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.

Do the risk factors for type 2 diabetes mellitus vary by location? A spatial analysis of health insurance claims in Northeastern Germany using kernel density estimation and geographically weighted regression

“…Geographic information systems (GIS) and spatial regression models at the ecological level have gained increasing attention in recent years as this approach allows an analysis of possible risk factors that are often unavailable on an individual level due to privacy restriction [15, 17]. For T2DM, this approach might help to identify the population groups, which are most in need for the provision of healthcare and access to preventive measures.…”

Detecting Hepatitis B and C by Combined Public Health and Primary Care Birth Cohort Testing

“…A higher prevalence was expected based on previous estimations in these hotspots (0.5% to 1%), 27,28 or other Dutch studies focusing on risk groups (0.3% to 4.8%), 32,34,38,39 and on (inter)national estimations that a substantial population is untested. [2][3][4][5] Estimates of the hidden HCV-infected population, however, are largely derived from mathematical models (43% to 72%).…”

“…28 The family practices agreeing to participate invited patients aged between 40 and 70 years for a HCV and HBV test. Blood samples were transported within 3 hours of collection to the laboratory.…”

“…Based on spatialepidemiological analyses of laboratory data, the estimated prevalence of HCV infection in the south of the Netherlands is 0.5%, and HCV infections are found to cluster in urban areas, with a prevalence of up to 1%. 27,28 These so-called hotspots are postal codes where there is a statistically significant elevated risk of diagnosed HCV infection (relative risk >4). In addition, an estimated 66% of HCV-infected persons are estimated to be untested and thus hidden to care.…”

Geographic Information Systems (GIS) in public health

“…Kauhl, B., Pilot, E., Rao, R., Gruebner, O., Schweikart, J., & Krafft, T. (2015). Estimating the spatial distribution of acute undifferentiated fever (AUF) and associated risk factors using emergency call data in India.…”