Regional variation in multimorbidity prevalence in British Columbia, Canada: a cross-sectional analysis of Canadian Community Health Survey data, 2015/16
Bibliographic record
Abstract
INTRODUCTION: Multimorbidity represents a major concern for population health and service delivery planners. Information about the population prevalence (absolute numbers and proportions) of multimorbidity among regional health service delivery populations is needed for planning for multimorbidity care. In Canada, health region-specific estimates of multimorbidity prevalence are not routinely presented. The Canadian Community Health Survey (CCHS) is a potentially valuable source of data for these estimates. METHODS: Data from the 2015/16 cycle of the CCHS for British Columbia (BC) were used to estimate and compare multimorbidity prevalence (3+ chronic conditions) through survey-weighted analyses. Crude frequencies and proportions of multimorbidity prevalence were calculated by BC Health Service Delivery Area (HSDA). Logistic regression was used to estimate differences in multimorbidity prevalence by HSDA, adjusting for known confounders. Multiple imputation using chained equations was performed for missing covariate values as a sensitivity analysis. The definition of multimorbidity was also altered as an additional sensitivity analysis. RESULTS: A total of 681 921 people were estimated to have multimorbidity in BC (16.9% of the population) in 2015/16. Vancouver (adj-OR = 0.65; 95% CI: 0.44-0.97) and Richmond (adj-OR = 0.55; 95% CI: 0.37-0.82) had much lower prevalence of multimorbidity than Fraser South (reference HSDA). Missing data analysis and sensitivity analysis showed results consistent with the main analysis. CONCLUSION: Multimorbidity prevalence estimates varied across BC health regions, and were lowest in Vancouver and Richmond after controlling for multiple potential confounders. There is a need for provincial and regional multimorbidity care policy development and priority setting. In this context, the CCHS represents a valuable source of information for regional multimorbidity analyses in Canada.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.013 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".