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Background Knowledge translation and dissemination are some of the main challenges that affect evidence-based medicine. Web 2.0 platforms promote the sharing and collaborative development of content. Executable knowledge tools, such as order sets, are a knowledge translation tool whose localization is critical to its effectiveness but a challenge for organizations to develop independently. Objective This paper describes a Web 2.0 resource, referred to as the collaborative network (TCN), for order set development designed to share executable knowledge (order sets). This paper also analyzes the scope of its use, describes its use through network analysis, and examines the provision and use of order sets in the platform by organizational size. Methods Data were collected from Think Research’s TxConnect platform. We measured interorganization sharing across Canadian hospitals using descriptive statistics. A weighted chi-square analysis was used to evaluate institutional size to share volumes based on institution size, with post hoc Cramer V score to measure the strength of association. Results TCN consisted of 12,495 order sets across 683 diagnoses or processes. Between January 2010 and March 2015, a total of 131 health care organizations representing 360 hospitals in Canada downloaded order sets 105,496 times. Order sets related to acute coronary syndrome, analgesia, and venous thromboembolism were most commonly shared. COVID-19 order sets were among the most actively shared, adjusting for order set lifetime. A weighted chi-square analysis showed nonrandom downloading behavior (P Conclusions In this paper, we have described and analyzed a Web 2.0 platform for the sharing of order set content with significant network activity. The robust use of TCN to access customized order sets reflects its value as a resource for health care organizations when they develop or update their own order sets.

BACKGROUND Knowledge translation and dissemination are some of the main challenges that affect evidence-based medicine. Web 2.0 platforms promote the sharing and collaborative development of content. Executable knowledge tools, such as order sets, are a knowledge translation tool whose localization is critical to its effectiveness but a challenge for organizations to develop independently. OBJECTIVE This paper describes a Web 2.0 resource, referred to as the collaborative network (TCN), for order set development designed to share executable knowledge (order sets). This paper also analyzes the scope of its use, describes its use through network analysis, and examines the provision and use of order sets in the platform by organizational size. METHODS Data were collected from Think Research’s TxConnect platform. We measured interorganization sharing across Canadian hospitals using descriptive statistics. A weighted chi-square analysis was used to evaluate institutional size to share volumes based on institution size, with post hoc Cramer V score to measure the strength of association. RESULTS TCN consisted of 12,495 order sets across 683 diagnoses or processes. Between January 2010 and March 2015, a total of 131 health care organizations representing 360 hospitals in Canada downloaded order sets 105,496 times. Order sets related to acute coronary syndrome, analgesia, and venous thromboembolism were most commonly shared. COVID-19 order sets were among the most actively shared, adjusting for order set lifetime. A weighted chi-square analysis showed nonrandom downloading behavior (P CONCLUSIONS In this paper, we have described and analyzed a Web 2.0 platform for the sharing of order set content with significant network activity. The robust use of TCN to access customized order sets reflects its value as a resource for health care organizations when they develop or update their own order sets.

Respiratory effects of ozone in the workplace have not been extensively studied. Our aim was to explore the relationship between daily average ozone levels and compensated acute respiratory problems among workers in Quebec between 2003 and 2010 using a time-stratified case-crossover design. Health data came from the Workers' Compensation Board. Daily concentrations of ozone were estimated using a spatiotemporal model. Conditional logistic regressions, with and without adjustment for temperature, were used to estimate odds ratios (ORs, per 1 ppb increase of ozone), and lag effects were assessed. Relationships with respiratory compensations in all industrial sectors were essentially null. Positive non-statistically significant associations were observed for outdoor sectors, and decreased after controlling for temperature (ORs of 0.98; 1.01 and 1.05 at Lags 0, 1 and 2 respectively). Considering the predicted increase of air pollutant concentrations in the context of climate change, closer investigation should be carried out on outdoor workers.

Background: Most studies that have assessed impacts on mortality of future temperature increases have relied on a small number of simulations and have not addressed the variability and sources of uncertainty in their mortality projections. Objectives: We assessed the variability of temperature projections and dependent future mortality distributions, using a large panel of temperature simulations based on different climate models and emission scenarios. Methods: We used historical data from 1990 through 2007 for Montreal, Quebec, Canada, and Poisson regression models to estimate relative risks (RR) for daily nonaccidental mortality in association with three different daily temperature metrics (mean, minimum, and maximum temperature) during June through August. To estimate future numbers of deaths attributable to ambient temperatures and the uncertainty of the estimates, we used 32 different simulations of daily temperatures for June–August 2020–2037 derived from three global climate models (GCMs) and a Canadian regional climate model with three sets of RRs (one based on the observed historical data, and two on bootstrap samples that generated the 95% CI of the attributable number (AN) of deaths). We then used analysis of covariance to evaluate the influence of the simulation, the projected year, and the sets of RRs used to derive the attributable numbers of deaths. Results: We found that < 1% of the variability in the distributions of simulated temperature for June–August of 2020–2037 was explained by differences among the simulations. Estimated ANs for 2020–2037 ranged from 34 to 174 per summer (i.e., June–August). Most of the variability in mortality projections (38%) was related to the temperature–mortality RR used to estimate the ANs. Conclusions: The choice of the RR estimate for the association between temperature and mortality may be important to reduce uncertainty in mortality projections. Citation: Benmarhnia T, Sottile MF, Plante C, Brand A, Casati B, Fournier M, Smargiassi A. 2014. Variability in temperature-related mortality projections under climate change. Environ Health Perspect 122:1293–1298; http://dx.doi.org/10.1289/ehp.1306954

Few studies have measured the effect of short-term exposure to industrial emissions on the respiratory health of children. Here we estimate the risk of hospitalization for asthma and bronchiolitis in young children associated with their recent exposure to emissions from an aluminum smelter. We used a case-crossover design to assess the risk of hospitalization, February 1999-December 2008, in relation to short-term variation in levels of exposure among children 0-4 years old living less than 7.5 km from the smelter. The percentage of hours per day that the residence of a hospitalized child was in the shadow of winds crossing the smelter was used to estimate the effect of wind-borne emissions on case and crossover days. Community-wide pollutant exposure was estimated through daily mean and daily maximum SO2 and PM2.5 concentrations measured at a fixed monitoring site near the smelter. Odds ratios (OR) were estimated using conditional logistic regressions. The risk of same-day hospitalization for asthma or bronchiolitis increased with the percentage of hours in a day that a child's residence was downwind of the smelter. For children aged 2-4 years, the OR was 1.27 (95% CI=1.03-1.56; n=103 hospitalizations), for an interquartile range (IQR) of 21% of hours being downwind. In this age group, the OR with PM2.5 daily mean levels was slightly smaller than with the hours downwind (OR: 1.22 for an IQR of 15.7 μg/m(3), 95% CI=1.03-1.44; n=94 hospitalizations). Trends were observed between hospitalizations and levels of SO2 for children 2-4 years old. Increasing short-term exposure to emissions from a Quebec aluminum smelter was associated with an increased risk of hospitalization for asthma and bronchiolitis in young children who live nearby. Estimating exposure through records of wind direction allows for the integration of exposure to all pollutants carried from the smelter stack.

Background: Systemic inflammation and oxidative stress have been suggested as mechanisms underlying health effects of ambient air pollutants yet few studies have assessed effects of air pollutants ...

Background: It is well established that short-term exposure to ambient air pollutants can exacerbate asthma, the role of early life or long-term exposure is less clear. We assessed the association between severe asthma exacerbations with both birth and annual exposure to outdoor air pollutants with a population-based cohort of asthmatic children in the province of Quebec (Canada). Method: Exacerbations of asthma occurring between 1 April 1996 and 31 March 2011 were defined as one hospitalization or emergency room visit with a diagnosis of asthma for children (

Background: Industrial plants emit air pollutants like fine particles (PM2.5), sulfur dioxide (SO2) and nitrogen dioxide (NO2) that may affect the health of individuals living nearby. Objective: To assess the effects of community exposure to air emissions of PM2.5, SO2, and NO2 from pulp mills, oil refineries, metal smelters, on respiratory hospital admissions in young children in Quebec (QC) and British Columbia (BC), Canada. Methods: We assessed QC, BC and pooled associations between the following estimates of exposure and hospital admissions for asthma and bronchiolitis in children aged 2–4 years for the years 2002–2010: i) Crude emission exposures at the residential postal codes of children, calculated by multiplying estimated daily emissions of PM2.5, SO2, or NO2 from all nearby (

Residential wood burning can be a significant wintertime source of ambient fine particles in urban and suburban areas. We developed a statistical model to predict minute (min) levels of particles with median diameter of

Objective To estimate the degree to which fine particulate (PM2.5) air pollution is associated with systemic autoimmune rheumatic diseases (SARDs). Methods We used population-based administrative data from Alberta (1993–2007) and Quebec (1989–2011). SARD algorithms included ≥2 physician billing codes, or ≥1 rheumatology billing code, or ≥1 hospitalization diagnostic code (for systemic lupus, Sjogren's Syndrome, scleroderma, polymyositis, dermatomyositis, or undifferentiated connective tissue disease). Bayesian hierarchical latent class regression models estimated the probability that any given resident was a SARD case, based on the algorithms. Mean 2001–2006 residential ambient PM2.5 levels were assigned using satellite-derived data for dissemination area regions in Alberta and CLSC regions in Quebec. The sum of individual level probabilities provided the estimated total cases per region in each province, according to age, sex, urban-versus-rural residence, income, and PM2.5 levels. In Alberta, we ran separate models for First-Nations (FN) and non-First Nations subgroups. Bayesian logistic regression modeling generated odds ratio (OR) estimates for being a SARD case, accounting concurrently for demographics, as well as an interaction term between age and sex. Results Our data suggested that the probability of being a SARD case was higher among females versus males and for residents aged >45 versus younger, with the highest ORs for older females. Independently, the odds of being a SARDs case increased with PM2.5 levels in both provinces. Conclusion Our data suggest that PM2.5 exposure may be associated with an increased risk of SARDs.

Industrial activities such as metal smelting, petroleum refining, and open mining emit air pollutants that can affect the health of surrounding communities. Few studies have assessed respiratory effects of acute exposure to industrial air emissions in children. In this study, we examined the association between daily exposure to air emissions from an industrial complex and hospitalizations for respiratory problems of children living nearby using a case crossover design. We used hospitalizations for respiratory problems of children under 5 years old living within 7.5 km of the industrial complex from January 1, 2001 to December 31, 2010. Pollutant exposure was estimated using daily mean and maximum concentrations of SO 2 and PM 2.5 at fixed monitoring stations located near the complex. We also calculated the daily percentage of hours that a child’s residence was downwind of the industrial complex as an indicator of exposure to emissions. Odds-ratios were adjusted for temperature, relative humidity and wind speed, and calculated using conditional logistic regressions, reported by increases of interquartile range. A significant positive association was found between hospitalization for asthma or bronchiolitis and the percentage of hours downwind (OR: 1.11, 95% CI=1.01–1.22) but large statistical variability was noted for associations with all three exposure metrics (OR maximum SO 2 levels: 1.06, 95% CI=0.98–1.15; OR daily maximum PM 2.5 levels: 0.97, 95% CI=0.86–1.09). The results suggest that exposure to the mixture of air pollutant emissions from an industrial complex may induce respiratory health problems in children residing nearby.

Background: Although it is well established that air pollutants can exacerbate asthma, the link with new asthma onset in children is less clear. Objective: We assessed the association between the onset of childhood asthma with both time of birth and time-varying exposures to outdoor air pollutants. Method: An open cohort of children born in the province of Québec, Canada, was created using linked medical–administrative databases. New cases of asthma were defined as one hospital discharge with a diagnosis of asthma or two physician claims for asthma within a 2 year period. Annual ozone (O3) levels were estimated at the child’s residence for all births 1999–2010, and nitrogen dioxide (NO2) levels during 1996–2006 were estimated for births on the Montreal Island. Satellite based concentrations of fine particles (PM2.5) were estimated at a 10 km × 10 km resolution and assigned to residential postal codes throughout the province (1996–2011). Hazard ratios (HRs) were assessed with Cox models for the exposure at the birth address and for the time-dependent exposure. We performed an indirect adjustment for secondhand smoke (SHS). Results: We followed 1,183,865 children (7,752,083 person-years), of whom 162,752 became asthmatic. After controlling for sex and material and social deprivation, HRs for an interquartile range increase in exposure at the birth address to NO2 (5.45 ppb), O3 (3.22 ppb), and PM2.5 (6.50 μg/m3) were 1.04 (95% CI: 1.02, 1.05), 1.11 (95% CI: 1.10, 1.12), and 1.31 (95% CI: 1.28, 1.33), respectively. Effects of O3 and PM2.5 estimated with time-varying Cox models were similar to those estimated using exposure at birth, whereas the effect of NO2 was slightly stronger (HR = 1.07; 95% CI: 1.05, 1.09). Conclusions: Asthma onset in children appears to be associated with residential exposure to PM2.5, O3 and NO2. Citation: Tétreault LF, Doucet M, Gamache P, Fournier M, Brand A, Kosatsky T, Smargiassi A. 2016. Childhood exposure to ambient air pollutants and the onset of asthma: an administrative cohort study in Québec. Environ Health Perspect 124:1276–1282; http://dx.doi.org/10.1289/ehp.1509838

To estimate the association between fine particulate (PM2.5) and nitrogen dioxide (NO2) pollution and systemic autoimmune rheumatic diseases (SARDs).Associations between ambient air pollution (PM2.5 and NO2) and SARDs were assessed using land-use regression models for Calgary, Alberta and administrative health data (1993-2007). SARD case definitions were based on ≥2 physician claims, or ≥1 rheumatology billing code; or ≥1 hospitalization code (for systemic lupus, Sjogren's Syndrome, scleroderma, polymyositis, dermatomyositis, or undifferentiated connective tissue disease). Bayesian hierarchical latent class regression models estimated the probability that each resident was a SARD case, based on these case definitions. The sum of individual level probabilities provided the estimated number of cases in each area. The latent class model included terms for age, sex, and an interaction term between age and sex. Bayesian logistic regression models were used to generate adjusted odds ratios (OR) for NO2 and PM2.5. pollutant models, adjusting for neighbourhood income, age, sex, and an interaction between age and sex. We also examined models stratified for First-Nations (FN) and non-FN subgroups.Residents that were female and/or aged45 had a greater probability of being a SARD case, with the highest OR estimates for older females. Independently, the odds of being a SARDs case increased with PM2.5 levels, but the results were inconclusive for NO2. The results stratified by FN and non-FN groups were not distinctly different.In this urban Canadian sample, adjusting for demographics, exposure to PM2.5 was associated with an increased risk of SARDs. The results for NO2 were inconclusive.

International audience; Objectives: We propose a novel approach to examine vulnerability in the relationship between heat and years of life lost and apply to neighborhood social disparities in Montreal and Paris. Methods: We used historical data from the summers of 1990 through 2007 for Montreal and from 2004 through 2009 for Paris to estimate daily years of life lost social disparities (DYLLD), summarizing social inequalities across groups. We used Generalized Linear Models to separately estimate relative risks (RR) for DYLLD in association with daily mean temperatures in both cities. We used 30 climate scenarios of daily mean temperature to estimate future temperature distributions (2021-2050). We performed random effect meta-analyses to assess the impact of climate change by climate scenario for each city and compared the impact of climate change for the two cities using a meta-regression analysis. Results: We show that an increase in ambient temperature leads to an increase in social disparities in daily years of life lost. The impact of climate change on DYLLD attributable to temperature was of 2.06 (95% CI: 1.90, 2.25) in Montreal and 1.77 (95% CI: 1.61, 1.94) in Paris. The city explained a difference of 0.31 (95% CI: 0.14, 0.49) on the impact of climate change. Conclusion: We propose a new analytical approach for estimating vulnerability in the relationship between heat and health. Our results suggest that in Paris and Montreal, health disparities related to heat impacts exist today and will increase in the future

Asthma is the most common chronic disease in childhood in developed countries and there is large geographic variation in asthma incidence and prevalence. Few cohort studies have assessed associatio...

Background: Ambient air ozone (O3) is a pulmonary irritant that has been associated with respiratory health effects including increased lung inflammation and permeability, airway hyperreactivity, respiratory symptoms, and decreased lung function. Estimation of O3 exposure is a complex task because the pollutant exhibits complex spatiotemporal patterns. To refine the quality of exposure estimation, various spatiotemporal methods have been developed worldwide. Objectives: We sought to compare the accuracy of three spatiotemporal models to predict summer ground-level O3 in Quebec, Canada. Methods: We developed a land-use mixed-effects regression (LUR) model based on readily available data (air quality and meteorological monitoring data, road networks information, latitude), a Bayesian maximum entropy (BME) model incorporating both O3 monitoring station data and the land-use mixed model outputs (BME-LUR), and a kriging method model based only on available O3 monitoring station data (BME kriging). We performed leave-one-station-out cross-validation and visually assessed the predictive capability of each model by examining the mean temporal and spatial distributions of the average estimated errors. Results: The BME-LUR was the best predictive model (R2 = 0.653) with the lowest root mean-square error (RMSE ;7.06 ppb), followed by the LUR model (R2 = 0.466, RMSE = 8.747) and the BME kriging model (R2 = 0.414, RMSE = 9.164). Conclusions: Our findings suggest that errors of estimation in the interpolation of O3 concentrations with BME can be greatly reduced by incorporating outputs from a LUR model developed with readily available data. Citation: Adam-Poupart A, Brand A, Fournier M, Jerrett M, Smargiassi A. 2014. Spatiotemporal modeling of ozone levels in Quebec (Canada): a comparison of kriging, land-use regression (LUR), and combined Bayesian maximum entropy–LUR approaches. Environ Health Perspect 122:970–976; http://dx.doi.org/10.1289/ehp.1306566

Background The evaluation of exposure to ambient temperatures in epidemiological studies has generally been based on records from meteorological stations which may not adequately represent local temperature variability. Here we propose a spatially explicit model to estimate local exposure to temperatures of large populations under various meteorological conditions based on satellite and meteorological data. Methods A general linear model was used to estimate surface temperatures using 15 LANDSAT 5 and LANDSAT 7 images for Quebec Province, Canada between 1987 and 2002 and spanning the months of June to August. The images encompassed both rural and urban landscapes and predictors included: meteorological records of temperature and wind speed, distance to major water bodies, Normalized Differential Vegetation Index (NDVI), land cover (built and bare land, water, or vegetation), latitude, longitude, and week of the year. Results The model explained 77% of the variance in surface temperature, accounting for both temporal and spatial variations. The standard error of estimates was 1.42°C. Land cover and NDVI were strong predictors of surface temperature. Conclusions This study suggests that a statistical approach to estimating surface temperature incorporating both spatially explicit satellite data and time-varying meteorological data may be relevant to assessing exposure to heat during the warm season in the Quebec. By allowing the estimation of space- and time-specific surface temperatures, this model may also be used to assess the possible impacts of land use changes under various meteorological conditions. It can be applied to assess heat exposure within a large population and at relatively fine-grained scale. It may be used to evaluate the acute health effect of heat exposure over long time frames. The method proposed here could be replicated in other areas around the globe for which satellite data and meteorological data is available.