Jillian Strayhorn
Jillian Strayhorn
Scroll
Assistant Professor of Social and Behavioral Sciences
-
Professional overview
-
Jillian C. Strayhorn, PhD is an Assistant Professor in the Department of Social and Behavioral Sciences at GPH and Associate Director of its Center for the Advancement and Dissemination of Intervention Optimization (cadio). She is a quantitative methodologist and decision scientist whose research focuses on the complex multi-criteria decision-making that goes into optimizing multicomponent interventions to achieve public health impact.
Dr. Strayhorn is an expert on the multiphase optimization strategy (MOST), a framework for optimizing behavioral, biobehavioral, and social-structural interventions. Her work in intervention optimization is highly interdisciplinary, bringing together ideas and methods from Bayesian statistics, health economics and multi-criteria decision analysis. The driving mission of this work is to enable more successful identification and advancement of high-value interventions capable of accomplishing complex objectives, including objectives that involve multiple outcomes, efficiency of resource use, or health equity. Dr. Strayhorn collaborates on applications of MOST across various areas of public health, including cancer risk reduction, smoking cessation, HIV, substance misuse, and mental health, among others.
Dr. Strayhorn earned her BA in Psychology, summa cum laude with distinction in all subjects, at Cornell University, and her PhD in Human Development and Family Studies at Pennsylvania State University, where she was the recipient of a Ruth L. Kirschstein NRSA predoctoral award (F31) from the National Institute on Drug Abuse . Her latest work has been published in Psychological Methods, Health Psychology, and Translational Behavioral Medicine.
-
Education
-
BA, Psychology, Cornell University, Ithaca, NYMS, Human Development and Family Studies, Pennsylvania State University, University Park, PAPhD, Human Development and Family Studies, Pennsylvania State University, University Park, PA
-
Honors and awards
-
Alumni Association Dissertation Award, Pennsylvania State University (2022)Student Optimization of Behavioral and Biobehavioral Interventions Research Award, Society of Behavioral Medicine (2021)Merrill Presidential Scholar Award, Cornell University (2014)Phi Beta Kappa Junior Inductee, Cornell University (2013)Robinson-Appel Humanitarian Award, Cornell University (2013)
-
Publications
Publications
Optimizing Multicomponent Interventions to Accomplish a Strategic Balance of Effectiveness and Ready Implementability: Latest Advances in the Multiphase Optimization Strategy.
AbstractStrayhorn, J., Collins, L., & Vanness, D. (n.d.).Publication year
2023AbstractMany of the behavioral and biobehavioral interventions with the greatest potential for public health impact are multicomponent, containing different intervention components intended to work together to produce one or more preferred health outcomes. However, for the potential of multicomponent interventions to be realized, these interventions must be not only effective on preferred outcomes but also readily implementable (i.e., affordable, scalable, and otherwise efficient in their use of the available resources). Components incur costs (in money, time, and/or any other limited resource), and building a multicomponent intervention that strategically balances effectiveness and cost(s) poses a complex decision problem. In this symposium, we demonstrate the latest advances in how the multiphase optimization strategy (MOST) can be used to approach this decision problem empirically. In particular, we show how the optimization randomized control trial, which usually (but not always) uses a design from the factorial family of experiments, i) estimates individual and combined effects for candidate intervention components and ii) illuminates tradeoffs between effectiveness and cost. Using three presentations, we describe an intervention optimization perspective for deciding which components merit inclusion in multicomponent interventions; demonstrate the latest advances in decision-making methods in MOST, as applied to make decisions about candidate components in an empirical example from HIV care; and offer our perspective on the important contrast between optimizing for a strategic balance of effectiveness and cost and using cost-effectiveness analysis to make policy decisions about interventions. We reserve time at the end for questions and open discussion.Raising expectations for D&I science: Intervention optimization as an opportunity to move toward implementability and equitability.
AbstractStrayhorn, J., Collins, L., Guastaferro, K., & Shelley, D. (n.d.).Publication year
2023AbstractThe emerging field of intervention optimization offers implementation science opportunities in several areas. In this session we offer an orientation to intervention optimization via the multiphase optimization strategy (MOST), followed by two presentations highlighting recent methodological developments in the intersection between intervention optimization and implementation science: first, in the optimization of multicomponent implementation strategies, and second, in optimizing to achieve not only effectiveness and implementability but also equitability (and even, we suggest, equitability in implementation). Panel chair Dr. Linda M. Collins and discussant Dr. Donna Shelley will then lead a discussion on implications and next steps for D&I science.Religiosity and teen birth rate in the United States
AbstractStrayhorn, J., Strayhorn, J. M., & Strayhorn, J. C. (n.d.).Publication year
2009Journal title
Reproductive healthVolume
6Page(s)
14AbstractThe children of teen mothers have been reported to have higher rates of several unfavorable mental health outcomes. Past research suggests several possible mechanisms for an association between religiosity and teen birth rate in communities.The multiphase optimization strategy (MOST) in child maltreatment prevention research
AbstractStrayhorn, J., Guastaferro, K., Strayhorn, J. C., & Collins, L. M. (n.d.).Publication year
2021Journal title
Journal of child and family studiesVolume
30Issue
10Page(s)
2481-2491AbstractEach year hundreds of thousands of children and families receive behavioral interventions designed to prevent child maltreatment; yet rates of maltreatment have not declined in over a decade. To reduce the prevalence and prevent the life-long negative consequences of child maltreatment, behavioral interventions must not only be effective, but also affordable, scalable, and efficient to meet the demand for these services. An innovative approach to intervention science is needed. The purpose of this article is to introduce the multiphase optimization strategy (MOST) to the field of child maltreatment prevention. MOST is an engineering-inspired framework for developing, optimizing, and evaluating multicomponent behavioral interventions. MOST enables intervention scientists to empirically examine the performance of each intervention component, independently and in combination. Using a hypothetical example of a home visiting intervention and artificial data, this article demonstrates how MOST may be used to optimize the of a parent-focused in-home intervention and the strategies of an intervention to increase completion rate to identify an intervention that is effective, efficient, economical, and scalable. We suggest that MOST will ultimately improve prevention science and hasten the progress of translational science to prevent child maltreatment.Using decision analysis for intervention value efficiency to select optimized interventions in the multiphase optimization strategy
AbstractStrayhorn, J., Strayhorn, J. C., Cleland, C. M., Vanness, D. J., Wilton, L., Gwadz, M., & Collins, L. M. (n.d.).Publication year
2024Journal title
Health psychology : official journal of the Division of Health Psychology, American Psychological AssociationVolume
43Issue
2Page(s)
89-100AbstractOptimizing multicomponent behavioral and biobehavioral interventions presents a complex decision problem. To arrive at an intervention that is both effective and readily implementable, it may be necessary to weigh effectiveness against implementability when deciding which components to select for inclusion. Different components may have differential effectiveness on an array of outcome variables. Moreover, different decision-makers will approach this problem with different objectives and preferences. Recent advances in decision-making methodology in the multiphase optimization strategy (MOST) have opened new possibilities for intervention scientists to optimize interventions based on a wide variety of decision-maker preferences, including those that involve multiple outcome variables. In this study, we introduce decision analysis for intervention value efficiency (DAIVE), a decision-making framework for use in MOST that incorporates these new decision-making methods. We apply DAIVE to select optimized interventions based on empirical data from a factorial optimization trial.Using factorial mediation analysis to better understand the effects of interventions
AbstractStrayhorn, J., Strayhorn, J. C., Collins, L. M., Brick, T. R., Marchese, S. H., Pfammatter, A. F., Pellegrini, C., & Spring, B. (n.d.).Publication year
2022Journal title
Translational behavioral medicineVolume
12Issue
1AbstractTo improve understanding of how interventions work or why they do not work, there is need for methods of testing hypotheses about the causal mechanisms underlying the individual and combined effects of the components that make up interventions. Factorial mediation analysis, i.e., mediation analysis applied to data from a factorial optimization trial, enables testing such hypotheses. In this commentary, we demonstrate how factorial mediation analysis can contribute detailed information about an intervention's causal mechanisms. We briefly review the multiphase optimization strategy (MOST) and the factorial experiment. We use an empirical example from a 25 factorial optimization trial to demonstrate how factorial mediation analysis opens possibilities for better understanding the individual and combined effects of intervention components. Factorial mediation analysis has important potential to advance theory about interventions and to inform intervention improvements.Value-efficiency: A tool for selecting optimized interventions
AbstractStrayhorn, J., & Vanness, D. (n.d.).Publication year
2025Journal title
Annals of Behavioral MedicineAbstract~