Lecture Notes: Cross-Sectional Studies

Overview

Measures health outcomes and exposures in a population at a specific point in time.
Provides a snapshot (point prevalence) of health outcomes , demographics (age, gender, education, income).
Describes conditions surrounding health outcomes and relevant exposures.

Quick and easy to conduct.
Enables study of multiple diseases and exposures simultaneously.
Estimating disease burden in a population.
Useful for prioritizing diseases for public health focus.
Can be a single point study or serial cross-sectional study to observe trends over time.

Difficult to establish temporality (whether exposure occurred before outcome).
- E.g., Cannot confirm if smoking caused lung cancer if cancer existed before smoking began.
Often rely on convenience sampling, leading to potential biases.
- Example: Surveying NFL fans for public funding opinions is not representative.
Not suitable for studying rare diseases as the whole population is measured, which may miss cases.
Generally considered less reliable than cohort and case-control studies, mainly hypothesis generating.

Common analysis tool: Odds Ratio (OR).
OR measures strength of association between exposure and health outcome.
Calculation involves a 2x2 table categorizing exposed/unexposed and diseased/non-diseased.
Formula: ( OR = \frac{(A \times B)}{(C \times D)} )
- Where A = exposed diseased, B = exposed non-diseased, C = unexposed diseased, D = unexposed non-diseased.
Interpretation of Odds Ratios:
- OR = 1: no difference (exposure does not affect risk).
- OR > 1: exposure may increase risk.
- OR < 1: exposure may reduce risk.
- Larger OR indicates greater risk (e.g., >2 meaningful, >4 very strong).