Estimation of Out-of-Pocket Expenditure on COVID-19 Management Among Patients Treated at Home, Iraq, 2020
Bibliographic record
Abstract
Background There is a global consensus that the socioeconomic impact of the COVID-19 crisis has had a substantial effect on health programs and health insurance, with losses of jobs and rising prices causing growing poverty. Objective This study aims to estimate the out-of-pocket expenditure spent on the management of patients with COVID-19 exclusively treated at home. Methods A cross-sectional study was conducted, and data were collected from participating patients with COVID-19 in Iraq through snowball sampling by using a questionnaire. Enrollment occurred from November 1 to December 31, 2020, and excluded individuals who were entering the hospitals. Results Among 589 participating patients with COVID-19, 328 (55.7%) were female. Female patients spent more than male patients to get cured of the illness; the mean amount of money spent by women was statistically higher than men (IQD 644,617 [US $402] and IQD 461,653 [US $307], respectively). The average total money expenditures spent was IQD 643,304 (US $428; range IQD 505,096-5,595,000 [US $336-US $3730]) among patients exclusively treated at home. The average money spent by patients with inadequate monthly income (IQD 901,424 [US $600], range IQD 220,000-5,260,000 [US $140-US $3500]) was significantly more than patients with adequate monthly income (IQD 613,252 [US $400], range IQD 48,000-5,500,000 [US $32-US $3600]). Patients with COVID-19 (25.5%) who had chronic diseases spent significantly more money (IQD 696,330 [US $460]) than those without the chronic disease (IQD 625,185 [US $416]). Conclusions Financial burdens affected the purchasing power and the economic situation on the management of patients with COVID-19 exclusively treated at home.
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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 | 0.003 |
| 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 | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".