Prevalence of Undiagnosed Type 2 Diabetes Mellitus and Its Associated Factors Among the Malaysian Population: The 2015 National Health and Morbidity Survey, Malaysia
Bibliographic record
Abstract
BACKGROUND: The prevalence of diabetes has increased dramatically in the last decade. Compounding the problem are undiagnosed cases of type 2 diabetes mellitus. These respondents are those who do not know that they have the disease. Undiagnosed cases have substantial implications as they are at more risk to develope fatal complications. This study aims to determine the prevalence of undiagnosed T2DM and to identify its associated factors in Malaysia.METHODS: A nationwide cross-sectional study was conducted involving 19,935 respondents. Two-stage stratified sampling design was used to select a representative sample of the Malaysian adult population. Face-to-face interviews using structured, validated questionnaires were used to obtain data from the respondents. Respondents who claimed that they were not diagnosed with diabetes before were asked to undergo a finger-prick test.RESULTS: The overall prevalence of T2DM was 17.5% while the prevalence of undiagnosed T2DM was 9.2% (n=2103). Respondents aged 60 years old & above had the highest percentage of undiagnosed T2DM at about 13.6 %, followed by those with no formal educational at 12.9%, among Indians were 11.9%, among female at 9.2%, among non-working citizen at 9.8%, widowed/divorced (12.0%), smokers (9.5%), obesity (13.6%) and hypertensive (12.8%). Multivariable analyses revealed that age group, ethnicity, education level, marital status, obesity and hypertensive were more likely to have undiagnosed T2DM.CONCLUSION: This study showed a high prevalence of undiagnosed T2DM in Malaysia. Factors associated with undiagnosed diabetes mellitus were obesity, age, ethnicity, educational level and hypertension. Screening is essential among adults aged 30 to 60 year old to enable early intervention and prevent development of serious complications of this disease.
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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 | 0.001 | 0.001 |
| 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".