Quantifying spatial variation in environmental and sociodemographic drivers of leptospirosis in the Dominican Republic using a geographically weighted regression model

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Mayfield, Angela M. Cadavid Restrepo, and 16 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6449611/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 25 Jul, 2025 Read the published version in Scientific Reports → Version 1 posted 13 You are reading this latest preprint version Abstract Spatial variation in drivers of leptospirosis transmission in the Dominican Republic is poorly understood. To inform targeted public health interventions, we aimed to identify household-level variations in leptospirosis drivers. We analysed data from 2078 participants in two provinces, Espaillat and San Pedro de Macoris (SPM), collected from a 2021 cross-sectional survey. We used geographically weighted regression to quantify associations between leptospirosis seropositivity and spatial environmental and sociodemographic data. In Espaillat, higher odds of seropositivity were associated with exposure to freshwater (OR 12.92;95%CI 1.36-122.29), a higher percentage of bare ground (OR 1.21;1.01–1.46) and river density (OR 1.53;1.14–2.06) surrounding the household. In SPM, rat exposure was associated with higher odds of seropositivity (OR 2.41;1.33–2.89). Higher community-level gross domestic product was associated with lower odds of seropositivity in both provinces. By identifying locally important drivers of transmission, our study provides evidence to support more tailored public health interventions to optimise the control and prevention of leptospirosis. Health sciences/Diseases/Infectious diseases Biological sciences/Computational biology and bioinformatics/Computational models Zoonosis Seroprevalence survey Risk factors spatial regression Caribbean region Figures Figure 1 Figure 2 Figure 3 Introduction Leptospirosis is a globally distributed zoonotic disease caused by pathogenic species of the Leptospira bacteria ( 1 ). An estimated 1.03 million human leptospirosis cases and 58900 deaths occur annually around the world ( 2 ). Latin America and the Caribbean account for one-third of all globally reported leptospirosis outbreaks ( 3 ). Estimated annual morbidity varies considerably, ranging from 3.9/100,000 population in South America to 50.7/100,000 population in the Caribbean ( 2 ). Human infection is primarily acquired through direct contact with urine or tissues of infected animals, or indirect exposure to contaminated soil or water ( 1 ). In tropical regions, the combination of warm climates, high rainfall and humidity, and informal settlements with limited infrastructure and sanitation access provides favourable conditions for leptospirosis transmission. The two primary epidemiological profiles include urban outbreaks triggered by heavy rainfall, flooding and other natural disasters that predominantly affect areas with poor infrastructure; and rural outbreaks primarily linked to endemic occupational exposures common in resource-poor areas ( 4 ). In 2020, the Dominican Republic (DR) reported 210 leptospirosis cases and 38 related deaths ( 5 ). Accurate population-level prevalence data are critical for effective public health planning and interventions. Moreover, locally based interventions should take into account that the occurrence of leptospirosis reflects the complex interaction among humans, reservoir animals and the environment ( 6 ), which varies across space. Statistical models that explore transmission risk factors and drivers of leptospirosis need to account for this spatial heterogeneity. Spatial models can therefore provide a more comprehensive understanding of disease patterns, allowing more informed and efficient decision-making. An enhanced population-level understanding of leptospirosis distribution can ensure that high-risk populations and locations are prioritised for support, optimizing the use of limited resources and potentially reducing overall health costs and health disparities. Results from our previous study in two target provinces showed an adjusted leptospirosis seroprevalence of 11.3% (95%CI10.8-13.0), using the microscopic agglutination test (MAT) ( 7 ). In the current study, we expand on this prior work by incorporating spatial data into our analysis and using spatially explicit statistical models. This study aimed to investigate spatial variation in environmental and sociodemographic risk factors and drivers of leptospirosis seroprevalence at the household level in the DR using generalised geographically weighted regression (GGWR) modelling. Our objective was to characterize risk factors and drivers of transmission on a fine spatial scale, which can be leveraged to inform tailored local public health interventions. Methods Setting The DR is in the Caribbean and occupies two-thirds of the island of Hispaniola, which it shares with Haiti. Due to its location and geophysical characteristics, the DR experiences frequent extreme weather events, including hurricanes and tropical storms (8). In local vulnerable areas, flooding, landslides and other natural disasters can lead to negative socioeconomic consequences and significant disease outbreaks (9). The DR is the second most populous country in the Caribbean region, with an estimated population of ~10.5 million in 2020 (5). Nearly 80% of the population resides in urban or semi-urban areas, but only about 20% of communities are classified as urban (9). In 2020, the population median age was 26.8 years with a ~50:50 male-to-female ratio (10). The country is divided into 31 provinces plus the Santo Domingo National District and subdivided into 155 municipalities, 386 district municipalities, 1565 sections and 12565 communities. Survey design Between 30 June and 12 October 2021, a nationally representative cross-sectional serosurvey was conducted in the DR. A detailed description of the survey design and data collection has been previously reported (11), and a summary of the study design has been included in the Supplementary information (Methods). The national survey included 6,683 participants, aged 6 to 97 years (median 40, interquartile range (IQR) 23-58 years), from all 31 provinces and the National District. In this study, we analysed data from two provinces, Espaillat in the northwest and San Pedro de Macoris (SPM) in the southeast. These two provinces were oversampled (n=2091) in the national study as they were linked to an ongoing clinical surveillance study investigating acute febrile illnesses and therefore provide more spatially granular data suitable for the current study (12) ( Figure 1 ). Survey data collection A trained field team interviewed and collected venous blood from all participants and captured Global Positioning System (GPS) coordinates of each household. Survey data collection procedure has been previously described (11) and more details on how variables were defined and measured are available in the Supplementary information (Methods). The interviews were conducted in Spanish, and Creole questionnaires and Creole speakers were available if requested. Venous blood samples were processed as sera and frozen at -80°C. MAT was used to detect anti- Leptospira antibodies. Serological analyses were performed at the US Centers for Disease Control and Prevention’s Zoonoses and Select Agent Laboratory, Bacterial Special Pathogens Branch, Atlanta, GA, USA. A panel of 20 pathogenic serovars were selected for the MAT panel, and titres of ≥1:100 were considered seropositive and indicative of prior infection. Spatial data collection Based on conceptual leptospirosis transmission frameworks (6), we integrated environmental, sociodemographic and census data with our survey data. Land cover was aggregated into five groups; crops correspond to human-planted cereals, grasses and crops; rangeland to open areas covered with homogeneous grasses; bare ground to areas of rock or soil with very sparse to no vegetation; trees to areas of dense vegetation; and built-up to human-made structures, major roads and rail networks (Supplementary Table 1). All spatial data considered for the analyses are described in the Supplementary information (Supplementary Figures 1–5, Supplementary Table 2). Using Esri® ArcGIS software v 10.8 (Esri® ArcMap 10.8.0.12790. Redlands, CA, USA) (13), spatial data layers (vector or raster formats) were overlaid with the survey data for data extraction. The survey data were represented as a vector layer, with each point-data signifying the geographical location of a surveyed household. Data analysis Generalised mixed-effect regression (GLMER) and GGWR were used to assess and quantify the odds ratio (OR) for leptospirosis seropositivity associated with each covariate. We employed a two-stage variable selection process to identify variables for inclusion in the final GLMER and GGWR models, conducted separately for each province. The variables retained in the final models were selected based on biological plausibility. In the first stage, bivariate mixed-effect models were fitted for each province separately (and combined) with the sampling community level included as a random effect. During the variable selection process, we identified that differences in seroprevalence between the two provinces were impacting the model (i.e. the results of the model with data from the two provinces combined, suggested all variables that occurred more frequently in the province with higher prevalence to be positively associated with leptospirosis seropositivity), thus we generated separate models for each region. Additionally, given the considerable geographic distance between the two provinces, applying one model to a combined dataset could not be appropriate. Variables with a P -value below 0.20 were included in the preliminary multivariable GLMER (Supplementary Table 3). Collinearity was assessed using the Variance Inflation Factor (VIF), and any variables with a VIF exceeding 10 in any multivariable model were excluded (Supplementary Table 4, Supplementary Table 5, and Supplementary Table 6). Starting with the variables with the highest VIF, each was individually excluded until the VIF of all remaining variables was less than 10. As the variables included in each province model were selected independently, the final set of variables was province-specific (Supplementary Table 7). For the GLMER models, associations were considered statistically significant if the 95% confidence interval (95%CI) of the estimated OR excluded one. For the GGWR models, results are shown as the median, minimum and maximum OR. R statistical programming language (R version 4.1.3, 2022-03-10) (14) was used for mixed effects models ( lme4 ) and GGWR models ( GWmodel ) (Supplementary Table 8). Ethics approval and consent to participate The 2021 field survey ethical approval was obtained from the National Council of Bioethics in Health (013-2019), the Institutional Review Board of Pedro Henríquez Ureña National University, Santo Domingo, DR; the Mass General Brigham Human Research Committee, Boston, USA (2019P000094); and the Human Research Ethics Committee of The University of Queensland (2022/HE001475), Brisbane, Australia. This research was conducted in accordance with the Declaration of Helsinki. Informed consent was obtained from all participants. For participants <18 years old, except emancipated minors, consent was obtained from the parent or legal guardian. Participants between 14-17 years old provided written assent and those between 7-13 years old provided verbal assent. For participants between 6-7, only parental consent was obtained. Study procedures and reporting adhered to the STROBE criteria for observational studies. Results After excluding participants with missing data, a total of 2,078 study participants from 23 communities across the two provinces were included in this analysis. The median age was 39 (23,56) years, 1,339 (64.0%) were female, and 43.5% were from rural communities. Overall, 237 (11.4%) participants were seropositive. Characteristics of participants from each province are shown in Table 1 . Fifty percent of participants included in SPM were under 35 years old, while in Espaillat the number of participants across the age groups was more evenly distributed. In Espaillat, a higher proportion of participants self-reported being farmers (7.4%) compared to SPM (1.2%), although the proportion who worked in outdoor environments was similar in both provinces. In Espaillat Province, 127 (15.8%) participants were seropositive and in SPM Province 110 (8.6%). Table 1. Characteristics of the study population by province, Dominican Republic, 2021. Population characteristics Espaillat San Pedro de Macoris Overall 802 (%) 1 276 (%) Age (years) Median (IQR) 44 (28, 61) 34 (21, 52) Category 5-19 101 (13) 285 (22) 20-34 176 (22) 354 (28) 35-49 191 (24) 268 (21) 50-64 174 (22) 218 (17) 65+ 160 (20) 151 (12) Gender Female 501 (62) 828 (65) Male 297 (37) 436 (34) Other 4 (<1) 12 (<1) Occupation Farmer 59 (7·4) 15 (1·2) Not-farmer 743 (93) 1,261 (99) Work Environment Indoor or Student or houseperson 447 (56) 701 (55) Outdoor 34 (4·2) 58 (4·5) Mixed indoor and outdoor 158 (20) 131 (10) Retired or unemployed 163 (20) 386 (30) Maximum educational level Primary or none 272 (34) 386 (30) Secondary, tertiary or technical 530 (66) 890 (70) Ethnicity Indigenous 99 (12) 227 (18) Mestizo 263 (33) 404 (32) Mulatto 389 (49) 638 (50) White or other 51 (6) 7 (<1) Setting Rural 467 (58) 437 (34) Urban 335 (42) 839 (66) Rat exposure No 798 (100) 949 (74) Yes 4 (<1) 327 (26) Non-spatial and spatial models In each province, the GLMER identified a different set of significant environmental and sociodemographic variables associated with leptospirosis seropositivity (Tables 2 and 3). Within and between each province, the GGWR models identified substantial spatial variation in the OR of leptospirosis seropositivity associated with each covariate. The range of variation and direction of association change between provinces and detailed results are presented below. Espaillat In the multivariable GLMER, variables associated with significantly higher OR of leptospirosis seropositivity included older age groups (reference 5-19 years), namely: 20-34 OR 3.52 (95%CI 1.02-12.19); 35-49 years OR 4.00 (1.16-13.77); 50-64 years (OR 3.67;1.04-12.93); and ≥65 years OR 7.14 (1.80-28.31). Male gender (OR 2.62;1.38-4.96), and exposure to freshwater (OR 12.92;1.36-122.29) also emerged as significant risk factors. The OR increased significantly with higher percentage of bare ground (OR 1.21;1.01-1.46) and greater river density (OR 1.53;1.14-2.06) within a 250-meter buffer surrounding the household ( Table 2 ). Table 2. Odds ratios (ORs) and 95% CI from the generalised linear mixed-effects regression (GLMER) and OR median and range from the generalised geographically weighted regression (GGWR) for leptospirosis seropositivity in Espaillat Province, Dominican Republic, 2021. GLMER OR (95%CI) GGWR Median (Min·-Max·) Age (years) 15-19 Ref Ref 20-34 3·52 (1·02-12·19) 2·83 (2·61-2·95) 35-49 4·00 (1·16-13·77) 3·04 (2·79-3·33) 50-64 3·67 (1·04-12·93) 3·13 (2·73-3·43) ≥65 7·14 (1·80-28·31) 4·24 (3·82-4·58) Gender Female Ref Ref Male 2·62 (1·38-4·96) 2·03 (1·91-2·16) Other 6·75 (0·35-130·38) 4·28 (4·05-4·77) Ethnic group Indigenous Ref Ref White or other 0·55 (0·22-1·33) 0·49 (0·42-0·63) Mestizo 0·57 (0·23-1·40) 0·61 (0·56-0·66) Mulatto 0·34 (0·07-1·62) 0·64 (0·63-0·66) Work Environment Indoor Ref Ref Mix Indoor and Outdoor 0·82 (0·37-1·81) 0·83 (0·77-0·89) Outdoor 1·33 (0·38-4·62) 1·49 (1·25-1·57) Students, retired, unemployed 1·13 (0·54-2·36) 1·18 (1·17-1·20) Educational level Secondary Ref Ref Primary or none 1·43 (0·73-2·80) 1·3 (1·27-1·31) Freshwater exposure No Ref Ref Yes 12·92 (1·36-122·29) 6·60 (5·73-7·43) Living in a flooding-risk area No Ref Ref Yes 1·25 (0·65-2·41) 1·19 (1·07-1·4) Socio-economic drivers Motorized time to health unity 1·32 (0·77-2·26) 1 1·21 (1·19-1·24) 1 GDP 0·61 (0·36-1·02) 2 0·67 (0·64-0·70) 2 Environmental drivers Bare ground percentage 1·21 (1·01-1·46) 3 1·13 (1·12-1·14) 3 Cropland percentage 1·26 (0·89-1·77) 3 1·18 (1·14-1·21) 3 River density 1·53 (1·14-2·06) 4 1·34 (1·31-1·37) 4 Average precipitation 1·63 (0·99-2·67) 5 1·37 (1·31-1·44) 5 1 Per 1-minute increase in the motorized travel. 2 Per 1,000,000 USD increase in the GDP. 3 Per each 1% increase in the land cover ground surrounding the household. 4 Per 1-metre increase in the total length of rivers surrounding the household. 5 Per 1mm increase in the 5-y average rainfall. Results for Espaillat Province GGWR model are presented in Figure 2 , showing the spatial variation in OR of variables significantly associated with leptospirosis seropositivity in the province-specific GLMER model. In the GGWR model, the widest range of variation of leptospirosis seropositivity OR across the province was associated with freshwater exposure (median OR 6.60, ranging from 5.73 to 7.43 across the study areas). In contrast, the OR associated with bare ground percentage had the lowest variation, ranging from 1.12 to 1.14 (median 1.13). San Pedro de Macoris In the multivariable GLMER, variables associated with significantly higher OR of leptospirosis seropositivity included older age groups (reference 5-19 years), namely: 20-34 years OR 5.15 (95%CI 1.45-8.33); 35-49 years OR 8.22 (2.30-29.36); 50-64 years (OR 6.49;1.74-24.22); and ≥65 years OR 9.56 (2.53-36.14). Male gender (OR 3.70;2.07-6.61) and exposure to rats compared (OR 2.28;1.13-4.61) were also significant risk factors in this province ( Table 3 ). Table 3. Odds ratios (ORs) and 95% CI from the generalised linear mixed-effects regression (GLMER) and OR median and range from the geographically weighted regression for leptospirosis seropositivity in San Pedro de Macoris Province, Dominican Republic, 2021 . GLMER OR (95%CI) GWR Median (Min·-Max·) Age (years) 5-19 Ref Ref 20-34 5·15 (1·45-8·33) 4·86 (3·14-5·27) 35-49 8·22 (2·30-29·36) 7·38 (5·42-7·50) 50-64 6·49 (1·74-24·22) 6·12 (4·7-6·37) ≥65 9·56 (2·53-36·14) 8·02 (7·41-8·72) Gender Female Ref Ref Male 3·70 (2·07-6·61) 3·34 (2·82-3·38) Other 2·40 (0·17-34·89) 1·91 (1·56-3·30) Ethnic group Indigenous Ref Ref Mestizo 0·96 (0·46-2·01) 1·11 (0·96-1·12) Mulatto 1·05 (0·49-2·24) 1·22 (0·93-1·32) Work Environment Indoors Ref Ref Mix Indoor and Outdoor 1·57 (0·70-3·54) 1·28 (1·23-2·42) Outdoor 1·05 (0·35-3·11) 0·74 (0·58-2·51) Students, retired, unemployed 1·45 (0·74-2·84) 1·44 (1·11-1·47) Educational level Secondary Ref Ref Primary or none 1·3 (0·74-2·29) 1·11 (1·09-1·32) Freshwater exposure No Ref Ref Yes 1·25 (0·61-2·58) 0·83 (0·73-0·93) Living in a flooding-risk area No Ref Ref Yes 0·71 (0·28-1·75) 2·74 (1·19-3·03) Rat exposure No Ref Ref Yes 2·28 (1·13-4·61) 2·41 (1·33-2·89) Socio-economic drivers Motorized time to health unity 1·24 (0·75-2·08) 1 1·16 (1·11-1·35) 1 GDP 0·78 (0·55-1·11) 2 0·82 (0·81-0·86) 2 Environmental drivers Cropland percentage 0·89 (0·62-1·27) 3 0·85 (0·84-0·87) 3 River density 0·71 (0·28-1·75) 4 0·61 (0·53-1·72) 4 Average precipitation 0·91 (0·52-1·59) 5 1·06 (0·80-1·15) 5 1 Per 1-minute increase in the motorized travel. 2 Per 1,000,000 USD increase in the GDP. 3 Per each 1% increase in the land cover ground surrounding the household. 4 Per 1-metre increase in the total length of rivers surrounding the household. 5 Per 1mm increase in the 5-y average rainfall. Results from the SPM Province GGWR model are presented in Figure 3 , showing the spatial variation in OR of variables significantly associated with leptospirosis seropositivity in the province-specific GLMER model. In the GGWR model for SPM, OR of leptospirosis seropositivity associated with being 20-34 years of age (reference 5-19 years) exhibited the widest variation across the province (4.86; 3.14-5.27). In contrast, the OR associated with percentage of crop area within a 250-meter buffer around the household had the lowest variation, ranging from 0.84 to 0.86 (median 0.85). Discussion 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 globally (17). 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 interventions (19). 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. Leptospirosis is traditionally considered an occupational disease (20), and young males are especially affected in resource-limited rural areas (21) 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 former (23). While leptospirosis seroprevalence studies typically report a peak in prevalence in young and middle-aged adults followed by a decrease in older age groups (17), 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 periods (24) with slower decay after repeated infections (1). 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 periods (25). 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 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 urbanization levels and primary economic activities might have impacted the relative importance of determinants between provinces. 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 both provinces, 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. This study provides a more comprehensive characterisation of leptospirosis drives in the DR; however, 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 generalization of our findings throughout the country and the Caribbean region. Furthermore, the questionnaire used in the survey collected self-reported ethnicity. We acknowledge that ethnicity is a complex concept, especially in countries with multiple heritages. However, there is growing evidence associating socially assigned race and health outcomes through discrimination and socioeconomic status (27) and showing that incomplete reporting of ethnic groups and race can limit actions on reducing inequalities (28). In addition, 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. Environmental variables included in this study were limited by publicly available data. Important risk factors such as farm animal density and proximity to sewage (22) 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. Additionally, 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 measures. 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. Finally, our analysis provided 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, prioritizing different strategies based on local conditions. For instance, some communities may benefit from interventions focusing on reducing freshwater exposure, while others 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. Declarations Acknowledgements We would like to thank the many study participants who volunteered to participate in this study. We would also like to thank the study staff who collected the field data, the Dominican Republic Ministry of Health and Social Assistance, and the Pedro Henriquez Ureña National University, for their commitment and support for the study. Finally, we would like to thank Dr Gregorio Antonio Rosario Michel and the valuable team working in the Servicio Geologico Nacional for providing the flooding-risk map. Author’s contributions Conceptualization, H.M., A.C.R., R.S-R., E.J.N. and C.L.L.; Data curation, B.M.M., H.M., C.T.P., M.C.E, M.S.A., D.D., S.G., W.D., F.P., G.A., L.C., B.H., M.B. and A.K.; Formal analysis, B.M.M., B.S., H.M., A.C.R., B.K.; Funding acquisition, E.J.N. and C.L.L..; Investigation, B.M.M., B.S., A.C.R., H.M., E.J.N. and C.L.L.; Methodology, B.M.M., B.S., H.M., and B.K.; Project administration, B.M.M., B.S. and C.L.L.; Resources, C.T.P., M.C.E, R.S-R, F.P., L.C., E.J.N. and C.L.L.; Supervision, A.C.R., E.J.N. and C.L.L; Validation, B.S., A.C.R., E.J.N. and C.L.L.; Visualization, B.M.M., B.S., A.C.R., E.J.N. and C.L.L.; Writing—original draft preparation, B.M.M.; Writing—review and editing, B.M.M., B.S., H.M., A.C.R., B.K., C.T.P., M.C.E, M.S.A., D.D., S.G., W.D., F.P., G.A., L.C., B.H., M.B., A.K., E.J.N. and C.L.L. Availability of data and materials A de-identified dataset analysed during the current study is available at https://github.com/enilles1/DR-Leptospirosis for the purpose of reproducing and building on the analyses. Competing interests The authors declare that they have no competing interests. Funding This research was funded by the US CDC U01, grant number U01GH002238. CLL was supported by Australian National Health and Medical Research Council Fellowships (1109035 and 1193826). CDC staff supported laboratory analysis. Funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. References Levett, P. N. & Leptospirosis Clin. Microbiol. Rev. ; 14 (2):296–326. (2001). Costa, F. et al. Global Morbidity and Mortality of Leptospirosis: A Systematic Review. PLoS Negl. Trop. Dis. 9 (9), e0003898 (2015). Munoz-Zanzi, C. et al. A systematic literature review of leptospirosis outbreaks worldwide, 1970–2012. Rev. Panam. Salud Publica . 44 , e78 (2020). Schneider, M. C. et al. Leptospirosis in Latin America: exploring the first set of regional data. Rev. Panam. Salud Publica . 41 , e81 (2017). Direccion de Estadistica Demograficas SyA. Anuario de Estadísticas Sociodemográficas. In: Oficina Nacional de Estadística, editor. 2022. (2021). Lau, C. & Jagals, P. A framework for assessing and predicting the environmental health impact of infectious diseases: a case study of leptospirosis. Rev. Environ. Health . 27 (4), 163–174 (2012). Nilles, E. J. et al. Seroepidemiology of human leptospirosis in the Dominican Republic: a multistage cluster survey, 2024. (2021). Oficina Nacional de Estadistica. in Evento naturales - Una mirada georreferenciada . (eds Economia) (Santo Domingo, 2023). Oficina Nacional de Estadistica. in Asentamientos humanos y salud ambiental . (eds Economia) (Santo Domingo, 2022). Oficina Nacional de Estadistica. República Dominicana: Estimaciones y proyecciones nacionales de población 1950–2100.. In: Departamento de Estadísticas Demográficas SyC, editor. Santo Domingo, Republica Dominicana (2015). Nilles, E. J. et al. SARS-CoV-2 seroprevalence, cumulative infections, and immunity to symptomatic infection – A multistage national household survey and modelling study, Dominican Republic, June–October 2021. Lancet Reg. Health - Americas . 16 , 100390 (2022). Nilles, E. J. et al. Monitoring Temporal Changes in SARS-CoV-2 Spike Antibody Levels and Variant-Specific Risk for Infection, Dominican Republic, March 2021–August 2022. Emerg. Infect. Disease J. 29 (4), 723 (2023). ESRI & ArcMap (2022). Available from: https://www.esri.com/en-us/arcgis/products/arcgis-pro/overview R Core Team. A language and environment for statistical computing (R Foundation for Statistical Computing, 2022). Tique, V. et al. Clinical and Epidemiological Status of Leptospirosis in a Tropical Caribbean Area of Colombia. Biomed. Res. Int. 2018 , 6473851 (2018). Briskin, E. A. et al. Seroprevalence, Risk Factors, and Rodent Reservoirs of Leptospirosis in an Urban Community of Puerto Rico, 2015. J. Infect. Dis. 220 (9), 1489–1497 (2019). Torgerson, P. R. et al. Global Burden of Leptospirosis: Estimated in Terms of Disability Adjusted Life Years. PLoS Negl. Trop. Dis. 9 (10), e0004122 (2015). Barcellos, C. & Sabroza, P. C. Socio-environmental determinants of the leptospirosis outbreak of 1996 in western Rio de Janeiro: A geographical approach. Int. J. Environ. Health Res. 10 (4), 301–313 (2000). Goarant, C. Leptospirosis: risk factors and management challenges in developing countries. Res. Rep. Trop. Med. 7 , 49–62 (2016). Waitkins, S. A. Leptospirosis as an occupational disease. Occup. Environ. Med. 43 (11), 721–725 (1986). Schneider, M. C. et al. Leptospirosis in Latin America: exploring the first set of regional data. Revista Panam. de Salud Pública . 41 , 1 (2017). Lau, C. L. et al. Leptospirosis in American Samoa–estimating and mapping risk using environmental data. PLoS Negl. Trop. Dis. 6 (5), e1669 (2012). Oficina, N. & de Estadistica, U. N. I. C. E. F. Dominican Republic Multiple Indicator Cluster Survey 2019. (2019). Rees, E. M. et al. Estimating the duration of antibody positivity and likely time of Leptospira infection using data from a cross-sectional serological study in Fiji. PLoS Negl. Trop. Dis. 16 (6), e0010506 (2022). Bierque, E., Thibeaux, R., Girault, D., Soupe-Gilbert, M. E. & Goarant, C. A systematic review of Leptospira in water and soil environments. PLoS One . 15 (1), e0227055 (2020). Hagan, J. E. et al. Spatiotemporal Determinants of Urban Leptospirosis Transmission: Four-Year Prospective Cohort Study of Slum Residents in Brazil. PLoS Negl. Trop. Dis. 10 (1), e0004275 (2016). White, K., Lawrence, J. A., Tchangalova, N., Huang, S. J. & Cummings, J. L. Socially-assigned race and health: a scoping review with global implications for population health equity. Int. J. Equity Health ; 19 (1). (2020). Routen, A. et al. Strategies to record and use ethnicity information in routine health data. Nat. Med. 28 (7), 1338–1342 (2022). Mayfield, H. J. et al. Use of geographically weighted logistic regression to quantify spatial variation in the environmental and sociodemographic drivers of leptospirosis in Fiji: a modelling study. Lancet Planet. Health . 2 (5), e223–e32 (2018). Widayani, P., Gunawan, T., Danoedoro, P. & Mardihusodo, S. J. Application of Geographically Weighted Regression for Vulnerable Area Mapping of Leptospirosis in Bantul District. Indonesian J. Geogr. 48 (2), 168–177 (2016). Additional Declarations No competing interests reported. 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Nilles","email":"","orcid":"","institution":"Brigham and Women's Hospital","correspondingAuthor":false,"prefix":"","firstName":"Eric","middleName":"J.","lastName":"Nilles","suffix":""},{"id":448355921,"identity":"7232f4d7-7520-43f1-b3cb-4de0fbf07f6d","order_by":19,"name":"Colleen L. Lau","email":"","orcid":"","institution":"University of Queensland Centre for Clinical Research (UQCCR), The University of Queensland","correspondingAuthor":false,"prefix":"","firstName":"Colleen","middleName":"L.","lastName":"Lau","suffix":""}],"badges":[],"createdAt":"2025-04-15 00:53:18","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6449611/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6449611/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1038/s41598-025-13413-5","type":"published","date":"2025-07-25T15:58:14+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":82145359,"identity":"dfc23bc0-978f-4654-bb38-7077e88e9fa0","added_by":"auto","created_at":"2025-05-07 06:52:40","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":115588,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eMap of the Caribbean region (a) and the Dominican Republic (b). \u003c/strong\u003eIn panel B the 31 provinces and the Municipal District Santo Domingo are shown, with the two provinces included in this study highlighted. Black dots represent the location of households included in the study.\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-6449611/v1/a88a04238b748d06e82cb584.png"},{"id":82147425,"identity":"6f3e62e7-bb31-4d1c-9734-a47e193a3b4b","added_by":"auto","created_at":"2025-05-07 07:00:40","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":385044,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eSpatial variation in odd ratios for leptospirosis seropositivity from geographically weighted regression, Espaillat Province.\u003c/strong\u003ea-d: Age groups [a) 20-34 years, b) 35-49 years, c) 50-64 years, d) ≥65 years], e) gender male, f) exposure to freshwater, g) the percentage of bare ground in a 250m buffer around the household, h) total river length in a 250m buffer around the household. Each dot represents a surveyed household, and colours represent OR at the household location for each covariate.\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-6449611/v1/08e175e7d519cca6858c95a6.png"},{"id":82147428,"identity":"ed3fabe8-68a4-41cb-b275-6391e32a64a8","added_by":"auto","created_at":"2025-05-07 07:00:40","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":279302,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eSpatial variation in odd ratios for leptospirosis from geographically weighted regression, San Pedro de Macoris Province.\u003c/strong\u003ea-d: Age groups [a) 20-34 years, b) 35-49 years, c) 50-64 years, d) ≥65 years], e) gender male, f) exposure to rats. Each dot represents a surveyed household, and colours represent odds ratios at the household location for each covariate.\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-6449611/v1/31d8d2f407d98d4fd0998f6f.png"},{"id":88506206,"identity":"64ad5c77-a408-435b-82b0-33e075ad0edc","added_by":"auto","created_at":"2025-08-07 07:32:34","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2272945,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6449611/v1/bf4c22c3-a903-40b0-8266-7a7bb7774cb9.pdf"},{"id":82148863,"identity":"b8c30476-67ad-4abe-ab3e-25e14a793b2a","added_by":"auto","created_at":"2025-05-07 07:08:40","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":707792,"visible":true,"origin":"","legend":"","description":"","filename":"GWRSupMaterialV2.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6449611/v1/67cdae399832fad0e9960be7.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Quantifying spatial variation in environmental and sociodemographic drivers of leptospirosis in the Dominican Republic using a geographically weighted regression model","fulltext":[{"header":"Introduction","content":"\u003cp\u003eLeptospirosis is a globally distributed zoonotic disease caused by pathogenic species of the \u003cem\u003eLeptospira\u003c/em\u003e bacteria (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e). An estimated 1.03\u0026nbsp;million human leptospirosis cases and 58900 deaths occur annually around the world (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e). Latin America and the Caribbean account for one-third of all globally reported leptospirosis outbreaks (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e). Estimated annual morbidity varies considerably, ranging from 3.9/100,000 population in South America to 50.7/100,000 population in the Caribbean (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e). Human infection is primarily acquired through direct contact with urine or tissues of infected animals, or indirect exposure to contaminated soil or water (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e). In tropical regions, the combination of warm climates, high rainfall and humidity, and informal settlements with limited infrastructure and sanitation access provides favourable conditions for leptospirosis transmission. The two primary epidemiological profiles include urban outbreaks triggered by heavy rainfall, flooding and other natural disasters that predominantly affect areas with poor infrastructure; and rural outbreaks primarily linked to endemic occupational exposures common in resource-poor areas (\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eIn 2020, the Dominican Republic (DR) reported 210 leptospirosis cases and 38 related deaths (\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e). Accurate population-level prevalence data are critical for effective public health planning and interventions. Moreover, locally based interventions should take into account that the occurrence of leptospirosis reflects the complex interaction among humans, reservoir animals and the environment (\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e), which varies across space. Statistical models that explore transmission risk factors and drivers of leptospirosis need to account for this spatial heterogeneity. Spatial models can therefore provide a more comprehensive understanding of disease patterns, allowing more informed and efficient decision-making. An enhanced population-level understanding of leptospirosis distribution can ensure that high-risk populations and locations are prioritised for support, optimizing the use of limited resources and potentially reducing overall health costs and health disparities.\u003c/p\u003e \u003cp\u003eResults from our previous study in two target provinces showed an adjusted leptospirosis seroprevalence of 11.3% (95%CI10.8-13.0), using the microscopic agglutination test (MAT) (\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e). In the current study, we expand on this prior work by incorporating spatial data into our analysis and using spatially explicit statistical models. This study aimed to investigate spatial variation in environmental and sociodemographic risk factors and drivers of leptospirosis seroprevalence at the household level in the DR using generalised geographically weighted regression (GGWR) modelling. Our objective was to characterize risk factors and drivers of transmission on a fine spatial scale, which can be leveraged to inform tailored local public health interventions.\u003c/p\u003e"},{"header":"Methods","content":"\u003cp\u003e\u003cem\u003eSetting\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eThe DR is in the Caribbean and occupies two-thirds of the island of Hispaniola, which it shares with Haiti. Due to its location and geophysical characteristics, the DR experiences frequent extreme weather events, including hurricanes and tropical storms (8). In local vulnerable areas, flooding, landslides and other natural disasters can lead to negative socioeconomic consequences and significant disease outbreaks (9).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe DR is the second most populous country in the Caribbean region, with an estimated population of ~10.5 million in 2020 (5). Nearly 80% of the population resides in urban or semi-urban areas, but only about 20% of communities are classified as urban (9). In 2020, the population median age was 26.8 years with a ~50:50 male-to-female ratio (10). The country is divided into 31 provinces plus the Santo Domingo National District and subdivided into 155 municipalities, 386 district municipalities, 1565 sections and 12565 communities. \u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eSurvey design\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eBetween 30 June and 12 October 2021, a nationally representative cross-sectional serosurvey was conducted in the DR. A detailed description of the survey design and data collection has been previously reported (11), and a summary of the study design has been included in the Supplementary information (Methods). The national survey included 6,683 participants, aged 6 to 97 years (median 40, interquartile range (IQR) 23-58 years), from all 31 provinces and the National District. In this study, we analysed data from two provinces, Espaillat in the northwest and San Pedro de Macoris (SPM) in the southeast. These two provinces were oversampled (n=2091) in the national study as they were linked to an ongoing clinical surveillance study investigating acute febrile illnesses and therefore provide more spatially granular data suitable for the current study (12) (\u003cstrong\u003eFigure 1\u003c/strong\u003e). \u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eSurvey data collection\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eA trained field team interviewed and collected venous blood from all participants and captured Global Positioning System (GPS) coordinates of each household. \u0026nbsp;Survey data collection procedure has been previously described (11) and more details on how variables were defined and measured are available in the Supplementary information (Methods). The interviews were conducted in Spanish, and Creole questionnaires and Creole speakers were available if requested.\u003c/p\u003e\n\u003cp\u003eVenous blood samples were processed as sera and frozen at -80\u0026deg;C.\u0026nbsp;MAT was used to detect anti-\u003cem\u003eLeptospira\u003c/em\u003e antibodies. Serological analyses were performed at the US Centers for Disease Control and Prevention\u0026rsquo;s Zoonoses and Select Agent Laboratory, Bacterial Special Pathogens Branch, Atlanta, GA, USA. A panel of 20 pathogenic serovars were selected for the MAT panel, and titres of \u0026ge;1:100 were considered seropositive and indicative of prior infection.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eSpatial data collection\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eBased on conceptual leptospirosis transmission frameworks (6), we integrated environmental, sociodemographic and census data with our survey data. Land cover was aggregated into five groups; \u003cem\u003ecrops\u003c/em\u003e correspond to human-planted cereals, grasses and crops; \u003cem\u003erangeland\u003c/em\u003e to open areas covered with homogeneous grasses; \u003cem\u003ebare ground\u003c/em\u003e to areas of rock or soil with very sparse to no vegetation; \u003cem\u003etrees\u003c/em\u003e to areas of dense vegetation; and \u003cem\u003ebuilt-up\u003c/em\u003e to human-made structures, major roads and rail networks (Supplementary Table 1). All spatial data considered for the analyses are described in the Supplementary information (Supplementary Figures 1\u0026ndash;5, Supplementary Table 2). Using Esri\u0026reg; ArcGIS software v 10.8 (Esri\u0026reg; ArcMap 10.8.0.12790. Redlands, CA, USA) (13), spatial data layers (vector or raster formats) were overlaid with the survey data for data extraction. The survey data were represented as a vector layer, with each point-data signifying the geographical location of a surveyed household.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eData analysis\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eGeneralised mixed-effect regression (GLMER) and GGWR were used to assess and quantify the odds ratio (OR) for leptospirosis seropositivity associated with each covariate. We employed a two-stage variable selection process to identify variables for inclusion in the final GLMER and GGWR models, conducted separately for each province. The variables retained in the final models were selected based on biological plausibility. In the first stage, bivariate mixed-effect models were fitted for each province separately (and combined) with the sampling community level included as a random effect. During the variable selection process, we identified that differences in seroprevalence between the two provinces were impacting the model (i.e. the results of the model with data from the two provinces combined, suggested all variables that occurred more frequently in the province with higher prevalence to be positively associated with leptospirosis seropositivity), thus we generated separate models for each region. Additionally, given the considerable geographic distance between the two provinces, applying one model to a combined dataset could not be appropriate. Variables with a \u003cem\u003eP\u003c/em\u003e-value below 0.20 were included in the preliminary multivariable GLMER (Supplementary Table 3). Collinearity was assessed using the Variance Inflation Factor (VIF), and any variables with a VIF exceeding 10 in any multivariable model were excluded (Supplementary Table 4, Supplementary Table 5, and Supplementary Table 6). Starting with the variables with the highest VIF, each was individually excluded until the VIF of all remaining variables was less than 10. \u0026nbsp;As the variables included in each province model were selected independently, the final set of variables was province-specific (Supplementary Table 7).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eFor the GLMER models, associations were considered statistically significant if the 95% confidence interval (95%CI) of the estimated OR excluded one. For the GGWR models, results are shown as the median, minimum and maximum OR. R statistical programming language (R version 4.1.3, 2022-03-10) (14) was used for mixed effects models (\u003cem\u003elme4\u003c/em\u003e) and GGWR models (\u003cem\u003eGWmodel\u003c/em\u003e) (Supplementary Table 8).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eEthics approval and consent to participate\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eThe 2021 field survey ethical approval was obtained from the National Council of Bioethics in Health (013-2019), the Institutional Review Board of Pedro Henr\u0026iacute;quez Ure\u0026ntilde;a National University, Santo Domingo, DR; the Mass General Brigham Human Research Committee, Boston, USA (2019P000094); and the Human Research Ethics Committee of The University of Queensland (2022/HE001475), Brisbane, Australia. This research was conducted in accordance with the Declaration of Helsinki.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eInformed consent was obtained from all participants. For participants \u0026lt;18 years old, except emancipated minors, consent was obtained from the parent or legal guardian. Participants between 14-17 years old provided written assent and those between 7-13 years old provided verbal assent. For participants between 6-7, only parental consent was obtained. Study procedures and reporting adhered to the STROBE criteria for observational studies.\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003eAfter excluding participants with missing data, a total of 2,078 study participants from 23 communities across the two provinces were included in this analysis. The median age was 39 (23,56) years, 1,339 (64.0%) were female, and 43.5% were from rural communities. Overall, 237 (11.4%) participants were seropositive. Characteristics of participants from each province are shown in \u003cstrong\u003eTable 1\u003c/strong\u003e. Fifty percent of participants included in SPM were under 35 years old, while in Espaillat the number of participants across the age groups was more evenly distributed. In Espaillat, a higher proportion of participants self-reported being farmers (7.4%) compared to SPM (1.2%), although the proportion who worked in outdoor environments was similar in both provinces. In Espaillat Province, 127 (15.8%) participants were seropositive and in SPM Province 110 (8.6%).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 1. Characteristics of the study population by province, Dominican Republic, 2021.\u003c/strong\u003e\u003c/p\u003e\n\u003ctable width=\"93%\"\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 50%;\"\u003e\n \u003cp\u003e\u003cstrong\u003ePopulation characteristics\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 18%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eEspaillat\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 30%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSan Pedro de Macoris\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 50%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eOverall\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 18%;\"\u003e\n \u003cp\u003e802 (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 30%;\"\u003e\n \u003cp\u003e1 276 (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 50%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAge (years)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 18%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 30%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 50%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMedian (IQR)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 18%;\"\u003e\n \u003cp\u003e44 (28, 61)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 30%;\"\u003e\n \u003cp\u003e34 (21, 52)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 50%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eCategory\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 18%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 30%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 50%;\"\u003e\n \u003cp\u003e5-19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 18%;\"\u003e\n \u003cp\u003e101 (13)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 30%;\"\u003e\n \u003cp\u003e285 (22)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 50%;\"\u003e\n \u003cp\u003e20-34\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 18%;\"\u003e\n \u003cp\u003e176 (22)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 30%;\"\u003e\n \u003cp\u003e354 (28)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 50%;\"\u003e\n \u003cp\u003e35-49\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 18%;\"\u003e\n \u003cp\u003e191 (24)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 30%;\"\u003e\n \u003cp\u003e268 (21)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 50%;\"\u003e\n \u003cp\u003e50-64\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 18%;\"\u003e\n \u003cp\u003e174 (22)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 30%;\"\u003e\n \u003cp\u003e218 (17)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 50%;\"\u003e\n \u003cp\u003e65+\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 18%;\"\u003e\n \u003cp\u003e160 (20)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 30%;\"\u003e\n \u003cp\u003e151 (12)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 50%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eGender\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 18%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 30%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 50%;\"\u003e\n \u003cp\u003eFemale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 18%;\"\u003e\n \u003cp\u003e501 (62)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 30%;\"\u003e\n \u003cp\u003e828 (65)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 50%;\"\u003e\n \u003cp\u003eMale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 18%;\"\u003e\n \u003cp\u003e297 (37)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 30%;\"\u003e\n \u003cp\u003e436 (34)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 50%;\"\u003e\n \u003cp\u003eOther\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 18%;\"\u003e\n \u003cp\u003e4 (\u0026lt;1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 30%;\"\u003e\n \u003cp\u003e12 (\u0026lt;1)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 50%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eOccupation\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 18%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 30%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 50%;\"\u003e\n \u003cp\u003eFarmer\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 18%;\"\u003e\n \u003cp\u003e59 (7\u0026middot;4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 30%;\"\u003e\n \u003cp\u003e15 (1\u0026middot;2)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 50%;\"\u003e\n \u003cp\u003eNot-farmer\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 18%;\"\u003e\n \u003cp\u003e743 (93)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 30%;\"\u003e\n \u003cp\u003e1,261 (99)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 50%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eWork Environment\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 18%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 30%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 50%;\"\u003e\n \u003cp\u003eIndoor or Student or houseperson\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 18%;\"\u003e\n \u003cp\u003e447 (56)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 30%;\"\u003e\n \u003cp\u003e701 (55)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 50%;\"\u003e\n \u003cp\u003eOutdoor\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 18%;\"\u003e\n \u003cp\u003e34 (4\u0026middot;2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 30%;\"\u003e\n \u003cp\u003e58 (4\u0026middot;5)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 50%;\"\u003e\n \u003cp\u003eMixed indoor and outdoor\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 18%;\"\u003e\n \u003cp\u003e158 (20)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 30%;\"\u003e\n \u003cp\u003e131 (10)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 50%;\"\u003e\n \u003cp\u003eRetired or unemployed\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 18%;\"\u003e\n \u003cp\u003e163 (20)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 30%;\"\u003e\n \u003cp\u003e386 (30)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 50%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMaximum educational level\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 18%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 30%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 50%;\"\u003e\n \u003cp\u003ePrimary or none\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 18%;\"\u003e\n \u003cp\u003e272 (34)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 30%;\"\u003e\n \u003cp\u003e386 (30)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 50%;\"\u003e\n \u003cp\u003eSecondary, tertiary or technical\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 18%;\"\u003e\n \u003cp\u003e530 (66)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 30%;\"\u003e\n \u003cp\u003e890 (70)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 50%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eEthnicity\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 18%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 30%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 50%;\"\u003e\n \u003cp\u003eIndigenous\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 18%;\"\u003e\n \u003cp\u003e99 (12)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 30%;\"\u003e\n \u003cp\u003e227 (18)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 50%;\"\u003e\n \u003cp\u003eMestizo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 18%;\"\u003e\n \u003cp\u003e263 (33)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 30%;\"\u003e\n \u003cp\u003e404 (32)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 50%;\"\u003e\n \u003cp\u003eMulatto\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 18%;\"\u003e\n \u003cp\u003e389 (49)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 30%;\"\u003e\n \u003cp\u003e638 (50)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 50%;\"\u003e\n \u003cp\u003eWhite or other\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 18%;\"\u003e\n \u003cp\u003e51 (6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 30%;\"\u003e\n \u003cp\u003e7 (\u0026lt;1)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 50%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSetting\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 18%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 30%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 50%;\"\u003e\n \u003cp\u003eRural\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 18%;\"\u003e\n \u003cp\u003e467 (58)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 30%;\"\u003e\n \u003cp\u003e437 (34)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 50%;\"\u003e\n \u003cp\u003eUrban\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 18%;\"\u003e\n \u003cp\u003e335 (42)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 30%;\"\u003e\n \u003cp\u003e839 (66)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 50%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eRat exposure\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 18%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 30%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 50%;\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 18%;\"\u003e\n \u003cp\u003e798 (100)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 30%;\"\u003e\n \u003cp\u003e949 (74)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 50%;\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 18%;\"\u003e\n \u003cp\u003e4 (\u0026lt;1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 30%;\"\u003e\n \u003cp\u003e327 (26)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cdiv align=\"center\"\u003e\u003cbr\u003e\u003c/div\u003e\n\u003cp\u003e\u003cem\u003eNon-spatial and spatial models\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eIn each province, the GLMER identified a different set of significant environmental and sociodemographic variables associated with leptospirosis seropositivity (Tables 2 and 3). Within and between each province, the GGWR models identified substantial spatial variation in the OR of leptospirosis seropositivity associated with each covariate. The range of variation and direction of association change between provinces and detailed results are presented below.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eEspaillat\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eIn the multivariable GLMER, variables associated with significantly higher OR of leptospirosis seropositivity included older age groups (reference 5-19 years), namely: 20-34 OR 3.52 (95%CI 1.02-12.19); 35-49 years OR 4.00 (1.16-13.77); 50-64 years (OR 3.67;1.04-12.93); and \u0026ge;65 years OR 7.14 (1.80-28.31). \u0026nbsp;Male gender (OR 2.62;1.38-4.96), and exposure to freshwater (OR 12.92;1.36-122.29) also emerged as significant risk factors. The OR increased significantly with higher percentage of bare ground (OR 1.21;1.01-1.46) and greater river density (OR 1.53;1.14-2.06) within a 250-meter buffer surrounding the household (\u003cstrong\u003eTable 2\u003c/strong\u003e).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 2. Odds ratios (ORs) and 95% CI from the generalised linear mixed-effects regression (GLMER) and OR median and range from the generalised geographically weighted regression (GGWR) for leptospirosis seropositivity in Espaillat Province, Dominican Republic, 2021.\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"586\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 161px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 198px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eGLMER OR (95%CI)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 227px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eGGWR Median (Min\u0026middot;-Max\u0026middot;)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 161px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAge (years)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 198px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 227px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 161px;\"\u003e\n \u003cp\u003e15-19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 198px;\"\u003e\n \u003cp\u003eRef\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 227px;\"\u003e\n \u003cp\u003eRef\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 161px;\"\u003e\n \u003cp\u003e20-34\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 198px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e3\u0026middot;52 (1\u0026middot;02-12\u0026middot;19)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 227px;\"\u003e\n \u003cp\u003e2\u0026middot;83 (2\u0026middot;61-2\u0026middot;95)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 161px;\"\u003e\n \u003cp\u003e35-49\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 198px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e4\u0026middot;00 (1\u0026middot;16-13\u0026middot;77)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 227px;\"\u003e\n \u003cp\u003e3\u0026middot;04 (2\u0026middot;79-3\u0026middot;33)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 161px;\"\u003e\n \u003cp\u003e50-64\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 198px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e3\u0026middot;67 (1\u0026middot;04-12\u0026middot;93)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 227px;\"\u003e\n \u003cp\u003e3\u0026middot;13 (2\u0026middot;73-3\u0026middot;43)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 161px;\"\u003e\n \u003cp\u003e\u0026ge;65\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 198px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e7\u0026middot;14 (1\u0026middot;80-28\u0026middot;31)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 227px;\"\u003e\n \u003cp\u003e4\u0026middot;24 (3\u0026middot;82-4\u0026middot;58)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 161px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eGender\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 198px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 227px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 161px;\"\u003e\n \u003cp\u003eFemale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 198px;\"\u003e\n \u003cp\u003eRef\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 227px;\"\u003e\n \u003cp\u003eRef\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 161px;\"\u003e\n \u003cp\u003eMale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 198px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e2\u0026middot;62 (1\u0026middot;38-4\u0026middot;96)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 227px;\"\u003e\n \u003cp\u003e2\u0026middot;03 (1\u0026middot;91-2\u0026middot;16)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 161px;\"\u003e\n \u003cp\u003eOther\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 198px;\"\u003e\n \u003cp\u003e6\u0026middot;75 (0\u0026middot;35-130\u0026middot;38)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 227px;\"\u003e\n \u003cp\u003e4\u0026middot;28 (4\u0026middot;05-4\u0026middot;77)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 161px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eEthnic group\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 198px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 227px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 161px;\"\u003e\n \u003cp\u003eIndigenous\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 198px;\"\u003e\n \u003cp\u003eRef\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 227px;\"\u003e\n \u003cp\u003eRef\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 161px;\"\u003e\n \u003cp\u003eWhite or other\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 198px;\"\u003e\n \u003cp\u003e0\u0026middot;55 (0\u0026middot;22-1\u0026middot;33)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 227px;\"\u003e\n \u003cp\u003e0\u0026middot;49 (0\u0026middot;42-0\u0026middot;63)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 161px;\"\u003e\n \u003cp\u003eMestizo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 198px;\"\u003e\n \u003cp\u003e0\u0026middot;57 (0\u0026middot;23-1\u0026middot;40)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 227px;\"\u003e\n \u003cp\u003e0\u0026middot;61 (0\u0026middot;56-0\u0026middot;66)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 161px;\"\u003e\n \u003cp\u003eMulatto\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 198px;\"\u003e\n \u003cp\u003e0\u0026middot;34 (0\u0026middot;07-1\u0026middot;62)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 227px;\"\u003e\n \u003cp\u003e0\u0026middot;64 (0\u0026middot;63-0\u0026middot;66)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 161px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eWork Environment\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 198px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 227px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 161px;\"\u003e\n \u003cp\u003eIndoor\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 198px;\"\u003e\n \u003cp\u003eRef\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 227px;\"\u003e\n \u003cp\u003eRef\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 161px;\"\u003e\n \u003cp\u003eMix Indoor and Outdoor\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 198px;\"\u003e\n \u003cp\u003e0\u0026middot;82 (0\u0026middot;37-1\u0026middot;81)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 227px;\"\u003e\n \u003cp\u003e0\u0026middot;83 (0\u0026middot;77-0\u0026middot;89)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 161px;\"\u003e\n \u003cp\u003eOutdoor\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 198px;\"\u003e\n \u003cp\u003e1\u0026middot;33 (0\u0026middot;38-4\u0026middot;62)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 227px;\"\u003e\n \u003cp\u003e1\u0026middot;49 (1\u0026middot;25-1\u0026middot;57)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 161px;\"\u003e\n \u003cp\u003eStudents, retired, unemployed\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 198px;\"\u003e\n \u003cp\u003e1\u0026middot;13 (0\u0026middot;54-2\u0026middot;36)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 227px;\"\u003e\n \u003cp\u003e1\u0026middot;18 (1\u0026middot;17-1\u0026middot;20)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" style=\"width: 359px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eEducational level\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 227px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 161px;\"\u003e\n \u003cp\u003eSecondary\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 198px;\"\u003e\n \u003cp\u003eRef\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 227px;\"\u003e\n \u003cp\u003eRef\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 161px;\"\u003e\n \u003cp\u003ePrimary or none\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 198px;\"\u003e\n \u003cp\u003e1\u0026middot;43 (0\u0026middot;73-2\u0026middot;80)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 227px;\"\u003e\n \u003cp\u003e1\u0026middot;3 (1\u0026middot;27-1\u0026middot;31)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 161px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eFreshwater exposure\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 198px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 227px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 161px;\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 198px;\"\u003e\n \u003cp\u003eRef\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 227px;\"\u003e\n \u003cp\u003eRef\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 161px;\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 198px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e12\u0026middot;92 (1\u0026middot;36-122\u0026middot;29)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 227px;\"\u003e\n \u003cp\u003e6\u0026middot;60 (5\u0026middot;73-7\u0026middot;43)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 161px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eLiving in a flooding-risk area\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 198px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 227px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 161px;\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 198px;\"\u003e\n \u003cp\u003eRef\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 227px;\"\u003e\n \u003cp\u003eRef\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 161px;\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 198px;\"\u003e\n \u003cp\u003e1\u0026middot;25 (0\u0026middot;65-2\u0026middot;41)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 227px;\"\u003e\n \u003cp\u003e1\u0026middot;19 (1\u0026middot;07-1\u0026middot;4)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 161px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSocio-economic drivers\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 198px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 227px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 161px;\"\u003e\n \u003cp\u003eMotorized time to health unity\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 198px;\"\u003e\n \u003cp\u003e1\u0026middot;32 (0\u0026middot;77-2\u0026middot;26)\u003csup\u003e1\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 227px;\"\u003e\n \u003cp\u003e1\u0026middot;21 (1\u0026middot;19-1\u0026middot;24)\u003csup\u003e1\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 161px;\"\u003e\n \u003cp\u003eGDP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 198px;\"\u003e\n \u003cp\u003e0\u0026middot;61 (0\u0026middot;36-1\u0026middot;02)\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 227px;\"\u003e\n \u003cp\u003e0\u0026middot;67 (0\u0026middot;64-0\u0026middot;70)\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" style=\"width: 359px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eEnvironmental drivers\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 227px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 161px;\"\u003e\n \u003cp\u003eBare ground percentage\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 198px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e1\u0026middot;21 (1\u0026middot;01-1\u0026middot;46)\u003csup\u003e3\u003c/sup\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 227px;\"\u003e\n \u003cp\u003e1\u0026middot;13 (1\u0026middot;12-1\u0026middot;14)\u003csup\u003e3\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 161px;\"\u003e\n \u003cp\u003eCropland percentage\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 198px;\"\u003e\n \u003cp\u003e1\u0026middot;26 (0\u0026middot;89-1\u0026middot;77)\u003csup\u003e3\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 227px;\"\u003e\n \u003cp\u003e1\u0026middot;18 (1\u0026middot;14-1\u0026middot;21)\u003csup\u003e3\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 161px;\"\u003e\n \u003cp\u003eRiver density\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 198px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e1\u0026middot;53 (1\u0026middot;14-2\u0026middot;06)\u003c/strong\u003e\u003csup\u003e4\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 227px;\"\u003e\n \u003cp\u003e1\u0026middot;34 (1\u0026middot;31-1\u0026middot;37)\u003csup\u003e4\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 161px;\"\u003e\n \u003cp\u003eAverage precipitation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 198px;\"\u003e\n \u003cp\u003e1\u0026middot;63 (0\u0026middot;99-2\u0026middot;67)\u003csup\u003e5\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 227px;\"\u003e\n \u003cp\u003e1\u0026middot;37 (1\u0026middot;31-1\u0026middot;44)\u003csup\u003e5\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003csup\u003e1\u003c/sup\u003ePer 1-minute increase in the motorized travel. \u003csup\u003e2\u003c/sup\u003ePer 1,000,000 USD increase in the GDP. \u003csup\u003e3\u003c/sup\u003ePer each 1% increase in the land cover ground surrounding the household. \u003csup\u003e4\u003c/sup\u003ePer 1-metre increase in the total length of rivers surrounding the household. \u003csup\u003e5\u003c/sup\u003ePer 1mm increase in the 5-y average rainfall.\u003c/p\u003e\n\u003cp\u003eResults for Espaillat Province GGWR model are presented in \u003cstrong\u003eFigure 2\u003c/strong\u003e, showing the spatial variation in OR of variables significantly associated with leptospirosis seropositivity in the province-specific GLMER model. In the GGWR model, the widest range of variation of leptospirosis seropositivity OR across the province was associated with freshwater exposure (median OR 6.60, ranging from 5.73 to 7.43 across the study areas). In contrast, the OR associated with bare ground percentage had the lowest variation, ranging from 1.12 to 1.14 (median 1.13).\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eSan Pedro de Macoris\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eIn the multivariable GLMER, variables associated with significantly higher OR of leptospirosis seropositivity included older age groups (reference 5-19 years), namely: 20-34 years OR 5.15 (95%CI 1.45-8.33); 35-49 years OR 8.22 (2.30-29.36); 50-64 years (OR 6.49;1.74-24.22); and \u0026ge;65 years OR 9.56 (2.53-36.14). Male gender (OR 3.70;2.07-6.61) and exposure to rats compared (OR 2.28;1.13-4.61) were also significant risk factors in this province (\u003cstrong\u003eTable 3\u003c/strong\u003e).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 3. Odds ratios (ORs) and 95% CI from the generalised linear mixed-effects regression (GLMER) and OR median and range from the geographically weighted regression for leptospirosis seropositivity in San Pedro de Macoris Province, Dominican Republic, 2021\u003c/strong\u003e\u003cstrong\u003e.\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"557\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 217px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 142px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eGLMER\u0026nbsp;\u003c/strong\u003eOR (95%CI)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 198px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eGWR\u0026nbsp;\u003c/strong\u003eMedian (Min\u0026middot;-Max\u0026middot;)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"3\" valign=\"bottom\" style=\"width: 557px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAge (years)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 217px;\"\u003e\n \u003cp\u003e5-19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 142px;\"\u003e\n \u003cp\u003eRef\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 198px;\"\u003e\n \u003cp\u003eRef\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 217px;\"\u003e\n \u003cp\u003e20-34\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 142px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e5\u0026middot;15 (1\u0026middot;45-8\u0026middot;33)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 198px;\"\u003e\n \u003cp\u003e4\u0026middot;86 (3\u0026middot;14-5\u0026middot;27)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 217px;\"\u003e\n \u003cp\u003e35-49\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 142px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e8\u0026middot;22 (2\u0026middot;30-29\u0026middot;36)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 198px;\"\u003e\n \u003cp\u003e7\u0026middot;38 (5\u0026middot;42-7\u0026middot;50)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 217px;\"\u003e\n \u003cp\u003e50-64\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 142px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e6\u0026middot;49 (1\u0026middot;74-24\u0026middot;22)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 198px;\"\u003e\n \u003cp\u003e6\u0026middot;12 (4\u0026middot;7-6\u0026middot;37)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 217px;\"\u003e\n \u003cp\u003e\u0026ge;65\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 142px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e9\u0026middot;56 (2\u0026middot;53-36\u0026middot;14)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 198px;\"\u003e\n \u003cp\u003e8\u0026middot;02 (7\u0026middot;41-8\u0026middot;72)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"3\" valign=\"bottom\" style=\"width: 557px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eGender\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 217px;\"\u003e\n \u003cp\u003eFemale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 142px;\"\u003e\n \u003cp\u003eRef\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 198px;\"\u003e\n \u003cp\u003eRef\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 217px;\"\u003e\n \u003cp\u003eMale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 142px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e3\u0026middot;70 (2\u0026middot;07-6\u0026middot;61)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 198px;\"\u003e\n \u003cp\u003e3\u0026middot;34 (2\u0026middot;82-3\u0026middot;38)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 217px;\"\u003e\n \u003cp\u003eOther\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 142px;\"\u003e\n \u003cp\u003e2\u0026middot;40 (0\u0026middot;17-34\u0026middot;89)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 198px;\"\u003e\n \u003cp\u003e1\u0026middot;91 (1\u0026middot;56-3\u0026middot;30)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"3\" style=\"width: 557px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eEthnic group\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 217px;\"\u003e\n \u003cp\u003eIndigenous\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 142px;\"\u003e\n \u003cp\u003eRef\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 198px;\"\u003e\n \u003cp\u003eRef\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 217px;\"\u003e\n \u003cp\u003eMestizo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 142px;\"\u003e\n \u003cp\u003e0\u0026middot;96 (0\u0026middot;46-2\u0026middot;01)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 198px;\"\u003e\n \u003cp\u003e1\u0026middot;11 (0\u0026middot;96-1\u0026middot;12)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 217px;\"\u003e\n \u003cp\u003eMulatto\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 142px;\"\u003e\n \u003cp\u003e1\u0026middot;05 (0\u0026middot;49-2\u0026middot;24)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 198px;\"\u003e\n \u003cp\u003e1\u0026middot;22 (0\u0026middot;93-1\u0026middot;32)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"3\" style=\"width: 557px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eWork Environment\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 217px;\"\u003e\n \u003cp\u003eIndoors\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 142px;\"\u003e\n \u003cp\u003eRef\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 198px;\"\u003e\n \u003cp\u003eRef\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 217px;\"\u003e\n \u003cp\u003eMix Indoor and Outdoor\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 142px;\"\u003e\n \u003cp\u003e1\u0026middot;57 (0\u0026middot;70-3\u0026middot;54)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 198px;\"\u003e\n \u003cp\u003e1\u0026middot;28 (1\u0026middot;23-2\u0026middot;42)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 217px;\"\u003e\n \u003cp\u003eOutdoor\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 142px;\"\u003e\n \u003cp\u003e1\u0026middot;05 (0\u0026middot;35-3\u0026middot;11)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 198px;\"\u003e\n \u003cp\u003e0\u0026middot;74 (0\u0026middot;58-2\u0026middot;51)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 217px;\"\u003e\n \u003cp\u003eStudents, retired, unemployed\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 142px;\"\u003e\n \u003cp\u003e1\u0026middot;45 (0\u0026middot;74-2\u0026middot;84)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 198px;\"\u003e\n \u003cp\u003e1\u0026middot;44 (1\u0026middot;11-1\u0026middot;47)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"3\" valign=\"bottom\" style=\"width: 557px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eEducational level\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 217px;\"\u003e\n \u003cp\u003eSecondary\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 142px;\"\u003e\n \u003cp\u003eRef\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 198px;\"\u003e\n \u003cp\u003eRef\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 217px;\"\u003e\n \u003cp\u003ePrimary or none\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 142px;\"\u003e\n \u003cp\u003e1\u0026middot;3 (0\u0026middot;74-2\u0026middot;29)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 198px;\"\u003e\n \u003cp\u003e1\u0026middot;11 (1\u0026middot;09-1\u0026middot;32)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"3\" valign=\"bottom\" style=\"width: 557px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eFreshwater exposure\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 217px;\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 142px;\"\u003e\n \u003cp\u003eRef\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 198px;\"\u003e\n \u003cp\u003eRef\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 217px;\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 142px;\"\u003e\n \u003cp\u003e1\u0026middot;25 (0\u0026middot;61-2\u0026middot;58)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 198px;\"\u003e\n \u003cp\u003e0\u0026middot;83 (0\u0026middot;73-0\u0026middot;93)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 217px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eLiving in a flooding-risk area\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 142px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 198px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003e\u0026nbsp;\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 217px;\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 142px;\"\u003e\n \u003cp\u003eRef\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 198px;\"\u003e\n \u003cp\u003eRef\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 217px;\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 142px;\"\u003e\n \u003cp\u003e0\u0026middot;71 (0\u0026middot;28-1\u0026middot;75)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 198px;\"\u003e\n \u003cp\u003e2\u0026middot;74 (1\u0026middot;19-3\u0026middot;03)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 217px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eRat exposure\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 142px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 198px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 217px;\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 142px;\"\u003e\n \u003cp\u003eRef\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 198px;\"\u003e\n \u003cp\u003eRef\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 217px;\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 142px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e2\u0026middot;28 (1\u0026middot;13-4\u0026middot;61)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 198px;\"\u003e\n \u003cp\u003e2\u0026middot;41 (1\u0026middot;33-2\u0026middot;89)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"3\" valign=\"bottom\" style=\"width: 557px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSocio-economic drivers\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 217px;\"\u003e\n \u003cp\u003eMotorized time to health unity\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 142px;\"\u003e\n \u003cp\u003e1\u0026middot;24 (0\u0026middot;75-2\u0026middot;08)\u003csup\u003e1\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 198px;\"\u003e\n \u003cp\u003e1\u0026middot;16 (1\u0026middot;11-1\u0026middot;35)\u003csup\u003e1\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 217px;\"\u003e\n \u003cp\u003eGDP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 142px;\"\u003e\n \u003cp\u003e0\u0026middot;78 (0\u0026middot;55-1\u0026middot;11)\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 198px;\"\u003e\n \u003cp\u003e0\u0026middot;82 (0\u0026middot;81-0\u0026middot;86)\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"3\" valign=\"bottom\" style=\"width: 557px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eEnvironmental drivers\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 217px;\"\u003e\n \u003cp\u003eCropland percentage\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 142px;\"\u003e\n \u003cp\u003e0\u0026middot;89 (0\u0026middot;62-1\u0026middot;27)\u003csup\u003e3\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 198px;\"\u003e\n \u003cp\u003e0\u0026middot;85 (0\u0026middot;84-0\u0026middot;87)\u003csup\u003e3\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 217px;\"\u003e\n \u003cp\u003eRiver density\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 142px;\"\u003e\n \u003cp\u003e0\u0026middot;71 (0\u0026middot;28-1\u0026middot;75)\u003csup\u003e4\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 198px;\"\u003e\n \u003cp\u003e0\u0026middot;61 (0\u0026middot;53-1\u0026middot;72)\u003csup\u003e4\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 217px;\"\u003e\n \u003cp\u003eAverage precipitation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 142px;\"\u003e\n \u003cp\u003e0\u0026middot;91 (0\u0026middot;52-1\u0026middot;59)\u003csup\u003e5\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 198px;\"\u003e\n \u003cp\u003e1\u0026middot;06 (0\u0026middot;80-1\u0026middot;15)\u003csup\u003e5\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003csup\u003e1\u003c/sup\u003ePer 1-minute increase in the motorized travel. \u003csup\u003e2\u003c/sup\u003ePer 1,000,000 USD increase in the GDP. \u003csup\u003e3\u003c/sup\u003ePer each 1% increase in the land cover ground surrounding the household. \u003csup\u003e4\u003c/sup\u003ePer 1-metre increase in the total length of rivers surrounding the household. \u003csup\u003e5\u003c/sup\u003ePer 1mm increase in the 5-y average rainfall.\u003c/p\u003e\n\u003cp\u003eResults from the SPM Province GGWR model are presented in \u003cstrong\u003eFigure 3\u003c/strong\u003e, showing the spatial variation in OR of variables significantly associated with leptospirosis seropositivity in the province-specific GLMER model. In the GGWR model for SPM, OR of leptospirosis seropositivity associated with being 20-34 years of age (reference 5-19 years) exhibited the widest variation across the province (4.86; 3.14-5.27). In contrast, the OR associated with percentage of crop area within a 250-meter buffer around the household had the lowest variation, ranging from 0.84 to 0.86 (median 0.85).\u0026nbsp;\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eOur 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 globally (17). 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 interventions (19). 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.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eLeptospirosis is traditionally considered an occupational disease (20), and young males are especially affected in resource-limited rural areas (21) 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 former (23). While leptospirosis seroprevalence studies typically report a peak in prevalence in young and middle-aged adults followed by a decrease in older age groups (17), 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 periods (24) with slower decay after repeated infections (1). However, these two factors are not unique to the DR, thus suggesting sustained exposure and transmission in older age groups.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eWater plays a crucial role in the transmission cycle of leptospirosis, with pathogenic \u003cem\u003eLeptospira\u003c/em\u003e capable of persisting in moist soil and freshwater for extended periods (25). 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 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 urbanization levels and primary economic activities might have impacted the relative importance of determinants between provinces. 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 both provinces, 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.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThis study provides a more comprehensive characterisation of leptospirosis drives in the DR; however, 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 generalization of our findings throughout the country and the Caribbean region. Furthermore, the questionnaire used in the survey collected self-reported ethnicity. We acknowledge that ethnicity is a complex concept, especially in countries with multiple heritages. However, there is growing evidence associating socially assigned race and health outcomes through discrimination and socioeconomic status (27) and showing that incomplete reporting of ethnic groups and race can limit actions on reducing inequalities (28). In addition, 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. \u0026nbsp;Environmental variables included in this study were limited by publicly available data. Important risk factors such as farm animal density and proximity to sewage (22) 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. \u0026nbsp;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. \u0026nbsp;Additionally, 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 measures. 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. Finally, our analysis provided household-level information regarding risk factors and drivers associated with leptospirosis transmission, identifying variation of transmission patterns on a fine spatial scale.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eOur 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, prioritizing different strategies based on local conditions. For instance, some communities may benefit from interventions focusing on reducing freshwater exposure, while others 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.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe would like to thank the many study participants who volunteered to participate in this study. We would also like to thank the study staff who collected the field data, the Dominican Republic Ministry of Health and Social Assistance, and the Pedro Henriquez Ure\u0026ntilde;a National University, for their commitment and support for the study. Finally, we would like to thank Dr Gregorio Antonio Rosario Michel and the valuable team working in the Servicio Geologico Nacional for providing the flooding-risk map.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor\u0026rsquo;s contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eConceptualization, H.M., A.C.R., R.S-R., E.J.N. and C.L.L.; Data curation, B.M.M., H.M., C.T.P., M.C.E, M.S.A., D.D., S.G., W.D., F.P., G.A., L.C., B.H., M.B. and A.K.; Formal analysis, B.M.M., B.S., H.M., A.C.R., B.K.; Funding acquisition, E.J.N. and C.L.L..; Investigation, B.M.M., B.S., A.C.R., H.M., E.J.N. and C.L.L.; Methodology, B.M.M., B.S., H.M., and B.K.; Project administration, B.M.M., B.S. and C.L.L.; Resources, C.T.P., M.C.E, R.S-R, F.P., L.C., E.J.N. and C.L.L.; Supervision, A.C.R., E.J.N. and C.L.L; Validation, B.S., A.C.R., E.J.N. and C.L.L.; Visualization, B.M.M., B.S., A.C.R., E.J.N. and C.L.L.; Writing\u0026mdash;original draft preparation, B.M.M.; Writing\u0026mdash;review and editing, B.M.M., B.S., H.M., A.C.R., B.K., C.T.P., M.C.E, M.S.A., D.D., S.G., W.D., F.P., G.A., L.C., B.H., M.B., A.K., E.J.N. and C.L.L.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eA de-identified dataset analysed during the current study is available at https://github.com/enilles1/DR-Leptospirosis for the purpose of reproducing and building on the analyses.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis research was funded by the US CDC U01, grant number U01GH002238. CLL was supported by Australian National Health and Medical Research Council Fellowships (1109035 and 1193826). CDC staff supported laboratory analysis. Funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.\u0026nbsp;\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eLevett, P. N. \u0026amp; Leptospirosis \u003cem\u003eClin. Microbiol. Rev.\u003c/em\u003e ;\u003cb\u003e14\u003c/b\u003e(2):296\u0026ndash;326. (2001).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCosta, F. et al. Global Morbidity and Mortality of Leptospirosis: A Systematic Review. \u003cem\u003ePLoS Negl. Trop. Dis.\u003c/em\u003e \u003cb\u003e9\u003c/b\u003e (9), e0003898 (2015).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMunoz-Zanzi, C. et al. A systematic literature review of leptospirosis outbreaks worldwide, 1970\u0026ndash;2012. \u003cem\u003eRev. Panam. 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Use of geographically weighted logistic regression to quantify spatial variation in the environmental and sociodemographic drivers of leptospirosis in Fiji: a modelling study. \u003cem\u003eLancet Planet. Health\u003c/em\u003e. \u003cb\u003e2\u003c/b\u003e (5), e223\u0026ndash;e32 (2018).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWidayani, P., Gunawan, T., Danoedoro, P. \u0026amp; Mardihusodo, S. J. Application of Geographically Weighted Regression for Vulnerable Area Mapping of Leptospirosis in Bantul District. \u003cem\u003eIndonesian J. Geogr.\u003c/em\u003e \u003cb\u003e48\u003c/b\u003e (2), 168\u0026ndash;177 (2016).\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Zoonosis, Seroprevalence survey, Risk factors, spatial regression, Caribbean region","lastPublishedDoi":"10.21203/rs.3.rs-6449611/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6449611/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eSpatial variation in drivers of leptospirosis transmission in the Dominican Republic is poorly understood. To inform targeted public health interventions, we aimed to identify household-level variations in leptospirosis drivers. We analysed data from 2078 participants in two provinces, Espaillat and San Pedro de Macoris (SPM), collected from a 2021 cross-sectional survey. We used geographically weighted regression to quantify associations between leptospirosis seropositivity and spatial environmental and sociodemographic data. In Espaillat, higher odds of seropositivity were associated with exposure to freshwater (OR 12.92;95%CI 1.36-122.29), a higher percentage of bare ground (OR 1.21;1.01\u0026ndash;1.46) and river density (OR 1.53;1.14\u0026ndash;2.06) surrounding the household. In SPM, rat exposure was associated with higher odds of seropositivity (OR 2.41;1.33\u0026ndash;2.89). Higher community-level gross domestic product was associated with lower odds of seropositivity in both provinces. By identifying locally important drivers of transmission, our study provides evidence to support more tailored public health interventions to optimise the control and prevention of leptospirosis.\u003c/p\u003e","manuscriptTitle":"Quantifying spatial variation in environmental and sociodemographic drivers of leptospirosis in the Dominican Republic using a geographically weighted regression model","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-05-07 06:52:35","doi":"10.21203/rs.3.rs-6449611/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-06-04T09:40:20+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-06-02T01:59:25+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-05-24T13:31:06+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"191718706785739574134186909537477379200","date":"2025-05-13T14:31:08+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"317177597065909678015409765912899663142","date":"2025-05-12T19:55:54+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"216294928166135929359332164343801394062","date":"2025-05-12T14:58:29+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"109970471586647053538979149425878420805","date":"2025-04-29T09:38:53+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"3837255515058804653167197839454598560","date":"2025-04-24T13:41:24+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-04-24T08:10:01+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-04-24T07:58:39+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2025-04-23T18:42:31+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-04-23T04:53:29+00:00","index":"","fulltext":""},{"type":"submitted","content":"Scientific Reports","date":"2025-04-15T00:47:00+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"40bd4da2-018e-434e-9487-0a7825c4f422","owner":[],"postedDate":"May 7th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[{"id":47720502,"name":"Health sciences/Diseases/Infectious diseases"},{"id":47720503,"name":"Biological sciences/Computational biology and bioinformatics/Computational models"}],"tags":[],"updatedAt":"2025-08-07T07:20:07+00:00","versionOfRecord":{"articleIdentity":"rs-6449611","link":"https://doi.org/10.1038/s41598-025-13413-5","journal":{"identity":"scientific-reports","isVorOnly":false,"title":"Scientific Reports"},"publishedOn":"2025-07-25 15:58:14","publishedOnDateReadable":"July 25th, 2025"},"versionCreatedAt":"2025-05-07 06:52:35","video":"","vorDoi":"10.1038/s41598-025-13413-5","vorDoiUrl":"https://doi.org/10.1038/s41598-025-13413-5","workflowStages":[]},"version":"v1","identity":"rs-6449611","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6449611","identity":"rs-6449611","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}