Course Learning Objectives
- Describe the main concepts in epidemiology.
- Describe the methodological approaches to measuring diseases in populations and assessing relationships between exposures and diseases.
- Describe how to use measures of association to investigate the relationship between exposure and disease.
- Describe the characteristics of different study designs, including cohort studies, case-control studies, and randomized trials and provide examples of when each study design would be appropriate and preferred.
- List the seven viewpoints that epidemiologists use to assess the likelihood of a causal exposure-disease relationship.
- Identify confounding and how to account for confounding to produce valid conclusions.
- Describe the most common strategies to control for confounding.
- Give examples of how the research question of interest will dictate how subjects are classified in terms of exposure and disease.
- Describe how to interpret the various measures of test performance.
- Summarize the common regression methods used in epidemiology.
- Describe the various types of surveillance systems and the ways in which global health surveillance can guide public health action.
Module Learning Objectives
Module 1: Introduction to Epidemiologic Methods and Quantitative Research
- Describe the main concepts in epidemiology.
- Describe the methodological approaches to measuring diseases in populations and assessing relationships between exposures and diseases.
- Give an example of a disease that is distributed unevenly in a population and what the distribution might tell you about the cases of disease.
- Define prevalence and incidence and describe the steps to measure each in a typical epidemiologic study.
- Explain how to compare disease risk between two groups and how to interpret these comparisons.
Module 2: Introduction to Statistical Decision Making
- Summarize data using standard measures of location and spread.
- List the graphical approaches to data display and identify how graphical displays can supplement formal statistical analysis.
- List and describe the standard measures of location and spread.
- Explain the relationship of hypothesis testing and independence of data.
- Define p-value and describe how p-values are used to assess the strength of statistical associations.
- Describe how to use measures of association to investigate the relationship between exposure and disease.
Module 3: Epidemiologic Study Designs
- Describe the characteristics of different study designs, including cohort studies, case-control studies, and randomized trials and provide examples of when each study design would be appropriate and preferred.
- Describe the differences between cross-sectional, retrospective, and prospective study designs.
- Describe the differences between observational studies and randomized trials, including their respective strengths and weaknesses.
- Explain how conclusions about exposure-disease relationships are drawn from different study designs.
Module 4: Causal Inference
- List the seven viewpoints that epidemiologists use to assess the likelihood of a causal exposure-disease relationship.
- Summarize the principles for inferring causal relationships from epidemiologic data.
- Explain why causes of disease do not need to be either necessary or sufficient.
Module 5: Data Management Practices in Health Research
- Define bias in epidemiologic studies and describe the main categories of bias.
- Identify confounding and how to account for confounding to produce valid conclusions.
- Describe the most common strategies to control for confounding.
- Form rate adjustment using a standard population.
Module 6: Measurement and Classification
- Give examples of how the research question of interest will dictate how subjects are classified in terms of exposure and disease.
- Compare and contrast the impacts of non-differential and differential (selective) misclassification.
- Define and describe how to calculate sensitivity, specificity, positive predictive value, and negative predictive value.
Module 7: Interpretation of Epidemiologic Studies and Decision Making
- Describe how to interpret the various measures of test performance.
- Explain how evidence from observational studies can be used to infer causal relations between exposures and disease incidence.
- Describe the criteria that should be used when deciding if a screening test should be used to detect disease.
Module 8: Multiple Variable Regression Models in Epidemiology
- Summarize the common regression methods used in epidemiology.
- Explain why multivariable regression models are used.
- Provide an interpretation of the odds ratio and hazard ratio estimates produced in logistic and survival analysis models.
- Describe how confounding is addressed in multivariable regression.
Module 9: Surveillance
- Define surveillance and describe the components of the definition.
- List the components of data collection for surveillance.
- List ways in which global health surveillance can guide public health action.
- Describe the various types of surveillance systems and their strengths and weaknesses.
- List the common attributes of surveillance systems.
- List the factors that should be considered when selecting a data collection method for a surveillance system.
- Identify some commonly used data sources in public health surveillance.
- Describe surveillance systems for emerging infectious diseases.
- Explain the purpose of surveillance during complex emergencies.
- Describe surveillance systems among refugee populations and informal settlements.
- Describe the advantages and disadvantages of using surveillance data for research purposes.
Module 10: Case Studies
In this final module of the course, there are two epidemiological case studies. These case studies will help you synthesize and practice the skills and concepts you have been learning over the last ten modules.