Project Highlights

This R01 study is conducting in-depth analyses using highly unique data sources from the Danish National Birth Cohort (DNBC) to investigate whether prenatal and postnatal exposures to Acetaminophen (N-acetyl-para-aminophenol, paracetamol, APAP) affect neurodevelopment from infancy through age 18.

The PEACH Study, a pilot project, aims to understand the use of prescription and over-the-counter medications during pregnancy. The pilot study is funded by the Yale Center for Clinical Investigation (YCCI) through the Lifespan Research pilot grant program.

Maternal autoimmune diseases confer higher risk of adverse pregnancy, birth and offspring neurodevelopmental outcomes. This includes women with rheumatoid arthritis , axial spondyloathritis, juvenile idiopathic arthritis, and psoriatic arthritis - together called chronic inflammatory arthritis (CIA). There is evidence that high CIA disease activity is associated with preterm births and smaller for gestational age infants, however it is unclear if adherence to pregnancy-compatible anti-rheumatic therapy at any trimester during pregnancy improves pregnancy outcomes.

Women are disproportionately affected by autoimmune diseases, 80% of individuals affected by autoimmune diseases are women and this includes conditions such as systemic lupus erythematosus, Sjogren’s syndrome, scleroderma, and immune-mediated myositis (Anti-nuclear antibody related connective tissue diseases/ARCTD). Anti-nuclear antibody (ANA) is a biomarker traditionally used to screen for autoimmune diseases and is a helpful screening biomarker among individuals who displays signs and symptoms of such conditions. Unfortunately, the value of ANA as a diagnostic aid is low and it is prone to false positivity. This results in delayed care among women with actual ARCTD.

Opioid use disorder (OUD) is a public health challenge that affects millions of people worldwide. Despite growing evidence supporting the effectiveness of medications for opioid use disorder (MOUD, including methadone, buprenorphine and extended-release naltrexone), access to these medications is still insufficient, with most patients remaining untreated. Therefore, enhancing knowledge about real-world effectiveness and guiding optimal use of MOUD in the clinical care of patients is of critical importance. This study, funded by NIH/NIDA K99/R00 award, leverages electronic heath records from the Veterans Affairs, state-of-the-art data models, machine learning algorithms and causal inference methods, to close these knowledge gaps and answer a set of timely questions centering around OUD care.

The aim of the project is to develop a collection of novel algorithms – The Target Trial Toolbox – for implementing the target trial framework. This framework is a design framework for causal inference from real world data. The framework is essential for pharmacoepidemiologic studies, but existing algorithms are fragmented. The toolbox will be grounded in Danish health data, with the ultimate objective of ensuring scalability on a global scale.