Model-based Assessment of the Effect of Contact Precautions Applied to Surveillance-detected Carriers of Carbapenemase-producing Enterobacteriaceae in Long-term Acute Care Hospitals.

BACKGROUND: An intervention that successfully reduced colonization and infection with carbapenemase-producing Enterobacteriaceae (CPE) in Chicago-area long-term acute-care hospitals included active surveillance and contact precautions. However, the specific effects of contact precautions applied to surveillance-detected carriers on patient-to-patient transmission are unknown, as other, concurrent intervention components or changes in facility patient dynamics also could have affected the observed outcomes. METHODS: Using previously published data from before and after the CPE intervention, we designed a mathematical model with an explicit representation of postintervention surveillance. We estimated preintervention to postintervention changes of 3 parameters: ß, the baseline transmission rate excluding contact precaution effects; δb, the rate of a CPE carrier progressing to bacteremia; and δc, the progression rate to nonbacteremia clinical detection. RESULTS: Assuming that CPE carriers under contact precautions transmit carriage to other patients at half the rate of undetected carriers, the model produced no convincing evidence for a postintervention change in the baseline transmission rate ß (+2.1% [95% confidence interval {CI}, -18% to +28%]). The model did find evidence of a postintervention decrease for δb (-41% [95% CI, -60% to -18%]), but not for δc (-7% [95% CI, -28% to +19%]). CONCLUSIONS: Our results suggest that contact precautions for surveillance-detected CPE carriers could potentially explain the observed decrease in colonization by itself, even under conservative assumptions for the effectiveness of those precautions for reducing cross-transmission. Other intervention components such as daily chlorhexidine gluconate bathing of all patients and hand-hygiene education and adherence monitoring may have contributed primarily to reducing rates of colonized patients progressing to bacteremia.

Attributable Cost and Length of Stay Associated with Nosocomial Gram-Negative Bacterial Cultures.

Few studies have estimated the excess inpatient costs due to nosocomial cultures of Gram-negative bacteria (GNB), and those that do are often subject to time-dependent bias. Our objective was to generate estimates of the attributable costs of the underlying infections associated with nosocomial cultures by using a unique inpatient cost data set from the U.S. Department of Veterans Affairs that allowed us to reduce time-dependent bias. Our study included data from inpatient admissions between 1 October 2007 and 30 November 2010. Nosocomial GNB-positive cultures were defined as clinical cultures positive for Acinetobacter, Pseudomonas, or Enterobacteriaceae between 48 h after admission and discharge. Positive cultures were further classified by site and level of resistance. We conducted analyses using both a conventional approach and an approach aimed at reducing the impact of time-dependent bias. In both instances, we used multivariable generalized linear models to compare the inpatient costs and length of stay for patients with and without a nosocomial GNB culture. Of the 404,652 patients included in the conventional analysis, 12,356 had a nosocomial GNB-positive culture. The excess costs of nosocomial GNB-positive cultures were significant, regardless of specific pathogen, site, or resistance level. Estimates generated using the conventional analysis approach were 32.0% to 131.2% greater than those generated using the approach to reduce time-dependent bias. These results are important because they underscore the large financial burden attributable to these infections and provide a baseline that can be used to assess the impact of improvements in infection control.

Extended models for nosocomial infection: parameter estimation and model selection.

We consider extensions to previous models for patient level nosocomial infection in several ways, provide a specification of the likelihoods for these new models, specify new update steps required for stochastic integration, and provide programs that implement these methods to obtain parameter estimates and model choice statistics. Previous susceptible-infected models are extended to allow for a latent period between initial exposure to the pathogen and the patient becoming themselves infectious, and the possibility of decolonization. We allow for multiple facilities, such as acute care hospitals or long-term care facilities and nursing homes, and for multiple units or wards within a facility. Patient transfers between units and facilities are tracked and accounted for in the models so that direct importation of a colonized individual from one facility or unit to another might be inferred. We allow for constant transmission rates, rates that depend on the number of colonized individuals in a unit or facility, or rates that depend on the proportion of colonized individuals. Statistical analysis is done in a Bayesian framework using Markov chain Monte Carlo methods to obtain a sample of parameter values from their joint posterior distribution. Cross validation, deviance information criterion and widely applicable information criterion approaches to model choice fit very naturally into this framework and we have implemented all three. We illustrate our methods by considering model selection issues and parameter estimation for data on methicilin-resistant Staphylococcus aureus surveillance tests over 1 year at a Veterans Administration hospital comprising seven wards.

Dynamic transmission models for economic analysis applied to health care-associated infections: A review of the literature.

BACKGROUND: Cost-effectiveness analyses are an important methodology in assessing whether a health care technology is suitable for widespread adoption. Common models used by economists, such as decision trees and Markov models, are appropriate for noninfectious diseases where treatment and exposure are independent. Diseases whose treatment and exposure are dependent require dynamic models to incorporate the nonlinear transmission effect. Two different types of models are often used for dynamic cost-effectiveness analyses: compartmental models and individual models. In this methodology-focused literature review, we describe each model type and summarize the literature associated with each using the example of health care-associated infections (HAIs). METHODS: We conducted a review of the literature to identify dynamic cost-effectiveness analyses that examined interventions to prevent or treat HAIs. To be included in the review, studies needed to have each of 3 necessary components: involve economics, such as cost-effectiveness analysis and evidence of economic theory, use a dynamic transmission model, and examine HAIs. RESULTS: Of the 9 articles published between 2005 and 2016 that met criteria to be included in our study, 3 used compartmental models and 6 used individual models. CONCLUSIONS: Very few published studies exist that use dynamic transmission models to conduct economic analyses related to HAIs and even fewer studies have used these models to perform cost-effectiveness analyses.

Incidence of Extended-Spectrum ß-Lactamase (ESBL)-Producing Escherichia coli and Klebsiella Infections in the United States: A Systematic Literature Review.

BACKGROUND Despite a reported worldwide increase, the incidence of extended-spectrum ß-lactamase (ESBL) Escherichia coli and Klebsiella infections in the United States is unknown. Understanding the incidence and trends of ESBL infections will aid in directing research and prevention efforts. OBJECTIVE To perform a literature review to identify the incidence of ESBL-producing E. coli and Klebsiella infections in the United States. DESIGN Systematic literature review. METHODS MEDLINE via Ovid, CINAHL, Cochrane library, NHS Economic Evaluation Database, Web of Science, and Scopus were searched for multicenter (≥2 sites), US studies published between 2000 and 2015 that evaluated the incidence of ESBL-E. coli or ESBL-Klebsiella infections. We excluded studies that examined resistance rates alone or did not have a denominator that included uninfected patients such as patient days, device days, number of admissions, or number of discharges. Additionally, articles that were not written in English, contained duplicated data, or pertained to ESBL organisms from food, animals, or the environment were excluded. RESULTS Among 51,419 studies examined, 9 were included for review. Incidence rates differed by patient population, time, and ESBL definition and ranged from 0 infections per 100,000 patient days to 16.64 infections per 10,000 discharges and incidence rates increased over time from 1997 to 2011. Rates were slightly higher for ESBL-Klebsiella infections than for ESBL-E. coli infections. CONCLUSION The incidence of ESBL-E. coli and ESBL-Klebsiella infections in the United States has increased, with slightly higher rates of ESBL-Klebsiella infections. Appropriate estimates of ESBL infections when coupled with other mechanisms of resistance will allow for the appropriate targeting of resources toward research, drug discovery, antimicrobial stewardship, and infection prevention. Infect Control Hosp Epidemiol 2017;38:1209-1215.

Incidence and Outcomes Associated With Infections Caused by Vancomycin-Resistant Enterococci in the United States: Systematic Literature Review and Meta-Analysis.

BACKGROUND Information about the health and economic impact of infections caused by vancomycin-resistant enterococci (VRE) can inform investments in infection prevention and development of novel therapeutics. OBJECTIVE To systematically review the incidence of VRE infection in the United States and the clinical and economic outcomes. METHODS We searched various databases for US studies published from January 1, 2000, through June 8, 2015, that evaluated incidence, mortality, length of stay, discharge to a long-term care facility, readmission, recurrence, or costs attributable to VRE infections. We included multicenter studies that evaluated incidence and single-center and multicenter studies that evaluated outcomes. We kept studies that did not have a denominator or uninfected controls only if they assessed postinfection length of stay, costs, or recurrence. We performed meta-analysis to pool the mortality data. RESULTS Five studies provided incidence data and 13 studies evaluated outcomes or costs. The incidence of VRE infections increased in Atlanta and Detroit but did not increase in national samples. Compared with uninfected controls, VRE infection was associated with increased mortality (pooled odds ratio, 2.55), longer length of stay (3-4.6 days longer or 1.4 times longer), increased risk of discharge to a long-term care facility (2.8- to 6.5-fold) or readmission (2.9-fold), and higher costs ($9,949 higher or 1.6-fold more). CONCLUSIONS VRE infection is associated with large attributable burdens, including excess mortality, prolonged in-hospital stay, and increased treatment costs. Multicenter studies that use suitable controls and adjust for time at risk or confounders are needed to estimate the burden of VRE infections. Infect Control Hosp Epidemiol. 2017;38:203-215.

Costs and Mortality Associated With Multidrug-Resistant Healthcare-Associated Acinetobacter Infections.

BACKGROUND Our objective was to estimate the per-infection and cumulative mortality and cost burden of multidrug-resistant (MDR) Acinetobacter healthcare-associated infections (HAIs) in the United States using data from published studies. METHODS We identified studies that estimated the excess cost, length of stay (LOS), or mortality attributable to MDR Acinetobacter HAIs. We generated estimates of the cost per HAI using 3 methods: (1) overall cost estimates, (2) multiplying LOS estimates by a cost per inpatient-day ($4,350) from the payer perspective, and (3) multiplying LOS estimates by a cost per inpatient-day from the hospital ($2,030) perspective. We deflated our estimates for time-dependent bias using an adjustment factor derived from studies that estimated attributable LOS using both time-fixed methods and either multistate models (70.4% decrease) or matching patients with and without HAIs using the timing of infection (47.4% decrease). Finally, we used the incidence rate of MDR Acinetobacter HAIs to generate cumulative incidence, cost, and mortality associated with these infections. RESULTS Our estimates of the cost per infection were $129,917 (method 1), $72,025 (method 2), and $33,510 (method 3). The pooled relative risk of mortality was 4.51 (95% CI, 1.10-32.65), which yielded a mortality rate of 10.6% (95% CI, 2.5%-29.4%). With an incidence rate of 0.141 (95% CI, 0.136-0.161) per 1,000 patient-days at risk, we estimated an annual cumulative incidence of 12,524 (95% CI, 11,509-13,625) in the United States. CONCLUSION The estimates presented here are relevant to understanding the expenditures and lives that could be saved by preventing MDR Acinetobacter HAIs. Infect Control Hosp Epidemiol 2016;1-7.

An Economic Analysis of Strategies to Control Clostridium Difficile Transmission and Infection Using an Agent-Based Simulation Model.

BACKGROUND: A number of strategies exist to reduce Clostridium difficile (C. difficile) transmission. We conducted an economic evaluation of "bundling" these strategies together. METHODS: We constructed an agent-based computer simulation of nosocomial C. difficile transmission and infection in a hospital setting. This model included the following components: interactions between patients and health care workers; room contamination via C. difficile shedding; C. difficile hand carriage and removal via hand hygiene; patient acquisition of C. difficile via contact with contaminated rooms or health care workers; and patient antimicrobial use. Six interventions were introduced alone and "bundled" together: (a) aggressive C. difficile testing; (b) empiric isolation and treatment of symptomatic patients; (c) improved adherence to hand hygiene and (d) contact precautions; (e) improved use of soap and water for hand hygiene; and (f) improved environmental cleaning. Our analysis compared these interventions using values representing 3 different scenarios: (1) base-case (BASE) values that reflect typical hospital practice, (2) intervention (INT) values that represent implementation of hospital-wide efforts to reduce C. diff transmission, and (3) optimal (OPT) values representing the highest expected results from strong adherence to the interventions. Cost parameters for each intervention were obtained from published literature. We performed our analyses assuming low, normal, and high C. difficile importation prevalence and transmissibility of C. difficile. RESULTS: INT levels of the "bundled" intervention were cost-effective at a willingness-to-pay threshold of $100,000/quality-adjusted life-year in all importation prevalence and transmissibility scenarios. OPT levels of intervention were cost-effective for normal and high importation prevalence and transmissibility scenarios. When analyzed separately, hand hygiene compliance, environmental decontamination, and empiric isolation and treatment were the interventions that had the greatest impact on both cost and effectiveness. CONCLUSIONS: A combination of available interventions to prevent CDI is likely to be cost-effective but the cost-effectiveness varies for different levels of intensity of the interventions depending on epidemiological conditions such as C. difficile importation prevalence and transmissibility.

Economic Analysis of Veterans Affairs Initiative to Prevent Methicillin-Resistant Staphylococcus aureus Infections.

INTRODUCTION: In an effort to reduce methicillin-resistant Staphylococcus aureus (MRSA) transmission through universal screening and isolation, the Department of Veterans Affairs (VA) launched the National MRSA Prevention Initiative in October 2007. The objective of this analysis was to quantify the budget impact and cost effectiveness of this initiative. METHODS: An economic model was developed using published data on MRSA hospital-acquired infection (HAI) rates in the VA from October 2007 to September 2010; estimates of the costs of MRSA HAIs in the VA; and estimates of the intervention costs, including salaries of staff members hired to support the initiative at each VA facility. To estimate the rate of MRSA HAIs that would have occurred if the initiative had not been implemented, two different assumptions were made: no change and a downward temporal trend. Effectiveness was measured in life-years gained. RESULTS: The initiative resulted in an estimated 1,466-2,176 fewer MRSA HAIs. The initiative itself was estimated to cost $207 million during this 3-year period, while the cost savings from prevented MRSA HAIs ranged from $27 million to $75 million. The incremental cost-effectiveness ratios ranged from $28,048 to $56,944/life-years. The overall impact on the VA's budget was $131-$179 million. CONCLUSIONS: Wide-scale implementation of a national MRSA surveillance and prevention strategy in VA inpatient settings may have prevented a substantial number of MRSA HAIs. Although the savings associated with prevented infections helped offset some but not all of the cost of the initiative, this model indicated that the initiative would be considered cost effective.

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

BACKGROUND: Standard estimates of the impact of Clostridium difficile infections (CDI) on inpatient lengths of stay (LOS) may overstate inpatient care costs attributable to CDI. In this study, we used multistate modeling (MSM) of CDI timing to reduce bias in estimates of excess LOS. METHODS: A retrospective cohort study of all hospitalizations at any of 120 acute care facilities within the US Department of Veterans Affairs (VA) between 2005 and 2012 was conducted. We estimated the excess LOS attributable to CDI using an MSM to address time-dependent bias. Bootstrapping was used to generate 95% confidence intervals (CI). These estimates were compared to unadjusted differences in mean LOS for hospitalizations with and without CDI. RESULTS: During the study period, there were 3.96 million hospitalizations and 43,540 CDIs. A comparison of unadjusted means suggested an excess LOS of 14.0 days (19.4 vs 5.4 days). In contrast, the MSM estimated an attributable LOS of only 2.27 days (95% CI, 2.14-2.40). The excess LOS for mild-to-moderate CDI was 0.75 days (95% CI, 0.59-0.89), and for severe CDI, it was 4.11 days (95% CI, 3.90-4.32). Substantial variation across the Veteran Integrated Services Networks (VISN) was observed. CONCLUSIONS: CDI significantly contributes to LOS, but the magnitude of its estimated impact is smaller when methods are used that account for the time-varying nature of infection. The greatest impact on LOS occurred among patients with severe CDI. Significant geographic variability was observed. MSM is a useful tool for obtaining more accurate estimates of the inpatient care costs of CDI.

Efficient parameter estimation for models of healthcare-associated pathogen transmission in discrete and continuous time.

We describe two novel Markov chain Monte Carlo approaches to computing estimates of parameters concerned with healthcare-associated infections. The first approach frames the discrete time, patient level, hospital transmission model as a Bayesian network, and exploits this framework to improve greatly on the computational efficiency of estimation compared with existing programs. The second approach is in continuous time and shares the same computational advantages. Both methods have been implemented in programs that are available from the authors. We use these programs to show that time discretization can lead to statistical bias in the underestimation of the rate of transmission of pathogens. We show that the continuous implementation has similar running time to the discrete implementation, has better Markov chain mixing properties, and eliminates the potential statistical bias. We, therefore, recommend its use when continuous-time data are available.

Improved hidden Markov model for nosocomial infections.

We propose a novel hidden Markov model (HMM) for parameter estimation in hospital transmission models, and show that commonly made simplifying assumptions can lead to severe model misspecification and poor parameter estimates. A standard HMM that embodies two commonly made simplifying assumptions, namely a fixed patient count and binomially distributed detections is compared with a new alternative HMM that does not require these simplifying assumptions. Using simulated data, we demonstrate how each of the simplifying assumptions used by the standard model leads to model misspecification, whereas the alternative model results in accurate parameter estimates.

A simulation-based assessment of strategies to control Clostridium difficile transmission and infection.

BACKGROUND: Clostridium difficile is one of the most common and important nosocomial pathogens, causing severe gastrointestinal disease in hospitalized patients. Although "bundled" interventions have been proposed and promoted, optimal control strategies remain unknown. METHODS: We designed an agent-based computer simulation of nosocomial C. difficile transmission and infection, which included components such as: patients and health care workers, and their interactions; room contamination via C. difficile shedding; C. difficile hand carriage and removal via hand hygiene; patient acquisition of C. difficile via contact with contaminated rooms or health care workers; and patient antimicrobial use. We then introduced six interventions, alone and "bundled" together: aggressive C. difficile testing; empiric isolation and treatment of symptomatic patients; improved adherence to hand hygiene and contact precautions; improved use of soap and water for hand hygiene; and improved environmental cleaning. All interventions were tested using values representing base-case, typical intervention, and optimal intervention scenarios. FINDINGS: In the base-case scenario, C. difficile infection rates ranged from 8-21 cases/10,000 patient-days, with a case detection fraction between 32%-50%. Implementing the "bundle" at typical intervention levels had a large impact on C. difficile acquisition and infection rates, although intensifying the intervention to optimal levels had much less additional impact. Most of the impact came from improved hand hygiene and empiric isolation and treatment of suspected C. difficile cases. CONCLUSION: A "bundled" intervention is likely to reduce nosocomial C. difficile infection rates, even under typical implementation conditions. Real-world implementation of the "bundle" should focus on those components of the intervention that are likely to produce the greatest impact on C. difficile infection rates, such as hand hygiene and empiric isolation and treatment of suspected cases.