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OBJECTIVES: To conduct a health economic evaluation of a proposed investment in urban bicycle infrastructure in Stockholm County, Sweden.

DESIGN: A cost-effectiveness analysis is undertaken from a healthcare perspective. Investment costs over a 50-year life cycle are offset by averted healthcare costs and compared with estimated long-term impacts on morbidity, quantified in disability-adjusted life years (DALYs). The results are re-calculated under different assumptions to model the effects of uncertainty.

SETTING: The Municipality of Stockholm (population 2.27 million) committed funds for bicycle path infrastructure with the aim of achieving a 15% increase in the number of bicycle commuters by 2030. This work is based on a previously constructed scenario, in which individual registry data on home and work address and a transport model allocation to different modes of transport identified 111 487 individuals with the physical capacity to bicycle to work within 30 min but that currently drive a car to work.

RESULTS: Morbidity impacts and healthcare costs attributed to increased physical activity, change in air pollution exposure and accident risk are quantified under the scenario. The largest reduction in healthcare costs is attributed to increased physical activity and the second largest to reduced air pollution exposure among the population of Greater Stockholm. The expected net benefit from the investment is 8.7% of the 2017 Stockholm County healthcare budget, and 3.7% after discounting. The economic evaluation estimates that the intervention is cost-effective and each DALY averted gives a surplus of €9933. The results remained robust under varied assumptions pertaining to reduced numbers of additional bicycle commuters.

CONCLUSION: Investing in urban infrastructure to increase bicycling as active transport is cost-effective from a healthcare sector perspective.

Introduction: Electric vehicles (EVs) are heavily promoted as beneficial for climate and health. In most studies, it is assumed that EVs contribution to urban air pollution is zero due to no tailpipe emissions, ignoring the contribution of non-exhaust particles (brake, tire and road wear), which are unregulated in EU. This study of Stockholm, Sweden, aims to 1) assess how a future vehicle fleet impacts concentrations of particles of size less than 2.5 μm (PM2.5) and evaluate the expected health outcomes economically and 2) compare this with CO2 savings.

Methods: Source specific dispersion models of exhaust and non-exhaust PM2.5 was used to estimate the population weighted concentrations. Thereafter exposure differences within a business as usual (BAU2035) and a fossil free fuel (FFF2035) scenario were used to assess expected health and economic impacts. The assessment considered both exhaust and non-exhaust emissions, considering the vehicle weight and the proportion of vehicles using studded winter tires. Health economic costs were retrieved from the literature and societal willingness to pay was used to value quality-adjusted life-years lost due to morbidity and mortality.

Results: The mean population weighted exhaust PM2.5 concentration decreased 0.012 μg/m3 (39%) in FFF2035 as compared to BAU2035. Assuming 50% higher road and tire wear PM2.5 emission because of higher weight among EVs and 30% less brake wear emissions, the estimated decrease in wear particle exposures were 0.152 (22%) and 0.014 μg/m3 (1.9%) for 0 and 30% use on studded winter tires, respectively. The resulting health economic costs were estimated to €217M and €32M, respectively. An increase by 0.079 μg/m3 (11%) was however estimated for 50% use of studded winter tires, corresponding to an €89M increase in health costs.

Conclusion: Considering both exhaust and wear generated particles, it is not straight forward that an increase of EVs will decrease the negative health impacts.

Long-term air pollution exposure increases the risk for cardiovascular disease, but little is known about the temporal relationships between exposure and health outcomes. This study aims to estimate the exposure-lag response between air pollution exposure and risk for ischemic heart disease (IHD) and stroke incidence by applying distributed lag non-linear models (DLNMs). Annual mean concentrations of particles with aerodynamic diameter less than 2.5 µm (PM2.5 ) and black carbon (BC) were estimated for participants in five Swedish cohorts using dispersion models. Simultaneous estimates of exposure lags 1–10 years using DLNMs were compared with separate year specific (single lag) estimates and estimates for lag 1–5-and 6–10-years using moving average exposure. The DLNM estimated no exposure lag-response between PM2.5 total, BC, and IHD. However, for PM2.5 from local sources, a 20% risk increase per 1 µg/m3 for 1-year lag was estimated. A risk increase for stroke was suggested in relation to lags 2–4-year PM2.5 and BC, and also lags 8–9-years BC. No associations were shown in single lag models. Increased risk estimates for stroke in relation to lag 1–5-and 6–10-years BC moving averages were observed. Estimates generally supported a greater contribution to increased risk from exposure windows closer in time to incident IHD and incident stroke.