HAI prevalence and positive predictive value

Eli’s excellent post about problems associated with use of ICD-9 codes to assess HAI burden can be extended to other surveillance approaches. That PPV erodes as disease prevalence drops (for HAIs, presumably through better prevention), is Diagnostics 101. So let’s take the example of laboratory-based tracking of C. difficile infection (CDI). The figure below is from a meta-analysis of CDI diagnostics published last year in Clinical Infectious Diseases. In most hospitals using PCR, positive test rates approach 15-20%. In these settings, the PPV is good (>80%). If a hospital or region were somehow able to push their CDI prevalence to below 5% in the tested population, however, PPV falls below 50% and gets worse as prevalence drops further. 

This is a good problem to have, generally speaking, but it does present problems for surveillance approaches as HAI rates drop (and also provides a reality check regarding “zero” targets, which are only achievable using subjective definitions that allow for human judgment/adjudication).

P.S. As a corollary, remember that when you extend CDI testing to patients with a low pre-test probability of disease (e.g. by repeat testing of those who initially test negative, by testing formed stool, or by performing "tests of cure"), you are both wasting resources and reducing PPV.