Conclusion London’s bicycle sharing system has positive health impacts overall, but these benefits are clearer for men than for women and for older users than for younger users. The potential benefits of cycling may not currently apply to all groups in all settings.

Results Over the year examined the users made 7.4 million cycle hire trips (estimated 71% of cycling time by men). These trips would mostly otherwise have been made on foot (31%) or by public transport (47%). To date there has been a trend towards fewer fatalities and injuries than expected on cycle hire bicycles. Using these observed injury rates, the population benefits from the cycle hire scheme substantially outweighed harms (net change −72 DALYs (95% credible interval −110 to −43) among men using cycle hire per accounting year; −15 (−42 to −6) among women; note that negative DALYs represent a health benefit). When we modelled cycle hire injury rates as being equal to background rates for all cycling in central London, these benefits were smaller and there was no evidence of a benefit among women (change −49 DALYs (−88 to −17) among men; −1 DALY (−27 to 12) among women). This sex difference largely reflected higher road collision fatality rates for female cyclists. At older ages the modelled benefits of cycling were much larger than the harms. Using background injury rates in the youngest age group (15 to 29 years), the medium term benefits and harms were both comparatively small and potentially negative.

With access to complete registration and usage data, we modelled the impact of the bicycle sharing system in London on the health of its users. Specifically, we sought to model both the mortality and the morbidity impacts of the scheme on male and female users of different ages, by estimating changes in their physical activity, road traffic injury rates, and exposure to air pollution.

Although the proliferation of bicycle sharing systems has attracted research attention, most studies have focused on the uptake of the scheme, the movement of bicycles around the city, or the characteristics and behaviour of users. 7 8 9 10 11 To our knowledge, only one study has modelled health impacts, estimating the effects of the Barcelona bicycle sharing system on all cause mortality. 12 This study found a large benefit to harm ratio in favour of the scheme but had limited data on usage of the scheme or on the likely personal characteristics of scheme users. The Barcelona evaluation also only assessed the health impacts on mortality and did not consider the effects on morbidity.

Physical inactivity is a major cause of morbidity and mortality, 1 and the creation of opportunities for safe, active mobility has been identified as one central feature of a “healthy city.” 2 Promoting a shift away from motorised vehicle travel towards walking and cycling would also be expected to yield additional health, economic, and environmental benefits, including reducing traffic congestion, noise, and the emission of greenhouse gases. 3 4 5 One way in which cities can seek to realise these benefits is by implementing bicycle sharing systems to facilitate short term bicycle rental in urban areas. Typically, users of these schemes can borrow a bicycle from any one of several self service stations and drop it off at any other station, making cycling into a form of public transport. Such schemes are increasingly popular around the world, having grown from five schemes in Europe in 2000 to 636 schemes (with an estimated 600 000 bicycles) in 49 countries in 2013. 6

Methods

The London cycle hire scheme The London cycle hire scheme is the local name for the bicycle sharing system that was launched in London in July 2010. The scheme operates 24 hours a day, 365 days a year, and initially comprised 5000 bicycles located across 315 docking stations in central London. Following an eastern extension in March 2012, the scheme grew to comprise 8000 bicycles at 571 docking stations. Users can either register online for an access key (registered users) or pay by credit or debit card at docking stations (casual users). See appendix 1 for maps and further details.

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. Table 1 Summary of key model inputs, data sources, and uncertainty and “what if” analyses View this table:

Usage of the London cycle hire scheme: operational data supplemented by survey data Transport for London provided data on operational usage for all cycle hire trips made between 30 July 2010 and 31 March 2012, including trip level data for the final 12 months. This trip level data included a unique ID for each user and the start and end time and location of each trip. It also included the sex and area of residence of registered users; no personal data were available for casual users. We estimated the age structure of cycle hire users and the modal shift attributable to cycle hire using the best data available—namely, two surveys conducted during July 2011 by Transport for London (2652 registered users in an online survey, 1034 casual users in an intercept survey). Both surveys recorded age and sex, and the online survey also asked respondents to report the duration of their most recent cycle hire trip and what alternative mode they would typically have used for that trip before cycle hire was introduced. Although there is no direct way to establish the representativeness of these samples, these survey data did generate accurate estimates of those values that could be cross checked against the operational data—for example, in the surveys, 76.6% of registered users were male (2031/2652) compared with 75.9% in the operational data (69 893/92 100).

Physical activity We modelled distributions of physical activity using marginal metabolic equivalent of tasks (MET) values (hours per year), for four different domains, each calculated separately by sex and age group. Three distributions were assumed to change: cycling on cycle hire bicycles increased by the amount observed in the operational data, whereas own bicycle cycling and walking decreased by the estimated duration displaced by cycle hire trips. We assumed the other domain, incorporating activity from work, sport, and house or garden tasks, to remain unchanged. To allow for the possibility that cycle hire may appeal more to those who are already somewhat active in other areas, we assumed the baseline activity levels of cycle hire users to lie between those of existing cyclists and those of the general population (see appendix 3). We took the median marginal MET values for each activity domain from a physical activity compendium,13 with values of 1.5 and under not counted towards total physical activity. Marginal MET values refer to the intensity of an activity minus 1 (the intensity of being at rest). The short walks involved in getting to a bus or underground stop in central London were assumed to be balanced out by the short walks involved in getting to a cycle hire docking station, and therefore were not included in the MET values lost from former trips on public transport. To generate a distribution of total physical activity we stochastically combined the distributions of METs from different domains. We modelled health impacts by comparing the median MET exposures for each 10th of this total distribution, with and without the changes attributed to the cycle hire scheme. A systematic overview14 provided relative risks for associations between physical activity and breast cancer, ischaemic heart disease, stroke, colon cancer, dementia, depression, and diabetes (see table 14 in appendix 3). Given evidence of a non-linear relation between physical activity and health,15 16 we assumed changes in risk of disease to be log linearly associated with a power transformation of the physical activity exposure (stochastically modelled with mode power 0.5, range 0.25-1). As a sensitivity analysis, we modelled the impact of physical activity on all cause mortality directly rather than through changes in individual diseases. We did this twice—firstly, using the estimated exposure response function from a recent systematic review16 and, secondly, taking relative risks for different levels of physical activity directly from a subsequent cohort study of 400 000 adults.17 We applied stochastic scaling factors to represent the observation that relative risk reductions are smaller at younger ages,17 probably as a result of the different composition of causes of deaths.

PM2.5 air pollution Among urban air pollutants, by far the largest health impact in Europe comes from PM2.5 (particles with a diameter of ≤2.5 μm).33 We estimated changes in the PM2.5 exposure rate associated with using the cycle hire scheme as: exposure rate=average PM2.5 concentration along route×ventilation rate×road position scaling factor×pollution composition factor. To calculate this, we modelled the most likely route for each observed cycle hire trip (fig 1⇓), and for four counterfactual modes (own bicycle; walking; car, van, motorcycle, or taxi; and bus). We modelled these routes using Routino (www.routino.org) software algorithms, calibrated to each mode (for example, cyclists will usually prefer cycle lanes and quieter roads, buses will avoid minor roads). We then estimated the exposure to PM2.5 along each route by applying published estimates of average 24 hour PM2.5 concentrations in 2008 in a 20 m2 grid across central London.31 For the London underground, we took data from academic papers18 and assumed they did not vary by route. We multiplied these concentrations by three sets of scaling factors to represent the facts that cyclists and pedestrians tend to inhale higher concentrations of pollutants because of their greater ventilation rates, whereas motorised road users tend to experience slightly higher pollution concentrations because of their proximity in the road to the emissions from other motor vehicles,19 and that the composition of PM2.5 pollution in the underground may render it less harmful to health than surface level PM2.5. Fig 1 Map of cycle hire zone showing estimated number of cycle hire trips in past year along each route and average PM2.5 concentrations. The March 2012 eastern extension (dashed line) was only operational in final month of data collection, hence fewer trips in that area. See figure on bmj.com for full extent of eastern extension To estimate a change in daily total exposure we multiplied the time spent travelling in each mode by that mode’s exposure rate, assuming that the exposure for the rest of the day was 14.91 μg m−3 (the 2008 average for inner London). To estimate the resulting impacts on cardiovascular disease, respiratory disease, and lung cancer, we used values recommended by the World Health Organization.22