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

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.