Clustering of Risk Behaviors and their Social Determinants among Primary School Learners in Beijing, China: A Cross-sectional Study

Clustering of health risk behaviors among school-going adolescents in Mymensingh district, Bangladesh

BACKGROUND

Adolescents frequently engage in risky behaviors that negatively influence their health and pose a serious public health concern. This study aimed to examine the clustering pattern of health risk behaviors among school-going adolescents in Bangladesh.

METHODS

A cross-sectional study was conducted from 15 April to 27 June 2022 among 412 school-going adolescents in Mymensingh district of Bangladesh through a convenience sampling technique. Data were collected via face-to-face interviews using a modified Global School-Based Student Health Survey (GSHS) 2021 questionnaire. Cluster membership was identified using the k-means clustering algorithm. The chi-square test was adopted to explore the association between sociodemographic variables and each cluster membership. The ordinal logistic regression model was employed to examine the predictors associated with cluster membership.

RESULTS

Most of the respondents were female (55.3%) and belonged to the 16-19 years (74.5%) age group. Three behavioral clusters were identified, including Cluster 1: Low-risk taker (50.2%), Cluster 2: Moderate risk taker (39.6%), and Cluster 3: High-risk taker (10.2%). Influential factors of high-risk behavior among adolescents were significantly associated with their age (p = 0.03), father's education who were illiterate (p = 0.02), monthly family income >30000 BDT (p = 0.04), parent's those were understanding their child's problems in most of the time (p = 0.001).

CONCLUSIONS

The study found that high-risk behaviors are significantly higher among late adolescents, those whose fathers are illiterate, whose monthly income is higher, those whose parents or guardians never realize their child's complications and worries, and those whose parents or guardians never recognize what they did in their leisure time. These findings will help to develop intervention programs, policies, strategies, and curricula in school by the experts following the necessity to adopt the adolescent toward healthy behavior and help to reduce the prevalence of health risk behavior.

Cluster analysis of risky behaviors among the youth in Western Iran: Determining correlates and comparing clusters based on severity of disability and attitude toward mental health help-seeking

BACKGROUND AND AIMS

The objectives of the study were (i) cluster analysis of risky behaviors; (ii) determining correlates; and (iii) comparing clusters with regard to the attitude toward mental health help seeking.

METHODS

The current cross-sectional study is a part of the first phase of the Persian Youth Cohort. From October 2014 to January 2017, 2990 individuals from Ravansar City in western Iran completed structured interviews and national and international standard questionnaires. The obtained data were analyzed using two-stage cluster analysis, multinomial logistic regression analysis, and Chi-square test.

RESULTS

This model provided three distinct clusters: (i) low-risk group with mild distress, (ii) high-risk group with high distress, and (iii) violent group with medium distress. Some sociodemographic and lifetime psychiatric disorders were the correlates of unhealthy clusters (P < 0.05). Compared to the reference cluster, a higher number of members in unhealthy clusters were suffering from medium to severe disability. Nevertheless, the participants in these clusters were less inclined to mental health help seeking.

CONCLUSIONS

More than half of the youth were suffering from suicidal and violent behaviors. Since high-risk participants are less inclined to mental health help seeking, the health policymakers can successfully utilize the results in planning general health programs.

A Latent Class Analysis of Health Lifestyles in Relation to Suicidality among Adolescents in Mauritius

Suicidality, which includes suicidal thoughts, planning, and suicide attempts, results mainly from a combination of psychological, sociological, and environmental factors. Despite a high prevalence of suicidality among adolescents in Africa, only a few studies have considered these factors simultaneously. The objective of the study was to identify the prevalence of suicidality, to draw up profiles of concomitant risks, and to examine the associations between these profiles and suicidality in Mauritius. This study used data from the 2017 Mauritian Global School-based Student Health Survey including 3012 adolescents with a mean age of 14.9 ± 1.4 years. Factors related to lifestyle such as consumptions of alcohol and tobacco, physical activity, violence, parental support, anxiety, and loneliness were considered. A latent class analysis was performed to identify the profiles. Finally, a modified Poisson regression analysis with generalized estimating equations, adjusted with sociodemographic characteristics, was used to assess the association between these profiles and suicidality. Overall, more than one in ten adolescents had at least one of the suicidality behaviors. Three profiles were identified: 1 = "low risk group" (63.9%); 2 = "problems with violence" (15.2%); 3 = "problems with violence, alcohol, tobacco and psychological distress" (20.9%). Profiles 2 and 3 were mainly made up of males. Adolescents under 15 represented the majority of individuals in profile 2. Finally, the risk of suicidality was higher in adolescents belonging to profiles 2 and 3 compared to profile 1 for the three suicidality behaviors (profile 3: Prevalence ratio (PR) for suicidal thoughts = 1.26, 95% CI = 1.19-1.34; PR for planning = 1.23, 95% CI = 1.17-1.30; PR for attempt = 1.23, 95% CI = 1.17-1.29). This study highlights the high prevalence of suicidality and a list of concomitant risks, emphasizing this suicidality in Mauritian adolescents. Therefore, these results recommend focusing preventive efforts toward a simultaneous consideration of these factors.

Clustering of health risk behaviors among adolescents in Kilifi, Kenya, a rural Sub-Saharan African setting

BACKGROUND

Adolescents tend to experience heightened vulnerability to risky and reckless behavior. Adolescents living in rural settings may often experience poverty and a host of risk factors which can increase their vulnerability to various forms of health risk behavior (HRB). Understanding HRB clustering and its underlying factors among adolescents is important for intervention planning and health promotion. This study examines the co-occurrence of injury and violence, substance use, hygiene, physical activity, and diet-related risk behaviors among adolescents in a rural setting on the Kenyan coast. Specifically, the study objectives were to identify clusters of HRB; based on five categories of health risk behavior, and to identify the factors associated with HRB clustering.

METHODS

A cross-sectional survey was conducted of a random sample of 1060 adolescents aged 13-19 years living within the area covered by the Kilifi Health and Demographic Surveillance System. Participants completed a questionnaire on health behaviors which was administered via an Audio Computer-Assisted Self-Interview. Latent class analysis on 13 behavioral factors (injury and violence, hygiene, alcohol tobacco and drug use, physical activity, and dietary related behavior) was used to identify clustering and stepwise ordinal logistic regression with nonparametric bootstrapping identified the factors associated with clustering. The variables of age, sex, education level, school attendance, mental health, form of residence and level of parental monitoring were included in the initial stepwise regression model.

RESULTS

We identified 3 behavioral clusters (Cluster 1: Low-risk takers (22.9%); Cluster 2: Moderate risk-takers (67.8%); Cluster 3: High risk-takers (9.3%)). Relative to the cluster 1, membership of higher risk clusters (i.e. moderate or high risk-takers) was strongly associated with older age (p<0.001), being male (p<0.001), depressive symptoms (p = 0.005), school non-attendance (p = 0.001) and a low level of parental monitoring (p<0.001).

CONCLUSION

There is clustering of health risk behaviors that underlies communicable and non-communicable diseases among adolescents in rural coastal Kenya. This suggests the urgent need for targeted multi-component health behavior interventions that simultaneously address all aspects of adolescent health and well-being, including the mental health needs of adolescents.