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Validation of a new predictive risk model: measuring the impact of major modifiable risks of death for patients and populations
Meeting Abstracts
Validation of a new predictive risk model: measuring the impact of major modifiable risks of death for patients and populations Emily Carnahan, Stephen S Lim, Eugene C Nelson, Catherine W Gillespie, Ali H Mokdad, Christopher J L Murray, Elliott S Fisher
Abstract Published Online June 17, 2013 Institute for Health Metrics and Evaluation, University of Washington, Seattle, WA, USA (E Carnahan BA, S S Lim PhD, C W Gillespie PhD, A H Mokdad PhD, Prof C J L Murray MD); and The Dartmouth Institute for Health Policy and Clinical Practice, Dartmouth Medical School, Lebanon, NH, USA (E C Nelson DSc, E S Fisher MD) Correspondence to: Emily Carnahan, Institute for Health Metrics and Evaluation, University of Washington, 2301 Fifth Avenue, Suite 600, Seattle, WA, USA 98121 [email protected]
Background Existing health risk measures are narrow in scope, often limited to specific diseases or clinical subpopulations. Modifiable risks account for a large fraction of death, but existing health risk measures do not produce estimates of the fraction of an individual’s risk that is potentially avoidable by clinical or behavioural intervention. The aim of this work is to develop and validate a risk prediction model to measure the modifiable risk of all-cause mortality for patients and populations. Methods We used data on the exposure distributions to 12 behavioural and biometric risks in the US population, mortality rates by cause, and estimates of the proportional hazards of risk factor exposure from systematic reviews to develop a risk model to estimate an adult’s 10-year total and avoidable mortality risk compared with an optimal population with no excess risk. We then compared predicted risk to observed mortality in 8253 National Health and Nutrition Examination Survey participants (1988–1994 and 1999–2004) aged 30 years and older with linked mortality data through 2006 to validate the model. Findings Predicted risk showed good discrimination; the area under the curve was 0·84 (SE 0·01) for both sexes. Preliminary results show that across deciles of predicted risk, mortality was accurately predicted in men (χ² 12·3, p=0·196) but slightly over predicted in the highest decile among women (χ² 22·8, p=0·002). Mortality risk was highly concentrated: among those aged 30–44 years, 5·1% (95% CI 4·1–6·0) of men and 5·9% (4·8–6·9) of women accounted for 25% of mortality risk. Interpretation The risk model accurately predicted individual all-cause mortality in a representative sample of the US population. It can be used to (a) counsel patients on what actions to take to protect their health, (b) identify patients at high risk of avoidable death, (c) quantify individual providers’ success at reducing the risk of populations served, and (d) monitor the impact of broad efforts to improve population health. Funding Institute for Health Metrics and Evaluation at the University of Washington. Contributors EC wrote the first draft of the abstract; all authors reviewed and revised. AHM and CWG developed the survey questions for the risk model. SSL, ECN, CJLM, and ESF designed the study and provided overall guidance. EC analysed and interpreted the data. Conflicts of interest We declare that we have no conflicts of interest. Acknowledgments The authors would like to thank Kelsey Pierce for her excellent project management and coordination throughout the development of the risk model.
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Validation of a new predictive risk model: measuring the impact of major modifiable risks of death for patients and populations Emily Carnahan, Stephen S Lim, Eugene C Nelson, Catherine W Gillespie, Ali H Mokdad, Christopher J L Murray, Elliott S Fisher
Abstract Published Online June 17, 2013 Institute for Health Metrics and Evaluation, University of Washington, Seattle, WA, USA (E Carnahan BA, S S Lim PhD, C W Gillespie PhD, A H Mokdad PhD, Prof C J L Murray MD); and The Dartmouth Institute for Health Policy and Clinical Practice, Dartmouth Medical School, Lebanon, NH, USA (E C Nelson DSc, E S Fisher MD) Correspondence to: Emily Carnahan, Institute for Health Metrics and Evaluation, University of Washington, 2301 Fifth Avenue, Suite 600, Seattle, WA, USA 98121 [email protected]
Background Existing health risk measures are narrow in scope, often limited to specific diseases or clinical subpopulations. Modifiable risks account for a large fraction of death, but existing health risk measures do not produce estimates of the fraction of an individual’s risk that is potentially avoidable by clinical or behavioural intervention. The aim of this work is to develop and validate a risk prediction model to measure the modifiable risk of all-cause mortality for patients and populations. Methods We used data on the exposure distributions to 12 behavioural and biometric risks in the US population, mortality rates by cause, and estimates of the proportional hazards of risk factor exposure from systematic reviews to develop a risk model to estimate an adult’s 10-year total and avoidable mortality risk compared with an optimal population with no excess risk. We then compared predicted risk to observed mortality in 8253 National Health and Nutrition Examination Survey participants (1988–1994 and 1999–2004) aged 30 years and older with linked mortality data through 2006 to validate the model. Findings Predicted risk showed good discrimination; the area under the curve was 0·84 (SE 0·01) for both sexes. Preliminary results show that across deciles of predicted risk, mortality was accurately predicted in men (χ² 12·3, p=0·196) but slightly over predicted in the highest decile among women (χ² 22·8, p=0·002). Mortality risk was highly concentrated: among those aged 30–44 years, 5·1% (95% CI 4·1–6·0) of men and 5·9% (4·8–6·9) of women accounted for 25% of mortality risk. Interpretation The risk model accurately predicted individual all-cause mortality in a representative sample of the US population. It can be used to (a) counsel patients on what actions to take to protect their health, (b) identify patients at high risk of avoidable death, (c) quantify individual providers’ success at reducing the risk of populations served, and (d) monitor the impact of broad efforts to improve population health. Funding Institute for Health Metrics and Evaluation at the University of Washington. Contributors EC wrote the first draft of the abstract; all authors reviewed and revised. AHM and CWG developed the survey questions for the risk model. SSL, ECN, CJLM, and ESF designed the study and provided overall guidance. EC analysed and interpreted the data. Conflicts of interest We declare that we have no conflicts of interest. Acknowledgments The authors would like to thank Kelsey Pierce for her excellent project management and coordination throughout the development of the risk model.
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