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

BACKGROUND: Variations 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. METHODS: A standardized asthma survey was administered to children in grades one and eight in Hamilton, Canada, in 1994-95 (N approximately 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. RESULTS: There 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. CONCLUSION: Our 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.

Extended follow-up and spatial analysis of the American Cancer Society study linking particulate air pollution and mortality.

We conducted an extended follow-up and spatial analysis of the American Cancer Society (ACS) Cancer Prevention Study II (CPS-II) cohort in order to further examine associations between long-term exposure to particulate air pollution and mortality in large U.S. cities. The current study sought to clarify outstanding scientific issues that arose from our earlier HEI-sponsored Reanalysis of the original ACS study data (the Particle Epidemiology Reanalysis Project). Specifically, we examined (1) how ecologic covariates at the community and neighborhood levels might confound and modify the air pollution-mortality association; (2) how spatial autocorrelation and multiple levels of data (e.g., individual and neighborhood) can be taken into account within the random effects Cox model; (3) how using land-use regression to refine measurements of air pollution exposure to the within-city (or intra-urban) scale might affect the size and significance of health effects in the Los Angeles and New York City regions; and (4) what exposure time windows may be most critical to the air pollution-mortality association. The 18 years of follow-up (extended from 7 years in the original study [Pope et al. 1995]) included vital status data for the CPS-II cohort (approximately 1.2 million participants) with multiple cause-of-death codes through December 31, 2000 and more recent exposure data from air pollution monitoring sites for the metropolitan areas. In the Nationwide Analysis, the influence of ecologic covariate data (such as education attainment, housing characteristics, and level of income; data obtained from the 1980 U.S. Census; see Ecologic Covariates sidebar on page 14) on the air pollution-mortality association were examined at the Zip Code area (ZCA) scale, the metropolitan statistical area (MSA) scale, and by the difference between each ZCA value and the MSA value (DIFF). In contrast to previous analyses that did not directly include ecologic covariates at the ZCA scale, risk estimates increased when ecologic covariates were included at all scales. The ecologic covariates exerted their greatest effect on mortality from ischemic heart disease (IHD), which was also the health outcome most strongly related with exposure to PM2.5 (particles 2.5 microm or smaller in aerodynamic diameter), sulfate (SO4(2-)), and sulfur dioxide (SO2), and the only outcome significantly associated with exposure to nitrogen dioxide (NO2). When ecologic covariates were simultaneously included at both the MSA and DIFF levels, the hazard ratio (HR) for mortality from IHD associated with PM2.5 exposure (average concentration for 1999-2000) increased by 7.5% and that associated with SO4(2-) exposure (average concentration for 1990) increased by 12.8%. The two covariates found to exert the greatest confounding influence on the PM2.5-mortality association were the percentage of the population with a grade 12 education and the median household income. Also in the Nationwide Analysis, complex spatial patterns in the CPS-II data were explored with an extended random effects Cox model (see Glossary of Statistical Terms at end of report) that is capable of clustering up to two geographic levels of data. Using this model tended to increase the HR estimate for exposure to air pollution and also to inflate the uncertainty in the estimates. Including ecologic covariates decreased the variance of the results at both the MSA and ZCA scales; the largest decrease was in residual variation based on models in which the MSA and DIFF levels of data were included together, which suggests that partitioning the ecologic covariates into between-MSA and within-MSA values more completely captures the sources of variation in the relationship between air pollution, ecologic covariates, and mortality. Intra-Urban Analyses were conducted for the New York City and Los Angeles regions. The results of the Los Angeles spatial analysis, where we found high exposure contrasts within the Los Angeles region, showed that air pollution-mortality risks were nearly 3 times greater than those reported from earlier analyses. This suggests that chronic health effects associated with intra-urban gradients in exposure to PM2.5 may be even larger between ZCAs within an MSA than the associations between MSAs that have been previously reported. However, in the New York City spatial analysis, where we found very little exposure contrast between ZCAs within the New York region, mortality from all causes, cardiopulmonary disease (CPD), and lung cancer was not elevated. A positive association was seen for PM2.5 exposure and IHD, which provides evidence of a specific association with a cause of death that has high biologic plausibility. These results were robust when analyses controlled (1) the 44 individual-level covariates (from the ACS enrollment questionnaire in 1982; see 44 Individual-Level Covariates sidebar on page 22) and (2) spatial clustering using the random effects Cox model. Effects were mildly lower when unemployment at the ZCA scale was included. To examine whether there is a critical exposure time window that is primarily responsible for the increased mortality associated with ambient air pollution, we constructed individual time-dependent exposure profiles for particulate and gaseous air pollutants (PM2.5 and SO2) for a subset of the ACS CPS-II participants for whom residence histories were available. The relevance of the three exposure time windows we considered was gauged using the magnitude of the relative risk (HR) of mortality as well as the Akaike information criterion (AIC), which measures the goodness of fit of the model to the data. For PM2.5, no one exposure time window stood out as demonstrating the greatest HR; nor was there any clear pattern of a trend in HR going from recent to more distant windows or vice versa. Differences in AIC values among the three exposure time windows were also small. The HRs for mortality associated with exposure to SO2 were highest in the most recent time window (1 to 5 years), although none of these HRs were significantly elevated. Identifying critical exposure time windows remains a challenge that warrants further work with other relevant data sets. This study provides additional support toward developing cost-effective air quality management policies and strategies. The epidemiologic results reported here are consistent with those from other population-based studies, which collectively have strongly supported the hypothesis that long-term exposure to PM2.5 increases mortality in the general population. Future research using the extended Cox-Poisson random effects methods, advanced geostatistical modeling techniques, and newer exposure assessment techniques will provide additional insight.

A land use regression model for predicting ambient concentrations of nitrogen dioxide in Hamilton, Ontario, Canada.

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 intraurban scale in an industrial setting. Models predicting exposure within smaller areas may lead to improved detection of health effects in epidemiologic studies.

Spatial analysis of the air pollution-mortality relationship in the context of ecologic confounders.

Lack of control for confounding by ecological covariates that may relate to sulfate air pollution and mortality was a key criticism of the two studies that were the focus of the Particle Reanalysis Project. To assess the validity of this criticism, we address the question: "Does sulfate air pollution exert health effects when the impact of other individual and ecologic variables thought to influence health is taken into account?" A related question arises from the possibility of autocorrelation in the mortality risks and ecologic covariates. Failure to control for autocorrelation can lead to false positive significance tests and may indicate bias resulting from a missing variable or group of variables. We control for more than 25 individual risk factors and for 20 ecologic variables representing environmental, socioeconomic, demographic, health- care, and lifestyle determinants of health in a two-stage multilevel analysis. Four modeling strategies are used to control for spatial autocorrelation. Of the 20 ecologic variables tested, only sulfate and sulfur dioxide are significant in models that incorporate spatial autocorrelation. Accounting for autocorrelation also reduces the size and certainty of the sulfate effect on mortality when compared to results generated from Cox models where independent observations are assumed. Confidence limits for the sulfate relative risk include unity in models that simultaneously control for sulfur dioxide and autocorrelation.

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 < or = 2.5 microm (PM(2.5)) 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.

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 mug/m 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.

Relation between income, air pollution and mortality: a cohort study.

BACKGROUND: Community levels of air pollution have been associated with variability in mortality rates, but previous studies have inferred exposure to pollutants on a citywide basis. We investigated mortality in relation to neighbourhood levels of income and air pollution in an urban area. METHODS: We identified 5228 people in the Hamilton-Burlington area of southern Ontario who had been referred for pulmonary function testing between 1985 and 1999. Nonaccidental deaths that occurred in this group between 1992 and 1999 were ascertained from the Ontario Mortality Registry. Mean household income was estimated by linking the subjects' postal codes with the 1996 census. Mean neighbourhood levels of total suspended particulates and sulfur dioxide were estimated by interpolation from data from a network of sampling stations. We used proportional hazards regression models to compute mortality risk in relation to income and pollutant levels, while adjusting for pulmonary function, body mass index and diagnoses of chronic disease. Household incomes and pollutant levels were each divided into 2 risk categories (low and high) at the median. RESULTS: Mean pollutant levels tended to be higher in lower-income neighbourhoods. Both income and pollutant levels were associated with mortality differences. Compared with people in the most favourable category (higher incomes and lower particulate levels), those with all other income-particulate combinations had a higher risk of death from nonaccidental causes (lower incomes and higher particulate levels: relative risk [RR] 2.62, 95% confidence interval [CI] 1.67-4.13; lower incomes and lower particulate levels: RR 1.82, 95% CI 1.30-2.55; higher incomes and higher particulate levels: RR 1.33, 95% CI 1.12-1.57). Similar results were observed for sulfur dioxide. The relative risk was lower at older ages. INTERPRETATION: Mortality rates varied by neighbourhood of residence in this cohort of people whose lung function was tested. Two of the broader determinants of health--income and air pollution levels--were important correlates of mortality in this population.