Calculated based on number of publications stored in Pure and citations from Scopus
1989 …2024

Research activity per year

Personal profile

Area of Expertise

Dr. Sambamoorthi is a health economist, who has dedicated her teaching, mentoring, and research efforts to improving population health by reducing disparities by gender, race/ethnicity, age, disability, and mental health. To support her research, Dr. Sambamoorthi has consistently received funding from federal agencies such as AHRQ, NIH, and the Veteran Health Administration (VHA) -the largest integrated healthcare system in the United States. Her work has also been supported by private foundations and private corporations. In recognition of her commitment to diversity, education, scholarship, and research, Dr. Sambamoorthi received the 2018 Women In Science and Health (WISH) Advanced Career Excellence award from West Virginia University (WVU). Dr. Sambamoorthi’s research focuses on improving population health through examination of healthcare access, quality, and outcomes using “real-world” large data. Her interest in interdisciplinary perspectives has led to numerous collaborative research publications and grants with physician researchers, epidemiologists, statisticians, psychologists, nurses, sociologists, policy and behavioral researchers. Dr. Sambamoorthi has over 230 collaborative publications in peer-reviewed high-impact journals. She uses diverse national and international databases (example: UK Biobank) for analysis and triangulation of research findings. Dr. Sambamoorthi has expertise in analyzing large national survey databases (NHIS, MEPS, BRFSS, NHANES, HRS, PSID and others), health insurance claims (Medicaid, Medicare – FFS and Advantage, Veteran Health Administration, and private insurance), and linked databases (SEER-Medicare, SEER-CAHPS, MCBS, Veteran Health Administration-Medicare) to answer timely healthcare delivery and policy questions. Dr. Sambamoorthi routinely applies advanced statistical and econometric techniques in her analysis of large data. These include multi-level modeling, generalized linear models, quantile regressions, and linear and non-linear decomposition techniques. To minimize selection bias inherent in observational data, she uses Heckman-selection, propensity score, inverse probability weighting, instrumental variable regressions, two-part models, and others. Dr. Sambamoorthi is one of the few scientists, who is applying machine learning methods to health outcomes research, an emerging area. With the transformational shift in population health landscape in terms of new payment models, the requirement of electronic health records (EHR), an emphasis on patient outcomes, integrated databases have become vital for population health analytics. Dr. Sambamoorthi has led efforts in linking data from multiple sources (e.g., EHR linked claims data). As these data often have Big-data features (volume, variety, and velocity), machine learning methods have become critical in obtaining “actionable” intelligence from these data. Dr. Sambamoorthi has been applying machine learning methods (e.g., Random Forest, XGboost) using R software for predicting health outcomes. Recently she has been applying natural language processing and text mining (using python software) to extract relevant clinical information from physician notes as well as to conduct machine-assisted systematic literature reviews. Dr. Sambamoorthi’s mentorship skills are well known and she is often referred to as "Mentor Extraordinaire" by her mentees. Her mentees have included graduate students, post-docs, junior faculty, and clinicians from different health professions. She has received outstanding mentoring awards from the American Public Health Association (2010), WVU Vice President’s Award (2017), WVU Health Sciences (2017), and WVU Distinction in Graduate Research Mentoring award in 2020.

Education/Academic qualification

Ph.D., Economics, University of Madras

… → Dec 1981

M.A., Economics, University of Madras

… → May 1976

B.A., Economics, University of Madras

… → May 1974


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