. 2024 Jan;3(1):100759.

doi: 10.1016/j.jacadv.2023.100759. Epub 2023 Dec 13.

The Science of Precision Prevention: Research Opportunities and Clinical Applications to Reduce Cardiovascular Health Disparities

Thomas A Pearson¹, Debbie Vitalis², Charlotte Pratt², Rebecca Campo², Antonis A Armoundas³, David Au⁴, Bettina Beech⁵, Olga Brazhnik², Christopher G Chute⁶, Karina W Davidson^{7

8}, Ana V Diez-Roux⁹, Lawrence J Fine², Davera Gabriel¹⁰, Peter Groenveld¹¹, Jaclyn Hall¹², Alison B Hamilton¹³, Hui Hu¹⁴, Heng Ji¹⁵, Amy Kind¹⁶, William E Kraus¹⁷, Harlan Krumholz¹⁸, George A Mensah¹⁹, Raina M Merchant¹¹, Dariush Mozaffarian²⁰, David M Murray²¹, Dianne Neumark-Sztainer²², Maya Petersen^{23

24}, David Goff²

Affiliations

¹ College of Medicine and College of Public Health and Health Professions, University of Florida Health Science Center, Gainesville, Florida, USA.
² Division of Cardiovascular Sciences, National Heart, Lung, and Blood Institute, National Institutes of Health, Bethesda, Maryland, USA.
³ Cardiovascular Research Center, Massachusetts General Hospital and Broad Institute, Massachusetts Institute of Technology, Cambridge, Massachusetts, USA.
⁴ Center of Innovation for Veteran-Centered and Value-Driven Care, University of Washington, Seattle, Washington, USA.
⁵ UH Population Health, University of Houston, Houston, Texas, USA.
⁶ Johns Hopkins Medicine, Institute for Clinical and Translational Research, Baltimore, Maryland, USA.
⁷ Institute of Health System Science, Feinstein Institutes for Medical Research, Northwell Health, New Hyde Park, New York, USA.
⁸ Feinstein Institutes for Medical Research, Northwell Health, Manhasset, New York, USA.
⁹ Urban Health Collaborative, Drexel Dornsife School of Public Health, Philadelphia, Pennsylvania, USA.
¹⁰ Biomedical Informatics and Data Science Section, Johns Hopkins University School of Medicine, Baltimore, Maryland, USA.
¹¹ Center for Health Care Transformation and Innovation, University of Pennsylvania Perelman School of Medicine, Philadelphia, Pennsylvania, USA.
¹² Department of Health Outcomes and Biomedical Informatics, Institute for Child Health Policy, College of Medicine, University of Florida, Gainesville, Florida, USA.
¹³ Center for the Study of Healthcare Innovation, Implementation & Policy, VA Greater Los Angeles Healthcare System, Los Angeles, California, USA.
¹⁴ Channing Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, Massachusetts, USA.
¹⁵ Department of Computer Science, University of Illinois Urbana-Champaign, Champaign, Illinois, USA.
¹⁶ Center for Health Disparities Research (CHDR), University of Wisconsin School of Medicine and Public Health, Madison, Wisconsin, USA.
¹⁷ Duke Molecular Physiology Institute, School of Medicine, Duke University, Durham, North Carolina, USA.
¹⁸ Institute for Social and Policy Studies, of Investigative Medicine and of Public Health (Health Policy), Yale University, New Haven, Connecticut, USA.
¹⁹ Center for Translation Research and Implementation Science, National Heart, Lung, and Blood Institute, National Institutes of Health, Bethesda, Maryland, USA.
²⁰ Friedman School of Nutrition Science & Policy, Tufts University, Medford, Massachusetts, USA.
²¹ Office of Disease Prevention, National Institutes of Health, Bethesda, Maryland, USA.
²² Division of Epidemiology and Community Health, School of Public Health, University of Minnesota, Minneapolis, Minnesota, USA.
²³ Division of Biostatistics, and UCSF-UC Berkeley Program in Computational Precision Health, School of Public Health, University of California-Berkeley, Berkeley, California, USA.
²⁴ University of California-San Francisco, San Francisco, California, USA.

PMID: 38375059
PMCID: PMC10876066
DOI: 10.1016/j.jacadv.2023.100759

The Science of Precision Prevention: Research Opportunities and Clinical Applications to Reduce Cardiovascular Health Disparities

Thomas A Pearson et al. JACC Adv. 2024 Jan.

. 2024 Jan;3(1):100759.

doi: 10.1016/j.jacadv.2023.100759. Epub 2023 Dec 13.

Authors

Affiliations

¹ College of Medicine and College of Public Health and Health Professions, University of Florida Health Science Center, Gainesville, Florida, USA.
² Division of Cardiovascular Sciences, National Heart, Lung, and Blood Institute, National Institutes of Health, Bethesda, Maryland, USA.
³ Cardiovascular Research Center, Massachusetts General Hospital and Broad Institute, Massachusetts Institute of Technology, Cambridge, Massachusetts, USA.
⁴ Center of Innovation for Veteran-Centered and Value-Driven Care, University of Washington, Seattle, Washington, USA.
⁵ UH Population Health, University of Houston, Houston, Texas, USA.
⁶ Johns Hopkins Medicine, Institute for Clinical and Translational Research, Baltimore, Maryland, USA.
⁷ Institute of Health System Science, Feinstein Institutes for Medical Research, Northwell Health, New Hyde Park, New York, USA.
⁸ Feinstein Institutes for Medical Research, Northwell Health, Manhasset, New York, USA.
⁹ Urban Health Collaborative, Drexel Dornsife School of Public Health, Philadelphia, Pennsylvania, USA.
¹⁰ Biomedical Informatics and Data Science Section, Johns Hopkins University School of Medicine, Baltimore, Maryland, USA.
¹¹ Center for Health Care Transformation and Innovation, University of Pennsylvania Perelman School of Medicine, Philadelphia, Pennsylvania, USA.
¹² Department of Health Outcomes and Biomedical Informatics, Institute for Child Health Policy, College of Medicine, University of Florida, Gainesville, Florida, USA.
¹³ Center for the Study of Healthcare Innovation, Implementation & Policy, VA Greater Los Angeles Healthcare System, Los Angeles, California, USA.
¹⁴ Channing Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, Massachusetts, USA.
¹⁵ Department of Computer Science, University of Illinois Urbana-Champaign, Champaign, Illinois, USA.
¹⁶ Center for Health Disparities Research (CHDR), University of Wisconsin School of Medicine and Public Health, Madison, Wisconsin, USA.
¹⁷ Duke Molecular Physiology Institute, School of Medicine, Duke University, Durham, North Carolina, USA.
¹⁸ Institute for Social and Policy Studies, of Investigative Medicine and of Public Health (Health Policy), Yale University, New Haven, Connecticut, USA.
¹⁹ Center for Translation Research and Implementation Science, National Heart, Lung, and Blood Institute, National Institutes of Health, Bethesda, Maryland, USA.
²⁰ Friedman School of Nutrition Science & Policy, Tufts University, Medford, Massachusetts, USA.
²¹ Office of Disease Prevention, National Institutes of Health, Bethesda, Maryland, USA.
²² Division of Epidemiology and Community Health, School of Public Health, University of Minnesota, Minneapolis, Minnesota, USA.
²³ Division of Biostatistics, and UCSF-UC Berkeley Program in Computational Precision Health, School of Public Health, University of California-Berkeley, Berkeley, California, USA.
²⁴ University of California-San Francisco, San Francisco, California, USA.

PMID: 38375059
PMCID: PMC10876066
DOI: 10.1016/j.jacadv.2023.100759

Abstract

Precision prevention embraces personalized prevention but includes broader factors such as social determinants of health to improve cardiovascular health. The quality, quantity, precision, and diversity of data relatable to individuals and communities continue to expand. New analytical methods can be applied to these data to create tools to attribute risk, which may allow a better understanding of cardiovascular health disparities. Interventions using these analytic tools should be evaluated to establish feasibility and efficacy for addressing cardiovascular disease disparities in diverse individuals and communities. Training in these approaches is important to create the next generation of scientists and practitioners in precision prevention. This state-of-the-art review is based on a workshop convened to identify current gaps in knowledge and methods used in precision prevention intervention research, discuss opportunities to expand trials of implementation science to close the health equity gaps, and expand the education and training of a diverse precision prevention workforce.

Keywords: data science; health equity; health promotion; implementation science; personalized medicine; precision analytics.

PubMed Disclaimer

Conflict of interest statement

The views expressed are those of the authors and do not necessarily reflect the views of the National Heart, Lung, and Blood Institute; the National Institutes of Health; or the U.S. Department of Health and Human Services. The authors have reported that they have no relationships relevant to the contents of this paper to disclose.

Figures

**Figure 1**
**Precision Health as the Data-driven Merger of Precision Medicine and Precision Public Health** Source: Pearson TA, Califf RM, Roper R, et al. Precision health analytics with predictive analytics and implementation research: JACC state-of-the-art review. *J Am Coll Cardiol*. 2020;76(3):306-320.

**Figure 2**
**Intersection of 3-Dimensions as the Conceptual Framework for Precision Prevention** Source: Division of Cardiovascular Sciences/National Heart, Lung, and Blood Institute.

**Figure 3**
**The National Institute of Minority Health Research Framework Including Domains and Levels of Influence** Source: NIMHD Research Framework Details, https://www.nimhd.nih.gov/about/overview/research-framework/nimhd-framework.html.

**Figure 4**
**Conceptual Model of Pathways in Which Residential Segregation Can Lead to Poor Health Outcomes** Source: Dr Ana V. Diez-Roux.

**Figure 5**
**Methodological Challenges in Health Studies of Spatial and Contextual Exposomes** Source: Hu H, Liu X, Zheng Y, et al. Methodological challenges in spatial and contextual exposome-health studies. *Crit Rev Environ Sci Technol*. 2023;53(7):827-846.

**Figure 6**
**Role of NIH BioData Catalyst Consortium Using Data Harmonization to Discover Diagnostic Tools and Therapeutic Options** Source: NHLBI BioData Catalyst Consortium. Presented at the Science of Precision Prevention: Research Opportunities and Clinical Applications to Reduce Disparities in Cardiovascular Health Workshop on December 5, 2022.

**Figure 7**
**Stakeholders and Benefits of Trustworthy Artificial Intelligence/Machine Learning in Health Care** Source: Designed by NHLBI Summer Fellow Erik Rozi and his mentor Dr Asif Rizwan.

**Figure 8**
**Modified Consolidated Framework for Implementation Research for Studying Process Design** Source: Rojas Smith L, Ashok M, Morss Dy S, et al. Contextual frameworks for research on the implementation of complex system Interventions. Rockville, MD: Agency for Healthcare Research and Quality; 2014. Report No. 14-EHC014-EF. https://www.ncdbi.nlm.nih.gov/books/NBK196199/pdf/Bookshelf_NBK196199.pdf.

**Figure 9**
**The NIH Stages Model of Behavior Intervention Development** Source: National Institute on Aging. NIH Stage Model for Behavioral Intervention Development. https://www.nia.nih.gov/research/dbsr/nih-stage-model-behavioral-intervention-development.

**Figure 10**
**Interaction of Genetics and Pharmacology in Personalized Lifestyle Medicine** Source: Genomic and Precision Medicine: Cardiovascular Disease, ISBN:9780128018125, 2017, Ginsburg et al, Chapter 2, Figure 2.

**Central Illustration**
**Dimensions of PrecisionPrevention**

See this image and copyright information in PMC

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The Science of Precision Prevention: Research Opportunities and Clinical Applications to Reduce Cardiovascular Health Disparities

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The Science of Precision Prevention: Research Opportunities and Clinical Applications to Reduce Cardiovascular Health Disparities

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