Public health · 2026
Association between behavioral-environmental risk patterns and multimorbidity of chronic non-communicable diseases among Chinese adolescents at schools.
СтатьяНазвание и аннотация: Перевод в очереди
Обработка статьи · этапы, ошибки и управление
Загрузка состояния…
Ошибки и ограничения
История · было → стало
Состояние обновляется каждые 5 секунд, пока блок открыт. Процент показывается только там, где известен объём работы. Пауза останавливает продолжение этапа; текущий запрос к источнику может завершиться.
Hu Zhiyun, Sun Xiaodong, Luo Chunyan, Du Wei, Yang Dongling, Xue Hui, Zhang Fengyun, Fan Lijun
Аннотация
OBJECTIVES: Multimorbidity, though traditionally discussed in older populations, is increasingly prevalent among adolescents. However, evidence on how co-occurring behavioral and environmental risks contribute to adolescent multimorbidity remains limited. This study aimed to identify distinct patterns of behavioral-environmental risks among Chinese adolescents and to examine how these patterns are associated with multimorbidity of chronic non-communicable diseases.
STUDY DESIGN: This was a cross-sectional study, utilizing data from 15,082 participants enrolled in the 2022 Shanghai "Surveillance for Common Diseases and Health Risk Factors among Students" project.
METHODS: Participants were selected using a cluster random sampling method. Adolescent multimorbidity was defined as the coexistence of six chronic non-communicable diseases (high blood pressure, high myopia, dental caries, abnormal spinal curvature, obesity, and depressive symptoms). Behavioral-environmental risk dimensions covered individual lifestyle behaviors (i.e., imbalanced diet, unhealthy food, smoking or drinking, excessive screen time, insufficient sleep, and physical inactivity), school-related environmental risks (untimely seat or desk adjustment, low frequency of eye exercises or physical education classes), and family environmental factors (non-nuclear family, low parental education, disadvantaged family economy, and household secondhand smoke exposure). Latent class analysis identified distinct risk patterns, and logistic regression models explored their associations with multimorbidity.
RESULTS: The mean age of participants was 14.94 (SD = 1.75), and 47.6% were girls. Three behavioral-environmental patterns emerged: "high behavioral and family-environmental risks (Class 1, 40.9%)", "high behavioral and school-environmental risks (Class 2, 22.3%)", and "relatively low behavioral-environmental risks (Class 3, 36.8%)". Compared with Class 3, adolescents in Class 1 (OR = 1.21-1.27, all p < 0.001) or Class 2 (OR = 1.20-1.28, all p < 0.001) had significantly higher odds of multimorbidity across all adjusted models.
CONCLUSIONS: School-aged adolescents exhibit distinct behavioral-environmental risk patterns, with differing associations with multimorbidity. Findings underscore the need for integrated health promotion strategies addressing individual behaviors and broader environmental influences to reduce multimorbidity among the youth.
STUDY DESIGN: This was a cross-sectional study, utilizing data from 15,082 participants enrolled in the 2022 Shanghai "Surveillance for Common Diseases and Health Risk Factors among Students" project.
METHODS: Participants were selected using a cluster random sampling method. Adolescent multimorbidity was defined as the coexistence of six chronic non-communicable diseases (high blood pressure, high myopia, dental caries, abnormal spinal curvature, obesity, and depressive symptoms). Behavioral-environmental risk dimensions covered individual lifestyle behaviors (i.e., imbalanced diet, unhealthy food, smoking or drinking, excessive screen time, insufficient sleep, and physical inactivity), school-related environmental risks (untimely seat or desk adjustment, low frequency of eye exercises or physical education classes), and family environmental factors (non-nuclear family, low parental education, disadvantaged family economy, and household secondhand smoke exposure). Latent class analysis identified distinct risk patterns, and logistic regression models explored their associations with multimorbidity.
RESULTS: The mean age of participants was 14.94 (SD = 1.75), and 47.6% were girls. Three behavioral-environmental patterns emerged: "high behavioral and family-environmental risks (Class 1, 40.9%)", "high behavioral and school-environmental risks (Class 2, 22.3%)", and "relatively low behavioral-environmental risks (Class 3, 36.8%)". Compared with Class 3, adolescents in Class 1 (OR = 1.21-1.27, all p < 0.001) or Class 2 (OR = 1.20-1.28, all p < 0.001) had significantly higher odds of multimorbidity across all adjusted models.
CONCLUSIONS: School-aged adolescents exhibit distinct behavioral-environmental risk patterns, with differing associations with multimorbidity. Findings underscore the need for integrated health promotion strategies addressing individual behaviors and broader environmental influences to reduce multimorbidity among the youth.