Chronic Disease Multimorbidity Among the Canadian Population: Prevalence and Associated Lifestyle Factors
Bibliographic record
Abstract
Abstract Background and Rationale With the increasing prevalence of most chronic diseases, multimorbidity is becoming an important public health concern in the Canadian population. The purpose of this study was to estimate the prevalence of multimorbidity in the general population based on 14 major chronic diseases and examine associations with lifestyle/behavioral factors.Methods: The data source was the 2015-2016 Canadian Community Health Survey (CCHS). The CCHS is a cross sectional, complex multi-stage survey based on information collected from 109,659 participants aged 12+, covering all provinces and territories. Multimorbidity was measured by counting the co-occurrence of two or more chronic diseases within a person. Multiple logistic regression was the primary analysis. Results: The prevalence of multimorbidity was 33%. Adjusting for sociodemographic variables, there was an increased odds of multimorbidity for those having a sedentary lifestyle (OR=1.06; CI:1.01-1.11) and being obese (OR=1.37;CI:1.32-1.43) or overweight (OR=2.65; CI: 2.54-2.76). . There were also significant interaction effects on multimorbidity,between sex and smoking, and immigration status and alcohol intake..Conclusion and Implications: Given the high prevalence of multimorbidity among the general Canadian population, policy makers and service providers should give more attention to the behavioral/lifestyle factors which significantly predicted multimorbidity. Policy and program efforts that promote a healthy lifestyle should be a priority.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".