Beyond borders: A systematic review and meta-analysis of human-specific faecal markers across geographical settings
Bibliographic record
Abstract
Human fecal waste is a global health risk associated with diarrheal diseases, responsible for approximately 1.2 million deaths annually. Microbial Source Tracking (MST) is a molecular method that evaluates environmental sources of fecal contamination, aiding quantification of this contamination and associated health risks. However, reported variations in global human gut microbiomes and geographic performance of human-specific fecal markers suggest that current MST targets may not have broad applicability across populations. This systematic review quantified the performance of human-specific fecal markers to identify those suitable for use across various geographic regions. We evaluated data from primary research articles, published before 18th October 2023, identified through PubMed, Scopus, and Web of Science using PRISMA guidelines. 103 studies published between 1995 and 2023, spanning 34 countries, 6 continents, and 4 climate zones met inclusion criteria, with quantifiable performance metrics (sensitivity, specificity or accuracy) and a geographic testing location. Extracted data was analyzed to establish marker performance across geographic locations, climate zones, and development status. Over 80% were conducted in High-Income Countries (HICs) and >50% in temperate zones, primarily in the USA (43%), Australia (24%), and Spain (19%). Bacteroides HF183 was the most commonly tested (n = 45 studies). However, no target consistently demonstrated sensitivity, specificity, and/or accuracy >80% across different settings. Consequently, a decision tree is presented supporting selection of appropriate human-specific markers for regional-specific baseline studies. This provides critical information to support new MST research, particularly in Low- and Middle-Income Countries (LMICs), assisting with informed decision and method selection for assessing risks of faecal derived pathogens.
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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 | 0.023 | 0.063 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.019 | 0.029 |
| Bibliometrics | 0.012 | 0.015 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.003 | 0.003 |
| 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".