PT  - JOURNAL ARTICLE
AU  - Ludden, Thomas
AU  - Shade, Lindsay
AU  - Thomas, Jeremy
AU  - de Hernandez, Brisa Urquieta
AU  - Mohanan, Sveta
AU  - Russo, Mark W.
AU  - Leonard, Michael
AU  - Zamor, Philippe J.
AU  - Patterson, Charity G.
AU  - Tapp, Hazel
TI  - Novel Models to Identify Census Tracts for Hepatitis C Screening Interventions
AID  - 10.3122/jabfm.2020.03.190305
DP  - 2020 May 01
TA  - The Journal of the American Board of Family
                Medicine
PG  - 407--416
VI  - 33
IP  - 3
4099  - http://www.jabfm.org/content/33/3/407.short
4100  - http://www.jabfm.org/content/33/3/407.full
SO  - J Am Board Fam Med2020 May 01; 33
AB  - 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.