Prevalence of chronic comorbidities in severe flavivirus infections
Bibliographic record
Abstract
Background: Flavivirus diseases such as dengue fever (DENV), West Nile virus (WNV), Zika and yellow fever represent a substantial global public health concern. Preexisting chronic conditions such as cardiovascular diseases, diabetes, obesity, and asthma were thought to predict risk of progression to severe infections. We aimed to quantify the prevalence of comorbidities in flavivirus diseases and to evaluate the relationship between these conditions and the severity of clinical viral expression. Methods & Materials: We conducted a comprehensive search in PubMed, Ovid MEDLINE(R), Embase and Embase Classic and grey literature databases to identify studies reporting prevalence estimates of comorbidities in flavivirus diseases. Study quality was assessed with the risk of bias tool. Subgroup analyses were undertaken to evaluate the prevalence estimates in different world regions. Results: We identified 65 studies as eligible for inclusion for DENV (47 studies) and WNV (18 studies). Obesity (prevalence: 24.5%, 95% CI: 18.6-31.6%), hypertension (17.5%, 13.6-22.1%) and diabetes (13.2%, 9.4-18.2%) were the most prevalent comorbidities in DENV. However, hypertension (45.0%, 39.1-51.0%), diabetes (24.7%, 20.2-29.8%) and heart diseases (25.6%, 19.5-32.7%) were the most prevalent in WNV. Prevalence rates varied markedly in the different world regions. There were ∼2- to 4-fold significantly higher prevalence of diabetes, hypertension and heart diseases in severe flavivirus cases compared to the non-severe ones. Conclusion: Findings of the present study may guide public health practitioners and clinicians to predict infection severity based on the presence of comorbidity, a critical public health measure that may avert severe disease outcome given the current dearth of a clear prevention practices for some flavivirus diseases.
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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 | 0.010 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.008 | 0.008 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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 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".