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Open Access Research

Comparison of different methods in analyzing short-term air pollution effects in a cohort study of susceptible individuals

Annette Peters1*, Stephanie von Klot1, Niklas Berglind23, Allmut Hörmann4, Hannelore Löwel1, Fredrik Nyberg25, Juha Pekkanen6, Carlo A Perucci7, Massimo Stafoggia7, Jordi Sunyer8, Pekka Tiittanen6 and Francesco Forastiere7

Author Affiliations

1 GSF-National Research Center for Environment and Health, Institute of Epidemiology, Neuherberg, Germany

2 Institute of Environmental Medicine, Karolinska Institutet, Sweden

3 Dept. of Occupational and Environmental Health, Stockholm County Council, Sweden

4 GSF-National Research Center for Environment and Health, Institute of Health Economics and Management, Neuherberg, Germany

5 AstraZeneca R&D, Mölndal, Sweden

6 Unit of Environmental Epidemiology, KTL – National Public Health Institute, Kuopio, Finland

7 Department of Epidemiology, Rome E Health Authority, Rome, Italy

8 IMIM, Barcelona, Spain

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Epidemiologic Perspectives & Innovations 2006, 3:10  doi:10.1186/1742-5573-3-10

Published: 9 August 2006

Abstract

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.

Conclusion

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.