A distributional regression approach to modeling the impact of structural and intermediary social determinants on communities burdened by tuberculosis in Eastern Amazonia - Brazil - Clóvis Luciano Giacomet; Antônio Carlos Vieira Ramos; Heriederson Sávio Dias Moura; Thaís Zamboni Berra; Yan Mathias Alves; Felipe Mendes Delpino; Jason E. Farley; Nancy R. Reynolds; Jonas Bodini Alonso; Titilade Kehinde Ayandeyi Teibo; Ricardo Alexandre Arcêncio (BMC, 2023)
Abstract Background Tuberculosis (TB) is a disease that is influenced by social determinants of health. However, the specific structural and intermediary determinants of TB in Eastern Amazonia remain unclear. Despite being rich in natural resources, the region faces significant challenges related to poverty, inequality, and neglected diseases. The objective of this study was to use mathematical modeling to evaluate the influence of structural and intermediary determinants of health on TB in Eastern Amazonia, Brazil. Methods This cross-sectional included all TB cases diagnosed and registered in the Notifiable Diseases Information System (SINAN) from 2001 to 2017. Data on social determinants were collected at the census tract level. The generalized additive model for location, scale, and shape (GAMLSS) framework was employed to identify the effect of social determinants on communities with a high TB prevalence. The Double Poisson distribution (DPO) was chosen, and inclusion of quadratic effects was tested. Results A total of 1730 individuals were diagnosed with TB and reported in SINAN during the analyzed period. The majority were female (59.3%), aged 31 to 59 years (47.6%), identified as blacks (67.9%), and had incomplete elementary education (46.6%). The prevalence of alcoholism was 8.6% and mental illness was 0.7%. GAMLSS analyses demonstrated that the risk of community incidence of TB is associated with the proportion of the population lacking basic sanitation, as well as with the age groups of 16-31 years and > 61 years. Conclusions The study highlights the strategic utility of GAMLSS in identifying high-risk areas for TB. Models should encompass a broader range of social determinants to inform policies aimed at reducing inequality and achieving the goals of the End TB strategy.