Economic Burden of COVID-19: A Systematic Review
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Objective: To review and qualitatively synthesize the evidence related to the economic burden of COVID-19, including healthcare resource utilization and costs. Methods: A systematic review of studies that assessed the economic burden [eg, direct costs, productivity, macroeconomic impact due to non-pharmaceutical interventions (NPIs) and equity] of COVID-19 was conducted by searches in EMBASE, MEDLINE, MEDLINE-IN-PROCESS, and The Cochrane Library, as well as manual searches of unpublished research for the period between January 2020 to February 2021. Single reviewer data extraction was confirmed independently by a second reviewer. Results: The screening process resulted in a total of 27 studies: 25 individual publications, and 2 systematic literature reviews, of narrower scopes, that fulfilled the inclusion criteria. The patients diagnosed with more severe COVID-19 were associated with higher costs. The main drivers for higher costs were consistent across countries and included ICU admission, in-hospital resource use such as mechanical ventilation, which lead to increase costs of $2082.65 ± 345.04 to $2990.76 ± 545.98. The most frequently reported indirect costs were due to productivity losses. On average, older COVID-19 patients incurred higher costs when compared to younger age groups. An estimation of a 20% COVID-19 infection rate based on a Monte Carlo simulation in the United States led to a total direct medical cost of $163.4 billion over the course of the pandemic. Conclusion: The COVID-19 pandemic has generated a considerable economic burden on patients and the general population. Preventative measures such as NPIs only have partial success in lowering the economic costs of the pandemic. Implementing additional preventative measures such as large-scale vaccination is vital in reducing direct and indirect medical costs, decreased productivity, and GDP losses.
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.
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 | 0.011 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.008 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.002 |
| 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 it