Prescription Duration After Drug Copay Changes in Older People: Methodological Aspects
Bibliographic record
Abstract
OBJECTIVES: Impact assessment of drug benefits policies is a growing field of research that is increasingly relevant to health care planning for older people. Some cost-containment policies are thought to increase noncompliance. This paper examines mechanisms that can produce spurious reductions in drug utilization measures after drug policy changes when relying on pharmacy dispensing data. Reference pricing, a copayment for expensive medications above a fixed limit, for angiotensin-converting enzyme(ACE) inhibitors in older British Columbia residents, is used as a case example. DESIGN: Time series of 36 months of individual claims data. Longitudinal data analysis, adjusting for autoregressive data. SETTING: Pharmacare, the drug benefits program covering all patients aged 65 and older in the province of British Columbia, Canada. PARTICIPANTS: All noninstitutionalized Pharmacare beneficiaries aged 65 and older who used ACE inhibitors between 1995 and 1997 (N = 119,074). INTERVENTION: The introduction of reference drug pricing for ACE inhibitors for patients aged 65 and older. MEASUREMENTS: Timing and quantity of drug use from a claims database. RESULTS: We observed a transitional sharp decline of 110% t a standard error of 30% (P = .02) in the overall utilization rate of all ACE inhibitors after the policy implementation; five months later, utilization rates had increased, but remained under the predicted prepolicy trend. Coinciding with the sharp decrease, we observed a reduction in prescription duration by 31% in patients switching to no-cost drugs. This reduction may be attributed to increased monitoring for intolerance or treatment failure in switchers, which in turn led to a spurious reduction in total drug utilization. We ruled out the extension of medication use over the prescribed duration through reduced daily doses (prescription stretching) by a quantity-adjusted analysis of prescription duration. CONCLUSION: The analysis of prescription duration after drug policy interventions may provide alternative explanations to apparent short-term reductions in drug utilization and adds important insights to time trend analyses of drug utilization data in the evaluation of drug benefit policy changes.
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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 | 0.143 | 0.291 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.005 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.002 | 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".