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Predictive risk algorithms in a population setting: an overview
  1. Douglas G Manuel1,2,3,4,5,6,7,
  2. Laura C Rosella6,7,8,
  3. Deirdre Hennessy1,2,
  4. Claudia Sanmartin2,
  5. Kumanan Wilson1,4,9
  1. 1Ottawa Hospital Research Institute, Ottawa, Ontario, Canada
  2. 2Health Analysis Division, Statistics Canada, Ottawa, Ontario, Canada
  3. 3The Department of Family Medicine, University of Ottawa, Ontario, Canada
  4. 4Department of Epidemiology and Community Medicine, University of Ottawa, Ontario, Canada
  5. 5C.T. Lamont Primary Health Care Research Centre and Bruyère Research Institute, Ottawa, Ontario, Canada
  6. 6The Institute for Clinical Evaluative Sciences, Toronto, Ontario, Canada
  7. 7Dalla Lana School of Public Health, University of Toronto, Toronto, Ontario, Canada
  8. 8Public Health Ontario, Toronto, Ontario
  9. 9Department of Medicine, University of Ottawa, Ontario, Canada
  1. Correspondence to Dr Douglas G Manuel, Ottawa Hospital Research Institute, Room 1-008 Administrative Services Building, 1053 Carling Ave. Ottawa ON K1Y 4E9, USA; dmanuel{at}ohri.ca

Abstract

Background The widespread use of risk algorithms in clinical medicine is testimony to how they have helped transform clinical decision-making. Risk algorithms have a similar but underdeveloped potential to support decision-making for population health.

Objective To describe the role of predictive risk algorithms in a population setting.

Methods First, predictive risk algorithms and how clinicians use them are described. Second, the population uses of risk algorithms are described, highlighting the strengths of risk algorithms for health planning. Lastly, the way in which predictive risk algorithms are developed is discussed briefly and a guide for algorithm assessment in population health presented.

Conclusion For the past 20 years, absolute and baseline risk has been a cornerstone of population health planning. The most accurate and discriminating method to generate such estimates is the use of multivariable risk algorithms. Routinely collected data can be used to develop algorithms with characteristics that are well suited to health planning and such data are increasingly available. The widespread use of risk algorithms in clinical medicine is testimony to how they have helped transform clinical decision-making. Risk algorithms have a similar but underdeveloped potential to support decision-making for population health.

  • Diabetes
  • prevention
  • public health
  • social inequalities
  • health policy
  • immunisation
  • public health policy

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Footnotes

  • Linked articles 201231, 201409.

  • Funding This work was supported by Population Health Improvement Research Network (grant number 06 548) and the Canadian Institutes for Health Research STAR team grant.

  • Competing interests None.

  • Provenance and peer review Not commissioned; externally peer reviewed.

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