Cardiovascular impact of the national program of publicly-funded access to dapagliflozin in Brazil
Bibliographic record
Abstract
OBJECTIVES: Sodium-glucose cotransporter-2 inhibitors can reduce the incidence of cardiovascular (CV) events in patients with type 2 diabetes. Financial barriers may preclude their use in clinical practice. The aim of this study is to evaluate the changes in CV events associated with the implementation of the Brazilian national program of publicly-funded access to dapagliflozin in 2021. STUDY DESIGN: Nationwide population-based study. METHODS: A nationwide population-based time-series analysis to examine trends in rates of CV mortality and hospitalizations before (2017-2019) and after (2022-2024) public funding of dapagliflozin was performed. Data were obtained from the Brazilian National Health registry (203,080,756 individuals). RESULTS: As compared to 2017-2019, the mortality rates from CV causes in the Brazilian population reduced in 2022-2024 following the publicly-funded access to dapagliflozin. As compared to 2017, the mortality rate from CV causes decreased by 4.7 % in 2022, 11.4 % in 2023, and 19.1 % in 2024. Importantly, an interrupted time-series analysis indicated that there was a significant change on mortality rates from CV causes between these time periods: slope -0.16 (95 % CI -0.49 to 0.16) for the 2017-2019 time period as compared to -0.74 (95 % CI -1.19 to -0.29, P = 0.039) for 2022-2024 indicating a steeper reduction on the trend of mortality from CV causes after this public health intervention. CONCLUSIONS: There was an important change (reduction) in the trends of adverse CV outcomes following the implementation of a national program of publicly-funded access to dapagliflozin. These data highlight that public health measures that improve affordability of medications can significantly impact mortality rates at a population level.
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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 | 0.003 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".