Determinants of Under-Five Mortality in Rural Empowered Action Group States in India: An Application of Cox Frailty Model

Abstract

Objectives : In India there has been a decline in overall under-five mortality, with some states still showing very high mortality rates. It is argued that there is family clustering in mortality among children aged <5 years. We explored the effects of programmable (proximate) determinants on under-five mortality by accounting for family-level clustering and adjusting for background variables using Cox frailty model in rural Empowered Action Group states (EAG) in India and compared results with standard models.

Methods: Analysis included 13,785 live births that occurred five years preceding the National Family Health Survey-3 (2005-06). The Cox frailty model and the traditional Cox proportional hazards models were used.

Results: The Cox frailty model showed that mother's age at birth, place of delivery, sex of the baby, composite variable of birth order and birth interval, baby size at birth, and breastfeeding were significant determinants of under- five mortality, after adjusting for the familial frailty effect. The hazard ratio was 1.41 (95% CI=I.I4-I.75) for children born to mothers aged 12-19 years compared to mothers aged 20-30 years, 1.42 (95% CI=I.I2-I.79) for small-sized than average-sized babies at birth, and I02 (95% CI=8I-I28) for non-breastfed than breastfed babies. Children had significantly lower mortality risks in the richest than poorest wealth quintile. The familial frailty effect was 2.86 in the rural EAG states. The hazard ratios for the determinants in all the three models were similar except the death of a previous child variable in the Cox frailty model, which had the highest R2 and lowest log-likelihood.

Conclusions and Public Health Implications: While planning for the child survival program in rural EAG states, parental competence which explains the unobserved familial effect needs to be considered along with significant programmable determinants. The frailty models that provide statistically valid estimates of the covariate effects are recommended, when observations are correlated.