Illness Severity and Outcomes Among Enteric Fever Cases From Bangladesh, Nepal, and Pakistan: Data From the Surveillance for Enteric Fever in Asia Project, 2016–2019
Bibliographic record
Abstract
BACKGROUND: Enteric fever can lead to prolonged hospital stays, clinical complications, and death. The Surveillance for Enteric Fever in Asia Project (SEAP), a prospective surveillance study, characterized the burden of enteric fever, including illness severity, in selected settings in Bangladesh, Nepal, and Pakistan. We assessed disease severity, including hospitalization, clinical complications, and death among SEAP participants. METHODS: We analyzed clinical and laboratory data from blood culture-confirmed enteric fever cases enrolled in SEAP hospitals and associated network laboratories from September 2016 to September 2019. We used hospitalization and duration of hospital stay as proxies for severity. We conducted a follow-up interview 6 weeks after enrollment to ascertain final outcomes. RESULTS: Of the 8705 blood culture-confirmed enteric fever cases enrolled, we identified 6 deaths (case-fatality ratio, .07%; 95% CI, .01-.13%), 2 from Nepal, 4 from Pakistan, and none from Bangladesh. Overall, 1.7% (90/5205) of patients recruited from SEAP hospitals experienced a clinical complication (Bangladesh, 0.6% [18/3032]; Nepal, 2.3% [12/531]; Pakistan, 3.7% [60/1642]). The most identified complications were hepatitis (n = 36), septic shock (n = 22), and pulmonary complications/pneumonia (n = 13). Across countries, 32% (2804/8669) of patients with hospitalization data available were hospitalized (Bangladesh, 27% [1295/4868]; Nepal, 29% [455/1595]; Pakistan, 48% [1054/2206]), with a median hospital stay of 5 days (IQR, 3-7). CONCLUSIONS: While defined clinical complications and deaths were uncommon at the SEAP sites, the high proportion of hospitalizations and prolonged hospital stays highlight illness severity and the need for enteric fever control measures, including the use of typhoid conjugate vaccines.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".