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Supplementary Table 1. Sample Size of each participated study, by case-control status and genotype platform. Supplementary Table 2. Association between fourteen environmental factors and the risk of breast cancer. Supplementary Table 3. Interactions between genes and fourteen environmental factors.

Genome-wide association studies (GWAS) have identified more than 200 susceptibility loci for breast cancer, but these variants explain less than a fifth of the disease risk. Although gene–environment interactions have been proposed to account for some of the remaining heritability, few studies have empirically assessed this.We obtained genotype and risk factor data from 46,060 cases and 47,929 controls of European ancestry from population-based studies within the Breast Cancer Association Consortium (BCAC). We built gene expression prediction models for 4,864 genes with a significant (P < 0.01) heritable component using the transcriptome and genotype data from the Genotype-Tissue Expression (GTEx) project. We leveraged predicted gene expression information to investigate the interactions between gene-centric genetic variation and 14 established risk factors in association with breast cancer risk, using a mixed-effects score test.After adjusting for number of tests using Bonferroni correction, no interaction remained statistically significant. The strongest interaction observed was between the predicted expression of the C13orf45 gene and age at first full-term pregnancy (PGXE = 4.44 × 10−6).In this transcriptome-informed genome-wide gene–environment interaction study of breast cancer, we found no strong support for the role of gene expression in modifying the associations between established risk factors and breast cancer risk.Our study suggests a limited role of gene–environment interactions in breast cancer risk.

Funding and acknowledgements.

Quantile-Quantile plot (Q-Q plot) of the aMiSTi p-values for each set of the GxE interactions.

BackgroundLyme disease is the most common vector-borne illness in the United States. Incidence is related to specific environmental conditions such as temperature, metrics of land cover, and species diversity.ObjectiveTo determine whether greenness, as measured by the Normalized Difference Vegetation Index (NDVI), and other selected indices of land cover were associated with the incidence of Lyme disease in the northeastern USA, 2000-2018.Materials and MethodsWe conducted an ecological analysis of incidence rates in counties of 15 “high” incidence states and the District of Columbia for 2000-2018. Annual counts of Lyme disease by county were obtained from the US Centers for Disease Control and values of NDVI were acquired from the Moderate Resolution Imaging Spectroradiometer instrument aboard Terra and Aqua Satellites. County-specific values of population density, area of land and water were obtained from the US Census. Using quasi-Poisson regression, multivariable associations were estimated between the incidence of Lyme disease NDVI, land cover variables, human population density, and calendar year.ResultsWe found that incidence increased by 7.1% per year (95% confidence interval: 6.8-8.2%). Land cover variables showed complex non-linear associations with incidence: average county-specific NDVI showed a ‘u-shaped” association, the standard deviation of NDVI showed a monotonic upward relationship, population density showed a decreasing trend, areas of land and water showed “n”-shaped relationships. We found an interaction between average and standard deviation of NDVI, with the highest average NDVI category, increased standard deviation of NDVI showed the greatest increase in rates.DiscussionThese associations cannot be interpreted as causal but indicate that certain patterns of land cover may have the potential to increase exposure to infected ticks and thereby may contribute indirectly to increased rates. Public health interventions could make use of these results in informing people where risks may be high.

Little is known about environmental factors that may increase the risk of prostate cancer. We estimated associations between incident prostate cancer and environmental concentrations of five ambient volatile organic compounds (VOCs): benzene; n-decane; ethylbenzene; hexane; and 1,2,4-trimethylbenzene.This study is based on a population-based case-control study of incident prostate cancer (PROtEuS) in men ≤ 75 years of age living in Montreal, Canada, in 2005 to 2012. We included 1172 cases and 1177 population controls. We had personal information, lifetime residential addresses, occupational exposures, and a variety of area-wide covariables. We inferred concentrations of the five VOCs using Bayesian geostatistical models using data from a dense environmental survey conducted in Montreal in 2005 to 2006. We used different sets of adjustments to estimate odds ratios (OR) and confidence intervals.We found nonlinear associations such that the ORs increased monotonically and then either flattened or fell off with increased exposures. The model that contained other environmental variables and contextual variables led to lower ORs and results were similar when we restricted analyses to controls recently screened or tested for prostate cancer or cases with low- or high-grade tumors. A change from the 5th to 25th percentile in mean environmental benzene levels led to an adjusted OR of 2.00 (95% confidence interval = 1.47, 2.71).We found positive associations between prostate cancer and concentrations of benzene and ethylbenzene, independently of previous testing for prostate cancer or tumor grade, suggesting that exposure to certain ambient VOCs may increase incidence.

Genome-wide association studies (GWAS) have identified more than 200 susceptibility loci for breast cancer, but these variants explain less than a fifth of the disease risk. Although gene–environment interactions have been proposed to account for some of the remaining heritability, few studies have empirically assessed this. We obtained genotype and risk factor data from 46,060 cases and 47,929 controls of European ancestry from population-based studies within the Breast Cancer Association Consortium (BCAC). We built gene expression prediction models for 4,864 genes with a significant (P < 0.01) heritable component using the transcriptome and genotype data from the Genotype-Tissue Expression (GTEx) project. We leveraged predicted gene expression information to investigate the interactions between gene-centric genetic variation and 14 established risk factors in association with breast cancer risk, using a mixed-effects score test. After adjusting for number of tests using Bonferroni correction, no interaction remained statistically significant. The strongest interaction observed was between the predicted expression of the C13orf45 gene and age at first full-term pregnancy (PGXE = 4.44 × 10−6). In this transcriptome-informed genome-wide gene–environment interaction study of breast cancer, we found no strong support for the role of gene expression in modifying the associations between established risk factors and breast cancer risk. Our study suggests a limited role of gene–environment interactions in breast cancer risk.

Results from ecological studies have suggested that air pollution increases the risk of developing and dying from COVID-19. Drawing causal inferences from the measures of association reported in ecological studies is fraught with challenges given biases arising from an outcome whose ascertainment is incomplete, varies by region, time, and across sociodemographic characteristics, and cannot account for clustering or within-area heterogeneity. Through a series of analyses, we illustrate the dangers of using ecological studies to assess whether ambient air pollution increases the risk of dying from, or transmitting, COVID-19.We performed an ecological analysis in the continental United States using county-level ambient concentrations of fine particulate matter (PMOur analyses revealed that the shape of the exposure-response curve between PMOur analyses indicated that ecological analyses are prone to showing spurious relationships between ambient air pollution and mortality from COVID-19 as well as the prevalence of HIV. We discuss the many potential biases inherent in any ecological-based analysis of air pollution and COVID-19.

Exposure to biocontaminants, such as dust mites, animal dander, bacteria, and mold, is associated with a range of health effects. This study identified household characteristics associated with indoor biocontaminant loadings in four Canadian cities. Floor dust was collected in 290 Canadian homes in Edmonton, Halifax, Montreal, and Windsor. The dust samples were analyzed for house dust mite allergens (Der f 1 and Der p 1), cat allergen (Fel d 1), cockroach allergen (Bla g 1), beta-(1,3)-D-glucan, and endotoxin. Household information was obtained through questionnaires and home inspections. We performed univariate and multivariate analyses to identify household determinants of biocontaminant loadings and mold odor presence. We observed large regional variations for all biocontaminants, except for cockroach allergen. The ranges of the contaminants measured in loadings and concentrations were similar to that of previous Canadian studies. Household characteristics including presence of carpeting, low floor cleaning frequency, older home age, presence of pets, and indoor relative humidity above 45% were positively associated with the presence of multiple indoor biocontaminants. High floor cleaning frequency and use of dehumidifiers were negatively associated with the presence of multiple indoor biocontaminants. Mold odor was positively associated with older home age, past water damage, and visible mold growth.

The objective of this paper was to estimate the inter-rater reliability of expert assessments of occupational exposures. An inter-rater reliability sub-study was conducted within a population-based case-control study of postmenopausal breast cancer. Detailed information on lifetime occupational histories was obtained from participants and two industrial hygienists assigned exposures to 185 jobs using a checklist of 293 agents. Experts rated exposure for each job-agent combination according to exposure status (unexposed/exposed), confidence that the exposure occurred (possible/probable/definite), intensity (low/medium/high), and frequency (% time per week). The statistical unit of observation was each job-agent assessment (185 jobs × 293 agents = 54,205 assessments per expert). Crude agreement, Gwet AC1/2 statistics, and Cohen's Kappa were used to estimate inter-rater agreement for confidence and intensity; for frequency, the intra-class correlation coefficient (ICC) was used. The majority of job-agent combinations were evaluated by the two experts to be not exposed (crude agreement98% of decisions). The degree of agreement between the experts for the confidence of exposure status was Gwet AC1/2 = 0.99 (95% CI: 0.99-0.99), and for intensity, a Gwet AC2 = 0.99 (95% CI: 0.99-0.99). For frequency, an ICC of 0.31 (95% CI: 0.26-0.35) was found. A sub-analysis restricted to job-agent combinations for which the two experts agreed on exposure status revealed a moderate agreement for confidence of exposure (Gwet AC2 = 0.66) and high agreement for intensity (Gwet AC2 = 0.96). For frequency, the ICC was 0.52 (95% CI: 0.47-0.57). A high level of inter-rater agreement was found for identifying exposures and for coding intensity, but agreement was lower for the coding of frequency of exposure.

Genome-wide analysis identifies 32 loci associated with breast cancer susceptibility, accounting for estrogen receptor, progesterone receptor and human epidermal growth factor receptor 2 status and tumor grade. Breast cancer susceptibility variants frequently show heterogeneity in associations by tumor subtype(1-3). To identify novel loci, we performed a genome-wide association study including 133,384 breast cancer cases and 113,789 controls, plus 18,908 BRCA1 mutation carriers (9,414 with breast cancer) of European ancestry, using both standard and novel methodologies that account for underlying tumor heterogeneity by estrogen receptor, progesterone receptor and human epidermal growth factor receptor 2 status and tumor grade. We identified 32 novel susceptibility loci (P < 5.0 x 10(-8)), 15 of which showed evidence for associations with at least one tumor feature (false discovery rate < 0.05). Five loci showed associations (P < 0.05) in opposite directions between luminal and non-luminal subtypes. In silico analyses showed that these five loci contained cell-specific enhancers that differed between normal luminal and basal mammary cells. The genetic correlations between five intrinsic-like subtypes ranged from 0.35 to 0.80. The proportion of genome-wide chip heritability explained by all known susceptibility loci was 54.2% for luminal A-like disease and 37.6% for triple-negative disease. The odds ratios of polygenic risk scores, which included 330 variants, for the highest 1% of quantiles compared with middle quantiles were 5.63 and 3.02 for luminal A-like and triple-negative disease, respectively. These findings provide an improved understanding of genetic predisposition to breast cancer subtypes and will inform the development of subtype-specific polygenic risk scores.

Lung and female breast cancers are highly prevalent worldwide. Although the association between exposure to ambient fine particulate matter (PM2.5 ) and lung cancer has been recognized, there is less evidence for associations with other common air pollutants such as nitrogen dioxide (NO2 ) and ozone (O3 ). Even less is known about potential associations between these pollutants and breast cancer. We conducted a population-based cohort study to investigate the associations of chronic exposure to PM2.5 , NO2 , O3 and redox-weighted average of NO2 and O3 (Ox ) with incident lung and breast cancer, using the Ontario Population Health and Environment Cohort (ONPHEC), which includes all long-term residents aged 35-85 years who lived in Ontario, Canada, 2001-2015. Incident lung and breast cancers were ascertained using the Ontario Cancer Registry. Annual estimates of exposures were assigned to the residential postal codes of subjects for each year during follow-up. We used Cox proportional-hazards models adjusting for personal- and neighborhood-level covariates. Our cohorts for lung and breast cancer analyses included ~4.9 million individuals and ~2.5 million women, respectively. During follow-up, 100,146 incident cases of lung cancer and 91,146 incident cases of breast cancer were diagnosed. The fully adjusted analyses showed positive associations of lung cancer incidence with PM2.5 (hazard ratio [HR] = 1.02 [95% CI: 1.01-1.05] per 5.3 μg/m3 ) and NO2 (HR = 1.05 [95% CI: 1.03-1.07] per 14 ppb). No associations with lung cancer were observed for O3 or Ox . Relationships between PM2.5 and NO2 with lung cancer exhibited a sublinear shape. We did not find compelling evidence linking air pollution to breast cancer.

Volatile organic compounds (VOCs) are components of the complex mixture of air pollutants within cities and can cause various adverse health effects. Therefore, it is necessary to understand their spatial distribution for exposure assessment in epidemiological studies.The objective was to model measured concentrations of five VOCs within the city of Montreal, Canada, developing spatial prediction models that can be used in health studies.We measured concentrations using 3M 3500 Organic Vapor Monitors, over 2-week periods, for three monitoring campaigns between 2005 and 2006 in over 130 locations in the city. Using GC/MSD (Gas Chromatography/Mass Selective Detector), we measured concentrations of benzene, n-decane, ethylbenzene, hexane, and trimethylbenzene. We fitted four different models that combine land-use regression and geostatistical methods to account for the potential spatial structure that remains after accounting for the land-use variables. The fitted models also accounted for possible variations in the concentration of air pollutants across campaigns.The highest concentrations for all VOCs were found in December with hexane being the most abundant followed by ethylbenzene. We obtained predicted surfaces for the VOCs for the three campaigns and mean surfaces across campaigns. We found higher concentrations of some VOCs along highways and in the Eastern part of Montreal, which is a highly industrialized area.Each of the fitted models captured the spatial and across-campaigns variability for each VOC, and we found that different VOCs required different model structures.

There is little data as to whether exposure to residential greenness is associated with the incidence of breast cancer. Lack of physical activity and obesity are two of the accepted risk factors for postmenopausal breast cancer and living near green areas may contribute to an active lifestyle and maintaining a normal body mass index and, consequently, residential greenness may be associated with lower incidence rates.The objective of this study was to determine whether there was an association between past exposure to residential greenness and the incidence of invasive postmenopausal breast cancer among Canadian women living in Montreal, Quebec, in the mid-2000s.We conducted a population-based, case-control study of incident postmenopausal breast cancer in Montreal, Canada, and herein we show analyses by level of greenness surrounding participants' homes. Incident cases were identified between 2008 and 2011 from all but one hospital that treated breast cancer in the Montreal area. Population controls were identified from provincial electoral lists of Montreal residents and frequency-matched to cases on age. Residential greenness was estimated using the maximum daily normalized difference vegetation index averaged over the growing season ("maximum NDVI"). Maximum NDVI was assigned at the home address of recruitment for the years 1992-1998 (about 15 years before diagnosis), and we measured subjects' personal information, exposure to NOWe found that the response functions between incident postmenopausal breast cancer and maximum NDVI were consistent with linearity. The age-adjusted and fully-adjusted ORs, per increase in the interquartile range (IQR=0.13) of maximum NDVI measured with a 250 m buffer around residences, were 0.95 (95%CI: 0.86-1.04) and 1.00 (95%CI: 0.84-1.11), respectively. For maximum NDVI measured using a 1000 m buffer (IQR=0.05), these were 0.98 (95%CI: 0.94-1.02) and 0.99 (95%CI: 0.95-1.03), respectively.Our findings suggest that exposure to NDVI evaluated where participants were interviewed is not associated with the risk of incident postmenopausal breast cancer.

Background: Epidemiologic studies indicate that early life arsenic exposures are linked to an increased risk of cardiovascular diseases. Different oxidation and methylation states of arsenic exist in the environment and are formed in vivo via the action of arsenic (+3 oxidation state) methyltransferase (As3MT). Methylated arsenicals are pro-atherogenic postnatally, but pre- and perinatal effects are unclear. This is particularly important because methylated arsenicals are known to cross the placenta. Objectives: We tested the effects of early life exposure to inorganic and methylated arsenicals on atherosclerotic plaque formation and its composition in apolipoprotein E knock-out (apoE−/−) mice and evaluated whether apoE−/− mice lacking As3MT expression were susceptible to this effect. Methods: We exposed apoE−/− or apoE−/−/As3MT−/− mice to 200 ppb inorganic or methylated arsenic in the drinking water from conception to weaning and assessed atherosclerotic plaques in the offspring at 18 wk of age. Mixed regression models were used to estimate the mean difference in each outcome relative to controls, adjusting for sex and including a random effects term to account for within-litter clustering. Results: Early life exposure to inorganic arsenic, and more profoundly methylated arsenicals, resulted in significantly larger plaques in the aortic arch and sinus in both sexes. Lipid levels in these plaques were higher without a substantial difference in macrophage numbers. Smooth muscle cell content was not altered, but collagen content was lower. Importantly, there were sex-specific differences in these observations, where males had higher lipids and lower collagen in the plaque, but females did not. In mice lacking As3MT, arsenic did not alter the plaque size, although the size was highly variable. In addition, control apoE−/−/As3MT−/− mice had significantly larger plaque size compared with control apoE−/−. Conclusion: This study shows that early life exposure to inorganic and methylated arsenicals is pro-atherogenic with sex-specific differences in plaque composition and a potential role for As3MT in mice. https://doi.org/10.1289/EHP8171

Objectives To estimate associations between fine particulate matter (PM2.5) and ozone and the onset of systemic autoimmune rheumatic diseases (SARDs). Methods An open cohort of over 6 million adults was constructed from provincial physician billing and hospitalization records between 2000 and 2013. We defined incident SARD cases (SLE, Sjogren’s syndrome, scleroderma, polymyositis, dermatomyositis, polyarteritis nodosa and related conditions, polymyalgia rheumatic, other necrotizing vasculopathies, and undifferentiated connective tissue disease) based on at least two relevant billing diagnostic codes (within 2 years, with at least 1 billing from a rheumatologist), or at least one relevant hospitalization diagnostic code. Estimated PM2.5 and ozone concentrations (derived from remote sensing and/or chemical transport models) were assigned to subjects based on residential postal codes, updated throughout follow-up. Cox proportional hazards models with annual exposure levels were used to calculate hazard ratios (HRs) for SARDs incidence, adjusting for sex, age, urban-versus-rural residence, and socioeconomic status. Results The adjusted HR for SARDS related to one interquartile range increase in PM2.5 (3.97 µg/m3) was 1.12 (95% confidence interval 1.08–1.15), but there was no clear association with ozone. Indirectly controlling for smoking did not alter the findings. Conclusions We found associations between SARDs incidence and PM2.5, but no relationships with ozone. Additional studies are needed to better understand interplays between the many constituents of air pollution and rheumatic diseases.

Chronic diseases, especially cancer, cardiovascular disease, and respiratory diseases, are the leading causes of morbidity and mortality worldwide. In Canada alone, chronic diseases account for 87% of total disability and 89% of all deaths (1). It is estimated that half of the Canadian population live currently with a chronic illness and more than 90% of them have these three diseases (1). Chronic diseases develop typically over long periods of time and have multiple risk factors. The major causes of cardiovascular disease include high cholesterol, high blood pressure, and obesity (2–6). For lung cancer and respiratory disease, tobacco smoking and exposure to toxic chemicals are important risk factors (7–15). More recently, ambient air pollution has been implicated in increasing the incidence and mortality from lung cancer and from cardio-pulmonary diseases (16–19). Present-day urban air pollution comprises hundreds of substances, including sulphur dioxide, ozone, nitrogen oxide, nitrogen dioxide, carbon monoxide, carbon dioxide, particulate matter, rubber dust, polycyclic aromatic hydrocarbons, and many different volatile organic compounds. Particles are a heterogeneous mixture of solid and liquid droplets with wide distributions of size and mass. Coarse particles, greater than 2.5 μm in median aerodynamic diameter, derive from a variety of sources including windblown dust and grinding operations; fine particles are primarily from the combustion of fossil fuels (20). Common constituents of particulates include elemental and organic carbon, sulphates, nitrates, pollen, microbial contaminants, and metals (20). Fine particles can react with sulphur dioxide and oxides of nitrogen in the atmosphere to form strong acids, such as sulphuric acid, nitric acid, hydrochloric acid, and acid aerosols (20). In addition, urban air also contains benzene and 1,3-butadiene that are considered carcinogenic (21). The health impact of outdoor air pollution became apparent during the smog episodes in London, England, (Figure 1) in the 1950s and in some other places. In London, the lethal fog, which occurred because a temperature inversion trapped heavy combustion-related emissions of particles and SO2 (traffic and coal-fired heating), resulted in approximately 3,000 more deaths than normal during the first three weeks of the smog event (Figure 2) (22). During five days of a smog episode in Donora, Pennsylvania, a small town of 14,000 residents, 20 people died and over 7,000 were hospitalized (23). These episodes demonstrated conclusively that the confluence of adverse weather conditions and extremely high levels of pollution from ambient particles and sulphur dioxide can cause immediate and dramatic increases in mortality (24;25). Figure 1: The London smog episode of 1952. A photograph during the day. Figure 2: The association between total suspended particles, sulphur dioxide, and nonaccidental deaths during 15 days in the London smog episode of 1952. The right hand vertical axis shows the number of deaths per day and the left hand axis represents concentrations ... In the subsequent decades, especially in economically developed countries, changes in fuels (e.g., low sulphur fuels), improved combustion technology, and regulations (e.g., the clean air acts in the UK, USA, and Canada) have led to significant reductions in the levels of ambient air pollution (Figure 3). Unfortunately, the situation in some other less developed countries is not encouraging. For example, Delhi, India, is subjected frequently to high levels of total suspended particulates, with an annual mean concentration well exceeding 600 μg/m3 (26). In many other parts of the world, such as Mexico City and Beijing (Figure 3), similarly high levels of total suspended particles are observed frequently (27;28). In contrast, the maximum annual level of total suspended particles in Windsor, Ontario, one of the most polluted cities in Canada, is usually below 120 μg/m3 (29). Figure 3: Photographs of air pollution in Montreal and Beijing. Panel A: a “clean” day in Montreal, August 27, 2002, mean concentration of fine particles 3 μg/m3. Panel B: a “dirty” day in Montreal, August 14, 2002, mean ...

Background: Studies have reported that ambient air pollution is associated with an increased risk of developing or dying from coronavirus-2 (COVID-19). Methodological approaches to investigate the health impacts of air pollution on epidemics should differ from those used for chronic diseases, but the methods used in these studies have not been appraised critically. Objectives: Our study aimed to identify and critique the methodological approaches of studies of air pollution on infections and mortality due to COVID-19 and to identify and critique the methodological approaches of similar studies concerning severe acute respiratory syndrome (SARS). Methods: Published and unpublished papers of associations between air pollution and developing or dying from COVID-19 or SARS that were reported as of 10 May 2020 were identified through electronic databases, internet searches, and other sources. Results: All six COVID-19 studies and two of three SARS studies reported positive associations. Two were time series studies that estimated associations between daily changes in air pollution, one was a cohort that assessed associations between air pollution and the secondary spread of SARS, and six were ecological studies that used area-wide exposures and outcomes. Common shortcomings included possible cross-level bias in ecological studies, underreporting of health outcomes, using grouped data, the lack of highly spatially resolved air pollution measures, inadequate control for confounding and evaluation of effect modification, not accounting for regional variations in the timing of outbreaks’ temporal changes in at-risk populations, and not accounting for nonindependence of outcomes. Discussion: Studies of air pollution and novel coronaviruses have relied mainly on ecological measures of exposures and outcomes and are susceptible to important sources of bias. Although longitudinal studies with individual-level data may be imperfect, they are needed to adequately address this topic. The complexities involved in these types of studies underscore the need for careful design and for peer review. https://doi.org/10.1289/EHP7411

BackgroundLiving in greener areas of cities was linked to increased physical activity levels, improved mental well-being, and lowered harmful environmental exposures, all of which may affect human health. However, whether living in greener areas may be associated with lower risk of cardiovascular disease incidence, progression, and premature mortality is unclear.ObjectivesWe conducted a cohort study to examine the associations between residential green spaces and the incidence of acute myocardial infarction (AMI) and heart failure (HF), post-AMI and HF hospital readmissions, and mortality.MethodsWe simultaneously followed four large population-based cohorts in Ontario, Canada, including the entire adult population, adults free of AMI and HF, and survivors of AMI or HF from 2000 to 2014. We estimated residential exposure to green spaces using satellite-derived observations and ascertained health outcomes using validated disease registries. We estimated the associations using spatial random-effects Cox proportional hazards models. We conducted various sensitivity analyses, including further adjusting for property values and performing exploratory mediation analysis.ResultsEach interquartile range increase in residential greenness was associated with a 7% [95% confidence interval (CI): 4%, 9%] decrease in incident AMI and a 6% (95% CI: 4%, 7%) decrease in incident HF. Residential greenness was linked to a ∼10% decrease in cardiovascular mortality in both adults free of AMI and HF and the entire adult population. These associations remained consistent in sensitivity analyses and were accentuated among younger adults. Additionally, we estimated that the decreases in AMI and HF incidence associated with residential greenness explained ∼53% of the protective association between residential greenness and cardiovascular mortality. Conversely, residential greenness was not associated with any delay in readmission or mortality among AMI and HF patients.ConclusionsLiving in urban areas with more green spaces was associated with improved cardiovascular health in people free of AMI and HF but not among individuals who have already developed these conditions. https://doi.org/10.1289/EHP6161.

Background: Living in greener areas of cities was linked to increased physical activity levels, improved mental well-being, and lowered harmful environmental exposures, all of which may affect human health. However, whether living in greener areas may be associated with lower risk of cardiovascular disease incidence, progression, and premature mortality is unclear. Objectives: We conducted a cohort study to examine the associations between residential green spaces and the incidence of acute myocardial infarction (AMI) and heart failure (HF), post-AMI and HF hospital readmissions, and mortality. Methods: We simultaneously followed four large population-based cohorts in Ontario, Canada, including the entire adult population, adults free of AMI and HF, and survivors of AMI or HF from 2000 to 2014. We estimated residential exposure to green spaces using satellite-derived observations and ascertained health outcomes using validated disease registries. We estimated the associations using spatial random-effects Cox proportional hazards models. We conducted various sensitivity analyses, including further adjusting for property values and performing exploratory mediation analysis. Results: Each interquartile range increase in residential greenness was associated with a 7% [95% confidence interval (CI): 4%, 9%] decrease in incident AMI and a 6% (95% CI: 4%, 7%) decrease in incident HF. Residential greenness was linked to a ∼10% decrease in cardiovascular mortality in both adults free of AMI and HF and the entire adult population. These associations remained consistent in sensitivity analyses and were accentuated among younger adults. Additionally, we estimated that the decreases in AMI and HF incidence associated with residential greenness explained ∼53% of the protective association between residential greenness and cardiovascular mortality. Conversely, residential greenness was not associated with any delay in readmission or mortality among AMI and HF patients. Conclusions: Living in urban areas with more green spaces was associated with improved cardiovascular health in people free of AMI and HF but not among individuals who have already developed these conditions. https://doi.org/10.1289/EHP6161

Background Exposure to road traffic noise has been linked to cardiometabolic complications, such as elevated blood pressure and glucose dysregulation. However, epidemiologic evidence linking road traffic noise to diabetes mellitus and hypertension remains scarce. We examined associations between road traffic noise and the incidence of diabetes mellitus and hypertension in Toronto, Canada. Methods and Results Using the Ontario Population Health and Environment Cohort, we conducted a retrospective, population‐based cohort study of long‐term residents of Toronto, aged 35 to 100 years, who were registered for provincial publicly funded health insurance, and were without a history of hypertension (n=701 174) or diabetes mellitus (n=914 607). Road traffic noise exposure levels were assessed by the equivalent continuous A‐weighted sound pressure level (dBA) for the 24‐hour day and the equivalent continuous A‐weighted sound pressure level for the night (11 pm –7 am) . Noise exposures were assigned to subjects according to their annual residential postal codes during the 15‐year follow‐up. We used random‐effect Cox proportional hazards models adjusting for personal and area‐level characteristics. From 2001 to 2015, each interquartile range increase in the equivalent continuous A‐weighted sound pressure level (dBA) for the 24‐hour day (10.0 dBA) was associated with an 8% increase in incident diabetes mellitus (95% CI, 1.07–1.09) and a 2% increase in hypertension (95% CI, 1.01–1.03). We obtained similar estimates with the equivalent continuous A‐weighted sound pressure level for the night (11 pm –7 am) . These results were robust to all sensitivity analyses conducted, including further adjusting for traffic‐related air pollutants (ultrafine particles and nitrogen dioxide). For both hypertension and diabetes mellitus, we observed stronger associations with the equivalent continuous A‐weighted sound pressure level (dBA) for the 24‐hour day among women and younger adults (aged Conclusions Long‐term exposure to road traffic noise was associated with an increased incidence of diabetes mellitus and hypertension in Toronto.

ObjectiveTo assess the associations between ambient temperatures and hospitalisations for coronary heart disease (CHD) and stroke.MethodsOur study comprised all residents living in Ontario, Canada, 1996–2013. For each of 14 health regions, we fitted a distributed lag non-linear model to estimate the cold and heat effects on hospitalisations from CHD, acute myocardial infarction (AMI), stroke and ischaemic stroke, respectively. These effects were pooled using a multivariate meta-analysis. We computed attributable hospitalisations for cold and heat, defined as temperatures above and below the optimum temperature (corresponding to the temperature of minimum morbidity) and for moderate and extreme temperatures, defined using cut-offs at the 2.5th and 97.5th temperature percentiles.ResultsBetween 1996 and 2013, we identified 1.4 million hospitalisations from CHD and 355 837 from stroke across Ontario. On cold days with temperature corresponding to the 1st percentile of temperature distribution, we found a 9% increase in daily hospitalisations for CHD (95% CI 1% to 16%), 29% increase for AMI (95% CI 15% to 45%) and 11% increase for stroke (95% CI 1% to 22%) relative to days with an optimal temperature. High temperatures (the 99th percentile) also increased CHD hospitalisations by 6% (95% CI 1% to 11%) relative to the optimal temperature. These estimates translate into 2.49% of CHD hospitalisations attributable to cold and 1.20% from heat. Additionally, 1.71% of stroke hospitalisations were attributable to cold. Importantly, moderate temperatures, rather than extreme temperatures, yielded the most of the cardiovascular burdens from temperatures.ConclusionsAmbient temperatures, especially in moderate ranges, may be an important risk factor for cardiovascular-related hospitalisations.

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 (O 3 ) and nitrogen dioxide (NO 2 ) of participants’ residences in Montreal, 1991–2002. Methods We used the following methods to predict spatially-resolved daily concentrations of O 3 and NO 2 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 O 3 and NO 2 . On a given day and postal code area the difference in the concentration assigned could be as high as 131 ppb for O 3 and 108 ppb for NO 2 . 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 O 3 and 0.81 (95%CI: 0.80, 0.81) for NO 2 , respectively. For this pair of methods the maximum difference on a given day and postal code area was 36 ppb for O 3 and 74 ppb for NO 2 . 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 O 3 , but not NO 2 , 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 O 3 and NO 2 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.

Background: Air pollution has been classified as a human carcinogen based largely on epidemiological studies of lung cancer. Recent research suggests that exposure to ambient air pollution increases the risk of female breast cancer especially in premenopausal women. Methods: Our objective was to determine the association between residential exposure to ambient nitrogen dioxide (NO2) and newly diagnosed cases of invasive breast cancer in a cohort of 89,247 women enrolled in the Canadian National Breast Screening Study between 1980 and 1985. Vital status and incident breast cancers through 2005 were determined through record linkage to the Canadian national mortality and cancer registries. Estimates of exposures to NO2 using participants’ addresses at time of entry into the study were derived from a national land use regression model. We classified women as reaching menopause according to information obtained at baseline. In addition, as we had no information from women on their menopausal status during the observation period, we conducted analyses using different cut-points for defining postmenopausal status (i.e., at 50 or at 52 years of age), and hence we had four non-independent cohorts. We computed rate ratios for the incidence of breast cancer and their 95% confidence intervals (CI) separately for premenopausal and postmenopausal women. Our Cox models used attained age as the time axis and the rate ratios were adjusted for several individual-level risk factors, including reproductive history, as well as census-based neighborhood-level characteristics. Results: The median concentration of NO2 was about 15 parts per billion (ppb). After adjusting for personal risk factors and contextual variables, we found no evidence of associations for the incidence of breast cancer in the postmenopausal cohorts. In premenopausal women, the rate ratio for an increase of 9.7 ppb (about the interquartile range) was 1.13 (95%CI: 0.94–1.37) for the 50 years of age cut-off for menopausal status and it was 1.17 (95%CI: 1.00–1.38) for the 52 years of age cut-off. Conclusions: Our findings suggest that exposure to low concentrations of NO2, a marker for traffic-related air pollution, increases the risk of premenopausal breast cancer, but not postmenopausal breast cancer. Keywords: Breast cancer, Air pollution, Nitrogen dioxide, Epidemiology, Cohort study

Breast cancer susceptibility variants frequently show heterogeneity in associations by tumor subtype. To identify novel loci, we performed a genome-wide association study (GWAS) including 133,384 breast cancer cases and 113,789 controls, plus 18,908 BRCA1 mutation carriers (9,414 with breast cancer) of European ancestry, using both standard and novel methodologies that account for underlying tumor heterogeneity by estrogen receptor (ER), progesterone receptor (PR), and human epidermal growth factor receptor 2 (HER2) status and tumor grade. We identified 32 novel susceptibility loci (P-8), 15 of which showed evidence for associations with at least one tumor feature (false discovery rate PIn-silico analyses showed these five loci contained cell-specific enhancers that differed between normal luminal and basal mammary cells. The genetic correlations between five intrinsic-like subtypes ranged from 0.35 to 0.80. The proportion of genome-wide chip heritability explained by all known susceptibility loci was 37.6% for triple-negative and 54.2% for luminal A-like disease. These findings provide an improved understanding of genetic predisposition to breast cancer subtypes and will inform the development of subtype-specific polygenic risk scores.

Breast cancer susceptibility variants frequently show heterogeneity in associations by tumor subtype1–3. To identify novel loci, we performed a genome-wide association study (GWAS) including 133,384 breast cancer cases and 113,789 controls, plus 18,908 BRCA1 mutation carriers (9,414 with breast cancer) of European ancestry, using both standard and novel methodologies that account for underlying tumor heterogeneity by estrogen receptor (ER), progesterone receptor (PR), and human epidermal growth factor receptor 2 (HER2) status and tumor grade. We identified 32 novel susceptibility loci (P < 5.0 × 10−8), 15 of which showed evidence for associations with at least one tumor feature (false discovery rate (FDR)