Using digital annual household survey data to prioritize high-risk villages for tuberculosis active case-finding

Background Tuberculosis (TB) active case-finding (ACF) among high-risk populations is recommended to detect the missing people with TB. In Rajasthan, India, a state with a high TB prevalence:notification ratio, leveraging digital annual health survey data could enhance ACF by targeting villages with a high burden of TB risk factors. Methods We conducted an ecological study across 19 districts of Rajasthan using data from the digital annual health survey. High-risk villages were identified based on three factors: multidimensional poverty index (MDPI), high proportion (>60%) of socially marginalized populations and geographic access (distance to primary health centre >7 km). Results The survey covered 24.6 million individuals across 20 803 villages. Thirty-five percent of individuals belonged to socially marginalized populations. At the household level, 39% used solid fuels, indicating potential exposure to indoor air pollution. Nine percent of villages had high poverty (MDPI >0.21) and 25% had a high proportion (>60%) of socially marginalized populations. Approximately 34% of villages had at least one of the three high-risk factors. Conclusions This study demonstrates the potential of existing digital annual survey data for targeted ACF. Further research is being planned to assess the yield of ACF in identified high-risk villages and to advocate for similar data-driven interventions in other settings.