Dementia diagnosis and prescription of antidementia drugs: An analysis of German claims data (2006–2016)
Bibliographic record
Abstract
BackgroundUse of claims data allows to analyze health service characteristics of dementia, which is one of the most frequent cognitive disorders in Germany and worldwide.ObjectiveThe study aimed at describing the variability in dementia diagnoses and in antidementia drug prescription pattern.MethodsWe analyzed data from a population-based sample of one of the largest German statutory health insurances. The cohort included 30,403 patients with incident dementia diagnosis from 2006-2016. We described frequencies, patterns, and interrelations of diagnoses (Alzheimer's disease (AD), vascular dementia, other specific dementia, unspecified dementia (UD), antidementia drugs (ADD), and professional groups. We described switches in diagnostic and medication patterns between index quarter and following quarters, and evaluated the prescriptions in relation to national guidelines.ResultsA total of 87% of patients received a diagnosis of UD in at least one quarter of insurance. In the quarter of incident diagnosis, 14% of patients received more than one diagnostic code of dementia, whereas over the course of observation, the majority of patients received more than one diagnostic code (61%). Most patients were diagnosed by a general practitioner without involving a specialist. All professional groups primarily made UD diagnoses except specialists who mainly diagnosed AD. Thirty-five percent of all patients and 67% of AD patients were prescribed an ADD at least once.ConclusionsSpecialists made the most specific diagnoses and prescribed most ADDs. A specialist consultation may be advisable, but only 34% of patients visited one. Many AD patients might be left untreated due to underdiagnosis or -treatment.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".