These data indicate an emerging US epidemic of HCV infection among young nonurban persons of predominantly white race. Reported incidence was higher in 2012 than 2006 in at least 30 states, with largest increases in nonurban counties east of the Mississippi River. Prescription opioid abuse at an early age was commonly reported and should be a focus for medical and public health intervention.

Due to shared routes of transmission, coinfection with both human immunodeficiency virus type 1 (HIV-1) and hepatitis C virus (HCV) is relatively common and results in accelerated liver disease, driving morbidity and mortality. Deaths related to HCV now exceed deaths related to HIV in the United States, and co-infected patients bear a significant proportion of that mortality. This burden may be addressed by novel antiviral therapies that promise increased rates of cure or by enhanced access to liver transplantation, but these are costly interventions. Ultimately, the future burden of coinfection is addressed by greater understanding of who is at risk for development of each infection, thus guiding preventive efforts. Key recent reports regarding the US burden of morbidity and mortality due to HCV and groups at risk for coinfection are reviewed, with a focus on recently described HCV occurring among young injection drug users and men who have sex with men. Given the lack of available vaccine against HCV, enhanced detection and surveillance is a vital component of our public health strategy to combat HCV.

BackgroundAutomatic identification of notifiable diseases from electronic medical records can potentially improve the timeliness and completeness of public health surveillance. We describe the development and implementation of an algorithm for prospective surveillance of patients with acute hepatitis B using electronic medical record data.MethodsInitial algorithms were created by adapting Centers for Disease Control and Prevention diagnostic criteria for acute hepatitis B into electronic terms. The algorithms were tested by applying them to ambulatory electronic medical record data spanning 1990 to May 2006. A physician reviewer classified each case identified as acute or chronic infection. Additional criteria were added to algorithms in serial fashion to improve accuracy. The best algorithm was validated by applying it to prospective electronic medical record data from June 2006 through April 2008. Completeness of case capture was assessed by comparison with state health department records.FindingsA final algorithm including a positive hepatitis B specific test, elevated transaminases and bilirubin, absence of prior positive hepatitis B tests, and absence of an ICD9 code for chronic hepatitis B identified 112/113 patients with acute hepatitis B (sensitivity 97.4%, 95% confidence interval 94–100%; specificity 93.8%, 95% confidence interval 87–100%). Application of this algorithm to prospective electronic medical record data identified 8 cases without false positives. These included 4 patients that had not been reported to the health department. There were no known cases of acute hepatitis B missed by the algorithm.ConclusionsAn algorithm using codified electronic medical record data can reliably detect acute hepatitis B. The completeness of public health surveillance may be improved by automatically identifying notifiable diseases from electronic medical record data.

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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.