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Cross-sectional studies are observational and evaluate exposure and outcome simultaneously — a snapshot of a single moment. They are the studies of frequency and prevalence measures.
30-second summary- Exposure and outcome measured at the same moment — a snapshot.
- It is the design of prevalence measures.
- Without temporality, it cannot establish causality.
📷 A single momentexposure + outcome measured together
Prevalence = existing cases ÷ evaluated population
No follow-up: everything is measured in the same “photo”. E.g.: what is the prevalence of diabetes among morbidly obese patients?
✓ Advantages: cheap and straightforward — no follow-up; good for chronic diseases.
✗ Disadvantages: unsuitable for acute conditions and weak for causality — without temporality (Hill’s criteria), you cannot tell what came first.
Does morbid obesity cause diabetes? A cross-sectional study cannot answer: it shows association at the moment of the photo, not the order of events.
Frequently asked questions
Can a cross-sectional study measure incidence?
No — incidence requires follow-up. The cross-sectional study measures prevalence: existing cases at one moment.
When is cross-sectional the best choice?
To size a problem (how many have the condition now), plan services and raise inexpensive hypotheses.
Can I infer causality from a cross-sectional study?
Very cautiously: without knowing what came first, the association may be reverse causation.
✍️Written by Francisco Tustumi, digestive system surgeon (FMUSP), associate editor of ABCD.
