Global Incidence of Human Shiga Toxin–Producing <i>Escherichia coli</i> Infections and Deaths: A Systematic Review and Knowledge Synthesis
Bibliographic record
Abstract
OBJECTIVES: Shiga toxin-producing Escherichia coli (STEC) are an important cause of foodborne disease, yet global estimates of disease burden do not exist. Our objective was to estimate the global annual number of illnesses due to pathogenic STEC, and resultant hemolytic uremic syndrome (HUS), end-stage renal disease (ESRD), and death. MATERIALS: We searched Medline, Scopus, SIGLE/OpenGrey, and CABI and World Health Organization (WHO) databases for studies of STEC incidence in the general population, published between January 1, 1990 and April 30, 2012, in all languages. We searched health institution websites for notifiable disease data and reports, cross-referenced citations, and consulted international knowledge experts. We employed an a priori hierarchical study selection process and synthesized results using a stochastic simulation model to account for uncertainty inherent in the data. RESULTS: We identified 16 articles and databases from 21 countries, from 10 of the 14 WHO Sub-Regions. We estimated that STEC causes 2,801,000 acute illnesses annually (95% Credible Interval [Cr.I.]: 1,710,000; 5,227,000), and leads to 3890 cases of HUS (95% Cr.I.: 2400; 6700), 270 cases of ESRD (95% Cr.I.: 20; 800), and 230 deaths (95% Cr.I.: 130; 420). Sensitivity analyses indicated these estimates are likely conservative. CONCLUSIONS: These are the first estimates of the global incidence of STEC-related illnesses, which have not been explicitly included in previous global burden of disease estimations. Compared to other pathogens with a foodborne transmission component, STEC appears to cause more cases than alveolar echinococcosis each year, but less than typhoid fever, foodborne trematodes, and nontyphoidal salmonellosis. APPLICATIONS: Given the persistence of STEC globally, efforts aimed at reducing the burden of foodborne disease should consider the relative contribution of STEC in the target population.
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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 | 0.013 | 0.048 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.011 | 0.009 |
| Bibliometrics | 0.025 | 0.023 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| 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".