Hypercholesterolemia Prevalence, Awareness, Treatment and Control among Adults in Malaysia: The 2015 National Health and Morbidity Survey, Malaysia
Bibliographic record
Abstract
BACKGROUND & OBJECTIVE: Dyslipidaemia is one of the main modifiable risk factors for cardiovascular disease (CVD). Therefore, it is crucial to examine the prevalence, awareness, treatment and control of hypercholesterolemia and its associated factors among adults in Malaysia.METHODS: We used data from 19,935 respondents aged 18 years and above who responded to the cholesterol module in the National Health and Morbidity Survey (NHMS) 2015. The survey employed a two-stage stratified sampling to select a representative sample of Malaysian adults. Descriptive statistics and multivariate logistic regression were used to analyse the data.RESULTS: The overall prevalence of hypercholesterolemia was 47.7%. Among those who were diagnosed to have hypercholesterolemia, only 19.2% were aware of their hypercholesterolemia status. Only a mere 12.7% (95% CI: 12.4 -13.1) among those who were aware were on treatment and out of which only 53.7% (95% CI: 50.1-57.2) had their cholesterol levels controlled. The prevalence of hypercholesterolemia was associated with gender, age, ethnicity, education level, occupation, marital status, obesity, hypertension and diabetes. Awareness and treatment of hypercholesterolemia saw a similar pattern (except for gender and locality). For control of hypercholesterolemia, the female gender and secondary education levels were the only significant associated factors.CONCLUSION: The overall high prevalence of hypercholesterolemia in addition to poor awareness, treatment and control are significant public health problems. Intensified health campaigns and programmes especially among high-risk groups should be implemented in order to reduce or prevent complications of hypercholesterolemia in the near future.
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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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".