Leptospirosis in American Samoa – estimating and mapping risk using environmental data

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dc.contributor.authorLau, Colleen L
dc.contributor.authorClements, Archie
dc.contributor.authorSkelly, Chris
dc.contributor.authorDobson, Annette
dc.contributor.authorSmythe, Lee D
dc.contributor.authorWeinstein, Philip
dc.date.accessioned2015-11-04T23:50:45Z
dc.date.available2015-11-04T23:50:45Z
dc.date.issued2012-05-29
dc.date.updated2015-12-11T08:41:55Z
dc.description.abstractBACKGROUND 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.
dc.description.sponsorshipThe seroprevalence study was funded by the School Population Health, the University of Queensland; a Graduate School Research Travel Grant from the University of Queensland; and the WHO/OIE/FAO Collaborating Centre for Reference and Research on Leptospirosis, Queensland Health Forensic and Scientific Services, Brisbane, Australia. This study of risk mapping was conducted as part of Dr. Colleen Lau’s Ph.D. degree at the School of Population Health, the University of Queensland. The Ph.D. was also supported by an Australian Postgraduate Award Scholarship, and a Graduate School Research Travel Grant from the University of Queensland.en_AU
dc.format11 pages
dc.identifier.issn1935-2735en_AU
dc.identifier.urihttp://hdl.handle.net/1885/16351
dc.publisherPublic Library of Science
dc.rights© 2012 Lau et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
dc.sourcePLoS Neglected Tropical Diseases
dc.subjectamerican samoa
dc.subjecthumans
dc.subjectleptospirosis
dc.subjectrisk assessment
dc.subjectseroepidemiologic studies
dc.subjecttopography, medical
dc.titleLeptospirosis in American Samoa – estimating and mapping risk using environmental data
dc.typeJournal article
dcterms.dateAccepted2012-04-18
local.bibliographicCitation.issue5en_AU
local.bibliographicCitation.lastpage11)
local.bibliographicCitation.startpagee1669en_AU
local.contributor.affiliationLau, Colleen L, University of Queensland, Australiaen_AU
local.contributor.affiliationClements, Archie, College of Medicine, Biology and Environment, CMBE Research School of Population Health, Natl Centre for Epidemiology & Population Health, The Australian National Universityen_AU
local.contributor.affiliationSkelly, Chris, University of Queensland, Australiaen_AU
local.contributor.affiliationDobson, Annette, University of Queensland, Australiaen_AU
local.contributor.affiliationSmythe, Lee D, Queensland Health Forensic and Scientific Services, Australiaen_AU
local.contributor.affiliationWeinstein, Philip, University of Queensland, Australiaen_AU
local.contributor.authoremailarchie.clements@anu.edu.auen_AU
local.contributor.authoruidu5611518en_AU
local.description.notesImported from ARIES. At the time of publication Archie C. A. Clements was affiliated with 1 School of Population Health, The University of Queensland, Herston, Australia.en_AU
local.identifier.absfor111706en_AU
local.identifier.ariespublicationU3488905xPUB4089en_AU
local.identifier.citationvolume6en_AU
local.identifier.doi10.1371/journal.pntd.0001669en_AU
local.identifier.essn1935-2735en_AU
local.identifier.scopusID2-s2.0-84863694120
local.identifier.uidSubmittedByu3488905en_AU
local.publisher.urlhttps://www.plos.org/en_AU
local.type.statusPublished Versionen_AU

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