Comparison of different methods in analyzing short-term air pollution effects in a cohort study of susceptible individuals.
BACKGROUND
Short-term fluctuations of ambient air pollution have been associated with exacerbation of cardiovascular disease. A multi-city study was designed to assess the probability of recurrent hospitalization in a cohort of incident myocardial infarction survivors in five European cities. The objective of this paper is to discuss the methods for analyzing short-term health effects in a cohort study based on a case-series.


METHODS
Three methods were considered for the analyses of the cohort data: Poisson regression approach, case-crossover analyses and extended Cox regression analyses. The major challenge of these analyses is to appropriately consider changes within the cohort over time due to changes in the underlying risk following a myocardial infarction, slow time trends in risk factors within the population, dynamic cohort size and seasonal variation.


RESULTS
Poisson regression analyses, case-crossover analyses and Extended Cox regression analyses gave similar results. Application of smoothing methods showed the capability to adequately model the complex time trends.


CONCLUSIONS
From a practical point of view, Poisson regression analyses are less time-consuming, and therefore might be used for confounder selection and most of the analyses. However, replication of the results with Cox models is desirable to assure that the results are independent of the analytical approach used. In addition, extended Cox regression analyses would allow a joint estimation of long-term and short-term health effects of time-varying exposures.
Peters
Annette
A
GSF-National Research Center for Environment and Health, Institute of Epidemiology, Neuherberg, Germany. peters@gsf.de
von Klot
Stephanie
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Berglind
Niklas
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Hörmann
Allmut
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Löwel
Hannelore
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Nyberg
Fredrik
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Pekkanen
Juha
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Perucci
Carlo A
CA
Stafoggia
Massimo
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Sunyer
Jordi
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Tiittanen
Pekka
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Forastiere
Francesco
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