Estimating the Economic Costs of Antidepressant Discontinuation during Pregnancy
Bibliographic record
Abstract
OBJECTIVE: Depression is a major public health concern that results in a wide range of economic costs to people, their families, and the health care system. Our study sought to determine the direct medical costs incurred by the Ontario government owing to cessation of antidepressant therapy during pregnancy. METHODS: We conducted an economic evaluation by making assumptions based on data obtained from Statistics Canada, federal and provincial government reports, and relevant depression literature. The analysis included the number of pregnant women with depression residing in Ontario and, subsequently, the number of those women who experienced depressive relapse during pregnancy owing to discontinuation of antidepressant medication. The cost of physician services, hospitalizations, and the birth of preterm and low birth weight infants (2 adverse outcomes associated with untreated depression during pregnancy) were also taken into consideration. RESULTS: An estimated 2953 pregnant women with depression in Ontario annually discontinue antidepressant therapy and subsequently have a depressive relapse. An estimated $20 546 982 is spent annually in Ontario on untreated maternal depression in pregnancy; this is the total after subtracting the cost of risks associated with treated depression during pregnancy ($3 144 053). CONCLUSIONS: Safe treatment options for the management of depression during pregnancy should be actively explored as treated depression translates into cost savings for the Ontario government and society as a whole. Beyond this cost, depression interferes with the quality of childrearing, maternal responsiveness to infants, and other determinants essential for optimal child development.
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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 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 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".