A SARS-CoV-2 Cluster in an Acute Care Hospital.

BACKGROUND: Little is known about clusters of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection in acute care hospitals. OBJECTIVE: To describe the detection, mitigation, and analysis of a large cluster of SARS-CoV-2 infections in an acute care hospital with mature infection control policies. DESIGN: Descriptive study. SETTING: Brigham and Women's Hospital, Boston, Massachusetts. PARTICIPANTS: Patients and staff with cluster-related SARS-CoV-2 infections. INTERVENTION: Close contacts of infected patients and staff were identified and tested every 3 days, patients on affected units were preemptively isolated and repeatedly tested, affected units were cleaned, room ventilation was measured, and specimens were sent for whole-genome sequencing. A case-control study was done to compare clinical interactions, personal protective equipment use, and breakroom and workroom practices in SARS-CoV-2-positive versus negative staff. MEASUREMENTS: Description of the cluster, mitigation activities, and risk factor analysis. RESULTS: Fourteen patients and 38 staff members were included in the cluster per whole-genome sequencing and epidemiologic associations. The index case was a symptomatic patient in whom isolation was discontinued after 2 negative results on nasopharyngeal polymerase chain reaction testing. The patient subsequently infected multiple roommates and staff, who then infected others. Seven of 52 (13%) secondary infections were detected only on second or subsequent tests. Eight of 9 (89%) patients who shared rooms with potentially contagious patients became infected. Potential contributing factors included high viral loads, nebulization, and positive pressure in the index patient's room. Risk factors for transmission to staff included presence during nebulization, caring for patients with dyspnea or cough, lack of eye protection, at least 15 minutes of exposure to case patients, and interactions with SARS-CoV-2-positive staff in clinical areas. Whole-genome sequencing confirmed that 2 staff members were infected despite wearing surgical masks and eye protection. LIMITATION: Findings may not be generalizable. CONCLUSION: SARS-CoV-2 clusters can occur in hospitals despite robust infection control policies. Insights from this cluster may inform additional measures to protect patients and staff. PRIMARY FUNDING SOURCE: None.

First confirmed cases of Middle East respiratory syndrome coronavirus (MERS-CoV) infection in the United States, updated information on the epidemiology of MERS-CoV infection, and guidance for the public, clinicians, and public health authorities - May 2014.

Since mid-March 2014, the frequency with which cases of Middle East respiratory syndrome coronavirus (MERS-CoV) infection have been reported has increased, with the majority of recent cases reported from Saudi Arabia and United Arab Emirates (UAE). In addition, the frequency with which travel-associated MERS cases have been reported and the number of countries that have reported them to the World Health Organization (WHO) have also increased. The first case of MERS in the United States, identified in a traveler recently returned from Saudi Arabia, was reported to CDC by the Indiana State Department of Health on May 1, 2014, and confirmed by CDC on May 2. A second imported case of MERS in the United States, identified in a traveler from Saudi Arabia having no connection with the first case, was reported to CDC by the Florida Department of Health on May 11, 2014. The purpose of this report is to alert clinicians, health officials, and others to increase awareness of the need to consider MERS-CoV infection in persons who have recently traveled from countries in or near the Arabian Peninsula. This report summarizes recent epidemiologic information, provides preliminary descriptions of the cases reported from Indiana and Florida, and updates CDC guidance about patient evaluation, home care and isolation, specimen collection, and travel as of May 13, 2014.

Invasive Pneumococcal Disease After 2 Decades of Pneumococcal Conjugate Vaccine Use.

OBJECTIVES: We sought to describe the evolving epidemiology of invasive pneumococcal disease (IPD) among children in Massachusetts, United States, over the last 2 decades during which sequential 7-valent pneumococcal conjugate vaccines (PCV7) and 13-valent PCVs (PCV13) were implemented. METHODS: Cases of IPD in children aged <18 years were detected between 2002 and 2021 through an enhanced population-based, statewide surveillance system. Streptococcus pneumoniae isolates from normally sterile sites were serotyped and evaluated for antimicrobial susceptibility. IPD incidence rates and rate ratios with 95% confidence intervals (CIs) were calculated. RESULTS: We identified 1347 IPD cases. Incidence of IPD in children aged <18 years declined 72% over 2 decades between 2002 and 2021 (incidence rate ratios 0.28, 95% CI 0.18-0.45). IPD rates continued to decline after replacement of PCV7 with PCV13 (incidence rate ratios 0.25, 95% CI 0.16-0.39, late PCV7 era [2010] versus late PCV13 era [2021]). During the coronavirus disease 2019 pandemic years, 2020 to 2021, the rate of IPD among children aged <18 years reached 1.6 per 100 000, the lowest incidence observed over the 20 years. In PCV13 era, approximately one-third of the IPD cases in children aged >5 years had at least 1 underlying condition (98, 30.3%). Serotypes 19A and 7F contributed 342 (48.9%) of all cases before implementation of PCV13 (2002-2010). Serotype 3 (31, 8.6%), and non-PCV13 serotypes 15B/C (39, 10.8%), 33F (29, 8.0%), 23B (21, 0.8%), and 35B (17, 4.7%) were responsible for 37.8% of cases in PCV13 era (2011-2021). Penicillin nonsusceptibility continued to decline (9.8% vs 5.3% in pre-/late PCV13 era, P = .003), however has become more common among non-PCV13 serotypes compared with vaccine serotypes (14.8% vs 1.4%, P < .001). CONCLUSIONS: Robust ongoing surveillance networks are critical for identifying emerging serotypes and development of next-generation vaccine formulations.

Using digital surveillance tools for near real-time mapping of the risk of infectious disease spread.

Data from digital disease surveillance tools such as ProMED and HealthMap can complement the field surveillance during ongoing outbreaks. Our aim was to investigate the use of data collected through ProMED and HealthMap in real-time outbreak analysis. We developed a flexible statistical model to quantify spatial heterogeneity in the risk of spread of an outbreak and to forecast short term incidence trends. The model was applied retrospectively to data collected by ProMED and HealthMap during the 2013-2016 West African Ebola epidemic and for comparison, to WHO data. Using ProMED and HealthMap data, the model was able to robustly quantify the risk of disease spread 1-4 weeks in advance and for countries at risk of case importations, quantify where this risk comes from. Our study highlights that ProMED and HealthMap data could be used in real-time to quantify the spatial heterogeneity in risk of spread of an outbreak.

Identification of Influenza Cases During the H1N1 Pandemic in Massachusetts Using Population-Based Hospital Discharge Data.

Objectives(1) To characterize the epidemiology of H1N1-related hospitalizations in Massachusetts; and (2) to compare characteristics of those hospitalized during periods of seasonal influenza activity and during the H1N1 pandemic. MethodsAuthors applied maximum and minimum criteria to the Massachusetts Hospital Discharge Database to identify H1N1-related hospitalizations. They constructed annual line graphs describing mean frequencies of influenza-like illness(ILI)-related discharges between 2005-2008, and compared these rates to early waves of H1N1 in 2009. ResultsDuring spring and summer 2009, there were significantly higher rates of ILI-related hospital discharges in Massachusetts compared to 2005-2008. Out of 359,344 total discharges between April 26-September 30,2009, H1N1-related hospitalizations ranged from 601 to 10,967 cases. Minimum criteria confirmed that H1N1 affected a younger population (50% were <18 years), with higher rates among African-Americans (18%) and Hispanics (23%) and higher rates of ICU admission (21%) compared to seasonal influenza (39%, 10%, 14%, and 17% respectively). ConclusionsThis is the first population-based assessment of epidemiological characteristics of hospitalized H1N1 cases in Massachusetts, and it is the first to include all possible hospitalized cases in the analysis. The authors confirm that large administrative data sets can detect hospitalizations for influenza during a pandemic, but estimated case counts vary widely depending on selection criteria used. Maximum criteria overestimated H1N1 activity, and those meeting minimum criteria resemble published accounts of H1N1-related hospitalizations closely.