Intro. to Epidemiology in Global Health Course run by E-learning Dept. Global Health (Online)

An in-depth orientation to the field of epidemiology for those seeking to conduct research or work on research studies in a global health context.

Faculty/Lecturer: 

• Brandon Guthrie, PhD, MPH

    Assistant Professor, Global Health

    Assistant Professor, Epidemiology

Overview

Are you interested in understanding distribution of disease and what factors affect risk of disease? This course gives an in-depth orientation to the field of epidemiology research in a global health context. You’ll get an understanding of how epidemiological methods are used to understand the distribution of disease within populations and what factors affect the risk of disease. Learn about important epidemiologic concepts, including how to describe disease risk, common study designs, bias and confounding, and the importance of appropriate measurement in epidemiological research. 

Sylubbus-Topics to be covered

1)      Introduction to Epidemiologic Methods and Quantitative Research

Understanding how and why diseases are distributed in populations and what factors are associated with disease relies on a set of epidemiologic methods that can be applied flexibly to many different settings. This unit introduces the main concepts in epidemiology and reviews the methodological approaches to measuring diseases in populations and assessing relationships between exposures and diseases.

2)      Introduction to Statistical Decision Making

This unit offers an introduction to core topics in statistics for the analysis of health-related data with emphasis on analyzing the most common epidemiologic study designs.

3)      Epidemiologic Study Designs

Epidemiologists employ a variety of observational and experimental study designs to investigate the distribution and causation of diseases. The primary types of epidemiologic studies will be introduced with examples to illustrate how investigators determine which study designs are best suited to addressing a set of scientific aims. The strengths and limitations of each study design are outlined for use in planning studies and interpreting and evaluating findings from published results.

4)      Causation, Bias, and Confounding

To identify causal relationships between exposure and disease it is sometimes necessary to rely on observational data to assess causation, which introduces important issues of bias inherent in observational studies. Interpretation of epidemiologic data is often complicated by the fact that other factors may distort the relationship between an exposure of interest and a given disease. Factors that are associated with both exposure and disease can induce what is called confounding, and if not appropriately compensated for, can obscure the true exposure-disease relationship.

5)      Measurement, Classification, and Misclassification

To understand the distribution of disease in populations and identify causal relationships between exposures and disease outcomes, epidemiologist must measure both exposure and disease in the population under study. Researchers must understand how to measure exposure and disease, and how the research question dictates the approach to classifying subjects as exposed or unexposed, diseased or non-diseased. What are the implications of misclassifications? How can sensitivity, specificity, positive predictive value, and negative predictive value be used to assess measurement instruments?

6)      Data Management Practices in Health Research

Epidemiologic studies produce data in a wide range of formats and structures. To make full use of the information gained in these studies, researchers should consider how study data will be stored and managed. Decisions about data management will depend on how the data are collected, who will be accessing the data, and what types of analyses will be performed. What factors effect data management strategies and outline techniques for effective data management at the time of collection, storage, processing, and analysis?

7)      Interpretation of Epidemiologic Studies and Decision Making

Findings from epidemiologic studies guide clinical and public health policy decisions, but sound decisions depend on a thorough understanding of relative strengths and weaknesses of different sources evidence. Study design, selection of study subjects, and differences between populations can greatly influence how research results are interpreted in the context of applied settings. Real-world examples demonstrate how epidemiologic principles can be used to synthesize evidence from different studies to evaluate the strength of evidence linking exposure and disease and to inform decisions about how to implement this knowledge into public health practice.

8)      Multiple variable regression models in epidemiology

Multivariate regression is a common approach used to analyze epidemiologic data that allows the investigator to simultaneously adjust for multiple confounders. This unit provides an introduction to multivariate regression and an overview of logistic and Cox regression methods. Particular emphasis is given to how odds ratios and hazard ratios from regression models should be reported and interpreted in the scientific literature.

9)      Qualitative Research Methods

Qualitative research methods are an important complement to the quantitative methods used by epidemiologists. Qualitative approaches are used to develop strategies to implement public health interventions, understand health decision making, and to follow-up on findings from quantitative studies. An introduction to qualitative research methods will provide a background for implementation of qualitative methods into epidemiologic research and public health practice. Practical examples will be used to illustrate the use of phenomenology and grounded theory methods, with a discussion of sampling and data collection.

10)  Analyzing Qualitative Data and Public Health Applications

Qualitative research employs data collection strategies that differ in important ways from quantitative research. The analysis of qualitative data involves analysis approaches tailored to the unique data structure of qualitative research and allows researchers to interpret and apply the results for these studies. Practical examples will be used to illustrate key topics including data management, coding, data analysis, and writing.         

* NOTE: course content is subject to change

Format

This online course has pre-recorded video lectures, readings, discussion forums, quizzes and three assignments. Unique username and password will be issued to each participant to get access into online learning management system by the University of Washington after they successfully admitted into the course.

You can participate in this course as an independent participant or as part of a local site. The course is taught in English. Participants should be comfortable with written and spoken English.

Eligibility

To be admitted to the course you must have a Bachelor’s-level degree (or equivalent) and experience in a health-related field. Proficiency in algebra is required.

Certification

 For those who successfully completed the course will receive a formal printed Certificate of Completion on vellum paper with University of Washington seal mailed to them through DHL courier services. We will ship them all together to your Site Coordinator for distribution.

Sample of certificate of completion- scanned copy

Further Information

Any additional enquiry and questions about this program you should contact Dr. Mohamed Y. Dualeh, MD via his email: info@drmohameddualeh.com and if possible discourse with his on phone:(+252 63 4417945 by texting him in WattsApp) regarding how to register, getting an assistance in application process while he is exercising as an official local resource for our participants acting as Site Coordinator for Somalia, UW Global Health Department.
Official website of the University: https://edgh.washington.edu/