Rapidly increasing body mass index among children, adolescents and young adults in a transitioning population, South Africa, 2008–15
Post-publication record
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Bibliographic record
Abstract
Background: There is a global epidemic of overweight and obesity; however, this rate of increase is even greater in some low- and middle-income countries (LMIC). South Africa (SA) is undergoing rapid socioeconomic and demographic changes that have triggered a rapid nutrition transition. The paper focuses on the recent rate of change of body mass index (BMI) among children, adolescents and young adults, further stratified by key sociodemographic factors. Methods: We analysed mean BMI of 28 247 individuals (including children) from 7301 households by age and year, from anthropometric data from four national cross-sectional (repeated panel) surveys using non-linear fitted curves and associated 95% confidence intervals. Results: From 2008 to 2015, there was rapid rise in mean BMI in the 6-25 age band, with the highest risk (3-4+ BMI unit increase) among children aged 8-10 years. The increase was largely among females in urban areas and of middle-high socioeconomic standing. Prominent gains were also observed in certain rural areas, with extensive geographical heterogeneity across the country. Conclusions: We have demonstrated a major deviation from the current understanding of patterns of BMI increase, with a rate of increase substantially greater in the developing world context compared with the global pattern. This population-wide effect will have major consequences for national development as the epidemic of related non-communicable disease unfolds, and will overtax the national health care budget. Our refined understanding highlights that risks are further compounded for certain groups/places, and emphasizes that urgent geographical and population-targeted interventions are necessary. These interventions could include a sugar tax, clearer food labelling, revised school feeding programmes and mandatory bans on unhealthy food marketing to children.The scenario unfolding in South Africa will likely be followed in other LMICs.
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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 | 0.001 |
| 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".