1. Instructor Resource Chapter 2 Copyright © Scott B. Patten, 2015. Permission granted for classroom use with Epidemiology for Canadian Students: Principles, Methods & Critical Appraisal (Edmonton: Brush Education Inc. www.brusheducation.ca).

2. Chapter 2. Epidemiological reasoning

3. Objectives State the “fundamental assumption” of epidemiological research. Explain the key concepts of association, proportion, prevalence, and point prevalence. Identify the 2 main sources of error in epidemiologic research: random error (sampling variability) systematic error (bias) Define critical appraisal.

4. The “fundamental assumption” Diseases do not distribute randomly in populations, but rather distribute in relation to their determinants. Therefore, determinants can be identified by studying distributions.

5. A classic example “Smoking and Carcinoma of the Lung: Preliminary Report,” Doll & Hill, 1950 compared lung cancer cases (disease) to controls exposure in question: smoking http://en.wikipedia.org/wiki/Austin_Bradford_Hill

6. Basic epidemiological parameters A parameter, in epidemiology, represents a characteristic of a population.

7. A basic parameter: prevalence Prevalence is the proportion of a population with a disease. Types of prevalence include: point prevalence period prevalence lifetime prevalence Usually, prevalence needs to be estimated from a sample.

8. Random error The law of large numbers ensures that prevalence estimated on data collected from random samples provides information about population prevalence. Even with perfect sampling procedures and measurement, estimates from small samples may be wrong by chance. Large samples are less likely to be seriously wrong due to sampling variability than small samples. If there are defects in study design (e.g., sampling or measurement), the estimates can be systematically wrong. This is called bias.

9. Critical Appraisal Critical appraisal comes into play when you read a study (and when you design and conduct a study). You read with a critical eye (seek to criticize). This involves searching for sources of error: random error due to sampling variability systematic sources of error due to study design defects

10. 1 x 2 Contingency table (population) Has diseaseNo diseaseRow totals ABN

11. 1 x 2 Contingency table (sample) Has diseaseNo diseaseRow totals abn

12. Standard error of an estimated proportion In this formula, p is the estimated prevalence What will happen as n gets bigger?

13. End