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Michael Jerrett

Publications and source records attributed to Michael Jerrett.

27 records · Page 2Linked to original sources
PubMed2003

Health, wealth, and air pollution: advancing theory and methods.

The effects of both ambient air pollution and socioeconomic position (SEP) on health are well documented. A limited number of recent studies suggest that SEP may itself play a role in the epidemiology of disease and death associated with exposure to air pollution. Together with evidence that poor and working-class communities are often more exposed to air pollution, these studies have stimulated discussion among scientists, policy makers, and the public about the differential distribution of the health impacts from air pollution. Science and public policy would benefit from additional research that integrates the theory and practice from both air pollution and social epidemiologies to gain a better understanding of this issue. In this article we aim to promote such research by introducing readers to methodologic and conceptual approaches in the fields of air pollution and social epidemiology; by proposing theories and hypotheses about how air pollution and socioeconomic factors may interact to influence health, drawing on studies conducted worldwide; by discussing methodologic issues in the design and analysis of studies to determine whether health effects of exposure to ambient air pollution are modified by SEP; and by proposing specific steps that will advance knowledge in this field, fill information gaps, and apply research results to improve public health in collaboration with affected communities.

Air Pollution↗
PubMedDate not supplied

Overview of the reanalysis of the Harvard Six Cities Study and American Cancer Society Study of Particulate Air Pollution and Mortality.

This article provides an overview of the Reanalysis Study of the Harvard Six Cities and the American Cancer Society (ACS) studies of particulate air pollution and mortality. The previous findings of the studies have been subject to debate. In response, a reanalysis team, comprised of Canadian and American researchers, was invited to participate in an independent reanalysis project to address the concerns. Phase I of the reanalysis involved the design of data audits to determine whether each study conformed to the consistency and accuracy of their data. Phase II of the reanalysis involved conducting a series of comprehensive analyses using alternative statistical methods. Alternative models were also used to identify covariates that may confound or modify the association of particulate air pollution as well as identify sensitive population subgroups. The audit demonstrated that the data in the original analyses were of high quality, as were the risk estimates reported by the original investigators. The sensitivity analysis illustrated that the mortality risk estimates reported in both studies were found to be robust against alternative Cox models. Detailed investigation of the covariate effects found a significant modifying effect of education and a relative risk of mortality associated with fine particles and declining education levels. The study team applied spatial analytic methods to the ACS data, resulting in various levels of spatial autocorrelations supporting the reported association for fine particles mortality of the original investigators as well as demonstrating a significant association between sulfur dioxide and mortality. Collectively, our reanalysis suggest that mortality may be attributable to more than one component of the complex mixture of ambient air pollutants for U.S. urban areas.

Air↗
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Selection of ecologic covariates in the American Cancer Society study.

The American Cancer Society (ACS) Study of the effects of long-term exposure to ambient air pollution on mortality used metropolitan areas to assign exposures to individual cohort members (Pope et al., 1995); these authors did not, however, control for any other place-specific variables in their analysis. Consequently, the study has been criticized on the basis that the association observed between air pollution and mortality may be confounded by other unmeasured ecologic covariates. To address this criticism, the reanalysis team selected a set of place-specific variables that measured determinants of health ranging from the biophysical environment to the social environment and the healthcare system. This article outlines the process by which place-specific ecologic covariates were selected; data measuring these variables were obtained and geographic boundaries for places were delineated. Issues involved in obtaining and using geographically based ecological data are examined within the context of the reanalysis of the ACS study. Both the ecological fallacy and the atomistic fallacy are addressed and an argument is made for the importance of studying the effects of place-specific variables that are integral or contextual in nature. Issues relating to the Modifiable Areal Unit Problem (MAUP) are explored with reference to using ZIP codes and data from a variety of sources. It is argued that differences in the geographical scale of variability for various pollutants may prove to be the key to distinguishing between their relative impacts on health and that multilevel analyses are essential for understanding the impact of social and environmental determinants of health. A number of determinants of health are then briefly examined in terms of their association with mortality, the appropriateness of their being measured at the metropolitan scale, and the availability of data for the 1980s from U.S. sources. Finally, the article presents the database of place-specific ecologic covariates that was incorporated into the ACS models during the reanalysis in order to account for the influence that place may have above and beyond ambient air pollution.

Air Pollutants↗
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The association between sulfate air pollution and mortality at the county scale: an exploration of the impact of scale on a long-term exposure study.

The American Cancer Society (ACS) Study and its reanalysis are built upon in order to examine the impact of scale on the observed relationship between sulfates and mortality. The limitations of the original ACS Study (Pope et al., 1995) and the reanalysis of this study (Krewski et al., 2000) are discussed; while the latter dealt with some issues in using ecological data, it did not address scale. Next, the article outlines the county-scale study, the methods used to aggregate data, and the two-stage analysis used to derive relative risk (RR). Finally, the results of working at the county scale are compared with those obtained by the reanalysis team using larger metropolitan areas. Less than half of the cohort used in the metropolitan study were used at the county scale because of the limited availability of sulfate monitors and because five-digit ZIP codes more accurately assigned individuals to geographical areas. Therefore, the county data should be considered as new and not as a reorganization of the original data set. The reanalysis and the county studies should be considered as two separate studies that took different scales as their basic organizing principle. The RR of all-cause mortality from sulfate exposure at the county scale was 1.50 (1.30, 1.73) compared with 1.25 (1.13, 1.37) at the metropolitan scale; for cardiopulmonary mortality, the RR was 1.75 (1.48, 2.08) at the county scale compared with 1.29 (1.15, 1.46) at the metropolitan scale. Because lung cancer mortality was low in some counties, the two-stage random effects model became unstable. At the county scale, the RR from sulfates was more robust to the inclusion of ecologic covariates. Other place-specific ecologic covariates were either insignificant or barely significant (with a lower 95% confidence limit near 0.99 or 1.00) when included in the two-stage regression model for all-cause mortality with sulfates. Moreover, no ecologic covariate changed the RR of all-cause mortality from sulfates by 25% or more. Both population change and unemployment rate affected the RR for cardiopulmonary mortality from sulfate exposure by 25% or more in the county-scale analysis. However, when these two variables were entered into a multiple covariate analysis, the RR from sulfates decreased but remained strongly significant. Sulfur dioxide was not an important covariate at the county scale. Thus, at the county scale, long-term exposure to sulfates appears to be more strongly associated with increased risk of all-cause and cardiopulmonary mortality than previously indicated by the ACS study and its reanalysis.

Air Pollution↗
PubMedDate not supplied

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.

Air Pollution↗
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Spatial regression models for large-cohort studies linking community air pollution and health.

Cohort study designs are often used to assess the association between community-based ambient air pollution concentrations and health outcomes, such as mortality, development and prevalence of disease, and pulmonary function. Typically, a large number of subjects are enrolled in the study in each of a small number of communities. Fixed-site monitors are used to determine long-term exposure to ambient pollution. The association between community average pollution levels and health is determined after controlling for risk factors of the health outcome measured at the individual level (i.e., smoking). We present a new spatial regression model linking spatial variation in ambient air pollution to health. Health outcomes can be measured as continuous variables (pulmonary function), binary variables (prevalence of disease), or time-to-event data (survival or development of disease). The model incorporates risk factors measured at the individual level, such as smoking, and at the community level, such as air pollution. We demonstrate that the spatial autocorrelation in community health outcomes, an indication of not fully characterizing potentially confounding risk factors to the air pollution--health association, can be accounted for through the inclusion of location in the deterministic component of the model assessing the effects of air pollution on health or through a distance-decay spatial autocorrelation function in the stochastic component of the model, or both. We present a statistical approach that can be implemented for very large cohort studies. Our methods are illustrated with an analysis of the American Cancer Society cohort to determine whether the prevalence of heart disease is associated with concentrations of sulfate particles. From a statistical point of view, it appears that a location surface in the deterministic component of the model was preferred to a distance-decay autocorrelation structure in the model's stochastic component.

Air Pollution↗
PubMedDate not supplied

Mortality and long-term exposure to ambient air pollution: ongoing analyses based on the American Cancer Society cohort.

This article provides an overview of previous analysis and reanalysis of the American Cancer Society (ACS) cohort, along with an indication of current ongoing analyses of the cohort with additional follow-up information through to 2000. Results of the first analysis conducted by Pope et al. (1995) showed that higher average sulfate levels were associated with increased mortality, particularly from cardiopulmonary disease. A reanalysis of the ACS cohort, undertaken by Krewski et al. (2000), found the original risk estimates for fine-particle and sulfate air pollution to be highly robust against alternative statistical techniques and spatial modeling approaches. A detailed investigation of covariate effects found a significant modifying effect of education with risk of mortality associated with fine particles declining with increasing educational attainment. Pope et al. (2002) subsequently reported results of a subsequent study using an additional 10 yr of follow-up of the ACS cohort. This updated analysis included gaseous copollutant and new fine-particle measurements, more comprehensive information on occupational exposures, dietary variables, and the most recent developments in statistical modeling integrating random effects and nonparametric spatial smoothing into the Cox proportional hazards model. Robust associations between ambient fine particulate air pollution and elevated risks of cardiopulmonary and lung cancer mortality were clearly evident, providing the strongest evidence to date that long-term exposure to fine particles is an important health risk. Current ongoing analysis using the extended follow-up information will explore the role of ecologic, economic, and, demographic covariates in the particulate air pollution and mortality association. This analysis will also provide insight into the role of spatial autocorrelation at multiple geographic scales, and whether critical instances in time of exposure to fine particles influence the risk of mortality from cardiopulmonary and lung cancer. Information on the influence of covariates at multiple scales and of critical exposure time windows can assist policymakers in establishing timelines for regulatory interventions that maximize population health benefits.

Air Pollution↗
PubMedDate not supplied

Geographies of risk in studies linking chronic air pollution exposure to health outcomes.

This article addresses the question of how to incorporate spatial processes into the assessment of chronic health effects from air pollution exposure. An analytic framework is developed around three related concepts: (1) the geography of susceptibility; (2) the geography of exposure; and (3) points of intersection between these two, termed the geography of risk. The article discusses how each concept encompasses many lower level issues such as meteorological dispersion of pollutants, time-space activity patterns, and population distributions of susceptible individuals in time and space. A key premise is that researchers should target studies with high degrees of overlap between geographies of exposure and susceptibility. Instances where the overlap remains incomplete, or systematically biased, usually produce attenuated or unreliable risk estimates, and some of this discordance may find expression in spatially autocorrelated residuals.

Air Pollutants↗