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Both models produced similar OR for the covariates, but GWLR also detected spatial variation in the effect of each covariate. The predictive risk Helen wang sex and the city. Lee Joon-Gi indicated that the highest risk was in the Helen wang sex and the city. Lee Joon-Gi of Viti Levu, and the agricultural region and southern end of Vanua Levu. Interpretation GWLR provided a valuable method Helen wang sex and the city. Lee Joon-Gi modelling spatial heterogeneity of covariates for leptospirosis infection and their relative importance over space. Results of GWLR could be used to inform more place-specific interventions, particularly for diseases with strong environmental or sociodemographic drivers of transmission.

In the Pacific islands, leptospirosis is responsible for substantial morbidity and mortality, and the incidence is among the highest in the world. Using multivariable regression modelling, the investigators identified individual behavioural and demographic risk factors including sex and ethnicity, and several socio-demographic and environmental covariates that were significantly associated with a higher probability of infection, including rural location, high poverty rate, high rainfall, close proximity to rivers, presence of pigs in the community, and high cattle density.

Findings from the study also suggested that the probability of infection varied significantly between regions and residential settings, and between individual communities in each type of residential setting. The ability to design and implement effective prevention strategies has been restricted by the inability of tools to accurately identify, predict, and forecast hotspots of transmission. Sociodemographic and environmental drivers can vary significantly over geographical spaces and between socioecological niches eg, urban vs rural areas, land use, sociodemographics, and ethnicityeven within the same country.

Although non-spatial regression methods are unable to capture variations in these relationships over geographical space, geographically weighted logistic regression GWLR accounts for the spatial heterogeneity of relationships by allowing the coefficient of each covariate to vary over space. We restricted the search to articles published in English, and did not identify any articles that used geographically weighted regression to model or map a combination of environmental and sociodemographic drivers of leptospirosis. Added value of this study We showed that GWLR and non-spatial, standard logistic regression models had equal power to predict hotspots of transmission as measured by the average area under the receiver operating curve.

However, GWLR was also useful for modelling spatial variation in the relationships between dependent and independent variables. Using GWLR, we identified significant geographical variation in the intensity of environmental and sociodemographic drivers, and provided new insights into leptospirosis transmission in Fiji. This study also added value to scientific literature on leptospirosis modelling and mapping by using GWLR to account for and demonstrate the spatial non-stationarity of covariates. Implications of all the available evidence Maps of the geographical variation in the odds ratios ie, relative importance of each covariate affecting leptospirosis provide a visually powerful way of conveying the findings to public health practitioners.

The information can be used to develop more targeted public health interventions, and prioritise strategies that are most likely to be effective in different locations. The concepts and methods used in this study could also be applied to other diseases, particularly those with strong environmental and sociodemographic determinants. Although standard multivariable regression methods provide good results when the strength and significance of the relationships between the dependent variable and the covariates are constant throughout the spatial distribution of the sample, they are not able to capture variations in these relationships over a geographical space. Geographically weighted regression is a spatial regression method that allows the coefficient of each covariate to vary over a geographical space.

The objectives of the study were to assess the performance of a GWLR model compared with a standard non-spatial logistic regression model; to determine if GWLR can provide additional insights into the eco-epidemiology of leptospirosis, particularly from a geographical perspective; and to identify potential public health interventions that were most likely to be effective in different parts of the country. Free personal radio that learns from your taste and connects you to others who like what you like! National library service for the blind and physically handicapped Gynecomastia center santa ana, ca medicinenet.

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