Spatial patterns and correlates of low birth weight in India: a climate and health perspective
Abstract
Background: Low birth weight (LBW) remains one of the most pressing maternal and child health challenges in India. The persistence of this burden across diverse geographic and climatic contexts suggests the influence of environmental factors, such as temperature and elevation, alongside various socioeconomic determinants. This study examines the spatial distribution and clustering of LBW and its correlates, with particular focus on mean annual temperature, elevation and various other socioeconomic determinants across Indian districts of India using NFHS-5 (2019–21).
Methods: This cross-sectional ecological spatial analysis used data from NFHS-5 (2019–21). Temperature-related analyses included 706 districts, with Lakshadweep excluded because mean annual temperature data were unavailable. District-level LBW prevalence and mean annual temperature were derived from NFHS-5 birth data and the DHS Geospatial Covariates dataset, respectively. Global Moran’s I and Anselin’s Local Moran’s I were used to assess spatial autocorrelation and identify clusters of LBW. Ordinary least squares, Spatial Lag Model, and Spatial Error Model (SEM) were estimated to examine district-level factors associated with LBW, with the SEM selected as the preferred model based on Akaike Information Criterion, Schwarz criterion, and log-likelihood values.
Results: The mean district-level LBW prevalence was 17.15%. At the state level, Punjab recorded the highest prevalence (22.4%) and Mizoram the lowest (4.0%). At the district-level, Dantewada in Chhattisgarh recorded the highest prevalence (36.6%) and Kohima in Nagaland the lowest (0.7%). Strong and statistically significant spatial clustering was observed (Moran’s I = 0.572, p < 0.001), with High-High clusters spanning a contiguous belt across Rajasthan, Madhya Pradesh, and Gujarat, and Low-Low clusters in Jammu & Kashmir, Ladakh, and the northeastern states. The SEM confirmed significant spatial dependence (Lambda = 0.652, p < 0.001). Mean annual temperature (β = 0.181, p = 0.023), maternal anaemia (β = 0.086, p < 0.001), maternal underweight (β = 0.144, p < 0.001), winter births (β = 0.174, p = 0.042), and institutional delivery (β = 0.067, p = 0.005) were positively associated with LBW, while adequate antenatal care coverage (β=-0.048, p < 0.001) was negatively associated with the outcome.
Conclusion: The observed district-level association between mean annual temperature and LBW, even after accounting for spatial dependence, highlights the potential relevance of climatic conditions to maternal health planning. Interventions addressing maternal anaemia, undernutrition, and antenatal care coverage, particularly in the high-burden districts of north-central India, are critical to reduce the LBW burden.
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BibTeX
@article{let2026spatial,
title = {{Spatial patterns and correlates of low birth weight in India: a climate and health perspective}},
author = {Subhojit Let and Vinothini Jayaraj and Sridevi Gnanasekaran and Geetha Mohan and Vinoth Rajendran},
journal = {BMC Public Health},
year = {2026},
month = oct,
doi = {10.1186/s12889-026-29955-w},
url = {https://link.springer.com/article/10.1186/s12889-026-29955-w},
note = {CC BY 4.0},
}
RIS
TY - JOUR
TI - Spatial patterns and correlates of low birth weight in India: a climate and health perspective
AU - Subhojit Let
AU - Vinothini Jayaraj
AU - Sridevi Gnanasekaran
AU - Geetha Mohan
AU - Vinoth Rajendran
PY - 2026
DA - 2026/10/20
JO - BMC Public Health
DO - 10.1186/s12889-026-29955-w
UR - https://link.springer.com/article/10.1186/s12889-026-29955-w
AB - Background: Low birth weight (LBW) remains one of the most pressing maternal and child health challenges in India. The persistence of this burden across diverse geographic and climatic contexts suggests the influence of environmental factors, such as temperature and elevation, alongside various socioeconomic determinants. This study examines the spatial distribution and clustering of LBW and its correlates, with particular focus on mean annual temperature, elevation and various other socioeconomic determinants across Indian districts of India using NFHS-5 (2019–21). Methods: This cross-sectional ecological spatial analysis used data from NFHS-5 (2019–21). Temperature-related analyses included 706 districts, with Lakshadweep excluded because mean annual temperature data were unavailable. District-level LBW prevalence and mean annual temperature were derived from NFHS-5 birth data and the DHS Geospatial Covariates dataset, respectively. Global Moran’s I and Anselin’s Local Moran’s I were used to assess spatial autocorrelation and identify clusters of LBW. Ordinary least squares, Spatial Lag Model, and Spatial Error Model (SEM) were estimated to examine district-level factors associated with LBW, with the SEM selected as the preferred model based on Akaike Information Criterion, Schwarz criterion, and log-likelihood values. Results: The mean district-level LBW prevalence was 17.15%. At the state level, Punjab recorded the highest prevalence (22.4%) and Mizoram the lowest (4.0%). At the district-level, Dantewada in Chhattisgarh recorded the highest prevalence (36.6%) and Kohima in Nagaland the lowest (0.7%). Strong and statistically significant spatial clustering was observed (Moran’s I = 0.572, p < 0.001), with High-High clusters spanning a contiguous belt across Rajasthan, Madhya Pradesh, and Gujarat, and Low-Low clusters in Jammu & Kashmir, Ladakh, and the northeastern states. The SEM confirmed significant spatial dependence (Lambda = 0.652, p < 0.001). Mean annual temperature (β = 0.181, p = 0.023), maternal anaemia (β = 0.086, p < 0.001), maternal underweight (β = 0.144, p < 0.001), winter births (β = 0.174, p = 0.042), and institutional delivery (β = 0.067, p = 0.005) were positively associated with LBW, while adequate antenatal care coverage (β=-0.048, p < 0.001) was negatively associated with the outcome. Conclusion: The observed district-level association between mean annual temperature and LBW, even after accounting for spatial dependence, highlights the potential relevance of climatic conditions to maternal health planning. Interventions addressing maternal anaemia, undernutrition, and antenatal care coverage, particularly in the high-burden districts of north-central India, are critical to reduce the LBW burden.
SN - 1471-2458
ER -