Population Health Science and the Challenges of Prediction
Authors: Sandro Galea, Katherine M. Keyes
Journal: Annals of Internal Medicine (2017)

Editorials3 October 2017Population Health Science and the Challenges of PredictionSandro Galea, MD, DrPH and Katherine M. Keyes, PhDSandro Galea, MD, DrPHFrom Boston University, Boston, Massachusetts, and Columbia University, New York, New York.Search for more papers by this author and Katherine M. Keyes, PhDFrom Boston University, Boston, Massachusetts, and Columbia University, New York, New York.Search for more papers by this authorAuthor, Article, and Disclosure Informationhttps://doi.org/10.7326/M17-1733 SectionsAboutFull TextPDF ToolsAdd to favoritesDownload CitationsTrack CitationsPermissions ShareFacebookTwitterLinkedInRedditEmail Peering into the future to accurately predict health outcomes is challenging and imprecise. Yet, this has not stopped the development of a broad array of models to predict future disease. The widespread availability of regression modeling approaches has made it seem easy to develop prediction models. A substantial body of literature documents the development, calibration, and validation of risk prediction models using large data sets. Typically, these models use individual patient risk factors to estimate the probability of a future health outcome. Patient-friendly Web sites provide tools that enable patients to calculate their own risk for such conditions as cardiovascular ...References1. Keyes KM, Galea S. Population Health Science. Oxford, UK: Oxford Univ Pr; 2016. Google Scholar2. Dalton JE, Perzynski AT, Zidar DA, Rothberg MB, Coulton CJ, Milinovich AT, et al. Accuracy of cardiovascular risk prediction varies by neighborhood socioeconomic position. A retrospective cohort study. Ann Intern Med. 2017;167:456-64. doi:10.7326/M16-2543 LinkGoogle Scholar3. Kuh D, Ben-Shlomo Y, Lynch J, Hallqvist J, Power C. Life course epidemiology. J Epidemiol Community Health. 2003;57:778-83. [PMID: 14573579] CrossrefMedlineGoogle Scholar4. Kaplan GA. What is the role of the social environment in understanding inequalities in health? Ann N Y Acad Sci. 1999;896:116-9. [PMID: 10681892] CrossrefMedlineGoogle Scholar5. Rothman KJ. Synergy and antagonism in cause-effect relationships. Am J Epidemiol. 1974;99:385-8. [PMID: 4841816] CrossrefMedlineGoogle Scholar6. Keyes KM, Galea S. Setting the Agenda for a New Discipline: Population Health Science [Editorial]. Am J Public Health. 2016;106:633-4. [PMID: 26959265] doi:10.2105/AJPH.2016.303101 CrossrefMedlineGoogle Scholar7. Keyes KM, Smith GD, Koenen KC, Galea S. The mathematical limits of genetic prediction for complex chronic disease. J Epidemiol Community Health. 2015;69:574-9. [PMID: 25648993] doi:10.1136/jech-2014-204983 CrossrefMedlineGoogle Scholar Author, Article, and Disclosure InformationAffiliations: From Boston University, Boston, Massachusetts, and Columbia University, New York, New York.Disclosures: Authors have disclosed no conflicts of interest. Forms can be viewed at www.acponline.org/authors/icmje/ConflictOfInterestForms.do?msNum=M17-1733.Corresponding Author: Sandro Galea, MD, DrPH, Boston University of Public Health, 715 Albany Street, Boston, MA 02118; e-mail, [email protected]edu.Current Author Addresses: Dr. Galea: Boston University School of Public Health, 715 Albany Street, Boston, MA 02118.Dr. Keyes: Columbia University Mailman School of Health, 722 West 168th Street, 724, New York, NY 10032.This article was published at Annals.org on 29 August 2017. PreviousarticleNextarticle Advertisement FiguresReferencesRelatedDetailsSee AlsoAccuracy of Cardiovascular Risk Prediction Varies by Neighborhood Socioeconomic Position Jarrod E. Dalton , Adam T. Perzynski , David A. Zidar , Michael B. Rothberg , Claudia J. Coulton , Alex T. Milinovich , Douglas Einstadter , James K. Karichu , and Neal V. Dawson Metrics Cited byA Machine Learning Approach to Predicting New‐onset Depression in a Military PopulationMethodological Challenges for Risk Prediction in Perinatal EpidemiologyOn Creating Positive Spillovers to Improve the Health of Populations: A Public Health of Consequence, February 2018 3 October 2017Volume 167, Issue 7Page: 511-512KeywordsBehaviorForecastingGenetic diseasesHeartMedical risk factorsPopulation geneticsPredictive modelingPrevention, policy, and public healthPublic policyRisk ratio ePublished: 29 August 2017 Issue Published: 3 October 2017 Copyright & PermissionsCopyright © 2017 by American College of Physicians. All Rights Reserved.PDF downloadLoading ...