A Markov Model Approach Assessing the Cost of Illness of Generalized Anxiety Disorder in Canada
Bibliographic record
Abstract
INTRODUCTION: Generalized anxiety disorder (GAD) is a chronic disease with waxing and waning of symptoms. This is the first comprehensive economic model developed to reflect the nature and course of GAD. METHODS: An incidence-based probabilistic Markov model was developed reflecting nine GAD health states (HS): clinical assessments (three HS), maintenance therapies (four HS), discontinuation (one HS), and death (one HS). A probability curve of the GAD onset (ages 18-80) determined entry into the model and assumed patients retained the diagnoses until death. Canadian Psychiatric Association (CPA) guidelines determined pharmacotherapy, with revisions/validation by an expert panel. Direct costs (clinician, pharmacotherapy, hospitalization) were retrieved from government publications. Remission was based on pooled-analysis of CPA-cited evidence. Remaining clinical rates, absenteeism, and hospitalization were retrieved from the literature. Direct costs were attributed throughout the model except for the discontinuation and death HS. Indirect costs (wage rate) were retrieved from government publications and the literature (absenteeism), and were attributed to patients with GAD ≤65 years of age. Results were discounted at 5% and results expressed in 2008 Canadian dollars. RESULTS: The mean lifetime cost of illness (COI) was estimated to be $31,213 (SD $9,100) per patient. The cost of absenteeism accounted for 96% of the mean COI. The mean age of onset was 31 years and approximately 19% did not respond to pharmacotherapy. Over 85% of patients discontinued treatment by the fourth cycle (2nd year of therapy). Over the course of the model, a mean of 53% of patients relapsed, with an average rate of 0.79 relapses per patient. On average and over a lifetime, the disorder went unmanaged over a period of 14 (SD 9) years. The model was most sensitive to absenteeism. CONCLUSION: GAD is a costly disease with a lifetime COI <$32k/patient, with absenteeism exerting a significant impact.
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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 | 0.003 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.011 | 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".