Integrated Genomic and Social Network Analyses of SARS-CoV-2 Transmission in the Healthcare Setting.

BACKGROUND: Infection prevention (IP) measures are designed to mitigate the transmission of pathogens in healthcare. Using large-scale viral genomic and social network analyses, we determined if IP measures used during the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) pandemic were adequate in protecting healthcare workers (HCWs) and patients from acquiring SARS-CoV-2. METHODS: We performed retrospective cross-sectional analyses of viral genomics from all available SARS-CoV-2 viral samples collected at UC San Diego Health and social network analysis using the electronic medical record to derive temporospatial overlap of infections among related viromes and supplemented with contact tracing data. The outcome measure was any instance of healthcare transmission, defined as cases with closely related viral genomes and epidemiological connection within the healthcare setting during the infection window. Between November 2020 through January 2022, 12 933 viral genomes were obtained from 35 666 patients and HCWs. RESULTS: Among 5112 SARS-CoV-2 viral samples sequenced from the second and third waves of SARS-CoV-2 (pre-Omicron), 291 pairs were derived from persons with a plausible healthcare overlap. Of these, 34 pairs (12%) were phylogenetically linked: 19 attributable to household and 14 to healthcare transmission. During the Omicron wave, 2106 contact pairs among 7821 sequences resulted in 120 (6%) related pairs among 32 clusters, of which 10 were consistent with healthcare transmission. Transmission was more likely to occur in shared spaces in the older hospital compared with the newer hospital (2.54 vs 0.63 transmission events per 1000 admissions, P < .001). CONCLUSIONS: IP strategies were effective at identifying and preventing healthcare SARS-CoV-2 transmission.

The Effect of Clostridioides difficile Diagnostic Stewardship Interventions on the Diagnosis of Hospital-Onset Clostridioides difficile Infections.

BACKGROUND: Public reporting of Clostridioides difficile infection (CDI) using laboratory-identified events has led some institutions to revert from molecular-based tests to less sensitive testing modalities. At one academic medical center, researchers chose to use nucleic acid amplification test alone in CDI diagnosis with institutional protocols aimed at diagnostic stewardship. METHODS: A single-center, quasi-experimental study was conducted to introduce and analyze the effects of various diagnostic stewardship interventions. In April 2017 an order report was created to inform providers of patients' recent bowel movements, laxative use, and prior Clostridioides difficile (CD) testing (Intervention 1). In November 2017 nursing staff were empowered to not send nondiarrheal stools for testing (Intervention 2). In February 2019, an interruptive alert was implemented to prevent testing that was not indicated (Intervention 3). CD testing rates and healthcare facility-onset CDI (HO-CDI) rates were compared before and after the interventions using one-way analysis of variance (ANOVA). RESULTS: At baseline, testing for CD after 3 days of admission was performed at mean ± standard deviation of 15.9 ± 1.7 tests/1,000 patient-days. After Intervention 1, it decreased to 12.1 ± 1.1 tests. This further decreased to 10.6 ± 0.8 after Intervention 2 and to 8.1 ± 0.1 after Intervention 3 (p < 0.001). HO-CDI cases per 10,000 patient-days declined from 12.7 ± 1.4 cases at baseline to 10.7 ± 1.2 after Intervention 1, to 8.7 ± 2.4 after Intervention 2, and to 5.8 ± 0.2 after Intervention 3 (p = 0.03). CONCLUSION: A multidisciplinary approach optimizing electronic health record support tools and leveraging nursing education can reduce both testing and HO-CDI rates while using the most sensitive testing modality.

Public Health Role of Academic Medical Center in Community Outbreak of Hepatitis A, San Diego County, California, USA, 2016-2018.

During 2016-2018, San Diego County, California, USA, experienced one of the largest hepatitis A outbreaks in the United States in 2 decades. In close partnership with local healthcare systems, San Diego County Public Health led a public health response to the outbreak that focused on a 3-pronged strategy to vaccinate, sanitize, and educate. Healthcare systems administered nearly half of the vaccinations delivered in San Diego County. At University of California San Diego Health, the use of informatics tools assisted with the identification of at-risk populations and with vaccine delivery across outpatient and inpatient settings. In addition, acute care facilities helped prevent further disease transmission by delaying the discharge of patients with hepatitis A who were experiencing homelessness. We assessed the public health roles that acute care hospitals can play during a large community outbreak and the critical nature of ongoing collaboration between hospitals and public health systems in controlling such outbreaks.

Evaluation of the National Healthcare Safety Network standardized infection ratio risk adjustment for healthcare-facility-onset Clostridioides difficile infection in intensive care, oncology, and hematopoietic cell transplant units in general acute-care hospitals.

OBJECTIVE: To evaluate the National Health Safety Network (NHSN) hospital-onset Clostridioides difficile infection (HO-CDI) standardized infection ratio (SIR) risk adjustment for general acute-care hospitals with large numbers of intensive care unit (ICU), oncology unit, and hematopoietic cell transplant (HCT) patients. DESIGN: Retrospective cohort study. SETTING: Eight tertiary-care referral general hospitals in California. METHODS: We used FY 2016 data and the published 2015 rebaseline NHSN HO-CDI SIR. We compared facility-wide inpatient HO-CDI events and SIRs, with and without ICU data, oncology and/or HCT unit data, and ICU bed adjustment. RESULTS: For these hospitals, the median unmodified HO-CDI SIR was 1.24 (interquartile range [IQR], 1.15-1.34); 7 hospitals qualified for the highest ICU bed adjustment; 1 hospital received the second highest ICU bed adjustment; and all had oncology-HCT units with no additional adjustment per the NHSN. Removal of ICU data and the ICU bed adjustment decreased HO-CDI events (median, -25%; IQR, -20% to -29%) but increased the SIR at all hospitals (median, 104%; IQR, 90%-105%). Removal of oncology-HCT unit data decreased HO-CDI events (median, -15%; IQR, -14% to -21%) and decreased the SIR at all hospitals (median, -8%; IQR, -4% to -11%). CONCLUSIONS: For tertiary-care referral hospitals with specialized ICUs and a large number of ICU beds, the ICU bed adjustor functions as a global adjustment in the SIR calculation, accounting for the increased complexity of patients in ICUs and non-ICUs at these facilities. However, the SIR decrease with removal of oncology and HCT unit data, even with the ICU bed adjustment, suggests that an additional adjustment should be considered for oncology and HCT units within general hospitals, perhaps similar to what is done for ICU beds in the current SIR.