Associations of neighborhood greenspace, and active living environments with autism spectrum disorders: A matched case-control study in Ontario, Canada.

BACKGROUND: Increasing evidence links early life residential exposure to natural urban environmental attributes and positive health outcomes in children. However, few studies have focused on their protective effects on the risk of autism spectrum disorder (ASD). The aim of this study was to investigate the associations of neighborhood greenspace, and active living environments during pregnancy with ASD in young children (≤6 years). METHODS: We conducted a population-based matched case-control study of singleton term births in Ontario, Canada for 2012-2016. The ASD and environmental data was generated using the Ontario Autism Spectrum Profile, the Better Outcomes Registry & Network Ontario, and Canadian Urban Environmental Health Research Consortium. We employed conditional logistic regressions to estimate the odds ratio (OR) between ASD and environmental factors characterizing selected greenspace metrics and neighborhoods conducive to active living (i.e., green view index (GVI), normalized difference vegetation index (NDVI), tree canopy, park proximity and active living environments index (ALE)). RESULTS: We linked 8643 mother-child pairs, including 1554 cases (18%). NDVI (OR 1.034, 0.944-1.024, per Inter Quartile Range [IQR] = 0.08), GVI (OR 1.025, 95% CI 0.953-1.087, per IQR = 9.45%), tree canopy (OR 0.992, 95% CI 0.903-1.089, per IQR = 6.24%) and the different categories of ALE were not associated with ASD in adjusted models for air pollution. In contrast, living closer to a park was protective (OR 0.888, 0.833-0.948, per 0.06 increase in park proximity index), when adjusted for air pollution. CONCLUSIONS: This study reported mixed findings showing both null and beneficial effects of green spaces and active living environments on ASD. Further investigations are warranted to elucidate the role of exposure to greenspaces and active living environments on the development of ASD.

Inequality in the Distribution of Air Pollution Attributable Mortality Within Canadian Cities.

Recent studies have identified inequality in the distribution of air pollution attributable health impacts, but to our knowledge this has not been examined in Canadian cities. We evaluated the extent and sources of inequality in air pollution attributable mortality at the census tract (CT) level in seven of Canada's largest cities. We first regressed fine particulate matter (PM2.5) and nitrogen dioxide (NO2) attributable mortality against the neighborhood (CT) level prevalence of age 65 and older, low income, low educational attainment, and identification as an Indigenous (First Nations, Métis, Inuit) or Black person, accounting for spatial autocorrelation. We next examined the distribution of baseline mortality rates, PM2.5 and NO2 concentrations, and attributable mortality by neighborhood (CT) level prevalence of these characteristics, calculating the concentration index, Atkinson index, and Gini coefficient. Finally, we conducted a counterfactual analysis of the impact of reducing baseline mortality rates and air pollution concentrations on inequality in air pollution attributable mortality. Regression results indicated that CTs with a higher prevalence of low income and Indigenous identity had significantly higher air pollution attributable mortality. Concentration index, Atkinson index, and Gini coefficient values revealed different degrees of inequality among the cities. Counterfactual analysis indicated that inequality in air pollution attributable mortality tended to be driven more by baseline mortality inequalities than exposure inequalities. Reducing inequality in air pollution attributable mortality requires reducing disparities in both baseline mortality and air pollution exposure.

Critical windows of exposure to air pollution and gestational diabetes: assessing effect modification by maternal pre-existing conditions and environmental factors.

BACKGROUND: Ambient air pollution has been associated with gestational diabetes (GD), but critical windows of exposure and whether maternal pre-existing conditions and other environmental factors modify the associations remains inconclusive. METHODS: We conducted a retrospective cohort study of all singleton live birth that occurred between April 1st 2006 and March 31st 2018 in Ontario, Canada. Ambient air pollution data (i.e., fine particulate matter with a diameter ≤ 2.5 µm (PM2.5), nitrogen dioxide (NO2) and ozone (O3)) were assigned to the study population in spatial resolution of approximately 1 km × 1 km. The Normalized Difference Vegetation Index (NDVI) and the Green View Index (GVI) were also used to characterize residential exposure to green space as well as the Active Living Environments (ALE) index to represent the active living friendliness. Multivariable Cox proportional hazards regression models were used to evaluate the associations. RESULTS: Among 1,310,807 pregnant individuals, 68,860 incident cases of GD were identified. We found the strongest associations between PM2.5 and GD in gestational weeks 7 to 18 (HR = 1.07 per IQR (2.7 µg/m3); 95% CI: 1.02 - 1.11)). For O3, we found two sensitive windows of exposure, with increased risk in the preconception period (HR = 1.03 per IQR increase (7.0 ppb) (95% CI: 1.01 - 1.06)) as well as gestational weeks 9 to 28 (HR 1.08 per IQR (95% CI: 1.04 -1.12)). We found that women with asthma were more at risk of GD when exposed to increasing levels of O3 (p- value for effect modification = 0.04). Exposure to air pollutants explained 20.1%, 1.4% and 4.6% of the associations between GVI, NDVI and ALE, respectively. CONCLUSION: An increase of PM2.5 exposure in early pregnancy and of O3 exposure during late first trimester and over the second trimester of pregnancy were associated with gestational diabetes whereas exposure to green space may confer a protective effect.

The Canadian Optimized Statistical Smoke Exposure Model (CanOSSEM): A machine learning approach to estimate national daily fine particulate matter (PM2.5) exposure.

Exposure to biomass smoke has been associated with a wide range of acute and chronic health outcomes. Over the past decades, the frequency and intensity of wildfires has increased in many areas, resulting in longer smoke episodes with higher concentrations of fine particulate matter (PM2.5). There are also many communities where seasonal open burning and residential wood heating have short- and long-term impacts on ambient air quality. Understanding the acute and chronic health effects of biomass smoke exposure requires reliable estimates of PM2.5 concentrations during the wildfire season and throughout the year, particularly in areas without regulatory air quality monitoring stations. We have developed a machine learning approach to estimate PM2.5 across all populated regions of Canada from 2010 to 2019. The random forest machine learning model uses potential predictor variables integrated from multiple data sources and estimates daily mean (24-hour) PM2.5 concentrations at a 5 km × 5 km spatial resolution. The training and prediction datasets were generated using observations from National Air Pollution Surveillance (NAPS) network. The Root Mean Squared Error (RMSE) between predicted and observed PM2.5 concentrations was 2.96 µg/m3 for the entire prediction set, and more than 96 % of the predictions were within 5 µg/m3 of the NAPS PM2.5 measurements. The model was evaluated using 10-fold, leave one-region-out, and leave-one-year-out cross-validations. Overall, CanOSSEM performed well but performance was sensitive to removal of large wildfire events such as the Fort McMurray interface fire in May 2016 or the extreme 2017 and 2018 wildfire seasons in British Columbia. Exposure estimates from CanOSSEM will be useful for epidemiologic studies on the acute and chronic health effects associated with PM2.5 exposure, especially for populations affected by biomass smoke where routine air quality measurements are not available.

Residential proximity to greenness and adverse birth outcomes in urban areas: Findings from a national Canadian population-based study.

BACKGROUND: Over the last decade, several studies have reported that residential proximity to vegetation, or 'greenness', is associated with improved birth outcomes, including for term birth weight (TBW), preterm birth (PTB), and small for gestational age (SGA). However, there remain several uncertainties about these possible benefits including the role of air pollution, and the extent to they are influenced socioeconomic status. METHODS: We addressed these gaps using a national population-based study of 2.2 million singleton live births in Canadian metropolitan areas between 1999 and 2008. Exposures to greenness, fine particulate matter (PM2.5), and nitrogen dioxide (NO2) were assigned to infants using the postal code of their mother's residence at the time of birth. The Normalized Difference Vegetation Index (NDVI) was used to characterize greenness, while estimates of ambient PM2.5 and NO2 were estimated using remote sensing, and a national land-use regression surface, respectively. Multivariable regression analysis was performed to describe associations between residential greenness and the birth outcomes. Stratified analyses explored whether these associations were modified by neighbourhood measures of socioeconomic status. RESULTS: Mothers who lived in greener areas had a lower risk of low TBW, PTB, and SGA babies. These associations persisted after adjustment for ambient NO2 and PM2.5. Specifically, in fully adjusted models, an interquartile range (IQR = 0.16) increase in the NDVI within a residential buffer of 250 m yielded odds ratios of 0.93 (95% confidence interval (CI): 0.92, 0.94), 0.94 (95% CI: 0.92, 0.95), and 0.94 (95% CI: 0.93, 0.95) for the outcomes of PTB, low TBW, and SGA, respectively. Similarly, an IQR increase in greenness was associated with a 16.3 g (95% CI: 15.3, 17.4) increase in TBW. We found inverse associations between greenness and the occurrence of adverse birth outcomes regardless of the socioeconomic status of the neighbourhood. INTERPRETATION: Our findings support the hypothesis that residential greenness contributes to healthier pregnancies, that these associations are independent from exposure to air pollution. , and that proximity to greenness benefits all mothers regardless of socioeconomic status.

Within-City Variation in Reactive Oxygen Species from Fine Particle Air Pollution and COVID-19.

Rationale: Evidence linking outdoor air pollution with coronavirus disease (COVID-19) incidence and mortality is largely based on ecological comparisons between regions that may differ in factors such as access to testing and control measures that may not be independent of air pollution concentrations. Moreover, studies have yet to focus on key mechanisms of air pollution toxicity such as oxidative stress. Objectives: To conduct a within-city analysis of spatial variations in COVID-19 incidence and the estimated generation of reactive oxygen species (ROS) in lung lining fluid attributable to fine particulate matter (particulate matter with an aerodynamic diameter ⩽2.5 µm [PM2.5]). Methods: Sporadic and outbreak-related COVID-19 case counts, testing data, population data, and sociodemographic data for 140 neighborhoods were obtained from the City of Toronto. ROS estimates were based on a mathematical model of ROS generation in lung lining fluid in response to iron and copper in PM2.5. Spatial variations in long-term average ROS were predicted using a land-use regression model derived from measurements of iron and copper in PM2.5. Data were analyzed using negative binomial regression models adjusting for covariates identified using a directed acyclic graph and accounting for spatial autocorrelation. Measurements and Main Results: A significant positive association was observed between neighborhood-level ROS and COVID-19 incidence (incidence rate ratio = 1.07; 95% confidence interval, 1.01-1.15 per interquartile range ROS). Effect modification by neighborhood-level measures of racialized group membership and socioeconomic status was also identified. Conclusions: Examination of neighborhood characteristics associated with COVID-19 incidence can identify inequalities and generate hypotheses for future studies.

Fine particulate matter concentration and composition and the incidence of childhood asthma.

BACKGROUND: Several studies have found positive associations between outdoor fine particulate air pollution (≤2.5 µm, PM2.5) and childhood asthma incidence. However, the impact of PM2.5 composition on children's respiratory health remains uncertain. OBJECTIVE: We examined whether joint exposure to PM2.5 mass concentrations and its major chemical components was associated with childhood asthma development. METHODS: We conducted a population-based cohort study by identifying 1,130,855 singleton live births occurring between 2006 and 2014 in the province of Ontario, Canada. Concentrations of PM2.5 and its seven major chemical components were assigned to participants based on their postal codes using chemical transport models and remote sensing. The joint impact of outdoor PM2.5 concentrations and its major components and childhood asthma incidence (up to age 6) were estimated using Cox proportional hazards models, allowing for potential nonlinearity. RESULTS: We identified 167,080 children who developed asthma before age 6. In adjusted models, outdoor PM2.5 mass concentrations during childhood were associated with increased incidence of childhood asthma (Hazard Ratio (HR) for each 1 µg/m3 increase = 1.026, 95% CI: 1.021-1.031). We found that the joint effects of PM2.5 and its components on childhood asthma incidence may be 24% higher than the conventional approach. Specific components/source markers such as black carbon, ammonium, and nitrate appeared to play an important role. CONCLUSIONS: Early life exposure to PM2.5 and its chemical components is associated with an increased risk of asthma development in children. The heterogeneous nature of PM2.5 should be considered in future health risk assessments.

Systematic review and meta-analysis of cohort studies of long term outdoor nitrogen dioxide exposure and mortality.

OBJECTIVE: To determine whether long term exposure to outdoor nitrogen dioxide (NO2) is associated with all-cause or cause-specific mortality. METHODS: MEDLINE, Embase, CENTRAL, Global Health and Toxline databases were searched using terms developed by a librarian. Screening, data extraction and risk of bias assessment were completed independently by two reviewers. Conflicts were resolved through consensus and/or involvement of a third reviewer. Pooling of results across studies was conducted using random effects models, heterogeneity among included studies was assessed using Cochran's Q and I2 measures, and sources of heterogeneity were evaluated using meta-regression. Sensitivity of pooled estimates to individual studies was examined and publication bias was evaluated using Funnel plots, Begg's and Egger's tests, and trim and fill. RESULTS: Seventy-nine studies based on 47 cohorts, plus one set of pooled analyses of multiple European cohorts, met inclusion criteria. There was a consistently high degree of heterogeneity. After excluding studies with probably high or high risk of bias in the confounding domain (n = 12), pooled hazard ratios (HR) indicated that long term exposure to NO2 was significantly associated with mortality from all/ natural causes (pooled HR 1.047, 95% confidence interval (CI), 1.023-1.072 per 10 ppb), cardiovascular disease (pooled HR 1.058, 95%CI 1.026-1.091), lung cancer (pooled HR 1.083, 95%CI 1.041-1.126), respiratory disease (pooled HR 1.062, 95%CI1.035-1.089), and ischemic heart disease (pooled HR 1.111, 95%CI 1.079-1.144). Pooled estimates based on multi-pollutant models were consistently smaller than those from single pollutant models and mostly non-significant. CONCLUSIONS: For all causes of death other than cerebrovascular disease, the overall quality of the evidence is moderate, and the strength of evidence is limited, while for cerebrovascular disease, overall quality is low and strength of evidence is inadequate. Important uncertainties remain, including potential confounding by co-pollutants or other concomitant exposures, and limited supporting mechanistic evidence. (PROSPERO registration number CRD42018084497).

Ambient ultrafine particle concentrations and incidence of childhood cancers.

BACKGROUND: Ambient air pollution has been associated with childhood cancer. However, little is known about the possible impact of ambient ultrafine particles (<0.1 µm) (UFPs) on childhood cancer incidence. OBJECTIVE: This study aimed to evaluate the association between prenatal and childhood exposure to UFPs and development of childhood cancer. METHODS: We conducted a population-based cohort study of within-city spatiotemporal variations in ambient UFPs across the City of Toronto, Canada using 653,702 singleton live births occurring between April 1, 1998 and March 31, 2017. Incident cases of 13 subtypes of paediatric cancers among children up to age 14 were ascertained using a cancer registry. Associations between ambient air pollutant concentrations and childhood cancer incidence were estimated using random-effects Cox proportional hazards models. We investigated both single- and multi-pollutant models accounting for co-exposures to PM2.5 and NO2. RESULTS: A total of 1,066 childhood cancers were identified. We found that first trimester exposure to UFPs (Hazard Ratio (HR) per 10,000/cm3 increase = 1.13, 95% CI: 1.03-1.22) was associated with overall cancer incidence diagnosed before 6 years of age after adjusting for PM2.5, NO2, and for personal and neighborhood-level covariates. Association between UFPs and overall cancer incidence exhibited a linear shape. No statistically significant associations were found for specific cancer subtypes. CONCLUSION: Ambient UFPs may represent a previously unrecognized risk factor in the aetiology of cancers in children. Our findings reinforce the importance of conducting further research on the effects of UFPs given their high prevalence of exposure in urban areas.

An ecological analysis of long-term exposure to PM2.5 and incidence of COVID-19 in Canadian health regions.

BACKGROUND: Ambient fine particulate matter (PM2.5) is associated with a wide range of acute and chronic health effects, including increased risk of respiratory infection. However, evidence specifically related to novel coronavirus disease (COVID-19) is limited. METHODS: COVID-19 case counts for 111 Canadian health regions were obtained from the COVID-19 Canada Open Data portal. Annual PM2.5 data for 2000-2016 were estimated from a national exposure surface based on remote sensing, chemical transport modelling and ground observations, and minimum and maximum temperature data for 2000-2015 were based on a national interpolated surface derived from thin-plate smoothing splines. Population counts and sociodemographic data by health region were obtained from the 2016 census, and health data (self-rated health and prevalence of smoking, obesity, and selected chronic diseases) by health region, were obtained from the Canadian Community Health Survey. Data on total number of COVID-19 tests and changes in mobility comparing post-vs. pre-introduction of social distancing measures were available by province. Data were analyzed using negative binomial regression models. RESULTS: After controlling for province, temperature, demographic and health characteristics and days since peak incidence by health region, long-term PM2.5 exposure exhibited a positive association with COVID-19 incidence (incidence rate ratio 1.07, 95% confidence interval 0.97-1.18 per µg/m3). This association was larger in magnitude and statistically significant in analyses excluding provinces that reported cases only for aggregated health regions, excluding health regions with less than median population density, and restricted to the most highly affected provinces (Quebec and Ontario). CONCLUSIONS: We observed a positive association between COVID-19 incidence and long-term PM2.5 exposure in Canadian health regions. The association was larger in magnitude and statistically significant in more highly affected health regions and those with potentially less exposure measurement error. While our results generate hypotheses for further testing, they should be interpreted with caution and require further examination using study designs less prone to bias.

Correction to: Systematic review and meta-analysis of case-crossover and time-series studies of short term outdoor nitrogen dioxide exposure and ischemic heart disease morbidity.

An amendment to this paper has been published and can be accessed via the original article.

Systematic review and meta-analysis of case-crossover and time-series studies of short term outdoor nitrogen dioxide exposure and ischemic heart disease morbidity.

BACKGROUND: Nitrogen dioxide (NO2) is a pervasive urban pollutant originating primarily from vehicle emissions. Ischemic heart disease (IHD) is associated with a considerable public health burden worldwide, but whether NO2 exposure is causally related to IHD morbidity remains in question. Our objective was to determine whether short term exposure to outdoor NO2 is causally associated with IHD-related morbidity based on a synthesis of findings from case-crossover and time-series studies. METHODS: MEDLINE, Embase, CENTRAL, Global Health and Toxline databases were searched using terms developed by a librarian. Screening, data extraction and risk of bias assessment were completed independently by two reviewers. Conflicts between reviewers were resolved through consensus and/or involvement of a third reviewer. Pooling of results across studies was conducted using random effects models, heterogeneity among included studies was assessed using Cochran's Q and I2 measures, and sources of heterogeneity were evaluated using meta-regression. Sensitivity of pooled estimates to individual studies was examined using Leave One Out analysis and publication bias was evaluated using Funnel plots, Begg's and Egger's tests, and trim and fill. RESULTS: Thirty-eight case-crossover studies and 48 time-series studies were included in our analysis. NO2 was significantly associated with IHD morbidity (pooled odds ratio from case-crossover studies: 1.074 95% CI 1.052-1.097; pooled relative risk from time-series studies: 1.022 95% CI 1.016-1.029 per 10 ppb). Pooled estimates for case-crossover studies from Europe and North America were significantly lower than for studies conducted elsewhere. The high degree of heterogeneity among studies was only partially accounted for in meta-regression. There was evidence of publication bias, particularly for case-crossover studies. For both case-crossover and time-series studies, pooled estimates based on multi-pollutant models were smaller than those from single pollutant models, and those based on older populations were larger than those based on younger populations, but these differences were not statistically significant. CONCLUSIONS: We concluded that there is a likely causal relationship between short term NO2 exposure and IHD-related morbidity, but important uncertainties remain, particularly related to the contribution of co-pollutants or other concomitant exposures, and the lack of supporting evidence from toxicological and controlled human studies.

Space-time hot spots of critically ill small for gestational age newborns and industrial air pollutants in major metropolitan areas of Canada.

We assessed the association of spatiotemporal hot spots of critically ill small for gestational age (ciSGA) newborns and industrial air emissions. Using neonatal admission data from the Canadian Neonatal Network between 2006 and 2010 (n = 32,836 infants), we aggregated maternal residential postal codes from nineteen census metropolitan areas (CMA) into space-time cubes and applied emerging hot spot analyses. Using National Pollutant Release Inventory data (n = 161 chemicals) and Environment Canada weather station data (n = 19 sites), we estimated monthly wind-dispersion of air emissions and calculated hot spots. We associated the patterns using logistic regression, with covariates for low socioeconomic status, NO2 pollution, and number of infants. A total of 5465 infants were identified as ciSGA and the larger CMAs had more and larger hot spots (i.e. accumulation of events in space and time). Seventy-eight industrial chemical hot spots were associated with ciSGA hot spots. The highest number of positive associations were for 28 different pollutants, which differed by CMA. Twenty-one were known or suspected developmental toxicants, such as particulate matter, carbon monoxide, heavy metals, and volatile organic compounds. Associations with hot spots of industrial chemical emissions were geographically specific and may help explain the space-time trends of ciSGA.

Health impact analysis of PM2.5 from wildfire smoke in Canada (2013-2015, 2017-2018).

Smoke from wildfires contains many air pollutants of concern and epidemiological studies have identified associations between exposure to wildfire smoke PM2.5 and mortality and respiratory morbidity, and a possible association with cardiovascular morbidity. For this study, a retrospective analysis of air quality modelling was performed to quantify the exposure to wildfire-PM2.5 across the Canadian population. The model included wildfire emissions from across North America for a 5-month period from May to September (i.e. wildfire season), between 2013 and 2015 and 2017-2018. Large variations in wildfire-PM2.5 were noted year-to-year, geospatially, and within fire season. The model results were then used to estimate the national population health impacts attributable to wildfire-PM2.5 and the associated economic valuation. The analysis estimated annual premature mortalities ranging from 54-240 premature mortalities attributable to short-term exposure and 570-2500 premature mortalities attributable to long-term exposure, as well as many non-fatal cardiorespiratory health outcomes. The economic valuation of the population health impacts was estimated per year at $410M-$1.8B for acute health impacts and $4.3B-$19B for chronic health impacts for the study period. The health impacts were greatest in the provinces with populations in close proximity to wildfire activity, though health impacts were also noted across many provinces indicating the long-range transport of wildfire-PM2.5. Understanding the population health impacts of wildfire smoke is important as climate change is anticipated to increase wildfire activity in Canada and abroad.