Statistical issues in epidemiologic studies of the health effects of ambient acid aerosols

Ware, James H.; Stram, Daniel O.

doi:10.2307/3315060

Cited by 4 publications

(2 citation statements)

References 22 publications

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“…First, the data were analyzed using the standard Cox proportional hazard survival model, with the implicit assumption that the observations were statistically independent after controlling for available risk factors measured on the individual (8). If the assumption of statistical independence is not valid, the uncertainty in the estimates of the association between air pollution and mortality may be understated (9)(10)(11)(12)(13). Second, missing or systematically mismeasured risk factors that may be correlated with air pollution could confound the pollution-mortality association (4)(5)(6).…”

Section: Discussionmentioning

confidence: 99%

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The spatial association between community air pollution and mortality: a new method of analyzing correlated geographic cohort data.

Burnett

Jerrett

et al. 2001

Environ Health Perspect

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We present a new statistical model for linking spatial variation in ambient air pollution to mortality. The model incorporates risk factors measured at the individual level, such as smoking, and at the spatial level, such as air pollution. We demonstrate that the spatial autocorrelation in community mortality rates, an indication of not fully characterizing potentially confounding risk factors to the air pollution-mortality association, can be accounted for through the inclusion of location in the model assessing the effects of air pollution on mortality. Our methods are illustrated with an analysis of the American Cancer Society cohort to determine whether all cause mortality is associated with concentrations of sulfate particles. The relative risk associated with a 4.2 microg/m(3) interquartile range of sulfate distribution for all causes of death was 1.051 (95% confidence interval 1.036-1.066) based on the Cox proportional hazards survival model, assuming subjects were statistically independent. Inclusion of community-based random effects yielded a relative risk of 1.055 (1.033, 1.077), which represented a doubling in the residual variance compared to that estimated by the Cox model. Residuals from the random-effects model displayed strong evidence of spatial autocorrelation (p = 0.0052). Further inclusion of a location surface reduced the sulfate relative risk and the evidence for autocorrelation as the complexity of the location surface increased, with a range in relative risks of 1.055-1.035. We conclude that these data display both extravariation and spatial autocorrelation, characteristics not captured by the Cox survival model. Failure to account for extravariation and spatial autocorrelation can lead to an understatement of the uncertainty of the air pollution association with mortality.

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Section: Discussionmentioning

confidence: 99%

“…Such an approach results in at least two somewhat related concerns. First, health responses can cluster by location (9). Clustering will induce a positive correlation of the response of subjects in the same location and thus suggests that there are one or more unmeasured or inadequately modeled risk factors specific to the location itself.…”

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The spatial association between community air pollution and mortality: a new method of analyzing correlated geographic cohort data.

Burnett

Jerrett

et al. 2001

Environ Health Perspect

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Spatial Regression Models for Large-Cohort Studies Linking Community Air Pollution and Health

Cakmak

Burnett

Jerrett

et al. 2003

Journal of Toxicology and Environmental Health, Part A

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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.

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A two-stage model for multiple time series data of counts

Berhane

Thomas²

2002

Biostatistics

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We propose a two-stage model for time series data of counts from multiple locations. This method fits first-stage model(s) using the technique of iteratively weighted filtered least squares (IWFLS) to obtain location-specific intercepts and slopes, with possible lagged effects via polynomial distributed lag modeling. These slopes and/or intercepts are then taken to a second-stage mixed-effects meta-regression model in order to stabilize results from various locations. The representation of the models from the stages into a combined mixed-effects model, issues of inference and choices of the parameters in modeling the lag structure are discussed. We illustrate this proposed model via detailed analysis on the effect of air pollution on school absenteeism based on data from the Southern California Children's Health Study.

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Statistical issues in epidemiologic studies of the health effects of ambient acid aerosols

Cited by 4 publications

References 22 publications

The spatial association between community air pollution and mortality: a new method of analyzing correlated geographic cohort data.

The spatial association between community air pollution and mortality: a new method of analyzing correlated geographic cohort data.

Spatial Regression Models for Large-Cohort Studies Linking Community Air Pollution and Health

A two-stage model for multiple time series data of counts

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