June 24 | 2019

Using maps to communicate environmental exposures and health risks: Review and best-practice recommendations. 

Stieb DM, Huang A, Hocking R, Crouse DL, Osornio-Vargas AR, Villeneuve PJ. 

Environ Res. 2019 May 31;176:108518. DOI:10.1016/j.envres.2019.05.049. [Epub ahead of print]



Graphical materials can be effective communication tools, and maps in particular are a potentially powerful means of conveying spatial information. Previous reviews have provided insights on the application of cartographic best practices, pitfalls to avoid, and considerations related to risk perception and communication, but none has reviewed primary studies of the effectiveness or utility of maps to users, nor have they addressed the issue from the perspective of health literacy, environmental health literacy, or public health ethics.


To systematically identify and review the literature pertaining to evaluation of maps in general, or specific map features, as environmental exposure and health risk communication tools; to formulate best-practice recommendations; and to identify future research priorities.


A health science librarian searched the literature for commentaries, reviews, and primary studies. Titles, abstracts, and full-text papers were screened for inclusion, and details of methods and results were extracted from 4 reviews and commentaries and 18 primary studies. This was supplemented by one additional review and 13 additional primary studies pertaining to use of maps for communication about wildfires and floods. One additional paper was identified by reviewing reference lists of all relevant papers.

RESULTS: and Discussion:

While there are significant gaps in the evidence, we formulated best practice recommendations highlighting the perspectives of health literacy and environmental health literacy. Key recommendations include: understanding the map developer’s societal role and mental model underlying map design; defining, understanding and iteratively engaging with map users; informing map design using key theoretical constructs; accounting for factors affecting risk perception; adhering to risk communication principles and cartographic best practices; and considering environmental justice and public health ethics implications. Recommendations for future research are also provided.

CIHR Data Analysis Grants | June 26th | 2019


The Canadian Institutes for Health Research has announced a new Operating Grant Competition for data analysis using existing databases and cohorts. The intent of this funding opportunity is to highlight and encourage the use of previously funded cohort, administrative, and survey data. There will be three funding streams; one stream in cancer prevention and control, another in reproductive, maternal, child, and youth health, as well as a stream in healthy cities intervention research.


CANUE hosted a webinar on June 26th (9 am pacific | 12 noon eastern) for researchers who would like more detailed information on our data holdings, partnerships with health data holders, and an opportunity to ask questions directly to the CANUE team.




June 18 | 2019

Accelerometer and GPS Data to Analyze Built Environments and Physical Activity. 

Tamura K, Wilson JS, Goldfeld K, Puett RC, Klenosky DB, Harper WA, Troped PJ.

Res Q Exerc Sport. 2019 Jun 14:1-8. DOI: 10.1080/02701367.2019.1609649. [Epub ahead of print]


Purpose: Most built environment studies have quantified characteristics of the areas around participants’ homes. However, the environmental exposures for physical activity (PA) are spatially dynamic rather than static. Thus, merged accelerometer and global positioning system (GPS) data were utilized to estimate associations between the built environment and PA among adults. Methods: Participants (N = 142) were recruited on trails in Massachusetts and wore an accelerometer and GPS unit for 1-4 days. Two binary outcomes were created: moderate-to-vigorous PA (MVPA vs. light PA-to-sedentary); and light-to-vigorous PA (LVPA vs. sedentary). Five built environment variables were created within 50-meter buffers around GPS points: population density, street density, land use mix (LUM), greenness, and walkability index. Generalized linear mixed models were fit to examine associations between environmental variables and both outcomes, adjusting for demographic covariates. Results: Overall, in the fully adjusted models, greenness was positively associated with MVPA and LVPA (odds ratios [ORs] = 1.15, 95% confidence interval [CI] = 1.03, 1.30 and 1.25, 95% CI = 1.12, 1.41, respectively). In contrast, street density and LUM were negatively associated with MVPA (ORs = 0.69, 95% CI = 0.67, 0.71 and 0.87, 95% CI = 0.78, 0.97, respectively) and LVPA (ORs = 0.79, 95% CI = 0.77, 0.81 and 0.81, 95% CI = 0.74, 0.90, respectively). Negative associations of population density and walkability with both outcomes reached statistical significance, yet the effect sizes were small. Conclusions: Concurrent monitoring of activity with accelerometers and GPS units allowed us to investigate relationships between objectively measured built environment around GPS points and minute-by-minute PA. Negative relationships between street density and LUM and PA contrast evidence from most built environment studies in adults. However, direct comparisons should be made with caution since most previous studies have focused on spatially fixed buffers around home locations, rather than the precise locations where PA occurs.

June 10 | 2019

Associations of combined exposures to surrounding green, air pollution and traffic noise on mental health.

Klompmaker JO, Hoek G, Bloemsma LD, Wijga AH, van den Brink C, Brunekreef B, Lebret E, Gehring U, Janssen NAH.

Environ Int. 2019 May 31;129:525-537. DOI: 10.1016/j.envint.2019.05.040 [Epub ahead of print]



Evidence is emerging that poor mental health is associated with the environmental exposures of surrounding green, air pollution and traffic noise. Most studies have evaluated only associations of single exposures with poor mental health.


To evaluate associations of combined exposure to surrounding green, air pollution and traffic noise with poor mental health.


In this cross-sectional study, we linked data from a Dutch national health survey among 387,195 adults including questions about psychological distress, based on the Kessler 10 scale, to an external database on registered prescriptions of anxiolytics, hypnotics & sedatives and antidepressants. We added data on residential surrounding green in a 300 m and a 1000 m buffer based on the Normalized Difference Vegetation Index (NDVI) and a land-use database (TOP10NL), modeled annual average air pollutant concentrations (including particulate matter (PM10, PM2.5), and nitrogen dioxide (NO2)) and modeled road- and rail-traffic noise (Lden and Lnight) to the survey. We used logistic regression to analyze associations of surrounding green, air pollution and traffic noise exposure with poor mental health.


In single exposure models, surrounding green was inversely associated with poor mental health. Air pollution was positively associated with poor mental health. Road-traffic noise was only positively associated with prescription of anxiolytics, while rail-traffic noise was only positively associated with psychological distress. For prescription of anxiolytics, we found an odds ratio [OR] of 0.88 (95% CI: 0.85, 0.92) per interquartile range [IQR] increase in NDVI within 300 m, an OR of 1.14 (95% CI: 1.10, 1.19) per IQR increase in NO2 and an OR of 1.07 (95% CI: 1.03, 1.11) per IQR increase in road-traffic noise. In multi exposure analyses, associations with surrounding green and air pollution generally remained but attenuated. Joint odds ratios [JOR], based on the Cumulative Risk Index (CRI) method, of combined exposure to air pollution, traffic noise and decreased surrounding green were higher than the ORs of single exposure models. Associations of environmental exposures with poor mental health differed somewhat by age.


Studies including only one of these three correlated exposures may overestimate the influence of poor mental health attributed to the studied exposure, while underestimating the influence of combined environmental exposures.