Assessment of Global Incidence and Mortality of Hospital-treated Sepsis: Current Estimates and Limitations
Bibliographic record
Abstract
RATIONALE: Reducing the global burden of sepsis, a recognized global health challenge, requires comprehensive data on the incidence and mortality on a global scale. OBJECTIVES: To estimate the worldwide incidence and mortality of sepsis and identify knowledge gaps based on available evidence from observational studies. METHODS: We systematically searched 15 international citation databases for population-level estimates of sepsis incidence rates and fatality in adult populations using consensus criteria and published in the last 36 years. MEASUREMENTS AND MAIN RESULTS: The search yielded 1,553 reports from 1979 to 2015, of which 45 met our criteria. A total of 27 studies from seven high-income countries provided data for metaanalysis. For these countries, the population incidence rate was 288 (95% confidence interval [CI], 215-386; τ = 0.55) for hospital-treated sepsis cases and 148 (95% CI, 98-226; τ = 0.99) for hospital-treated severe sepsis cases per 100,000 person-years. Restricted to the last decade, the incidence rate was 437 (95% CI, 334-571; τ = 0.38) for sepsis and 270 (95% CI, 176-412; τ = 0.60) for severe sepsis cases per 100,000 person-years. Hospital mortality was 17% for sepsis and 26% for severe sepsis during this period. There were no population-level sepsis incidence estimates from lower-income countries, which limits the prediction of global cases and deaths. However, a tentative extrapolation from high-income country data suggests global estimates of 31.5 million sepsis and 19.4 million severe sepsis cases, with potentially 5.3 million deaths annually. CONCLUSIONS: Population-level epidemiologic data for sepsis are scarce and nonexistent for low- and middle-income countries. Our analyses underline the urgent need to implement global strategies to measure sepsis morbidity and mortality, particularly in low- and middle-income countries.
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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 | 0.163 | 0.363 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.007 | 0.005 |
| Bibliometrics | 0.017 | 0.020 |
| Science and technology studies | 0.001 | 0.005 |
| Scholarly communication | 0.006 | 0.007 |
| Open science | 0.006 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".