September 23 | 2019

Assessing the micro-scale environment using Google Street View: the Virtual Systematic Tool for Evaluating Pedestrian Streetscapes (Virtual-STEPS). 

Steinmetz-Wood M, Velauthapillai K, O’Brien G, Ross NA.

BMC Public Health. 2019 Sep 10;19(1):1246. doi: 10.1186/s12889-019-7460-3




Altering micro-scale features of neighborhood walkability (e.g., benches, sidewalks, and cues of social disorganization or crime) could be a relatively cost-effective method of creating environments that are conducive to active living. Traditionally, measuring the micro-scale environment has required researchers to perform observational audits. Technological advances have led to the development of virtual audits as alternatives to observational field audits with the enviable properties of cost-efficiency from elimination of travel time and increased safety for auditors. This study examined the reliability of the Virtual Systematic Tool for Evaluating Pedestrian Streetscapes (Virtual-STEPS), a Google Street View-based auditing tool specifically designed to remotely assess micro-scale characteristics of the built environment.


We created Virtual-STEPS, a tool with 40 items categorized into 6 domains (pedestrian infrastructure, traffic calming and streets, building characteristics, bicycling infrastructure, transit, and aesthetics). Items were selected based on their past abilities to predict active living and on their feasibility for a virtual auditing tool. Two raters performed virtual and field audits of street segments in Montreal neighborhoods stratified by the Walkscore that was used to determine the ‘walking-friendliness’ of a neighborhood. The reliability between virtual and field audits (n = 40), as well as inter-rater reliability (n = 60) were assessed using percent agreement, Cohen’s Kappa statistic, and the Intra-class Correlation Coefficient.


Virtual audits and field audits (excluding travel time) took similar amounts of time to perform (9.8 versus 8.2 min). Percentage agreement between virtual and field audits, and for inter-rater agreement was 80% or more for the majority of items included in the Virtual-STEPS tool. There was high reliability between virtual and field audits with Kappa and ICC statistics indicating that 20 out of 40 (50.0%) items had almost perfect agreement and 13 (32.5%) items had substantial agreement. Inter-rater reliability was also high with 17 items (42.5%) with almost perfect agreement and 11 (27.5%) items with substantial agreement.


Virtual-STEPS is a reliable tool. Tools that measure the micro-scale environment are important because changing this environment could be a relatively cost-effective method of creating environments that are conducive to active living.

September 9 | 2019

Prenatal exposure to traffic-related air pollution, the gestational epigenetic clock and risk of early-life allergic sensitization.

Sbihi H, Jones MJ, MacIsaac JL, Brauer M, Allen RW, Sears MR, Subbarao P, Mandhane PJ, Moraes TJ, Azad MB, Becker AB, Brook JR, Kobor MS, Turvey SE. 

J Allergy Clin Immunol. 2019 Aug 27. pii: S0091-6749(19)31101-7. [Epub ahead of print] DOI: 10.1016/j.jaci.2019.07.047



Prenatal exposure to traffic-related air pollution is associated with an increased risk of allergic sensitization by modifying in utero development and altering the gestational epigenetic clock.

September 3 | 2019

Ambient Air Pollution and the Risk of Atrial Fibrillation and Stroke: A Population-Based Cohort Study.

Shin S, Burnett RT, Kwong JC, Hystad P, van Donkelaar A, Brook JR, Goldberg MS, Tu K, Copes R, Martin RV, Liu Y, Kopp A, Chen H.

Environ Health Perspect. 2019 Aug;127(8):87009. Epub 2019 Aug 26. DOI:10.1289/EHP4883




Although growing evidence links air pollution to stroke incidence, less is known about the effect of air pollution on atrial fibrillation (AF), an important risk factor for stroke.


We assessed the associations between air pollution and incidence of AF and stroke. We also sought to characterize the shape of pollutant-disease relationships.


The population-based cohort comprised 5,071,956 Ontario residents, age 35-85 y and without the diagnoses of both outcomes on 1 April 2001 and was followed up until 31 March 2015. AF and stroke cases were ascertained using health administrative databases with validated algorithms. Based on annual residential postal codes, we assigned 5-y running average concentrations of fine particulate matter ([Formula: see text]), nitrogen dioxide ([Formula: see text]), and ozone ([Formula: see text]) from satellite-derived data, a land-use regression model, and a fusion-based method, respectively, as well as redox-weighted averages of [Formula: see text] and [Formula: see text] ([Formula: see text]) for each year. Using Cox proportional hazards models, we estimated the hazard ratios (HRs) and 95% confidence intervals (95% CIs) of AF and stroke with each of these pollutants, adjusting for individual- and neighborhood-level variables. We used newly developed nonlinear risk models to characterize the shape of pollutant-disease relationships.


Between 2001 and 2015, we identified 313,157 incident cases of AF and 122,545 cases of stroke. Interquartile range increments of [Formula: see text], [Formula: see text], [Formula: see text], and [Formula: see text] were associated with increases in the incidence of AF [HRs (95% CIs): 1.03 (1.01, 1.04), 1.02 (1.01, 1.03), 1.01 (1.00, 1.02), and 1.01 (1.01, 1.02), respectively] and the incidence of stroke [HRs (95% CIs): 1.05 (1.03, 1.07), 1.04 (1.01, 1.06), 1.05 (1.03, 1.06), and 1.05 (1.04, 1.06), respectively]. Associations of similar magnitude were found in various sensitivity analyses. Furthermore, we found a near-linear association for stroke with [Formula: see text], whereas [Formula: see text], [Formula: see text]-, and [Formula: see text] relationships exhibited sublinear shapes.


Air pollution was associated with stroke and AF onset, even at very low concentrations.

August 12 | 2019

Evaluation of a method to indirectly adjust for unmeasured covariates in the association between fine particulate matter and mortality.

Erickson AC, Brauer M, Christidis T, Pinault L, Crouse DL, van Donkelaar A, Weichenthal S, Pappin A, Tjepkema M, Martin RV, Brook JR, Hystad P, Burnett RT.

Environ Res. 2019 Aug;175:108-116. doi: 10.1016/j.envres.2019.05.010 Epub 2019 May 11.




Indirect adjustment via partitioned regression is a promising technique to control for unmeasured confounding in large epidemiological studies. The method uses a representative ancillary dataset to estimate the association between variables missing in a primary dataset with the complete set of variables of the ancillary dataset to produce an adjusted risk estimate for the variable in question. The objective of this paper is threefold: 1) evaluate the method for non-linear survival models, 2) formalize an empirical process to evaluate the suitability of the required ancillary matching dataset, and 3) test modifications to the method to incorporate time-varying exposure data, and proportional weighting of datasets.


We used the association between fine particle air pollution (PM2.5) with mortality in the 2001 Canadian Census Health and Environment Cohort (CanCHEC, N = 2.4 million, 10-years follow-up) as our primary dataset, and the 2001 cycle of the Canadian Community Health Survey (CCHS, N = 80,630) as the ancillary matching dataset that contained confounding risk factor information not available in CanCHEC (e.g., smoking). The main evaluation process used a gold-standard approach wherein two variables (education and income) available in both datasets were excluded, indirectly adjusted for, and compared to true models with education and income included to assess the amount of bias correction. An internal validation for objective 1 used only CanCHEC data, whereas an external validation for objective 2 replaced CanCHEC with the CCHS. The two proposed modifications were applied as part of the validation tests, as well as in a final indirect adjustment of four missing risk factor variables (smoking, alcohol use, diet, and exercise) in which adjustment direction and magnitude was compared to models using an equivalent longitudinal cohort with direct adjustment for the same variables.


At baseline (2001) both cohorts had very similar PM2.5 distributions across population characteristics, although levels for CCHS participants were consistently 1.8-2.0 μg/m3 lower. Applying sample-weighting largely corrected for this discrepancy. The internal validation tests showed minimal downward bias in PM2.5 mortality hazard ratios of 0.4-0.6% using a static exposure, and 1.7-3% when a time-varying exposure was used. The external validation of the CCHS as the ancillary dataset showed slight upward bias of -0.7 to -1.1% and downward bias of 1.3-2.3% using the static and time-varying approaches respectively.


The CCHS was found to be fairly well representative of CanCHEC and its use in Canada for indirect adjustment is warranted. Indirect adjustment methods can be used with survival models to correct hazard ratio point estimates and standard errors in models missing key covariates when a representative matching dataset is available. The results of this formal evaluation should encourage other cohorts to assess the suitability of ancillary datasets for the application of the indirect adjustment methodology to address potential residual confounding.

August 6 | 2019

Acute Blood Pressure and Cardiovascular Effects of Near-Roadway Exposures with and without N95-Respirators. 

Morishita M, Wang L, Speth K, Zhou N, Bard RL, Li F, Brook JR, Rajagopalan S, Brook RD.

Am J Hypertens. 2019 Jul 27. pii: hpz113. [Epub ahead of print] DOI: 10.1093/ajh/hpz113



The risk for cardiovascular events increases within hours of near-roadway exposures. We aimed to determine the traffic-related air pollutants (TRAP) and biological mechanisms involved and if reducing particulate matter<2.5 µm (PM2.5) inhalation is protective.


Fifty healthy-adults underwent multiple 2-hour near-roadway exposures (Tuesdays-Fridays) in Ann Arbor during 2 separate weeks (randomized to wear an N95-respirator during one week). Monday both weeks, participants rested 2-hours in an exam room (once wearing an N95-respirator). Brachial blood pressure, aortic hemodynamics and heart rate variability were repeatedly-measured during exposures. Endothelial function (reactive hyperemia index [RHI]) was measured post-exposures (Thursdays). Black carbon (BC), total particle count (PC), PM2.5, noise and temperature were measured throughout exposures.


PM2.5 (9.3±7.7 µg/m3), BC (1.3±0.6 µg/m3), PC (8375±4930 particles/cm3) and noise (69.2±4.2 dB) were higher (p-values<0.01) and aortic hemodynamic parameters trended worse while near-roadway (p-values<0.15 versus exam room). Other outcomes were unchanged. Aortic hemodynamics trended towards improvements with N95-respirator usage while near-roadway (p-values<0.15 versus no-use), whereas other outcomes remained unaffected. Higher near-roadway PC and BC exposures were associated with increases in aortic augmentation pressures (p-values<0.05) and trends toward lower RHI (p-values<0.2). N95-respirator usage did not mitigate these adverse responses (non-significant pollutant-respirator interactions). Near-roadway outdoor-temperature and noise were also associated with cardiovascular changes.


Exposure to real-world combustion-derived particulates in TRAP, even at relatively-low concentrations, acutely worsened aortic hemodynamics. Our mixed findings regarding the health benefits of wearing N95-respirators support that further studies are needed to validate if they adequately-protect against TRAP given their growing worldwide usage.

July 29 | 2019

Particulate matter air pollution and national and county life expectancy loss in the USA: A spatiotemporal analysis.

James E. Bennett , Helen Tamura-Wicks , Robbie M. Parks, Richard T. Burnett, C. Arden Pope III, Matthew J. Bechle, Julian D. Marshall, Goodarz Danaei, Majid Ezzati. 

PLOS | Medicine Published: July 23, 2019




Exposure to fine particulate matter pollution (PM2.5) is hazardous to health. Our aim was to directly estimate the health and longevity impacts of current PM2.5 concentrations and the benefits of reductions from 1999 to 2015, nationally and at county level, for the entire contemporary population of the contiguous United States.

Methods and findings

We used vital registration and population data with information on sex, age, cause of death, and county of residence. We used four Bayesian spatiotemporal models, with different adjustments for other determinants of mortality, to directly estimate mortality and life expectancy loss due to current PM2.5 pollution and the benefits of reductions since 1999, nationally and by county. The covariates included in the adjusted models were per capita income; percentage of population whose family income is below the poverty threshold, who are of Black or African American race, who have graduated from high school, who live in urban areas, and who are unemployed; cumulative smoking; and mean temperature and relative humidity. In the main model, which adjusted for these covariates and for unobserved county characteristics through the use of county-specific random intercepts, PM2.5 pollution in excess of the lowest observed concentration (2.8 μg/m3) was responsible for an estimated 15,612 deaths (95% credible interval 13,248–17,945) in females and 14,757 deaths (12,617–16,919) in males. These deaths would lower national life expectancy by an estimated 0.15 years (0.13–0.17) for women and 0.13 years (0.11–0.15) for men. The life expectancy loss due to PM2.5was largest around Los Angeles and in some southern states such as Arkansas, Oklahoma, and Alabama. At any PM2.5 concentration, life expectancy loss was, on average, larger in counties with lower income and higher poverty rate than in wealthier counties. Reductions in PM2.5 since 1999 have lowered mortality in all but 14 counties where PM2.5 increased slightly. The main limitation of our study, similar to other observational studies, is that it is not guaranteed for the observed associations to be causal. We did not have annual county-level data on other important determinants of mortality, such as healthcare access and quality and diet, but these factors were adjusted for with use of county-specific random intercepts.


According to our estimates, recent reductions in particulate matter pollution in the USA have resulted in public health benefits. Nonetheless, we estimate that current concentrations are associated with mortality impacts and loss of life expectancy, with larger impacts in counties with lower income and higher poverty rate.

July 22 | 2019

Spatial variations in ambient ultrafine particle concentrations and risk of congenital heart defects.

Lavigne E, Lima I, Hatzopoulou M, Van Ryswyk K, Decou ML, Luo W, van Donkelaar A, Martin RV, Chen H, Stieb DM, Crighton E, Gasparrini A, Elten M, Yasseen AS 3rd, Burnett RT, Walker M, Weichenthal S.

Environ Int. 2019 Jul 1;130:104953. DOI: 10.1016/j.envint.2019.104953 [Epub ahead of print]



Cardiovascular malformations account for nearly one-third of all congenital anomalies, making these the most common type of birth defects. Little is known regarding the influence of ambient ultrafine particles (<0.1 μm) (UFPs) on their occurrence.


This population-based study examined the association between prenatal exposure to UFPs and congenital heart defects (CHDs).


A total of 158,743 singleton live births occurring in the City of Toronto, Canada between April 1st 2006 and March 31st 2012 were identified from a birth registry. Associations between exposure to ambient UFPs between the 2nd and 8th week post conception when the foetal heart begins to form and CHDs identified at birth were estimated using random-effects logistic regression models, adjusting for personal- and neighbourhood-level covariates. We also investigated multi-pollutant models accounting for co-exposures to PM2.5, NO2 and O3.


A total of 1468 CHDs were identified. In fully adjusted models, UFP exposures during weeks 2 to 8 of pregnancy were not associated with overall CHDs (Odds Ratio (OR) per interquartile (IQR) increase = 1.02, 95% CI: 0.96-1.08). When investigating subtypes of CHDs, UFP exposures were associated with ventricular septal defects (Odds Ratio (OR) per interquartile (IQR) increase = 1.13, 95% CI: 1.03-1.33), but not with atrial septal defect (Odds Ratio (OR) per interquartile (IQR) increase = 0.89, 95% CI: 0.74-1.06).


This is the first study to evaluate the association between prenatal exposure to UFPs and the risk of CHDs. UFP exposures during a critical period of embryogenesis were associated with an increased risk of ventricular septal defect.

July 15 | 2019

Interdisciplinary-driven hypotheses on spatial associations of mixtures of industrial air pollutants with adverse birth outcomes. 

Jesus Serrano-Lomelin, Charlene C. Nielsen, M. Shazan M. Jabbar, Osnat Wine, Colin Bellinger, Paul J. Villeneuvee, Dave Stieb, Nancy Aelicks, Khalid Aziz, Irena Buka, Sue Chandra, Susan Crawford, Paul Demers, Anders C. Erickson, Perry Hystad, Manoj Kumar, Erica Phipps, Prakesh S. Shah, YanYuan, Osmar R. Zaiane, Alvaro R. Osornio-Vargas.

Environment International Volume 131, October 2019,




Adverse birth outcomes (ABO) such as prematurity and small for gestational age confer a high risk of mortality and morbidity. ABO have been linked to air pollution; however, relationships with mixtures of industrial emissions are poorly understood. The exploration of relationships between ABO and mixtures is complex when hundreds of chemicals are analyzed simultaneously, requiring the use of novel approaches.


We aimed to generate robust hypotheses spatially linking mixtures and the occurrence of ABO using a spatial data mining algorithm and subsequent geographical and statistical analysis. The spatial data mining approach aimed to reduce data dimensionality and efficiently identify spatial associations between multiple chemicals and ABO.


We discovered co-location patterns of mixtures and ABO in Alberta, Canada (2006–2012). An ad-hoc spatial data mining algorithm allowed the extraction of primary co-location patterns of 136 chemicals released into the air by 6279 industrial facilities (National Pollutant Release Inventory), wind-patterns from 182 stations, and 333,247 singleton live births at the maternal postal code at delivery (Alberta Perinatal Health Program), from which we identified cases of preterm birth, small for gestational age, and low birth weight at term. We selected secondary patterns using a lift ratio metric from ABO and non-ABO impacted by the same mixture. The relevance of the secondary patterns was estimated using logistic models (adjusted by socioeconomic status and ABO-related maternal factors) and a geographic-based assignment of maternal exposure to the mixtures as calculated by kernel density.


From 136 chemicals and three ABO, spatial data mining identified 1700 primary patterns from which five secondary patterns of three-chemical mixtures, including particulate matter, methyl-ethyl-ketone, xylene, carbon monoxide, 2-butoxyethanol, and n-butyl alcohol, were subsequently analyzed. The significance of the associations (odds ratio > 1) between the five mixtures and ABO provided statistical support for a new set of hypotheses.


This study demonstrated that, in complex research settings, spatial data mining followed by pattern selection and geographic and statistical analyses can catalyze future research on associations between air pollutant mixtures and adverse birth outcomes.

July 10 | 2019

Air pollution, lung function and COPD: results from the population-based UK Biobank study.

Dany Doiron, Kees de Hoogh, Nicole Probst-Hensch, Isabel Fortier, Yutong Cai, Sara De Matteis, Anna L. Hansell.

European Respiratory Journal 2019;


Ambient air pollution increases the risk of respiratory mortality but evidence for impacts on lung function and chronic obstructive pulmonary disease (COPD) is less well established. The aim was to evaluate whether ambient air pollution is associated with lung function and COPD, and explore potential vulnerability factors.

We used UK Biobank data on 3 03 887 individuals aged 40–69 years, with complete covariate data and valid lung function measures. Cross-sectional analyses examined associations of Land Use Regression-based estimates of particulate matter (PM2.5, PM10 and PMcoarse) and nitrogen dioxide (NO2) concentrations with forced expiratory volume in 1 s (FEV1), forced vital capacity (FVC), the FEV1/FVC ratio, and COPD (FEV1/FVC <lower limit of normal). Effect modification was investigated for sex, age, obesity, smoking status, household income, asthma status, and occupations previously linked to COPD.

Higher exposures to each pollutant were significantly associated with lower lung function. A 5 µg·m−3 increase in PM2.5 concentration was associated with lower FEV1 (−83.13 mL [95%CI: −92.50, −73.75]) and FVC (−62.62 mL [95%CI: −73.91, −51.32]). COPD prevalence was associated with higher concentrations of PM2.5 (OR 1.52 [95%CI: 1.42, 1.62], per 5 µg·m−3), PM10 (OR 1.08 [95%CI: 1.00, 1.16], per 5 µg·m−3), and NO2 (OR 1.12 [95%CI: 1.10, 1.14], per 10 µg·m−3), but not with PMcoarse. Stronger lung function associations were seen for males, individuals from lower income households, and “at-risk” occupations, and higher COPD associations for obese, lower income, and non-asthmatic participants.

Ambient air pollution was associated with lower lung function and increased COPD prevalence in this large study.

July 2 | 2019

Interaction between neighborhood walkability and traffic-related air pollution on hypertension and diabetes: The CANHEART cohort.

Howell NA, Tu JV, Moineddin R, Chen H, Chu A, Hystad P, Booth GL.

Environ Int. 2019 Jun 19:104799. DOI: 10.1016/j.envint.2019.04.070




Living in unwalkable neighborhoods has been associated with heightened risk for diabetes and hypertension. However, highly walkable environments may have higher concentrations of traffic-related air pollution, which may contribute to increased cardiovascular disease risk. We therefore aimed to assess how walkability and traffic-related air pollution jointly affect risk for hypertension and diabetes.


We used a cross-sectional, population-based sample of individuals aged 40-74 years residing in selected large urban centres in Ontario, Canada on January 1, 2008, assembled from administrative databases. Walkability and traffic-related air pollution (NO2) were assessed using validated tools and linked to individuals based on neighborhood of residence. Logistic regression was used to estimate adjusted associations between exposures and diagnoses of hypertension or diabetes accounting for potential confounders.


Overall, 2,496,458 individuals were included in our analyses. Low walkability was associated with higher odds of hypertension (lowest vs. highest quintile OR = 1.34, 95% CI: 1.32, 1.37) and diabetes (lowest vs. highest quintile OR = 1.25, 95% CI: 1.22, 1.29), while NO2exhibited similar trends (hypertension: OR = 1.09 per 10 p.p.b., 95% CI: 1.08, 1.10; diabetes: OR = 1.16, 95% CI: 1.14, 1.17). Significant interactions were identified between walkability and NO2 on risk for hypertension (p < 0.0001 and diabetes (p < 0.0001). At higher levels of pollution (40 p.p.b.), differences in the probability of hypertension (lowest vs. highest walkability quintile: 0.26 vs. 0.25) or diabetes (lowest vs. highest walkability quintile: 0.15 vs. 0.15) between highly walkable and unwalkable neighborhoods were diminished, compared to differences observed at lower levels of pollution (5 p.p.b.) (hypertension, lowest vs. highest walkability quintile: 0.21 vs. 0.13; diabetes, lowest vs. highest walkability quintile: 0.09 vs. 0.06).


Walkability and traffic-related air pollution interact to jointly predict risk for hypertension and diabetes. Although walkable neighborhoods appear to have beneficial effects, they may accentuate the harmful effects of air pollution on cardiovascular risk factors.