Economic affordability of medicines and its impact on households in Russia
Bibliographic record
Abstract
The article studies the financial affordability of medicines for Russian families and its impact on the well-being of the population. Affordability of medicines depends on market conditions and government guarantees for free provision of medicines. Estimated drug consumption in the Russian Federation is based on microeconomic data from the nationwide Russia Longitudinal Monitoring Survey (RLMS-HSE) and pharmaceutical market data from DSM-Group. To assess the impact of pharmaceutical spending on poverty, disposable cash income per family member after deduction of medicines cost was calculated taking into account the poverty line. More than 2/3 of medicines in the Russian Federation are purchased by households on the commercial market. According to the study, every fifth household was at risk of poverty due to spending on medicines. The risk of poverty due to pharmaceutical costs is high in low-income families. Payments for medicines increase the number of families with out-of-pocket medical expenses by 5 times. Catastrophic spending on medicines in the Russian Federation reached its lowest level in 2021 according to the Universal Healthcare Coverage (UHC) criteria of the World Health Organization (WHO). There were no families that spent more than 100% of their income on medicines, and only 1.2% of families spent more than a quarter of their income. Every twentieth family refused to buy medicines at least once a year. In groups where the cost of medicines exceeded 10% of income, every tenth family refused to buy medicines. When the cost exceeded 25%, almost every fifth family refused to buy medicines. Thus, the rising prices and expenses for medicines are forcing Russian families to save on medicines and on their health. Recommendations for expanding the affordability of medicines for Russian families are given in the conclusion.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".