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).
J Am Board Fam Med. 2020 May-Jun;33(3):407-416. doi: 10.3122/jabfm.2020.03.190305.
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.
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.
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, < .001). A significant relationship also existed with the HCV infection rate for households with no/limited English use (B = -0.24, = .001), no car use (B = 0.036, < .001), living below the poverty line (B = 0.014, = .009), and median household income (B = -0.00, = .009).
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.
增加筛查力度和开发有效的抗病毒治疗方法，显著改善了丙型肝炎（HCV）患者的预后。然而，据信美国仍有许多人未被诊断出患有 HCV。使用代表需要 HCV 筛查的高危人群和健康社会决定因素（SDOH）的变量进行地理空间建模，可以识别出患有 HCV 的高危人群。
进行了文献回顾，以确定与 HCV 感染风险患者相关的变量。收集了两组变量：HCV 传播风险和 SDOH 需求水平。将变量组合到每个组的指数中，然后在普查区层面（n = 233）进行映射。使用多元线性回归分析和 Pearson 相关系数验证模型。
确定了 4 个 HCV 传播风险变量和 12 个 SDOH 需求水平变量。在这两个指标中，确定了 21 个高风险普查区，其得分至少比平均值高出 2 个标准差。回归分析显示与 HCV 感染率和药物使用流行率呈显著相关（B = 0.78，<.001）。与使用英语能力有限/无的家庭 HCV 感染率（B = -0.24，<.001）、无汽车使用（B = 0.036，<.001）、生活在贫困线以下（B = 0.014，<.009）和家庭中位数收入（B = -0.00，<.009）也存在显著关系。
地理空间模型确定了高优先级的普查区，可以用来绘制 HCV 高危人群的地图，否则这些人群可能无法被识别。这将为 HCV 高危患者的未来有针对性的筛查和链接到护理干预提供依据。
