Impact of salt intake reduction on CVD mortality in Costa Rica: A scenario modelling study
Bibliographic record
Abstract
Cardiovascular diseases (CVD) represent the leading cause of death in Costa Rica and high blood pressure was associated with a mortality rate of 29% in 2018. The average household sodium intake in the country is also two times higher than the World Health Organization recommendation. The objective of this study was to estimate the impact of reducing salt intake on CVD mortality in Costa Rica using a scenario simulation model. The Preventable Risk Integrated ModEl (PRIME) was used to estimate the number of deaths that would be averted or delayed in the Costa Rican population by following the national and the international guidelines to reduce salt consumption, according to two scenarios: A) 46% reduction and B) 15% reduction, both at an energy intake of 2171 kcal. The scenarios estimated that between 4% and 13%, respectively, of deaths due to CVD would be prevented or postponed. The highest percentages of deaths prevented or postponed by type of CVD would be related to Coronary heart disease (39% and 38%, respectively), Hypertensive disease (32% and 33%, respectively), and Stroke (22% in both). The results demonstrate that reducing salt consumption could prevent or postpone an important number of deaths in Costa Rica. More support for existing policies and programs urges.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.002 | 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".