Epidemiological models and related simulation results for understanding of contraceptive adoption in India

SN Dwivedi and KR Sundaram

Department of Biostatistics, All India Institute of Medical Sciences, Ansari Nagar, New Delhi-110029, India.


    Abstract
 Top
 Abstract
 Introduction
 Materials and Methods
 Results
 Conclusions
 References
 
Background For the first time, models using multilevel analysis of Indian data and related simulation results are reported. They take hierarchical structure into account and incorporate variables from all levels to get correct analysis and proper interpretation of data on current contraceptive use (including sterilization and modern methods).

Methods The data from an Indian State, Uttar Pradesh (UP), collected by the National Family Health Survey (NFHS) conducted during 10 October 1992 to 22 February 1993 was used. For model I, 7851 currently married women who were neither pregnant nor had continuing post-partum amenorrhoea (PPA) were considered. For model II, these women with at least one child (n = 6748) were used. Two-level logistic regression analysis was carried out for which women's level (level 1) and PSU (Primary Sampling Unit) level (level 2) variables were considered. The results were considered significant at the 5% level of significance. Simulation analysis using each model was also carried out.

Results Model I reveals that those more likely to adopt contraception were women exposed to a TV message (odds ratio [OR] = 1.3; 95% CI : 1.1–1.6); whose houses were pucca (bricks and mortar) (OR = 1.3; 95% CI : 1.1–1.5); who were educated to high school level and above (OR = 2.9; 95% CI : 2.2–3.7); whose husbands were literate with schooling of >=11 years (OR = 1.7; 95% CI : 1.4–2.1); and who had >=2 living sons (OR = 2.2; 95% CI : 1.1–4.4). Muslim and other religious women were less likely than Hindu women to adopt contraception (OR = 0.5; 95% CI : 0.4–0.6). Also, the PSU level availability of all weather road was positively associated with contraceptive adoption (OR = 1.4; 95% CI : 1.1–1.7). The PSU level variance, which is the unexplained PSU level variation after controlling for the considered characteristics, was significantly higher. The simulation results revealed that public health education (a TV message) was found to be more effective among less educated women. The PSU level availability of all weather road was as effective as public health education. Similar results were evident from the analysis of second data set (model II) with the noticeable finding that those whose last child is surviving are most likely to adopt contraception (OR = 8.82; 95% CI : 1.01–77.38).

Conclusions These results reveal that the survival status of the last child has a marked effect on the adoption of contraception in UP. They further support the idea that public health education (a TV message) is more effective among less educated women. Also, the PSU level presence of all weather road is equally effective. Consideration of higher level variables provides not only more accurate results but also important public health clues to help the policy planners.

Keywords Multilevel analysis, hierarchical, currently married, post-partum amenorrhoea, significant, PSU level

Accepted 30 September 1999


    Introduction
 Top
 Abstract
 Introduction
 Materials and Methods
 Results
 Conclusions
 References
 
The National Family Planning Program in India was established in 1952 due to increasing concern over the rapidly growing population, which at that time was still less than 400 million. As a result of the continuing efforts made by national, international and voluntary organizations, considerable progress has been made in reducing the birth rate. During the same period, the infant mortality rate was also reduced substantially.1 Today knowledge of family planning is almost universal, and almost half of the couples are using some means of contraception. However, the government supported family planning programme is dominated by voluntary sterilization, especially female sterilization, which accounts for 75% of all contraceptive use.2 Clearly, India is well into its demographic transition with reduced and declining fertility and mortality levels. In spite of this, the population growth has remained high, and relatively constant during the last 20 years, at a rate of 2% per year.1 According to the US Agency For International Development (USAID), India's current population of more than 900 million, which is increasing rapidly by 18 million a year, is expected to exceed one billion by the year 2000 (USAID, 1995: Report ‘Indo-US collaboration in population’ presented at the annual meeting of the Indian Association for the study of population, March 25–27, Lucknow, India). The 1991 census showed 139.1 million people in the single state Uttar Pradesh (UP), a number that represents an annual growth of 2.3% over the prior decade. At this rate of growth, the next decade could see UP's population reach 174.6 million. Such an increase will significantly fuel population growth for the country as a whole3 and compromises efforts by the government to improve the well-being of the people.

Raising contraceptive prevalence is viewed as one mechanism to lower fertility levels and eventually reduce population growth. However, despite the efforts of the National Family Planning Program to increase contraceptive uptake, the national average remains at 40.6%.2 As India's largest state, UP merits special attention from the family planning programme because of its high contribution to the Indian population growth and also has the lowest current contraceptive adoption (19.8%) among currently married women (other than Nagaland, 13.0%). This justifies USAID's special efforts in UP: (i) the ‘Innovations in Family Planning Services’ (IFPS) project;4 (ii) the UP District Level Baseline Surveys;5 (iii) the PERFORM Evaluation System project;3 and (iv) the Male Reproductive Health Survey (MRHS),6 along with other operation research programmes.

Contraceptive use is the result of interactions among a complex set of demand and supply aspects of family planning. Even the most recent research does not draw clear conclusions as to which aspects are most effective.7,8 The answer is likely to be very region-specific. A number of international, national and voluntary research organizations remain committed to data gathering and utilization. Considering secondary and/or primary data, a number of publications9–12 based on micro/macro data analysis are available mainly dealing with differentials, regional variations and its determinants. They have emphasized the need for regional studies. Since it is not clear what factors are most effective in achieving the goal of higher contraceptive uptake, especially in UP, there is a need to understand the epidemiology.

In many areas of public health research including contraceptive adoption, the data structures are often hierarchical in nature. Until recently, there has been generally two classical statistical procedures to deal with them. The first is to disaggregate all higher order variables to the individual level and carry out the analysis at individual level. Thus, the assumption of the independence of observations that is basic to classical statistical technique becomes invalid. On the other hand, the second is to aggregate individual level variables to a higher level and do the analysis at the higher level. Thus, all the within-group information, which may account for as much as 80% or 90% of the total variation, is discarded before the analysis is carried out. As a consequence, relations between aggregated variables are often much stronger giving distorted interpretation at the individual level.13–20 Also, a number of qualitative/ behavioural characteristics may not be either available or adequately measured, particularly those as perceived by the client/community. Almost all survey data sets involve some type of cluster sampling where groups of individuals from randomly chosen segments of a population are interviewed. However, generally, statistical methods are used that assume that the data were obtained from a simple random sample. A number of recent papers in the epidemiological literature contend that ignoring the above issues (unobserved community effects) in data analysis produces downward biases in the standard errors of the estimated parameters which leads to erroneous estimates of the impact of individual variables.15–17 However, these issues, which are considered to be of great importance in epidemiological studies, are rarely addressed in analysis.

This study deals with relatively newly developed multilevel analysis that takes hierarchical structure into account and makes it possible to incorporate variables from all levels which leads to correct analysis and proper interpretation of the data. Unobserved community effects are also taken into account. It also takes into account the relative importance of a woman's individual characteristics and those of the area in which she lives which gives important clues to strengthening ensuing public health programmes. The main objectives of the present study were: (i) to work out models for contraceptive adoption in UP using a relatively new technique, ‘Multilevel Analysis’; and (ii) to use the developed models in simulation analysis leading to better epidemiological understanding.


    Materials and Methods
 Top
 Abstract
 Introduction
 Materials and Methods
 Results
 Conclusions
 References
 
Data
The data used in the present study are from the National Family Health Survey (NFHS) conducted in Uttar Pradesh in 1992– 1993. The sample design adopted for the NFHS is a systematic, two-stage stratified sample of households. The NFHS in UP is a state representative survey of ever-married women aged 13–49. The main objective of the survey was to collect reliable and up-to-date information on family planning, fertility, mortality, and maternal and child health providing state level estimates. Another important objective was to provide high quality data to academicians and researchers for undertaking analytical research.2,21

The NFHS in UP was conducted between 10 October 1992 and 22 February 1993, in 242 selected rural area Primary Sampling Units (PSU) and 96 urban area PSU. In rural areas, the 1981 Census list of villages served as the sampling frame, and a two-stage sample design was adopted with the selection of villages (PSU) as the first stage and households in selected villages as the next stage. In urban areas, the list of Census Enumeration Blocks provided by the Registrar General of India for 1991 served as the sampling frame. Accordingly, a two-stage sample design was adopted: selection of Census Enumeration Blocks (PSU) followed by selection of households in each of the selected PSU. The selection of PSU was systematic with probability proportional to size (PPS). The households to be interviewed were selected from the household lists using systematic sampling with equal probability. On an average, in a rural area, 30 households were selected from PSU with <300 households, and 40 from larger PSU with >=300 households. In urban areas, on an average, 20 households per PSU were selected. In total, 11 438 ever-married women aged 13–49 from 10 110 households were interviewed. More details are available in the state level report for UP released in October 1994.21

For the present study, two data sets were used: (i) all currently married women who were neither pregnant nor had continuing post-partum amenorrhoea (PPA); and (ii) all currently married women with at least one child who were neither pregnant nor had continuing PPA. Only women whose records were complete for all variables considered in the analysis were included. There were, however, very few women with incomplete records (less than one per thousand). Accordingly, 7851 (set I) and 6748 (set II) currently married women were included in analysis who were neither pregnant nor had continuing PPA. In addition, the second set of women had at least one child making it possible to assess the impact of survival status of the last child on contraceptive use.

In view of poor contraceptive adoption in UP with the majority of couples going for sterilization and also taking into account the results from a series of exploratory models, in order to have meaningful observations, current contraceptive use (including sterilization, modern methods etc) was considered a dependent variable. Similarly, taking into account theoretical considerations as well as the results from a series of exploratory models, individual level variables considered in the analysis were: woman's present age in years; length of marriage in years; residential status (rural/urban); religion (Hindu/others); type of house (kuchcha + semi-pucca/pucca [built from bricks and mortar]); electricity in the house (no/yes); hearing a TV message about contraception (no/yes); working status (no/yes); education (illiterate/primary school/middle school/>=high school); husband's education (illiterate, 1–4/5–7/8–10/>=11 years); number of living sons (0/1/2/>=3) and number of ever born male children (0/1/2/3/>=4). The survey, in addition to the standard set of questions on individual level socio-demographic and other variables, included higher level questions on households/PSU/ district/state. Since the higher level data were being organized by Macro International (USA), the available data did not allow us to consider district level variables. However, PSU level data could be obtained and considered in the analysis. These were: availability of all weather road in the locality (no/yes); distance of locality from primary school (>=2 km/<2 km) and distance of locality from health centre (>=3 km/<3 km). After much exploration with alternative forms, it was decided that these forms of the variables were most appropriate. In this study, household level characteristics were not considered because the sample sizes within each household were too small to permit meaningful estimates. In the analysis of the second data set an additional variable, namely survival status of last child, was also included.

Methodology
As a special case of the general framework,14,18 and as used by others,12,17 this study used multilevel logistic regression to estimate the individual and area factors that influence current contraceptive use. The model fitted takes the form

log[pij/(1 – pij)] = xij a + wj b + uj + eij

where pij is the probability that woman i in PSU j uses some method of contraception; xij and wj are vectors of individual and PSU level characteristics respectively; and a and b are vectors of estimated parameter coefficients. uj (~N(0, {sigma}u2)) is an error term at the PSU level and eij (~N(0, {sigma}2)) is an error term at individual level. This model is an example of what is called a two-level model—individual women (level 1) are nested within PSU (level 2). The purpose of this approach is to control for the correlation between women in a particular PSU. The PSU error term (uj) in the model gives an indication of the variation after controlling for the individual level characteristics. In this study, the model is estimated using the computer program MLn for multilevel analysis.22 However, univariate analysis including rate ratio (RR) and 95% CI was carried out using STATA software.23 The results were considered significant at 5% level of significance.

Under the simulation analysis using models I and II, the estimated probabilities of contraceptive use for a particular variable were calculated by holding all other variables at their mean. Similarly, these probabilities for a particular combination of variables are also calculated by holding all remaining variables at their mean.


    Results
 Top
 Abstract
 Introduction
 Materials and Methods
 Results
 Conclusions
 References
 
In all, data from 7851 (set I) and 6748 (set II) eligible women were analysed in two data sets. Further the set II women were considered to assess the impact of survival of last child on contraceptive adoption (model II).

Table 1Go shows the percentage of contraceptive users in every sub-category of variable in relation to data set I. This Table also displays the unadjusted RR and its 95% CI estimate of contraceptive use in various categories of a variable compared to the reference category. Table 1Go clearly indicates that urban dwellers were more likely to use contraceptives than their rural counterparts (RR = 2.18; 95% CI : 2.01–2.36). Similarly, women living in pucca houses (RR = 2.41; 95% CI : 2.23–2.61), who had electricity (RR = 2.11; 95% CI : 1.95–2.29), and who received a TV message on contraception (RR = 2.70; 95% CI : 2.52–2.90), were more likely to use contraceptive methods. Also, contraceptive adoption was highly associated positively with the number of living sons and number of ever born male children. Husband's education also had a significant positive association with contraceptive adoption. Working women had a significantly higher chance (14%) of contraceptive adoption but women who were not Hindu had 46% less chance (significant) of contraceptive use (Table 1Go). The PSU level characteristics such as presence of all weather road (RR = 1.89; 95% CI : 1.74–2.05); primary school <=2 km (RR = 1.65; 95% CI : 1.50–1.81); and health centre <=3 km (RR = 1.58; 95% CI : 1.44–1.74) showed significant positive relationships with contraceptive adoption.


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Table 1 Distribution of currently married women excluding pregnant and continuing post-partum amenorrhoea (Set I) who practice contraception, by selected individual characteristics and related rate ratio (RR) and 95% CI
 
Table 2Go embodies two-level analysis (model I) for contra-ceptive adoption using women who were neither pregnant nor had continuing PPA (set I). It displays the RR and its 95% CI estimate of contraceptive use in various categories of a variable in comparison to the reference category, adjusted for age, marital duration and also for the remaining considered variables. The results shown in Table 2Go clearly reveal that the adjusted chance of contraceptive adoption decreased in relation to every variable. This adjustment demonstrated an insignificant association of contraceptive adoption in relation to residence (RR = 0.97; 95% CI : 0.70–1.35), and working status of women (RR = 1.05; 95% CI : 0.89–1.23). Also, among the PSU level variables, only availability of all weather road in the locality was found to contribute significantly towards contraceptive adoption even after adjustment (RR = 1.40; 95% CI : 1.09–1.70). The PSU level variance reported for model 1 in Table 2Go, which is meant as the unexplained PSU level variation after controlling for the considered characteristics, was also significant. This variation may be the result of variables that have not been considered, or in some cases, cannot be observed.


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Table 2 Rate ratio (RR) and 95% CI estimates for two-level logistic model of contraceptive use versus non-use using currently married women but excluding those pregnant and with continuing post-partum amenorrhoea (Model I)
 
Similar to the results for data set I, Table 3Go exhibits the percentage of contraceptive users in every sub-category of variable in relation to women with at least one ever born child and who were neither pregnant nor had continuing PPA (data set II). The unadjusted RR and its 95% CI estimate of contraceptive use in various categories of a variable in comparison to reference category are also presented in this Table. As mentioned earlier, to assess the impact of survival status of last child on contraceptive use, only women with at least one ever born child were included in this analysis which obviously increases the chance of contraceptive use. This is clear from the comparative results in Tables 1 and 3GoGo, which demonstrate higher contraceptive prevalence in each and every sub-category of variable in Table 3Go in comparison with Table 1Go. However, the pattern of contraceptive use among various categories of a particular variable remained the same. Accordingly, the unadjusted RR and 95% CI in relation to each variable were almost comparable with those for data set I except in relation to the number of living sons as well as the number of ever born male children which may be attributed to the obvious change in reference categories. Survival of the last child made a significant contribution (RR = 1.78; 95% CI : 1.52–2.08) towards contraceptive adoption (Table 3Go).


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Table 3 Distribution of currently married women with at least one ever born child excluding pregnant women and those with continuing post-partum amenorrhoea (Set II) who practice contraception by selected individual characteristics and related rate ratio (RR) and 95% CI
 
The adjusted RR and 95% CI estimates of contraceptive use related to data set II (model II) in relation to each variable (Table 4Go) were similar to those true in case of data set I (Table 2Go). Even after adjustment, survival of the last child exhibited a significant contribution towards contraceptive adoption (RR = 8.82; 95% CI : 1.01–77.38). The PSU level variance reported for model II in Table 4Go, which is meant as the unexplained PSU level variation after controlling for the considered characteristics, was comparably lower than that for model I but still significant. This again emphasizes the role of unconsidered/ unknown variables in the analysis.


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Table 4 Rate ratio (RR) and 95% CI estimates for two-level logistic model of contraceptive use versus non-use using currently married women with at least one ever born child (Model II)a
 
Table 5Go demonstrates the estimated probabilities of contraceptive adoption by a woman having particular characteristics, once other characteristics are retained at their average level in the respective models, I and II. As a result of the inclusion of women with at least one child in data set II, the probabilities under model II are consistently higher than those under model I. These results under model II clearly show that women with high school education (0.51), exposed to a TV message (0.33), having literate husbands (0.32–0.39), >=2 living sons (0.33– 0.37), >=2 ever born male children (0.44–0.71), whose last child is surviving (0.34), are comparatively more likely to adopt contraception. This Table provides epidemiological understanding of contraceptive adoption by a woman with particular characteristics when all the other variables are controlled in the multivariate analysis.


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Table 5 Estimated probabilities of contraceptive use, by selected individual characteristics, according to models I and II
 
Table 6Go also describes the epidemiology of contraceptive adoption by a woman with a combination of characteristics when all the remaining variables are controlled at their average level in the multilevel models. As evident from the Table under models I and II, irrespective of the education levels of each partner, probability of contraceptive use increases with increasing number of living sons. However, both models provide almost same probability of contraceptive use for women with one living son as well as that for women with two living sons. However, for women with no living son, model II provides a consistently higher probability of contraception use than that from model I. Interestingly this difference is almost constant rather than instead of the fact that the probability of contraception increases with increase in educational status of partners. It clearly indicates the positive role of surviving last child on contraceptive adoption. Also, the probability of contraceptive adoption becomes highest once the women have high school education irrespective of whether their husbands have 1–4 years schooling or even more. The role of women's education becomes more clear once we substitute the variable TV message in place of partner's education—women who have a high school education and are exposed to a TV message are more likely to adopt contraception than those whose husbands have >=11 years education as well as being exposed to the TV message. However, education of the partner contributes more towards contraceptive adoption than exposure to TV message. TV message and availability of all weather road contribute almost equally towards contraceptive adoption.


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Table 6 Estimated probabilities of contraceptive use, by selected combinations of individual characteristics, according to models I and II
 

    Conclusions
 Top
 Abstract
 Introduction
 Materials and Methods
 Results
 Conclusions
 References
 
There have been numerous individual/district/state level analyses dealing with the differentials/determinants of contraceptive adoption, but to our knowledge, in India, no study has so far tried to analyse data either consisting of these many variables or using the more appropriate procedure of the present study. It is well known that studies involving a large number of important variables categorized suitably combined with the appropriate analytical procedure will provide more valid and stable results. The results of this study, however, confirm earlier findings from India that the use of contraceptives is affected by a host of individual and community characteristics.

This study provides the central finding that those women whose last child is surviving, were nine times more likely to adopt contraception. However, looking at the 95% CI, the very high upper confidence limit indicates instability in the adjusted RR. This may be because in the north Indian population until a couple has achieved at least a living son, a surviving last daughter does not encourage contraceptive adoption. The instability in view of the very high upper confidence limit is also evident from the non-significant results related to number of ever born male children in model II; contrary to the significant results in model I. Also, those likely to adopt contraception had >=2 living sons; were educated to high school level and above; had literate husbands with schooling of >=11 years; and were exposed to a TV message. Muslim and other religious women were less likely than Hindu women to adopt contraception. The simulation results further reveal that public health education (a TV message) was found to be more effective among less educated women.

The PSU level availability of all weather road was positively associated with contraceptive adoption. This may be attributed to the fact that availability of all weather road may be an indication of supply of as well use of facilities related to family welfare programme and health. Further, this was as effective as public health education. The PSU level variance, which is the unexplained PSU level variation after controlling for the considered characteristics, was found to be significantly higher. In summary, there is need to consider more PSU level variables. Also, consideration of district level variables in the analysis may further refine the results.

These analyses can not fully identify the pathways by which these variables influence contraceptive use. For example, family planning programmes may not be targetted so strongly in areas with high levels of religious practice because of the antipathy of religious and local leaders, or an aversion to change may exist among the population.12 However, these analyses do suggest that contraceptive adoption depends on the current levels of willingness to accept and to institute change in a community, which may be affected by literacy and utilization of health facilities.


    Acknowledgments
 
The authors are thankful to the All India Institute of Medical Sciences, New Delhi, for its timely financial support and other facilities which were necessary to carry out this study. The East-West Center, Hawaii also deserves thanks for allowing us to use the MLn package on its license. Prof. KB Pathak, Director, International Institute for Population Sciences, Mumbai, India, provided helpful comments. Finally, Mr Rajvir Singh, Dept. of Biostatistics, AIIMS, New Delhi and Dr S Rajaram, Population Foundation of India, New Delhi also deserve thanks for their help in data analysis and report preparation.


    References
 Top
 Abstract
 Introduction
 Materials and Methods
 Results
 Conclusions
 References
 
1 Health Information of India. An annual publication of the Directorate General of Health Services, Government of India, 1994.

2 National Family Health Survey. A Final Report of the National Family Health Survey, 1992–93. Bombay, India: International Institute for Population Sciences, 1995.

3 PERFORM Survey. A Final Report on the Performance Indicators for the Innovations in Family Planning Services Project 1995. The Evaluation Project, Carolina Population Center, Chapel Hill, USA, 1996.

4 Innovations in Family Planning Services Project. USAID, New Delhi: USAID/India Program Summary, 1995.

5 Evaluation Project. Uttar Pradesh District Level Baseline Surveys. The Evaluation Project, Carolina Population Center, Chapel Hill, USA, 1996.

6 Male Reproductive Health Survey. Uttar Pradesh: Male Reproductive Health Survey 1995–1996. The Evaluation Project, Carolina Population Center, Chapel Hill, USA, 1996.

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11 Dwivedi SN. Contribution of some socio-economic variables towards explaining the level of adoption of various family planning devices in India during 1987. Demography India 1992;21:239–45.

12 Amin S, Diamond I, Steele F. Contraception and Religious Practice in Bangladesh. The Population Council, New York, NY, Working Paper No.83, 1996, pp.1–35.

13 Bryk AS, Raudenbush SW. Hierarchical linear models. Applications and Data Analysis Methods. Sage Publications, 1992.

14 Goldstein H. Multilevel Statistical Models. London: Edward Arnold, 1995.

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22 Rasbash J, Woodhouse G. MLn Command Reference. London: Institute of Education, 1995.

23 Stata Corp. Stata Statistical Software. Release 4.0. College Station, TX: Stata Corporation, 1995.





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