When patients have to pay a share of drug costs: effects on frequency of physician visits, hospital admissions and filling of prescriptions
Bibliographic record
Abstract
BACKGROUND: Previous research has shown that patient cost-sharing leads to a reduction in overall health resource utilization. However, in Canada, where health care is provided free of charge except for prescription drugs, the converse may be true. We investigated the effect of prescription drug cost-sharing on overall health care utilization among elderly patients with rheumatoid arthritis. METHODS: Elderly patients (> or = 65 years) were selected from a population-based cohort with rheumatoid arthritis. Those who had paid the maximum amount of dispensing fees (200 dollars) for the calendar year (from 1997 to 2000) were included in the analysis for that year. We defined the period during which the annual maximum co-payment had not been reached as the "cost-sharing period" and the one beyond which the annual maximum co-payment had been reached as the "free period." We compared health services utilization patterns between these periods during the 4 study years, including the number of hospital admissions, the number of physician visits, the number of prescriptions filled and the number of prescriptions per physician visit. RESULTS: Overall, 2968 elderly patients reached the annual maximum cost-sharing amount at least once during the study periods. Across the 4 years, there were 0.38 more physician visits per month (p < 0.001), 0.50 fewer prescriptions filled per month (p = 0.001) and 0.52 fewer prescriptions filled per physician visit (p < 0.001) during the cost-sharing period than during the free period. Among patients who were admitted to the hospital at least once, there were 0.013 more admissions per month during the cost-sharing period than during the free period (p = 0.03). INTERPRETATION: In a predominantly publicly funded health care system, the implementation of cost-containment policies such as prescription drug cost-sharing may have the unintended effect of increasing overall health utilization among elderly patients with rheumatoid arthritis.
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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 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".