Prevalence and factors associated with depression among higher secondary school adolescents of Pokhara Metropolitan, Nepal: a cross-sectional study

Bhattarai, Deepa; Shrestha, Nisha; Paudel, Shishir

doi:10.1136/bmjopen-2020-044042

Cited by 35 publications

(44 citation statements)

References 36 publications

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“…Schoolchildren’s mental health problems including depression and anxiety have been studied by many researchers [ 12 - 16 ]. Research studies have reported that depression and anxiety lead to several negative consequences on children’s health development, such as functional impairment and poor cognitive development, social development, and educational and academic performance.…”

Section: Introductionmentioning

confidence: 99%

Assessment and Prediction of Depression and Anxiety Risk Factors in Schoolchildren: Machine Learning Techniques Performance Analysis

Qasrawi¹,

VicunaPolo²,

Al-Halawa³

et al. 2022

JMIR Form Res

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Background Depression and anxiety symptoms in early childhood have a major effect on children’s mental health growth and cognitive development. The effect of mental health problems on cognitive development has been studied by researchers for the last 2 decades. Objective In this paper, we sought to use machine learning techniques to predict the risk factors associated with schoolchildren’s depression and anxiety. Methods The study sample consisted of 3984 students in fifth to ninth grades, aged 10-15 years, studying at public and refugee schools in the West Bank. The data were collected using the health behaviors schoolchildren questionnaire in the 2013-2014 academic year and analyzed using machine learning to predict the risk factors associated with student mental health symptoms. We used 5 machine learning techniques (random forest [RF], neural network, decision tree, support vector machine [SVM], and naive Bayes) for prediction. Results The results indicated that the SVM and RF models had the highest accuracy levels for depression (SVM: 92.5%; RF: 76.4%) and anxiety (SVM: 92.4%; RF: 78.6%). Thus, the SVM and RF models had the best performance in classifying and predicting the students’ depression and anxiety. The results showed that school violence and bullying, home violence, academic performance, and family income were the most important factors affecting the depression and anxiety scales. Conclusions Overall, machine learning proved to be an efficient tool for identifying and predicting the associated factors that influence student depression and anxiety. The machine learning techniques seem to be a good model for predicting abnormal depression and anxiety symptoms among schoolchildren, so the deployment of machine learning within the school information systems might facilitate the development of health prevention and intervention programs that will enhance students’ mental health and cognitive development.

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Section: Introductionmentioning

confidence: 99%

Assessment and Prediction of Depression and Anxiety Risk Factors in Schoolchildren: Machine Learning Techniques Performance Analysis

Qasrawi¹,

VicunaPolo²,

Al-Halawa³

et al. 2022

JMIR Form Res

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show abstract

“…According to the study findings, the demographic characteristics such as type of family and living status of parents had significant relationship with the level of depression among the participants at p = 0.01. In contrast, the study shows that the adolescents who were in joint family showed higher depressive symptoms [13]. According to the relationship found with demographic data of the participants, the type of family and living status of the parents had greater influence with the level of depression.…”

Section: Discussionmentioning

confidence: 68%

“…The survey discovered a significant frequency of depression among high school students, with 44.2% of participants suffering with depression. Furthermore, 25.3% were diagnosed with mild depression, while 18.9% were diagnosed with serious depression [13]. Malik et al found a 52.9% incidence of depression among teenage students in Hariyana, India, and another study in Bihar, India, found a 49.2% prevalence of depression [14,15].…”

Section: Discussionmentioning

confidence: 99%

Prevalence of Depression and Its’ Risk Factors among Higher Secondary School Students

Alshahrani

Paulsamy

Panneerselvam

et al. 2021

JPRI

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As adolescence is a transitional stage in which a person's physical and psychological development and transformation are limited to the time between puberty and legal adulthood. It is commonly associated with the onset of a variety of mental health or adjustment problems. This study was to find out the incidence of depression and the factors that contribute to it among higher secondary school students. A descriptive, cross-sectional study was conducted among 100 adolescents who were selected by simple random sampling technique. The socio-demographic information, Perceived Social Support scale and Patient Health Questionnaire (PHQ-9) were adopted to collect the data. Descriptive and Inferential statistics was used to analyse the collected data. In this study, 52% of the adolescents did not have depression, whereas 18% had moderate, 8% had moderate to severe depression and 2% of them had severe depression. Nearly 68% of students had a higher social support perception. The type of family and living status of parents had significant relationship with the level of depression among adolescents at p = 0.01. The study concludes that depression and mental distress among these adolescents may be alleviated by boosting their perceived degree of social support and self-esteem through a variety of methods such as family support, counselling at schools and workshops on self – management skills etc.

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“…20 Besides, low self-esteem, family history of depression, female sex, number of traumatic experiences, and disturbing family environment are also associated risk factors for depressive and anxiety disorders. 28,[31][32][33]…”

Section: Discussionmentioning

confidence: 99%

Burden of Depressive and Anxiety Disorders in Nepal, 1990-2017: An Analysis of Global Burden of Disease Data

Budhathoki

Paudel

et al. 2021

Preprint

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Objectives: This study aims to describe the burden of depressive and anxiety disorders in Nepal in terms of incidence, prevalence, YLDs, and DALYs by age and sex. Design: An ecological study on disease burden. Methods: We used the publicly available Global Burden of Disease (GBD) data from 1990 to 2017. The age and sex-specific incidence, prevalence rate, Years Lived with Disability (YLDs), and Disability Adjusted Life Years (DALYs) were examined among per 100,000 populations. Results: Females had a higher prevalence of depressive 4094.4 (95% uncertainty interval [UI]: 3761.9-4470.3 per 100,000) and anxiety 4496.8 (95% UI: 4171.9-4837.9 per 100,000) disorders. The prevalence of major depressive disorders was comparatively higher in females (2766.7) than males (1822.9). Females also had higher YLDs for both depressive and anxiety disorders. In 2017, higher DALYs of anxiety disorder were found in the females of 45-49 years (630.1). Childhood sexual abuse was found to be the main risk factor for depressive disorder, contributing to 32.5 DALYs in both sexes. Bullying, victimization had contributed to 26.7 DALYs of anxiety. Conclusions: The high burden of disorders in females would support in identifying major mental health challenges in Nepal and develop plans and various preventive, curative, and rehabilitative strategies.

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Prevalence and factors associated with depression among higher secondary school adolescents of Pokhara Metropolitan, Nepal: a cross-sectional study

Cited by 35 publications

References 36 publications

Assessment and Prediction of Depression and Anxiety Risk Factors in Schoolchildren: Machine Learning Techniques Performance Analysis

Assessment and Prediction of Depression and Anxiety Risk Factors in Schoolchildren: Machine Learning Techniques Performance Analysis

Prevalence of Depression and Its’ Risk Factors among Higher Secondary School Students

Burden of Depressive and Anxiety Disorders in Nepal, 1990-2017: An Analysis of Global Burden of Disease Data

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