table 2. Background characteristics by response for 6,206 adults 50 years and older living permanently in the Agincourt sub-district, 2006

Variables Sex (%) Men Women Mean age (SD) Age group (years) 50
59 60
69 70
79 80

p-Value for difference Nonrespondents Respondents respondents vs. non(N4,085) (N 2,121) respondents

1,012 (24.8) 3,073 (75.2) 66.6 (10.6) 1,297 1,221 1,077 490

(31.7) (29.9) (26.4) (12.0)

Education level (%) No formal education 2,601 (65.8) Less than or equal 757 (19.2) to 6 years More than 6 years 594 (15.0) Marital status (%) Single 2,223 (54.4) Current partnership 1,862 (45.6) Household asset score (%) First quintile 629 (15.9) Second quintile 753 (18.9) Third quintile 766 (19.3) Fourth quintile 841 (21.2) Fifth quintile 978 (24.6) Mean number of 7.0 (4.1) household members (SD) Household members 32.1 (25.9) aged 50 years and over (SD) Nationality of origin South African Mozambican

926 (43.7) 1,195 (56.3) 64.8 (11.3) 923 546 413 238

B0.001 B0.001

(43.5) (25.7) (19.5) (11.2)

B0.001

1,038 (67.5) 218 (14.1)

B0.001

292 (18.9) 1,125 (53.0) 996 (47.0)

0.302

313 (18.5) 312 (18.5) 330 (19.5) 329 (19.5) 405 (24.0) 7.4 (4.6)

0.125

28.9 (25.9)

0.002

B0.001

2,972 (72.8) 1,111 (27.2)

1,399 (66.0) 720 (34.0)

B0.001

Occupational status in 2004 Working 503 (14.6) Not working 2,930 (85.3)

481 (28.8) 1,189 (71.2)

B0.001

Citation: Global Health Action Supplement 2, 2010. DOI: 10.3402/gha.v3i0.2126

Cross-sectional survey of older people in rural South Africa

Table 3a. Demographic variables by sex [n, (%)] for 4,085 adults aged 50 and over in Agincourt sub-district, 2006 p-Value for difference between Variable Sex (%) Mean Age in years (95% CI)

Males

Females

1,012 (24.8) 67.8 (67.1, 68.5)

3,073 (75.2)

Total

male and female

4085 (100)

pB0.001

66.1 (65.7, 66.4)

Age group (years) 50
59

275 (27.2)

1,022 (33.3)

1,297 (31.7)

df3

60
69

321 (31.7)

900 (29.3)

1,221 (29.9)

p0.001

70
79 80

269 (26.6) 147 (14.5)

808 (26.3) 343 (11.2)

1,077 (26.4) 490 (12.0)

Partnership status In a partnership

771 (76.2)

1,091 (35.5)

1,862 (45.6)

df1

Currently single

241 (23.8)

1,982 (64.5)

2,223 (54.4)

pB0.001

No education

549 (54.2)

2,052 (66.8)

2,601 (63.7)

df3

Less than 6 years Six years or more

214 (21.1) 209 (20.6)

543 (17.1) 385 (12.5)

757 (18.5) 594 (14.5)

40 (4.0)

93 (3.0)

133 (3.3)

159 (15.7) 167 (16.5)

470 (15.3) 586 (19.1)

629 (15.4) 753 (18.4)

Education level

Missing data

pB0.001

Household asset score (quintiles) First (lowest) Second Third

171 (16.9)

595 (19.4)

766 (18.7)

Fourth

212 (20.9)

629 (20.5)

841 (20.6)

Fifth (highest)

279 (27.6)

699 (22.7)

978 (23.9)

24 (2.4)

94 (3.1)

118 (2.9)

Missing data

df5 p0.016

Household with and without people aged less than 50 years With under 50

853 (84.3)

2841 (92.5)

3694 (90.4)

Without under 50

159 (15.7)

232 (7.5)

391 (9.6)

df1

South African

767 (75.9)

2,205 (71.8)

2,972 (72.8)

df1

Mozambican

244 (24.1)

867 (28.2)

1,111 (27.2)

p0.011

pB0.001

Nationality of origin

Occupational status in 2004 Working

169 (19.7)

334 (13.0)

503 (14.7)

Not working

690 (80.3)

2,240 (87.0)

2,930 (85.4)

questions (each with multiple response categories) to produce the final health score. The health score is then transformed to a scale of 0
100. IRT models the relationship between a person’s reported Health Status and their probability of responding to each question in a multi-item scale. A key feature of IRT modelling is that item parameter estimates should be invariant to group membership (i.e. each item functions similarly across groups of people from different cultures) (26). To measure disability levels we used the WHODAS II (World Health Organization Disability Assessment Schedule II) scale that assesses day-to-day functioning in six activity domains. There are 10 questions with multiple

df1 pB0.001

response options. Measurement of functionality was calculated by asking participants about difficulty experienced performing certain activities during the past 30 days, and transformed into the WHODASi score for functional ability as described above. QoL was measured using the Word Health Organisation Quality of Life (WHOQoL) scale. WHO defines QoL as ‘the individual’s perception of their position in life in the context of the culture and value systems in which they live and in relation to their goals, expectations, standards and concerns’ (27, 28). QoL domains include questions on self-rated general health and questions on satisfaction. The WHOQoL score is presented on a scale of 8
40

Citation: Global Health Action Supplement 2, 2010. DOI: 10.3402/gha.v3i0.2126

27

F. Xavier Go´mez-Olive´ et al.

Table 3b. Demographic variables by age group for 4,085 adults aged 50 and over in Agincourt sub-district, 2006 Age groups Sample distribution Mean (95% CI)

50
59, N (%)

60
69, N (%)

1,297 (31.8)

1,221 (29.9)

54.5 (54.4
54.7)

64.8 (64.6
64.9)

70
79, N (%) 1,077 (26.4)

80, N (%) 490 (12)

Total N (%)

p-Value

4,085 (100)

74.5 (74.3
74.7) 84.9 (84.6
85.3)

Sex Male

275 (21.2)

321 (26.3)

269 (25.0)

147 (30.0)

1,012 (24.8)

1,022 (78.8)

900 (73.7)

808 (75.0)

343 (70.0)

3,073 (75.2)

In a partnership

732 (56.4)

615 (50.4)

374 (34.7)

141 (28.8)

1,862 (45.6)

df3

Currently single

565 (43.6)

606 (49.6)

703 (65.3)

349 (71.2)

2,223 (54.4)

pB0.001

df9

Female

df3 p0.001

Marital status

Education level No formal education

630 (48.6)

736 (60.3)

844 (78.4)

391 (79.8)

2,601 (63.7)

Primary or less than six years

304 (23.4)

253 (20.7)

144 (13.4)

56 (11.4)

757 (18.5)

Six years or more

316 (24.4)

193 (15.8)

61 (5.7)

24 (4.9)

594 (14.5)

47 (3.6)

39 (3.2)

28 (2.6)

19 (3.9)

133 (3.3)

First (lowest)

198 (15.3)

153 (12.5)

186 (17.3)

92 (18.8)

629 (15.4)

df15

Second

233 (18.0)

198 (16.2)

220 (20.4)

102 (20.8)

753 (18.4)

p B0.001

Third

238 (18.4)

246 (20.2)

199 (18.5)

83 (16.9)

766 (18.8)

Missing

pB0.001

Socio-economic quintiles

Fourth

258 (19.9)

258 (21.1)

231 (21.5)

94 (19.2)

841 (20.6)

Fifth (highest) Missing

337 (26.0) 33 (2.5)

326 (26.7) 40 (3.3)

217 (20.2) 24 (2.2)

98 (20.0) 21 (4.3)

978 (23.9) 118 (2.9)

1,206 (93.0) 91 (7.0)

1,123 (92.0) 98 (8.0)

964 (89.5) 113 (10.5)

401 (81.8) 89 (18.2)

3,694 (90.4) 391 (9.6)

df3 pB0.001

South African

957 (73.8)

919 (75.3)

740 (68.7)

356 (72.7)

2,972 (72.8)

df3

Mozambican

339 (26.2)

301 (24.7)

337 (31.3)

134 (27.4)

1,111 (27.2)

p0.003

Adult in the household Youth plus older Only older Nationality

Occupational status Working

284 (26.4)

160 (15.3)

44 (4.9)

15 (3.6)

Not working

791 (73.6)

883 (84.7)

859 (95.1)

397 (96.4)

(where 8 is the best QoL) and transformed to a 0
100 scale corresponding to the other scores.

503 (14.7) 2,930 (85.4)

df3 pB0.001

model sequentially and then discarded if the effect was not significant at the level of p0.1.

Data entry and analysis We entered data using CSPro 3.1 data entry programme (http://www.census.gov/ipc/www/cspro/index.html) which includes validation checks, and data was then extracted to Stata 10.1 (College Station, TX, USA) for analysis. Logistic regression was performed to assess the relation between potentially associated factors and confounders, and the three outcomes, i.e. health score, functionality (WHODASi) and quality of life (WHOQoL). We first carried out a univariate analysis with each of the census variables and then constructed a multivariate model based on the results of the univariate analyses (Tables 5, 7 and 9). Variables which were significantly related to the outcome measures in a univariate analysis were introduced into the

28

Ethical clearance Ethical clearance for the MRC/WITS Rural Public Health and Health Transitions Research Unit
Health and Socio-Demographic Surveillance System (Agincourt)
census and modules has been granted by the Committee for Research on Human Subjects (Medical) of the University of the Witwatersrand, Johannesburg, South Africa (Ref No. M960720). Ethical clearance for the Agincourt-INDEPTH Study on Global Ageing and Adult Health was given by the Committee for Research on Human Subjects (Medical) of the University of the Witwatersrand, Johannesburg, South Africa (Ref No. R14/49). Citation: Global Health Action Supplement 2, 2010. DOI: 10.3402/gha.v3i0.2126

Cross-sectional survey of older people in rural South Africa

Table 4. Range of Health Status (quintiles) by demographic variables [n, (%)] for 4,085 adults aged 50 and over in Agincourt sub-district, 2006 Health status quintile Variable

1 (poorest)

2

3

4

5 (best)

p-Value

Sex df 4

Male

160 (15.8)

170 (16.8)

175 (17.3)

215 (21.2)

292 (28.8)

Female

641 (20.9)

597 (19.4)

562 (18.3)

639 (20.8)

634 (20.6)

pB0.001

50
59

170 (13.1)

240 (18.5)

220 (17)

315 (24.3)

352 (27.1)

df12

60
69

183 (15)

209 (17.1)

239 (19.6)

283 (23.2)

307 (25.1)

pB0.001

70
79

270 (25.1)

207 (19.2)

202 (18.8)

193 (17.9)

205 (19)

80 and over

178 (36.3)

111 (22.7)

76 (15.5)

63 (12.9)

Age group (years)

62 (12.7)

Partnership In a partnership

277 (14.9)

341 (18.3)

328 (17.6)

411 (22.1)

505 (27.1)

Currently single

524 (23.6)

426 (19.2)

409 (18.4)

443 (19.9)

421 (18.9)

df 4 pB0.001

Education level df 8

No education

590 (22.7)

500 (19.2)

475 (18.3)

510 (19.6)

526 (20.2)

Less than 6 years

120 (15.9)

140 (18.5)

147 (19.4)

166 (21.9)

184 (24.3)

Six years or more

65 (10.9)

97 (16.3)

96 (16.2)

159 (26.8)

177 (29.8)

First (lowest)

126 (20.0)

120 (19.1)

111 (17.7)

131 (20.8)

141 (22.4)

df16

Second

159 (21.1)

148 (19.7)

138 (18.3)

155 (20.6)

153 (20.3)

p0.321

Third

145 (18.9)

135 (17.6)

147 (19.2)

163 (21.3)

176 (23.0)

Fourth Fifth (highest)

164 (19.5) 179 (18.3)

177 (21.1) 165 (16.9)

152 (18.1) 160 (16.4)

163 (19.4) 219 (22.4)

185 (22.0) 255 (26.1)

pB0.001

Household asset score (quintiles)

Household with and without people aged less than 50 With under 50 Without under 50

696 (18.8) 105 (26.9)

702 (19) 65 (16.6)

671 (18.2) 66 (16.9)

787 (21.3) 67 (17.1)

838 (22.7) 88 (22.5)

df4 p0.003

South African

623 (21.0)

558 (18.8)

506 (17.0)

619 (20.8)

666 (22.4)

df4

Mozambican

178 (16.0)

209 (18.8)

229 (20.6)

235 (21.1)

260 (23.4)

Nationality of origin p0.003

Occupational status in 2004 Working Not working

59 (11.7)

74 (14.7)

93 (18.5)

119 (23.7)

158 (31.4)

612 (20.9)

569 (19.4)

518 (17.7)

612 (20.9)

619 (21.1)

Results From the 6,206 people aged 50 years and over selected from the 2005 census, 4,085 (65.8%) responded to a questionnaire. Of those that did not complete a questionnaire, 1,616 (26.0%) were absent at the time of the interview, 218 (3.5%) had died, 47 (0.75%) declined to take part and 240 (3.9%) were unable to answer the questions (mainly due to different health conditions). A comparison of respondents and non-respondents (Table 2) shows that non-respondents were significantly younger (mean age 64.8 vs. 66.6, pB0.001), included a higher proportion of men (43.7% vs. 24.8%, pB0.001) and were better educated. There were no differences in

df 4 pB0.001

marital status or socio-economic status, but respondents included significantly more South Africans than Mozambicans and proportionally more people who were currently not working (85.3% vs. 71.2%; pB0.001). About 85% of respondents were ‘currently not working’, but the majority of these were not formally ‘unemployed’ (i.e. actively searching for work but not finding it). The 5.7% of people who were formally unemployed included 15% of those aged 50
59 and 4.3% of those aged 60
69 (data not shown). Among the respondents, there were significant differences between men and women in all the variables (Table 3a). Only a quarter of the respondents were men (24.8%), and

Citation: Global Health Action Supplement 2, 2010. DOI: 10.3402/gha.v3i0.2126

29

F. Xavier Go´mez-Olive´ et al.

Table 5. Factors associated with poor Health Statusa score for 4,085 adults aged 50 and over in Agincourt sub-district, 2006 Univariate model OR (95% CI)

Multivariate model OR (95% CI)

1 1.42 (1.23, 1.64)

1 1.30 (1.09, 1.55)

50
59 60
69

1 1.13 (0.97, 1.32)

1 1.05 (0.88, 1.26)

70
79

1.81 (1.53, 2.13)

1.46 (1.19, 1.78)

80

3.09 (2.45, 3.89)

2.59 (1.97, 3.40)

Education level No formal education

1.97 (1.64, 2.35)

1.62 (1.31, 2.00)

Less than 6 years

1.51 (1.22, 1.88)

1.42 (1.12, 1.79)

Variables Sex Male Female Age group (years)

Six years or more

1

1

Marital status Single

1.52 (1.34, 1.72)

In current partnership

1

1.18 (1.01, 1.37) 1

Household with and without people aged less than 50 With under 50 1 Not included in the final model Without under 50

1.19 (0.97, 1.48)

Household asset score First quintile (lowest)

1.23 (1.01, 1.51)

Not included in the final model

Second quintile

1.36 (1.12, 1.65)

Third quintile

1.18 (0.98, 1.43)

Fourth quintile

1.33 (1.11, 1.60)

Fifth quintile (highest)

1

Nationality of origin South African

1

Mozambican

0.95 (0.82, 1.09)

1 0.76 (0.64, 0.91)

Occupational status in 2004 Working Not working

1 1.69 (1.40, 2.05)

1 1.29 (1.06, 1.59)

a

IRT (Item Response Theory) used when measuring health status. The Health Status scale was divided in quintiles. The best Health Status was defined as those in the two highest quintiles, while the worst Health Status was defined as those in the three lower quintiles.

the men were older (67.8 years vs. 66.1 years; p B0.001), more likely to be in a current partnership (76.2% vs. 35.5%; pB0.001) and more likely to be in paid employment. Demographic variables presented by age group (Table 3b) show that the proportion of males increased

30

with age (21.2% in 50
59 age group vs. 30% in the 80 age group; p0.001); the younger age group was better educated (24.4% in the 50
59 age group vs. 4.9% in 80 have 6 years or more of formal education; pB0.001); the two younger age groups have higher socio-economic status (26.0 and 26.7% in the younger groups vs. 20.2 and 20.0% in the older age groups; p B0.001). Table 4 shows the range of Health Status responses by each of the demographic variables, while Table 5 shows the results of univariate and multivariate logistic regression analysis examining the odds of reporting a Health Status in one of the bottom two quintiles. Household asset score, household age structure and nationality of origin did not show a significant association in univariate analysis. In the final multivariate model, women had a 30% higher risk than men (odds ratio (OR) 1.30, 95% confidence interval (CI) 1.09, 1.55) of reporting a low Health Status. Older age (OR2.59, 95% CI 1.97, 3.40), lower education level (OR1.62, 95% CI 1.31, 2.00), single marital status (OR1.18, 95% CI 1.01, 1.37) and not working at present (OR 1.29, 95% CI 1.06, 1.59) were also all related to a poorer Health Status. People of Mozambican origin were 24% less likely to report a Health Status in the bottom two quintiles (OR 0.76, 95% CI 0.64, 0.91). The quintiles for self-reported ability (WHODASi score) are shown in Table 6, while Table 7 shows the results of univariate and multivariate logistic regression analysis examining the odds of reporting a WHODASi score in one of the bottom two quintiles (poorer selfreported functioning). In multivariate analysis, women were more likely to be in the bottom two quintiles of selfreported functioning (OR1.38, 95% CI 1.14, 1.66), as were older people (OR 2.92, 95% CI 2.25, 3.78), those with less education (OR1.57, 95% CI 1.26,1.97), those not in a current partnership (OR 1.25, 95% CI 1.06, 1.46) and those who were not working (OR1.33, 95% CI 1.06, 1.66). Although women were significantly more likely than men to be in the lowest two quintiles of self-reported QoL
WHOQoL (Table 8), this effect disappeared after adjusting for other variables, as did the effect of household age structure and nationality of origin (Table 9). In the final multivariate model, older age (OR 1.35, 95% CI 1.06, 1.74), lack of education (OR 1.39, 95% CI 1.11, 1.73), not being in a current partnership (OR 1.28, 95% CI 1.10, 1.49), having a low household asset score (OR1.52, 95% CI 1.19, 1.94) and not working at present (OR 1.32; 95% CI 1.07, 1.64) were all associated with a higher odds of being in one of the lower two quintiles for WHOQoL (Table 9).

Discussion In this study we describe the well-being and functionality of the population aged 50 and over in the Agincourt Citation: Global Health Action Supplement 2, 2010. DOI: 10.3402/gha.v3i0.2126

Cross-sectional survey of older people in rural South Africa

Table 6. WHODASia by demographic variables [n, (%)] for 4,085 adults aged 50 and over in Agincourt sub-district, 2006 WHODASi quintile Variable

1 (high ability)

2

3

4

5 (low ability)

p-Value

Sex Male

328 (32.4)

184 (18.2)

165 (16.3)

160 (15.8)

175 (17.3)

Female

701 (22.8)

542 (17.6)

526 (17.1)

642 (20.9)

662 (21.5)

df 4 pB0.001

Age group (years) 50
59

398 (30.7)

264 (20.4)

220 (17)

256 (19.7)

159 (12.3)

60
69

364 (29.8)

238 (19.5)

210 (17.2)

217 (17.8)

192 (15.7)

70
79

198 (18.4)

177 (16.4)

188 (17.5)

233 (21.6)

281 (26.1)

69 (14.1)

47 (9.6)

73 (14.9)

96 (19.6)

205 (41.8)

545 (29.3) 484 (21.8)

369 (19.8) 357 (16.1)

323 (17.4) 368 (16.6)

343 (18.4) 459 (20.7)

282 (15.2) 555 (25.0)

df 4 pB0.001

df 8

80 and over

df12 pB0.001

Partnership In a partnership Currently single Education level No education

583 (22.4)

419 (16.1)

443 (17)

539 (20.7)

617 (23.7)

Less than 6 years Six years or more

214 (28.3) 206 (34.7)

149 (19.7) 130 (21.9)

127 (16.8) 99 (16.7)

147 (19.4) 89 (15)

120 (15.9) 70 (11.8)

pB0.001

168 (26.7) 181 (24)

98 (15.6) 139 (18.5)

105 (16.7) 129 (17.1)

127 (20.2) 153 (20.3)

131 (20.8) 151 (20.1)

df16 p0.218

Household asset score (quintiles) First (lowest) Second Third

184 (24)

157 (20.5)

123 (16.1)

136 (17.8)

166 (21.7)

Fourth

191 (22.7)

148 (17.6)

148 (17.6)

176 (20.9)

178 (21.2)

Fifth (highest)

281 (28.7)

166 (17)

170 (17.4)

179 (18.3)

182 (18.6)

940 (25.5)

662 (17.9)

631 (17.1)

720 (19.5)

741 (20.1)

89 (22.8)

64 (16.4)

60 (15.4)

82 (21)

96 (24.6)

Household with and without people aged less than 50 With under 50 Without under 50

df 4 p0.199

Nationality of origin South African

719 (24.2)

535 (18)

522 (17.6)

560 (18.8)

636 (21.4)

Mozambican

309 (27.8)

191 (17.2)

169 (15.2)

241 (21.7)

201 (18.1)

Occupational status in 2004 Working Not working

179 (35.6)

98 (19.5)

85 (16.9)

81 (16.1)

60 (11.9)

686 (23.4)

523 (17.9)

502 (17.1)

574 (19.6)

645 (22.0)

df 4 p0.005

df 4 pB0.001

a

WHODASi: Using the World Health Organization Disability Assessment Schedule II (WHODAS II) the variable scale was inverted and divided into quintiles.

Health and Socio-demographic Surveillance Site by measuring three main variables (scores) that flow from a more basic health status assessment (Health Status) through to more complex functioning of the person (WHODASi) and then to the person’s satisfaction with their life (WHOQoL). Women were 30% more likely than men to report a poor state of health (low Health Status). Other factors associated with a worse Health Status were aged above 70 years, lower levels of formal education, being single and currently not working. On the other hand, being of Mozambican origin is related to a better-reported Health

Status. As with the Health Status, women were more likely to report poorer functionality (WHODASi) than men. Age significantly affected functionality only from 70 years of age. People aged 80 and over had a threefold increase in risk of reporting poorer functionality. Progressively lower levels of education related to a gradual increase in functional problems. Being single or ‘not working at present’ were also associated with worse functionality. There was no gender difference in QoL. However, our analysis showed the following factors related to lower QoL: older age group, no formal education, being single and currently not working.

Citation: Global Health Action Supplement 2, 2010. DOI: 10.3402/gha.v3i0.2126

31

F. Xavier Go´mez-Olive´ et al.

Table 7. Factors associated with poor self-reported functioning (WHODASia) for 4,085 adults aged 50 and over in Agincourt sub-district, 2006

Variables

Univariate model OR (95% CI)

Multivariate model OR (95% CI)

Sex Male Female

1 1.49 (1.28, 1.73)

1 1.38 (1.14, 1.66)

Age group (years) 50
59 60
69 70
79 80

1 1.07 (0.90, 1.27) 1.94 (1.64, 2.29) 3.38 (2.73, 4.20)

1 1.00 (0.83, 1.21) 1.62 (1.32, 1.99) 2.92 (2.25, 3.78)

Education level No formal education Less than 6 years Six years or more

2.19 (1.80, 2.67) 1.49 (1.18, 1.88) 1

1.57 (1.26, 1.97) 1.33 (1.03, 1.72) 1

Marital status Single In current partnership

1.66 (1.46, 1.88) 1

1.25 (1.06, 1.46) 1

HH with and without people aged less than 50 With under 50 1 Not included in the final model Without under 50 1.28 (1.03, 1.57) Household asset score (quintiles) First quintile (lowest) 1.24 (1.03, 1.50) Second quintile Third quintile Fourth quintile Fifth quintile (highest) Nationality of origin South African Mozambican

Not included in the final model

1.11 (0.91, 1.35) 1.16 (0.95, 1.41) 1.19 (0.97, 1.46) 1 1

Not included in the final model

0.98 (0.85, 1.13)

Occupational status in 2004 Working 1 Not working 1.83 (1.48, 2.25)

1 1.33 (1.06, 1.66)

a

WHODASi: Using the World Health Organization Disability Assessment Schedule II (WHODAS II) the variable scale was inverted and divided into quintiles. ORs reflect odds for those in the two lowest quintiles of functionality.

Finally there was a gradient in the expected direction in the relationship between lower QoL and lower socioeconomic status measured by household asset score. Our data show that women report significantly poorer functionality for both Health Status and WHODASi, the two measures that include variables of functionality, although they do not report a lower QoL. There are several possible explanations for this. Women may objectively have poorer functionality but do not regard this as a problem, or women may be more active in the home than their retired partners and therefore more aware of a change in functionality, or women may be more aware of

32

their own health and therefore report health problems in a higher proportion than men. At present, the data are not available to explore this issue further. The oldest age group (people aged 70 and over) reported worst QoL and functioning. However, the age group 60
69 years presented no significant difference in Health Status and functioning measures compared with the 50
59 year age group. Moreover, they reported a significantly better QoL than the younger 50
59 age group. This may be related to the fact that women who retire at 60 and men at 65 are still in good health. In addition, they receive old-age grants (pensions) which allows them a better life with higher food security and, importantly, with greater capacity to help children in their households who then enjoy higher food security and better schooling (29). At older ages (70 and over), Health Status and functioning had deteriorated and they reported worse levels of both variables despite still receiving pension grant. The household asset score was created as a proxy for household socio-economic status. The asset data used in this study were collected in 2005, a year earlier than the study was conducted. Our data did not show any relation between this score and either the Health Status or the WHODASi. However, the household asset score is significantly related to the WHOQoL that measures satisfaction with one’s life. This could mean that people’s socio-economic status has no relation to being physically and socially functional, but impacts on how satisfied people are with their life and expectations (30). Unemployment among Agincourt’s adult population (including both permanent and temporary residents) is 36%, representing 29% of men and 46% of women (Collinson, personal communication). In our study sample, 85% of all respondents were ‘not currently working’, but only 5.7% were formally unemployed. There is a significant relationship between currently not working and Health Status, WHODASi and WHOQoL even after controlling for age group. Other work in the Agincourt study site has shown residents of Mozambican origin to be a vulnerable subgroup (24, 31). We thus expected Mozambican nationality to have a significant relationship with low Health Status, low WHODASi and low WHOQoL. However, no relationship with WHOQoL and WHODASi was found, and being Mozambican was associated with less likelihood of reporting a lower Health Status, meaning that those of Mozambican origin reported feeling in better health than their South African counterparts. This may be related to a healthy immigrant selectivity that may decrease over coming years (32). The Agincourt HDSS includes individuals living permanently in the area and those that spend more than 6 months per year outside the study area but remain linked to their rural households. Some permanent Citation: Global Health Action Supplement 2, 2010. DOI: 10.3402/gha.v3i0.2126

Cross-sectional survey of older people in rural South Africa

Table 8. WHOQoLa by demographic variables [n (%)] for 4,085 adults aged 50 and over in Agincourt sub-district, 2006 WHOQoL quintile Variable

1 (high)

2

3

4

5 (low)

p-Value

Sex Male

244 (24.2)

217 (21.5)

168 (16.6)

171 (16.9)

210 (20.8)

df4

Female

566 (18.4)

623 (20.3)

608 (19.8)

678 (22.1)

596 (19.4)

pB0.001

Age group (years) 50
59

269 (20.8)

274 (21.1)

246 (19.0)

261 (20.1)

246 (19.0)

df12

60
69

279 (22.9)

281 (23.0)

238 (19.5)

257 (21.0)

165 (13.5)

pB0.001

70
79

185 (17.2)

214 (19.9)

209 (19.4)

225 (20.9)

242 (22.5)

77 (15.7)

71 (14.5)

83 (16.9)

106 (21.6)

153 (31.2)

371 (19.9)

371 (19.9)

292 (15.7)

df4

405 (18.2)

478 (21.5)

514 (23.1)

pB0.001

80 and over Partnership In a partnership

432 (23.2)

Single

378 (17.0)

394 (21.2) 446 (20.1)

Education level No education

454 (17.5)

508 (19.5)

513 (19.7)

565 (21.7)

558 (21.5)

df8

Less than 6 years

169 (22.3)

163 (21.5)

131 (17.3)

164 (21.7)

129 (17.0)

pB0.001

Six years or more

157 (26.4)

151 (25.4)

102 (17.2)

91 (15.3)

93 (15.7)

Household asset score (quintiles) 94 (14.9)

128 (20.4)

117 (18.6)

135 (21.5)

155 (24.6)

df16

Second

First (lowest)

119 (15.8)

158 (20.1)

144 (19.1)

168 (22.3)

164 (21.8)

pB0.001

Third

162 (21.1)

155 (20.2)

141 (18.4)

177 (23.1)

131 (17.1)

Fourth

157 (18.7)

183 (21.8)

157 (18.7)

174 (20.7)

169 (20.1)

Fifth (highest)

269 (27.6)

200 (20.5)

187 (19.1)

165 (16.9)

155 (15.9)

Household with and without people aged less than 50 With under 50 Without under 50

735 (19.9)

772 (20.9)

708 (19.2)

768 (20.8)

710 (19.2)

78 (20.0)

68 (17.4)

68 (17.4)

81 (20.7)

96 (24.6)

df4 p0.099

Nationality of origin South African

624 (21)

617 (20.8)

559 (18.8)

587 (19.7)

585 (19.7)

df4

Mozambican

189 (17.0)

223 (20.1)

215 (19.4)

262 (23.6)

221 (19.9)

p0.014

Occupational status in 2004 Working

136 (27.0)

114 (22.7)

95 (18.9)

86 (17.1)

72 (14.3)

Not working

568 (19.4)

603 (20.6)

566 (19.3)

614 (21.0)

579 (19.8)

df4 pB0.001

a

WHOQoL: The World Health Organization Quality of Life score was calculated and then divided into quintiles.

residents work in the surrounding area making it difficult to find them at home. In this study, 76% of nonrespondents were not found at home for interview despite three visits to the household. Men participate in the labour force more than women, and the nonrespondents represented nearly 50% of all men and 30% of all women expected to participate in the study. Table 2 shows that non-respondents included twice the proportion of workers compared to respondents. Moreover, 69% of workers among the non-respondent group were aged between 50 and 59 years (data not shown). Those who out-migrate permanently from the study area

(around 3% of the total population per year) are not followed up and so it is not possible to measure their impact on the health status and functionality of the remaining population. Thus, the study may have underestimated the reported health of the population given that the results show the health status of those that live most of the year in the study area. This study presents the first population-based data from South Africa on Health Status, functionality and WHOQoL. Other studies have focused on specific diseases (33, 34) or on defining the best domains with which to evaluate QoL and Health Status (30).

Citation: Global Health Action Supplement 2, 2010. DOI: 10.3402/gha.v3i0.2126

33

F. Xavier Go´mez-Olive´ et al.

Table 9. Factors associated with poor quality of life (WHOQoL) a for 4,085 adults aged 50 and over in Agincourt sub-district, 2006

Variables

Univariate model OR Multivariate model OR (95% CI) (95% CI)

Sex Male Female Age group (years) 50
59 60
69 70
79 80 Education level No formal education Less than 6 years Six years or more Marital status Now single In current partnership

1

Not included in the final model

1.17 (1.01, 1.35) 1 0.82 (0.70, 0.97) 1.19 (1.01, 1.37) 1.74 (1.41, 2.15)

1 0.73 (0.61, 0.89) 0.91 (0.75, 1.11) 1.35 (1.06, 1.74)

1.69 (1.40, 2.05)

1.39 (1.11, 1.73)

1.41 (1.12, 1.77)

1.33 (1.04, 1.70)

1

1

1.46 (1.28, 1.65) 1

1

1.28 (1.10, 1.49) 1

1.52 1.49 1.29 1.34

(1.19, (1.19, (1.04, (1.09, 1

1.94) 1.86) 1.61) 1.66)

Not included in the final model

1.18 (1.03, 1.36)

Occupational status in 2004 Working 1 Not working 1.50 (1.22, 1.83)

1 1.32 (1.07, 1.64)

a

WHOQoL: The World Health Organization Quality of Life score was calculated and then divided into quintiles. A better quality of life was defined as those included in the three lowest quintiles; while a worse quality of life was defined as those included in the two highest quintiles.

Measuring health status, functionality and QoL at the population level in older people is important to understand the health, welfare and social support needs of this growing proportion of the population. As the Agincourt population continues to age, along with millions living in similar rural settings, it will become increasingly important for health and social services to adapt and improve in order to provide effective care for a growing older population

34

We thank the study participants, field team and local authorities. Special thanks to Dr. Oscar Franco (Warwick University, UK) and to Ms. Marguerite Schneider (Human Sciences Research Council, RSA) for providing useful comments for the improvement of the manuscript. This study was funded by the National Institute on Aging of the National Institutes of Health, USA and by the Wellcome Trust, UK (Grant Nos. 058893/Z/99/A and 069683/Z/02/ Z). It was carried out in collaboration with the World Health Organization.

The authors have not received any funding or benefits from industry to conduct this study.

References

Household asset score (quintiles) First (lowest) 1.76 (1.43, 2.16) Second 1.62 (1.33, 1.97) Third 1.38 (1.13, 1.68) Fourth 1.41 (1.17, 1.71) Fifth (highest) 1

Mozambican

Acknowledgements

Conflict of interest and funding

Household with and without people aged less than 50 With under 50 1 Not included in the final model Without under 1.24 (1.01, 1.53) 50

Nationality of origin South African

with significantly impaired functionality and other health problems. We plan to continue to monitor the health and well-being of older people. This will provide information on how societal changes are affecting their health and wellbeing, assist policy makers to predict demand for health services, and inform the development of appropriate and cost-effective health and social services.

1. United Nations Department of Economics and Social Affairs. Population division. World population prospects: the 2008 revision. New York: United Nations; 2009. 2. Murray CJ, Lopez AD. Alternative projections of mortality and disability by cause 1990
2020: global burden of disease study. Lancet 1997; 349: 1498
504. 3. Velkoff VA, Kowal PR. US Census Bureau, current population reports, P95/07-1. Population aging in Sub-Saharan Africa: demographic dimensions 2006. Washington, DC: U.S. Government Printing Office; 2007. 4. Min SK, Kim KI, Lee CI, Jung YC, Suh SY, Kim DK. Development of the Korean versions of WHO Quality of Life scale and WHOQoL-BREF. Qual Life Res 2002; 11: 593
600. 5. Strong K, Mathers C, Leeder S, Beaglehole R. Preventing chronic diseases: how many lives can we save? Lancet 2005; 366: 1578
82. 6. World Health Organization. Preventing chronic diseases
a vital investment. Geneva: WHO; 2005. 7. Nguyen N, Holodniy M. HIV infection in the elderly. Clin Interv Aging 2008; 3: 453
72. 8. Quinn TC. HIV epidemiology and the effects of antiviral therapy on long-term consequences. AIDS 2008; 22: S7
12. 9. Wittenberg M, Collinson MA. Household transitions in rural South Africa, 1996
2003. Scand J Public Health Suppl 2007; 69: 130
7. 10. Schatz EJ. ‘‘Taking care of my own blood’’: older women’s relationships to their households in rural South Africa. Scand J Public Health Suppl 2007; 69: 147
54. 11. Statistics South Africa. Mid-year population estimates, South Africa: 2006. Available from: http://www.statssa.gov.za [cited 27 May 2010]. 12. Statistics South Africa. Mid-year population estimates, South Africa: 2009. Available from: http://www.statssa.gov.za [cited 27 May 2010]. 13. Collinson MA, Tollman SM, Kahn K. Migration, settlement change and health in post-apartheid South Africa: triangulating Citation: Global Health Action Supplement 2, 2010. DOI: 10.3402/gha.v3i0.2126

Cross-sectional survey of older people in rural South Africa

14.

15.

16.

17.

18.

19.

20.

21.

22.

23. 24.

health and demographic surveillance with national census data. Scand J Public Health Suppl 2007; 69: 77
84. Kahn K, Garenne ML, Collinson MA, Tollman SM. Mortality trends in a new South Africa: hard to make a fresh start. Scand J Public Health Suppl 2007; 69: 26
34. Zwang J, Garenne M, Kahn K, Collinson M, Tollman SM. Trends in mortality from pulmonary tuberculosis and HIV/ AIDS co-infection in rural South Africa (Agincourt). Trans R Soc Trop Med Hyg 2007; 101: 893
8. Tollman SM, Kahn K, Sartorius B, Collinson MA, Clark SJ, Garenne ML. Implications of mortality transition for primary health care in rural South Africa: a population-based surveillance study. Lancet 2008; 372: 893
901. Menken J, Cohen B, Mann AS, Ezeh A, Kaseke E, KuateDefo B, et al. Aging in Sub-Saharan Africa: recommendations for furthering research. In: Cohen B, Menken J, eds. Aging in Sub-Saharan Africa: recommendations for furthering research. Panel on Policy Research and Data Needs to Meet the Challenge of Aging in Africa. Washington, DC: The National Academies Press; 2006, pp. 9
51. Lam D, Murray L, Vimal R. Labor force withdrawal of the elderly in South Africa. In: Cohen B, Menken J, eds. Aging in Sub-Saharan Africa: recommendations for furthering research. Panel on Policy Research and Data Needs to Meet the Challenge of Aging in Africa. Washington, DC: The National Academies Press; 2006 pp. 214
49. Samson M MacQuene K van Niekerk I. Inter-regional inequality facility. Policy brief 1. Social grants. South Africa; 2006. Available from: http://www.odi.org.uk/resources/download/ 1067.pdf [cited 30 May 2010]. Department of Social Development (The Government of South Africa). Old age pensions. Available from: http://www.capegate way.gov.za/eng/directories/services/11586/47491 [cited 30 May 2010]. Collinson MA, Mokoena O, Mgiba N, Kahn K, Tollman SM, Garenne M, et al. Chapter 16: Agincourt Demographic Surveillance System (Agincourt DSS). In: Sankoh O, Kahn K, Mwageni E, eds. Population, health and survival at INDEPTH sites. Ottawa: IDRC; 2002. Kahn K, Tollman SM, Collinson MA, Clark SJ, Twine R, Clark BD, et al. Research into health, population and social transitions in rural South Africa: data and methods of the Agincourt Health and Demographic Surveillance System. Scand J Public Health 2007; 35: 8
20. WHO. Available from: http://www.who.int/healthinfo/systems/ sage/en/index1.html [cited 7 September 2009]. Hargreaves JR, Collinson MA, Kahn K, Clark SJ, Tollman SM. Childhood mortality among former Mozambican refugees and

25.

26. 27.

28.

29.

30.

31.

32.

33.

34.

their hosts in rural South Africa. Int J Epidemiol 2004; 33: 1271
8. Cieza A, Bickenbach J, Chatterji S. The ICF as a conceptual platform to specify and discuss health and health-related concepts. Gesundheitswesen 2008; 70: e47
56. Reeve B. Applying item response theory to enhance health outcomes assessment. Qual Life Res 2007; 16: 1
3. WHOQOL Group. Study protocol for the World Health Organization project to develop a Quality of Life assessment instrument (WHOQoL). Qual Life Res 1993; 2: 153
9. WHOQOL Group. The World Health Organization Quality of Life assessment (WHOQoL): position paper from the World Health Organization. Soc Sci Med 1995; 41: 1403
9. Case A, Menendez A. Does money empower the elderly? Evidence from the Agincourt demographic surveillance site, South Africa. Scand J Public Health Suppl 2007; 69: 157
64. Jelsma J, Mkoka S, Amosun SL. Health-related quality of life (HRQoL) domains most valued by urban IsiXhosa-speaking people. Qual Life Res 2008; 17: 347
55. Kahn K, Collinson M, Hargreaves J, Clark S, Tollman S. Socioeconomic status and child mortality in a rural sub-district of South Africa. In: de Savigny D, Debpuur C, Mwageni E, Nathan R, Razzaque A, Setel P, eds. Measuring health equity in small areas-findings from Demographic Surveillance Sites. Farnham, Surrey: Ashgate Publishing; 2005, pp. 67
86. Jasso G, Massey DS, Rosenzweig MR, Smith JP. Immigrant Health Selectivity and Acculturation. In: Anderson NB, Bulatao RA, Cohen B, eds. Critical perspectives on racial and ethnic differences in health in late life. Washington, DC: National Academy Press; 2004, pp. 227
66. Phaladze NA, Human S, Dlamini SB, Hulela EB, Hadebe IM, Sukati NA, et al. Quality of life and the concept of ‘‘living well’’ with HIV/AIDS in sub-Saharan Africa. J Nurs Scholar 2005; 37: 120
6. Westaway MS, Rheeder P, Gumede T. The effect of type 2 diabetes mellitus on health-related quality of life (HRQoL). Curationis 2001; 24: 74
8.

*F. Xavier Go´mez-Olive´ MRC/Wits Rural Public Health and Health Transitions Research Unit (Agincourt) School of Public Health Faculty of Health Sciences University of the Witwatersrand 7 York Road, Parktown 2193 Johannesburg, South Africa Email: [email protected]

Citation: Global Health Action Supplement 2, 2010. DOI: 10.3402/gha.v3i0.2126

35

INDEPTH WHO-SAGE Supplement æ

Health status and quality of life among older adults in rural Tanzania Mathew A. Mwanyangala1,2*, Charles Mayombana1,2,3, Honorathy Urassa1,2, Jensen Charles1,2, Chrizostom Mahutanga1,2, Salim Abdullah1,2,3 and Rose Nathan1,2,3 1

Ifakara Site Health Institute, Ifakara, Morogoro, Tanzania; 2INDEPTH Network, Accra, Ghana; Mikocheni Office, Ifakara Health Institute, Tanzania

3

Background: Increasingly, human populations throughout the world are living longer and this trend is developing in sub-Saharan Africa. In developing African countries such as Tanzania, this demographic phenomenon is taking place against a background of poverty and poor health conditions. There has been limited research on how this process of ageing impacts upon the health of older people within such lowincome settings. Objective: The objective of this study is to describe the impacts of ageing on the health status, quality of life and well-being of older people in a rural population of Tanzania. Design: A short version of the WHO Survey on Adult Health and Global Ageing questionnaire was used to collect information on the health status, quality of life and well-being of older adults living in Ifakara Health and Demographic Surveillance System, Tanzania, during early 2007. Questionnaires were administered through this framework to 8,206 people aged 50 and over. Results: Among people aged 50 and over, having good quality of life and health status was significantly associated with being male, married and not being among the oldest old. Functional ability assessment was associated with age, with people reporting more difficulty in performing routine activities as age increased, particularly among women. Reports of good quality of life and well-being decreased with increasing age. Women were significantly more likely to report poor quality of life (odds ratio 1.31; p B0.001, 95% CI 1.15
1.50). Conclusions: Older people within this rural Tanzanian setting reported that the ageing process had significant impacts on their health status, quality of life and physical ability. Poor quality of life and well-being, and poor health status in older people were significantly associated with marital status, sex, age and level of education. The process of ageing in this setting is challenging and raises public health concerns. Keywords: health status; quality of life; older people; ageing; Health and Demographic Surveillance System; INDEPTH WHO-SAGE

Access the supplementary material to this article: INDEPTH WHO-SAGE questionnaire (including variants of vignettes), a data dictionary and a password-protected dataset (see Supplementary files under Reading Tools online). To obtain a password for the dataset, please send a request with ‘SAGE data’ as its subject, detailing how you propose to use the data, to [email protected]

Received: 20 November 2009; Revised: 29 March 2010; Accepted: 8 July 2010; Published: 27 September 2010

uman populations throughout the world are living longer than ever before
but this is a relatively new phenomenon in developing countries. It is estimated that nearly 63% of the population aged 60 and over are living in developing countries, and further projected that by 2050 nearly 1.5 billion older

H

people will reside in developing countries (1). The number of older people is growing rapidly in sub-Saharan Africa (2). Changes in the ageing process within developing countries have been observed through shifts in population age composition. This process is associated with rapid declines in fertility and mortality (3). In the

Global Health Action 2010. # 2010 Mathew A. Mwanyangala et al. This is an Open Access article distributed under the terms of the Creative Commons 36 Attribution-Noncommercial 3.0 Unported License (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. Citation: Global Health Action Supplement 2, 2010. DOI: 10.3402/gha.v3i0.2142

Health status and quality of life among adults in Tanzania

near future, larger older populations will become ubiquitous in Africa (1, 4, 5). Tanzania has a total population of 34 million of whom 4% are aged 50 and over. It is also among the countries in sub-Saharan Africa with at least 1 million older people, and this proportion is projected to rise to 10% of the total population by 2050 (6, 7). Furthermore, the absolute number of people entering the older cohort is increasing (7). In developing African countries such as Tanzania, many older people reach retirement age after a lifetime of poverty and deprivation, poor access to health care and poor diet. This situation can leave them with insufficient personal savings as a consequence of a fragile earning history (8, 9). In most developing countries, formal social security systems have only limited coverage and inadequate benefit payments (10, 11). As a result, the majority of older people depend on family support networks, a reality that is well appreciated in most parts of sub-Saharan Africa (12
14). Furthermore, it is recognised that traditional social security systems are evolving, attenuating and rapidly disappearing due to pressures from urbanisation, industrialisation and HIV/AIDS (15). At the same time it is widely reported that older people have more substantial inter-individual variability in health related to age than do younger people (16, 17). The health care system spends a small fraction of the budget on treating older adult illness and access to care is limited and not a policy priority in most developing countries (6, 18
20). Within developing countries the demographic transition towards older populations is likely to constrain future health care systems. The attitude of health care providers towards older people makes their situation even more difficult. It has been reported that older people in Tanzania are frequently mistreated by health care providers when they seek care (21). Although provision of free health services to older people is stipulated in the Tanzanian National Ageing policy,

many older people still do not access these services due to inability to prove their age, aggravated by the limited availability of health services, equipment and expertise (6). The economies of rural Tanzanian settings are predominantly supported by subsistence agriculture, which provides little or no pension coverage and limited health care services. The age structure of these settings is already being impacted by the emigration of younger people to urban areas and the return of older people to rural environments from urban areas on retirement. Current health challenges and existing policies act to hide the situation of older people. A large body of research has described the process of ageing using contrasting perspectives: demographic characteristics, physical health, cognitive impairment, disability and self-perceived health of older people in developed countries (22
24). In the developing world, studies of population ageing have been focused primarily on Asia and Latin America. In Tanzania there has been limited research on explaining process of societal ageing and impact on the health of older people, especially in rural settings where people are most beset by poverty and poor health conditions. This study aims to describe the impact of ageing on the health status and well-being of older people in a rural Tanzanian population using data collected by the Ifakara Health Institute’s Health and Demographic Surveillance System (HDSS) in collaboration with the INDEPTH Network and the WHO Survey on Adult Health and Global Ageing (SAGE). Our aim was to provide a better understanding of the health and well-being of older people in developing countries. The resulting information will provide a baseline for examining the relationship between ageing and other health outcomes during demographic transition in these settings. This will help to raise awareness about the predicament of older people, support possible policy interventions and stimulate further research.

Fig. 1. Maps of Africa, Tanzania and the Ifakara HDSS area. Citation: Global Health Action Supplement 2, 2010. DOI: 10.3402/gha.v3i0.2142

37

Mathew A. Mwanyangala et al.

Design Geography of the HDSS area The Ifakara HDSS area is located in southern Tanzania in parts of the Kilombero and Ulanga districts, both in the Morogoro region (latitude 8.08
8.68 S and longitude 35.98
36.68 E), as shown in Fig. 1. The Ifakara DSS covers an area of 2,400 km2 in the Kilombero Valley. The HDSS site was initiated in September 1996. A baseline census was conducted between September and December 1996 in 25 villages covering a population of about 93,000 people living in 19,000 households. Since January 1997, each household has been visited once every 4 months to record births and pregnancies, deaths and migrations. In order to document community-based causes of death, the HDSS has conducted verbal autopsies since 2002. The area is predominately rural with scattered households. Many local houses have brick walls but only 34% have a corrugated iron roof. The main ethnic groups are Wapogoro, Wandamba, Wabena, Wahehe and Wambunga, with several other smaller groups. Most of the inhabitants are Christian or Muslim. All residents speak the Kiswahili language. Subsistence farming of maize, rice and cassava occupies the majority of the population. Fishing is also common both for local consumption and shipping to other towns within the country. Data collection In January 2007, all households with people aged 50 and over were identified from the Ifakara HDSS database. These households were subsequently visited to interview these older people. The questionnaires and the consent forms were translated to Kiswahili. All field workers were trained for 3 days prior to conducting the interviews, including 1 day of tool piloting. Surveys started in the middle of January 2007 and ended in April 2007. During field work, interviewers were closely supervised by field supervisors who accompanied them on interviews, performed spot-checks and re-interviewed where appropriate. Also, desk checks on the completed questionnaires were done to identify errors before computer data entry. All questionnaires that raised queries were returned to interviewers for clarification in the field. Data entry was conducted using a double entry system in CSPro. Verbal informed consent was obtained from all older people who participated in this study. All individuals were interviewed using the WHO-abbreviated survey instrument short module adapted from the full SAGE questionnaire: the health status and associated vignette questions plus Activities of Daily Living (ADL)-type questions (following the WHO Disability Assessment Scale version II [WHODAS-II] model), and questions on subjective well-being as measured by the 8-item version of the World Health Organization Quality of Life (WHOQoL)

38

instrument. A copy of the INDEPTH WHO
SAGE summary questionnaire is available as a supplementary file. Additional data targeted for inclusion into the final data set, derived directly from the HDSS, included sociodemographic characteristics, such as age, sex, education, marital status, socio-economic status and household information, such as the household size.

Health status information Health status scores were calculated based on health responses in eight health domains covering affect, cognition, interpersonal activities and relationships, mobility, pain, self-care, sleep/energy and vision. Each domain included at least two questions. Asking more than one question about difficulties in a given domain provides more robust assessments of individual health levels and reduces measurement error for any single response item. Item Response Theory (IRT) was used to score the responses to the health questions using a partial credit model which served to generate a composite health status score (25, 26). An item calibration was obtained for each item. In order to determine how well each item contributed to common global health measurement, chisquared fit statistics were calculated. The calibration for each of the health items was taken into account and the raw scores were transformed through Rasch modelling into a continuous cardinal scale where a score of zero represents worst health and a maximum score of 100 represents best health. More details on the application of the IRT approach to computing patient-reported health outcomes are described in Chang and Reeve, and Kyobungi (27
31). The IRT has been judged as among the most efficient, reliable and valid methods to evaluate measures of health (32
37). Quality of life and well-being In this study we define quality of life as individual perceptions of life in the context of local culture and value systems, as well as in relation to goals, expectations, standards and concerns. An 8-item version of the WHOQoL instrument was used to assess perceived well-being (38). This is a cross-culturally valid instrument for comprehensively assessing overall subjective wellbeing, yet is also very brief. It recognises that health and quality of life are strongly associated yet distinct concepts. Results from the 8-items were summed to get an overall WHOQoL score which was then transformed to a 0
100 scale, similar to the health status score. The WHOQoL instruments have been used in other studies of older people in Africa (39, 40). Functional status assessment Personal functioning was assessed through the standardised 12-item WHODAS-II. It is a well-tested instrument, with published psychometric properties, and a good Citation: Global Health Action Supplement 2, 2010. DOI: 10.3402/gha.v3i0.2142

Health status and quality of life among adults in Tanzania

predictor of global disability (41
43). The WHODAS is compatible with the International Classification of Functioning, Disability and Health (ICF) and contains many of the most commonly asked ADL and Instrumental Activities of Daily Living (IADL) questions. The WHODAS instrument also provides an assessment of severity of disability. Results from the 12-items were summed to get an overall WHODAS score, which was then transformed to a 0
100 scale, with zero representing no disability. Since this scale runs counterintuitively to the WHOQoL and health status scores, it was inverted to a scale designated here as WHODASi, in which 100 represents the best situation, i.e. no disability, and which thus represents a measure of functional ability.

Table 1. Background characteristics of study subjects Variables

Respondents Non-respondentsa (n 5,131)

(n3,075)

Sex (%) Men

47.8

52

Women

52.2

48

Mean age (years) (SD)

62.6 (9.2)

61.3 (7.8)

Age group (years) 50
59

43.7

48.5

60
69 70
79

32.8 18.2

33.2 17.9

5.3

0.3

39.3 56.6

41.4 45.2

4.1

13.3

80 and over Education level (%)

Socio-economic status of households The socio-economic status of households was assessed by constructing a household wealth index based on household asset ownership, level of education of the head of household and household characteristics, as proposed and validated by Filmer (44). Data on asset ownership were collected within the HDSS framework. Data analysis Data were analysed using Stata version 10. Simple crosstabulations and multivariate analysis were done to describe the situation of ageing, health status, physical disability, quality of life and well-being of older people. The median values for health status, WHOQoL and WHODASi were computed, and used to define cut-off points for assessing good or poor status. Mean scores were calculated for different sex and age groups. In order to investigate the factors associated with health and quality of life, univariate and multivariate models were run. In both models, social and demographic variables were fitted as possible explanatory variables. Principal component analysis (PCA) was conducted on household characteristics and asset ownership data to investigate associations between these variables at the household level. Wealth index quartiles were constructed to investigate associations between health status and household wealth. Results A total of 8,206 older people from 3,914 households were identified from the Ifakara DHSS. In visits, 63% were successfully interviewed (n5,131). The majority of nonresponders were men (52%) in the 50
59 age group. The reasons for non-response included hearing impairment, out-migration, refusal, death and absence during the day of the interview. Characteristics of responders and nonresponders are shown in Table 1. Among those interviewed, the majority were women (n 2,668). The mean age of respondents was 62.5 years Citation: Global Health Action Supplement 2, 2010. DOI: 10.3402/gha.v3i0.2142

No formal education Less than or equal to six years More than six years Marital status (%) Currently single

33.3

29.0

In current partnership

66.7

71.0

Socio-economic quartile (%) Lowest quartile

19.2

19.6

Second quartile

19.4

23.7

Third quartile

21.1

19.9

Highest quartile

40.3

36.7

Mean no. of household

10.4 (6.0)

members (sd) Percentage of household

22.9

members aged 50 years and over a

Includes those listed in the HDSS database who had outmigrated or died prior to interview visit, and those who did not respond for other reasons.

with a standard deviation of 9.2. The majority of people in this study were within the 50
59 age group, and 67% of the respondents were married, while 39% of respondents had no formal education. In the majority of households (54%), less than 25% of household members were 50 years old or above. The mean size of households where older people lived was 10.4 (standard deviation 6.0). Only 2% of households were composed solely of older people living on their own.

Functional status assessment and quality of life The mean and median quality of life scores (WHOQoL) were 68.2 and 68.8, respectively, with the proportion below the median decreasing with increasing age (Table 2). The mean and median functional ability scores (WHODASi)

39

Mathew A. Mwanyangala et al.

Table 2. Distribution of quality of life (WHOQoL) and functional ability (WHODASi) outcomes by age and sex Variables

Men (n2,463)

Women (n 2,668)

Mean WHOQoL score (SD) 50
59 years

69.3 (5.6)

68.8 (6.6)

60
69 years

68.4 (5.9)

67.6 (6.9)

70
79 years 80 years and over

67.0 (7.3) 64.3 (7.1)

67.2 (9.4) 66.1 (11.7)

Percentage of respondents with WHOQoL less than median 50
59 years 60
69 years

28.8 39.1

37.0 50.3

70
79 years

52.8

59.7

80 years and over

67.9

71.2

Mean WHODASi score (SD) 50
59 years 90.4 (13.4)

Discussion 87.5 (14.4)

60
69 years

87.1 (14.9)

82.2 (16.2)

70
79 years

80.5 (18.1)

74.0 (21.3)

80 years and over

68.4 (22.1)

59.0 (24.9)

Percentage of respondents with WHODASi less than median 50
59 years

35.0

43.9

60
69 years

45.2

61.2

70
79 years

62.0

73.5

80 years and over

82.1

86.5

were 84 and 90, respectively. Functional ability was lower among women than men in all age groups.

Distributions of health status The median health status score of the surveyed population was 68.4. Health status was associated with age and gender (Table 3). Poor health status was associated with increasing age and among women.

Table 3. Distribution of self-reported health status outcomes by age and sex Variables

Men (n2,463)

Women (n2,668)

Mean health status score (SD) 50
59 years

74.5 (13.0)

72.1(12.1)

60
69 years

71.5 (12.2)

68.4 (10.3)

70
79 years

67.1 (11.2)

64.5 (11.0)

80 years and over

61.3 (10.2)

58.5 (9.2)

Percentage of respondents with health status less than median 50
59 years

34.8

41.3

60
69 years

43.8

54.2

70
79 years

60.0

66.8

80 years and over

82.7

84.7

40

Factors associated with poor quality of life and health status Odds ratios for below-median quality of life and health status showed significant associations with being female, older and unmarried (Tables 4 and 5). Women were more likely to report poor health as well as being scored for lower quality of life than men. Lower quality of life was also significantly associated with the two lower socioeconomic quartiles. However, no association between socio-economic status and self-reported health was evident in multivariate analysis controlling for other factors (Table 5). Age composition within households and education were not appreciably associated with either quality of life or health status in multivariate analyses.

This study observed that among older adults men reported better health status than women, and that health status, quality of life and physical ability deteriorated markedly with increasing age. This is in line with empirical knowledge of the physiological processes of ageing and linked to disease and ill health. These results underscore the reality of existing gender biases in relation to economic power, which may be the product of lower levels of education and savings, and the poorer life-time earning histories many women have (45). The results are consistent with those reported recently by the Tanzanian Ministry of Health and Social Welfare, which found that older people make up around one-third of all disabled people in Tanzania (46). Higher quality of life and good health status was associated with being married, a high level of education and higher socio-economic status of the household. This reinforces the hypothesis that individual health is improved by education, possibly due to having greater access to information on health, better eating habits and self-care (47, 48). These results reveal sex differences in longevity, with larger numbers of women than men aged 50 and over, despite their poorer health outcomes. The mean household size of 10 observed for households containing older people in this study area is broadly reflective of socio-cultural practices in rural areas of most countries in sub-Saharan Africa, where older people tend to live in extended family households rather than independently (49). This is reflective of the current Tanzania Ageing policy which prioritises family as the basic institution of care and support for older people (50). Few studies have been conducted on adult health and ageing in Tanzania. The approach of assessing individual health status based on self-reported health status has been criticised by various scholars, and it has been suggested that self-reported health status should not be used to estimate disease prevalence and identify individuals with disease (47, 51). Thus, although the current Citation: Global Health Action Supplement 2, 2010. DOI: 10.3402/gha.v3i0.2142

Health status and quality of life among adults in Tanzania

Table 4. Factors associated with below-median quality of life (WHOQoL) Variables

Univariate model (OR and 95% CI)

p-value

Multivariate model (OR and 95% CI)

p-value

pB0.001

1.27 (1.11
1.45)

pB0.001

Sex Men

1

Women

1.37 (1.22
1.53)

Age group (years) 50
59

1

60
69

1.63 (1.43
1.86)

pB0.001

1 1.57 (1.38
1.80)

pB0.001

70
79

2.60 (2.22
3.04)

pB0.001

2.37 (2.01
2.80)

pB0.001

80

4.52 (3.44
5.92)

pB0.001

4.33 (3.26
5.75)

pB0.001

Education level No formal education

1.63 (1.22
2.19)

p0.001

1.17 (0.86
1.60)

p0.315

Less than or equal to six years

1.46 (1.30
1.64)

pB0.001

1.03 (0.76
1.39)

p0.845

More than six years

1

1.19 (1.04
1.37)

p0.010

Marital status Now single

1.62 (1.44
1.82)

In current partnership

1

pB0.001

10

Proportion aged 50 years and over in the same household (%) B25

0.79 (0.63
0.98)

p0.035

0.92 (0.69
1.23)

p0.575

25
49

0.80 (0.63
1.00)

p0.049

0.96 (0.75
1.23)

p0.749

50
74 575

0.86 (0.65
1.13) 1

p0.272

1.05 (0.83
1.33) 1

p0.697

0.71 (0.61
0.82) 0.61 (0.52
0.71)

pB0.001 pB0.001

0.71 (0.69
0.99) 0.62 (0.63
0.87)

p0.042 pB0.001

Third quartile

0.81 (0.70
0.94)

p0.006

0.75 (0.75
1.03)

p0.118

Highest quartile

1

Socio-economic quartile Lowest quartile Second quartile

study indicates a clear association between older people’s perception of age and health, further medically based studies are required to confirm the health burden of older people in rural Tanzania. Following up this sample over time would be useful to see how these data relate to subsequent health outcomes. Several studies have shown socio-economic status to be associated with older people’s health status, quality of life and well-being (52
54). However, the current study also detected an association between household socioeconomic status and quality of life, but not between wealth and self-reported health description. Similar observations have been documented elsewhere (55), and may be due to the fact that household asset-based wealth indices can be unrelated to individual health status, depending on which member of the household is head and who owns assets (56). Although Tanzania is the second country in Africa to have a national Ageing policy, after Mauritius, many issues related to older people are not yet fully defined. For example, even in the National Strategy for Poverty Citation: Global Health Action Supplement 2, 2010. DOI: 10.3402/gha.v3i0.2142

1

Reduction (57), older people are not fully considered. Older people are widely recognised as being a valuable source of information, knowledge and experience. Thus, attempts should be made to consider and improve their health status and quality of life within this and other rural settings in Tanzania and other developing countries.

Conclusion The health status and quality of life of older people in rural Tanzania is reduced significantly during the ageing process. Perceptions of physical disability also increase with age in this population. Poor quality of life and well-being, and health status in older people are significantly related to marital status, sex and age. Specifically, quality of life decreases with age, and women experience poorer quality of life and a greater burden of physical disability than men. Thus, the process of ageing presents a clear public health challenge in this setting.

41

Mathew A. Mwanyangala et al.

Table 5. Factors associated with below-median health status responses Variables

Univariate model (OR and 95% CI)

p-value

Multivariate model (OR and 95% CI)

p-value

Sex Men

1

Women

1.28 (1.14
1.44)

1 pB0.001

1.33 (1.15
1.52)

pB0.001

Age group (years) 50
59

1

60
69

1.56 (1.36
1.180)

pB0.001

1 1.57 (1.36
1.81)

70
79

2.98 (2.53
3.49)

pB0.001

2.96 (2.50
3.51)

pB0.001

80

8.95 (6.71
11.96)

pB0.001

8.96 (6.64
12.09)

pB0.001

pB0.001

Education level No formal education

1.74 (1.27
2.40)

p0.001

1.24 (0.88
1.74)

p0.27

Less than or equal to 6 years

1.32 (0.97
1.82)

p0.082

1.25 (0.90
1.74)

p0.180

More than 6 years

1

1

Marital status Now single

1.57 (1.39
1.77)

In current partnership

1

pB0.001

1.16 (1.00
1.33)

p0.045

1

Proportion aged 50 years and over in the same household (%) B25

0.94 (0.74
1.20)

p0.633

1.21 (0.93
1.58)

25
49

1.06 (0.82
1.37)

p0.644

1.21 (0.92
1.59)

p0.147 p0.162

50
74 575%

1.11 (0.79
1.56) 1

p0.558

1.17(0.82
1.68) 1

p0.384

1.13 (0.95
1.34) 0.89 (0.75
1.60)

p0.176 p0.206

0.92 (0.78
1.08)

p0.296

Socio-economic quartile Lowest quartile Second quartile

0.92 (0.78
1.08) 0.72 (0.62
0.85)

p0.293 pB0.001

Third quartile

0.84 (0.72
0.97)

p0.022

Highest quartile

1

Acknowledgements We would like to thank the Kilombero and Ulanga district councils for their support to the Ifakara HDSS. We extend our gratitude to the leadership of Mlabani village for allowing us to pilot test the survey tools. We highly appreciate the hard work and commitment of the HDSS field and data management teams. We are indebted to the respondents who voluntarily offered their time for interviews and shared the useful information without which the survey would not have been possible. We are thankful to the INDEPTH Network and WHO Survey on Adult Health and Global Ageing (SAGE).

Conflict of interest and funding Funding support for the HDSS was provided by the Swiss Development Corporation, Norvatis Foundation, USAID and the Tanzanian Ministry of Health and Social Welfare, which is highly appreciated.

References 1. UN. World population prospects: the 2000 revision. New York: UN; 2001. 2. Mba CJ. Nigeria’s ageing population: a call for attention. J Int Instit Ageing 2001; 12: 15
24.

42

1

3. Mbamaonyeukwu CJ. Africa’s ageing populations. J Int Instit Ageing 2001; 11: 2
7. 4. Mba C. Determinants of living arrangements of Lesotho’s older persons female population. J Int Women’s Stud 2002; 3: 1
22. 5. United Nations, Population Division, Affairs. DoEaS. Population ageing. New York: United Nations, Population Division, Affairs; 1999. 6. URT. The Ministry of labour, youth Development and Sports National Aging Policy. In: Ministry of labour, Youth Development and Sports, eds. United Republic of Tanzania Population and Housing cenus. Dar es Salaam: United Republic of Tanzania; 2003, pp. 3
7. 7. URT. Population and housing census. Dar es Salaam: United Republic of Tanzania, National Bureau of Statistics; 2002. 8. Charton KE, Rose D. Nutrition among older adults in Africa: the situation at the beginning of the millennium. J Nutri 2001; 131: 245
85. 9. Kimokoti RW, Hamer DH. Nutrition, health, and aging in sub Saharan Africa. J Nutrition 2008 2008; 66: 611
23. 10. Bailey C. Governance of social security schemes: social security documentation. International Social Security Association 2000; 21: 71
113. 11. Colin G, Turner J, Bailey C, Latulippe D. Social security pensions: development and reform. Geneva: International Labour Office; 2000. Citation: Global Health Action Supplement 2, 2010. DOI: 10.3402/gha.v3i0.2142

Health status and quality of life among adults in Tanzania

12. Van de Walle E. African households: censuses and surveys. London: Word Bank; 2006. 13. Kaseke E. An overview of formal and informal social security systems in Africa. USA and Johannesburg, South Africa: National Academy of Sciences; 2004. 14. Deaton A, Paxson CH. Patterns of aging in Thailand and Coˆ te d’Ivoire. In: Wise DA, ed. Topics in the economics of aging. Chicago, IL: University of Chicago Press; 1992, pp. 163
206. 15. Tostensen A. Towards feasible social security systems in subSaharan Africa. In: Grimm M, ed. Bergen: World Bank; 2004, p. 14. 16. Fialova D, Onder G. Medication errors in elderly people: contributing factors and future perspectives. Br J Clin Pharmacol 2009; 67: 641
5. 17. Bedard M, Gibbons C, Dubois S. The needs of rural and urban young, middle-aged and older adults with a serious mental illness. Can J Rural Med 2007; 12: 167
75. 18. Feachem RGA, Kjellstrom T, Murray CJL, Over M, Phillips MA. The health of adults in the developing world. New York: Oxford University Press; 1992. 19. Poullier JP, Hernandez P, Kawabata K. Health systems performance assessment: debates, methods, and empiricism. In: Evans ICJLMaDR, ed. National health accounts: concepts, data sources, and methodology. Geneva: World Health Organization; 2003, pp. 185
93. 20. Tollman S, Doherty J, Mulligan JA. General primary care. In: Jamison DT, Jamison JG, Measham AR, Alleyne G, Claeson M, Evans DB, Jha P, Mills A, Musgrove P, ed. Disease control priorities in developing countries. Washington, DC: International Bank for Reconstruction and Development and the World Bank; 2006, pp. 1193
209. 21. HelpAge. HelpAge International targets 2005
2010. Johannesburg: HelpAge; 2007. 22. O’ Reilly D, Rosato M, Petterson C. Self reported health and mortality: ecological analysis on based on electoral wards across the United Kingdom. BMJ 2005; 331: 988
9. 23. Xie J, Mathews FE, Jagger C, Bond J, Brayne C. The oldest old in England and Wales: a descriptive analysis based on MRC cognitive function and ageing study. Age and Ageing 2008; 37: 396
402. 24. Xu X, Jensen G. Health effects of managed care among the near older persons. J Aging Health 2008; 18: 507. 25. Wilson TR, Alexander DJ, Kind P. Measurement of healthrelated quality of life in the early follow-up of colon and rectal cancer. Dis Colon Rectum 2006; 49: 1692
702. 26. Andrich D. Controversy and the Rasch model: a characteristic of incompatible paradigms? Med Care 2004; 42: I7
6. 27. Chang C, Reeve B. Item responses theory and its applications to patient-reported outcomes measurements. Eval Health Prof 2005; 28: 264
82. 28. Kyobutungi C, Ezeh AC, Zulu E, Falkingham J. HIV/AIDS and the health of older people in the slums of Nairobi, Kenya: results from a cross sectional survey. BMC Public Health 2009; 9: 153. 29. Dolan P, Lee H, King D, Metchelfe R. How does NICE value health? BMJ 2009; 339: b2577. 30. Kahneman D, Krueger AB, Schkade DA, Schwarz N, Stone AA. A survey method for characterizing daily life experience: the day reconstruction method. Science 2004; 306: 1776
80. 31. Salomon JA, Murray CJL. A multi-method approach to measuring health-state evaluations. Health Economics 2003; 13: 281
90. 32. Ware JE Jr., Kosinski M, Bjorner JB, Bayliss MS, Batenhorst A, Dahlof CG et al. Applications of computerized adaptive testing (CAT) to the assessment of headache impact. Qual Life Res 2003; 12: 935
52. Citation: Global Health Action Supplement 2, 2010. DOI: 10.3402/gha.v3i0.2142

33. Saliba D, Orlando M, Wenger NS, Hays RD, Rubenstein LZ. Identifying a short functional disability screen for older persons. J Gerontol A Biol Sci Med Sci 2000; 55: M750
6. 34. Saha TD, Compton WM, Pulay AJ, Stinson FS, Ruan WJ, Smith SM et al. Dimensionality of DSM-IV nicotine dependence in a national sample: an item response theory application. Drug Alcohol Depend 2009; 108: 21
8. 35. Revicki DA, Cella DF. Health status assessment for the twentyfirst century: item response theory, item banking and computer adaptive testing. Qual Life Res 1997; 6: 595
600. 36. Jenkinson C, Fitzpatrick R, Garratt A, Peto V, Stewart-Brown S. Can item response theory reduce patient burden when measuring health status in neurological disorders? Results from Rasch analysis of the SF-36 physical functioning scale (PF-10). J Neurol Neurosurg Psychiatry 2001; 71: 220
4. 37. Reeve BB, Hays RD, Bjorner JB, Cook KF, Crane PK, Teresi JA et al. Psychometric evaluation and calibration of healthrelated quality of life item banks: plans for the Patient-Reported Outcomes Measurement Information System (PROMIS). Med Care 2007; 45: S22
31. 38. Schmidt P, Clouth J, Haggenmuller L, Naber D, Reitberger U. Constructing an index for the Subjective Well-being Under Neuroleptics scale (SWN), short form: applying structural equation modeling for testing reliability and validity of the index. Qual Life Res 2006; 15: 1191
202. 39. Gureje O, Kola L, Afolabi E, Olley BO. Determinants of quality of life of elderly Nigerians: results from the Ibadan study of ageing. Afr J Med Med Sci 2008; 37: 239
47. 40. Sen A. Public action and the quality of life in developing countries. Oxf Bull Econ Stat 1981; 43: 287
319. 41. O’Donovan DK. Angle on disability. Lancet 1989; 2: 866. 42. Sousa RM, Ferri CP, Acosta D, Albanese E, Guerra M, Huang Y et al. Contribution of chronic diseases to disability in elderly people in countries with low and middle incomes: a 10/66 dementia research group population-based survey. Lancet 2009; 374: 1821
30. 43. Luciano JV, Ayuso-Mateos JL, Fernandez A, Aguado J, Serrano-Blanco A, Roca M et al. Utility of the twelve-item World Health Organization Disability Assessment Schedule II (WHO-DAS II) for discriminating depression ‘‘caseness’’ and severity in Spanish primary care patients. Qual Life Res 2009; 19: 97
101. 44. Filmer D, Pritchett LH. Estimating wealth effects without expenditure data
or tears: an application to educational enrolments in states of India. Demography 2001; 38: 115
32. 45. Kinsella K, Phillips DR. Global aging: the challenge of success. Washington, DC: Population Reference Bureau; 2005. 46. Welfare MoHaS. Tanzania Disability survey. Johannesburg: World bank; 2009. 47. Goldman N, Glei DA. Sex differences in relationship between DHEAS and health experimental. Gerontology 2007; 42: 979
87. 48. Herd P, Goesling B, House JS. Socioeconomic position and health: the differential effects of education versus income on the onset versus progression of health problems. Journal of Health and Social Behavior 2007; 48: 223
38. 49. Van de Walle E. African households: censuses and surveys. London: World Bank; 2006. 50. United Republic of Tanzania. The Ministry of Labour, Youth Development and Sports National Aging Policy. In: Ministry of Labour, Youth Development and Sports, ed. Dar es Salaam: United Republic of Tanzania; 2003. 51. Coster W, Ludlow L, Mancini M. Using IRT variable maps to enrich understanding of rehabilitation data. J Outcome Meas 1999; 3: 123
33.

43

Mathew A. Mwanyangala et al.

52. Marmot M, Brunner E. Cohort profile: the Whitehall II Study. International Journal of Epidemiology 2005; 34: 251
56. 53. Ye L, Waite LJ. The impact of childhood and adult SES on physical, mental, and cognitive well-being in later life. Journals of Gerontology Series B: Psychological Science and Social Science 2005; 60: S93
S101. 54. Meich RA, Hauser RM. Socioeconomic status and health at midlife: a comparison of educational attainment with occupation-based indicators. Annals of Epidemiology 2001; 11: 75
84. 55. Tavallaii SA, Einollahi B, Azizabadi Farahani M, Namdari M. Socioeconomic links to health-related quality of life, anxiety, and depression in kidney transplant recipients. Iran J Kidney Dis 2009; 3: 40
4.

44

56. Smith V, Goldman N. Socioeconomic differences in health among older adults in Mexico. Social Science and Medicine 2007; 65: 1372
85. 57. United Republic of Tanzania national Strategy for Growth and Reduction of Poverty; 2005. Available from: http://www. siteresources.worldbank.org/INTPRS1/Resources/TanzaniaPRSP (June-2005).pdf [cited 3 May 2010]. *Mathew A. Mwanyangala Ifakara Site Health Institute P.O. Box 53, Ifakara, Morogoro, Tanzania Email: [email protected]

Citation: Global Health Action Supplement 2, 2010. DOI: 10.3402/gha.v3i0.2142

INDEPTH WHO-SAGE Supplement


The health and well-being of older people in Nairobi’s slums Catherine Kyobutungi1,2*, Thaddaeus Egondi1,2 and Alex Ezeh1,2 1

African Population & Health Research Centre, Nairobi, Kenya; 2INDEPTH Network, Accra, Ghana

Background: Globally, it is estimated that people aged 60 and over constitute more than 11% of the population, with the corresponding proportion in developing countries being 8%. Rapid urbanisation in subSaharan Africa (SSA), fuelled in part by rural
urban migration and a devastating HIV/AIDS epidemic, has altered the status of older people in many SSA societies. Few studies have, however, looked at the health of older people in SSA. This study aims to describe the health and well-being of older people in two Nairobi slums. Methods: Data were collected from residents of the areas covered by the Nairobi Urban Health and Demographic Surveillance System (NUHDSS) aged 50 years and over by 1 October 2006. Health status was assessed using the short SAGE (Study on Global AGEing and Adult Health) form. Mean WHO Quality of Life (WHOQoL) and a composite health score were computed and binary variables generated using the median as the cut-off. Logistic regression was used to determine factors associated with poor quality of life (QoL) and poor health status. Results: Out of 2,696 older people resident in the NUHDSS surveillance area during the study period, data were collected on 2,072. The majority of respondents were male, aged 50
60 years. The mean WHOQoL score was 71.3 (SD 6.7) and mean composite health score was 70.6 (SD 13.9). Males had significantly better QoL and health status than females and older respondents had worse outcomes than younger ones. Sex, age, education level and marital status were significantly associated with QoL, while slum of residence was significantly associated with health status. Conclusion: The study adds to the literature on health and well-being of older people in SSA, especially those in urban informal settlements. Further studies are needed to validate the methods used for assessing health status and to provide comparisons from other settings. Health and Demographic Surveillance Systems have the potential to conduct such studies and to evaluate health and well-being over time. Keywords: Nairobi; slum settlements; older people; ageing; well-being; quality of life; INDEPTH WHO-SAGE

Access the supplementary material to this article: INDEPTH WHO-SAGE questionnaire (including variants of vignettes), a data dictionary and a password-protected dataset (see Supplementary files under Reading Tools online). To obtain a password for the dataset, please send a request with ‘SAGE data’ as its subject, detailing how you propose to use the data, to [email protected]

Received: 17 November 2009; Revised: 27 June 2010; Accepted: 8 July 2010; Published: 27 September 2010

he proportion of older people is increasing worldwide. Globally, it is estimated that people aged 60 and over currently constitute more than 11% of the population; over 20% in developed nations and about 8% in developing ones. The proportion of older people globally is expected to double to 22% by 2050 (1). In Africa, people aged 60 and over account for only 5% of the population; this is projected to increase to 11% by 2050 (2). In this study setting, people aged 60 and over constituted 1.6% of the population, and those aged 50

T

and over constituted 4.9% of the population under surveillance. It is estimated that people aged 60 and over in Kenya as a whole constituted 4.0% of the total population in 2005 and this proportion is expected to increase to 4.5% by 2015 and to 9.3% by 2050 (3). Older people will therefore form an increasingly important subgroup in numeric terms in developing nations. Older people have traditionally been held in high esteem in many African societies for their wisdom, role as heads of families and roles in conflict resolution. More

Global Health Action 2010. # 2010 Catherine Kyobutungi et al. This is an Open Access article distributed under the terms of the Creative Commons Attribution-Noncommercial 3.0 Unported License (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. Citation: Global Health Action Supplement 2, 2010. DOI: 10.3402/gha.v3i0.2138

45

Catherine Kyobutungi et al.

recently, older people have been involved in the fight against HIV/AIDS, especially in their role as caregivers for HIV-infected family members and orphans left behind by deceased relatives. On the other hand, older people have not been spared by the direct effects of HIV/AIDS. A recent AIDS indicator survey in Kenya shows that the HIV prevalence among the 50
54 years age group is 8% (similar for both males and females). The prevalence for females is similar to that in the 45
49 age group while for males, the prevalence is higher in the 50
54 age group. The HIV prevalence in urban areas is also higher than in rural areas (8.9% vs. 7.0%) (4) and even higher (11.4%) in the study area according to a recent survey (APHRC, unpublished data). Apart from HIV/AIDS, older people are also most affected by chronic degenerative diseases. This implies that in Kenya and many other countries in sub-Saharan Africa (SSA), older people most probably bear a dual burden of disease. Population ageing is occurring in a context of rapid urbanisation in SSA. Africa is urbanising at a rate faster than any other region in the world and by 2030 more than half of the SSA population will live in urban areas (5). The pace of urbanisation in many SSA countries has not been matched by economic growth. In fact, in countries like Kenya, urbanisation has been rapid amid economic stagnation. This has resulted in an increase in the number and size of informal settlements or slums in many cities. It is estimated that more than 70% of urban residents in SSA live in slum or slum-like conditions. In Kenya, this percentage is about 71% (6). The informal nature of these settlements means that they are underserved by the public sector in the provision of basic amenities and services including health, education, water and sanitation, and garbage collection services. Slums are also characterised by high levels of unemployment, overcrowding, insecurity, greater involvement in risky sexual practices, social fragmentation, and high levels of mobility (7
9). Studies from different SSA countries have shown that slum residents have worse health outcomes than their rural counterparts (10
13). For example, childhood mortality in poor urban areas of Zambia and Malawi is higher than in rural and peri-urban areas (11, 14). Desperate living conditions and lack of livelihood opportunities could predispose residents to risky health-related behaviours such as high alcohol consumption, unsafe sex, smoking and other substance abuse. All these factors have adverse effects on health which may be compounded by poor access to health services. Ageing in an urban setting, especially a slum settlement, poses its own challenges. These include weak social networks, neglect and loss of respect and stature that are often accorded older people in more stable communities. It should be expected, therefore, that older people in slum settlements have poor or even poorer health outcomes just like other sub-populations therein.

46

As the HIV/AIDS pandemic rages in SSA and as slums grow in a rapidly urbanising continent, it is important that the impact of these processes on older people is assessed and addressed. The intersection between the HIV/AIDS pandemic, population ageing and uncontrolled urbanisation in SSA will have far-reaching consequences on the social, economic and health spheres of societies. Despite the evident need to understand issues that affect older people in SSA, relative to other demographic trends, ageing in Africa has only recently started receiving attention in research and policy-making. There is a near absence of policies and programmes targeting older people in most countries in SSA (15), and Kenya is no exception. Health policies and programmes are geared towards the traditional vulnerable groups of women of reproductive age and children. The current National Health Sector Strategic Plan however recognises that older people have special needs that are different from other adults and hence spells out specific interventions for older people (16). In addition to regular curative and preventive services, such interventions include annual screening and provision of curative services for degenerative diseases, and counselling for lifestyle changes. It remains to be seen whether these interventions have been translated into real programmes that serve older people in health facilities. The fact that older people have been long neglected in many policies and programmes in Kenya means that there is a dearth of research on their health and wellbeing. This study therefore aims to fill the gap in ageing research in Africa by describing the health and well-being of older people living in two Nairobi slums.

Methods Study setting The study was conducted in two slum communities where the African Population and Health Research Centre (APHRC) is implementing the longitudinal Nairobi Urban Health and Demographic Surveillance System (NUHDSS). The NUHDSS covers large parts of the two slums of Korogocho and Viwandani in Nairobi City, Kenya’s capital and commercial centre. Both communities are informal settlements located about 5
10 km from the city centre. The population under surveillance as of 1 January 2007 was 59,513 individuals living in 21,993 households. The NUHDSS started after an initial census in August 2002. Since January 2003, data on core demographic events (births, deaths, in- and out-migrations) have been collected and updated every 4 months during routine Health and Demographic Surveillance System (HDSS) rounds.

Citation: Global Health Action Supplement 2, 2010. DOI: 10.3402/gha.v3i0.2138

The health and well-being of older people in Nairobi’s slums

Data collection Data for this study was collected from all residents of the NUHDSS who were aged 50 years and over as of 1 October 2006. Eligible participants (n 2,696) were identified from the most up-to-date NUHDSS database at the time. Data were collected on 2,072 respondents who had complete interviews and only these were included in the analysis. Out of the 624 who were not interviewed, 102 refused to be interviewed, 27 had died, 213 had out-migrated and no contact was made with the rest for various reasons including absence of a competent respondent, entire household absent for prolonged periods and unknown whereabouts. The final response rate was 84.4% after omitting the 240 older people later found to have died or out-migrated. Data were collected in the framework of a larger study on the linkages between urbanisation, migration, poverty and health over the life course. An intervieweradministered questionnaire was used to collect data. Interviewers had a minimum education level of Form 4 (12 years of schooling) and were residents in the NUHDSS area. They were trained over a five-day period followed by two days of field testing. Each group of five interviewers was supervised by a team leader who manually edited all completed forms, conducted random spot checks on at least 5% of forms filled by each field worker under his/her supervision, and offered additional training whenever necessary. Self-reported health status was assessed using the short form of the individual SAGE (Study on Global Ageing and Adult Health) questionnaire, available as a Supplementary File to this paper. Details of how this tool was developed, validated and adapted for use in this survey are described elsewhere (17). In brief, this form has sections on health status descriptions in eight domains of health including mobility, self-care, affect, vision, pain and discomfort, sleep/energy, interpersonal activities and cognition. Typically, questions ask about how much difficulty the respondent had had in the preceding 30 days with tasks or activities in the eight domains. Responses range from no difficulty to extreme difficulty on a five-item scale. In addition, the SAGE form has questions on functioning assessment using items in the Activities of Daily Living / Instrumental Activities of Daily Living (ADL/IADL) tool as well as on Subjective Well-being and Quality of Life (QoL). This paper focuses on two measures of self-reported health status: QoL and health status scores. The QoL was assessed using the World Health Organization Quality of Life tool (WHOQoL) score, on a scale from 0 to 100 where 100 is the best QoL. Details of how this is computed are described elsewhere (17). Health status scores were computed using Item Response Theory (IRT) parameter estimates in Winsteps†, a Rasch measurement software package (http://www.winsteps.com). More de-

tails on how scores for this study were derived are provided elsewhere (17). In brief, IRT uses Maximum Likelihood Estimation methods to model the relationship between a person’s health status and their probability of responding to each question in a multi-item scale. Each item is modelled to have a set of parameters which describe the relationship between the item and the measured construct as well as how the item functions within a population. The health score is then transformed to a scale of 0 to 100 (where 100 is the best health status). More details on the application of the IRT approach to computing patient-reported health outcomes are available in the paper by Chang and Reeve (18).

Statistical analysis Descriptive analyses were conducted for both measures of health. For WHOQoL, mean scores were computed for different categories of respondents. The different categories include: sex (male, female), age (age groups: 50
59, 60
69, 70
79, 80), educational level (no formal education, up to 6 years of formal education, more than 6 years of education), marital status (in current partnership, never married, separated, divorced and widowed), wealth index (quintiles), whether respondent stays alone (Yes, No) and proportion of people aged 50 years and over in the same household (B25%, 25
49%, 50
74%, 75%). In addition, the proportion of respondents in each category with a WHOQoL score less than the median was computed. For the health status score, mean scores were also calculated and the proportion of respondents falling below the overall median score was calculated for each category of respondents. Exploratory analyses were conducted to determine the factors associated with poor QoL and poor health status. For both measures of health, respondents who had scores below the median were categorised as having poor QoL or poor health, respectively. In order to investigate the effect of non-response, we fitted a logistic regression model using response status as the outcome and key socioeconomic and demographic characteristics as explanatory variables. A completed interview was defined as response while an incomplete interview for a participant determined to be resident in the study area at any time during the survey was considered non-response. Gender, education and wealth index were found to be associated with non-response. The predicted probability of responding was calculated for every individual in the data using the fitted model. Once the predicted probability was calculated, its inverse became the weight for that observation. The computed weights were re-adjusted to approximately add up to the sample size. These weights were included in subsequent univariate and multivariable logistic regressions using the categorical health outcomes described above to adjust for non-response. The variables found to be associated with

Citation: Global Health Action Supplement 2, 2010. DOI: 10.3402/gha.v3i0.2138

47

Catherine Kyobutungi et al.

non-response were also included in the model as predictors. Results are presented for the models adjusted for non-response.

Results The descriptive characteristics of the study participants are shown in Table 1. The characteristics of nonrespondents are also shown. Demographic characteristics for non-respondents were obtained from the existing Table 1. Background characteristics of study subject (respondents and non-respondents)

Variables Sex (%) Men

Respondents

Non-respondents

(N 2,072)

(N384)

NUHDSS database. Marital status for non-respondents could not be established since this variable is not routinely collected and may change over time. There were no major differences between respondents and nonrespondents except for wealth index, where a larger proportion of non-respondents fell in the poorest wealth quintile compared to respondents, and living arrangements, whereby a quarter of the respondents were staying alone compared to more than a third of non-respondents. These differences were both statistically significant (pB 0.001). Among both respondents and non-respondents, there were more males than females and the majority of respondents were in the 50
59 year age group. A majority of the respondents had at least six or more years of schooling. The average number of household members for the respondents was about four members per household compared to about three for non-respondents.

1,327 (64.4%)

302 (79.1%)

745 (36.0%)

80 (20.9%)

59.2 (9.06)

57.1 (7.5)

50
59 years

1,358 (65.4%)

283(73.9%)

60
69 years

458 (22.1%)

69 (18.0%)

70
79 years 80 years and over

163 (7.9%) 93 (4.5%)

23 (6.0%) 8 (2.1%)

No formal education

571 (28.7%)

77 (21.4%)

Less than or equal to 6 years

562 (28.2%)

81 (22.5%)

Table 2. Distribution of WHOQoL and Health Status Scores by age and sex

More than 6 years

858 (43.2%)

202 (56.1%)

Variables

662 (32.0%)




1,410 (68.1%)




Women Mean age (SD) Age group

The distribution of WHOQoL and health status scores The distribution of WHOQoL and health status scores is shown in Table 2. The median values used as cut-offs were 71.9 for WHOQoL and 67.5 for health status. The higher the WHOQoL score, the better the QoL, and the higher the health status scores, the better the health status. The mean WHOQoL score was lower for older

Education level (%)

Marital status (%) Now single In current partnership

Mean WHOQoL score (SD)

Wealth index (%) First quintile (Poorest) Second quintile

518 (25.0%) 206 (10.0%)

177 (46.3%) 6 (1.6%)

Third quintile

514 (24.8%)

16(4.2%)

Fourth quintile

453 (21.9%)

Fifth quintile (Least poor) 380 (18.4%) Mean number of household 4.12 (3.19)

No

48

70.9 (6.3) 68.3 (6.6)

70
79 years

71.1 (6.2)

65.7 (7.2)

80 years and over

67.3 (9.1)

63.8 (8.5)

Proportion of respondents with WHOQoL below the median 50
59 years 32.0% 45.8% 60
69 years

43.9%

64.6%

70
79 years

51.9%

79.8%

80 years and over

71.1%

78.2%

3.0 (2.5)

Mean health status score (SD) 0.52 (0.34)

0.62 (0.3)

496 (24.0%)

140 (36.5%)

1,576 (76.0%)

244 (63.5%)

Site of residence (%) Korogocho Viwandani

73.1 (5.8) 71.9 (6.4)

69 (18.1%)

years and over (SD) Stays alone Yes

50
59 years 60
69 years

114 (29.8%)

members (SD) Proportion of household members aged 50

Men (n 1,331) Women (n 747)

1,462 (70.6%) 610 (29.4%)

214 (55.7%) 170 (44.3%)

50
59 years

74.7 (13.9)

69.7 (12.5)

60
69 years

71.0 (12.9)

63.9 (10.6)

70
79 years

69.0 (13.3)

60.2 (10.6)

80 years and over

59.3 (15.9)

56.6 (10.9)

Proportion of respondents with health status score below the median 50
59 years

33.3%

50.8%

60
69 years

49.1%

73.5%

70
79 years

46.8%

83.3%

80 years and over

79.0%

90.9%

Citation: Global Health Action Supplement 2, 2010. DOI: 10.3402/gha.v3i0.2138

The health and well-being of older people in Nairobi’s slums

respondents but with some sex differences. Female respondents had, on average, appreciably lower WHOQoL scores than their male counterparts in the same age group. Similar effects were observed when the proportions of respondents with a WHOQoL score below the median were considered, with poorer QoL associated with women and older age groups. A similar pattern to that observed for WHOQoL scores was observed with the health status scores. The average health status scores decreased with increasing age and females have lower scores than males, indicating worse health status. The proportion with health status scores below the median increased with age, particularly among females. The results for the two measures of self-reported health status consistently showed that health status and QoL deteriorated in both sexes as people got older and that females had significantly worse health outcomes than males.

Table 3. Factors associated with poor quality of life

Variables

Multivariate model

(OR and 95% CI)

(OR and 95% CI)

Site Viwandani

0.59 (0.49
0.72)

0.85 (0.68
1.07)

Korogocho

1.00

1.00

Sex Men

0.44 (0.36
0.53)

0.78 (0.61
1.01)

Women (Ref)

1.00

1.00

50
59 years

1.00

1.00

60
69 years

1. 97 (1.59
2.45)

1.55 (1.22
1.96)

70
79 years

3.59 (2.48
4.95)

2.06(1.40
3.02)

80 years and over

5.42 (3.33
8.81)

2.94 (1.71
5.02)

No formal

3.07 (2.46
3.82)

1.68 (1.29
2.18)

education Less than or equal

1.73 (1.39
2.16)

1.25 (0.98
1.60)

1.00

1.00

1.00

1.00

Age group

Education level

Factors associated with poor QoL and poor health status Both univariate and adjusted logistic regression results using WHOQoL as the outcome are presented in Table 3. Male respondents were significantly less likely to have poor WHOQoL compared to females in the univariate models. However in adjusted models, this effect was attenuated and was of borderline statistical significance. An age gradient, consistent with the descriptive results, is observed in the logistic regression models. In adjusted models, the oldest respondents (80) had almost three times the risk of having poor QoL as the youngest respondents (50
59 years). An education gradient was also observed whereby individuals with no education or less than 6 years of education were more likely to report poor QoL compared to those with more than 6 years of education. This association was significant in both univariate and adjusted models. Marital status was found to be associated with QoL. Respondents who were in some kind of partnership were least likely to report poor QoL. Separated and widowed respondents had significantly worse QoL than those in partnership. There was no significant relationship between the proportion of older people living in a household and QoL. Wealth index had an inverted-V relationship with QoL. In adjusted models, respondents in the poorest and least poor quintiles had similar odds of reporting poor QoL while those in the second quintile had higher odds of poor QoL. Only the odds ratio for being in the second quintile approached statistical significance. The results on factors associated with poor selfreported heath state are presented in Table 4. Poor health status was associated with gender, age, educational level and marital status among older people. As observed with QoL, male respondents were less likely to report poor

Univariate model

to 6 years More than 6 years (Ref) Marital status In current partnership (Ref) Never married

1.63 (1.04
2.54)

1.17 (0.71
1.92)

Separated

2.12 (1.47
3.04)

1.55 (1.04
2.31)

Divorced

2.31 (1.40
3.80)

1.52 (0.87
2.64)

Widowed

2.79 (2.20
3.52)

1.52 (1.12
2.07)

Proportion aged 50 years and over in the same household B25%

0.96 (0.76
1.20)

1.03 (0.80
1.34)

25
49%

0.96 (0.76
1.21)

1.01 (0.78
1.31)

50
74% ]75% (Ref)

0.68 (0.53
0.88) 1.00

0.72 (0.54
0.96) 1.00

First quintile

0.96 (0.73
1.26)

1.01 (0.74
1.37)

Second quintile Third quintile

2.18 (1.61
2.95) 1.46 (1.10
1.93)

1.37 (0.98
1.91) 1.22 (0.90
1.65)

Wealth Index

Fourth quintile

1.29 (0.98
1.71)

1.06 (0.78
1.44)

Fifth quintile (Ref)

1.00

1.00

health as compared to female counterparts (Adjusted odds ratio: 0.69, 95% CI: 0.54
0.89) and the oldest respondents were close to six times as likely to report poor health as the youngest in adjusted models. Individuals with no formal education were more likely to report poor health compared to those with more than 6 years of education. Individuals who were never married were almost twice as likely to report poor health status compared to those who were in partnership while

Citation: Global Health Action Supplement 2, 2010. DOI: 10.3402/gha.v3i0.2138

49

Catherine Kyobutungi et al.

Table 4. Factors associated with poor health status

Variables

Univariate model

Multivariate model

(OR and 95% CI)

(OR and 95% CI)

Site Viwandani

0.38 (0.31
0.46)

0.50 (0.40
0.63)

Korogocho

1.00

1.00

Sex Men

0.36 (0.30
0.43)

0.67 (0.52
0.86)

Women

1.00

1.00

50
59 years

1.00

1.00

60
69 years

2.32 (1.86
2.88)

1.83 (1.43
2.34)

70
79 years

3.06 (2.17
4.31)

1.73 (1.17
2.60)

80 years and over

9.47 (5.20
17.26)

5.66 (3.00
10.69)

3.27 (2.62
4.08)

1.50 (1.16
1.96)

education Less than or equal to 1.77 (1.42
2.20)

1.19 (0.94
1.52)

Age group

Education level No formal

6 years More than 6 years

1.00

1.00

Marital status In current partnership 1.00

1.00

(Ref) Never married

2.86 (1.79
4.56)

1.88 (1.10
3.19)

Separated

1.91 (1.33
2.74)

1.24 (0.82
1.89)

Divorced

2.42 (1.46
4.01)

1.45 (0.83
2.53)

Widowed

3.48 (2.72
4.43)

1.59 (1.16
2.18)

Proportion aged 50 years and over in the same household B25%

1.09 (0.86
1.37)

1.10 (0.80
1.43)

25
49%

1.08 (0.85
1.36)

1.11 (0.85
1.46)

50
74%

0.92 (0.71
1.18)

0.97 (0.72
1.29)

]75%

1.00

1.00

First quintile

0.78 (0.60
1.03)

1.02 (0.75
1.40)

Second quintile

1.78 (1.32
2.40)

1.12 (0.80
1.57)

Third quintile Fourth quintile

1.31 (1.00
1.73) 1.16 (0.88
1.52)

1.05 (0.77
1.42) 0.88 (0.65
1.19)

Fifth quintile

1.00

1.00

Wealth index

widowed individuals were 1.6 times more likely. The wealth index and proportion of people aged 50 years and over in the household were not significantly associated with reported health status.

Discussion Kenya, like many SSA countries, has been hard hit by the HIV/AIDS epidemic. During the 1980s, Kenya’s child mortality declined steadily until the 1990s, when a reversal in the trend was observed (19). The reversal in childhood mortality coincided with an economic crisis

50

and could have been exacerbated by the growth of the HIV/AIDS epidemic. As a result, Kenya is a country still in the early stages of the health transition. However, as non-communicable diseases gain a foothold in SSA, it is unlikely that the country will follow a uni-directional path towards the second and third stages of the health transition. While there is paucity of data on the magnitude of the non-communicable disease burden in the country, studies show that the prevalence of risk factors for these illnesses is increasing (20). Within the study setting, there is a high mortality burden from HIV/ AIDS (21) but in the absence of morbidity studies, it is hard to quantify the extent to which the country could be enduring a dual burden of disease characterised by high mortality and morbidity from both infectious diseases and non-communicable diseases as has been suggested. The proportion of older people in the study area is lower than the national estimate (3) and this is due to the fact that more young people in the economically productive age groups migrate and stay in the city to find work and economic opportunities. For similar reasons, in all age groups except the population under 15 years, the number of males is more than double that of females in the study area. Since migrants into the NUHDSS constitute a very large proportion of residents, sex differences are even greater at older ages since older females are less likely to migrate and historically, more males migrated to cities. These reasons partly explain why we have a high proportion of older people (25%) staying alone. Other reasons for this observation may include widowhood especially among females, divorce or separation or split households where other family members are left in rural areas while the older person works in the city (22). The study area has a sex and age distribution which is unlike the national one but is similar to the distribution for Nairobi city (Fig. 1). The population pyramids in Fig. 1 (a) and (b) both show a predominance of the 20
29 year age groups among males and females and significant narrowing of the pyramid after the age of 50 years which is more pronounced among females. The sex and age distribution is also different between the two slums because Viwandani slum, being near the industrial area, is mostly inhabited by migrant male labourers seeking job opportunities in the surrounding industries. Older people who are less likely to find employment in the industries are therefore less likely to reside in Viwandani and prefer Korogocho and other slums where they are mostly engaged in informal businesses. Qualitative research in the Nairobi slums where the study was conducted shows that older people play several important roles in society. They are considered fair arbitrators in disputes within families and in the community. They are also considered to have a wealth of experience and wisdom and hence their advice is sought

Citation: Global Health Action Supplement 2, 2010. DOI: 10.3402/gha.v3i0.2138

The health and well-being of older people in Nairobi’s slums

(b) Nairobi City, 1999b

(a) Study site: Korogocho and Viwandani, 2002a

80+

80+ 70–74

Males

70–74

Females Males Males

60–64

Females Females

60–64

50–54

50–54

40–44

40–44

30–34

30–34

20–24

20–24

10–14

10–14 0–4

0–4 10.0

8.0

6.0

4.0

2.0

0.0

2.0

4.0

6.0

8.0

10.0

10.0

8.0

6.0

4.0

2.0

0.0

2.0

4.0

6.0

8.0

10.0

Percentage

Percentage

(c) National population pyramid for Kenya, 1999b 80+

70–74

Males

Females

60–64

50–54

40–44

30–34

20–24

10–14

0–4 10.0

8.0

6.0

4.0

2.0

0.0

2.0

4.0

6.0

8.0

10.0

Percentage

a

Source: APHRC NUHDSS data.

bSource: Ref. (22). Fig. 1. Population pyramids for the study area, Nairobi City and the whole of Kenya

on various issues. Older people are also perceived as important in community development initiatives where they provide leadership and counsel though they are also perceived by some as gatekeepers and impediments to development. During community crises, they play a leading role in mobilising the community (22). These roles are in addition to more traditional roles of heads of household, breadwinners and care givers for grandchildren. However, older people are also more vulnerable in these settings due to altered family structures and

living arrangements. Almost 25% of the respondents live alone and are therefore more likely to be deprived of social support structures. The HIV/AIDS epidemic in SSA has also led to an increased number of orphans, most of whom are cared for by grandparents who are likely to be older people (23). In the study area, 19.5% of respondents were looking after children below the age of 15 years. Out of these 1,019 children, 770 were either orphans or their parents’ whereabouts were unknown.

Citation: Global Health Action Supplement 2, 2010. DOI: 10.3402/gha.v3i0.2138

51

Catherine Kyobutungi et al.

Older people in many parts of SSA have been engaged in efforts to mitigate the effects of HIV/AIDS due to the increased mortality of people in the reproductive and more economically productive age groups. The high HIV/ AIDS and tuberculosis burden in the study area (20) means that the chronic ill-health associated with these conditions has led to role reversal whereby older people are providing care to their ill and dying family members. About 7% of respondents were caring for someone with a prolonged illness at the time of the interview while another 6% had cared for someone in the past 3 years. Such responsibilities, coupled with economic adversity, may negatively affect the health and well-being of older people. With respect to the specific findings, in both univariate and multivariate analysis for the measure of self-reported health, females have worse outcomes than males at all age groups; these deteriorate, as expected, with age. Older female disadvantage in health status has been described in industrialised country settings (24
26), and so our findings add to the body of evidence supporting this association. Korogocho respondents have significantly worse health outcomes than Viwandani residents. Other studies in the NUHDSS have shown similar findings in other age groups but it is unclear what the underlying reasons are since both slums have poor environmental sanitation and poor access to social services. Viwandani is however inhabited by mostly labour migrants seeking employment in the nearby industrial area and hence there are more employment opportunities. In addition, a larger proportion of residents in Viwandani stay for short periods and then move on compared to Korogocho. It is possible that residents do not stay long enough to be exposed to the hazardous slum environment or that, in the Viwandani cash-based economy, economically unsuccessful migrants, who could potentially have worse outcomes, move elsewhere and leave behind the more successful ones. This is apparent in the characteristics of non-respondents, who are more likely to be from Viwandani and also more likely to be in the poorest wealth quintile. A migrant tracking study that assesses reasons for migration out of the slums and post-migration economic and health status, while logistically extremely challenging, would be helpful in clarifying these issues. As expected, a clear age gradient is observed for both measures of health status; however the gradient is steeper for the self-reported health status than for QoL. Marital status has a significant effect on health outcomes though the pattern of the effect differs for the two health outcomes. In both cases, married respondents or those in partnership have better health outcomes than other respondents. The relationship between being married and well-being has long been established (26, 27), albeit with other health outcomes, as has the association between poor health outcomes and widowhood and never married status.

52

The association between wealth index and QoL is an inverted V-shape but this variable had no significant association with reported health status. This could be explained by the lower response rates among the poorest wealth quintiles compared to other quintiles. On the other hand, in an environment with high levels of deprivation, it is possible that differences in wealth are marginal in real terms and have no tangible impact on health outcomes. Self-reported measures of health status have not been widely used in SSA in general nor in Kenya in particular. Their validity as a measure of health has therefore not been established, but the finding of steep age and education gradients with worse female health scores point to a good degree of internal validity. It is known that the validity of self-reported measures of health and their reliability are influenced by underlying socio-cultural factors including basic and health literacy, cultural perceptions of illness, disability and health status among others (28, 29). Further studies including vignettes should investigate the influence of such factors on the validity of self-reported health in this population. On the other hand, the longitudinal framework offered by demographic surveillance sites offers a unique opportunity to validate these measures by assessing their performance against objective measures of health and in predicting mortality. The absence of similar studies in the country and in the region makes it hard to interpret some of the findings. However, comparison with findings from other HDSS sites may shed more light. Other important research questions include the coping strategies and factors associated with resilience and healthy ageing among older people in resource-deprived settings as well as coping strategies in the absence of strong contributory national social security funds. The study adds to the limited body of literature regarding health and well-being of older people in SSA and especially those in urban informal settlements. Further studies are needed to validate the methods used for assessing health status and to provide comparisons on which the health of the older urban poor can be judged.

Acknowledgements This research uses data partly collected under the Urbanisation, Poverty and Health Dynamics (UPHD) Research Programme in the Nairobi Urban Health and Demographic Surveillance System (NUHDSS). We are also grateful to the WHO-SAGE group for availing the SAGE instrument which was used in data collection and for their support in the analysis and interpretation of the data. We also wish to acknowledge the contribution of the APHRC’s dedicated field and data management teams, and the residents of Korogocho and Viwandani for their continued participation in the NUHDSS.

Citation: Global Health Action Supplement 2, 2010. DOI: 10.3402/gha.v3i0.2138

The health and well-being of older people in Nairobi’s slums

Conflict of interest and funding The UPHD Research Programme is funded by the Wellcome Trust UK (grant number GR078530AIA). Work in the NUHDSS has been supported by grants from the William and Flora Hewlett Foundation and the Rockefeller Foundation. We acknowledge funding from the National Institutes of Health which enabled us to collect the data on health status assessment.

References 1. United Nations Department of Economic and Social Affairs/ Population Division. World population ageing 2007. New York: United Nations Department of Economic and Social Affairs/ Population Division; 2007. 2. United Nations Department of Economic and Social Affairs/ Population Division. Population ageing and development. Available from: http://www.un.org/esa/population/publications/ ageing/ageing2009.htm [cited 24 June 2010]. 3. United Nations Department of Economic and Social Affair / Population Division. World population prospects: the 2008 revision. New York: United Nations Department of Economic and Social Affair/Population Division; 2008. 4. National AIDS and STD Control Program MoHK. Kenya AIDS Indicator Survey 2007: preliminary report. Nairobi: Ministry of Health; 2008. 5. United Nations Department of Economic and Social Affairs/ Population Division. World urbanization prospects: the 2007 revision. New York: United Nations Department of Economic and Social Affairs/Population Division; 2008. 6. United Nations Human Settlements Programme (UN-HABITAT). The challenge of slums: global report on human settlements. London and Sterling, VA: Earthscan Publications Ltd.; 2003. 7. African Population and Health Research Centre. Population and health dynamics in Nairobi informal settlements. Nairobi: African Population and Health Research Centre; 2002. 8. Lamba D. The forgotten half; environmental health in Nairobi’s poverty areas. Environ Urban 1994; 6: 164
8. 9. Todaro M. Urbanization and rural-urban migration: theory and policy. Economic development in the Third World. New York: Longman; 1989, pp. 263
89. 10. Brockerhoff M, Brennan E. The poverty of cities in developing countries. Popul Dev Rev 1998; 24: 75
114. 11. Madise N, Diamond I. Determinants of infant mortality in Malawi: an analysis to control for death clustering within families. J Biosoc Sci 1995; 27: 95
106. 12. Magadi M, Zulu E, Brockerhoff M. The inequality of maternal health care in urban sub-Saharan Africa in the 1990s. Popul Stud (Camb) 2003; 57: 347
66. 13. Taffa N. A comparison of pregnancy and child health outcomes between teenage and adult mothers in the slums of Nairobi, Kenya. Int J Adolesc Med Health 2003; 15: 321
9.

14. Madise N, Banda E, Benaya K. Infant mortality in Zambia: socioeconomic and demographic correlates. Soc Biol 2003; 50: 148
66. 15. Kollapan J. The rights of older people
African perspectives. Available from: http://www.globalaging.org/elderrights/world/ 2008/africa.pdf [cited 3 March 2009]. 16. Ministry of Health
Kenya. Reversing the trends: The Second National Health Sector Strategic Plan-NHSSP-II, 2005-2010. Nairobi, Kenya: Ministry of Health; 2005. 17. Kowal P, Kahn K, Ng N, Naidoo N, Abdullah S, Bawah A, et al. Ageing and adult health status in eight low-income countries: the INDEPTH WHO-SAGE collaboration. Global Health Action Supplement 2, 2010. DOI: 10.3402/gha.v3i0.5302. 18. Chang C, Reeve B. Item response theory and its applications to patient-reported outcomes measurement. Eval Health Prof 2005; 28: 264
82. 19. Central Bureau of Statistics, Ministry of Health, Macro R. Kenya Demographic and Health Survey 2003. Calverton, MD: CBS, MoH and ORC Macro; 2004. 20. Kyobutungi C, Ziraba AK, Ezeh A, Ye Y. The burden of disease profile of residents of Nairobi’s slums: results from a Demographic Surveillance System. Popul Health Metr 2008; 6: doi: 10.1186/1478-7954-1186-1181. 21. de Laat J, Archambault C. Child well-being, social amenities, and imperfect information: shedding light on family migration to urban slums. New York: PAA; 2007. 22. Ministry of Finance and Planning, Central Bureau of Statistics. Kenya Population Census 1999. Nairobi: Central Bureau of Statistics; 2001. 23. Arber S, Cooper H. Gender differences in health in later life: the new paradox? Soc Sci Med 1999; 48: 61
76. 24. Lahelma E, Martikainen P, Rahkonen O, Silventoinen K. Gender differences in ill health in Finland: patterns, magnitude and change. Soc Sci Med 1999; 48: 7
19. 25. Matthews S, Hertzman C, Ostry A, Power C. Gender, work roles and psychosocial work characteristics as determinants of health. Soc Sci Med 1998; 46: 1417
24. 26. Coombs RH. Marital status and personal well-being: a literature review. Fam Relat 1991; 40: 97
102. 27. Johnson NJ, Backlund E, Sorlie PD, Loveless CA. Marital status and mortality: the national longitudinal mortality study. Ann Epidemiol 2000; 10: 224
38. 28. Carr AJ, Gibson B, Robinson PG. Measuring quality of life: is quality of life determined by expectations or experience? BMJ 2001; 322: 1240
3. 29. Carr AJ, Higginson IJ. Are quality of life measures patient centred? BMJ 2001; 322: 1357
60. *Catherine Kyobutungi African Population & Health Research Center Longonot Road, Upper Hill P.O. Box 10787, GPO 00100, Nairobi, Kenya Tel: 254 20 2720400 Fax: 254 20 2720380 Email: [email protected]

Citation: Global Health Action Supplement 2, 2010. DOI: 10.3402/gha.v3i0.2138

53

INDEPTH WHO-SAGE Supplement æ

Self-reported health and functional limitations among older people in the Kassena-Nankana District, Ghana Cornelius Debpuur1,2*, Paul Welaga1,2, George Wak1,2 and Abraham Hodgson1,2 1

Navrongo Health and Demographic Surveillance System, Navrongo, Ghana; 2INDEPTH Network, Accra, Ghana

Background: Ghana is experiencing significant increases in its ageing population, yet research on the health and quality of life of older people is limited. Lack of data on the health and well-being of older people in the country makes it difficult to monitor trends in the health status of adults and the impact of social policies on their health and welfare. Research on ageing is urgently required to provide essential data for policy formulation and programme implementation. Objective: To describe the health status and identify factors associated with self-rated health (SRH) among older adults in a rural community in northern Ghana. Methods: The data come from a survey on Adult Health and Ageing in the Kassena-Nankana District involving 4,584 people aged 50 and over. Survey participants answered questions pertaining to their health status, including self-rated overall health, perceptions of well-being and quality of life, and self-reported assessment of functioning on a range of different health domains. Socio-demographic information such as age, sex, marital status and education were obtained from a demographic surveillance database. Results: The majority of older people rated their health status as good, with the oldest old reporting poorer health. Multivariate regression analysis showed that functional ability and sex are significant factors in SRH status. Adults with higher levels of functional limitations were much more likely to rate their health as being poorer compared with those having lower disabilities. Household wealth was significantly associated with SRH, with wealthier adults more likely to rate their health as good. Conclusion: The depreciation in health and daily functioning with increasing age is likely to increase people’s demand for health care and other services as they grow older. There is a need for regular monitoring of the health status of older people to provide public health agencies with the data they need to assess, protect and promote the health and well-being of older people. Keywords: self-reported health status; functional limitations; older people; INDEPTH WHO-SAGE; adult health; KassenaNankana District; Ghana

Access the supplementary material to this article: INDEPTH WHO-SAGE questionnaire (including variants of vignettes), a data dictionary and a password-protected dataset (see Supplementary files under Reading Tools online). To obtain a password for the dataset, please send a request with ‘‘SAGE data’’ as its subject, detailing how you propose to use the data, to [email protected]

Received: 27 November 2009; Revised: 4 June 2010; Accepted: 8 July 2010; Published: 27 September 2010

lthough population ageing is often associated with industrialised societies such as Europe, America and Japan, the phenomenon is gradually gaining attention in the developing world. Advances in public health and the associated improvement in life expectancy has increased the proportion of the aged

A

population in the developing world. It is expected that the proportion of older people will grow rapidly in many parts of the developing world, including sub-Saharan Africa (1, 2). The rapid growth of the aged population poses various challenges. Chronic diseases and disability are disproportionately high among older people. Thus, a

Global Health Action 2010. # 2010 Cornelius Debpuur et al. This is an Open Access article distributed under the terms of the Creative Commons 54 Attribution-Noncommercial 3.0 Unported License (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. Citation: Global Health Action Supplement 2, 2010. DOI: 10.3402/gha.v3i0.2151

Self-reported health and functional limitations among older people in Ghana

growing elderly population will increase the demand for health care and other social services. Due to their low economic development, inadequate health infrastructure and limited social security programmes, meeting the needs of older people in the developing world and especially in sub-Saharan Africa is and will be difficult. This is likely to be compounded by the erosion of the traditional family support systems for older people. Policies and programmes that address the health and other needs of the growing aged population are urgently needed to ensure successful ageing and functional independence of the aged. However, health research in developing countries (including Ghana) has been and continues to be heavily focused on younger population groups. As such, the extent of ageing, the health needs of the ageing population, as well as the implications of national policies for the health and welfare of the aged are poorly understood and yet to be well appreciated. Questions about changing health over the life course and compression of morbidity demand an empirical basis for analysis, particularly in the context of planning and preparing social protection mechanisms (health and pension systems) to meet the demands of this growing population group. The ongoing World Health Organization’s global Study on Adult Health and Ageing (SAGE) provides an important platform for generating empirical data on ageing and health transition for policy formulation and programme implementation, especially in sub-Saharan Africa. Four African countries
Ghana, South Africa, Tanzania and Kenya
are participating in this programme of research, and have conducted various surveys on ageing and adult health using comparable instruments. This article draws on data from a survey conducted in the Kassena-Nankana District of northern Ghana as part of this global programme of research on ageing. We describe the health status of older people based on their own reports on various aspects of their health. We then examine factors associated with self-rated health (SRH) among older people. In particular, we examine whether perceived disability in various activities of living influences rating of one’s health status. The social, ecological and economic circumstances of the district are more representative of the northern ecological zone of Ghana as well as other Sahelian populations to the north of Ghana than of the southern and coastal zones of the country (3). The results of this study therefore have relevance for our understanding of the health of older people in Ghana and beyond.

Methods The setting The Kassena-Nankana District1 (KND) in the Upper East region of Ghana is located at the northern-most part

of the country and shares a boundary with Burkina Faso to the north. Since 1993, the Navrongo Health Research Centre (NHRC) has been operating a demographic surveillance system in this area. The district lies between latitudes 10.5 and 11.08 N and longitudes 1.0 and 1.58 W (4). The land is relatively flat and covers an area of 1,675 km2, with altitude of between 200 and 400 m above mean sea level. Located in the Guinea savanna belt, the ecology of the study area is typically Sahelian, with a short rainy season from April to September and a prolonged dry season from October to March. The mean annual rainfall is about 1,300 mm, with the heaviest rains occurring in August. Monthly temperatures range from 20 to 408C, with the mean annual minimum and maximum being 22.8 and 34.48C, respectively. Data from demographic surveillance estimated the population of the district as at end of June 2007 to be 147,536 with females constituting 53%, giving a M:F ratio of 0.89. About 38% of the population is under 15 years old, while those aged 65 and over constitute 4.7%. This gives a dependency ratio of 74.5%. The district is largely rural with dispersed settlements. There are two main ethnic groups
the Kassenas and the Nankanas
with other ethnic groups forming about 5% of the population. Although mortality and fertility are high, there have been declines since the 1990s. For instance, the crude death rate declined from 18.7 to 10.4 per 1,000 between 1997 and 2007, while the crude birth rate fell from 29.4 to 26.2 per 1,000 and the total fertility rate from 5.0 to 4.0 during the same period. The economy of the district is largely agrarian with about 90% of the population dependent on subsistence agriculture. Major crops grown are cereals such as millet, maize, sorghum and rice. The Tono irrigation dam as well as several dug-out dams in various communities facilitate irrigated farming and dry-season gardening. Rearing of animals like cattle, goats, sheep and poultry form part of the agricultural activities. Due to the dependence on agriculture and declining agricultural yields, poverty is endemic in the area. The district has a poor road network and transportation in many parts is limited to bicycles and occasional vehicles. Typically, movement within communities is by foot and use of bicycles. Recently, however, there has been an increase in the use of motor bikes, especially in the urban part of the district. Health facilities in the district include one hospital (located in Navrongo), six health centres, three clinics and several chemist’s shops. In addition to these static health facilities, community health officers have been deployed to several communities to offer door-to-door services to the people. As part of recent efforts to promote access to basic health services, a national health insurance scheme has been instituted and district mutual health insurance schemes are operational in all districts of the country. The main causes of morbidity in the study area are

Citation: Global Health Action Supplement 2, 2010. DOI: 10.3402/gha.v3i0.2151

55

Cornelius Debpuur et al.

malaria, gastroenteritis and acute respiratory infection. Periodic outbreaks of epidemic meningococcal meningitis have been recorded in the district. Service provision data suggest an increasing prevalence of hypertension and diabetes, and there is need for more systematic documentation of the type and prevalence of non-communicable diseases among adults. The Adult Health and Ageing study being implemented in the district as part of the INDEPTH WHO
SAGE initiative will contribute towards highlighting the health situation of adults and inform health care delivery in the district. The survey reported here is the first district-wide population-based survey of adults to collect information on self-reported health status among persons aged 50 and over, and thus provides baseline data for monitoring and evaluating adult health.

Data The data for this study come from the summary version of the INDEPTH WHO-SAGE Adult Health and Ageing Survey implemented by the NHRC. The Adult Health and Ageing Survey is an INDEPTH Network multi-site activity in collaboration with the World Health Organization’s Study on global AGEing and Adult Health (SAGE). The survey forms part of efforts by the INDEPTH Network to establish a longitudinal database on older people to inform policies related to their well-being. Ethical approval for the study was obtained from the ethics committee of the Ghana Health Service as well as the institutional review board of the NHRC. Community approval was obtained from the chiefs and elders. Written consent from individuals was obtained before interview. The summary version of the SAGE study primarily targeted older people (50 and over), although smaller samples of adults 18
49 years were also included. A single-stage simple random sample of 6,074 older people (50 years and over) and 1,360 younger adults (18
49 years) in the Kassena-Nankana District was drawn using the Health and Demographic Surveillance System (HDSS) database as a sampling frame. The data collection was integrated into the routine HDSS data collection round that took place between January and April 2007. Trained HDSS interviewers visited households and conducted face-to-face interviews with selected individuals. The questionnaire was written in English although the interviews were conducted in the local languages of respondents. Translation of the questions in to Kassim and Nankam
the two principal languages in the district
(and back translation from the local languages into English) as well as pre-testing of the questionnaire was done as part of interviewer training. The questions asked in the survey were grouped under two sections
Health Status Descriptions, and Subjective Well-being and Quality of Life. Items under Health

56

Status Descriptions included overall rating of health, questions on eight domains of health (mobility, self-care, pain and discomfort, cognition, interpersonal activities, sleep/energy, affect and vision), as well as functional assessment questions. Vignettes for health status descriptions were included in the Full SAGE survey but not in the Summary version. Under the Subjective Well-being and Quality of Life section, respondents were asked questions on their thoughts about their life situation. Almost all the questions in the questionnaire had 5-point scale response categories. Background information on age, education, marital status of each respondent as well as household information were obtained from the routine HDSS data. Standardised self-reported surveys of health have contributed immensely to the understanding of the health status of elderly people in the developed world and Asia. However, such studies (particularly those focusing on older people) are rare in sub-Saharan Africa. The data reported in this article will contribute towards bridging the knowledge gap on the health status of older people in sub-Saharan Africa and the developing world at large.

Outcome variables The primary outcome of interest in this study is overall SRH status. This is based on respondents’ assessment of their current health status on a 5-point scale in response to the question: ‘In general, how would you rate your health today?’ Response categories were: very good, good, moderate, bad and very bad. Barely 10% of respondents rated their health as very good and few rated their health either as ‘bad’ (4.8%) or ‘very bad’ (0.2%). Almost half (49.4%) reported their health as ‘good’, while 36.6% rated their health as moderate. From this we created a dichotomous measure coded 0 if response was ‘very good’ or ‘good’ and 1 if response was ‘moderate’, ‘bad’ or ‘very bad’. This simple measure of health status has been used in population-based epidemiological research, and has been identified as a powerful predictor of morbidity and mortality (5
7). In dichotomising SRH in our analysis, we follow the lead of previous researchers who adopted a similar approach (5,7
9) and the observation by Manor et al. (10) that such dichotomisation does not make any difference. Other indicators of health status examined in this study are overall health status and self-reported functional limitations. The overall health status of individuals was assessed based on responses to questions in eight domains of health covering affect, cognition, interpersonal activities and relationships, mobility, pain, self-care, sleep/energy, and vision. At least two questions were asked in each domain, thus providing more robust assessments of individual health levels and reducing measurement error for any single self-reported item. An Citation: Global Health Action Supplement 2, 2010. DOI: 10.3402/gha.v3i0.2151

Self-reported health and functional limitations among older people in Ghana

overall health status score (HSS) for each respondent was derived from responses to these various items using Item Response Theory (IRT) parameter estimates in Winsteps, a Rasch measurement software package (http://www.win steps.com). The health score is then transformed to a scale of 0
100 (where 0 represents the worst health and 100 the best health status). Based on self-reports of difficulty in carrying out various activities contained in the health status descriptions section of the questionnaire, an index of overall disability (WHO Disability Assessment Scale
WHO DAS) was constructed. Self-reported functioning was assessed through the standardised 12-item WHODAS, Version 2 (11). On a 5-point scale, respondents rated their level of difficulty in carrying out various activities. These responses were used to create a score of overall disability; the score was then transformed to a scale ranging from 0 (no disability) to 100 (greatest disability). In effect, WHODAS is an overall summary of one’s perceived difficulties in carrying out various functions of daily living. A higher score indicates greater perceived difficulty in carrying out daily functions, while a lower score indicates lower perceived difficulty in functioning. In order to make this score conceptually consistent with the HSS, it was inverted to a score designated here as WHODASi, so that a higher score (on a 0
100 scale) represents better functioning. In the analyses we grouped WHODASi into quintiles to represent levels of functional ability.

Analysis The analysis is in two parts. First, we describe the health status of older people based on three indicators: overall SRH, an index of self-reported functional ability (WHODASi) and an overall HSS. In the second part of the analysis, we explore factors related to poor SRH using logistic regression. In this analysis we are particularly interested in the influence of reported functional ability (WHODASi) on self-related health status. Functional ability is an important dimension of health and an individual’s assessment of ability to perform basic daily activities is likely to influence SRH. However, the magnitude of the influence of functional limitations on SRH may be mitigated by factors such as the cause and duration of disability, awareness of co-morbidity and access to assistive devices. Generally, we expect that adults with greater functional disability will rate their health poorer than those with lower disability. We controlled for confounders such as age, ever attended school, marital status, relationship to household head, socioeconomic status and proportion of household members aged 50 or over. These factors have been identified as significant factors in self-reported health, as have age and gender differences (5, 12). Similarly, marital status, education, socioeconomic status and social support have been identified as relevant factors in health status (13). We include relationship to household head and proportion of older people in household as crude indicators of social support.

Socio-demographic variables Socio-demographic information on respondents was obtained from routine demographic surveillance data including sex, age, education, marital status, relationship to head of household, number of older people in the household and household economic status. Age was categorised into three subgroups: 50
59, 60
69 and 70. Marital status was categorised as married or unmarried. Educational status was categorised as never attended school or ever attended school. In the analysis those who have never attended school are referred to as having no formal education, while those who have ever attended school are described as having some formal education. The socioeconomic status of households was assessed in terms of wealth quintiles based on possessions and housing characteristics. The five quintiles represent poorest, poorer, poor, less poor and least poor households. In terms of relationship to the head of household, respondents were described as head, spouse of head, parent of head or other relation to head of household. The number of older people in the household was expressed as a proportion of the total number of people in the household and grouped into quartiles for the analysis.

Results Although a sample of adults aged 18
49 years were interviewed using the summary version of the SAGE Adult Health Survey, our analysis in this article is limited to older participants in the survey. Of the 6,074 older people targeted for survey, 4,584 were successfully interviewed (a response rate of 75.5%). A major reason for non-participation was the inability of the interviewers to meet the targeted respondent after at least three visits to the household. Other reasons include migration, death and inaccurate information. In Table 1 we compare respondents and non-respondents in terms of background characteristics (sex, age, education, marital status, relationship to household head, socioeconomic quintile of household, average household size and proportion of household members aged 50 years and over). The data indicate that compared with respondents, non-respondents were largely male, slightly younger, unmarried, more educated and from relatively less poor households. These are likely to be more active and mobile and hence are more likely to be away from home during the survey. To the extent that our respondents are not representative of the older population of the district, our results may have limited generalisability.

Citation: Global Health Action Supplement 2, 2010. DOI: 10.3402/gha.v3i0.2151

57

Cornelius Debpuur et al.

Table 1. Background characteristics of 4,584 adult respondents and 1,437 non-respondents aged 50 and over in northern Ghana

Variables

Respondents (n 4,584)

Non-respondents (n1,437)

Sex (%) Men Women

39.0 61.0

44.9 55.1

Mean age (SD)

62.5 (9.1)

61.4 (9.0)

Age group (%) 50
59 years

43.0

50.2

60
69 years

35.9

30.8

70
79 years

16.6

14.8

4.5

4.2

80 years and over

Table 2. Proportions reporting poor self-rated health among 4,584 adults aged 50 and over in northern Ghana Variables

Men (%)

Women (%)

All (%)

Sex

Education level (%) No formal education

90.7

85.2

Less than or equal to

3.9

3.5

5.4

11.5

6 years More than 6 years

The percentage of older people reporting poor health increased with age among both men and women. However, the greatest differentials in SRH were observed in terms of levels of functional disability. The proportion of respondents reporting poor health was substantially higher among those also reporting low functional ability, both in men and women. Whereas less than one in five participants in the highest category of functional ability reported poor health, more than three in four of those in the lowest category of functional ability reported poor

Men





35.2

Women





45.2

50
59

26.7

37.7

33.5

60
69

33.7

48.8

43.4

70 years and over

50.7

58.8

54.9

Age group (years)

Marital status (%) Now single

46.3

50.5

In current partnership

53.7

49.5

Socioeconomic quintile (%)

Education level No formal education

37.1

46.3

42.9

Some formal education

25.5

36.4

29.8

First quintile

27.5

23.5

Second quintile

24.4

18.8

Marital status

Third quintile

21.9

20.4

Now single

41.1

48.5

47.3

Fourth quintile

18.7

21.5

In current partnership

33.9

40.6

36.6

7.4

15.7

Fifth quintile

Relationship to household head (%)

Relationship to household head Head

34.7

44.9

38.2

Head

51.0

52.3

Spouse

14.8

41.5

40.8

Spouse

21.1

15.4

Parent

38.1

52.2

51.3

Parent Other relation

13.8 14.2

10.9 21.4

Other relation

43.5

47.0

46.1

Mean number of house-

6.6 (4.6)

6.2 (5.1)

hold members (SD) Mean proportion of

0.4 (0.2)

0.4 (0.3)

household members aged 50 and over (SD)

The majority of respondents (61%) were female, and the average age was 62.5 years, with men (average age of 63.7 years) being older than the women (average age of 61.7 years). Nearly three-quarters of the respondents were aged below 70, and less than 10% had ever attended school. About half of the respondents were married, while a similar proportion were heads of households. Overall, less than half of the respondents (42%) rated their overall health as poor, with slightly more women (45%) than men (35%) reporting poor health (Table 2).

58

Proportion of household members aged 50 and over (%) B25

32.9

45.4

40.3

25
49

37.2

44.3

41.4

50
74

32.6

44.4

40.4

575

46.4

53.2

50.7

Socioeconomic quintile Poorest quintile

36.7

45.7

41.8

Second quintile

36.8

52.0

45.7

Third quintile

37.6

46.3

43.1

Fourth quintile Least poor quintile

30.1 29.1

41.3 34.8

37.5 32.7

WHODASi quintile Highest ability quintile

13.8

20.0

16.8

Second quintile Third quintile

23.3 35.4

28.3 44.1

26.4 40.8

Fourth quintile

54.9

57.6

56.7

Lowest ability quintile

75.6

77.6

76.9

Citation: Global Health Action Supplement 2, 2010. DOI: 10.3402/gha.v3i0.2151

Self-reported health and functional limitations among older people in Ghana

Mean WHODASi functional ability score

health (Fig. 1). Other differentials were also observed in terms of education, marital status and household socioeconomic status. The proportion of household population aged 50 and over is included to indicate social support within the household. Households with more than half of members aged 50 or over have a greater proportion of elderly dependents and possibly less social support, hence SRH in such households could be poorer than in less-dependent households. Table 3 shows results of WHODASi and HSS for older men and women in the Kassena-Nankana District by age category. A higher WHODASi score indicates a higher level of functional ability compared to a lower score. The mean WHODASi for the sample is 70.9 (73.7 for men and 69.1 for women), with the mean score decreasing with age such that the oldest respondents had lowest functional ability. Thus, the reported level of functional ability decreased with age. This pattern was observed among both men and women (Fig. 2). Generally, the WHODASi score was lower among women compared to men of comparable age. The age
sex pattern in functional limitations is evident in the proportion of older people whose WHODASi scores were below the median for the overall sample. Higher proportions of participants in the older age groups had scores below the median than their younger counterparts. Similarly, more women in each age group had WHODASi scores below the median (72.2) compared to men. For the overall HSS a higher score indicates better health than a lower score. The mean HSS score for the sample was 64.0 with men scoring slightly higher (65.8) than women (62.8) as shown in Table 3. In terms of age, younger age groups tended to report better health (higher mean HSS) than their older counterparts; while more women than men in each age group reported HSS below the median (63.5).

80

70

60

50

40 50–54

55–59

60–64

65–69

70–74

75–79

80 +

Age group Poor self-rated health

Good self-rated health

Fig. 1. Mean WHODASi functional ability score, by age group and self-rated health, among 4,584 adults aged 50 and over in northern Ghana.

These three indicators measure different dimensions of health, and although SRH, WHODASi and HSS are related, none is completely determined by the others. SRH and WHODASi are positively related with correlation of 0.49, while SRH and HSS are similarly correlated (0.50). The highest correlation is found between WHODASi and HSS (0.84). On the basis of SRH, WHODASi and HSS, reported health status declined with age and was slightly worse among women than men. We explored the association between functional disability and SRH among older people while controlling for selected socio-demographic factors such as sex, age, education, marital status, relationship to head of household, proportion of people aged 50 and over in the household and socioeconomic quintile of the household. Table 4 presents logistic regression results with poor SRH as the outcome variable. In the univariate model most factors (except proportion of household members aged 50 and over, and household socioeconomic status) had a significant association with SRH status. Individuals with lower functional ability levels were more likely to report poor health than their colleagues with better functional ability. Men appeared less likely to report poor health than women. Other researchers have suggested that women’s poorer rating of their health may be indicative of greater sensitivity to health conditions rather than a female health disadvantage (7). The oldest old were much more likely to rate their health poorly than those 50
59 years old. Similarly those with no education were more likely to report poor health than those with some education; being single was associated with reports of poor health. In terms of relationship to the household head, those who were parents of or otherwise related to the head appeared more likely to report poor health compared to the household heads themselves. In the multivariate model, the effects of WHODASi remained significant, with respondents in the higher disability quintiles much more likely to report poor health status than those in the lowest disability quintile. In other words, adults with greater functional limitations were more likely to rate their health as poor compared to those with less functional limitations. The other factors that had significant effect on SRH were sex and household wealth quintile. Women were more likely than men to rate their health as poor, while older people in the two higher wealth quintiles were less likely to rate their health as poor compared to their counterparts in the least wealthy quintile. The effects of age were barely significant after allowing for WHODASi, although older adults appeared more likely to report poor health than their younger colleagues. These results suggest that functional disability is the primary factor associated with overall SRH among older people in the Kassena-Nankana District. The influence

Citation: Global Health Action Supplement 2, 2010. DOI: 10.3402/gha.v3i0.2151

59

Cornelius Debpuur et al.

Table 3. Distribution of WHODASi functional ability score and health status score by age and sex among 4,584 adults aged 50 and over in northern Ghana Men (n1,789)

Women (n 2,795)

All (n 4,854)

50
59 years

79.7 (15.7)

74.8 (15.0)

76.6 (15.4)

60
69 years

74.5 (16.9)

67.6 (16.8)

70.1 (17.2)

70 years and over All ages

63.6 (20.9) 73.7 (18.7)

58.1 (18.7) 69.1 (18.7)

60.7 (20.0) 70.9 (18.1)

Variables Mean WHODASi score (SD)

Proportion of respondents with WHODASi less than median 50
59 years 60
69 years

29.4 41.8

40.1 57.7

36.2 51.9

70 years and over

63.7

75.2

69.7

All ages

42.5

53.0

48.9

66.4 (8.3)

Mean health status score (SD) 50
59 years

68.4 (9.4)

65.2 (7.4)

60
69 years

65.9 (8.7)

62.1 (7.4)

63.5 (8.1)

70 years and over

61.7 (9.3)

58.3 (7.3)

59.9 (8.5)

All ages

65.8 (9.6)

62.8 (7.8)

64.0 (8.7)

34.8

Proportion with health status score less than median 50
59 years

27.2

39.1

60
69 years

39.6

57.5

51.0

70 years and over

59.9

77.2

68.8

All ages

39.9

52.8

47.8

Mean WHODASi functional ability score

of functional limitations on SRH observed in this study is consistent with findings from other studies (9, 14, 15). Other significant determinants of SRH were sex and household wealth quintile. Although age, education and marital status appeared to be significant in the univariate analysis, their significance eroded when other variables were controlled for in the multivariate analysis. Unlike

80

70

60

50

40 50–54

55–59

60–64

65–69

70–74

75–79

80+

Age group Men

Women

Fig. 2. Mean WHODASi functional ability score, by age group and sex, among 4,584 adults aged 50 and over in northern Ghana.

60

other studies, it appears that these factors are not important determinants of SRH in this population.

Discussion Data on adult health status, particularly the health of older people in sub-Saharan Africa, are required to monitor trends in the health status of adults and the extent to which social and health policies impact on older people. One relatively easy way of generating such data is through population surveys of self-reported health. The implementation of such surveys has contributed immensely to the understanding of ageing and transitions in health with age in the developed world and Asia. Although self-reported health is subjective, it has been found to be a good predictor of future health care use and mortality. In 2007, the NHRC conducted a survey on ageing and adult health in the Kassena-Nankana District of Ghana as part of the INDEPTH WHO-SAGE Adult Health Study. The survey collected information on selfreported health among adults in the district. Data from this survey have been analysed to describe the health status as well as identify factors associated with SRH status among older people in this rural setting. Our results indicate that the majority of older people rated their overall health as good. However, women were more likely than men to rate their health as poor. A similar pattern was observed with regard to reported Citation: Global Health Action Supplement 2, 2010. DOI: 10.3402/gha.v3i0.2151

Self-reported health and functional limitations among older people in Ghana

Table 4. Factors associated with poor self-rated health among 4,584 adults aged 50 and over in northern Ghana Univariate model Variables

Multivariate model

(OR and [95% CI]) (OR and [95% CI])

WHODASi quintile Highest ability

1.00

1.00

quintile Second quintile

1.78 [1.43
2.22]**

1.65 [1.32
2.07]**

Third quintile

3.42 [2.72
4.23]**

3.18 [2.56
3.96]**

Fourth quintile

6.51 [5.18
8.17]**

5.76 [4.54
7.32]**

Lowest ability

16.56 [13.1
20.9]** 14.23 [11.1
18.3]**

quintile Sex Men

1.00

1.00

Women

1.54 [1.37
1.75]**

1.40 [1.38
1.73]**

Age group (year) 50
59

1

1

60
69 70 years and over

1.52 [1.32
1.74]** 2.41 [2.06
2.82]**

1.12 [0.96
1.31] 1.24 [1.02
1.51]*

No formal education

1.00

1.00

Some formal education

0.56 [0.46
0.69]**

0.92 [0.71
1.17]

1.56 [1.32
1.75]** 1

0.94 [0.78
1.13] 1

Education level

Marital status Now single In current partnership

Relationship to household head Head

1

1

Spouse Parent

1.11 [0.96
1.30] 1.70 [1.43
2.03]**

0.94 [0.75
1.19] 1.00 [0.80
1.26]

Other relation

1.38 [1.15
1.66]**

1.06 [0.85
1.33]

Proportion of household members aged 50 and over (%) B25 1.00 1.00 25
49

1.04 [0.90
1.20]

50
74

1.00 [0.85
1.18]

1.00 [0.85
1.18] 0.96 [0.80
1.17]

575

1.52 [1.23
1.88]**

1.49 [1.16
1.92]**

Poorest quintile

1.00

1.00

Second quintile

1.17 [0.99
1.38]

1.12 [0.93
1.35]

Third quintile

1.05 [0.88
1.25]

0.95 [0.78
1.15]

Fourth quintile

0.83 [0.70
1.00]

0.74 [0.60
0.90]**

Least poor quintile

0.67 [0.52
0.87]**

0.65 [0.48
0.88]**

Socioeconomic quintile

*pB0.05; **pB0.001.

functional disability and overall health score. Functional disability was higher among women compared to men. Among both men and women, older adults were more likely to report functional disability. Adults with higher functional disability were more likely to rate their health

as poor compared to those with lower disability. Multivariate regression results showed that levels of functional disability, sex and household wealth quintile had significant influence on SRH status. The findings in this study are comparable with the results of previous studies in various parts of the world. Earlier studies have noted the existence of sociodemographic differentials in SRH. Research evidence suggests that men generally report fewer diseases and fewer limitations in activities of daily living at older ages than their female counterparts. Women are more likely to rate their health poorer and to report more functional limitations and disability than men (16
20). Irrespective of sex, however, older age is related to higher odds of reporting health problems and various studies have observed that older adults tend to rate their health poorer than their younger colleagues (16, 21, 22). Lower socioeconomic status is associated with worse morbidity, mortality and self-reported health in older persons (23). Other factors such as marital status, socioeconomic status and education are also known to affect health status (24, 25), although marital status and education did not appear significant in our analysis. Older people in this district face considerable health challenges like their colleagues elsewhere. As our results indicate, there is considerable increase in functional limitations and poor health with age, with more women tending to report health problems than their male counterparts. Unfortunately however, older people in the Kassena-Nankana District do not only have to deal with functional limitations, but also have to deal with infectious diseases such as malaria and gastroenteritis. What is more, they grapple with these health challenges in a context of inadequate health care and weak social support systems. Public policy and health interventions that promote healthier lifestyles and improve access to health care are required to improve the health and quality of life of older people. In spite of increasing urbanisation, the majority of Ghana’s older people live in rural areas where health and social services are inadequate. Education and information on healthy living need to be made available to the general population to enhance prevention and control of chronic conditions. Programmes need to focus attention on promoting healthy ageing. Bold policy decisions are also needed to integrate ageing and adult health issues into all aspects of national planning and development. Some observers have noted that the concerns of older people remain marginalised in Ghana’s social and economic debates (21). There is the need to marshal evidence on the health situation of older people in the country and to use this evidence to advocate for programmes and policies to address the health care and other needs of older people. This study has highlighted the situation of older people in one of the rural districts in Ghana, and it is hoped that this will broaden the evidence

Citation: Global Health Action Supplement 2, 2010. DOI: 10.3402/gha.v3i0.2151

61

Cornelius Debpuur et al.

on the health status of older Ghanaians and contribute towards effective policy formulation in the country. Results from the national SAGE study conducted in the country around the same time as our study will provide a broader national picture on the health status of older people. For the purposes of monitoring the health status of older people, such studies need to be conducted periodically and in a variety of settings. This initial survey has demonstrated the feasibility of conducting population-based health surveys of adults in rural Ghana. The results of the analyses are generally consistent with other studies and indicate the scope for monitoring population health using selfassessments of health. There is the opportunity for follow-up and longitudinal analysis anchored on the HDSS platform existing in the district. Future analyses will explore the relationship between SRH and morbidity and mortality in this population. The INDEPTH WHO-SAGE Adult Health Research platform (of which this study is a part) is uniquely placed to contribute towards an understanding of the relationship between SRH and subsequent morbidity and mortality in the region. Subsequent analysis of SRH and mortality from INDEPTH Network sites will contribute to the literature on this topic, which is currently under-researched in sub-Saharan Africa.

Conclusion As in other developing countries, the population of older people in Ghana is increasing steadily. Despite the increasing number of older people in the country, however, very little is known about their health status, especially for those in rural areas. This lack of knowledge impedes development and implementation of policies and programmes as well as evaluation of the impact of social and health policies on older people. Ghana is participating in the WHO multi-country SAGE. The data presented in this study form part of this global study. Our results suggest that the ageing process in this district is consistent with what has been observed in other parts of the world. SRH declines with age among both men and women. It appears that with increasing age there is a decline in health which is manifest in increasing functional disability. This depreciation in health and daily functioning increases the demand for health care and other services by older people. Therefore, steps need to be taken to address the health care and other needs of older people. Health policies and programmes that improve functional capacity and well-being for older people are particularly urgent. There is also the need for regular monitoring and assessment of the health status of older people to provide public health agencies with the data they need to assess, protect, and promote the health and wellbeing of older people. The present study will serve as a

62

baseline for monitoring trends in the health status of older people in the Kassena-Nankana District.

Acknowledgements The authors would like to thank the people of the Kassena-Nankana District, especially all the men and women who agreed to be interviewed, for their support and participation in the study. We are grateful to the NHRC staff who collected and processed the data for this study.

Conflict of interest and funding This project was supported by a grant from the INDEPTH WHO-SAGE study and the INDEPTH Network. The authors would like to acknowledge the INDEPTH Network for their financial support.

Note 1. In 2008 the Kassena-Nankana District was split into two districts
Kassena-Nankana and KassenaNankana West districts. In this article we use the original name of the district to refer to the two districts.

References 1. United Nations Department of Economic and Social Affairs (DESA). Population Division. World population prospects: the 2006 revision, highlights. United Nations. New York, NY: DESA; 2007. 2. Rosenberg M. Overview of life expectancy. Available from: http://geography.about.com/od/populationgeography/a/lifeexpec tancy.htm [cited 26 August 2008]. 3. Adongo BP, Phillips JF, Kajihara B, Fayorsey C, Debpuur C, Binka FN. Cultural factors constraining the introduction of family planning among the Kassena-Nankana of northern Ghana. Soc Sci Med 1997; 45: 1789
804. 4. Nyarko P, Wontuo P, Nazzar A, Phillips J, Ngom P, Binka F. In: Sankoh O, Kahn K, Mwageni E, Ngom P, Nyarko P, eds. Population and health in developing countries. Population, health, and survival at INDEPTH sites, Vol. 1. Ottawa: International Development Research Centre; 2002, pp. 247
56. 5. Idler EL, Benyamini Y. Self-rated health and mortality: a review of twenty-seven community studies. J Health Soc Behav 1997; 38: 21
37. 6. Frankenberg E, Jones NR. Self-rated health and mortality: does the relationship extend to a low income setting? J Health Soc Behav 2004; 45: 441
52. 7. Kuhn R, Rahman O, Menken J. Survey measures of health: how well do self-reported and observed indicators measure health and predict mortality? In: Cohen B, Menken J, eds. Committee on population, division of behavioral and social sciences and education. Washington, DC: The National Academies Press; 2006, pp. 314
41. 8. Deeg DJ, Kriegsman DM. Concepts of self-rated health: specifying the gender difference in mortality risk. Gerontologist 2003; 43: 376
86. 9. Murata C, Kondo T, Tamakoshi K, Yatsuya H, Toyoshima H. 2006. Determinants of self-rated health: could health status explain the association between self-rated health and mortality? Arch Gerontol Geriatr 2006; 43: 369
80. Citation: Global Health Action Supplement 2, 2010. DOI: 10.3402/gha.v3i0.2151

Self-reported health and functional limitations among older people in Ghana

10. Manor O, Matthews S, Power C. Dichotomous or categorical response? Analysing self-rated health and lifetime social class. Int J Epidemiol 2000; 29: 149
57. 11. World Health Organization. Measuring health and disability: a manual for the World Health Organization Disability Assessment Schedule (WHODAS 2.0). Geneva: World Health Organization; 2009. 12. Zimmer Z, Natividad J, Lin HS, Chayovan N. A cross national examination of the determinants of self-assessed health. J Health Soc Behav 2000; 41: 465
81. 13. Kuate-Defo B. Interactions between socioeconomic status and living arrangements in predicting gender-specific health status among the elderly in Cameroon. In: Cohen B, Menken J, eds. Committee on population, division of behavioral and social sciences and education. Washington, DC: The National Academies Press; 2006, pp. 276
313. 14. Idler EL. Gender differences in self-rated health, in mortality, and in the relationship between the two. Gerontologist 2003; 43: 372
5. 15. Damian J, Ruigomez A, Pastor V, Martin-Moreno JM. Determinants of self assessed health among Spanish older people living at home. J Epidemiol Community Health 1999; 53: 412
6. 16. Shi J, Liu M, Zhang Q, Lu M, Quan H. Male and female adult population health status in China: a cross-sectional national survey. BMC Public Health 2008; 8: 277. Available from: http:// www.biomedcentral.com/content/pd/1471-2459-8-277.pdf [cited 6 April 2009]. 17. Tajvar M, Arab M, Montazeri A. Determinants of healthrelated quality of life in elderly in Tehran, Iran. BMC Central public Health 2008; 8: 323. Available from: http://www.biomed central.com/content/pdf/1471-2458-8-323.pdf [cited 6 April 2009].

18. Knurowski T, Lazic D, van Djik JP, Geckova AM, TobiaszAdamczyk B, van den Heuvel WJA. Survey of health status and quality of life of the elderly in Poland and Croatia. Croat Med J 2004; 45: 750
6. 19. Olsen KM, Dahl SA. Health differences between European countries. Soc Sci Med 2007; 64: 1665
78. 20. Zimmer Z. Poverty, wealth inequality, and health among older adults in rural Cambodia. Soc Sci Med 2008; 66: 57
71. 21. Mba CJ. The health condition of older women in Ghana: a case study of Accra City. J Int Women’s Stud [online] 2006; 8: 176
189. Available from: http://www.bridgew.edu/soas/jiws/Nov 06/index.htm. [cited 16 February 2009]. 22. Omar MR, Arthur JB. Self-reported health among older Bangladeshis: how good a health indicator is it? Gerontologist 2003; 43: 856
63. 23. Lawlor DA, Sterne JAC. Socioeconomic inequalities in health. BMJ 2007; 334: 963
4. 24. Waite L, Gallagher M. The case for marriage: why married people are happier, healthier, and better off financially. New York: Doubleday; 2000. 25. Williams K, Umberson D. Marital status, marital transitions, and health: a gendered life course perspective. J Health Soc Behav 2004; 45: 81
98. *Cornelius Debpuur Navrongo Health Research Centre P.O. Box 114 Navrongo, UER, Ghana Tel: 233 74222310 Fax: 233 74222320 Email: [email protected]

Citation: Global Health Action Supplement 2, 2010. DOI: 10.3402/gha.v3i0.2151

63

INDEPTH WHO-SAGE Supplement


Patterns of health status and quality of life among older people in rural Viet Nam Hoang Van Minh1,2*, Peter Byass3#, Nguyen Thi Kim Chuc1,2 and Stig Wall3# 1

Faculty of Public Health, Hanoi Medical University, Hanoi, Viet Nam; 2INDEPTH Network, Accra, Ghana; 3Department of Public Health and Clinical Medicine, Umea˚ Centre for Global Health Research, Umea˚ University, Umea˚, Sweden

Background: To effectively and efficiently respond to the growing health needs of older people, it is critical to have an indepth understanding about their health status, quality of life (QoL) and related factors. This paper, taking advantage of the INDEPTH WHO-SAGE study on global ageing and adult health, aims to describe the pattern of health status and QoL among older adults in a rural community of Viet Nam, and examine their associations with some socio-economic factors. Methods: The study was carried out in the Bavi District, a rural community located 60 km west of Hanoi, the capital, within the Epidemiological Field Laboratory of Bavi (FilaBavi). Face-to-face household interviews were conducted with people aged 50 years and over who lived in the FilaBavi area. The interviews were performed by trained surveyors from FilaBavi using a standard summary version SAGE questionnaire. Both descriptive and analytical statistics were used to examine the patterns of health status and QoL, and associations with socio-economic factors. Results: Higher proportions of women reported both poor health status and poor QoL compared to men. Age was shown to be a factor significantly associated with poor health status and poor QoL. Higher educational level was a significant positive predictor of both health status and QoL among the study subjects. Higher economic status was also associated with both health status and QoL. The respondents whose families included more older people were significantly less likely to have poor QoL. Conclusion: The findings reveal problems of inequality in health status and QoL among older adults in the study setting by sex, age, education and socio-economic status. Given the findings, actions targeted towards improving the health of disadvantaged people (women, older people and lower education and economic status) are needed in this setting. Keywords: older people; health status; quality of life; rural; Viet Nam; INDEPTH WHO-SAGE

Access the supplementary material to this article: INDEPTH WHO-SAGE questionnaire (including variants of vignettes), a data dictionary and a password-protected dataset (see Supplementary files under Reading Tools online). To obtain a password for the dataset, please send a request with ‘‘SAGE data’’ as its subject, detailing how you propose to use the data, to [email protected]

Received: 5 March 2010; Revised: 13 May 2010; Accepted: 8 July 2010; Published: 27 September 2010

uring the past few decades, under the forces of a demographic transition characterised by declining fertility rates and increasing life expectancy, the proportion of people in the world

D #

Deputy Editor, Peter Byass, Chief Editor, Stig Wall, have not participated in the review and decision process for this paper.

population who reach middle age and beyond is increasing sharply (1
3). Developing countries are currently ageing much faster than industrialised countries (3, 4). In 2002, almost 400 million people aged 60 and over lived in the developing world. By 2025, it may rise to 840 million representing 70% of all older people worldwide (2, 3).

Global Health Action 2010. # 2010 Hoang Van Minh et al. This is an Open Access article distributed under the terms of the Creative Commons Attribution- 64 Noncommercial 3.0 Unported License (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. Citation: Global Health Action Supplement 2, 2010. DOI: 10.3402/gha.v3i0.2124

WHO-SAGE study on older adults in rural community of Viet Nam

Population ageing in low- and middle-income countries has special implications for many public services, especially for health care, in these countries. The health care systems of many developing countries are still focused on childhood and infectious diseases as well as reproductive health services. But the ageing population leads to increasing demands for care that addresses chronic health conditions (2). Nowadays, in all countries, and in low- and middle-income countries in particular, measures to help older people remain healthy and active are urgently needed (2, 4). To effectively and efficiently respond to the growing health needs of older populations, it is critical to have an indepth understanding about their health conditions, quality of life (QoL) and related socio-economic factors. Viet Nam, a developing country in South-East Asia, is undergoing demographic transition and experiencing rapid population ageing. The proportion of people aged 50 years and over rose from 12.6% in 2000 to 14.1% in 2005 and will account for 18.9% of the total population in 2015 (3). In Viet Nam, the number of older people living in rural areas is about 3.5 times higher than those living in urban areas (5). About 44% of older Vietnamese are working, but mostly in agricultural activities which provide low and unstable incomes. Other sources of income, including pension and social assistance benefits, are significant factors to reduce risks for older people. However, the coverage of the current social protection system in Viet Nam is not adequate (6). Life expectancy in Viet Nam reached 72.2 years in 2005, a relatively high level compared to the nation’s economic conditions. However, the average healthy life expectancy was far lower, at 58.2 years and ranked 116 among 174 countries in the world (7). Health care for the older people in Viet Nam has been improved, but the accessibility for vulnerable and low-income older people is still low, and the poorer shoulder greater burdens of health care costs in terms of percentage of household expenditure (8). As in other developing countries, little empirical research has been conducted in Viet Nam on the health status, QoL and related socio-economic status among older people. This article, therefore, taking advantage of the INDEPTH WHO-SAGE study on global ageing and adult health (9), aims to describe the patterns of health status and QoL among older adults in a rural community of Viet Nam, and examine their associations with some socio-economic factors.

Methods Study design and setting This was a population-based cross-sectional study, carried out in Bavi District, a rural community located 60 km west of Hanoi, the capital, within the Epidemiological Field Laboratory of Bavi (FilaBavi). The FilaBavi Health Citation: Global Health Action Supplement 2, 2010. DOI: 10.3402/gha.v3i0.2124

and Demographic Surveillance System (HDSS), supported by Sida/SAREC, was established in 1999 with a sample of around 50,000 individuals from the Bavi District. People aged 50 and over accounted for about 17% of the total population under surveillance. The surveyed population includes three distinct groups: those in mountainous areas, highlands, and riverside or island dwellers (10). The FilaBavi HDSS is a member of the INDEPTH network (11).

Data collection Face-to-face household interviews were planned for all people aged 50 years and over who lived in the FilaBavi area between the end of 2006 and the beginning of 2007. The interviews were done by trained surveyors from FilaBavi using a summary version of the SAGE questionnaire (available as a Supplementary File to this paper). Further details of the study methodology are available separately (9). The questionnaire was translated into the local language and pre-tested before official use. Spot-checks and re-checks on sample data were conducted by supervisors for quality control. Measurements Outcome variable Self-reported health status and QoL among the study subjects were outcome variables. Health status scores were calculated based on self-reported health levels in eight health domains covering: affect, cognition, interpersonal activities and relationships, mobility, pain, self-care, sleep/ energy, and vision. Each domain included at least two questions. Asking more than one question about difficulties in a given domain provides more robust assessments of individual health levels and reduces measurement error for any single self-reported item. Health status scores were computed by using Item Response Theory (IRT) parameter estimates in Winsteps†, a Rasch measurement software package (http://www.winsteps. com). Higher health status scores within a 0
100 scale imply better health status. Respondents who had health status scores below the median were categorised as having poor health status. QoL was assessed by using the eightitem version of the World Health Organization Quality of Life instrument (WHOQoL). Results from the eight items were summed to get an overall WHOQoL score which was then transformed into a 0
100 scale. The higher the WHOQoL score, the better the QoL. Respondents who had WHOQoL scores less than the median were considered as having poor QoL. More details on how scores for this study were derived are given elsewhere (9). Explanatory variables Explanatory variables included sex (male, female), age (grouped as 50
59, 60
69, 70
79, 80 years), educational level (no formal education, up to 6 years of formal

65

Hoang Van Minh et al.

education, more than 6 years of education), marital status (in current partnership, never married, separated, divorced and widowed), wealth index (quintiles), whether respondent stays alone (yes or no) and proportion of people aged 50 years and over in the same household (B25%, 25
49%, 50
74%, 75%). The wealth quintiles were constructed by using principal component analysis techniques (12). Variables used included household income, the area of land owned, type of house, materials of roof and floor, toilet facilities, electricity and water supplies, and ownership of a range of durable assets for each household.

Data analysis Both descriptive and analytical statistics were performed. The data analysis began with calculation of frequencies and percentages of the variables of interest. Multivariable logistic regressions were then carried out to examine the association between health status and QoL with the selected explanatory variables.

Table 1. Background characteristics of study subjects (respondents and non-respondents) Respondents

Non-respondents

(n8,535)

(n339)

Gender Male (%)

3,469 (40.6)

140 (42.6)

Female (%)

5,066 (59.4)

189 (57.4)

50
59 (%)

3,221 (37.7)

148 (45)

60
69 (%) 70
79 (%)

2,258 (26.5) 2,086 (24.4)

Age (years)

80 and over (%) Mean age (SD)

87 (26.3) 45 (13.8)

970 (11.4)

49 (14.9)

65.3 (10.7)

63.7 (19.2)

Education No formal education (%)

878 (10.3)

85 (25.9)

Primary orB6 years (%)

4,190 (49.1)

112 (33.9)

More than 6 years (%)

3,467 (40.6)

132 (40.2)

In current partnership (%) 5,895 (69.1)

215 (65.5)

Now single (%)

114 (34.5)

Marital status

Ethical considerations The protocol of this study was approved by the Scientific Board of FilaBavi. All subjects in the study were asked for their written informed consent before collecting data, and they had complete right to withdraw from the study at any time without disadvantage.

Results Characteristics of the study populations Of the total 8,874 people aged 50 and over living in the study setting at the time of the survey, there were 8,535 who participated in the study (amounting to 96%). Four percent of subjects were unable to participate as they were away (2.3%) or were not healthy enough to take part in the survey (1.7%). The background characteristics of potential study subjects (respondents and non-respondents) are described in Table 1. There were no significant differences in socio-economic characteristics between the respondents and the non-respondents. Distribution of health status and WHOQoL scores Table 2 presents the distribution of health status scores of the study population by age and sex. The overall mean health status score was 66.2 and median 65.0. In both sexes, the average health status scores decreased with age. Men had higher health status scores than women of the same age group. Overall, the proportion of respondents with below-median health status among men and women was 39.1 and 58.3%, respectively. A similar pattern was observed for QoL. The overall mean WHOQoL score was 61.2, median 62.5. In both sexes, the average WHOQoL score decreased with age. Women had lower WHOQoL scores than men of the

66

2,640 (30.9)

Economic status of household Poorest quintile 1,209 (14.2)

41 (12.4)

Second quintile

1,548 (18.2)

Third quintile

1,787 (21)

65 (19.9)

Fourth quintile

1,996 (23.4)

81 (24.6)

Least poor quintile

1,976 (23.2)

Mean number of

4.2 (2)

52 (15.7)

90 (27.4) 4.3 (1.9)

household members (SD) Proportion of household members aged 50 and

50.7 (28.9)

49.5 (28.8)

over (SD)

same age group. Overall, the proportion of respondents with poor QoL among men and women was 38.4 and 52.4%, respectively (Table 3). Fig. 1 shows the distribution of the study subjects by health status and QoL. Women were shown to have poorer health status than men. About 25.9% of men and 40% of women reported having both poor health status and poor QoL.

Factors associated with poor health status and poor quality of life (QoL) Multivariate logistic regression analyses of the association between poor health status and poor QoL, and socioeconomic status are shown in Table 4. Men were shown to be significantly less likely to have poor health status compared to women. Older respondents had poorer health status than those younger. People with lower educational levels had a significantly higher probability of having poor health status than those with higher educational levels. Citation: Global Health Action Supplement 2, 2010. DOI: 10.3402/gha.v3i0.2124

WHO-SAGE study on older adults in rural community of Viet Nam

Men

Table 2. Distribution of health status scores by age and sex among 8,535 adults aged 50 years and over in northern rural Viet Nam

Variables

Women (n5,066)

48.3

50.0 40.0

40.0

(%)

Men (n3,469)

30.0

29.3 25.9

Mean health status score (SD) 50
59 years 60
69 years

72.5 (11.5) 68.8 (9.9)

68.8 (9.4) 64.8 (7.9)

70
79 years

65.3 (9.2)

61.7 (8.2)

80 years and over

60.1 (8.8)

57.6 (8.2)

All ages

60.1 (8.8)

57.6 (8.2)

68.9 (11.0)

64.4 (8.4)

50
59 years (%)

24.4

35.4

60
69 years (%)

33.1

51.7

70
79 years (%)

50.1

67.1

80 years and over (%) All ages (%)

70.0 35.9

81.5 54.2

Proportion of respondents with health status score below the median

Study subjects in the lowest wealth quintile were more likely to have poor health status than those belonging to the highest wealth quintile. There was no significant association between poor health status and marital status or the proportion of older people living in a household. Similar to the pattern of health status, poor QoL was shown to be significantly associated with women, older ages, lower educational levels and lower economic status. Table 4 shows that the respondents whose families had more older people ( ]75% people aged 50 years and over in the same household) were significantly less likely to have poor QoL. Women, older people, those with lower Table 3. Distribution of WHOQoL scores by age and sex among 8,535 adults aged 50 years and over in northern rural Viet Nam Variables

Men (n 3,469)

Women (n 5,066)

Mean QoL score (SD) 50
59 years

65.7 (12.7)

62.2 (12.3)

60
69 years 70
79 years

64.1 (13.2) 61.9 (14.1)

60.9 (12.6) 57.7 (13.2)

80 years and over

56.6 (14.1)

53.8 (14.0)

All ages

63.7 (13.5)

59.5 (13.2)

Proportion of respondents with WHOQoL score below median 50
59 years (%) 32.0 45.1 60
69 years (%)

37.3

70
79 years (%)

44.5

48.6 58.3

80 years and over (%)

60.9

66.2

All ages (%)

38.4

52.4

Citation: Global Health Action Supplement 2, 2010. DOI: 10.3402/gha.v3i0.2124

Women

60.0

18.3

20.0 13.2

12.5 12.4

10.0 – Poor health and poor quality of life

Poor health and non- Non- poor health and Non- poor health and non- poor quality of poor quality of life poor quality of life life

Fig. 1. Distribution (%) of study subjects by health status and quality of life, among 8,535 adults aged 50 years and over in northern rural Viet Nam.

educational level, without any marital partnership, with fewer older people in the family and with lower economic status were more likely to have both poor health status and poor QoL.

Discussion This article describes the pattern of health status and QoL among older adults in a rural community of Viet Nam. It reveals socio-economic inequalities in health status and QoL among older adults in the study setting. We found that a higher proportion of women reported both poor health status and poor QoL compared to men. The findings are in line with recent studies on health status from other Asian countries such as Pakistan (13), Bangladesh (14) and Singapore (15). Gender inequality in health has been well documented in the international literature (16). The findings are also consistent with results from previous studies on QoL that reported female disadvantages in both emotional and subjective well-being (17
20). One likely explanation could be that women are more likely to suffer from conditions that are debilitating but not fatal. The paradox is that women report poorer health but live longer, and this is true in almost every country in the world (21). Age was shown to be a factor significantly associated with poor health status and poor QoL. This has been consistently shown in previous studies (13
15). In our setting, chronic diseases were shown to be more prevalent among women and older people (22). We found that higher educational levels were significant positive predictors of both health status and QoL among the study subjects. Education is well known as an important factor for health, both among men and women, particularly in rural areas. The findings are consistent with previous studies (13
15). Education is assumed to have a positive effect on health status since persons with more education are assumed to be better informed about health matters, diet and disease

67

Hoang Van Minh et al.

Table 4. Factors associated with poor health status and poor quality of life among 8,535 adults aged 50 years and over in northern rural Viet Nam OR with 95% CI Variables

Poor health

Poor QoL

Poor health and poor QoL

Gender Men

0.7 (0.6
0.8)a

0.8 (0.7
0.9)a

0.8 (0.7
0.9)a

Women

1

1

1

50
59 years

1

1

1

60
69 years

1.5 (1.3
1.7)a

1.0 (0.9
1.1)

70
79 years

a

Age group 1.3 (1.1
1.5)a

a

1.9 (1.6
2.2)a

4.6 (3.7
5.7)

a

1.8 (1.5
2.1)

3.0 (2.4
3.6)a

No formal education

2.7 (2.2
3.3)a

2.1 (1.7
2.5)a

2.3 (1.9
2.9)a

Less than or equal to 6 years

1.6 (1.4
1.7)a

1.5 (1.4
1.7)a

1.6 (1.4
1.8)a

More than 6 years

1

1

1

Now single

1

1

1

In current partnership

0.9 (0.8
1.0)

0.9 (0.8
1.0)

0.8 (0.7
0.9)a

1.2 (1.0
1.4)

1.6 (1.4
1.9)a

1.4 (1.2
1.6)a

1.1 (1.0
1.3)

a

1.4 (1.2
1.6)a

a

80 years and over

2.4 (2.1
2.8)

a

1.2 (1.1
1.4)

Educational level

Marital status

Proportion of people aged 50 years and over in the same household B25% 25
49%

1.6 (1.4
1.9)

50
74%

1.2 (1.0
1.4)

1.5 (1.3
1.7)

1.4 (1.2
1.6)a

]75%

1

1

1

1.7 (1.4
2.0)a

3.2 (2.7
3.8)a

2.5 (2.1
3.0)a

a

Socio-economic quintile Poorest quintile Second quintile Third quintile

1.2 (1.0
1.4) 1.2 (1.0
1.4)

2.0 (1.8
2.4) 1.7 (1.5
2.0)a

1.6 (1.4
1.9)a 1.5 (1.3
1.8)a

Fourth quintile

1.1 (1.0
1.3)

1.6 (1.4
1.9)a

1.5 (1.3
1.7)a

Least poor quintile

1

1

1

a

Significant results (95% CI does not include 1).

prevention measures leading to better health conditions, consequently leading to higher QoL. Similarly, improvements in economic status are also likely to raise both health status and QoL. In addition to providing means for purchasing health care, higher economic status can provide better nutrition, housing and recreational opportunities. The findings are consistent with previous studies in Asia (13
15), in Europe (23) and the Americas (24). This study also revealed a positive effect of having more older people living in the same family. This positive effect may be the result of mutually practised health beliefs and behaviours, shared physical environments and interpersonal relations between the older people in the same family. We need to note some limitations of this study. Firstly, the cross-sectional nature of the data limited our ability

68

to understand causal mechanisms that resulted in particular heath status and QoL outcomes among the study population. Secondly, low educational level and the presence of impaired cognition in older people might have led to inaccuracies in the self-reported data. Our careful training and field supervision would have overcome this problem to some extent. In summary, this study provides cross-sectional evidence on patterns of health status and QoL among older adults in rural Viet Nam. The findings reveal problems of inequality in health status and QoL among older adults in the study setting by sex, age, education and economic status. Given these findings, actions to enhance the health of disadvantaged people (women, the elderly, less educated and lower economic status) are needed in this setting. Citation: Global Health Action Supplement 2, 2010. DOI: 10.3402/gha.v3i0.2124

WHO-SAGE study on older adults in rural community of Viet Nam

Acknowledgements This research was supported by FAS, the Swedish Council for Social and Work Life Research, Grant No. 2003-0075. We would like to thank INDEPTH WHO-SAGE for support and contribution of the SAGE instrument.

Conflict of interest and funding The authors have not received any funding or benefits from industry to conduct this study.

References 1. Omran AR. The epidemiological transition theory revisited thirty year later. World Health Statistic Quart 1998; 51: 99
119. 2. World Health Organization. Active aging: a policy framework. Geneva: World Health Organization; 2002. 3. United Nations. World population prospects: the 2006 revision and world urbanization prospects; 2006. Available from: http:// esaunorg/unpp [cited 10 December 2009]. 4. Shrestha LB. Population aging in developing countries. Health Aff (Millwood) 2000; 19: 204
12. 5. UNFPA Viet Nam. How can Vietnam respond to population ageing? Hanoi: UNFPA; 2009. 6. Giang TL, Dfau WD. Demographic changes and the long-term pension finances in Vietnam: a stochastic actuarial assessment. J Popul Ageing 2009; 1: 125
51. 7. Ministry of Health of Vietnam. Joint annual health review 2008: health financing in Vietnam. Hanoi: Ministry of Health of Vietnam; 2008. 8. Giang TL. Health status and accessibility to health facilities of the elderly in Viet Nam. Soc Insur Rev 2008; 7: 37
43. 9. Kowal P, Kahn K, Ng N, Naidoo N, Abdullah S, Bawah A, et al. Ageing and adult health status in eight low-income countries: the INDEPTH WHO-SAGE collaboration. Global Health Action Supplement 2, 2010. DOI: 10.3402/gha.v3i0.5302 10. Chuc NTK, Diwan VK. FilaBavi, a demographic surveillance site, an epidemiological field laboratory in Vietnam. Scand J Public Health 2003; 31: 3
7. 11. INDEPTH Network. Population and health in developing countries. Volume 1: population, health, and survival. Ottawa: INDEPTH Network, IDRC; 2002. 12. Vyas S, Kumaranayake L. Constructing socio-economic status indices: how to use principal components analysis. Health Policy Plan 2006; 21: 459
68.

Citation: Global Health Action Supplement 2, 2010. DOI: 10.3402/gha.v3i0.2124

13. Ahmad K, Jafar T, Chaturvedi N. Self-rated health in Pakistan: results of a national health survey. BMC Public Health 2005; 5: 51. 14. Rahman MO, Barsky AJ. Self-reported health among older Bangladeshis: how good a health indicator is it? Gerontologist 2003; 43: 856
63. 15. Lim W-Y, Ma S, Heng D, Bhalla V, Chew S. Gender, ethnicity, health behaviour & self-rated health in Singapore. BMC Public Health 2007; 7: 184. 16. Vlassoff C. Gender inequalities in health in the third world: uncharted ground. Soc Sci Med 1994; 39: 1249
59. 17. Netuveli G, Blane D. Quality of life in older ages. 2008; ldn003. Br Med Bull 2008; 85: 113
26. 18. Smith A, Sim J, Scharf T, Phillipson C. Determinants of quality of life amongst older people in deprived neighbourhoods. Ageing Soc 2004; 24: 793
814. 19. Kabir ZN, Tishelman C, Agu¨ero-Torres H, Chowdhury AMR, Winblad B, Ho¨jer B. Gender and rural
urban differences in reported health status by older people in Bangladesh. Arch Gerontol Geriatrics 2003; 37: 77
91. 20. Srapyan Z, Armenian HK, Petrosyan V. Health-related quality of life and depression among older people in Yerevan, Armenia: a comparative survey of retirement home and household residents aged 65 years old and over. Age Ageing 2006; 35: 190
3. 21. Mascitelli L, Pezzetta F, Sullivan JL. Why women live longer than men: sex differences in longevity. Gend Med 2006; 3: 341. 22. Hoang Van M, Dao Lan H, Kim Bao G. Self-reported chronic diseases and associated sociodemographic status and lifestyle risk factors among rural Vietnamese adults. Scand J Public Health 2008; 36: 629
34. 23. Aberg-Yngwe M, Diderichsen F, Whitehead M, Holland P, Burstrom B. The role of income differences in explaining social inequalities in self-rated health in Sweden and Britain. J Epidemiol Community Health 2001; 55: 556
61. 24. Smith KV, Goldman N. Socioeconomic differences in health among older adults in Mexico. Soc Sci Med 2007; 65: 1372
85. *Hoang Van Minh Faculty of Public Health Hanoi Medical University No 1, Ton That Tung, Dong Da Hanoi, Viet Nam Tel: 84 48523798 Fax: 84 45745070 Email: [email protected]

69

INDEPTH WHO-SAGE Supplement æ

Socio-demographic differentials of adult health indicators in Matlab, Bangladesh: self-rated health, health state, quality of life and disability level Abdur Razzaque1,2*, Lutfun Nahar3, Masuma Akter Khanam4 and Peter Kim Streatfield1,2 1

Matlab Health and Demographic Surveillance System, ICDDR,B, Mohakhali, Dhaka, Bangladesh; INDEPTH Network, Accra, Ghana; 3Department of Social Science, East West University, Dhaka, Bangladesh; 4Chronic Disease Unit, ICDDR,B, Dhaka, Bangladesh 2

Background: Mortality has been declining in Bangladesh since the mid- twentieth century, while fertility has been declining since the late 1970s, and the country is now passing through the third stage of demographic transition. This type of demographic transition has produced a huge youthful population with a growing number of older people. For assessing health among older people, this study examines self-rated health, health state, quality of life and disability level in persons aged 50 and over. Data and methods: This is a collaborative study between the World Health Organization Study on global AGEing and adult health and the International Network for the Demographic Evaluation of Populations and Their Health in developing countries which collected data from eight countries. Two sources of data from the Matlab study area were used: health indicator data collected as a part of the study, together with the ongoing Health and Demographic Surveillance System (HDSS) data. For the survey, a total of 4,000 randomly selected people aged 50 and over (HDSS database) were interviewed. The four health indicators derived from these data are self-rated health (five categories), health state (eight domains), quality of life (eight items) and disability level (12 items). Self-rated health was coded as dummy while scores were calculated for the rest of the three health indicators using WHO-tested instruments. Results: After controlling for all the variables in the regression model, all four indicators of health (self-rated health, health state, quality of life and disability level) documented that health was better for males than females, and health deteriorates with increasing age. Those people who were in current partnerships had generally better health than those who were single, and better health was associated with higher levels of education and asset score. Conclusions: To improve the health of the population it is important to know health conditions in advance rather than just before death. This study finds that all four health indicators vary by socio-demographic characteristics. Hence, health intervention programmes should be targeted to those who suffer and are in the most need, the aged, female, single, uneducated and poor. Keywords: adult health; self-rated health; health state; quality of life; disability; Matlab; Bangladesh; INDEPTH WHO-SAGE

Access the supplementary material to this article: INDEPTH WHO-SAGE questionnaire (including variants of vignettes), a data dictionary and a password-protected dataset (see Supplementary files under Reading Tools online). To obtain a password for the dataset, please send a request with ‘SAGE data’ as its subject, detailing how you propose to use the data, to [email protected]

Received: 10 December 2009; Revised: 3 June 2010; Accepted: 8 July 2010; Published: 27 September 2010

M

ortality has been declining in Bangladesh since the mid-twentieth century, while fertility has been declining since the late 1970s, and the

country is now passing through the third stage of demographic transition (1). This type of demographic transition has produced a huge youthful population and

Global Health Action 2010. # 2010 Abdur Razzaque et al. This is an Open Access article distributed under the terms of the Creative Commons Attribution- 70 Noncommercial 3.0 Unported License (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. Citation: Global Health Action Supplement 2, 2010. DOI: 10.3402/gha.v3i0.4618

Socio-demographic differentials of adult health indicators in Matlab

a growing number of older people. Due to such an age structure, the population is now experiencing a double disease burden; over 50% of deaths in Matlab are now due to non-infectious diseases (2). Bangladesh is one of the 20 developing countries with the largest numbers of older people, and by 2025 Bangladesh, along with four other Asian countries, will account for about half of the world’s older population (3). In fact, population increase among those aged 65 and over was negligible in Bangladesh during the first half of the twentieth century, but it increased substantially during the second half (2.4 million) and it is projected to increase by 20.8 million during the first half of the twenty-first century (4). As social security is almost non-existent for older people in Bangladesh (pension for government and semigovernment employees 5%, and governmental support for elderly people 10%), older people usually live in extended households and depend primarily on adult children for economic support and personal care (5). However, the traditional family support system for older people is under pressure due to the increasing outmigration of household members to cities, and women’s labour force participation outside the home, causing vulnerability for older people. In Bangladesh about 50% of the population fall below the poverty line, and so older people are likely to be in ill health, in social isolation and in poverty (6). Moreover, the majority of the older people live in rural areas where there is no specialised care service for older people in health facilities (Upazila Health Complex). Based on Matlab data, it was documented earlier that the prevalence of chronic morbidity was 75% among older people (last 3 months) while it was about 50% (last 1 month) for acute morbidity (7); 2.1% of older males and 3.6% of females could not use a toilet without help. As costs associated with assessing health status of a population are high, there is a need for low-cost health indicators, particularly for developing countries. Currently, some low-cost health indicators are available for developed countries that are good predictors of mortality and functional ability (8
11), but such indicators are rare for the developing countries. Based on the Matlab Health and Socio-economic Status Survey of Bangladesh, (12) it was reported that adults of this community can effectively assess their own health even with poor education and low levels of interaction with the modern health system. The current study has collected data on four indicators of health using a summary version (SAGE
INDEPTH) of the full WHO-SAGE questionnaire: self-rated health, health state, quality of life and disability level. The study will examine these four health indicators for people aged 50 and over, and their relationship with various

socio-demographic characteristics as well as the interrelationship of these health indicators.

Methods Setting Data for this study come from Matlab Upazila (subdistrict) where the International Centre for Diarrhoeal Disease Research, Bangladesh (ICDDR,B) has maintained a field station since 1963. Matlab is a rural area located about 55 km south-east of Dhaka. The area is a low-lying deltaic plain intersected by the tidal river Gumti and its numerous canals. In the past, major modes of transport within the area were walking, country boat and in some cases small steamer or launch. However, in recent years most of the villages have become accessible by rickshaw. Farming is the dominant occupation, except in a few villages where fishing is the means of livelihood (13). Most of the farmers are in marginal situations with less than a hectare of land and 40% of them are landless. For many families, sharecropping and work on others’ land on a daily wage basis have become the main sources of livelihood. Some people work in mills and factories in different towns and cities but their families live in the study area. Rural
urban out-migration is about 5% in recent years, while it is about 1% for international migration; however, these rates were much lower in the 1980s (3.3% vs. 0.3%). Women are largely restricted to activities in the home, with relatively little opportunity to venture outside the home, although these restrictions have decreased in recent years. Rice constitutes the staple food and is harvested three times annually. Rates of illiteracy are high and are higher among older people. Since 1966 the ICDDR,B has maintained a Health and Demographic Surveillance System (HDSS) in the Matlab area covering about 225,000 people. The surveillance system collects data on births, deaths, migrations, marriages, divorces and household divisions (14), and also collects cross-sectional socio-economic data which are available for 1974, 1982, 1996 and 2005. The HDSS data are of high quality because they have been collected during regular household visits (every 2 weeks until 1997, every month between 1998 and 2006 and every 2 months since then) by the Community Health Research Workers (CHRWs). Since October 1977, half of the surveillance area has been exposed to Maternal and Child Health and Family Planning (MCH-FP/ICDDR,B service area) services while the other half is a comparison area (15, 13). These two areas are almost similar in socio-economic conditions but differ in access to the MCH-FP programme. Beginning in 1996, the community-based maternity care service of the ICDDR,B service area was gradually phased out and replaced by a facility-based strategy of sub-centres.

Citation: Global Health Action Supplement 2, 2010. DOI: 10.3402/gha.v3i0.4618

71

Abdur Razzaque et al.

However, these health services are targeted mainly at mother and child health and not to older population, except for services for diarrhoeal diseases. In fact, treatment for diarrhoea has been provided from the Matlab field hospital since the beginning and such service is open to all irrespective of place of residence. The history of modern medicine is rather short in Bangladesh, since it did not reach the rural population until after World War II. During 1947
1970 the physical infrastructure for delivering health services by the then government was mainly urban-based, and such services were more curative than preventive in nature. The government accepted primary health care as a national health objective in 1978, since when the health care system has been reoriented to provide essential care to the general mass of the population. Funding for the health sector increased significantly from the early 1980s, with new facilities including Maternal and Child Welfare Centres in urban and sub-urban areas, Upazila Health Complexes at Upazila level and Family Welfare Centres at Union level (16). In Matlab town the government runs a 31-bed free general hospital with nine doctors (Upazila Health Complex) along with several Family Welfare Centres, each with a sub-assistant Community Medical Officer and a Family Welfare Visitor. Except for the service from Upazila Health Complex, all other services are targeted to maternal and child health. Finally, there are across the country both private practitioners (qualified and unqualified), private clinics (in big cities) and traditional practitioners (Ayurvedic, Unani and Homoeopathy); these services cover the population across all age groups.

Data and methods This is a multi-country study between the World Health Organization Study on global AGEing and adult health (SAGE) and the International Network for the Demographic Evaluation of Populations and Their Health in developing countries (INDEPTH), and collected data from eight countries of Africa and Asia. Two sources of data from the Matlab study area were used: survey data collected as a part of the study and the ongoing HDSS data. For the survey, questionnaires were received from the SAGE
INDEPTH and piloted in the field after translating into local languages. A total of 4,000 people 50 years and older, out of 31,400, were selected randomly from the HDSS database (ICDDR,B-service area); a sample from half of the HDSS area was selected to minimise travel time to visit the sample households. The survey was conducted by a team of college-graduate females with data collection experience. Interviewers received extensive training on data collection, particularly about asking questions on sensitive topics and on the data collection tools designed for the survey. The interviews were conducted at the residence of the respondent by

72

face-to-face interview and contact with absentees was attempted three times. As a quality check, about 2% of samples were re-interviewed by an independent field worker/supervisor and feedback was incorporated accordingly. Based on the survey data, four health indicators were calculated: self-rated health, health state, quality of life and disability level. Self-rated health was a categorical variable (five categories), health state was measured through eight domains (affect, cognition, interpersonal activities and relationship, mobility, pain, self-care, sleep/ energy, and vision), quality of life was measured through eight items and disability was assessed through 12 items (17). Self-rated health was coded as a dummy while scores were calculated using the WHO-tested instruments for health status, quality of life and disability level. All three of these scores were transformed into 0
100 scales on which higher scores indicate better outcomes [better health status, better quality of life (WHOQoL) and better functional ability (WHODASi)]. Analyses were undertaken using both bivariate and multivariate methods. The dependent variable was dichotomous for self-rated health and involved continuous scores for health state, quality of life and disability level. The independent variables were age of respondent, sex, marital status, proportion of people aged above 50 in the household, education level and asset quintiles. Age was grouped into four (50
59, 60
69, 70
79 and 80 and over), completed years schooling into three (none, 1
5 and 6 years or more), marital status into two (now single and in current partnership) and proportion of people aged above 50 in household into four groups (B0.25, 0.25
0.49, 0.50
0.74 and 0.75 or more). Asset index was calculated based on a number of consumer items (radio, watch, etc.), dwelling characteristics (wall and roof material) and type of drinking water and toilet facilities (18). For this study we have studied first to fifth quintiles as poorest to richest. For examining the interrelationship between two variables, self-rated health was grouped into two categories (very good, good, moderate1 and bad/very bad0); health status (IRT health 555.20 and55.21); quality of life (WHOQoL 580.0 0 and 80.01); disability level (WHODASi 581.00 and81.01); x2-tests were performed for significance level.

Results About two-fifths of the sample belonged to the age group 50
59 years while about one-fifth were aged 70 and over (Table 1). Educational level was low, with about 55% illiterate and only about 15% had six or more years of schooling. About 25% of people were single, 30% of household members were 50 years or older and mean household size was slightly over 5. Sample households are not equally distributed across quintiles, with more from

Citation: Global Health Action Supplement 2, 2010. DOI: 10.3402/gha.v3i0.4618

Socio-demographic differentials of adult health indicators in Matlab

Table 1. Background characteristics (%) of the study population in Matlab, Bangladesh

Table 2. Distribution of health indicators by age and sex for 4,037 adults aged 50 and over in Matlab, Bangladesh

Respondents Non-respondents Variables

Men

Women (N 2,021)

(N31,425)

Men

49.9

47.4

50
59 years

87.2

77.4

Women

50.1

52.6

60
69 years 70
79 years

77.9 64.4

60.1 42.9

50
59

45.3

44.0

80 years and over

48.9

24.2

60
69 70
79

33.8 17.1

34.0 17.3

3.8

4.7

Sex

Indicators

(N2,016)

(N3,990)

Self-rated health (Percentage of very good/good/moderate)

Age group (years)

80 and over

Mean health status (score) 50
59 years

Education level No formal education Less than or equal to 6 years

56.3 28.7

57.4 28.7

More than 6 years

14.9

13.9

Marital status Now single In current partnership

23.8

29.7

76.2

70.3

First quintile Second quintile

15.2 16.6

13.6 16.8

Third quintile

17.5

20.3

Fourth quintile

23.2

23.5

Fifth quintile

27.4

25.9

5.4

4.9

18.6

16.6

members Percentage of household

57.7

62.2

55.4

70
79 years

59.2

51.3

80 years and over

55.6

50.7

50
59 years

80.3

77.3

60
69 years

79.0

74.7

70
79 years

77.8

72.1

80 years and over

76.4

71.4

50
59 years

84.0

62.1

60
69 years

76.1

54.5

70
79 years

66.3

45.8

80 years and over

54.6

42.0

Mean quality of life (score)

Mean functional ability level (score)

Socio-economic quintile

Mean number of household

65.7

60
69 years

members aged 50 years and over

the fourth and fifth quintiles, because the quintiles are population-based. In fact, sample characteristics are comparable to the population characteristics. All four measures of health indicator (self-rated health, health state, quality of life and disability level) indicated that health was better for males than females irrespective of age categories and health deteriorated gradually as age increased (Table 2). For self-rated health, the proportion with good health declined from 87.2% to 48.9% for males and 77.4% to 24.2% for females between age groups 50
59 and 80 years and over; while for health status, the mean score declined from 65.7 to 55.6 for males and 57.7 to 50.7 for females between these two age groups. For quality of life, the mean score decreased from 80.3 to 76.4 for males and 77.3 to 71.4 for females between age groups 50
59 and 80 years and over; while for functional ability level, the mean score decreased from 84.0 to 54.6 for males and 62.1 to 42.0 for females between these two age groups.

Table 3 shows multivariate relationships for selfrated health and health status by socio-demographic characteristics. After controlling for all other variables in the regression model (logistic), males reported significantly better health (2.19 times) than females; health got significantly worse as age increased (7.70 times better for age group 50
59 and reduced to 2.07 times for age group 70
79 compared to age group 80 years and over); educated people had significantly better health than uneducated (0.74 times for those with no formal education and 0.87 times for those less or equal to 6 years compared to those with six or more years of education); and health got significantly worse as socio-economic status declined (0.74 times for first quintile to fifth quintile). For health status, after controlling for all other variables in the regression model (linear regression), the score for males increased by 7.07 per unit change in the female score; for age group 50
59, the score increased by 8.76 per unit change and 2.51 times per unit change for age group 70
79 compared to those in age group 80 years and over; for no formal education the score declined by 1.22 per unit change and by 0.74 per unit change for those with less or equal to six years compared to those with more than six years of schooling; for single persons the score declined by 0.08 per unit change of those in a current partnership; and for first

Citation: Global Health Action Supplement 2, 2010. DOI: 10.3402/gha.v3i0.4618

73

Abdur Razzaque et al.

Table 3. Multivariate models of factors associated with self-rated health (logistic regression) and health state (linear regression) for 4,037 adults aged 50 and over in Matlab, Bangladesh Variables

Self-rated health (Exponent of b and 95% CI)

Health status (b coefficient and 95% CI)

Sex (ref: women) Men

2.19 (1.83, 2.62)**

7.07 (6.48, 7.66)**

50
59 years

7.70 (5.34, 11.09)**

8.76 (7.44, 10.07)**

60
69 years 70
79 years

4.06 (2.84, 5.08)** 2.07 (1.43, 2.99)**

5.95 (4.64, 7.25)** 2.51 (1.15, 3.87)**

Age group (ref: 80 years and over)

Education level (ref: more than 6 years) No formal education

0.74 (0.57, 0.95)*

1.22 ( 1.98, 0.46)**

Less or equal to 6 years

0.87 (0.67, 1.13)

0.74 ( 1.51, 0.03)***

0.97 (0.79, 1.18)

0.08 ( 0.78, 0.63)

Marital status (ref: in current partnership) Now single

Proportion aged 50 years and over in the household (ref: ]0.75) 0.25

1.06 (0.82, 1.38)

0.03 ( 0.93, 0.87)

0.25
0.49

0.97 (0.75, 1.25)

0.08 ( 0.95, 0.79)

0.50
0.74

0.83 (0.63, 1.10)

0.55 ( 1.51, 0.41)

Socio-economic quintile (ref: Fifth quintile) First quintile

0.74 (0.58, 0.94)*

1.04 ( 1.85, 0.23)*

Second quintile

0.81 (0.64, 1.02)***

1.33 ( 2.10, 0.57)**

Third quintile Fourth quintile

0.78 (0.62, 0.98)* 0.93 (0.76, 1.15)

0.90 ( 1.64, 0.15)* 0.56 ( 1.24, 0.11)

*P B0.05; **P B0.01; ***P B0.10.

socio-economic quintile the score declined by 1.04 per unit change compared to those in fifth quintile. Table 4 shows the multivariate relationship of quality of life (WHOQoL) and disability level (WHODASi) by socio-demographic characteristics. After controlling for all other variables in the regression model (linear regression), the WHOQoL score for males increased by 2.01 per unit change in female score; for age group 50
59 the score increased by 3.42 per unit change and by 0.87 per unit change for age group 70
79 compared to those in age group 80 years or more; for single persons the score decreased by 4.04 per unit change of those in a current partnership; for no formal education the score decreased by 0.81 per unit change, and by 0.31 per unit change for those with less or equal to 6 years compared to those with six years or more schooling; and for the first socioeconomic quintile the score decreased by 2.95 per unit change and by 0.93 per unit change for those in the fourth quintile compared to those in the fifth quintile. For functional ability level, after controlling for all other variables in the regression model (linear regression), the score for males increased by 20.17 per unit change in the female score; for age group 50
59 the score increased by 25.49 per unit change and by 8.96 per unit change for

74

those in age group 70
79 compared to those 80 years or more; for no formal education the score decreased by 4.31 per unit change and by 2.66 per unit change for those with less or equal to 6 years compared to those with six or more years of schooling; and for the first socioeconomic quintile the score decreased by 2.32 per unit change compared to those in fifth quintile. All four health indicators (self-rated health, health state, quality of life and disability level) show that males, those who were younger, educated and those in higher socio-economic groups reported better health, compared to females, older age groups, illiterates and those in lower socio-economic groups. Table 5 shows the interrelationship of different health indicators. Results show that all four health indicators are highly significantly related to each other.

Discussion Bangladesh is currently passing through the third stage of demographic transition, where both fertility and mortality rates are at relatively low levels. Such as demographic transition has produced a huge youthful population with a growing number of older people (4), where disease patterns are changing from infectious to

Citation: Global Health Action Supplement 2, 2010. DOI: 10.3402/gha.v3i0.4618

Socio-demographic differentials of adult health indicators in Matlab

Table 4. Multivariate models (linear regression) of factors associated with quality of life and functional ability level for 4,037 adults aged 50 and over in Matlab, Bangladesh Variables

Quality of life (b coefficient and 95% CI)

Functional ability level (b coefficient and 95% CI)

Sex (ref: women) Men

2.01 (1.68, 2.34)**

20.17 (18.82, 21.52)**

50
59 years

3.42 (2.69, 4.16)**

25.49 (22.50, 28.48)**

60
69 years 70
79 years

2.07 (1.34, 2.80)** 0.87 (0.11 1.63)*

18.08 (15.00, 21.06)** 8.96 (5.86, 12.06)**

Age group (ref: 80 years and over)

Education level (ref: more than 6 years) No formal education Less or equal to 6 years

0.81 ( 1.23, 0.38)**

4.31 ( 6.05, 2.57)**

0.31 ( 0.75, 0.11)

2.66 ( 4.42, 0.89)**

Marital status Now single (ref: in current partnership)

4.04 ( 4.43, 3.64)**

0.19 ( 1.42, 1.82)

Proportion aged 50 years and over in the household (ref: ]0.75) 0.25

0.23 ( 0.74, 0.26)

0.25
0.49

0.04 ( 0.53, 0.44)

0.50
0.74

0.04 ( 0.57, 0.50)

0.34 ( 1.70, 2.40) 0.80 ( 1.19, 2.80) 0.37 ( 2.57, 1.83)

Socio-economic quintile (ref: least poor quintile) Poorest quintile

2.95 ( 3.41, 2.50)**

2.32 ( 4.17, 0.48)*

Second quintile

2.29 ( 2.71, 1.86)**

2.04 ( 3.76, 0.30)*

Third quintile Fourth quintile

1.40 ( 1.82, 0.98)** 0.93 ( 1.31, 0.55)**

1.46 ( 3.16, 0.23) 0.72 ( 2.27, 0.81)

*P B0.05; **P B0.01.

non-infectious (2). Traditionally, older people are viewed in this society as an integral part of the family and used to enjoy absolute authority over the younger generation; however, the status of older people is under pressure due to demographic, social and economic change (19). As a result of mortality decline during the past few decades, life span has increased significantly in Bangladesh but it is not known whether health status has improved during the increased life span. The study found that all four health indicators (self-rated health, health state,

quality of life and ability level) deteriorated with increasing age. The finding is in agreement with a recent study from Matlab that the prevalence of chronic disease increased with age (20). It is likely that this population will need more support (physical/co-residence, social and economic) as the number of older people is increasing rapidly along with an increase in chronic diseases. In Bangladesh, older females survive better than males (2) but health indicators from the current study (selfrated health, health state, quality of life and disability

Table 5. Inter-relationship of different health indicators in order persons, Matlab, Bangladesh Quality of life

Disability level

Health state

Self-rated health

Quality of life Disability level

x2 526.7

Health state

x2 355.8

x2 645.6

Self-rated health

P B0.001 x2 313.3

PB0.001 x2 303.8

x2 499.2

P B0.001

PB0.001

PB0.001

P B0.001

Note: Health indicators (categories): self-rated health (very good, good, moderate1 and bad/very bad0); Health status (IRT health 55.21 and 555.20); Quality of life (WHOQoL 580.00 and80.01); Disability level (WHODASi 581.00 and81.01).

Citation: Global Health Action Supplement 2, 2010. DOI: 10.3402/gha.v3i0.4618

75

Abdur Razzaque et al.

level) demonstrate that females are worse-off than males during old age. However, it was reported that the health disadvantage for women reflect their ‘greater sensitivity’ to health conditions (12). In this society, where women continue to be valued less than men as documented in the past (21), older women’s health reflects their lifelong experience of discrimination, deprivation and neglect (6). Traditionally, older women also own fewer assets and have less control over family income, and a recent study from Matlab reported that females experience more chronic disease than their male counterparts (20). All four health indicators documented that health is better among educated/rich than uneducated/poor people. The finding is also in agreement with mortality patterns, in which educated/rich people had lower mortality than uneducated/poor (2). Some years ago, it was reported (22) that socio-economic differentials in mortality indicate that a degree of success has been achieved in one section of the community that has not been achieved in others. In Matlab (20), it has been documented that some chronic diseases (stroke, heart disease, diabetes) increase with increased education while others (joint pain, pulmonary, hypertension, cancer) decrease. All four health indicators were found to be interrelated and these indicators also showed similar patterns by socio-demographic characteristics. This indicates that these health indicators, although measuring different dimensions of health, had some common characteristics. Preliminary analysis of the same dataset show that these four health indicators are also predictors of subsequent mortality (23). To improve the health of the population, it is important to know their health status in advance rather than just before death. The findings of this study have policy implications in terms of assessing the overall burden of diseases and effectiveness of health systems. Moreover, the study indicates that health intervention programmes should be targeted to those who suffer and need most: the older, female and uneducated/poor people.

Conflict of interest and funding The authors have not received any funding or benefits from industry to conduct this study.

References 1. Bairagi R, Datta AK. Demographic transition in Bangladesh: what happened in the twentieth century and what will happen next? Asia Pac Popul J 2001; 16: 3
16. 2. Razzaque A, Carmichael GA, Streatfield PK. Adult mortality in Matlab: levels, trends and determinants. Asian Popul Stud 2009; 5: 85
100. 3. East
West Centre. The future of population of Asia. Honolulu: East
West Centre; 2002.

76

4. Razzaque A. Population growth of Bangladesh: past, present and future. South Asian J Popul Health (submitted for publication). 5. Chaklader H, Haque M, Kabir M. Socio-economic situation of urban elderly population: evidence from a micro study. In: Sattar MA, Kabir M, Elahi KM, Abedin S, eds. The elderly: contemporary issues. Dhaka: Dhaka Presidency Press; 2003, pp. 1
13. 6. Chaklader H, Kabir M. What we know about elderly population’s health problems: evidences from rural Bangladesh villages. In: Sattar MA, Kabir M, Elahi KM, Abedin S, eds. The elderly: contemporary issues. Dhaka: Dhaka Presidency Press; 2003,. p. 134
53. 7. Mostafa G, Streatfield PK. Health implications of an aging Bangladeshi population. In: Sattar MA, Kabir M, Elahi KM, Abedin S, eds. The elderly: contemporary issues. Dhaka: Dhaka Presidency Press; 2003,. p. 33
53. 8. Idle EI, Benyamini, Y. Self-rated health and mortality: a review of 20-seven community studies. J Health Soc Behav 1987; 38: 21
37. 9. Idle EI, Kasl SV. Health perceptions and survival: do global evaluations of health status really predict mortality? J Gerontol 1991; 46: 555
65. 10. Idle EI, Kasl SV. Self-ratings of health: do they also predict changes in functional ability? J Gerontol Ser B Psychol Sci Soc Sci 1995; 50: S344
53. 11. Frankenberg E, Jones N. Self-rated health and mortality: does the relationship extend to a low-income setting? Minneapolis, MN: Population Association of America; 2003. 12. Kuhn R, Rahman O, Menken J. Survey measures of health: how well do self-reported and observed indicators measure health and predict mortality? In: Cohen B, Menken J, eds. Aging in Sub-Saharan Africa: recommendations for furthering research. Washington, DC: National Academies Press; 2006, pp. 314
41. 13. Van Ginneken J, Bairagi R, de Francisco A, Sarder AM, Vaughan P. Health and demographic surveillance in Matlab: past, present and future, special publican No. 72. Dhaka: ICDDR,B; 1998. 14. Razzaque A, Streatfield PK. Matlab demographic surveillance system, Bangladesh. In: Sankoh OA, Kahn K, Mwageni E, Ngom P, Nyarko P, eds. Population and health in developing countries. Vol. 1. Ottawa: IDRC & INDEPTH; 2002, pp. 287
95. 15. Bhatia S, Mosley WH, Faruque ASG, Chakraborty J. The Matlab family planning-health services project. Stud Fam Plann 1980; 11: 202
11. 16. DGHS (Directorate General of Health Services). Bangladesh Health Services-1989. Government of the People’s Republic of Bangladesh: DGHS; 1990. 17. Kowal P, Kahn K, Ng N, Naidoo N, Abdullah S, Bawah A, et al. Ageing and adult health status in eight low-income countries: the INDEPTH WHO-SAGE collaboration. Global Health Action Supplement 2, 2010: DOI: 10.3402/gha.v3i0. 5302. 18. Razzaque A, Streatfield PK, Gwatkin D. Does health intervention improve socioeconomic inequalities of neonatal, infant and child mortality? Evidence from Matlab, Bangladesh. Int J Equity Health 2007; 6: 4. DOI: 10.1186/1475-9276-6-4. 19. Kabir ZN. Traditional norms vs reality: the case of elderly. Paper presented at the women’s health conference, Singapore, 15 May 1999. 20. Razzaque A, Ali Ashraf, et al. Self-reported common chronic diseases and their relationship with body mass index: evidence from ICDDR,B field sites; 2010 (unpublished).

Citation: Global Health Action Supplement 2, 2010. DOI: 10.3402/gha.v3i0.4618

Socio-demographic differentials of adult health indicators in Matlab

21. Chen LC, Huq E, D’Souza S. Sex bias in the family allocation of food and health care in rural Bangladesh. Popul Dev Rev 1981; 7: 55
70. 22. Antonovsky A, Bernstein J. Social class and infant mortality. Soc Sci Med 1977; 11: 453
70. 23. Razzaque A, Mustafa AHMG, et al. Self-rated health and subsequent mortality: evidence from Matlab, Bangladesh; 2010 (unpublished).

*Abdur Razzaque HDSU ICDDR,B Mohakhali, Dhaka-1212 Bangladesh Email: [email protected]

Citation: Global Health Action Supplement 2, 2010. DOI: 10.3402/gha.v3i0.4618

77

INDEPTH WHO-SAGE Supplement æ

Health and quality of life among older rural people in Purworejo District, Indonesia Nawi Ng1,2,3*#, Mohammad Hakimi2,3, Peter Byass1#, Siswanto Wilopo2,3 and Stig Wall1# 1

Department of Public Health and Clinical Medicine, Centre for Global Health Research, Epidemiology and Global Health, Umea˚ University, Umea˚, Sweden; 2Purworejo Health and Demographic Surveillance System, Faculty of Medicine, Gadjah Mada University, Yogyakarta, Indonesia; 3INDEPTH Network, Accra, Ghana

Introduction: Increasing life expectancy and longevity for people in many highly populated low- and middleincome countries has led to an increase in the number of older people. The population aged 60 years and over in Indonesia is projected to increase from 8.4% in 2005 to 25% in 2050. Understanding the determinants of healthy ageing is essential in targeting health-promotion programmes for older people in Indonesia. Objective: To describe patterns of socio-economic and demographic factors associated with health status, and to identify any spatial clustering of poor health among older people in Indonesia. Methods: In 2007, the WHO Study on global AGEing and adult health (SAGE) was conducted among 14,958 people aged 50 years and over in Purworejo District, Central Java, Indonesia. Three outcome measures were used in this analysis: self-reported quality of life (QoL), self-reported functioning and disability, and overall health score calculated from self-reported health over eight health domains. The factors associated with each health outcome were identified using multivariable logistic regression. Purely spatial analysis using Poisson regression was conducted to identify clusters of households with poor health outcomes. Results: Women, older age groups, people not in any marital relationship and low educational and socioeconomic levels were associated with poor health outcomes, regardless of the health indices used. Older people with low educational and socio-economic status (SES) had 3.4 times higher odds of being in the worst QoL quintile (OR 3.35; 95% CI 2.73
4.11) as compared to people with high education and high SES. This disadvantaged group also had higher odds of being in the worst functioning and most disabled quintile (OR1.67; 95% CI 1.35
2.06) and the lowest overall health score quintile (OR 1.66; 95% CI1.36
2.03). Poor health and QoL are not randomly distributed among the population over 50 years old in Purworejo District, Indonesia. Spatial analysis showed that clusters of households with at least one member being in the worst quintiles of QoL, functioning and health score intersected in the central part of Purworejo District, which is a semi-urban area with more developed economic activities compared with other areas in the district. Conclusion: Being female, old, unmarried and having low educational and socio-economic levels were significantly associated with poor self-reported QoL, health status and disability among older people in Purworejo District. This study showed the existence of geographical pockets of vulnerable older people in Purworejo District, and emphasized the need to take immediate action to address issues of older people’s health and QoL.

Access the supplementary material to this article: INDEPTH WHO-SAGE questionnaire (including variants of vignettes), a data dictionary and a password-protected dataset (see Supplementary files under Reading Tools online). To obtain a password for the dataset, please send a request with ‘‘SAGE data’’ as its subject, detailing how you propose to use the data, to [email protected] Keywords: adult health; health status; clustering; quality of life; disability; ageing; Purworejo; Indonesia; INDEPTH WHO-SAGE

Received: 3 November 2009; Revised: 28 June 2010; Accepted: 8 July 2010; Published: 27 September 2010

#

Editor, Nawi Ng, Deputy Editor, Peter Byass, Chief Editor, Stig Wall, have not participated in the review and decision process for this paper.

Global Health Action 2010. # 2010 Nawi Ng et al. This is an Open Access article distributed under the terms of the Creative Commons Attribution- 78 Noncommercial 3.0 Unported License (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. Citation: Global Health Action Supplement 2, 2010. DOI: 10.3402/gha.v3i0.2125

Health of older people in Indonesia

dvances in public health and medical technologies have driven population growth in the last century. Increasing life expectancy and longevity in many highly populated low- and middle-income countries has led to an increased number of older people. In 2006, about 500 million people (7.5%) of the 6.5 billion world population were aged 65 years and over, and this number is projected to double by 2030, to represent 12.5% of the global population (1, 2). During 2007
2050, the population in low- and middle-income countries is projected to increase by 61% (3). By 2030, the population aged 65 years and over is projected to increase by 140% in developing countries (1, 4). About 53% of this older population lives in Asia, home to 61% of the world’s population. Indonesian population structure has shifted significantly towards an ageing population since 1950. The total fertility rate (TFR) has decreased from 5.5 in 1950
1955 to 2.4 children per woman in 2000
2005. Life expectancy has increased from 37.5 to 68.6 years during the same period. As a consequence, the population aged 60 years and over increased from 6.2% in 1950 to 8.4% in 2005 and is projected to increase to 23.7% in 2050 (5). The Indonesia National Socio-Economic Survey in 2004 showed variation in the proportion of older people across the provinces in Indonesia ranging from 2% in Papua to 12.8% in Yogyakarta. The proportion of older people in Central Java was about 9.5%. The survey also showed that about one-third of those over 60 years reported an illness during the month prior to the survey with no differences between rural and urban areas (6). The expected growth in the ageing population in Indonesia poses significant challenges to the health system and government. Currently, the health system focuses more on battling infectious diseases such as malaria, tuberculosis, diarrhoea and dengue fever. Resources have not been allocated proportionally to the larger and increasingly threatening burden of chronic non-communicable diseases such as heart diseases, stroke, diabetes, cancer and hypertension (7). Changing family structure and patterns of work and retirement pose immediate economic challenges, particularly to the social insurance systems. The pensions and social insurance system only cover a small percentage of the Indonesian population who work in the formal sector, which excludes most of the older population. Indonesian social insurance schemes, which are limited to covering formal workers in productive age groups and poor population sectors, are not designed to anticipate an ageing population (8). The lack of a social safety net increases the vulnerability of older people to poor health and quality of life (QoL), mostly due to the threat of chronic illness from noncommunicable diseases, and lack of financial support for accessing health care.

A

Citation: Global Health Action Supplement 2, 2010. DOI: 10.3402/gha.v3i0.2125

Indonesian older people play an important role in their families and their society. In traditional Javanese society, older parents typically co-reside with one of their youngest children, usually a daughter (extended family), who accepts responsibility to take care of them until they die. Well-off older persons provide key intergenerational support for families (9), have high social status and are respected in their communities. Javanese people highly respect older people because of the value placed on lineage. Though many of the older people in Indonesia, particularly those who are widowed, live in poverty, they also contribute significantly to the rural economy; many engage actively in agricultural industries as non-skilled labour. Most of them, particularly women, have low education. Older people who are still working are less economically dependent on their next-of-kin (10). Elderly care and intergenerational relationships have become an emerging issue, particularly for those who live in urban areas, as societal values change from extended family to nuclear family structures, and younger generations become more mobile in search of better career opportunities. A significant amount of research and literature on older people in Indonesia is available, mainly from anthropological studies focusing on the socio-cultural aspects of ageing, intergenerational relationships and changes in family structure and support for older people (10
13). However, studies on health status and QoL among the older population are largely lacking, and very little is known about morbidity among Indonesia’s older population (6, 14). Self-reported health has been identified as a strong predictor of morbidity and subsequent mortality (15, 16). While evidence has mainly come from developed countries, it can also be extended to low-income settings such as Indonesia, as shown by Frankenberg and Jones in the panel data analysis of the Indonesia Family Life Survey (IFLS) in 1993, 1997 and 2000. The IFLS data shows that individuals who perceived their health as poor are more likely to die, and the association remains even after being adjusted for physical function, physical illness and depression, weight, height and indicators of high blood pressure (17). An understanding of older people’s health and well-being will provide important information on any special health care needs and demand for services, and this knowledge can be used to guide planning of health interventions and programmes (18). The primary objectives of this study are to describe patterns of socio-economic and demographic factors that determine the health status of older people in Indonesia. The secondary objective is to identify the clustering pattern of poor health among them. Knowledge on the determinants of health status and spatial distribution of poor health will help to improve our understanding of older people’s health, thus providing evidence for the

79

Nawi Ng et al.

district authorities in promoting better health status and developing targeted interventions for disadvantaged populations in their specific geographical areas.

Methods Study area and participants The WHO Study on global AGEing and adult health (SAGE) (19) was conducted in a functioning Health and Demographic Surveillance System (HDSS) site in Purworejo District, Central Java, Indonesia. The Purworejo HDSS is a member of the INDEPTH Network, which consists of 38 HDSS sites in Africa, Asia and Oceania (http://www.indepth-network.org). The district is located between longitudes 1098 and 1108E and latitude 78S, about 60 km from Yogyakarta City. It covers an area of 1,035 km2, spanning a diverse geographical area from the coast in the south to the mountains in the north. The district has 750,000 inhabitants (26% under 15 years old, 63% in the economically productive age group and 11% over 65 years old). Eleven percent of those over six years of age have had no formal education. About 89% of the population live in rural areas. The Purworejo HDSS has been running since 1994 covering a total of 600,000 person-years of observation (20). In 2006, the total population under surveillance was 55,000 (13,443 households living in 128 enumeration areas). The study was conducted between January and June 2007. We identified and invited all adults aged 50 years and over to participate in the study, a total of 14,958 people. Instruments This study used the modified and shortened version of the INDEPTH WHO-SAGE questionnaire (19), consisting of subjective well-being and QoL, function and disability, and health status description modules. All the questionnaires were translated into Bahasa Indonesia and were pilot-tested during November
December 2006. Data collection and management Household visits were conducted by trained surveyors who administrated the survey questionnaire. Supervisors conducted spot-checks and revisits to 5% of the participants to ensure the quality of data obtained. All questionnaires were checked and validated by field supervisors and then sent to the central office in Gadjah Mada University, Yogyakarta, for data entry. Data entry was conducted in D-Entry software and the SAGE data was linked to the surveillance database. Double entry was also conducted on 5% of total questionnaires. Demographic variables (such as age, highest level of education completed, marital status, household size and proportion of person over 50 years old within household) and geographic coordinates of each household were extracted from the surveillance database. The SAGE dataset was

80

also linked to data from the household socio-economic survey conducted in 2004. The socio-economic survey collected data on household characteristics and ownership of non-disposable and disposable goods, and socioeconomic status (SES) quintiles were derived through principal component analysis (21). The final merged dataset was converted into STATA data format for data analysis.

Data analysis Three outcome measures were used in the analysis: selfreported QoL, self-reported problems in functioning and disability, and overall health status. Each of those measures was developed as composite indices from series of validated questions (22). The composite index for self-reported QoL was adapted from the WHO Quality of Life (WHOQoL) tool (23). The index was derived from eight questions assessing respondent’s thoughts about their life and life situation, satisfaction with themselves and their health, ability to perform daily living activities, personal relationships, living conditions and overall life. Answers to the Likert scale were summed up and later transformed to a 0
100 scale with 0 representing the worst QoL and 100 representing the best QoL. Questions to assess problems in functioning and disability were adapted from the WHO Disability Assessment Schedule (WHODAS) 12-item instrument (24). The series of questions assessed any difficulties faced by the respondents in performing different daily life activities due to their health conditions. The responses were collected on the Likert scale and different weights were assigned to responses from different questions. The total score was then inverted to transform it to an index between 0 and 100, with 0 representing extreme problems or complete disability and 100 representing a total absence of disability, termed WHODASi. The use of WHODAS in the INDEPTH WHO-SAGE study has been described elsewhere (22). Overall health status was measured using self-reported health derived from eight health domains, including affect, cognition, interpersonal relationships, mobility, pain, self-care, sleep/energy, and vision (19). Two questions in each domain, which measured the difficulties faced by the respondents in performing activities, were put to the respondents and responses were collected using a five-response scale. Item response theory with partial credit model was used to generate a composite health status score. Following each item calibration using chisquared fit statistics to evaluate its contribution to the composite health score, the raw composite score was transformed through Rasch modelling into a continuous cardinal scale, with 0 representing worst health and 100 representing best health (22). The psychometric Citation: Global Health Action Supplement 2, 2010. DOI: 10.3402/gha.v3i0.2125

Health of older people in Indonesia

properties of the health score have been evaluated elsewhere (25). All the three continuous indices (WHOQoL, WHO DASi and overall health status) were later categorised into quintiles, independently. The three outcome measures were defined as being in the worst quintiles for QoL, functioning and disability, and overall health, as defined by the three indices, respectively. Sociodemographic and economic factors associated with being in the worst quintile of each health outcome were identified through multivariable logistic regression. The SES quintiles were later regrouped into low (first and second), middle (third) and high (fourth and fifth) quintiles. Educational levels were defined as low (no formal education), medium (less than 6 years of education) and high (at least 6 years of education). As there was moderate correlation between educational level and socio-economic groups, we combined the educational and socio-economic groups into five categories in the analysis. The regression analysis was performed separately for each outcome measure. All analyses were conducted using STATA statistical software version 10.0. The SAGE data containing individual observations on the health outcomes was transformed into household level data, by counting the number of individuals in each household belonging to the worst quintile of each index. This household level data was later merged with the geographical coordinates in the surveillance area. The purely spatial analysis using Poisson probability modelling was conducted to identify clusters of households with at least one member being in the worst quintile of QoL, disability and health score, independently. The total number of people aged 50 years and over in each household was used as the population in the analysis. Monte Carlo hypothesis testing was used with 999 replications and a significance level of 0.05. The risk estimates for each cluster were identified. The analysis was conducted using SaTScanTM software, version 7.0 (26). The Research Ethics Committee at Gadjah Mada University and Purworejo District Health Offices approved the SAGE study in Purworejo District, Indonesia. Documented informed consent was obtained from each individual prior to the study.

Over half of the study participants were women (54%), and the majority (84%) had less than 6 years of education. Only 7.2% of the study participants were aged 80 years and over. The data showed that 29% of the participants were not in a marital relationship but most of the participants did not live alone. The average number of household members was 3.5. As the study covered all older people in the surveillance area, the household socio-economic quintiles presented in this study Table 1. Background characteristics of respondents and non-respondents among adults aged 50 years and over in Purworejo, Indonesia

Variables

A total of 14,958 individuals aged 50 years and over were visited, with data obtained from 12,459 individuals (83%). Cleaned and complete data from 11,753 individuals were available for analysis. The background characteristics of the respondents and the non-respondents (n 2,564) were presented in Table 1. Reasons for not participating in the study included: could not be reached after two visit attempts (81%), refusal (8.3%), died (5%) and out-migration (5.7%). Citation: Global Health Action Supplement 2, 2010. DOI: 10.3402/gha.v3i0.2125

Non-respondents

(N11,753)

(N 2,564)

Sex, n (%) Men

5,420 (46.1)

1,285 (50.1)

Women

6,333 (53.9)

1,278 (49.9)

64.1 (9.4)

65.5 (11.5)

Age, mean (standard deviation) Age group, n (%) 50
59 years

4,344 (36.9)

928 (36.2)

60
69 years

4,045 (33.3)

709 (27.7)

70
79 years 80 years and over

2,644 (22.7) 720 (7.2)

595 (23.2) 331 (12.9)

3,440 (29.6) 6,459 (54.7)

659 (27.4) 1,257 (52.2)

More than 6 years

1,854 (15.7)

491 (20.4)

Marital status, n (%) In current partnership

8,400 (71.0)

1,925 (77.6)

3,353 (29.0)

556 (22.4)

Education level, n (%) No formal education Less than or equal to 6 years

Being single

Socio-economic quintile, n (%) First quintile 2,394 (20.4)

225 (17.1)

Second quintile

2,317 (19.8)

259 (19.6)

Third quintile

2,390 (20.3)

248 (18.8)

Fourth quintile

2,387 (20.3)

303 (23.0)

Fifth quintile

2,265 (19.2)

285 (21.6)

Number of household

Results

Respondents

3.5 (1.7)

3.5 (1.8)

member, mean (standard deviation) Proportion of household member aged 50 years and over, n (%) 995 (8.6)

324 (12.9)

25
49%

3,288 (28.0)

646 (25.6)

50
74%

3,853 (32.6)

733 (29.1)

]75%

3,617 (30.9)

818 (32.5)

B25%

Note: All figures were weighted to the Purworejo HDSS population in 2007.

81

Nawi Ng et al.

reflected the quintiles in the whole surveillance population (Table 1). Table 2 presents summary statistics of three different health indices of WHOQoL, WHODASi and overall health status scores across different age groups and sexes. Overall, a higher proportion of women aged over 50 years in Purworejo District were categorised in the worst quintiles of health indices as compared to men. These patterns were observed consistently in all age groups. A larger discrepancy in functioning and disability and health status was observed across age groups in men and women. The differences of QoL index were, however, less prominent across age groups in men and women. The results showed that function, QoL and overall health status decreased substantially among the oldest agegroup, with more than 50% of those over 80 belonging to the worst function and disability and overall health status quintiles. Being in the older age group, having low education and being in a low socio-economic group, and not being in a marital relationship were significantly associated with higher odds of being in the worst quintiles for QoL, functioning and disability, and overall health, respectively. The multivariable analysis showed that respondents aged over 80 years were more than 3.3 times more likely to be in the worst quintile of QoL compared to those aged between 50 and 59 years. They were 12.6 and 10.6 times more likely to be in the worst functioning and overall health score quintiles, respectively. The education and socio-economic gradient was also prominent for QoL reporting, with individuals in the low SES group who had a low level of education being 3.4 times more likely be in the worst quintile of QoL compared to those with high education in the high SES group (Table 3 and Fig. 1). The overall effects of low SES and education were less prominent, though statistically significant, for being in the worst disability and overall health status quintiles. The spatial analysis revealed clusters of households with at least one member being in the worst quintile of QoL, functioning and disability, and overall health, respectively (Fig. 2). Clusters of households with a member being in the worst quintile of self-reported QoL were identified in the northern part of the district, which is a mainly hilly and mountainous area. This area is less developed, less urbanised and contains many households categorised in the poorest socio-economic quintile. In contrast, the clusters of households with at least one member being in the worst quintile of overall health status were identified in the mid-southern part of the district, mainly highly populated semi-urban and coastal areas. This part of Purworejo District is mainly low land covering four main sub-districts of Bayan, Banyuurip, Kutoarjo and Purworejo. These are the four most populated sub-districts in Purworejo District with a population density ranging from 918 to

82

Table 2. Distribution of health indices by age-group and sex among 11,753 adults aged 50 years and over in Purworejo District, 2007 Indices

Men

Women

50
59 years 60
69 years

75.5 (75.3
75.7) 74.6 (74.3
74.8)

75.1 (74.9
75.3) 73.9 (73.7
74.1)

70
79 years

73.3 (72.9
73.6)

72.6 (72.3
72.9)

80 years and over

71.7 (70.9
72.4)

71.5 (70.7
72.3)

WHO Quality of Life (QoL) score Mean score (95% CI)

Percentage in the worst quintile (95% CI) 50
59 years

11.8 (10.4
13.2)

60
69 years

17.3 (15.5
19.1)

14.7 (13.2
16.1) 22.0 (20.3
23.7)

70
79 years

25.9 (23.5
28.4)

32.0 (29.6
34.4)

80 years and over

37.4 (32.4
42.3)

42.9 (37.7
48.1)

WHO Disability Assessment Schedule (WHODASi) score Mean score (95% CI) 50
59 years

93.2 (92.8
93.6)

91.2 (90.8
91.7)

60
69 years

88.4 (87.7
89.0)

84.2 (83.6
84.9)

70
79 years

81.0 (80.0
81.9)

76.2 (75.2
77.2)

80 years and over

70.9 (68.7
73.1)

66.4 (64.1
68.7)

Percentage in the worst quintile (95% CI) 50
59 years

5.5 (4.5
6.5)

8.8 (7.7
10.0)

60
69 years

14.3 (12.6
15.9)

23.4 (21.6
25.1)

70
79 years 80 years and over

28.0 (25.4
30.5) 52.0 (46.8
57.1)

40.1 (37.5
42.6) 59.2 (54.0
64.3)

50
59 years 60
69 years

77.3 (76.9
77.8) 73.0 (72.5
73.5)

74.7 (74.3
75.1) 69.9 (69.5
70.3)

70
79 years

68.4 (67.9
69.0)

66.0 (65.6
66.5)

80 years and over

64.1 (63.2
65.1)

62.9 (61.9
63.8)

Overall health score Mean score (95% CI)

Percentage in the worst quintile (95% CI) 50
59 years

6.0 (5.0
7.0)

10.6 (9.4
11.9)

60
69 years

15.6 (13.9
17.2)

27.2 (25.4
29.1)

70
79 years

30.7 (28.1
33.3)

43.6 (41.0
46.1)

80 years and over

50.4 (45.3
55.5)

60.8 (55.7
65.9)

Note: All figures were weighted to the Purworejo HDSS population in 2007.

1,700 inhabitants per km2. Most households in these areas fall within the richest socio-economic quintile with the majority of people over 50 having had at least six years of education.

Discussion In addition to risks for the oldest old, our study showed that people with low levels of education and SES had higher odds of having poorer self-reported QoL and health. Economic instability during old age may Citation: Global Health Action Supplement 2, 2010. DOI: 10.3402/gha.v3i0.2125

Citation: Global Health Action Supplement 2, 2010. DOI: 10.3402/gha.v3i0.2125

Table 3. Three different models in assessing factors associated with poor health indices among 11,753 adults aged 50 years and over in Purworejo District, 2007 Model 1: Being in the worst QoL

Model 2: Being in the worst WHODASi

Model 3: Being in the worst health status

quintile as outcome

quintile as outcome

score quintile as outcome

Unadjusted ORs

Adjusted ORs

Unadjusted ORs

Adjusted ORs

Unadjusted ORs

Adjusted ORs

(95% CI)

(95% CI)

(95% CI)

(95% CI)

(95% CI)

(95% CI)

Men

1

1

1

1

1

1

Women

1.30 (1.19
1.42)

1.13 (1.02
1.26)

1.57 (1.44
1.73)

1.39 (1.25
1.55)

1.69 (1.55
1.85)

1.50 (1.35
1.66)

50
59 years

1

1

1

1

1

1

60
69 years

1.62 (1.45
1.83)

1.41 (1.25
1.59)

3.09 (2.69
3.55)

2.75 (2.38
3.17)

3.09 (2.71
3.52)

2.73 (2.38
3.11)

70
79 years

2.69 (2.38
3.04)

2.09 (1.83
2.39)

6.74 (5.86
7.75)

5.54 (4.76
6.44)

6.55 (5.73
7.48)

5.34 (4.63
6.16)

80 years and over

4.38 (3.69
5.22)

3.32 (2.75
4.01)

12.6 (10.3
15.5)

13.6 (11.4
16.4)

10.6 (8.69
12.9)

Variables Sex

Age group

16.1 (13.3
19.4)

Marital status Being single

1.86 (1.7
2.05)

1.32 (1.16
1.49)

2.74 (2.5
3.01)

1.56 (1.38
1.77)

2.79 (2.55
3.06)

1.56 (1.38
1.76)

In current partnership

1

1

1

1

1

1

Percentage aged 50 years and over in the household B25%

0.88 (0.74
1.05)

0.85 (0.64
1.13)

1.06 (0.9
1.25)

0.76 (0.57
1.02)

1.02 (0.87
1.19)

0.81 (0.60
1.07)

25%
49%

0.84 (0.75
0.94)

1.05 (0.87
1.27)

0.80 (0.71
0.89)

0.92 (0.75
1.13)

0.73 (0.65
0.82)

0.90 (0.73
1.09)

50%
74%

0.73 (0.65
0.82)

0.96 (0.84
1.10)

0.68 (0.61
0.76)

0.93 (0.81
1.07)

0.64 (0.57
0.71)

0.89 (0.78
1.02)

]75%

1

1

1

1

1

1

0.96 (0.93
0.99)

1.04 (0.99
1.09)

0.98 (0.95
1.01)

1.08 (1.02
1.13)

0.96 (0.93
0.98)

1.05 (1.00
1.10)

High SES, high education

1

1

1

1

1

1

High SES, low-middle

1.78 (1.46
2.16)

1.37 (1.12
1.68)

2.31 (1.92
2.79)

1.36 (1.12
1.66)

2.33 (1.95
2.79)

1.39 (1.15
1.68)

Middle SES, all education

2.22 (1.82
2.71)

1.77 (1.44
2.16)

2.36 (1.95
2.87)

1.44 (1.18
1.77)

2.25 (1.87
2.71)

1.37 (1.12
1.66)

levels Low SES, middle-high

2.81 (2.32
3.41)

2.47 (2.03
3.01)

1.77 (1.46
2.15)

1.27 (1.04
1.57)

1.81 (1.51
2.18)

1.30 (1.07
1.58)

5.11 (4.21
6.21)

3.35 (2.73
4.11)

4.15 (3.42
5.03)

1.67 (1.35
2.06)

4.21 (3.50
5.06)

1.66 (1.36
2.03)

Family size Education and SES

education Low SES, low education

83

Note: WHOQoL, World Health Organization Quality of Life; WHODASi, World Health Organization Disability Assessment Schedule. All analyses were weighted to the Purworejo HDSS population in 2007.

Health of older people in Indonesia

education

Nawi Ng et al.

Fig. 1. The odds ratio for poor health among different education and socio-economic groups among 11,753 adults aged 50 years and over in Purworejo District, 2007.

potentially be more of a threat to the urban older than to their rural counterparts. The majority of older Javanese in our study were still engaged in agricultural production and were typically more economically productive and stable compared to their urban counterparts. Our data reaffirmed the results from the IFLS conducted in 1993 that showed older Indonesian men and women often remain economically active; males and younger age groups were more active than women and older age groups. The IFLS data indicated that older men who coresided still worked about 30 hours per week, while those who did not co-reside worked about 38 hours per week. The IFLS data also showed that the availability of

intergenerational financial transfer does not necessarily influence parent’s labour supply (27). Family and local community support for older people is still reliable in rural Java. Only a very small proportion of older Indonesians receive a pension as their source of income (about 13% of males and 4% of females in 1985 with no significant change since then). Those who receive a pension are mainly urban dwellers who had worked in government sectors, the military or industries. Pensions are not paid to urban poor or traditional agricultural workers (14). The National Social Security Law for poor people, proposed by the government in 2004, has yet to be agreed by the legislative body and operationalised by Cluster of households with at least one member being in the worst quintiles of quality of life Centre: 109.972 °E, 7.640 °S Radius: 13.2 km RR: 1.51 (p