Gerard Hoek

Association between mortality and indicators of traffic-related air pollution in the Netherlands: a cohort study

BACKGROUND Long-term exposure to particulate matter air pollution has been associated with increased cardiopulmonary mortality in the USA. We aimed to assess the relation between traffic-related air pollution and mortality in participants of the Netherlands Cohort study on Diet and Cancer (NLCS), an ongoing study. METHODS We investigated a random sample of 5000 people from the full cohort of the NLCS study (age 55-69 years) from 1986 to 1994. Long-term exposure to traffic-related air pollutants (black smoke and nitrogen dioxide) was estimated for the 1986 home address. Exposure was characterised with the measured regional and urban background concentration and an indicator variable for living near major roads. The association between exposure to air pollution and (cause specific) mortality was assessed with Coxs proportional hazards models, with adjustment for potential confounders. FINDINGS 489 (11%) of 4492 people with data died during the follow-up period. Cardiopulmonary mortality was associated with living near a major road (relative risk 1.95, 95% CI 1.09-3.52) and, less consistently, with the estimated ambient background concentration (1.34, 0.68-2.64). The relative risk for living near a major road was 1.41 (0.94-2.12) for total deaths. Non-cardiopulmonary, non-lung cancer deaths were unrelated to air pollution (1.03, 0.54-1.96 for living near a major road). INTERPRETATION Long-term exposure to traffic-related air pollution may shorten life expectancy.

Air pollution and lung cancer incidence in 17 European cohorts: prospective analyses from the European Study of Cohorts for Air Pollution Effects (ESCAPE)

BACKGROUND Ambient air pollution is suspected to cause lung cancer. We aimed to assess the association between long-term exposure to ambient air pollution and lung cancer incidence in European populations. METHODS This prospective analysis of data obtained by the European Study of Cohorts for Air Pollution Effects used data from 17 cohort studies based in nine European countries. Baseline addresses were geocoded and we assessed air pollution by land-use regression models for particulate matter (PM) with diameter of less than 10 μm (PM10), less than 2·5 μm (PM2·5), and between 2·5 and 10 μm (PMcoarse), soot (PM2·5absorbance), nitrogen oxides, and two traffic indicators. We used Cox regression models with adjustment for potential confounders for cohort-specific analyses and random effects models for meta-analyses. FINDINGS The 312 944 cohort members contributed 4 013 131 person-years at risk. During follow-up (mean 12·8 years), 2095 incident lung cancer cases were diagnosed. The meta-analyses showed a statistically significant association between risk for lung cancer and PM10 (hazard ratio [HR] 1·22 [95% CI 1·03-1·45] per 10 μg/m(3)). For PM2·5 the HR was 1·18 (0·96-1·46) per 5 μg/m(3). The same increments of PM10 and PM2·5 were associated with HRs for adenocarcinomas of the lung of 1·51 (1·10-2·08) and 1·55 (1·05-2·29), respectively. An increase in road traffic of 4000 vehicle-km per day within 100 m of the residence was associated with an HR for lung cancer of 1·09 (0·99-1·21). The results showed no association between lung cancer and nitrogen oxides concentration (HR 1·01 [0·95-1·07] per 20 μg/m(3)) or traffic intensity on the nearest street (HR 1·00 [0·97-1·04] per 5000 vehicles per day). INTERPRETATION Particulate matter air pollution contributes to lung cancer incidence in Europe. FUNDING European Communitys Seventh Framework Programme.

Effects of long-term exposure to air pollution on natural-cause mortality: an analysis of 22 European cohorts within the multicentre ESCAPE project

BACKGROUND Few studies on long-term exposure to air pollution and mortality have been reported from Europe. Within the multicentre European Study of Cohorts for Air Pollution Effects (ESCAPE), we aimed to investigate the association between natural-cause mortality and long-term exposure to several air pollutants. METHODS We used data from 22 European cohort studies, which created a total study population of 367,251 participants. All cohorts were general population samples, although some were restricted to one sex only. With a strictly standardised protocol, we assessed residential exposure to air pollutants as annual average concentrations of particulate matter (PM) with diameters of less than 2.5 μm (PM2.5), less than 10 μm (PM10), and between 10 μm and 2.5 μm (PMcoarse), PM2.5 absorbance, and annual average concentrations of nitrogen oxides (NO2 and NOx), with land use regression models. We also investigated two traffic intensity variables-traffic intensity on the nearest road (vehicles per day) and total traffic load on all major roads within a 100 m buffer. We did cohort-specific statistical analyses using confounder models with increasing adjustment for confounder variables, and Cox proportional hazards models with a common protocol. We obtained pooled effect estimates through a random-effects meta-analysis. FINDINGS The total study population consisted of 367,251 participants who contributed 5,118,039 person-years at risk (average follow-up 13.9 years), of whom 29,076 died from a natural cause during follow-up. A significantly increased hazard ratio (HR) for PM2.5 of 1.07 (95% CI 1.02-1.13) per 5 μg/m(3) was recorded. No heterogeneity was noted between individual cohort effect estimates (I(2) p value=0.95). HRs for PM2.5 remained significantly raised even when we included only participants exposed to pollutant concentrations lower than the European annual mean limit value of 25 μg/m(3) (HR 1.06, 95% CI 1.00-1.12) or below 20 μg/m(3) (1.07, 1.01-1.13). INTERPRETATION Long-term exposure to fine particulate air pollution was associated with natural-cause mortality, even within concentration ranges well below the present European annual mean limit value. FUNDING European Communitys Seventh Framework Program (FP7/2007-2011).

Estimating long-term average particulate air pollution concentrations: application of traffic indicators and geographic information systems.

Background. As part of a multicenter study relating traffic-related air pollution with incidence of asthma in three birth cohort studies (TRAPCA), we used a measurement and modelling procedure to estimate long-term average exposure to traffic-related particulate air pollution in communities throughout the Netherlands; in Munich, Germany; and in Stockholm County, Sweden. Methods. In each of the three locations, 40–42 measurement sites were selected to represent rural, urban background and urban traffic locations. At each site and fine particles and filter absorbance (a marker for diesel exhaust particles) were measured for four 2-week periods distributed over approximately 1-year periods between February 1999 and July 2000. We used these measurements to calculate annual average concentrations after adjustment for temporal variation. Traffic-related variables (eg, population density and traffic intensity) were collected using Geographic Information Systems and used in regression models predicting annual average concentrations. From these models we estimated ambient air concentrations at the home addresses of the cohort members. Results. Regression models using traffic-related variables explained 73%, 56% and 50% of the variability in annual average fine particle concentrations for the Netherlands, Munich and Stockholm County, respectively. For filter absorbance, the regression models explained 81%, 67% and 66% of the variability in the annual average concentrations. Cross-validation to estimate the model prediction errors indicated root mean squared errors of 1.1–1.6 &mgr;g/m3 for PM2.5 and 0.22–0.31 *10−5m−1 for absorbance. Conclusions. A substantial fraction of the variability in annual average concentrations for all locations was explained by traffic-related variables. This approach can be used to estimate individual exposures for epidemiologic studies and offers advantages over alternative techniques relying on surrogate variables or traditional approaches that utilize ambient monitoring data alone.

Air pollution and development of asthma, allergy and infections in a birth cohort

Few studies have addressed associations between traffic-related air pollution and respiratory disease in young children. The present authors assessed the development of asthmatic/allergic symptoms and respiratory infections during the first 4 yrs of life in a birth cohort study (n = ∼4,000). Outdoor concentrations of traffic-related air pollutants (nitrogen dioxide PM2.5, particles with a 50% cut-off aerodynamic diameter of 2.5 μm and soot) were assigned to birthplace home addresses with a land-use regression model. They were linked by logistic regression to questionnaire data on doctor-diagnosed asthma, bronchitis, influenza and eczema and to self-reported wheeze, dry night-time cough, ear/nose/throat infections and skin rash. Total and specific immunoglobulin (Ig)E to common allergens were measured in a subgroup (n = 713). Adjusted odds ratios (95% confidence intervals) per interquartile pollution range were elevated for wheeze (1.2 (1.0–1.4) for soot), doctor-diagnosed asthma (1.3 (1.0–1.7)), ear/nose/throat infections (1.2 (1.0–1.3)) and flu/serious colds (1.2 (1.0–1.4)). No consistent associations were observed for other end-points. Positive associations between air pollution and specific sensitisation to common food allergens (1.6 (1.2–2.2) for soot), but not total IgE, were found in the subgroup with IgE measurements. Traffic-related pollution was associated with respiratory infections and some measures of asthma and allergy during the first 4 yrs of life.

Black carbon as an additional indicator of the adverse health effects of airborne particles compared with PM10 and PM2.5.

Background: Current air quality standards for particulate matter (PM) use the PM mass concentration [PM with aerodynamic diameters ≤ 10 μm (PM10) or ≤ 2.5 μm (PM2.5)] as a metric. It has been suggested that particles from combustion sources are more relevant to human health than are particles from other sources, but the impact of policies directed at reducing PM from combustion processes is usually relatively small when effects are estimated for a reduction in the total mass concentration. Objectives: We evaluated the value of black carbon particles (BCP) as an additional indicator in air quality management. Methods: We performed a systematic review and meta-analysis of health effects of BCP compared with PM mass based on data from time-series studies and cohort studies that measured both exposures. We compared the potential health benefits of a hypothetical traffic abatement measure, using near-roadway concentration increments of BCP and PM2.5 based on data from prior studies. Results: Estimated health effects of a 1-μg/m3 increase in exposure were greater for BCP than for PM10 or PM2.5, but estimated effects of an interquartile range increase were similar. Two-pollutant models in time-series studies suggested that the effect of BCP was more robust than the effect of PM mass. The estimated increase in life expectancy associated with a hypothetical traffic abatement measure was four to nine times higher when expressed in BCP compared with an equivalent change in PM2.5 mass. Conclusion: BCP is a valuable additional air quality indicator to evaluate the health risks of air quality dominated by primary combustion particles.

Particulate Air Pollution and Risk of ST-Segment Depression During Repeated Submaximal Exercise Tests Among Subjects With Coronary Heart Disease The Exposure and Risk Assessment for Fine and Ultrafine Particles in Ambient Air (ULTRA) Study

Background—Daily variations in ambient particulate air pollution have been associated with cardiovascular mortality and morbidity. We therefore assessed the associations between levels of the 3 main modes of urban aerosol distribution and the occurrence of ST-segment depressions during repeated exercise tests. Methods and Results—Repeated biweekly submaximal exercise tests were performed during 6 months among adult subjects with stable coronary heart disease in Helsinki, Finland. Seventy-two exercise-induced ST-segment depressions >0.1 mV occurred during 342 exercise tests among 45 subjects. Simultaneously, particle mass <2.5 &mgr;m (PM2.5) and the number concentrations of ultrafine particles (particle diameter 10 to 100 nm [NC0.01–0.1]) and accumulation mode particles (100 to 1000 nm [NC0.1–1]) were monitored at a central site. Levels of particulate air pollution 2 days before the clinic visit were significantly associated with increased risk of ST-segment depression during exercise test. The association was most consistent for measures of particles reflecting accumulation mode particles (odds ratio 3.29; 95% CI, 1.57 to 6.92 for NC0.1–1 and 2.84; 95% CI, 1.42 to 5.66 for PM2.5), but ultrafine particles also had an effect (odds ratio 3.14; 95% CI, 1.56 to 6.32), which was independent of PM2.5. Also, gaseous pollutants NO2 and CO were associated with an increased risk for ST-segment depressions. No consistent association was observed for coarse particles. The associations tended to be stronger among subjects who did not use &bgr;-blockers. Conclusions—The present results suggest that the effect of particulate air pollution on cardiovascular morbidity is at least partly mediated through increased susceptibility to myocardial ischemia.

Long-term effects of traffic-related air pollution on mortality in a Dutch cohort (NLCS-AIR study)

Background Several studies have found an effect on mortality of between-city contrasts in long-term exposure to air pollution. The effect of within-city contrasts is still poorly understood. Objectives We studied the association between long-term exposure to traffic-related air pollution and mortality in a Dutch cohort. Methods We used data from an ongoing cohort study on diet and cancer with 120,852 subjects who were followed from 1987 to 1996. Exposure to black smoke (BS), nitrogen dioxide, sulfur dioxide, and particulate matter ≤mu;M2.5), as well as various exposure variables related to traffic, were estimated at the home address. We conducted Cox analyses in the full cohort adjusting for age, sex, smoking, and area-level socioeconomic status. Results Traffic intensity on the nearest road was independently associated with mortality. Relative risks (95% confidence intervals) for a 10-μg/m3 increase in BS concentrations (difference between 5th and 95th percentile) were 1.05 (1.00–1.11) for natural cause, 1.04 (0.95–1.13) for cardiovascular, 1.22 (0.99–1.50) for respiratory, 1.03 (0.88–1.20) for lung cancer, and 1.04 (0.97–1.12) for mortality other than cardiovascular, respiratory, or lung cancer. Results were similar for NO2 and PM2.5, but no associations were found for SO2. Conclusions Traffic-related air pollution and several traffic exposure variables were associated with mortality in the full cohort. Relative risks were generally small. Associations between natural-cause and respiratory mortality were statistically significant for NO2 and BS. These results add to the evidence that long-term exposure to ambient air pollution is associated with increased mortality.

Development of Land Use Regression Models for PM2.5, PM2.5 Absorbance, PM10 and PMcoarse in 20 European Study Areas; Results of the ESCAPE Project

Land Use Regression (LUR) models have been used increasingly for modeling small-scale spatial variation in air pollution concentrations and estimating individual exposure for participants of cohort studies. Within the ESCAPE project, concentrations of PM(2.5), PM(2.5) absorbance, PM(10), and PM(coarse) were measured in 20 European study areas at 20 sites per area. GIS-derived predictor variables (e.g., traffic intensity, population, and land-use) were evaluated to model spatial variation of annual average concentrations for each study area. The median model explained variance (R(2)) was 71% for PM(2.5) (range across study areas 35-94%). Model R(2) was higher for PM(2.5) absorbance (median 89%, range 56-97%) and lower for PM(coarse) (median 68%, range 32- 81%). Models included between two and five predictor variables, with various traffic indicators as the most common predictors. Lower R(2) was related to small concentration variability or limited availability of predictor variables, especially traffic intensity. Cross validation R(2) results were on average 8-11% lower than model R(2). Careful selection of monitoring sites, examination of influential observations and skewed variable distributions were essential for developing stable LUR models. The final LUR models are used to estimate air pollution concentrations at the home addresses of participants in the health studies involved in ESCAPE.

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.