Open AccessHeterogeneity of adolescent health risk behaviors in rural western China: A latent class analysis
Author(s) -
Yuanyi Ji,
Huanyu Xu,
Yu Zhang,
Qiaolan Liu
Publication year - 2018
Publication title -
plos one
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.99
H-Index - 332
ISSN - 1932-6203
DOI - 10.1371/journal.pone.0199286
Subject(s) - latent class model , demography , multinomial logistic regression , logistic regression , environmental health , psychological intervention , medicine , rural area , china , gerontology , psychology , geography , psychiatry , statistics , mathematics , archaeology , pathology , machine learning , sociology , computer science
Background Adolescent health risk behaviors are a public health priority given their prevalence and their associations with chronic diseases and life quality in adulthood. This study examined the heterogeneity of adolescent health risk behaviors and the associations between demographic characteristics and subgroup membership in rural western China. Methods In fall 2015, 2805 students from rural middle schools in Sichuan Province were surveyed using the Health-Related Behavior Questionnaire for Adolescents. Latent class analysis (LCA) was used to identify subgroups of adolescents with distinct patterns of health risk behaviors. Differences in class membership related to selected demographic characteristics were examined using multinomial logistic regression analysis. Results A four-class model emerged: (1) high-risk group (n = 108, 4.0%), (2) high-physical-inactivity and suicide-risk group (n = 340, 12.1%), (3) moderate-risk group (n = 897, 32.0%), and (4) low-risk group (n = 1460, 52.1%). The multinomial logistic regression analysis revealed that boys and adolescents with poor parental relationships and high allowances (spending money) were significantly more likely to be in the high-risk group than the low-risk group. Conclusions Adolescents in rural western China are a heterogeneous population requiring different tailored and effective interventions.