Air pollution and mortality in a large, representative U.S. cohort: multiple-pollutant analyses, and spatial and temporal decompositions
Notice bibliographique
Résumé
Abstract Background Cohort studies have documented associations between fine particulate matter air pollution (PM 2.5 ) and mortality risk. However, there remains uncertainty regarding the contribution of co-pollutants and the stability of pollution-mortality associations in models that include multiple air pollutants. Furthermore, it is unclear whether the PM 2.5 -mortality relationship varies spatially, when exposures are decomposed according to scale of spatial variability, or temporally, when effect estimates are allowed to change between years. Methods A cohort of 635,539 individuals was compiled using public National Health Interview Survey (NHIS) data from 1987 to 2014 and linked with mortality follow-up through 2015. Modelled air pollution exposure estimates for PM 2.5 , other criteria air pollutants, and spatial decompositions (< 1 km, 1–10 km, 10–100 km, > 100 km) of PM 2.5 were assigned at the census-tract level. The NHIS samples were also divided into yearly cohorts for temporally-decomposed analyses. Cox proportional hazards models were used to estimate hazard ratios (HRs) and 95% confidence intervals (CIs) in regression models that included up to six criteria pollutants; four spatial decompositions of PM 2.5 ; and two- and five-year lagged mean PM 2.5 exposures in the temporally-decomposed cohorts. Meta-analytic fixed-effect estimates were calculated using results from temporally-decomposed analyses and compared with time-independent results using 17- and 28-year exposure windows. Results In multiple-pollutant analyses, PM 2.5 demonstrated the most robust pollutant-mortality association. Coarse fraction particulate matter (PM 2.5–10 ) and sulfur dioxide (SO 2 ) were also associated with excess mortality risk. The PM 2.5 -mortality association was observed across all four spatial scales of PM 2.5 , with higher but less precisely estimated HRs observed for local (< 1 km) and neighborhood (1–10 km) variations. In temporally-decomposed analyses, the PM 2.5 -mortality HRs were stable across yearly cohorts. The meta-analytic HR using two-year lagged PM 2.5 equaled 1.10 (95% CI 1.07, 1.13) per 10 μg/m 3 . Comparable results were observed in time-independent analyses using a 17-year (HR 1.13, CI 1.09, 1.16) or 28-year (HR 1.09, CI 1.07, 1.12) exposure window. Conclusions Long-term exposures to PM 2.5 , PM 2.5–10 , and SO 2 were associated with increased risk of all-cause and cardiopulmonary mortality. Each spatial decomposition of PM 2.5 was associated with mortality risk, and PM 2.5 -mortality associations were consistent over time.
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 enseignantsNi 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.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,001 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 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 tête enseignante, 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 ».