Premature Mortality of 2050 High Bike Use Scenarios in 17 Countries

Julen Egiguren , M.J. Nieuwenhuijsen , and David Rojas-Rueda

Julen Egiguren

¹ ISGlobal, Centre for Research in Environmental Epidemiology (CREAL), Barcelona, Spain

² Universitat Pompeu Fabra (UPF), Barcelona, Spain

Find articles by Julen Egiguren

M.J. Nieuwenhuijsen

¹ ISGlobal, Centre for Research in Environmental Epidemiology (CREAL), Barcelona, Spain

² Universitat Pompeu Fabra (UPF), Barcelona, Spain

³ Municipal Institute of Medical Research (IMIM-Hospital del Mar), Barcelona, Spain

⁴ CIBER Epidemiología y Salud Pública (CIBERESP), Madrid, Spain

Find articles by M.J. Nieuwenhuijsen

David Rojas-Rueda

¹ ISGlobal, Centre for Research in Environmental Epidemiology (CREAL), Barcelona, Spain

² Universitat Pompeu Fabra (UPF), Barcelona, Spain

¹ ISGlobal, Centre for Research in Environmental Epidemiology (CREAL), Barcelona, Spain

² Universitat Pompeu Fabra (UPF), Barcelona, Spain

³ Municipal Institute of Medical Research (IMIM-Hospital del Mar), Barcelona, Spain

⁴ CIBER Epidemiología y Salud Pública (CIBERESP), Madrid, Spain

⁵ Department of Environmental and Radiological Health Sciences, Colorado State University, Fort Collins, Colorado, USA

Corresponding author.

Address correspondence to David Rojas-Rueda, Colorado State University, Environmental Health Building, 1601 Campus Delivery, Fort Collins, CO 80523 USA. Telephone: (970) 491-7038; Fax: (970) 491-2940. Email: [email protected]

Supplemental Material is available online ( https://doi.org/10.1289/EHP9073 ).

The authors declare they have no actual or potential competing financial interests.

Note to readers with disabilities: $negative 859$ $negative 379$ $negative 1571$ $negative 10747$ $negative 4732$ $negative 19633$ 114%Canada $negative 144$ $negative 1205$ $negative 807$ $negative 1794$ 224%China $negative 4127$ $negative 2694$ $negative 6332$ $negative 25153$ $negative 15209$ $negative 40530$ 24%Denmark $negative 144$ $negative 102$ $negative 214$ 17%Egypt $negative 399$ $negative 180$ $negative 710$ $negative 4241$ $negative 1796$ $negative 7702$ 106%France $negative 160$ $negative 108$ $negative 241$ $negative 2132$ $negative 1452$ $negative 3195$ 106%Germany $negative 206$ $negative 145$ $negative 301$ $negative 2749$ $negative 1941$ $negative 4008$ 109%India $negative 6987$ $negative 4349$ $negative 10787$ $negative 87337$ $negative 54350$ $negative 134832$ 162%Indonesia $negative 1437$ $negative 737$ $negative 2326$ $negative 17968$ $negative 9211$ $negative 29071$ 143%Italy $negative 100$ $negative 146$ $negative 1257$ $negative 788$ $negative 1814$ 101%Japan $negative 181$ $negative 122$ $negative 274$ $negative 2271$ $negative 1520$ $negative 3435$ 113%Mexico $negative 571$ $negative 204$ $negative 944$ $negative 7133$ $negative 2554$ $negative 12037$ 268%Netherlands $negative 357$ $negative 247$ $negative 536$ 17%Russia $negative 1302$ $negative 900$ $negative 1940$ $negative 16274$ $negative 11246$ $negative 24262$ 153%South Africa $negative 707$ $negative 473$ $negative 1045$ $negative 8839$ $negative 5836$ $negative 13058$ 117%United Kingdom $negative 185$ $negative 128$ $negative 274$ $negative 2308$ $negative 1602$ $negative 3421$ 107%United States of America $negative 1227$ $negative 816$ $negative 1865$ $negative 15309$ $negative 10199$ $negative 23308$ 218%Total $negative 18589$ $negative 11396$ $negative 28969$ $negative 205424$ $negative 123592$ $negative 322850$ 89%

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Note: HUI, high uncertainty interval; LUI, low uncertainty interval.

Scenario 2

If HBU levels are achieved by 2050, and 100% of these future bike trips replace car trips, the estimated premature annual deaths avoided, based on the model assumptions, could be 205,424 (95% UI: 123,592, 322,850) among the 17 countries (ambitious scenario) ( Table 2 ). This means 89% more annual premature deaths avoided due to bike use in the HBU in comparison with the current 2050 bike trends (assuming 100% of car–bike substitution). In absolute numbers, the top 5 countries with the largest benefits will be India [87,337 annual premature deaths avoided (95% UI: 54,350, 134,832)], China [25,153 annual premature deaths avoided (95% UI: 15,209, 40,530)], Indonesia [17,968 annual premature deaths avoided (95% UI: 9,211, 29,071)], Russia [16,274 annual premature deaths avoided (95% UI: 11,246, 24,262)] and the United States [15,309 annual premature deaths avoided (95% UI: 10,199, 23,308)].

Discussion

This study found that HBU in urban populations across 17 countries by 2050 could prevent up to 205,424 annual premature deaths if 100% of these future bike trips replace car trips. In a conservative scenario, if only 8% of these future bike trips replace car trips, HBU could prevent 18,589 annual premature deaths by 2050 among the urban populations of 17 countries. In all the countries and scenarios, the reductions of premature mortality (due to physical activity) related to bike use outweighed the increments of premature mortality due to air pollution inhalation and traffic incidents, with an average benefit:risk ratio among the 17 countries of 32:1.

To our knowledge, this is the first study assessing the health impacts of future and global biking scenarios. This study included urban populations from 17 countries (Brazil, Canada, China, Denmark, Egypt, France, Germany, India, Indonesia, Italy, Japan, Mexico, Netherlands, Russia, South Africa, United Kingdom, and the United States) across 5 continents and considered mechanical and e-bikes. This study provides a systematic assessment comparing biking trends and HBU scenarios for 2050.

The results of this study are in accordance with findings from previous biking HIAs using similar health exposures (physical activity, air pollution, and traffic fatalities) ( Mueller et al. 2018a ; Otero et al. 2018 ; Rojas-Rueda et al. 2012 , 2013 , 2011 , 2016 ; Stevenson 2017 ; Woodcock et al. 2014 ; Zapata-Diomedi et al. 2017 ). Unlike previous studies that have been focused on single cities ( Rojas-Rueda et al. 2012 , 2016 ; Woodcock et al. 2014 ; Zapata-Diomedi et al. 2017 ) or a comparison of few cities ( Stevenson 2017 ), our study focused on the national urban populations in comparing 17 countries, providing a global perspective of similar biking scenarios in the future (2050), including the health impacts in low- and middle-income countries. Like previous studies, this HIA focused on car trip replacement, considering that shifting car trips to active transportation could have larger health benefits, in addition to other important climate and environmental co-benefits ( Rojas-Rueda et al. 2013 , 2016 ).

This study found that the health impacts of biking vary among countries. In the most realistic of our scenarios, Scenario 1, using the average (8%) reported car–bike substitution related to multiple bike interventions among 26 cities in China, Europe, and North America, we estimated that in 2050 an HBU scenario could result in the avoidance of 18,589 annual premature deaths, ranging from 2.35 annual premature deaths per 100,000 bicyclists in Denmark to 38.82 in South Africa. The estimated differences, comparing a similar number of bicyclists across countries, could be explained by the difference between trip distances, speed, days traveled per year, average levels of physical activity, air quality, traffic fatalities, and baseline mortality rate in each country. If national and local stakeholders could improve traffic safety, air quality, and bike usability, larger reductions in premature mortality related to biking scenarios could be expected in the future. In addition, we found that if more bike trips replaced car trips in each country, benefits could increase by 1,105% (comparing Scenario 1, 18,589 annual premature deaths avoided vs. Scenario 2, 205,424 annual premature deaths avoided). This scenario comparison highlights the importance of car-trip substitution policies for health, and the combination of bike policies (e.g., bike lanes, bike parking, bike-sharing systems, etc.) with policies aiming to reduce car use (e.g., parking pricing and reduction, congestion pricing, etc.).

We performed our analysis distinguishing between e-bikes and mechanical bikes. E-bikes offer different levels of electric assistance ( Otero et al. 2018 ). In our analysis was assumed that e-bikes were used in a “standard assistance” function. “Standard assistance” e-bikes consider 90% of the physical activity needed to ride a bike compared with that needed to ride a mechanical bike (e-bikes’ 6.12 METs vs. 6.8 METs of mechanical bikes) ( Otero et al. 2018 ). In addition, we also assumed a higher traffic fatality for kilometers traveled by e-bike compared with mechanical bikes ( Otero et al. 2018 ). Fewer mortality benefits have been found among e-bikes in previous studies ( Otero et al. 2018 ), mainly due to the lower number of expected trips compared with mechanical bikes. In our study, e-bikes had the largest estimated increment of avoided premature deaths compared with mechanical bikes by 2050 (173% vs. 90%) due to the expected increase in future e-bike sales. We also found that in all countries and scenarios, the reductions in premature mortality from e-bike use related to physical activity outweigh the increments of premature mortality due to traffic fatalities and air pollution inhalation, similar to findings for mechanical bikes.

Physical activity resulted in the largest health impacts in our analysis. Physical activity can prevent several noncommunicable diseases, such as cardiovascular disease, diabetes mellitus, colon and breast cancers, and dementia, among others, which can also reduce the overall mortality ( Rojas-Rueda et al. 2013 ). This study focused on all-cause mortality as a health outcome because it has been suggested as the outcome with the largest health impacts, in comparison with morbidity, in previous HIAs of active transportation ( Mueller et al. 2015 ; Otero et al. 2018 ; Rojas-Rueda et al. 2013 ). The TAPAS tool for biking estimated the health impacts of physical activity using a nonlinear DRF from a meta-analysis of cohort studies ( Woodcock et al. 2011 ) and calibrated with the corresponding physical activity levels reported by the adult population in each country and applied to the exposure levels by each scenario and country assessed. The nonlinear function considers that people who already were physically active would gain fewer reductions in premature mortality in comparison with those who are more sedentary. This nonlinear approach results in fewer mortality benefits compared with using a linear DRF ( Rojas-Rueda et al. 2016 ).

The air pollution assessment included in this study was based only on $particulate matter begin subscript 2.5 end subscript$ inhalation during the trip. Although changes in modal share, especially shifts from car to bike, are expected to produce changes in air pollution emissions and concentrations at the city and regional levels ( Rojas-Rueda et al. 2012 ), these air quality and health-related impacts were not in the scope of this study, and we focused only on the $particulate matter begin subscript 2.5 end subscript$ biking exposure during the trip. $particulate matter begin subscript 2.5 end subscript$ was chosen because it was expected to produce the largest health burden in comparison with other air pollutants, such as ${NO}_{2}$ or black carbon ( Rojas-Rueda et al. 2013 ). We also decided not to include these other pollutants in the analysis because these are highly correlated and produce similar health outcomes ( Otero et al. 2018 ). We found differences between the health impacts associated with $particulate matter begin subscript 2.5 end subscript$ among the urban populations from 17 countries and scenarios. These differences can be explained by the variability in air pollution levels, trip characteristics (duration and frequency), and physical activity among the countries and scenarios. Annual average $particulate matter begin subscript 2,5 end subscript$ concentrations among the 17 countries included ranged from $109.5 micrograms per cubic meter$ in Egypt to $6.87 micrograms per cubic meter$ in Canada ( WHO 2019 ). If national and local authorities implement and promote policies to improve air quality to those levels recommended by the WHO ( $particulate matter begin subscript 2.5 end subscript$ $annual average concentration less than 10 micrograms per cubic meter$ ( WHO 2006 ), the expected reductions in premature mortality in our scenarios could be larger.

The traffic fatality assessment quantified fatal traffic incidents per billion kilometers traveled. To create this model, we used the reported national road safety estimates provided by the WHO in each country ( WHO 2009 , 2013 , 2015 , 2018 ). This study only took into account the traffic fatality risk by mode of transport (car, bike, and e-bike) but did not assess the impacts related to other traffic risk factors, such as traffic route, traveler age, or sex, due to the lack of data on these specific characteristics from traffic safety records from each country. From the 17 countries included, 13 reported a higher risk of traffic fatalities per kilometers traveled by bike compared with travel in a car ( WHO 2009 , 2013 , 2015 , 2018 ). China, India, Russia, and South Africa reported a higher risk for traffic fatalities per kilometer traveled in a car compared with travel by bike ( WHO 2009 , 2013 , 2015 , 2018 ). These greater traffic risks in cars vs. on bikes in China, India, Russia, and South Africa could be explained by the low traffic safety levels in those countries, resulting from having fewer national car safety policies in comparison with high-income countries, in addition to having older vehicle fleets and poorer transport infrastructure ( Vinand and Reich 2014 ). Another explanation could be the possibility of an incomplete record of traffic safety data ( WHO 2018 ). To provide a better estimation of the traffic fatalities in these countries, we ran a sensitivity analysis using the estimated traffic fatalities records instead of the reported national data, both provided by the WHO ( WHO 2009 , 2013 , 2015 , 2018 ).

The results of our analysis are consistent with previous studies assessing car–bike trip substitution. Otero et al. found a reduction in premature deaths in multiple bike-sharing systems across European cities. According to this study, the 12 largest bike-sharing systems in Europe could avoid up to 73 annual premature deaths with an economic value of 225 million Euros if 100% of bike trips replaced car trips ( Otero et al. 2018 ). Furthermore, it has been suggested that the health impacts of car–bike trip substitution on mortality should be considered in the long term, after 3 or 4 years of achieving the specific car–bike substitution level ( Otero et al. 2018 ). These effects on mortality would be mainly due to the impacts of physical activity and air pollution, which have to be maintained over time before the effects would be perceptible ( Rojas-Rueda et al. 2013 ).

Moreover, to improve health in cities, other urban interventions in addition to bike interventions should also be considered. In Barcelona, Spain, the Superblock model has been implemented to promote sustainable mobility and an active lifestyle ( Mueller et al. 2020 ). It has been suggested that this built environment intervention could also help prevent premature mortality ( Mueller et al. 2020 ). Overall, each city has unique geographic, environmental, and sociocultural characteristics that influence the health status of its inhabitants. Identifying and improving these characteristics may help promote healthier cities ( Mueller et al. 2018b ).

As in all quantitative HIAs, our study was limited by the availability of data and the necessity to make assumptions to model likely scenarios. In terms of the scenarios modeled, the forecast for 2050 was based on a previously published report from the Global High Shift Cycling study that estimated the bike use scenarios for several countries and all regions around the world ( Mason et al. 2015 ). Due to the lack of transport and health data to perform the assessment in all the countries reported by the Global High Shift Cycling study, we could include only 17 countries from those reported in this study ( Mason et al. 2015 ). The lack of data was particularly common in low- and middle-income countries, such as countries in Africa and the Middle East ( WHO 2018 ). The Global High Shift Cycling study forecast biking and transport scenarios based on modal share predictions estimating the number of trips per person per day, distance traveled per mode, and the urban population projections for 2050 ( Mason et al. 2015 ). The Global High Shift Cycling study was limited by the availability of transport estimations at the national level and the need to include transport assumptions in their analysis, such as the use of constant average trip lengths and speed across modeled years ( Mason et al. 2015 ).

Another limitation was the lack of specific modal shift data from car to bike in the Global High Shift Cycling study ( Mason et al. 2015 ). For this reason, in addition to the 2050 HBU scenario provided by the Global High Shift Cycling study, we created additional subscenarios to capture the variability in the expected car–bike substitution levels in the future years in all the countries (Scenarios 1–4). These car–bike substitution scenarios were based on data available from 26 cities from China ( Ma et al. 2019 ), Europe ( Bjørnarå et al. 2019 ; Oakil et al. 2016 ; Otero et al. 2018 ; Scheepers et al. 2014 ), and the United States ( Scheepers et al. 2014 ), where previous studies reported the car–bike substitution levels of multiple urban bike interventions, such as the implementation of bike-sharing systems, bike infrastructure, and bike promotion and education. Although this data combined different cities, we acknowledge that these estimations could not reflect the reality of car–bike substitution across the globe, mainly because this data comes primarily from high-income countries, with only three cities coming from China ( Ma et al. 2019 ). Based on the data for these 26 cities, we estimated that on average multiple bike interventions could support an average 8% car–bike substitution, which we used in Scenario 1. We also used the maximum (35%) and minimum (0.46%) reported car–bike substitution among the 26 cities and used these as a reference for our Scenarios 3 and 4, respectively. We also estimated the health impacts of a hypothetical scenario, based on “what if” 100% of the future bike trips were to replace cars trips, to provide an overall picture of the health opportunities related to large biking interventions (Scenario 2).

In the sensitivity analysis using a 2050 mortality rate (instead of 2017) for those 20–64 y old, in Scenario 1, we found a slightly lower number of premature deaths avoided. This finding can be explained because mortality rates in the countries analyzed (similarly to the global trend) are expected to improve by 2050 ( IHME 2020 ).

When we use a 2050 mortality rate but include people age 20 y and older, assuming that an aging population will also affect the cyclist population, we found that the number of annual premature deaths prevented increased importantly. This finding can be explained because including older age groups (such as 65 y and older) will increase the mortality rates and affect the number of premature deaths that could be prevented if bike use increases by 2050. These sensitivity analyses, considering those age 20 y and older, also highlight the importance of promoting and implement active transportation policies to support older age groups. In addition, in this study, we assessed the uncertainty of our estimates, providing UIs that were composed by the variability of the input data, using the changes (maximum and minimum) and the CIs from the DRF from air pollution and physical activity.

To achieve HBU among the urban population, the following policies have shown to bring a quick increase in biking levels: retrofitting biking infrastructure onto existing roads to create backbone networks in arterial streets, small residential streets, and intercity roads; implementation of bike-share systems in large cities; laws and enforcement practices to better protect active transport; investment in walking facilities and public transport to offer transport options that can be combined with bike trips; elimination of policies that support additional motorized vehicle use, such as free parking and fuel subsidies; and establishment of congestion pricing, vehicle-kilometers traveled fees, and development impact fees to charge a price for driving ( Mason et al. 2015 ). In our analysis, we have also quantified an intermediate year (2030) to provide a vision of the health opportunities that HBU scenarios could provide if those are achieved sooner (see Tables S19–S30).

General recommendations can be extracted from this study for national and local authorities, health practitioners, and researchers. Regarding local and national transport authorities, this study supports the implementation of strong bike and car substitution policies as key interventions for healthy and sustainable development. Furthermore, transport authorities should consider systematic data collection through travel surveys and traffic counts at national and local levels, prioritizing data on modal share, number of trips per person/mode/day, trip frequency, duration, length, and transport mode shifts. These data will help policymakers and stakeholders to understand travel behavior and plan for specific transport interventions. Also, these data should be harmonized among cities, countries, and regions and published in an open-access format. We also found a lack of needed transport and health data from low- and middle-income countries that authorities and researchers could help collect and harmonize. The need for such data is especially important because low- and middle-income countries face a faster population growth ( IHME 2020 ), increasing epidemiological transition to noncommunicable diseases ( IHME n.d. ), and rapid urbanization ( Stevenson 2017 ). For health practitioners, these study supports the prioritization of interdisciplinary collaborations among urban and transport planners and health practitioners, considering current and future active transport policies for health promotion and prevention, improving collaborations on road traffic safety, air quality, physical activity, and transport and health equity.

Conclusions

We found that global bike use may provide important reductions in premature mortality by 2050. If biking trips above current trends (high bike use scenario) are achieved by 2050, 205,424 annual premature deaths could be prevented among Brazil, Canada, China, Denmark, Egypt, France, Germany, India, Indonesia, Italy, Japan, Mexico, Netherlands, Russia, South Africa, the United Kingdom, and the United States. The mortality benefits of physical activity drive the health impacts estimated in this study. In all countries and scenarios analyzed, the benefits of physical activity outweighed the mortality risks related to air pollution inhalation and road traffic fatalities. Future reductions in premature mortality of bike use will depend on current and future transport and built environment policies, promoting active transportation, car–bike substitution, air quality, and traffic safety. Implementing ambitious urban policies supporting biking and car–bike substitution should be considered key public health interventions for a healthy urban design.

Supplementary Material

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References

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