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Background: At the start of the COVID-19 pandemic, the DC Department of Health (DC Health) mandated new case reporting for early outbreak detection: (1) weekly healthcare personnel (HCP) absenteeism line lists indicating staff absent for confirmed or suspected SARS-CoV-2, (2) daily line lists of all SARS-CoV-2–positive inpatients, and (3) hospital contact tracing. Between March 27, 2020, and December 31, 2020, DC Health detected 36 confirmed and 14 suspected hospital outbreaks, of which only 18% (8 confirmed and 1 suspect) were known to the affected hospital. DC Health learned which outbreaks warranted early or aggressive intervention by tracking outbreak characteristics across its jurisdiction. This allowed prioritization of during surges when it was difficult for DC Health and hospital staff to investigate every outbreak. Methods: Potential outbreaks in short-stay and inpatient rehabilitation hospitals were flagged after identifying SARS-CoV-2 hospital-onset (HO) inpatients or staff clusters on line lists. Variables of interest in line lists included specimen collection and hospital admission dates, units or departments, and patient contact. Facility contact tracing by infection preventionists further verified epidemiological links among cases. Outbreak details were systematically tracked in a locally developed REDCap database and were analyzed if they had an initial case, outbreak start date, or an investigation start date in 2020. Frequency procedures, SQL statements, and date calculations were computed using SAS Enterprise Guide version 8.3 software. Results: Confirmed outbreaks had an average of 6.92 (range, 0–32) HCP and 2.58 (range, 0–22) patient cases, with 69% being confirmed-HO cases and 31% probable HO. Moreover, 53% of confirmed outbreaks occurred in the following departments: cardiac, behavioral health, intensive care, and environmental services (EVS)/facilities. All of these departments had recurrent outbreaks. Behavioral health, medical and cardiac units had the highest number of patient cases. On average, confirmed outbreak investigations lasted 24.6 days, with outbreaks prolonged in the ICU (40.25 days) and the medical unit (37.67 days). Top triggers for investigations ultimately classified as confirmed outbreaks were (1) positive symptomatic HCP, (2) confirmed-HO cases, and (3) exposures from positive HCP. Conclusions: The dynamic nature of COVID-19 created challenges in detecting and responding to hospital outbreaks. Developing a low-resource outbreak tracking system helped identify outbreak types and triggers that warranted early or aggressive interventions. Understanding the characteristics of hospital outbreaks was critical for maximizing infection control resources during surges of infectious disease outbreaks, such as COVID-19. Hospitals or local health departments could adapt this system to meet their needs.
Background: In June 2019, the Maryland Department of Health (MDH) was notified of a hospitalized patient with Candida auris bloodstream infection. The MDH initiated a contact investigation to identify additional patients with C. auris colonization. Many of the contacts had been discharged home from the hospital and were therefore not available for screening. Healthcare facilities in Maryland, Virginia, and Washington, DC, submit patient data to a regional health information exchange (HIE) called the Chesapeake Regional Information System for our Patients (CRISP). CRISP includes a notification system that alerts providers when flagged patients have healthcare encounters. We aimed to use this system to identify discharged C. auris contacts on their next inpatient encounter to rapidly screen them and to detect new cases. Methods:C. auris contacts were defined as patients located on an inpatient unit on the same day, receiving wound care from the same team, or having a procedure in the same operating room on the same day as the index patient or any patients subsequently identified as having C. auris infection or colonization detected either during the normal course of clinical care or through screening. Contacts who remained hospitalized were screened during inpatient point prevalence surveys (PPSs). Contacts discharged to postacute-care facilities were screened by facility staff. Contacts who had been discharged home were flagged in CRISP, and MDH staff received CRISP encounter alerts when these patients were readmitted. MDH staff then contacted the admitting facilities to recommend screening for C. auris. Axilla and groin swabs were collected and tested by rt-PCR at the Mid-Atlantic Regional Antibiotic Resistance Laboratory Network laboratory. Results: As of October 8, 2019, 4,017 contacts were identified. Among these, 936 (23%) contacts at 56 healthcare facilities (33 acute-care hospitals and 23 postacute-care facilities) were screened for C. auris, and 10 patients with C. auris colonization were identified (1.1% of contacts who underwent C. auris screening). Of these, 6 (60%) were identified through CRISP notification and 4 (40%) were identified by PPSs conducted in acute-care hospitals. Conclusions: In this ongoing C. auris outbreak, a large proportion of colonized patients was identified using an electronic encounter notification system within a regional HIE. This approach was effective for identifying opportunities to screen contacts at their next healthcare encounter and can augment other means of case detection, like PPSs. HIEs should incorporate mechanisms to facilitate contact tracing for public health investigations.
Carbapenem-resistant Enterobacteriaceae (CRE) are a significant clinical and public health concern. Understanding the distribution of CRE colonization and developing a coordinated approach are key components of control efforts. The prevalence of CRE in the District of Columbia is unknown. We sought to determine the CRE colonization prevalence within healthcare facilities (HCFs) in the District of Columbia using a collaborative, regional approach.
This study included 16 HCFs in the District of Columbia: all 8 acute-care hospitals (ACHs), 5 of 19 skilled nursing facilities, 2 (both) long-term acute-care facilities, and 1 (the sole) inpatient rehabilitation facility.
Inpatients on all units excluding psychiatry and obstetrics-gynecology.
CRE identification was performed on perianal swab samples using real-time polymerase chain reaction, culture, and antimicrobial susceptibility testing (AST). Prevalence was calculated by facility and unit type as the number of patients with a positive result divided by the total number tested. Prevalence ratios were compared using the Poisson distribution.
Of 1,022 completed tests, 53 samples tested positive for CRE, yielding a prevalence of 5.2% (95% CI, 3.9%–6.8%). Of 726 tests from ACHs, 36 (5.0%; 95% CI, 3.5%–6.9%) were positive. Of 244 tests from long-term-care facilities, 17 (7.0%; 95% CI, 4.1%–11.2%) were positive. The relative prevalence ratios by facility type were 0.9 (95% CI, 0.5–1.5) and 1.5 (95% CI, 0.9–2.6), respectively. No CRE were identified from the inpatient rehabilitation facility.
A baseline CRE prevalence was established, revealing endemicity across healthcare settings in the District of Columbia. Our study establishes a framework for interfacility collaboration to reduce CRE transmission and infection.
Infect Control Hosp Epidemiol 2017;38:921–929
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