Trends and Regional Differences in the Use of Maintenance Inhaled Medications in COPD: A Population-Based Study
Notice bibliographique
Résumé
This was a retrospective cohort study in the Canadian province of British Columbia (BC). We included adults aged \(\ge\) 35 with COPD based on a validated case definition and analyzed trends from 2010 to 2020. We used demographics databases and pharmacy records for all outpatient dispensed medications regardless of the payer. Within this cohort, the “index date” was the first dispensation of any (overall) or specific (each drug class) maintenance inhaled medication and marked the beginning of follow-up. Geographic regions across BC are organized into 16 distinct health services delivery areas for planning, reporting, and implementing provincial health policies. The primary outcome was the proportion of maintenance inhaled medication users among prevalent COPD patients, overall and by medication class across those regions. Single therapies consisted of individual use of ICS, LABA, and LAMA. Combination-therapies included either single-inhalers with multiple ingredients or separate inhalers with ≥ 14 days of overlap. Heterogeneity in medication use across regions over calendar years was visualized using boxplots. To adjust for the contribution of patient characteristics, we fitted negative binomial models (with logarithmic link function) with the total number of users as the outcome and region (dummy-coded), age, sex, urban/rural residence, and socio-economic status (SES, neighborhood income quintiles) as independent variables. We generated rate ratios (RR) and 95% confidence intervals. Heterogeneity across regions was tested using a likelihood ratio test. The study received ethics approval from Human Ethics Board at University of British Columbia (H23-00607). Over 11 years, the number of COPD patients using any medication increased from 77,273 to 83,157; however, the proportion of users declined from 59.1 to 41.7% (average decline 1.7%/year; Fig. 1 A). Among single-inhaler therapies, ICS was used by 33.4% patients and showed the fastest decline (average 9.6%/year). Single-inhaler LABA was used by 2.3% of patients (average decline of 5.7%/year). Single-inhaler LAMA was used by 19% (increased by 1.8%/year). Among combination-therapies, 39.1% of COPD patients used ICS + LABA, followed by triple-therapy (ICS + LAMA + LABA, 14.7%) and LAMA + LABA (6.4%). The proportion of ICS + LABA users declined by 2.9%/year, whereas LAMA + LABA and triple-therapy users rose annually by 43.6% and 4.4%, respectively. Annual proportion of maintenance inhaled medication users ( A ), further classified by single-therapies: LAMA ( B ), LABA ( C ), ICS ( D ), and combination-therapies LAMA + LABA ( E ), ICS + LABA ( F ), and LAMA + LABA + ICS ( G ) among patients with COPD in British Columbia, Canada, from 2010 to 2020, stratified by geographic region (HSDA). Abbreviations: COPD, chronic obstructive pulmonary disease; ICS, inhaled corticosteroids; LABA, long-acting beta2-agonists; LAMA, long-acting muscarinic antagonists; HSDA, Health Services Delivery Area; Q1, quartile 1; Q3, quartile 3. Note: Each dot represents an HSDA. The horizontal line cutting through the plot is the overall provincial average. The median is the line separating the upper (white) and lower (dark gray) boxes. Combination-therapies are based either on single-inhalers containing multiple ingredients or from separate inhalers with 14 days of overlap. Annual percentage change is obtained from a negative binomial regression model. Females, younger patients, and those with either lowest (compared to those in second and third quintiles) or highest (compared to the lowest category) SES were more likely to use maintenance inhaled medications. There was significant regional variability in medication use after controlling for patient characteristics ( p < 0.01). Compared to the reference region, the RR of medication use ranged from 0.80 to 1.20 across regions (Fig. 2 B). Geographic map of the regions (HSDA) ( A ) and forest plots of adjusted RRs and 95% CI of maintenance inhaled medication use ( B ). CI, confidence interval. The negative binominal regression models were adjusted for age group, sex, socio-economic status, and area of residence. Abbreviations: HSDA, Health Services Delivery Area.
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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 | 0,001 | 0,003 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,001 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,002 | 0,006 |
| Études des sciences et des technologies | 0,002 | 0,001 |
| Communication savante | 0,001 | 0,000 |
| Science ouverte | 0,002 | 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 ».