Precision Prevention: Using Data to Target the Right Intervention at the Right Intensity in the Right Community at the Right Time

Abstract

Objectives: This survey paper summarizes the recent trend of "Precision Prevention" in public health, focusing on significant developments in informatics to enable targeted prevention and improved public health.

Methods: Given relatively limited use of the term "Precision Prevention" in the literature to date, com-bined with significant developments in this space outside of peer reviewed literature, the topic was ill-suited for a systematic review approach. Instead, the co-authors used a narrative review approach, combining related search terms and complementary expertise to develop and refine sub-topics to be included. Each section was then written using a combination of prior knowledge and specific relevant search terms.

Results: The paper opens with an explanation of the term "precision prevention", including its origins and relationship to other concepts such as precision medicine. It then provides an overview of types of data relevant to precision prevention, as well as how those data are collected in different contexts and through different modalities. The authors then describe the HL7 Gravity Project, a multi-stakeholder public collaborative project aimed at data standardization in the social determinants space. Finally, the authors present how those data types are used across the spectrum from clinical care to target outreach for human services, to data-driven health policy.

Conclusions: Precision prevention, targeting the right intervention to the right population at the right time, is now recognized as of vital importance, particularly in light of the COVID-19 pandemic's spotlight on health disparities and societal consequences. Optimizing interventions targeted at different communities and populations will require novel and innovative collection, use, and dissemination of data, information, and knowledge. The talent and skills of the international informatics community are critical for success in this work.

Figure 1:

Relative contribution of socioeconomic factors, physical environment, health behaviors, and health care toward the health of an individual. (Figure source: https://coveragetoolkit.org/health-equity-and-the-national-dpp/defining-health-equity ). Prepared under Cooperative Agreement Number 5NU38OT000225-04, funded by the Centers for Disease Control and Prevention).

Figure 2:

The Social Determinants of Health (SDOH) Ecosystem, as depicted in [ 22 ].