Use of Cardiovascular Disease Secondary Prevention Medications in Four Middle East Countries in a Community Setting
Bibliographic record
Abstract
Background: Evidence-based International clinical practice guidelines, universally recommend secondary prevention medications for those with previous cardiovascular disease (CVD). There is limited data on the community use of these medications in the Middle East (ME). Objectives: This study assesses the use and predictors of evidence based secondary prevention medications in individuals with a history of CVD [coronary heart disease (CHD) or stroke]. Methods: Between 2005 and 2015, we enrolled 11,228 individuals aged between 35-70 years from 52 urban and 35 rural communities from four ME countries, United Arab Emirates (n = 1499), Kingdom of Saudi Arabia (n = 2046), Occupied Palestinian Territory (n = 1668) and Islamic Republic of Iran (n = 6013). With standardized questionnaires, we report estimates of medication use in those with CVD at national level and the independent predictors of their utilization through a multivariable analysis model. Results: Of the total ME cohort, 614 (5.5%) had CVD, of which 115 (1.0%) had stroke, 523 (4.7%) had CHD and 24 (0.2%) had both. The mean age of those with CVD was 56.6 ± 8.8 years and 269 (43.8%) were female. Overall, only 23.5% of those with CVD reported using three or more proven secondary prevention medications, and a substantial proportion (stroke 27.8%, CHD 25.8%) did not take any of these medications. In a fully adjusted analysis, increasing age, female gender, higher education, higher wealth in individual household, residence in a higher income country as well as being obese, hypertensive or diabetic were independent predictors of medication use. Conclusion: The use of secondary prevention medication is low in ME and has not reached the modest recommended WHO target of 50% use of 3 or more medications. Independent factors of higher use were, better socioeconomic status (household wealth, country wealth and education) and better contact and accessibility to health care (increasing age, female gender, obesity, diabetes and hypertension).
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".