HIV Testing, Linkage and Retention in Care: Contextual Factors and Disparities

描述（由申请人提供）：本研究的目的是确定人们获得医疗保健的背景（即，健康护理环境）或生命（即，居住环境）和五项成果（艾滋病毒检测、接受tes结果、与艾滋病毒/艾滋病护理的联系、继续接受艾滋病毒护理和艾滋病毒病毒载量）中的每一项。艾滋病毒/艾滋病在少数种族/族裔（特别是黑人和拉丁美洲人）和老年人（即，50岁及以上）表明，他们可能会遇到障碍，导致艾滋病毒的早期检测和他们的艾滋病的差距。因此，我们还将检查种族/民族和年龄较大（年龄>50岁）的结果差异。 借鉴社会行为科学，该研究将为临床实践提供信息，以促进不同群体，社区条件和成年阶段的护理公平。本研究的主要目的是：（1）基于基层医疗患者的电子病历，采用Logistic回归多水平随机效应模型，检验基层医疗就诊期间医疗背景、居住背景和HIV检测之间的关系;和（2）分别审查保健环境、居住环境和接受艾滋病毒检测结果之间的关系以及与艾滋病毒/艾滋病护理的联系，使用具有随机效应的多水平逻辑回归和考克斯比例风险模型，在新诊断为艾滋病毒阳性的管理式护理登记者中。次要具体目标是确定是否与种族/民族或年龄相关（即，年龄<50 vs. >50岁）的差异存在于这些关系中。 基于我们在艾滋病毒检测和护理方面的工作， 在一个整合了公共卫生关键种族实践和医疗保健利用行为模型的模型的指导下，这项为期四年的研究基于该地区最大的管理式医疗机构注册的成年人的电子病历（EMR）。我们将汇总五年（2007-2011年）的数据，以检查所有接受初级保健的患者的艾滋病毒检测情况，并检查所有新诊断为艾滋病毒阳性的患者接受艾滋病毒检测结果的情况、与艾滋病毒/艾滋病护理的联系和在艾滋病毒/艾滋病护理中的保留情况以及艾滋病毒病毒载量。该研究将包括对患者居住环境的四个多层次分析（例如，邻里HIV流行率）和医疗保健环境（例如，在初级保健就诊期间进行艾滋病毒检测（目标1）、在被诊断为艾滋病毒阳性的人中获得艾滋病毒检测结果和与艾滋病毒/艾滋病护理的联系（目标2）、在艾滋病毒/艾滋病护理中的保留率和诊断后一年内的艾滋病毒RNA病毒载量方面，对患者人群的特征进行了评估。我们将在EMR中使用个人和地理空间代码链接到：（1）包含每个提供商详细信息的文件（例如，人口统计学，专业）;（2）来自疾病控制和预防中心的关于每个邮政编码的艾滋病毒流行率和艾滋病毒检测点的公共数据;（3）2010年人口普查社会经济数据（例如，集中的贫困）;以及（4）全球定位系统软件，用于计算从患者的家到其提供者的距离。这种跨学科、跨机构的合作使一个由新的和经验丰富的研究人员组成的多样化团队的专业知识得到了充分的利用。

DESCRIPTION (provided by applicant): The purpose of this study is to determine if associations exist between the contexts in which people obtain healthcare (i.e., healthcare context) or live (i.e., residential context) and each of five outcomes (HIV testing, receipt of tes results, linkage to HIV/AIDS care, retention in HIV care and HIV viral load). The epidemiology of HIV/AIDS among racial/ethnic minorities (specifically, Blacks and Latinos) and older adults (i.e., 50 and older) suggests they may encounter barriers that contribute to disparities in early detection of HIV and in their prognoses. Hence, we will also examine disparities in the outcomes by race/ethnicity and older age (age >50). Drawing on the socio-behavioral sciences, the study will inform clinical practice in ways that promote equity in care across diverse groups, neighborhood conditions and stages of adulthood. The study's primary Specific Aims are to: (1) Examine relations between healthcare context, residential context and HIV testing during primary care visits using logistic regression multilevel models with random effects based on primary care patients' electronic medical records; and, (2) Examine relations between healthcare context, residential context and receipt of HIV test results and linkage to HIV/AIDS care, respectively, among managed care enrollees newly diagnosed as HIV-positive using multilevel logistic regression and Cox proportional hazards models with random effects. The secondary Specific Aim is to determine if racial/ethnic- or age-related (i.e., aged <50 vs. >50 years) disparities exist in these relationships. Building on our work on HIV testing and care, and guided by a model integrating the Public Health Critical Race Praxis and Behavioral Model of Healthcare Utilization, the four-year study based on the electronic medical records (EMRs) of adults enrolled in the largest managed care organization in the region. We will pool data over five years (2007-2011) to examine HIV testing among all patients presenting for primary care, and to examine receipt of HIV test results, linkage to and retention in HIV/AIDS care as well as HIV viral load among all patients newly diagnosed as HIV-positive. The study will comprise four multilevel analyses of patients' residential contexts (e.g., neighborhood HIV prevalence) and healthcare contexts (e.g., characteristics of the patient population) relative to HIV testing durin primary care visits (Aim 1), receipt of HIV test results and linkage to HIV/AIDS care among those diagnosed as HIV-positive (Aim 2), retention in HIV/AIDS care and HIV RNA viral load up to one-year post diagnosis. We will use personal and geospatial codes in the EMRs to link to: (1) files containing detailed information on each provider (e.g., demographics, specialty); (2) public data from the Centers for Disease Control and Prevention on HIV prevalence and HIV test sites in each zip code; (3) 2010 Census socioeconomic data (e.g., concentrated poverty) for each zip code; and, (4) global positioning system software to calculate the distance from a patient's home to their provider. This interdisciplinary, inter- institutional collaboration leveraes the expertise of a diverse team of new and seasoned investigators.