Temporal Trends in the Prevalence of Child Undernutrition in China From 2000 to 2019, With Projections of Prevalence in 2030: Cross-Sectional Analysis
Bibliographic record
Abstract
BACKGROUND: Although the problem of malnutrition among children in China has greatly improved in recent years, there is a gap compared to developed countries, and there are differences between provinces. Research on long-term comprehensive trends in child growth failure (CGF) in China is needed for further improvement. OBJECTIVE: The purpose of this study was to examine trends in stunting, wasting, and underweight among children younger than 5 years in China from 2000 to 2019, and predict CGF till 2030. METHODS: We conducted a cross-sectional analysis using data from the local burden of disease (LBD) database. Using Joinpoint Regression Software, we examined trends in CGF among children younger than 5 years in China from 2000 to 2019, and predicted the trends of prevalence in 2030, using the Holt-Winters model with trends but without seasonal components. The assessment was performed with Stata 17 (StataCorp). Data were analyzed from October 17, 2023, to November 22, 2023. RESULTS: In 2019, the prevalences of stunting, wasting, and underweight decreased to 12%, 3%, and 4%, respectively (decreases of 36.9%, 25.0%, and 42.9%, respectively, compared with the values in 2000). The prevalence of CGF decreased rapidly from 2000 to 2010, and the downward trend slowed down after 2010. Most provinces had stagnated processes of trends after 2017. The age group with the highest stunting prevalence was children aged 1 to 4 years, and the highest prevalence of wasting and underweight was noted in early neonatal infants. From 2000 to 2019, the prevalence of CGF declined in all age groups of children. The largest relative decrease in stunting and underweight was noted in children aged 1 to 4 years, and the largest decrease in wasting was noted in early neonatal infants. The prevalences of stunting, wasting, and underweight in China are estimated to decrease to 11.4%, 3.2%, and 4.1%, respectively, by 2030. China has nationally met the World Health Organization's Global Nutrition Targets for 2030 for stunting but not for wasting. CONCLUSIONS: This study provides data on the prevalence and trends of CGF among children younger than 5 years and reports declines in CGF. There remain areas with slow progress in China. Most units have achieved the goal for stunting prevalence but not wasting prevalence.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| 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".