Healthcare costs and hospitalizations in <scp>US</scp> patients with type 2 diabetes and cardiovascular disease: A retrospective database study ( <scp>OFFSET</scp> )
Bibliographic record
Abstract
AIM: To investigate the budget implications of treatment with glucagon-like peptide-1 receptor agonists (GLP-1 RAs) versus other glucose-lowering treatment (here termed 'standard of care' [SoC]) during 2012-2019. MATERIALS AND METHODS: GLP-1 RA-naïve adults with type 2 diabetes (T2D) in the IBM MarketScan database with at least one glucose-lowering medication claim within 6 months after their first cardiovascular disease (CVD) hospitalization were included (index date was the date of first claim for a GLP-1 RA for the GLP-1 RA group, and the date of the first claim, independent of medication type, for the SoC group). Monthly healthcare costs and hospitalization risk over 12 months postindex date were compared for those who initiated a GLP-1 RA posthospitalization versus those with a claim for any other glucose-lowering medication. RESULTS: Postindex date, mean observed total costs were lower for patients receiving a GLP-1 RA compared with SoC ($3853 vs. $4288). In adjusted analysis, both groups had similar total healthcare costs (P = .56). This was driven by significantly lower inpatient and outpatient costs and higher drug costs in the GLP-1 RA group compared with SoC (P < .001). Risks of all-cause (adjusted hazard ratio: 0.85) and CVD-related hospitalization (0.76) were significantly lower in the GLP-1 RA group compared with SoC (P < .001). Similar results were observed in a subgroup with atherosclerotic CVD. CONCLUSIONS: These findings suggest that, in US patients with T2D and a CVD-related hospitalization, the added medical cost of treatment with GLP-1 RAs is offset by lower inpatient and outpatient care costs, resulting in budget neutrality against SoC.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".