Using Latent Class Analysis to Identify Health Lifestyle Profiles and Their Association with Suicidality among Adolescents in Benin

Abstract

Youth suicidality is considerably prevalent in low- and middle-income countries, including Benin. Factors such as psychosocial distress, socio-environmental factors, and health risk behaviors are associated with suicidality. However, little is known about how these factors co-occur in these countries. An analysis of these factors taken together would help to identify the profiles most at risk and better target prevention policies. Our study aimed to identify profiles related to these factors and their association with suicidality among adolescents in Benin. Data from the 2016 Global School-Based Student Health Survey were used, and factors related to lifestyle (tobacco and alcohol consumption and physical activity), physical violence, parental support, and psychological distress were studied. Latent class analysis was used to identify the profiles, and a modified Poisson regression with generalized estimating equations, adjusted for sociodemographic characteristics, was performed to assess the association between these profiles and suicidality. The survey results show that globally, 13.8% of the adolescents (n = 2536) aged 11 to 18 had thought about suicide, 15.6% had planned suicide, and 15.6% had attempted suicide. Four profiles were identified: a low-risk group, one with psychological distress problems, a group with violence problems, and one with alcohol, tobacco, and violence problems. The risk of suicidality, in terms of ideation, planning, or attempting, was higher for adolescents in Profiles 2, 3, and 4 than those in Profile 1 (p < 0.05). Adolescents in Profile 2 were particularly affected by this increased risk (prevalence ratio (PR) for ideation = 1.13, 95% CI = 1.03–1.23; PR for planning = 1.12, 95% CI = 1.04–1.22; PR for attempting = 1.09, 95% CI = 1.01–1.17). This study highlights the typical profiles that may be linked with suicidality among adolescents in Benin. A holistic consideration of these factors could help in planning better preventive measures to reduce suicidality among adolescents in Benin.

Suicide attempts among students of higher education, Nelson Mandela Bay Municipality, South Africa

“…17 However, the prevalence of suicide attempts in this study is lower than the 22.2%, 18.5% and 23.2% reported in schoolbased surveys in Ghana, Mozambique and Benin respectively. 18,19,20 The higher prevalences may be attributed to higher proportion of participants: younger than 18 years; and anxiety and bullying experienced in the previous studies.…”

Conceptualising the correlates of adolescent suicidal behaviour in Benin: evidence from a national survey

“…For instance, evidence from 59 LMICs reported a prevalence of 17% [25]. Also, a previous study in Benin reported the prevalence of suicide behaviour as 20% [10].…”

“…To a greater extent, conceptualising the factors that are linked to adolescent suicidal behaviours is crucial for mental health promotion and suicide prevention. Additionally, recent studies on suicidal behaviour among adolescents in Benin have not fully conceptualised these protective and risk factors from the broader socio-demographic and psychosocial characteristics of participants [5,10]. Therefore, this study sought to examine extensively multidimensional factors that serve as associated protective or risks for adolescent suicidal behaviour among adolescents in Benin.…”

A study on differences about the influencing factors of depressive symptoms between medical staff and residents during 2022 city-wide temporary static management period to fighting against COVID-19 pandemic in Shanghai

“…The cut-off point for PHQ-9 is 10 points which could identify high-risk individuals for depression episodes ( 3 ). Yet trajectories of depressive episodes are complicated and might originate from the interaction of internal and external factors, including genetic, psychological, and environmental risk factors ( 4 ). Early screening of depressive symptoms by cut-off value may decrease the sensitivity of the screening test.…”