• Thomas Ludden, Lindsay Shade, Jeremy Thomas, Brisa Urquieta de Hernandez, Sveta Mohanan, Mark W Russo, Michael Leonard, Philippe J Zamor, Charity G Patterson, and Hazel Tapp.
• From Department of Family Medicine, Atrium Health, Charlotte, NC (TL, LS, JT, SM, HT); Department of Hepatology, Atrium Health, Charlotte, NC (MWR, PJZ); Department of Infectious Diseases, Atrium Health, Charlotte, NC (ML); Community Health, Atrium Health, Charlotte, NC (BUH); School of Health and Rehabilitation Sciences, University of Pittsburgh, Pittsburgh, PA (CGP). Tom.Ludden@atriumhealth.org.
• J Am Board Fam Med. 2020 May 1; 33 (3): 407-416.

BackgroundIncreased screening efforts and the development of effective antiviral treatments have led to marked improvement in hepatitis C (HCV) patient outcomes. However, many people in the United States are still believed to have undiagnosed HCV. Geospatial modeling using variables representing at-risk populations in need of screening for HCV and social determinants of health (SDOH) provide opportunities to identify populations at risk of HCV.MethodsA literature review was conducted to identify variables associated with patients at risk for HCV infection. Two sets of variables were collected: HCV Transmission Risk and SDOH Level of Need. The variables were combined into indices for each group and then mapped at the census tract level (n = 233). Multiple linear regression analysis and the Pearson correlation coefficient were used to validate the models.ResultsA total of 4 HCV Transmission Risk variables and 12 SDOH Level of Need variables were identified. Between the 2 indexes, 21 high-risk census tracts were identified that scored at least 2 standard deviations above the mean. The regression analysis showed a significant relationship with HCV infection rate and prevalence of drug use (B = 0.78, P < .001). A significant relationship also existed with the HCV infection rate for households with no/limited English use (B = -0.24, P = .001), no car use (B = 0.036, P < .001), living below the poverty line (B = 0.014, P = .009), and median household income (B = -0.00, P = .009).ConclusionsGeospatial models identified high-priority census tracts that can be used to map high-risk HCV populations that may otherwise be unrecognized. This will allow future targeted screening and linkage-to-care interventions for patients at high risk of HCV.© Copyright 2020 by the American Board of Family Medicine.

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