To investigate the associations between air pollution and mortality, focusing on associations below current European Union, United States, and World Health Organization standards and guidelines.
...Pooled analysis of eight cohorts.
Multicentre project Effects of Low-Level Air Pollution: A Study in Europe (ELAPSE) in six European countries.
325 367 adults from the general population recruited mostly in the 1990s or 2000s with detailed lifestyle data. Stratified Cox proportional hazard models were used to analyse the associations between air pollution and mortality. Western Europe-wide land use regression models were used to characterise residential air pollution concentrations of ambient fine particulate matter (PM
), nitrogen dioxide, ozone, and black carbon.
Deaths due to natural causes and cause specific mortality.
Of 325 367 adults followed-up for an average of 19.5 years, 47 131 deaths were observed. Higher exposure to PM
, nitrogen dioxide, and black carbon was associated with significantly increased risk of almost all outcomes. An increase of 5 µg/m
in PM
was associated with 13% (95% confidence interval 10.6% to 15.5%) increase in natural deaths; the corresponding figure for a 10 µg/m
increase in nitrogen dioxide was 8.6% (7% to 10.2%). Associations with PM
, nitrogen dioxide, and black carbon remained significant at low concentrations. For participants with exposures below the US standard of 12 µg/m
an increase of 5 µg/m
in PM
was associated with 29.6% (14% to 47.4%) increase in natural deaths.
Our study contributes to the evidence that outdoor air pollution is associated with mortality even at low pollution levels below the current European and North American standards and WHO guideline values. These findings are therefore an important contribution to the debate about revision of air quality limits, guidelines, and standards, and future assessments by the Global Burden of Disease.
Summary 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/m3 ). For PM2·5 the HR was 1·18 (0·96–1·46) per 5 μg/m3 . 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/m3 ) 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 Community's Seventh Framework Programme.
To further quantify the association between physical activity (PA) after breast cancer diagnosis and all-cause mortality, breast cancer mortality and/or breast cancer recurrence.
PubMed was searched ...until November 2017 for observational studies investigating any type of PA in association with total mortality, breast cancer mortality and/or breast cancer recurrence among women with breast cancer diagnosis. Pooled hazard ratios (HRs) and 95% confidence intervals (CIs) were estimated using random-effects models for highest versus lowest categories of PA. Ten studies were included in the meta-analysis. During an average follow-up ranging from 3.5 to 12.7 years there were 23,041 breast cancer survivors, 2,522 deaths from all causes, 841 deaths from breast cancer and 1,398 recurrences/remissions. Compared to women in the lowest recreational PA level (lowest quintile/quartile), women in the highest level had a lower risk of all-cause mortality (HR = 0.58, 95% CIs: 0.45–0.75; 8 studies), of death from breast cancer (HR = 0.60, 95% CIs 0.36–0.99; 5 studies) and a lower, albeit non-significant, risk of recurrence (HR = 0.79, 95% CIs 0.60–1.05; 5 studies). There was evidence of heterogeneity between the studies evaluating recreational PA and total mortality (Ι2 = 52.4%) and even higher for breast cancer mortality (Ι2 = 77.7%) or recurrence (Ι2 = 66.4%).
Highest recreational PA after breast cancer diagnosis was associated with lower all-cause and breast cancer mortality. This finding probably reflects the favorable impact of PA on cardiovascular mortality, and a possible favorable role on breast cancer survival, though reverse causation cannot be excluded.
•Physical activity was associated with survival among breast cancer survivors.•Post-diagnosis physical activity was inversely associated with all-cause mortality.•Post-diagnosis physical activity was inversely associated with breast-cancer mortality.•The inverse association with breast cancer recurrence was consistent but not significant.
Summary 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/m3 was recorded. No heterogeneity was noted between individual cohort effect estimates (I2 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/m3 (HR 1·06, 95% CI 1·00–1·12) or below 20 μg/m3 (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 Community's Seventh Framework Program (FP7/2007–2011).
In order to investigate associations between air pollution and adverse health effects consistent fine spatial air pollution surfaces are needed across large areas to provide cohorts with comparable ...exposures. The aim of this paper is to develop and evaluate fine spatial scale land use regression models for four major health relevant air pollutants (PM2.5, NO2, BC, O3) across Europe.
We developed West-European land use regression models (LUR) for 2010 estimating annual mean PM2.5, NO2, BC and O3 concentrations (including cold and warm season estimates for O3). The models were based on AirBase routine monitoring data (PM2.5, NO2 and O3) and ESCAPE monitoring data (BC), and incorporated satellite observations, dispersion model estimates, land use and traffic data. Kriging was performed on the residual spatial variation from the LUR models and added to the exposure estimates. One model was developed using all sites (100%). Robustness of the models was evaluated by performing a five-fold hold-out validation and for PM2.5 and NO2 additionally with independent comparison at ESCAPE measurements. To evaluate the stability of each model's spatial structure over time, separate models were developed for different years (NO2 and O3: 2000 and 2005; PM2.5: 2013).
The PM2.5, BC, NO2, O3 annual, O3 warm season and O3 cold season models explained respectively 72%, 54%, 59%, 65%, 69% and 83% of spatial variation in the measured concentrations. Kriging proved an efficient technique to explain a part of residual spatial variation for the pollutants with a strong regional component explaining respectively 10%, 24% and 16% of the R2 in the PM2.5, O3 warm and O3 cold models. Explained variance at fully independent sites vs the internal hold-out validation was slightly lower for PM2.5 (65% vs 66%) and lower for NO2 (49% vs 57%). Predictions from the 2010 model correlated highly with models developed in other years at the overall European scale.
We developed robust PM2.5, NO2, O3 and BC hybrid LUR models. At the West-European scale models were robust in time, becoming less robust at smaller spatial scales. Models were applied to 100 × 100 m surfaces across Western Europe to allow for exposure assignment for 35 million participants from 18 European cohorts participating in the ELAPSE study.
•Robust PM2.5, NO2, BC and O3 hybrid LUR models at a 100x100 m resolution for Western Europe were developed•Models included large scale SAT and CTM estimates and fine scale traffic and land use and were further improved with kriging•Models were robust in time at European scale, becoming less robust at smaller spatial scales.
Empirical spatial air pollution models have been applied extensively to assess exposure in epidemiological studies with increasingly sophisticated and complex statistical algorithms beyond ordinary ...linear regression. However, different algorithms have rarely been compared in terms of their predictive ability.
This study compared 16 algorithms to predict annual average fine particle (PM2.5) and nitrogen dioxide (NO2) concentrations across Europe. The evaluated algorithms included linear stepwise regression, regularization techniques and machine learning methods. Air pollution models were developed based on the 2010 routine monitoring data from the AIRBASE dataset maintained by the European Environmental Agency (543 sites for PM2.5 and 2399 sites for NO2), using satellite observations, dispersion model estimates and land use variables as predictors. We compared the models by performing five-fold cross-validation (CV) and by external validation (EV) using annual average concentrations measured at 416 (PM2.5) and 1396 sites (NO2) from the ESCAPE study. We further assessed the correlations between predictions by each pair of algorithms at the ESCAPE sites.
For PM2.5, the models performed similarly across algorithms with a mean CV R2 of 0.59 and a mean EV R2 of 0.53. Generalized boosted machine, random forest and bagging performed best (CV R2~0.63; EV R2 0.58–0.61), while backward stepwise linear regression, support vector regression and artificial neural network performed less well (CV R2 0.48–0.57; EV R2 0.39–0.46). Most of the PM2.5 model predictions at ESCAPE sites were highly correlated (R2 > 0.85, with the exception of predictions from the artificial neural network). For NO2, the models performed even more similarly across different algorithms, with CV R2s ranging from 0.57 to 0.62, and EV R2s ranging from 0.49 to 0.51. The predicted concentrations from all algorithms at ESCAPE sites were highly correlated (R2 > 0.9). For both pollutants, biases were low for all models except the artificial neural network. Dispersion model estimates and satellite observations were two of the most important predictors for PM2.5 models whilst dispersion model estimates and traffic variables were most important for NO2 models in all algorithms that allow assessment of the importance of variables.
Different statistical algorithms performed similarly when modelling spatial variation in annual average air pollution concentrations using a large number of training sites.
•Multiple statistical algorithms with very different assumptions were compared.•Despite the difference in modeling frameworks, predictions among the models exhibit generally good agreement.•The use of an external evaluation dataset strengthens evaluation by cross-validation.
AbstractObjectiveTo assess short term mortality risks and excess mortality associated with exposure to ozone in several cities worldwide.DesignTwo stage time series analysis.Setting406 cities in 20 ...countries, with overlapping periods between 1985 and 2015, collected from the database of Multi-City Multi-Country Collaborative Research Network.PopulationDeaths for all causes or for external causes only registered in each city within the study period.Main outcome measuresDaily total mortality (all or non-external causes only).ResultsA total of 45 165 171 deaths were analysed in the 406 cities. On average, a 10 µg/m3 increase in ozone during the current and previous day was associated with an overall relative risk of mortality of 1.0018 (95% confidence interval 1.0012 to 1.0024). Some heterogeneity was found across countries, with estimates ranging from greater than 1.0020 in the United Kingdom, South Africa, Estonia, and Canada to less than 1.0008 in Mexico and Spain. Short term excess mortality in association with exposure to ozone higher than maximum background levels (70 µg/m3) was 0.26% (95% confidence interval 0.24% to 0.28%), corresponding to 8203 annual excess deaths (95% confidence interval 3525 to 12 840) across the 406 cities studied. The excess remained at 0.20% (0.18% to 0.22%) when restricting to days above the WHO guideline (100 µg/m3), corresponding to 6262 annual excess deaths (1413 to 11 065). Above more lenient thresholds for air quality standards in Europe, America, and China, excess mortality was 0.14%, 0.09%, and 0.05%, respectively.ConclusionsResults suggest that ozone related mortality could be potentially reduced under stricter air quality standards. These findings have relevance for the implementation of efficient clean air interventions and mitigation strategies designed within national and international climate policies.
•Studies with information on PM2.5 and NO2 measurement error structures were reviewed.•We derived outdoor source personal exposure to compare with ambient concentrations.•Outdoor sources contribute ...44% to total personal exposure to PM2.5 and 74% for NO2.•Mean difference (measurement error) was 5.72 μg/m3 for PM2.5 and 7.17 ppb for NO2.•Error variability was also greater for NO2. Error correlation was not reported.
The use of proxy exposure estimates for PM2.5 and NO2 in air pollution studies instead of personal exposures, introduces measurement error, which can produce biased epidemiological effect estimates. Most studies consider total personal exposure as the gold standard. However, when studying the effects of ambient air pollution, personal exposure from outdoor sources is the exposure of interest.
We assessed the magnitude and variability of exposure measurement error by conducting a systematic review of the differences between personal exposures from outdoor sources and the corresponding measurements for ambient concentrations in order to increase understanding of the measurement error structures of the pollutants.
We reviewed the literature (ISI Web of Science, Medline, 2000–2016) for English language studies (in any age group in any location (NO2) or Europe and North America (PM2.5)) that reported repeated measurements over time both for personal and ambient PM2.5 or NO2 concentrations. Only a few studies reported personal exposure from outdoor sources. We also collected data for infiltration factors and time-activity patterns of the individuals in order to estimate personal exposures from outdoor sources in every study.
Studies using modelled rather than monitored exposures were excluded. Type of personal exposure monitor was assessed. Random effects meta-analysis was conducted to quantify exposure error as the mean difference between “true” and proxy measures.
Thirty-two papers for PM2.5 and 24 for NO2 were identified. Outdoor sources were found to contribute 44% (range: 33–55%) of total personal exposure to PM2.5 and 74% (range: 57–88%) to NO2. Overall estimates of personal exposure (24-hour averages) from outdoor sources were 9.3 μg/m3 and 12.0 ppb for PM2.5 and NO2 respectively, while the corresponding difference between these exposures and the ambient concentrations (i.e. the measurement error) was 5.72 μg/m3 and 7.17 ppb. Our findings indicated also higher error variability for NO2 than PM2.5. Large heterogeneity was observed which was not explained sufficiently by geographical location or age group of the study sample.
Relying only on information available in published studies led to some limitations: the contribution of outdoor sources to total personal exposure for NO2 had to be inferred, individual variation in exposure misclassification was unavailable and instrument error could not be addressed. The larger magnitude and variability of errors for NO2 compared with PM2.5 has implications for biases in the health effect estimates of multi-pollutant epidemiological models. Results suggest that further research is needed regarding personal exposure studies and measurement error bias in epidemiological models.
Few multi-city studies in Asian developing countries have examined the acute health effects of ambient nitrogen dioxide (NO2). In the China Air Pollution and Health Effects Study (CAPES), we ...investigated the short-term association between NO2 and mortality in 17 Chinese cities. We applied two-stage Bayesian hierarchical models to obtain city-specific and national average estimates for NO2. In each city, we used Poisson regression models incorporating natural spline smoothing functions to adjust for long-term and seasonal trend of mortality, as well as other time-varying covariates. We examined the associations by age, gender and education status. We combined the individual-city estimates of the concentration–response curves to get an overall NO2–mortality association in China. The averaged daily concentrations of NO2 in the 17 Chinese cities ranged from 26μg/m3 to 67μg/m3. In the combined analysis, a 10-μg/m3 increase in two-day moving averaged NO2 was associated with a 1.63% 95% posterior interval (PI), 1.09 to 2.17, 1.80% (95% PI, 1.00 to 2.59) and 2.52% (95% PI, 1.44 to 3.59) increase of total, cardiovascular, and respiratory mortality, respectively. These associations remained significant after adjustment for ambient particles or sulfur dioxide (SO2). Older people appeared to be more vulnerable to NO2 exposure. The combined concentration–response curves indicated a linear association. Conclusively, this largest epidemiologic study of NO2 in Asian developing countries to date suggests that short-term exposure to NO2 is associated with increased mortality risk.
► Few multi-city studies in Asia have examined the acute health effects of NO2. ► We observed significant effects of NO2 on daily mortality in 17 Chinese cities. ► Age, but not gender or socioeconomic status, may modify the health effects of NO2.
Few comparisons between causal inference and traditional approaches have been performed. We applied “causal” and “traditional” methods to investigate the association between long-term air pollution ...exposure (PM2.5 and NO2) and mortality.
We analyzed pooled data from eight well-characterized cohorts and one administrative cohort. We defined the generalized propensity score (GPS) as the conditional likelihood of exposure given confounders, and derived corresponding inverse-probability weights (IPW). We applied Cox-proportional hazard models weighted by IPW, adjusted for GPS, and directly adjusting for all confounders.
In IPW models, PM2.5 5 µg/m3 increases were associated with hazard ratios (HR) = 1.141 (95% confidence interval (CI): 1.107, 1.176) and 1.050 (1.014, 1.088) in the pooled and administrative cohorts. Corresponding estimates for traditional Cox models were 1.132 (1.107, 1.158) and 1.057 (1.025, 1.089). Almost identical results were found for all approaches and both pollutants, when unbalanced covariates were adjusted for in causal models.
Traditional and causal approaches provided consistent associations between long-term exposure to air pollution and mortality.
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