Annual cost of HAIs in the US = $10 billion

A new paper in JAMA Internal Medicine aims to give us an update on the impact of healthcare associated infections in the US. Given all the focus on HAIs, particularly with the rollout of value based purchasing, this is an important study. The investigators performed a systematic review, which included papers that were published this year, to determine attributable cost estimates, used NHSN data for incidence estimates, then performed Monte Carlo simulation. Total cost of the 5 major infections (CLABSI, CAUTI, VAP, SSI and C. difficile) was estimated to be $9.8 billion per year. The table below shows the breakdown by infection type.

Eli may want to comment on the methodology of the study and the validity of the results, but I suspect these numbers will be cited frequently.


  1. I cannot intelligently comment on the other HAI outcomes, but the cost and length of stay (LOS) estimates for CLABSI in the above table appear highly inflated. The studies used by the authors to derive these estimates suffer from a number of methodological flaws. The fixed and variable cost estimates in these studies are not separated out. Fixed costs, in general, have minimal impact on decision-makers and it is far more productive to focus on liberated bed-days from a hospital infection preventionist perspective. Unfortunately, estimated attributable excess-LOS in the studies included in this paper are severely biased. Most failed to account for timing of CLABSI relative to overall LOS (time-dependent bias), many utilized matching which often results in bias from variables omitted from the matching process, and none of the studies adequately accounted for competing risks. More contemporary studies by Graves and Beyersmann have done a better job in accounting for these methodological challenges and I am surprised these studies were not included in this analysis. My concern with papers like these is that they create false expectations of the anticipated benefits of preventative interventions which undercuts infection control efforts in the long-term. In my opinion, our energy is better focused on generating data on sounder methodological footing.

  2. Agree 100% with the above comment. Those who have cared for patients with SSI and CLABSI would recognize the biased estimates quickly. For example, who would think CLABSI cost 2x more than an SSI and that both result in the same excess LOS? Why would the CLABSI estimates be so high? As Chris Crnich said, most of included CLABSI studies counted the entire hospital stay in their cost/LOS estimates since the exact timing of CLABSI is hard to estimate from administrative data.

  3. This article is the best thing to happen to industry and my spam filter since the IOM report. Three solicitation emails this morning.
    Agree with the above comments. The 2.7 days ALOS for CLABSI suggested by Barnett et al (ICHE 2010 1106-1114) is not only more conservative, but more likely given how CLABSIs can be managed outside acute care settings. These biased cost estimate reports remind me of the boy who cried wolf. The IP program that uses these numbers in cost-justification for enhanced (or these days maintained) resources should not be surprised when their proposal is viewed with a healthy degree of skepticism.

  4. I'm glad we are having a talk about this. I had hoped the days of disseminating scary BIG numbers were behind us. I am not sure they help us choose the best infection control programs to invest in. Another paper done by Adrian Barnett (Value in Health 14 (2011) 381-386) that applied methods developed the Freiburg group (Jan Beyersmann & Martin Wolkewitz) showed that ignoring time bias gave an extra stay of 11.23 days, and accounting for it showed 1.35 days. Also, To back up cjcrnich's point, preventing an infection does not release much cash, but instead we extra bed days available in the system. Their $ value depends on what else we can do with them.

  5. Hospital acquired infection is a real issue throughout the world today. One hospital in Riverside, CA implemented the Steiros Algorithm. After implementation, housewide CAUTI rates per 1000 device days dropped from 5.51 to 1.28.resulting a 79%reduction. Cardiothoracic sternal wound infections went from 4.9% to 0.34%. An Orthopedic Surgeon in Omaha, NE uses the Steiros Algorithm protocol, bathing patients with Steirolotion. He has not had a SSI infection in over 3 years. These people are people are making real progress in reducing hospital acquired infection. I don't know why everyone is not doing this.


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