Mixed-effects model estimates leptospirosis incidence in the Caribbean, highlighting public health needs for small island states.
Introduction Previous studies up to early 2000s found that leptospirosis incidence in humans was high across the Caribbean region (CR), yet up-to-date and reliable surveillance data are scarce. Limited research capacity in the region has further contributed to less robust characterisation of transmission drivers, perpetuating a cycle of neglect. To address these gaps and support evidence-based public health responses, this study aims to update incidence estimates in the CR by integrating data from multiple data sources, including peer-reviewed publications, surveillance reports and environmental and sociodemographic datasets. Methodology/Principal findings We used mixed-effects hierarchical negative binomial models at the country/territory-year level, incorporating covariates (precipitation, temperature, gross domestic product, biodiversity loss, human footprint, population density, populations exposed to crop areas and frequency of extreme weather events) to estimate annual case numbers by country/territory. Temporal patterns in case fatality rate (CFR) were modelled using locally estimated scatterplot smoothing regressions. Between 2001 and 2023, we estimated 38,659 (95%CI 26,006–56,527) cases in the region. Annual incidence (cases/100,000 population) with small- to medium-sized population islands exhibiting the highest estimated incidence (Guadeloupe 23.0 (95%CI 17.2-30.3) and Saint Vincent and the Grenadines 21.3 (95%CI 11.3-36.0)). Estimated CFR increased over the study period, from 8.8% (95%CI 4.1-13.4) in 2001 to 12.2% (2.2-22.2 in 2022). Conclusions/Significance Leptospirosis remains an important public health concern in the CR, where small island developing states bear a disproportionate burden of the disease. These findings underscore the urgent need for strengthening surveillance systems and laboratory capacity in the region, particularly in small states, to provide accurate data to prioritise public health and environmental health interventions to reduce transmission and improve diagnosis and treatment.
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