MétaCan
Menu
Retour à la cohorte
Enregistrement W2361455150

Air pollution and health: a European and North American approach (APHENA).

2009· article· en· W2361455150 sur OpenAlexaboutno aff
Klea Katsouyanni, Samet Jm, Anderson Hr, Richard Atkinson, Le Tertre A, S Médina, Evangelia Samoli, Giota Touloumi, Burnett Rt, Daniel Krewski, Tim Ramsay, Francesca Dominici, Rd Peng, Joel Schwartz, Antonella Zanobetti

Notice bibliographique

RevuePubMed · 2009
Typearticle
Langueen
DomaineEnvironmental Science
ThématiqueAir Quality and Health Impacts
Établissements canadiensnon disponible
Organismes subventionnairesnon disponible
Mots-clésAir pollutionPollutionConsistency (knowledge bases)GeographyEnvironmental healthRegional scienceEnvironmental planningComputer scienceMedicine
DOInon disponible

Résumé

récupéré en direct d'OpenAlex

INTRODUCTION: This report provides the methodology and findings from the project: Air Pollution and Health: a European and North American Approach (APHENA). The principal purpose of the project was to provide an understanding of the degree of consistency among findings of multicity time-series studies on the effects of air pollution on mortality and hospitalization in several North American and European cities. The project included parallel and combined analyses of existing data. The investigators sought to understand how methodological differences might contribute to variation in effect estimates from different studies, to characterize the extent of heterogeneity in effect estimates, and to evaluate determinants of heterogeneity. The APHENA project was based on data collected by three groups of investigators for three earlier studies: (1) Air Pollution and Health: A European Approach (APHEA), which comprised two multicity projects in Europe. (Phase 1 [APHEA1] involving 15 cities, and Phase 2 [APHEA2] involving 32 cities); (2) the National Morbidity, Mortality, and Air Pollution Study (NMMAPS), conducted in the 90 largest U.S. cities; and (3) multicity research on the health effects of air pollution in 12 Canadian cities. METHODS: The project involved the initial development of analytic approaches for first-stage and second-stage analyses of the time-series data and the subsequent application of the resulting methods to the time-series data. With regard to the first-stage analysis, the various investigative groups had used conceptually similar approaches to the key issues of controlling for temporal confounding and temperature; however, specific methods differed. Consequently, the investigators needed to establish a standard protocol, but one that would be linked to prior approaches. Based on exploratory analyses and simulation studies, a first-stage analysis protocol was developed that used generalized linear models (GLM) with either penalized splines (PS) or natural splines (NS) to adjust for seasonality, with 3, 8, or 12 degrees of freedom (df) per year and also the number of degrees of freedom chosen by minimizing the partial autocorrelation function (PACF) of the model's residuals. For hospitalization data, the approach for model specification followed that used for mortality, accounting for seasonal patterns, but also, for weekend and vacation effects, and for epidemics of respiratory disease. The data were also analyzed to detect potential thresholds in the concentration-response relationships. The second-stage analysis used pooling approaches and assessed potential effect modification by sociodemographic characteristics and indicators of the pollution mixture across study regions. Specific quality control exercises were also undertaken. Risks were estimated for two pollutants: particulate matter - 10 pm in aerodynamic diameter (PM10) and ozone (O3). RESULTS: The first-stage analysis yielded estimates that were relatively robust to the underlying smoothing approach and to the number of degrees of freedom. The first-stage APHENA results generally replicated the previous independent analyses performed by the three groups of investigators. PM10 effects on mortality risk estimates from the APHEA2 and NMMAPS databases were quite close, while estimates from the Canadian studies were substantially higher. For hospitalization, results were more variable without discernable patterns of variation among the three data sets. PM10 effect-modification patterns, explored only for cities with daily pollution data (i.e., 22 in Europe and 15 in the U.S.), were not entirely consistent across centers. Thus, the levels of pollutants modified the effects differently in Europe than in the United States. Climatic variables were important only in Europe. In both Europe and the United States, a higher proportion of older persons in the study population was associated with increased PM10 risk estimates, as was a higher rate of unemployment - the sole indicator of socioeconomic status uniformly available across the data sets. APHENA study results on the effects of O3 on mortality were less comprehensive than for PM10 because the studies from the three regions varied in whether they analyzed data for the full year or only for the summer months. The effects tended to be larger for summer in Europe and the United States. In the United States they were lower when controlled for PM10. The estimated effect of O3 varied by degrees of freedom and across the three geographic regions. The effects of O3 on mortality were larger in Canada, and there was little consistent indication of effect modification in any location. CONCLUSIONS: APHENA has shown that mortality findings obtained with the new standardized analysis were generally comparable to those obtained in the earlier studies, and that they were relatively robust to the data analysis method used. For PM10, the effect-modification patterns observed were not entirely consistent between Europe and the United States. For O3, there was no indication of strong effect modification in any of the three data sets.

Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.

Comment cette classification a été obtenuedéplier

Prédiction distillée sur la base complète

Imitation des enseignants

Ni prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.

score de la tête « metaresearch » (Codex)0,001
score de la tête « metaresearch » (Gemma)0,000
Version: codex-gemma-dda1882f352aStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Observationnel · Signal consensuel: aucune
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,946
Score d'incertitude au seuil0,281

Scores Codex et Gemma par catégorie

CatégorieCodexGemma
Métarecherche0,0010,000
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0000,000
Bibliométrie0,0000,000
Études des sciences et des technologies0,0000,000
Communication savante0,0000,000
Science ouverte0,0000,000
Intégrité de la recherche0,0000,000
Charge utile insuffisante (le modèle a refusé de juger)0,0000,000

Scores machine (provisoires)

Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.

Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.

Tête enseignante Opus0,062
Tête enseignante GPT0,265
Écart entre enseignants0,203 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découle

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.

Les modèles n’ont appliqué aucune catégorie : rien dans la taxonomie ne correspondait à ce travail.
Devis d'étudeObservationnel
Domainenon disponible
GenreEmpirique

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations168
Publié2009
Routes d'admission1
Résumé présentoui

Explorer davantage

Même revuePubMedMême sujetAir Quality and Health ImpactsTravaux en français237 207