Modelling the impact of changes to prescription medicine cost-sharing schemes among middle aged and older adults
Bibliographic record
Abstract
OBJECTIVE: To assess impacts of government changes to prescription medicine co-payments on individuals' out-of-pocket expenditure. METHODS: Participants were community-dwelling adults primarily aged ≥56 years from The Irish Longitudinal Study on Ageing study. Ireland has two prescription cost-sharing schemes: the General Medical Services (GMS) scheme (primarily low-income populations), involving low monthly payment caps and co-payments, and the Drugs Payment Scheme (DPS) (for others), with higher caps and no co-payment limit. We modelled changes to these schemes implemented between 2016 and 2022 using 2016 data, assessing out-of-pocket prescription medicine expenditure using descriptive statistics and regression analysis. RESULTS: Among 4,155 participants with out-of-pocket prescription medicine expenditure, estimated mean annual prescription medicine expenditure for GMS-eligible participants reduced from €117 (95 %CI = €114-120) to €55 (95 %CI = €54-€56) due to post-2016 changes. For DPS-eligible participants, it reduced from €719 (95 %CI = €694-€744) to €555 (95 %CI = €541-€569). CONCLUSIONS: Co-payment changes led to average savings of €62 for GMS-eligible participants and €174 for DPS-eligible participants. Although absolute savings were smaller for GMS participants, as the scheme is primarily for low-income populations and the relative expenditure reduction was greater for GMS- than DPS-eligible participants, these savings were likely more impactful for GMS-eligible participants. Further reductions in monthly caps and co-payment charges, particularly for low-income populations, warrant consideration.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 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 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".