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  • A method for the inclusion of physical activity-related health benefits in cost-benefit analysis of built environment initiatives

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    Veerman171814.pdf (776.1Kb)
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    Author(s)
    Zapata-Diomedi, Belen
    Gunn, Lucy
    Giles-Corti, Billie
    Shiell, Alan
    Veerman, J Lennert
    Griffith University Author(s)
    Veerman, Lennert L.
    Zapata Diomedi, Belen
    Year published
    2018
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    Abstract
    The built environment has a significant influence on population levels of physical activity (PA) and therefore health. However, PA-related health benefits are seldom considered in transport and urban planning (i.e. built environment interventions) cost-benefit analysis. Cost-benefit analysis implies that the benefits of any initiative are valued in monetary terms to make them commensurable with costs. This leads to the need for monetised values of the health benefits of PA. The aim of this study was to explore a method for the incorporation of monetised PA-related health benefits in cost-benefit analysis of built environment ...
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    The built environment has a significant influence on population levels of physical activity (PA) and therefore health. However, PA-related health benefits are seldom considered in transport and urban planning (i.e. built environment interventions) cost-benefit analysis. Cost-benefit analysis implies that the benefits of any initiative are valued in monetary terms to make them commensurable with costs. This leads to the need for monetised values of the health benefits of PA. The aim of this study was to explore a method for the incorporation of monetised PA-related health benefits in cost-benefit analysis of built environment interventions. Firstly, we estimated the change in population level of PA attributable to a change in the built environment due to the intervention. Then, changes in population levels of PA were translated into monetary values. For the first step we used estimates from the literature for the association of built environment features with physical activity outcomes. For the second step we used the multi-cohort proportional multi-state life table model to predict changes in health-adjusted life years and health care costs as a function of changes in PA. Finally, we monetised health-adjusted life years using the value of a statistical life year. Future research could adapt these methods to assess the health and economic impacts of specific urban development scenarios by working in collaboration with urban planners.
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    Journal Title
    PREVENTIVE MEDICINE
    Volume
    106
    DOI
    https://doi.org/10.1016/j.ypmed.2017.11.009
    Copyright Statement
    © 2018 Elsevier. Licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International Licence (http://creativecommons.org/licenses/by-nc-nd/4.0/) which permits unrestricted, non-commercial use, distribution and reproduction in any medium, providing that the work is properly cited.
    Subject
    Sports science and exercise
    Health services and systems
    Public health
    Publication URI
    http://hdl.handle.net/10072/383479
    Collection
    • Journal articles

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