Barnett, Adrian G.

Beyersmann, Jan

Cooper, Ben

Crnich, Christopher

Graves, Nicholas

Halton, Kate

Harbarth, Stephan

Samore, Matthew

Wolkewitz, Martin

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INFECTION CONTROL AND HOSPITAL EPIDEMIOLOGY SEPTEMBER 2 0 1 1 , VOL. 3 2 , NO. 9 L E T T E R S T O T H E E D I T O R The Importance of Good Data, Analysis, and Interpretation for Showing the Economics of Reducing Healthcare-Associated Infection To the Editor—In a recent review article in Infection Control and Hospital Epidemiology, Umscheid et al1 summarized pub-lished data on incidence rates of catheter-associated blood-stream infection (CABSI), catheter-associated urinary tract infection (CAUTI), surgical site infection (SSI), and venti-lator-associated pneumonia (VAP); estimated how many cases are preventable; and calculated the savings in hospital costs and lives that would result from preventing all preventable cases. Providing these estimates to policy makers, political leaders, and health officials helps to galvanize their support for infection prevention programs. Our concern is that im-portant limitations of the published studies on which Um-scheid and colleagues built their findings are incompletely addressed in this review. More attention needs to be drawn to the techniques applied to generate these estimates. The ambitious goal of Umscheid and colleagues was to synthesize all evidence on the effectiveness of interventions to prevent CABSI, VAP, CAUTI, and SSI. The scope of this review might be too broad to allow a sufficiently detailed appraisal of the evidence found. Preventability is a moving target because technologies and behaviors change, and in-ferences about causal effects depend on an explicit specifi-cation of alternatives.2 Other evidence synthesis studies for infection control interventions have been narrower in scope and more cautious in interpreting the data. Gould et al3 reg-istered a review of the effectiveness of hand hygiene inter-ventions with the Cochrane Collaboration. Of 48 articles and 1 thesis revealed by the search strategy, only 2 studies were included. The rest were excluded because they were small scale or poorly controlled or because their long-term impacts were not assessed. Some of the studies included by Umscheid et al that report infection control effectiveness had significant methodological flaws. Harris et al4 discuss the challenges that researchers face when trying to estimate the effectiveness of infection prevention. The information that Umscheid et al use to estimate the annual number of infections should be treated with caution. Large variability in the quality of infection surveillance data for CABSI was found by Lin et al.5 Whether other types of infection are surveyed with greater accuracy is unknown. None of the studies used to estimate the clinical and eco-nomic consequences of these infections appropriately ac-counted for length of stay or risk of death, either because the timing of infection was ignored altogether or because of con-ditioning on the future.6 The consequence is that results are biased upward.7 Barnett et al8 showed that 11.23 extra days in the hospital were attributable to a case of healthcare-associated infection when the timing of infection was ignored and that, when the timing of infection was appropriately included, the additional duration of hospital stay was reduced to 1.35 days. Problems of bias may also arise when attributing death risk to infection. Umscheid et al used cost data prepared by hospital ac-countants to make an economic argument. This is inappro-priate, because accountants and economists have different objectives and require different information.9 Accounting costs are unlikely to capture the opportunity cost of infec-tions. The alternate use of resources released from reduced infection is easily misinterpreted when presented as cash sav-ings to hospitals.10 The findings of Umscheid et al1 were that between 23,545 and 53,483 lives would be saved every year as a result of preventing preventable cases of CABSI, VAP, CAUTI, and SSI. Annual cost savings to hospitals would be between $3.43 billion and $23.44 billion. Using a conservative estimate of $5.5 million per statistical life saved,11 the total economic benefit from eliminating preventable infections suggested to policy makers is between $136 billion and $341 billion an-nually. We suggest that flaws in the underlying data used to make these estimates exaggerate the result and will improperly raise expectations about the value of future investments in prevention programs. A potential negative consequence for the infection control community is reduced credibility. An-other risk associated with reporting such large numbers is that government health regulators might use them to justify withholding large cash reimbursements to hospitals when (preventable) infections do happen, and this might create an economic incentive to underreport infections,12 which is a quality-reducing activity. Relentless growth in healthcare costs means that decision makers will demand high-quality economic evidence prior to committing resources to healthcare programs. Reliable eco-nomic arguments are important for obtaining extra resources and even for keeping existing ones. Those working toward reducing the number of infections should carefully craft eco-nomic arguments on the basis of sound methods and good quality data,13 and they should use the evidence base to build sustainable and cost-effective infection control programs. A C K N O W L E D G M E N T S Potential conflicts of interest. All authors report no conflicts of interest rel-evant to this article. Nicholas Graves, PhD;1 Adrian G. Barnett, PhD;1 Kate Halton, PhD;1 Christopher Crnich, MD, MS;2 https:/www.cambridge.org/core/terms. https://doi.org/10.1086/661600Downloaded from https:/www.cambridge.org/core. University of Basel Library, on 30 May 2017 at 20:26:50, subject to the Cambridge Core terms of use, available at9 2 8 INFECTION CONTROL AND HOSPITAL EPIDEMIOLOGY SEPTEMBER 2 0 1 1 , VOL. 3 2 , NO. 9 Ben Cooper, PhD;3 Jan Beyersmann, PhD;4 Martin Wolkewitz, PhD;4 Matthew Samore, MD;5 Stephan Harbarth, MD, MS6 Affiliations: 1. School of Public Health and Institute for Health and Bio-medical Innovation, Queensland University of Technology, Brisbane, Aus-tralia; 2. Infectious Disease Clinic, University of Wisconsin Hospital and Clinics, Madison, Wisconsin; 3. Faculty of Tropical Medicine, Mahidol Uni-versity, Bangkok, Thailand; 4. Department of Medical Biometry and Statis-tics, Institute of Medical Biometry and Medical Informatics, University Med-ical Center Freiburg, Freiburg, Germany; 5. Division of Clinical Epidemiology, Biomedical Informatics, University of Utah, Salt Lake City, Utah; 6. Service prevention et contr61e de l'infection, Hopitaux Universi-taires de Geneve, Geneva, Switzerland. Address correspondence to Nicholas Graves, PhD, School of Public Health and Institute for Health and Biomedical Innovation, Queensland University of Technology, 60 Musk Avenue, Brisbane, QLD, 4059, Australia (n.graves@qut.edu.au). Infect Control Hosp Epidemiol 2011;32(9):927-928 © 2011 by The Society for Healthcare Epidemiology of America. All rights reserved. 0899-823X/2011/3209-0017$15.00. DOI: 10.1086/661600 R E F E R E N C E S 1. Umscheid CA, Mitchell MD, Doshi JA, Agarwal R, Williams K, Brennan PJ. Estimating the proportion of healthcare-associated infections that are reasonably preventable and the related mor-tality and costs. Infect Control Hosp Epidemiol 2011;32(2): 101-114. 2. Hernan MA. A definition of causal effect for epidemiological research. / Epidemiol Community Health 2004;58(4):265-271. 3. Gould DJ, Moralejo D, Drey N, Chudleigh JH. Interventions to improve hand hygiene compliance in patient care. Cochrane Database Syst Rev 2010;(9):CD005186. 4. Harris AD, Lautenbach E, Perencevich E. A systematic review of quasi-experimental study designs in the fields of infection control and antibiotic resistance. Clin Infect Dis 2005;41(1): 77-82. 5. Lin MY, Hota B, Khan YM, et al. Quality of traditional sur-veillance for public reporting of nosocomial bloodstream in-fection rates. JAMA 2010;304(18):2035-2041. 6. De Angelis J, Beyersmann J, Murthy S, Harbarth S. Estimating the impact of healthcare-associated infections on length of stay and costs. Clin Microbiol Infect 2010;16:1729-1735. 7. Crnich CJ. Estimating excess length of stay due to central line-associated bloodstream infection: separating the wheat from the chaff. Infect Control Hosp Epidemiol 2010;31(11): 1115-1117. 8. Barnett AG, Beyersmann J, Allignol A, Rosenthal VD, Graves N, Wolkewitz M. The time-dependent bias and its effect on extra length of stay due to nosocomial infection. Value Health 2011; 14(2):381-386. 9. Graves N, Harbath S, Beyersmann J, Barnett A, Halton K, Coo-per B. Estimating the cost of health care-associated infections: mind your p's and q's. Clin Infect Dis 2010;50(7):1017-1021. 10. Graves N. Economics and preventing hospital-acquired infec-tion. Emerg Infect Dis 2004;10(4):561-566. 11. Kniesner TJ, Viscusi WK, Woock C, Ziliak JP. Pinning down the value of statistical life. IZA DP no. 3107. Published 2007. http: //ftp.iza.org/dp3107.pdf. Accessed July 27, 2011. 12. Graves N, McGowan JE. Nosocomial infection, the deficit re-duction act, and incentives for hospitals. JAMA 2008;300(13): 1577-1579. 13. Halton K, Graves N. Economics of preventing catheter-related bloodstream infection? Emerg Infect Dis 2007;13(6):815-823. Reply to Graves et al To the Editor—We appreciate the interest by Graves et al1 regarding our recent article estimating the proportion of healthcare-associated infections (HAIs) that are reasonably preventable and the related mortality and costs.2 In their let-ter, Graves and colleagues suggest that our estimates were intended to galvanize support for infection prevention pro-grams and were generated using studies with important lim-itations. We wish to address these concerns in this response. Our analysis was originally performed in 2008 for the So-ciety for Healthcare Epidemiology of America (SHEA), to be included in its written testimony on HAIs to Congress.3 To inform its testimony, SHEA requested that we review the published literature to estimate the proportion of HAIs that might be preventable. This was a critical question, because the federal government was considering a policy of nonpay-ment for HAIs as an incentive to reduce HAIs.4 Although some believed this was an effective strategy to reduce HAIs, others were concerned that not all HAIs were preventable and that the incentive under consideration would present challenges to hospitals caring for patients at high risk for HAIs.5 To estimate the proportion of preventable HAIs in the most efficient and accurate manner, we used an up-to-date federally funded systematic review that examined the effec-tiveness of single and multimodal interventions on HAI pre-vention6 as well as the most recent and valid estimates of HAI incidence.7 We also conducted our own systematic review of studies examining the incremental costs of individual HAIs.2 The dilemma at the time was whether to make an estimate based on data of limited quality or to avoid making such an estimate because of the data limitations and take the chance that other estimates derived using a less scientific approach would inform the policy discussion. SHEA opted to inform the discussion with the best estimates available from the published literature, so the intent of our analysis and our subsequent article was to present those estimates while high-lighting their key limitations and caveats. To ensure that we provided the most accurate and gen-eralizable data on the effectiveness of HAI prevention inter-ventions, we estimated ranges of preventability and included the lowest and highest risk reductions reported by only those studies that were conducted in the United States, were pub-lished within the previous 10 years, and received a quality score of moderate or good from the federally sponsored sys-tematic review.6 Of the 64 studies originally included in the https:/www.cambridge.org/core/terms. https://doi.org/10.1086/661600Downloaded from https:/www.cambridge.org/core. University of Basel Library, on 30 May 2017 at 20:26:50, subject to the Cambridge Core terms of use, available at

The Importance of Good Data, Analysis, and Interpretation for Showing the Economics of Reducing Healthcare-Associated Infection

The Importance of Good Data, Analysis, and Interpretation for Showing the Economics of Reducing Healthcare-Associated Infection

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