Intergenerational Transmission of Obesity from Mothers to Their Offspring: Trends and Associated Factors Derived from the Malaysian National Health and Morbidity Survey (NHMS)
Bibliographic record
Abstract
Along with the increasing overweight and obesity trends among adults and children globally, numerous studies have suggested a strong association between maternal overweight and obesity among their offspring. We sought to report the prevalence and associated factors of intergenerational overweight and obesity among mother-child pairs in Malaysia from 2006 to 2015. Data were analysed from three waves of the Malaysian National Health and Morbidity Survey, a population-based cross-sectional study conducted in 2006, 2011 and 2015. A mother and the youngest child from each household formed 'mother-child pairs' and were grouped according to their body mass index categories. A multivariable logistic regression model was performed to determine the factors associated with overweight mother/overweight child pairs (OWM/OWC), with normal weight mother/normal weight child pairs (NWM/NWC) as the reference group. The prevalence of OWM/OWC increased from 15.3% to 21.7%, while the prevalence of NWM/NWC decreased from 28.4% to 23.8% between 2006 and 2015. Older maternal age and having primary and secondary education levels were positively associated with OWM/OWC. Conversely, older child age, Chinese ethnicity, large household size and low-income households were inversely associated with OWM/OWC. In conclusion, intergenerational weight gain is a worrisome trend in Malaysia. These findings may help in guiding priority setting for obesity prevention strategies in Malaysia.
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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 | 0.001 |
| 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".