Sex-disaggregated effectiveness data reporting in COVID-19 vaccine research: a systematic review
Bibliographic record
Abstract
BACKGROUND: Sex and gender are believed to influence vaccine response. Yet, the relationship between sex and gender and COVID-19 vaccine efficacy is poorly understood and remains under-investigated. METHODS: We conducted a systematic review to determine whether and to what extent post-approval COVID-19 vaccine effectiveness (VE) studies report sex-disaggregated VE data. We searched four publication and pre-publication databases and additional grey literature sources for relevant published/preprint studies released between 1 January 2020 and 1 October 2021 (i.e., pre-Omicron era). We included observational studies providing VE estimates for one or more licensed/approved COVID-19 vaccines and including both males and females. Two reviewers independently assessed study eligibility, extracted data, and assessed risk-of-bias through a modified version of Cochrane's ROBINS-I tool. A qualitative data synthesis was performed. RESULTS: Here we show that, among 240 eligible publications, 68 (28.3%) do not report the sex distribution among participants. Only 21/240 (8.8%) studies provide sex-disaggregated VE estimates, and high between-study heterogeneity regarding design, target population, outcomes, and vaccine type/timing prevent the assessment of sex in determining COVID-19 VE across studies. CONCLUSIONS: Our findings indicate that few COVID-19 vaccine research publications account for sex. Improved adherence to recommended reporting guidelines will ensure that the evidence generated can be used to better understand the relationship between sex and gender and VE.
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How this classification was reachedexpand
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 | 0.060 | 0.199 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.008 | 0.000 |
| Bibliometrics | 0.002 | 0.007 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.000 | 0.003 |
| Insufficient payload (model declined to judge) | 0.000 | 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; both teacher heads 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".