How Much Do Mental Health and Substance Use/Addiction Affect Use of General Medical Services? Extent of Use, Reason for Use, and Associated Costs
Bibliographic record
Abstract
OBJECTIVE: To measure service use and costs associated with health care for patients with mental health (MH) and substance use/addiction (SA) problems. METHODS: A 5-year cross-sectional study (2007-2012) of administrative health care data was conducted (average annual sample size = 123,235 adults aged >18 years who had a valid Ontario health care number and used at least 1 service during the year; 55% female). We assessed average annual use of primary care, emergency departments and hospitals, and overall health care costs for patients identified as having MH only, SA only, co-occurring MH and SA problems (MH+SA), and no MH and/or SA (MH/SA) problems. Total visits/admissions and total non-MH/SA visits (i.e., excluding MH/SA visits) were regressed separately on MH, SA, and MH+SA cases compared to non-MH/SA cases using the 2011-2012 sample ( N = 123,331), controlling for age and sex. RESULTS: Compared to non-MH/SA patients, MH/SA patients were significantly ( P < 0.001) more likely to visit primary care physicians (1.82 times as many visits for MH-only patients, 4.24 for SA, and 5.59 for MH+SA), use emergency departments (odds, 1.53 [MH], 3.79 [SA], 5.94 [MH+SA]), and be hospitalized (odds, 1.59 [MH], 4.10 [SA], 7.82 [MH+SA]). MH/SA patients were also significantly more likely than non-MH/SA patients to have non-MH/SA-related visits and accounted for 20% of the sample but over 30% of health care costs. CONCLUSIONS: MH and SA are core issues for all health care settings. MH/SA patients use more services overall and for non-MH/SA issues, with especially high use and costs for MH+SA patients.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| 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".