Inhaler Formulary Change in COPD and the Association with Exacerbations, Health Care Utilization, and Costs
Bibliographic record
Abstract
Rationale: Prescription formularies specify which medications are available to patients. Formularies change frequently, potentially forcing patients to switch medications for nonclinical indications (nonmedical switching). Nonmedical switching is known to impact disease control and adherence. The consequences of nonmedical switching have not been rigorously studied in COPD. Methods: We conducted a cohort study of Veterans with COPD on inhaler therapy in January 2016 when formoterol was removed from the Department of Veterans Affairs (VA) national formulary. A 2-point difference-in-differences analysis using multivariable negative binomial and generalized linear models was performed to estimate the association of the formulary change with patient outcomes in the 6 months before and after the change. Our primary outcome was the number of COPD exacerbations in 6 months, with secondary outcomes of total health care encounters and encounter-related costs in 6 months. Results: We identified 10,606 Veterans who met our inclusion criteria, of which 409 (3.9%) experienced nonmedical switching off formoterol. We did not identify a change in COPD exacerbations (-0.04 exacerbations; 95% confidence interval [CI] -0.12, 0.03) associated with the formulary change. In secondary outcome analysis, we did not observe a change in the number of health care encounters (-0.12 visits; 95% CI -1.00, 0.77) or encounter-related costs ($369; 95% CI -$1141, $1878). Conclusions: Among COPD patients on single inhaler therapy, nonmedical inhaler switches due to formulary discontinuation of formoterol were not associated with changes in COPD exacerbations, encounters, or encounter-related costs. Additional research is needed to confirm our findings in more severe disease and other settings.
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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 | 0.002 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".