The University of Queensland, School of Public Health, Herston Road, Herston, 4006, Brisbane, Queensland, Australia.
RMIT University, Healthy Liveable Cities Group, Centre for Urban Research, Melbourne, 3000, Victoria, Australia.
Prev Med. 2018 Jan;106:224-230. doi: 10.1016/j.ypmed.2017.11.009. Epub 2017 Nov 8.
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.
建筑环境对人群体力活动（PA）水平有重大影响，进而影响健康。然而，在交通和城市规划（即建筑环境干预）的成本效益分析中，很少考虑与 PA 相关的健康益处。成本效益分析意味着需要用货币价值来衡量任何举措的效益，以便将其与成本进行比较。这就需要对 PA 的健康效益进行货币化估值。本研究旨在探索一种将与 PA 相关的货币化健康益处纳入建筑环境干预措施成本效益分析的方法。首先，我们估计了由于干预措施导致建筑环境变化而导致的人群 PA 水平的变化。然后，将 PA 人群水平的变化转化为货币价值。对于第一步，我们使用文献中的估计值来关联建筑环境特征与体力活动结果。对于第二步，我们使用多队列比例多状态生命表模型来预测 PA 变化对健康调整生命年和医疗保健成本的影响。最后，我们使用统计生命年的价值来货币化健康调整生命年。未来的研究可以通过与城市规划者合作，采用这些方法来评估特定城市发展情景的健康和经济影响。
