Accounting for health inequities in the design of contact tracing interventions: a rapid review
Bibliographic record
Abstract
Abstract Context Contact tracing has been a central COVID-19 transmission control measure. However, without the consideration of the needs of specific populations, public health interventions can exacerbate health inequities. Purpose The purpose of this rapid review was to determine if and how health inequities were included in the design of contact tracing interventions in epidemic settings. Method We conducted a search of the electronic databases MEDLINE and Web of Science. Our inclusion criteria included articles that: (i) described the design of contact tracing interventions, (ii) have been published between 2013 and 2020 in English, French, Spanish, Chinese, or Portuguese, (iii) and included at least 50% of empiricism, according to the Automated Classifier of Texts on Scientific Studies (ATCER) tool. We relied on various tools to extract data. Result Following the titles and abstracts screening of 230 articles, 39 articles met the inclusion criteria. Only seven references were retained after full text review. None of the selected studies considered health inequities in the design of contact tracing interventions. Conclusion The use of tools/concepts for incorporating health inequities, such as the REFLEX-ISS tool, and “proportionate universalism” when designing contact tracing interventions, would enable practitioners, decision makers, and researchers to better consider health inequities.
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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 | 0.030 | 0.127 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.006 |
| Bibliometrics | 0.017 | 0.014 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 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".