Dapagliflozin vs non‐SGLT‐2i treatment is associated with lower healthcare costs in type 2 diabetes patients similar to participants in the DECLARE‐TIMI 58 trial: A nationwide observational study
Bibliographic record
Abstract
AIMS: To investigate how the cardiovascular (CV) risk benefits of dapagliflozin translate into healthcare costs compared with other non-sodium-glucose cotransporter-2 inhibitor glucose-lowering drugs (oGLDs) in a real-world population with type 2 diabetes (T2D) that is similar to the population of the DECLARE-TIMI 58 trial. METHODS: Patients initiating dapagliflozin or oGLDs between 2013 and 2016 in Swedish nationwide healthcare registries were included if they fulfilled inclusion and exclusion criteria of the DECLARE-TIMI 58 trial (DECLARE-like population). Propensity scores for the likelihood of dapagliflozin initiation were calculated, followed by 1:3 matching with initiators of oGLDs. Per-patient cumulative costs for hospital healthcare (in- and outpatient) and for drugs were calculated from new initiation until end of follow-up. RESULTS: A total of 24 828 patients initiated a new GLD; 6207 initiated dapagliflozin and 18 621 initiated an oGLD. After matching based on 96 clinical and healthcare cost variables, groups were balanced at baseline. Mean cumulative 30-month healthcare cost per patient was similar in the dapagliflozin and oGLD groups ($11 807 and $11 906, respectively; difference, -$99; 95% CI, -$629, $483; P = 0.644). Initiation of dapagliflozin rather than an oGLD was associated with significantly lower hospital costs (-$658; 95% CI, -$1169, -$108; P = 0.024) and significantly higher drug costs ($559; 95% CI, $471, $648; P < 0.001). Hospital cost difference was related mainly to fewer CV- and T2D-associated complications with use of dapagliflozin compared with use of an oGLD (-$363; 95% CI, -$665, -$61; P = 0.008). CONCLUSION: In a nationwide, real-world, DECLARE-like population, dapagliflozin was associated with lower hospital costs compared with an oGLD, mainly as a result of reduced rates of CV- and T2D-associated complications.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".