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Cited by 583Air Quality and Health ImpactsStatistical Methods in EpidemiologyClimate Change and Health Impacts

Model Choice in Time Series Studies of Air Pollution and Mortality

Peng, Roger D., Dominici, Francesca, Louis, Thomas A.·2006·Journal of the Royal Statistical Society Series A: Statistics in Society
DOI 10.1111/j.1467-985x.2006.00410.x
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Abstract

Summary: Multicity time series studies of particulate matter and mortality and morbidity have provided evidence that daily variation in air pollution levels is associated with daily variation in mortality counts. These findings served as key epidemiological evidence for the recent review of the US national ambient air quality standards for particulate matter. As a result, methodological issues concerning time series analysis of the relationship between air pollution and health have attracted the attention of the scientific community and critics have raised concerns about the adequacy of current model formulations. Time series data on pollution and mortality are generally analysed by using log-linear, Poisson regression models for overdispersed counts with the daily number of deaths as outcome, the (possibly lagged) daily level of pollution as a linear predictor and smooth functions of weather variables and calendar time used to adjust for time-varying confounders. Investigators around the world have used different approaches to adjust for confounding, making it difficult to compare results across studies. To date, the statistical properties of these different approaches have not been comprehensively compared. To address these issues, we quantify and characterize model uncertainty and model choice in adjusting for seasonal and long-term trends in time series models of air pollution and mortality. First, we conduct a simulation study to compare and describe the properties of statistical methods that are commonly used for confounding adjustment. We generate data under several confounding scenarios and systematically compare the performance of the various methods with respect to the mean-squared error of the estimated air pollution coefficient. We find that the bias in the estimates generally decreases with more aggressive smoothing and that model selection methods which optimize prediction may not be suitable for obtaining an estimate with small bias. Second, we apply and compare the modelling approaches with the National Morbidity, Mortality, and Air Pollution Study database which comprises daily time series of several pollutants, weather variables and mortality counts covering the period 1987–2000 for the largest 100 cities in the USA. When applying these approaches to adjusting for seasonal and long-term trends we find that the Study's estimates for the national average effect of PM10 at lag 1 on mortality vary over approximately a twofold range, with 95% posterior intervals always excluding zero risk.

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  • The discrepancy in the results for 3 df/year between the NS and PS approaches has a theoretical basis related to the tradeoff between the parametric and nonparametric components of the PS models in explaining variation in the outcome variable.

    “The discrepancy in the results for 3 df/year between the NS and PS approaches has a theoretical basis related to the tradeoff between the parametric and nonparametric components of the PS models in explaining variation in the outcome variable (Peng et al. 2006; Speckman 1988).”

    From Acute effects of ambient ozone on mortality in Europe and North America: results from the APHENA study (2013) · cites this paper as Peng et al. 2006

  • Various regression models and parameter settings affect regression coefficients.

    “Earlier simulation studies have shown that various regression models and parameter setting in the models (e.g. df selection, convergence criteria) would affect the regression coefficients (Dominici et al. 2002; Peng et al. 2006).”

    From Differentiating the effects of fine and coarse particles on daily mortality in Shanghai, China (2007) · cites this paper as Peng et al. 2006

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Authors
Peng, R. D., Dominici, F., & Louis, T. A.
Affiliations
Johns Hopkins University (US)
Corresponding author
Peng, Roger D.
ORCID
Venue
Journal of the Royal Statistical Society Series A: Statistics in Society
Publisher
Oxford University Press (OUP)
Published
2006-02-14
Volume
169
Issue
2
Pages
179-203
ISSN
  • 0964-1998 (ISSN-L)
  • 1467-985X
DOI
10.1111/j.1467-985x.2006.00410.x
OpenAlex
W2134434400
Cited by
583
References
70
Citations by year
2026: 25 · 2025: 29 · 2024: 14 · 2023: 21 · 2022: 33 · 2021: 52 · 2020: 54 · 2019: 32 · 2018: 24 · 2017: 43 · 2016: 30 · 2015: 33 · 2014: 38 · 2013: 41 · 2012: 33
FWCI
11.67
Type
journal article
Language
en
Open access
closed
Licence
academic.oup.com/journals/pages/open_access/funder_policies/chorus/standard_publication_model (vor)
Locations
  • Journal of the Royal Statistical Society Series A (Statistics in Society) (publishedVersion)
  • RePEc: Research Papers in Economics (submittedVersion)
  • Collection of Biostatistics Research Archive (COBRA) (submittedVersion)
Topics
  • Physical Sciences › Environmental Science › Health, Toxicology and Mutagenesis › Air Quality and Health Impacts
  • Physical Sciences › Mathematics › Statistics and Probability › Statistical Methods in Epidemiology
  • Physical Sciences › Environmental Science › Health, Toxicology and Mutagenesis › Climate Change and Health Impacts
Keywords
model selection, model uncertainty, air pollution mortality, particulate matter, PM10, confounding adjustment, seasonal patterns, long-term trends, meteorological parameters, overdispersion, ambient air quality standards

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