Particulate air pollution and chronic obstructive pulmonary disease patients: an assessment of exposure and cardiovascular health effects
Notice bibliographique
Résumé
Epidemiologic studies have repeatedly demonstrated associations between particulate air pollution and adverse health effects. One concern in time-series studies is the assessment of exposure of the study population using fixed site outdoor measurements. To address the issue of exposure misclassification, we evaluate the relationship between ambient and personal particulate concentrations of a population expected to be at risk of particle health effects. Biologically plausible mechanisms of particle health effects are also lacking; thus, we evaluate several cardiovascular outcomes of our population. Sampling was conducted within the Vancouver metropolitan area during April-September 1998. Sixteen subjects (non-smoking, ages 54-86) with physician-diagnosed COPD wore personal PM2.5 monitors for seven, randomly spaced, 24-hour periods. Time-activity logs, dwelling characteristics data, blood pressure (BP) and 24-hour ambulatory ECG recordings were obtained for each subject. Daily 24-hour ambient PM10 and PM2.5 concentrations were measured at five fixed sites spaced throughout the study region. Sulfate, a marker of ambient combustion-source particulate, was measured in all PM2.5 samples. Regression analyses were conducted to assess the relationship between personal and ambient levels. Ambient concentrations were expressed either as an average of the five values obtained for each day of personal sampling, or the concentration obtained at the site closest to each subject's home. The median Pearson's r of individual regressions between personal and average ambient PM2.5 concentrations was 0.48 (range: -0.68 to 0.83). Using sulfate as the exposure metric, the median correlation was 0.96 (range: 0.66 to 1.00). The mean personal to ambient concentration ratio of all samples was 1.75 for PM2.5 and 0.75 for sulfate. Use of the closest ambient site did not improve the median correlation of the group for either exposure variable. Inclusion of time-activity and dwelling characteristics data in a regression model for PM2.5 exposure improved model fit, but was not highly predictive (R²: 0.27). The model for sulfate was predictive (R²: 0.82) as personal exposures were largely explained by ambient levels. BP, supraventricular ectopic beats (SVE), heart rate (HR) and heart rate variability (HRV) indices, were regressed against exposure. Temperature, relative humidity, carbon monoxide, ozone and bronchodilator use were tested for confounding. Decreases in BP and increases in SVEs were observed with increasing exposure. HR and HRV models produced inconsistent results and were unstable upon the addition of secondary variables. These results indicate a relatively low degree of correlation between personal and ambient concentrations for PM2.5 compared with a high correlation when using sulfate as a marker of outdoor combustion-source particulate. These data also suggest that BP and SVE are sensitive cardiovascular indicators, however the implications of our findings remain to be assessed. [Scientific formulae used in this abstract could not be reproduced.]
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 machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,002 | 0,005 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,002 | 0,002 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,001 | 0,001 |
| Science ouverte | 0,000 | 0,001 |
| Intégrité de la recherche | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,001 | 0,000 |
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.
score_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 découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».