Economic Burden of Cardiovascular Disease in Type 2 Diabetes: A Systematic Review
Bibliographic record
Abstract
BACKGROUND: Cardiovascular diseases (CVDs) constitute major comorbidities in type 2 diabetes mellitus (T2DM), contributing substantially to treatment costs for T2DM. An updated overview of the economic burden of CVD in T2DM has not been presented to date. OBJECTIVE: To systematically review published articles describing the costs associated with treating CVD in people with T2DM. METHODS: Two reviewers searched MEDLINE, Embase, and abstracts from scientific meetings to identify original research published between 2007 and 2017, with no restrictions on language. Studies reporting direct costs at either a macro level (e.g., burden of illness for a country) or a micro level (e.g., cost incurred by one patient) were included. Extracted costs were inflated to 2016 values using local consumer price indexes, converted into US dollars, and presented as cost per patient per year. RESULTS: Of 81 identified articles, 24 were accepted for analysis, of which 14 were full articles and 10 abstracts. Cardiovascular comorbidities in patients with T2DM incurred a significant burden at both the population and patient levels. From a population level, CVD costs contributed between 20% and 49% of the total direct costs of treating T2DM. The median annual costs per patient for CVD, coronary artery disease, heart failure, and stroke were, respectively, 112%, 107%, 59%, and 322% higher compared with those for T2DM patients without CVD. On average, treating patients with CVD and T2DM resulted in a cost increase ranging from $3418 to $9705 compared with treating patients with T2DM alone. CONCLUSIONS: Globally, CVD has a substantial impact on direct medical costs of T2DM at both the patient and population levels.
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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 | 0.004 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.008 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".