Catastrophic healthcare expenditures among Iranian households: a systematic review and meta-analysis
Bibliographic record
Abstract
Purpose Protecting households against financial risks of healthcare services is one of the main functions of health systems. The purpose of this paper is to provide a pooled estimate of the prevalence of catastrophic healthcare expenditures (CHE) among households in Iran. Design/methodology/approach Both international (PubMed, Scopus and Clarivate Analytics (previously known as the Institute for Scientific Information)) and Iranian (Scientific Information Database, Iranmedex and Magiran) scientific databases were searched for published studies on CHE among Iranian households. The following keywords in Persian and English language were used as keywords for the search: “catastrophic healthcare costs,” “catastrophic health costs,” “impoverishment due to health costs,” “fair financial contribution,” “prevalence,” “frequency” and “Iran” with and without “health system”. The I2-test and χ2-based Q-test suggested heterogeneity in the reported prevalence among the qualified studies; thus, a random-effects model was used to estimate the overall prevalence of CHE among households in Iran. Findings A total of 24 studies with a cumulative sample of 301,097 households were included in the study. The estimated pooled prevalence of CHE among households was 7 percent (95 percent confidence interval: 6–8 percent). Meta-regression analysis indicated that the prevalence of CHE was inversely related to the sample size (p<0.05). The results did not suggest a significant association between the prevalence of CHE and the year of data collection. Originality/value The findings revealed that the prevalence of CHE among Iranian households is significantly higher than 1 percent, which is the goal set out in Iran’s fourth five-year development plan. This warrants further policy interventions to protect households from incurring CHE in Iran.
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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 | 0.013 | 0.027 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.013 | 0.025 |
| Bibliometrics | 0.010 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| 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".