Agreement Between Medico-Administrative Database Algorithms and Survey-Based Diagnoses for Depression and Anxiety in Older Adults
Bibliographic record
Abstract
Objectives: This study aimed to assess the concordance between depression and anxiety case definitions derived from algorithms based on medico-administrative data and structured interviews aligned with the fourth edition of the Diagnostic and Statistical Manual of Mental Disorders (DSM-IV) criteria in older adults. Methods: We analyzed data from 1405 primary care older adults (≥65 years) from the Étude sur la Santé des Aînés (ESA)-Services cohort (2011–2013) in Quebec, Canada, who had available survey and medico-administrative data. Cases of depression and anxiety were identified using algorithms incorporating combinations of hospitalization records, physician-visit claims, and medication claims for antidepressants or anxiolytics. The agreement was assessed with the kappa statistics (κ), and the algorithms’ sensitivity, specificity, and positive and negative predictive values were calculated using the case definitions derived from the DSM-IV-aligned ESA-Services interviews as the gold standard. Results: Agreements between the algorithms and the interviews were fair (κ: 0.06–0.22) for depression gooand slight (κ: 0.02–0.09) for anxiety. The algorithms had low sensitivity (2–39.7% for depression and 1.4–39.9% for anxiety) but high specificity (84.5–99.6% for depression and 73–99.2% for anxiety), depending on the algorithm. Conclusions: The agreement between algorithms based on administrative data and DSM-IV-aligned interviews for anxiety or depressive disorders was low. The two methods identified older adults with different characteristics. Despite these discrepancies, algorithms with high specificity provide valuable insights into healthcare utilization patterns associated with these disorders.
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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 | 0.086 | 0.173 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 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 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".