Relationship between community prevalence of obesity and associated behavioral factors and community rates of influenza‐related hospitalizations in the United States
Bibliographic record
Abstract
BACKGROUND: Findings from studies examining the association between obesity and acute respiratory infection are inconsistent. Few studies have assessed the relationship between obesity-related behavioral factors, such as diet and exercise, and risk of acute respiratory infection. OBJECTIVE: To determine whether community prevalence of obesity, low fruit/vegetable consumption, and physical inactivity are associated with influenza-related hospitalization rates. METHODS: Using data from 274 US counties, from 2002 to 2008, we regressed county influenza-related hospitalization rates on county prevalence of obesity (BMI ≥ 30), low fruit/vegetable consumption (<5 servings/day), and physical inactivity (<30 minutes/month recreational exercise), while adjusting for community-level confounders such as insurance coverage and the number of primary care physicians per 100,000 population. RESULTS: A 5% increase in obesity prevalence was associated with a 12% increase in influenza-related hospitalization rates [adjusted rate ratio (ARR) 1.12, 95% confidence interval (CI) 1.07, 1.17]. Similarly, a 5% increase in the prevalence of low fruit/vegetable consumption and physical inactivity was associated with an increase of 12% (ARR 1.12, 95% CI 1.08, 1.17) and 11% (ARR 1.11, 95% CI 1.07, 1.16), respectively. When all three variables were included in the same model, a 5% increase in prevalence of obesity, low fruit/vegetable consumption, and physical inactivity was associated with 6%, 8%, and 7% increases in influenza-related hospitalization rates, respectively. CONCLUSIONS: Communities with a greater prevalence of obesity were more likely to have high influenza-related hospitalization rates. Similarly, less physically active populations, with lower fruit/vegetable consumption, tended to have higher influenza-related hospitalization rates, even after accounting for obesity.
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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 | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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".