Do We Adequately Control for Unmeasured Confounders When Estimating the Short-term Effect of Air Pollution on Mortality?
Rent the article at a discountRent now
* Final gross prices may vary according to local VAT.Get Access
Numerous time series studies have quantified the potential association between daily variations in air pollution and daily variations in non-accidental deaths. In order to account for the presence of unmeasured confounders, a smooth function of time trend is typically used as a proxy for these variables. We shed light on the validity of the results obtained by using this approach. Specifically, we use data from the National Morbidity, Mortality and Air Pollution Study database, and carry out a carefully designed simulation study. Our findings suggest that the use of a smooth function of time trend cannot fully account for the presence of unmeasured confounders, especially when their impact is strong relatively to the effect of air pollution, and when several unobservables are not included in the model.
- Dominici, F., McDermott, A., Daniels, M. J., Zeger, S. L., & Samet, J. M. (2003). Revised analysis of the national morbidity mortality air pollution study: Part II. Cambridge: The Health Effects Institute.
- Hastie, T., & Tibshirani, R. (1990). Generalized additive models. London: Chapman and Hall.
- He, S., Mazumdar, S., & Arena, V. C. (2006). A comparative study of the use of GAM and GLM in air pollution research. Environmetrics, 17, 81–93. CrossRef
- McCullagh, P., & Nelder, J. A. (1989). Generalized linear models. London: Chapman and Hall.
- Peng, D. R., Dominici, F., & Louis, A. T. (2006). Model choice in time series studies of air pollution and mortality. Journal of the Royal Statistical Society Series A, 169, 179–203.
- Peng, R. D., & Dominici, F. (2008). Statistical methods for environmental epidemiology with R: A case study in air pollution and health. New York: Springer.
- Peng, R. D., & Welty, L. J. (2004). The NMMAPSdata package. R News, 4, 10–14.
- Pope, C. A., & Burnett, R. T. (2007). Confounding in air pollution epidemiology. Epidemiology, 18, 424–426. CrossRef
- Samet, J., Zeger, S., Dominici, F., Curriero, F., Coursac, I., Dockery, D., et al. (2000). The national morbidity, mortality, and air pollution study. Part II: Morbidity and mortality, from air pollution in the United States. HEI Project, 96–97, 5–47.
- Schwartz, J., Zanobetti, A., & Bateson, T. (2003). Morbidity and mortality among elderly residents of cities with daily PM measurements. In Revised analyses of time-series studies of air pollution and health (pp. 25–58). Cambridge: Health Effects Institute.
- Wood, S. N. (2006). Generalized additive models: An introduction with R. London: Chapman and Hall.
- Wood, S. N. (2008). Fast stable direct fitting and smoothness selection for generalized additive models. Journal of the Royal Statistical Society Series B, 70, 495–518. CrossRef
- Do We Adequately Control for Unmeasured Confounders When Estimating the Short-term Effect of Air Pollution on Mortality?
Water, Air, & Soil Pollution
Volume 218, Issue 1-4 , pp 347-352
- Cover Date
- Print ISSN
- Online ISSN
- Springer Netherlands
- Additional Links
- Adverse health effects
- Air pollution
- Generalized additive models
- Unmeasured confounding
- Industry Sectors
- Author Affiliations
- 1. Department of Statistical Science, University College London, Gower Street, London, WC1E 6BT, UK
- 2. Department of Health Services Research and Policy, London School of Hygiene and Tropical Medicine, Keppel Street, London, WC1E 7HT, UK