<?xml version='1.0' encoding='UTF-8'?><xml><records><record><source-app name="HighWire" version="7.x">Drupal-HighWire</source-app><ref-type name="Journal Article">17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Ludden, Thomas</style></author><author><style face="normal" font="default" size="100%">Shade, Lindsay</style></author><author><style face="normal" font="default" size="100%">Thomas, Jeremy</style></author><author><style face="normal" font="default" size="100%">de Hernandez, Brisa Urquieta</style></author><author><style face="normal" font="default" size="100%">Mohanan, Sveta</style></author><author><style face="normal" font="default" size="100%">Russo, Mark W.</style></author><author><style face="normal" font="default" size="100%">Leonard, Michael</style></author><author><style face="normal" font="default" size="100%">Zamor, Philippe J.</style></author><author><style face="normal" font="default" size="100%">Patterson, Charity G.</style></author><author><style face="normal" font="default" size="100%">Tapp, Hazel</style></author></authors><secondary-authors></secondary-authors></contributors><titles><title><style face="normal" font="default" size="100%">Novel Models to Identify Census Tracts for Hepatitis C Screening Interventions</style></title><secondary-title><style face="normal" font="default" size="100%">The Journal of the American Board of Family
                Medicine</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2020</style></year><pub-dates><date><style  face="normal" font="default" size="100%">2020-05-01 00:00:00</style></date></pub-dates></dates><pages><style  face="normal" font="default" size="100%">407-416</style></pages><doi><style  face="normal" font="default" size="100%">10.3122/jabfm.2020.03.190305</style></doi><volume><style face="normal" font="default" size="100%">33</style></volume><issue><style face="normal" font="default" size="100%">3</style></issue><abstract><style  face="normal" font="default" size="100%">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 &lt; .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 &lt; .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.</style></abstract></record></records></xml>