Seek and ye shall find....

I found this article from today’s NY Times to be very interesting, both as a critique of our ability to detect foodborne illness, and as a corollary to problems inherent in the detection and reporting of healthcare-associated infections (HAIs).

The gist of the piece is that when a state has an excellent foodborne illness surveillance and investigation system (e.g. Minnesota), that state will find a disproportionate number of foodborne illnesses and outbreaks (now you might be saying, “Duh!”).  But if one looks at the foodborne disease data without any knowledge of disparities in quality of surveillance, detection and investigation, Minnesota looks like a cesspool of foodborne disease!  In fact, Minnesota may be the safest place in the U.S. to eat, because they are more likely than any other state to quickly detect and investigate foodborne disease outbreaks.

Similarly, those hospitals that do the best surveillance are more likely to detect HAIs when they occur.  As just one example, the best way to avoid detecting Legionella infections is to fail to test for Legionella among patients with healthcare-associated pneumonia.  Because Legionella won’t grow on the routine culture media used for respiratory specimens, a concerted effort must be made to request specific testing (using enriched selective media or the urinary antigen test).

Other examples are more nuanced, but suffice to say there are also many ways to fail to detect ventilator associated pneumonias, catheter-associated bloodstream infections, and surgical site infections.

How to address this problem?  One way is to perform external validations (or audits) of each hospital’s surveillance data.  For example, the CDC is working with states to develop validation methods for data submitted to the National Healthcare Safety Network (NHSN), which is extremely important since states with mandatory reporting legislation usually utilize NHSN as the reporting mechanism.  But such validation is complicated, time-consuming and expensive. 

An alternative approach would be to directly monitor data from microbiology laboratories or pharmacies, to correlate with (and validate) reported infection rates—or even as a surrogate for those rates.  For example, if a hospital reported zero nosocomial bloodstream infections but exceeded a certain rate of blood culture positivity (for cultures drawn > 48 hours after hospital admission), that might trigger further investigation…..similarly, parameters could be established to compare reported SSI rates with post-operative antimicrobial use data.

There is no simple and inexpensive solution, but addressing this problem will become increasingly important as HAI rates become public across the country.