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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".