Systematic literature review of the health economic implications of early detection by screening populations at risk for type 2 diabetes
Bibliographic record
Abstract
BACKGROUND: Undetected/uncontrolled diabetes is associated with substantial morbidity and mortality and consequent costs. Early detection through screening identifies patients at risk, allowing for earlier treatment initiation. OBJECTIVES: To determine the economic impact of screening for type 2 diabetes (T2DM). DATA SOURCES: We systematically reviewed health economic analyses of screening programs for T2DM/pre-diabetes. STUDY ELIGIBILITY CRITERIA: Published between 2000 and 2015 in any language. Articles must have reported costs of screening, test/patient outcomes and cost-effectiveness. PARTICIPANTS AND INTERVENTIONS: Any type of screening (universal, targeted, opportunistic) was accepted. METHODS: Data were extracted from Scopus/Medline/Embase, then tabulated. RESULTS: There were 137 studies identified, 108 rejected; 29 were analyzed. Screening types included 18 universal, 8 targeted and 8 opportunistic. One study screened for pre-diabetes, 16 for T2DM and 12 examined both. Fourteen (48%) reported costs of screening only, 9 (31%) costs of screening combined with interventions and 6 (21%) presented all costs separately. Screening was compared to no screening in 13 studies (45%); screening was cost-effective in 8 (62%), not cost-effective in 4 (31%) and neither in 1 (8%). When comparing different screening methods, 6 found targeted screening was cost-effective compared with universal screening (none found the opposite), 2 found opportunistic superior to universal. Sensitivity analyses generally confirmed primary findings. Cost drivers included prevalence of T2DM/pre-diabetes, type of blood test used and uptake of testing. For optimal cost-effectiveness, screening for both T2DM and pre-diabetes should be initiated around age 45-50, with repeated testing every 5 years. CONCLUSIONS/IMPLICATIONS: Targeted screening appears to be cost-effective compared to universal screening.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".