January 14 | 2020

Mortality-Air Pollution Associations in Low-Exposure Environments (MAPLE): Phase 1.

Brauer M, Brook JR, Christidis T, Chu Y, Crouse DL, Erickson A, Hystad P, Li C, Martin RV, Meng J, Pappin AJ, Pinault LL, Tjepkema M, van Donkelaar A, Weichenthal S, Burnett RT.

Res Rep Health Eff Inst. 2019 Nov;(203):1-87. https://www.healtheffects.org/system/files/brauer-rr-203-phase1-report_0.pdf



Fine particulate matter (particulate matter ≤2.5 μm in aerodynamic diameter, or PM2.5) is associated with mortality, but the lower range of relevant concentrations is unknown. Novel satellite-derived estimates of outdoor PM2.5 concentrations were applied to several large population-based cohorts, and the shape of the relationship with nonaccidental mortality was characterized, with emphasis on the low concentrations (<12 μg/m3) observed throughout Canada.


Annual satellite-derived estimates of outdoor PM2.5 concentrations were developed at 1-km2 spatial resolution across Canada for 2000-2016 and backcasted to 1981 using remote sensing, chemical transport models, and ground monitoring data. Targeted ground-based measurements were conducted to measure the relationship between columnar aerosol optical depth (AOD) and ground-level PM2.5. Both existing and targeted ground-based measurements were analyzed to develop improved exposure data sets for subsequent epidemiological analyses.

Residential histories derived from annual tax records were used to estimate PM2.5 exposures for subjects whose ages ranged from 25 to 90 years. About 8.5 million were from three Canadian Census Health and Environment Cohort (CanCHEC) analytic files and another 540,900 were Canadian Community Health Survey (CCHS) participants. Mortality was linked through the year 2016. Hazard ratios (HR) were estimated with Cox Proportional Hazard models using a 3-year moving average exposure with a 1-year lag, with the year of follow-up as the time axis. All models were stratified by 5-year age groups, sex, and immigrant status. Covariates were based on directed acyclical graphs (DAG), and included contextual variables (airshed, community size, neighborhood dependence, neighborhood deprivation, ethnic concentration, neighborhood instability, and urban form). A second model was examined including the DAG-based covariates as well as all subject-level risk factors (income, education, marital status, indigenous identity, employment status, occupational class, and visible minority status) available in each cohort. Additional subject-level behavioral covariates (fruit and vegetable consumption, leisure exercise frequency, alcohol consumption, smoking, and body mass index [BMI]) were included in the CCHS analysis.

Sensitivity analyses evaluated adjustment for covariates and gaseous copollutants (nitrogen dioxide [NO2] and ozone [O3]), as well as exposure time windows and spatial scales. Estimates were evaluated across strata of age, sex, and immigrant status. The shape of the PM2.5-mortality association was examined by first fitting restricted cubic splines (RCS) with a large number of knots and then fitting the shape-constrained health impact function (SCHIF) to the RCS predictions and their standard errors (SE). This method provides graphical results indicating the RCS predictions, as a nonparametric means of characterizing the concentration-response relationship in detail and the resulting mean SCHIF and accompanying uncertainty as a parametric summary.

Sensitivity analyses were conducted in the CCHS cohort to evaluate the potential influence of unmeasured covariates on air pollution risk estimates. Specifically, survival models with all available risk factors were fit and compared with models that omitted covariates not available in the CanCHEC cohorts. In addition, the PM2.5 risk estimate in the CanCHEC cohort was indirectly adjusted for multiple individual-level risk factors by estimating the association between PM2.5 and these covariates within the CCHS.


Satellite-derived PM2.5 estimates were low and highly correlated with ground monitors. HR estimates (per 10-μg/m3 increase in PM2.5) were similar for the 1991 (1.041, 95% confidence interval [CI]: 1.016-1.066) and 1996 (1.041, 1.024-1.059) CanCHEC cohorts with a larger estimate observed for the 2001 cohort (1.084, 1.060-1.108). The pooled cohort HR estimate was 1.053 (1.041-1.065). In the CCHS an analogous model indicated a HR of 1.13 (95% CI: 1.06-1.21), which was reduced slightly with the addition of behavioral covariates (1.11, 1.04-1.18). In each of the CanCHEC cohorts, the RCS increased rapidly over lower concentrations, slightly declining between the 25th and 75th percentiles and then increasing beyond the 75th percentile. The steepness of the increase in the RCS over lower concentrations diminished as the cohort start date increased. The SCHIFs displayed a supralinear association in each of the three CanCHEC cohorts and in the CCHS cohort.

In sensitivity analyses conducted with the 2001 CanCHEC, longer moving averages (1, 3, and 8 years) and smaller spatial scales (1 km2 vs. 10 km2) of exposure assignment resulted in larger associations between PM2.5 and mortality. In both the CCHS and CanCHEC analyses, the relationship between nonaccidental mortality and PM2.5 was attenuated when O3 or a weighted measure of oxidant gases was included in models. In the CCHS analysis, but not in CanCHEC, PM2.5 HRs were also attenuated by the inclusion of NO2. Application of the indirect adjustment and comparisons within the CCHS analysis suggests that missing data on behavioral risk factors for mortality had little impact on the magnitude of PM2.5-mortality associations. While immigrants displayed improved overall survival compared with those born in Canada, their sensitivity to PM2.5 was similar to or larger than that for nonimmigrants, with differences between immigrants and nonimmigrants decreasing in the more recent cohorts.


In several large population-based cohorts exposed to low levels of air pollution, consistent associations were observed between PM2.5 and nonaccidental mortality for concentrations as low as 5 μg/m3. This relationship was supralinear with no apparent threshold or sublinear association.

January 6 | 2020

Drop-And-Spin Virtual Neighborhood Auditing: Assessing Built Environment for Linkage to Health Studies.

Plascak JJ, Rundle AG, Babel RA, Llanos AAM, LaBelle CM, Stroup AM, Mooney SJ.

Am J Prev Med. 2020 Jan;58(1):152-160. DOI: 10.1016/j.amepre.2019.08.032



Various built environment factors might influence certain health behaviors and outcomes. Reliable, resource-efficient methods that are feasible for assessing built environment characteristics across large geographies are needed for larger, more robust studies. This paper reports the item response prevalence, reliability, and rating time of a new virtual neighborhood audit protocol, drop-and-spin auditing, developed for assessment of walkability and physical disorder characteristics across large geographic areas.


Drop-and-spin auditing, a method where a Google Street View scene was rated by spinning 360° around a point location, was developed using a modified version of the virtual audit tool Computer Assisted Neighborhood Visual Assessment System. Approximately 8,000 locations within Essex County, New Jersey were assessed by 11 trained auditors. Using a standardized protocol, 32 built environment items per a location within Google Street View were audited. Test-retest and inter-rater κ statistics were from a 5% subsample of locations. Data were collected in 2017-2018 and analyzed in 2018.


Roughly 70% of Google Street View scenes had sidewalks. Among those, two thirds were in good condition. At least 5 obvious items of garbage or litter were present in 41% of Google Street View scenes. Maximum test-retest reliability indicated substantial agreement (κ ≥0.61) for all items. Inter-rater reliability of each item, generally, was lower than test-retest reliability. The median time to rate each item was 7.3 seconds.


Compared with segment-based protocols, drop-and-spin virtual neighborhood auditing is quicker and similarly reliable for assessing built environment characteristics. Assessment of large geographies may be more feasible using drop-and-spin virtual auditing.

December 16 | 2019

Early Life Exposure to Air Pollution and Incidence of Childhood Asthma, Allergic Rhinitis and Eczema.

To T, Zhu J, Stieb D, Gray N, Fong I, Pinault L, Jerrett M, Robichaud A, Ménard R, van Donkelaar A, Martin RV, Hystad P, Brook JR, Dell S.

Eur Respir J. 2019 Dec 5. pii: 1900913. DOI: 10.1183/13993003.00913-2019  [Epub ahead of print]




There is growing evidence that air pollution may contribute to the development of childhood asthma and other allergic diseases. In this follow-up of the Toronto Child Health Evaluation Questionnaire (T-CHEQ) study, we examined associations between early life exposures to air pollution and incidence of asthma, allergic rhinitis and eczema from birth through adolescence.


1286 T-CHEQ participants were followed from birth until outcome, March 31, 2016, or loss-to-follow-up with a mean of 17 years of follow-up. Concentrations of NO2, O3 and PM2.5 from January 1, 1999, to December 31, 2012 were assigned to participants based on their postal codes at birth using ground observations, chemical/meteorological models, remote sensing and land use regression (LUR) models. Study outcomes included incidence of physician-diagnosed asthma, allergic rhinitis and eczema. Cox proportional hazard regression models were used to estimate hazard ratios (HR) per interquartile range of exposures and outcomes, adjusting for potential confounders.


HRs of 1.17 (95%CI: 1.05, 1.31) for asthma and 1.07 (95%CI: 0.99, 1.15) for eczema were observed for total oxidants (O3 and NO2) at birth. No significant increase in risk was found for PM2.5.


Exposures to oxidant air pollutants (O3 and NO2), but not PM2.5 were associated with an increased risk of incident asthma and eczema in children. This suggests that improving air quality may contribute to the prevention of asthma and other allergic disease in childhood and adolescence.

Making the Most of Residential History | February 4th | 2020


It’s a fact – people move! Join our panel of experts to hear more about how this impacts environmental health research, and how you can take advantage of residential history data now in Canada’s major cohorts.

Why do we care about residential history?

  • Paul Villeneuve,  Professor in the School of Mathematics and Statistics, with appointments in the Department of Health Sciences and in the Departments of Health Sciences and Neurosciences at Carleton University
Statistics Canada residential history program. 
  • Michael Tjepkema, Principal Researcher, Statistics Canada, Division of Health Analysis
CANUE data and cohorts with residential history.
  • Dany Doiron, Research Associate, Respiratory Epidemiology and Clinical Research Unit, Research Institute of the McGill University Health Centre (RI-MUHC); CANUE data linkage expert.
Case Study – Examples from the Canadian Census Health and Environment Cohort.
  • Dan Crouse, Research Associate, New Brunswick Institute for Research, Data and Training; University of New Brunswick Department of Sociology.
Case Study – Examples from the BC Generations CPTP cohort.
  • Trevor Dummer, Co-National Scientific Director of the Canadian Partnership for Tomorrow Project (CPTP).

December 9 | 2019

Effects of greenspace morphology on mortality at the neighbourhood level: a cross-sectional ecological study.

Huaqing Wang, MSc, Prof Louis G Tassinary, PhD

The Lancet Planetary Health  VOLUME 3, ISSUE 11, PE460-E468, NOVEMBER 01, 2019

November, 2019 DOI:https://doi.org/10.1016/S2542-5196(19)30217-7




The association between urban greenspace and mortality risk is well known, but less is known about how the spatial arrangement of greenspace affects population health. We aimed to investigate the relation between urban greenspace distribution and mortality risk.


We did a cross-sectional study in Philadelphia, PA, USA, using high-resolution landcover data for 2008 from the Pennsylvania Spatial Data Access database. We calculated landscape metrics to measure the greenness, fragmentation, connectedness, aggregation, and shape of greenspace, including and omitting green areas 83·6 m2 or smaller, using Geographical Information System and spatial pattern analysis programs. We analysed all-cause and cause-specific mortality (related to heart disease, chronic lower respiratory diseases, and neoplasms) recorded in 2006 for 369 census tracts (small geographical areas with a population of 2500–8000 people). We did negative binomial regression and principal component analyses to assess associations between landscape spatial metrics and mortality, controlling for geographical, demographic, and socioeconomic factors.


A 1% increase in the percentage of greenspace was predicted to reduce all-cause mortality by 0·419% (95% CI 0·050–0·777), with no effect on cause-specific mortality. All-cause mortality was negatively associated with the area of greenspace. A 1 m2 increase in the mean area of greenspace led to a 0·011% (95% CI 0·004–0·018) fall in all-cause mortality and a 0·019% (0·007–0·032) decrease in cardiac mortality; considering only green areas larger than 83·6 m2 would contribute to a 0·002% (95% CI 0·001–0·003) decrease in all-cause mortality and a 0·003% (0·001–0·006) reduction in cardiac deaths. Census tracts with more connected, aggregated, coherent, and complex shape greenspaces had a lower risk of all-cause and cause-specific mortality. The negative association between articulated landscape parcels and all-cause mortality varied with age and education, such that the relation was stronger for census tracts with a higher percentage of older and less well-educated adults.


A significant modest association exists between the spatial distribution of greenspace in cities and mortality risk. The overall amount of greenspace alone is probably failing to capture significant variance in local health outcomes and, thus, environment-based health planning should consider the shape, form, and function of greenspace.

December 4 | 2019

Examining the Shape of the Association between Low Levels of Fine Particulate Matter and Mortality across Three Cycles of the Canadian Census Health and Environment Cohort.

Pappin AJ, Christidis T, Pinault LL, Crouse DL, Brook JR, Erickson A, Hystad P, Li C, Martin RV, Meng J, Weichenthal S, van Donkelaar A, Tjepkema M, Brauer M, Burnett RT.

Environ Health Perspect. 2019 Oct;127(10):107008. doi: 10.1289/EHP5204 Epub 2019 Oct 22.



Ambient fine particulate air pollution with aerodynamic diameter ≤2.5 μm (PM2.5) is an important contributor to the global burden of disease. Information on the shape of the concentration-response relationship at low concentrations is critical for estimating this burden, setting air quality standards, and in benefits assessments.


We examined the concentration-response relationship between PM2.5 and nonaccidental mortality in three Canadian Census Health and Environment Cohorts (CanCHECs) based on the 1991, 1996, and 2001 census cycles linked to mobility and mortality data.


Census respondents were linked with death records through 2016, resulting in 8.5 million adults, 150 million years of follow-up, and 1.5 million deaths. Using annual mailing address, we assigned time-varying contextual variables and 3-y moving-average ambient PM2.5 at a 1×1 km spatial resolution from 1988 to 2015. We ran Cox proportional hazards models for PM2.5 adjusted for eight subject-level indicators of socioeconomic status, seven contextual covariates, ozone, nitrogen dioxide, and combined oxidative potential. We used three statistical methods to examine the shape of the concentration-response relationship between PM2.5 and nonaccidental mortality.


The mean 3-y annual average estimate of PM2.5 exposure ranged from 6.7 to 8.0 μg/m3 over the three cohorts. We estimated a hazard ratio (HR) of 1.053 [95% confidence interval (CI): 1.041, 1.065] per 10-μg/m3 change in PM2.5 after pooling the three cohort-specific hazard ratios, with some variation between cohorts (1.041 for the 1991 and 1996 cohorts and 1.084 for the 2001 cohort). We observed a supralinear association in all three cohorts. The lower bound of the 95% CIs exceeded unity for all concentrations in the 1991 cohort, for concentrations above 2 μg/m3 in the 1996 cohort, and above 5 μg/m3 in the 2001 cohort.


In a very large population-based cohort with up to 25 y of follow-up, PM2.5 was associated with nonaccidental mortality at concentrations as low as 5 μg/m3.

November 25 | 2019

Green spaces and mortality: a systematic review and meta-analysis of cohort studies.

David Rojas-Rueda, PhD, Prof Mark J Nieuwenhuijsen, PhD, Mireia Gascon, PhD, Daniela Perez-Leon, MD, Pierpaolo Mudu, PhD

The Lancet Planetary Health  VOLUME 3, ISSUE 11, PE469-E477, NOVEMBER 01, 2019  DOI:https://doi.org/10.1016/S2542-5196(19)30215-3




Green spaces have been proposed to be a health determinant, improving health and wellbeing through different mechanisms. We aimed to systematically review the epidemiological evidence from longitudinal studies that have investigated green spaces and their association with all-cause mortality. We aimed to evaluate this evidence with a meta-analysis, to determine exposure-response functions for future quantitative health impact assessments.


We did a systematic review and meta-analysis of cohort studies on green spaces and all-cause mortality. We searched for studies published and indexed in MEDLINE before Aug 20, 2019, which we complemented with an additional search of cited literature. We included studies if their design was longitudinal; the exposure of interest was measured green space; the endpoint of interest was all-cause mortality; they provided a risk estimate (ie, a hazard ratio [HR]) and the corresponding 95% CI for the association between green space exposure and all-cause mortality; and they used normalised difference vegetation index (NDVI) as their green space exposure definition. Two investigators (DR-R and DP-L) independently screened the full-text articles for inclusion. We used a random-effects model to obtain pooled HRs. This study is registered with PROSPERO, CRD42018090315.


We identified 9298 studies in MEDLINE and 13 studies that were reported in the literature but not indexed in MEDLINE, of which 9234 (99%) studies were excluded after screening the titles and abstracts and 68 (88%) of 77 remaining studies were excluded after assessment of the full texts. We included nine (12%) studies in our quantitative evaluation, which comprised 8 324 652 individuals from seven countries. Seven (78%) of the nine studies found a significant inverse relationship between an increase in surrounding greenness per 0·1 NDVI in a buffer zone of 500 m or less and the risk of all-cause mortality, but two studies found no association. The pooled HR for all-cause mortality per increment of 0·1 NDVI within a buffer of 500 m or less of a participant’s residence was 0·96 (95% CI 0·94–0·97; I2, 95%).


We found evidence of an inverse association between surrounding greenness and all-cause mortality. Interventions to increase and manage green spaces should therefore be considered as a strategic public health intervention.

November 22 | 2019

Evaluating the Sensitivity of PM2.5-Mortality Associations to the Spatial and Temporal Scale of Exposure Assessment.

Crouse DL, Erickson AC, Christidis T, Pinault L, van Donkelaar A, Li C, Meng J, Martin RV, Tjepkema M, Hystad P, Burnett R, Pappin A, Brauer M, Weichenthal S.

Epidemiology. 2019 Nov 4. doi: 10.1097/EDE.0000000000001136 [Epub ahead of print]




The temporal and spatial scales of exposure assessment may influence observed associations between fine particulate air pollution (PM2.5) and mortality but few studies have systematically examined this question.


We followed 2.4 million adults in the 2001 Canadian Census Health and Environment Cohort for nonaccidental and cause-specific mortality between 2001-2011. We assigned PM2.5 exposures to residential locations using satellite-based estimates and compared three different temporal moving averages (1-year, 3-year, and 8-year) and three spatial scales (1-km, 5-km, and 10-km) of exposure assignment. In addition, we examined different spatial scales based on age, employment status, and urban/rural location, as well as adjustment for O3, NO2, or their combined oxidant capacity (Ox).


In general, longer moving averages resulted in stronger associations between PM2.5 and mortality. For nonaccidental mortality, we observed a hazard ratio of 1.11 (95% CI: 1.08, 1.13) for the 1-year moving average compared to 1.23 (95% CI: 1.20, 1.27) for the 8-year moving average. Respiratory and lung cancer mortality were most sensitive to the spatial scale of exposure assessment with stronger associations observed at smaller spatial scales. Adjustment for oxidant gases attenuated associations between PM2.5 and cardiovascular mortality and strengthened associations with lung cancer. Despite these variations, PM2.5 was associated with increased mortality in nearly all of the models examined.


These findings support a relationship between outdoor PM2.5 and mortality at low concentrations and highlight the importance of longer exposure windows, more spatially resolved exposure metrics, and adjustment for oxidant gases in characterizing this relationship.

November 11 | 2019

Association Between Neighborhood Walkability and Predicted 10-Year Cardiovascular Disease Risk: The CANHEART (Cardiovascular Health in Ambulatory Care Research Team) Cohort.

Howell NA, Tu JV, Moineddin R, Chu A, Booth GL. 

J Am Heart Assoc. 2019 Nov 5;8(21):e013146. Epub 2019 Oct 31. DOI:10.1161/JAHA.119.013146


Background Individuals living in unwalkable neighborhoods appear to be less physically active and more likely to develop obesity, diabetes mellitus, and hypertension. It is unclear whether neighborhood walkability is a risk factor for future cardiovascular disease. Methods and Results We studied residents living in major urban centers in Ontario, Canada on January 1, 2008, using linked electronic medical record and administrative health data from the CANHEART (Cardiovascular Health in Ambulatory Care Research Team) cohort. Walkability was assessed using a validated index based on population and residential density, street connectivity, and the number of walkable destinations in each neighborhood, divided into quintiles (Q). The primary outcome was a predicted 10-year cardiovascular disease risk of ≥7.5% (recommended threshold for statin use) assessed by the American College of Cardiology/American Heart Association Pooled Cohort Equation. Adjusted associations were estimated using logistic regression models. Secondary outcomes included measured systolic blood pressure, total and high-density lipoprotein cholesterol levels, prior diabetes mellitus diagnosis, and current smoking status. In total, 44 448 individuals were included in our analyses. Fully adjusted analyses found a nonlinear relationship between walkability and predicted 10-year cardiovascular disease risk (least [Q1] versus most [Q5] walkable neighborhood: odds ratio =1.09, 95% CI: 0.98, 1.22), with the greatest difference between Q3 and Q5 (odds ratio=1.33, 95% CI: 1.23, 1.45). Dose-response associations were observed for systolic blood pressure, high-density lipoprotein cholesterol, and diabetes mellitus risk, while an inverse association was observed with smoking status. Conclusions In our setting, adults living in less walkable neighborhoods had a higher predicted 10-year cardiovascular disease risk than those living in highly walkable areas.

November 4 | 2019

Comparison of land use regression and random forests models on estimating noise levels in five Canadian cities.

Liu Y, Goudreau S, Oiamo T, Rainham D, Hatzopoulou M, Chen H, Davies H, Tremblay M, Johnson J, Bockstael A, Leroux T, Smargiassi A.

Environ Pollut. 2019 Oct 10:113367. DOI:10.1016/j.envpol.2019.113367 [Epub ahead of print]


Chronic exposure to environment noise is associated with sleep disturbance and cardiovascular diseases. Assessment of population exposed to environmental noise is limited by a lack of routine noise sampling and is critical for controlling exposure and mitigating adverse health effects. Land use regression (LUR) model is newly applied in estimating environmental exposures to noise. Machine-learning approaches offer opportunities to improve the noise estimations from LUR model. In this study, we employed random forests (RF) model to estimate environmental noise levels in five Canadian cities and compared noise estimations between RF and LUR models. A total of 729 measurements and 33 built environment-related variables were used to estimate spatial variation in environmental noise at the global (multi-city) and local (individual city) scales. Leave one out cross-validation suggested that noise estimates derived from the RF global model explained a greater proportion of variation (R2: RF = 0.58, LUR = 0.47) with lower root mean squared errors (RF = 4.44 dB(A), LUR = 4.99 dB(A)). The cross-validation also indicated the RF models had better general performance than the LUR models at the city scale. By applying the global models to estimate noise levels at the postal code level, we found noise levels were higher in Montreal and Longueuil than in other major Canadian cities.