Trends and predictions to 2030 in demographic structures and metabolic health for children and adolescents in China: analysis of national school health surveys from 2000 to 2019

Background

Understanding the changing metabolic health burden among children and adolescents is crucial for current and future public health resource allocation in China, particularly given rapid population ageing. We aimed to estimate trends in the metabolic burden in children and adolescents aged 7–18 years from 2000 to 2030, using overweight, obesity, and hypertension as proxy indicators.

Methods

We extracted age, sex, height, weight, and blood pressure data for Han children and adolescents aged 7–18 years, as recorded in five cycles of the Chinese National Surveys on Students Constitution and Health in the years 2000, 2005, 2010, 2014, and 2019. We used demographic indicators reported by the Seventh National Population Census in 2020 to represent the demographic situation in 2019 and UN population estimates and projections for China to derive the national age structure from 2000 to 2030. We calculated the 2019 age-standardised prevalence rates of overweight and obesity, hypertension, comorbid overweight and obesity with hypertension, severe obesity, and severe hypertension. Direct standardisation was applied to adjust for the effect of changes in population structures and derive age-specific prevalence estimates from 2000 to 2030. A population development index that captures demographic trends while accounting for the influence of age structure was calculated from birth rate, death rate, and proportions of the population aged 0–14 years and older than 65 years. Correlation coefficients (r) and corresponding p values for the association between the population development index and metabolic burden were calculated with general linear regression models. Multinomial regressions were applied to model age-specific and sex-specific prevalence rates as a function of time. We used decomposition analysis to evaluate the individual contributions of age-specific prevalence, age distribution, and population growth to the net change in case numbers.

Findings

The final analysis of national survey data included 1 106 416 observations. In 2019, the age-standardised prevalence rates were 21·5% (95% CI 21·3–21·7) for overweight and obesity, 16·6% (16·4–16·8) for hypertension, 5·5% (5·4–5·6) for overweight and obesity with hypertension, 1·6% (1·5–1·6) for severe obesity, and 2·1% (2·0–2·2) for severe hypertension. China's population of children and adolescents aged 7–18 years is predicted to decrease from 276 million in 2000 to 181 million in 2030 (–34·4%). Between 2000 and 2030, we estimate increases of 39·0 million (180·6%) cases of overweight and obesity, 7·1 million (131·5%) cases of overweight and obesity with hypertension, 4·3 million (430·0%) cases of severe obesity, and 1·2 million (34·3%) cases of severe hypertension. Between 2000 and 2030, we estimate a slight decrease of 0·3 million (–0·8%) cases of hypertension. A significant negative association between population development index and metabolic burden was observed for 2019 (r=–0·485, p=0·0062) and projected for 2030 (r=–0·417, p=0·020). Decomposition analysis indicated that rising age-specific prevalence is the primary driver of increasing numbers of metabolic cases, partially offset by population decline.

Interpretation

In the context of China's declining youth populations, increases in the prevalence, clinical severity, and absolute case numbers of overweight and obesity with hypertension signal a worsening metabolic health burden. Beyond public health policies to shape healthier lifestyle patterns, enhanced efforts are needed to prepare China's primary health-care system and optimise the allocation of paediatric health-care resources.

Funding

National Key R&D Program of China, National Natural Science Foundation of China, Beijing Natural Science Foundation, Peking University Talent Introduction Program Project, Clinical Medicine Plus X-Young Scholars Project of Peking University, UK Medical Research Council, and the Abdul Latif Jameel Institute for Disease and Emergency Analytics at Imperial College London, funded by a donation from Community Jameel.