I am an infectious disease and preventive medicine physician and my interests include studying the epidemiology of communicable diseases and and the practice of public health. I’m a clinical assistant professor in the Department of Epidemiology at the School of Public Health and in the Division of Infectious Diseases at the Medical School. My work also focuses on connecting physicians to public health practice; I serve as the Program Director for the Preventive Medicine Residency and I lead a certificate program in population health and health equity for physicians in training. I’m also an associate editor for the American Journal of Preventive Medicine. My research focuses on the epidemiology and prevention of infectious disease, including communicable diseases in the broader public health setting.
Dr. Fleischer’s research focuses on how the broader socioeconomic and policy environments impact health disparities and the health of vulnerable populations, in the U.S. and around the world. Through this research, her group employs various analytic techniques to examine data at multiple levels (country-level, state-level, and neighborhood-level), emphasizing the role of structural influences on individual health. Her group applies advanced epidemiologic, statistical, and econometric methods to this research, including survey methodology, longitudinal data analysis, hierarchical modeling, causal inference, systems science, and difference-in-difference analysis. Dr. Fleischer leads two NCI-funded projects focused on the impact of tobacco control policies on health equity in the U.S.
My research focuses on issues in data collection with hard-to-reach populations. In particular, she examines 1) nontraditional sampling approaches for minority or stigmatized populations and their statistical properties and 2) measurement error and comparability issues for racial, ethnic and linguistic minorities, which also have implications for cross-cultural research/survey methodology. Most recently, my research has been dedicated to respondent driven sampling that uses existing social networks to recruit participants in both face-to-face and Web data collection settings. I plan to expand my research scope in examining representation issues focusing on the racial/ethnic minority groups in the U.S. in the era of big data.
My research lies in cutting-edge methodology development in streams of Bayesian statistics, complex survey inference, missing data imputation, causal inference, and data confidentiality protection. I have extensive collaboration experiences with health services researchers and epidemiologists to improve healthcare and public health practice, and have been providing statistical support to solve sampling and analysis issues on health and social science surveys.
John E. Marcotte, PhD is a statistician and data security expert. His research concerns data sharing, data security, data management, disclosure, health policy, nursing staffing and patient outcomes. He has over 25 years of experience implementing computing systems and performing quantitative analysis. During his career, Marcotte has served as a quantitative researcher, biostatistician, data archivist, data security officer and computing director. Among Marcotte’s statistical fortes are linear and logistic regression, survival analysis and sampling while his computing specialties include secure systems, high performance systems and numerical methods. He has collaborated with social and natural scientists as well as nurses and physicians. Marcotte regularly presents at professional conferences and contributes to invited panels on data security and disclosure. He has formal training in Demography, Statistics and Computer Science.
I research how humans behave by observing the things we say, what we do, and who we are. My research combines linguistic analysis and network science together to understand behavior in its natural social context. I collaborate with colleagues from areas such as Psychology, Linguistics, Digital Humanities, and Sociology to improve our theories using data-driven insights and methodologies.
Image caption: Indians use online matrimonial websites to complement the traditional arranged marriage process. Data from these websites can reveal widespread attitudes on caste identity through individuals signaling their openness to marrying someone from a different caste, i.e., intercaste marriage. This figure shows a comparison of demographic factors affecting openness to intercaste marriage in family-posted (left) versus self-posted (right) matrimonial profiles on a major Indian website. Values for each factor reflect a logistic regression coefficients for predicting whether that individual will be open to intercaste marriage. The difference that social status as a function of education, income, affluence, and to some degree caste, drive attitudes, where lower social status individuals are less open to intercaste marriage. Significance levels for model coefficients are reported as ‘***’ for p<0.001 , ‘**’ p<0.01 , and ‘*’ p<0.05, and bars show standard errors. This figure is taken from a paper by
Ashwin Rajadesingan, Ramaswami Mahalingam, David Jurgens, “Smart, Responsible, and Upper Caste Only:Measuring Caste Attitudes through Large-Scale Analysis of Matrimonial Profiles” in the Proceedings of the AAAI International Conference on Web and Social Media (ICWSM), 2019.
The long temporal and large spatial scales of ecological systems make controlled experimentation difficult and the amassing of informative data challenging and expensive. The resulting sparsity and noise are major impediments to scientific progress in ecology, which therefore depends on efficient use of data. In this context, it has in recent years been recognized that the onetime playthings of theoretical ecologists, mathematical models of ecological processes, are no longer exclusively the stuff of thought experiments, but have great utility in the context of causal inference. Specifically, because they embody scientific questions about ecological processes in sharpest form—making precise, quantitative, testable predictions—the rigorous confrontation of process-based models with data accelerates the development of ecological understanding. This is the central premise of my research program and the common thread of the work that goes on in my laboratory.
My research is primarily focused around 1) machine learning methods for understanding healthcare delivery and outcomes in the population, 2) analyses of correlated data (e.g. longitudinal and clustered data), and 3) survival analysis and competing risks analyses. We have developed tree-based and ensemble regression methods for censored and multilevel data, combination classifiers using different types of learning methods, and methodology to identify representative trees from an ensemble. These methods have been applied to important areas of biomedicine, specifically in patient prognostication, in developing clinical decision-making tools, and in identifying complex interactions between patient, provider, and health systems for understanding variations in healthcare utilization and delivery. My substantive areas of research are cancer and pediatric cardiovascular disease.
Dr. Morckel uses spatial and statistical methods to examine ways to improve quality of life for people living in shrinking, deindustrialized cities in the Midwestern United States. She is especially interested in the causes and consequences of population loss, including issues of vacancy, blight, and neighborhood change.
Kevin J. Dombkowski, DrPH., MS, is Research Professor with the Child Health Evaluation and Research (CHEAR) Center within the University of Michigan Department of Pediatrics. He is a health services researcher working extensively with public health information systems and large administrative claims databases.
Kevin’s primary research focus is conducting population-based interventions aimed at improving the health of children, especially those with chronic conditions. Much of his work has focused on evaluating the feasibility and accuracy of using administrative claims data to identify children with chronic conditions by linking these data with clinical and public health systems. Many of these projects have linked claims, immunization registries, newborn screening, birth records and death records to conduct population-based evaluations of health services. He has also applied these approaches to assess the statewide prevalence of chronic conditions such as asthma, sickle cell disease, and inflammatory bowel disease in Michigan as well as other states. Kevin is currently collaborating with Michigan State University on the design and development of the Flint Registry information architecture.
Kevin’s research interests also include registry-based interventions to improve the timeliness of vaccinations through automated reminder and recall systems. He has led numerous collaborations with the Michigan Department of Health and Human Services (MDHHS), including several CDC-funded initiatives using the Michigan Care Improvement Registry (MCIR). Through this collaboration, Kevin tested a statewide intervention aimed at increasing influenza vaccination among children with chronic conditions during the 2009 influenza pandemic. Kevin is currently collaborating with MDHHS to evaluate MCIR data quality as immunization providers across Michigan adopt real-time, bi-directional messaging between electronic health records (EHRs) and MCIR. As PI of a CDC-funded project, Kevin is evaluating the costs and benefits of electronic interoperability between EHRs and MCIR. He is also conducting a statewide evaluation of blood lead testing result data reported by electronic laboratory systems to MDHHS.