Health Informatics to Model the Scott County HIV Outbreak

摘要 新冠肺炎病例和死亡人数在美国农村地区激增。在四月的最后两周 2020年，美国农村县每10万人中的COVID-19病例平均数上升了125%，但仅 68%的城市。在同一时期，农村地区与COVID-19相关的死亡人数上升了169% 而在城市地区，这一比例为113%。截至2020年5月4日，格鲁吉亚有28,945例新冠肺炎确诊病例， 1，186例与COVID-19相关的死亡。格鲁吉亚是一个以农业为主的州。农村艾滋病毒感染者 部分由于免疫系统受损和高比例的共病健康， 条件农村艾滋病毒感染者与共病的物质使用障碍（SUD）是特别高的风险，新冠肺炎- 19感染许多农村艾滋病毒感染者+SUD缺乏获得医疗和心理护理的机会， 接受艾滋病毒和SUD治疗的距离，经历高比率的情绪障碍， 歧视，偏见和耻辱有关他们的艾滋病毒状况，性身份，和SUD。格鲁吉亚是一个 研究农村PLHIV + SUD中COVID-19风险的最佳状态。2017年，格鲁吉亚拥有最高的 任何州每10万居民的艾滋病毒感染率。目前，格鲁吉亚在COVID-19数量上排名第12位 病例，因COVID-19住院的第五位，但COVID-19检测率全国第七慢。这 这项研究的科学前提是，迄今为止，大多数COVID-19研究都是在城市中心进行的; 已知（i）农村PLHIV + SUD中COVID-19的发病率，（ii）该地区COVID-19感染的预测因素 组，以及（iii）农村PLHIV + SUD参与避免感染的预防行为类型。这 这项研究将在格鲁吉亚东北部的100名农村艾滋病毒感染者中进行前瞻性纵向队列研究，其中50%的人 有活动性SUD（最有可能的阿片类药物使用障碍）。这项研究将在格鲁吉亚的卫生区进行 10，其中所有10个县都被列为“农村”，9个是心理健康专业人员短缺地区，8个是 是初级保健专业人员短缺的地区。为了最大限度地提高受试者的安全性，所有数据将使用 创新的远程评估方法。在Wilson和Cleary的生活质量模型的指导下， 将在基线和3、6和9个月随访时完成评估，收集：（1）生物学数据：CD 4 计数、艾滋病毒病毒载量、病毒性肝炎状态;（2）行为/心理社会数据：烟草和大麻的使用、电子烟 实践、抑郁症状、应对COVID-19相关压力的方式以及应对自我效能;以及 (3)环境数据：住房状况和教养系统的参与情况。调查还将评估 COVID-19预防行为数据（例如，用肥皂和水洗手;保持社交距离）。分析 将比较农村PLHIV + SUD和农村PLHIV无SUD的参与COVID-19预防的比率 行为。逻辑回归分析将确定该组中COVID-19感染的预测因素。 研究结果可以为农村艾滋病毒感染者的COVID-19预防干预措施的制定提供信息， 根据农村艾滋病毒感染者+未成年人的独特需求制定干预措施。

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.