The effect of helminth infection on vaccine responses in humans and animal models: A systematic review and meta‐analysis
Bibliographic record
Abstract
Abstract Vaccination has potential to eliminate infectious diseases. However, parasitic infections such as helminths may hinder vaccines from providing optimal protection. We reviewed existing literature on the effects of helminth infections and their treatment on vaccine responses in humans and animals. We searched literature until 31 January 2022 in Medline, EMBASE, Global health, Scopus, and Web of science; search terms included WHO licensed vaccines and human helminth types. Standardized mean differences (SMD) in vaccine responses between helminth infected and uninfected or anthelminthic treated and untreated individuals were obtained from each study with suitable data for meta‐analysis, and combined using a random effects model. Analysis was stratified by whether helminth exposure was direct or prenatal and by vaccine type. This study is registered with PROSPERO (CRD42019123074). Of the 4402 articles identified, 37 were included in the review of human studies and 24 for animal experiments. For human studies, regardless of vaccine type, overall SMD for helminth uninfected/treated, compared to infected/untreated, was 0.56 (95% CI 0.04–1.07 and I 2 = 93.5%) for direct helminth exposure and 0.01 (95% CI −0.04 to 0.07 and I 2 = 85.9%) for prenatal helminth exposure. Effects of anthelminthic treatment were inconsistent, with no overall benefit shown. Results differed by vaccine type, with responses to live vaccines most affected by helminth exposure. For animal studies, the most affected vaccine was BCG. This result indicates that helminth‐associated impairment of vaccine responses is more severe for direct, than for prenatal, helminth exposure. Further research is needed to ascertain whether deworming of individuals before vaccination may help improve responses.
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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 | 0.020 | 0.047 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.023 | 0.041 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| 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".