Obesity and Respiratory Hospitalizations During Influenza Seasons in Ontario, Canada: A Cohort Study
Bibliographic record
Abstract
BACKGROUND: Previous studies suggest that obesity may be a risk factor for complications from pandemic influenza A(H1N1) infection. We aimed to examine the association between obesity and respiratory hospitalizations during seasonal influenza epidemics and to determine the extent of this association among individuals without established risk factors for serious complications due to influenza infection. METHODS: We conducted a cohort study over 12 influenza seasons (1996-1997 through 2007-2008) of 82545 respondents to population health surveys in Ontario, Canada. We included individuals aged 18-64 years who had responded to a survey within 5 years prior to the start of an influenza season. We used logistic regression to examine the association between self-reported body mass index (BMI) and hospitalization for selected respiratory diseases (pneumonia and influenza, acute respiratory diseases, and chronic lung diseases), both in the entire cohort and stratified by chronic condition status. RESULTS: Obese class I (BMI, 30-34.9) (odds ratio [OR], 1.45 [95% confidence interval {CI}, 1.03-2.05]) and obese class II or III (BMI, ≥35) individuals (OR, 2.12 [95% CI, 1.45-3.10]) were more likely than normal weight individuals to have a respiratory hospitalization during influenza seasons. Among obese class II or III individuals, the association was present both for those without previously identified risk factors (OR, 5.10 [95% CI, 2.53-10.24]) and for those with 1 risk factor (OR, 2.11 [95% CI, 1.10-4.06]). CONCLUSIONS: Severely obese individuals with and without chronic conditions are at increased risk for respiratory hospitalizations during influenza seasons. They should be considered a priority group for preventive influenza measures, such as vaccination and treatment with antiviral medications.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".