November 26 | 2018

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Association of Prenatal Exposure to Air Pollution With Autism Spectrum Disorder. 

Pagalan L, Bickford C, Weikum W, Lanphear B, Brauer M, Lanphear N, Hanley GE, Oberlander TF, Winters M. 

JAMA Pediatr. 2018 Nov 19. doi: 10.1001/jamapediatrics.2018.3101. [Epub ahead of print] 10.1001/jamapediatrics.2018.3101

 

Abstract

IMPORTANCE: The etiology of autism spectrum disorder (ASD) is poorly understood, but prior studies suggest associations with airborne pollutants.

OBJECTIVE: To evaluate the association between prenatal exposures to airborne pollutants and ASD in a large population-based cohort.

DESIGN, SETTING, AND PARTICIPANTS: This population-based cohort encompassed nearly all births in Metro Vancouver, British Columbia, Canada, from 2004 through 2009, with follow-up through 2014. Children were diagnosed with ASD using a standardized assessment with the Autism Diagnostic Interview-Revised and Autism Diagnostic Observation Schedule. Monthly mean exposures to particulate matter with a diameter less than 2.5 µm (PM2.5), nitric oxide (NO), and nitrogen dioxide (NO2) at the maternal residence during pregnancy were estimated with temporally adjusted, high-resolution land use regression models. The association between prenatal air pollution exposures and the odds of developing ASD was evaluated using logistic regression adjusted for child sex, birth month, birth year, maternal age, maternal birthplace, and neighborhood-level urbanicity and income band. Data analysis occurred from June 2016 to May 2018.

EXPOSURES: Mean monthly concentrations of ambient PM2.5, NO, and NO2 at the maternal residence during pregnancy, calculated retrospectively using temporally adjusted, high-resolution land use regression models.

MAIN OUTCOMES AND MEASURES: Autism spectrum disorder diagnoses based on standardized assessment of the Autism Diagnostic Interview-Revised and Autism Diagnostic Observation Schedule. The hypothesis being tested was formulated during data collection.

RESULTS: In a cohort of 132 256 births, 1307 children (1.0%) were diagnosed with ASD by the age of 5 years. The final sample size for the PM2.5-adjusted model was 129 439 children, and for NO and NO2, it was 129 436 children; of these, 1276 (1.0%) were diagnosed with ASD. Adjusted odds ratios for ASD per interquartile range (IQR) were not significant for exposure to PM2.5 during pregnancy (1.04 [95% CI, 0.98-1.10] per 1.5 μg/m3 increase [IQR] in PM2.5) or NO2 (1.06 [95% CI, 0.99-1.12] per 4.8 ppb [IQR] increase in NO2) but the odds ratio was significant for NO (1.07 [95% CI, 1.01-1.13] per 10.7 ppb [IQR] increase in NO). Odds ratios for male children were 1.04 (95% CI, 0.98-1.10) for PM2.5; 1.09 (95% CI, 1.02-1.15) for NO; and 1.07 (95% CI, 1.00-1.13) for NO2. For female children, they were for 1.03 (95% CI, 0.90-1.18) for PM2.5; 0.98 (95% CI, 0.83-1.13) for NO; and 1.00 (95% CI, 0.86-1.16) for NO2.

CONCLUSIONS AND RELEVANCE: In a population-based birth cohort, we detected an association between exposure to NO and ASD but no significant association with PM2.5 and NO2.

URBAN FORM METRICS FROM HIGH RESOLUTION SATELLITE AND STREET-LEVEL IMAGERY

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We are getting ready to start some intensive work on developing urban form metrics from high resolution satellite and street-level imagery. We will be hosting two virtual meetings to develop a comprehensive list of metrics and applications that are high on the priority list for CANUE members. Your feedback will help us develop a work program for CANUE staff in the coming months.

We will be hosting the same meeting on Dec 4th  (9am to 10:30 am pacific) and Dec 6th (12 noon to 1:30pm pacific).

REGISTER HERE  for your preferred date.

If you are unable to attend on either date, please email info@canue.ca with the following message: “please add me to the working group for machine learning and high resolution imagery”. You can provide feedback prior to the meetings, and we will send a summary report in early January.

November 19 | 2018

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The health risks and benefits of cycling in urban environments compared with car use: health impact assessment study.

David Rojas-Rueda, Audrey de Nazelle, Marko Tainio, Mark J Nieuwenhuijsen

 

BMJ 2011; 343 doi: https://doi.org/10.1136/bmj.d4521

 

Abstract

Objective To estimate the risks and benefits to health of travel by bicycle, using a bicycle sharing scheme, compared with travel by car in an urban environment.

Design Health impact assessment study.

Setting Public bicycle sharing initiative, Bicing, in Barcelona, Spain.

Participants 181 982 Bicing subscribers.

Main outcomes measures The primary outcome measure was all cause mortality for the three domains of physical activity, air pollution (exposure to particulate matter <2.5 μm), and road traffic incidents. The secondary outcome was change in levels of carbon dioxide emissions.

Results Compared with car users the estimated annual change in mortality of the Barcelona residents using Bicing (n=181 982) was 0.03 deaths from road traffic incidents and 0.13 deaths from air pollution. As a result of physical activity, 12.46 deaths were avoided (benefit:risk ratio 77). The annual number of deaths avoided was 12.28. As a result of journeys by Bicing, annual carbon dioxide emissions were reduced by an estimated 9 062 344 kg.

Conclusions Public bicycle sharing initiatives such as Bicing in Barcelona have greater benefits than risks to health and reduce carbon dioxide emissions.

 

November 12 | 2018

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A Web-Based Survey of Residents’ Views on Advocating with Patients for a Healthy Built Environment in Canada. 

Matthew Cruickshank and Marcus Law

International Journal of Family Medicine Volume 2014, Article ID 458184, 7 pages

http://dx.doi.org/10.1155/2014/458184

 

Abstract

Purpose. To determine family medicine residents’ perceived knowledge and attitudes towards the built environment and their responsibility for health advocacy and to identify their perceived educational needs and barriers to patient education and advocacy.

Methods. A web-based survey was conducted in Canada with University of Toronto family medicine residents. Data were analyzed descriptively.

Results. 93% agreed or strongly agreed that built environment significantly impacts health. 64% thought educating patients on built environment is effective disease prevention; 52% considered this a role of family physicians. 78% reported that advocacy for built environment is effective disease prevention; 56% perceived this to be the family physician’s role. 59% reported being knowledgeable to discuss how a patient’s environment may affect his/her health; 35% reported being knowledgeable to participate in community discussions on built environment. 78% thought education would help with integration into practice. Inadequate time (92%), knowledge (73%), and remuneration (54%) were barriers.

Conclusions. While residents perceived value in education and advocacy as disease prevention strategies and acknowledged the importance of a healthy built environment, they did not consider advocacy towards this the family physician’s role. Barrier reduction and medical education may contribute to improved advocacy, ultimately improving physical activity levels and patient health outcomes.

STUDENT TRAVEL AWARD RECIPIENTS

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Congratulations to our latest travel award recipients:

 

 

 

 

 

 

 

November 5 | 2018

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Mapping Air Pollution with Google Street View Cars: Efficient Approaches with Mobile Monitoring and Land Use Regression.

Messier KP, Chambliss SE, Gani S, Alvarez R, Brauer M, Choi JJ, Hamburg SP, Kerckhoffs J, LaFranchi B, Lunden MM, Marshall JD, Portier CJ, Roy A, Szpiro AA, Vermeulen RCH, Apte JS.

Environ Sci Technol. 2018 Oct 24. doi: DOI:10.1021/acs.est.8b03395 [Epub ahead of print]

 

Abstract 

Air pollution measurements collected through systematic mobile monitoring campaigns can provide outdoor concentration data at high spatial resolution. We explore approaches to minimize data requirements for mapping a city’s air quality using mobile monitors with “data-only” versus predictive modeling approaches. We equipped two Google Street View cars with 1-Hz instruments to collect nitric oxide (NO) and black carbon (BC) measurements in Oakland, CA. We explore two strategies for efficiently mapping spatial air quality patterns through Monte Carlo analyses. First, we explore a “data-only” approach where we attempt to minimize the number of repeated visits needed to reliably estimate concentrations for all roads. Second, we combine our data with a land use regression-kriging (LUR-K) model to predict at unobserved locations; here, measurements from only a subset of roads or repeat visits are considered. Although LUR-K models did not capture the full variability of on-road concentrations, models trained with minimal data consistently captured important covariates and general spatial air pollution trends, with cross-validation R2 for log-transformed NO and BC of 0.65 and 0.43. Data-only mapping performed poorly with few (1-2) repeated drives but obtained better cross-validation R2 than the LUR-K approach within 4 to 8 repeated drive days per road segment.