This is a basic course in epidemiology which also covers a variety of analytic topics not commonly addressed in elementary statistics courses. This course will introduce students to the field of epidemiology. Students will critically evaluate articles in the epidemiologic literature and examine epidemiologic methods including:
- data collection
- study design and statistical analysis
- relative risk
- contingency tables
- logistic and Poisson
- measurement error and exposure misclassification
- imputation of missing values
- multilevel regression models in epidemiology
- Explain basic concepts in descriptive epidemiology such as incidence, prevalence, mortality, morbidity and effect.
- Distinguish among common epidemiological designs such as case-control, cohort, cross-sectional and randomized controlled studies.
- Identify major categories of bias that can affect the validity of epidemiological studies.
- Apply common measures of association such as relative risk, odds and odds ratios, attributable risk, attributable risk percentage and population attributable risk to epidemiological data.
- Employ common tools of epidemiological statistics such as logistic and Poisson regression and become oriented to multilevel regression models to analyze appropriate data.
Admission to the Professional Specialization Certificate in Population Health Data Analysis or permission of the Faculty Advisor.
Working knowledge of how to interpret P values, null hypothesis and confidence intervals and some experience with SAS, Excel, linear regression models and natural logarithms including exponentiation using a calculator or the “exp” function in Excel.
A solid grasp of high school math skills, including the ability to use logarithms capably and to work competently with percentages and ratios.
Instructor: Laura Holder
Laura Holder is a Senior Analyst with the Analytics and Special Projects team at the Western Office of the Canadian Institute for Health Information (CIHI). She completed her M.Sc. in Epidemiology and Biostatistics in the Public Health Sciences Department at Queen’s University, Ontario. Her graduate research focused on how best to employ statistical methodologies to handle missing data in complex survey settings and the impact these methodological choices have on the conclusions drawn in studies. Prior to her role at CIHI, Laura worked as an analyst with the Institute for Clinical and Evaluative Sciences (ICES), where she used administrative health data to conduct the analyses for a broad range of studies. These included international comparisons of childhood mortality, usage of the emergency department for mental health issues, validation of liver disease case definitions, and maternal outcomes for incarcerated women. In her current role at CIHI, Laura and her team work to support the analytic capacity of Canada’s western jurisdictions.
This program has been developed by Population Data BC in partnership with the Division of Continuing Studies, University of Victoria.
For further details, visit the Division of Continuing Studies webpage.