Identifying and characterizing hepatitis C virus hotspots in Massachusetts: a spatial epidemiological approach

Abstract

BackgroundHepatitis C virus (HCV) infections have increased during the past decade but little is known about geographic clustering patterns.MethodsWe used a unique analytical approach, combining geographic information systems (GIS), spatial epidemiology, and statistical modeling to identify and characterize HCV hotspots, statistically significant clusters of census tracts with elevated HCV counts and rates. We compiled sociodemographic and HCV surveillance data (n = 99,780 cases) for Massachusetts census tracts (n = 1464) from 2002 to 2013. We used a five-step spatial epidemiological approach, calculating incremental spatial autocorrelations and Getis-Ord Gi* statistics to identify clusters. We conducted logistic regression analyses to determine factors associated with the HCV hotspots.ResultsWe identified nine HCV clusters, with the largest in Boston, New Bedford/Fall River, Worcester, and Springfield (p < 0.05). In multivariable analyses, we found that HCV hotspots were independently and positively associated with the percent of the population that was Hispanic (adjusted odds ratio [AOR]: 1.07; 95% confidence interval [CI]: 1.04, 1.09) and the percent of households receiving food stamps (AOR: 1.83; 95% CI: 1.22, 2.74). HCV hotspots were independently and negatively associated with the percent of the population that were high school graduates or higher (AOR: 0.91; 95% CI: 0.89, 0.93) and the percent of the population in the “other” race/ethnicity category (AOR: 0.88; 95% CI: 0.85, 0.91).ConclusionWe identified locations where HCV clusters were a concern, and where enhanced HCV prevention, treatment, and care can help combat the HCV epidemic in Massachusetts. GIS, spatial epidemiological and statistical analyses provided a rigorous approach to identify hotspot clusters of disease, which can inform public health policy and intervention targeting. Further studies that incorporate spatiotemporal cluster analyses, Bayesian spatial and geostatistical models, spatially weighted regression analyses, and assessment of associations between HCV clustering and the built environment are needed to expand upon our combined spatial epidemiological and statistical methods.

Applying core theory and spatial analysis to identify hepatitis C virus infection “core areas” in British Columbia, Canada

“…We identified and characterized HCV infection core areas in BC where there is a significant clustering of HCV cases primarily in urban areas, but also in population centres supporting rural and remote communities. We identified one major cluster persisting for nearly 20 years which could be considered a core area for HCV; However, most studies on spatial analysis of HCV 29,55 do not take HCV testing into account. Additionally, social and material deprivations are important determinants of HCV infection 42 and often not available for analysis in most studies.…”

“…In our analyses, testing was higher in areas with high number of HCV cases indicating the association between testing and HCV diagnoses (data not shown). However, most studies on spatial analysis of HCV do not take HCV testing into account. Additionally, social and material deprivations are important determinants of HCV infection and often not available for analysis in most studies .…”

“…Previous spatial epidemiological research on HCV has focused on the spatial distribution of HCV, spatiotemporal distribution of HCV clusters, HCV‐related morbidity and mortality and HIV‐HCV co‐infection using a variety of spatial analytic methods. Although these studies have concentrated on characterizing HCV infection and hotspots, none of them have defined “core areas” per se for HCV infection.…”

Spatial Clustering and Analysis on Hepatitis C Virus Infections in Egypt

“…On the other hand, there are works implied GIS in their HCV diagnosis like the study of Thomas and co. [20], it presented a new analytical model combing GIS, five-step spatial epidemiological approach, and logistic regressions to spatially identify the hotspot clusters of HCV reported infections and their characteristics in Massachusetts through the years from 2002 to 2013 displayed by dot density and choropleth maps. In a similar study, [21] had implied GIS with GWPR (Geographically Weighted Poisson Regression) model to display the spatial heterogeneity of HCV disease risk in the southern of Netherlands and to quantify the impact of socio-economic determinants on its prevalence.…”

Áreas de risco para a ocorrência de hanseníase e sua relação com os determinantes sociais em município da região de fronteira Brasil, Paraguai e Argentina

“…Onde: A finalidade da aplicação dessa técnica reside no fato de que as distâncias dos agrupamentos espaciais ficam mais pronunciados, sendo necessária a aplicação do "Incremental Spatial Autocorrelation" (ISA) para identificar a melhor distância a ser utilizada (STOPKA et al, 2017;ZANG et al, 2015). Nessa etapa testou-se 30 distâncias para verificação daquela que mais se adequasse aos casos, sendo a melhor encontrada em 3.283 metros, com z-score de 4,47 (p=0,001).…”