Instructor Resource Chapter 10 Copyright © Scott B. Patten, Permission granted for classroom use with Epidemiology for Canadian Students: Principles, Methods & Critical Appraisal (Edmonton: Brush Education Inc.

Chapter 10. Cross- sectional studies

Objectives Define cross-sectional studies. Differentiate between the potential descriptive and analytical goals of cross-sectional studies. Describe the following measures of association: prevalence differences, prevalence ratios, prevalence odds ratios, and specific types of linear equations. Explain how to interpret measures of association calculated from cross-sectional data. List strengths and weaknesses of cross-sectional studies.

Critical appraisal This chapter is our first examination of a specific study design. Identification of study designs is a key step in critical appraisal of research reports. Critical appraisal is more than just reading a study and intuitively trying to identify problems with it. Critical appraisal involves asking and answering a series of key questions. An early step in critical appraisal should be identification of the study design.

Strategies for classifying study design A common (but sometimes problematic) approach to classifying studies is based on the motivations of the investigators. This approach asks the question: did these investigators have descriptive or analytical goals? The problem is that studies can have mixed goals and motivations may not be clearly stated. A more reliable procedure for study-design classification is based on actual features of the study.

What is a cross-sectional study? Cross-sectional studies are studies in which all data are collected at a single point in time. Prevalence studies are usually cross-sectional.

What is a cross-sectional study? (continued) Obviously, no study can occur exactly in a single instant. A prevalence study may take months or even years to conduct. Conceptually, however, these studies take place at a point in time: the study design neither to looks back into the past (retrospectively), nor forward into the future (prospectively). Note that a cross-sectional study can look back in time with its questions (e.g., it may ask adults questions about abuse when they were children), but the design is neither retrospective nor prospective.

Examples of cross-sectional studies The Canadian Community Health Survey general health “iterations” specialized “iterations”: e.g., nutrition, aging

More examples See abstracts at:

How cross-sectional studies look at associations For the next few slides, consider the classic 2 x 2 contingency table: DiseaseNo Disease Exposedab Nonexposedcd

Prevalence differences Key point: A prevalence difference is an example of a measure of association because it embodies a comparison between 2 simpler parameters, which are prevalence in exposed and nonexposed groups.

Prevalence ratios

Prevalence odds ratios

Specific types of linear equations

Strengths of the cross- sectional study design Cross-sectional studies: provide valuable snapshots of disease describe disease occurrence across multiple variables: they can examine multiple diseases and exposures are cost efficient: these studies are often relatively inexpensive and practical are invulnerable to attrition

Weaknesses of the cross- sectional study design Cross-sectional studies: lack causal inference: temporality is usually unclear provide no determination of risk are inefficient for rare diseases are insensitive to time-dependent frequency changes

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