FC15-02 - Health costs trends of those with and without mental health problems
Bibliographic record
Abstract
FWe report prevalence and cost results for 8 years of administrative billing data, comparing the health costs of groups with and without mental health problems. Methods A data cube containing registration and visit data for all mental health cases was constructed and matched on age and sex in a ratio of 1:8 with non-mental health cases (n TOTAL = 681,676). Four groups emerged in the final dataset: Group 1 - Those with mental health problems treated in publicly funded tertiary care (n = 61,479); Group2 - those with mental health problems treated in their doctors’ offices (n = 272,120); Group 3 - those with mental health problems treated in publicly funded tertiary care without treatment in their doctors’ offices (15,135); and Group 4 - those without mental health problems (n = 332,942). Results At present we have examined the Physician billing data for those old and younger than or equal to 18 years of age, with the overall finding that the health costs (total costs - mental health costs) were greater for those with mental health problems in Group 1 Case ($3,039 average per individual (API) over 8 years) and Group 2 comparison ($2,554 API over 8 years) as compared to Group 3 case ($1326) and Group 4 comparison ($1089) API over 8 years). Conclusions Having a mental health problem has a profound impact on health-related expenditures.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 0.005 |
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".