Objectives: HCWs are recommended to wipe the computers with alcohol wipes before clinical use. Compliance assessment by direct observation is resource intensive. We used ATP measurement as a surrogate to assess the compliance to preutilization cleaning of computers. Methods: We conducted a pilot study to determine the median relative light unit (RLU) value reflective of preutilization cleaning of the computers. We identified values of <250, 250–500, and >500 RLU to reflect cleaned, probably cleaned, and not cleaned computers, respectively. Subsequently, we conducted a cross-sectional study of the computers in the inpatient wards in Tan Tock Seng Hospital and National Centre for Infectious Diseases. Using 3M Clean-Trace ATP swabs, we tested 5 computers in each ward: 2 computers on wheels, 2 from the nursing station, and 1 at the patients’ room entrance. All analyses were conducted using STATA version 15 software. Results: Between October 4 and 10, 2021, we collected 219 samples from 219 computers. Among them, 44 (20.1%) were cleaned, 49 (22.4%) were probably cleaned, and 126 (57.5%) computers were not cleaned. Higher compliance to computer cleaning was observed in COVID-19 wards [85 ATP samples; cleaned, 37 (43.5%); probably cleaned, 26 (30.6%); not cleaned, 22 (25.9%)] compared with non–COVID-19 wards [134 ATP samples; cleaned, 7 (5.2%); probably cleaned, 23 (17.2%); not cleaned, 104 (77.6%)] (P < .01). No significant difference was observed in compliance with cleaning computers between the ICU [30 ATP samples; cleaned, 7 (23.3%); probably cleaned, 4 (13.3%); not cleaned, 19 (63.3%)] and general wards [189 ATP samples; cleaned, 37 (19.6%); probably cleaned, 45 (23.8%); not cleaned, 107 (56.6%)] (P = .47). Conclusions: ATP swab tests can be used as a surrogate marker to assess compliance to pre-utilization cleaning of computers. Enhanced awareness of environmental hygiene may explain the higher compliance to computer cleaning observed in COVID-19 wards.

Objectives: The use of handwashing sinks for activities other than hand hygiene (HH) is associated with higher rates of β-lactamase–producing Enterobacteriaceae. However, little has been published about the handwashing sink activities in Singapore hospitals. We explored the handwashing sink activities in a tertiary-care hospital in Singapore. Methods: Five trained shadow observers conducted this observational study between December 18 and 21, 2018 (6 hours per day: 07:00–09:00, 09:30–11:30, and 12:30–14:30) in acute-care general wards. We divided the handwashing sink activities by healthcare workers (HCWs) and non-HCWs (ie, visitors, caregivers, and relatives) and by HH- and non–HH-related activities. We used Stata version 15 software for the analysis. The study was approved by the Institutional Review Board of the National Healthcare Group, Singapore (DSRB no. 2020/01257). Results: In total, 657 handwashing sink activities were recorded [HCWs, 475 (72.3%) and non-HCWs, 182 (27.7%)]. Of the 475 HCW handwashing sink activities, 451 (94.9%) were HH-related, 10 (2.1%) were for patient nutrition, 7 (1.5%) were for environmental care, 6 (1.3%) were for medical equipment cleaning, and 1 (0.2%) was patient personal-item cleaning. Of the 182 handwashing sink activities by non-HCWs, 117 (64.3%) were HH related, 30 (16.5%) were for patient nutrition, 21 (11.5%) were for personal hygiene, 14 (7.7%) were patient personal-item cleaning. The distribution of handwashing sink activities differed significantly (P < .01) between HCWs and non-HCWs. The odds of non–HH-related handwashing sink activities among non-HCWs was 10× higher than among HCWs (OR, 10.44; 95% CI, 5.98–18.23; P < .01). Conclusions: Handwashing sinks use for non–HH-related activities is higher among non-HCWs than HCWs. Further studies are needed to understand the impact of non-HH handwashing sink activities on nosocomial infections and ways to reduce them.

Objectives: In healthcare facilities, environmental reservoirs of CPE are associated with CPE outbreaks. In the newly built NCID building, we studied the introduction of CPE in the aqueous environment. Methods: We sampled the aqueous environments (ie, sink, sink strainer, and shower drain-trap with Copan E-swabs and sink P-trap water) of 4 NCID wards (ie, 2 multidrug-resistant organism (MDRO) wards and 2 non-MDRO wards). Two sampling cycles (cycle 1, June–July 2019 and cycle 2, September–November 2019) were conducted in all 4 wards. Cycle 3 (November 2020) was conducted in 1 non-MDRO ward to investigate CPE colonization from previous cycles. Enterobacterales were identified using MALDI-TOF MS and underwent phenotypic (mCIM and eCIM) and confirmatory PCR tests for CPE. Results: We collected 448, 636, and 96 samples in cycles 1, 2, and 3, respectively. MDRO and non-MDRO wards were operational for 1 and 7 months during the first sampling cycle. The CPE prevalence rates in MDRO wards were 1.67% (95% CI, 0.46% – 4.21%) in cycle 1 and 1.76% (95% CI, 0.65% – 3.80%) in cycle 2. In the aqueous environments in MDRO wards, multiple species were detected (cycle 1: 2 K. pneumoniae, 1 E. coli, and 1 S. marcescens; cycle 2: 5 K. pneumoniae and 1 R. planticola), and multiple genotypes were detected (cycle 1: 3 blaOXA48; cycle 2: 5 blaOXA48 and 1 blaKPC). The CPE prevalence in non-MDRO wards was 1.92% (95% CI, 0.53%–4.85%) in cycle 1. The prevalence rate increased by 5.51% (95% CI, 1.99%–9.03%) to 7.43% (95% CI, 4.72%–11.04%; P = .006) in cycle 2, and by another 2.98% (95% CI, −3.82% to 9.79%) to 10.42% (95% CI, 5.11% – 18.3%; P = .353) in cycle 3. Only blaOXA48 S. marcescens were detected in all cycles (except 1 blaOXA48 K. pneumoniae in cycle 2) in the non-MDRO ward. Conclusions: CPE established rapidly in the aqueous environment of NCID wards, more so in MDRO wards than non-MDRO wards. Longitudinal studies to understand the further expansion of the CPE colonization and its impact on patients are needed.

Objectives: In this study, we compared the performance of a rapid polymerase chain reaction (PCR) method in detecting carbapenemase-producing organisms (CPOs) and its impact on infection prevention and control (IPC) measures compared with a culture PCR method. Methods: All patients requiring CPO screening were included. Rectal swabs were collected with double rayon swabs (Copan 139C). They were simultaneously analyzed for the presence of CPOs using rapid PCR assay (Xpert Carba-R assay, Cepheid, Sunnyvale, CA) and a culture–PCR method (ChromID CARBA-SMART, bioMerieux, Marcy-l’Etoile, France). For CARBA-SMART, only colored colonies (ie, Enterobacterales) were evaluated for CPOs according to the prevailing institutional protocol. We tracked time to CPO detection. Using CPO positivity from either the rapid PCR or the culture PCR method as the gold standard, we calculated the sensitivity and specificity of both tests. We calculated the number of epidemiologically linked contacts generated when the first test results were known. We prospectively followed the ward census to identify the putative additional number of contacts generated by the later known result. Contacts were patients who shared the same ward (with overlapping time) as the CPO patients. Results: Between April 2019 and June 2020, culture PCR method detected CPOs in 316 (1.3%) of 24,514 samples (blaOXA48, N = 211; blaNDM, N = 51; blaIMI, N = 21; blaIMP, N = 10; blaKPC, N = 9; mixed genotypes, N = 14). The rapid PCR test detected CPOs in 605(2.5%) of 24,514 samples (blaOXA48, N = 266; blaNDM, N = 161; blaIMP, N = 99; blaVIM, N = 29; blaKPC, N = 15; mixed genotypes, N = 35). The sensitivity of direct PCR and culture PCR methods were 94.2% (95% CI, 92.1%–95.8%) and 43.5% (95% CI, 39.6%–47.4%), respectively. Both tests had 100% specificity. The median times to detection for the rapid PCR and culture PCR methods were 3–4 hours and 4 days, respectively. Compared with rapid PCR, the culture PCR method generated additional 7,415 contacts when it also tested positive for CPOs and an additional 23,135 contacts when it tested negative for CPOs. Conclusions: In our study, the rapid PCR test was more sensitive, identified CPO faster, and generated fewer epidemiologically linked contacts than the culture PCR method.

Objectives: The increase in carbapenemase-producing organism (CPO) transmission among hospitalized patients is a growing concern. Studies investigating the transmission of CPO to epidemiologically linked contacts are scarce. We conducted an interim subgroup analysis of the ongoing multicenter household transmission of CPO in Singapore (CaPES-C) study to identify the acquisition rate of CPO among epidemiologically linked contacts of hospitalized CPO patients. Methods: This multicenter prospective cohort study was conducted between January and December 2021. We recruited CPO-positive patients and their epidemiologically linked contacts. Stool samples were collected from the patients at baseline, day 3, day 7, and at weeks 2, 3, 4, 5, 6, 12, 24, 36, and 48. Additionally, a sample was collected at the time of discharge from the hospital. Xpert Carba-R test was used to detect CPO genotypes in the stool samples. In this interim analysis, we calculated the acquisition rate of CPO among the epidemiologically linked hospital contacts of CPO positive patients using Stata version 15 software. Results: We recruited 22 (56.4%) CPO-positive index patients [blaNDM, n = 7 (31.8%); blaIMP, n = 3 (13.6%); blaOXA-48, n = 10 (45.5%), others, n = 2 (9.1%)] and 14 (35.9%) epidemiologically linked hospital contacts. The median age of CPO-positive patients was 72.5 years (IQR, 62–82) and 15 (68.2%) were female. The median age for the epidemiologically linked contacts was 82.5 years (IQR, 70–85) and 4 (28.6%) were female. After 1,082 patient days, 2 (14.3%) epidemiologically linked contacts tested positive for CPO giving an acquisition rate of 1.85 per 1,000 patient days (95% CI, 0.46 – 7.39). One of these participants acquired a concordant genotype (blaOXA-48) at day 7 and the other acquired a discordant genotype (CPO positive index, blaIMP; epidemiologically linked contact, blaNDM) at week 12 of follow-up. Conclusions: This small interim analysis revealed a high conversion rate among epidemiologically linked hospital contacts. A larger study is needed to understand the influence of genotypes, hospital environment, and human behavior on the transmission of CPO in hospitals.

Objectives: High-touch surface (HTS) cleaning is critical to prevent healthcare-associated infections. However, HTS definitions and cleaning frequency vary across guidelines. We conducted a scoping review of published guidelines on HTS definitions and recommended cleaning frequency in inpatient wards. Methods: We searched national and societal guidelines on Google and PubMed using the following search terms: [(environmental cleaning/disinfection/housekeeping/sanitization), (hospital/healthcare/infection control prevention/inpatient/acute care), and (practice/guideline/guidance/methodology/protocol)]. We compared the guidelines’ HTS definitions, recommended cleaning frequency, and supporting evidence. Results: In total, 9 environmental cleaning guidelines were included: Centers for Disease Control and Prevention (CDC 2020); Asia Pacific Society of Infection Control (APSIC 2013); International Society for Infectious Diseases (ISID 2018); Joint Commission Resources (JCR 2018); National Health Service, United Kingdom (NHSUK 2021); Public Health Agency, Northern Ireland (PHANI 2016); Public Health Ontario, Canada (PHOC 2018); National Health and Medical Research Council, Australia (NHMRC 2019); Ministry of Health, Singapore (MOH 2013). These 6 guidelines identified 31 types of HTS: bed rails and frames [mentioned by 6 of 6 guidelines]; call bells, doorknobs and handles (5 of 6 guidelines); bedside tables and handles, light switches, overbed and tray tables, and sinks and faucet handles (4 of 6 guidelines); chairs and chair arms, edges of privacy curtains, IV infusion pumps and poles, keyboards, medical equipment, monitoring equipment, and telephones (3 of 6 guidelines); assist bars, counters, elevator buttons, toilet seats and flushes, transport equipment, and wall areas around the toilet (2 of 6 guidelines); and bedpan cleaners, beds, blankets, commodes/bedpans, dispensers, documents, mattresses, monitors, mouse, pillows, and touch screens (1 of 6 guidelines). The JCR, NHMRC, NHSUK guidelines did not define HTSs. The 6 guidelines recommended at least daily cleaning for HTSs, but ISID, JCR, and NHSUK guidelines did not mention HTS cleaning frequency. The CDC guidelines further specified at least once daily cleaning for inpatient wards and private toilets and twice daily for public or shared toilets. None of the guidelines cited any references for HTS cleaning frequency recommendations. Conclusions: There is no uniformity in HTS definitions among 6 guidelines, and the recommended HTS cleaning frequency in these guidelines was not supported by published evidence. Studies exploring optimal cleaning frequency of HTSs are needed.

Objectives: Over the past 2 years, many infection prevention and control (IPC) resources have been diverted to manage the COVID-19 pandemic. Its impact on the incidence of antimicrobial-resistant organisms has not been adequately studied. We investigated the impact of the pandemic on the incidence of carbapenem-resistant Enterobacterales (CRE) in Singapore. Methods: We extracted data on unique CRE isolates (clinical and/or surveillance cultures) and patient days for 6 public hospitals in Singapore from the carbapenemase-producing Enterobacteriaceae (CaPES) study group database, and we calculated the monthly incidence of CRE (per 10,000 patient days). Interrupted time-series (ITS) analysis was conducted with the pre–COVID-19 period defined as before February 2020, and the COVID-19 period defined as after February 2020. Statistical analyses were performed using Stata version 15 software. Results: From January 2017 to March 2021, 6,770 CRE isolates and 9,126,704 patient days were documented. The trend in CRE monthly incidence increased significantly during the pre–COVID-19 period (0.060; 95% CI, 0.033–0.094; P < .001) but decreased during the COVID-19 period (−0.183; 95% CI, −0.390 to 0.023; P = .080) without stepwise change in the incidence (−1.496; 95% CI, −3.477 to 0.485; P = .135). The trend in monthly incidence rate of CRE clinical cultures increased significantly during the pre–COVID-19 period (0.046; 95% CI, 0.028–0.064; P < .001) and decreased significantly during COVID-19 period (−0.148; 95% CI, −0.249 to −0.048; P = .048) with no stepwise change in the incidence (−0.063; 95% CI, −0.803 to 0.677; P = .864). The trend in monthly incidence rate of CRE surveillance cultures decreased during the pre–COVID-19 period (−0.020; 95% CI, −0.062 to 0.022; P = .341) and the COVID-19 period (−0.067; 95% CI, −0.291to 0.158; P = .552) without stepwise change in the incidence (−1.327; 95% CI, −3.535 to 0.881; P = .233). Conclusions: The rate of CRE in clinical cultures decreased during COVID-19 but not the rate in surveillance cultures. Further studies are warranted to study the impact of COVID-19 on CREs.

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We estimated the annual bed days lost and economic burden of healthcare-associated infections to Singapore hospitals using Monte Carlo simulation. The mean (standard deviation) cost of a single healthcare-associated infection was S$1,809 (S$440) [or US$1,362 (US$331)]. This translated to annual lost bed days and economic burden of 55,978 (20,506) days and S$152.0 million (S$37.1 million) [or US$114.4 million (US$27.9 million)], respectively.

Background:Candida auris is an emerging nosocomial fungal pathogen causing invasive illness and outbreaks worldwide. A major issue regarding C. auris is that it can be misidentified unless appropriate technology is used. We conducted a survey of available methods for identification of C. auris in 21 hospital laboratories in India regarding their protocols for prevention of C. auris infection. Methods: The survey was an adaptation of a similar survey conducted for the Connecticut Laboratory Response Network in 2017. We mailed the survey to 30 microbiologists and ID physicians, and 21 of them from 12 states responded. All respondents were from private acute-care and teaching hospitals. The responses were analyzed and compared to the Connecticut study. Results: Of 21 hospitals, 19 (90.5%) can identify C. auris in house. Also, 18 (85.7%) have identified C. auris in the past 18 months. Species level identification was done only for blood cultures in all hospitals. Only 5 (26%) laboratories speciated Candida spp isolated from other sites such as respiratory and urinary specimens. Automated systems were used like Vitek 2 in 16 (84.2%), Phoenix BD in 2(10.5%) and Microscan in 1(5.26%) laboratory. MALDI-TOF MS and PCR for identification were used in 2 laboratories. Antifungal susceptibility testing is done in-house in 19 (90.5%) laboratories. Only 10 (52.6%) responding hospitals from India had infection prevention protocols for C. auris, and 9 (47.4%) of them isolated patients. The major challenges for infection prevention with C. auris are absence of screening in high-risk patients (66.7%), misidentification by automated systems (84.2%), and inability to speciate from nonsterile sites underestimates the prevalence (100%). Conclusions: There is an urgent need to enhance the capacity of hospital laboratories to detect C. auris early, and to implement infection prevention measures. In both studies early detection is the key and as suggested by the US authors, challenges can be overcome through collaboration between hospitals and referral laboratories when resources are limited. This optimizes laboratory capacity and prevents global spread through colonized patients. The limitation of this study is that data from public hospitals are unknown and larger studies are needed.

Funding: None

Disclosures: None

Genetically distinct isolates of New Delhi metallo-β-lactamase (NDM)–producing Enterobacteriaceae were identified from the clinical cultures of 6 patients. Screening of shared-ward contacts identified 2 additional NDM-positive patients. Phylogenetic analysis proved that 1 contact was a direct transmission while the other was unrelated to the index, suggesting hidden routes of transmission.

Infect Control Hosp Epidemiol 2016;37:987–990