Leprosy in Brazil remains closely tied to poverty, inequality, and unequal access to health and sanitation infrastructure, making it a revealing case for studying social and demographic disparities across territory. This study analyzes the socioeconomic, demographic, sanitary, and environmental determinants of the disease in 1,793 municipalities in the Northeast between 2002 and 2023. A two-dimensional fixedeffects spatial panel was used, preceded by Exploratory Spatial Data Analysis (ESDA), Moran's Global Index, and local indicators of spatial association (LISA). Among the alternative spatial models estimated, the Spatial Durbin Model (SDM) was selected based on AIC/BIC criteria and diagnostic tests. The results show a persistent positive spatial autocorrelation of leprosy, with clusters concentrated in socially vulnerable areas. Education, income inequality, sanitation conditions, infant mortality, vaccination coverage, and climatic factors proved relevant in explaining disease incidence. Indirect effects indicate that the socioeconomic and demographic characteristics of neighboring municipalities significantly influence local leprosy levels, revealing spatial externalities in poverty, inequality, and public-service provision. The findings reinforce the need for regionally coordinated social and health policies to address the territorial roots of disease transmission.
BT - Associação Nacional dos Centros de Pós-Graduação em Economia DA - 10/2026 LA - ENG M3 - Article N2 -Leprosy in Brazil remains closely tied to poverty, inequality, and unequal access to health and sanitation infrastructure, making it a revealing case for studying social and demographic disparities across territory. This study analyzes the socioeconomic, demographic, sanitary, and environmental determinants of the disease in 1,793 municipalities in the Northeast between 2002 and 2023. A two-dimensional fixedeffects spatial panel was used, preceded by Exploratory Spatial Data Analysis (ESDA), Moran's Global Index, and local indicators of spatial association (LISA). Among the alternative spatial models estimated, the Spatial Durbin Model (SDM) was selected based on AIC/BIC criteria and diagnostic tests. The results show a persistent positive spatial autocorrelation of leprosy, with clusters concentrated in socially vulnerable areas. Education, income inequality, sanitation conditions, infant mortality, vaccination coverage, and climatic factors proved relevant in explaining disease incidence. Indirect effects indicate that the socioeconomic and demographic characteristics of neighboring municipalities significantly influence local leprosy levels, revealing spatial externalities in poverty, inequality, and public-service provision. The findings reinforce the need for regionally coordinated social and health policies to address the territorial roots of disease transmission.
PY - 2026 SP - 1 EP - 20 T2 - Associação Nacional dos Centros de Pós-Graduação em Economia TI - Poverty, Inequality, and the Territorial Geography of Leprosy in Northeastern Brazil: A Spatial Panel Analysis UR - https://www.anpec.org.br/encontro/2026/submissao/files_I/i12-7e8157c47900636d86df79d27a28a4cd.pdf ER -