Spatio-temporal dynamics of leptospirosis in Brazil between 2010 and 2023: Identifying high-risk regions and gender-specific patterns.

Abstract

BACKGROUND: Leptospirosis is a globally neglected zoonotic disease with heightened transmission in tropical regions. Brazil bears a disproportionate burden in Latin America, driven by socio-economic vulnerability, urban infrastructure deficiencies, occupational exposure, and hydrological factors such as rainfall and flooding. Despite its significance, spatio-temporal patterns of leptospirosis across Brazil's diverse regions remain poorly understood. This study examines the spatial distribution, temporal dynamics, and gender-specific patterns of leptospirosis risk in Brazil from 2010 to 2023. METHODS: We conducted a national-level retrospective analysis using confirmed leptospirosis cases and population data aggregated at the microregional level (n=558) from official surveillance systems. Descriptive statistics assessed overall trends. Spatio-temporal scan statistics with a discrete Poisson model identified high-risk clusters. Bayesian spatio-temporal models estimated annual relative risks (RRs) and 95% credible intervals for total, male, and female populations. An interactive dashboard was developed for dissemination. RESULTS: A total of 48,190 cases were reported, with males accounting for 80.1%. Temporal variation was notable, with peaks in 2011 and 2018 and a sharp decline during the COVID-19 pandemic. Thirty significant clusters were identified, mainly in Acre and southern Brazil. RR estimates confirmed persistent endemicity in these regions, with some microregions exceeding RR>20. Gender-disaggregated analyses revealed divergent trends between males and females in several areas. CONCLUSION: This study highlights marked spatial heterogeneity and gender disparities in leptospirosis risk across Brazil. Persistent hotspots underscore the need for long-term, localized interventions. High-resolution spatio-temporal modeling and interactive tools can inform equitable, data-driven disease control strategies in endemic regions.