Essays on Child Mental Health: Estimating Diagnostic Errors, Latent Risk, and Long-term Trajectories

Understanding diagnostic decision-making for child mental illness is the central focus of my dissertation research. I combine multiple quasi-experimental methods and machine learning tools with novel individual-level tax and survey data linkages to explore these issues. Over three papers, I address several sources of diagnostic and treatment errors in child mental illness, stemming from school and home environments. In the first paper, I study how a child's school starting age generates over-diagnosis of ADHD in young-for-grade and male students and missed diagnoses in relatively older students, especially females. Importantly, I demonstrate that teacher special education training mitigates these age-based assessment errors. The second paper investigates multiple sources of under-and over-utilization of mental health care by combining a machine learning-derived mental health risk prediction with multiple exogenous changes in diagnosis and treatment likelihood. These changes include the effect of school starting age on diagnosis and the role of Quebec's pharmaceutical insurance plan expansion on treatment uptake. I identify that over-diagnosis may be driven by over-weighting externalized behaviours, which are more common in males. At the same time, I additionally find that removing out-of-pocket costs increases treatment rates, specifically for high-risk children. The final paper expands upon this work to understand the long-term trajectories of children with mental health issues and heterogeneous treatment effects at the population level. I find that measured gaps in educational attainment, social assistance receipt and income due to child mental illness persist well into adulthood. I find evidence that diagnosis and treatment worsen human capital outcomes like educational attainment and unemployment for low-risk individuals. Nevertheless, treatment can help close mental health-related socioeconomic inequalities, but only for very high-risk children. Together these papers outline the growing issue of mental illness in childhood and its long-term implications. They highlight the potential for diagnostic mistakes or misallocation of care and the role of new data-driven tools to alleviate some of these medical errors. In my concluding chapter, I highlight some of the strengths and limitations of machine learning tools for improving mental illness detection and treatment going forward and point towards critical work required to make these tools useful for clinical practice.

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