Problem of setting up a clinico-environmental predictive model for early detection of tuberculosis in Guinea

Abstract

Tuberculosis remains a major cause of morbidity and mortality worldwide, particularly in resource-limited countries such as Guinea, where the incidence was 175 cases per 100,000 inhabitants in 2021. Early detection of tuberculosis is hampered by unequal access to care, inadequate health infrastructures and low awareness among the population.

This editorial explores the development of a clinico-environmental predictive model for early detection of tuberculosis in Guinea. This model integrates clinical factors (duration of cough, HIV status) and environmental factors (promiscuity, pollution) to identify high-risk individuals. The aim is to make screening more effective by targeting vulnerable populations and optimizing resource allocation.

International experience shows that the integration of clinical and environmental variables improves tuberculosis detection, notably in China and other low-resource countries. In Guinea, a model based on spatial and socio-demographic data could enable better identification of epidemic outbreaks and strategic orientation of public health interventions.

However, several challenges remain, including the collection of reliable data, validation of the model in the Guinean context, and socio-cultural barriers linked to the stigmatization of screening. A participatory approach involving communities and a clear ethical framework are essential to guarantee the acceptability and effectiveness of this strategy.

The development of a predictive model adapted to the Guinean context represents an opportunity to strengthen the fight against tuberculosis. Its implementation will require political commitment, investment in infrastructure and integration into national public health strategies in order to maximize its impact on reducing the incidence of the disease.