Gender, family, and the nutritional status of children in three culturally contrasting states of India
Introduction
The United Nations (1994) reported that over half of the underweight children in the world are living in South Asia. Underweight children are at increased risk of both mortality and morbidity (Mosley & Cowley, 1991; Gage & Zansky, 1995), the increased risk of mortality rising with the degree of malnutrition. In addition, there is evidence to suggest that children who are malnourished at an early age are more likely to have reduced educational attainment (Pollit, 1984). Clearly then, there is a need to tackle the problem of malnutrition in the sub-continent. The 1992–93 National Family Health Survey (NFHS) is the first national-level sample survey to examine the nutritional status of young children in India. It provides an opportunity to study the associations between a range of demographic and socio-economic indicators and childhood nutritional status.
This paper uses regression modelling to examine the weight for age z-scores of children under the age of four years in the three Indian states of Maharashtra, Tamil Nadu and Uttar Pradesh using the NFHS data. Previous research has shown that there are marked differences in mortality and health outcomes between the north and the south of the country (Dyson & Moore, 1983; Arnold, Choe, & Roy, 1996; Agnihotri, 1997; Basu, 1990; and Florencio (1993), Kishor (1995)). The states of Uttar Pradesh in the northeast, Maharashtra in the southwest zone of India, and Tamil Nadu in the southeast were chosen to provide a sample that takes into account regional diversities. Florencio, 1980 suggests that because very young children rely on other family members for food, their nutritional status should be studied within the context of the household. We therefore examine the weight for age z-scores of children with particular reference to the clustering of underweight children in certain familial types. This analysis aims to highlight any differences that may be found between children living in joint families with mothers-in-law and those living in other types of family structure. A number of demographic and socio-economic explanatory variables that are applicable to the child at the family and the individual level will be explored. A random effects model is used to control for the fact that some children in the sample belong to the same family and therefore share the same family-level characteristics. In addition the existence of sex differences in nutritional status is examined.
The regional differences in marital systems and family formation identified by Dyson and Moore (1983) have been carefully considered when selecting states for analysis. The states of Maharashtra, Tamil Nadu and Uttar Pradesh (Fig. 1) include a range of cultural settings for examining the influence of family structure, clustering of weight for age z-scores within families, and gender disparities in weight for age z-scores. Table 1 gives some basic demographic indicators for each state. Uttar Pradesh, in the north of India, is less demographically developed and urbanised than the other two states. The disparity between the demographic and socio-economic conditions of men and women in Uttar Pradesh is greater than that in either Maharashtra or Tamil Nadu.
Section snippets
Clustering of underweight children within families
Since children living in the same family often share both a biological inheritance and a nurturing experience, it is likely that their health and mortality outcomes are correlated and, indeed, the clustering of health and mortality outcomes within families has been well researched. Studies that have examined infant and child mortality include Guo (1993), Zenger (1993), Ronsmans (1995), Madise and Diamond (1995), and Curtis and Steele (1996). Other work has investigated the clustering of health
Data
The Indian NFHS was representative of 99% of the Indian population and was designed to explore the demographic and socioeconomic determinants of fertility, family planning and maternal and child health. In Tamil Nadu, 3948 ever-married women aged 13–49 years were interviewed; in Maharashtra, 4106; and in Uttar Pradesh, 11,438. The NFHS collected weight and height data for children in Uttar Pradesh and Maharashtra. However, in Tamil Nadu only the weights of children were measured and not height.
Statistical modelling
Linear regression modelling was used to determine the effect of familial structure, gender and familial clustering on weight for age z-scores controlling for a range of other demographic, health, sanitation, and socio-economic variables. The regression analysis was performed separately for children aged 1–11 months at the time of the survey and those aged 12–48 months. This method of analysis allows the examination of predictor variables that are more likely to have an impact on nutritional
Results
Table 3 shows the proportion of children aged under four years who survived to the survey date who were underweight or severely underweight. All three states exhibit low weight for age z-scores. Maharashtra has a slightly higher proportion of children who are both underweight and severely underweight than Uttar Pradesh, and Tamil Nadu has the lowest level. However, it is possible that the proportion of children observed to be underweight in Uttar Pradesh is underestimated compared to those of
Discussion
The results show that a number of behavioural, morbidity, and socio-economic factors are associated with lower weight for age z-scores. It is also clear from Table 4, Table 5 that the significant covariates varied between the states, though the size of the child at birth and whether the child was still breastfeeding at the time of the survey were significantly associated with the weight for age z-score in all three states. This discussion focuses on the following covariates of substantive
Conclusion
This paper has identified a number of socio-economic and behavioural factors that influence the nutritional status of children in Tamil Nadu, Uttar Pradesh and Maharashtra. Both males and females show a high probability of being underweight in all three states studied with nearly 50% of the sample showing weight for age z-scores more than two standard deviations below the NCHS reference population used. However, no gender differences in weight for age z-scores are observed, indicating that
Acknowledgements
Paula Griffiths would like to thank the Economic and Social Research Council of the United Kingdom (award number R00429534127) for supporting financially a substantial proportion of her contribution to the work reported in this paper. The authors would also like to acknowledge the very perceptive comments of several anonymous referees.
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