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Integrating Data and Implementation Science to Accelerate HIV Prevention, Treatment, and Care


Lori A. J. Scott-Sheldon, Ph.D.
Division of AIDS Research


This concept aims to encourage participatory data science and implementation science research to accelerate HIV prevention, treatment, and care.


Rapidly expanding data, tools, and technologies provide a unique opportunity to advance HIV research by forecasting the anticipated benefits and consequences of evidence-based interventions prior to implementation. The promise of using “big data” to more precisely identify the prevention, diagnosis, treatment, and response needed to end the HIV epidemic has been identified by the U.S. Department of Health and Human Services, but these data-driven insights must also be considered in light of the complex social-environmental context in which people live, work, and interact with others. Optimizing data-driven discoveries by engaging community members, and using implementation science methods, is critical for delivering precision public health that will have a meaningful impact on the HIV epidemic.

Conventional data and implementation science research efforts have focused on identifying strategies for improving the uptake of evidence-based HIV interventions, but these scientific efforts have largely operated in isolation which hinders scientific discovery. Complex public health problems require collaborative and cross-disciplinary approaches to understand the interplay of dynamic and evolving individual, community, social, and structural factors that influence HIV outcomes and disparities over time and allow for a deeper understanding of the strategies associated with improved HIV outcomes. System science approaches that include stakeholders are critical for ensuring that HIV prevention and treatment strategies are rooted in the local context, address local priorities, and have real-world impact. Collaborative, participatory, and team-based system science research can leverage the strengths and expertise of a diverse group of stakeholders, implementing partners, and data and implementation scientists to accelerate efforts to end the HIV epidemic.

This concept aims to challenge conventional data and implementation science approaches by encouraging HIV research that will promote participatory data and implementation science research with the goals of (1) using data science methods to model complex systems, including the social and structural determinants of health, that help identify more targeted HIV prevention, treatment, and care interventions and implementation strategies; (2) using novel measurement approaches and modern statistical methods to evaluate the implementation of data-driven discoveries; and (3) integrating meaningful engagement of community and implementing partners at every stage from problem definition, model development, testing, and evaluation, and intervention implementation. Inherent in this concept is the need to integrate team science and system science principles and practices that clearly identify the prevention, diagnosis, treatment, and response required for a meaningful and sustained impact on the HIV epidemic. NIMH encourages team science and system science methods to address HIV and mental health priorities across the prevention and care continuum.