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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.

Introduction: Emerging studies have implicated air pollution in the neurodegenerative processes. Less is known about the influence of air pollution, especially at the relatively low levels, on developing dementia. We conducted a population-based cohort study in Ontario, Canada, where the concentrations of pollutants are among the lowest in the world, to assess whether air pollution exposure is associated with incident dementia. Methods: The study population comprised all Ontario residents who, on 1 April 2001, were 55–85years old, Canadian-born, and free of physician-diagnosed dementia (~2.1 million individuals). Follow-up extended until 2013. We used population-based health administrative databases with a validated algorithm to ascertain incident diagnosis of dementia as well as prevalent cases. Using satellite observations, land-use regression model, and an optimal interpolation method, we derived long-term average exposure to fine particulate matter (≤2.5μm in diameter) (PM2.5), nitrogen dioxide (NO2), and ozone (O3), respectively at the subjects' historical residences based on a population-based registry. We used multilevel spatial random-effects Cox proportional hazards models, adjusting for individual and contextual factors, such as diabetes, brain injury, and neighborhood income. We conducted various sensitivity analyses, such as lagging exposure up to 10years and considering a negative control outcome for which no (or weaker) association with air pollution is expected. Results: We identified 257,816 incident cases of dementia in 2001–2013. We found a positive association between PM2.5 and dementia incidence, with a hazard ratio (HR) of 1.04 (95% confidence interval (CI): 1.03–1.05) for every interquartile-range increase in exposure to PM2.5. Similarly, NO2 was associated with increased incidence of dementia (HR=1.10; 95% CI: 1.08–1.12). No association was found for O3. These associations were robust to all sensitivity analyses examined. These estimates translate to 6.1% of dementia cases (or 15,813 cases) attributable to PM2.5 and NO2, based on the observed distribution of exposure relative to the lowest quartile in concentrations in this cohort. Discussion: In this large cohort, exposure to air pollution, even at the relative low levels, was associated with higher dementia incidence. Keywords: Fine particulate matter, Nitrogen dioxides, Ozone, Dementia, Cohort

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.

In this study, we estimated the transportation-related emissions of nitrogen oxides (NO x ) at an individual level for a sample of the Montreal population. Using linear regression, we quantified the associations between NO x emissions and selected individual attributes. We then investigated the relationship between individual emissions of NO x and exposure to nitrogen dioxide (NO 2 ) concentrations derived from a land-use regression model. Factor analysis and clustering of land-uses were used to test the relationships between emissions and exposures in different Montreal areas. We observed that the emissions generated per individual are positively associated with vehicle ownership, gender, and employment status. We also noted that individuals who live in the suburbs or in peripheral areas generate higher emissions of NO x but are exposed to lower NO 2 concentrations at home and throughout their daily activities. Finally, we observed that for most individuals, NO 2 exposures based on daily activity locations were often slightly more elevated than NO 2 concentrations at the home location. We estimated that between 20% and 45% of individuals experience a daily exposure that is largely different from the concentration at their home location. Our findings are relevant to the evaluation of equity in the generation of transport emissions and exposure to traffic-related air pollution. We also shed light on the effect of accounting for daily activities when estimating air pollution exposure.

Introduction Dysfunction of the autonomic nervous system is one of the postulated pathways linking short-term exposure to air pollution to adverse cardiovascular outcomes. A hypothesis is that exposure to air pollution decreases heart rate variability, a recognized independent predictor of poorer cardiovascular prognosis. Methods We conducted a structured review of panel studies published between 1946 and July 2015 of the association between ambient air pollution and parameters of heart rate variability reflecting autonomic nervous function. We focused on exposure to mass concentrations of fine particles (PM 2.5 ), nitrogen dioxide (NO 2 ), and ozone (O 3 ) , and four commonly used indices of heart rate variability (HRV): standard deviation of all normal-to-normal intervals (SDNN); root mean square of successive differences in adjacent normal-to-normal intervals (RMSSD); high frequency power (HF); and low frequency power (LF). We searched bibliographic databases and references of identified articles and abstracted characteristics of their design and conduct, and synthesized the quantitative findings in graphic form according to health condition of the study population and the functional form of the HRV indices used in the regression analyses. Results A total of 33 panel studies were included: 31, 12, and 13 studies were used to investigate ambient exposure to PM 2.5 , NO 2 and O 3 , respectively. We found substantial variation across studies in terms of design characteristics and statistical methodologies, and we identified some studies that may have had methodological and statistical issues. Because many panel studies were not comparable to each other, meta-analyses were not generally possible, although we were able to pool the results obtained amongst older adults who had cardiovascular disease for the 24-h average concentrations of PM 2.5 prior to the heart rate variability measurements. In studies of PM 2.5 among older adults with cardiovascular disease, logarithmic transformations of the HRV indices were used in ten studies. Negative associations across all HRV indices were found in 60–86% of these studies for periods of exposures ranging from 5-min to 5-days. The pooled percent changes for an increase of 10 μg/m 3 in the 24-h prior to the measurements of HRV were: −2.11% for SDNN (95% confidence interval (95%CI): −4.00, −0.23%), −3.29% for RMSSD (95%CI: −6.32, −0.25%), −4.76% for LF (95%CI: −12.10, 2.58%), and −1.74% for HF (95%CI: −7.79, 4.31%). No transformations were used in seven studies of PM 2.5 among older adults with cardiovascular disease, and we found for absolute differences pooled changes in the HRV indices, for an increase of 10 μg/m 3 , of −0.31 ms for SDNN (95%CI: −1.02, 0.41 ms) and −1.22 ms for RMSSD (95%CI: −2.37; −0.07 ms). For gaseous pollutants, negative associations over periods of exposure ranging from 5-min or to 5-days prior to the heart rate variability measurements were reported in 71–83% of studies of NO 2 and 57–100% of studies of O 3 , depending of the indices of heart rate variability. However, many of these studies had statistical or methodological issues, and in the few studies without these issues the confidence intervals were relatively wide and mostly included the null. Conclusions and discussion We were not persuaded by the results that there was an association between PM 2.5 and any of the four indices of heart rate variability. For NO 2 and O 3 the number of high-quality studies was insufficient to draw any definite conclusions. Further panel studies with improved design and methodologies are needed to help establish or refute an association between ambient exposure to air pollution and heart rate variability.

This study investigates the variability in near-road concentrations of ultra-fine particles (UFP). Our results are based on a mobile data collection campaign conducted in 2012 in Montreal, Canada using instrumented bicycles and covering approximately 475 km of unique roadways. The spatial extent of the data collected included a diverse array of roads and land use patterns. Average concentrations of UFP per roadway segment varied greatly across the study area (1411–192,340 particles/cm 3 ) as well as across the different visits to the same segment. Mixed effects linear regression models were estimated for UFP (R 2 = 43.80%), incorporating a wide range of predictors including land-use, built environment, road characteristics, and meteorology. Temperature and wind speed had a large negative effect on near-road concentrations of UFP. Both the day of the week and time of day had a significant effect with Tuesdays and afternoon periods positively associated with UFP. Since UFP are largely associated with traffic emissions and considering the wide spatial extent of our data collection campaign, it was impossible to collect traffic volume data. For this purpose, we used simulated data for traffic volumes and speeds across the region and observed a positive effect for volumes and negative effect for speed. Finally, proximity to truck routes was also associated with higher UFP concentrations.

Urban canyons increase overall air pollution concentrations at the street level. Yet, due to the difficulty in characterizing such geometric features over a regional area, they are rarely included in the evaluation of population exposures. This study combines measurements of ultrafine particles (UFP) with land-use regression techniques in order to capture the determinants of near-road concentrations of UFP while specifically accounting for urban canyon effects. Using the mean UFP concentration measured within a set of urban canyons (over 1000 road segments) as the dependent variable, we investigated the effects of a number of urban canyon descriptors (width, height, contiguity, vegetation, and road class) through a multivariate linear regression. Our models are adjusted for the effects of meteorology. The signs and values of the regression coefficients are qualitatively consistent with what is already known about urban canyons: increased building height (b height = 0.004; 95% CI: 0.001, 0.007) along with decreased canyon width (b width = −0.007; 95% CI: −0.012, −0.002) serve to worsen the air quality therein, while increased tree cover (b trees = −1.762; 95% CI: −2.236, −1.289) improves air quality. We also observed that the difference in UFP levels on both sides of the canyon increased with increasing street width (b width = 0.113; 95% CI: 0.061, 0.165), and with wind direction more perpendicular to the street. We conclude that it is possible to include urban canyon effects in the development of air pollution exposure surfaces thus refining the assessment of population exposure to air pollution in urban microenvironments.

There is a paucity of studies investigating adult leukemia and air pollution. To address this gap, we analyzed data from a Canadian population-based case-control study conducted in 1994-1997. Cases were 1064 adults with incident leukemia and controls were 5039 healthy adults. We used data from satellites and fixed-site monitoring stations to estimate residential concentrations of NO2 and fine particulate matter (PM2.5) for the period prior to diagnosis, starting in 1975 and ending in 1994. We modeled the average annual exposure of each subject. Odds ratios (OR) and their 95% confidence intervals (CI) were estimated using logistic regression, adjusted for age, gender, province, smoking, education, body mass index, income, and self-reported exposures to ionizing radiation and benzene. We found an 'n-shaped' response function between exposure to NO2 and all forms of leukemia: from the tenth percentile to the median (4.51 to 14.66 ppb), the OR was 1.20; 95% CI: 0.97-1.48 and from the 75th percentile to the 90th (22.75 to 29.7 ppb), the OR was 0.79; 95% CI 0.68-0.93. For PM2.5 we found a response function consistent with a linear model, with an OR per 10 μg/m(3) of 0.97 (95% CI 0.75-1.26). For chronic lymphocytic leukemia we found response functions that were consistent with a simple linear model, with an OR per 5 ppb of NO2 of 0.93 (95% CI 0.86-1.00) and an OR per 10 μg/m(3) of PM2.5 of 0.62 (95% CI 0.42-0.93). In summary, for chronic lymphocytic leukemia we found no evidence of an association with air pollution and with all forms of leukemia we found weak evidence of an association only at low concentrations of NO2. It is possible that these inconsistent results may have arisen because of unaccounted urban/rural differences or possibly from a selection effect, especially among controls.

Genome-wide association studies have identified SNPs near ZNF365 at 10q21.2 that are associated with both breast cancer risk and mammographic density. To identify the most likely causal SNPs, we fine mapped the association signal by genotyping 428 SNPs across the region in 89,050 European and 12,893 Asian case and control subjects from the Breast Cancer Association Consortium. We identified four independent sets of correlated, highly trait-associated variants (iCHAVs), three of which were located within ZNF365. The most strongly risk-associated SNP, rs10995201 in iCHAV1, showed clear evidence of association with both estrogen receptor (ER)-positive (OR = 0.85 [0.82-0.88]) and ER-negative (OR = 0.87 [0.82-0.91]) disease, and was also the SNP most strongly associated with percent mammographic density. iCHAV2 (lead SNP, chr10: 64,258,684:D) and iCHAV3 (lead SNP, rs7922449) were also associated with ER-positive (OR = 0.93 [0.91-0.95] and OR = 1.06 [1.03-1.09]) and ER-negative (OR = 0.95 [0.91-0.98] and OR = 1.08 [1.04-1.13]) disease. There was weaker evidence for iCHAV4, located 5' of ADO, associated only with ER-positive breast cancer (OR = 0.93 [0.90-0.96]). We found 12, 17, 18, and 2 candidate causal SNPs for breast cancer in iCHAVs 1-4, respectively. Chromosome conformation capture analysis showed that iCHAV2 interacts with the ZNF365 and NRBF2 (more than 600 kb away) promoters in normal and cancerous breast epithelial cells. Luciferase assays did not identify SNPs that affect transactivation of ZNF365, but identified a protective haplotype in iCHAV2, associated with silencing of the NRBF2 promoter, implicating this gene in the etiology of breast cancer.

Objective The acute cardiorespiratory effects of air quality among children living in areas with considerable heavy industry have not been well investigated. We conducted a panel study of children with asthma living in proximity to an industrial complex housing two refineries in Montreal, Quebec, in order to assess associations between their personal daily exposure to air pollutants and changes in pulmonary function and selected indicators of cardiovascular health. Methods Seventy-two children with asthma age 7–12 years in 2009–2010 participated in this panel study for a period of 10 consecutive days. They carried a small backpack for personal monitoring of sulphur dioxide (SO 2 ), benzene, fine particles (PM 2.5 ), nitrogen dioxide (NO 2 ) and polycyclic aromatic hydrocarbons (PAHs) and underwent daily spirometry and cardiovascular testing (blood pressure, pulse rate and oxygen saturation). To estimate these associations, we used mixed regression models, adjusting for within-subject serial correlation, and for the effects of a number of personal and environmental variables (e.g., medication use, ethnicity, temperature). Results Children with asthma involved in the study had relatively good pulmonary function test results (mean FEV 1 compared to standard values: 89.8%, mean FVC: 97.6%, mean FEF 25–75 : 76.3%). Median diastolic, systolic blood pressures and oxygen saturation were 60/94 mm Hg and 99%, respectively. Median personal concentrations of pollutants were NO 2 , 5.5 ppb; benzene, 2.1 µg/m 3 ; PM 2.5 , 5.7 µg/m 3 ; and total PAH, 130 µg/m 3 . Most personal concentrations of SO 2 were below the level of detection. No consistent associations were observed between cardio-pulmonary indices and personal exposure to PM 2.5 , NO 2 and benzene, although there was a suggestion for a small decrease in respiratory function with total concentrations of PAHs (e.g., adjusted association with FVC: −9.9 ml per interquartile range 95%CI: −23.4, 3.7). Conclusions This study suggests that at low daily average levels of exposure to industrial emissions, effects on pulmonary and cardiovascular functions in children with asthma may be difficult to detect over 10 consecutive days.

Long-term exposure to ambient air pollution has been linked to cardiovascular mortality, but the associations with incidence of major cardiovascular diseases are not fully understood, especially at low concentrations. We aimed to investigate the associations between exposure to fine particulate matter (PM2.5), nitrogen dioxide (NO2), ozone (O3), redox-weighted average of NO2 and O3 (Ox) and incidence of congestive heart failure (CHF) and acute myocardial infarction (AMI). Our study population included all long-term residents aged 35–85 years who lived in Ontario, Canada, from 2001 to 2015 (~5.1 million). Incidence of CHF and AMI were ascertained from validated registries. We assigned estimates of annual concentrations of pollutants to the residential postal codes of subjects for each year during follow-up. We estimated hazard ratios (HRs) and 95% CIs for each pollutant separately using Cox proportional hazards models. We examined the shape of concentration-response associations using shape-constrained health impact functions. From 2001 to 2015, there were 422,625 and 197,628 incident cases of CHF and AMI, respectively. In the fully adjusted analyses, the HRs of CHF corresponding to each interquartile range increase in exposure were 1.05 (95% CI: 1.04–1.05) for PM2.5, 1.02 (95% CI: 1.01–1.04) for NO2, 1.03 (95% CI: 1.02–1.03) for O3, and 1.02 (95% CI: 1.02–1.03) for Ox, respectively. Similarly, exposure to PM2.5, O3, and Ox were positively associated with AMI. The concentration-response relationships were different for individual pollutant and outcome combinations (e.g., for PM2.5 the relationship was supralinear with CHF, and linear with AMI). Keywords: Ambient air pollution, Incidence, Cardiovascular events, Congestive heart failure, Acute myocardial infarction, Concentration-response curves

The 10q26 locus in the second intron of FGFR2 is the locus most strongly associated with estrogen-receptor-positive breast cancer in genome-wide association studies. We conducted fine-scale mapping in case-control studies genotyped with a custom chip (iCOGS), comprising 41 studies (n = 89,050) of European ancestry, 9 Asian ancestry studies (n = 13,983), and 2 African ancestry studies (n = 2,028) from the Breast Cancer Association Consortium. We identified three statistically independent risk signals within the locus. Within risk signals 1 and 3, genetic analysis identified five and two variants, respectively, highly correlated with the most strongly associated SNPs. By using a combination of genetic fine mapping, data on DNase hypersensitivity, and electrophoretic mobility shift assays to study protein-DNA binding, we identified rs35054928, rs2981578, and rs45631563 as putative functional SNPs. Chromatin immunoprecipitation showed that FOXA1 preferentially bound to the risk-associated allele (C) of rs2981578 and was able to recruit ERα to this site in an allele-specific manner, whereas E2F1 preferentially bound the risk variant of rs35054928. The risk alleles were preferentially found in open chromatin and bound by Ser5 phosphorylated RNA polymerase II, suggesting that the risk alleles are associated with changes in transcription. Chromatin conformation capture demonstrated that the risk region was able to interact with the promoter of FGFR2, the likely target gene of this risk region. A role for FOXA1 in mediating breast cancer susceptibility at this locus is consistent with the finding that the FGFR2 risk locus primarily predisposes to estrogen-receptor-positive disease. © 2013 The American Society of Human Genetics.

This study investigated associations between long-term exposure to ambient volatile organic compounds (VOCs) and mortality. 58,760 Toronto residents (≥35 years of age) were selected from tax filings and followed from 1982 to 2004. Death information was extracted using record linkage to national mortality data. Land-use regression surfaces for benzene, n-hexane, and total hydrocarbons were generated from sampling campaigns in 2002 and 2004 and assigned to residential addresses in 1982. Cox regression was used to estimate relationships between each VOC and non-accidental, cardiovascular, and cancer mortality. Positive associations were observed for each VOC. In multi-pollutant models the benzene and total hydrocarbon signals were strongest for cancer. The hazard ratio for cancer that corresponded to an increase in the interquartile range of benzene (0.13 μg/m(3)) was 1.06 (95% CI = 1.02-1.11). Our findings suggest ambient concentrations of VOCs were associated with cancer mortality, and that these exposures did not confound our previously reported associations between NO2 and cardiovascular mortality.

We developed a web-based route planning tool for cyclists in Montreal, Canada, using spatial monitoring data for ambient nitrogen dioxide (NO2). With this tool, we estimated exposures to NO2 along shortest routes and lower exposure alternatives using origin-destination survey data. On average, exposures were estimated to be lower by 0.76 ppb (95% CI: 0.72, 0.80) relative to the shortest route, with decreases of up to 6.1 ppb for a single trip. Cumulative exposure levels (ppb km) decreased by approximately 4%. In general, the benefits of decreased exposure could be achieved with little increase (less than 1 km) in the overall route length.

In this paper we explore the air pollution levels along types of bicycle facilities using a NO2 land use model previously developed for Montreal. We explore potential associations between bicycle volumes through signalized intersections and pollution levels at those intersections. We further investigate this relationship through the comparison of over thirty cycling corridors as well as an evaluation of the potential exposure of cyclists to air pollution along five routes. We observe NO2 concentrations to be positively correlated with bicycle flows at the intersection level. We also observe that corridors with either a bicycle path or cycle track generally rank higher in terms of bicycle volume and also have higher NO2 concentrations than corridors without bicycle facilities. This indicates that intersections and bicycle facilities with a large number of cyclists are also those characterized with the highest air pollution levels.

Land use regression has been used in epidemiologic studies to estimate long-term exposure to air pollution within cities. The models are often developed toward the end of the study using recent air pollution data. Given that there may be spatially-dependent temporal trends in urban air pollution and that there is interest for epidemiologists in assessing period-specific exposures, especially early-life exposure, methods are required to extrapolate these models back in time. We present herein three new methods to back-extrapolate land use regression models. During three two-week periods in 2005–2006, we monitored nitrogen dioxide (NO 2 ) at about 130 locations in Montreal, Quebec, and then developed a land-use regression (LUR) model. Our three extrapolation methods entailed multiplying the predicted concentrations of NO 2 by the ratio of past estimates of concentrations from fixed-site monitors, such that they reflected the change in the spatial structure of NO 2 from measurements at fixed-site monitors. The specific methods depended on the availability of land use and traffic-related data, and we back-extrapolated the LUR model to 10 and 20 years into the past. We then applied these estimates to residential information from subjects enrolled in a case–control study of postmenopausal breast cancer that was conducted in 1996. Observed and predicted concentrations of NO 2 in Montreal decreased and were correlated in time. The estimated concentrations using the three extrapolation methods had similar distributions, except that one method yielded slightly lower values. The spatial distributions varied slightly between methods. In the analysis of the breast cancer study, the odds ratios were insensitive to the method but varied with time: for a 5 ppb increase in NO 2 using the 2006 LUR the odds ratio (OR) was about 1.4 and the ORs in predicted past concentrations of NO 2 varied (OR∼1.2 for 1985 and OR∼1.3–1.5 for 1996). Thus, the ORs per unit exposure increased with time as the range and variance of the spatial distributions decreased, and this is due partly to the regression coefficient being approximately inversely proportional to the variance of exposure. Changing spatial variability complicates interpretation and this may have important implications for the management of risk. Further studies are needed to estimate the accuracy of the different methods.

Concentrations of traffic-related air pollution can be highly variable at the local scale and can have substantial seasonal variability. This study was designed to provide estimates of intra-urban concentrations of ambient nitrogen dioxide (NO2) in Montreal, Canada, that would be used subsequently in health studies of chronic diseases and long-term exposures to traffic-related air pollution. We measured concentrations of NO2 at 133 locations in Montreal with passive diffusion samplers in three seasons during 2005 and 2006. We then used land use regression, a proven statistical prediction method for describing spatial patterns of air pollution, to develop separate estimates of spatial variability across the city by regressing NO2 against available land-use variables in each of these three periods. We also developed a “pooled” model across these sampling periods to provide an estimate of an annual average. Our modelling strategy was to develop a predictive model that maximized the model R2. This strategy is different from other strategies whose goal is to identify causal relationships between predictors and concentrations of NO2. Observed concentrations of NO2 ranged from 2.6 ppb to 31.5 ppb, with mean values of 12.6 ppb in December 2005, 14.0 ppb in May 2006, and 8.9 ppb in August 2006. The greatest variability was observed during May. Concentrations of NO2 were highest downtown and near major highways, and they were lowest in the western part of the city. Our pooled model explained approximately 80% of the variability in concentrations of NO2. Although there were differences in concentrations of NO2 between the three sampling periods, we found that the spatial variability did not vary significantly across the three sampling periods and that the pooled model was representative of mean annual spatial patterns.

Some neighbourhoods in urban areas are characterised by concentrations of socially and materially deprived populations. Additionally, levels of ambient air pollution in a city can be variable at the local scale and can create disparities in air quality between neighbourhoods. Socioeconomic and physical characteristics of neighbourhood environments can affect the health and well-being of local residents. In this paper we identify whether neighbourhoods in Montreal, Canada characterised by social and material deprivation have higher levels of ambient air pollution than do others. We collected two-week integrated samples of nitrogen dioxide (NO 2 ) at 133 sites in Montreal during three seasons between 2005 and 2006. We used these data in a geographic information system, along with data describing characteristics of land use, roads, and traffic, to create a spatial model of predicted mean annual concentrations of NO 2 across Montreal. Next, we collected neighbourhood socioeconomic information for 501 census tracts and overlaid their boundaries on the pollution surface. We calculated Pearson correlation coefficients and 95% confidence intervals (CI) between neighbourhood-level indicators of deprivation and levels of ambient NO 2 . We found associations between concentrations of NO 2 and neighbourhood-level indicators of material deprivation, including median household income, and with indicators of social deprivation, including proportion of people living alone. We identified specific neighbourhoods that were characterised by a double burden of high levels of deprivation and high concentrations of ambient NO 2 . Because of the particular social geography in Montreal, we found that not all deprived neighbourhoods had high levels of pollution and that some affluent neighbourhoods in the downtown core had high levels. Our results underscore the importance of considering social contexts in interpreting general associations between social and environmental risks to population health.

Objectives Recent studies suggest that persons with congestive heart failure (CHF) may be at higher risk for the short-term effects of air pollution. We carried out this daily diary panel study in Montreal, Quebec, to determine whether indicators of self-reported health status and shortness of breath at night were associated with selected health-related and dietary factors, weather conditions, and air pollution. Methods Thirty-one subjects with CHF participated in this study in 2002 and 2003. Over a 2-month period, they measured their oxygen saturation, pulse rate, weight, and temperature each morning and recorded these and other data in a daily diary. Every morning they recorded on visual analogue scales their assessments of their general health, shortness of breath, and weakness, their weight, temperature and other data in a daily diary. Air pollution and weather conditions were obtained from fixed-site monitoring stations. This paper deals only with the daily self-reported health outcomes of general health and shortness of breath. We made use of mixed regression models, adjusting for within-subject serial correlation and temporal trends, to determine the association between oxygen saturation and pulse rate and health-related, dietary and environmental variables. Depending on the model, we accounted for the effects of a variety of health-related and dietary variables as well as NO2, ozone, maximum temperature and change in barometric pressure at 8:00AM from the previous day. Results Of the many associations for self-reported general health and shortness of breath, we found only a few statistically significant predictors, although increases in many variables showed decrements in self-reported general health and shortness of breath. The statistically significant associations with general health were increases in daily pulse rate and body weight, higher maximum ambient air temperature, higher relative humidity, and ozone (on the concurrent day). Statistically significant predictors of worsened shortness of breath at night were increases in blood pressure, body weight, and higher maximum ambient air temperature (lag 0 and 1 days). We also found that there was little confounding between environmental variables and health and dietary factors. Conclusions The findings from the present investigation suggest that certain health-related indices and environmental conditions affect self-reported health and shortness of breath in CHF patients, although larger studies are needed to confirm these findings.

We discuss Bukowski's hypothesis that unmeasured, individual risk factors, notably stress caused by being in traffic or at work, may confound the association found in many time-series studies between air pollution and cardiovascular outcomes. In replying to these concerns, we describe the methods used to carry out time-series studies and discuss whether it is reasonable to believe that stress can confound the association. Our analysis suggests that these factors are unlikely to cause bias in time-series studies, although it may be important in panel and other longitudinal studies. Specifically, we argue that: (1) persons most likely to be susceptible to the effects of air pollution, notably the very young and the elderly, and those suffering from certain chronic diseases (e.g., congestive heart failure), are unlikely to be highly exposed to traffic and therefore to traffic-related stress and (2) over lengthy periods of time and sub-populations, levels of individual stress are unlikely to act coherently to create an association with ambient air pollution.

Context and objective Asthma among children is a major public health problem worldwide. There are increasing number of studies suggesting a possible association between allergenic pollen and exacerbations of asthma. In the context of global climate change, a number of future climate and air pollution scenarios predict increases in concentrations of pollen, an extension of the pollen season, and an increase in the allergenicity of pollen. The goal of the present study is to evaluate the short-term effects of exposure to grass and weed pollen on emergency department visits and readmissions for asthma among children aged 0–9 years living in Montreal between April and October, 1994–2004. Methodology and results Time-series analyses were carried out using parametric log-linear overdispersed Poisson models that were adjusted for temporal variations, daily weather conditions (temperature, atmospheric pressure), and gaseous air pollutants (ozone and nitrogen dioxide). We have found positive associations between emergency department visits and concentrations of grass pollen 3 days after exposure. The effect of grass pollen was higher on emergency department readmissions as compared to initial visits. Weak negative associations were found between weed pollen (including ragweed pollen) and emergency department visits 2 days after exposure. Conclusion The data indicate that among children, emergency department visits increased with increasing concentrations of grass pollen.

The objective of these analyses was to determine whether highways significantly influence ambient concentrations of NO2 at distances greater than 200 m. NO2 was sampled for 14 consecutive days in May 2003 at 67 sites across Montreal, Canada. The association between logarithmic concentrations of NO2 and land-use variables was assessed using multiple regressions. Locations less than 100 m from the nearest highways were excluded, leaving 61 data points. Then, locations less than 200 m were excluded, leaving 55 data points. Excluding sampling locations located less than 100 or 200 m from the nearest highway did not substantially change the regression parameters. NO2 was still significantly associated with both the distance from nearest highway and the traffic count on the nearest highway. These findings indicate that the negative association found between distance from highways and NO2 concentration in several land-use regression studies in Europe and North America was not generated solely by the high concentrations found in the immediate vicinity of highways.

PURPOSE: We used Poisson regression methods to examine the relation between temporal changes in the levels of fine particulate air pollution (PM2.5) and the risk of mortality among participants of the Harvard Six Cities longitudinal study. METHODS: Our analyses were based on 1430 deaths that occurred between 1974 and 1991 in a cohort that accumulated 105,714 person-years of follow-up. For each city, indices of PM2.5 were derived using daily samples. Individual level data were collected on several risk factors including: smoking, education, body mass index (BMI), and occupational exposure to dusts. Time-dependent indices of PM2.5 were created across 13 calendar periods (< 1979, 1979, 1980, … , 1989, ≥ 1990) to explore whether recent or chronic exposures were more important predictors of mortality. RESULTS: The relative risk (RR) of mortality calculated using Poisson regression based on average city-specific exposures that remained constant during follow-up was 1.31 [95% confidence interval (CI) = 1.12–1.52] per 18.6 μg/m3 of PM2.5. This result was similar to the risk calculated using the Cox model (RR = 1.26, 95% CI = 1.08–1.46). The RR of mortality was attenuated when the Poisson regression model included a time-dependent estimate of exposure (RR = 1.19, 95% CI = 1.04–1.36). There was little variation in RR across time-dependent indices of PM2.5. CONCLUSIONS: The attenuated risk of mortality that was observed with a time-dependent index of PM2.5 is due to the combined influence of city-specific variations in mortality rates and decreasing levels of air pollution that occurred during follow-up. The RR of mortality associated with PM2.5 did not depend on when exposure occurred in relation to death, possibly because of little variation between the time-dependent city-specific exposure indices.

This study was undertaken to determine whether variations in concentrations of particles in the ambient air of Montreal, Quebec, during the period 1984 to 1993, were associated with daily variations in nonaccidental mortality. Fixed-site air pollution monitors in Montreal provided daily mean levels of various measures of particulates and gaseous pollutants. Total sulfates were also measured daily (1986-1993) at a monitoring station 150 km southeast of the city (Sutton, Quebec). We estimated associations for PM(2.5), PM(10), total suspended particles, coefficient of haze (COH), extinction coefficient, and sulfates. We used coefficient of haze, extinction coefficient, and Sutton sulfates to predict fine particles and sulfates for days that were missing. To estimate the associations between nonaccidental mortality and ambient air particles, we regressed the logarithm of daily counts of nonaccidental mortality on the daily mean levels for the above measures of particulates, after accounting for seasonal and subseasonal fluctuations in the mortality time series, non-Poisson dispersion, weather variables, and gaseous pollutants. There were 140,939 residents of Montreal who died during the study period. We found evidence of associations between daily nonaccidental deaths and most measures of particulate air pollution. For example, the mean percentage increase (MPC) for an increase of total suspended particles of 28.57 microg/m(3) (interquartile range, IQ), evaluated at lag 0 days, was 1.86% (95% confidence interval (CI): 0.00-3.76%), and for an increase of coefficient of haze (IQ=18.5 COH units per 327.8 linear m) the MPC was 1.44% (95% CI: 0.75-2.14%). These results are similar to findings from other studies (the mean percentage increase in nonaccidental deaths for a 100 microg/m(3) increase in daily total suspended particles was 6.7%). We also found increases for fine particles and for inhalable particles, but the confidence intervals included unity. All measures of sulfates showed increased daily mortality; e.g., the MPC for sulfates from fine particles (IQ=3.51 microg/m(3)) was 1.86% (95% CI: 0.40-3.35%). We generally found higher excesses in daily mortality for persons 65 years of age and for exposures averaged across lags 0, 1, and 2 days. The slope of the association between daily mortality and ambient air particles in Montreal, which has lower levels of pollution than most major urban centers, is similar to that reported in most other industrialized cities. This study therefore provides further evidence that the association is linear and that any threshold effect, should it exist, would be found at lower levels of air pollution than those found in Montreal.

This study was undertaken to determine whether variations in concentrations of particulates in the ambient air of Montreal, Quebec, during the period 1984 to 1993, were associated with daily variations in cause-specific daily mortality. Fixed-site air pollution monitors in Montreal provided daily mean levels of various measures of particles and gaseous pollutants. Total sulfate was also measured daily (1986-1993) at a monitoring station 150 km southeast of the city (Sutton, Quebec). We used coefficient of haze (COH), extinction coefficient, and sulfate from the Sutton station to predict fine particles and sulfate from fine particles for days that were missing. We estimated associations between cause-specific mortality and PM(2.5), PM(10), predicted fine particles and fine sulfate particles, total suspended particles, coefficient of haze, extinction coefficient, and total sulfate measured at the Sutton station. We selected a set of underlying causes of death, as recorded on the death certificates, as the endpoint and then regressed the logarithm of daily counts of cause-specific mortality on the daily mean levels for the above measures of particulates, after accounting for seasonal and subseasonal fluctuations in the mortality time series, non-Poisson dispersion, weather variables, and gaseous pollutants. We found positive and statistically significant associations between the daily measures of ambient particle mass and sulfate mass and the deaths from respiratory diseases and diabetes. The mean percentage change in daily mortality (MPC), evaluated at the interquartile range for pollutants averaged over the day of death and the preceding 2 days, for deaths from respiratory diseases was MPC(COH)=6.90% (95% CI: 3.69-10.21%), MPC(Predicted PM2.5)= 9.03% (95% CI: 5.83- 12.33%), and MPC(Sutton sulfate)=4.64% (95% CI: 2.46-6.86%). For diabetes, the corresponding estimates were MPC(COH)=7.50% (95% CI: 1.96-13.34%), MPC(Predicted PM2.5)=7.59% (95% CI: 2.36-13.09%), and MPC(Sutton sulfate)=4.48% (95% CI: 1.08-7.99%). Among individuals older than 65 years at time of death, we found consistent associations across our metrics of particles for neoplasms and coronary artery diseases. Associations with sulfate mass were also found among elderly persons who died of cardiovascular diseases and of lung cancer. These associations were consistent with linear relationships. The associations found for respiratory diseases and for cardiovascular diseases, especially in the elderly, are in line with some of the current hypotheses regarding mechanisms by which ambient particles may increase daily mortality. The positive associations found for cancer and for diabetes may be understood through a general hypothesis proposed by Frank and Tankersley, who suggested that persons in failing health may be at higher risk for external insults through the failure of regulating physiological set points. The association with diabetes may be interpreted in light of recent toxicological findings that inhalation of urban particles in animals increases blood pressure and plasmatic levels of endothelins that enhance vasoconstriction and alter electrophysiology. Further research to confirm these findings and to determine whether they are causal is warranted.

Question Does short term exposure to ambient ozone increase mortality in the United States? Study design Cross sectional time series. Main results A 10 parts per billion by volume (ppb) increase in daily ambient ozone levels in the previous week significantly increased all-cause and cardiovascular and respiratory mortality (all-cause: 0.52%, 95% posterior interval 0.27% to 0.77%; cardiovascular and respiratory: 0.64%, 95% posterior interval 0.31% to 0.98%). The findings were robust after adjustment for particulate matter, weather, seasonality, and long term trends. Authors’ conclusions A 10 ppb increase in daily ambient ozone levels in the previous week increased all-cause and cardiovascular and respiratory mortality.

Using data from the Quebec birth registration file, case-control analyses were conducted in order to evaluate the risk of low birth weight (< 2500 g), very low birth weight (< 1500 g), preterm birth (< 37 completed weeks), and small for gestational age (< third percentile) among infants born to women living near a municipal solid waste landfill site in a densely populated area of Montreal, Quebec. Potential exposure to vapors and gases (biogas) was defined in terms of exposure zones around the site. A set of reference areas was selected to be similar to these exposure zones on a number of key sociodemographic factors. Odds ratios (OR) adjusted for variables on the birth file were calculated using logistic regression. Low birth weight was significantly elevated in the exposure zone proximal to the site (adjusted OR = 1.20; 1107 exposed cases; 95% confidence interval: 1.04-1.39). Excess risks were also observed for small for gestational age, but the association was not as strong as for low birth weight (adjusted OR = 1.09; 951 exposed cases; 95% confidence interval: 0.96-1.24). No significant positive associations were observed for very low birth weight or for preterm birth. Because it was not possible to evaluate the effects of all potentially important confounding factors and because detailed environmental exposure assessments were not available, it is not possible to conclude definitively whether low birth weight and small for gestational age are associated with exposure to biogas. Further studies at this and at other landfill sites are warranted in order to confirm or refute these observations and to investigate other possible adverse reproductive outcomes.