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.

Consumption of sugar-sweetened beverages (SSB) by infants and young children are less explored in Asian populations. The Growing Up in Singapore Towards healthy Outcomes cohort study examined associations between SSB intake at 18 months and 5 years of age, with adiposity measures at 6 years of age. We studied Singaporean infants/children with SSB intake assessed by FFQ at 18 months of age (n 555) and 5 years of age (n 767). The median for SSB intakes is 28 (interquartile range 5·5–98) ml at 18 months of age and 111 (interquartile range 57–198) ml at 5 years of age. Association between SSB intake (100 ml/d increments and tertile categories) and adiposity measures (BMI standard deviation scores (sd units), sum of skinfolds (SSF)) and overweight/obesity status were examined using multivariable linear and Poisson regression models, respectively. After adjusting for confounders and additionally for energy intake, SSB intake at age 18 months were not significantly associated with later adiposity measures and overweight/obesity outcomes. In contrast, at age 5 years, SSB intake when modelled as 100 ml/d increments were associated with higher BMI by 0·09 (95 % CI 0·02, 0·16) sd units, higher SSF thickness by 0·68 (95 % CI 0·06, 1·44) mm and increased risk of overweight/obesity by 1·2 (95 % CI 1·07, 1·23) times at age 6 years. Trends were consistent with SSB intake modelled as categorical tertiles. In summary, SSB intake in young childhood is associated with higher risks of adiposity and overweight/obesity. Public health policies working to reduce SSB consumption need to focus on prevention programmes targeted at young children.