Prevalence and patterns of chronic disease multimorbidity and associated determinants in Canada
Bibliographic record
Abstract
INTRODUCTION: Multimorbidity is increasingly recognized as a key issue in the prevention and management of chronic diseases. We examined the prevalence and correlates of chronic disease multimorbidity in the general adult Canadian population in relation to age and other key determinants. METHODS: We extracted data from the Canadian Community Health Survey 2011/12 on 105 416 Canadian adults. We analysed the data according to the number of multimorbidities (defined as 2+ or 3+ diseases from a list of 9) and examined the determinants of multimorbidity using regression analyses. RESULTS: Our findings show that 12.9% of Canadians report 2+ chronic diseases and 3.9% report 3+ chronic diseases. Those reporting 3 or more chronic diseases were more likely to be female, older, living in the lowest income quintile and to have not completed high school. In the overall population, social deprivation is associated with a 3.7 odds of multimorbidity, but when examined across age groups, the odds of multimorbidity were notably higher in middle age, 7.5 for those aged 35 to 49 years and 5.4 for those aged 50 to 64 years. DISCUSSION: As the proportion of Canadians living with multiple chronic diseases increases, we need to assess chronic disease from a holistic perspective that captures multimorbidity and upstream factors, to facilitate broader and more context-appropriate associations with healthy living, quality of life, health care costs and mortality. Special consideration should be given to the role that social deprivation plays in the development of multimorbidity. Canadians living in the lowest socioeconomic group are not only more likely to develop multimorbidity, but the onset of multimorbidity is also likely to be significantly earlier.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.008 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.002 | 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".