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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.

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.

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.

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 Montréal, the city of Québec, Surrey, and Toronto. For cardiovascular and respiratory causes, people aged 65 and older had significantly higher mortality risks during extreme heat events in Montréal, and both Montréal 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. [ABSTRACT FROM AUTHOR]

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. Key Messages: More than half of the population in Montreal, Toronto, and Vancouver had access to at least one cooling centre within a 15‐minute walk.Areas of high marginalization were more likely to have cooling centre access than areas of low marginalization.Residential instability was the most important factor explaining the spatial patterning of cooling centre access. [ABSTRACT FROM AUTHOR]

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 heatvulnerable 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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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 FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Sports are a popular and effective way to illustrate physics principles. Baseball in particular presents a number of opportunities to motivate student interest and teach concepts. Several articles have appeared in this journal on this topic, illustrating a wide variety of areas of physics. In addition, several websites and an entire book are available. In this paper we describe a student-designed project that illustrates the relative surface gravity on the Earth, Sun, and other solar system bodies using baseball. We describe the project and its results here as an example of a simple, fun, and student-driven use of baseball to illustrate an important physics principle.

This research applies a Bayesian multivariate modeling approach to analyze the spatiotemporal patterns of physical disorder, social disorder, property crime, and violent crime at the small‐area scale. Despite crime and disorder exhibiting similar spatiotemporal patterns, as hypothesized by broken windows and collective efficacy theories, past studies often analyze a single outcome and overlook the correlation structures between multiple crime and disorder types. Accounting for five covariates, the best‐fitting model partitions the residual risk of each crime and disorder type into one spatial shared component, one temporal shared component, and type‐specific spatial, temporal, and space–time components. The shared components capture the underlying spatial pattern and time trend common to all types of crime and disorder. Results show that population size, residential mobility, and the central business district are positively associated with all outcomes. The spatial shared component is found to explain the largest proportion of residual variability for all types of crime and disorder. Spatiotemporal hotspots of crime and disorder are examined to contextualize broken windows theory. Applications of multivariate spatiotemporal modeling with shared components to ecological crime theories and crime prevention policy are discussed. [ABSTRACT FROM AUTHOR]