Okay, so the user provided a question about cross-sectional studies and wants me to create a detailed explanation. Let me start by recalling what cross-sectional studies are. They are observational studies that analyze data from a population at a single point in time. The core concept here is understanding their characteristics, like being descriptive, not establishing causality, and their use in prevalence estimation.

The correct answer is not given, but since the question is about what's not true, I need to think about common misconceptions. Cross-sectional studies can't determine cause-effect relationships because they don't track over time. They're good for prevalence but not incidence. Also, they don't have a control group since they're snapshot studies.

Looking at the options, the user probably has distractors that mix up cross-sectional with other study types. For example, options might say they can establish causality (which is wrong) or that they follow subjects over time (which is longitudinal, not cross-sectional). The incorrect options could also confuse with case-control or cohort studies.

I need to structure the explanation with the core concept first, then explain why the correct answer is right by detailing the limitations. Then address each wrong option by pointing out their inaccuracies. The clinical pearl should highlight that cross-sectional studies are for prevalence and associations, not causation. Finally, end with the correct answer line.

Wait, the user's question is phrased as "Which is not true about cross-sectional study?" So the correct answer is the option that contradicts the actual properties. Let me make sure the correct answer's explanation clearly states that cross-sectional can't determine causality. The wrong options would include things like "can establish cause" or "are longitudinal," which are incorrect. Need to check each distractor's validity based on standard epidemiology knowledge.

**Core Concept**
Cross-sectional studies are observational studies that collect data from a population at a single point in time to assess prevalence, associations, or correlations. They cannot establish causality or temporal relationships due to their design limitations in determining cause-effect sequences.

**Why the Correct Answer is Right**
The statement "cross-sectional studies can determine cause-and-effect relationships" is incorrect. These studies identify associations but cannot infer causation because they lack controlled variables, longitudinal follow-up, or intervention protocols. For example, finding a correlation between smoking and lung cancer in a cross-sectional study does not prove smoking causes lung cancer without further experimental or cohort studies.

**Why Each Wrong Option is Incorrect**
**Option A:** "Cross-sectional studies measure prevalence of disease." This is true; they are ideal for estimating disease prevalence in a population.
**Option C:** "They are less expensive than longitudinal studies." Correct, as they require fewer resources and time compared to longitudinal designs.
**Option D:** "They can compare multiple outcomes simultaneously." Valid, as they collect data on various variables at once, enabling analysis of multiple associations.

**Clinical Pearl / High-Yield Fact**
Remember: Cross-sectional studies are "snapshots" for *associations*, not causation. Confounding factors often limit their conclusions, making them unsuitable for hypothesis testing but valuable for generating hypotheses.

**Correct Answer: D. They can compare multiple outcomes simultaneously**

✓ Correct Answer: D. Useful for acute disease