Multimorbidity in large Canadian urban centres: A multilevel analysis of pooled 2015–2018 cross-sectional cycles of the Canadian Community Health Survey
Bibliographic record
Abstract
BACKGROUND: There is limited knowledge on how the prevalence of multimorbidity varies within and across major Canadian urban centres. The objective of this study was to investigate the between-neighbourhood variation in the prevalence of multimorbidity in Canada's large urban centres, controlling for compositional effects associated with individual-level demographic and socioeconomic factors. METHODS: = 100,803). Census tracts (CTs) were used as a measure of neighbourhood. To assess the between-neighbourhood differences in multimorbidity prevalence, we fitted three sequential random intercept logistic regression models. RESULTS: During the 2015-2018 period, 8.1% of residents of large urban centres had multimorbidity. The results from the unadjusted model indicate that 13.4% of the total individual variance in multimorbidity could be attributed to the between-neighbourhood differences. After adjustment for overall characteristics of the CMAs in which these neighbourhoods are located, as well as for individual-level demographic and socioeconomic factors related to compositional effects, 11.0% of the individual variance in multimorbidity could still be attributed to the between-neighbourhood differences. CONCLUSION: There is significant and substantial geographic variation in multimorbidity prevalence across neighbourhoods in Canada's large urban centres. Residing in some neighbourhoods could be associated with increased odds of having multimorbidity, even after accounting for overall characteristics of the CMAs in which these neighbourhoods are located, as well as individual-level factors.
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 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 | 0.004 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".