Deconstructing Disparities in Lupus Pregnancies - PROJECT SUMMARY / ABSTRACT In a disease like systemic lupus erythematosus (SLE) that primarily affects women of childbearing age, social determinants of health (SDOH) and clinical factors significantly impact adverse pregnancy outcomes (APOs). While race has been recognized as a pivotal SDOH in APOs in SLE, there is a dearth of data regarding the influence of other SDOH. The objective of this mentored patient-oriented research career development award is to determine the contribution of SDOH to the risk of APOs in SLE. The central hypothesis is that multiple discrete SDOH contribute to risk of APOs in SLE patients. The primary outcome is a combination of fetal morbidity and CDC-defined maternal morbidity, and the secondary outcome is a combination of in-hospital maternal and fetal mortality. With the Candidate’s experience in health disparities research and her mentoring team’s expertise in SLE, qualitative research and machine learning, the Candidate will test the hypotheses with the following aims: 1) To quantify the associations and interactions of race and income with APOs in patients with SLE; 2) To identify important neighborhood-level SDOH associated with APOs in patients with SLE; and 3) To determine the ability of individual-level SDOH in predicting APOs in patients with SLE. The Candidate will use US nationwide data (~51,000 SLE pregnancies) and machine learning in data from five distinct and geographically diverse US states to determine the association between race and neighborhood-level income, as well as understand the impact of other neighborhood-level SDOH on APOs in patients with SLE. Further, she will collect both prospective individual-level SDOH data at two New York centers and perform qualitative interviews with stakeholders (recently pregnant patients with SLE, rheumatologists, high-risk obstetricians, and social workers) to contextualize the results and to elicit unmeasured SDOH. Through the strategic utilization of multi-dimensional data, at the end of this project, Dr. Bella Mehta (awardee) will have identified the most meaningful SDOH that contribute to APO risk in SLE both at an individual- and community-level. Over the course of this mentored research career development award, Dr. Mehta will obtain advanced training in the application of established and innovative methods for machine learning, acquire a broad understanding of the concepts and methods of qualitative research, and develop proficiency in assessment of reproductive health policies and programs. Data and skills from this study will provide the foundation for an R01 to disentangle the role of different SDOH in SLE pregnancy outcomes and develop a robust risk prediction tool in large SLE cohorts and other rheumatic conditions. Thus, patients who need intervention can be identified early, and future strategies and policies can be developed and implemented to reduce health disparities and improve outcomes in individual patients.
