Evaluating the Absolute and Relative Income ...

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Chapter 10

Evaluating the Absolute and Relative Income Hypotheses in an Exploratory Analysis of Deaths in the Health and Lifestyle Survey Kelvyn Jones Craig Duncan Lizbeth Twigg

Introduction: Income and Mortality Absolute and Relative Income In studying the links between income and mortality we need to recognise two key hypotheses. In the absolute income hypothesis, the higher an individual income, the lower the risk of mortality. In the relative income hypothesis, the individual's health is additionally affected by the distribution of income within society so that living in a place with grossly unequal income distribution is anticipated to lead to a worse health experience (see Chapter 5 of this volume). This chapter attempts to review the evidence for the importance of both these hypotheses using data from the U K representative Health and Lifestyle Survey employing both individual and ecological measures of income. Despite the considerable policy implications, there has been comparatively little work undertaken in the British context on the absolute income hypothesis. However, U S A studies have found an inverse non-linear relation between household income and early mortality, poorer income being associated with poorer health. Previous studies are reviewed in detail elsewhere (Jones et al., 2001). The relative income hypothesis has been subject to considerable research interest in recent years. This can be seen as part of a wider critique of reductionist, individualised risk-factor epidemiology and the promotion of what Susser (1998: 609) has called a 'multilevel eco-epidemiology' in which health depends not only on individual characteristics but also on the setting, or 'ecology', in which individuals live and work. In terms of the health and relative income hypothesis, the most 219

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The Geography of Health Inequalities in the Developed World

sustained work lias come from Wilkinson and the fundamental arguments of his Unhealthy Societies (1996) can be summarised in two propositions: • •

In the developed world, the most egalitarian, rather than the richest, countries have the best health. The most important links between disease and income inequality are psychosocial operating through the pathway of social cohesion.

For him, it is not the absolute standard of living in advanced economies that affects a population's health experience but relative inequality which affects levels of isolation, anxiety and insecurity with the key causal pathway being chronic stress (Wilkinson, 1998; Kawachi and Kennedy, 1999). Aggregate Studies and the Ecological Fallacy It is important to stress that these conclusions are reached on the basis of a growing body of empirical work. Wilkinson (1996) summarises a range of studies which suggest that greater income inequality is linked to higher population mortality and this association remains even when the average income of the population is taken into account. It is not the wealthiest countries that have the highest life expectancy but those with the smallest spread of incomes. A number of aggregate studies have examined the potential effects of relative inequality within a country. These include the American studies of Kaplan et al. (1996), Kennedy et al. (1996), Kennedy et al. (1998), Kawachi and Kenndey (1997), and Lynch et al. (1998), all of which found a substantial correlation between some aspect of health and income inequality. In the British context there has been much less work on the relative income hypothesis. Instead some composite measure of 'deprivation' (consisting of such variables as 'unemployment', 'overcrowding', 'car-ownership', 'publichousing', or 'lower social class') is frequently related to health outcomes. Characteristic British aggregate studies of the effects of variations in deprivation include Barnes et al. (1993), BenShlomo etal. (1996) and Boyle etal. (1999). A noticeable feature of these relative income and deprivation studies is that they have used aggregate data for places, be they wards, states or countries, to explore contextual effects and consequently they inevitably run the risk of committing the ecological fallacy. Aggregate analysis is essentially incapable of distinguishing the contextual — the difference a place makes — from the compositional — what is in a place. It is unclear whether it is social characteristics of the individual or the social characteristics of the place that is influencing mortahty; the two are confounded in an aggregate analysis. Gravelle (1998) has given this familiar argument a new twist, showing mathematically that a non-linear relationship between mortality risk and individual income within an area will artifactually lead to an association between mortahty and inequality when areas are compared. If he is correct, we do not need the relative income hypothesis to explain the aggregate results; they would be a result of

/

Evaluating the Absolute and Relative Income Hypotheses

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absolute income. According to thds critique, it is not inequality between people that is causal, but a lack of individual income that affects health and longevity.' Micro-macro Studies It is clear that this debate cannot be addressed using aggregate data analysis. Wagstaff and van Doorslaer (2000) provide a devastating critique of how much of the existing research based on aggregate studies cannot hope to discriminate between competing hypotheses. Improved research requires access to individual data, and to a multilevel form of analysis that permits the simultaneous study of both the micro-relations of income and mortality within areas, and the macro-relations between areas (Jones and Duncan, 1995). Reviewing the literature, there appear to have been six studies to date that have simultaneously analysed micro and macro data on income and health outcomes, and we now provide a brief commentary on each of them. Fiscella and Franks (1997) analysed data on 14,407 subjects and found that the effect for community income inequality for U S counties on premature mortality disappeared when account was taken of individual household income. Unfortunately, this study based its measure of income inequality on the survey itself, which results in a small sample size for each measure of community inequality. Consequently, the attenuation of the effect for income inequality may be caused by measurement error due to the imprecision of the higher-level variable. The study did not use a multi-level model to explicitly analyse the two levels of variation, and the study was not designed to ensure that the selected sample was representative of the communities. Finally, during data collection, the income data was truncated at $25,000 and this may effect the calculation of the Gini scores of income inequality. Kennedy et al. (1998) examined the relationship between self-rated health for individuals and State-level income inequality using a sample in excess of 200,000 respondents. They adjusted for a wide range of variables including 'race', gender, household income and smoking status. They used Gini coefficients as measures of income inequality for 50 U S A States and analysed the data in the S U D A A N package, so that the standard errors were corrected for the nested structure of the data. The effects for income inequality were 'modest' (particularly in comparison to the effects for individual income), but significant (perhaps not surprising given the sample size). The effects of inequality were most marked (odds ratio of 1.33) for those with the lowest individual incomes. The study did not take into account the average income of the state in which the respondent lived. As one may anticipate that this omitted variable and the Gini values may be correlated, it may be that the effect of inequality is overestimated. Daly etal. (1998) analysed the American Panel Study of Income Dynamics for two time periods. Using logistic, non-multi-level, analysis with 'controls' for age, 'race', sex and median state income, they found that no inequality-mortality relation was more than two times the standard error, indeed virtually all were less than their

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The Geography of Health Inequalities in the Developed World

standard errors. They referred to the absence of significant effects as 'surprising' and noted that 'the effects sizes are small'. They concluded that: considerable experimentation with subgroups classified by household income showed that the middle-income non-elderly were the only group for whom inequality has a statistically significant (and in this case) detrimental effect on mortality. (Daly et al., 1998: 333) There are real problems of data mining here, and the fact that the size, and in some cases the relationship, changes between the two time periods suggests that the results must be viewed with caution. Moreover, there are less than 400 deaths in each study, and we would anticipate that the majority would be to those aged 65 and above, rather than those in younger age groups in which the significant effects were found. Also, as the significant results are only for the middle-income group, the number of deaths must be quite small. The study does not report any logistic regression diagnostics (in the manner of Jones et al., 2000) to see whether the effects are due to a few highly influential observations, nor the size of the individual income effect, nor the effect for median state income reported, as attention solely focuses on income inequahties. Anderson et al. (1999) used the American National Longitudinal Mortality Study for 239,187 persons over a 11-year foUow-up to study the effects of individual family income and census tract median income. They fitted separate models for men and women, blacks and whites, younger and older age groups. Although they did not use multi-level techniques, they corrected the standard errors in their models for sample clustering. They concluded that: although family income has a stronger association with mortality than census tract, our results indicate that, more broadly, area socio-economic status makes a unique and substantial contribution to mortahty. (Anderson et al., 1999: 42) The most marked effects for tiact median income were found for blacks aged 25-64 at the start of the study and no measure of income inequality was included in this study. Subramanian et al. (2001) use an explicit multi-level approach to examine variations in self-rated health. Data from the analysis comes from the 1993-1994 Behavioral Risk Factor Surveillance System (nearly 145,000 respondents) for the response and a range of individual predictors, while the 1986-1990 General Social Surveys provide data on income and 'social capital' at the state level. There are three higher-level variables: per capita median income; a Gini coefficient for income inequality, and a 'mistrust' variable. They find at the individual level that low-income, smoking and being black are strongly related to self-rated poor health. After taking account of a range of individual factors there is some between-state variation that is differential for income groups with the largest between-state variation being for the lowest income. Usmg the expected variance of the standard logistic distribution, it is possible to calculate the degree of variation, after taking account of individual factors, th&t lies at the state level. Using the results in Tables 3 and 4, the percentages

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are small at 1.65,1.02 and 0.90 for low, middle and high-income groups respectively. Significant effects for median income and mistrust were found but the Ginicoefficient was only significant in relation to the highest income category. For lowand middle-income groups self-related health is not different between an equal and an unequal state, but the affluent report better health from living in high inequality states. This study was unable to investigate the size and nature of the variations at levels between the individual and the state. Soobader andLeClere (1999) analysed respondents' self-assessed perceived health from the 1989-1991 National Health Interview Survey hnked to the 1990 US Census. They corrected the standard errors of their models for the nested structure of their data. They found at the tract level that there was some effect for income inequality when individual income was included as well as either median income or percentage in poverty, but it was only the worst upper quartile of areas in terms of inequality where the effect occurred. At the larger county level, income inequality had a stronger independent effect. In contrast the effects of area absolute income (both for median income and per cent in poverty) were more marked at the census tract rather than the county scale. They argue that at the finer scale of the census tract income inequality is mediated by individual processes.

Research Questions In reviewing this growing body of research, there arise a number of questions that need to be asked about both the absolute and relative income hypotheses in the British context. Absolute Income Hypothesis •

What is the nature of the relationship? Is it a regular gradient or does it involve thresholds and non-linearity? The shape of the relation has implications for the psychosocial argument as the lack of a threshold between markers of socialeconomic position has been taken to imply that material conditions are not a strong determinant of health inequalities. The form of the relationship will also have implications for the relative-income hypothesis.

Relative Income Hypothesis •

Does place inequality have an effect after taking account of individual income? We are unaware of any published studies that have examined the relative income hypothesis within Britain with the exception of the aggregate analysis of Stanistreet et al. (1999) who found mortality was related to both mean income and income inequality on the basis of data imputed from the 1991 New Earnings Survey to 366 EngUsh local government districts.

224 •



• •



The Geography of Health Inequalities in the Developed World Is it the average level of income in a place that is more closely related to mortality or is the spread of income within a place more important? In aggregate work on variations within the U S A , area average income is often taken as a 'confounder' to be partialled out in the investigation of regional variations. This may be^ue to die mistaken belief that controlling for average income is a substitute for having individual income in the model. Clearly the effects of both 'average' and 'spread' need to be examined simultaneously after taking account of individual income. Is the relationship with relative income linear or non-linear? Studies have tended to presume a linear relationship, and have not explored non-linearities. In contrast to individual income, a large-scale US study of income of zip-code area of residence (Davey Smith et al., 1996a, 1996b) did not show much flattening of the relation at high levels of area income. The published graphs of the betweencountry and between-state variations do not show evidence of non-linearity. The plot shown in Stanistreet et al. (1999) supports a linear relationship for English districts. How should inequalities be measured? On balance it seems wise to use a range of indicators in an exploratory study. What scale of area should be used for the analysis? Wilkinson (1996: 81) clearly believes that the relationship should occur at larger scales. Soobader and LeClere (1999) argue that income inequality manifests itself through residential spatial segregation of poor and rich households. Y/hen there is low inequality, sharing of resources occurs so that the poor benefit from facilities shared with the rich. However, at extreme levels of inequality, the rich and poor share neither physical spaces nor communal services. This in turn feeds into, and is sustained by, the decay of social capital as local societies break down. They hypothesise, and fmd that, at the finest spatial scale (the census tract) individual socio-economic characteristics absorb most of the effect of income inequality, but at the countylevel income inequality will 'generate more economic segregation with associated geographic consequences' (1999: 736). These findings are supported in the British context by Boyle et al. (1999) who found that as a finer scale was adopted within ward as opposed to within locality, there is increasing homogeneity and the association between relative deprivation and morbidity is weaker. Ideally, as recognised by Kaplan et al. (1996: 1002) the study of the effects of relative income need to be undertaken for a range of geographical units. Are there significant interactions between relative income and individual income? Do poor people in poor places do particularly badly? Do relative income inequalities affect everybody, or do they impact differentially on the poor as opposed to those on middle and high incomes? This question is relatively unexamined. However, in a study of chronic illness, Jones and Duncan (1995) found some evidence of interaction between individual social position and the deprivation of the area in which people lived, while Diez-Roux et al. (2000) argue, and to some extent find, that the effects of inequality are greatest for those on low income.

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The present study aims to explore some of these questions concerning absolute and relative income through the analysis of data collected in the U K Health and Lifestyle Survey (HALS).

Data Sources and Coding The H A L S represents one of the most comprehensive studies of the health of the adult U K population to date (Cox, 1988). The original sample of 9,003 was initially interviewed in 1984-1985 and the original respondents were 'flaggecr* to provide the subsequent date and cause of death (Cox, 1997). The present analysis is based on 8,720 individuals followed to May 1997; we do not include 283 individuals who have been lost to follow-up. The respondents were nested within 396 electoral wards and 207 parliamentary constituencies. We have grouped the parliamentary constituencies into 22 regions, which represent a breakdown of the U K ' s standard regions that takes account of urban/metropohtan and rural distinctions (Jones et al., 1992). No attempt has been made to take into account the migration of respondents. Individual-level Data The variables used in the analysis are summarised in Table 10.1. The outcome measure is whether the respondent remained alive or had died as of May 1997. The predictor variables refer either to individuals, the parliamentary constituency, or the region in which the respondents lived at the outset of the study. The individual-level variables consist, in addition to income, of three groups: demographic (age and sex); behavioural (smoking, alcohol, diet and exercise); and socio-structural (social class and tenure). More detail on the derivation of these variables is to be found in Jones and Duncan (1995). The household net income data were collected by asking respondents to place themselves in one specified net (after tax) income band from a choice of 12 which was listed in random order on the interview schedule. We converted these categories into a continuous scale, using the midpoints of the income categories employing the method of Ecob and Davey Smith (1999) and Davey Smith et al. (1996a, b) for dealing with extreme incomes. The resultant values were also converted into household equivalent income using the McClements (1977) scale. This divides the net household income by a factor which is dependent on the presence of a spouse, dependent children and number of adults in the household. The data include a number of cases for which the respondent did not reply to the income question. A categorical term representing this 'missing income' category has been included in all analyses to deal with possible bias by item non-response. Area-level Data The areal data were derived in two ways and at two scales. The first approach aggregated the JLALS raw income data to derive a set of income measures for regions

226 Table 10.1

The Geography of Health Inequalities in the Developed World Variables used in the analysis

Variable

Variable categories

Variable notes

Inidividual-level data (n = 8720) Response variable Died

1 = alive (83.7%, 7303) 0 = dead (16.3%, 1417)

Continuous predictor variables Age Income Raw income

Equivahsed income

Categorical predictor variables Sex Female (56.7%, 4951) Male (43.3%, 3769) Smoking Non-smoker (67.1%, 5859) Smoker (32.9%, 2861) Exercise Active (41.6%, 3625) Inactive (58%, 5056) Unknown (0.4%, 39) Healthy eater (61.9%, 5401) Diet Unhealthy eater (38.1%, 3319) low (55.7%, 4856) Alcohol High (13.4%, 1165) Other (30.8%, 2699) III Manual (34.6%, 3014) Social class I / n (27.3%, 2383) m Non-manual (14%, 1218)

Death as of May 1997

Years as of 1984 (mean = 46, min = 19, max = 97) £'s per week Net weekly income, after tax but including benefits, pensions and other income (mean = £145.5, median =£115.5) Raw income deflated for household size and composition (mean=£115.7, median=£98.9)

Regular smoker Vigorous exercise in last two weeks Four or more 'bad habits' High drinker: > 14 units per week for females > 21 units per week for males

r V / V ( 2 2 % , 1930)

Tenure

Unknown (2%, 175) Owner-occupied (64.1 %, 5619) Local Authority (27.7%, 2418) Private rented (7.8%, 683)

Continued

Evaluating the Absolute and Relative Income Hypotheses

Variable

Variable categories

227

Variable notes

Parliamentary constituency -- level data (n = 207) Mean income

Mean income (HALS)

Robin Hood index (HALS) Gini index (HALS)

£s per week derived from the National Shopping survey (mean = £247, min = £157, max = £354) £s per week derived from aggregating HALS (mean = £156, min = £77, max = £283) Derived from aggregating HAJLS (mean = 24, min = 14, max = 36) HALS (mean = 32, min = 20, max = 47)

Regional level data (n = 22) Mean income

Mean income (HALS)

Robin Hood index (HALS) Gini index (HALS)

£s per week derived from the National Shopping survey (mean = £239, min = £195, max = £309) £s per week derived from aggregating HALS (mean = £149, min = £122, max = £201) Derived from aggregating HALS (mean = 25, min = 21, max = 28) Derived from aggregating HALS (mean = 34, min = 31, max = 38)

Note: The base category used in the modelUng is given in bold; figures in braclcets refer to percentages and sample sizes for each category; percentages may not add to one hundred because of rounding.

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The Geography of Health Inequalities in the Developed World

and parliamentary constituencies. For the regions, the minimal sample size in a region was 110 known income responses with a median of 285. The parliamentary constituency aggregation is far more precarious and likely to be troubled by samphng error, being based on a range of 20 to 50 known income responses, with a median of 36. The second approach used the National Shopping Survey (NSS), and represents the average household weekly income, 1990-1991 for the parliamentary constituency (Butler and Kavanagh, 1992). The means from each survey were highly correlated for both regions (0.89) and, to a lesser extent, the parliamentary constituencies (0.61). The H A L S data were also used to derive a range of inequality measures. The Gini coefficient is an often used measure, which is given by the area between the actual income distribution and the diagonal of equal incomes. Zero means everybody gets an equivalent share, while perfect inequality is given by a Gini coefficient of one. It was calculated by die method detailed in Marsh (1988: 90). The Robin Hood index is another measure that is often used. As its name suggests it reflects the percentage amount that would have to be transferred from the rich to ^ e poor to achieve an equal income (Kennedy et al., 1996). A t both the regional and parliamentary constituency level, the Gini and Robin Hood indices are very highly correlated (0.98, and 0.96 respectively). Moreover both indices are correlated to approximately the same extent with the percentage income shares, with the highest correlation being with the share that 60 to 70 per cent of the population receives. A t the parliamentary constituency level, and acknowledging sample size problems, the two indices are very highly correlated (0.96). Comparing U S A states and U K regions reveals that the latter have a lower Robin Hood index overall (25 compared to Kennedy et al.'s (1996) figure of 30) but about the same range, 22 (Industrial South Wales) to 28 (East Central Scotland) as compared to 27 to 34 for the U S A . At the level of the state and the region, the U S A is more unequal than Britain but the variation around the overall figure is comparable. There is some correlation between mean income and inequality (the Gini and Robin Hood indexes), but the correlation is not strong, around 0.2 at the Parliamentary Constituency level, and 0.3 at the regional level. Poverty is not strongly related to inequality.

Analysis The GAM Model The usual binomial model is not used for the binary response (dead or alive), as a generalised additive model is preferred. Such a model is non-parametric in that it makes no assumptions about the underlying functional form between the response and a continuous predictor, rather a data-derived 'smooth' is fitted to the data after taking into account other included predictor variables. The degree of smoothing is controlled by a tuning parameter known as the 'equivalent degrees of freedom'.

Evaluating the Absolute and Relative Income Hypotheses

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When this is set to 1, the smoothest possible relationship, a straight-Une, is imposed on the data. Increasing the degrees of freedom allows the data to increasingly determine the shape of the fitted relation. In this case a generalised additive model with a logit link and a binomial error-term was used (Jones and Wrigley, 1995). The initial exploratory fitting was undertaken in S-Plus (Chambers and Hastie, 1992) using the G A M object. This procedure does not take the multi-level structure of respondents nested in parliamentary constituencies and regions into account, and specific models were later re-estimated as multi-level models to ensure correct standard errors, and to assess the size and nature of any remaining between-place variation. The model-building strategy was deliberately exploratory and sought to assess the effect of the inclusion of variables (and their interactions) on the improvement in the fit of the model. However, an automatic stepwise selection was not used, rather a number of theoretically interesting models were developed with particular attention being paid to determining the nature of the income relationship. Household Income The summary results are given in Table 10.2 where '+' means that a predictor enters a model as an additive term, while ':' signifi«is an interaction; and's' represents a nonparametric smooth. The aim is to reduce the deviance or 'badness of fit' of the model significantly by including relevant variables in their appropriate functional form. Block 0 represents a base model which includes age and sex; it is against this initial model that changes in deviance (Adeviance), changes in degrees of freedom (Ad.f.) and the resultant significance (Prob X-) are at first judged. In the base model, the logodds of the response (dead or alive) are related to a quadratic in age and sex, with an interaction between age and a male contrast. Previous work had found this to be a parsimonious model (Jones etal., 2000). Block 1 is concerned with exploring the relations between mortality and household income. Model 2 includes a linear term for household net income and a term for unknown income: that is when the respondent did not reply to the income question. There is a significant reduction in the deviance (17.94) when these two terms are included; household net income in its linear form has a significant effect on subsequent mortality. Model 3 allows a non-linear relationship with net income with equivaJent degrees of freedom set to 3. There is a significant reduction in the deviance (10.31) in comparison to model 2, providing clear evidence that the income relationship cannot be adequately described by a linear model. Further models (not presented) with lesser smoothing were also tried (degrees of freedom set to 4,5 and 6) but no further significant improvement was found. Model 4 has the same functional form as model 3 but equivalised income replaces net income. Comparing the deviances, the better prediction is given by net household income, and it is this variable that is used in all subsequent analyses. Model 5 represents a parametric model in which household net income is included as a logarithm. This can be readily interpreted as an equal improvement i n mortality experience for a similar

Table 10.2 Block

Sequential G A M analysis Model

Terms

0

1

Age + Male + Age^ +Male: Age

1 Individual income

2

1+s (Household nelincome, df= 1) + Unknown income 1 + s(Household net income, df = 3) + Unknown income 1 + s (Household equivalised income, df = 3) + Unknown income 1 + Log,g net income + Unknown income 1 + 4-fold stepped household net income

3 4 5 6 2 Constituency income

7 8 9

O ADev

Adf

Prob(X2)

17.94

2

0.00

28.25

2

0.00

17.80

4

0.00

21.35

2

0.00

33.34

3

22.71 26.84 24.59

1 3 4

0.00 0.00 0.00

25.06 21.57

3 1

0.00 0.00

10 11

6 + s (NSS constituency mean income, df= 1) 6 + s (NSS constituency mean income, df= 3) 6 + 4-fold stepped household net income: NSS constituency mean income 6 + 4-fo!d stepped NSS constituency mean income 6 + s (HALS constituency mean income, df = 1)

3 Regional income

12 13

10+ s (NSS regional mean income, df= 1) 10+ s (HALS regional mean income, df= 1)

0.67 1.67

1 1

0.41 0.20

4 Inequalities

14 15 16 17

10 + Constituency Gini index 10 + Constituency Robin Hood index 10 +Regional Gini index 10 + Regional Robin Hood index

0,^9 0.76 0.01 0.02

1 1 1 1

0.44 0.38 0.95 0.90

Comparison

ADev

Adf

Prob(X2)

3 versus 2

10.31

2

0.00

3 versus 5

6.90

2

0.03

6 versus 2

15.39

1

0.00

8 versus 7 9 versus 7

4.09 1.88

2 3

0.11 0.59

10 versus 7

2.34

2

0.31

I

KC3

b

Ob

5 Behaviours

18 19 20

1 + Smoking + Exercise + Diet + Alcohol 1 + Smoking + Exercise + Diet + Alcohol + 4-fold stepped Household net income 1 + Smoking + Exercise + Diet + Alcohol + 4-fold stepped Household net income + 4-fold stepped NSS constituency mean income

102.18 119.18

5 8

• 0.00 0.00

19 versus 18

17.00

3

0.00

135.40

11

0.00

20 versus 19

16.22

3

0.00

Continued

Block 6 Sociostructural

Model 21 22 23

24

Terms

ADev

1 + Social class + Tenure 92.94 1 + Social class + Tenure + Smoking + 159.76 Exercise + Diet+Alcohol 1 + Social class + Tenure + Smoking + 164.60 Exercise + Diet + Alcohol + 4-fold stepped Household net income 1 + Social class + Tenure + Smoking + 176.60 Exercise + Diet+Alcohol + 4-fold stepped Household net income + 4-fold stepped NSS constituency mean income

Note: From block 1 onwards the model includes terms from a previous model.

Adf

ProbCX^)

Comparison

ADev

Adf

Prob(X2)

6 11

0.00 0.00

22 versus 21

66.82

5

0.00

14

0.00

23 versus 22

4.84

3

0.18

17

0.00

24 versus 23

11.99

3

0.01

Co

fx

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The Geography of Health Inequalities in the Developed World

proportional increase in income. This form of the relation has often previously been used (for example, Backlund et al, 1996, 1999), and while it has a smaller deviance than the linear model, it is not as good a fit to the data as model 3. The final model in Block 1, model 6 involves a stepped relationship for income that distinguishes those below the lower quartile, those between the lower quartile and the median, and those above the median. That is a four-fold classification is used: poor (£14.74 to £66), low (£87.5 to £115.5), high (over £155.5), and unknown. This model was a significant improvement over the linear model 2; it has a lower deviance than model 3, while consuming one less degree of freedom. A model in which a stepped relation was allowed within the higher-income category (above and below the upper quartile) did not result in a significant improvement over the four-group stepped model. Moreover, no significant improvements in the fit (at the 0.05 significance level) were obtained when interactions between male and income, and over 60 years and income were included in the model. Moreover, the stepped income variables retained their importance when an indicator representing unemployment at the time of the original survey was included in the model. Model 6 was the most parsimonious model and was used in all subsequent analyses. In summary there is support for the absolute income hypothesis, with the underlying relationship for absolute income taking a non-linear form. Area Income Block 2 builds on model 6 by additionally including parliamentary constituency mean income to explore the relative income hypothesis. In model 7, NSS parliamentary constituency mean income makes a significant improvement in the fit for a linear term, but the non-linear term of model 8 fails to cause a further significant improvement at even the 0.10 level. Model 9 shows that there are no significant interactions between household net income in its stepped form and linear parliamentary constituency income. Model 10 uses a model in which NSS parliamentary constituency mean income is defined in a fourfold stepped manner (based on quartiles with breaks at £221, £244, £269). There is no significant improvement over the linear form of model 7. The final model in the block shows that a similar improvement in the fit over model 6 is achieved by using HALS-derived parhamentary constituency mean income as that obtained from the NSS. In block 3, regional income is added to model 10 which already includes household and parliamentary constituency income, both in stepped form for comparability. Neither NSS (model 12) nor HALS-derived regional means (model 13) make a significant improvement to the model. Block 7 explores the effects for two measures of inequality (Gini and Robin Hood) at both the parliamentary constituency and regional scale. No significant effects are found and this result was confirmed when a range of percentile measures of income inequality (10 to 90 in steps of 10) were included one by one in a set of models. Admittedly, at the constituency level the inequality measures are likely to be relatively unreliable.

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233

On the basis of blocks 2 to 4, there is support for the relative income hypothesis but only in a particular form. Mortality is affected by mean income of the locality, but not of the region, even when account is taken of individual income. There does not appear to be any evidence that non-linearities are involved, nor that poor people in poor areas are especially vulnerable or protected, nor that there are significant effects for parliamentary constituency and regional inequalities when parliamentary constituency mean income is taken into account. The relationship between mortality and relative income appears to be at the more local scale, rather than at the regional, and involves averages and not spread. Including Behaviours and Socio-Structural Variables Block 5 assesses the income relationships when behavioural variables are taken into account. From model 18 it is clear that the behavioural variables significantly and substantially reduce the deviance when included in the simple age-sex model 1. However, it is also clear (from models 19 and 20) that the inclusion of both stepped individual income and parliamentary constituency mean income makes an additional significant contribution even when the model already includes behaviours. Models (not presented) were also fitted to evaluate interactions between the behaviours and individual and area income, but no significant effects were found. Block 6 presents the results when further measures of socio-economic position, social class and housing tenure, are included in the model. Model 21 includes these socio-structural variables in the age-sex model, while models 22 to 24 sequentially and additionally include the behaviours, household stepped income and parliamentary constituency stepped income. Including social class and tenure in the age-sex model results in a highly significant reduction in the deviance; a more detailed examination of the changes reveals that tenure is associated with the biggest change. When the behavioural variables are included there is a further substantial and significant reduction in the deviance (model 22 compared to model 21). However, individual household income in its stepped form, does not bring any further significant reduction; but four-fold parliamentary constituency mean income does (model 24 versus model 23). In summary, the striking feature of blocks 5 and 6 is that, when account is taken of two socio-structural variables and four health-related behaviours, area income in the form of parliamentary constituency mean income remains a significant contributor to the variations in individual mortality. Further, explorations revealed that the effects for neither household nor parliamentary constituency mean income interacted with any of the behaviours, nor with tenure and class. Four key models (1,10, 20 and 24) were re-estimated as a multi-level logistic model using the MLwiN software to take account of the nested structure of individuals within wards within parliamentary constituencies within regions (Goldstein et al., 1998). It was only in the age-sex model 1 that significant betweenarea variations were found. In the other models all the higher-level variances are close

234

The Geography of Health Inequalities in the D ev eloped World

to zero. Consequently, essentially the same results are derived as from a non-multilevel analysis.

Discussion Functional Fonnsfor the Income Variables The salient results for the income variables, before taking account of behavioural and social-structural variables, are shown in Figure 10.1 which displays the estimates as well as the 95 per cent confidence bands. The graphs on the left show the results for household income, while on the right there are plots for parliamentary constituency mean income. Graphs (a) and (b) are derived from model 3 and model 8 respectively, in which three degrees of freedom are allowed in each case for the smoothing parameter. There appears to be a marked curved relationship between the log-odds of mortality and individual income with basically a linear relationship with parhamentary constituency mean income. Graphs (c) and (d) are derived from model 10 and show the fourfold stepped relationship between mortality and household and parliamentary constituency income. The results confirm the marked non-linearity for household income with the highest income group having a significantly reduced risk of mortality. In contrast there is a 'dose-response' relationship between mortality and parliamentary constituency income, with each stepped increase in mean income bringing a reduction in the log-odds of mortality. A marked feature of British work on socio-economic markers, such as occupational class, is the consistency in which a linear relationship with a regular gradient is found; there has generally been no support for a break or threshold in the relation (Macintyre, 1994). This has been taken to imply that it is relative deprivation rather than absolute deprivation that underlies inequalities in health. In contrast, a major finding of this study is that the income-mortality relation takes a non-linear form, with a flattening off of the relation at higher household income. These results supports the general concave form of the relationship found in American studies of mortaUty (Backlund et al., 1996, 1999), but the nature of tiiis relationship is not fully captured by a logarithmic relation which has been widely used in such work. The non-linear form of the income-mortality relationship is well described as a stepped relation. High income is unambiguously associated with increased longevity. Those above median income do not appear to be differentiated in their mortahty experience. There is no evidence that the shape of this relation is different for males and females, or for those above and below 60 years of age. These results for household income clearly add support to Gravelle's speculations (Gravelle, 1998). However, the results for parliamentary constituency income contradict Gravelle's concerns that the ecological relation is merely an artefact. There really does appear to be robust evidence of an area effect of income on mortality, albeit in terms of general level of areal income rather than inequahty per se.

235

Evaluating the Absolute and Relative Income Hypotheses (a) Smooth (df=3)

(b) Smooth (df=3)

Constituency income

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Log-odds of dying by income for households and parliamentary constituencies

236

The Geography of Health Inequalities in the Developed World

Size of Effects Including behavioural and structural factors in the model results in a major conceptual difficulty in terms of the explanatory status of variables and what should be regarded as an 'exposure' and what should be regarded as a 'confounder' (Clayton and Hills, 1993; Chapter 27). 'Controlling for behaviours' is problematic i f such behaviours are seen as a causal pathway between income and health. Similarly, income can be seen as an intervening variable between social-status and health. We follow Macintyre's (1997) 'soft' approach, accepting that behavioural and asset differences do not explain away social inequalities but contribute to them. Income, social position and behaviour are not seen as mutually exclusive, rather we can anticipate that they are interrelated. The size and nature of the effects are most easily appreciated from the plots of the odds ratios and associated 95 per cent confidence bands in Figure 10.2 and 10.3. Figure 10.2 is derived from model 20 in which 'healthy' behaviours, and high income for both households and parliamentary constituencies are taken as the base category, but social class and tenure are not included in the model. The increasing odds of dying associated with decreasing household income is discernible from the plot, but it is noticeable that the effects of a three-fold increase between 'rich' and 'poor' found in other studies (Backlund etal., 1996; Stronks etal., 1998) is not matched. However, stepped household income does remain substantial even when account is taken of the possibly intervening behavioural variables. As pointed out earlier, the area income plot for parliamentary constituency mean income shows a much clearer case for a dose-response relationship, with those living in the poorest quartile of the distribution having odds of over 150, a figure that is exactiy the same as the elevated odds for poorest household income quartile. In terms of the behavioural variables, there are sizeable and significant effects for regular smoking, lacking regular exercise and having unhealthy dietary habits, with the strongest effect for smoking. Of the behavioural variables, the least substantial effect is for alcohol. Calculating probabihties from these results shows that the likelihood of dying by May 1997 for a male who was 60 years of age in 1984, had above median income, lived in a 'rich' parUamentary constituency and took exercise, did not smoke, had low alcohol consumption and did not have a poor diet is 13 per cent. This rises to 33 per cent i f he is a regular smoker, drinks more than 21 units of alcohol per week, is inactive and has four or more unhealthy dietary habits. However, i f he exhibits these poor behaviours but is also in the lowest income quartile, the probability (reflecting the multiplicative nature of the log-odds model) rises to 42 per cent. Moreover, i f he additionally lives in a parliamentary constituency in the poorest category of mean income, the probability of death rises to 52 per cent. In marked contrast, the probability for a 60 year-old female with all tiie favourable characteristics is less than 7 per cent. A distinctive feature of Figure 10.3, which is based on model 24, is the effects for the rented tenure sector, both public and private, which are substantial even when

Evaluating the Absolute and Relative Income Hypotheses

237

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account is taken of behaviour. Social class does not show the clear-cut pattern that is usually found as die highest odds are for the HI non-manual category. The very high rate for unknown social class is associated with a very large confidence band as there are only 175 respondents so categorised. These respondents, to which no social class category could be assigned, are characterised by a youthful profile (10 years younger than the overall sample average) and a high degree of unemployment (three times the

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23 8

The Geography of Health Inequalities in the Developed World

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