A systematic review of the economic burden of diabetes mellitus: contrasting perspectives from high and low middle-income countries
Bibliographic record
Abstract
Introduction: Diabetes increases preventative sickness and costs healthcare and productivity. Type 2 diabetes and macrovascular disease consequences cause most diabetes-related costs. Type 2 diabetes greatly costs healthcare institutions, reducing economic productivity and efficiency. This cost of illness (COI) analysis examines the direct and indirect costs of treating and managing type 1 and type 2 diabetes mellitus. Methodology: According to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines, Cochrane, PubMed, Embase, CINAHL, Scopus, Medline Plus, and CENTRAL were searched for relevant articles on type 1 and type 2 diabetes illness costs. The inquiry returned 873 2011-2023 academic articles. The study included 42 papers after an abstract evaluation of 547 papers. Results: Most articles originated in Asia and Europe, primarily on type 2 diabetes. The annual cost per patient ranged from USD87 to USD9,581. Prevalence-based cost estimates ranged from less than USD470 to more than USD3475, whereas annual pharmaceutical prices ranged from USD40 to more than USD450, with insulin exhibiting the greatest disparity. Care for complications was generally costly, although costs varied significantly by country and problem type. Discussion: This study revealed substantial heterogeneity in diabetes treatment costs; some could be reduced by improving data collection, analysis, and reporting procedures. Diabetes is an expensive disease to treat in low- and middle-income countries, and attaining Universal Health Coverage should be a priority for the global health community.
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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 | 0.019 | 0.076 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.009 | 0.009 |
| Bibliometrics | 0.018 | 0.021 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.002 |
| 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".