Establishing syndromic surveillance of gastrointestinal infections in emergency departments using routine emergency department data and validating it against laboratory-based surveillance, Germany, January 2019 – June 2023
Bibliographic record
Abstract
2. Structured Abstract Background Gastrointestinal infections in Germany account for 24.5 million outpatient visits annually. Surveillance of gastrointestinal infections in emergency departments strengthens timely outbreak detection and disease trend monitoring. Aim We developed a syndrome definition for automated syndromic surveillance of gastrointestinal infections in emergency departments, and validated it against statutory laboratory-based surveillance. Methods To develop a syndrome definition, we selected presenting complaints (Canadian Emergency Department Information System) and diagnoses (ICD-10). We validated the definition through time series and cross-correlation analysis, comparing trends between syndromic and laboratory-based surveillance. We analysed German emergency department registry (AKTIN) data and included emergency departments that continuously transferred (01/2019-06/2023) data. As reference we combined statutory norovirus-gastroenteritis, rotavirus-gastroenteritis, campylobacteriosis and salmonellosis notifications. Results Our syndrome definition combined presenting complaints (diarrhoea, vomiting and nausea) and diagnoses (Intestinal infectious diseases). Accordingly, in 7 emergency departments with n = 864,353 visits, 2.1% ( n = 18,158) were gastrointestinal infection cases. Of those, 57% ( n = 10,424) were female, with 23% 0–19 years ( n = 4,108) and 23% 20–29 years ( n = 4,116) old. We visually observed similar gastrointestinal infection trends in both surveillance systems. The cross-correlation was 0.73 (95%-confidence interval 0.61–0.85; p <0.001) at lag −1, indicating a 1-week relative reporting delay of laboratory-based surveillance. Conclusion The coherent trends and significant cross-correlation validated our syndrome definition, which adequately captures gastrointestinal infection cases in emergency departments. Our novel automated surveillance complements laboratory-based surveillance, while offering advantages regarding timeliness and reduced workload. Therefore, it will be implemented in national routine surveillance.
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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 | 0.005 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".