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Reporting a prevalence of depression that is higher than expected due to test specificity indicates that the measurement may be reflecting false positives rather than an accurate representation of true cases of depression in the population. Test specificity refers to the ability of the test to correctly identify those without the condition; therefore, if a test has low specificity, it means that it is more likely to incorrectly classify individuals as having depression when they do not actually have it.

Consequently, an observed prevalence that exceeds what is typically expected could be primarily attributed to those false positive results derived from the testing process. This emphasizes the importance of understanding how test characteristics can influence prevalence estimates, highlighting that the elevated prevalence is not necessarily indicative of a true increase in depression cases within the population being studied.