The impact of eliminating out-of-pocket payments for medicines on low-income households: a controlled interrupted time series analysis using linked administrative data from British Columbia
Bibliographic record
Abstract
BACKGROUND: There is interest in reducing out-of-pocket payments for prescription medicines, but the effects of such interventions remain unclear. OBJECTIVE: To study the impact of changes to the public prescription drug insurance program in British Columbia (BC), Canada that eliminated copayments for low-income households. METHODS: We used administrative data from 2017 to 2021 from Population Data BC and a controlled interrupted time-series design to examine a 2019 policy that eliminated copayments for households with incomes below $13,750. Households with incomes over $45,000-who experienced no changes in public coverage-served as a control. Our primary outcomes were prescription drug expenditures and the number of prescriptions dispensed. We also conducted a pre-post analysis to study impacts on dispensing and expenditures across therapeutic classes. RESULTS: The intervention cohort included 9,095 patients representing 8,011 households with an average age of 48.4. The control cohort included 820,395 patients representing 471,778 households with an average age of 51.1. Copayment elimination led to a level increase of $3.85 (95 % CI: $1.13 - $7.03) in monthly drug expenditures and had no impact on the trend. The mean number of prescriptions dispensed had a level increase of 0.07 (95 % CI: 0.04 - 0.09) and the rate of dispensing increased by 0.006 prescriptions monthly (95 % CI: 0.002 - 0.010). Copayment elimination was associated with increased expenditures and dispensing across most therapeutic classes. INTERPRETATION: Copayment elimination for low-income households in BC led to significant increases in prescription drug expenditures and dispensing across drug classes. Eliminating copayments appears to be effective at improving access to medicines for lower-income families.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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) | 0.000 | 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 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".