Background: Nigeria is among the top 5 countries that have the highest gap between people reported as diagnosed and estimated to have developed Tuberculosis (TB). To bridge this gap, there is a need for innovative approaches to identify geographical areas at high risk of TB transmission and targeted Active Case Finding (ACF) interventions. Leveraging community level data together with granular sociodemographic contextual information can unmask local hotspots which could be otherwise missed. We evaluated if this approach helps to reach communities with higher numbers of undiagnosed TB.
Methodology: We conducted a retrospective analysis of the data generated from an ACF intervention program in 4 south-western states in Nigeria. We subdivided wards (smallest administrative level in Nigeria) into further smaller population clusters. ACF sites and their respective TB screening outputs were mapped to these population clusters. We then combined this data with open source high resolution contextual data to train a Bayesian inference model. The model predicted TB positivity rates on the community level (population cluster level), and these were visualised on a customised geoportal for use by the local teams to identify communities at high risk of TB transmission and plan ACF interventions. The TB positivity yield (proportion) observed at model-predicted hotspots was compared with the yield obtained at other sites identified based on aggregated notification data.
Results: The yield in population clusters that were predicted to have high TB positivity rates by the model was at least 1.75 times higher (Chi-Squared test p-value