Systematic review and meta-analysis of epidemiologic data on infectious disease in relation to exposure to twelve perfluoroalkyl substances (PFAS)
Bibliographic record
Abstract
BACKGROUND: While some per- and polyfluoroalkyl substances (PFAS) are immunosuppressants, whether they have an adverse effect on infectious disease morbidity is unclear. We conducted a systematic review and meta-analysis of epidemiologic data on the association between an incremental increase in serum concentration of any of 12 PFAS and the risk or rate of infectious disease (ID). METHODS: From 25 reports representing 18 unique study populations, we conducted meta-analyses stratified on exposure type (log-transformed or absolute scale) and outcome type (risk or rate). To synthesize data that could not be combined with meta-analysis due to different exposure or outcome types, we additionally conducted vote counting and calculated combined p-values. RESULTS: A small positive association between PFAS exposure and ID risk or rate was more frequently reported than not, though in the synthesized data statistical significance was present only in a few instances. The meta-analyses and combined p-value analyses had many similar findings. In the combined p-value analyses, statistically significant positive associations were noted between Perfluorononanoic acid and lower respiratory tract infection (LRTI) event rates, Perfluorooctanesulfonamide and LRTI event rates and LRTI risk and rates combined, Perfluorooctanoic acid and Perfluorodecanoic acid with all ID risk and rates combined, and Perfluoroundecanoic acid with all ID risk. CONCLUSION: We identified moderate evidence of positive associations that were of variable size but usually small; the certainty of evidence was, however, generally low or very low and diminished by the possible influences of multiple testing and covariance among results not accounted for in the analyses. PROSPERO REGISTRATION: CRD42024551990.
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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 | 0.062 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.019 | 0.038 |
| Bibliometrics | 0.011 | 0.012 |
| 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) | 0.003 | 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".