Multimorbidity prevalence in the general population: the role of obesity in chronic disease clustering
Bibliographic record
Abstract
BACKGROUND: The role of obesity in the prevalence and clustering of multimorbidity, the occurrence of two or more chronic conditions, is understudied. We estimated the prevalence of multimorbidity by obesity status, and the interaction of obesity with other predictors of multimorbidity. METHODS: Data from adult respondents (18 years and over) to the Health Quality Council of Alberta 2012 Patient Experience Survey were analyzed. Multivariable regression models were fitted to test for associations. RESULTS: The survey sample included 4803 respondents; 55.8% were female and the mean age was 47.8 years (SD, 17.1). The majority (62.0%) of respondents reported having at least one chronic condition. The prevalence of multimorbidity, including obesity, was 36.0% (95% CI, 34.8 - 37.3). The prevalence of obesity alone was 28.1% (95% CI 26.6 - 29.5). Having obesity was associated with more than double the odds of multimorbidity (odds ratio = 2.2, 95% CI 1.9 - 2.7) compared to non-obese. CONCLUSIONS: The prevalence of multimorbidity in the general population is high, but even higher in obese than non-obese persons. These findings may be relevant for surveillance, prevention and management strategies for multimorbidity.
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 | 0.004 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".