Modelling the effect of compliance with WHO salt recommendations on cardiovascular disease mortality and costs in Brazil
Bibliographic record
Abstract
INTRODUCTION: Cardiovascular diseases (CVDs) represent the main cause of death among non-communicable diseases (NCDs) in Brazil, and they have a high economic impact on health systems. Most populations around the world, including Brazilians, consume excessive sodium, which increases blood pressure and the risk of CVDs. OBJECTIVE: To model the estimated deaths and costs associated with CVDs, which are mediated by increased blood pressure attributable to excessive sodium consumption in adults from the perspective of the Brazilian public health system in 2017. METHODS: We employed two macrosimulation methods, using top-down approaches and based on the same relative risks. The models estimated the mortality and costs-of-illness attributable to excessive sodium intake and mediated by hypertension for adults aged over 30 years in 2017. Direct healthcare cost data (inpatient care, outpatient care and medications) were extracted from the Ministry of Health information systems and official records. RESULTS: In 2017, an estimated 46,651 deaths from CVDs could have been prevented if the average sodium consumption had been reduced to 2 g/day in Brazil. Premature deaths related to excessive sodium consumption caused 575,172 Years of Life Lost and US$ 752.7 million in productivity losses to the economy. In the same year, the National Health System's costs of hospitalizations, outpatient care and medication for hypertension attributable to excessive sodium consumption totaled US$192.1 million. The main causes of death and costs associated with CVDs were coronary heart disease and stroke, followed by hypertensive disease, heart failure and aortic aneurysm. CONCLUSION: Excessive sodium consumption is estimated to account for 15% of deaths by CVDs and to 14% of the inpatient and outpatient costs associated with CVD. It also has high societal costs in terms of premature deaths. CVDs are a leading cause of disease and economic burden on the global, regional and country levels. As a largely preventable and treatable conditions, CVDs require the strengthening of cost-effective policies, supported by evidence, including modeling studies, to reduce the costs relating to illness borne by the Brazilian public health system and society.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".