Elizabeth Conlisk

Elizabeth Conlisk, professor of public health, received her B.A. from the University of Notre Dame and M.S. and Ph.D. in epidemiology from Cornell University. Conlisk has a wide range of public health experience at local, state, national, and international agencies, including the Instituto de Nutricion de Centro America y Panama in Guatemala, the Centers for Disease Control and Prevention and the North Carolina Department of Health.

Conlisk came to Hampshire in 2001 and teaches classes in the areas of public health, epidemiology, and statistics. Her research focuses on various topics in cancer prevention, especially cervical cancer screening and tobacco use.

She is also the director of the Five College Program in Culture, Health and Science, an interdisciplinary certificate program for students interested in both the socio-political and biologic bases of health.

Recent and Upcoming Courses

Food and Health
This course explores the complex and often controversial role of food in health promotion and disease prevention. The primary goals of the course are to learn to think critically about dietary research and to be more discerning about epidemiologic research in general. Readings will be drawn from the primary and secondary scientific literature as well as the popular media. Dietary exposures will range from the micro to the macro and will include specific nutrients, foods, dietary patterns, public health programs, public policies and agricultural practices. We will also explore topics related to undernutrition, such as the role of nutritional status in infectious disease and the effectiveness of nutrition intervention programs.
Epidemiology
NS 248 is an introduction to the principles and practice of epidemiology and the use of data in program planning and policy development. 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 past century; they also form small groups to design and conduct a small epidemiologic study on campus. The major assignments 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 their own design.
Field Study
Data Analysis in the Health Sciences
This hands-on course provides an overview of the statistics and data analyses commonly used in epidemiologic and medical research. The primary goals are to learn to develop a testable hypothesis, identify appropriate analyses and correctly interpret and communicate the results, orally and in writing. Students will spend the first half of the semester analyzing health datasets of various sizes and structures, and gaining practice with basic statistical tests (t-test, ANOVA, chi-square, regression) and measures of effect (relative risk, odds ratio). Students will then work independently, developing and executing analytical plans for data they have collected or have accessed on their own. This is an ideal course for Division III students who will be analyzing quantitative data for their research as well as students who want to develop their statistical skills through extensive practice. There are no prerequisites for the course, though an introductory course in statistics is strongly encouraged.
Statistics
This course will be an introduction to descriptive and inferential statistics, with examples drawn from the fields of ecology, agriculture, public health, and clinical medicine. The approach will mainly be applied and hands-on; students will complete a workbook of statistical problems, collect and analyze data as a class, design and carry out small individual projects, do weekly problem sets plus revisions, and read and interpret data from the literature. We will learn to use common computer packages for statistical analysis: Excel and Minitab. Topics will include description, estimation, and basic techniques for hypothesis testing: z-scores, t-tests, chi-square, correlation, regression, one-way and two-way analysis of variance, and odds ratios. More advanced techniques such as multi-way anovas and multiple regression will also be briefly noted. We will also discuss the role of statistics in the scientific method and the philosophy of science, although the emphasis of the course will be on practical applications in design and analysis.