Assessing the Costs and Benefits of Insuring Psychological Services as Part of Medicare for Depression in Canada
Bibliographic record
Abstract
OBJECTIVE: The study estimated costs and effects associated with increasing access to publicly funded psychological services for depression in a public health care system. METHODS: Discrete event simulation modeled clinical events (relapse, recovery, hospitalizations, suicide attempts, and suicide), health service use, and cost outcomes over 40 years in a population with incident depression. Parameters included epidemiologic and economic data from the literature and data from a secondary analysis of the 2012 Canadian Community Health Survey on mental health. Societal costs were measured with the human capital approach. Analyses estimated the incremental cost-effectiveness ratio associated with improved access to psychological services among individuals not receiving adequate mental health care and reporting an unmet need for such care compared with present use of health services for mental health reasons. RESULTS: Over 40 years, increased access to mental health services in a simulated population of adults with incident depression would lead to significantly lower lifetime prevalence of hospitalizations (27.9% versus 30.2% base case) and suicide attempts (14.1% versus 14.6%); fewer suicides (184 versus 250); a per-person gain of .17 quality-adjusted life years; and average societal cost savings of $2,590 CAD per person (range $1,266-$6,320). Publicly funding psychological services would translate to additional costs of $123,212,872 CAD ($67,709,860-$190,922,732) over 40 years. Savings to society would reach, on average, $246,997,940 CAD ($120,733,356-$602,713,120). CONCLUSIONS: In Canada, every $1 invested in covering psychological services would yield $2.00 ($1.78 to $3.15) in savings to society. Covering psychological services as part of Medicare for individuals with an unmet need for mental health care would pay for itself.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".