Using Decision Trees to Examine Environmental and Behavioural Factors Associated with Youth Anxiety, Depression, and Flourishing.
Katelyn BattistaKaren Allison PatteLiqun DiaoJoel A DubinScott T LeatherdalePublished in: International journal of environmental research and public health (2022)
Modifiable environmental and behavioural factors influence youth mental health; however, past studies have primarily used regression models that quantify population average effects. Decision trees are an analytic technique that examine complex relationships between factors and identify high-risk subgroups to whom intervention measures can be targeted. This study used decision trees to examine associations of various risk factors with youth anxiety, depression, and flourishing. Data were collected from 74,501 students across Canadian high schools participating in the 2018-2019 COMPASS Study. Students completed a questionnaire including validated mental health scales and 23 covariates. Decision trees were grown to identify key factors and subgroups for anxiety, depression, and flourishing outcomes. Females lacking both happy home life and sense of connection to school were at greatest risk for higher anxiety and depression levels. In contrast with previous literature, behavioural factors such as diet, movement and substance use did not emerge as differentiators. This study highlights the influence of home and school environments on youth mental health using a novel decision tree analysis. While having a happy home life is most important in protecting against youth anxiety and depression, a sense of connection to school may mitigate the negative influence of a poor home environment.
Keyphrases
- mental health
- physical activity
- sleep quality
- healthcare
- mental illness
- risk factors
- randomized controlled trial
- depressive symptoms
- young adults
- decision making
- magnetic resonance
- type diabetes
- systematic review
- high school
- computed tomography
- big data
- climate change
- cancer therapy
- deep learning
- magnetic resonance imaging
- skeletal muscle
- cross sectional