REVIEW OF CARDIOVASCULAR DISEASE PRIMARY PREVENTION INTERVENTIONS IN LOW- AND MIDDLE-INCOME COUNTRIES
Bibliographic record
Abstract
Background The burden of cardiovascular diseases (CVD) in low- and middle-income countries (LMIC) has increased greatly. Primary prevention of CVD has been shown to work in developed countries, and is urgently needed in LMIC. Objective This literature review aims to evaluate the effectiveness of primary prevention strategies for CVD in LMIC. Methods Studies were obtained via a detailed search using Ovid MEDLINE©, EMBASE and Global Health databases, published in English between 1946 and 2013. Included studies were primary research articles aimed at the primary prevention of CVD in LMIC participants over age 15 years. We included studies of interventions targeted to the general population or to groups with specific levels of risk for CVD. Results Of 1002 studies that were identified through the initial search, 12 studies met the inclusion criteria. The interventions at the general population level mainly consisted of health promotion and education through media, as well as policy implementation and community outreach. The risk-group focused interventions used pharmaceutical treatment of cardiovascular risk factors or CVD education and lifestyle counselling as preventive measures. All but two studies showed significant improvement in at least one cardiovascular risk factor. Primary prevention strategies used in high-risk groups showed greater benefits in reducing physiological cardiovascular risk factors when compared to population-based interventions. Conclusion Primary prevention interventions have been shown to be effective at lowering cardiovascular risk factors in LMIC. In general, the included studies indicate that health promotion and education are the most successful prevention strategies. However, due to heterogeneity between the included studies (e.g. different populations or treatment modalities), no valid conclusions on the comparative effectiveness of specific study interventions can be made.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.005 | 0.024 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.004 |
| Bibliometrics | 0.010 | 0.012 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
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".