Tuberculosis (TB) and HIV coinfection represents a global and national public health challenge, with TB being the leading cause of mortality among people living with HIV/AIDS (PLHIV/AIDS), and HIV increasing the risk of active TB by about 20-fold. Brazil is a priority country for this coinfection according to the World Health Organization (WHO). Spatial analysis is essential to understand geographic distribution of diseases, identify risk areas, and guide interventions, based on analysis of data from the Notifiable Diseases Information System (SINAN). To analyze and describe the spatial dynamics of HIV-TB coinfection in the health macroregions of the state of Bahia from 2016 to 2024. Ecological, descriptive, quantitative study analyzing the distribution of HIV–TB coinfection cases in Bahia between 2016 and 2024, using the nine health macroregions defined by SESAB as units of analysis. Data were obtained from SINAN, including all notified cases in the period, excluding inconsistent records. Data were organized according to the territorial division of the state’s macroregions and analyzed to describe spatial distribution and risk patterns using epidemiological and demographic variables. Between 2016 and 2024, 4,225 HIV-TB coinfection cases were notified in Bahia, distributed across macroregions. Statewide spatial dynamics showed a much higher number of cases in the Eastern macroregion (Salvador), with 2,783 notified cases, followed by the Southern macroregion (Ilhéus) with 430 cases, the Extreme South (Teixeira de Freitas) with 304 cases, and the Center-East (Feira de Santana) with 254 cases. The Center-North (Jacobina), West (Barreiras), and Northeast (Alagoinhas) macroregions had the lowest notifications, with 73, 64, and 46 cases, respectively. These data demonstrate unequal and heterogeneous distribution. Findings indicate the need to strengthen integration between TB and HIV control programs, with emphasis on active HIV screening in all TB cases, and to improve access to diagnostic services, especially in macroregions with lower absolute numbers, which may conceal underreporting scenarios.
Barreto et al. (Sun,) studied this question.