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<p>Maternal health outcomes are associated with a complex interplay of healthcare accessibility, socioeconomic status, and environmental conditions, with disparities often concentrated in specific geographic regions. This study employs a Geographic Information System (GIS)-based framework to analyze spatial and temporal patterns of maternal health cases in Saveetha Hospital catchment, Tamil Nadu, India, using 2023 patient-level data from Saveetha Medical College and Hospital. A total of 483 cases were geocoded and visualized using a kriging-based smoothing surface to illustrate spatial clustering of patient origins. Results show persistent spatial clustering of cases in the northeastern coastal districts, which spatially correspond with areas of poorer healthcare accessibility, lower antenatal care coverage, and higher socioeconomic and environmental risk scores. Seasonal peaks in cases corresponded to the southwest and northeast monsoon periods, indicating a temporal association with climatic conditions. The integration of spatial analytics with environmental and socioeconomic datasets underscores GIS as a powerful decision-support tool for identifying high-risk zones and informing targeted, equity-focused interventions. This approach provides an evidence-based framework for policymakers to optimize healthcare resource allocation, address social determinants, and improve maternal health outcomes in peri-urban and rural contexts.</p>

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Geospatial analysis of maternal healthcare utilization and socioenvironmental determinants in Tamil Nadu, India

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年3月26日

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  • Mohammad Suhail Meer,
  • Ranganathan Sandhya

摘要

Maternal health outcomes are associated with a complex interplay of healthcare accessibility, socioeconomic status, and environmental conditions, with disparities often concentrated in specific geographic regions. This study employs a Geographic Information System (GIS)-based framework to analyze spatial and temporal patterns of maternal health cases in Saveetha Hospital catchment, Tamil Nadu, India, using 2023 patient-level data from Saveetha Medical College and Hospital. A total of 483 cases were geocoded and visualized using a kriging-based smoothing surface to illustrate spatial clustering of patient origins. Results show persistent spatial clustering of cases in the northeastern coastal districts, which spatially correspond with areas of poorer healthcare accessibility, lower antenatal care coverage, and higher socioeconomic and environmental risk scores. Seasonal peaks in cases corresponded to the southwest and northeast monsoon periods, indicating a temporal association with climatic conditions. The integration of spatial analytics with environmental and socioeconomic datasets underscores GIS as a powerful decision-support tool for identifying high-risk zones and informing targeted, equity-focused interventions. This approach provides an evidence-based framework for policymakers to optimize healthcare resource allocation, address social determinants, and improve maternal health outcomes in peri-urban and rural contexts.