IS TREATMENT OF DEPRESSION COST-EFFECTIVE IN PEOPLE WITH DIABETES? A SYSTEMATIC REVIEW OF THE ECONOMIC EVIDENCE
Bibliographic record
Abstract
OBJECTIVES: Depression is common in diabetes and linked to a wide range of adverse outcomes. UK policy indicates that depression should be treated using conventional psychological treatments in a stepped care framework. This review aimed to identify current economic evidence of psychological treatments for depression among people with diabetes. METHOD: Electronic search strategies (conducted in MEDLINE, EMBASE, PsycINFO, CINAHL, NHS EED) combined clinical and economic search terms to identify full economic evaluations of the relevant interventions. Prespecified screening and inclusion criteria were used. Standardized data extraction and critical appraisal were conducted and the results summarized qualitatively. RESULTS: Excluding duplicates, 1,516 studies for co-morbid depression and diabetes were screened. Four economic evaluations were identified. The studies found that the interventions improved health status, reduced depression and were cost-effective compared with usual care. The studies were all U.S.-based and evaluated collaborative care programs that included psychological therapies. Critical appraisal indicated limitations with the study designs, analysis and results for all studies. CONCLUSIONS: The review highlighted the paucity of evidence in this area. The four studies indicated the potential of interventions to reduce depression and be cost-effective compared with usual care. Two studies reported costs per QALY gained of USD 267 to USD 4,317, whilst two studies reported the intervention dominated usual care, with net savings of USD 440 to USD 612 and net gains in patient free days or QALYs.
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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 | 0.016 | 0.084 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.009 | 0.009 |
| Bibliometrics | 0.009 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".