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Health and Population Perspectives and Issues 37 (3 & 4), 76-87, 2014

IDENTIFYING THE FACTORS INFLUENCING INSTITUTIONAL AND NON-INSTITUTIONAL DELIVERY PRACTICES IN SLUMS OF SHILLONG CITY Sanku Dey*, Enayetur Raheem**, Preenan Sarkar***

ABSTRACT An attempt has been made in this paper to determine the factors which resulted in preference for Institutional and Non-Institutional deliveries in slum areas of Shillong city. Cross-sectional study was conducted in slums of Shillong City. From a total of 17605 slum dwellers distributed in different proportions in sixteen wards in Shillong city, a representative sample of 1300 slum households was selected from thirty one localities, using an appropriate statistical formula. From these 1300 households, 1417 women were identified as married women eligible for the study. Information of 1417 married women was analyzed to interpret the place of delivery of slum women in relation to respondent’s religion, education, occupation, family income, age at marriage etc. SAS/STAT software was used to analyze the data. The method is based on the well-known statistical technique of factor analysis by which we essentially find out the principal component of the group consisting of various indicators in descending order of their importance. Results from factor analysis show that the total number of ever born children, demand for male child, economic status and customs and religious practices influence both institutional and non-institutional deliveries. Key words: PCA, Factor analysis, Polychoric correlation, Survey, Delivery practices.

National Population Policy of India, 2000, emphasizes safe motherhood and thereby institutional deliveries. However the target of achieving 80 per cent institutional deliveries and 100% deliveries attended by trained personnel is yet to be realized. Perhaps this is due to the fact that the facilities for institutional *St. Anthony’s College, Shillong, India **University of Northern Colorado, Greeley, CO, USA. ***Banaras Hindu University, Uttar Pradesh, India.

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delivery on a mass scale require massive health infrastructure investments. It is a well-known fact that child birth in a medical institution under the care of trained health-workers enhances child survival and reduces the risk of maternal mortality. In India, both child mortality (especially neonatal mortality) and maternal mortality are high. Seven out of every 100 children born in India die before reaching age one, and approximately five out of every 1,000 mothers who become pregnant die due to pregnancy related complicacies and childbirth1. Maternal mortality is a global problem2. According to assessment of trends in maternal mortality for 181 countries from 1980–2008, it was estimated to be 342,900 maternal deaths worldwide in 2008 decreasing from 526,300 in 1980. More than 50 per cent of all maternal deaths were only from six countries in 2008 (India, Nigeria, Pakistan, Afghanistan, Ethiopia, and the Democratic Republic of Congo)3,4. Many low and middle income countries tried their best to optimize key and effective maternal health interventions to improve maternal health5. The place of delivery is also closely related with maternal and neonatal outcomes6-8. It is a proven fact that childbirth in a hospital attended by trained medical staff reduces the rates of maternal and neonatal mortality and morbidity than home births7,9, notwithstanding, the belief of our society that pregnancy is a condition which does not require medical attention10. Thus, in order to promote institutional delivery, various maternity benefit schemes had been launched by the union government. However, the likelihood of child delivery in a medical institution is influenced not only by use of antenatal-care services but also by other factors such as mother’s age, education, family income, exposure to mass media, access to health services etc.10-14 The need for region specific study from time to time regarding issues having regional variation is well understood. The purpose of this article is to study the factors, which determine the preference for institutional and non-institutional deliveries in slum areas of Shillong city. For this, we have applied Exploratory Factor Analysis (EFA) technique to identify the factors responsible for the preference for institutional and non-institutional deliveries. METHODOLOGY A cross-sectional study was conducted in slums of Shillong City during April 2010 to March 201215. From 17605 slum dwellers distributed in sixteen wards, a representative sample of 1300 slum households was selected from fourteen wards. In order to reach the respondents, i.e. currently married women of reproductive age (MWRA), a two-stage random sampling procedure was followed. In the first step, stratification was done according to municipal wards. In the second step a random sample of proportionate size was drawn from each of the wards. A total 77

of 1417 married women were interviewed using pre-structured, pretested oral questionnaire. Responses of 1417 married women were analyzed to interpret the place of delivery (institutional vs non-institutional) of slum women in relation to total children ever born, wanted male children by couple, wanted female children by couple, respondent’s religion, education, occupation, family income, age at marriage, use family planning methods, and husband’s education. The statistical analysis in this paper was performed using SAS/STAT software, Version 9.3 of the SAS System for Windows. In this paper, the well-known factor analysis technique was used to examine the interrelationships among the variables. The basic assumption of the factor analysis is that dimensions or factors can be used to explain complex phenomena. The mathematical model for factor analysis is similar to that of multiple regression equation, and each variable is expressed as a linear combination of factors that are not actually observed. A brief description of the variables used in this study is given in TABLE 1. The variables in our data set are all ordinal or binary except for WantMale and WantFem which are count variables. In order to fit a common factor model to the data, we first obtained polychoric correlation matrix and then used PROC FACTOR in SAS. This procedure is also known as latent trait modelling. TABLE 1 CHARACTERISTICS OF RESPONDENTS BY PLACES OF DELIVERY N (%) Institutional Delivery N=1211 (85.46%)

Non-Institutional Delivery N=206 (14.54%)

1: 1-2 Children 2: 3-4 Children 3: 5 or more children

581 (47.98%) 434 (35.84%) 196 (16.18%)

59 (28.64%) 73 (35.44%) 74 (35.93%)

1: Hindu 2: Muslim 3: Sikh 4: Christian 5: Buddhist 6. Other

789 (65.15%) 108 (8.92&) 78 (6.44%) 223 (18.41%) 12 (0.99%) 1(0.08%)

133 (64.56%) 25 (12.14%) 17 (8.25%) 28 (13.59%) 3 (1.46%) -

Variables

Code

Total children ever born (grouped)

Respondent’s religion

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N (%) Variables

Code

Institutional Delivery N=1211 (85.46%)


Respondent’s Education

1: Primary 2: Middle School 3: High School 4: Higher Secondary 5: Degree 6: PG and above 7: No Education

497 (41.07%) 292 (22.11%) 129 (10.65%) 60 (4.96%) 31 (2.56%) 1 (0.08%) 201 (16.60%)

98 (47.57%) 34 (16.50%) 22 (10.68%) 4 (1.94%) 1 (0.49%) 47 (22.82%)

Respondent’s Husband’s Education

1: Primary 2: Middle School 3: High School 4: Higher Secondary 5: Degree 6. PG and above 7: No Education

535 (44.18%) 302 (24.94%) 194 (16.02%) 89 (7.35%) 65 (5.37%) 10 (0.83%) 16 (1.32%)

109 (52.91%) 47 (22.82%) 26 (12.62%) 8 (3.88%) 11 (5.34%) 5 (2.43%)

Respondent’s Occupation

1: Housewife 2: Service 3: Wage Earner 4: Business 5: Agriculture 6: Other

499 (41.21%) 35 (2.89%) 141 (11.64%) 40 (3.30%) 4 (0.33%) 492 (40.63%)

84 (40.78%) 5 (2.43%) 28 (13.59%) 11 (5.34%) 1 (0.49%) 77 (37.38%)

Respondent’s Husband’s Occupation

1: Service 2: Wage Earner 3: Business 4: Agriculture 5: Other

167 (13.79%) 269 (22.21%) 149 (12.30%) 5 (0.41%) 621 (51.28%)

24 (11.65%) 45 (21.48%) 32 (15.53%) 2 (0.97%) 103 (50.00%)

Family Income of the Respondent (Taka)

1: Below 2,000 2: 2,000 – 4,000 3: 4,000 – 8,000 4: 8,000 – 10,000 5: 10,000 – 15,000 6: 15,000 and above

148 (12.22%) 857 (70.77%) 114 (9.41%) 33 (2.73%) 45 (3.72%) 14 (1.16%)

39 (18.93%) 136 (66.02%) 18 (8.74%) 6 (2.91%) 7 (3.40%) -

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N (%) Variables

Code

Institutional Delivery N=1211 (85.46%)


Wanted Male Child by Couple

1 2 3 4 5

712 (58.79%) 424 (35.01%) 71 (5.86%) 3 (0.25%) 1 (0.08%)

85 (41.26%) 92 (44.66%) 23 (11.17%) 6 (2.91%) -

Wanted Female Child by Couple

0 1 2 3 4 5

28 (2.31%) 1068 (88.19%) 101 (8.34%) 10 (0.83%) 2 (0.17%) 2 (0.17%)

5 (2.43%) 177 (85.92%) 19 (9.22%) 2 (0.97%) 3 (1.46%) -

884 (39.97%) 727 (60.03%)

56 (27.18%) 150 (72.82%)

493 (40.71%) 620 (51.20%) 98 (8.09%)

111 (53.88%) 85 (41.26%) 10 (4.85%)

Use Family Planning Yes Method No Respondent’s Age at Marriage

Below 18 years 18-25 years 25 years and above

FINDINGS AND DISCUSSION We used several factor extraction techniques including Principal Components Method (PC), Iterated Principal Components Method (IPC), and Maximum Likelihood (ML) Method. Extracted factors were rotated using varimax and promax methods. Maximum likelihood approach ended up with Heywood case, and PC and IPC approach resulted in similar conclusion. Therefore, we only report the results of PC factor extraction method. The criterion for the number of factors to be extracted was that of eigenvalue of each variable factor had to be equal to or greater than one. This is known as the MINEIGEN criterion. These methods gave us a set of independent interpretable/meaningful factors. Since our objective was to identify factors that influence institutional and noninstitutional delivery practices, we performed separate analysis for institutional deliveries and non-institutional deliveries. After splitting the data, we tested it for suitability of Principal Component analysis using Kaiser-Meyer-Olkin Measure of Sampling Adequacy (KMO). The calculated value of KMO was found to be 0.50 for non-institutional deliveries, and 0.55 for institutional deliveries.

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FIGURE 1 SCREE PLOT AND PERCENTAGE OF VARIANCE EXPLAINED BY THE NUMBER OF FACTORS FOR INSTITUTIONAL DELIVERIES

Scree plot and percentage of variance explained by the number of factors for the institutional delivery data is shown in. Scree plot for non-institutional deliveries are not shown. For both institutional and non-institutional data sets, four factors were extracted based on MINEIGEN criterion. For institutional deliveries, the resulting factors explained 55 per cent of the total variability while it was 61 per cent for non-institutional deliveries. Rotated factor loadings along with their communalities (specific variances) of each of the variables for institutional delivery data are shown in Table 2 and for non-institutional delivery data are shown in Table 3. For both data sets, factor loadings that are smaller than 0.4 were suppressed to enhance readability of the loadings.

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Respondent’s Family Income

Respondent’s Religion

Use Family Planning Method

Respondent’s Education


FamInc

Religion

FamPlan

Edn

HusEd

Values less than 0.4 are not printed.

% Variance explained

Respondent’s Occupation

Respondent’s Age at Marriage

AgeAtMar

Occu

Wanted Female Children by Couple

WantFem


Wanted Male Children by Couple

WantMale

HusOcc

Total Children Ever Born

Child

Factor 2 . . . . 0.7333 0.6063 -0.6633 . . . -0.4056 25.84

Factor 1 0.8911 0.7454 0.5459 -0.5291 . . . . . . . 33.13

Rotated Factor Pattern

21.35

.

.

0.5899

0.7492

.

.

.

0.5032

.

.

19.68

0.4791

0.8650

.

.

.

.

.

.

.

.

Factor 3 Factor 4 . .

0.4391

0.7498

0.3929

0.6391

0.4676

0.3993

0.5530

0.5588

0.4329

0.5742

0.7965

Communality

ROTATED FACTOR LOADINGS FOR INSTITUTIONAL DELIVERIES ALONG WITH THEIR COMMUNALITIES AND PERCENTAGE OF VARIANCE EXPLAINED

TABLE 2

FIGURE 2 FACTOR LOADINGS PLOT FOR ROTATED FACTOR 1 AND FACTOR 2 FOR INSTITUTIONAL DELIVERIES

The percentage of total variance is used as an index to determine how well a particular factor solution accounts for what all the variables together represent. For the institutional delivery data, Factors 1, 2, 3, and 4 explain 33.13 per cent, 25.84 per cent, 21.35 per cent, 19.68 per cent variation respectively. The corresponding figures for non-institutional data are 37.28 per cent, 21.92 per cent, 21.13 per cent and 19.67 per cent. Clearly, first two factors explain most of the overall variation for both data sets. In order to visually see the contributions of variables on each factor, their loadings on Factor 1 and Factor 2 are plotted in Figure 2 for institutional delivery data and in Figure 3 for non-institutional delivery data. Communalities show the amount of variance in a variable by the four factors taken together. The size of the communality is a useful index for assessing how much variance in a particular variable is accounted by the factor solution. The highest communality was found for the variable that describes ‘total children ever born’. This variable has the highest communality for both institutional (0.7965) and non-institutional (0.8234) data sets. This implies that the number of 83

children ever born to the respondents has the highest impact. The second largest communality belongs to ‘Respondent’s Education’ (0.7498) for the institutional delivery data while for the non-institutional delivery data it is the ‘respondent’s age at marriage’ (0.7166). TABLE 3 ROTATED FACTOR LOADINGS FOR NON-INSTITUTIONAL DELIVERIES ALONG WITH THEIR COMMUNALITIES AND PERCENTAGE OF VARIANCE EXPLAINED Factor Pattern Factor 1 Factor 2 Factor 3 Factor 4 Communality Child FamPlan

Total Children Ever 0.8497 Born Use Family Plan0.7482 ning Method

.

.

.

0.8234

.

.

.

0.6642

.

.

.

0.5747

.

.

0.6232

WantMale

Wanted male chil0.6759 dren by couple

WantFem

Wanted Female 0.6377 -0.4332 Children by Couple

Religion Occu AgeAtMar FamInc

Respondent’s Religion Respondent’s Occupation Respondent’s age at marriage R e s p o n d e n t ’s Family Income

.

0.6353

.

.

0.6675

.

0.4663

.

.

0.3349

.

0.4640

0.6426

.

0.7166

.

.

0.5302

0.4030

0.5803

HusOcc


.

.

-0.5291

.

0.6221

HusEd


.

.

.

0.5329

0.4852

Edn

Respondent’s Edu-0.4508 cation

.

.

-0.5621

0.6292

21.92

21.13

19.67

% Variance Explained

37.28

Values less than 0.4 are not printed.

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Naming the Factors In this study, we have identified four factors that influence institutional and noninstitutional delivery practices. They are discussed in the following. We also tried to explain the overall variability using three factors; however, we retained four factors in order to be able to better interpret the findings. Factor 1: Fertility and demand for male child Overall, fertility and demand for male child has emerged as a notable factor accounting for 33.13% of institutional deliveries and 37.28% of non-institutional deliveries. For institutional deliveries, total children ever born and wanted male child load highly on this factor. On the other hand, total children ever born and use of family planning method loads highly, and demand for male and female child loads moderately on this factor for non-institutional deliveries. Factor 2: Economic status Economic status is the second important factor for families that went for institutional deliveries. This factor is a contrast between the respondent’s and her husband’s occupation, and family income. This factor explains 25.84 per cent of total variation. It has the highest loading on husband’s occupation indicating a strong impact on choosing the place of delivery for their pregnant wives. As for the non-institutional deliveries, respondent’s religion and occupation played a role in defining this factor. Overall, factor 2 is hard to interpret for these families who opted to non-institutional deliveries. FIGURE 3 FACTOR LOADINGS PLOT FOR ROTATED FACTOR 1 AND FACTOR 2 FOR NONINSTITUTIONAL DELIVERIES

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Factor 3: Social and Religious factor Respondent’s religion and family planning methods load highly on Factor 3 which defines the factor for institutional deliveries. On the other hand, this factor is a contrast between age at marriage and husband’s occupation making it difficult for interpretation. In conclusion, we can say that social factors play a major role for institutional and non-institutional deliveries practices. Factor 4: Educational Status Lastly, the Factor 4 can be named ‘Educational status’ since it has higher loadings for respondent’s education and husband’s education for both institutional and non-institutional delivery practices. However, for non-institutional deliveries, the factor explains a contrast between respondent’s education and husband’s education. This factor explains roughly equal amount of variability, 19.68% and 19.67%, respectively, for institutional and non-institutional delivery practices. CONCLUSION The findings of the present study broadly suggest that it is possible to achieve improved percentages for institutional deliveries by promoting antenatal check-ups and associated counselling. Among the other predictor variables considered, children ever born have a strong positive effect of institutional delivery. On the other hand, the reasons for non-institutional deliveries may be customs and religious practices, monetary problem, spontaneous delivery, homely atmosphere16. No doubt, the utilization of health facilities for delivering the newborn babies has increased over the years but still 14.7 per cent of the total deliveries were conducted at home15. Even though the residents have easy access to various health facilities the state of affairs in the slums of Shillong city is a cause of concern. Hence, Government/Non-Government organizations can play an important role in promoting institutional deliveries by enhancing the educational level of couples and spreading awareness of reproductive health and safe deliveries. REFERENCES 1. 2.

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