Dr. Melissa Jay Smith's statistical research interests are in the development of Bayesian methods for disease mapping and mediation analysis. The goal of disease mapping is to create reliable model-based estimates of disease risk in small areas such as ZIP codes or counties. By visualizing these estimates on a map, public health professionals and policymakers can determine where levels of a disease are high and low and effectively allocate limited resources or plan prevention efforts. Her recent work focuses on a new Bayesian method for estimating age-adjusted incidence and mortality rates when there are excess zeros in the dataset due to the low-prevalence of a disease and small population sizes in rural areas.
Dr. Smith also conducts research in the area of mediation analysis. The purpose of a mediation analysis is to better understand the mechanism through which an exposure affects a health outcome. In epidemiological studies, mediation analyses are also frequently used to understand why disease incidence or mortality differs between groups. Dr. Smith develops and evaluates statistical methods for conducting these types of analyses.
In addition to her statistical research, Dr. Smith is interested in interdisciplinary studies that use electronic health records for health outcome prediction and comparative effectiveness research. Previously, she worked at a machine learning start-up company focused on predicting sepsis and patient decompensation in the hospital using data from patients’ charts. In this position, she managed multiple databases of electronic health records, created statistical tables and figures, and drafted publications and case studies.
Keywords: Bayesian statistics, spatial statistics, disease mapping, mediation analysis, health disparities, electronic health records.