Risk factors for serious outcomes associated with influenza illness in high‐ versus low‐ and middle‐income countries: Systematic literature review and meta‐analysis
Bibliographic record
Abstract
AIM: To determine factors associated with a serious outcome (hospital admission or severe outcome: critical care or death) and associated with illness caused by laboratory-confirmed influenza, with a specific interest in low- and middle-income countries (LMIC). METHOD: Databases were searched on 11 March 2016 for reports of influenza and factors associated with mortality or morbidity in humans, with no language restrictions. Pooled risks were estimated using random-effects models. RESULTS: Despite the heterogeneity of results across studies, known risk factors for serious disease were associated with both hospital admission and severe outcomes (critical care and/or death). In LMIC, but not in high income countries (HIC), pregnant women, people with HIV/AIDS and children < 5 years old (compared with older children) were at increased risk of a severe outcome. Also, although all patients with neurological conditions were at higher risk of severe outcomes than those without, children were at higher risk than adults and children who lived in a LMIC were at significantly higher risk than those living in HIC. Adults were more likely than children to suffer a severe outcome if they had diabetes or a hematologic condition, were obese or had liver disease. Asthma is a risk factor for hospital admission but not for severe outcomes. CONCLUSION: Known risk factors for serious disease remain important predictors of hospital admission and severe outcomes with few differences between HIC and LMIC countries. These differences likely reflect differences in health-seeking behaviours and health services, but high heterogeneity between studies limits conclusions about the effect size.
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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 | 0.008 | 0.023 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.014 | 0.033 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".