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Enregistrement W2342047644

Identifying subgroups of the general population that may be susceptible to short-term increases in particulate air pollution: a time-series study in Montreal, Quebec.

2000· article· en· W2342047644 sur OpenAlexaffabout
Gol'dberg Ms, Bailar Jc, Burnett Rt, Brook, Robyn Tamblyn, Yvette Bonvalot, Pierre Ernst, Flegel Km, Singh Rk, Valois Mf

Notice bibliographique

RevuePubMed · 2000
Typearticle
Langueen
DomaineEnvironmental Science
ThématiqueAir Quality and Health Impacts
Établissements canadiensUniversité LavalInstitut National de la Recherche Scientifique
Organismes subventionnairesnon disponible
Mots-clésParticulatesAerodynamic diameterAir pollutionEnvironmental scienceHazePopulationAtmospheric sciencesPollutantSulfateMeteorologyPollutionAir pollutantsAerosolDemographyGeographyChemistry
DOInon disponible

Résumé

récupéré en direct d'OpenAlex

This study was undertaken in order to shed light on which groups of the general population may be susceptible to the effects of ambient particles. The objectives of the study were (1) to determine whether concentrations of particles in the ambient air of Montreal, Quebec, were associated with daily all-cause and cause-specific mortality in the period 1984 to 1993, and (2) to determine whether groups of the population had higher than average risks of death from exposure to particles. From the network of fixed-site air pollution monitors in Montreal we obtained daily mean levels of various measures of particles, gaseous pollutants, and weather variables measured at Dorval International Airport. We also used measurements of sulfate from an acid rain monitoring station 150 km southeast of the city (Sutton, Quebec). We estimated associations for particulate matter (PM) with an aerodynamic diameter of 10 microns or smaller (PM10), or 2.5 microns or smaller (PM2.5), total suspended particles (TSP), coefficient of haze (COH), an extinction coefficient, and sulfate. Because substantial data for fine particles were missing, we developed a regression model to predict PM2.5 and to predict sulfate from PM2.5. In the main body of the report, we present results for COH, predicted PM2.5, and sulfate. Detailed results for all pollutants are included in Appendices H through O, which are available on request from Health Effects Institute and from the HEI web site at www.healtheffects.org. To address the first objective, we made use of the underlying causes of death among all 140,939 residents of Montreal who died between 1984 and 1993. We regressed the logarithm of daily counts of cause-specific mortality on the daily mean levels for a variety of measures of particles, accounting for seasonal and subseasonal fluctuations in the mortality time series, overdispersion, and weather factors. To address the second objective, we developed algorithms to define conditions that subjects had prior to death, with the focus on cardiopulmonary diseases. These algorithms were based on information retained on the databases of the universal Quebec Health Insurance Plan (QHIP). The databases include records of all procedures (e.g., type of surgery), physician visits, and consultations carried out by all physicians in Quebec. For persons > or = 65 years and for all recipients of social assistance the prescription database contains records of all pharmaceuticals dispensed (type of medication, dose, quantity). For each group of conditions defined, we used the same statistical model that was used in the analyses of all nonaccidental causes of death. In the analyses of cause-specific mortality, we found evidence of associations for all nonaccidental causes of death and specific causes of death--cancer, coronary artery disease, respiratory diseases, and diabetes--that were consistent across most metrics of ambient air particle concentrations, evaluated as the 3-day mean of particle concentrations measured on the day of death (lag 0) and on each of the two days before death (lag 1, lag 2). Associations for all cardiovascular diseases combined were found only with sulfate. As well, we generally found increased daily mortality for persons 65 years of age and over. The results for all nonaccidental causes of death are similar to findings from other studies; the mean percent increase in mortality for a 100 micrograms/m3 increase in daily TSP at lag 0 was 6.7%. In the analyses of the groups defined from the QHIP data, there was little evidence of associations with air pollutants among persons who before death were classified as having acute or chronic upper respiratory diseases, airways diseases, hypertension, acute coronary artery diseases, and cerebrovascular diseases. On the other hand, we found consistent increases across most types of ambient particles for persons who had cancer, acute lower respiratory diseases, any form of cardiovascular disease, chronic coronary artery diseases, and congestive heart failure. As well, we found an association for individuals who did not have any cardiovascular disease, lower respiratory diseases, and cancer. This latter group consisted of persons who had no interactions with the health care system one year before death (12%) and individuals with a wide variety of potentially fatal diseases (52%), including neurological conditions (12%), diabetes (8%), cardiac dysrhythmias (8%), dementia (6%), organic psychotic disorders (6%), and anemias (4%). As statistical power was reduced in the analyses presented above, differences between groups (e.g., < 65 and > or = 65 year age groups) were not usually statistically significant. The association with diabetes has not been reported previously, and this needs to be replicated in other studies. (ABSTRACT TRUNCATED)

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: Observationnel
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,303
Score d'incertitude au seuil0,774

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 Opus0,050
Tête enseignante GPT0,293
Écart entre enseignants0,242 · 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

Citations106
Publié2000
Routes d'admission2
Résumé présentoui

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