The COVID‐19 Pandemic Economic Implications in Iran: A National Survey Assessing Catastrophic Health Expenditures
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Bibliographic record
Abstract
Introduction: The COVID‐19 pandemic caused many financial crises in households worldwide. This study aimed to quantify the COVID‐19‐related catastrophic costs (CCC) in Iran during the pandemic. Methods: In this national survey, a total of 2006 households from 10 provinces of Iran were selected using a multistage random cluster sampling. The data were collected on COVID‐19 prevention, inpatient, outpatient, and income loss costs, and the household income and wealth information using a validated researcher‐constructed questionnaire in 2022. We calculated the probability of the CCC with and without coping strategies. We analyzed data using logistic regression models and estimated the CCC for other provinces using the 2021 Household Income and Expenditures Survey. Results: The CCC was 3.19% with coping strategies and 5.38% without coping strategies. The CCC positively correlated with the COVID‐19 inpatient ( β = 2.324, 95% CI [1.65 to 2.997]) and outpatient ( β = 1.797, 95% CI [1.165 to 2.430]) service utilization. Access to the basic ( β = −0.687, 95% CI [−1.248 to −0.109]) and complementary ( β = −1.201, 95% CI [−2.612 to 0.210]) health insurance decreased the risk of the CCC. The highest and lowest probabilities of estimated CCC were observed in Sistan and Baluchistan (8.57%) and Tehran (2.1%) provinces, respectively. Conclusion: The COVID‐19 pandemic imposed an additional financial burden on households. The pandemic provided important lessons for health policymakers about the effectiveness of the health financing protection system during the crisis and the scarcity of health resources. Supply and demand of services are unbalanced in the outbreaks, and insurance systems might fall into failure due to the shortage of services, black markets, and price inflation.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it