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:




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.