BACKGROUND: Health care-associated infections due to multidrug-resistant organisms (MDROs), such as methicillin-resistant Staphylococcus aureus (MRSA) and Clostridioides difficile (CDI), place a significant burden on our health care infrastructure.
OBJECTIVE: Screening for MDROs is an important mechanism for preventing spread but is resource intensive. The objective of this study was to develop automated tools that can predict colonization or infection risk using electronic health record (EHR) data, provide useful information to aid infection control, and guide empiric antibiotic coverage.
METHODS: We retrospectively developed a machine learning model to detect MRSA colonization and infection in undifferentiated patients at the time of sample collection from hospitalized patients at the University of Virginia Hospital. We used clinical and nonclinical features derived from on-admission and throughout-stay information from the patient\'s EHR data to build the model. In addition, we used a class of features derived from contact networks in EHR data; these network features can capture patients\' contacts with providers and other patients, improving model interpretability and accuracy for predicting the outcome of surveillance tests for MRSA. Finally, we explored heterogeneous models for different patient subpopulations, for example, those admitted to an intensive care unit or emergency department or those with specific testing histories, which perform better.
RESULTS: We found that the penalized logistic regression performs better than other methods, and this model\'s performance measured in terms of its receiver operating characteristics-area under the curve score improves by nearly 11% when we use polynomial (second-degree) transformation of the features. Some significant features in predicting MDRO risk include antibiotic use, surgery, use of devices, dialysis, patient\'s comorbidity conditions, and network features. Among these, network features add the most value and improve the model\'s performance by at least 15%. The penalized logistic regression model with the same transformation of features also performs better than other models for specific patient subpopulations.
CONCLUSIONS: Our study shows that MRSA risk prediction can be conducted quite effectively by machine learning methods using clinical and nonclinical features derived from EHR data. Network features are the most predictive and provide significant improvement over prior methods. Furthermore, heterogeneous prediction models for different patient subpopulations enhance the model\'s performance.

A systematic approach to contact investigations has long been a cornerstone of interrupting the transmission of tuberculosis in community settings. This paper describes the implementation of a systematic 10-step contact investigation within an acute care setting during a multistate outbreak of healthcare-associated tuberculosis. A systematic approach to contact investigations might have applicability to the prevention of other communicable infections within healthcare settings.

BACKGROUND: This study examines the impact of the COVID-19 pandemic on health care-associated infection (HAI) incidence in low- and middle-income countries (LMICs).
METHODS: Patients from 7 LMICs were followed up during hospital intensive care unit (ICU) stays from January 2019 to May 2020. HAI rates were calculated using the International Nosocomial Infection Control Consortium (INICC) Surveillance Online System applying the Centers for Disease Control and Prevention\'s National Healthcare Safety Network (CDC-NHSN) criteria. Pre-COVID-19 rates for 2019 were compared with COVID-19 era rates for 2020 for central line-associated bloodstream infections (CLABSIs), catheter-associated urinary tract infections (CAUTIs), ventilator-associated events (VAEs), mortality, and length of stay (LOS).
RESULTS: A total of 7,775 patients were followed up for 49,506 bed days. The 2019 to 2020 rate comparisons were 2.54 and 4.73 CLABSIs per 1,000 central line days (risk ratio [RR] = 1.85, p = .0006), 9.71 and 12.58 VAEs per 1,000 mechanical ventilator days (RR = 1.29, p = .10), and 1.64 and 1.43 CAUTIs per 1,000 urinary catheter days (RR = 1.14; p = .69). Mortality rates were 15.2% and 23.2% for 2019 and 2020 (RR = 1.42; p < .0001), respectively. Mean LOS for 2019 and 2020 were 6.02 and 7.54 days (RR = 1.21, p < .0001), respectively.
CONCLUSIONS: This study documents an increase in HAI rates in 7 LMICs during the first 5 months of the COVID-19 pandemic and highlights the need to reprioritize and return to conventional infection prevention practices.

The Centers for Disease Control and Prevention\'s (CDC\'s) National Healthcare Safety Network (NHSN) is the most widely used health care-associated infection (HAI) and antimicrobial use and resistance surveillance program in the United States. Over 37,000 health care facilities participate in the program and submit a large volume of surveillance data. These data are used by the facilities themselves, the CDC, and other agencies and organizations for a variety of purposes, including infection prevention, antimicrobial stewardship, and clinical quality measurement. Among the summary metrics made available by the NHSN are standardized infection ratios, which are used to identify HAI prevention needs and measure progress at the national, regional, state, and local levels.
To extend the use of geospatial methods and tools to NHSN data, and in turn to promote and inspire new uses of the rendered data for analysis and prevention purposes, we developed a web-enabled system that enables integrated visualization of HAI metrics and supporting data.
We leveraged geocoding and visualization technologies that are readily available and in current use to develop a web-enabled system designed to support visualization and interpretation of data submitted to the NHSN from geographically dispersed sites. The server-client model-based system enables users to access the application via a web browser.
We integrated multiple data sets into a single-page dashboard designed to enable users to navigate across different HAI event types, choose specific health care facility or geographic locations for data displays, and scale across time units within identified periods. We launched the system for internal CDC use in January 2019.
CDC NHSN statisticians, data analysts, and subject matter experts identified opportunities to extend the use of geospatial methods and tools to NHSN data and provided the impetus to develop NHSNViz. The development effort proceeded iteratively, with the developer adding or enhancing functionality and including additional data sets in a series of prototype versions, each of which incorporated user feedback. The initial production version of NHSNViz provides a new geospatial analytic resource built in accordance with CDC user requirements and extensible to additional users and uses in subsequent versions.

UNASSIGNED: Patients hospitalized with coronavirus disease 2019 (COVID-19) are at increased risk of health care-associated infections (HAIs), especially with prolonged hospital stays. We sought to identify incidence, antimicrobial susceptibilities, and outcomes associated with bacterial/fungal secondary infections in a large cohort of patients with COVID-19.
UNASSIGNED: We evaluated adult patients diagnosed with COVID-19 between 2 March and 31 May 2020 and hospitalized >24 hours. Data extracted from medical records included diagnoses, vital signs, laboratory results, microbiological data, and antibiotic use. Microbiologically confirmed bacterial and fungal pathogens from clinical cultures were evaluated to characterize community- and health care-associated infections, including describing temporal changes in predominant organisms on presentation and throughout hospitalization. Univariable and multivariable logistic regression analyses were performed to investigate risk factors for HAIs.
UNASSIGNED: A total of 3028 patients were included and accounted for 899 positive clinical cultures. Overall, 516 (17%) patients with positive cultures met criteria for infection. Community-associated coinfections were identified in 183 (6%) patients, whereas HAIs occurred in 350 (12%) patients. Fifty-seven percent of HAIs were caused by gram-negative bacteria and 19% by fungi. Antibiotic resistance increased with longer hospital stays, with incremental increases in the proportion of vancomycin resistance among enterococci and ceftriaxone and carbapenem resistance among Enterobacterales. Intensive care unit stay, invasive mechanical ventilation, and steroids were associated with HAIs.
UNASSIGNED: HAIs occur in a small proportion of patients hospitalized with COVID-19 and are most often caused by gram-negative and fungal pathogens. Antibiotic resistance is more prevalent with prolonged hospital stays. Antimicrobial stewardship is imperative in this population to minimize unnecessary broad-spectrum antibiotic use.

Coronavirus disease 2019 (COVID-19) represents an important infectious complication associated with high mortality rates in patients with hematologic diseases. There have not been published any epidemiologic studies from Czech Republic so far.
This study is the first analysis of patients with hematologic malignancies and bone marrow failure syndromes treated at single hematology center in the Czech Republic between March 1 and December 31, 2020, in whom COVID-19 infection was confirmed.
The sample comprised 96 patients aged 26 to 84 years (median, 66.0 years). At the time of their COVID-19 diagnosis, 75 patients (78.1%) were treated for hematologic diseases. Twenty-seven patients (28.1%) in the sample had complete remission (CR) of their hematologic disease. They were nonsignificantly more likely to have asymptomatic to moderate COVID-19 infection than those who failed to achieve CR (74.1% vs. 56.5%; P = .06). A more severe course of the infection was significantly correlated with older age (P = .047). Lung involvement was also statistically significantly associated with older age (P = .045). Over the study period, a total of 15 patients died. Age greater than 60 years was significantly associated with deaths from COVID-19 (P = .036), with failure to achieve CR having a statistically nonsignificant impact on mortality (P = .22).
These results confirm the prognostic significance of age for achieving treatment response of hematologic disease as well as the severity and mortality of COVID-19 in hematology patients.

Mobile phones may be contaminated with nosocomial pathogens such as methicillin-resistant Staphylococcus aureus (MRSA). The aim of this study was to investigate the MRSA contamination rate on doctors\' hospital-use-only mobile phones and the efficacy of 222-nm ultraviolet light (UV) disinfection.
We investigated the MRSA contamination rate of doctors\' hospital-use-only mobile phones, as well as the reduction in MRSA counts on plastic plates and aerobic bacteria (AB) on mobile phones before and after exposure to 222-nm UV irradiation.
Five (10%) of the 50 mobile phones investigated were contaminated with MRSA. Exposure to 0.1 mJ/cm2 222-nm UVC irradiation for 1.5 and 2.5 min (9 and 15 mJ/cm2) achieved mean log10 MRSA colony-forming units reductions of 2.91 and 3.95, respectively. Exposure to 9 mJ/cm2 222-nm UVC irradiation (0.1 mW/cm2 for 1.5 minutes) significantly reduced AB contamination on mobile phones (P < .001).
The use of 222-nm UV disinfection resulted in effective in vitro reduction of MRSA and significantly reduced AB contamination of mobile phone surfaces.

BACKGROUND: The cluster detection of health care-associated infections (HAIs) is crucial for identifying HAI outbreaks in the early stages.
OBJECTIVE: We aimed to verify whether multisource surveillance based on the process data in an area network can be effective in detecting HAI clusters.
METHODS: We retrospectively analyzed the incidence of HAIs and 3 indicators of process data relative to infection, namely, antibiotic utilization rate in combination, inspection rate of bacterial specimens, and positive rate of bacterial specimens, from 4 independent high-risk units in a tertiary hospital in China. We utilized the Shewhart warning model to detect the peaks of the time-series data. Subsequently, we designed 5 surveillance strategies based on the process data for the HAI cluster detection: (1) antibiotic utilization rate in combination only, (2) inspection rate of bacterial specimens only, (3) positive rate of bacterial specimens only, (4) antibiotic utilization rate in combination + inspection rate of bacterial specimens + positive rate of bacterial specimens in parallel, and (5) antibiotic utilization rate in combination + inspection rate of bacterial specimens + positive rate of bacterial specimens in series. We used the receiver operating characteristic (ROC) curve and Youden index to evaluate the warning performance of these surveillance strategies for the detection of HAI clusters.
RESULTS: The ROC curves of the 5 surveillance strategies were located above the standard line, and the area under the curve of the ROC was larger in the parallel strategy than in the series strategy and the single-indicator strategies. The optimal Youden indexes were 0.48 (95% CI 0.29-0.67) at a threshold of 1.5 in the antibiotic utilization rate in combination-only strategy, 0.49 (95% CI 0.45-0.53) at a threshold of 0.5 in the inspection rate of bacterial specimens-only strategy, 0.50 (95% CI 0.28-0.71) at a threshold of 1.1 in the positive rate of bacterial specimens-only strategy, 0.63 (95% CI 0.49-0.77) at a threshold of 2.6 in the parallel strategy, and 0.32 (95% CI 0.00-0.65) at a threshold of 0.0 in the series strategy. The warning performance of the parallel strategy was greater than that of the single-indicator strategies when the threshold exceeded 1.5.
CONCLUSIONS: The multisource surveillance of process data in the area network is an effective method for the early detection of HAI clusters. The combination of multisource data and the threshold of the warning model are 2 important factors that influence the performance of the model.

UNASSIGNED: Microbial bio-burden on high-touch surfaces in patient rooms may lead to acquisition of health care-associated infections in acute care hospitals. This study examined the effect of a novel copper-impregnated solid material (16%-20% copper oxide in a polymer-based resin) on bacterial contamination on high-touch surfaces in patient rooms in an acute care hospital.
UNASSIGNED: Five high-touch surfaces were sampled for aerobic bacterial colonies (ABCs) 3 times per day over a 3-day period in 16 rooms with copper installed and 16 rooms with standard noncopper laminate installed on high-touch surfaces. A Bayesian multilevel negative binomial regression model was used to compare ABC plate counts from copper-impregnated surfaces with standard hospital laminate surfaces.
UNASSIGNED: The mean and median (interquartile range [IQR]) ABC counts from copper-impregnated surfaces were 25.5 and 11 (4-27), and for standard hospital laminate surfaces they were 60.5 and 29 (10-74.3). The negative binomial regression model-estimated incidence rate for ABC counts on plates taken from copper-impregnated surfaces was 0.40 (0.21-0.70) times the incidence rate of ABC counts on plates taken from standard hospital laminate surfaces.
UNASSIGNED: Copper-impregnated solid surfaces may reduce the level of microbial contamination on high-touch surfaces in patient rooms in the acute care environment, as our study demonstrated a decline in microbial bio-burden on samples taken from copper-impregnated compared with standard hospital laminate high-touch surfaces.

Psychological safety is a critical factor in team learning that positively impacts patient safety. We sought to examine the influence of psychological safety on using recommended health care-associated infection (HAI) prevention practices within US hospitals.
We mailed surveys to infection preventionists in a random sample of nearly 900 US acute care hospitals in 2017. Our survey asked about hospital and infection control program characteristics, organizational factors, and the use of practices to prevent common HAIs. Hospitals that scored 4 or 5 (5-point Likert scale) on 7 psychological safety questions were classified as high psychological safety. Using sample weights, we conducted multivariable regression to determine associations between psychological safety and the use of select HAI prevention practices.
Survey response rate was 59%. High psychological safety was reported in approximately 38% of responding hospitals, and was associated with increased odds of regularly using urinary catheter reminders or stop-orders and/or nurse-initiated urinary catheter discontinuation (odds ratio, 2.37; P = .002) for catheter-associated urinary tract infection prevention, and regularly using sedation vacation (odds ratio, 1.93; P = .04) for ventilator-associated pneumonia prevention.
We provide a snapshot of psychological safety in US hospitals and how this characteristic influences the use of select HAI prevention practices. A culture of psychological safety should be considered an integral part of HAI prevention efforts.