Out-of-pocket prescription medicine expenditure amongst community-dwelling adults: findings from The Irish Longitudinal Study on Ageing (TILDA) in 2016
Bibliographic record
Abstract
Background The number of prescription medicines prescribed to older adults is increasing in Ireland and other countries. This is leading to higher out-of-pocket prescription medicine expenditure for older adults, which has several negative consequences including cost-related non-adherence. This study aimed to characterise out-of-pocket prescription medicine payments, and examine their relationship with entitlements, multimorbidity and adherence. Methods This cross-sectional study used 2016 data from a nationally-representative sample of adults in Ireland aged 50 years or less. Descriptive statistics and regression models were used to describe out-of-pocket prescription medicine payments and assess the association between out-of-pocket prescription medicine payments and the following variables: healthcare entitlements, multimorbidity, and cost-related non-adherence. Results There were 5,668 eligible participants. Median annual out-of-pocket prescription medicine expenditure was 144 euro (IQR: 0-312 euro). A generalised linear model showed that, amongst those with out-of-pocket prescription medicine expenditure, having fewer healthcare entitlements was associated with 4.74 (95% CI: 4.37-5.15) times higher out-of-pocket prescription medicine expenditure. Overall, 1.7% (n=89) of participants reported cost-related non-adherence in the previous year. A multivariable model found no significant associations between any variables and cost-related non-adherence. Conclusions Those with entitlements to subsidised prescription medicines had much lower out-of-pocket prescription medicine expenditure. This highlights the benefits of expanding healthcare entitlements and ensuring uptake of entitlements by those with eligibility.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".