Perry Hystad

Living near major roads and the incidence of dementia, Parkinson's disease, and multiple sclerosis: a population-based cohort study

BACKGROUND Emerging evidence suggests that living near major roads might adversely affect cognition. However, little is known about its relationship with the incidence of dementia, Parkinsons disease, and multiple sclerosis. We aimed to investigate the association between residential proximity to major roadways and the incidence of these three neurological diseases in Ontario, Canada. METHODS In this population-based cohort study, we assembled two population-based cohorts including all adults aged 20-50 years (about 4·4 million; multiple sclerosis cohort) and all adults aged 55-85 years (about 2·2 million; dementia or Parkinsons disease cohort) who resided in Ontario, Canada on April 1, 2001. Eligible patients were free of these neurological diseases, Ontario residents for 5 years or longer, and Canadian-born. We ascertained the individuals proximity to major roadways based on their residential postal-code address in 1996, 5 years before cohort inception. Incident diagnoses of dementia, Parkinsons disease, and multiple sclerosis were ascertained from provincial health administrative databases with validated algorithms. We assessed the associations between traffic proximity and incident dementia, Parkinsons disease, and multiple sclerosis using Cox proportional hazards models, adjusting for individual and contextual factors such as diabetes, brain injury, and neighbourhood income. We did various sensitivity analyses, such as adjusting for access to neurologists and exposure to selected air pollutants, and restricting to never movers and urban dwellers. FINDINGS Between 2001, and 2012, we identified 243 611 incident cases of dementia, 31 577 cases of Parkinsons disease, and 9247 cases of multiple sclerosis. The adjusted hazard ratio (HR) of incident dementia was 1·07 for people living less than 50 m from a major traffic road (95% CI 1·06-1·08), 1·04 (1·02-1·05) for 50-100 m, 1·02 (1·01-1·03) for 101-200 m, and 1·00 (0·99-1·01) for 201-300 m versus further than 300 m (p for trend=0·0349). The associations were robust to sensitivity analyses and seemed stronger among urban residents, especially those who lived in major cities (HR 1·12, 95% CI 1·10-1·14 for people living <50 m from a major traffic road), and who never moved (1·12, 1·10-1·14 for people living <50 m from a major traffic road). No association was found with Parkinsons disease or multiple sclerosis. INTERPRETATION In this large population-based cohort, living close to heavy traffic was associated with a higher incidence of dementia, but not with Parkinsons disease or multiple sclerosis. FUNDING Health Canada (MOA-4500314182).

The impact of daily mobility on exposure to traffic-related air pollution and health effect estimates

Epidemiological studies of traffic-related air pollution typically estimate exposures at residential locations only; however, if study subjects spend time away from home, exposure measurement error, and therefore bias, may be introduced into epidemiological analyses. For two study areas (Vancouver, British Columbia, and Southern California), we use paired residence- and mobility-based estimates of individual exposure to ambient nitrogen dioxide, and apply error theory to calculate bias for scenarios when mobility is not considered. In Vancouver, the mean bias was 0.84 (range: 0.79–0.89; SD: 0.01), indicating potential bias of an effect estimate toward the null by ∼16% when using residence-based exposure estimates. Bias was more strongly negative (mean: 0.70, range: 0.63–0.77, SD: 0.02) when the underlying pollution estimates had higher spatial variation (land-use regression versus monitor interpolation). In Southern California, bias was seen to become more strongly negative with increasing time and distance spent away from home (e.g., 0.99 for 0–2 h spent at least 10 km away, 0.66 for ≥10 h spent at least 40 km away). Our results suggest that ignoring daily mobility patterns can contribute to bias toward the null hypothesis in epidemiological studies using individual-level exposure estimates.

Creating National Air Pollution Models for Population Exposure Assessment in Canada

Background: Population exposure assessment methods that capture local-scale pollutant variability are needed for large-scale epidemiological studies and surveillance, policy, and regulatory purposes. Currently, such exposure methods are limited. Methods: We created 2006 national pollutant models for fine particulate matter [PM with aerodynamic diameter ≤ 2.5 μm (PM2.5)], nitrogen dioxide (NO2), benzene, ethylbenzene, and 1,3-butadiene from routinely collected fixed-site monitoring data in Canada. In multiple regression models, we incorporated satellite estimates and geographic predictor variables to capture background and regional pollutant variation and used deterministic gradients to capture local-scale variation. The national NO2 and benzene models are evaluated with independent measurements from previous land use regression models that were conducted in seven Canadian cities. National models are applied to census block-face points, each of which represents the location of approximately 89 individuals, to produce estimates of population exposure. Results: The national NO2 model explained 73% of the variability in fixed-site monitor concentrations, PM2.5 46%, benzene 62%, ethylbenzene 67%, and 1,3-butadiene 68%. The NO2 model predicted, on average, 43% of the within-city variability in the independent NO2 data compared with 18% when using inverse distance weighting of fixed-site monitoring data. Benzene models performed poorly in predicting within-city benzene variability. Based on our national models, we estimated Canadian ambient annual average population-weighted exposures (in micrograms per cubic meter) of 8.39 for PM2.5, 23.37 for NO2, 1.04 for benzene, 0.63 for ethylbenzene, and 0.09 for 1,3-butadiene. Conclusions: The national pollutant models created here improve exposure assessment compared with traditional monitor-based approaches by capturing both regional and local-scale pollution variation. Applying national models to routinely collected population location data can extend land use modeling techniques to population exposure assessment and to informing surveillance, policy, and regulation.

Ambient PM2.5, O3, and NO2 Exposures and Associations with Mortality over 16 Years of Follow-Up in the Canadian Census Health and Environment Cohort (CanCHEC)

Background Few studies examining the associations between long-term exposure to ambient air pollution and mortality have considered multiple pollutants when assessing changes in exposure due to residential mobility during follow-up. Objective We investigated associations between cause-specific mortality and ambient concentrations of fine particulate matter (≤ 2.5 μm; PM2.5), ozone (O3), and nitrogen dioxide (NO2) in a national cohort of about 2.5 million Canadians. Methods We assigned estimates of annual concentrations of these pollutants to the residential postal codes of subjects for each year during 16 years of follow-up. Historical tax data allowed us to track subjects’ residential postal code annually. We estimated hazard ratios (HRs) for each pollutant separately and adjusted for the other pollutants. We also estimated the product of the three HRs as a measure of the cumulative association with mortality for several causes of death for an increment of the mean minus the 5th percentile of each pollutant: 5.0 μg/m3 for PM2.5, 9.5 ppb for O3, and 8.1 ppb for NO2. Results PM2.5, O3, and NO2 were associated with nonaccidental and cause-specific mortality in single-pollutant models. Exposure to PM2.5 alone was not sufficient to fully characterize the toxicity of the atmospheric mix or to fully explain the risk of mortality associated with exposure to ambient pollution. Assuming additive associations, the estimated HR for nonaccidental mortality corresponding to a change in exposure from the mean to the 5th percentile for all three pollutants together was 1.075 (95% CI: 1.067, 1.084). Accounting for residential mobility had only a limited impact on the association between mortality and PM2.5 and O3, but increased associations with NO2. Conclusions In this large, national-level cohort, we found positive associations between several common causes of death and exposure to PM2.5, O3, and NO2. Citation Crouse DL, Peters PA, Hystad P, Brook JR, van Donkelaar A, Martin RV, Villeneuve PJ, Jerrett M, Goldberg MS, Pope CA III, Brauer M, Brook RD, Robichaud A, Menard R, Burnett RT. 2015. Ambient PM2.5, O3, and NO2 exposures and associations with mortality over 16 years of follow-up in the Canadian Census Health and Environment Cohort (CanCHEC). Environ Health Perspect 123:1180–1186; http://dx.doi.org/10.1289/ehp.1409276

Residential Greenness and Birth Outcomes: Evaluating the Influence of Spatially Correlated Built-Environment Factors

Background: Half the world’s population lives in urban areas. It is therefore important to identify characteristics of the built environment that are beneficial to human health. Urban greenness has been associated with improvements in a diverse range of health conditions, including birth outcomes; however, few studies have attempted to distinguish potential effects of greenness from those of other spatially correlated exposures related to the built environment. Objectives: We aimed to investigate associations between residential greenness and birth outcomes and evaluate the influence of spatially correlated built environment factors on these associations. Methods: We examined associations between residential greenness [measured using satellite-derived Normalized Difference Vegetation Index (NDVI) within 100 m of study participants’ homes] and birth outcomes in a cohort of 64,705 singleton births (from 1999–2002) in Vancouver, British Columbia, Canada. We also evaluated associations after adjusting for spatially correlated built environmental factors that may influence birth outcomes, including exposure to air pollution and noise, neighborhood walkability, and distance to the nearest park. Results: An interquartile increase in greenness (0.1 in residential NDVI) was associated with higher term birth weight (20.6 g; 95% CI: 16.5, 24.7) and decreases in the likelihood of small for gestational age, very preterm (< 30 weeks), and moderately preterm (30–36 weeks) birth. Associations were robust to adjustment for air pollution and noise exposures, neighborhood walkability, and park proximity. Conclusions: Increased residential greenness was associated with beneficial birth outcomes in this population-based cohort. These associations did not change after adjusting for other spatially correlated built environment factors, suggesting that alternative pathways (e.g., psychosocial and psychological mechanisms) may underlie associations between residential greenness and birth outcomes. Citation: Hystad P, Davies HW, Frank L, Van Loon J, Gehring U, Tamburic L, Brauer M. 2014. Residential greenness and birth outcomes: evaluating the influence of spatially correlated built-environment factors. Environ Health Perspect 122:1095–1102; http://dx.doi.org/10.1289/ehp.1308049

Long-term residential exposure to air pollution and lung cancer risk.

Background: There is accumulating evidence that air pollution causes lung cancer. Still, questions remain about exposure misclassification, the components of air pollution responsible, and the histological subtypes of lung cancer that might be produced. Methods: We investigated lung cancer incidence in relation to long-term exposure to three ambient air pollutants and proximity to major roads, using a Canadian population-based case-control study. We compared 2,390 incident, histologically confirmed lung cancer cases with 3,507 population controls in eight Canadian provinces from 1994 to 1997. We developed spatiotemporal models for the whole country to estimate annual residential exposure to fine particulate matter (PM2.5), nitrogen dioxide (NO2), and ozone (O3) over a 20-year exposure period. We carried out a subanalysis in urban centers, using exposures derived from fixed-site air pollution monitors, and also examined traffic proximity measures. Hierarchical logistic regression models incorporated a comprehensive set of individual and geographic covariates. Results: The increase in lung cancer incidence (expressed as fully adjusted odds ratios [ORs]) was 1.29 (95% confidence interval = 0.95–1.76) with a ten-unit increase in PM2.5 (&mgr;g/m3), 1.11 (1.00–1.24) with a ten-unit increase in NO2 (ppb), and 1.09 (0.85–1.39) with a ten-unit increase in O3 (ppb). The urban monitor-based subanalyses generally supported the national results, with larger associations for NO2 (OR = 1.34; 1.07–1.69) per 10 ppb increase. No dose-response trends were observed, and no clear relationships were found for specific histological cancer subtypes. There was the suggestion of increased risk among those living within 100 m of highways, but not among those living near major roads. Conclusions: Lung cancer incidence in this Canadian study was increased most strongly with NO2 and PM2.5 exposure. Further investigation is needed into possible effects of O3 on development of lung cancer.

Exploring pathways linking greenspace to health: Theoretical and methodological guidance

Background In a rapidly urbanizing world, many people have little contact with natural environments, which may affect health and well‐being. Existing reviews generally conclude that residential greenspace is beneficial to health. However, the processes generating these benefits and how they can be best promoted remain unclear. Objectives During an Expert Workshop held in September 2016, the evidence linking greenspace and health was reviewed from a transdisciplinary standpoint, with a particular focus on potential underlying biopsychosocial pathways and how these can be explored and organized to support policy‐relevant population health research. Discussions Potential pathways linking greenspace to health are here presented in three domains, which emphasize three general functions of greenspace: reducing harm (e.g. reducing exposure to air pollution, noise and heat), restoring capacities (e.g. attention restoration and physiological stress recovery) and building capacities (e.g. encouraging physical activity and facilitating social cohesion). Interrelations between among the three domains are also noted. Among several recommendations, future studies should: use greenspace and behavioural measures that are relevant to hypothesized pathways; include assessment of presence, access and use of greenspace; use longitudinal, interventional and (quasi)experimental study designs to assess causation; and include low and middle income countries given their absence in the existing literature. Cultural, climatic, geographic and other contextual factors also need further consideration. Conclusions While the existing evidence affirms beneficial impacts of greenspace on health, much remains to be learned about the specific pathways and functional form of such relationships, and how these may vary by context, population groups and health outcomes. This Report provides guidance for further epidemiological research with the goal of creating new evidence upon which to develop policy recommendations. HighlightsAlthough it appears that greenspace benefits health, the pathways are unclear.We have organized pathways into three domains that emphasize greenspace functions.Pathways likely intertwine and vary by context, populations and health outcomes.We identify diverse challenges in measurement and analysis that require attention.Research guided by our discussion will better efforts to enable greenspace‐related health benefits.

Exposure to traffic-related air pollution and the risk of developing breast cancer among women in eight Canadian provinces: a case-control study.

A few recent studies have reported positive associations between long-term exposure to traffic-related air pollution and the incidence of breast cancer. We capitalized on an existing Canadian multi-site population-based case-control study to further investigate this association. We used the National Enhanced Cancer Surveillance System, a population-based case-control study conducted in eight of 10 Canadian provinces from 1994 to 1997. A total of 1569 breast cancer cases and 1872 population controls who reported at least 90% complete self-reported addresses over the 1975-1994 exposure period were examined. Mean exposure levels to nitrogen dioxide (NO2) (an indicator of traffic-related air pollution) were estimated for this period using three different measures: (1) satellite-derived observations; (2) satellite-derived observations scaled with historical fixed-site measurements of NO2; and (3) a national land-use regression (LUR) model. Proximity to major roads was also examined. Using unconditional logistic regression, stratified by menopausal status, we estimated odds ratios (ORs) adjusted for many individual-level and contextual breast cancer risk factors. We observed positive associations between incident breast cancer and all three measures of NO2 exposure from 1975 to 1994. In fully adjusted models for premenopausal breast cancer, a 10ppb increase in NO2 exposure estimated from the satellite-derived observations, the scaled satellite-derived observations, and the national LUR model produced ORs of 1.26 (95% confidence intervals (CIs): 0.92-1.74), 1.32 (95% CI: 1.05-1.67) and 1.28 (95% CI: 0.92-1.79). For postmenopausal breast cancer, we found corresponding ORs of 1.10 (95% CI: 0.88-1.36), 1.10 (95% CI: 0.94-1.28) and 1.07 (95% CI: 0.86-1.32). Substantial heterogeneity in the ORs was observed across the eight Canadian provinces and reduced ORs were observed when models were restricted to women who had received routine mammography examinations. No associations were found for road proximity measures. This study provides some support for the hypothesis that traffic-related air pollution may be associated with the development of breast cancer, especially in premenopausal women. With the few studies available, further research is clearly needed.

Sense of community-belonging and health-behaviour change in Canada

Background Research indicates that primary prevention targeting individual behaviours should incorporate contextual factors. The objectives of this study are to examine the role of community-belonging and contextual factors on health-behaviour change in Canada, and whether the influence of community-belonging on behaviour change varies by specific types of behaviours and contextual factors. Methods Data on individual-level community-belonging, socio-demographics and self-rated health were obtained for 119 693 respondents from the 2007/2008 Canadian Community Health Survey located within 100 health regions across Canada. Contextual factors were based on health-region groupings of socio-economic determinants of health. Multilevel models were used to estimate the influence of community-belonging and health-region contextual factors on general, and specific, health-behaviour changes in the past year. Results After controlling for individual and contextual factors, community-belonging showed a positive dose–response relationship with health-behaviour change. Health-region contextual factors were only slightly associated with behaviour change; however, the influence of community-belonging on behaviour change showed significant variability based on health-region contextual factors. The influence of community-belonging also varied by specific health-behaviour changes, but for most prominent health behaviours (exercise, weight loss and improved diet) the effect was consistent. Conclusions Community-belonging was strongly related to health-behaviour change in Canada and may be an important component of population health prevention strategies. Efforts to increase community-belonging, however, need to be considered along with contextual factors.

Spatiotemporal air pollution exposure assessment for a Canadian population-based lung cancer case-control study

BackgroundFew epidemiological studies of air pollution have used residential histories to develop long-term retrospective exposure estimates for multiple ambient air pollutants and vehicle and industrial emissions. We present such an exposure assessment for a Canadian population-based lung cancer case-control study of 8353 individuals using self-reported residential histories from 1975 to 1994. We also examine the implications of disregarding and/or improperly accounting for residential mobility in long-term exposure assessments.MethodsNational spatial surfaces of ambient air pollution were compiled from recent satellite-based estimates (for PM2.5 and NO2) and a chemical transport model (for O3). The surfaces were adjusted with historical annual air pollution monitoring data, using either spatiotemporal interpolation or linear regression. Model evaluation was conducted using an independent ten percent subset of monitoring data per year. Proximity to major roads, incorporating a temporal weighting factor based on Canadian mobile-source emission estimates, was used to estimate exposure to vehicle emissions. A comprehensive inventory of geocoded industries was used to estimate proximity to major and minor industrial emissions.ResultsCalibration of the national PM2.5 surface using annual spatiotemporal interpolation predicted historical PM2.5 measurement data best (R2 = 0.51), while linear regression incorporating the national surfaces, a time-trend and population density best predicted historical concentrations of NO2 (R2 = 0.38) and O3 (R2 = 0.56). Applying the models to study participants residential histories between 1975 and 1994 resulted in mean PM2.5, NO2 and O3 exposures of 11.3 μg/m3 (SD = 2.6), 17.7 ppb (4.1), and 26.4 ppb (3.4) respectively. On average, individuals lived within 300 m of a highway for 2.9 years (15% of exposure-years) and within 3 km of a major industrial emitter for 6.4 years (32% of exposure-years). Approximately 50% of individuals were classified into a different PM2.5, NO2 and O3 exposure quintile when using study entry postal codes and spatial pollution surfaces, in comparison to exposures derived from residential histories and spatiotemporal air pollution models. Recall bias was also present for self-reported residential histories prior to 1975, with cases recalling older residences more often than controls.ConclusionsWe demonstrate a flexible exposure assessment approach for estimating historical air pollution concentrations over large geographical areas and time-periods. In addition, we highlight the importance of including residential histories in long-term exposure assessments.For submission to: Environmental Health