Vitamin D deficiency in Malaysian adolescents aged 13 years: findings from the Malaysian Health and Adolescents Longitudinal Research Team study (MyHeARTs)
Bibliographic record
Abstract
OBJECTIVE: To determine the prevalence of vitamin D deficiency (<37.5 nmol/L) among young adolescents in Malaysia and its association with demographic characteristics, anthropometric measures and physical activity. DESIGN: This is a cross-sectional study among Form 1 (year 7) students from 15 schools selected using a stratified random sampling design. Information regarding sociodemographic characteristics, clinical data and environmental factors was collected and blood samples were taken for total vitamin D. Descriptive and multivariable logistic regression was performed on the data. SETTING: National secondary schools in Peninsular Malaysia. PARTICIPANTS: 1361 students (mean age 12.9±0.3 years) (61.4% girls) completed the consent forms and participated in this study. Students with a chronic health condition and/or who could not understand the questionnaires due to lack of literacy were excluded. MAIN OUTCOME MEASURES: Vitamin D status was determined through measurement of sera 25-hydroxyvitamin D (25(OH)D). Body mass index (BMI) was classified according to International Obesity Task Force (IOTF) criteria. Self-reported physical activity levels were assessed using the validated Malay version of the Physical Activity Questionnaire for Older Children (PAQ-C). RESULTS: Deficiency in vitamin D was seen in 78.9% of the participants. The deficiency was significantly higher in girls (92.6%, p<0.001), Indian adolescents (88.6%, p<0.001) and urban-living adolescents (88.8%, p<0.001). Females (OR=8.98; 95% CI 6.48 to 12.45), adolescents with wider waist circumference (OR=2.64; 95% CI 1.65 to 4.25) and in urban areas had higher risks (OR=3.57; 95% CI 2.54 to 5.02) of being vitamin D deficient. CONCLUSIONS: The study shows a high prevalence of vitamin D deficiency among young adolescents. Main risk factors are gender, ethnicity, place of residence and obesity.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.006 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".