Several factors can contribute to overestimation in epidemiological studies:
1. Selection Bias: This occurs when the sample is not representative of the population. For instance, if a study on a disease focuses mainly on patients in hospitals, the disease may appear more prevalent than it is in the general population. 2. Information Bias: Errors in data collection, such as inaccurate self-reporting or misclassification of disease status, can lead to overestimation. 3. Confounding Variables: If not properly controlled, confounders can distort the true relationship between exposure and outcome. 4. Publication Bias: Studies with significant findings are more likely to be published, which can lead to a skewed understanding of the disease's prevalence or impact.
