Health impact modelling
We modelled the health impacts of the London cycle hire scheme by comparing the effects of the scheme against a counterfactual scenario in which it did not exist. Health impacts were modelled through changes in physical activity and exposure to air pollution (using a comparative risk assessment approach) and in road traffic injuries (using a risk and travel time based approach). Our primary outcome metric was lifelong change in non-discounted, non-age weighted disability adjusted life years (DALYs), calculated as the sum of years of life lost owing to premature mortality (YLL) and years of healthy life lost due to disability (YLD). We calculated impacts in terms of the effect across the users’ life course from one year changes in incidence of disease and injury. For example, if the cycle hire scheme averted one fatality per year in a given age group, the gain in DALYs assigned to that year would include all the future years of healthy life that would be expected for someone in that age group. All changes in exposure were assumed only to affect people in their current 10 to 15 year age band—that is, we did not include lags to capture possible longer term effects.
Our modelling used a revised version of the integrated transport and health impact modelling tool,3 4 implemented in Analytica Lumina 4.4 (see appendix 2 for details of the model’s specification). This included stochastic uncertainty analyses around key parameter estimates. It also included deterministic “what if” sensitivity analyses, examining the sensitivity of our findings to key aspects of the London context (for example, background air pollution and injury rates) and so exploring the generalisability of our findings to other settings. Table 1⇓ summarises the key modelling data sources and sensitivity analyses, with details provided in appendix 3.
Health impact modellingWe modelled the health impacts of the London cycle hire scheme by comparing the effects of the scheme against a counterfactual scenario in which it did not exist. Health impacts were modelled through changes in physical activity and exposure to air pollution (using a comparative risk assessment approach) and in road traffic injuries (using a risk and travel time based approach). Our primary outcome metric was lifelong change in non-discounted, non-age weighted disability adjusted life years (DALYs), calculated as the sum of years of life lost owing to premature mortality (YLL) and years of healthy life lost due to disability (YLD). We calculated impacts in terms of the effect across the users’ life course from one year changes in incidence of disease and injury. For example, if the cycle hire scheme averted one fatality per year in a given age group, the gain in DALYs assigned to that year would include all the future years of healthy life that would be expected for someone in that age group. All changes in exposure were assumed only to affect people in their current 10 to 15 year age band—that is, we did not include lags to capture possible longer term effects.Our modelling used a revised version of the integrated transport and health impact modelling tool,3 4 implemented in Analytica Lumina 4.4 (see appendix 2 for details of the model’s specification). This included stochastic uncertainty analyses around key parameter estimates. It also included deterministic “what if” sensitivity analyses, examining the sensitivity of our findings to key aspects of the London context (for example, background air pollution and injury rates) and so exploring the generalisability of our findings to other settings. Table 1⇓ summarises the key modelling data sources and sensitivity analyses, with details provided in appendix 3.
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