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Abstract Details
Novel Models to Identify Census Tracts for Hepatitis C Screening Interventions
J Am Board Fam Med. May-Jun 2020;33(3):407-416. doi: 10.3122/jabfm.2020.03.190305.
Thomas Ludden1, Lindsay Shade2, Jeremy Thomas2, Brisa Urquieta de Hernandez2, Sveta Mohanan2, Mark W Russo2, Michael Leonard2, Philippe J Zamor2, Charity G Patterson2, Hazel Tapp2
Author information
1From 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.
2From 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).
Abstract
Background: Increased 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.
Methods: A 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.
Results: A 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).
Conclusions: Geospatial 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.