Long-term exposure to traffic-related air pollution and non-accidental mortality: A systematic review and meta-analysis.

BACKGROUND: The health effects of traffic-related air pollution (TRAP) continue to be of important public health interest across the globe. Following its 2010 review, the Health Effects Institute appointed a new expert Panel to systematically evaluate the epidemiological evidence regarding the associations between long-term exposure to TRAP and selected health outcomes. This paper describes the main findings of the systematic review on non-accidental mortality. METHODS: The Panel used a systematic approach to conduct the review. An extensive search was conducted of literature published between 1980 and 2019. A new exposure framework was developed to determine whether a study was sufficiently specific to TRAP, which included studies beyond the near-roadway environment. We performed random-effects meta-analysis when at least three estimates were available of an association between a specific exposure and outcome. We evaluated confidence in the evidence using a modified Office of Health Assessment and Translation (OHAT) approach, supplemented with a broader narrative synthesis. RESULTS: Thirty-six cohort studies were included. Virtually all studies adjusted for a large number of individual and area-level covariates-including smoking, body mass index, and individual and area-level socioeconomic status-and were judged at a low or moderate risk for bias. Most studies were conducted in North America and Europe, and a few were based in Asia and Australia. The meta-analytic summary estimates for nitrogen dioxide, elemental carbon and fine particulate matter-pollutants with more than 10 studies-were 1.04 (95% CI 1.01, 1.06), 1.02 (1.00, 1.04) and 1.03 (1.01, 1.05) per 10, 1 and 5 µg/m3, respectively. Effect estimates are interpreted as the relative risk of mortality when the exposure differs with the selected increment. The confidence in the evidence for these pollutants was judged as high, because of upgrades for monotonic exposure-response and consistency across populations. The consistent findings across geographical regions, exposure assessment methods and confounder adjustment resulted in a high confidence rating using a narrative approach as well. CONCLUSIONS: The overall confidence in the evidence for a positive association between long-term exposure to TRAP and non-accidental mortality was high.

Long-term exposure to traffic-related air pollution and stroke: A systematic review and meta-analysis.

BACKGROUND: Stroke remains the second cause of death worldwide. The mechanisms underlying the adverse association of exposure to traffic-related air pollution (TRAP) with overall cardiovascular disease may also apply to stroke. Our objective was to systematically evaluate the epidemiological evidence regarding the associations of long-term exposure to TRAP with stroke. METHODS: PubMed and LUDOK electronic databases were searched systematically for observational epidemiological studies from 1980 through 2019 on long-term exposure to TRAP and stroke with an update in January 2022. TRAP was defined according to a comprehensive protocol based on pollutant and exposure assessment methods or proximity metrics. Study selection, data extraction, risk of bias (RoB) and confidence assessments were conducted according to standardized protocols. We performed meta-analyses using random effects models; sensitivity analyses were assessed by geographic area, RoB, fatality, traffic specificity and new studies. RESULTS: Nineteen studies were included. The meta-analytic relative risks (and 95% confidence intervals) were: 1.03 (0.98-1.09) per 1 µg/m3 EC, 1.09 (0.96-1.23) per 10 µg/m3 PM10, 1.08 (0.89-1.32) per 5 µg/m3 PM2.5, 0.98 (0.92; 1.05) per 10 µg/m3 NO2 and 0.99 (0.94; 1.04) per 20 µg/m3 NOx with little to moderate heterogeneity based on 6, 5, 4, 7 and 8 studies, respectively. The confidence assessments regarding the quality of the body of evidence and separately regarding the presence of an association of TRAP with stroke considering all available evidence were rated low and moderate, respectively. CONCLUSION: The available literature provides low to moderate evidence for an association of TRAP with stroke.

Long-term exposure to traffic-related air pollution and selected health outcomes: A systematic review and meta-analysis.

The health effects of traffic-related air pollution (TRAP) continue to be of important public health interest. Following its well-cited 2010 critical review, the Health Effects Institute (HEI) appointed a new expert Panel to systematically evaluate the epidemiological evidence regarding the associations between long-term exposure to TRAP and selected adverse health outcomes. Health outcomes were selected based on evidence of causality for general air pollution (broader than TRAP) cited in authoritative reviews, relevance for public health and policy, and resources available. The Panel used a systematic approach to search the literature, select studies for inclusion in the review, assess study quality, summarize results, and reach conclusions about the confidence in the evidence. An extensive search was conducted of literature published between January 1980 and July 2019 on selected health outcomes. A new exposure framework was developed to determine whether a study was sufficiently specific to TRAP. In total, 353 studies were included in the review. Respiratory effects in children (118 studies) and birth outcomes (86 studies) were the most commonly studied outcomes. Fewer studies investigated cardiometabolic effects (57 studies), respiratory effects in adults (50 studies), and mortality (48 studies). The findings from the systematic review, meta-analyses, and evaluation of the quality of the studies and potential biases provided an overall high or moderate-to-high level of confidence in an association between long-term exposure to TRAP and the adverse health outcomes all-cause, circulatory, ischemic heart disease and lung cancer mortality, asthma onsetin chilldren and adults, and acute lower respiratory infections in children. The evidence was considered moderate, low or very low for the other selected outcomes. In light of the large number of people exposed to TRAP - both in and beyond the near-road environment - the Panel concluded that the overall high or moderate-to-high confidence in the evidence for an association between long-term exposure to TRAP and several adverse health outcomes indicates that exposures to TRAP remain an important public health concern and deserve greater attention from the public and from policymakers.

Impact of preferential sampling on exposure prediction and health effect inference in the context of air pollution epidemiology.

Preferential sampling has been defined in the context of geostatistical modeling as the dependence between the sampling locations and the process that describes the spatial structure of the data. It can occur when networks are designed to find high values. For example, in networks based on the U.S. Clean Air Act monitors are sited to determine whether air quality standards are exceeded. We study the impact of the design of monitor networks in the context of air pollution epidemiology studies. The effect of preferential sampling has been illustrated in the literature by highlighting its impact on spatial predictions. In this paper, we use these predictions as input in a second stage analysis, and we assess how they affect health effect inference. Our work is motivated by data from two United States regulatory networks and health data from the Multi-Ethnic Study of Atherosclerosis and Air Pollution. The two networks were designed to monitor air pollution in urban and rural areas respectively, and we found that the health analysis results based on the two networks can lead to different scientific conclusions. We use preferential sampling to gain insight into these differences. We designed a simulation study, and found that the validity and reliability of the health effect estimate can be greatly affected by how we sample the monitor locations. To better understand its effect on second stage inference, we identify two components of preferential sampling that shed light on how preferential sampling alters the properties of the health effect estimate.