Background: Reliable prediction of clinical progression over time can improve the outcomes of depression. Little work has been done integrating various risk factors for depression, to determine the combinations of factors with the greatest utility for identifying which individuals are at the greatest risk. Method: This study demonstrates that data-driven machine learning (ML) methods such as RE-EM (Random Effects/Expectation Maximization) trees and MERF (Mixed Effects Random Forest) can be applied to reliably identify variables that have the greatest utility for classifying subgroups at greatest risk for depression. 185 young adults completed measures of depression risk, including rumination, worry, negative cognitive styles, cognitive and coping flexibilities, and negative life events, along with symptoms of depression. We trained RE-EM trees and MERF algorithms and compared them to traditional linear mixed models (LMMs) predicting depressive symptoms prospectively and concurrently with cross-validation. Results: Our results indicated that the RE-EM tree and MERF methods model complex interactions, identify subgroups of individuals and predict depression severity comparable to LMM. Further, machine learning models determined that brooding, negative life events, negative cognitive styles, and perceived control were the most relevant predictors of future depression levels. Conclusions: Random effects machine learning models have the potential for high clinical utility and can be leveraged for interventions to reduce vulnerability to depression.
Objectives:To examine the prevalence and treatment utilization of patients diagnosed with Depression and Anxiety Disorders (DAD) based on Kentucky Medicaid 2012-2019 datasets.Methods:The study was based on Kentucky Medicaid claims data from 2012 through 2019 for patients 14 years and older. We constructed yearly patient-level databases using ICD_9 CM and ICD_10 CM codes to identify the patients with DAD, using the Current Procedure Terminology (CPT) codes to identify individual psychotherapy and group psychotherapy and using the National drug codes to categorize pharmacotherapy. Based on these data, we constructed summary tables that reflected the trends in prevalence of DAD across eight Kentucky Medicaid regions and for different demographic subgroups. Next, we implemented logistic regression on the constructed yearly patient-level data to formally assess the impact of risk factors and treatments on the prevalence of DAD. The potential risk factors included age, gender, race/ethnicity, geographic characteristics, comorbidities such as alcohol use disorder and tobacco use.Results:The prevalence of DAD increased from 30.84% in 2012 to 36.04% in 2019. The prevalence of DAD was significantly higher in patients with the following characteristics: non-Hispanic white, females, aged between 45 and 54 years old, living in rural areas, having alcohol use disorder, and using tobaccos. Other than 2013, the utilization of pharmacotherapy maintained at about 62%. The utilization of psychotherapy increased over years from 24.4% in 2012 to 36.5% in 2019. Overall, the utilization of any treatment slightly increased from 70.9% in 2012 to 73.3% in 2019 except a drastic decline in 2013 due to the reduction of benzodiazepine prescription. Patients being whites, females, and living in rural areas were more likely to use pharmacotherapy, and patients living in rural areas were less likely to use psychotherapy than those residing in urban areas.Conclusion:The prevalence of DAD has increased over time from 2012 to 2019. The utilization of pharmacotherapy maintained at 62% over eight years except 2013, and the utilization of psychotherapy has steadily increased over time.
