Data-driven Identification of Communities with High Levels of Tuberculosis Infection in the Democratic Republic of Congo

When access to diagnosis and treatment of tuberculosis is disrupted by poverty or unequal access to health services, marginalized communities not only endorse the burden of preventable deaths, but also suffer from the dramatic consequences of a disease which impacts one's ability to access education and minimal financial incomes. Unfortunately, these pockets are often left unrecognized in the flow of data collected in national tuberculosis reports, as localized hotspots are diluted in aggregated reports focusing on notified cases. Such system is therefore profoundly inadequate to identify these marginalized groups, which urgently require adapted interventions. We computed an estimated incidence-rate map for the South-Kivu province of the Democratic Republic of Congo, a province of 5.8 million inhabitants, leveraging available data including notified incidence, level of access to health care and exposition to identifiable risk factors. These estimations were validated in a prospective multi-centric study. We could demonstrate that combining different sources of openly-available data allows to precisely identify pockets of the population which endorses the biggest part of the burden of disease. We could precisely identify areas with a predicted annual incidence higher than 1%, a value three times higher than the national estimates. While hosting only 2.5% of the total population, we estimated that these areas were responsible for 23.5% of the actual tuberculosis cases of the province. The bacteriological results obtained from systematic screenings strongly correlated with the estimated incidence (r = 0.86), and much less with the incidence reported by epidemiological reports (r = 0.77), highlighting the inadequacy of these reports when used alone to guide disease control programs.