Staphylococcus aureus bacteremia mortality across country income groups: A secondary analysis of a systematic review
Bibliographic record
Abstract
ObjectivesStaphylococcus aureus bacteremia (SAB) is a common infection worldwide. We compared SAB mortality in low- and middle-income countries (LMIC) versus high-income countries (HIC) in a meta-analysis.MethodsWe searched MEDLINE, Embase, and Cochrane Database of Systematic Reviews from 1991-2021 and included observational, single-country studies on patients with positive blood cultures for S. aureus. The main outcome was the proportion of patients with SAB who died in the hospital. A generalized linear mixed random-effects model was used to pool estimates, and a meta-regression was used to adjust for study-level characteristics.ResultsA total of 332 studies involving 517,671 patients in 39 countries were included. No study was conducted in a low-income country. Only 33 (10%) studies were performed in middle-income countries (MIC), which described 6,216 patients. The pooled in-hospital mortality was 32.4% (95% confidence interval [CI] 27.2%-38.2%, T2 = 0.3063) in MIC and 22.3% (95% CI 20.1%-24.6%, T2 = 0.3257) in HIC. In a meta-regression model, MIC had higher in-hospital mortality (adjusted odds ratio 1.37, 95% CI 1.11-1.71; P = 0.0042) than HIC.ConclusionIn SAB studies, LMIC are poorly represented. In-hospital mortality was significantly higher in MIC than in HIC. Research should be conducted in LMIC to characterize differences in care processes driving the mortality gap.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.005 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 teacher head, 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".