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Objective:We investigated real-world vaccine effectiveness for Oxford-AstraZeneca (ChAdOx1) and CoronaVac against laboratory-confirmed severe acute respiratory coronavirus virus 2 (SARS-CoV-2) infection among healthcare workers (HCWs).Methods:We conducted a retrospective cohort study among HCWs (aged ≥18 years) working in a private healthcare system in Brazil between January 1, 2021 and August 3, 2021, to assess vaccine effectiveness. We calculated vaccine effectiveness as 1 − rate ratio (RR), with RR determined by adjusting Poisson models with the occurrence of SARS-CoV-2 infection as the outcome and the vaccination status as the main variable. We used the logarithmic link function and simple models adjusting for sex, age, and job types.Results:In total, 13,813 HCWs met the inclusion criteria for this analysis. Among them, 6,385 (46.2%) received the CoronaVac vaccine, 5,916 (42.8%) received the ChAdOx1 vaccine, and 1,512 (11.0%) were not vaccinated. Overall, COVID-19 occurred in 6% of unvaccinated HCWs, 3% of HCWs who received 2 doses of CoronaVac vaccine, and 0.7% of HCWs who received 2 doses of ChAdOx1 vaccine (P < .001). In the adjusted analyses, the estimated vaccine effectiveness rates were 51.3% for CoronaVac, and 88.1% for ChAdOx1 vaccine. Both vaccines reduced the number of hospitalizations, the length of hospital stay, and the need for mechanical ventilation. In addition, 19 SARS-CoV-2 samples from 19 HCWs were screened for mutations of interest. Of 19 samples, 18 were the γ (gamma) variant.Conclusions:Although both COVID-19 vaccines (viral vector and inactivated virus) can significantly prevent COVID-19 among HCWs, CoronaVac was much less effective. The COVID-19 vaccines were also effective against the dominant γ variant.

Data visualization refers to the techniques used to communicate information by encoding it as visual objects (eg, points, lines, or bars) contained in graphics. The recent acceleration in informatics technology has made it possible to obtain and process large amounts of data. Although data visualization can provide insights from large datasets, it can also help simplify messaging, making information more accessible for healthcare stakeholders. The field of data visualization is constantly evolving, and new techniques are frequently being created. However, evidence regarding the best way to visualize data in the fields of infection prevention and antimicrobial stewardship is limited. We provide an overview of data visualization theory and history, as well as recommendations for creating graphs for infection prevention and antimicrobial stewardship.

The global outbreak of invasive Mycobacterium chimaera infections associated with heater-cooler devices (HCDs) presented several important and unique challenges. To mitigate the risk of infection, we removed the HCDs from operating rooms (ORs) at our hospital and since that time (4.5 years ago) we have had no new cases.

Background: Central-line–associated bloodstream infection (CLABSI) rates have steadily decreased as evidence-based prevention bundles were implemented. Bone marrow transplant (BMT) patients are at increased risk for CLABSI due to immunosuppression, prolonged central-line utilization, and frequent central-line accesses. We assessed the impact of an enhanced prevention bundle on BMT nonmucosal barrier injury CLABSI rates. Methods: The University of Iowa Hospitals & Clinics is an 811-bed academic medical center that houses the only BMT program in Iowa. During October 2018, we added 3 interventions to the ongoing CLABSI prevention bundle in our BMT inpatient unit: (1) a standardized 2-person dressing change team, (2) enhanced quality daily chlorhexidine treatments, and (3) staff and patient line-care stewardship. The bundle included training of nurse champions to execute a team approach to changing central-line dressings. Standard process description and supplies are contained in a cart. In addition, 2 sets of sterile hands and a second person to monitor for breaches in sterile procedure are available. Site disinfection with chlorhexidine scrub and dry time are monitored. Training on quality chlorhexidine bathing includes evaluation of preferred product, application per product instructions for use and protection of the central-line site with a waterproof shoulder length glove. In addition to routine BMT education, staff and patients are instructed on device stewardship during dressing changes. CLABSIs are monitored using NHSN definitions. We performed an interrupted time-series analysis to determine the impact of our enhanced prevention bundle on CLABSI rates in the BMT unit. We used monthly CLABSI rates since January 2017 until the intervention (October 2018) as baseline. Because the BMT changed locations in December 2018, we included both time points in our analysis. For a sensitivity analysis, we assessed the impact of the enhanced prevention bundle in a hematology-oncology unit (March 2019) that did not change locations. Results: During the period preceding bundle implementation, the CLABSI rate was 2.2 per 1,000 central-line days. After the intervention, the rate decreased to 0.6 CLABSI per 1,000 central-line days (P = .03). The move in unit location did not have a significant impact on CLABSI rates (P = .85). CLABSI rates also decreased from 1.6 per 1,000 central-line days to 0 per 1,000 central-line days (P < .01) in the hematology-oncology unit. Conclusions: An enhanced CLABSI prevention bundle was associated with significant decreases in CLABSI rates in 2 high-risk units. Novel infection prevention bundle elements should be considered for special populations when all other evidence-based recommendations have been implemented.Funding: NoneDisclosures: None

The learning hospital is distinguished by ceaseless evolution of erudition, enhancement, and implementation of clinical best practices. We describe a model for the learning hospital within the framework of a hospital infection prevention program and argue that a critical assessment of safety practices is possible without significant grant funding. We reviewed 121 peer-reviewed manuscripts published by the VCU Hospital Infection Prevention Program over 16 years. Publications included quasi-experimental studies, observational studies, surveys, interrupted time series analyses, and editorials. We summarized the articles based on their infection prevention focus, and we provide a brief summary of the findings. We also summarized the involvement of nonfaculty learners in these manuscripts as well as the contributions of grant funding. Despite the absence of significant grant funding, infection prevention programs can critically assess safety strategies under the learning hospital framework by leveraging a diverse collaboration of motivated nonfaculty learners. This model is a valuable adjunct to traditional grant-funded efforts in infection prevention science and is part of a successful horizontal infection control program.

BACKGROUNDRecent studies have shown that using no-touch disinfection technologies (ie, ultraviolet light [UVL] or hydrogen peroxide vapor [HPV] systems) can limit the transmission of nosocomial pathogens and prevent healthcare-associated infections (HAIs). To investigate these findings further, we performed a systematic literature review and meta-analysis on the impact of no-touch disinfection methods to decrease HAIs.METHODSWe searched PubMed, CINAHL, CDSR, DARE and EMBASE through April 2017 for studies evaluating no-touch disinfection technology and the nosocomial infection rates forClostridium difficile, methicillin-resistantStaphylococcus aureus(MRSA), vancomycin-resistant enterococci (VRE), and other multidrug-resistant organisms (MDROs). We employed random-effect models to obtain pooled risk ratio (pRR) estimates. Heterogeneity was evaluated with I2estimation and the Cochran Q statistic. Pooled risk ratios forC. difficile, MRSA, VRE, and MDRO were assessed separately.RESULTSIn total, 20 studies were included in the final review: 13 studies using UVL systems and 7 studies using HPV systems. When the results of the UVL studies were pooled, statistically significant reduction insC. difficileinfection (CDI) (pRR, 0.64; 95% confidence interval [CI], 0.49–0.84) and VRE infection rates (pRR, 0.42; 95% CI, 0.28–0.65) were observed. No differences were found in rates of MRSA or gram-negative multidrug-resistant pathogens.CONCLUSIONSUltraviolet light no-touch disinfection technology may be effective in preventing CDI and VRE infection.Infect Control Hosp Epidemiol2018;39:20–31

Background: Manual cleaning is the recommended method of environmental disinfection; it plays a key role in the prevention of healthcare-associated infections. Recently, automated no-touch disinfection technologies, such as ultraviolet (UV) light, have been proposed as a supplement to manual cleaning. However, UV light adds time to the cleaning process and may decrease the quality of manual cleaning. We evaluated the impact of adding UV light on the quality of manual cleaning and on room turnover times. Methods: During January–September 2019, we assessed the thoroughness of disinfection cleaning (TDC) of environmental surfaces in rooms identified for discharge. According to hospital policy, contact precautions rooms use UV light after manual cleaning with an EPA-approved sporicidal agent (bleach). Non–contact precautions rooms are disinfected using quaternary ammonium only. Rooms were identified after patient admission, selected randomly, and marked once discharge orders were placed. Fluorescent markers were applied on high-touch surfaces before discharge and were assessed after the cleaning process was completed. TDC scores were defined as the percentage of cleaned surfaces of the total of examined surfaces. UV-light disinfection time is determined automatically based on room size. We compared TDC scores and manual cleaning times between contact precautions rooms and noncontact precautions rooms. We also calculated UV-light cycle durations. Results: We assessed 2,383 surfaces in 24 contact precautions rooms with UV-light disinfection and 201 noncontact precautions rooms without UV-light disinfection. The TDC score was similar in contact precautions rooms (243 of 273 surfaces) and noncontact precautions rooms (1,835 of 2,110 surfaces; 89% vs 87%). The median manual cleaning time for contact precautions rooms was 56 minutes (IQR, 37–79), and for noncontact precautions rooms the median manual cleaning time was 33 minutes (IQR, 22–43). UV-light use added a median of 49 minutes (IQR, 35–67) to the overall cleaning process. The median turnover time for contact precautions rooms was 156 minutes (IQR, 87–216) versus 58 minutes (IQR, 40–86) in noncontact precautions room. Conclusions: In a setting with an objective assessment of environmental cleaning, there was no difference in quality of manual cleaning between contact precautions rooms (UV light) and noncontact precautions rooms (UV light). Adding UV light following manual disinfection increased the overall cleaning time and delayed room availability.Funding: NoneDisclosures: None

Background: Including infection preventionists (IPs) in hospital design, construction, and renovation projects is important. According to the Joint Commission, “Infection control oversights during building design or renovations commonly result in regulatory problems, millions lost and even patient deaths.” We evaluated the number of active major construction projects at our 800-bed hospital with 6.0 IP FTEs and the IP time required for oversight. Methods: We reviewed construction records from October 2018 through October 2019. We classified projects as active if any construction occurred during the study period. We describe the types of projects: inpatient, outpatient, non–patient care, and the potential impact to patient health through infection control risk assessments (ICRA). ICRAs were classified as class I (non–patient-care area and minimal construction activity), class II (patients are not likely to be in the area and work is small scale), class III (patient care area and work requires demolition that generates dust), and class IV (any area requiring environmental precautions). We calculated the time spent visiting construction sites and in design meetings. Results: During October 2018–October 2019, there were 51 active construction projects with an average of 15 active sites per week. These sites included a wide range of projects from a new bone marrow transplant unit, labor and delivery expansion and renovation, space conversion to an inpatient unit to a project for multiple air handler replacements. All 51 projects were classified as class III or class IV. We visited, on average, 4 construction sites each week for 30 minutes per site, leaving 11 sites unobserved due to time constraints. We spent an average of 120 minutes weekly, but 450 minutes would have been required to observe all 15 sites. Yearly, the required hours to observe these active construction sites once weekly would be 390 hours. In addition to the observational hours, 124 hours were spent in design meetings alone, not considering the preparation time and follow-up required for these meetings. Conclusions: In a large academic medical center, IPs had time available to visit only a quarter of active projects on an ongoing basis. Increasing dedicated IP time in construction projects is essential to mitigating infection control risks in large hospitals.Funding: NoneDisclosures: None

Background: Central lines (CL) are widely used in the inpatient setting and central-line–associated bloodstream infection (CLABSI) is a serious complication of CL use. Because CL insertion site inflammation (ISI) may precede the onset of CLABSI, we aimed to define ISI, to determine whether ISI was associated with CLABSI, and to develop an automated surveillance system for ISI. Methods: We extracted electronic medical records (EMRs) of adult patients hospitalized at the University of Iowa Hospitals & Clinics during January 2015–December 2018. Nurses routinely document CL insertion-site characteristics in specifically designed flow sheets in the EMR. An ISI was counted every time ≥1 of the following signs were documented during CL assessments: edema, erythema, induration, tenderness, or drainage. A 1:2 case-control investigation was performed by matching nonmucosal barrier injury (non-MBI) CLABSI patients (cases) to patients without a CLABSI diagnosis (controls). We matched for age (±10 years), sex, date (±30 days), inpatient unit, central-line days, and central-line type (temporary vs permanent). The main exposure of interest was having an ISI on or before CLABSI onset. CLABSIs were determined using CDC NHSN definitions. We then created a metric: ISI days (defined as the number of days with ≥1 ISI documented) and plotted ISI incidence (ISI days per central-line days) to quantify the burden of ISIs and to determine whether ISI and non-MBI CLABSI incidences were collinear. An automated surveillance system for ISI was created using structured query language queries to the EMR data repository and Tableau software. Results: During 2015–2018, we detected 194 CLABSI cases that were matched to 338 controls. CLABSI patients had greater odds of having an ISI (OR, 2.3; 95% CI, 1.3–4.0). Over the study period, ISI incidence decreased from ~80 to ~50 ISI days per 1,000 CL days. Non-MBI CLABSI rates also decreased from ~1.5 to ~1.0 CLABSIs per 1,000 CL days. Conclusions: ISI incidence is associated with non-MBI CLABSI incidence. Because ISI incidence is higher than CLABSI incidence, surveillance for ISI could be a more sensitive indicator for monitoring the impact of CLABSI prevention practices. Automated surveillance for novel process metrics is a promising infection prevention tool.Funding: NoneDisclosures: None

Background: The CDC recently updated recommendations on tuberculosis (TB) screening in healthcare facilities, suggesting the discontinuation of annual TB screening. However, hospitals may opt to continue based on their local TB epidemiology. We assessed TB infection control parameters in our facility to guide the implementation of the new CDC recommendations. Methods:We retrieved data for patients with an International Classification of Disease, Tenth Revision (ICD-10) code for TB treated at the University of Iowa Hospitals and Clinics during 2016–2019. We supplemented our search with microbiology data: culture or PCR for Mycobacterium tuberculosis. Based on manual chart review, we adjudicated each patient as active TB, latent TB, previously treated TB, unclear history, or no TB. We further labeled active TB cases based on their risk of transmission (pulmonary or extrapulmonary cases that underwent an aerosol generating procedure). We then calculated the number of exposure events associated with those patients and tuberculin skin test (TST) conversion rates among the exposed. Results: During 2016–2019, we identified 197 patients based on ICD-10 codes. In total, 10 additional patients were detected by microbiology data review. Of these 207 patients, 48 (23.2%) had active TB: lung, n = 24 (50%); lymph node, n = 9 (19%); bone or spine, n = 5 (10%); eye, n = 3 (6%); disseminated, n = 2 (4%); pleura, n = 2 (4%); skin abscess, n = 2 (4%); and meningitis, n = 1 (2%). Of the 24 pulmonary patients, 6 (25%) had either a positive smear or a cavity on imaging. In total, 159 patients were excluded: no TB, n = 22 (14%); latent TB, n = 27 (17%); old or treated TB, n = 93 (58%); and unclear history, n = 9 (6%). Of the 48 cases with active TB, 31 (65%) were deemed potentially infectious. Also, 10 cases (32%) led to the exposure of 204 healthcare workers (HCWs). Baseline and postexposure TST were available for 179 HCWs (88%); 72 (35%) followed up in the employee health clinic within the 8–12 weeks after exposure. Of 161 HCWs with a negative TST at baseline, no conversions occurred. Of 18 HCWs with positive TST at baseline, no HCW developed symptoms during the observation period. Conclusions: Nearly one-third of infectious TB cases led to HCW exposures in a low-incidence setting. However, no TST conversions or active TB infections were seen. Exposure and conversion rates are useful indicators of TB infection control in healthcare facilities and may help guide implementation of the new CDC TB control recommendations.Funding: NoneDisclosures: None

Background: Candidemia is a leading cause of bloodstream infections (BSIs), and community-onset candidemia is being recognized as a public health problem. In the era of electronic health records (EHRs), we can use machine learning to detect patterns in patient data that may predict infections. Objective: We aimed to predict community-onset candidemia in patients admitted to the University of Iowa Hospital & Clinics (UIHC) using machine-learning algorithms. Methods: We retrospectively reviewed data for patients admitted to UIHC during 2015–2018. All adult inpatients who had a requested blood culture were included. Candidemia was defined as a blood culture positive for Candida within 48 hours after admission. Variables of interest were extracted from the EHR: age, sex, body mass index, and month of admission. We also included comorbidities upon admission defined by the International Classification of Diseases, 10th Revision, Clinical Modification (ICD-10-CM): cardiovascular diseases, neurological disorders, chronic pulmonary disease, dementia, rheumatoid disease, peptic ulcer disease, liver disease, diabetes mellitus, hypothyroidism, renal failure, coagulopathy, obesity, weight loss, fluid and electrolyte disorders, anemia, alcohol abuse, drug abuse, psychiatric diseases, malignancy, and HIV/AIDS. We calculated Charlson and Elixhauser scores based on ICD-10-CM codes. We also included prehospitalization conditions (90 days before admission): Candida-positive cultures from sites other than blood, antibiotics/antifungals, hemodialysis, central lines, corticosteroids, surgeries, and intensive care unit (ICU) admissions. Mode and median imputation were used for missing information. Random forests with resampled training sets were used for prediction, and results were evaluated using 10-fold cross validation. Results: In total, 30,528 adult admissions were extracted; 73 admissions had an episode of candidemia (Candida from sites other than blood, use of a central line, and recent use of antibiotics/antifungals. The model’s area under the receiver operating characteristic curve was 0.72. Conclusions: Preadmission patient characteristics predicted community-onset candidemia. Machine-learning models may help detect patients eligible for screening for candidemia and prompt empiric antifungal therapy in high-risk patients in the first 48 hours of their admission.Funding: NoneDisclosures: None

The performance of a hospital- and community-onset Clostridium difficile infection definition using administrative data with a present-on-admission indicator was compared with definitions using clinical surveillance. For hospital-onset C. difficile infection, there was moderate sensitivity (68%) and high specificity (93%); for community-onset, sensitivity and specificity were high (both 85%).Infect Control Hosp Epidemiol 2015;36(7):838–840

Objective.To evaluate the impact of state laws on reporting of healthcare-associated infections on central line-associated bloodstream infection (CLABSI) rates.Design.Retrospective, cross-sectional study.Methods.Hospital-level administrative and Hospital Compare data were collected on University HealthSystem Consortium hospitals. An ordered probit regression model assessed the association between state legislation and CLABSI standardized infection ratio (SIR). The main independent variable was a state legislation variable concerning 3 legal requirements (data submission, reporting of data to the public, inclusion of facility identifiers in public reports) and was coded for hospitals accordingly located in a state that did not have CLABSI reporting, located in a state that had CLABSI reporting legislation and met 3 legal requirements, or located in a state that had CLABSI reporting but did not meet the 3 legal requirements. A secondary analysis ascertained whether the mean state SIR values differed among the 3 legislation groups.Results.There were 159 hospitals included; 92 were located in states that had CLABSI reporting and met 3 requirements, 33 were located in states that had reporting but did not meet the 3 requirements, and 34 were in states that had no legislation. There was no effect of state legislation group on CLABSI SIR. There were no significant differences in the mean state CLABSI SIRs among the legislation groups.Conclusions.In this sample of academic medical centers, there was no evidence of an effect of state HAI laws on CLABSI occurrence. The impact of state legislation may be lessened by other CLABSI prevention initiatives.

Objective.To compare the efficacy of universal gloving with emollient-impregnated gloves with standard contact precautions for the control of multidrug-resistant organisms (MDROs) and to measure the effect on healthcare workers' (HCWs') hand skin health.Design.Prospective before-after trial.Setting.An 18-bed surgical intensive care unit.Methods.During phase 1 (September 2007 through March 2008) standard contact precautions were used. During phase 2 (March 2008 through September 2008) universal gloving with emollient-impregnated gloves was used, and no contact precautions. Patients were screened for vancomycin-resistantEnterococcus(VRE) and methicillin-resistantStaphylococcus aureus(MRSA). HCW hand hygiene compliance and hand skin health and microbial contamination were assessed. The incidences of device-associated infection andClostridium difficileinfection (CDI) were determined.Results.The rate of compliance with contact precautions (phase 1) was 67%, and the rate of compliance with universal gloving (phase 2) was 78% (P= .01). Hand hygiene compliance was higher during phase 2 than during phase 1 (before patient care, 40% vs 35% of encounters;P= .001; after patient care, 63% vs 51% of encounters;P< .001). No difference was observed in MDRO acquisition. During phases 1 and 2, incidences of device-related infections, in number of infections per 1,000 device-days, were, respectively, 3.7 and 2.6 for bloodstream infection (P= .10), 8.9 and 7.8 for urinary tract infection (P= .10), and 1.0 and 1.1 for ventilator-associated pneumonia (P= .09). The CDI incidence in phase 1 and in phase 2 was, respectively, 2.0 and 1.4 cases per 1,000 patient-days (P= .53). During phase 1, 29% of HCW hand cultures were MRSA positive, compared with 13% during phase 2 (P= .17); during phase 1, 2% of hand cultures were VRE positive, compared with 0 during phase 2 (P= .16). Hand skin health improved during phase 2.Conclusions.Compared with contact precautions, universal gloving with emollient-impregnated gloves was associated with improved hand hygiene compliance and skin health. No statistically significant change in the rates of device-associated infection, CDI, or patient MDRO acquisition was observed. Universal gloving may be an alternative to contact precautions.

Although isolated cases of malaria still may be acquired in the United States,’ imported cases in international travelers are primarily responsible for approximately 1,000 cases reported annually to the Centers for Disease Control and Prevention (CDC). In the decade ending in 1988, more than 50 malaria-associated deaths were reported, most of them theoretically preventable. Moreover, it is estimated that only 30% to 60% of malaria cases are actually reported to the CDC.In recent years, an increase in infections due toPlasmodium falciparum, which accounts for the most severe form of malaria, has been noted. This increase is multifactorial in origin and is associated with an increase in travel to malarious areas, difficulties with chemoprophylaxis (including compliance and adverse effects), and increasing resistance to multiple antimalarial agents. Thus, careful counseling of the traveler and appropriate chemoprophylaxis remain vitally important.

Nosocomial infections remain a significant problem for the healthcare system in the United States. At least 5% of patients will acquire an infection during their hospital stay, extending hospitalization by four days per infection, directly accounting for an estimated 60,000 deaths per year and an additional $10 billion of the amount spent on healthcare.Previous studies have demonstrated that pathogenic bacteria can be recovered from the hands of healthcare workers and transmitted to patients. Moreover, a recent study has shown that hand carriage of organisms and subsequent contact may be the most important mechanism of nosocomial infection transmission in the intensive care unit setting.

Needle recapping has been shown to be one of the leading causes of needlestick injuries. Frequency of recapping has not been reported. This study was designed to determine the frequency of needle recapping by nursing personnel and the effect of bedside needle disposal units on the frequency of recapping and needlesticks. Seventy-four nurses carrying out 312 activities involving use of needles were observed. The subjects were not aware of the nature of the study. The recapping frequency was 93.9%. The study was repeated after educational programs and following installation of a hospital-wide bedside needle disposal system. Fifty-three nurses performing 151 activities with needles were observed. Frequency of recapping was 94%. There was no significant difference in the rate of recapping or needlestick injuries after installation of the new needle disposal system. Educational programs regarding recapping, a very common practice, may be ineffective. Alternate methods for preventing nee-dlesticks may be necessary.