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Leptospirosis in American Samoa--estimating and mapping risk using environmental data.

Lau CL, Clements AC, Skelly C, Dobson AJ, Smythe LD, Weinstein P - PLoS Negl Trop Dis (2012)

Bottom Line: Goodness of fit of models was measured using area under the curve of the receiver operating characteristic, and the percentage of cases correctly classified as seropositive.Environmental predictors of seroprevalence included living below median altitude of a village, in agricultural areas, on clay soil, and higher density of piggeries above the house.Models had acceptable goodness of fit, and correctly classified ∼84% of cases.

View Article: PubMed Central - PubMed

Affiliation: School of Population Health, The University of Queensland, Herston, Australia. colleen.lau@uqconnect.edu.au

ABSTRACT

Background: The recent emergence of leptospirosis has been linked to many environmental drivers of disease transmission. Accurate epidemiological data are lacking because of under-diagnosis, poor laboratory capacity, and inadequate surveillance. Predictive risk maps have been produced for many diseases to identify high-risk areas for infection and guide allocation of public health resources, and are particularly useful where disease surveillance is poor. To date, no predictive risk maps have been produced for leptospirosis. The objectives of this study were to estimate leptospirosis seroprevalence at geographic locations based on environmental factors, produce a predictive disease risk map for American Samoa, and assess the accuracy of the maps in predicting infection risk.

Methodology and principal findings: Data on seroprevalence and risk factors were obtained from a recent study of leptospirosis in American Samoa. Data on environmental variables were obtained from local sources, and included rainfall, altitude, vegetation, soil type, and location of backyard piggeries. Multivariable logistic regression was performed to investigate associations between seropositivity and risk factors. Using the multivariable models, seroprevalence at geographic locations was predicted based on environmental variables. Goodness of fit of models was measured using area under the curve of the receiver operating characteristic, and the percentage of cases correctly classified as seropositive. Environmental predictors of seroprevalence included living below median altitude of a village, in agricultural areas, on clay soil, and higher density of piggeries above the house. Models had acceptable goodness of fit, and correctly classified ∼84% of cases.

Conclusions and significance: Environmental variables could be used to identify high-risk areas for leptospirosis. Environmental monitoring could potentially be a valuable strategy for leptospirosis control, and allow us to move from disease surveillance to environmental health hazard surveillance as a more cost-effective tool for directing public health interventions.

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Related in: MedlinePlus

Predicted leptospirosis seroprevalence based on environmental variables.Predicted values were calculated using Model A, based on four environmental variables (altitude, piggeries, vegetation, and soil type).
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pntd-0001669-g004: Predicted leptospirosis seroprevalence based on environmental variables.Predicted values were calculated using Model A, based on four environmental variables (altitude, piggeries, vegetation, and soil type).

Mentions: Using Model A, based on environmental risk factors only (Figure 4)


Leptospirosis in American Samoa--estimating and mapping risk using environmental data.

Lau CL, Clements AC, Skelly C, Dobson AJ, Smythe LD, Weinstein P - PLoS Negl Trop Dis (2012)

Predicted leptospirosis seroprevalence based on environmental variables.Predicted values were calculated using Model A, based on four environmental variables (altitude, piggeries, vegetation, and soil type).
© Copyright Policy
Related In: Results  -  Collection

Show All Figures
getmorefigures.php?uid=PMC3362644&req=5

pntd-0001669-g004: Predicted leptospirosis seroprevalence based on environmental variables.Predicted values were calculated using Model A, based on four environmental variables (altitude, piggeries, vegetation, and soil type).
Mentions: Using Model A, based on environmental risk factors only (Figure 4)

Bottom Line: Goodness of fit of models was measured using area under the curve of the receiver operating characteristic, and the percentage of cases correctly classified as seropositive.Environmental predictors of seroprevalence included living below median altitude of a village, in agricultural areas, on clay soil, and higher density of piggeries above the house.Models had acceptable goodness of fit, and correctly classified ∼84% of cases.

View Article: PubMed Central - PubMed

Affiliation: School of Population Health, The University of Queensland, Herston, Australia. colleen.lau@uqconnect.edu.au

ABSTRACT

Background: The recent emergence of leptospirosis has been linked to many environmental drivers of disease transmission. Accurate epidemiological data are lacking because of under-diagnosis, poor laboratory capacity, and inadequate surveillance. Predictive risk maps have been produced for many diseases to identify high-risk areas for infection and guide allocation of public health resources, and are particularly useful where disease surveillance is poor. To date, no predictive risk maps have been produced for leptospirosis. The objectives of this study were to estimate leptospirosis seroprevalence at geographic locations based on environmental factors, produce a predictive disease risk map for American Samoa, and assess the accuracy of the maps in predicting infection risk.

Methodology and principal findings: Data on seroprevalence and risk factors were obtained from a recent study of leptospirosis in American Samoa. Data on environmental variables were obtained from local sources, and included rainfall, altitude, vegetation, soil type, and location of backyard piggeries. Multivariable logistic regression was performed to investigate associations between seropositivity and risk factors. Using the multivariable models, seroprevalence at geographic locations was predicted based on environmental variables. Goodness of fit of models was measured using area under the curve of the receiver operating characteristic, and the percentage of cases correctly classified as seropositive. Environmental predictors of seroprevalence included living below median altitude of a village, in agricultural areas, on clay soil, and higher density of piggeries above the house. Models had acceptable goodness of fit, and correctly classified ∼84% of cases.

Conclusions and significance: Environmental variables could be used to identify high-risk areas for leptospirosis. Environmental monitoring could potentially be a valuable strategy for leptospirosis control, and allow us to move from disease surveillance to environmental health hazard surveillance as a more cost-effective tool for directing public health interventions.

Show MeSH
Related in: MedlinePlus