Identification of Physician-Diagnosed Alzheimer’s Disease and Related Dementias in Population-Based Administrative Data: A Validation Study Using Family Physicians’ Electronic Medical Records
Bibliographic record
Abstract
BACKGROUND: Population-based surveillance of Alzheimer's and related dementias (AD-RD) incidence and prevalence is important for chronic disease management and health system capacity planning. Algorithms based on health administrative data have been successfully developed for many chronic conditions. The increasing use of electronic medical records (EMRs) by family physicians (FPs) provides a novel reference standard by which to evaluate these algorithms as FPs are the first point of contact and providers of ongoing medical care for persons with AD-RD. OBJECTIVE: We used FP EMR data as the reference standard to evaluate the accuracy of population-based health administrative data in identifying older adults with AD-RD over time. METHODS: This retrospective chart abstraction study used a random sample of EMRs for 3,404 adults over 65 years of age from 83 community-based FPs in Ontario, Canada. AD-RD patients identified in the EMR were used as the reference standard against which algorithms identifying cases of AD-RD in administrative databases were compared. RESULTS: The highest performing algorithm was "one hospitalization code OR (three physician claims codes at least 30 days apart in a two year period) OR a prescription filled for an AD-RD specific medication" with sensitivity 79.3% (confidence interval (CI) 72.9-85.8%), specificity 99.1% (CI 98.8-99.4%), positive predictive value 80.4% (CI 74.0-86.8%), and negative predictive value 99.0% (CI 98.7-99.4%). This resulted in an age- and sex-adjusted incidence of 18.1 per 1,000 persons and adjusted prevalence of 72.0 per 1,000 persons in 2010/11. CONCLUSION: Algorithms developed from health administrative data are sensitive and specific for identifying older adults with AD-RD.
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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 | 0.020 | 0.049 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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 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".