Core Concepts in Pharmacoepidemiology: Principled Use of Artificial Intelligence and Machine Learning in Pharmacoepidemiology and Healthcare Research.

Artificial intelligence (AI) and machine learning (ML) are important tools across many fields of health and medical research. Pharmacoepidemiologists can bring essential methodological rigor and study design expertise to the design and use of these technologies within healthcare settings. AI/ML-based tools also play a role in pharmacoepidemiology research, as we may apply them to answer our own research questions, take responsibility for evaluating medical devices with AI/ML components, or participate in interdisciplinary research to create new AI/ML algorithms.

The price paradox of biosimilar-like long-acting insulin.

Objectives: To describe the uptake and out-of-pocket (OOP) costs of Basaglar, the first long-acting insulin biosimilar, in a commercially insured population in the United States.

Study Design: Retrospective analysis of commercial pharmacy claims and pharmacy co-payment offsets.

Methods: We assessed Basaglar uptake by examining trends in the composition of the long-acting insulin market in the United States from 2014 to 2018.

Evaluation of VTE, MACE, and Serious Infections Among Patients with RA Treated with Baricitinib Compared to TNFi: A Multi-Database Study of Patients in Routine Care Using Disease Registries and Claims Databases.

Introduction: The aim of this work is to evaluate baricitinib safety with respect to venous thromboembolism (VTE), major adverse cardiovascular events (MACE), and serious infection relative to tumor necrosis factor inhibitors (TNFi) in patients with rheumatoid arthritis (RA).

Methods: Patients with RA from 14 real-world data sources (three disease registries, eight commercial and three government health insurance claims databases) in the United States (n = 9), Europe (n = 3), and Japan (n = 2) were analyzed using a new user active comparator design. Propensity score matching (1:1) controlled for potential confounding.

Long-term use of immunosuppressive medicines and in-hospital COVID-19 outcomes: a retrospective cohort study using data from the National COVID Cohort Collaborative.

Background: Many individuals take long-term immunosuppressive medications. We evaluated whether these individuals have worse outcomes when hospitalised with COVID-19 compared with non-immunosuppressed individuals.

Methods: We conducted a retrospective cohort study using data from the National COVID Cohort Collaborative (N3C), the largest longitudinal electronic health record repository of patients in hospital with confirmed or suspected COVID-19 in the USA, between Jan 1, 2020, and June 11, 2021, within 42 health systems.

Characteristics of Copayment Offsets for Prescription Drugs in the United States.

Importance: Despite ongoing debate regarding the high prices that patients pay for prescription drugs, to our knowledge, little is known regarding the use of coupons, vouchers, and other types of copayment "offsets" that reduce patients' out-of-pocket drug spending. Although offsets reduce patients' immediate cost burden, they may encourage the use of higher-cost products and diminish health insurers' ability to optimize pharmaceutical value.

Objective: To examine the drugs most commonly covered by offsets, the percentage of out-of-pocket costs covered by offsets, and the characteristics of patients using offsets for retail pharmacy transactions in the United States in 2017 through 2019.

Fentanyl and fentanyl analogs in the illicit stimulant supply: Results from U.S. drug seizure data, 2011-2016.

Background: U.S. research examining the illicit drug supply remains rare even though the information could help reduce overdoses.