Estimating multimorbidity prevalence with the Canadian Chronic Disease Surveillance System
Bibliographic record
Abstract
INTRODUCTION: The Public Health Agency of Canada's Canadian Chronic Disease Surveillance System (CCDSS) uses a validated, standardized methodology to estimate prevalence of individual chronic diseases, such as diabetes. Expansion of the CCDSS for surveillance of multimorbidity, the co-occurrence of two or more chronic diseases, could better inform health promotion and disease prevention. The objective of this study was to assess the feasibility of using the CCDSS to estimate multimorbidity prevalence. METHODS: We used administrative health data from seven provinces and three territories and five validated chronic conditions (i.e. cardiovascular disease, respiratory disease, mental illness, hypertension and diabetes) to estimate multimorbidity prevalence. We produced age-standardized (using Canada's 1991 population) and age-specific estimates for two multimorbidity definitions: (1) two or more conditions, and (2) three or more conditions from the five validated conditions, by sex, fiscal year and geography. RESULTS: Among Canadians aged 40 years and over in the fiscal year 2011/12, the prevalence of two or more and three or more chronic conditions was 26.5% and 10.2%, respectively, which is comparable to other estimates based on administrative health data. The increase in multimorbidity prevalence with increasing age was similar across provinces. The difference in prevalence for males and females varied by province and territory. We observed substantial variation in estimates over time. Results were consistent for the two definitions of multimorbidity. CONCLUSION: The CCDSS methodology can produce comparative estimates of multimorbidity prevalence across provinces and territories, but there are challenges in using it to estimate temporal trends. Further expansion of the CCDSS in the number and breadth of validated case definitions will improve the accuracy of multimorbidity surveillance for the Canadian population.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".