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We describe the association between job roles and coronavirus disease 2019 (COVID-19) among healthcare personnel. A wide range of hazard ratios were observed across job roles. Medical assistants had higher hazard ratios than nurses, while attending physicians, food service workers, laboratory technicians, pharmacists, residents and fellows, and temporary workers had lower hazard ratios.
We describe COVID-19 cases among nonphysician healthcare personnel (HCP) by work location. The proportion of HCP with coronavirus disease 2019 (COVID-19) was highest in the emergency department and lowest among those working remotely. COVID-19 and non–COVID-19 units had similar proportions of HCP with COVID-19 (13%). Cases decreased across all work locations following COVID-19 vaccination.
Background: Whether working on COVID-19 designated units put healthcare workers (HCWs) at higher risk of acquiring COVID-19 is not fully understood. We report trends of COVID-19 incidence among nonphysician HCWs and the association between the risk of acquiring COVID-19 and work location in the hospital. Methods: The University of Iowa Hospitals & Clinics (UIHC) is an 811-bed, academic medical center serving as a referral center for Iowa. We retrospectively collected COVID-19–associated data for nonphysician HCWs from Employee Health Clinic between June 1st 2020 and July 31th 2021. The data we abstracted included age, sex, job title, working location, history of COVID-19, and date of positive COVID-19 test if they had a history of COVID-19. We excluded HCWs who did not have a designated working location and those who worked on multiple units during the same shift (eg, medicine resident, hospitalist, etc) to assess the association between COVID-19 infections and working location. Job titles were divided into the following 5 categories: (1) nurse, (2) medical assistant (MA), (3) technician, (4) clerk, and (5) others (eg patient access, billing office, etc). Working locations were divided into the following 6 categories: (1) emergency department (ED), (2) COVID-19 unit, (3) non–COVID-19 unit, (4) Clinic, (5) perioperative units, and (6) remote work. Results: We identified 6,971 HCWs with work locations recorded. During the study period, 758 HCWs (10.8%) reported being diagnosed with COVID-19. Of these 758 COVID-19 cases, 658 (86.8%) were diagnosed before vaccines became available. The location with the highest COVID-19 incidence was the ED (17%), followed by both COVID-19 and non–COVID-19 units (12.7%), clinics (11.0%), perioperative units (9.4%) and remote work stations (6.6%, p Conclusions: Strict and special infection control strategies may be needed for HCWs in the ED, especially where vaccine uptake is low. The administrative control of HCWs working remotely may be associated with a lower incidence of COVID-19. Given that the difference in COVID-19 incidence among HCWs by location was lower and comparable after the availability of COVID-19 vaccines, facilities should make COVID-19 vaccination mandatory as a condition of employment for all HCWs, especially in areas where the COVID-19 incidence is high.
Background: The IDSA has a clinical definition for catheter-related bloodstream infection (CRBSI) that requires ≥1 set of blood cultures from the catheter and ≥1 set from a peripheral vein. However, because blood cultures obtained from a central line may represent contamination rather than true infection, many institutions discourage blood cultures from central lines. We describe blood culture ordering practices in patients with a central line. Methods: The University of Iowa Hospitals & Clinics is an academic medical center with 860 hospital beds. We retrospectively collected data for blood cultures obtained from adult patients (aged ≥18 years) in the emergency department or an inpatient unit during 2020. We focused on the first blood cultures obtained during each admission because they are usually obtained before antibiotic initiation and are the most important opportunity to diagnose bacteremia. We classified blood-culture orders as follows: CRBSI workup, non-CRBSI sepsis workup, or incomplete workup. We defined CRBSI workup as ≥1 blood culture from a central line and ≥1 peripheral blood culture (IDSA guidelines). We defined non-CRBSI sepsis workup as ≥2 peripheral blood cultures without cultures from a central line because providers might have suspected secondary bacteremia rather than CRBSI. We defined incomplete workup as any order that did not meet the CRBSI or non-CRBSI sepsis workup. This occurred when only 1 peripheral culture was obtained or when ≥1 central-line culture was obtained without peripheral cultures. Results: We included 1,150 patient admissions with 4,071 blood cultures. In total, 349 patient admissions with blood culture orders (30.4%) met CRBSI workup. 62.8% were deemed non-CRBSI sepsis workup, and 6.9% were deemed an incomplete workup. Stratified by location, ICUs had the highest percentage of orders with incomplete workups (8.8%), followed by wards (7.2%) and the emergency department (5.1%). In total, 204 patient admissions had ≥1 positive blood culture (17.7%). The most frequently isolated organisms were Staphylococcus epidermidis (n = 33, 16.2%), Staphylococcus aureus (n = 16, 7.8%), and Escherichia coli (n = 15, 7.4%) Conclusions: Analysis of blood culture data allowed us to identify units at our institute that were underperforming in terms of ordering the necessary blood cultures to diagnose CRBSI. Being familiar with CRBSI guidelines as well as decreasing inappropriate ordering will help lead to early and proper diagnosis of CRBSI which can reduce its morbidity, mortality, and cost.
We analyzed blood-culture practices to characterize the utilization of the Infectious Diseases Society of America (IDSA) recommendations related to catheter-related bloodstream infection (CRBSI) blood cultures. Most patients with a central line had only peripheral blood cultures. Increasing the utilization of CRBSI guidelines may improve clinical care, but may also affect other quality metrics.
We described the epidemiology of bat intrusions into a hospital and subsequent management of exposures during 2018–2020. Most intrusions occurred in older buildings during the summer and fall months. Hospitals need bat intrusion surveillance systems and protocols for bat handling, exposure management, and intrusion mitigation.
Background: COVID-19 in hospitalized patients may be the result of community acquisition or in-hospital transmission. Molecular epidemiology can help confirm hospital COVID-19 transmission and outbreaks. We describe large COVID-19 clusters identified in our hospital and apply molecular epidemiology to confirm outbreaks. Methods: The University of Iowa Hospitals and Clinics is an 811-bed academic medical center. We identified large clusters involving patients with hospital onset COVID-19 detected during March–October 2020. Large clusters included ≥10 individuals (patients, visitors, or HCWs) with a laboratory confirmed COVID-19 diagnosis (RT-PCR) and an epidemiologic link. Epidemiologic links were defined as hospitalization, work, or visiting in the same unit during the incubation or infectious period for the index case. Hospital onset was defined as a COVID-19 diagnosis ≥14 days from admission date. Admission screening has been conducted since May 2020 and serial testing (every 5 days) since July 2020. Nasopharyngeal swab specimens were retrieved for viral whole-genome sequencing (WGS). Cluster patients with a pairwise difference in ≤5 mutations were considered part of an outbreak. WGS was performed using Oxford Nanopore Technology and protocols from the ARTIC network. Results: We identified 2 large clusters involving patients with hospital-onset COVID-19. Cluster 1: 2 hospital-onset cases were identified in a medical-surgical unit in June 2020. Source and contact tracing revealed 4 additional patients, 1 visitor, and 13 employees with COVID-19. Median age for patients was 62 (range, 38–79), and all were male. In total, 17 samples (6 patients, 1 visitor, and 10 HCWs) were available for WGS. Cluster 2: A hospital-onset case was identified via serial testing in a non–COVID-19 intensive care unit in September 2020. Source investigation, contact tracing, and serial testing revealed 3 additional patients, and 8 HCWs. One HCW also had a community exposure. Patient median age was 60 years (range, 48–68) and all were male. In total, 11 samples (4 patients and 7 HCWs) were sequenced. Using WGS, cluster 1 was confirmed to be an outbreak: WGS showed 0–5 mutations in between samples. Cluster 2 was also an outbreak: WGS showed less diversity (0–3 mutations) and ruled out the HCW with a community exposure (20 mutations of difference). Conclusion: Whole-genome sequencing confirmed the outbreaks identified using classic epidemiologic methods. Serial testing allowed for early outbreak detection. Early outbreak detection and implementation of control measures may decrease outbreak size and genetic diversity.
Background: Bats are recognized as important vectors in disease transmission. Frequently, bats intrude into homes and buildings, increasing the risk to human health. We describe bat intrusions and exposure incidents in our hospital over a 3-year period. Methods: The University of Iowa Hospitals and Clinics (UIHC) is an 811-bed academic medical center in Iowa City, Iowa. Established in 1928, UIHC currently covers 209,031.84 m2 (~2,250,000 ft2) and contains 6 pavilions built between 1928 and 2017. We retrospectively obtained bat intrusion calls from the infection prevention and control program call database at UIHC during 2018–2020. We have also described the event management for intrusions potentially associated with patient exposures. Results: In total, 67 bat intrusions occurred during 2018–2020. The most frequent locations were hallways or lounges 28 (42%), nonclinical office spaces 19 (14%), and stairwells 8 (12%). Most bat intrusions (65%) occurred during the summer and fall (June–November). The number of events were 15 in 2018, 28 in 2019, and 24 in 2020. We observed that the number of intrusions increased with the age of each pavilion (Figure 1). Of 67 intrusions, 2 incidents (3%) were associated with potential exposure to patients. In the first incident, reported in 2019, the bat was captured in a patient care area and released before an investigation of exposures was completed and no rabies testing was available. Also, 10 patients were identified as having had potential exposure to the bat. Among them, 9 patients (90%) received rabies postexposure prophylaxis. In response to this serious event, we provided facility-wide education on our bat control policy, which includes the capture and safe handling of the bat, assessment of potential exposures, and potential need for rabies testing. We also implemented a bat exclusion project focused on the exterior of the oldest hospital buildings. The second event, 1 patient was identified to have potential exposure to the bat. The bat was captured, tested negative for rabies, no further action was needed. Conclusions: Bat intrusions can be an infection prevention and control challenge in facilities with older buildings. Hospitals may need animal intrusion surveillance systems, management protocols, and remediation efforts.
Background: Hospital semiprivate rooms may lead to coronavirus disease 2019 (COVID-19) patient exposures. We investigated the risk of COVID-19 patient-to-patient exposure in semiprivate rooms and the subsequent risk of acquiring COVID-19. Methods: The University of Iowa Hospitals & Clinics is an 811-bed tertiary care center. Overall, 16% of patient days are spent in semiprivate rooms. Most patients do not wear masks while in semiprivate rooms. Active COVID-19 surveillance included admission and every 5 days nasopharyngeal SARS-CoV-2 polymerase chain reaction (PCR) testing. We identified inpatients with COVID-19 who were in semiprivate rooms during their infectious periods during July–December 2020. Testing was repeated 24 hours after the first positive test. Cycle threshold (Ct) values of the two tests (average Ct <30), SARS-CoV-2 serology results, clinical assessment, and COVID-19 history were used to determine patient infectiousness. Roommates were considered exposed if in the same semiprivate room with an infectious patient. Exposed patients were notified, quarantined (private room), and follow-up testing was arranged (median seven days). Conversion was defined as having a negative test followed by a subsequent positive within 14 days after exposure. We calculated the risk of exposure: number of infectious patients in semiprivate rooms/number of semiprivate patient-days (hospitalization days in semiprivate rooms). Results: There were 16,427 semiprivate patient days during July–December 2020. We identified 43 COVID-19 inpatients who roommates during their infectious periods. Most infectious patients (77%) were male; the median age was 67 years; and 22 (51%) were symptomatic. Most were detected during active surveillance: admission testing (51%) and serial testing (28%). There were 57 exposed roommates. The risk of exposure was 3 of 1,000 semiprivate patient days. In total, 16 roommates (28%) did not complete follow-up testing. Of 41 exposed patients with follow-up data, 8 (20%) converted following their exposure. Median time to conversion was 5 days. The risk of exposure and subsequent conversion was 0.7 of 1,000 semiprivate patient days. Median Ct value of the source patient was 20 for those who converted and 23 for those who did not convert. Median exposure time was 45 hours (range, 3–73) for those who converted and 12 hours (range, 1–75) for those who did not convert. Conclusions: The overall risk of exposure in semiprivate rooms was low. The conversion rate was comparable to that reported for household exposures. Lower Ct values and lengthier exposures may be associated with conversion. Active COVID-19 surveillance helps early detection and decreases exposure time.
Background: Hospitalized patients may unknowingly carry severe acute respiratory coronavirus virus 2 (SARS-CoV-2), even if they are admitted for other reasons. Because SARS-CoV-2 may remain positive by reverse-transcriptase polymerase chain reaction (RT-PCR) for months after infection, patients with a positive result may not necessarily be infectious. We aimed to determine the frequency of SARS-CoV-2 infections in patients admitted for reasons unrelated to coronavirus disease 2019 (COVID-19). Methods: The University of Iowa Hospitals and Clinics is an 811-bed tertiary-care center. We use a nasopharyngeal SARS-CoV-2 RT-PCR to screen admitted patients without signs or symptoms compatible with COVID-19. Patients with positive tests undergo a repeat test to assess cycle threshold (Ct) value kinetics. We reviewed records for patients with positive RT-PCR screening admitted during July–October 2020. We used a combination of history, serologies, and RT-PCR Ct values to assess and qualify likelihood of infectiousness: (1) likely infectious, if Ct values were <29, or (2) likely not infectious, if 1 or both samples had Cts <30 with or without a positive SARS-CoV-2 antinucleocapsid IgG/IgM test or history of a positive result in the past 90 days. Contact tracing was only conducted for patients likely to be infectious. We describe the isolation duration and contact tracing data. Results: In total, 6,447 patients were tested on hospital admission for any reason (persons under investigation or admitted for reasons other than COVID-19). Of these, 240 (4%) had positive results, but 65 (27%) of these were admitted for reasons other than COVID-19. In total, 55 patients had Ct values available and were included in this analysis. The median age was 56 years (range, 0–91), 28 (51%) were male, and 12 (5%) were children. The most frequent admission syndromes were neurological (36%), gastrointestinal (16%), and trauma (16%). Our assessment revealed 23 likely infections (42%; 14 definite, 9 possible) and 32 cases likely not infectious (58%). The mean Ct for patients who were likely infectious was 22; it was 34 for patients who were likely not infectious. Mean duration of in-hospital isolation was 6 days for those who were likely infectious and 2 days for those who were likely not infectious. We detected 8 individuals (1 healthcare worker and 7 patients) who were exposed to a likely infectious patient. Conclusions: SARS-CoV-2 infection in patients hospitalized for other reasons was infrequent. An assessment of the likelihood of infectiousness including history, RT-PCR Cts, and serology may help prioritize patients in need of isolation and contact investigations.
Background: Coronavirus disease 2019 (COVID-19) is caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). SARS-CoV-2 RNA can be detected by real-time reverse-transcription polymerase chain reaction (RT-PCR) for several weeks after infection. Discerning persistent RT-PCR positivity versus reinfection is challenging and the frequency of COVID-19 reinfections is unknown. We aimed to determine the frequency of clinically suspected reinfection in our center and confirm reinfection using viral whole-genome sequencing (WGS). Methods: The University of Iowa Hospitals and Clinics (UIHC) is an 811-bed academic medical center. Patients with respiratory complaints undergo COVID-19 RT-PCR using nasopharyngeal swabs. The RT-PCR (TaqPath COVID-19 Combo kit) uses 3 targets (ORF1ab, S gene, and N gene). We identified patients with previous laboratory-confirmed COVID-19 who sought care for new respiratory complaints and underwent a repeated SARS-CoV-2 test at least 45 days from their first positive test. We then identified patients with median RT-PCR cycle threshold (Ct) values. Results: During the study period, 13,603 patients had a SARS-CoV-2– positive RT-PCR. Of these, 296 (2.2%) had a clinical visit for new onset of symptoms and a repeated RT-PCR assay >45 days from the first test. Moreover, 29 patients (9.8%) had a positive RT-PCR assay in the repeated testing. Ct values were available for samples from 25 patients; 7 (28%) had Ct values. Conclusions: In patients with a recent history of COVID-19 infection, repeated testing for respiratory symptoms was infrequent. Some had a SARS-CoV-2–positive RT-PCR assay on repeated testing, but only 1 in 4 had Ct values suggestive of a reinfection. We confirmed 1 case of reinfection using WGS.
Background: The COVID-19 pandemic has affected healthcare systems worldwide, but the impact on infection prevention and control (IPC) programs has not been fully evaluated. We assessed the impact of the COVID-19 pandemic on IPC consultation requests. Methods: The University of Iowa Hospitals & Clinics comprises an 811-bed hospital that admits >36,000 patients yearly and >200 outpatient clinics. Questions about IPC can be addressed to the Program of Hospital Epidemiology via e-mail, in person, or through our phone line. We routinely record date and time, call source, reason for the call, and estimated time to resolve questions for all phone line requests. We defined calls during 2018–2019 as the pre–COVID-19 period and calls from January to December 2020 as the COVID-19 period. Results: In total, 6,564 calls were recorded from 2018 to 2020. In the pre–COVID-19 period (2018–2019), we received a median of 71 calls per month (range, 50–119). The most frequent call sources were inpatient units (n = 902; 50%), department of public health (n = 357; 20%), laboratory (n = 171; 9%), and outpatient clinics (n = 120; 7%) (Figure 1). The most common call topics were isolation and precautions (n = 606; 42%), outside institutions requests (n = 324; 22%), environmental and construction (n = 148; 10%), and infection exposures (n = 149; 10%). The most frequent infection-related calls were about tuberculosis (17%), gram-negative organisms (14%), and influenza (9%). During the COVID-19 period, the median monthly call volume increased 500% to 368 per month (range, 149–829). Most (83%) were COVID-19 related. The median monthly number of COVID-19 calls was 302 (range, 45–674). The median monthly number of non–COVID-19 calls decreased to 56 (range, 36–155). The most frequent call sources were inpatient units (57%), outpatient clinics (16%), and the department of public health (5%). Most calls concerned isolation and precautions (50%) and COVID-19 testing (20%). The mean time required to respond to each question was 10 minutes (range, 2–720). The biggest surges in calls during the COVID-19 period were at the beginning of the pandemic (March 2020) and during the hospital peak COVID-19 census (November 2020). Conclusions: In addition to supporting a proactive COVID-19 response, our IPC program experienced a 500% increase in consultation requests. Planning for future bioemergencies should include creative strategies to provide additional resources to increase response capacity within IPC programs.
The incidence of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) exposure in shared patient rooms was low at our institution: 1.8 per 1,000 shared-room patient days. However, the secondary attack rate (21.6%) was comparable to that reported in household exposures. Lengthier exposures were associated with SARS-CoV-2 conversion. Hospitals should implement measures to decrease shared-room exposures.
Patients admitted to the hospital may unknowingly carry severe acute respiratory coronavirus virus 2 (SARS-CoV-2), and hospitals have implemented SARS-CoV-2 admission screening. However, because SARS-CoV-2 reverse-transcription polymerase chain reaction (RT-PCR) assays may remain positive for months after infection, positive results may represent active or past infection. We determined the prevalence and infectiousness of patients who were admitted for reasons unrelated to COVID-19 but tested positive for SARS-CoV-2 on admission screening.
We conducted an observational study at the University of Iowa Hospitals & Clinics from July 7 to October 25, 2020. All patients admitted without suspicion of COVID-19 were included, and medical records of those with a positive admission screening test were reviewed. Infectiousness was determined using patient history, PCR cycle threshold (Ct) value, and serology.
In total, 5,913 patients were screened and admitted for reasons unrelated to COVID-19. Of these, 101 had positive admission RT-PCR results; 36 of these patients were excluded because they had respiratory signs/symptoms on admission on chart review. Also, 65 patients (1.1%) did not have respiratory symptoms. Finally, 55 patients had Ct values available and were included in this analysis. The median age of the final cohort was 56 years and 51% were male. Our assessment revealed that 23 patients (42%) were likely infectious. The median duration of in-hospital isolation was 5 days for those likely infectious and 2 days for those deemed noninfectious.
SARS-CoV-2 was infrequent among patients admitted for reasons unrelated to COVID-19. An assessment of the likelihood of infectiousness using clinical history, RT-PCR Ct values, and serology may help in making the determination to discontinue isolation and conserve resources.
Background: Antimicrobial prophylaxis is one of the strongest surgical site infection (SSI) prevention measures. Current guidelines recommend the use of cefazolin as antimicrobial prophylaxis for abdominal hysterectomy procedures. However, there is growing evidence that anaerobes play a role in abdominal hysterectomy SSIs. We assessed the impact of adding anaerobic coverage on abdominal hysterectomy SSI rates in our institution. Methods: The University of Iowa Hospitals & Clinics is an 811-bed academic medical center that serves as a referral center for Iowa and neighboring states. Each year, ~33,000 major surgical operations are performed here, and on average, 600 are abdominal hysterectomies. Historically, patients have received cefazolin only, but beginning November 2017, patients undergoing abdominal hysterectomy received cefazolin + metronidazole for antimicrobial prophylaxis. Order sets within the electronic medical record were modified, and education was provided to surgeons, anesthesiologists, and other ordering providers. Procedures and subsequent SSIs were monitored and reported using National Healthcare Safety Network (NHSN) definitions. Infection rates are calculated using all depths (superficial, deep and organ space) and by deep and organ space only, as this is how they are publicly reported. We used numerator (SSIs) and denominator (number of abdominal hysterectomy procedures) data from the NHSN from January 2015 through September 2019. We performed an interrupted time-series analysis to determine how the addition of metronidazole was associated with abdominal hysterectomy SSIs (all depths, and deep and organ space). Results: From January 2015 through October 2017, the hysterectomy SSI rates were 3.2% (all depths) and 1.5% (deep and organ space). After the adjustment was made to antimicrobial prophylaxis in November 2017, the rates decreased to 1.6% (all depths) and 0.6% (deep and organ space). Of the SSIs with pathogens identified, the proportion of anaerobes decreased from 59% to 25% among all depths and from 82% to 50% among deep and organ-space SSIs. The rate of SSI decline after the intervention was statistically significant (P = .01) for deep and organ-space infections but not for all depths (P = .73). Conclusions: The addition of anaerobic coverage with metronidazole was associated with a decrease in deep and organ-space abdominal hysterectomy SSI rates at our institution. Hospitals should assess the microbiology of abdominal hysterectomy SSIs and should consider adding metronidazole to their antimicrobial prophylaxis.
Background: In December of 2019, the World Health Organization reported a novel coronavirus (severe acute respiratory coronavirus virus 2 [SARS-CoV-2)]) causing severe respiratory illness originating in Wuhan, China. Since then, an increasing number of cases and the confirmation of human-to-human transmission has led to the need to develop a communication campaign at our institution. We describe the impact of the communication campaign on the number of calls received and describe patterns of calls during the early stages of our response to this emerging infection. Methods: The University of Iowa Hospitals & Clinics is an 811-bed academic medical center with >200 outpatient clinics. In response to the coronavirus disease 2019 (COVID-19) outbreak, we launched a communications campaign on January 17, 2020. Initial communications included email updates to staff and a dedicated COVID-19 webpage with up-to-date information. Subsequently, we developed an electronic screening tool to guide a risk assessment during patient check in. The screening tool identifies travel to China in the past 14 days and the presence of symptoms defined as fever >37.7°C plus cough or difficulty breathing. The screening tool was activated on January 24, 2020. In addition, university staff contacted each student whose primary residence record included Hubei Province, China. Students were provided with medical contact information, signs and symptoms to monitor for, and a thermometer. Results: During the first 5 days of the campaign, 3 calls were related to COVID-19. The number of calls increased to 18 in the 5 days following the implementation of the electronic screening tool. Of the 21 calls received to date, 8 calls (38%) were generated due to the electronic travel screen, 4 calls (19%) were due to a positive coronavirus result in a multiplex respiratory panel, 4 calls (19%) were related to provider assessment only (without an electronic screening trigger), and 2 calls (10%) sought additional information following the viewing of the web-based communication campaign. Moreover, 3 calls (14%) were for people without travel history but with respiratory symptoms and contact with a person with recent travel to China. Among those reporting symptoms after travel to China, mean time since arrival to the United States was 2.7 days (range, 0–11 days). Conclusion: The COVID-19 outbreak is evolving, and providing up to date information is challenging. Implementing an electronic screening tool helped providers assess patients and direct questions to infection prevention professionals. Analyzing the types of calls received helped tailor messaging to frontline staff.
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.
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.