Starting to Learn About the Costs of Nosocomial Infections in the New Millennium: Where Do We Go From Here?

Abstract

In this issue of Infection Control and Hospital Epidemiology, three articles report various aspects of the costs associated with nosocomial infections. Hollenbeak et al.1 use four different statistical methods to determine the impact of nonrandom selection on the attributable cost of surgical-site infection (SSI) following coronary artery bypass graft (CABG) surgery. In this study, they analyzed data from 41 cases of deep chest infections following CABG surgery at a Midwestern community medical center and compared them with data from 160 randomly selected uninfected controls. Using the hospital’s cost accounting system, they compared the costs of infected and uninfected patients by a t test in a matched and unmatched comparison, by regression analysis, and using Heckman’s twostage method. Unmatched comparison demonstrated $20,012 in excess cost for the infected patients and after 1:1 matching on age, gender, diabetes, renal insufficiency, and length of surgical procedure, the excess cost for the infected patients was $19,579, which was $433 lower than the unmatched estimate. Forward stepwise logistic regression was then used to estimate the attributable cost of SSI by controlling for other patientand process-related risk factors that may affect the cost of care, including severity of illness, duration of surgery, gender, obesity, congestive heart failure, diabetes, reexploration for bleeding, and use of an intra-aortic balloon pump. Use of an intra-aortic balloon pump was the only variable besides SSI that significantly affected the cost of CABG surgery. After controlling for the use of an intra-aortic balloon pump, the authors found that deep chest SSI increased cost by $19,311. The authors then used Heckman’s two-stage procedure to control for the impact of nonrandom selection, which may affect the cost of SSIs. For example, many of the factors that increase the risk of SSIs may also independently increase the cost of care, such as diabetes, heart failure, severity of illness, renal failure, and duration of surgery. The two-stage method corrects for the possibility of nonrandom selection when assessing cost impact. Using this method, the authors found that the economic impact of deep chest SSI was $14,211, approximately $6,000 (30%) lower than the cost estimates defined by the other methods. However, the coefficient on the hazard function was not statistically significant, implying that the cost estimates from the three other methods did not suffer from selection bias. Thus, the cost estimates produced by unmatched comparison, matched comparison, and linear regression are reasonable to use. This study provides excellent, recent data on the cost of deep chest SSI in a community medical center and thus can be used by infection control specialists, epidemiologists, and administrators to understand the financial impact of SSIs. It also outlines some fairly simple methods that can be used by these same groups to understand what the costs of infections are in their own hospitals. This study was done on one surgical procedure in one hospital during a period of increased infection rates. What we do not know is whether these costs would be approximately the same during periods of lower infection rates, or whether costs are different for low-frequency endemic infections compared with high-frequency infections or outbreaks. We also do not know how these costs compare with costs in different types of hospitals in different geographic areas. Clearly, additional studies are necessary to determine the variations in cost in different types of hospitals and in different regions. Studies also need to be done to determine how the type of insurance (Medicare,