A distributional regression approach to modeling the impact of structural and intermediary social determinants on communities burdened by tuberculosis in Eastern Amazonia - Brazil

BackgroundTB is a disease affected by social determinants of health; however, it is unclear what its structural and intermediary determinants are in Eastern Amazonia. The region contains many natural resources, yet it suffers drastically from poverty, inequality, and neglected diseases. Here, we aimed to employ mathematical modeling to evaluate the influence of structural and intermediary determinants of health on TB in Eastern Amazonia - Brazil. MethodsWe conducted an ecological study. We considered cases diagnosed of TB and collected data by census tract to measure the social determinants. We applied the generalized additive model for location, scale, and shape (GAMLSS) framework to identify the effect of social determinants on communities with a high prevalence of TB. The Double Poisson distribution (DPO) was selected and we tested the inclusion of quadratic effects. Results1,730 people were selected. The majority were female (59.3%), aged 31 to 59 years (47.6%), blacks (67.9%), schooling level of 5 to 8 years (18.7%). Prevalence of alcoholism was 8.6% and mental illness 0.7%. The GAMLSS analyses showed that the risk of community incidence of TB is associated with the proportion of the population without basic sanitation and also with the age groups 16-31 years and > 61 years. ConclusionsThe study revealed that GAMLSS is an strategic tool to identify territories at greatest risk for TB. Models should have broader scope to include social determinants so as to better inform policy to reduce inequality and achieving the goal of the End TB strategy.