Alzheimer's Disease Linkage to Real-World Evidence (AD-LINE) Study: Linking Claims Data to Phase 3 GRADUATE Study of Gantenerumab.

Background: Linking data from clinical trials and real-world claims may improve the robustness of trial data and provide information on the health, economic, and societal impacts of a disease.

Objective: To report on the feasibility of linking trial data to Medicare claims data in early symptomatic Alzheimer's disease (AD) in the US.

Design And Setting: Alzheimer's Disease Linkage to Real-World Evidence (AD-LINE) was a noninterventional cohort study that included participants recruited from the GRADUATE program whose trial data were linked to their Medicare claims.

Characteristics and Healthcare Burden of Patients with Schizophrenia Treated in a US Integrated Healthcare System.

Background: Schizophrenia is one of 15 major causes of disability worldwide and is responsible for more than USD 150 billion in annual healthcare costs in the United States. Although the burden of schizophrenia as measured by healthcare resource utilization (HRU) is known to be considerable, data generally come from claims databases or healthcare systems/payors representing only a subset of patients, such as Medicare/Medicaid recipients. A broader understanding of HRU across the schizophrenia patient population would help identify underserved groups and inform strategies for improving healthcare delivery.

The Impact of Once-Monthly Paliperidone Palmitate on Healthcare Utilization Among Patients With Schizophrenia Treated in an Integrated Healthcare System: A Retrospective Mirror-Image Study.

Introduction: Previous evidence demonstrated that patients with schizophrenia consumed substantial healthcare resources in an integrated healthcare system. This study evaluated the impact of initiating once-monthly paliperidone palmitate (PP1M) on healthcare resource utilization (HRU) among patients with schizophrenia treated in a US integrated healthcare system.

Methods: This retrospective study used electronic medical records from Atrium Health.

Development of a Behavioral Health Stigma Measure and Application of Machine Learning for Classification.

Given the growing public health importance of measuring the change in mental health stigma over time, the goal of this study was to demonstrate the potential for using machine learning as a tool to analyze patterns of social stigma as a complement to traditional research methods. A total of 1,904 participants were recruited through Sona Systems, Ltd (Tallinn, Estonia), an experiment management system for online research, to complete a self-reported survey. The collected data were used to develop a new measure of mental (behavioral) health stigma.