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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., Competing Interests: Conflicting and Competing Interests: None, (© Copyright 2020 by the American Board of Family Medicine.)

Introduction: A key element for reducing health care costs and improving community health is increased access to primary care and preventative health services. Geographic information systems (GIS) have the potential to assess patterns of health care utilization and community-level attributes to identify geographic regions most in need of primary care access., Methods: GIS, analytical hierarchy process, and multiattribute assessment and evaluation techniques were used to examine attributes describing primary care need and identify areas that would benefit from increased access to primary care services. Attributes were identified by a collaborative partnership working within a practice-based research network using tenets of community-based participatory research. Maps were created based on socioeconomic status, population density, insurance status, and emergency department and primary care safety-net utilization., Results: Individual and composite maps identified areas in our community with the greatest need for increased access to primary care services., Conclusions: Applying GIS to commonly available community- and patient-level data can rapidly identify areas most in need of increased access to primary care services. We have termed this a Multiple Attribute Primary Care Targeting Strategy. This model can be used to plan health services delivery as well as to target and evaluate interventions designed to improve health care access.

Background: Hispanics are the largest and fastest growing minority group in the United States. Charlotte, NC, had the 4th fastest growing Hispanic community in the nation between 1990 to 2000. Gaining understanding of the patterns of health care use for this changing population is a key step toward designing improved primary care access and community health., Methods: The Multiple Attribute Primary Care Targeting Strategy process was applied to key patient- and community-level attributes describing the Charlotte Hispanic community. Maps were created based on socioeconomic status, population density, insurance status, and use of the emergency department as a primary care safety net. Each of these variables was weighed and added to create a single composite map., Results: Individual attribute maps and the composite map identified geographic locations where Hispanic community members would most benefit from increased access to primary care services., Conclusions: Using the Multiple Attribute Primary Care Targeting Strategy process we were able to identify geographic areas within our community where many Hispanic immigrants face barriers to accessing appropriate primary care services. These areas can subsequently be targeted for interventions that improve access to primary care and reduce emergency department use. The geospatial model created through this process can be monitored over time to determine the effectiveness of these interventions.