Evaluation of the hypercholesterolemia care cascade and compliance with NCEP-ATP III guidelines in Iran based on the WHO STEPS survey
Bibliographic record
Abstract
INTRODUCTION: Noncommunicable diseases (NCDs), particularly cardiovascular disease (CVD), are the leading cause of death worldwide, with hypercholesterolemia being a major risk factor for CVD. This study evaluated the hypercholesterolemia care cascade in Iran-including prevalence, diagnosis, treatment coverage, and effectiveness-using the National Cholesterol Education Program Adult Treatment Panel III (NCEP-ATP III) guidelines. METHODS: This cross-sectional study drew on data from the 2021 Iran STEPS survey, which employed a systematic cluster sampling of adults aged ≥ 18 years across all provinces in Iran. Hypercholesterolemia was defined per NCEP-ATP III thresholds (LDL ≥ 160 mg/dL, total cholesterol ≥ 240 mg/dL, HDL ≤ 40 mg/dL, or ongoing lipid-lowering therapy). Weighted descriptive statistics were calculated, and Poisson regression with robust variance estimated crude and adjusted prevalence ratios for optimal lipid control among those treated. The 10-year CVD risk was determined using the Framingham Risk Score, stratifying participants into low (< 10%), intermediate (10-20%), and high (> 20%) risk categories. RESULTS: Out of 18,074 participants, 10,582 (55.32%, 95% CI: 54.29-56.35) met NCEP-ATP III criteria for hypercholesterolemia. Among these, only 20.61% (19.55-21.72) were receiving pharmacological treatment. Treatment coverage was notably lower in males (13.15%, 11.98-14.40) than females (29.12%, 27.35-30.96). Statins were the most commonly used medication (11.43% of males, 25.87% of females). Of those receiving treatment, 52.85% (females) and 53.93% (males) achieved optimal LDL, while 76.98% (females) and 81.06% (males) attained total cholesterol < 200 mg/dL. However, only 19.89% (females) and 3.97% (males) met the HDL > 60 mg/dL goal. The 10-year CVD risk was < 10% in 57.79% of participants, 10-20% in 33.27%, and > 20% in 8.94%. CONCLUSION: Despite a high prevalence of hypercholesterolemia in Iran, treatment coverage remains suboptimal, particularly among males and working-age adults. Although most treated individuals achieve favorable LDL and total cholesterol levels, gaps persist in achieving optimal HDL targets. These findings underscore the need for strengthened screening, treatment, and adherence strategies-alongside broader preventive measures-to reduce the burden of hypercholesterolemia and CVD in Iran.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".