Correcting for bias when estimating the cost of hospital-acquired infection: an analysis of lower respiratory tract infections in non-surgical patients

Graves, Nicholas; Weinhold, Diana; Roberts, J. A. Fraser

doi:10.1002/hec.967

Cited by 31 publications

(22 citation statements)

References 37 publications

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“…The estimated coefficients for equations (1) and (2) are shown in Tables II and III and all of the estimated coefficients are significant at 1 percent level. INFECT and LOS affect each other positively, confirming findings reported elsewhere in the literature (Graves et al, 2005;Plowman et al, 2001). Specifically, INFECT increases LOS by 9.3 days and an increase in LOS by one day increases the probability of catching an infection in the hospital by 0.0137.…”

Section: Resultssupporting

confidence: 91%

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Hospital length of stay and probability of acquiring infection

Hassan

Tuckman

Patrick

et al. 2010

Intl J of Pharm & Health Mrkt

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2010),"The adoption of technology orientation in healthcare delivery: Case study of a large-scale hospital and healthcare system's electronic health record", If you would like to write for this, or any other Emerald publication, then please use our Emerald for Authors service information about how to choose which publication to write for and submission guidelines are available for all. Please visit www.emeraldinsight.com/authors for more information. About Emerald www.emeraldinsight.comEmerald is a global publisher linking research and practice to the benefit of society. The company manages a portfolio of more than 290 journals and over 2,350 books and book series volumes, as well as providing an extensive range of online products and additional customer resources and services.Emerald is both COUNTER 4 and TRANSFER compliant. The organization is a partner of the Committee on Publication Ethics (COPE) and also works with Portico and the LOCKSS initiative for digital archive preservation. AbstractPurpose -Hospital-acquired infection (HAI) poses important health and financial problems for society. Understanding the causes of infection in hospital care is strategically important for hospital administration for formulating effective infection control programs. The purpose of this paper is to show that hospital length of stay (LOS) and the probability of developing an infection are interdependent. Design/methodology/approach -A two-equation model was specified for hospital LOS and the incidence of infection. Using the patient-level data of hospital discharge in the State of New Jersey merged with other data, the parameters of the two equations were estimated using a simultaneous estimation method. Findings -It was found that extending the LOS by one day increases the probability of catching an infection by 1.37 percent and the onset of infection increases average LOS by 9.32 days. The estimation indicates that HAI elongates LOS increasing the cost of a hospital stay.Research limitations/implications -The findings imply that studies on cost of HAI that do not properly control for the simultaneity of these two variables, will result in a biased estimation of cost. Originality/value -The study produces quantitative estimation of the extent of interdependency of hospital LOS and the probability of catching an infection.

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Section: Resultssupporting

confidence: 91%

“…Our analysis confirms that LOS and HAI are interdependent as described in Graves et al (2005). It also offers a better understanding of the process by which costs increase than has been previously available.…”

Section: Discussionsupporting

confidence: 85%

Hospital length of stay and probability of acquiring infection

Hassan

Tuckman

Patrick

et al. 2010

Intl J of Pharm & Health Mrkt

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“…The appropriate modelling of endogenous predictor variables is a critical challenge with this analytical method. Using data previously collected by Plowman et al [30] on 899 patients from a district general hospital in the UK, Graves et al [31] used a two-stage instrumental variable estimation strategy to overcome the bias from endogenous variables to estimate the costs associated with lower respiratory tract infections. On the basis of the results of the regression analysis, the authors tested the presence of nasogastric tube and oxygen therapy as instruments, because of the evidence that both were risk factors for the development of HAI, but neither of them was a determinant of LOS.…”

Section: Matching Design Linear Regression Models and Instrumental Vmentioning

confidence: 99%

Estimating the impact of healthcare-associated infections on length of stay and costs

Angelis

Murthy

Beyersmann

et al. 2010

Clinical Microbiology and Infection

128

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Healthcare-associated infections (HAIs) unquestionably have substantial effects on morbidity and mortality. However, quantifying the exact economic burden attributable to HAIs still remains a challenging issue. Inaccurate estimations may arise from two major sources of bias. First, factors other than infection may affect patients' length of stay (LOS) and healthcare utilization. Second, HAI is a time-varying exposure, as the infection can impact on LOS and costs only after the infection has started. The most frequent mistake in previously published evidence is the introduction of time-dependent information as time-fixed, on the assumption that the impact of such exposure on the outcome was already present on admission. Longitudinal and multistate models avoid time-dependent bias and address the time-dependent complexity of the data. Appropriate statistical methods are important in analysis of excess costs and LOS associated with HAI, because informed decisions and policy developments may depend on them.

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“…3, 18 Graves and Weinhold 12,19 describe the problem in detail and report preliminary attempts at a solution, using an instrumental variables method. Controlling bias from endogenous variables and interpreting the results of an unbiased model is a methodological challenge for future research.…”

mentioning

confidence: 99%

Effect of Healthcare-Acquired Infection on Length of Hospital Stay and Cost

Graves

Weinhold

Tong

et al. 2007

Infect. Control Hosp. Epidemiol.

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151

128

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Objective.To estimate the independent effect of a single lower respiratory tract infection, urinary tract infection, or other healthcare-acquired infection on length-of-stay and variable costs and to demonstrate the bias from omitted variables that is present in previous estimates.Design.Prospective cohort study.Setting.A tertiary care referral hospital and regional district hospital in southeast Queensland, Australia.Patients.Adults aged 18 years or older with a minimum inpatient stay of 1 night who were admitted to selected clinical specialities.Results.Urinary tract infection was not associated with an increase in length of hospital stay or variable costs. Lower respiratory tract infection was associated with an increase of 2.58 days in the hospital and variable costs of AU$24, whereas other types of infection were associated with an increased length of stay of 2.61 days but not with variable costs. Many other factors were found to be associated with increased length of stay and variable costs alongside healthcare-acquired infection. The exclusion of these variables caused a positive bias in the estimates of the costs of healthcare-acquired infection.Conclusions.The existing literature may overstate the costs of healthcare-acquired infection because of bias, and the existing estimates of excess costs may not make intuitive sense to clinicians and policy makers. Accurate estimates of the costs of healthcare-acquired infection should be made and used in appropriately designed decision-analytic economic models (ie, cost-effectiveness models) that will make valid and believable predictions of the economic value of increased infection control.

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Correcting for bias when estimating the cost of hospital-acquired infection: an analysis of lower respiratory tract infections in non-surgical patients

Cited by 31 publications

References 37 publications

Hospital length of stay and probability of acquiring infection

Hospital length of stay and probability of acquiring infection

Estimating the impact of healthcare-associated infections on length of stay and costs

Effect of Healthcare-Acquired Infection on Length of Hospital Stay and Cost

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