Health inequities related to vaccination: An evidence map of potentially influential factors and systematic review of interventions
Bibliographic record
Abstract
The National Advisory Committee on Immunization (NACI) makes recommendations for vaccines in Canada. To inform considerations for equity when making recommendations, the NACI Secretariat developed a matrix of factors that may influence vaccine equity. To inform the matrix we mapped the evidence for P2ROGRESS And Other factors potentially associated with unequal levels of illness or death from vaccine-preventable diseases (VPDs) and systematically reviewed the evidence for interventions aimed at reducing inequities. In October 2019 we searched Medline, Embase, and CINAHL. Two reviewers agreed on the included studies. Our primary outcomes were VPD-related hospitalizations and deaths. Secondary outcomes were differential vaccine access, and exposure, susceptibility, severity, and consequences of VPDs. Two reviewers appraised the certainty of evidence. We mapped the evidence for P2ROGRESS And Other factors and summarized the findings descriptively. We summarized the interventions narratively. We identified 413 studies reporting on P2ROGRESS And Other factors. The most commonly investigated factors included age (n = 374, 89%), pre-existing conditions (n = 179, 42%), and gender identity or sex (n = 144, 34%). We identified 2 trials investigating the effects of interventions. One (n = 1249) provided very low certainty evidence that staff vaccination policies may reduce hospitalizations and deaths from influenza among private care home residents. The other (n not reported) provided very low certainty evidence that universal vaccination by nurses in clinics may reduce hospitalizations for rotavirus gastroenteritis compared with vaccination by physicians or no intervention. There is a large body of studies reporting on hospitalizations and deaths from VPDs stratified by P2ROGRESS And Other factors. We found only two trials examining the effects of interventions on hospitalization for or mortality from VPDs. This review has been helpful to NACI and will be helpful to similar organizations aiming to systematically identify and target health inequities through the development of vaccine program recommendations.
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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 | 0.065 | 0.250 |
| Meta-epidemiology (narrow) | 0.003 | 0.003 |
| Meta-epidemiology (broad) | 0.014 | 0.014 |
| Bibliometrics | 0.051 | 0.054 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.007 | 0.008 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.003 | 0.003 |
| 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; 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".