Beschreibung:
While the presence of disparities in healthcare is well documented, the mechanisms of such disparities are less understood, particularly in relation to mental health. This paper develops and estimates a structural model of diagnosis for the most prevalent child mental health condition, Attention Deficit Hyperactivity Disorder (ADHD). The model incorporates both patient and physician influences to highlight four key mechanisms of mental health diagnosis: underlying prevalence of ADHD symptoms, mental healthcare utilization, diagnostic uncertainty, and disutility from diagnostic errors. I estimate gender-specific model parameters using novel doctor note data together with machine learning and natural language processing techniques. In raw comparisons, male patients are 2.3 times more likely to be diagnosed with ADHD than female ones. Counterfactual simulations using model estimates show that less than half of this diagnostic disparity can be explained by differences in underlying symptom prevalence by gender. Through this exercise, I find that physicians view missed diagnosis to be costlier than misdiagnosis, especially for their male patients. Back of the envelope calculations suggest that reducing ADHD diagnostic errors could save $27.6-$52.8 billion dollars nationally.