Main Determinants of Supplementary Health Insurance Demand: (Case of Iran)
Bibliographic record
Abstract
INTRODUCTION: In the majority of developing countries, the volume of medical insurance services, provided by social insurance organizations is inadequate. Thus, supplementary medical insurance is proposed as a means to address inadequacy of medical insurance. Accordingly, in this article, we attempted to provide the context for expansion of this important branch of insurance through identification of essential factors affecting demand for supplementary medical insurance. METHOD: In this study, two methods were used to identify essential factors affecting choice of supplementary medical insurance including Classification and Regression Trees (CART) and Bayesian logit. To this end, Excel® software was used to refine data and R® software for estimation. The present study was conducted during 2012, covering all provinces in Iran. Sample size included 18,541 urban households, selected by Statistical Center of Iran using 3-stage cluster sampling approach. In this study, all data required were collected from the Statistical Center of Iran. RESULTS: In 2012, an overall 8.04% of the Iranian population benefited from supplementary medical insurance. Demand for supplementary insurance is a concave function of age of the household head, and peaks in middle-age when savings and income are highest. The present study results showed greater likelihood of demand for supplementary medical insurance in households with better economic status, higher educated heads, female heads, and smaller households with greater expected medical expenses, and household income is the most important factor affecting demand for supplementary medical insurance. CONCLUSION: Since demand for supplementary medical insurance is hugely influenced by households' economic status, policy-makers in the health sector should devise measures to improve households' economic or financial access to supplementary insurance services, by identifying households in the lower economic deciles, and increasing their financial ability to pay. Moreover, insurance companies should adjust their insurance policy according to clients' needs, household characteristics, and their incomes.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".