Meningitis in the Southeast Region: A Spatial Analysis

Meningitis is a disease of the central nervous system that causes inflammation of the meninges and subarachnoid space. Usually caused by viruses, it may also have bacterial etiology, which is associated with higher morbidity and mortality despite the availability of vaccines. To analyze the spatial distribution of meningitis incidence and correlate it with socioeconomic factors in the Southeast region from 2015 to 2024. Ecological, exploratory study using DATASUS data on meningitis cases in municipalities of the Southeast region states between 2015 and 2024. Incidence rates per 100,000 inhabitants were calculated for each city. The TerraView and GeoDa (public access) programs were used to quantify the global Moran’s Index (IMg), an estimator of spatial autocorrelation. Thematic maps and Moran maps were constructed, with the bivariate Moran Index (IMb) as estimator. Independent variables were the Brazilian Deprivation Index (IBP) and Human Development Index (HDI), provided by Fiocruz and IBGE, respectively. A 5% alpha level was adopted. A total of 69,782 cases were recorded in the 1,668 municipalities of the region, with a rate of 82.3/100,000 inhabitants, peaking in 2018 (11.9/100,000) and lowest in 2024 (2.7/100,000). The IMg for the period rate was positive and significant (IMg = 0.17; p = 0.01), with clusters in the regions of São Paulo, Rio de Janeiro, Campinas and Linhares. The IMb between incidence rate and HDI was significant (IMb = 0.15; p<0.01), as was the IMb with IBP (IMb = -0.21; p<0.01), identifying a pattern in which both high HDI (124 cities) and low IBP (141 cities) ‒ areas with greater socioeconomic development ‒ are possibly associated with higher notification of the disease. Moran maps showed more cities with high incidence correlating with better socioeconomic indicators (low IBP and high HDI), with clusters in the regions of São Paulo, Campinas, Vale do Ribeira, Araçatuba and Uberlândia. These findings may be related to greater hospital capacity in these cities. It was possible to identify municipalities with elevated rates, providing subsidies to municipal and regional managers for interventions. These findings may be associated with better access to health services.