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.

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 (PM_2.5) 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 PM_2.5 total, BC, and IHD. However, for PM_2.5 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 PM_2.5 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.

Background: Alzheimer’s disease (AD) and other dementias currently represent the fifth most common cause of death in the world, according to the World Health Organization, with a projected future increase as the proportion of the elderly in the population is growing. Air pollution has emerged as a plausible risk factor for AD, but studies estimating dementia cases attributable to exposure to fine particulate matter (PM_2.5) air pollution and resulting monetary estimates are lacking. Methods: We used data on average population-weighted exposure to ambient PM_2.5 for the entire population of Sweden above 30 years of age. To estimate the annual number of dementia cases attributable to air pollution in the Swedish population above 60 years of age, we used the latest concentration response functions (CRF) between PM_2.5 exposure and dementia incidence, based on ten longitudinal cohort studies, for the population above 60 years of age. To estimate the monetary burden of attributable cases, we calculated total costs related to dementia, including direct and indirect lifetime costs and intangible costs by including quality-adjusted life years (QALYs) lost. Two different monetary valuations of QALYs in Sweden were used to estimate the monetary value of reduced quality-of-life from two different payer perspectives. Results: The annual number of dementia cases attributable to PM_2.5 exposure was estimated to be 820, which represents 5% of the annual dementia cases in Sweden. Direct and indirect lifetime average cost per dementia case was estimated to correspond € 213,000. A reduction of PM_2.5 by 1 μg/m³ was estimated to yield 101 fewer cases of dementia incidences annually, resulting in an estimated monetary benefit ranging up to 0.01% of the Swedish GDP in 2019. Conclusion: This study estimated that 5% of annual dementia cases could be attributed to PM_2.5 exposure, and that the resulting monetary burden is substantial. These findings suggest the need to consider airborne toxic pollutants associated with dementia incidence in public health policy decisions.

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 (PM_2.5) and evaluate the expected health outcomes economically and 2) compare this with CO2 savings. Methods: Source specific dispersion models of exhaust and non-exhaust PM_2.5 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 PM_2.5 concentration decreased 0.012 μg/m³ (39%) in FFF2035 as compared to BAU2035. Assuming 50% higher road and tire wear PM_2.5 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.

Air pollution's short-term effects on a wide range of health outcomes have been studied extensively, primarily focused on vulnerable groups (e.g., children and the elderly). However, the air pollution effects on the adult working population through sick leave have received little attention. This study aims to 1) estimate the associations between particulate matter ≤2.5 μm³ (PM_2.5) and sick leave episodes and 2) calculate the attributable number of sick leave days and the consequential productivity loss in the City of Stockholm, Sweden. Individual level daily sick leave data was obtained from Statistics Sweden for the years 2011-2019. Daily average concentrations of PM_2.5 were obtained from the main urban background monitoring station in Stockholm. A case-crossover study design was applied to estimate the association between short-term PM_2.5 and onset of sick leave episodes. Conditional logistic regression was used to estimate the relative increase in odds of onset per 10 μg/m³ of PM_2.5, adjusting for temperature, season, and pollen. A human capital method was applied to estimate the PM_2.5 attributable productivity loss. In total, 1.5 million (M) individual sick leave occurrences were studied. The measured daily mean PM_2.5 concentration was 4.2 μg/m³ (IQR 3.7 μg/m³). The odds of a sick leave episode was estimated to increase by 8.5% (95% CI: 7.8-9.3) per 10 μg/m³ average exposure 2-4 days before. Sub-group analysis showed that private sector and individuals 15-24 years old had a lower increase in odds of sick leave episodes in relation to PM_2.5 exposure. In Stockholm, 4% of the sick leave episodes were attributable to PM_2.5 exposure, corresponding to €17 M per year in productivity loss. Our study suggests a positive association between PM_2.5 and sick leave episodes in a low exposure area.