Systematic review and meta-analysis of cohort studies of long term outdoor nitrogen dioxide exposure and mortality.

OBJECTIVE: To determine whether long term exposure to outdoor nitrogen dioxide (NO2) is associated with all-cause or cause-specific mortality. METHODS: MEDLINE, Embase, CENTRAL, Global Health and Toxline databases were searched using terms developed by a librarian. Screening, data extraction and risk of bias assessment were completed independently by two reviewers. Conflicts were resolved through consensus and/or involvement of a third reviewer. Pooling of results across studies was conducted using random effects models, heterogeneity among included studies was assessed using Cochran's Q and I2 measures, and sources of heterogeneity were evaluated using meta-regression. Sensitivity of pooled estimates to individual studies was examined and publication bias was evaluated using Funnel plots, Begg's and Egger's tests, and trim and fill. RESULTS: Seventy-nine studies based on 47 cohorts, plus one set of pooled analyses of multiple European cohorts, met inclusion criteria. There was a consistently high degree of heterogeneity. After excluding studies with probably high or high risk of bias in the confounding domain (n = 12), pooled hazard ratios (HR) indicated that long term exposure to NO2 was significantly associated with mortality from all/ natural causes (pooled HR 1.047, 95% confidence interval (CI), 1.023-1.072 per 10 ppb), cardiovascular disease (pooled HR 1.058, 95%CI 1.026-1.091), lung cancer (pooled HR 1.083, 95%CI 1.041-1.126), respiratory disease (pooled HR 1.062, 95%CI1.035-1.089), and ischemic heart disease (pooled HR 1.111, 95%CI 1.079-1.144). Pooled estimates based on multi-pollutant models were consistently smaller than those from single pollutant models and mostly non-significant. CONCLUSIONS: For all causes of death other than cerebrovascular disease, the overall quality of the evidence is moderate, and the strength of evidence is limited, while for cerebrovascular disease, overall quality is low and strength of evidence is inadequate. Important uncertainties remain, including potential confounding by co-pollutants or other concomitant exposures, and limited supporting mechanistic evidence. (PROSPERO registration number CRD42018084497).

The Oakville Oil Refinery Closure and Its Influence on Local Hospitalizations: A Natural Experiment on Sulfur Dioxide.

Background: An oil refinery in Oakville, Canada, closed over 2004⁻2005, providing an opportunity for a natural experiment to examine the effects on oil refinery-related air pollution and residents' health. Methods: Environmental and health data were collected for the 16 years around the refinery closure. Toronto (2.5 million persons) and the Greater Toronto Area (GTA, 6.3 million persons) were used as control and reference populations, respectively, for Oakville (160,000 persons). We compared sulfur dioxide and age- and season-standardized hospitalizations, considering potential factors such as changes in demographics, socio-economics, drug prescriptions, and environmental variables. Results: The closure of the refinery eliminated 6000 tons/year of SO2 emissions, with an observed reduction of 20% in wind direction-adjusted ambient concentrations in Oakville. After accounting for trends, a decrease in cold-season peak-centered respiratory hospitalizations was observed for Oakville (reduction of 2.2 cases/1000 persons per year, p = 0.0006 ) but not in Toronto (p = 0.856) and the GTA (p = 0.334). The reduction of respiratory hospitalizations in Oakville post closure appeared to have no observed link to known confounders or effect modifiers. Conclusion: The refinery closure allowed an assessment of the change in community health. This natural experiment provides evidence that a reduction in emissions was associated with improvements in population health. This study design addresses the impact of a removed source of air pollution.

Estimates and 25-year trends of the global burden of disease attributable to ambient air pollution: an analysis of data from the Global Burden of Diseases Study 2015.

BACKGROUND: Exposure to ambient air pollution increases morbidity and mortality, and is a leading contributor to global disease burden. We explored spatial and temporal trends in mortality and burden of disease attributable to ambient air pollution from 1990 to 2015 at global, regional, and country levels. METHODS: We estimated global population-weighted mean concentrations of particle mass with aerodynamic diameter less than 2·5 µm (PM2·5) and ozone at an approximate 11 km × 11 km resolution with satellite-based estimates, chemical transport models, and ground-level measurements. Using integrated exposure-response functions for each cause of death, we estimated the relative risk of mortality from ischaemic heart disease, cerebrovascular disease, chronic obstructive pulmonary disease, lung cancer, and lower respiratory infections from epidemiological studies using non-linear exposure-response functions spanning the global range of exposure. FINDINGS: Ambient PM2·5 was the fifth-ranking mortality risk factor in 2015. Exposure to PM2·5 caused 4·2 million (95% uncertainty interval [UI] 3·7 million to 4·8 million) deaths and 103·1 million (90·8 million 115·1 million) disability-adjusted life-years (DALYs) in 2015, representing 7·6% of total global deaths and 4·2% of global DALYs, 59% of these in east and south Asia. Deaths attributable to ambient PM2·5 increased from 3·5 million (95% UI 3·0 million to 4·0 million) in 1990 to 4·2 million (3·7 million to 4·8 million) in 2015. Exposure to ozone caused an additional 254 000 (95% UI 97 000-422 000) deaths and a loss of 4·1 million (1·6 million to 6·8 million) DALYs from chronic obstructive pulmonary disease in 2015. INTERPRETATION: Ambient air pollution contributed substantially to the global burden of disease in 2015, which increased over the past 25 years, due to population ageing, changes in non-communicable disease rates, and increasing air pollution in low-income and middle-income countries. Modest reductions in burden will occur in the most polluted countries unless PM2·5 values are decreased substantially, but there is potential for substantial health benefits from exposure reduction. FUNDING: Bill & Melinda Gates Foundation and Health Effects Institute.

Approximations for Estimating Change in Life Expectancy Attributable to Air Pollution in Relation to Multiple Causes of Death Using a Cause Modified Life Table.

There is considerable debate as to the most appropriate metric for characterizing the mortality impacts of air pollution. Life expectancy has been advocated as an informative measure. Although the life-table calculus is relatively straightforward, it becomes increasingly cumbersome when repeated over large numbers of geographic areas and for multiple causes of death. Two simplifying assumptions were evaluated: linearity of the relation between excess rate ratio and change in life expectancy, and additivity of cause-specific life-table calculations. We employed excess rate ratios linking PM2.5 and mortality from cerebrovascular disease, chronic obstructive pulmonary disease, ischemic heart disease, and lung cancer derived from a meta-analysis of worldwide cohort studies. As a sensitivity analysis, we employed an integrated exposure response function based on the observed risk of PM2.5 over a wide range of concentrations from ambient exposure, indoor exposure, second-hand smoke, and personal smoking. Impacts were estimated in relation to a change in PM2.5 from 19.5 µg/m(3) estimated for Toronto to an estimated natural background concentration of 1.8 µg/m(3) . Estimated changes in life expectancy varied linearly with excess rate ratios, but at higher values the relationship was more accurately represented as a nonlinear function. Changes in life expectancy attributed to specific causes of death were additive with maximum error of 10%. Results were sensitive to assumptions about the air pollution concentration below which effects on mortality were not quantified. We have demonstrated valid approximations comprising expression of change in life expectancy as a function of excess mortality and summation across multiple causes of death.

Indirect adjustment for multiple missing variables applicable to environmental epidemiology.

OBJECTIVES: Develop statistical methods for survival models to indirectly adjust hazard ratios of environmental exposures for missing risk factors. METHODS: A partitioned regression approach for linear models is applied to time to event survival analyses of cohort study data. Information on the correlation between observed and missing risk factors is obtained from ancillary data sources such as national health surveys. The relationship between the missing risk factors and survival is obtained from previously published studies. We first evaluated the methodology using simulations, by considering the Weibull survival distribution for a proportional hazards regression model with varied baseline functions, correlations between an adjusted variable and an adjustment variable as well as selected censoring rates. Then we illustrate the method in a large, representative Canadian cohort of the association between concentrations of ambient fine particulate matter and mortality from ischemic heart disease. RESULTS: Indirect adjustment for cigarette smoking habits and obesity increased the fine particulate matter-ischemic heart disease association by 3%-123%, depending on the number of variables considered in the adjustment model due to the negative correlation between these two risk factors and ambient air pollution concentrations in Canada. The simulations suggested that the method yielded small relative bias (<40%) for most cohort designs encountered in environmental epidemiology. CONCLUSIONS: This method can accommodate adjustment for multiple missing risk factors simultaneously while accounting for the associations between observed and missing risk factors and between missing risk factors and health endpoints.

An integrated risk function for estimating the global burden of disease attributable to ambient fine particulate matter exposure.

BACKGROUND: Estimating the burden of disease attributable to long-term exposure to fine particulate matter (PM2.5) in ambient air requires knowledge of both the shape and magnitude of the relative risk (RR) function. However, adequate direct evidence to identify the shape of the mortality RR functions at the high ambient concentrations observed in many places in the world is lacking. OBJECTIVE: We developed RR functions over the entire global exposure range for causes of mortality in adults: ischemic heart disease (IHD), cerebrovascular disease (stroke), chronic obstructive pulmonary disease (COPD), and lung cancer (LC). We also developed RR functions for the incidence of acute lower respiratory infection (ALRI) that can be used to estimate mortality and lost-years of healthy life in children < 5 years of age. METHODS: We fit an integrated exposure-response (IER) model by integrating available RR information from studies of ambient air pollution (AAP), second hand tobacco smoke, household solid cooking fuel, and active smoking (AS). AS exposures were converted to estimated annual PM2.5 exposure equivalents using inhaled doses of particle mass. We derived population attributable fractions (PAFs) for every country based on estimated worldwide ambient PM2.5 concentrations. RESULTS: The IER model was a superior predictor of RR compared with seven other forms previously used in burden assessments. The percent PAF attributable to AAP exposure varied among countries from 2 to 41 for IHD, 1 to 43 for stroke, < 1 to 21 for COPD, < 1 to 25 for LC, and < 1 to 38 for ALRI. CONCLUSIONS: We developed a fine particulate mass-based RR model that covered the global range of exposure by integrating RR information from different combustion types that generate emissions of particulate matter. The model can be updated as new RR information becomes available.

Cardiovascular impacts and micro-environmental exposure factors associated with continuous personal PM2.5 monitoring.

The US Environmental Protection Agency's (US EPA) Detroit Exposure and Aerosol Research Study (DEARS) has provided extensive data on human exposures to a wide variety of air pollutants and their impact on human health. Previous analyses in the DEARS revealed select cardiovascular (CV) health outcomes such as increase in heart rate (HR) associated with hourly based continuous personal fine particulate matter (PM2.5) exposures in this adult, non-smoking cohort. Examination of time activity diary (TAD), follow-up questionnaire (FQ) and the continuous PM2.5 personal monitoring data provided the means to more fully examine the impact of discreet human activity patterns on personal PM2.5 exposures and changes in CV outcomes. A total of 329 343 min-based PM2.5 personal measurements involving 50 participants indicated that ∼75% of these total events resulted in exposures <35 µg/m(3). Cooking and car-related events accounted for nearly 10% of the hourly activities that were identified with observed peaks in personal PM2.5 exposures. In-residence cooking often resulted in some of the highest incidents of 1 min exposures (33.5-17.6 µg/m(3)), with average peaks for such events in excess of 209 µg/m(3). PM2.5 exposure data from hourly based personal exposure activities (for example,, cooking, cleaning and household products) were compared with daily CV data from the DEARS subject population. A total of 1300 hourly based lag risk estimates associated with changes in brachial artery diameter and flow-mediated dilatation (BAD and FMD, respectively), among others, were defined for this cohort. Findings indicate that environmental tobacco smoke (ETS) exposures resulted in significant HR changes between 3 and 7 h following the event, and exposure to smells resulted in increases in BAD on the order of 0.2-0.7 mm/µg/m(3). Results demonstrate that personal exposures may be associated with several biological responses, sometimes varying in degree and direction in relation to the extent of the exposure.

Impact of personal and ambient-level exposures to nitrogen dioxide and particulate matter on cardiovascular function.

This work explored the association between nitrogen dioxide (NO(2)) and PM(2.5) components with changes in cardiovascular function in an adult non-smoking cohort. The cohort consisted of 65 volunteers participating in the US EPA's Detroit Exposure and Aerosol Research Study (DEARS) and a University of Michigan cardiovascular sub-study. Systolic and diastolic blood pressure (SBP, DBP), heart rate (HR), brachial artery diameter (BAD), brachial artery flow-mediated dilatation (FMD) and nitroglycerin-mediated arterial dilatation (NMD) were collected by in-home examinations. A maximum of 336 daily environmental and health effect observations were obtained. Daily potassium air concentrations were associated with significant decreases in DBP (-0.0447 mmHg/ng/m(3) ± 0.0132, p = 0.0016, lag day 0) among participants compliant with the personal monitoring protocol. Personal NO(2) exposures resulted in significant changes in BAD (e.g., 0.0041 mm/ppb ± 0.0019, p = 0.0353, lag day 1) and FMD (0.0612 ± 0.0235, p = 0.0103, lag day 0) among other findings.

Exploration of the rapid effects of personal fine particulate matter exposure on arterial hemodynamics and vascular function during the same day.

BACKGROUND: Levels of fine particulate matter [≤ 2.5 µm in aerodynamic diameter (PM(2.5))] are associated with alterations in arterial hemodynamics and vascular function. However, the characteristics of the same-day exposure-response relationships remain unclear. OBJECTIVES: We aimed to explore the effects of personal PM(2.5) exposures within the preceding 24 hr on blood pressure (BP), heart rate (HR), brachial artery diameter (BAD), endothelial function [flow-mediated dilatation (FMD)], and nitroglycerin-mediated dilatation (NMD). METHODS: Fifty-one nonsmoking subjects had up to 5 consecutive days of 24-hr personal PM(2.5) monitoring and daily cardiovascular (CV) measurements during summer and/or winter periods. The associations between integrated hour-long total personal PM(2.5) exposure (TPE) levels (continuous nephelometry among compliant subjects with low secondhand tobacco smoke exposures; n = 30) with the CV outcomes were assessed over a 24-hr period by linear mixed models. RESULTS: We observed the strongest associations (and smallest estimation errors) between HR and TPE recorded 1-10 hr before CV measurements. The associations were not pronounced for the other time lags (11-24 hr). The associations between TPE and FMD or BAD did not show as clear a temporal pattern. However, we found some suggestion of a negative association with FMD and a positive association with BAD related to TPE just before measurement (0-2 hr). CONCLUSIONS: Brief elevations in ambient TPE levels encountered during routine daily activity were associated with small increases in HR and trends toward conduit arterial vasodilatation and endothelial dysfunction within a few hours of exposure. These responses could reflect acute PM(2.5)-induced autonomic imbalance and may factor in the associated rapid increase in CV risk among susceptible individuals.

Differences in blood pressure and vascular responses associated with ambient fine particulate matter exposures measured at the personal versus community level.

BACKGROUND: Higher ambient fine particulate matter (PM2.5) levels can be associated with increased blood pressure and vascular dysfunction. OBJECTIVES: To determine the differential effects on blood pressure and vascular function of daily changes in community ambient- versus personal-level PM2.5 measurements. METHODS: Cardiovascular outcomes included vascular tone and function and blood pressure measured in 65 non-smoking subjects. PM2.5 exposure metrics included 24 h integrated personal- (by vest monitors) and community-based ambient levels measured for up to 5 consecutive days (357 observations). Associations between community- and personal-level PM2.5 exposures with alterations in cardiovascular outcomes were assessed by linear mixed models. RESULTS: Mean daily personal and community measures of PM2.5 were 21.9±24.8 and 15.4±7.5 µg/m³, respectively. Community PM2.5 levels were not associated with cardiovascular outcomes. However, a 10 µg/m³ increase in total personal-level PM2.5 exposure (TPE) was associated with systolic blood pressure elevation (+1.41 mm Hg; lag day 1, p<0.001) and trends towards vasoconstriction in subsets of individuals (0.08 mm; lag day 2 among subjects with low secondhand smoke exposure, p=0.07). TPE and secondhand smoke were associated with elevated systolic blood pressure on lag day 1. Flow-mediated dilatation was not associated with any exposure. CONCLUSIONS: Exposure to higher personal-level PM2.5 during routine daily activity measured with low-bias and minimally-confounded personal monitors was associated with modest increases in systolic blood pressure and trends towards arterial vasoconstriction. Comparable elevations in community PM2.5 levels were not related to these outcomes, suggesting that specific components within personal and background ambient PM2.5 may elicit differing cardiovascular responses.

Extended follow-up and spatial analysis of the American Cancer Society study linking particulate air pollution and mortality.

We conducted an extended follow-up and spatial analysis of the American Cancer Society (ACS) Cancer Prevention Study II (CPS-II) cohort in order to further examine associations between long-term exposure to particulate air pollution and mortality in large U.S. cities. The current study sought to clarify outstanding scientific issues that arose from our earlier HEI-sponsored Reanalysis of the original ACS study data (the Particle Epidemiology Reanalysis Project). Specifically, we examined (1) how ecologic covariates at the community and neighborhood levels might confound and modify the air pollution-mortality association; (2) how spatial autocorrelation and multiple levels of data (e.g., individual and neighborhood) can be taken into account within the random effects Cox model; (3) how using land-use regression to refine measurements of air pollution exposure to the within-city (or intra-urban) scale might affect the size and significance of health effects in the Los Angeles and New York City regions; and (4) what exposure time windows may be most critical to the air pollution-mortality association. The 18 years of follow-up (extended from 7 years in the original study [Pope et al. 1995]) included vital status data for the CPS-II cohort (approximately 1.2 million participants) with multiple cause-of-death codes through December 31, 2000 and more recent exposure data from air pollution monitoring sites for the metropolitan areas. In the Nationwide Analysis, the influence of ecologic covariate data (such as education attainment, housing characteristics, and level of income; data obtained from the 1980 U.S. Census; see Ecologic Covariates sidebar on page 14) on the air pollution-mortality association were examined at the Zip Code area (ZCA) scale, the metropolitan statistical area (MSA) scale, and by the difference between each ZCA value and the MSA value (DIFF). In contrast to previous analyses that did not directly include ecologic covariates at the ZCA scale, risk estimates increased when ecologic covariates were included at all scales. The ecologic covariates exerted their greatest effect on mortality from ischemic heart disease (IHD), which was also the health outcome most strongly related with exposure to PM2.5 (particles 2.5 microm or smaller in aerodynamic diameter), sulfate (SO4(2-)), and sulfur dioxide (SO2), and the only outcome significantly associated with exposure to nitrogen dioxide (NO2). When ecologic covariates were simultaneously included at both the MSA and DIFF levels, the hazard ratio (HR) for mortality from IHD associated with PM2.5 exposure (average concentration for 1999-2000) increased by 7.5% and that associated with SO4(2-) exposure (average concentration for 1990) increased by 12.8%. The two covariates found to exert the greatest confounding influence on the PM2.5-mortality association were the percentage of the population with a grade 12 education and the median household income. Also in the Nationwide Analysis, complex spatial patterns in the CPS-II data were explored with an extended random effects Cox model (see Glossary of Statistical Terms at end of report) that is capable of clustering up to two geographic levels of data. Using this model tended to increase the HR estimate for exposure to air pollution and also to inflate the uncertainty in the estimates. Including ecologic covariates decreased the variance of the results at both the MSA and ZCA scales; the largest decrease was in residual variation based on models in which the MSA and DIFF levels of data were included together, which suggests that partitioning the ecologic covariates into between-MSA and within-MSA values more completely captures the sources of variation in the relationship between air pollution, ecologic covariates, and mortality. Intra-Urban Analyses were conducted for the New York City and Los Angeles regions. The results of the Los Angeles spatial analysis, where we found high exposure contrasts within the Los Angeles region, showed that air pollution-mortality risks were nearly 3 times greater than those reported from earlier analyses. This suggests that chronic health effects associated with intra-urban gradients in exposure to PM2.5 may be even larger between ZCAs within an MSA than the associations between MSAs that have been previously reported. However, in the New York City spatial analysis, where we found very little exposure contrast between ZCAs within the New York region, mortality from all causes, cardiopulmonary disease (CPD), and lung cancer was not elevated. A positive association was seen for PM2.5 exposure and IHD, which provides evidence of a specific association with a cause of death that has high biologic plausibility. These results were robust when analyses controlled (1) the 44 individual-level covariates (from the ACS enrollment questionnaire in 1982; see 44 Individual-Level Covariates sidebar on page 22) and (2) spatial clustering using the random effects Cox model. Effects were mildly lower when unemployment at the ZCA scale was included. To examine whether there is a critical exposure time window that is primarily responsible for the increased mortality associated with ambient air pollution, we constructed individual time-dependent exposure profiles for particulate and gaseous air pollutants (PM2.5 and SO2) for a subset of the ACS CPS-II participants for whom residence histories were available. The relevance of the three exposure time windows we considered was gauged using the magnitude of the relative risk (HR) of mortality as well as the Akaike information criterion (AIC), which measures the goodness of fit of the model to the data. For PM2.5, no one exposure time window stood out as demonstrating the greatest HR; nor was there any clear pattern of a trend in HR going from recent to more distant windows or vice versa. Differences in AIC values among the three exposure time windows were also small. The HRs for mortality associated with exposure to SO2 were highest in the most recent time window (1 to 5 years), although none of these HRs were significantly elevated. Identifying critical exposure time windows remains a challenge that warrants further work with other relevant data sets. This study provides additional support toward developing cost-effective air quality management policies and strategies. The epidemiologic results reported here are consistent with those from other population-based studies, which collectively have strongly supported the hypothesis that long-term exposure to PM2.5 increases mortality in the general population. Future research using the extended Cox-Poisson random effects methods, advanced geostatistical modeling techniques, and newer exposure assessment techniques will provide additional insight.

The effect of censoring on cancer risk estimates based on the Canadian National Dose Registry of occupational radiation exposure.

Cohort studies represent an important epidemiological tool for exploring the potential adverse health effects of low-dose exposure to ionizing radiation in the workplace. Analyses of data from the National Dose Registry of Canada have suggested that occupational radiation exposure leads to increased risk of several specific types of cancer, as well as increased overall risk of cancer. An important aspect of such studies is the censoring in recorded exposures induced by dosimetry detection limits. Such a censoring effect can lead to significant underestimation of cumulative doses which, in turn, can result in overestimation of the excess cancer risk associated with occupational radiation exposure. In this article, we present analytic results, supported by a simulation study, on the magnitude of overestimation of risk based on the additive relative risk model used in the analysis of the NDR data that can occur due to censoring. Our results indicate that overestimation of risk is modest, being less than 20% in all situations considered here. Because censoring also results in ovestimation of the precision of the risk estimates, the significance levels of Wald-type statistical tests for increased risk based on the ratio of the estimate to its standard error are virtually unaffected by censoring. These results suggest that although the application of the additive excess relative risk model in the presence of censoring may lead to some overestimation of risk, the model does not lead to invalid conclusions regarding the association between occupational radiation exposure and cancer risk based on data from the NDR.