Health Informatics to Model the Scott County HIV Outbreak

摘要 美国农村地区的新冠肺炎病例和死亡人数正在激增。在四月的最后两周 2020年，美国农村地区每10万人平均罹患新冠肺炎的人数上升了125%，但只有 在城市县占68%。在同一时期，农村地区与新冠肺炎相关的死亡人数上升了169% 相比之下，城市地区的这一比例为113%。截至2020年5月4日，格鲁吉亚有28,945例新冠肺炎和 1,186例与新冠肺炎相关的死亡。佐治亚州是一个以农村为主的州。农村艾滋病毒携带者(PLHIV) 新冠肺炎面临风险，部分原因是免疫系统受损和高并存健康率 条件。患有共病物质使用障碍(SODS)的农村PLHIV患COVID的风险特别高。 19感染。许多农村PLHIV+肥皂水缺乏获得医疗和心理护理的机会，必须四处旅行 接受HIV和SUD治疗的距离，经历情绪障碍的高比率，以及经历 与他们的艾滋病毒状况、性认同和性功能障碍有关的歧视、偏见和耻辱。乔治亚州是一个 研究新冠肺炎在农村PLHIV+肥皂病中的风险的合适状态。2017年，佐治亚州是最高的 任何州每10万居民的艾滋病毒流行率。目前，佐治亚州的新冠肺炎数量排名第12 5例，因新冠肺炎而住院，但新冠肺炎检测率在全国排名第7。这 该研究的科学前提是，到目前为止，大多数新冠肺炎研究都是在城市中心进行的；很少 已知(I)农村PLHIV+肥皂水中新冠肺炎的比率，(Ii)在这一地区预测新冠肺炎感染的因素 以及(Iii)农村PLHIV+肥皂水参与以避免感染的预防行为类型。这 这项研究将汇集佐治亚州东北部100名农村艾滋病病毒感染者的前瞻性纵向队列，其中50% 有活跃的SUD(最有可能是阿片类药物使用障碍)。这项研究将在佐治亚州的卫生区进行 10个，其中10个县全部被归类为农村，9个是精神卫生专业人员短缺地区，8个 是初级保健专业人员短缺的地区。为了最大限度地提高参与者的安全，所有数据都将使用 创新远程评估方法。在威尔逊和克利里的生活质量模型的指导下，参与者 将在基线和3个月、6个月和9个月的随访中完成评估，收集：(1)生物数据：CD4 计数、艾滋病毒病毒载量、病毒性肝炎状况；(2)行为/心理数据：烟草和大麻使用、蒸发 做法、抑郁症状、应对新冠肺炎相关压力的方式和应对自我效能；以及 (3)环境数据：住房状况和惩教系统参与情况。调查还将评估 新冠肺炎预防行为数据(例如，用肥皂和水洗手；社交距离)。分析 将在新冠肺炎预防方面将农村PLHIV+肥皂与不使用肥皂的农村PLHIV进行比较 行为。Logistic回归分析将确定这一组中新冠肺炎感染的预测因素。 研究结果可以为新冠肺炎农村艾滋病预防干预措施的发展提供信息，并可能 将干预措施与农村艾滋病毒+肥胖症的独特需求联系起来。

ABSTRACT COVID-19 cases and deaths are surging in rural areas of the United States. In the last two weeks of April 2020, the average number of COVID-19 cases per 100,000 persons rose 125% in rural U.S. counties but only 68% in urban counties. During this same time period, COVID-19 related deaths rose 169% in rural areas compared to 113% in urban areas. As of May 4 2020, Georgia had 28,945 confirmed cases of COVID-19 and 1,186 COVID-19-related deaths. Georgia is a predominantly rural state. Rural people living with HIV (PLHIV) are at-risk for COVID-19 due, in part, to compromised immune systems and high rates of comorbid health conditions. Rural PLHIV with comorbid substance use disorders (SUDs) are at particularly high risk for COVID- 19 infection. Many rural PLHIV + SUDs lack access to medical and psychological care, must travel vast distances to receive HIV and SUD treatments, experience high rates of mood disorders, and experience discrimination, prejudice, and stigma related to their HIV-status, sexual-identity, and SUD. Georgia is an opportune state in which to study risk for COVID-19 in rural PLHIV + SUDs. In 2017, Georgia had the highest HIV prevalence rate per 100,000 residents of any state. Currently, Georgia ranks 12th in number of COVID-19 cases, 5th in hospitalizations due to COVID-19, but has the nation's 7th slowest COVID-19 testing rate. This study's scientific premise is that, to date, most COVID-19 research has been conducted in urban centers; little is known about (i) rates of COVID-19 in rural PLHIV + SUDs, (ii) factors predictive of COVID-19 infection in this group, and (iii) types of preventive behaviors in which rural PLHIV + SUDs engage to avoid infection. This study will assemble a prospective longitudinal cohort of 100 rural PLHIV in northeast Georgia, 50% of whom have an active SUD (most likely opioid use disorders). The study will be conducted in Georgia's Health District 10, in which all ten counties are classified as “rural,” 9 are mental health professional shortage areas, and 8 are primary care professional shortage areas. To maximize participant safety, all data will be collected using innovative remote assessment methodologies. Guided by Wilson and Cleary's model of life quality, participants will complete assessments at baseline and 3-, 6- and 9-month follow-up that collect: (1) biologic data: CD4 count, HIV viral load, viral hepatitis status; (2) behavioral/psychosocial data: tobacco and marijuana use, vaping practices, depressive symptoms, ways of coping with COVID-19-related stress, and coping self-efficacy; and (3) environmental data: housing status and correctional systems involvement. Surveys will also assess COVID-19 prevention behavior data (e.g., washing hands with soap and water; social distancing). Analyses will compare rural PLHIV + SUDs to rural PLHIV without SUDs on rates of engaging in COVID-19 preventive behaviors. Logistic regression analyses will identify factors predictive of COVID-19 infection in this group. Study findings can inform the development of COVID-19 prevention interventions for rural PLHIV and possibly contextualize interventions for the unique needs of rural PLHIV + SUDs.