Comparison of 2 <i>Clostridium difficile</i> Surveillance Methods National Healthcare Safely Network's Laboratory-Identified Event Reporting Module versus Clinical Infection Surveillance

Gase, Kathleen A.; Haley, Valerie B.; Xiong, Kuangnan; Antwerpen, Carole Van; Stricof, Rachel L.

doi:10.1086/669509

Cited by 31 publications

(40 citation statements)

References 14 publications

(6 reference statements)

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“…33 We classified any laboratory test for CDI as an infection, and we were unable to verify the presence of clinical symptoms; however, agreement between clinical surveillance and LabID events has been estimated at~80%. 34 In addition, there were no standard testing practices across the included facilities during the study period. Therefore, the sensitivity and specificity of testing algorithms may have varied over time and across facilities.…”

Section: Discussionmentioning

confidence: 99%

Excess Length of Stay Attributable to Clostridium difficile Infection (CDI) in the Acute Care Setting: A Multistate Model

Stevens

Khader

Nelson

et al. 2015

Infect. Control Hosp. Epidemiol.

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Background. Evaluation of the economic impact of interventions designed to reduce the incidence of Clostridium difficile infection (CDI) depends on accurate estimates of healthcare resource use attributable to infection. Studies that do not account for the timing of infection overestimate attributable length of stay (LOS). The purpose of this study was to evaluate the excess LOS due to CDI using a multi-state model Methods. We conducted a retrospective cohort study of all patients hospitalized on a general medical, surgical, or intensive care unit within the US Department of Veterans Affairs (VA) health care system between January 1, 2005 and December 31, 2012. A diagnosis of CDI was based on a positive laboratory result by enzyme immunoassay or cytotoxin test. A multi-state approach, implemented through the etmpackage in R, was used to estimate the excess LOS attributable to CDI while accounting for the timing of infection. Confidence intervals were estimated using bootstrapping techniques. A composite outcome of discharge or death was used. Estimates from the multistate model were compared to traditional estimates, including crude comparisons, ordinary least squares (OLS), and a generalized linear model (GLM) with a log link and gamma distributionResults. During the study period, 4.2 million Veterans were followed up for nearly 22 million patient-days of observation. CDIs were observed in 43,661 (1%) of hospitalizations. The median LOS among patients with and without CDI was 10 (IQR: 17) and 3 (IQR: 4) days, respectively, for a crude difference of 7 days ( p < .0001). OLS regression returned an estimated excess LOS of 14.3 (95%CI: 14.0 -14.6) days. The estimated increase in LOS from the GLM model was 7.0 (95% CI: 6.9 -7.1) days. When accounting for the timing of infection in a multi-state model, the attributable LOS was estimated to be 2.28 (95% CI: 2.14 -2.41) daysConclusion. CDI significantly contributes to LOS in the acute care setting but the magnitude of its estimated impact is substantially smaller with methods that account for the time-varying nature of infection. Further efforts to control for time-varying confounding will refine these estimates. Estimates of CDI costs should be based on more accurate measures of attributable LOS to avoid overstating the benefit of interventions to reduce CDI

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Section: Discussionmentioning

confidence: 99%

Excess Length of Stay Attributable to Clostridium difficile Infection (CDI) in the Acute Care Setting: A Multistate Model

Stevens

Khader

Nelson

et al. 2015

Infect. Control Hosp. Epidemiol.

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“…Traditional surveillance for CDI requires manual chart review and is labor intensive, subjective, and expensive. 3 …”

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confidence: 99%

A Comparison Between National Healthcare Safety Network Laboratory-Identified Event Reporting versus Traditional Surveillance for Clostridium difficile Infection

Durkin

Baker

Dicks

et al. 2014

Infect. Control Hosp. Epidemiol.

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Objective Hospitals in the National Healthcare Safety Network began reporting laboratory-identified (LabID) Clostridium difficile infection (CDI) events in January 2013. Our study quantified the differences between the LabID and traditional surveillance methods. Design Cohort study. Setting A cohort of 29 community hospitals in the southeastern United States. Methods A period of 6 months (January 1, 2013, to June 30, 2013) of prospectively collected data using both LabID and traditional surveillance definitions were analyzed. CDI events with mismatched surveillance categories between LabID and traditional definitions were identified and characterized further. Hospital-onset CDI (HO-CDI) rates for the entire cohort of hospitals were calculated using each method, then hospital-specific HO-CDI rates and standardized infection ratios (SIRs) were calculated. Hospital rankings based on each CDI surveillance measure were compared. Results A total of 1,252 incident LabID CDI events were identified during 708,551 patient-days; 286 (23%) mismatched CDI events were detected. The overall HO-CDI rate was 6.0 vs 4.4 per 10,000 patient-days for LabID and traditional surveillance, respectively (P < .001); of 29 hospitals, 25 (86%) detected a higher CDI rate using LabID compared with the traditional method. Hospital rank in the cohort differed greatly between surveillance measures. A rank change of at least 5 places occurred in 9 of 28 hospitals (32%) between LabID and traditional CDI surveillance methods, and for SIR. Conclusions LabID surveillance resulted in a higher hospital-onset CDI incidence rate than did traditional surveillance. Hospital-specific rankings varied based on the HO-CDI surveillance measure used. A clear understanding of differences in CDI surveillance measures is important when interpreting national and local CDI data.

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“…When initiating a new HAI data submission requirement, a state will have to decide which surveillance method should be used. 18 Furthermore, several HAI are reported by most states (i.e., CLABSI, MRSA, and C. difficile ) and this raises concerns about the need for rapid harmonization and consensus across states that report the same information (an issue that will become increasingly important as more states require the reporting of additional infection types). This is highlighted by the publication of NHSN reports comparing the results of these standardized data on infection prevention quality measures.…”

Section: Discussionmentioning

confidence: 99%

State-Mandated Reporting of Health Care–Associated Infections in the United States

et al. 2014

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Over the past decade, most US states and territories began mandating that acute care hospitals report healthcare-associated infections (HAI) to their departments of health. Trends in state HAI law enactment and data submission requirements were determined using systematic legal review; state HAI coordinators were contacted to confirm collected data. As of January 31, 2013, 37 US states and territories (71%) had adopted laws requiring HAI data submission; most were enacted and became effective in 2006 and 2007. Most states with HAI laws required reporting of central line-associated bloodstream infections in adult ICUs (92%), and about half required reporting of methicillin-resistant Staphylococcus aureus and Clostridium difficile infections (54% and 51%, respectively). Overall, data submission requirements were found to vary across states. Considering the facility and state resources needed to comply with HAI reporting mandates, future studies should focus on whether these laws have had the desired impact of reducing infection rates.

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Comparison of 2 Clostridium difficile Surveillance Methods National Healthcare Safely Network's Laboratory-Identified Event Reporting Module versus Clinical Infection Surveillance

Cited by 31 publications

References 14 publications

Excess Length of Stay Attributable to Clostridium difficile Infection (CDI) in the Acute Care Setting: A Multistate Model

Excess Length of Stay Attributable to Clostridium difficile Infection (CDI) in the Acute Care Setting: A Multistate Model

A Comparison Between National Healthcare Safety Network Laboratory-Identified Event Reporting versus Traditional Surveillance for Clostridium difficile Infection

State-Mandated Reporting of Health Care–Associated Infections in the United States

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