Using Decision Trees to Examine the Influence of the School Environment on Youth Mental Health

Abstract

Youth mental health is a current public health priority in Canada, with nearly one in four young people living with a mental illness. The contextual school environment can be particularly influential given the considerable amount of time that youth spend in school. Schools are seen as ideal settings for prevention and early intervention initiatives. While a myriad of practices and programs are being implemented across schools to address student mental health, there is limited and contradictory evidence on their effectiveness. Most available research has been conducted using statistical techniques that have limited ability to account for the complex interactions between co-occurring environmental influences. While machine learning techniques such as decision trees are well suited for this type of analysis, they are relatively underused in public health research. The overall objective of this dissertation was to use decision tree analysis to further our understanding of the influence of the school contextual environment on youth depression, anxiety, and psychosocial wellbeing. Specific objectives were to (1) compare the performance of decision trees to traditional regression models in the context of health survey data, (2) determine which environmental and behavioural factors are most influential on mental health outcomes, and (3) determine which, if any, combinations of school mental health practices are associated with better student mental health. These objectives were addressed through three manuscripts using student- and school-level data from the 2017-18 and 2018-19 waves of the COMPASS study. The first manuscript provided a methodological overview and application of two decision tree techniques: classification and regression trees and conditional inference trees. Decision tree model performance was compared to traditional linear and logistic regression. All techniques showed general agreement in the identification of key differentiating factors across five outcomes. Tree models had slightly lower prediction accuracy than regression models but were more parsimonious. Unlike traditional regression methods, decision trees allowed for the identification of non-linear associations and differential impacts among high-risk subgroups. The second manuscript used cross-sectional student-level data to examine associations of various environmental and behavioural risk factors with youth anxiety, depression, and flourishing levels. Having a happy home life and sense of school connection were identified as key protective factors, while behavioural factors such as diet, movement, and substance use did not emerge as important differentiators. Females lacking both happy home life and sense of connection to school were at greatest risk for higher anxiety and depression levels. These results highlighted the importance of the home and school environments and suggested that a sense of connection to school may help to mitigate the negative influence of a poor home environment. The third manuscript used longitudinal student- and school-level data to examine variation in school mental health practices as well as associations between changes in these practices and youth anxiety, depression, and flourishing levels. Decision trees were used to comprehensively examine whether any combination of practice and service changes were associated with mental health outcomes. While substantial variability was seen in the mental health practices and services offered between schools and across years, decision tree analysis found no combinations of changes that meaningfully contributed to better student mental health outcomes. These results suggested that incremental practice changes were not effective and highlighted the need for more comprehensive school mental health approaches. This dissertation used a novel decision tree approach to expand our knowledge of the influence of the school contextual environment on youth depression, anxiety, and psychosocial wellbeing. These findings have important implications for practice, as they suggest that schools can enhance student mental health through initiatives that foster a supportive school environment and sense of connection. These findings further support calls for comprehensive school health programming by showing that current tactics of incremental and sporadic practices changes at the individual school level are ineffective. This dissertation also provides a framework for future research, as the decision tree approach used here can be applied to other public health domains to examine complex interactions and identify high-risk subgroups. Further, the ability to comprehensively examine permutations of simultaneously changing factors makes decision trees a compelling tool for natural experiment evaluation. In addition to answering important research questions regarding the influence of school context on youth mental health, this dissertation work highlights the potential power in combining machine learning methods with large population health surveillance data.