Increased risk of bacterial infections among the obese with chronic diseases
Bibliographic record
Abstract
OBJECTIVES: It has been well understood that cigarette smokers have an increased risk of infections; however, the association between obesity and infections has not been well explored in general population. METHODS: The analysis was based on data from the Canadian Community Health Survey - Healthy Aging (2008-2009), and included a total of 30 763 Canadians aged 45 years or older. Information on demographic data, body mass index (BMI), smoking status, chronic condition(s) and antibiotics use during the past month were collected. Logistic regression analysis was used to determine the associations of obesity and smoking with antibiotics use and adjusted for potential confounders. RESULTS: Overall, 6.6% used antibiotics in the previous month. Compared with those of normal weight, overweight and obese individuals were more likely to use antibiotics after adjustment for confounders, with odds ratios (ORs) of 1.28 (95% CI: 1.08, 1.50) and 1.25 (95% CI: 1.04, 1.50), respectively. When stratified by presence/absence of chronic condition(s), the associations were only significant among those with chronic condition(s) and the adjusted ORs were 1.30 (95% CI: 1.09, 1.55) for the overweight and 1.43 (95% CI: 1.18, 1.73) for the obese. Current smokers had an increased risk of antibiotics use when compared with non-smokers. The adjusted OR for smoking was similar for people with or without chronic condition(s), but was significant only for those with chronic condition(s) (OR 1.41, 95% CI: 1.16, 1.73). CONCLUSIONS: Overweight and obese Canadians aged 45 years or more, especially in those with chronic condition(s), had an increased risk of bacterial infections than their normal weight counterparts. The reasons for the modifying effect of chronic condition(s) on the association between body weight and infections were discussed.
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 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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".