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

Abstract

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.