Martina S. Ragettli

Improving health through policies that promote active travel: A review of evidence to support integrated health impact assessment

BACKGROUND Substantial policy changes to control obesity, limit chronic disease, and reduce air pollution emissions, including greenhouse gasses, have been recommended. Transportation and planning policies that promote active travel by walking and cycling can contribute to these goals, potentially yielding further co-benefits. Little is known, however, about the interconnections among effects of policies considered, including potential unintended consequences. OBJECTIVES AND METHODS We review available literature regarding health impacts from policies that encourage active travel in the context of developing health impact assessment (HIA) models to help decision-makers propose better solutions for healthy environments. We identify important components of HIA models of modal shifts in active travel in response to transport policies and interventions. RESULTS AND DISCUSSION Policies that increase active travel are likely to generate large individual health benefits through increases in physical activity for active travelers. Smaller, but population-wide benefits could accrue through reductions in air and noise pollution. Depending on conditions of policy implementations, risk tradeoffs are possible for some individuals who shift to active travel and consequently increase inhalation of air pollutants and exposure to traffic injuries. Well-designed policies may enhance health benefits through indirect outcomes such as improved social capital and diet, but these synergies are not sufficiently well understood to allow quantification at this time. CONCLUSION Evaluating impacts of active travel policies is highly complex; however, many associations can be quantified. Identifying health-maximizing policies and conditions requires integrated HIAs.

Natural-Cause Mortality and Long-Term Exposure to Particle Components: An Analysis of 19 European Cohorts within the Multi-Center ESCAPE Project

Background Studies have shown associations between mortality and long-term exposure to particulate matter air pollution. Few cohort studies have estimated the effects of the elemental composition of particulate matter on mortality. Objectives Our aim was to study the association between natural-cause mortality and long-term exposure to elemental components of particulate matter. Methods Mortality and confounder data from 19 European cohort studies were used. Residential exposure to eight a priori–selected components of particulate matter (PM) was characterized following a strictly standardized protocol. Annual average concentrations of copper, iron, potassium, nickel, sulfur, silicon, vanadium, and zinc within PM size fractions ≤ 2.5 μm (PM2.5) and ≤ 10 μm (PM10) were estimated using land-use regression models. Cohort-specific statistical analyses of the associations between mortality and air pollution were conducted using Cox proportional hazards models using a common protocol followed by meta-analysis. Results The total study population consisted of 291,816 participants, of whom 25,466 died from a natural cause during follow-up (average time of follow-up, 14.3 years). Hazard ratios were positive for almost all elements and statistically significant for PM2.5 sulfur (1.14; 95% CI: 1.06, 1.23 per 200 ng/m3). In a two-pollutant model, the association with PM2.5 sulfur was robust to adjustment for PM2.5 mass, whereas the association with PM2.5 mass was reduced. Conclusions Long-term exposure to PM2.5 sulfur was associated with natural-cause mortality. This association was robust to adjustment for other pollutants and PM2.5. Citation Beelen R, Hoek G, Raaschou-Nielsen O, Stafoggia M, Andersen ZJ, Weinmayr G, Hoffmann B, Wolf K, Samoli E, Fischer PH, Nieuwenhuijsen MJ, Xun WW, Katsouyanni K, Dimakopoulou K, Marcon A, Vartiainen E, Lanki T, Yli-Tuomi T, Oftedal B, Schwarze PE, Nafstad P, De Faire U, Pedersen NL, Östenson C-G, Fratiglioni L, Penell J, Korek M, Pershagen G, Eriksen KT, Overvad K, Sørensen M, Eeftens M, Peeters PH, Meliefste K, Wang M, Bueno-de-Mesquita HB, Sugiri D, Krämer U, Heinrich J, de Hoogh K, Key T, Peters A, Hampel R, Concin H, Nagel G, Jaensch A, Ineichen A, Tsai MY, Schaffner E, Probst-Hensch NM, Schindler C, Ragettli MS, Vilier A, Clavel-Chapelon F, Declercq C, Ricceri F, Sacerdote C, Galassi C, Migliore E, Ranzi A, Cesaroni G, Badaloni C, Forastiere F, Katsoulis M, Trichopoulou A, Keuken M, Jedynska A, Kooter IM, Kukkonen J, Sokhi RS, Vineis P, Brunekreef B. 2015. Natural-cause mortality and long-term exposure to particle components: an analysis of 19 European cohorts within the Multi-Center ESCAPE Project. Environ Health Perspect 123:525–533; http://dx.doi.org/10.1289/ehp.1408095

Investigating the Use Of Portable Air Pollution Sensors to Capture the Spatial Variability Of Traffic-Related Air Pollution.

Advances in microsensor technologies for air pollution monitoring encourage a growing use of portable sensors. This study aims at testing their performance in the development of exposure surfaces for nitrogen dioxide (NO2) and ozone (O3). In Montreal, Canada, a data-collection campaign was conducted across three seasons in 2014 for 76 sites spanning the range of land uses and built environments of the city; each site was visited from 6 to 12 times, for 20 min, using NO2 and O3 sensors manufactured by Aeroqual. Land-use regression models were developed, achieving R(2) values of 0.86 for NO2 and 0.92 for O3 when adjusted for regional meteorology to control for the fact that all of the locations were not monitored at the same time. A total of two exposure surfaces were then developed for NO2 and O3 as averages over spring, summer, and fall. Validation against the fixed-station data and previous campaigns suggests that Aeroqual sensors tend to overestimate the highest NO2 and O3 concentrations, thus increasing the range of values across the city. However, the sensors suggest a good performance with respect to capturing the spatial variability in NO2 and O3 and are very convenient to use, having great potential for capturing temporal variability.

Performance of Multi-City Land Use Regression Models for Nitrogen Dioxide and Fine Particles

Background: Land use regression (LUR) models have been developed mostly to explain intraurban variations in air pollution based on often small local monitoring campaigns. Transferability of LUR models from city to city has been investigated, but little is known about the performance of models based on large numbers of monitoring sites covering a large area. Objectives: We aimed to develop European and regional LUR models and to examine their transferability to areas not used for model development. Methods: We evaluated LUR models for nitrogen dioxide (NO2) and particulate matter (PM; PM2.5, PM2.5 absorbance) by combining standardized measurement data from 17 (PM) and 23 (NO2) ESCAPE (European Study of Cohorts for Air Pollution Effects) study areas across 14 European countries for PM and NO2. Models were evaluated with cross-validation (CV) and hold-out validation (HV). We investigated the transferability of the models by successively excluding each study area from model building. Results: The European model explained 56% of the concentration variability across all sites for NO2, 86% for PM2.5, and 70% for PM2.5 absorbance. The HV R2s were only slightly lower than the model R2 (NO2, 54%; PM2.5, 80%; PM2.5 absorbance, 70%). The European NO2, PM2.5, and PM2.5 absorbance models explained a median of 59%, 48%, and 70% of within-area variability in individual areas. The transferred models predicted a modest-to-large fraction of variability in areas that were excluded from model building (median R2: NO2, 59%; PM2.5, 42%; PM2.5 absorbance, 67%). Conclusions: Using a large data set from 23 European study areas, we were able to develop LUR models for NO2 and PM metrics that predicted measurements made at independent sites and areas reasonably well. This finding is useful for assessing exposure in health studies conducted in areas where no measurements were conducted. Citation: Wang M, Beelen R, Bellander T, Birk M, Cesaroni G, Cirach M, Cyrys J, de Hoogh K, Declercq C, Dimakopoulou K, Eeftens M, Eriksen KT, Forastiere F, Galassi C, Grivas G, Heinrich J, Hoffmann B, Ineichen A, Korek M, Lanki T, Lindley S, Modig L, Mölter A, Nafstad P, Nieuwenhuijsen MJ, Nystad W, Olsson D, Raaschou-Nielsen O, Ragettli M, Ranzi A, Stempfelet M, Sugiri D, Tsai MY, Udvardy O, Varró MJ, Vienneau D, Weinmayr G, Wolf K, Yli-Tuomi T, Hoek G, Brunekreef B. 2014. Performance of multi-city land use regression models for nitrogen dioxide and fine particles. Environ Health Perspect 122:843–849; http://dx.doi.org/10.1289/ehp.1307271

Transport-related measures to mitigate climate change in Basel, Switzerland: A health-effectiveness comparison study

BACKGROUND Local strategies to reduce green-house gases (GHG) imply changes of non-climatic exposure patterns. OBJECTIVE To assess the health impacts of locally relevant transport-related climate change policies in Basel, Switzerland. METHODS We modelled change in mortality and morbidity for the year 2020 based on several locally relevant transport scenarios including all decided transport policies up to 2020, additional realistic and hypothesized traffic reductions, as well as ambitious diffusion levels of electric cars. The scenarios were compared to the reference condition in 2010 assumed as status quo. The changes in non-climatic population exposure included ambient air pollution, physical activity, and noise. As secondary outcome, changes in Disability-Adjusted Life Years (DALYs) were put into perspective with predicted changes of CO2 emissions and fuel consumption. RESULTS Under the scenario that assumed a strict particle emissions standard in diesel cars and all planned transport measures, 3% of premature deaths could be prevented from projected PM2.5 exposure reduction. A traffic reduction scenario assuming more active trips provided only minor added health benefits for any of the changes in exposure considered. A hypothetical strong support to electric vehicles diffusion would have the largest health effectiveness given that the energy production in Basel comes from renewable sources. CONCLUSION The planned local transport related GHG emission reduction policies in Basel are sensible for mitigating climate change and improving public health. In this context, the most effective policy remains increasing zero-emission vehicles.

Cross-sectional associations between air pollution and chronic bronchitis: an ESCAPE meta-analysis across five cohorts

Background This study aimed to assess associations of outdoor air pollution on prevalence of chronic bronchitis symptoms in adults in five cohort studies (Asthma-E3N, ECRHS, NSHD, SALIA, SAPALDIA) participating in the European Study of Cohorts for Air Pollution Effects (ESCAPE) project. Methods Annual average particulate matter (PM10, PM2.5, PMabsorbance, PMcoarse), NO2, nitrogen oxides (NOx) and road traffic measures modelled from ESCAPE measurement campaigns 2008–2011 were assigned to home address at most recent assessments (1998–2011). Symptoms examined were chronic bronchitis (cough and phlegm for ≥3 months of the year for ≥2 years), chronic cough (with/without phlegm) and chronic phlegm (with/without cough). Cohort-specific cross-sectional multivariable logistic regression analyses were conducted using common confounder sets (age, sex, smoking, interview season, education), followed by meta-analysis. Results 15 279 and 10 537 participants respectively were included in the main NO2 and PM analyses at assessments in 1998–2011. Overall, there were no statistically significant associations with any air pollutant or traffic exposure. Sensitivity analyses including in asthmatics only, females only or using back-extrapolated NO2 and PM10 for assessments in 1985–2002 (ECRHS, NSHD, SALIA, SAPALDIA) did not alter conclusions. In never-smokers, all associations were positive, but reached statistical significance only for chronic phlegm with PMcoarse OR 1.31 (1.05 to 1.64) per 5 µg/m3 increase and PM10 with similar effect size. Sensitivity analyses of older cohorts showed increased risk of chronic cough with PM2.5abs (black carbon) exposures. Conclusions Results do not show consistent associations between chronic bronchitis symptoms and current traffic-related air pollution in adult European populations.

Projections of temperature-related excess mortality under climate change scenarios

Summary Background Climate change can directly affect human health by varying exposure to non-optimal outdoor temperature. However, evidence on this direct impact at a global scale is limited, mainly due to issues in modelling and projecting complex and highly heterogeneous epidemiological relationships across different populations and climates. Methods We collected observed daily time series of mean temperature and mortality counts for all causes or non-external causes only, in periods ranging from Jan 1, 1984, to Dec 31, 2015, from various locations across the globe through the Multi-Country Multi-City Collaborative Research Network. We estimated temperature–mortality relationships through a two-stage time series design. We generated current and future daily mean temperature series under four scenarios of climate change, determined by varying trajectories of greenhouse gas emissions, using five general circulation models. We projected excess mortality for cold and heat and their net change in 1990–2099 under each scenario of climate change, assuming no adaptation or population changes. Findings Our dataset comprised 451 locations in 23 countries across nine regions of the world, including 85 879 895 deaths. Results indicate, on average, a net increase in temperature-related excess mortality under high-emission scenarios, although with important geographical differences. In temperate areas such as northern Europe, east Asia, and Australia, the less intense warming and large decrease in cold-related excess would induce a null or marginally negative net effect, with the net change in 2090–99 compared with 2010–19 ranging from −1·2% (empirical 95% CI −3·6 to 1·4) in Australia to −0·1% (−2·1 to 1·6) in east Asia under the highest emission scenario, although the decreasing trends would reverse during the course of the century. Conversely, warmer regions, such as the central and southern parts of America or Europe, and especially southeast Asia, would experience a sharp surge in heat-related impacts and extremely large net increases, with the net change at the end of the century ranging from 3·0% (−3·0 to 9·3) in Central America to 12·7% (−4·7 to 28·1) in southeast Asia under the highest emission scenario. Most of the health effects directly due to temperature increase could be avoided under scenarios involving mitigation strategies to limit emissions and further warming of the planet. Interpretation This study shows the negative health impacts of climate change that, under high-emission scenarios, would disproportionately affect warmer and poorer regions of the world. Comparison with lower emission scenarios emphasises the importance of mitigation policies for limiting global warming and reducing the associated health risks. Funding UK Medical Research Council.

Health Impacts of Active Transportation in Europe

Policies that stimulate active transportation (walking and bicycling) have been related to heath benefits. This study aims to assess the potential health risks and benefits of promoting active transportation for commuting populations (age groups 16–64) in six European cities. We conducted a health impact assessment using two scenarios: increased cycling and increased walking. The primary outcome measure was all-cause mortality related to changes in physical activity level, exposure to fine particulate matter air pollution with a diameter <2.5 μm, as well as traffic fatalities in the cities of Barcelona, Basel, Copenhagen, Paris, Prague, and Warsaw. All scenarios produced health benefits in the six cities. An increase in bicycle trips to 35% of all trips (as in Copenhagen) produced the highest benefits among the different scenarios analysed in Warsaw 113 (76–163) annual deaths avoided, Prague 61 (29–104), Barcelona 37 (24–56), Paris 37 (18–64) and Basel 5 (3–9). An increase in walking trips to 50% of all trips (as in Paris) resulted in 19 (3–42) deaths avoided annually in Warsaw, 11(3–21) in Prague, 6 (4–9) in Basel, 3 (2–6) in Copenhagen and 3 (2–4) in Barcelona. The scenarios would also reduce carbon dioxide emissions in the six cities by 1,139 to 26,423 (metric tonnes per year). Policies to promote active transportation may produce health benefits, but these depend of the existing characteristics of the cities. Increased collaboration between health practitioners, transport specialists and urban planners will help to introduce the health perspective in transport policies and promote active transportation.

Application of land use regression modelling to assess the spatial distribution of road traffic noise in three European cities

Noise prediction models and noise maps are used to estimate the exposure to road traffic noise, but their availability and the quality of the noise estimates is sometimes limited. This paper explores the application of land use regression (LUR) modelling to assess the long-term intraurban spatial variability of road traffic noise in three European cities. Short-term measurements of road traffic noise taken in Basel, Switzerland (n=60), Girona, Spain (n=40), and Grenoble, France (n=41), were used to develop two LUR models: (a) a “GIS-only” model, which considered only predictor variables derived with Geographic Information Systems; and (b) a “Best” model, which in addition considered the variables collected while visiting the measurement sites. Both noise measurements and noise estimates from LUR models were compared with noise estimates from standard noise models developed for each city by the local authorities. Model performance (adjusted R2) was 0.66–0.87 for “GIS-only” models, and 0.70–0.89 for “Best” models. Short-term noise measurements showed a high correlation (r=0.62–0.78) with noise estimates from the standard noise models. LUR noise estimates did not show any systematic differences in the spatial patterns when compared with those from standard noise models. LUR modelling with accurate GIS source data can be a promising tool for noise exposure assessment with applications in epidemiological studies.

Statistical modeling of the spatial variability of environmental noise levels in Montreal, Canada, using noise measurements and land use characteristics.

The availability of noise maps to assess exposure to noise is often limited, especially in North American cities. We developed land use regression (LUR) models for LAeq24h, Lnight, and Lden to assess the long-term spatial variability of environmental noise levels in Montreal, Canada, considering various transportation noise sources (road, rail, and air). To explore the effects of sampling duration, we compared our LAeq24h levels that were computed over at least five complete contiguous days of measurements to shorter sampling periods (20 min and 24 h). LUR models were built with General Additive Models using continuous 2-min noise measurements from 204 sites. Model performance (adjusted R2) was 0.68, 0.59, and 0.69 for LAeq24h, Lnight, and Lden, respectively. Main predictors of measured noise levels were road-traffic and vegetation variables. Twenty-minute non-rush hour measurements corresponded well with LAeq24h levels computed over 5 days at road-traffic sites (bias: −0.7 dB(A)), but not at rail (−2.1 dB(A)) nor at air (−2.2 dB(A)) sites. Our study provides important insights into the spatial variation of environmental noise levels in a Canadian city. To assess long-term noise levels, sampling strategies should be stratified by noise sources and preferably should include 1 week of measurements at locations exposed to rail and aircraft noise.