Tropical Medicine and Infectious Disease (Apr 2024)

Effectiveness of Using AI-Driven Hotspot Mapping for Active Case Finding of Tuberculosis in Southwestern Nigeria

  • Abiola Alege,
  • Sumbul Hashmi,
  • Rupert Eneogu,
  • Vincent Meurrens,
  • Anne-Laure Budts,
  • Michael Pedro,
  • Olugbenga Daniel,
  • Omokhoudu Idogho,
  • Austin Ihesie,
  • Matthys Gerhardus Potgieter,
  • Obioma Chijioke Akaniro,
  • Omosalewa Oyelaran,
  • Mensah Olalekan Charles,
  • Aderonke Agbaje

DOI
https://doi.org/10.3390/tropicalmed9050099
Journal volume & issue
Vol. 9, no. 5
p. 99

Abstract

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Background: Nigeria is among the top five 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 that could be otherwise missed. This work evaluated whether this approach helps to reach communities with higher numbers of undiagnosed TB. Methodology: A retrospective analysis of the data generated from an ACF intervention program in four southwestern states in Nigeria was conducted. Wards (the smallest administrative level in Nigeria) were further subdivided into smaller population clusters. ACF sites and their respective TB screening outputs were mapped to these population clusters. This data were then combined 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 (p-value Conclusions: The community-level Bayesian predictive model has the potential to guide ACF implementers to high-TB-positivity areas for finding undiagnosed TB in the communities, thus improving the efficiency of interventions.

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