Evidence map›Paper›PMID 41669497›Full record

ArticleHealth science reports2026

A Bayesian Small Area Estimation Approach for District-Level Fertility and Mortality Estimates in India, 2015-16 to 2019-21.

Laxmi Kant Dwivedi, Somnath Jana, Shekhar Chauhan, Pooja Arora, Priyanka Dixit, Mrigesh Bhatia

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Article in Health science reports, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

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1 · What the graph read from it

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1 citing paper in PubMed.

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5 · Who and what money

Authors and funding

6 authors.

Laxmi Kant DwivediDepartment of Survey Research & Data Analytics International Institute for Population Sciences Mumbai India.ORCID https://orcid.org/0000-0001-9737-2844
Somnath JanaInternational Institute for Population Sciences Mumbai India.ORCID https://orcid.org/0009-0000-5870-5247
Shekhar ChauhanClaude Pepper Center, Florida State University.
Pooja AroraInternational Institute for Population Sciences Mumbai India.
Priyanka DixitSchool of Health Systems Studies Tata Institute for Social Sciences (TISS) Mumbai India.
Mrigesh BhatiaDepartment of Health Policy London School of Economics London UK.ORCID https://orcid.org/0000-0001-9366-142X

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

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.

Indexed as

bayesiandistrict‐levelIndiainfant mortality rateneonatal mortality rateNFHS‐4 & NFHS‐5small area estimationtotal fertility rateunder‐five mortality rate

Identifiers

PMID41669497
PMCPMC12883338

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.