Residential surrounding greenness and the incidence of childhood asthma: Findings from a population-based cohort in Ontario, Canada.

Several epidemiological studies have investigated the possible role that living in areas with greater amounts of greenspace has on the incidence of childhood asthma. These findings have been inconsistent, and few studies explored the relevance of timing of exposure. We investigated the role of residential surrounding greenness on the risk of incident asthma using a population-based retrospective cohort study. We included 982,131 singleton births in Ontario, Canada between 2006 and 2013. Two measures of greenness, the Normalized Difference Vegetation Index (NDVI) and the Green View Index (GVI), were assigned to the residential histories of these infants from pregnancy through to 12 years of age. Longitudinally-based diagnoses of asthma were determined by using provincial administrative health data. The extended Cox hazards model was used to characterize associations between greenness measures and asthma (up to age 12 years) while adjusting for several risk factors. In a fully adjusted model, that included a term for traffic-related air pollution (NO2), we found no association between an interquartile range increase (0.08) of the NDVI during childhood and asthma incidence (HR = 0.99; 95 % CI = 0.99-1.01). In contrast, we found that an 0.08 increase in NDVI during childhood reduced the risk of asthma in children 7-12 years of age by 14 % (HR = 0.86, 95 % CI:0.79-0.95). Seasonal differences in the association between greenness and asthma were noted. Our findings suggest that residential proximity to greenness reduces the risk of asthma in children aged 7-12.

Changes in industrial air pollution and the onset of childhood asthma in Quebec, Canada.

Ambient air pollution has been associated with asthma onset and exacerbation in children. Whether improvement in air quality due to reduced industrial emissions has resulted in improved health outcomes such as asthma in some localities has usually been assessed indirectly with studies on between-subject comparisons of air pollution from all sources and health outcomes. In this study we directly assessed, within small areas in the province of Quebec (Canada), the influence of changes in local industrial fine particulate matter (PM2.5), nitrogen dioxide (NO2), and sulfur dioxide (SO2) concentrations, on changes in annual asthma onset rates in children (≤12 years old) with a longitudinal ecological design. We identified the yearly number of new cases of childhood asthma in 1282 small areas (census tracts or local community service centers) for the years 2002, 2004, 2005, 2006, and 2015. Annual average concentrations of industrial air pollutants for each of the geographic areas, and three sectors (i.e., pulp and paper mills, petroleum refineries, and metal smelters) were estimated by the Polair3D chemical transport model. Fixed-effects negative binomial models adjusted for household income were used to assess associations; additional adjustments for environmental tobacco smoke, background pollutant concentrations, vegetation coverage, and sociodemographic characteristics were conducted in sensitivity analyses. The incidence rate ratios (IRR) for childhood asthma onset for the interquartile increase in total industrial PM2.5, NO2, and SO2 were 1.016 (95% confidence interval, CI: 1.006-1.026), 1.063 (1.045-1.090), and 1.048 (1.031-1.080), respectively. Positive associations were also found with pollutant concentrations from most individual sectors. Results suggest that changes in industrial pollutant concentrations influence childhood asthma onset rates in small localities.

Associations between environmental noise and myocardial infarction and stroke: Investigating the potential mediating effects of hypertension.

BACKGROUND: We investigated whether hypertension may be a mediator in the pathway linking environmental noise exposure to incident MI and stroke. METHODS: Separately for MI and stroke, we built two population-based cohorts from linked health administrative data. Participants were residents of Montreal (Canada) between 2000 and 2014, aged 45 years and older who were free of hypertension and MI or stroke at time of entry. MI, stroke and hypertension were ascertained from validated case definitions. Residential long-term environmental noise exposure, expressed as the annual mean level acoustic equivalent 24 h (LAeq24h), was estimated from a land use regression model. We performed mediation analysis based on the potential outcomes framework. We used a Cox proportional hazards model for the exposure-outcome model and a logistic regression for the exposure-mediator model. In sensitivity analysis we applied a marginal structural approach to estimate the natural direct and indirect effects. RESULTS: Each cohort included approximately 900 000 individuals, with 26 647 incident cases of MI and 16 656 incident cases of stroke. 36% of incident MI and 40% of incident stokes had previously developed hypertension. The estimated total effect per interquartile range increase (from 55.0 to 60.5 dB A) in the annual mean LAeq24h was 1.073 (95% confidence interval (CI): 1.070-1.077) for both MI for stroke. We found no evidence of exposure-mediator interaction for both outcomes. The relationships between environmental noise and MI and stroke was not mediated by hypertension. CONCLUSIONS: This population-based cohort study suggests that the main route by which environmental noise exposure may cause MI or stroke is not through hypertension.

Integrating random forests and propagation models for high-resolution noise mapping.

The adverse effects of long-term exposure to environmental noise on human health are of increasing concern. Noise mapping methods such as spatial interpolation and land use regression cannot capture complex relationships between environmental conditions and noise propagation or attenuation in a three-dimension (3D) built environment. In this study, we developed a hybrid approach by combining a traffic propagation model and random forests (RF) machine learning algorithm to map the total environment noise levels for daily average, daytime, nighttime, and day-evening-nighttime at 30 m × 30 m resolution for the island of Montreal, Canada. The propagation model was used to predict traffic noise surfaces using road traffic flow, 3D building information, and a digital elevation model. The traffic noise estimates were compared with ground-based sound-level measurements at 87 points to extract residuals between total environmental noise and traffic noise. Residuals at these points were fit to RF models with multiple environmental and geographic predictor variables (e.g., vegetation index, population density, brightness of nighttime lights, land use types, and distances to noise contour around the airport, bus stops, and road intersections). Using the sound-level measurements as baseline data, the prediction errors, i.e., mean error, mean absolute error, and root mean squared error of daily average noise levels estimated by our hybrid approach was -0.03 dB(A), 2.67 dB(A), and 3.36 dB(A). Combining deterministic and stochastic models can provide accurate total environmental noise estimates for large geographic areas where sound-level measurements are available.

Tree characteristics and environmental noise in complex urban settings - A case study from Montreal, Canada.

Field studies have shown that dense tree canopies and regular tree arrangements reduce noise from a point source. In urban areas, noise sources are multiple and tree arrangements are rarely dense. There is a lack of data on the association between the urban tree canopy characteristics and noise in complex urban settings. Our aim was to investigate the spatial variation of urban tree canopy characteristics, indices of vegetation abundance, and environmental noise levels. Using Light Detection and Ranging point cloud data for 2015, we extracted the characteristics of 1,272,069 public and private trees across the island of Montreal, Canada. We distinguished needle-leaf from broadleaf trees, and calculated the percentage of broadleaf trees, the total area of the crown footprint, the mean crown centroid height, and the mean volume of crowns of trees that were located within 100m, 250m, 500m, and 1000m buffers around 87 in situ noise measurement sites. A random forest model incorporating tree characteristics, the normalized difference vegetation index (NDVI) values, and the distances to major urban noise sources (highways, railways and roads) was employed to estimate variation in noise among measurement locations. We found decreasing trends in noise levels with increases in total area of the crown footprint and mean crown centroid height. The percentages of increased mean squared error of the regression models indicated that in 500m buffers the total area of the crown footprint (29.2%) and the mean crown centroid height (12.6%) had a stronger influence than NDVI (3.2%) in modeling noise levels; similar patterns of influence were observed using other buffers. Our findings suggest that municipal initiatives designed to reduce urban noise should account for tree features, and not just the number of trees or the overall amount of vegetation.

Hot weather and risk of drowning in children: Opportunity for prevention.

The link between outdoor temperature and risk of drowning in children is poorly understood. The objective of this study was to determine the association between elevated temperature and the chance of drowning in children and adolescents. We used a case-crossover study design to assess 807 fatal and nonfatal drowning-related hospitalisations among children aged 0 to 19 years in Quebec, Canada between 1989 and 2015. The primary exposure measure was maximum temperature the day of drowning. We estimated odds ratios and 95% confidence intervals (CI) for the association of temperature with drowning by age group (<2, 2-4, 5-9, 10-19 years), adjusted for precipitation, relative humidity, and holidays. Elevated temperature was associated with greater odds of drowning. Compared with 15 °C, a temperature of 30 °C was associated with 6 times the chance of drowning between 0 and 19 years of age (95% CI 4.40-8.16). The association was not modified by characteristics such as age or location of drowning. Relative to 15 °C, a temperature of 30 °C was associated with 3.75 times the odds of drowning in pools (95% CI 1.85-7.63) and 12.44 times the odds of drowning in other bodies of water (95% CI 3.53-43.81). Associations persisted even after implementation of a policy to restrict access to private pools in 2010. These findings suggest that hot weather is strongly associated with the risk of drowning in children aged 0 to 19 years. Interventions to prevent drowning in children should be enhanced during hot days, and not only around pools.

Air pollution from industries and asthma onset in childhood: A population-based birth cohort study using dispersion modeling.

BACKGROUND: Despite evidence that ambient air pollution may play a role in the development of asthma, little is known about the potential contribution of industrial emissions. OBJECTIVE: We used a population-based birth cohort to investigate the association between asthma onset in childhood and residential exposure to industrial emissions, estimated from atmospheric dispersion modeling. METHODS: The study population comprised all children born in the province of Quebec, Canada, 2002-2011. Asthma onset were ascertained from health administrative databases with validated algorithms. We used atmospheric dispersion modeling to develop time-varying annual mean concentration of ambient PM2.5, NO2 and SO2 at participants' residence from industries. For each pollutant, we assessed the association between industrial emissions exposure and childhood asthma onset using Cox proportional hazard model, adjusted for sex, material and social deprivation and calendar year. Sensitivity analysis included adjusting for long-term regional and traffic-related ambient PM2.5 and NO2, and assessing potential confounding by unmeasured secondhand smoke. RESULTS: The cohort included 722,667 children and 66,559 incident cases of asthma. For all pollutants, we found a non-linear association between childhood asthma onset and residential ambient air pollutant concentration from industries, with stronger effects at lower concentrations. A change from 25th to the 75th percentile in the mean annual ambient concentration of PM2.5 (0.13 µg/m3), NO2 (1.0 µg/m3) and SO2 (1.6 µg/m3) from industrial emissions was associated with a 19% (95% CI: 17-20%), 21% (95% CI: 19-23%) and 23% (95% CI: 21-24%) increase in the risk of asthma onset in children, respectively. For PM2.5 and NO2, associations were persisting after adjustments for long-term regional PM2.5 and traffic-related NO2 ambient concentration. CONCLUSION: Residential exposure to industrial emissions estimated from dispersion modeling was associated with asthma onset in childhood. Importantly, associations were stronger at lower concentrations and independent from those of other sources, thus adding up to the burden of regional and traffic-related air pollution.

Exposure to ambient air pollutants and the onset of dementia in Québec, Canada.

BACKGROUND: Effects of air pollutants are related to oxidative stress which is also linked to the pathogenesis of dementia including Alzheimer's and related diseases. OBJECTIVE: We assessed associations between exposure to air pollutants and the onset of dementia; the association with the distance between residence and major roads was also assessed for the island of Montreal. METHODS: We created an open cohort of adults aged 65 years and older starting in 2000 and ending in 2012 in the province of Québec, Canada using linked medico-administrative databases. New cases of dementia were defined based on a validated algorithm. Annual residential levels of nitrogen dioxide (NO2) and fine particles (PM2.5) at residential levels were estimated for each year of follow up using estimates based on satellite images and ground air monitoring data. Hazard ratios (HRs) were assessed with Extended (time dependent exposure) Cox models with age as the time axis and stratified for sex, for the annual exposure level at each residential address. Models were adjusted for the calendar year, area-wide social and material deprivation indexes and for NO2 or PM2.5; they were also indirectly adjusted for smoking. RESULTS: 1,807,133 persons (13,242,270 person-years) were followed and 199,826 developed dementia. From models (adjusted for calendar year, social and material deprivation indexes), HRs for an interquartile range (IQR) increase in time-varying exposure to NO2 (IQR 13.26 ppb), PM2.5 (IQR 3.90 µg/m³), and distance to major roads (IQR 150 m, in Montreal only), were 1.005 (CI 95% 0.994-1.017), 1.016 (CI 95% 1.003-1.028) and 0.969 (CI 95% 0.958-0.980), respectively. CONCLUSIONS: Results suggest that the onset of dementia may be related to residential exposure to PM2.5, NO2, and distance to major roads.

Environmental and health impacts of transportation and land use scenarios in 2061.

We compared numbers of trips and distances by transport mode, air pollution and health impacts of a Business As Usual (BAU) and an Ideal scenario with urban densification and reductions in car share (76%-62% in suburbs; 55%-34% in urban areas) for the Greater Montreal (Canada) for 2061. We estimated the population in 87 municipalities using a demographic model and population projections. Year 2031 (Y2031) trips (from mode choice modeling) and distances were used to estimate those of Y2061. Emissions of nitrogen dioxide (NO2) and carbon dioxide (CO2) were estimated and NO2 used with dispersion modeling to estimate concentrations. Walking and Public Transit (PT) use and corresponding distances walked in Y2061 were >70% higher for the Ideal scenario vs the BAU, while car share and distances were <40% lower. NO2 levels were slightly lower in the Ideal scenario vs the BAU, but always higher in the urban core. Health impacts, summarized with disability adjusted life years (DALY), differed between urban and suburb areas but globally, the Ideal scenario reduced the impacts of the Y2061 BAU by 33% DALY. Percentages of car and PT trips were similar for the Y2031 and Y2061 BAU but kms travelled by car, CO2 and NO2 increased, due to increased populations. Drastic measures to decrease car share appear necessary to substantially reduce impacts of transportation.

Long-term exposure to a mixture of industrial SO2, NO2, and PM2.5 and anti-citrullinated protein antibody positivity.

BACKGROUND: Studies of associations between industrial air emissions and rheumatic diseases, or diseases-related serological biomarkers, are few. Moreover, previous evaluations typically studied individual (not mixed) emissions. We investigated associations between individual and combined exposures to industrial sulfur dioxide (SO2), nitrogen dioxide (NO2), and fine particles matter (PM2.5) on anti-citrullinated protein antibodies (ACPA), a characteristic biomarker for rheumatoid arthritis (RA). METHODS: Serum ACPA was determined for 7600 randomly selected CARTaGENE general population subjects in Quebec, Canada. Industrial SO2, NO2, and PM2.5 concentrations, estimated by the California Puff (CALPUFF) atmospheric dispersion model, were assigned based on residential postal codes at the time of sera collection. Single-exposure logistic regressions were performed for ACPA positivity defined by 20 U/ml, 40 U/ml, and 60 U/ml thresholds, adjusting for age, sex, French Canadian origin, smoking, and family income. Associations between regional overall PM2.5 exposure and ACPA positivity were also investigated. The associations between the combined three industrial exposures and the ACPA positivity were assessed by weighted quantile sum (WQS) regressions. RESULTS: Significant associations between individual industrial exposures and ACPA positivity defined by the 20 U/ml threshold were seen with single-exposure logistic regression models, for industrial emissions of PM2.5 (odds ratio, OR = 1.19, 95% confidence intervals, CI: 1.04-1.36) and SO2 (OR = 1.03, 95% CI: 1.00-1.06), without clear associations for NO2 (OR = 1.01, 95% CI: 0.86-1.17). Similar findings were seen for the 40 U/ml threshold, although at 60 U/ml, the results were very imprecise. The WQS model demonstrated a positive relationship between combined industrial exposures and ACPA positivity (OR = 1.36, 95% CI: 1.10-1.69 at 20 U/ml) and suggested that industrial PM2.5 may have a closer association with ACPA positivity than the other exposures. Again, similar findings were seen with the 40 U/ml threshold, though 60 U/ml results were imprecise. No clear association between ACPA and regional overall PM2.5 exposure was seen. CONCLUSIONS: We noted positive associations between ACPA and industrial emissions of PM2.5 and SO2. Industrial PM2.5 exposure may play a particularly important role in this regard.

Residential noise exposure and the longitudinal risk of hospitalization for depression after pregnancy: Postpartum and beyond.

OBJECTIVE: Depression is a major public health concern, but the link with the built environment is unclear. We sought to determine the relationship between residential noise during pregnancy and later risk of severe depression in women. METHODS: We analyzed a population-based cohort of 140,456 women with no documented history of mental illness who were pregnant in Montreal between 2000 and 2016. We obtained residential noise estimates (LAeq. 24 h, Lden, Lnight) from land use regression models, and followed the women over time for up to 18 years after pregnancy to identify subsequent hospitalizations for depression or other mental disorders. We used Cox regression to compute hazard ratios and 95% confidence intervals (CI) adjusted for maternal characteristics. RESULTS: There were 8.0 incident hospitalizations for depression and 16.4 for other mental disorders per 10,000 person-years in women exposed to an LAeq. 24 h of 60-64.9 dB(A). The incidence was lower for noise at < 55 dB(A), with 7.4 hospitalizations for depression and 13.8 for other mental disorders per 10,000 person-years. Compared with 50 dB(A), an LAeq. 24 h of 60 dB(A) was associated with 1.16 times (95% CI 0.84-1.62) the risk of depression hospitalization, and 1.34 times (95% CI 1.04-1.74) the risk of other mental disorders. Associations were more prominent for Lnight, with 1.32 times (95% CI 1.08-1.63) the risk of depression hospitalization at 60 dB(A) and 1.68 times the risk (95% CI 1.05-2.67) at 70 dB(A). CONCLUSIONS: Pregnant women exposed to noise, especially nighttime noise, have a greater risk of hospitalization for depression and other mental disorders later in life. Residential noise may be a risk factor for depression after pregnancy.

The associations between malaria, interventions, and the environment: a systematic review and meta-analysis.

BACKGROUND: Malaria transmission is driven by multiple factors, including complex and multifaceted connections between malaria transmission, socioeconomic conditions, climate and interventions. Forecasting models should account for all significant drivers of malaria incidence although it is first necessary to understand the relationship between malaria burden and the various determinants of risk to inform the development of forecasting models. In this study, the associations between malaria risk, environmental factors, and interventions were evaluated through a systematic review. METHODS: Five electronic databases (CAB Abstracts, EMBASE, Global Health, MEDLINE and ProQuest Dissertations & Theses) were searched for studies that included both the effects of the environment and interventions on malaria within the same statistical model. Studies were restricted to quantitative analyses and health outcomes of malaria mortality or morbidity, outbreaks, or transmission suitability. Meta-analyses were conducted on a subset of results using random-effects models. RESULTS: Eleven studies of 2248 potentially relevant articles that met inclusion criteria were identified for the systematic review and two meta-analyses based upon five results each were performed. Normalized Difference Vegetation Index was not found to be statistically significant associated with malaria with a pooled OR of 1.10 (95% CI 0.07, 1.71). Bed net ownership was statistically associated with decreasing risk of malaria, when controlling for the effects of environment with a pooled OR of 0.75 (95% CI 0.60, 0.95). In general, environmental effects on malaria, while controlling for the effect of interventions, were variable and showed no particular pattern. Bed nets ownership, use and distribution, have a significant protective effect while controlling for environmental variables. CONCLUSIONS: There are a limited number of studies which have simultaneously evaluated both environmental and interventional effects on malaria risk. Poor statistical reporting and a lack of common metrics were important challenges for this review, which must be addressed to ensure reproducibility and quality research. A comprehensive or inclusive approach to identifying malaria determinants using standardized indicators would allow for a better understanding of its epidemiology, which is crucial to improve future malaria risk estimations.

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.

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.

Spatial modeling of daily concentrations of ground-level ozone in Montreal, Canada: A comparison of geostatistical approaches.

Ground-level ozone (O3) is a powerful oxidizing agent and a harmful pollutant affecting human health, forests and crops. Estimating O3 exposure is a challenge because it exhibits complex spatiotemporal patterns. The aim in this study was to provide high-resolution maps (100 × 100 m) of O3 for the metropolitan area of Montreal, Canada. We assessed the kriging with external drift (KED) model to estimate O3 concentration by synoptic weather classes for 2010. We compared these results with ordinary kriging (OK), and a simple average of 12 monitoring stations. We also compared the estimates obtained for the 2010 summer with those from a Bayesian maximum entropy (BME) model reported in the literature (Adam-Poupart et al., 2014). The KED model with road and vegetation density as covariates showed good performance for all six synoptic classes (daily R2 estimates ranging from 0.77 to 0.92 and RMSE from 2.79 to 3.37 ppb). For the summer of 2010, the model using KED demonstrated the best results (R2 = 0.92; RMSE = 3.14 ppb), followed by the OK model (R2 = 0.85, RMSE = 4 ppb). Our results showed that errors appear to be substantially reduced with the KED model. This may increase our capacity of linking O3 levels to health problems by means of improved assessments of ambient exposures. However, future work integrating the temporal dependency in the data is needed to not overstate the performance of the KED model.

Estimating the health benefits of planned public transit investments in Montreal.

BACKGROUND: Since public transit infrastructure affects road traffic volumes and influences transportation mode choice, which in turn impacts health, it is important to estimate the alteration of the health burden linked with transit policies. OBJECTIVE: We quantified the variation in health benefits and burden between a business as usual (BAU) and a public transit (PT) scenarios in 2031 (with 8 and 19 new subway and train stations) for the greater Montreal region. METHOD: Using mode choice and traffic assignment models, we predicted the transportation mode choice and traffic assignment on the road network. Subsequently, we estimated the distance travelled in each municipality by mode, the minutes spent in active transportation, as well as traffic emissions. Thereafter we estimated the health burden attributed to air pollution and road traumas and the gains associated with active transportation for both the BAU and PT scenarios. RESULTS: We predicted a slight decrease of overall trips and kilometers travelled by car as well as an increase of active transportation for the PT in 2031 vs the BAU. Our analysis shows that new infrastructure will reduce the overall burden of transportation by 2.5 DALYs per 100,000 persons. This decrease is caused by the reduction of road traumas occurring in the inner suburbs and central Montreal region as well as gains in active transportation in the inner suburbs. CONCLUSION: Based on the results of our study, transportation planned public transit projects for Montreal are unlikely to reduce drastically the burden of disease attributable to road vehicles and infrastructures in the Montreal region. The impact of the planned transportation infrastructures seems to be very low and localized mainly in the areas where new public transit stations are planned.

Association between quantity and duration of snowfall and risk of myocardial infarction.

BACKGROUND: Although aggregate data suggest a link between snowfall and myocardial infarction (MI), individual risk has yet to be assessed. We evaluated the association between quantity and duration of snowfall and the risk of MI using nonaggregated administrative health data. METHODS: We used a case-crossover study design to investigate the association between snowfall and hospital admission or death due to MI in the province of Quebec, Canada, between November and April during 1981-2014. The main exposure measures were quantity (in centimetres) and duration (in hours) of snowfall by calendar day. We computed odds ratios (ORs) and 95% confidence intervals (CIs) for the association between daily snowfall and MI, adjusted for minimum daily temperatures. RESULTS: In all, 128 073 individual hospital admissions and 68 155 deaths due to MI were included in the analyses. The likelihood of MI was increased the day after a snowfall among men but not among women. Compared with 0 cm, 20 cm of snowfall was associated with an OR of 1.16 for hospital admission (95% CI 1.11-1.21) and 1.34 for death (95% CI 1.26-1.42) due to MI the following day among men. Corresponding ORs among women were 1.01 (95% CI 0.95-1.07) and 1.04 (95% CI 0.96-1.13). Similar but smaller associations were observed for snowfall duration (0 h v. 24 h) and MI. INTERPRETATION: Both the quantity and duration of snowfall were associated with subsequent risk of hospital admission or death due to MI, driven primarily by an effect in men. These data have implications for public health initiatives in regions with snowstorms.

Risk of hospitalization for fire-related burns during extreme cold weather.

BACKGROUND: Environmental factors are important predictors of fires, but no study has examined the association between outdoor temperature and fire-related burn injuries. We sought to investigate the relationship between extremely cold outdoor temperatures and the risk of hospitalization for fire-related burns. MATERIAL AND METHODS: We carried out a time-stratified case-crossover study of 2470 patients hospitalized for fire-related burn injuries during cold months between 1989 and 2014 in Quebec, Canada. The main exposure was the minimum outdoor temperature on the day of and the day before the burn. We computed odds ratios (OR) and 95% confidence intervals (CI) to evaluate the relationship between minimum temperature and fire-related burns, and assessed how associations varied across sex and age. RESULTS: Exposure to extreme cold temperature was associated with a significantly higher risk of hospitalization for fire-related burns. Compared with 0°C, exposure to a minimum temperature of -30°C was associated with an OR of 1.51 (95% CI 1.22-1.87) for hospitalization for fire-related burns. The associations were somewhat stronger for women, youth, and the elderly. Compared with 0°C, a minimum temperature of -30°C was associated with an OR for fire-related burn hospitalization of 1.65 for women (95% CI 1.13-2.40), 1.60 for age < 25 years (95% CI 1.02-2.52), and 1.73 for age ≥ 65 years (95% CI 1.08-2.77). DISCUSSION: Extremely cold outdoor temperature is a risk factor for fire-related burns. Measures to prevent fires should be implemented prior to the winter season, and enhanced during extreme cold.

Industrial air emissions, and proximity to major industrial emitters, are associated with anti-citrullinated protein antibodies.

OBJECTIVE: To determine the association of anti-citrullinated antibodies (ACPA) with the ambient air pollutants fine particulate matter (PM2.5) and sulfur dioxide (SO2). METHODS: The CARTaGENE first-wave cohort includes 20,000 general population subjects from Quebec (Canada). On a sample of unselected 1586 subjects, we determined serum, ACPA and performed multivariable logistic regression, for the outcome of positive ACPA, assessing for independent effects of our air pollution variables, adjusting for age, sex, smoking, and French Canadian origin. Two models assessed distance to main industrial emitters of PM2.5, and of SO2, and two models assessed tons of SO2 and of PM2.5 annual emissions. We also assessed associations with PM2.5 regional ambient concentrations estimated with satellite imagery. RESULTS: Adjusted analyses suggested a positive association between annual industrial PM2.5 and SO2 emissions and the presence of ACPA antibodies (OR: 1.02, 95%CI 1.00-1.04 per 10t of PM2.5 and 100t of SO2). The data were also consistent with a negative association between the presence of ACPA, and distance to a major industrial emitter of both PM2.5 and SO2. We found no association with PM2.5 estimates of ambient levels. CONCLUSIONS: These analyses suggest that exposure to industrial emissions of air pollutants is related to ACPA positivity.

Comparison of spatiotemporal prediction models of daily exposure of individuals to ambient nitrogen dioxide and ozone in Montreal, Canada.

BACKGROUND: In previous studies investigating the short-term health effects of ambient air pollution the exposure metric that is often used is the daily average across monitors, thus assuming that all individuals have the same daily exposure. Studies that incorporate space-time exposures of individuals are essential to further our understanding of the short-term health effects of ambient air pollution. OBJECTIVES: As part of a longitudinal cohort study of the acute effects of air pollution that incorporated subject-specific information and medical histories of subjects throughout the follow-up, the purpose of this study was to develop and compare different prediction models using data from fixed-site monitors and other monitoring campaigns to estimate daily, spatially-resolved concentrations of ozone (O3) and nitrogen dioxide (NO2) of participants' residences in Montreal, 1991-2002. METHODS: We used the following methods to predict spatially-resolved daily concentrations of O3 and NO2 for each geographic region in Montreal (defined by three-character postal code areas): (1) assigning concentrations from the nearest monitor; (2) spatial interpolation using inverse-distance weighting; (3) back-extrapolation from a land-use regression model from a dense monitoring survey, and; (4) a combination of a land-use and Bayesian maximum entropy model. We used a variety of indices of agreement to compare estimates of exposure assigned from the different methods, notably scatterplots of pairwise predictions, distribution of differences and computation of the absolute agreement intraclass correlation (ICC). For each pairwise prediction, we also produced maps of the ICCs by these regions indicating the spatial variability in the degree of agreement. RESULTS: We found some substantial differences in agreement across pairs of methods in daily mean predicted concentrations of O3 and NO2. On a given day and postal code area the difference in the concentration assigned could be as high as 131ppb for O3 and 108ppb for NO2. For both pollutants, better agreement was found between predictions from the nearest monitor and the inverse-distance weighting interpolation methods, with ICCs of 0.89 (95% confidence interval (CI): 0.89, 0.89) for O3 and 0.81 (95%CI: 0.80, 0.81) for NO2, respectively. For this pair of methods the maximum difference on a given day and postal code area was 36ppb for O3 and 74ppb for NO2. The back-extrapolation method showed a higher degree of disagreement with the nearest monitor approach, inverse-distance weighting interpolation, and the Bayesian maximum entropy model, which were strongly constrained by the sparse monitoring network. The maps showed that the patterns of agreement differed across the postal code areas and the variability depended on the pair of methods compared and the pollutants. For O3, but not NO2, postal areas showing greater disagreement were mostly located near the city centre and along highways, especially in maps involving the back-extrapolation method. CONCLUSIONS: In view of the substantial differences in daily concentrations of O3 and NO2 predicted by the different methods, we suggest that analyses of the health effects from air pollution should make use of multiple exposure assessment methods. Although we cannot make any recommendations as to which is the most valid method, models that make use of higher spatially resolved data, such as from dense exposure surveys or from high spatial resolution satellite data, likely provide the most valid estimates.