An economic evaluation of targeted case-finding strategies for identifying postnatal depression: A model-based analysis comparing common case-finding instruments
Bibliographic record
Abstract
BACKGROUND: Half of women with postnatal depression (PND) are not identified in routine care. We aimed to estimate the cost-effectiveness of PND case-finding in women with risk factors for PND. METHODS: A decision tree was developed to represent the one-year costs and health outcomes associated with case-finding and treatment for PND. The sensitivity and specificity of case-finding instruments, and prevalence and severity of PND, for women with ≥1 PND risk factor were estimated from a cohort of postnatal women. Risk factors were history of anxiety/depression, age < 20 years, and adverse life events. Other model parameters were derived from published literature and expert consultation. Case-finding for high-risk women only was compared with no case-finding and universal case-finding. RESULTS: More than half of the cohort had one or more PND risk factor (57.8 %; 95 % CI 52.7 %-62.7 %). The most cost-effective case-finding strategy was the Edinburgh Postnatal Depression Scale with a cut-off of ≥10 (EPDS-10). Among high-risk women, there is a high probability that EPDS-10 case-finding for PND is cost-effective compared to no case-finding (78.5 % at a threshold of £20,000/QALY), with an ICER of £8146/QALY gained. Universal case-finding is even more cost-effective at £2945/QALY gained (versus no case-finding). There is a greater health improvement with universal rather than targeted case-finding. LIMITATIONS: The model includes costs and health benefits for mothers in the first year postpartum, the broader (e.g. families, societal) and long-term impacts are also important. CONCLUSIONS: Universal PND case-finding is more cost-effective than targeted case-finding which itself is more cost-effective than not case-finding.
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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 | 0.031 | 0.061 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.006 | 0.011 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.007 | 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".