The Distribution of Mental Health Service Costs for Depression in the Alberta Population
Bibliographic record
Abstract
OBJECTIVES: In Canada, most mental health services are embedded in the public health care system. Little is known of the cost distribution within the mental health population. Our study aims to estimate the depression care costs of patients with a depression diagnosis, ranking them by the increasing total depression health care costs. METHODS: For fiscal year 2007/08, we extracted administrative health care records from across the continuum, including physicians, outpatient services, and hospitals. Using a unique patient identifier, all service costs were merged for each person. Costs were summed by service categories and then divided by the served population into 10 equal-size groups. Further, we divided costs in the top decile into 10 percentile groups. RESULTS: There were 208 167 people (5.9% of Albertans) who had at least 1 health care visit for depression. The total cost for depression treatment services was $114.5 million, an average $550 per treated person. In the first 9 deciles, most costs were for general practitioners. By the ninth decile, cost per person was about $400. Within the tenth decile, costs increased regularly, and in the top 1 percentile (1% of patients) there was an increase of cost per patient to $25 826 from $5792 in the previous percentile. CONCLUSION: Per person costs were highly skewed. Until the ninth decile, the cost increased slowly, consisting of mainly physician costs. In the last decile, costs increased substantially, mainly because of hospitalizations. Thus both primary care and specialist care play key roles.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".