Lifestyle Interventions to Prevent Type 2 Diabetes: A Systematic Review of Economic Evaluation Studies
Bibliographic record
Abstract
Objective. To summarize key findings of economic evaluations of lifestyle interventions for the primary prevention of type 2 diabetes (T2D) in high-risk subjects. Methods. We conducted a systematic review of peer-reviewed original studies published since January 2009 in English, French, and Spanish. Eligible studies were identified through relevant databases including PubMed, Medline, National Health Services Economic Evaluation, CINHAL, EconLit, Web of sciences, EMBASE, and the Latin American and Caribbean Health Sciences Literature. Studies targeting obesity were also included. Data were extracted using a standardized method. The BMJ checklist was used to assess study quality. The heterogeneity of lifestyle interventions precluded a meta-analysis. Results. Overall, 20 studies were retained, including six focusing on obesity control. Seven were conducted within trials and 13 using modeling techniques. T2D prevention by physical activity or diet or both proved cost-effective according to accepted thresholds, except for five inconclusive studies, three on diabetes prevention and two on obesity control. Most studies exhibited limitations in reporting results, primarily with regard to generalizability and justification of selected sensitivity parameters. Conclusion. This confirms that lifestyle interventions for the primary prevention of diabetes are cost-effective. Such interventions should be further promoted as sound investment in the fight against diabetes.
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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 | 0.020 | 0.069 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.012 | 0.009 |
| Bibliometrics | 0.011 | 0.011 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| 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".