Natalie Bareis, PhD, LMSW

My clinical work and expertise in epidemiological and biostatistical methods have informed multiple studies using the NIMH Data Archive (NDA) and large nationally representative datasets (the CDC’s National Health and Nutrition Examination Survey (NHANES), the Health and Retirement Study (HRS), the National Comorbidity Survey Replication (NCS-R)). In addition, I was project manager of the Columbia University site of the Mental and Substance Use Disorders Prevalence Study (MDPS), a novel nationally representative psychiatric epidemiological survey with a primary focus on identifying up-to-date prevalence estimates of schizophrenia spectrum disorders in the US population. My current work utilizing national Medicaid claims data that identified variation in prescribing patterns of psychotropics for individuals with schizophrenia spectrum disorders between states, as well as identifying means of suicide among this population, and clozapine prescription, has laid the foundation for my K23 award. In that project I am using machine learning methods with claims and electronic health record data to identify unique clinical characteristics, or profiles, of individuals that are associated with heterogeneous responses to treatments prescribed for schizophrenia spectrum disorders. Using these data-driven methods we are identifying personalized treatment rules that predict outcomes of clinical importance to individuals receiving these treatments to ensure a patient-centered perspective that aligns with the heart of social work.