Our study identified considerable spatial variation in the sociodemographic and environmental drivers of leptospirosis seropositivity within and between the two provinces investigated in the DR, requiring the construction of specific models for each province. Despite this variation, older age groups and male gender were associated with higher odds of leptospirosis seropositivity across both provinces, in accordance with previously reported higher burden of disease among males in the Caribbean^15,16 and globally¹⁷. While there was some overlap in the variables included in the final province-specific GGWR models, there were crucial differences in the final set of variables and their association with leptospirosis seropositivity. The importance of risk factors frequently associated with leptospirosis such as freshwater and rat exposure, and outdoor work environment^16,18, varied substantially between the two provinces, illustrating the important contribution that spatial analyses can make for informing more targeted and precise public health interventions¹⁹. In this sense, while in Espaillat effectiveness of public health interventions could benefit from focusing on guidance regarding contact with freshwater, in SPM measures to reduce and control rat population (e.g.: waste management) would have greater impact. The final models for each province included different sets of variables as well as different definitions of categories for some of variables, such as varying buffer sizes and aggregation strategies. For example, river density was extracted using a 250 m buffer in Espaillat, while a 500 m buffer was used in SPM, reflecting differences in the spatial scale at which environmental drivers demonstrated the strongest correlation with transmission risk. Additionally, some continuous variables were aggregated differently across provinces to account for non-linear relationships, such as categorisation based on quartile distributions, further tailoring the models to local data characteristics. While these differences limit direct comparisons of each driver between provinces, they enhance the ability to detect context-specific drivers of leptospirosis, supporting more precise and locally relevant public health interventions.

Leptospirosis is traditionally considered an occupational disease²⁰, and young males are especially affected in resource-limited rural areas²¹ where work-related activities, such as animal husbandry and agriculture, take place in outdoor environments^4,20,22. However, in our study, the association between leptospirosis seropositivity and outdoor work environment was not significant in the GLMER model for both provinces. The GGWR models indicated differences between the two provinces, with increased OR of leptospirosis seropositivity associated with outdoor work environments in Espaillat but not in SPM. This could be due to the predominance of farm-related activities in the former²³. While leptospirosis seroprevalence studies typically report a peak in prevalence in young and middle-aged adults followed by a decrease in older age groups¹⁷, our results diverge from these findings. In Espaillat, the GGWR revealed a continuous rise in OR across age groups, while in SPM, two peaks were reported (35–49 and ≥ 65 years) indicating a complex age-specific risk profile in the DR. Partially, this unique profile could be explained by the association of recurrent exposures throughout life and antibodies lasting long periods²⁴ with slower decay after repeated infections¹. However, these two factors are not unique to the DR, thus suggesting sustained exposure and transmission in older age groups.

Water plays a crucial role in the transmission cycle of leptospirosis, with pathogenic Leptospira capable of persisting in moist soil and freshwater for extended periods²⁵. Heavy rainfall, cyclones, and flooding events have been associated with leptospirosis outbreaks in many different environmental settings around the world^1,18. Studies show that floods, cyclones and extreme rainfall events might become more frequent as the world becomes warmer, creating more favourable conditions for leptospirosis transmission. In addition to traditionally recognised high-risk freshwater exposure, there is growing evidence that recreational exposure to previously considered low-risk freshwater (e.g., waterfalls and rivers) during sports such as triathlon, kayaking, and whitewater rafting can also contribute to outbreaks [ref], highlighting the multifaceted nature of water-related risk. In this context, unpacking spatial variation of the importance of specific drivers could be fundamental to the success of targeted public health interventions. In Espaillat, results from the GGWR identified freshwater exposure as an important risk factor, and other water-related variables, such as river density and average precipitation in the last five years, were associated with increased OR across this province. However, in SPM, water-related variables were not associated with leptospirosis seroprevalence. Differences in urbanisation levels and primary economic activities might have impacted the relative importance of determinants between provinces. In Espaillat, additionally to having a larger proportion of population living in rural areas compared to SPM (54.7% and 5.9%, respectively), animal husbandry is the main farming activity, while in SPM agricultural practices is distributed across animal husbandry and crop production. Recent studies conducted in slum settlements in Latin America found no evidence of the association between flooding and other water exposure and leptospirosis cases^16,26, suggesting that the impact of water-related events on leptospirosis prevalence might be non-linear and vary between specific contexts. In urban settings, leptospirosis transmission is mostly associated with poor sanitation, proximity to sewage, solid waste collection, and an increased rat population^16,18. In our study, rat exposure exhibited a strong positive association with seropositivity in SPM but not in Espaillat. In the latter, the absence of seropositive participants who reported positive exposure limited the inclusion of this covariate in the final province-specific model. Leptospirosis is highly associated with poverty in rural and urban settings^2,17. In Espaillat, a higher GDP at the community-level was associated with lower OR of leptospirosis seropositivity, suggesting that poverty might be an important determinant of infection. In SPM, the nonlinear association between GDP and leptospirosis seropositivity required the analysis to be conducted by aggregating GDP in groups based on quartile distribution. Yet, no quartile was significantly associated with leptospirosis seropositivity.

While our findings offer valuable insights into the spatial dynamics of leptospirosis transmission, certain aspects of the study design and data availability inevitably shaped the scope of our conclusions. First, due to the cross-sectional design of this study, temporal patterns and trends could not be assessed; therefore, our study might not reflect any recent epidemiological changes in the transmission patterns. Second, the analysis was restricted to only two of the 31 provinces plus Santo Domingo National District. As our results show, leptospirosis drivers and risk factors vary across space, limiting the generalisation of our findings throughout the country and the Caribbean region. Third, some questionnaire variables could also have benefited from greater detail. The design of a household survey is always a balance between the level of detail we would like to have, and the number and complexity of questions that a field team can reasonably be expected to ask each participant. For instance, rat exposure was recorded as a binary variable, without capturing frequency or intensity, which may have limited our ability to detect more nuanced associations. Similarly, while freshwater exposure included a comprehensive characterisation of the type of exposure, the sample size may have constrained our ability to fully explore its relationship with seropositivity. Additionally, the questionnaire collected self-reported ethnicity, yet the interpretation of this results can be complex, especially in countries with multiple heritages. It is important to notice that there is growing evidence associating socially assigned race and health outcomes through discrimination and socioeconomic status²⁷ and showing that incomplete reporting of ethnic groups and race can limit actions on reducing inequalities²⁸. In this study, we used a robust variable selection procedure, in which this variable was selected for the final model. Nevertheless, results from the final model did not identify significant differences in leptospirosis seropositivity and ethnic groups. Fourth, our analysis was conducted by aggregating all serogroups, but transmission pathways, reservoirs mammals, and risk factors might differ between serogroups. Combining serogroups for our analyses might have obscured specific risk factors, which can be crucial for targeted public health interventions. Fifth, environmental variables included in this study were limited by publicly available data. Important risk factors such as farm animal density and proximity to sewage²² were not included, as data were mostly not available, or when available, the spatial resolution was limited to the province level and not suitable for our analysis. This limitation might have impacted model performance differently between the two provinces. In SPM, besides older age groups and male gender, exposure to rats was the only variable significantly associated with leptospirosis seropositivity in the GLMER models, suggesting the existence of relevant risk factors and drivers in this province that were not captured by our model. Finally, differences in variable selection and representation between the province-specific models—such as buffer sizes and categorisation of continuous variables—also limit direct comparisons. However, this tailored approach allowed us to identify highly localised risk factors, which are essential for informing context-specific public health strategies. To ensure the inclusion of relevant variables in each province, we searched multiple data sources to obtain a comprehensive dataset of climatic, environmental and sociodemographic factors that can be spatially linked to our survey data. One of the strengths of our study is the detailed data extraction process; for most of the spatially linked variables, we explored multiple approaches to extract the data. Our analysis provided individual and household-level information regarding risk factors and drivers associated with leptospirosis transmission, identifying variation of transmission patterns on a fine spatial scale.

Our results contribute to a better understanding of leptospirosis epidemiology in the DR. Similarly to studies conducted in South-East Asia and Western Pacific regions we unveil the variation in the importance of local drivers of leptospirosis transmission^29,30. By doing so, this research highlights the need for tailored public health interventions that can vary on a fine spatial scale. Effective control measures must adapt to the specific risk factors in each province and community, prioritising different strategies based on local conditions. For instance, some communities may benefit from interventions focusing on reducing freshwater exposure, while others may benefit from controlling rat populations. The success of public health actions depends on knowing which factors most significantly impact each community, enabling more informed, efficient and impactful decision-making.