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The Impact of 51 Risk Factors on Life Expectancy in Canada: Findings from a New Risk Prediction Model Based on Data from the Global Burden of Disease Study

Author

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  • Jacek A. Kopec

    (School of Population and Public Health, University of British Columbia, Vancouver, BC V6T 1Z3, Canada
    Arthritis Research Canada, Vancouver, BC V5Y 3P2, Canada)

  • Eric C. Sayre

    (Arthritis Research Canada, Vancouver, BC V5Y 3P2, Canada)

  • Benajir Shams

    (Fraser Health Authority, Surrey, BC V3T 0H1, Canada)

  • Linda C. Li

    (Arthritis Research Canada, Vancouver, BC V5Y 3P2, Canada
    Department of Physical Therapy, University of British Columbia, Vancouver, BC V6T 1Z3, Canada)

  • Hui Xie

    (Arthritis Research Canada, Vancouver, BC V5Y 3P2, Canada
    Faculty of Health Sciences, Simon Fraser University, Burnaby, BC V5A 1S6, Canada)

  • Lynne M. Feehan

    (Department of Physical Therapy, University of British Columbia, Vancouver, BC V6T 1Z3, Canada)

  • John M. Esdaile

    (Arthritis Research Canada, Vancouver, BC V5Y 3P2, Canada
    Department of Medicine, University of British Columbia, Vancouver, BC V5Z 1M9, Canada)

Abstract

The aims of this study were (1) to develop a comprehensive risk-of-death and life expectancy (LE) model and (2) to provide data on the effects of multiple risk factors on LE. We used data for Canada from the Global Burden of Disease (GBD) Study. To create period life tables for males and females, we obtained age/sex-specific deaths rates for 270 diseases, population distributions for 51 risk factors, and relative risk functions for all disease-exposure pairs. We computed LE gains from eliminating each factor, LE values for different levels of exposure to each factor, and LE gains from simultaneous reductions in multiple risk factors at various ages. If all risk factors were eliminated, LE in Canada would increase by 6.26 years for males and 5.05 for females. The greatest benefit would come from eliminating smoking in males (2.45 years) and high blood pressure in females (1.42 years). For most risk factors, their dose-response relationships with LE were non-linear and depended on the presence of other factors. In individuals with high levels of risk, eliminating or reducing exposure to multiple factors could improve LE by several years, even at a relatively advanced age.

Suggested Citation

  • Jacek A. Kopec & Eric C. Sayre & Benajir Shams & Linda C. Li & Hui Xie & Lynne M. Feehan & John M. Esdaile, 2022. "The Impact of 51 Risk Factors on Life Expectancy in Canada: Findings from a New Risk Prediction Model Based on Data from the Global Burden of Disease Study," IJERPH, MDPI, vol. 19(15), pages 1-21, July.
    • Handle: RePEc:gam:jijerp:v:19:y:2022:i:15:p:8958-:d:869749
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    References listed on IDEAS

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    1. Gregor Stiglic & Majda Pajnkihar, 2015. "Evaluation of Major Online Diabetes Risk Calculators and Computerized Predictive Models," PLOS ONE, Public Library of Science, vol. 10(11), pages 1-14, November.
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