The Impact of Cost Sharing on Antidepressant Use Among Older Adults in British Columbia
Bibliographic record
Abstract
OBJECTIVE: Antidepressant therapies are underused among older adults and could be further curtailed by patient cost-sharing requirements. The authors studied the effects of two sequential cost-sharing policies in a large, stable population of all British Columbia seniors: change from full prescription coverage to 10-25 dollars copayments (copay) in January 2002 and replacement with income-based deductibles and 25% coinsurance in May 2003. METHODS: PharmaNet data were used to calculate monthly dispensing of antidepressants (in imipramine-equivalent milligrams) among all British Columbia residents age 65 and older beginning January 1997 through December 2005. Monthly rates of starting and stopping antidepressants were calculated. Population-level patterns over time were plotted, and the effects of implementing cost-sharing policies on antidepressant use, initiation, and stopping were examined in segmented linear regression models. RESULTS: Implementation of the copay policy was not associated with significant changes in level of antidepressant dispensing or the rate of dispensing growth. Subsequent implementation of the income-based deductible policy also did not lead to a significant change in dispensing level but led to a significant (p=.02) decrease in the rate of growth of antidepressant dispensing. The copay policy was associated with a significant (p=.01) drop in the frequency of antidepressant initiation among persons with depression. Income-based deductibles reduced the rate of increase in antidepressant initiation over time. Implementation of the copay and income-based deductible policies did not have significant effects on stopping rates. CONCLUSIONS: Introducing new forms of medication cost sharing appears to have the potential to reduce some use and initiation of antidepressant therapy by seniors. The clinical consequences of such reduced use need to be clarified.
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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 | 0.001 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.001 | 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".