Economic evaluation of COVID-19 rapid antigen screening programs in the workplace
Bibliographic record
Abstract
BACKGROUND: Diagnostic testing has been pivotal in detecting SARS-CoV-2 infections and reducing transmission through the isolation of positive cases. We quantified the value of implementing frequent, rapid antigen (RA) testing in the workplace to identify screening programs that are cost-effective. METHODS: To project the number of cases, hospitalizations, and deaths under alternative screening programs, we adapted an agent-based model of COVID-19 transmission and parameterized it with the demographics of Ontario, Canada, incorporating vaccination and waning of immunity. Taking into account healthcare costs and productivity losses associated with each program, we calculated the incremental cost-effectiveness ratio (ICER) with quality-adjusted life year (QALY) as the measure of effect. Considering RT-PCR testing of only severe cases as the baseline scenario, we estimated the incremental net monetary benefits (iNMB) of the screening programs with varying durations and initiation times, as well as different booster coverages of working adults. RESULTS: Assuming a willingness-to-pay threshold of CDN$30,000 per QALY loss averted, twice weekly workplace screening was cost-effective only if the program started early during a surge. In most scenarios, the iNMB of RA screening without a confirmatory RT-PCR or RA test was comparable or higher than the iNMB for programs with a confirmatory test for RA-positive cases. When the program started early with a duration of at least 16 weeks and no confirmatory testing, the iNMB exceeded CDN$1.1 million per 100,000 population. Increasing booster coverage of working adults improved the iNMB of RA screening. CONCLUSIONS: Our findings indicate that frequent RA testing starting very early in a surge, without a confirmatory test, is a preferred screening program for the detection of asymptomatic infections in workplaces.
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 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 | 0.008 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".