The India Health and Climate Resilience Fund asked us to build a district learning architecture across four sites: Chamarajanagar in Karnataka, Dhubri in Assam, Khunti in Jharkhand, and West Singhbhum in Jharkhand. The deliverables included district base papers — structured profiles that would anchor the Fund's fellowship programme and serve as reference documents for grantees working in those geographies.
Writing them took longer than anticipated. Not because the data did not exist, but because it existed in formats that could not be read against each other.
Each district has a health profile. Each has climate data. The two sets of information are held by different arms of government, structured around different administrative geographies, and updated on different schedules. The foundational problem in climate-health research at the district level in India is not a data scarcity problem. It is a data integration problem.
Take Dhubri, in lower Assam. Flood risk data from the Brahmaputra Board covers embankment failure zones and inundation histories, but does so at the river-segment level, not the block or panchayat level. NFHS-5 gives us block-level data on anaemia, child nutrition, and healthcare utilisation for Assam, but the geographies do not map cleanly onto the flood zones. District hospital data — available through the Health Management Information System — records admissions by diagnosis and month, but the HMIS categories predate the climate-health analytical frame, so "diarrhoeal disease" in July does not automatically flag as flood-related even when the correlation is obvious to anyone who has looked at both datasets simultaneously.
Chamarajanagar presented a different version of the same problem. It is one of Karnataka's most climate-stressed districts: high heat exposure, tribal population with limited health system access, and the Biligiriranga Hills creating microclimatic variation that aggregate district figures erase entirely. The Karnataka Health Department's HMIS data showed the district's health system performing adequately on process indicators — facility availability, staffing ratios, immunisation coverage. Community health data from NFHS-5 told a more complicated story on outcomes: stunting, institutional delivery, and fever treatment-seeking rates all showed marked intra-district variation that correlated with forest dependency and distance to town.
Khunti and West Singhbhum sit in Jharkhand's mining belt. The climate-health story there is layered: heat stress from deforestation, water contamination downstream of mining operations, and tuberculosis rates that remain among the highest in the country in a state that carries a disproportionate share of India's adivasi population. The data systems that track mining-related environmental exposure are administered by the Ministry of Mines and the Jharkhand State Pollution Control Board. The data systems that track TB incidence and treatment outcomes are in the National TB Elimination Programme. These are not cross-referenced anywhere in the administrative system.
The value of the district base papers was not that they resolved this integration problem. They did not. What they did was force us to attempt the integration, document where it failed, and be explicit about what we were inferring versus what the data actually supported. That discipline — writing down what you cannot say and why — turned out to be one of the most useful outputs for Fund grantees, who were designing programmes in these geographies and needed to know the evidentiary floor before claiming outcomes.
A secondary finding: the districts where climate-health interdependence was most obvious to lived observation were often the districts where the administrative data most obscured it. Heavily stressed systems produce coping adaptations — in health-seeking, in food strategies, in mobility — that standard indicators are not designed to detect. The district base paper methodology, which combined secondary data synthesis with field visits and key informant interviews, was better placed to surface those adaptations than any single data source would have been.
The Climate Hazards Group InfraRed Precipitation with Station data (CHIRPS) and ERA5 reanalysis data were used for climate layers. Health system data came from HMIS and NFHS-5 district factsheets. Nutrition data was cross-referenced with Poshan Tracker where available.