bluuebunny/crossref_metadata_2025_split huggingface dataset:
Utilizing a Risk-Based Criteria Framework to Identify Infection Prevention and Control Risks in Australian Dental Settings
Authors: Robb Kylie 
Published: November 2020

Background: A dental practitioner must comply with the Dental Board of Australia’s guidelines on infection control. In this project, we developed a risk-based criteria framework to assess a practitioner’s infection prevention and control (IPC) systems and processes. This project allowed for the provision of the highest standard of infection control continuing education and advice relevant to the needs of members of the Australian Dental Association (NSW Branch). Methods: A review of 1,050 continuing professional development (CPD) IPC course evaluation forms was conducted to determine the key IPC areas that participants have the most difficulty with. All core IPC documents that practitioners are expected to understand and comply with were determined and any regulator- and profession-led compliance checklists were collated. These data were consolidated to generate a risk-based criteria framework that was then applied to 99 private, office-based, dental practices to determine IPC compliance. Results: After the review of 99 dental practices, the total aggregate compliance score was 78%, and the 15 key IPC areas were ranked from highest to lowest. These data assisted with the development of a full-day IPC course focusing on the top 5 risks in each category. The five areas of opportunity identified were Hand Hygiene (52%), Surgical Procedures and Aseptic Technique (59%), Documentation, Policy and Knowledge (61%), Sharps (72%), Steam Sterilisers (72%). Conclusions: This project identified key IPC risks for office-based dental practices from the capture of performance-based data. This data formed a targeted education framework that prioritized areas of opportunity to improve IPC standards in Australian dental practices.Funding: NoneDisclosures: None
Variability and Trends in Blood Culture Utilization, US Hospitals, 2012–2017
Authors: Hatfield Kelly , McCarthy Natalie , Reddy Sujan , Baggs James , Epstein Lauren , Kazakova Sophia , Olubajo Babatunde , Wolford Hannah , Jernigan John 
Published: November 2020

Background: Microbiology data are utilized to quantify epidemiology and trends in pathogens, antimicrobial resistance, and bloodstream infections. Understanding variability and trends in rates of hospital-level blood culture utilization may be important for interpreting these findings. Methods: We used clinical microbiology results and discharge data to identify monthly blood culture rates from US hospitals participating in the Premier Healthcare Database during 2012–2017. We included all discharges from months where a hospital reported at least 1 blood culture with microbiology and antimicrobial susceptibility results. Blood cultures drawn on or before day 3 were defined as admission cultures (ACs); blood cultures collected after day 3 were defined as a postadmission cultures (PACs). The AC rate was defined as the proportion of all hospitalizations with an AC. The PAC rate was defined as the number of days with a PAC among all patient days. Generalized estimating equation regression models that accounted for hospital-level clustering with an exchangeable correlation matrix were used to measure associations of monthly rates with hospital bed size, teaching status, urban–rural designation, region, month, and year. The AC rates were modeled using logistic regression, and the PAC rates were modeled using a Poisson distribution. Results: We included 11.7 million hospitalizations from 259 hospitals, accounting for nearly 52 million patient days. The median annual hospital-level AC rate was 27.1%, with interhospital variation ranging from 21.1% (quartile 1) to 35.2% (quartile 3) (Fig. 1). Multivariable models revealed no significant trends over time (P = .74), but statistically significant associations between AC rates with month (P < .001) and region (P = .003), associations with teaching status (P = .063), and urban-rural designation (P = .083) approached statistical significance. There was no association with bed size (P = .38). The median annual hospital-level PAC rate was 11.1 per 1,000 patient days, and interhospital variability ranged from 7.6 (quartile 1) to 15.2 (quartile 3) (Fig. 2). Multivariable models of PAC rates showed no significant trends over time (P = .12). We found associations between PAC rates with month (P = .016), bed size (P = .030), and teaching status (P = .040). PAC rates were not associated with urban–rural designation (P = .52) or region (P = .29). Conclusions: Blood culture utilization rates in this large cohort of hospitals were unchanged between 2012 and 2017, though substantial interhospital variability was detected. Although both AC and PAC rates vary by time of year and potentially by teaching status, AC rates vary by geographic characteristics whereas PAC rates vary by bed size. These factors are important to consider when comparing rates of bloodstream infections by hospital.Funding: NoneDisclosures: None
Verification of Healthcare Personnel Immunity as a Strategy for Measles Preparedness
Authors: Snyders Rachael , Babcock Hilary , Blank Christopher 
Published: November 2020

Background: Immunization resistance is fueling a resurgence of vaccine-preventable diseases in the United States, where several large measles outbreaks and 1,282 measles cases were reported in 2019. Concern about these measles outbreaks prompted a large healthcare organization to develop a preparedness plan to limit healthcare-associated transmission. Verification of employee rubeola immunity and immunization when necessary was prioritized because of transmission risk to nonimmune employees and role of the healthcare personnel in responding to measles cases. Methods: The organization employs ∼31,000 people in diverse settings. A multidisciplinary team was formed by infection prevention, infectious diseases, occupational health, and nursing departments to develop the preparedness plan. Immunity was monitored using a centralized database. Employees without evidence of immunity were asked to provide proof of vaccination, defined by the CDC as 2 appropriately timed doses of rubeola-containing vaccine, or laboratory confirmation of immunity. Employees were given 30 days to provide documentation or to obtain a titer at the organization’s expense. Staff with negative titers were given 2 weeks to coordinate with the occupational heath department for vaccination. Requests for medical or religious accommodations were evaluated by occupational heath staff, the occupational heath medical director, and the human resources department. All employees were included, though patient-interfacing employees in departments considered higher risk were prioritized. These areas were the emergency, dermatology, infectious diseases, labor and delivery, obstetrics, and pediatrics departments. Results: At the onset of the initiative in June 2019, 4,009 employees lacked evidence of immunity. As of November 2019, evidence of immunity had been obtained for 3,709 employees (92.5%): serological evidence of immunity was obtained for 2,856 (71.2%), vaccine was administered to 584 (14.6%), and evidence of previous vaccination was provided by 269 (6.7%). Evidence of immunity has not been documented for 300 (7.5%). The organization administered 3,626 serological tests and provided 997 vaccines, costing ∼$132,000. Disposition by serological testing is summarized in Table 1. Conclusions: A measles preparedness strategy should include proactive assessment of employees’ immune status. It is possible to expediently assess a large number of employees using a multidisciplinary team with access to a centralized database. Consideration may be given to prioritization of high-risk departments and patient-interfacing roles to manage workload.Funding: NoneDisclosures: None
You Cannot Manage What You Cannot Measure—Developing Central Sterilizing Performance Metrics
Authors: Stout Savanna , Helms Angela , Murphy Rebecca 
Published: November 2020

Background: Metrics are critical to healthcare, particularly in areas that are highly process oriented. Our hospital’s central sterilizing department functions on standardized processes that have almost no industry specific measurements. Furthermore, the department had no formal internal method for process performance evaluation. A multiphase 6 Sigma DMAIC project was initiated in February 2019 in the central sterilizing department, with ‘phase 1’ being the development of performance metrics to evaluate current state and track future improvement. Methods: A DMAIC team was formed and completed a data inventory that included identifying all existing data sources pertaining to central sterilizing. Sources identified as reliable included count of daily surgical cases, counts of defects based on surgical services communication logs, number of surgical trays processed in the central sterilizing department, and the number of unsterile trays in the department at the start of each 7:00 a.m. shift. Results: A multidisciplinary team including surgeons and senior leadership formed the DMAIC team. Using identified data sources and input from frontline staff and leadership, official metric definitions were created. Weekly defect rates were chosen as a measurement and translated into the number of ready-for-use surgical trays at the time of case setup for every 100 surgical cases. Data from November 2018 through January 20, 2019, demonstrated the mean ready-for-use surgical trays at the time of case setup: for every 100 surgical cases, 96 the surgical trays was ready for use. Further analysis of the data over time demonstrated that variability was out of control. Defects were stratified into 12 categories. Moreover, 6 defects were creating 80% of the issues in trays not being ready: ‘hole in wrapper,’ ‘biohazardous debris,’ ‘nonbiohazardous debris,’ ‘missing instrument,’ ‘incorrect/mislabeled instrument trays,’ and ‘missing indicators.’ Results for unsterile trays at 7:00 a.m. showed that, on average, the central sterilizing department had 100 or more trays at the start of each 7:00 a.m. shift. Conclusions: Without a formal method for data collection, defined metrics, and ongoing analysis, evaluation of performance is based on anecdotal conclusions resulting in missed opportunities for improvement and, subsequently, opportunities for improved patient safety. Regularly published visible dashboards provide a true picture of performance, allowing staff to identify unwanted variation and to put interventions in place so that future variation is better controlled and or prevented. Additionally, having solid metrics, even when no industry ones exist, aid in measuring the effectiveness of improvement efforts, such as the DMAIC project, as they move into subsequent phases.Funding: NoneDisclosures: None
Evaluation of Autoclaving CAT: A Infectious Substance Packaging during the Ebola Crisis in the United States
Authors: Krisiunas Edward , Vallone-Hood Gina , Woodard Alan , Norrell Neil 
Published: November 2020

Background: During the 2014 US Ebola crisis, an unprecedented amount of biohazardous waste was generated during patient care; healthcare facilities were overwhelmed by large volumes of waste. Few facilities had on-site waste treatment; therefore, waste was packaged and sent off site for incineration and disposal in Port Arthur, Texas, at a significant cost. Shipping this waste off site required the use of the US Department of Transportation (USDOT) Hazardous Materials (HMR 49 CFR) category A triple packaging for infectious substances. The most common treatment method for biohazardous waste in the United States is via commercial autoclaves. Because Category A waste packaging had not been tested to ensure effective treatment, we conducted autoclave efficacy studies to evaluate the various types of Category A packaging containing surrogate Ebola waste. If successful, this would potentially provide additional treatment options in the United States. Methods: Testing was conducted at commercial locations in 3 states: New York, Pennsylvania, and Florida. Various types of Category A packaging were obtained (Fig. 1). Waste loads were comprised of Ebola patient treatment material and included personal protective equipment, sharps containers, suction canisters, drapes, and associated items. Configured packaging was placed into autoclave bins to be processed. Each package tested included a biological indicator, a class 5 integrator. Where possible, thermocouples were added to record the thermoprofile of the waste. Initially, a modified cycle was tested (a prevacuum cycle followed by exposure to steam at 138°C for 60 minutes) and a postvacuum cycle. Cycle times were adjusted based upon initial results. Results: The initial New York autoclave was tested from a cold start (no vessel preheating) resulting in a failure to obtain efficacy (Fig. 1). Successful results were no growth in recovered biological indicators, acceptable color change in integrators, and reaching and holding temperatures >121°C for 30 minutes. After making modifications to treatment cycles, which included preheating of vessels, multiple prevacuum steps and a hold time of 60 minutes, successful results were achieved in testing conducted at the treatment plants in Pennsylvania and Florida. Conclusions: Commercial autoclaves can be effective in treating Category A biohazardous waste. Each autoclave considered for treatment requires a validation process using the types of packaging containing the waste, biological indicators, integrators, and thermocouples to present a complete assessment of the treatment process.Funding: Three companies provided support for this study: Approved Storage & Waste Hauling, Daniels International, and ProMed Solutions.Disclosures: Edward Krisiunas reports contracted research for Future Health Care Systems, Daniels Sharpsmart, and ProMed Solutions.
Hand Hygiene in the Era of Big Data: We Can Now See What We Have Been Missing
Authors: DiGiorgio Megan , Moore Lori , Robbins Greg , Parker Albert , Arbogast James 
Published: November 2020

Background: Hand hygiene (HH) has long been a focus in the prevention of healthcare-associated infections. The limitations of direct observation, including small sample size (often 20–100 observations per month) and the Hawthorne effect, have cast doubt on the accuracy of reported compliance rates. As a result, hospitals are exploring the use of automated HH monitoring systems (AHHMS) to overcome the limitations of direct observation and to provide a more robust and realistic estimation of HH behaviors. Methods: Data analyzed in this study were captured utilizing a group-based AHHMS installed in a number of North American hospitals. Emergency departments, overflow units, and units with <1 year of data were excluded from the study. The final analysis included data from 58 inpatient units in 10 hospitals. Alcohol-based hand rub and soap dispenses HH events (HHEs) and room entries and exits (HH opportunities (HHOs) were used to calculate unit-level compliance rates. Statistical analysis was performed on the annual number of dispenses and opportunities using a mixed effects Poisson regression with random effects for facility, unit, and year, and fixed effects for unit type. Interactions were not included in the model based on interaction plots and significance tests. Poisson assumptions were verified with Pearson residual plots. Results: Over the study period, 222.7 million HHOs and 99 million HHEs were captured in the data set. There were an average of 18.7 beds per unit. The average number of HHOs per unit per day was 3,528, and the average number of HHEs per unit per day was 1,572. The overall median compliance rate was 35.2 (95% CI, 31.5%–39.3%). Unit-to-unit comparisons revealed some significant differences: compliance rates for medical-surgical units were 12.6% higher than for intensive care units (P < .0001). Conclusions: This is the largest HH data set ever reported. The results illustrate the magnitude of HHOs captured (3,528 per unit per day) by an AHHMS compared to that possible through direct observation. It has been previously suggested that direct observation samples between 0.5% to 1.7% of all HHOs. In healthcare, it is unprecedented for a patient safety activity that occurs as frequently as HH to not be accurately monitored and reported, especially with HH compliance as low as it is in this multiyear, multicenter study. Furthermore, hospitals relying on direct observation alone are likely insufficiently allocating and deploying valuable resources for improvement efforts based on the scant information obtained. AHHMSs have the potential to introduce a new era in HH improvement.Funding: GOJO Industries, Inc., provided support for this study.Disclosures: Lori D. Moore and James W. Arbogast report salary from GOJO.
Accuracy of the NHSN Central-Line–Associated Bloodstream Infections (CLABSIs) Definition: The Experience of Two Geographically Proximal Hospitals
Authors: Muto Carlene , Bailey Pamela Louise , Patrick Amie , Rittmann Barry John , Pryor Rachel , Cooper Kaila , Doll Michelle Elizabeth , Stevens Michael , Bearman Gonzalo 
Published: November 2020

Background: Central-line–associated blood stream infections (CLABSIs) are linked with significant morbidity and mortality. A NHSN laboratory-confirmed bloodstream infection (LCBSI) has specific criteria to ascribe an infection to the central line or not. The criteria used to associate the pathogen to another site are restrictive. This objective to better classify CLABSIs using enhanced criteria to gain a comprehensive understanding of the error so that appropriate reduction efforts are utilized. Methods: We conducted a retrospective review of medical records with NHSN-identified CLABSI from July 2017 to December 2018 at 2 geographically proximate hospitals. Trained infectious diseases personnel from tertiary-care academic medical centers, the University of Virginia Health System, a 600-bed medical center in Charlottesville, Virginia, and Virginia Commonwealth University Health System with 865 beds in Richmond, Virginia, reviewed charts. We defined “overcaptured” or O-CLABSI into different categories: O-CLABSI-1 is bacteremia attributable to a primary infectious source; O-CLABSI-2 is bacteremia attributable to neutropenia with gastrointestinal translocation not meeting mucosal barrier injury criteria; O-CLABSI-3 is a positive blood culture attributable to a contaminant; and O-CLABSI-4 is a patient injecting line, though not officially documented. Descriptive analyses were performed using the χ2 and the Fisher exact tests. Results: We found a large number of O-CLABSIs on chart review (79 of 192, 41%). Overall, 56 of 192 (29%) LCBSIs were attributable to a primary infectious source not meeting NHSN definition. O-CLABSI proportions between the 2 hospitals were statistically different; hospital A identified 34 of 59 (58%) of their NHSN-identified CLABSIs as O-CLABSIs, and hospital B identified a 45 of 133 (34%) as O-CLABSIs (P = .0020) (Table 1). When comparing O-CLABSI types, hospital B had a higher percentage of O-CLABSI-1 compared to hospital B: 76% versus 64%. Hospital A had a higher proportion of O-CLABSI-2: 21 versus 7%. Hospitals A and B had similar proportion of O-CLABSI-3: 15% versus 18%. These values were all statistically significant (P < .0001). Discussions: The results of these 2 geographically proximate systems indicate that O-CLABSIs are common. Attribution can vary significantly between institutions, likely depending on differences in incidence of true CLABSI, patient populations, protocols, and protocol compliance. These findings have implications for interfacility comparisons of publicly reported data. Most importantly, erroneous attribution can result in missed opportunity to direct patient safety efforts to the root cause of the bacteremia and could lead to inappropriate treatment.Funding: NoneDisclosures: Michelle Doll, Research Grant from Molnlycke Healthcare
Estimating the Contribution of a Contaminated Wheelchair to Pathogen Spread With an Agent-Based Model
Authors: Wilson Amanda , Donskey Curtis , Verhougstraete Marc , Reynolds Kelly 
Published: November 2020

Background: Wheelchairs can contribute to healthcare-associated infection transmission due to direct contact with patients and healthcare workers and due to wide spatial movement in facilities. Objective: We utilized location data of a wheelchair to inform an agent-based model for estimating the contribution of a single contaminated patient ride in a wheelchair to subsequent environmental contamination and to estimate the potential for wheelchair disinfection between patients to disrupt this spread. Methods: The destination and origin of wheelchairs were tracked in several facility locations: specialty care services, long-term care, radiology, acute care, common spaces, domiciliary, and outpatient clinics. An agent-based model was developed in which the probability of the wheelchair traveling directly from one location to another was informed by wheelchair origin and destination data. We assumed that the first patient’s hands were contaminated with methicillin-resistant Staphylococcus aureus (MRSA). For each patient trip, each simulated patient made contact with the wheelchair arm rests and a surface in the destination location. To evaluate potential exposures of uninfected patients, all patients riding in the wheelchair after the contaminated patient were assumed to be uncontaminated. In total, 50 patient rides were simulated. The concentration and number of contaminated surfaces in each hospital area were compared in addition to the average concentration of MRSA on patient hands over time. The intervention simulation involved a disinfection of wheelchair armrests with 90%, 70%, or 50% efficacy. Results: The 3 areas that had the largest estimated number of contaminated surfaces after 50 wheelchair trips following the first patient assumed to be infected were specialty care services, long-term care, and acute care. This finding was consistent with the paths that were most frequented by the wheelchair. Without cleaning between patients, the fiftieth patient to use the wheelchair had an average MRSA concentration of 41.5 CFU/cm2. With cleaning between patients, assuming a 50% cleaning efficacy, average MRSA concentration on the hands for the fiftieth patient was reduced to 7.4 ×10-14 CFU/cm2. Conclusions: We have demonstrated that cleaning, even with efficacies as low as 50%, may protect patients using contaminated wheelchairs from potential pathogen exposures. This study also demonstrates that tracking portable equipment can be useful not only for exposure modeling but also for predicting where the largest number of surfaces contaminated via portable equipment routes may be found. Future steps include performing a sensitivity analysis to evaluate the influence of spatial assumptions.Funding: NoneDisclosures: None