October 15 | 2018

Association between residential self-selection and non-residential built environment exposures.

Howell NA, Farber S, Widener MJ, Allen J, Booth GL.

Health Place. 2018 Oct 1;54:149-154 DOI: 10.1016/j.healthplace.2018.08.009

Abstract

Studies employing ‘activity space’ measures of the built environment do not always account for how individuals self-select into different residential and non-residential environments when testing associations with physical activity. To date, no study has examined whether preferences for walkable residential neighborhoods predict exposure to other walkable neighborhoods in non-residential activity spaces. Using a sample of 9783 university students from Toronto, Canada, we assessed how self-reported preferences for a walkable neighborhood predicted their exposure to other walkable, non-residential environments, and further whether these preferences confounded observed walkability-physical activity associations. We found that residential walkability preferences and non-residential walkability were significant associated (β = 0.42, 95% CI: (0.37, 0.47)), and further that these preferences confounded associations between non-residential walkability exposure and time spent walking (reduction in association = 10.5%). These results suggest that self-selection factors affect studies of non-residential built environment exposures.

October 9 | 2018

Who has access to urban vegetation? A spatial analysis of distributional green equity in 10 US cities. 

Lorien Nesbitt, Michael J. Meitner, Cynthia Girling, Stephen R.J. Sheppard, Yuhao Lua.

Landscape and Urban Planning Volume 181, January 2019, Pages 51-79

https://doi.org/10.1016/j.landurbplan.2018.08.007

Abstract 

This research examines the distributional equity of urban vegetation in 10 US urbanized areas using very high resolution land cover data and census data. Urban vegetation is characterized three ways in the analysis (mixed vegetation, woody vegetation, and public parks), to reflect the variable ecosystem services provided by different types of urban vegetation. Data are analyzed at the block group and census tract levels using Spearman’s correlations and spatial autoregressive models. There is a strong positive correlation between urban vegetation and higher education and income across most cities. Negative correlations between racialized minority status and urban vegetation are observed but are weaker and less common in multivariate analyses that include additional variables such as education, income, and population density. Park area is more equitably distributed than mixed and woody vegetation, although inequities exist across all cities and vegetation types. The study finds that education and income are most strongly associated with urban vegetation distribution but that various other factors contribute to patterns of urban vegetation distribution, with specific patterns of inequity varying by local context. These results highlight the importance of different urban vegetation measures and suggest potential solutions to the problem of urban green inequity. Cities can use our results to inform decision making focused on improving environmental justice in urban settings.

October 1 | 2018

Capturing the spatial variability of noise levels based on a short-term monitoring campaign and comparing noise surfaces against personal exposures collected through a panel study.

Fallah-Shorshani M, Minet L, Liu R, Plante C, Goudreau S, Oiamo T, Smargiassi A, Weichenthal S, Hatzopoulou M.

Environ Res. 2018 Aug 17;167:662-672. DOI: 10.1016/j.envres.2018.08.021 

 

Abstract

Environmental noise can cause important cardiovascular effects, stress and sleep disturbance. The development of appropriate methods to estimate noise exposure within a single urban area remains a challenging task, due to the presence of various transportation noise sources (road, rail, and aircraft). In this study, we developed a land-use regression (LUR) approach using a Generalized Additive Model (GAM) for LAeq (equivalent noise level) to capture the spatial variability of noise levels in Toronto, Canada. Four different model formulations were proposed based on continuous 20-min noise measurements at 92 sites and a leave one out cross-validation (LOOCV). Models where coefficients for variables considered as noise sources were forced to be positive, led to the development of more realistic exposure surfaces. Three different measures were used to assess the models; adjusted R2 (0.44-0.64), deviance (51-72%) and Akaike information criterion (AIC) (469.2-434.6). When comparing exposures derived from the four approaches to personal exposures from a panel study, we observed that all approaches performed very similarly, with values for the Fractional mean bias (FB), normalized mean square error (NMSE), and normalized absolute difference (NAD) very close to 0. Finally, we compared the noise surfaces with data collected from a previous campaign consisting of 1-week measurements at 200 fixed sites in Toronto and observed that the strongest correlations occurred between our predictions and measured noise levels along major roads and highway collectors. Our validation against long-term measurements and panel data demonstrates that manual modifications brought to the models were able to reduce bias in model predictions and achieve a wider range of exposures, comparable with measurement data.

September 24 | 2018

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

Burr WS, Dales R, Liu L, Stieb D, Smith-Doiron M, Jovic B, Kauri LM, Shin HH.

Int J Environ Res Public Health. 2018 Sep 17;15(9). pii: E2029. DOI:10.3390/ijerph15092029

Abstract

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.

Lessons Learned: Moving Walkability to Policy and Practice | October 16 | 2018

9am – 10am pacific | 12 noon – 1pm eastern

REGISTER NOW

Utilitarian walkability by 1km buffered postal code – Prepared by Urban Design 4 Health Ltd and Toronto Public Health
The Walkable City: Neighbourhood Design and Preferences, Travel Choices and Health, April 2012 Toronto Public Health


Hear about Dr. Frank’s recent collaborative work in Metro Vancouver, linking detailed data on neighbourhood walkability, regional transit and park access with Type 2 Diabetes, cardiovascular disease, hypertension, stress, and sense of community relationships across a range of age and income cohorts, followed by a broader discussion of  walkability research and future directions.

 

Lawrence Frank is Professor in Sustainable Transportation and Public Health at UBC and specializes in the interaction between land use, travel behavior, air quality; and health.  He coined the term “walkability” in the early – mid 90’s; his work led to WalkScore and has been cited over 26,000 times making him one of the 2 most cited planning academics globally. Thompson and Reuters has listed him in the top 1% globally since 2014 as a highly cited researcher.  Dr. Frank has published over 150 peer reviewed articles and reports and co-authored two of the leading books – Heath and Community Design and Urban Sprawl and Public Health which helped to map out the field emerging at the nexus of planning and health.

EXPERT WEBINAR: OCTOBER 16 | 2018

Don’t miss Dr. Larry Frank’s webinar: Lessons Learned: Moving Walkability to Policy and Practice
9am- 10am pacific | 12 noon – 1 pm eastern

September 17 | 2018

Evaluating the Impact of Neighborhood Characteristics on Differences between Residential and Mobility-Based Exposures to Outdoor Air Pollution. 

Fallah-Shorshani M, Hatzopoulou M, Ross NA, Patterson Z, Weichenthal S.

Environ Sci Technol. 2018 Aug 29. DOI: 10.1021/acs.est.8b02260

 

Abstract

Epidemiological studies often assign outdoor air pollution concentrations to residential locations without accounting for mobility patterns. In this study, we examined how neighborhood characteristics may influence differences in exposure assessments between outdoor residential concentrations and mobility-based exposures. To do this, we linked residential location and mobility data to exposure surfaces for NO2, PM2.5, and ultrafine particles in Montreal, Canada for 5452 people in 2016. Mobility data were collected using the MTL Trajet smartphone application (mean: 16 days/subject). Generalized additive models were used to identify important neighborhood predictors of differences between residential and mobility-based exposures and included residential distances to highways, traffic counts within 500 m of the residence, neighborhood walkability, median income, and unemployment rate. Final models including these parameters provided unbiased estimates of differences between residential and mobility-based exposures with small root-mean-square error values in 10-fold cross validation samples. In general, our findings suggest that differences between residential and mobility-based exposures are not evenly distributed across cities and are greater for pollutants with higher spatial variability like NO2. It may be possible to use neighborhood characteristics to predict the magnitude and direction of this error to better understand its likely impact on risk estimates in epidemiological analyses.

 

Travel Awards Available

Post-doctoral, graduate and undergraduate students who are developing data for CANUE, or who have used our data to produce new research are eligible to apply for a student travel award.

We will provide up to $2,500 toward the costs of presenting at a conference (travel, hotel, and registration fees) or visiting a research team at another institution for training. Applications will be reviewed by a Committee of CANUE members within 30 days of the submission deadline.

 

TWO AWARDS AVAILABLE – APPLICATION DEADLINE OCTOBER 31ST | 2018

APPLY NOW


 

September 10 | 2018

Global estimates of mortality associated with long-term exposure to outdoor fine particulate matter.

Burnett R, Chen H, Szyszkowicz M, Fann N, Hubbell B, Pope CA 3rd, Apte JS, Brauer M, Cohen A, Weichenthal S, Coggins J, Di Q, Brunekreef B, Frostad J, Lim SS, Kan H, Walker KD, Thurston GD, Hayes RB, Lim CC, Turner MC, Jerrett M, Krewski D, Gapstur SM, Diver WR, Ostro B, Goldberg D, Crouse DL, Martin RV, Peters P, Pinault L, Tjepkema M, van Donkelaar A, Villeneuve PJ, Miller AB, Yin P, Zhou M, Wang L, Janssen NAH, Marra M, Atkinson RW, Tsang H, Quoc Thach T, Cannon JB, Allen RT, Hart JE, Laden F, Cesaroni G, Forastiere F, Weinmayr G, Jaensch A, Nagel G, Concin H, Spadaro JV.

Proc Natl Acad Sci U S A. 2018 Sep 4. pii: 201803222. [Epub ahead of print]  DOI:10.1073/pnas.1803222115

 

Abstract

Exposure to ambient fine particulate matter (PM2.5) is a major global health concern. Quantitative estimates of attributable mortality are based on disease-specific hazard ratio models that incorporate risk information from multiple PM2.5 sources (outdoor and indoor air pollution from use of solid fuels and secondhand and active smoking), requiring assumptions about equivalent exposure and toxicity. We relax these contentious assumptions by constructing a PM2.5-mortality hazard ratio function based only on cohort studies of outdoor air pollution that covers the global exposure range. We modeled the shape of the association between PM2.5 and nonaccidental mortality using data from 41 cohorts from 16 countries-the Global Exposure Mortality Model (GEMM). We then constructed GEMMs for five specific causes of death examined by the global burden of disease (GBD). The GEMM predicts 8.9 million [95% confidence interval (CI): 7.5-10.3] deaths in 2015, a figure 30% larger than that predicted by the sum of deaths among the five specific causes (6.9; 95% CI: 4.9-8.5) and 120% larger than the risk function used in the GBD (4.0; 95% CI: 3.3-4.8). Differences between the GEMM and GBD risk functions are larger for a 20% reduction in concentrations, with the GEMM predicting 220% higher excess deaths. These results suggest that PM2.5 exposure may be related to additional causes of death than the five considered by the GBD and that incorporation of risk information from other, nonoutdoor, particle sources leads to underestimation of disease burden, especially at higher concentrations.

September 5 | 2018

Diabetes status and susceptibility to the effects of PM2.5 exposure on cardiovascular mortality in a national Canadian cohort.

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

Epidemiology. 2018 Aug 1. DOI: 10.1097/EDE.0000000000000908 [Epub ahead of print]

Abstract

BACKGROUND: Diabetes is infrequently coded as the primary cause of death but may contribute to cardiovascular disease (CVD) mortality in response to fine particulate matter (PM2.5) exposure. We analyzed all contributing causes of death to examine susceptibility of diabetics to CVD mortality from long-term exposure.

METHODS: We linked a subset of the 2001 Canadian Census Health and Environment Cohort (CanCHEC) with 10 years of follow-up to all causes of death listed on death certificates. We used survival models to examine the association between CVD deaths (n=123,500) and exposure to PM2.5 among deaths that co-occurred with diabetes (n=20,600) on the death certificate. More detailed information on behavioral covariates and diabetes status at baseline available in the Canadian Community Health Survey (CCHS) – mortality cohort (n=12,400 CVD deaths, with 2,800 diabetes deaths) complemented the CanCHEC analysis.

RESULTS: Among CanCHEC subjects, co-mention of diabetes on the death certificate increased the magnitude of association between CVD mortality and PM2.5 (HR=1.51 [1.39-1.65] per 10 μg/m) – versus all CVD deaths (HR=1.25 [1.21-1.29]) or CVD deaths without diabetes (HR=1.20 [1.16-1.25]). Among CCHS subjects, diabetics who used insulin or medication (included as proxies for severity) had higher HR estimates for CVD deaths from PM2.5 (HR=1.51 [1.08-2.12]) relative to the CVD death estimate for all respondents (HR=1.31 [1.16-1.47]).

CONCLUSIONS: Mention of diabetes on the death certificate resulted in higher magnitude associations between PM2.5 and CVD mortality, specifically amongst those who manage their diabetes with insulin or medication. Analyses restricted to the primary cause of death likely underestimate the role of diabetes in air pollution-related mortality.