Do community demographics, environmental characteristics and access to care affect risks of developing ACOS and mortality in people with asthma?
Notice bibliographique
Résumé
Individuals with asthma and chronic obstructive pulmonary disease (COPD) overlap syndrome (ACOS) have a more rapid decline in lung function, more frequent exacerbations and worse quality of life than those with asthma or COPD alone [1–3]. Various risk factors may be associated with the development of ACOS, such as smoking history and status, obesity, comorbidity and indoor and outdoor environmental exposures [1, 4–6]. The risk of developing ACOS may vary substantially by region, since demographic and environmental risk factors and community characteristics are not geographically homogeneous. Here, we use population-based data to estimate the incidence of ACOS in the asthma population and to measure the association between demographic factors, community-level characteristics and environmental factors and the risk of incident ACOS and all-cause mortality while accounting for spatial autocorrelation. Material deprivation increases ACOS and death risk in people with asthma; air pollution may also increase death risk Health administrative data were provided by the Institute for Clinical Evaluative Sciences (ICES) and air pollution data were provided by the Ministry of the Environment and Climate Change (MOECC). Neither ICES nor the MOECC had any role in study design, analysis, interpretation of data, or writing of the report. No endorsement by ICES or the MOECC is intended or should be inferred. Practice locations of currently certified respirologists were obtained from the website of the College of Physicians and Surgeons of Ontario ([www.cpso.on.ca/][1]). Practice location of currently certified asthma/COPD educators and certified respiratory therapists were provided by the College of Respiratory Therapists of Ontario. Finally, the geographic locations of pulmonary function testing laboratories or spirometry clinics were obtained from the Canadian Lung Association ( ). Kristian Larsen received a Postdoctoral Fellowship, in part, through the Hospital for Sick Children Research Training Centre and the CRRN. Andrea Gershon holds a New Investigator Career Award from CIHR and was also the recipient of 2015 Early Career Achievement Award of the Assembly on Behavioural Science and Health Services Research, American Thoracic Society. Teresa To was the recipient of the 2016 Meritorious Service Award of the Ontario Lung Association. Author contributions are as follows. T. To initiated and designed the study, conducted the spatial modelling and statistical analysis, interpreted findings and drafted the manuscript. J. Zhu compiled the data and conducted statistical analysis. K. Larsen compiled the geographical variables and conducted GIS mapping. L.Y. Feldman and K. Ryckman conducted a search of the literature, summarised relevant study findings and reviewed the manuscript. All authors interpreted findings, reviewed and commented on drafts, have seen and approved the final version. [1]: http://www.cpso.on.ca/
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 | 0,010 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,001 |
| Méta-épidémiologie (sens large) | 0,001 | 0,002 |
| Bibliométrie | 0,001 | 0,002 |
| Études des sciences et des technologies | 0,001 | 0,000 |
| Communication savante | 0,001 | 0,002 |
| Science ouverte | 0,001 | 0,001 |
| Intégrité de la recherche | 0,002 | 0,002 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,004 | 0,001 |
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 ».