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).
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, the standard dichotomous measures of retention (e.g., in/out of care) are coarse classifications that ignore the diversity and complexity of individual care trajectories. The goal of this study is to describe these complex patterns of care and identify the most common HIV care trajectories as a more nuanced, data-driven approach to classifying patients into different 'care classes.' We will evaluate how health outcomes differ by care class as well as which barriers to care are typically associated with each class, which will inform interventions that are targeted and tailored to HIV+ patients (and care patterns) at greatest risk for adverse outcomes.
|Alarid-Escudero, Fernando; MacLehose, Richard F; Peralta, Yadira et al. (2018) Nonidentifiability in Model Calibration and Implications for Medical Decision Making. Med Decis Making 38:810-821|
|Easterly, C W; Alarid-Escudero, F; Enns, E A et al. (2018) Revisiting assumptions about age-based mixing representations in mathematical models of sexually transmitted infections. Vaccine 36:5572-5579|
|Alarid-Escudero, F; Enns, E A; MacLehose, R F et al. (2018) Force of infection of Helicobacter pylori in Mexico: evidence from a national survey using a hierarchical Bayesian model. Epidemiol Infect 146:961-969|
|Krijkamp, Eline M; Alarid-Escudero, Fernando; Enns, Eva A et al. (2018) Microsimulation Modeling for Health Decision Sciences Using R: A Tutorial. Med Decis Making 38:400-422|
|Enns, Eva Andrea; Kao, Szu-Yu; Kozhimannil, Katy Backes et al. (2017) Using Multiple Outcomes of Sexual Behavior to Provide Insights Into Chlamydia Transmission and the Effectiveness of Prevention Interventions in Adolescents. Sex Transm Dis 44:619-626|
|Enns, Eva A; Reilly, Cavan S; Virnig, Beth A et al. (2016) Potential Impact of Integrating HIV Surveillance and Clinic Data on Retention-in-Care Estimates and Re-Engagement Efforts. AIDS Patient Care STDS 30:409-15|