PHDA 02 - Epidemiological Statistics
"I found Epidemiological Statistics (PHDA02) useful to my work as a research associate at the Centre for Addictions Research of BC. I would recommend this course for anyone who is working in public health and uses epidemiological methods in their employment.
The course covered a wide range of topics from calculating relative risk to meta-analysis. The instructor took the time to ensure all students were able to do the assignments and the course reading provided useful references."
Gina Martin, Research Associate, Centre for Addictions Research of BC
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: Allison Scott
Allison Scott is a Research Scientist with the Child and Youth Data Laboratory (CYDL). She completed her PhD in the Department of Epidemiology, Biostatistics and Occupational Health at McGill University. There she partnered with community members and service providers to conduct a mixed methods study on pregnancy in homeless youth. This study utilized administrative data to estimate the rate of pregnancy in street youth and qualitative, in-depth interviews to understand how identity, isolation and belonging impacted the young women’s contraception and pregnancy-related decisions. Prior to working at PolicyWise, Allison was an Epidemiologist at Alberta Health, where she was involved in communicable disease surveillance, creating novel administrative data based vaccine efficacy estimates for the influenza vaccine, and developing and analyzing survey content to estimate the association between a history of homelessness and health. Allison loves to conduct policy-relevant research, travel and cook.
This course is offered in partnership with the Division of Continuing Studies and Department of Geography at the University of Victoria.