Out-of-pocket pharmaceutical expenditure and its determinants among Iranian households with elderly members: a double-hurdle model
Bibliographic record
Abstract
OBJECTIVES: The population of older adults continues to grow in Iran, with pharmaceutical costs as a leading driver of household health-related costs. The present study was conducted to estimate the out-of-pocket pharmaceutical expenditure and its socioeconomic predictors among households with the elderly in Iran. METHOD: This study is a secondary analysis using 2019 national household expenditure and income survey data in Iran. The sample size was 9381 households with at least one member older than 65. The double-hurdle model in STATA 16 was used to examine the association between independent variables and households' out-of-pocket pharmaceutical expenditures. RESULTS: The mean out-of-pocket pharmaceutical expenditures for each household with elderly member was $8065 per year. There was a positive association between the (female) gender of the household head, urban residence, employment status, insurance expenditure and a higher level of education of the head of the household with the out-of-pocket pharmaceutical expenditures (P < 0.05). The income of elderly households did not affect these expenditures (P > 0.05). CONCLUSIONS: This study showed that the socioeconomic characteristics of elderly families not only influenced their decision to enter the medicine market, but also the rate of medicine purchase. It is helpful to manage and control the pharmaceutical costs among the elderly.
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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 | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.005 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 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".