Association of Mental Health with Health Care Use and Cost: A Population Study
Bibliographic record
Abstract
OBJECTIVE: To compare the health costs of groups with and without psychiatric diagnoses (PDs) using 9 years of physician billing data. METHODS: A dataset containing registration data for all patients receiving public mental health service was constructed and subsequently matched, on age and sex, in a final patient to comparison patient ratio of 1:8, with health care users who did not receive treatment in the mental health system. Three groups emerged: a patient PD group-patients with psychiatric disorders treated in public mental health care (n = 76 677); a comparison patient PD group-comparison patients with PDs treated in physicians only (n = 277 627); and a patient- comparison patient non-PD group-patients (treated in specialized publicly funded care or by their physician) without PDs (n = 329 177). Examining over 42 million billing records for all of these patients, we compared the average number of visits and the average health only (nonpsychiatric) billing cost per each patient during the 9-year study period across the groups. RESULTS: Among all health care users in the data, the health costs (Total Costs - Mental Health Costs) were greater on average for the patients with PD group ($3437) and the comparison patient PD group ($3265), compared with patient-comparison patient non-PD group ($1345). Forty-six percent of the comparison sample had a PD. CONCLUSIONS: Having a mental health problem is related to greater health-related expenditures. This has important policy implications on how mental health resources are constructed and rationed within the health care system.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".