Air quality regulations and programs are vital for protecting the public from harms caused by air pollution. To support these actions, numerous epidemiological studies have sought to identify the pollutants most responsible for adverse outcomes. These studies often used statistical adjustments for copollutants in outcome regression models, a practice also commonly applied to assess interactions between copollutants. Here, we highlight possible pitfalls of multipollutant analyses. Indiscriminate copollutant adjustment can induce noncausal associations through collider adjustment, distorting effect estimates for individual air pollutants. We describe the underlying mechanisms and provide empirical evidence on how such bias may realistically influence the relationships between air pollution and health outcomes from a well-characterized Canadian national cohort alongside a simulation study. Additionally, we discuss strategies to mitigate the impact of this bias. Given the widespread interest in multipollutant approaches among the scientific and policy communities, greater caution is needed when conducting and interpreting research on multiple pollutants.

Background:It is not clear whether the increased mortality pattern observed in a prior analysis of the Canadian Census Health and Environment Cohorts for HIV/AIDS, diabetes, prostate cancer, and uterine cancer among Black adults is reflected in incident hospitalization (a marker of severity) or the diagnosis of these diseases, nor is it clear whether disparities exist regarding early screening and survivability. Methods:To understand the paths that contribute to differential mortality patterns, standard Cox proportional hazard models were used to assess the incidence risk of diagnosis (uterine and prostate cancer) and incident hospitalization (HIV and diabetes) among 161,520 Black adults, compared with 6,866,070 White adults. Competing risk regression was used to evaluate the cumulative risk of death for the four disease outcomes since diagnosis or hospitalization. For the observed differential cancer mortality, mediation analysis was conducted to investigate the role of cancer diagnosis at follow-up (a proxy for delayed diagnosis that is not entirely indicative of late-stage cancer). Results:Across all examined outcomes, except for uterine cancer, Black adults had elevated incident diagnoses or hospitalizations compared with White adults. Notably, Black males demonstrated a risk of incident prostate cancer and hospitalizations from HIV and diabetes twice as high relative to White males. For Black females, the risk of incident HIV hospitalization was 12 times as high. However, Black females were 15% less likely to be diagnosed with uterine cancer, compared with White females. Cumulative mortality risk analysis showed significantly lower survivability (two times lower) among Black females diagnosed with uterine cancer, relative to White females. Delayed diagnosis mediated a marginally higher proportion of the total differential uterine cancer mortality among Black females (14.9%; 95% confidence interval [CI]: 10.5% to 23.1%), compared with White females (8.9%; 95% CI: 6.3% to 13.9%). Interpretation:This study unveils substantial parallels between heightened incidence risk and relative mortality for most of the four explored outcomes between Black and White adults in Canada. Notably, the study highlights a lower incident diagnosis of uterine cancer among Black females, despite a relatively higher uterine cancer mortality. Three in every 20 uterine cancer deaths were mediated through the time of uterine cancer diagnosis (relatively delayed in Black females), underscoring the need for targeted interventions and early detection strategies to address health disparities in this population.

The objectives of this study were to quantify the small-area associations between heat and mortality and to characterize the spatial patterns of mortality risks at hot temperatures. Daily mortality and temperature data were retrieved for the cities of Montreal, Toronto, and Vancouver during the summer months between 2018 and 2022. Spatial distributed lag non-linear models quantified the associations between temperature and mortality at the small-area scale. Heat-mortality hotspots were identified based on the relative risks of mortality at hot temperatures. The spatial patterns of mortality at hot temperatures were described using small-area sociodemographic and environmental characteristics. Hot temperatures were associated with elevated relative risks of mortality in Montreal and Vancouver compared to median summer temperatures. At 95th percentile temperatures, 38

Background The impact of past air quality improvements on health and equity at low pollution levels near the revised WHO air quality guidelines remains largely unknown. Less is known about the influence of simultaneous reductions in multiple major pollutants. Leveraging real-world improvements in air quality across Canada, we sought to directly evaluate their health benefits by quantifying the impact of a joint shift in three criteria pollutants on mortality in a national cohort. Methods In this population-based cohort study, we assembled a cohort of 27 million adults living in Canada in 2007 who were followed up through 2016. Annual mean concentrations of fine particulate matter (PM25), nitrogen dioxide (NO2), and ozone (O-3) were assigned to participants' residential locations. For each pollutant individually and combined, we conducted a causal analysis of the impact of the decadal shift in annual exposure from the pre-baseline level (2004-06) on the risk of non-accidental mortality using the parametric g-formula, a structural causal model. To check the robustness of our results, we conducted multiple sensitivity analyses, including exploring alternative exposure scenarios. We also evaluated differential benefits across regions and socio-demographic subgroups. Findings Between 2007 and 2016, annual mean exposures to PM25 and NO2 decreased (from 71 mu g/m(3) [SD 23] to 55 mu g/m(3) [19] for PM25 and from 111 ppb [SD 66] to 80 ppb [49] for NO2), whereas O-3 declined initially and then rebounded (from 386 [SD 83] ppb to 360 [60] ppb and then 381 [54] ppb). Compared to pre-baseline (2004-06) levels, the joint change in the pollution exposures beginning in 2007 resulted in, per million population, 70 (95% CI 29-111) fewer deaths by 2009, 416 (283-549) fewer deaths by 2012, and 609 (276-941) fewer deaths by 2016, corresponding to a -07% change in mortality risk over the decade. Stratified analyses showed greater beneficial impacts in men, adults aged 50 years and older, low income-earners, and residents in regions undergoing substantial air quality improvements. Had all regions experienced pollution reductions similar to the most improved region, approximately three times as many deaths would have been averted (2191 fewer deaths per million). Conversely, if the observed air quality improvements had been delayed in all regions by 3 years, there would have been 429 more deaths per million by 2016. Interpretation In Canada, substantial health gains were associated with air quality improvements at levels near the revised WHO guidelines between 2007 and 2016, with notable heterogeneity observed across socio-demographic subgroups and regions. These findings indicate that modest declines in air pollution can considerably improve health and equity, even in low-exposure environments.

The use of exclusionary discipline (ExD) was compared for U.S. public pre-kindergarten (Pre-K) and K-12 grades within the same school. ExD rates were 10 times higher in K-12 than in Pre-K when calculated for all schools but ExD rates were comparable for schools that reported at least one case of ExD in Pre-K. Additionally, disparities in the use of ExD were observed for both Pre-K and K-12 schools: higher rates were found for boys relative to girls, for Black children relative to White children, and for charter schools relative to non-charter schools. While most schools did not use ExD, when they did, they did so at high rates. The use of ExD appears to be associated with school-level factors including a culture of use.

Background Extreme heat has significant impacts on mortality. In Canada, past research has analyzed the degree to which non-accidental mortality increases during single extreme heat events; however, few studies have considered multiple causes of death and the impacts of extreme heat events on mortality over longer time periods. Data and methods Daily death counts attributable to non-accidental, cardiovascular, and respiratory causes were retrieved for the 12 most populous cities in Canada from 2000 to 2020. Generalized additive models were applied to quantify daily mortality risks for people aged younger than 65 years and for those aged 65 years and older in each city and for each cause of death. Model results were used to calculate the change in mortality risks and the number of excess deaths attributable to extreme heat during extreme heat events. Results Elevated mortality risks were observed during extreme heat events in most cities for non-accidental and respiratory causes. The impacts of extreme heat on non-accidental mortality were typically greater for people aged 65 and older than for those aged younger than 65. Significantly higher non-accidental mortality risks were observed during extreme heat events for people aged 65 and older in Montr & eacute;al, the city of Qu & eacute;bec, Surrey, and Toronto. For cardiovascular and respiratory causes, people aged 65 and older had significantly higher mortality risks during extreme heat events in Montr & eacute;al, and both Montr & eacute;al and Toronto, respectively. In the 12 cities, approximately 670 excess non-accidental deaths, 115 excess cardiovascular deaths, and 115 excess respiratory deaths were attributable to extreme heat events during the study period. Mortality risks during extreme heat events were generally higher in cities with larger proportions of renter households and fewer extreme heat events. Interpretation This study estimates the longer -term impacts of extreme heat events on three mortality outcomes in a set of large Canadian cities. As climate change causes more frequent and intense extreme heat events, and as policy makers aim to reduce the health impacts of heat, it is important to understand how and where extreme heat affects health.

Cooling centres provide respite, safety, and social support during extreme heat events for populations that do not have the resources to own or operate in-home air conditioning. The objective of this study was to measure the spatial accessibility of cooling centres and analyze the associations between cooling centre access and marginalization in Montreal, Toronto, and Vancouver, Canada. The potential spatial accessibility of cooling centres within a 15-minute walk was measured at the dissemination area scale using the two-step floating catchment area method. A two-stage modelling approach was used to analyze the associations between cooling centre access and marginalization. Approximately 62%, 58%, and 54% of the populations in Montreal, Toronto, and Vancouver had access to at least one cooling centre. In Montreal and Vancouver, high marginalization areas were more likely to have cooling centre access than low marginalization areas. Of the areas with cooling centre access, smaller access scores were observed in areas with high residential instability. Approximately one-fifth of the areas in each city had no cooling centre access and high marginalization, and may be considered for future cooling centres or programs that improve accessibility to existing centres.

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.

Background:Household air conditioning is one of the most effective approaches for reducing the health impacts of heat exposure; however, few studies have measured the prevalence of household air conditioning in Canada.Data and methods:Data were obtained from the 2017 Canadian Community Health Survey and the 2017 Households and the Environment Survey. Statistics Canada linked the survey respondents and created survey weights. Four heat-vulnerable populations were defined: older adults, older adults living alone, older adults with at least one health condition associated with reduced thermoregulation and older adults living alone and with a health condition associated with reduced thermoregulation. Weighted ratios and logistic regression models were used to analyze person-level air conditioning rates for national, regional and heat-vulnerable populations.Results:Approximately 61% of the national population had household air conditioning. Regional rates ranged between 32% in British Columbia and 85% in Ontario. People living alone and people who did not own a home were significantly less likely to have air conditioning in Canada and in most regions. One heat vulnerable group, older adults living alone, had significantly lower air conditioning rates compared with the national and Ontario averages, at 56% and 81%, respectively.Interpretation:This study is the first to quantify air conditioning prevalence in Canada at the person-level. The results of this study may inform heat-health policies and climate change adaptation strategies that aim to identify populations with high risks of heat-related mortality or morbidity and low access to household air conditioning.

Emissions of fine particulate matter (PM2.5) from human activities have been linked to substantial disease burdens, but evidence regarding how reducing PM2.5 at its sources would improve public health is sparse. We followed a population-based cohort of 2.7 million adults across Canada from 2007 through 2016. For each participant, we estimated annual mean concentrations of PM2.5 and the fractional contributions to PM2.5 from the five leading anthropogenic sources at their residential address using satellite observations in combination with a global atmospheric chemistry transport model. For each source, we estimated the causal effects of six hypothetical interventions on 10-y nonaccidental mortality risk using the parametric g-formula, a structural causal model. We conducted stratified analyses by age, sex, and income. This cohort would have experienced tangible health gains had contributions to PM2.5 from any of the five sources been reduced. Compared with no intervention, a 10% annual reduction in PM2.5 contributions from transportation and power generation, Canada's largest and fifth-largest anthropogenic sources, would have prevented approximately 175 (95%CI: 123-226) and 90 (95%CI: 63-117) deaths per million by 2016, respectively. A more intensive 50% reduction per year in PM2.5 contributions from the two sources would have averted 360 and 185 deaths per million, respectively, by 2016. The potential health benefits were greater among men, older adults, and low-income earners. In Canada, where PM2.5 levels are among the lowest worldwide, reducing PM2.5 contributions from anthropogenic sources by as little as 10% annually would yield meaningful health gains.

Residential income segregation is a spatial manifestation of social inequality and is an important factor that influences access to resources, services, and amenities. In general, past research analyzing income segregation has applied index-based methods to describe the separation of low-income households at one spatial scale; however, existing studies have not yet considered how income segregation varies across multiple income classes, spatial scales, and local contexts. This study applies a multilevel multigroup modeling approach to explore the global and local patterns of income segregation between dissemination areas (micro-scale), census tracts (meso-scale), and neighborhoods (macro-scale) in Toronto, Canada. A global model that estimates the overall multiscale segregation of five income classes finds that the most affluent families had the highest levels of segregation and that the segregation of all income classes was strongest at the macro- and micro-scales. A local model that allows the micro-scale segregation measures to vary geographically shows that higher-income families were less segregated in the city center than in the inner suburbs, that middle-income families were highly segregated in areas serviced by public transit, and that almost all income classes had high levels of segregation in disadvantaged neighborhoods prioritized for investment by local policymakers. The methodological and substantive contributions of this study for understanding the complex patterns of income segregation are discussed.

This research explores the socioeconomic composition of sixteen wind farm communities in Ontario, Canada, for wind farms commissioned between 2006 and 2012. Past research has shown that wind farms are disproportionately developed in socioeconomically disadvantaged areas and that socioeconomic factors influence wind farm support, an important factor in wind farm planning. This research finds that wind farm communities do not exhibit characteristics of disadvantage compared to host counties. Investigating the association between when wind farms were commissioned and community-scale characteristics, this research observes that communities with wind farms operational before 2009 had significantly lower median income compared to communities with wind farms operational after 2009. This provides one perspective on how community-scale characteristics may shape wind farm planning, specifically the influence of local opposition and financial incentives on the location of wind farm developments.

The purpose of this study was to investigate if and how the associations between social support availability (SSA) and cognitive function varied across urban, rural, and geographical regions in Canada. Data from a population-level sample of community-dwelling adults aged 45-85 years were obtained from the baseline Tracking Cohort of the Canadian Longitudinal Study on Aging. The associations between SSA and two domains of cognitive function, memory and executive function, were analyzed using multilevel regression models. SSA was positively and significantly associated with both executive function and memory. We found SSA had stronger positive associations with executive function among participants living in rural areas compared to urban areas in all geographical regions; however, geographical variation in the associations between SSA and memory were not supported by model results. Understanding how the associations between cognitive function and modifiable risk factors, including SSA, vary across geographical contexts is important for developing policies and programs to support healthy aging.

This research replicates in Phoenix, Arizona a study originally conducted by DiMaggio et al. (2020) that investigated the associations between positive COVID-19 tests and demographic, socioeconomic, and racial characteristics in New York City at the ZIP Code Tabulation Area level. We extend that work through a conceptual replication that introduces covariates appropriate to Phoenix, AZ. Our direct replication, which focuses on that city's first wave of COVID-19 (May 31, 2020 to August 1, 2020), demonstrates that the framework used by DiMaggio et al. can be transferred across cities, but also identifies specification decisions that need careful consideration. Our conceptual replication identifies the proportion of Hispanic residents, rather than that of Black/African American residents, to be a key predictor of positive COVID-19 testing. This finding sheds light on the dynamics of race during the pandemic.

Neighborhood socioeconomic disadvantage is a measure of socio-spatial inequality that has been shown to be associated with a variety of social, economic, and health outcomes. Existing studies that explore the local patterning of disadvantage often construct composite indices that summarize the interactions between multiple dimensions of social status, but do not consider if, and how, disadvantage exhibits spatial structure. This study applies a Bayesian multivariate factor analytic modeling approach to examine the spatial structure of socioeconomic disadvantage in Toronto, Canada. Socioeconomic disadvantage is modeled as an area-based composite index associated with three variables measuring low income, low-educational attainment, and low occupational status, and a series of models with different assumptions regarding the spatial structure of disadvantage are compared. The best-fitting model shows that the prevalence of low-income households has the strongest positive association with disadvantage and that spatial clustering is three times more important than spatial heterogeneity for explaining the spatial structure of disadvantage. The implications of this study for analyzing multivariate spatial data and for understanding the interactions amongst multiple dimensions of disadvantage are discussed.

The number of days missed due to suspensions (DMS) was analyzed in a national sample of K-12 public schools in the U.S. In the 2017-2018 national sample, about 11 million days of school were missed due to suspension. Rates of DMS varied across the regions of the U.S., from state to state, and from school to school (greater in nonelementary and non-charter public schools). Additionally, disparities were found with higher rates found for boys relative to girls and for Black children relative to White children. Regression analyses revealed that school characteristics explained the greatest amount of variance in rates of DMS, although enrollment characteristics also accounted for a significant amount of the variance. It was concluded that there is a need to for more research to better understand the factors that predict DMS and its adverse outcomes for students and to help develop effective prevention and intervention efforts to reduce its use, especially to reduce disparities in its use.

Educational materials focused on spatial data analysis often feature mathematical descriptions of methods and step-by-step instructions of software tools, but infrequently discuss the set of decisions involved in specifying a statistical model. Failing to consider model specification may lead to specification searching, or the process of repeating analyses to obtain results that meet the criteria thought to be required for publication, and the disproportionate reporting of false-positive results in the academic literature. This article proposes that the specification curve - a meta-analytical technique that visualizes the specifications and results from a large set of justifiable and plausible statistical models - be used as a pedagogical tool to teach (spatial) data analysis and explore the geographic uncertainties that arise when specifying and interpreting spatial regression models. An example specification curve that focuses on two common specification decisions in a spatial regression model, specifically selecting predictor variables and constructing the spatial weight matrix, is illustrated. Strategies for using the specification curve in educational contexts to develop analytical plans, reflect on the generalizability of research findings, and highlight issues of replicability and publication bias are proposed.

ABSTRACT The spatial patterning of crime hotspots provides place-based information for the design, allocation, and implementation of crime prevention policies and programmes. However, most spatial hotspot identification methods are univariate, analyse a single crime type, and do not consider if hotspots are shared amongst multiple crime types. This study applies a Bayesian spatial shared component model to identify crime-general and crime-specific hotspots for violent crime and property crime at the small-area scale. The spatial shared component model jointly analyzes both violent crime and property crime and separates the area-specific risks of each crime type into one shared component, which captures the underlying crime-general spatial pattern common to both crime types, and one type-specific component, which captures the crime-specific spatial pattern that diverges from the shared pattern. Crime-general and crime-specific hotspots are classified based on the posterior probability estimates of the shared and type-specific components, respectively. Results show that the crime-general pattern explains approximately 81% of the total variation of violent crime and 70% of the total variation of property crime. Crime-general hotspots are found to be more frequent than crime-specific hotspots, and property crime-specific hotspots are more frequent than violent crime-specific hotspots. Crime-general and crime-specific hotspots are areas that may be targeted with comprehensive initiatives designed for multiple crime types or specialized initiatives designed for a single crime type, respectively.

This repository contains the data, model code, and initial values for a spatial factor analysis model described in: The spatial structure of socioeconomic disadvantage: a Bayesian multivariate spatial factor analysis. International Journal of Geographical Information Science.The International Journal of Geographical Information Science is available at: https://www.tandfonline.com/toc/tgis20/currentAbstract:Neighborhood socioeconomic disadvantage is a measure of socio-spatial inequality that has been shown to be associated with a variety of social, economic, and health outcomes. Existing studies that explore the local patterning of disadvantage often construct composite indices that summarize the interactions between multiple dimensions of social status, but do not consider if, and how, disadvantage exhibits spatial structure. This study applies a Bayesian multivariate factor analytic modeling approach to examine the spatial structure of socioeconomic disadvantage in Toronto, Canada. Socioeconomic disadvantage is modeled as an area-based composite index associated with three variables measuring low income, low educational attainment, and low occupational status, and a series of models with different assumptions regarding the spatial structure of disadvantage are compared. The best-fitting model shows that the prevalence of low-income households has the strongest positive association with disadvantage and that spatial clustering is three times more important than spatial heterogeneity for explaining the spatial structure of disadvantage. The implications of this study for analyzing multivariate spatial data and for understanding the interactions amongst multiple dimensions of disadvantage are discussed.

OBJECTIVE:This study examines the association between community-level marginalization and emergency room (ER) wait time in Ontario.METHODS:Data sources included ER wait time data and Ontario Marginalization Index scores. Linear regression models were used to quantify the association.RESULTS:A positive association between total marginalization and overall, high-acuity and low-acuity ER wait time was found. Considering specific marginalization dimensions, we found positive associations between residential instability and ER wait time and negative associations between dependency and ER wait time.CONCLUSIONS:Reductions in community-level marginalization may impact ER wait time. Future studies using individual-level data are necessary.