Division of Epidemiology
Epidemiology involves studying the patterns and distribution of diseases and healthrelated events within populations in an attempt to understand their determinants and associated factors and apply this knowledge to prevention and control. Infectious and non-infectious diseases are examined from the perspective of etiology, transmission, surveillance, biomonitoring, screening, and health interventions. Studies targeting patient populations can address questions of disease diagnostics, patient prognosis, and treatment/intervention efficacy with the goal of contributing to evidence-based medicine and practice.
Courses Offered by Division Faculty
Epidemiological Methods
This course will cover epidemiological measurements, study designs used in epidemiological studies, analysis of bias and confounding, how to critically analyze a published report, how to write epidemiology-related articles for peer-reviewed journals and how to publish it. Class lectures along with in-class learning exercises, in-class discussion, variety of practical examples from published literature based on health-related problems, homework assignments, text books and reading materials help students to achieve the desired goals.
Research Methods in Public Health
This course is based on knowledge and expertise gained by the MPH students in introductory courses and extends that knowledge further using literature review, formulating research questions and hypotheses, fundamentals of research study designs, sampling methods, data collection, data analysis, report writing, dissemination of the results, writing research protocol and grant proposal, and evaluating public health programs. This course will introduce quantitative, qualitative, mixed method and participatory approaches to research.
Clinical Epidemiology
Students will gain the competence in clinical epidemiology necessary for a hospital-based clinical researcher or research assistant to solve clinical questions, generating rigorous scientific evidence based on clinical data. All course activities will focus on the application of epidemiological methods to handle data, especially those obtained from clinical settings. Students will become familiar with the characteristics of various clinical study designs as they relate to diagnosis, prognosis, treatment, prevention, and risk. Students will learn how to prepare study protocols, perform analyses, and present their clinical research, as well as master the basic statistical methods involved.
Chronic Disease Epidemiology
Morbidity and mortality rates in chronic disease have increased. Consequently, there is increasing importance for early detection, management of disease, and prevention. This class will give students an opportunity to assess the efficiency of examinations and medical treatments and learn about adverse events caused by over-diagnosis and over-treatment. We also will take a look at measurement errors (noise) versus real changes (signal) in screening tests over time and the utility of it in determining appropriate monitoring intervals.
Molecular Epidemiology
This course focus on understanding various phenotypes (e.g. disease, clinical outcomes, etc.) and its relationship with genetic, environmental, and lifestyle factors using indicators at the molecular level (biomarkers). The identification of molecular markers also has the potential to improve our understanding of disease pathogenesis. This course will provide an overview of pertinent methodological issues specific to the molecular epidemiology of chronic diseases and of available molecular technologies in this era of “omics”, all within the context of relevant examples.
Systematic Reviews and Meta-analyses
Combined lectures and computer lab exercises 135 minutes per week for 15 weeks will cover theories related research question for meta-analysis, literature search, quality of included studies, data abstraction techniques, statistical models appropriate for pooling extracted data, how to assess heterogeneity among studies, interpret data analysis output, rationale behind subgroup analysis, publication bias, power analysis and evaluation of published meta-analysis.