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The Spatial Distribution of Hepatitis C Virus Infections and Associated Determinants - An Application of a Geographically Weighted Poisson Regression for Evidence-Based Screening Interventions in Hotspots

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@article{7442a1d0260145c0944d1138ab18c3ad,
title = "The Spatial Distribution of Hepatitis C Virus Infections and Associated Determinants - An Application of a Geographically Weighted Poisson Regression for Evidence-Based Screening Interventions in Hotspots",
abstract = "BackgroundHepatitis C Virus (HCV) infections are a major cause for liver diseases. A large proportion of these infections remain hidden to care due to its mostly asymptomatic nature. Population-based screening and screening targeted on behavioural risk groups had not proven to be effective in revealing these hidden infections. Therefore, more practically applicable approaches to target screenings are necessary. Geographic Information Systems (GIS) and spatial epidemiological methods may provide a more feasible basis for screening interventions through the identification of hotspots as well as demographic and socio-economic determinants.MethodsAnalysed data included all HCV tests (n = 23,800) performed in the southern area of the Netherlands between 2002-2008. HCV positivity was defined as a positive immunoblot or polymerase chain reaction test. Population data were matched to the geocoded HCV test data. The spatial scan statistic was applied to detect areas with elevated HCV risk. We applied global regression models to determine associations between population-based determinants and HCV risk. Geographically weighted Poisson regression models were then constructed to determine local differences of the association between HCV risk and population-based determinants.ResultsHCV prevalence varied geographically and clustered in urban areas. The main population at risk were middle-aged males, non-western immigrants and divorced persons. Socio-economic determinants consisted of one-person households, persons with low income and mean property value. However, the association between HCV risk and demographic as well as socio-economic determinants displayed strong regional and intra-urban differences.DiscussionThe detection of local hotspots in our study may serve as a basis for prioritization of areas for future targeted interventions. Demographic and socio-economic determinants associated with HCV risk show regional differences underlining that a one-size-fits-all approach even within small geographic areas may not be appropriate. Future screening interventions need to consider the spatially varying association between HCV risk and associated demographic and socio-economic determinants.",
keywords = "GENERAL-POPULATION, COST-EFFECTIVENESS, HCV INFECTION, UNITED-STATES, RISK-FACTORS, PREVALENCE, DISEASE, SEROPREVALENCE, IDENTIFICATION, EPIDEMIOLOGY",
author = "B. Kauhl and J. Heil and C.J.P.A. Hoebe and J. Schweikart and T. Krafft and N.H.T.M. Dukers-Muijrers",
year = "2015",
month = "9",
day = "9",
doi = "10.1371/journal.pone.0135656",
language = "English",
volume = "10",
journal = "PLOS ONE",
issn = "1932-6203",
publisher = "Public Library of Science",
number = "9",

}

RIS

TY - JOUR

T1 - The Spatial Distribution of Hepatitis C Virus Infections and Associated Determinants - An Application of a Geographically Weighted Poisson Regression for Evidence-Based Screening Interventions in Hotspots

AU - Kauhl, B.

AU - Heil, J.

AU - Hoebe, C.J.P.A.

AU - Schweikart, J.

AU - Krafft, T.

AU - Dukers-Muijrers, N.H.T.M.

PY - 2015/9/9

Y1 - 2015/9/9

N2 - BackgroundHepatitis C Virus (HCV) infections are a major cause for liver diseases. A large proportion of these infections remain hidden to care due to its mostly asymptomatic nature. Population-based screening and screening targeted on behavioural risk groups had not proven to be effective in revealing these hidden infections. Therefore, more practically applicable approaches to target screenings are necessary. Geographic Information Systems (GIS) and spatial epidemiological methods may provide a more feasible basis for screening interventions through the identification of hotspots as well as demographic and socio-economic determinants.MethodsAnalysed data included all HCV tests (n = 23,800) performed in the southern area of the Netherlands between 2002-2008. HCV positivity was defined as a positive immunoblot or polymerase chain reaction test. Population data were matched to the geocoded HCV test data. The spatial scan statistic was applied to detect areas with elevated HCV risk. We applied global regression models to determine associations between population-based determinants and HCV risk. Geographically weighted Poisson regression models were then constructed to determine local differences of the association between HCV risk and population-based determinants.ResultsHCV prevalence varied geographically and clustered in urban areas. The main population at risk were middle-aged males, non-western immigrants and divorced persons. Socio-economic determinants consisted of one-person households, persons with low income and mean property value. However, the association between HCV risk and demographic as well as socio-economic determinants displayed strong regional and intra-urban differences.DiscussionThe detection of local hotspots in our study may serve as a basis for prioritization of areas for future targeted interventions. Demographic and socio-economic determinants associated with HCV risk show regional differences underlining that a one-size-fits-all approach even within small geographic areas may not be appropriate. Future screening interventions need to consider the spatially varying association between HCV risk and associated demographic and socio-economic determinants.

AB - BackgroundHepatitis C Virus (HCV) infections are a major cause for liver diseases. A large proportion of these infections remain hidden to care due to its mostly asymptomatic nature. Population-based screening and screening targeted on behavioural risk groups had not proven to be effective in revealing these hidden infections. Therefore, more practically applicable approaches to target screenings are necessary. Geographic Information Systems (GIS) and spatial epidemiological methods may provide a more feasible basis for screening interventions through the identification of hotspots as well as demographic and socio-economic determinants.MethodsAnalysed data included all HCV tests (n = 23,800) performed in the southern area of the Netherlands between 2002-2008. HCV positivity was defined as a positive immunoblot or polymerase chain reaction test. Population data were matched to the geocoded HCV test data. The spatial scan statistic was applied to detect areas with elevated HCV risk. We applied global regression models to determine associations between population-based determinants and HCV risk. Geographically weighted Poisson regression models were then constructed to determine local differences of the association between HCV risk and population-based determinants.ResultsHCV prevalence varied geographically and clustered in urban areas. The main population at risk were middle-aged males, non-western immigrants and divorced persons. Socio-economic determinants consisted of one-person households, persons with low income and mean property value. However, the association between HCV risk and demographic as well as socio-economic determinants displayed strong regional and intra-urban differences.DiscussionThe detection of local hotspots in our study may serve as a basis for prioritization of areas for future targeted interventions. Demographic and socio-economic determinants associated with HCV risk show regional differences underlining that a one-size-fits-all approach even within small geographic areas may not be appropriate. Future screening interventions need to consider the spatially varying association between HCV risk and associated demographic and socio-economic determinants.

KW - GENERAL-POPULATION

KW - COST-EFFECTIVENESS

KW - HCV INFECTION

KW - UNITED-STATES

KW - RISK-FACTORS

KW - PREVALENCE

KW - DISEASE

KW - SEROPREVALENCE

KW - IDENTIFICATION

KW - EPIDEMIOLOGY

U2 - 10.1371/journal.pone.0135656

DO - 10.1371/journal.pone.0135656

M3 - Article

VL - 10

JO - PLOS ONE

T2 - PLOS ONE

JF - PLOS ONE

SN - 1932-6203

IS - 9

M1 - e0135656

ER -