Multimorbidity and out-of-pocket expenditure on medicine in Europe: Longitudinal analysis of 13 European countries between 2013 and 2015
Bibliographic record
Abstract
Background Many European Health Systems are implementing or increasing levels of cost-sharing for medicine in response to the growing constrains on public spending on health despite their negative impact on population health due to delay in seeking care. Objective This study aims to examine the relationships between multimorbidity (two or more coexisting chronic diseases, CDs), complex multimorbidity (three or more CDs impacting at least three different body systems), and out-of-pocket expenditure (OOPE) for medicine across European nations. Methods This study utilized data on participants aged 50 years and above from two recent waves of the Survey of Health, Aging, and Retirement in Europe conducted in 2013 (n = 55,806) and 2015 (n = 51,237). Pooled cross-sectional and longitudinal study designs were used, as well as a two-part model, to analyse the association between multimorbidity and OOPE for medicine. Results The prevalence of multimorbidity was 50.4% in 2013 and 48.2% in 2015. Nearly half of those with multimorbidity had complex multimorbidity. Each additional CD was associated with a 34% greater likelihood of incurring any OOPE for medicine (Odds ratio = 1.34, 95% CI = 1.31–1.36). The average incremental OOPE for medicine was 26.4 euros for each additional CD (95% CI = 25.1–27·7), and 32.1 euros for each additional body system affected (95% CI 30.6–33.7). In stratified analyses for country-specific quartiles of household income the average incremental OOPE for medicine was not significantly different across groups. Conclusion Between 2013 and 2015 in 13 European Health Systems increased prevalence of CDs was associated with greater likelihood of having OOPE on medication and an increase in the average amount spent when one occurred. Monitoring this indicator is important considering the negative association with treatment adherence and subsequent effects on health.
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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 | 0.005 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| 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".