Temporal Trends in Cardiovascular Health Status Among Chinese School-Aged Children From 1989 to 2018: Multiwave Cross-Sectional Analysis
Bibliographic record
Abstract
BACKGROUND: Despite the release of updated metrics for Life's Essential 8 (LE8), key indicators for assessing cardiovascular health (CVH) status, there is currently no report on their distribution among Chinese children. OBJECTIVE: This study aimed to assess the nationwide distribution of CVH in Chinese school-aged children using LE8 scores and analyze temporal changes in these scores over time. METHODS: Participants aged 7 to 19 years from 11 waves (between 1989 and 2018) of the China Health and Nutrition Survey were included in this study. LE8 components were grouped into 2 domains of health behaviors (diet, physical activity, nicotine exposure, sleep) and health factors (BMI, blood lipids, blood glucose, blood pressure). Scores of overall CVH and each LE8 metric were calculated individually. Temporal changes were assessed with joint point regression models by rural and urban living residence. The causal relationships between health behaviors and health factors that changed the most over time were built with cross-lagged panel models. RESULTS: A total of 21,921 participants, 52.6% (n=11,537) of whom were male, who had data for at least 4 CVH components were included in the analysis. The mean age was 13 (SD 3.6) years. The overall CVH score remained stable in most regions, with the lowest found in Shandong from East China, which had a mean between 67 (SD 10.9) and 67.2 (SD 12.4). In contrast, the highest score was found in Guizhou from Southwest China, with a mean between 71.4 (SD 10.8) and 74.3 (SD 10.3). In rural areas, the diet score decreased significantly from 1997 onward with a speed of 0.18 (95% CI: 0.15-0.21; P<.001) per year, and the BMI score decreased significantly from 2005 onward with a speed of 0.56 (95% CI 0.44-0.68; P<.001) per year. In urban areas, the diet score decreased from 1994 onward with a speed of 0.03 (95% CI: 0.001-0.07; P=.04) per year, and the BMI score decreased from 2002 onward with a speed of 0.63 (95% CI 0.47-0.79; P<.001) per year. The sleep score dropped constantly in both urban and rural areas, with a speed of 0.69 (95% CI 0.58-0.80; P<.001) and 0.69 (95% CI: 0.52-0.86; P<.001) per year, respectively. A decline in the diet score led to a decline in the BMI score with a coefficient of 0.190 (95% CI 0.030-0.351; P=.02), while a decline in the BMI score led to a decline in sleep health with a coefficient of 0.089 (95% CI 0.010-0.168; P=.03). CONCLUSIONS: Chinese school-aged children and adolescents were generally of moderate CVH status, but mutual influences existed between CVH metrics. Dietary interventions should be prioritized for promoting overall CVH in the future.
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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 | 0.004 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.006 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".