Search

Introduction: Recent publications have called for substantial improvements in the design, conduct, analysis and reporting of prediction models. Publication of study protocols, with prespecification of key aspects of the analysis plan, can help to improve transparency, increase quality and protect against increased type I error. Valid population-based risk algorithms are essential for population health planning and policy decision-making. The purpose of this study is to develop, evaluate and apply cardiovascular disease (CVD) risk algorithms for the population setting. Methods and analysis: The Ontario sample of the Canadian Community Health Survey (2001, 2003, 2005; 77 251 respondents) will be used to assess risk factors focusing on health behaviours (physical activity, diet, smoking and alcohol use). Incident CVD outcomes will be assessed through linkage to administrative healthcare databases (619 886 person-years of follow-up until 31 December 2011 ). Sociodemographic factors (age, sex, immigrant status, education) and mediating factors such as presence of diabetes and hypertension will be included as predictors. Algorithms will be developed using competing risks survival analysis. The analysis plan adheres to published recommendations for the development of valid prediction models to limit the risk of overfitting and improve the quality of predictions. Key considerations are fully prespecifying the predictor variables; appropriate handling of missing data; use of flexible functions for continuous predictors; and avoiding data-driven variable selection procedures. The 2007 and 2009 surveys (approximately 50 000 respondents) will be used for validation. Calibration will be assessed overall and in predefined subgroups of importance to clinicians and policymakers. Ethics and dissemination: This study has been approved by the Ottawa Health Science Network Research Ethics Board. The findings will be disseminated through professional and scientific conferences, and in peer-reviewed journals. The algorithm will be accessible electronically for population and individual uses. [ABSTRACT FROM AUTHOR]

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. INSETS: Case example of risk prediction in clinical medicined algorithms.;Case example of population use of a clinical risk algorithm. [ABSTRACT FROM AUTHOR]