Research into health inequalities has established socioeconomic circumstances as important determinants of health.1–4 Different indicators of socioeconomic position measure different aspects of social stratification and are associated with measures of health in slightly different ways.5–8 Education, income, and occupational position are considered to be the standard markers of socioeconomic position and are widely used in health inequalities research.6 The role of education is often seen to be less important than markers of adult socioeconomic position.7,9,10 As most of the evidence on health inequalities comes from the developed world,1–5,7,8,11 there is little systematic evidence on the role played by education in developing countries. Recent work on Korean data suggests that education is a strong predictor of mortality,12,13 probably due to its stronger link with occupational position and material well-being in Korea compared with western countries.14 This “antecedent” role of education,15,16 linking it to future life chances, is likely to be particularly manifest in developing countries.
The purpose of the present analysis is to examine the association between adult education and child mortality in India. Article 45 of the Indian Constitution ensures “provision for free and compulsory education” until the age of 14 years, but many adults in India have received no education.17 Higher level of adult education has been shown to be associated with lower child mortality in India17–20 and other developing countries.21–23 However, it is unclear whether education remains associated with child mortality in India after taking into consideration the influence of other markers of socioeconomic disadvantage, here assessed by caste, household wealth, and urbanization. Caste is a traditional measure of social position in India and differs from other indicators in that it is both endogamous and hereditary.24,25 Caste continues to be linked to socioeconomic disadvantage, despite affirmative action program in India that reserves government jobs and seats in educational institutes for the disadvantaged castes.26 Thus, there are the expected associations between caste and poor health in India.25,27 Material factors, measured here as household wealth, have been shown to be important for child health in India and other developing countries.28–30 Finally, the concentration on poverty31 and the lack of access to healthcare in rural areas32 makes urbanization an important variable to consider. Our aim in this paper is to first examine the association between adult education and child mortality in analysis adjusted for other socioeconomic markers, and then to explore whether these markers act as effect modifiers of the association between adult education and child mortality.
Data are drawn from the nationally representative 1998–1999 Indian National Family Health Survey.33 The data were obtained by face-to-face interviews with the head of the household, conducted in 1 of the 18 major Indian languages, in the respondent's own home. Information was obtained on a range of health, demographic, and socioeconomic measures for each member of the household. Head of household provided data on all measures for all the members of the household, deceased or alive. This method of data collection is economical and has been shown to be accurate when compared with self-reported data in India.34 The survey response rate ranged from 89% to almost 100%, with 24 of the 26 states having a rate of more than 94%.33 The 1991 census list of wards served as the sampling frame. The census wards were stratified according to geographic region, size, percent of males working in the nonagricultural sector, percent belonging to the disadvantaged castes, and female literacy. A subset of these variables, depending on the state under consideration, was used to list wards. Subsequently, wards were selected with probability proportional to size based on the 1991 census. These wards formed the primary sampling unit, which were villages or groups of villages in rural areas and wards or municipal localities in urban areas (N = 3211). The households to be interviewed were then selected with equal probability from the household list in each primary sampling unit, using systematic sampling. Further details are available elsewhere.35
The respondent to the household survey was asked about the number of living resident members of the household as well as the number who had died within the 2 years preceding the survey (1997–1998). For each deceased household member, information was obtained on sex and age at death. Child mortality was considered to be death by the age of 5 years for the analysis reported in this paper. Thus, mortality was an outcome even though it was estimated from a cross-sectional survey. Such indirect methods of mortality assessment have been used in demographic studies, and their suitability is widely accepted.36
Education (measured as years of schooling) was available for the head of household (male in 90% of cases) as well as the spouse. The years of schooling were categorized into 3 groups: no schooling (0 years of schooling), primary and lower secondary (1–8 years), and higher secondary (9 years or more) education. The top category (9 years or more) reflects education until at least age 14.
Caste was based on the head-of-household's self-identification (scheduled caste, scheduled tribe, other backward class, other caste, or no caste group).27 At the bottom of the hierarchy are the low-status castes (scheduled castes) and indigenous groups (scheduled tribes). Scheduled castes are the lowest castes in the traditional Hindu caste hierarchy (eg, “untouchables” or Dalits), and as a consequence experience intense social and economic segregation and disadvantage. Occupationally, most scheduled castes are landless agricultural laborers or are engaged in what were traditionally considered to be ritually polluting occupations such as cleaning toilets, removing dead animals, working with leather, and cremating the dead. Scheduled tribes consist of approximately 700 tribes that tend to be geographically isolated and have limited economic and social interaction with the rest of the population. They are ethnically distinct and live in physical isolation from other caste groups. “Other backward class” comprises a diverse collection of “intermediate” castes that are considered low in the traditional caste hierarchy but above the scheduled castes and tribes. “Other caste” is the default residual group (ie, persons who do not belong to a scheduled caste, scheduled tribe, or other backward class) that enjoys higher status in the caste hierarchy. Groups for whom caste was not likely to be applicable (eg, Muslims, Christians, or Buddhists) and participants who did not report any caste affiliation in the survey were classified under “no caste.”
Household wealth was measured by household assets and material possessions. These included consumer durables (clock/watch, ceiling fan, bicycle, radio, television, sewing machine, refrigerator, motorcycle/car), characteristics of the dwelling (toilet facilities, source of drinking water, electricity, number of rooms, separate kitchen, cooking fuel used), and land ownership. Asset ownership indices have been used in many studies as a reliable and valid proxy measure for wealth and standard of living.37 The National Family Health Survey standard-of-living index was adapted using “proportionate possession weighting”38 to yield a weighted standard-of-living index. The weights for each item were derived on the basis of the proportion of households owning the particular item. Thus, for example, if 40 of 100 households in the sample owned a radio, then a radio would get a weight of 60 (100 − 40). Weights for each item were summed into a linear index and households were allocated a final household wealth score. These were then converted into quintiles for the analysis.
Urbanization was characterized by the number of residents and the urban or rural nature of the neighborhood. Households were classified as being located in a large city (population ≥1 million), small city (population 100,000 to 1 million), town (population ≤100,000), or village and rural area.
In addition to age and sex of the children, we used information on the state in which the respondents lived. State refers to the administrative groupings within India. Data used in the analyses come from all 26 states that existed in 1998–1999.
We used 2-level multilevel logistic regression model,39 with individuals comprising level 1 and the local area (here the primary sampling unit) comprising level 2, to account for clustering at the level of the primary sampling unit. The effect for clustering was tested using the likelihood ratio test. The null hypothesis was that the random-intercept variance is equal to zero; the resulting P value was then halved.39
We examined the association between the 5 measures of socioeconomic position and child mortality in analysis first adjusted for age and sex (model I) and then also adjusted for state (model II). “State” is treated as a separate covariate as there are substantial socioeconomic and mortality differences among the states, and we wanted to remove this confounding influence on the association between socioeconomic position and child mortality. Given the fact that the sex ratio is unfavorable for females in India,40 we tested sex differences in the association between measures of socioeconomic position and child mortality by fitting an interaction term between each socioeconomic variable and sex using the likelihood-ratio test. A lack of observed sex differences allowed us to combine boys and girls for further analysis.
Subsequent analyses were pursued only for the 2 measures of education: education of head of household and education of spouse. The purpose of these analyses was 2-fold. First we examined whether the age-, sex-, and state-adjusted association (model A) between adult education and child mortality remained after adjustment (model B) for caste, household wealth, and urbanization, separately and then all together. We then examined whether caste, household wealth, and urbanization acted as effect modifiers of the association between adult education and child mortality. Effect modification was tested by fitting an interaction term, on a multiplicative scale, between the measure of education and caste, then household wealth, and finally urbanization. The models were fitted using the NLMIXED procedure of the SAS software by maximizing an approximation to the likelihood integrated over the random effects (SAS Institute, Cary, NC). The method for approximating the integral was adaptive Gauss-Hermite quadrature. A dual quasi-Newton algorithm was used to carry out the maximization.
Overall, there were no differences between boys and girls (data not shown), leading us to combine them in the analyses. Data on 9044 children (Table 1) were excluded due to missing values on the measures of education. Child mortality in the excluded group (3.5%) was the same as mortality among children included in the analysis (3.5%). Data on both measures of adult education were missing for 7172 children; the mortality in this group was 3.6%. Only a third of children overall were under 1 year of age, but they made up a large proportion of the dead in both the missing data (73%) and those included in further analysis (73%). Overall, 66,367 children were included in the analysis presented in this paper; details are presented in Table 1. For 26% of children the head of household had 9 or more years of education; the corresponding figure for the spouses’ education was 11%; 65% of spouses had no schooling whatsoever. Over 60% of children were from the backward classes (scheduled caste, scheduled tribe, and other backward caste) and a third (33%) were from the upper classes, represented in the table by other caste. Nearly three quarters of all children (74%) lived in villages.
Table 2 shows the association between adult education and 3 other measures of socioeconomic position. The median years of education for the head of household was closely linked to caste, household wealth, and the measure of urbanization. For the spouse the association was clearly graded for the measure of urbanization, but the association was evident only for household wealth in the top quintile. The median years of education of the spouse was zero in all caste groups. The 66,367 children included in the analysis came from 38,833 households, with every household contributing a mean of 1.7 children. Deaths among the children occurred in 2144 households, 93% of these households reported only 1 death. The variance estimates for the primary sampling unit suggested there was clustering of deaths at the primary sampling unit level (P value for all models was less than 0.001).
The association between all the measures of socioeconomic position used in this study and child mortality is presented in Table 3. The interaction term for sex ranged from 0.15 to 0.54, indicating no strong evidence that the association between measures of socioeconomic position and child mortality differed as a function of the sex of the child. Model 1 was adjusted for the effect of age and sex and took account the clustering at the primary sampling unit level; model 2 was further adjusted for the confounding effects of state. In fully adjusted models (model 2), 9 years or more of education for the head of household had a protective association with child mortality (odds ratio [OR] = 0.54; 95% confidence interval [CI] = 0.48–0.62); the same was true for spouse's education (0.44; 0.36–0.54). The reference category for analysis of caste was the “scheduled caste” group. The other backward caste (0.85; 0.75–0.97) and the other caste group (0.70; 0.61–0.80) had lower child mortality compared with the scheduled caste group. The association between household wealth and child mortality was finely graded, with the lowest mortality in the wealthiest group (0.27; 0.22–0.32). Finally, urbanization was also associated with child mortality. Compared with children who lived in villages, mortality was lower among town (0.64; 0.54–0.75), small city (0.49; 0.37–0.64), and large city dwellers (0.56; 0.44–0.72).
Table 4 presents results of further analysis on education of the head of household and child mortality. Model A accounted for clustering at the primary sampling unit and was adjusted for age, sex, and state (model 2 in Table 3). Subsequently, this model was adjusted for caste, household wealth, and urbanization, individually and then all together (model B) to remove the mediating or confounding effect of these measures of socioeconomic position. The interaction term was used to assess effect modification by caste, household wealth, or urbanization on the association between education of head of household and child mortality. Nine years or more of education for the head of household was associated with lower child mortality in analysis adjusted for all covariates and caste (0.58; 0.51–0.66). Similar results were obtained in analyses adjusted for household wealth (0.80; 0.69–0.91) or urbanization (0.58; 0.51–0.66). In analyses adjusted for covariates in model A plus caste, household wealth, and urbanization, 9 years or more of education for the head of household was associated with 19% lower child mortality (0.81; 0.70–0.93).
Table 5 repeats the analysis reported in Table 4 using the measure of education for the spouse. Nine years or more of education had a protective effect on child mortality in analyses adjusted for caste (0.47; 0.38–0.58), for household wealth (0.74; 0.59–0.92), or for urbanization (0.51; 0.41–0.63). The results for spousal education differ from that of head of household in that even 1–8 years of education had a protective association with child mortality in analyses adjusted for caste (0.73; 0.65–0.83), household wealth (0.87; 0.77–0.99), or urbanization (0.74; 0.65–0.83). The association between spouse's education and child mortality was similar in different caste (P = 0.54), household wealth (P = 0.34), and urbanization (P = 0.54). Nine years of more of education for the spouse was associated with 25% (0.75; 0.60–0.94) lower child mortality when adjusted for caste, household wealth, and urbanization.
A composite measure of education, constructed using the average of the 2 measures, shows 9 or more years of education to be associated with lower child mortality in analysis adjusted for age, sex, and state (0.42; 0.0.35–0.51) and also when adjustments were made for other measures of socioeconomic position (0.77; 0.63–0.94).
The purpose of the present analyses was 2-fold. First, we examined whether there remains an association between adult education and child mortality after adjustments for other sources of socioeconomic disadvantage in India. Our results show that for 2 measures of adult education used (education of head of the household and of the spouse) the highest category of education was associated with lower child mortality. These effects were weakened but not completely attenuated after adjustments for caste, household wealth, and urbanization. It is not possible to infer an independent effect of education because of the possibility of residual association. Measurement error in strongly correlated confounders will almost guarantee such a residual effect.42 However, household wealth and location of residence (urbanization), are probably on the causal pathway between education and health outcomes, in which case adjustment for them constitutes over adjustment.
Second, we looked for effect modification of the association between adult education and child mortality by markers of socioeconomic disadvantage—caste, household wealth, and urbanization. Such interactions could have implications for public policies on education. If parents’ education were beneficial to child health only for the already advantaged, then further education would do little to improve population health. We found no evidence of such interactions; the benefits of adult education for child mortality were similar across caste groups. We checked this further by examining this association in analysis stratified by caste. These results support those obtained using the interaction term—higher education was indeed associated similarly with lower child mortality within all caste groups. Indeed, we did not observe any effect modification of the association between adult education and child mortality in these data. Thus, the education level of adults in the household is equally beneficial for child mortality outcomes in different caste and wealth groups, and in urban and rural neighborhoods. The implication for India, and perhaps for groups that have traditionally faced socioeconomic disadvantage elsewhere, is that education matters for health outcomes.
We studied the role of adult education in the context of 3 measures of socioeconomic position—caste, household wealth, and urbanization. There are important differences between these measures. Caste is generally unchanging throughout the life course. Household wealth needs to be considered because in some past studies, material factors have fully explained the association between education and child mortality.28–30 Finally, urbanization is an important measure; an effect as large as we found may be unique to developing or poor countries. However, these several measures of socioeconomic position are not interchangeable; they show the complex sources of socioeconomic disadvantage in India. Any public health message on the benefits of education needs also to consider these other aspects of socioeconomic circumstances.
Education influences adult health through material, psychosocial, and behavioral pathways.30,42 Research on child mortality suggests parental education is associated with better child care, better health care utilization, and higher value being placed on children,43 with greater autonomy44 and discipline17 as further mechanisms through which maternal education influences child health. We cannot clearly address the independent effects of maternal and paternal education in these data. The data were collected in interviews with the “head of the household,” which could be a grand-parent or parent. While this imprecision should not interfere with our broad conclusions on education, they do not allow a careful separation of the effects of father's and mother's education.
These data have other limitations. Child mortality rates in our sample are lower between birth and 5 years (35 of 1000) than for India as a whole (85 of 1000)45; this suggests that the most disadvantaged groups are not fully represented in our sample. We were not able to examine cause-specific mortality and it is possible that education has a stronger link with some causes of death than with others. Thus, generalizations cannot be made for all causes of child mortality. Furthermore, the 2-year time frame for mortality does not allow us to capture the year-to-year instability in mortality, likely to be prevalent in developing countries. Nevertheless, it is highly unlikely that the general pattern of results between adult education and child mortality varies from 1 year to the next. Finally, the analytic strategy used in this study does not allow causal inferences to be made either about the role of education or about the pathways through which education influences health.46
A prerequisite to reducing health inequalities is ascertainment of the relative importance of factors that underlie these inequalities. There appears to be a general consensus regarding the importance of education to both economic development and health, leading the United Nations to declare the years 2005–2014 to be the “Decade for Education for Sustainable Development.”47 India, like many other developing countries, is ridden with multiple inequalities and it is often difficult to identify 1 factor that could substantially improve survival chances among children. Education is different from other markers of socioeconomic position in that it is in a realm of public policy, with governments setting targets for the population. Nine years of education is a modest goal, guaranteed by Article 45 of the Indian Constitution for all in India. In India, education appears to be associated with lower child mortality even after some of the sources of social advantage and disadvantage are taken into account.
1. Fox AJ. Health Inequalities in European Countries.
Aldershot, UK: Gower Publishing Company Limited; 1989.
2. Townsend P, Davidson N, Whitehead M. Inequalities in Health: The Black Report and the Health Divide.
London: Penguin Books; 1992.
3. Lynch JW, Kaplan GA, Shema SJ. Cumulative impact of sustained economic hardship on physical, cognitive, psychological, and social functioning. N Engl J Med
4. Kitagawa EM, Hauser PM. Differential Mortality in the United States: A Study in Socieoeconomic Epidemiology.
Cambridge: Harvard University Press; 1973.
5. Bartley M, Sacker A, Firth D, et al. Understanding social variation in cardiovascular risk factors in women and men: the advantage of theoretically based measures. Soc Sci Med
6. Galobardes B, Shaw M, Lawlor DA, et al. Indicators of socioeconomic position (part 1). J Epidemiol Community Health
7. Davey SG, Hart C, Hole D, et al. Education and occupational social class: which is the more important indicator of mortality risk? J Epidemiol Community Health
8. Geyer S, Hemstrom O, Peter R, et al. Education, income, and occupational class cannot be used interchangeably in social epidemiology. Empirical evidence against a common practice. J Epidemiol Community Health
9. Agardh E, Ahlbom A, Andersson T, et al. Socio-economic position at three points in life in association with type 2 diabetes and impaired glucose tolerance in middle-aged Swedish men and women. Int J Epidemiol
10. Daly MC, Duncan GJ, McDonough P, et al. Optimal indicators of socioeconomic status for health research. Am J Public Health
11. Beebe-Dimmer J, Lynch JW, Turrell G, et al. Childhood and adult socioeconomic conditions and 31-year mortality risk in women. Am J Epidemiol
12. Khang YH, Lynch JW, Kaplan GA. Health inequalities in Korea: age- and sex-specific educational differences in the 10 leading causes of death. Int J Epidemiol
13. Khang YH, Kim HR. Explaining socioeconomic inequality in mortality among South Koreans: an examination of multiple pathways in a nationally representative longitudinal study. Int J Epidemiol
14. Son M. Commentary: why the educational effect is so strong in differentials of mortality in Korea? Int J Epidemiol
15. Singh-Manoux A, Clarke P, Marmot M. Multiple measures of socio-economic position and psychosocial health: proximal and distal measures. Int J Epidemiol
16. Singh-Manoux A. Commentary: modeling multiple pathways to explain social inequalities in health and mortality. Int J Epidemiol
17. Basu AM, Stephenson R. Low levels of maternal education and the proximate determinants of childhood mortality: a little learning is not a dangerous thing. Soc Sci Med
18. Bang AT, Bang RA, Baitule SB, et al. Effect of home-based neonatal care and management of sepsis on neonatal mortality: field trial in rural India. Lancet
19. Kravdal O. Child mortality in India: the community-level effect of education. Popul Stud (Camb)
20. Das Gupta M. Death clustering, mother's education and the determinants of child mortality in rural Punjab, India. Popul Stud (Camb)
21. Caldwell J, McDonald P. Influence of maternal education on infant and child mortality: levels and causes. Health Policy Educ
22. Majumder AK, May M, Pant PD. Infant and child mortality determinants in Bangladesh: are they changing? J Biosoc Sci
23. Mosley WH, Chen LC. An analytical framework for the study of child survival in developing countries. 1984. Bull World Health Organ
24. Dumont L. Homo Hierarchicus: The Caste System and Its Implications.
London, England: Weidenfeld & Nicholson; 1970.
25. Mohindra KS, Haddad S, Narayana D. Women's health in a rural community in Kerala, India: do caste and socioeconomic position matter? J Epidemiol Community Health
26. Deshpande A. Caste at birth? Redefining disparity in India. Rev Dev Econ
27. Subramanian SV, Nandy S, Irving M, et al. The mortality divide in India: the differential contributions of gender, caste, and standard of living across the life course. Am J Public Health
28. Desai S, Alva S. Maternal education and child health: is there a strong causal relationship? Demography
29. Frenzen PD, Hogan DP. The impact of class, education, and health care on infant mortality in a developing society: the case of rural Thailand. Demography
30. van Oort FV, van Lenthe FJ, Mackenbach JP. Material, psychosocial, and behavioural factors in the explanation of educational inequalities in mortality in The Netherlands. J Epidemiol Community Health
31. Ghosh M. Agrarian structure and rural poverty in India. J Contemporary Asia
32. Borah BJ. A mixed logit model of health care provider choice: analysis of NSS data for rural India. Health Econ
33. International Institute of Population Sciences. National Family Health Survey 1998–99. Mumbai, India: International Institute of Population Sciences; 2000.
34. Mohan D, Neufeld K, Chopra A, et al. Agreement between head of household informant and self-report in a community survey of substance use in India. Drug Alcohol Depend
35. National Family Health Survey 1998–1999. Available at: http://www.nfhsindia.org/
. Accessed March 30, 2007.
36. Hill K. Approaches to the measurement of childhood mortality: a comparative review. Popul Index
37. Filmer D, Pritchett LH. Estimating wealth effects without expenditure data or tears: an application to educational enrollments in states of India. Demography
38. Gordon D, Pantazis C. Breadline Britain in the 1990s.
Aldershot, UK: Ashgate Publishing; 1997.
39. Snijders TAB, Bosker RJ. Multilevel Analysis, an Introduction to Basic and Advanced Multilevel Modeling
. London: Sage Publications Ltd; 1999.
40. Sharma DC. Widespread concern over India's missing girls. Selective abortion and female infanticide cause girl-to-boy ratios to plummet. Lancet
41. Phillips AN, Smith GD. How independent are “independent” effects? Relative risk estimation when correlated exposures are measured imprecisely. J Clin Epidemiol
42. Ross CE, Wu C-L. The links between education and health. Am Sociol Rev
43. Caldwell JC. Education as a factor in mortality decline. An examination of Nigerian data. Popul Stud
44. Bourne KL, Walker GM. The differential effect of mothers’ education on mortality of boys and girls in India. Popul Stud
45. Mortality Country Fact Sheet 2006, India. Available at: http://www.who.int/whosis/mort/profiles/mort_searo_ind_india.pdf/
. Accessed March 30, 2007.
46. Kaufman JS, Maclehose RF, Kaufman S. A further critique of the analytic strategy of adjusting for covariates to identify biologic mediation. Epidemiol Perspect Innov