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Introduction: Chronic infection with hepatitis C virus (HCV) is a leading cause of liver disease and infectious disease deaths. While recent and emerging treatment options for HCV patients have enabled higher rates of sustained virologic response (SVR), the demographic, clinical, geographic, and payer characteristics of the estimated 3.4 million chronic HCV patients in the USA are poorly understood. The goal of this study was to create a dataset describing the current HCV patient landscape in the USA., Methods: Data from two large national laboratory companies representing the majority of US patients screened for HCV antibody and/or tested for HCV RNA from 2013 through 2016 were organized into the present study dataset. Age, gender, payer channel, 3-digit ZIP code and ordering physician specialty, and 3-digit ZIP code information were available for all patients. Among RNA-positive patients, additional clinical characteristics included HCV genotype, fibrosis stage, renal function, and HIV status. Initiating treatment and attaining cure were imputed using data-driven algorithms based on successive RNA viral load measurements., Results: The number of RNA-positive HCV patients increased from 200,066 patients in 2013 to 469,550 in 2016. The availability of clinical data measurements and rates of treatment initiation increased over the study period, indicating improved care engagement for HCV patients. Treatment and cure rates varied by age, disease severity, geographic location, and payer channel. Sensitivity and specificity of the cure prediction algorithms were consistently above 0.90, validating the robustness of the data imputation approach., Conclusion: This is the largest, most comprehensive dataset available to describe the current US HCV patient landscape. Our results highlight that the epidemiology of HCV is evolving with an increasing number of patients who are younger and have milder disease than described in previous years. Results of this study should help guide efforts toward the elimination of HCV in this country. Future work will focus on factors associated with varying treatment and cure patterns and describing recent changes in the HCV patient landscape., Funding: AbbVie. Plain language summary available for this article.

Background: To help broaden the use of machine-learning approaches in health services research, we provide an easy-to-follow framework on the implementation of random forests and apply it to identify quality of care (QC) patterns correlated with treatment receipt among Medicare disabled patients with hepatitis C virus (HCV)., Methods: Using Medicare claims 2006-2009, we identified 1936 patients with 6 months continuous enrollment before HCV diagnosis. We ran a random forest on 14 pretreatment QC indicators, extracted the forest's representative tree, and aggregated its terminal nodes into 4 QC groups predictive of treatment. To explore determinants of differential QC receipt, we compared patient-level and county-level (linked AHRF data) characteristics across QC groups., Results: The strongest predictors of treatment included "liver biopsy," "HCV genotype testing," "specialist visit," "HCV viremia confirmation," and "iron overload testing." High QC [n=360, proportion treated (pt)=33.3%] was defined for patients with at least 2 from the above-mentioned metrics. Good QC patients (n=302, pt=12.3%) had either "HCV genotype testing" or "specialist visit," whereas fair QC (n=282, pt=7.1%) only had "HCV viremia confirmation." Low QC patients (n=992, pt=2.5%) had none of the selected metrics. The algorithm accuracy of predicting treatment was 70% sensitivity and 78% specificity. HIV coinfection, drug abuse, and residence in counties with higher supply of hospitals with immunization and AIDS services correlated with lower QC., Conclusions: Machine-learning techniques could be useful in exploring patterns of care. Among Medicare disabled HCV patients, the receipt of more QC indicators was associated with higher treatment rates. Future research is needed to assess determinants of differential QC receipt.

Unlabelled: Patient- and county-level characteristics associated with advanced liver disease (ALD) at hepatitis C virus (HCV) diagnosis were examined in three Medicare cohorts: (1) elderly born before 1945; (2) disabled born 1945-1965; and (3) disabled born after 1965. We used Medicare claims (2006-2009) linked to the Area Health Resource Files. ALD was measured over the period of 6 months before to 3 months after diagnosis. Using weighted multivariate modified Poisson regression to address generalizability of findings to all Medicare patients, we modeled the association between contextual characteristics and presence of ALD at HCV diagnosis. We identified 1,746, 3,351, and 592 patients with ALD prevalence of 28.0%, 23.0%, and 15.0% for birth cohorts 1, 2, and 3. Prevalence of drug abuse increased among younger birth cohorts (4.2%, 22.6%, and 35.6%, respectively). Human immunodeficiency virus coinfection (prevalence ratio [PR] = 0.63; 95% confidence interval [CI]: 0.50-0.80; P = 0.001), dual Medicare/Medicaid eligibility (PR = 0.89; 95% CI: 0.80-0.98; P = 0.017), residence in counties with higher median household income (PR = 0.82; 95% CI: 0.71-0.95; P = 0.008), higher density of primary care providers (PR = 0.84; 95% CI: 0.73-0.98; P = 0.022), and more rural health clinics (PR = 0.90; 0.81-1.01; P = 0.081) were associated with lower ALD risk. End-stage renal disease (PR = 1.41; 95% CI: 1.21-1.63; P = 0.001), alcohol abuse (PR = 2.57; 95% CI: 2.33-2.84; P = 0.001), hepatitis B virus (PR = 1.32; 95% CI: 1.09-1.59; P = 0.004), and Midwest residence (PR = 1.22; 95% CI: 1.05-1.41; P = 0.010) were associated with higher ALD risk. Living in rural counties with high screening capacity was protective in the elderly, but associated with higher ALD risk among the disabled born 1945-1965., Conclusions: ALD prevalence patterns were complex and were modified by race, elderly/disability status, and the extent of health care access and screening capacity in the county of residence. These study results help inform treatment strategies for HCV in the context of coordinated models of care., (© 2015 by the American Association for the Study of Liver Diseases.)

We examined whether interferon treatment is associated with reduced metabolic/vascular complications in hepatitis C virus patients. The study had historical prospective cohort design using Maryland Medicaid administrative data (2006-2009). The end point was the incidence rate of mild, severe and combined mild/severe events from the Diabetes Complications Severity Index (DCSI). Interferon-treated and -untreated hepatitis C virus patients were matched on baseline covariates. Using multivariate counting process Cox regressions, we modeled the association between interferon receipt of at least 24 weeks and DCSI events incidence rate. Treated whites had similar rate of mild DCSI events, significantly 64% (p < 0.01) lower rate of severe DCSI events, and overall 29% (p = 0.14) lower rate of mild/severe DCSI events, compared with untreated whites. Compared with untreated blacks, treated blacks had a similar rate of DSCI events. Future studies should confirm our findings and should include important clinical variables such as viral genotype, virologic count and achieving sustained virologic response.