PP66 Increasing Burden Of Out-Of-Pocket Healthcare Expense On Patients
Bibliographic record
Abstract
Introduction We conducted an analysis of the key factors triggering cost-sharing mechanisms to understand the status of out-of-pocket (OOP) healthcare expense in the United States (US), Europe, and emerging markets and better appreciate the implications of OOP healthcare expense on patients’ health management. Methods A review of literature and databases including The Organisation for Economic Co-operation and Development (OECD) and World Bank was performed to understand different cost-sharing mechanisms, factors triggering OOP expenditure and the country-wise trends of OOP expenditure. Additionally, the impact of OOP expenditure on healthcare budget and on patients in terms of medication adherence, uptake of newer therapies and generic substitution was explored. Results The findings reveal that patients are concerned about rising healthcare OOP costs, and we observed an increase of 134 percent in the number of articles published on OOP from 2005 to 2017. The percentage of household spending that goes OOP as healthcare expense is higher in Brazil, Russia, India, and China (BRIC countries; ~11 percent) compared to France, Germany, Italy, United Kingdom, US, Japan, and Canada (G7 countries; ~2 percent). In addition, OOP expenditure increased with age (1.9 percent of take home income in 55-64 age group versus 1.2 percent in 18-25 age group) and is higher in the low-income population (2.8 percent of take home income versus 1 percent in high-income group). Whereas, increasing OOP expenditure reduces the overall healthcare expenditure due to generic substitution (28 percent reduction) and reduction in excessive consumption of supplementary medicines, it also reduces patient adherence (~20 percent decline in dispensed prescriptions) and may foster a reluctance to adopt newer therapies. Conclusions The population groups most impacted by increasing OOP expense are the older population, those in the low-income bracket and in poorer countries. While OOP expense may help in the effective and judicious utilization of healthcare system resources and medicines usage, its implementation requires a cautious and considered approach.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.005 | 0.007 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.017 | 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".