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

Area of Science:

• Public Health
• Epidemiology
• Health Informatics

Background:

• Tuberculosis (TB) active case-finding (ACF) is crucial for detecting missed cases, especially in high-prevalence regions like Rajasthan, India.
• Existing digital health survey data offers a potential resource for enhancing ACF strategies.
• Targeting high-risk populations and villages can optimize resource allocation for TB control.

Purpose of the Study:

• To evaluate the utility of digital annual health survey data for identifying high-risk villages for TB ACF in Rajasthan.
• To determine the prevalence of key risk factors (poverty, social marginalization, geographic access) in villages within Rajasthan.

Main Methods:

• An ecological study was conducted using data from a digital annual health survey across 19 districts of Rajasthan.
• High-risk villages were defined by the presence of multidimensional poverty index (MDPI) >0.21, >60% socially marginalized populations, or distance >7 km to a primary health centre.
• Data from 24.6 million individuals across 20,803 villages were analyzed.

Main Results:

• Approximately 34% of villages exhibited at least one of the identified high-risk factors for TB.
• Nine percent of villages reported high poverty (MDPI >0.21), and 25% had a high proportion of socially marginalized populations.
• 35% of the surveyed population belonged to socially marginalized groups, and 39% of households used solid fuels.

Conclusions:

• Digital annual health survey data can be effectively utilized for targeted TB active case-finding.
• The study highlights the feasibility of using existing data infrastructure to identify high-risk areas for TB interventions.
• Further research is recommended to assess ACF yield in identified villages and promote data-driven strategies in other regions.