Socio-economic Status and Clustering of Child Deaths in Rural Punjab

In recent years several studies of child mortality have been focused on the problem of death clustering or inter-family heterogeneity in child mortality (Das Gupta 1990; Curtis, Diamond and McDonald 1993; Guo 1993; Ronsmans 1993; Zaba and David 1994). This was indicated by earlier analyses of World Fertility Survey data (Meegama 1980; Hobcraft, McDonald and Rutstein 1985), as well as in other studies in various settings. ' Heterogeneity can have considerable implications for reproductive health and child survival programmes. In India, as in many other countries, health services are made available largely in response to demand. If child deaths are heavily concentrated in some families, this would suggest that substantial improvements in child mortality could be achieved by adopting the more costeffective techniques of focusing health-care resources specifically on the sub-group of families with a high risk of child death. Clustering needs to be examined carefully because there are several reasons why observed heterogeneity in risks could be an artifact of the method of data analysis. Thus, we might expect clustering when we analyse data aggregated from different locations, as is often done with countrylevel data. Living conditions of residents in different areas may be very different, and could affect mortality (e.g. through differences in access to health services, nutrition, and exposure to disease). The same considerations apply to local studies in a setting in which mortality has changed over time; in this case aggregation of the experience of different age groups of women will have an effect similar to that of aggregating samples from different regions. Another reason for clustering within a population is that mortality may differ in families in different socio-economic status groups. Thus, Guo (1993) found that most of the clustering in Guatemalan families could be explained by the household's economic status and the mother's education. As he points out, the discovery of clustering owing to such well-known factors does not add much to our understanding of child mortality (Caldwell 1979; Ware 1984; Cleland and van Ginneken 1988). Clustering in any group of families will disappear after all the relevant factors in child mortality have been controlled; it is of interest only if it points to factors that have hitherto not been analysed. In this paper we explore aternative models to test for clustering and apply them to data for Punjab in groups stratified by socio-economic status, mother's education, and child's age at death. The child's sex is also included because it is known that in Punjab, girls in families with more than one daughter suffer selective discrimination and excess mortality (Das Gupta 1987), and this could account for some clustering. Significant evidence of clustering is found only in the lowest socioeconomic and education groups. We also explore the relationship between familial risk and familybuilding factors such as short birth intervals and high-parity births, as these are well known to be associated with higher child mortality. It appears that this association may be more an effect than a cause of clustering of child deaths.