Association between sub-optimal medication adherence and risk of COPD diagnosis in patients with physician-diagnosed asthma: A propensity score-based analysis
Bibliographic record
Abstract
Background: This study investigated the independent effect of medication adherence (MA) and severity of physician-diagnosis of asthma on the risk of COPD. Methods: The Population data BC was used to identify asthma patients aged 18 years and older between 1998 and 1999. The primary event was time-to-COPD diagnosis during the follow-up period (2000 to 2018). The proportion of days covered (PDC) was used as a surrogate measure for medication adherence (MA) assessed at optimal-level (≥ 0.80), intermediate-level (0.50-0.79), and low-level (< 0.5) of adherence. A propensity adjusted analysis with Marginal Structural Cox (MSC) model was employed to estimate the adjusted hazard ratios (aHR) and 95% confidence intervals (95% CI) for the effect of medication adherence and asthma severity over time. Results: At cohort entry, the sample included 68,211 asthma patients with an overall mean age of 48.2 years. The 18-year incidence of COPD in asthma patients was 9.8 per 1000 persons per year. In an inverse weighted propensity adjusted analysis of the MSC model, higher MA levels were significantly associated with decreased risk of COPD as follows: optimal-level (aHR: 0.19, 95% CI: 0.17-0.24) and intermediate-level (aHR: 0.20, 95% CI: 0.18, 0.23) compared to the low-level adherence group. A significant increase in COPD risk was observed in severe asthma patients with low medication adherence (aHR: 1.72, 95% CI: 1.52-1.93), independent of other patient factors. Conclusion: Healthcare providers should intensify interventions aimed at improving Medication adherence in severe asthma patients.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".