Norm Finkelstein

Spatial analysis of air pollution and mortality in Los Angeles.

Background: The assessment of air pollution exposure using only community average concentrations may lead to measurement error that lowers estimates of the health burden attributable to poor air quality. To test this hypothesis, we modeled the association between air pollution and mortality using small-area exposure measures in Los Angeles, California. Methods: Data on 22,905 subjects were extracted from the American Cancer Society cohort for the period 1982–2000 (5,856 deaths). Pollution exposures were interpolated from 23 fine particle (PM2.5) and 42 ozone (O3) fixed-site monitors. Proximity to expressways was tested as a measure of traffic pollution. We assessed associations in standard and spatial multilevel Cox regression models. Results: After controlling for 44 individual covariates, all-cause mortality had a relative risk (RR) of 1.17 (95% confidence interval = 1.05–1.30) for an increase of 10 &mgr;g/m3 PM2.5 and a RR of 1.11 (0.99–1.25) with maximal control for both individual and contextual confounders. The RRs for mortality resulting from ischemic heart disease and lung cancer deaths were elevated, in the range of 1.24–1.6, depending on the model used. These PM results were robust to adjustments for O3 and expressway exposure. Conclusion: Our results suggest the chronic health effects associated with within-city gradients in exposure to PM2.5 may be even larger than previously reported across metropolitan areas. We observed effects nearly 3 times greater than in models relying on comparisons between communities. We also found specificity in cause of death, with PM2.5 associated more strongly with ischemic heart disease than with cardiopulmonary or all-cause mortality.

A GIS - environmental justice analysis of particulate air pollution in Hamilton, Canada

The authors address two research questions: (1) Are populations with lower socioeconomic status, compared with people of higher socioeconomic status, more likely to be exposed to higher levels of particulate air pollution in Hamilton, Ontario, Canada? (2) How sensitive is the association between levels of particulate air pollution and socioeconomic status to specification of exposure estimates or statistical models? Total suspended particulate (TSP) data from the twenty-three monitoring stations in Hamilton (1985–94) were interpolated with a universal kriging procedure to develop an estimate of likely pollution values across the city based on annual geometric means and extreme events. Comparing the highest with the lowest exposure zones, the interpolated surfaces showed more than a twofold increase in TSP concentrations and more than a twentyfold difference in the probability of exposure to extreme events. Exposure estimates were related to socioeconomic and demographic data from census tract areas by using ordinary least squares and simultaneous autoregressive (SAR) models. Control for spatial autocorrelation in the SAR models allowed for tests of how robust specific socioeconomic variables were for predicting pollution exposure. Dwelling values were significantly and negatively associated with pollution exposure, a result robust to the method of statistical analysis. Low income and unemployment were also significant predictors of exposure, although results varied depending on the method of analysis. Relatively minor changes in the statistical models altered the significant variables. This result emphasizes the value of geographical information systems (GIS) and spatial statistical techniques in modelling exposure. The result also shows the importance of taking spatial autocorrelation into account in future justice – health studies.

Modeling the Intraurban Variability of Ambient Traffic Pollution in Toronto, Canada

The objective of this paper is to model determinants of intraurban variation in ambient concentrations of nitrogen dioxide (NO2) in Toronto, Canada, with a land use regression (LUR) model. Although researchers have conducted similar studies in Europe, this work represents the first attempt in a North American setting to characterize variation in traffic pollution through the LUR method. NO2 samples were collected over 2 wk using duplicate two-sided Ogawa passive diffusion samplers at 95 locations across Toronto. Independent variables employed in subsequent regression models as predictors of NO2 were derived by the Arc 8 geographic information system (GIS). Some 85 indicators of land use, traffic, population density, and physical geography were tested. The final regression model yielded a coefficient of determination (R2) of .69. For the traffic variables, density of 24-h traffic counts and road measures display positive associations. For the land use variables, industrial land use and counts of dwellings within 2000 m of the monitoring location were positively associated with NO2. Locations up to 1500 m downwind of major expressways had elevated NO2 levels. The results suggest that a good predictive surface can be derived for North American cities with the LUR method. The predictive maps from the LUR appear to capture small-area variation in NO2 concentrations. These small-area variations in traffic pollution are probably important to the exposure experience of the population and may detect health effects that would have gone unnoticed with other exposure estimates.

Do socioeconomic characteristics modify the short term association between air pollution and mortality? Evidence from a zonal time series in Hamilton, Canada.

Study objective: To assess the short term association between air pollution and mortality in different zones of an industrial city. An intra-urban study design is used to test the hypothesis that socioeconomic characteristics modify the acute health effects of ambient air pollution exposure. Design: The City of Hamilton, Canada, was divided into five zones based on proximity to fixed site air pollution monitors. Within each zone, daily counts of non-trauma mortality and air pollution estimates were combined. Generalised linear models (GLMs) were used to test mortality associations with sulphur dioxide (SO2) and with particulate air pollution measured by the coefficient of haze (CoH). Main results: Increased mortality was associated with air pollution exposure in a citywide model and in intra-urban zones with lower socioeconomic characteristics. Low educational attainment and high manufacturing employment in the zones significantly and positively modified the acute mortality effects of air pollution exposure. Discussion: Three possible explanations are proposed for the observed effect modification by education and manufacturing: (1) those in manufacturing receive higher workplace exposures that combine with ambient exposures to produce larger health effects; (2) persons with lower education are less mobile and experience less exposure measurement error, which reduces bias toward the null; or (3) manufacturing and education proxy for many social variables representing material deprivation, and poor material conditions increase susceptibility to health risks from air pollution.

A cohort study of traffic-related air pollution and mortality in Toronto, Ontario, Canada.

Background Chronic exposure to traffic-related air pollution (TRAP) may contribute to premature mortality, but few studies to date have addressed this topic. Objectives In this study we assessed the association between TRAP and mortality in Toronto, Ontario, Canada. Methods We collected nitrogen dioxide samples over two seasons using duplicate two-sided Ogawa passive diffusion samplers at 143 locations across Toronto. We calibrated land use regressions to predict NO2 exposure on a fine scale within Toronto. We used interpolations to predict levels of particulate matter with aerodynamic diameter ≤ 2.5 μm (PM2.5) and ozone levels. We assigned predicted pollution exposures to 2,360 subjects from a respiratory clinic, and abstracted health data on these subjects from medical billings, lung function tests, and diagnoses by pulmonologists. We tracked mortality between 1992 and 2002. We used standard and multilevel Cox proportional hazard models to test associations between air pollution and mortality. Results After controlling for age, sex, lung function, obesity, smoking, and neighborhood deprivation, we observed a 17% increase in all-cause mortality and a 40% increase in circulatory mortality from an exposure contrast across the interquartile range of 4 ppb NO2. We observed no significant associations with other pollutants. Conclusions Exposure to TRAP was significantly associated with increased all-cause and circulatory mortality in this cohort. A high prevalence of cardiopulmonary disease in the cohort probably limits inference of the findings to populations with a substantial proportion of susceptible individuals.

A Land Use Regression Model for Predicting Ambient Concentrations of Nitrogen Dioxide in Hamilton, Ontario, Canada

Abstract This paper reports on the development of a land use regression (LUR) model for predicting the intraurban variation of traffic-related air pollution in Hamilton, Ontario, Canada, an industrial city at the western end of Lake Ontario. Although land use regression has been increasingly used to characterize exposure gradients within cities, research to date has yet to test whether this method can produce reliable estimates in an industrialized location. Ambient concentrations of nitrogen dioxide (NO2)were measured for a 2-week period in October 2002 at >100 locations across the city and subsequently at 30 of these locations in May 2004 to assess seasonal effects. Predictor variables were derived for land use types, transportation, demography, and physical geography using geographic information systems. The LUR model explained 76% of the variation in NO2. Traffic density, proximity to a highway, and industrial land use were all positively correlated with NO2 concentrations, whereas open land use and distance from the lake were negatively correlated with NO2. Locations downwind of a major highway resulted in higher NO2 levels. Cross-validation of the results confirmed model stability over different seasons. Our findings demonstrate that land use regression can effectively predict NO2 variation at the intra-urban scale in an industrial setting. Models predicting exposure within smaller areas may lead to improved detection of health effects in epidemiologic studies.

Spatial analysis for environmental health research: concepts, methods, and examples.

Spatial analysis can illuminate environmental health research in two ways. First, spatial analysis may suggest possible causal factors in disease pathogenesis. Association between disease and place may imply that the population living there either possesses inherent traits that make it more susceptible to disease or experiences elevated exposure to a risk factor such as air pollution. Second, spatial analysis can help identify how populations adapt and relate to their environment. This knowledge may lead to improved understanding of how people perceive and avoid health risks of environmental origin. The potential for spatial analysis to uncover these aspects of the association between health and the environment is limited by data and methodological problems that are discussed in the article. To familiarize researchers and policymakers with this increasingly important approach, we review spatial-analytic methods under three headings: visualization, exploration, and modeling. We use illustrative examples to assist readers in understanding the strengths and weaknesses of specific methods.

Spatial analysis of air pollution and childhood asthma in Hamilton, Canada: comparing exposure methods in sensitive subgroups

BackgroundVariations in air pollution exposure within a community may be associated with asthma prevalence. However, studies conducted to date have produced inconsistent results, possibly due to errors in measurement of the exposures.MethodsA standardized asthma survey was administered to children in grades one and eight in Hamilton, Canada, in 1994–95 (N ~1467). Exposure to air pollution was estimated in four ways: (1) distance from roadways; (2) interpolated surfaces for ozone, sulfur dioxide, particulate matter and nitrous oxides from seven to nine governmental monitoring stations; (3) a kriged nitrogen dioxide (NO2) surface based on a network of 100 passive NO2 monitors; and (4) a land use regression (LUR) model derived from the same monitoring network. Logistic regressions were used to test associations between asthma and air pollution, controlling for variables including neighbourhood income, dwelling value, state of housing, a deprivation index and smoking.ResultsThere were no significant associations between any of the exposure estimates and asthma in the whole population, but large effects were detected the subgroup of children without hayfever (predominately in girls). The most robust effects were observed for the association of asthma without hayfever and NO2LUR OR = 1.86 (95%CI, 1.59–2.16) in all girls and OR = 2.98 (95%CI, 0.98–9.06) for older girls, over an interquartile range increase and controlling for confounders.ConclusionOur findings indicate that traffic-related pollutants, such as NO2, are associated with asthma without overt evidence of other atopic disorders among female children living in a medium-sized Canadian city. The effects were sensitive to the method of exposure estimation. More refined exposure models produced the most robust associations.