Precision-weighted estimates of neonatal, post-neonatal and child mortality for 640 districts in India, National Family Health Survey 2016

Abstract

Background The conventional indicators of infant and under-five mortality are aggregate deaths occurring in the first year and the first five years, respectively. Monitoring deaths by <1 month (neonatal), 1-11 months (post-neonatal), and 12-59 months (child) can be more informative given various etiological causes that may require different interventions across these three mutually exclusive periods. For optimal resource allocation, it is also necessary to track progress in robust estimates of child survival at a smaller geographic and administrative level. Methods Data on 259 627 children came from the 2015-2016 Indian National Family Health Survey. We used a random effects model to account for the complex survey design and sampling variability, and predicted district-specific probabilities of neonatal, post-neonatal, and child mortality. The resulting precision-weighted estimates are more reliable as they pool information and borrow strength from other districts that share the same state membership. The Pearson correlation and Spearman’s rank correlation were assessed for the three mortality estimates, and the Moran’s I measure was used to detect spatial clustering of high burden districts for each outcome. Results The majority of under-five deaths was disproportionately concentrated in the neonatal period. Across all districts, the predicted probability of neonatal, post-neonatal, and child mortality varied from 6.0 to 63.9 deaths, 3.8 to 47.6 deaths, and 1.7 to 11.8 deaths per 1000 live births, respectively. The overall correlation between district-wide probabilities of mortality for the three mutually exclusive periods was moderate (Pearson correlation = 0.47-0.58, Spearman’s rank correlation = 0.58-0.64). For each outcome, a relatively strong spatial clustering was detected across districts that transcended state boundaries (Moran’s I = 0.61-0.76). Conclusions Sufficiently breaking down the under-five mortality to distinct age groups and using the precision-weighted estimations to monitor performances at smaller geographic and administrative units can inform more targeted interventions and foster accountability to improve child survival.

Trends and geographic variability in gender inequalities in child mortality and stunting in India, 2006–2016

“…Researchers have also pointed out that gender patterns in child mortality differ substantially by age; neonatal mortality has different underlying causes as well as different outcomes than mortality in later childhood (Kim et al, 2020;Million Death Study et al, 2010). Preventable deaths in the former largely reflect maternal nutrition, antenatal care and unsafe delivery and thus are less mediated by genderspecific patterns in childcare (Million Death Study et al, 2010).…”

Progress on Sustainable Development Goal indicators in 707 districts of India: a quantitative mid-line assessment using the National Family Health Surveys, 2016 and 2021

“…In order to partially account for the fact that survey clusters were post hoc assigned to the 2021 NFHS geometry, we used a precision-weighted multilevel modeling strategy to derive estimates for the indicators. 35 , 36 We specified a four-level discrete binary random effects logistic model that preserved the original complex multi-stage survey design. …”