Measuring the Protective Effect of Health Insurance Coverage on Out-of-Pocket Expenditures During the COVID-19 Pandemic in the Peruvian Population
Bibliographic record
Abstract
Health insurance coverage is expected to protect individuals from out-of-pocket (OOP) expenditures, potentially preventing them from falling into poverty. However, to date, the effect of health insurance on OOP spending during the coronavirus disease 2019 (COVID-19) pandemic has not been fully explored. This study aimed to estimate differences in the proportion and the amount of OOP expenditures among Peruvians during the pre- and post-mandatory lockdown response to COVID-19 in 2020 according to the health insurance coverage status.This study utilized repeated cross-sectional data from the National Household Survey on Living and Poverty Conditions (ENAHO) from the first quarter of 2017 until the fourth quarter of 2020. The outcomes were (i) the proportion of individuals who incurred OOP expenditures and (ii) the monetary value of OOP expenditures. An interrupted time series analysis (ITS) and a quasi-experimental difference-in-difference (DID) analysis were performed to examine the outcomes among the control (individuals without health insurance) and treatment groups (individuals with health insurance) after the COVID-19 pandemic.ITS analysis showed that the proportion of individuals reporting OOP expenditures after implementation of mandatory lockdown due to COVID-19 in Peru decreased in both groups, but no difference in the slope trend was found (P=.916). The average quarterly amount of OOP spending increased in both groups, but no difference in the slope trend was found (P=.073). Lastly, the DID analysis showed that the mandatory lockdown was associated with a higher amount of OOP, but there was no evidence to indicate that the higher amount was different between the control and treatment groups.The mandatory lockdown in response to the COVID-19 was associated with a higher amount of OOP expenditures and a lower likelihood of incurring OOP expenditures. However, our findings suggest that health insurance coverage does not lower OOP expenditures or reduce the likelihood of incurring OOP expenditures.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".