http://www.cnr.it/ontology/cnr/individuo/prodotto/ID184464
Analysis of health outcome time series data in epidemiological studies (Articolo in rivista)
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- Analysis of health outcome time series data in epidemiological studies (Articolo in rivista) (literal)
- Anno
- 2004-01-01T00:00:00+01:00 (literal)
- Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#doi
- 10.1002/env.623) (literal)
- Alternative label
Giota Touloumi1*; Richard Atkinson 2; Alain Le Tertre 3; Evangelia Samoli 1; Joel Schwartz 4; Christian Schindler 5; Judith M. Vonk 6; Giuseppe Rossi 7; Marc Saez 8; Daniel Rabszenko 9; and Klea Katsouyanni 1 (2004)
Analysis of health outcome time series data in epidemiological studies
in Environmetrics (London Ont., Online); John Wiley & Sons Ltd., Chichester (Regno Unito)
(literal)
- Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#autori
- Giota Touloumi1*; Richard Atkinson 2; Alain Le Tertre 3; Evangelia Samoli 1; Joel Schwartz 4; Christian Schindler 5; Judith M. Vonk 6; Giuseppe Rossi 7; Marc Saez 8; Daniel Rabszenko 9; and Klea Katsouyanni 1 (literal)
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- 1 Department of Hygiene and Epidemiology, University of Athens Medical School, Athens, Greece
2 Department of Public Health Sciences, St. George's Hospital Medical School, University of London, U.K.
3 Institut de Veille Sanitaire, Paris, France
4 Department of Environmental Health, Harvard School of Public Health, Boston, U.S.A.
5 Universitaet Basel, Institut fuer Sozial-und Praeventivmedizin, Switzerland
6 University of Groningen, The Netherlands
7 Fisiologia Clinica C.N.R., Unit of Epidemiology and Biostatistics, Pisa, Italy
8 Research Group on Statistics, Applied Economics and Health (GRECS), University of Girona, Spain
9 National Institute of Hygiene, Department of Medical Statistics, Population Studies Laboratory, Poland (literal)
- Titolo
- Analysis of health outcome time series data in epidemiological studies (literal)
- Abstract
- Several recent studies have reported significant health effects of air pollution even at low levels of air pollutants.
These studies have been criticized for the statistical methods and for inconsistency in results between cities. An
important development in air pollution epidemiology has come from multicenter studies. Within the APHEA-2
project we have developed a statistical methodology to evaluate short-term health effects of air pollution using
data from 30 cities across Europe. For the analysis, a hierarchical modelling approach was adopted and
implemented in two stages: (a) data from each city were analyzed separately to allow for local differences,
using generalized additive Poisson regression models; (b) city-specific effects estimates were regressed on cityspecific
covariates to obtain an overall estimate and to explore heterogeneity across cities. In order to illustrate our
methodology we present results for PM10 effects. It was found that a 10 mg/m3 increase in PM10 or NO2
concentrations is associated with a 0.67% (95% CI: 0.50 to 0.90) and 0.33% (0.20 to 0.40) increase in total
mortality, respectively. After mutual adjustment, the PM10 effect was reduced by 40% and that of NO2 by 20%,
but both pooled estimates remained significant. Long-term mean NO2 concentrations act as an effect modifier for
PM10 effects, even after adjustment for NO2 confounding effects. In the second stage we explored two different
models for combining the adjusted for NO2, PM10 effects across cities: bivariate, which accounts for within-city
correlation of PM10 and NO2; and univariate, which ignores this correlation. Both models gave broadly the same
results. (literal)
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