Investigation of a Prolonged and Large Outbreak of Healthcare-Associated Mucormycosis Cases in an Acute Care Hospital-Arkansas, June 2019-May 2021.

Background: Outbreaks of healthcare-associated mucormycosis (HCM), a life-threatening fungal infection, have been attributed to multiple sources, including contaminated healthcare linens. In 2020, staff at Hospital A in Arkansas alerted public health officials of a potential HCM outbreak. Methods: We collected data on patients at Hospital A who had invasive mucormycosis during January 2017-June 2021 and calculated annual incidence of HCM (defined as mucormycosis diagnosed within ≥7 days after hospital admission). We performed targeted environmental assessments, including linen sampling at the hospital, to identify potential sources of infection. Results: During the outbreak period (June 2019-June 2021), 16 patients had HCM; clinical features were similar between HCM patients and non-HCM patients. Hospital-wide HCM incidence (per 100 000 patient-days) increased from 0 in 2018 to 3 in 2019 and 6 in 2020. For the 16 HCM patients, the most common underlying medical conditions were hematologic malignancy (56%) and recent traumatic injury (38%); 38% of HCM patients died in-hospital. Healthcare-associated mucormycosis cases were not epidemiologically linked by common procedures, products, units, or rooms. At Hospital A and its contracted offsite laundry provider, suboptimal handling of laundered linens and inadequate environmental controls to prevent mucormycete contamination were observed. We detected Rhizopus on 9 (9%) of 98 linens sampled at the hospital, including on linens that had just arrived from the laundry facility. Conclusions: We describe the largest, single-center, HCM outbreak reported to date. Our findings underscore the importance of hospital-based monitoring for HCM and increased attention to the safe handling of laundered linens.

Severe acute respiratory coronavirus virus 2 (SARS-CoV-2) outbreak investigation in a hospital emergency department-California, December 2020-January 2021.

We describe a large outbreak of severe acute respiratory coronavirus virus 2 (SARS-CoV-2) involving an acute-care hospital emergency department during December 2020 and January 2021, in which 27 healthcare personnel worked while infectious, resulting in multiple opportunities for SARS-CoV-2 transmission to patients and other healthcare personnel. We provide recommendations for improving infection prevention and control.

Risk Factors for SARS-CoV-2 Infection Among US Healthcare Personnel, May-December 2020.

To determine risk factors for coronavirus disease (COVID-19) among US healthcare personnel (HCP), we conducted a case-control analysis. We collected data about activities outside the workplace and COVID-19 patient care activities from HCP with positive severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) test results (cases) and from HCP with negative test results (controls) in healthcare facilities in 5 US states. We used conditional logistic regression to calculate adjusted matched odds ratios and 95% CIs for exposures. Among 345 cases and 622 controls, factors associated with risk were having close contact with persons with COVID-19 outside the workplace, having close contact with COVID-19 patients in the workplace, and assisting COVID-19 patients with activities of daily living. Protecting HCP from COVID-19 may require interventions that reduce their exposures outside the workplace and improve their ability to more safely assist COVID-19 patients with activities of daily living.

COVID-19 Vaccine Uptake Among Residents and Staff Members of Assisted Living and Residential Care Communities-Pharmacy Partnership for Long-Term Care Program, December 2020-April 2021.

OBJECTIVES: In December 2020, CDC launched the Pharmacy Partnership for Long-Term Care Program to facilitate COVID-19 vaccination of residents and staff in long-term care facilities (LTCFs), including assisted living (AL) and other residential care (RC) communities. We aimed to assess vaccine uptake in these communities and identify characteristics that might impact uptake. DESIGN: Cross-sectional study. SETTING AND PARTICIPANTS: AL/RC communities in the Pharmacy Partnership for Long-Term Care Program that had ≥1 on-site vaccination clinic during December 18, 2020-April 21, 2021. METHODS: We estimated uptake using the cumulative number of doses of COVID-19 vaccine administered and normalizing by the number of AL/RC community beds. We estimated the percentage of residents vaccinated in 3 states using AL census counts. We linked community vaccine administration data with county-level social vulnerability index (SVI) measures to calculate median vaccine uptake by SVI tertile. RESULTS: In AL communities, a median of 67 residents [interquartile range (IQR): 48-90] and 32 staff members (IQR: 15-60) per 100 beds received a first dose of COVID-19 vaccine at the first on-site clinic; in RC, a median of 8 residents (IQR: 5-10) and 5 staff members (IQR: 2-12) per 10 beds received a first dose. Among 3 states with available AL resident census data, median resident first-dose uptake at the first clinic was 93% (IQR: 85-108) in Connecticut, 85% in Georgia (IQR: 70-102), and 78% (IQR: 56-91) in Tennessee. Among both residents and staff, cumulative first-dose vaccine uptake increased with increasing social vulnerability related to housing type and transportation. CONCLUSIONS AND IMPLICATIONS: COVID-19 vaccination of residents and staff in LTCFs is a public health priority. On-site clinics may help to increase vaccine uptake, particularly when transportation may be a barrier. Ensuring steady access to COVID-19 vaccine in LTCFs following the conclusion of the Pharmacy Partnership is critical to maintaining high vaccination coverage among residents and staff.

Comparative Impact of Individual Quarantine vs. Active Monitoring of Contacts for the Mitigation of COVID-19: a modelling study.

BACKGROUND: Voluntary individual quarantine and voluntary active monitoring of contacts are core disease control strategies for emerging infectious diseases, such as COVID-19. Given the impact of quarantine on resources and individual liberty, it is vital to assess under what conditions individual quarantine can more effectively control COVID-19 than active monitoring. As an epidemic grows, it is also important to consider when these interventions are no longer feasible, and broader mitigation measures must be implemented. METHODS: To estimate the comparative efficacy of these case-based interventions to control COVID-19, we fit a stochastic branching model to reported parameters for the dynamics of the disease. Specifically, we fit to the incubation period distribution and each of two sets of the serial interval distribution: a shorter one with a mean serial interval of 4.8 days and a longer one with a mean of 7.5 days. To assess variable resource settings, we consider two feasibility settings: a high feasibility setting with 90% of contacts traced, a half-day average delay in tracing and symptom recognition, and 90% effective isolation; and low feasibility setting with 50% of contacts traced, a two-day average delay, and 50% effective isolation. FINDINGS: Our results suggest that individual quarantine in high feasibility settings where at least three-quarters of infected contacts are individually quarantined contains an outbreak of COVID-19 with a short serial interval (4.8 days) 84% of the time. However, in settings where this performance is unrealistically high and the outbreak continues to grow, so too will the burden of the number of contacts traced for active monitoring or quarantine. When resources are prioritized for scalable interventions such as social distancing, we show active monitoring or individual quarantine of high-risk contacts can contribute synergistically to mitigation efforts. INTERPRETATION: Our model highlights the urgent need for more data on the serial interval and the extent of presymptomatic transmission in order to make data-driven policy decisions regarding the cost-benefit comparisons of individual quarantine vs. active monitoring of contacts. To the extent these interventions can be implemented they can help mitigate the spread of COVID-19.

US-county level variation in intersecting individual, household and community characteristics relevant to COVID-19 and planning an equitable response: a cross-sectional analysis.

OBJECTIVES: To illustrate the intersections of, and intercounty variation in, individual, household and community factors that influence the impact of COVID-19 on US counties and their ability to respond. DESIGN: We identified key individual, household and community characteristics influencing COVID-19 risks of infection and survival, guided by international experiences and consideration of epidemiological parameters of importance. Using publicly available data, we developed an open-access online tool that allows county-specific querying and mapping of risk factors. As an illustrative example, we assess the pairwise intersections of age (individual level), poverty (household level) and prevalence of group homes (community-level) in US counties. We also examine how these factors intersect with the proportion of the population that is people of colour (ie, not non-Hispanic white), a metric that reflects histories of US race relations. We defined 'high' risk counties as those above the 75th percentile. This threshold can be changed using the online tool. SETTING: US counties. PARTICIPANTS: Analyses are based on publicly available county-level data from the Area Health Resources Files, American Community Survey, Centers for Disease Control and Prevention Atlas file, National Center for Health Statistic and RWJF Community Health Rankings. RESULTS: Our findings demonstrate significant intercounty variation in the distribution of individual, household and community characteristics that affect risks of infection, severe disease or mortality from COVID-19. About 9% of counties, affecting 10 million residents, are in higher risk categories for both age and group quarters. About 14% of counties, affecting 31 million residents, have both high levels of poverty and a high proportion of people of colour. CONCLUSION: Federal and state governments will benefit from recognising high intrastate, intercounty variation in population risks and response capacity. Equitable responses to the pandemic require strategies to protect those in counties at highest risk of adverse COVID-19 outcomes and their social and economic impacts.

Individual quarantine versus active monitoring of contacts for the mitigation of COVID-19: a modelling study.

BACKGROUND: Voluntary individual quarantine and voluntary active monitoring of contacts are core disease control strategies for emerging infectious diseases such as COVID-19. Given the impact of quarantine on resources and individual liberty, it is vital to assess under what conditions individual quarantine can more effectively control COVID-19 than active monitoring. As an epidemic grows, it is also important to consider when these interventions are no longer feasible and broader mitigation measures must be implemented. METHODS: To estimate the comparative efficacy of individual quarantine and active monitoring of contacts to control severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), we fit a stochastic branching model to reported parameters for the dynamics of the disease. Specifically, we fit a model to the incubation period distribution (mean 5·2 days) and to two estimates of the serial interval distribution: a shorter one with a mean serial interval of 4·8 days and a longer one with a mean of 7·5 days. To assess variable resource settings, we considered two feasibility settings: a high-feasibility setting with 90% of contacts traced, a half-day average delay in tracing and symptom recognition, and 90% effective isolation; and a low-feasibility setting with 50% of contacts traced, a 2-day average delay, and 50% effective isolation. FINDINGS: Model fitting by sequential Monte Carlo resulted in a mean time of infectiousness onset before symptom onset of 0·77 days (95% CI -1·98 to 0·29) for the shorter serial interval, and for the longer serial interval it resulted in a mean time of infectiousness onset after symptom onset of 0·51 days (95% CI -0·77 to 1·50). Individual quarantine in high-feasibility settings, where at least 75% of infected contacts are individually quarantined, contains an outbreak of SARS-CoV-2 with a short serial interval (4·8 days) 84% of the time. However, in settings where the outbreak continues to grow (eg, low-feasibility settings), so too will the burden of the number of contacts traced for active monitoring or quarantine, particularly uninfected contacts (who never develop symptoms). When resources are prioritised for scalable interventions such as physical distancing, we show active monitoring or individual quarantine of high-risk contacts can contribute synergistically to mitigation efforts. Even under the shorter serial interval, if physical distancing reduces the reproductive number to 1·25, active monitoring of 50% of contacts can result in overall outbreak control (ie, effective reproductive number <1). INTERPRETATION: Our model highlights the urgent need for more data on the serial interval and the extent of presymptomatic transmission to make data-driven policy decisions regarding the cost-benefit comparisons of individual quarantine versus active monitoring of contacts. To the extent that these interventions can be implemented, they can help mitigate the spread of SARS-CoV-2. FUNDING: National Institute of General Medical Sciences, National Institutes of Health.

Estimated Demand for US Hospital Inpatient and Intensive Care Unit Beds for Patients With COVID-19 Based on Comparisons With Wuhan and Guangzhou, China.

Importance: Sustained spread of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has happened in major US cities. Capacity needs in cities in China could inform the planning of local health care resources. Objectives: To describe and compare the intensive care unit (ICU) and inpatient bed needs for patients with coronavirus disease 2019 (COVID-19) in 2 cities in China to estimate the peak ICU bed needs in US cities if an outbreak equivalent to that in Wuhan occurs. Design, Setting, and Participants: This comparative effectiveness study analyzed the confirmed cases of COVID-19 in Wuhan and Guangzhou, China, from January 10 to February 29, 2020. Exposures: Timing of disease control measures relative to timing of SARS-CoV-2 community spread. Main Outcomes and Measures: Number of critical and severe patient-days and peak number of patients with critical and severe illness during the study period. Results: In Wuhan, strict disease control measures were implemented 6 weeks after sustained local transmission of SARS-CoV-2. Between January 10 and February 29, 2020, patients with COVID-19 accounted for a median (interquartile range) of 429 (25-1143) patients in the ICU and 1521 (111-7202) inpatients with serious illness each day. During the epidemic peak, 19 425 patients (24.5 per 10 000 adults) were hospitalized, 9689 (12.2 per 10 000 adults) were considered in serious condition, and 2087 (2.6 per 10 000 adults) needed critical care per day. In Guangzhou, strict disease control measures were implemented within 1 week of case importation. Between January 24 and February 29, COVID-19 accounted for a median (interquartile range) of 9 (7-12) patients in the ICU and 17 (15-26) inpatients with serious illness each day. During the epidemic peak, 15 patients were in critical condition and 38 were classified as having serious illness. The projected number of prevalent critically ill patients at the peak of a Wuhan-like outbreak in US cities was estimated to range from 2.2 to 4.4 per 10 000 adults, depending on differences in age distribution and comorbidity (ie, hypertension) prevalence. Conclusions and Relevance: Even after the lockdown of Wuhan on January 23, the number of patients with serious COVID-19 illness continued to rise, exceeding local hospitalization and ICU capacities for at least a month. Plans are urgently needed to mitigate the consequences of COVID-19 outbreaks on the local health care systems in US cities.

The effect of human mobility and control measures on the COVID-19 epidemic in China.

The ongoing coronavirus disease 2019 (COVID-19) outbreak expanded rapidly throughout China. Major behavioral, clinical, and state interventions were undertaken to mitigate the epidemic and prevent the persistence of the virus in human populations in China and worldwide. It remains unclear how these unprecedented interventions, including travel restrictions, affected COVID-19 spread in China. We used real-time mobility data from Wuhan and detailed case data including travel history to elucidate the role of case importation in transmission in cities across China and to ascertain the impact of control measures. Early on, the spatial distribution of COVID-19 cases in China was explained well by human mobility data. After the implementation of control measures, this correlation dropped and growth rates became negative in most locations, although shifts in the demographics of reported cases were still indicative of local chains of transmission outside of Wuhan. This study shows that the drastic control measures implemented in China substantially mitigated the spread of COVID-19.