Non-adherence to COPD medications and its association with adverse events: A longitudinal population based cohort study of older adults
Notice bibliographique
Résumé
OBJECTIVE: To determine the association between non-adherence to long term chronic obstructive pulmonary disease (COPD) medications and COPD related emergency department (ED) visits and hospitalizations in patients with incident COPD, utilizing time varying measures of adherence as well as accounting for time-varying confounding impacted by prior adherence. STUDY DESIGN AND SETTING: We conducted a population-based retrospective cohort study between 2007-2017 among individuals aged 66 years and older with incident COPD using multiple linked administrative health databases from the province of Ontario, Canada. Adherence to COPD medications was measured using time varying proportion of days covered based on insurance claims for medications dispensed at community pharmacies. The parametric g-formula was used to assess the association between time-varying adherence (in the last 90-days) to COPD medications and risk of COPD related hospitalizations and ED visits while accounting for time varying confounding by COPD severity. RESULTS: Overall, 60,251 individuals with incident COPD were included; mean age was 76 (SD 7) and 59% were male. Mean adherence over the entire follow-up was 23% (SD 0.3). There were 7248 (12%) COPD related ED visits (2.8 events per 100 person years [PY]) and 9188 (15%) COPD related hospitalizations (3.5 events per 100 PY). Compared to those with 0% 90-day adherence, those with adherence between 1-33% had a 19% decreased risk of COPD related ED visits (adjusted risk ratio[aRR]:0.81, 95% confidence interval [CI]:0.78-0.83), those with adherence between 34%-67% had a 18% decreased risk (aRR: 0.82, 95% CI: 0.77-0.85) while those with 68%-100% 90-day adherence had a 63% increased risk of COPD related ED visits (aRR: 1.63, 95% CI: 1.47-1.78). Nearly identical results were obtained for COPD specific hospitalizations. CONCLUSION: After accounting for time varying confounding by COPD severity, the highest time varying 90-days adherence was associated with an increased risk of both COPD related ED visits and hospitalizations compared to the lowest adherence categories. Differences in COPD severity between adherence categories, perception of need for medication management in the higher adherence categories, and potential residual confounding makes it difficult to disentangle the independent effects of adherence from the severity of the condition itself.
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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 | 0,002 | 0,004 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,000 |
| Bibliométrie | 0,000 | 0,001 |
| 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 ».