Congenital Syphilis in Brazil: Municipal Antenatal Care, Primary Health Care Capacity, and Territorial Inequalities, 2015–2024

Abstract Objectives To describe spatial patterns of gestational (GS) and congenital syphilis (CS) in Brazil and assess municipal antenatal care, sociodemographic, and primary-care correlates of CS notifications after accounting for GS burden. Methods We conducted a nationwide ecological study using routinely collected SINAN, SINASC, and CNES data (2015–2024). Spatial analyses used empirical Bayes smoothing, Global Moran’s I, and LISA. Associations were estimated with a Bayesian negative-binomial spatiotemporal model including municipal random effects, annually replicated Besag fields, a first-order random walk, and an estimated coefficient for GS burden. Sensitivity analyses evaluated formulations, adjacency, priors, and age composition. Results Spatial analyses included 55,699 municipality-years from 5,570 municipalities; the primary model included 28,436 observations from 3,625 municipalities. CS showed recurrent high–high clusters in the North and Northeast. Per 10-percentage-point increase, fewer than seven antenatal visits (adjusted ratio 1.10, 95% CrI 1.08–1.11) and births to women recorded as Black or Brown (1.07, 1.06–1.08) were positively associated with expected CS notifications, whereas low maternal education was inversely associated (0.93, 0.92–0.95). Each additional registered Family Health Strategy team per 10,000 population was inversely associated (0.89, 0.87–0.91), although this estimate was model-sensitive. GS elasticity was 0.72 (0.71–0.74); estimates were stable under KNN-5. Conclusions for Practice CS remained spatially concentrated, and municipal antenatal-care utilization, sociodemographic composition, and primary-care capacity were associated with expected CS notifications after accounting for GS burden. These ecological findings may help identify territories where care delivery, primary-care capacity, and surveillance warrant closer assessment. They do not estimate individual transmission risk or establish causal effects.