Impact of a household-level deductible on prescription drug use among lower-income adults: a quasi-experimental study
Bibliographic record
Abstract
BACKGROUND: Several Canadian public drug plans have income-based deductibles, but we have limited data on their impact, particularly for vulnerable populations. Therefore, we studied the impact of deductibles in British Columbia's Fair PharmaCare program on drug use among lower-income adults. METHODS: We used a quasi-experimental regression discontinuity design to study the impact of BC rules that impose no deductible before receiving public coverage on households with incomes less than $15 000, a deductible of 2% of household income on those with incomes between $15 000 and $30 000, and a deductible of 3% of household income on those with incomes above $30 000. We studied the impact of these thresholds on public and total drug expenditures between 2003 and 2015 using 24 million person-years of data. RESULTS: Both thresholds decreased the proportion of beneficiaries receiving benefits, by 0.33 (95% confidence interval [CI] -0.34 to -0.30) and 0.05 (95% CI -0.064 to -0.032) respectively. There were also substantial reductions in the extent of public drug plan expenditures ($59.94 [95% CI -74.74 to -45.14] and $26.12 [95% CI -39.78 to -12.46], respectively). The change at the $15 000 threshold reduced patient drug expenditures by $26.00 (95% CI -45.48 to -6.51), or 7.2%. In contrast, we found no statistically significant change in total expenditures when households moved from a deductible of 2% to 3% at the $30 000 threshold. INTERPRETATION: Income-based deductibles considerably affected the extent of public subsidy for prescription drugs. For lower-income households making around $15 000, the deductible led to a reduction of 7.2% in overall drug use and costs. Although deductibles are a useful tool to limit public expenditures, policy-makers should be cautious in their use among vulnerable populations.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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 teacher head, 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".