Environmental risk factors for Lyme disease identified with geographic information systems.

Abstract

OBJECTIVES. A geographic information system was used to identify and locate residential environmental risk factors for Lyme disease. METHODS. Data were obtained for 53 environmental variables at the residences of Lyme disease case patients in Baltimore County from 1989 through 1990 and compared with data for randomly selected addresses. A risk model was generated combining the geographic information system with logistic regression analysis. The model was validated by comparing the distribution of cases in 1991 with another group of randomly selected addresses. RESULTS. In crude analyses, 11 environmental variables were associated with Lyme disease. In adjusted analyses, residence in forested areas (odds ratio [OR] = 3.7, 95% confidence interval [CI] = 1.2, 11.8), on specific soils (OR = 2.1, 95% CI = 1.0, 4.4), and in two regions of the county (OR = 3.5, 95% CI = 1.6, 7.4) (OR = 2.8, 95% CI = 1.0, 7.7) was associated with elevated risk of getting Lyme disease. Residence in highly developed regions was protective (OR = 0.3, 95% CI = 0.1, 1.0). The risk of Lyme disease in 1991 increased with risk categories defined from the 1989 through 1990 data. CONCLUSIONS. Combining a geographic information system with epidemiologic methods can be used to rapidly identify risk factors of zoonotic disease over large areas.

Spatial scale modulates the strength of ecological processes driving disease distributions

“…The third pathogen, the chytrid fungus Batrachochytrium dendrobatidis (Bd), is considered one of the deadliest organisms on the planet because of its association with hundreds of amphibian extinctions in the last half century (21,22). We chose to model the spatial factors affecting these pathogens because (i) spatially explicit datasets of their distributions were available (but were not available for other pathogens or other organisms in general; see Methods); (ii) they span a diversity of taxa (a virus, bacterium, and fungus) and transmission modes (WNV and Lyme are mosquito-and tick-borne, respectively, and Bd is a directly transmitted, water-borne pathogen), and infect various types of hosts (endothermic and ectothermic), increasing the generality of our findings; (iii) they are widespread generalists throughout the United States, providing a spatial extent great enough to conduct largescale analyses; (iv) their abundances or prevalences appear to be partially controlled by a common biotic factor, the richness of potential hosts (19,21,23,24), and by common abiotic factors, including climate and vegetation (20,25,26); and, finally, (v) understanding emerging diseases is of critical importance to biodiversity conservation and human health. Our goal was not to develop and put forth the best possible model to explain the spread of these diseases but rather to test whether spatial scale influences which types of ecological processes are important.…”

Anthropogenic and environmental risk factors for rabies occurrence in Bhutan

“…It is therefore important to understand the risk factors for disease occurrence. Socio-demographic, anthropogenic and environmental factors have been assessed to understand the epidemiology of various infectious diseases in epidemiologic research and have provided useful information as predictors of disease occurrence (Glass et al, 1995;Weiss and McMichael, 2004;Hu et al, 2007;Mongoh et al, 2007;Ward et al, 2009). For example, the estimated equine West Nile Virus attack rate in Texas (USA) was best described by environmental features such as lakes, forests and cultivated areas .…”

Monitoring Method of Cow Anthrax Based on Gis and Spatial Statistical Analysis

“…GIS is particularly well suited for studying these associations because of its spatial analysis and display capabilities. Recently GIS has been used in the surveillance and monitoring of vector-borne diseases (Glass GE, et al, 1995;Beck LR, et al, 1994;Richards FO, et al,1993;Clarke KC, et al, 1991), water borne diseases (Braddock M, et al,1994), in environmental health (Barnes S and Peck A. 1994;Wartenberg D, et al, 1993;Wartenberg D. et al,1992), and the analysis of disease policy and planning (Marilyn O Ruiz, et al,2004).Spatial analysis function of GIS can be widened and strengthened by using spatial statistical analysis, allowing Spatial Statistical Analysis for the deeper exploration, analysis, manipulation and interpretation of spatial pattern and spatial correlation of the animal disease.…”