Dean of Natural Science, Cognitive Science and Critical Social Inquiry and Professor of Public Health
Professor Conlisk teaches classes in epidemiology, statistics, public health, and topics that lie at the intersection of culture and biology. Her research focuses on cancer prevention, especially cervical cancer screening in Latin America. She has served on the Hampshire Board of Trustees and as the director of the Five College Program in Culture, Health and Science, an interdisciplinary certificate program for students interested in the socio-political and biologic bases of health.
Is an introduction to the principles and practice of epidemiology, the core science of public health and the primary tool for measuring health disparities. The course covers the major concepts usually found in a graduate-level introductory course in epidemiology: outbreak investigations, study design, measures of effect, internal and external validity, reliability, and causal inference. Assigned readings are drawn from a standard textbook and the primary literature. In addition, students read case studies and work step-by-step through major epidemiologic investigations of the 20th century, including the first studies linking smoking and lung cancer; the controversies regarding HIV screening in the early years of the AIDS epidemic; and the emergence of a mysterious syndrome eventually linked to a health supplement. Students also form small groups to design and conduct a small epidemiologic study on campus. The major assignments for the course are four case studies; regular response papers/worksheets on the readings; a critique of a primary paper; a poster presentation of the on-campus study; and a proposal for an epidemiologic study of the student's choosing. Key words: epidemiology, public health, health disparities, data science
This course is an introduction to descriptive and inferential statistics with examples drawn primarily from the fields of medicine, public health, and ecology. The approach is applied and hands-on; students are expected to complete two problem sets each week, collect and analyze data as a class, and design and carry out their own examples of each analysis in four review exercises. We cover description, estimation and hypothesis testing (z-scores, t-tests, chi-square, correlation, regression, and analysis of variance). More advanced techniques such as multi-way ANOVA and multiple regression are noted but not covered in detail. We also discuss the role of statistics in causal inference though the emphasis of the course is on practical applications in design and analysis. The course text is The Basic Practice of Statistics by David S. Moore; students use the statistical package Minitab to conduct data analyses.