Large-scale infectious disease testing programs have little consideration for equity: findings from a scoping review
Bibliographic record
Abstract
OBJECTIVE: This scoping review aimed to identify how equity has been considered in large-scale infectious disease testing initiatives. STUDY DESIGN AND SETTING: Large-scale testing interventions are instrumental for infectious disease control and a central tool for the coronavirus 19 (COVID-19) pandemic. We searched Web of Science: core collection, Embase and Medline in June 2021 and followed the Preferred Reporting Items for Systematic Reviews and Meta-Analyses recommendations for scoping reviews. We critically analyzed the content of all included articles. RESULTS: Our search resulted in 2448 studies of which 86 were included for data extraction after screening. Of the included articles, 80% reported on COVID-19 -related screening programs. None of the studies presented a formal definition of (in)equity in testing, however, 71 articles did indirectly include elements of equity through the justification of their target population. Of these 71 studies, 58% articles indirectly alluded to health equity according to the PROGRESS-Plus framework, an acronym used to identify a list of socially stratifying characteristics driving inequity in health outcomes. CONCLUSION: The studies included in our scoping review did not explicitly consider equity in their design or evaluation which is imperative for the success of infectious disease testing programs.
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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 | 0.075 | 0.274 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.006 | 0.008 |
| Bibliometrics | 0.017 | 0.025 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.008 | 0.009 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.003 | 0.003 |
| 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".