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Geographic Information System (GIS) for mapping diagnostic services and sub-district administrative boundaries in
Mathew Joseph Valamparampil1, A V Gayathri2, Sanjeev Nair3
1Sree Chitra Tirunal Institute for Medical Sciences and Technology (SCTIMST), Kerala, India.
Background:
Tuberculosis (TB) remains a leading cause of death globally, with India accounting for 25% of the total disease burden. A significant barrier to elimination is delayed or inequitable access to diagnostic services, often accelerated by a lack of granular administrative health boundaries for effective planning. Additionally, identifying the exact locations of TB diagnostic facilities is relevant for understanding the geographic coverage of diagnostic services.
Methods:
- Diverse types of data were obtained from multiple health and non-health sources. The study mapped 77 Tuberculosis Units (TUs) in Kerala by standardising heterogeneous LSGI and district-level administrative data into a unified GIS database. 642 government and private TB diagnostic facilities were available in Kerala during the study period. Researchers utilised ArcGIS to integrate these spatial boundaries and performed topology checks to ensure accuracy before plotting 642 geocoded diagnostic facilities. QGIS-generated Euclidean buffers were applied to these locations to visualise and analyse geographic inequities in service accessibility relative to population density.
Results:
The study successfully created the first digital sub-district health boundaries (TU level) in India for TB. The number of TUs ranged from 3 to 8 across various districts of Kerala. Mapping revealed a non-uniform distribution of TB diagnostic facilities, with high concentrations in densely populated coastal and central regions (up to 6540 persons per $kmˆ2). Conversely, facilities were sparse in the eastern hilly and northern districts. Mapping revealed that a 2-km buffer leaves many populated areas underserved, but a 4-km buffer covers nearly all residents, indicating that moderate travel distances significantly improve geographic accessibility.
Conclusion:
GIS as a critical tool for public health, providing the first sub-district level digital health boundaries in India to address geographic inequities in TB diagnostic access. Integrating GIS into the National Tuberculosis Elimination Program (NTEP) provides actionable evidence to address intra-district disparities and improve resource allocation. The created digital TU boundaries are available as an open-source resource to support future health research and policy planning.
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