Factors That Predict Differences in Childhood Mortality in Nigerian Communities: A Prognostic Model

J Pediatr. 2016 Jan:168:144-150.e1. doi: 10.1016/j.jpeds.2015.09.057. Epub 2015 Oct 24.

Abstract

Objective: To identify predictors of variations of childhood mortality between Nigerian communities and to identify high-risk communities where childhood mortality was higher than expected.

Study design: Secondary analysis of the 2013 Nigeria Demographic and Health Survey data using prognostic univariable and multivariable mixed Poisson regression models. Likelihood ratio test, Hosmer-Lemeshow goodness-of-fit, and variance inflation factor were used to evaluate the fitness of the final model.

Results: The final adjusted model revealed that communities with high rating of multiple childhood deprivation (relative risk 1.14, 95% CI 1.09-1.19) and maternal socioeconomic deprivation (relative risk 1.22, 95% CI 1.14-1.29) were associated significantly with the risk of childhood mortality. Communities with enhanced maternal hospital-based health-seeking behaviors and more advantageous environmental conditions had reduced risks of childhood mortality. Similarly, children living in communities with high ethnic diversity were significantly less likely to die before their fifth birthday (relative risk 0.96, 95% CI 0.94-0.97). About 64% of the observed heterogeneity in childhood mortality in these communities was explained by the final model. Eleven of the 896 communities had higher than expected childhood mortality rates during the study period.

Conclusions: Of the 31 482 children included in this survey, 2886 had died before their fifth birthday (128 deaths per 1000 live births). There are variations in childhood mortality across Nigerian communities that are not determined only by health system functions but also by factors beyond the scope of health authorities and healthcare delivery systems.

Publication types

  • Comparative Study
  • Research Support, U.S. Gov't, Non-P.H.S.

MeSH terms

  • Child
  • Child Mortality*
  • Humans
  • Models, Statistical*
  • Nigeria / epidemiology
  • Prognosis
  • Residence Characteristics
  • Risk