P1‐565: ESTIMATING THE FUTURE PREVALENCE OF DEMENTIA IN CANADA: AN INTERNAL VALIDATION OF SHORT‐TERM PROJECTION METHODS USING ADMINISTRATIVE HEALTH DATA
Bibliographic record
Abstract
Globally, dementia projections are being increasingly used to support related policy development and healthcare planning. In Canada, retrospective data on diagnosed dementia have been available since 2016 through the Canadian Chronic Disease Surveillance System (CCDSS), which uses linked administrative health data to generate evidence on 20+ chronic conditions. In this study, we assessed the internal validity of CCDSS short-term projection methods for estimating future dementia prevalence. CCDSS dementia data from 2001–2002 to 2015–2016 and adjusted population estimates from the national Census were used to calculate observed and projected age-standardized prevalence rates (ASPRs). We used 5/10 years of historical data to project the last 5 years of available data. Assuming that the number of prevalent cases next year equals the number of prevalent cases surviving to the end of the current year plus incident cases for next year, we tested the following core scenarios: 2010–2011 incidence and all-cause mortality rates continue; 5/10-year average incidence and all-cause mortality rates continue; and trends in incidence and all-cause mortality rates over 5/10 years continue. Sensitivity analyses assumed a continuation of past minimum/maximum incidence and all-cause mortality rates. Projection accuracy was quantified using the absolute percentage error (APE) between projected vs observed ASPRs in 2015–2016 and median APE (medAPE) for 2011–2012 to 2015–2016. The predictive accuracy of the projections was high. The medAPEs for the core scenarios ranged from 0.6%-4.7% in females and 0.3%-2.1% in males. The best core scenario in females and in males had a medAPE<1%, resulting in an ASPR of 6.8% projected vs 6.7% observed for both sexes combined in 2015–2016. The best core scenario was based on the 10-year average incidence and all-cause mortality rates in females and a continuation of the 2010–2011 rates in males. Overall, scenarios based on trends did not improve predictive accuracy. Using 10 years of historical data seemed preferable in females, but the reverse was observed in males. Simple methods using administrative health data can generate accurate short-term projections of dementia prevalence in Canada. A longer time series is needed to validate CCDSS methods for longer term projections.
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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 | 0.043 | 0.156 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".