Defining Depression and Anxiety in Individuals With Rheumatic Diseases Using Administrative Health Databases: A Systematic Review
Bibliographic record
Abstract
OBJECTIVE: To conduct a systematic review to describe how administrative health databases have been used to study depression and anxiety in patients with rheumatic diseases and to synthesize the case definitions that have been applied. METHODS: Search strategies to identify articles evaluating depression and anxiety among individuals with rheumatic diseases were employed in Medline, Embase, CINAHL, Cochrane Database of Systematic Reviews, and PsycINFO. Studies included were those using administrative health data and reporting case definitions for depression and anxiety using International Classification of Diseases (ICD) codes. We extracted information on study design and objectives, administrative health database, specific data sources (e.g., inpatient, pharmacy records), ICD codes, operational definitions, and validity of case definitions. RESULTS: Of the 36 studies included in this review, all studies assessed depression, and 13 studies (36.1%) evaluated anxiety. A number of specific ICD-9/10 codes were consistently applied to identify depression and anxiety, but the overall combination of ICD codes and operational definitions varied across studies. Twenty-four studies reported operational definitions, and 19 of these studies (79.2%) combined claims from more than 1 type of administrative data source (e.g., inpatient, outpatient). Validated case definitions were used by 6 studies (16.7%), with sensitivity estimates for depression and anxiety case definitions ranging from 33% to 74% and 42% to 76%, respectively. CONCLUSION: We identified numerous case definitions used to evaluate depression and anxiety among individuals with rheumatic diseases within administrative health databases. Recommendations include using case definitions with demonstrated validity as well as operationalizing case definitions within multiple data sources.
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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 | 0.014 | 0.075 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.008 | 0.008 |
| Bibliometrics | 0.019 | 0.018 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 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".