Heterogeneity of adolescent health risk behaviors in rural western China: A latent class analysis

Abstract

BackgroundAdolescent 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.MethodsIn 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.ResultsA 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.ConclusionsAdolescents in rural western China are a heterogeneous population requiring different tailored and effective interventions.

Clusters of alcohol and drug use and other health-risk behaviors among Thai secondary school students: a latent class analysis

“…Consistent with research in other countries, this study suggests that most adolescents could go through this transitional period successfully as the highest proportion of adolescents were in this low-risk group [ 3 , 13 , 34 ]. For example, Noel and colleagues found that the majority (80% of a four-class LCA model) of students in secondary schools in New Zealand were ‘healthy’ and did not engage in health-risk behaviors or suffer from emotional health concerns [ 13 ] and Ji and colleagues found the highest proportion (52% of a four-class LCA model) of middle school students in rural western China was in the low-risk class with lowest probabilities for all risk behaviors such as binge drinking, self-injurious behaviors, suicide and accidental injury [ 34 ]. One explanation of the high proportion of class 1 members in our study may be that these students were still young with about 50% being 13 years and under and studying in year 7 so they had not experienced those risk behaviors yet.…”

“…Additionally, the probabilities of tobacco smoking, behaviors related to violence or unintentional injuries, and suicidal behaviors were high in this group. The clustering of alcohol consumption with these behaviors was similar to other studies [ 8 , 13 , 34 ] and emphasized the consequences of alcohol drinking among adolescents such as risky sexual activities [ 38 ], school absenteeism [ 39 ], and automobile accidents [ 38 , 40 ]. Drinking and smoking most often start in adolescence.…”

Beyond Their HIV Status: the Occurrence of Multiple Health Risk Behavior Among Adolescents from a Rural Setting of Sub-Saharan Africa

“…Our findings of the four class solution are similar to reports from other studies on HRB clustering conducted among rural Chinese [79], Canadian [80], and Dutch adolescents [11]. The proportion of adolescents in the highest risk class (5.9%) in this present study was close to composition of the highest risk group (4%) among rural Chinese adolescents [79], whose mean age (14.7 years) was also similar to the age of our participants (13.7 years). However, these findings should be interpreted cautiously since the other studies did not focus on adolescents living with HIV, were from different geographical settings, and measured behaviors that were different from the ones reported in this study.…”

“…This segment of adolescents may potentially represent a vulnerable sub-population with shared cumulative psychosocial risk factors which underlie an unhealthy lifestyle [10]. Our findings of the four class solution are similar to reports from other studies on HRB clustering conducted among rural Chinese [79], Canadian [80], and Dutch adolescents [11]. The proportion of adolescents in the highest risk class (5.9%) in this present study was close to composition of the highest risk group (4%) among rural Chinese adolescents [79], whose mean age (14.7 years) was also similar to the age of our participants (13.7 years).…”

Identifying clusters of health risk behaviors and their predictors in adult survivors of childhood cancer: A report from the French Childhood Cancer Survivor Study

“…LCA is a statistical tool used to identify homogeneous, mutually exclusive groups (or “classes”) existing in a heterogeneous population. Study participants were grouped by their endorsement patterns, allowing for two informative parameters to emerge: (a) the probability of being in a given class for each individual (posterior class probability) and (b) the probability of a response to a certain indicator, given a participant's membership in a latent class (variable‐class probabilities) 30 . The selection of the optimal number of classes was achieved through fit indices, such as the Bayesian information criterion (BIC), the adjusted Bayesian information criterion (aBIC), the Bozdogan's consistent AIC (CAIC) and Entropy 31‐33 .…”