Consumer out-of-pocket spending for pharmaceuticals in Kazakhstan: implications for sectoral reform
Bibliographic record
Abstract
What do consumers pay for pharmaceuticals in a transition economy, and who is hit hardest? Kazakhstan is in the midst of emerging from a Soviet Union state to a market economy. It has seen a significant dip in Gross Domestic Product and available revenues for health as a result. New sources of revenues, such as out-of-pocket payments, both formal and informal, have become widespread. In this paper we use the results of a 1996 Living Standards survey jointly sponsored by the World Bank and the Kazakhstan Government to examine patterns of prescribed pharmaceutical spending. We use a two-part regression model that is utilized to adjust for the skewness of non-spenders and heavy utilizers. Results suggest that upper-income groups spend more in absolute terms, but low-income groups pay a higher share of their income for pharmaceuticals. Pharmaceutical expenditure is positively related to poor health status, chronic illness and rural area residence. Our estimates suggest that on average people in rural areas spend 16% more than people in urban areas. The analysis shows that certain types of illnesses impose significant out-of-pocket burden for consumers - gynaecologic as well as intestinal and cardiac. The findings can be used for developing and designing a new 10-year World Bank-financed programme for restructuring the health sector. They also suggest the need for prioritizing rural care, as well as covering pharmaceuticals for specific types of care interventions and certain demographic groups.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".