Reporting of health equity considerations in vaccine trials for COVID-19: a methodological review
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVES: An emerging body of randomized controlled trials (RCTs) on COVID-19 vaccines has served as the evidence base for public health decision-making. While it is recommended that RCTs report results by health equity stratifiers to reduce bias in health care and gaps in research, it is unknown whether this was done in COVID-19 vaccine trials. To critically examine the use of health equity stratifiers in COVID-19 vaccine trials. STUDY DESIGN AND SETTING: We conducted a methodological review of published COVID-19 vaccine trials available in the COVID-19 living Network Meta-Analysis systematic review database through February 8, 2023. Based on the PROGRESS-Plus framework, we examined the following health equity stratifiers: place of residence, race/ethnicity, occupation, gender/sex, religion, education, socio-economic status, social capital, age, disability, features of relationships, and temporary situations. We assessed each study in duplicate according to three criteria for comprehensive health-equity reporting: 1) describing participants, 2) reporting equity-relevant results, and 3) discussing equity-relevant implications of trial findings. RESULTS: We reviewed 144 trial manuscripts. The most frequently used PROGRESS-Plus stratifiers to describe participants were age (100%), place of residence (100%), gender/sex (99%), and race/ethnicity (64%). Age was most often used to disaggregate or adjust results (67%), followed by gender or sex (35%). Discussions of equity-relevant implications often indicated limited generalizability of results concerning age (40% of studies). Half (47%) of the studies considered at least one health equity stratifier for all three criteria. No trials included stratifiers related to religion, socioeconomic status, sexual orientation, or features of relationships. CONCLUSION: COVID-19 vaccine trials provided a limited description of health equity stratifiers as defined by PROGRESS-Plus and infrequently disaggregated results or discussed the study implications as they related to health equity. Considering the health disparities exacerbated during the pandemic, increased uptake of PROGRESS-Plus in RCTs would support a more nuanced understanding of health disparities and better inform actions to improve health equity.
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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 | 0.370 | 0.691 |
| Meta-epidemiology (narrow) | 0.003 | 0.003 |
| Meta-epidemiology (broad) | 0.014 | 0.024 |
| Bibliometrics | 0.010 | 0.009 |
| Science and technology studies | 0.001 | 0.005 |
| Scholarly communication | 0.009 | 0.007 |
| Open science | 0.005 | 0.007 |
| Research integrity | 0.006 | 0.006 |
| Insufficient payload (model declined to judge) | 0.006 | 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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".