Mathematical modeling and analysis of HIV care trajectories and retention in care challenges

 描述（由申请人提供）：伊娃恩斯，博士，是在卫生政策和管理在公共卫生的明尼苏达大学学院谁应用工程概念，如优化，资源分配和数学建模，以解决艾滋病毒和其他传染病的治疗和预防的挑战部门的助理教授。这个K25奖将支持博士恩斯与指导和培训，她需要有效和有意义地整合她的定量工程背景到一个独立的公共卫生研究事业。在数学建模、生物统计学和行为研究领域的多学科导师团队的指导下，恩斯博士将（1）通过应用生物统计学和数据分析技术的高级培训来补充她的定量工程背景，以更好地根据现有数据对数学模型进行参数化;（2）熟悉行为理论和研究方法，以更好地将健康行为和相关的背景驱动因素纳入数学模型;以及（3）应用这些技能来解决HIV护理中的保留问题，最终目标是制定R 01提案，以评估旨在改善整个HIV护理连续体结果的干预组合的成本效益。 恩斯博士的长期研究目标是通过确定资源的最佳分配来解决艾滋病毒护理，治疗和预防挑战，从而改善患者和人群的健康结果。这K25提案是关于改善艾滋病毒护理的保留，这仍然是美国有效艾滋病毒管理的主要挑战。研究一再发现，与定期、持续的护理相比，艾滋病毒初级护理的保留不佳与死亡率和其他并发症的风险增加有关。然而，二分法的入/出护理指定是粗略的分类，忽略了个体护理轨迹的多样性和复杂性。该提案的目的是：（1）利用先进的生物统计方法，对艾滋病毒阳性患者经历的不同艾滋病毒护理轨迹及其相关健康影响进行数据驱动的表征;（2）评估艾滋病毒护理轨迹与背景因素之间的关联通过对艾滋病毒阳性患者的调查，了解可能影响护理模式的（结构、诊所和个人）因素;以及（3）使用艾滋病毒传播、进展和参与护理的数学模型，检查不同艾滋病毒护理轨迹的人口水平影响以及干预措施的潜在影响。这项研究将提供一个全面的艾滋病毒护理动态的特点，将告知资源应如何有针对性的和量身定制的患者（和护理模式）在最大的风险为不利的结果。这些发现将为设计改善艾滋病毒阳性患者护理保留的干预措施提供信息，并提供一个框架，用于描述可应用于其他艾滋病毒护理环境或更广泛地应用于其他慢性疾病护理的护理模式（例如，糖尿病）。

 DESCRIPTION (provided by applicant): Eva Enns, PhD, is an Assistant Professor in the Division of Health Policy and Management at the University of Minnesota School of Public Health who applies engineering concepts, such as optimization, resource allocation, and mathematical modeling, to address treatment and prevention challenges for HIV and other infectious diseases. This K25 award will support Dr. Enns with the mentorship and training she needs to effectively and meaningfully integrate her quantitative engineering background into an independent public health research career. Under the guidance of a multidisciplinary mentorship team of leaders in mathematical modeling, biostatistics, and behavioral research, Dr. Enns will (1) complement her quantitative engineering background with advanced training in applied biostatistics and data analysis techniques to better parameterize mathematical models based on the available data; (2) become familiar with behavioral theories and research methods to better incorporate health behaviors and associated contextual drivers into mathematical models; and (3) apply these skills to address issues of retention in HIV care, with the ultimate goal of developing an R01 proposal to evaluate the cost-effectiveness of a portfolio of interventions aimed at improving outcomes across the HIV care continuum. Dr. Enns' long-term research objective is to improve patient and population health outcomes by identifying the optimal allocation of resources to address HIV care, treatment, and prevention challenges. This K25 proposal is concerned with improving retention in HIV care, which continues to be a major challenge to effective HIV management in the US. Studies have repeatedly found that sub-optimal retention in HIV primary care is associated with increased risks of mortality and other complications compared to regular, ongoing care. However, dichotomous in/out of care designations are coarse classifications that ignore the diversity and complexity of individual care trajectories. The aims of this proposal are: (1) to develop a data-driven characterization of the different HIV care trajectories experienced by HIV+ patients and their associated health impacts using advanced biostatistical methods; (2) to assess the association between HIV care trajectories and contextual factors (structural, clinic, and individual) that may influence care patterns through a survey of HIV+ patients; and (3) to examine the population-level implications of different HIV care trajectories and the potential impact of interventions using a mathematical model of HIV transmission, progression, and engagement in care. This study will provide a comprehensive characterization of HIV care dynamics that will inform how resources should be targeted and tailored to patients (and care patterns) at greatest risk for adverse outcomes. These findings will inform the design of interventions for improving retention in care among HIV+ patients, as well as providing a framework for characterizing patterns of care that could be applied to other HIV care settings or more broadly to the care of other chronic diseases (e.g., diabetes).