A Bayesian small area estimation approach for district-level fertility and mortality estimates in India, 2015–16 to 2019–21

Abstract

Background and Aims: Fertility and child mortality are critical public health indicators in India, directly influencing health policy, planning, and intervention effectiveness. The relationship between declining fertility and decreasing child mortality has been widely debated. This study aims to estimate and compare fertility and child mortality rates at the district level using data from the National Family Health Survey (NFHS) rounds 4 and 5, with a focus on understanding regional trends and their implications for health interventions. Methods: The study investigates four key demographic indicators: Total Fertility Rate (TFR), Neonatal Mortality Rate (NMR), Infant Mortality Rate (IMR), and Under‐Five Mortality Rate (U5MR). Fertility and child mortality rates were estimated using Bayesian methods, aligned with Demographic and Health Survey (DHS) standards. Fertility rates were computed in two stages: first, birth history data were transformed into table of birth, followed by the calculation of fertility rates through Poisson regression models at the district level. Results: Between 2015–16 and 2019–21, the number of districts with a TFR below 1.6 increased from 21 to 166, while number of districts with a TFR between 1.6 and 2.1 remained stable at 328. Additionally, the number of districts with an NMR under 10 per 1,000 live births grew from 79 to 140. The study found a strong association between the reduction in child mortality and the decline in fertility rates. Conclusion: This study suggests that addressing regional variations in fertility and child mortality rates could enhance the effectiveness of health interventions in India. Policymakers should prioritize expanding access to family planning and maternal‐child health services. The availability of district‐level data will support more targeted and effective health policies tailored to local needs.