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

HIV 护理轨迹的数学建模和分析以及护理挑战的保留

• 批准号: 9230340
• 负责人: Eva A Enns
• 金额: $ 13万
• 依托单位: UNIVERSITY OF MINNESOTA
• 依托单位国家: 美国
• 财政年份: 2015
• 资助国家: 美国
• 起止时间: 2015-03-01 至 2020-02-29
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9230340
• 关键词: AIDS/HIV problem; Address; Adverse event; Award; Behavior; Behavioral; Behavioral Research; Biometry; Biostatistical Methods; Budgets; Caring; Case Study; Chronic Disease; Classification; Clinic; Clinic Visits; Clinical; Communicable Diseases; Communities; Complement; Complex; Continuity of Patient Care; Cost Effectiveness Analysis; County; Data; Data Analyses; Data Set; Diabetes Mellitus; Diagnosis; Disease Progression; Doctor of Philosophy; Engineering; Frequencies; General Population; Goals; HIV; HIV diagnosis; Health; Health Care Costs; Health Policy; Health behavior; Housing; Imprisonment; Individual; Infection; Intervention; Label; Laboratories; Link; Mathematics; Measures; Medical; Medical center; Mentorship; Methods; Minnesota; Modeling; Morbidity - disease rate; Outcome; Patient Care; Patients; Pattern; Patterns of Care; Persons; Population; Prevalence; Prevention; Primary Health Care; Process; Provider; Public Health Schools; Quality of Care; Quality of life; Records; Research; Research Methodology; Resource Allocation; Resources; Risk; Specific qualifier value; Substance abuse problem; Surveys; Techniques; Testing; Time; Training; Transportation; Treatment Failure; Universities; Viral Load result; adverse outcome; antiretroviral therapy; base; career; contextual factors; cost; cost-effectiveness evaluation; design; experience; improved; improved outcome; mathematical analysis; mathematical model; migration; mortality; multidisciplinary; novel; patient oriented; patient population; population health; prevent; professor; public health relevance; public health research; retention rate; skills; theories; therapy design; transmission process; viral rebound

 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).