Salt substitutes and premature cardiovascular deaths in Africa: a modelling study
Bibliographic record
Abstract
Abstract Background/Introduction Hypertension is a leading cause of death worldwide, with many African countries experiencing significantly low rates of treatment and control. Lower sodium potassium-enriched salt substitutes (LSSS) are considered a cost-effective strategy for managing hypertension. However, the potential impact of widely adopting LSSS in Africa remains unclear. Purpose This study aimed to predict the long-term impact of a government-led strategy to gradually replace regular salt (100% sodium chloride) with LSSS (75% sodium chloride + 25% potassium chloride) over a ten-year period, focusing on hypertension and premature cardiovascular deaths across all African countries. Methods We developed a multicohort proportional multistate lifetable model in R Studio to simulate the impact of the LSSS strategy. Blood pressure distributions and epidemiological data were sourced from the Global Burden of Disease 2021 study. Meta-analyses of randomized controlled trials and cohort studies were used to estimate the effects of LSSS on blood pressure and related cardiovascular risk. To account for uncertainty in the model’s results, probabilistic sensitivity analysis was performed using Monte Carlo simulations. Results Our model projects that LSSS could reduce the age-standardized prevalence of hypertension in Africa by an average of 36%, with relative reductions ranging from 22% in South Africa to 61% in Ethiopia. By 2050, widespread adoption of these LSSS could decrease the probability of premature cardiovascular deaths in women by between 9.8% (95% uncertainty interval [UI]: 8.1% to 10.9%) in Ethiopia to 14.0% (95% UI: 10.3% to 17.8%) in Cameroon. For men, the predicted reductions in the probability of premature cardiovascular deaths range from 9.6% (95% UI: 8.4% to 11.7%) in Libya to 13.9% (95% UI: 10.2% to 17.7%) in Cameroon. Conclusions A continent-wide shift from current regular salt to LSSS could greatly lessen the impact of hypertension and cardiovascular disease in Africa.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".