Biomedical
The apparent prevalence, the true prevalence
Parham Habibzadeh,
Peer Reviewed
Abstract
Serologic tests are important for conducting seroepidemiologic and prevalence studies. However, the tests used are typically imperfect and produce false-positive and false-negative results. This is why the seropositive rate (apparent prevalence) does not typically reflect the true prevalence of the disease or condition of interest. Herein, we discuss the way the true prevalence could be derived from the apparent prevalence and test sensitivity and specificity. A computer simulation based on the Monte-Carlo algorithm was also used to further examine a situation where the measured test sensitivity and specificity are also uncertain. We then complete our review with a real example. The apparent prevalence observed in many prevalence studies published in medical literature is a biased estimation and cannot be interpreted correctly unless we correct the value.
Key Questions
What is apparent prevalence?
Apparent prevalence refers to the proportion of individuals in a population who test positive for a disease or condition using a specific diagnostic test. However, due to the possibility of false-positive and false-negative results, this measure may not accurately reflect the true prevalence of the disease.
How can true prevalence be estimated from apparent prevalence?
True prevalence can be estimated by adjusting the apparent prevalence based on the sensitivity and specificity of the diagnostic test used. The formula is:
True Prevalence = (Apparent Prevalence + Specificity - 1) / (Sensitivity + Specificity - 1).
This adjustment accounts for the inaccuracies inherent in the test, providing a more accurate estimate of the actual disease prevalence in the population.
Why is it important to distinguish between apparent and true prevalence?
Distinguishing between apparent and true prevalence is crucial for accurate public health assessments and policy-making. Relying solely on apparent prevalence without adjustment can lead to overestimation or underestimation of disease burden, potentially resulting in misallocation of resources and ineffective intervention strategies.
What role do sensitivity and specificity play in prevalence estimation?
Sensitivity (the ability of a test to correctly identify those with the disease) and specificity (the ability to correctly identify those without the disease) are critical parameters in adjusting apparent prevalence to estimate true prevalence. High sensitivity and specificity reduce the number of false results, leading to more accurate prevalence estimates.
