Contact Tracing for Mpox Clade II Cases Associated with Air Travel - United States, July 2021-August 2022.

Monkeypox virus (MPXV) can spread among humans through direct contact with lesions, scabs, or saliva; via respiratory secretions; and indirectly from fomites; via percutaneous injuries; and by crossing the placenta to the fetus during pregnancy. Since 2022, most patients with mpox in the United States have experienced painful skin lesions, and some have had severe illness. During 2021-2022, CDC initiated aircraft contact investigations after receiving reports of travelers on commercial flights with probable or confirmed mpox during their infectious period. Data were collected 1) during 2021, when two isolated clade II mpox cases not linked to an outbreak were imported into the United States by international travelers and 2) for flights arriving in or traveling within the United States during April 30-August 2, 2022, after a global clade II mpox outbreak was detected in May 2022. A total of 113 persons (100 passengers and 13 crew members) traveled on 221 flights while they were infectious with mpox. CDC developed definitions for aircraft contacts based on proximity to mpox cases and flight duration, sent information about these contacts to U.S. health departments, and received outcome information for 1,046 (68%) of 1,538 contacts. No traveler was found to have acquired mpox via a U.S. flight exposure. For persons with mpox and their contacts who had departed from the United States, CDC forwarded contact information as well as details about the exposure event to destination countries to facilitate their own public health investigations. Findings from these aircraft contact investigations suggest that traveling on a flight with a person with mpox does not appear to constitute an exposure risk or warrant routine contact tracing activities. Nonetheless, CDC recommends that persons with mpox isolate and delay travel until they are no longer infectious.

On alert for Ebola: public health risk assessment of travellers from Uganda to the USA during the 2022 outbreak.

BACKGROUND: On 20 September 2022, the Ugandan Ministry of Health declared an outbreak of Ebola disease caused by Sudan ebolavirus. METHODS: From 6 October 2022 to 10 January 2023, Centers for Disease Control and Prevention (CDC) staff conducted public health assessments at five US ports of entry for travellers identified as having been in Uganda in the past 21 days. CDC also recommended that state, local and territorial health departments ('health departments') conduct post-arrival monitoring of these travellers. CDC provided traveller contact information, daily to 58 health departments, and collected health department data regarding monitoring outcomes. RESULTS: Among 11 583 travellers screened, 132 (1%) required additional assessment due to potential exposures or symptoms of concern. Fifty-three (91%) health departments reported receiving traveller data from CDC for 10 114 (87%) travellers, of whom 8499 (84%) were contacted for monitoring, 1547 (15%) could not be contacted and 68 (1%) had no reported outcomes. No travellers with high-risk exposures or Ebola disease were identified. CONCLUSION: Entry risk assessment and post-arrival monitoring of travellers are resource-intensive activities that had low demonstrated yield during this and previous outbreaks. The efficiency of future responses could be improved by incorporating an assessment of risk of importation of disease, accounting for individual travellers' potential for exposure, and expanded use of methods that reduce burden to federal agencies, health departments, and travellers.

SARS-CoV-2 Cases Reported on International Arriving and Domestic Flights: United States, January 2020-December 2021.

Objectives. To describe trends in the number of air travelers categorized as infectious with SARS-CoV-2 (severe acute respiratory syndrome coronavirus 2; the virus that causes COVID-19) in the context of total US COVID-19 vaccinations administered, and overall case counts of SARS-CoV-2 in the United States. Methods. We searched the Quarantine Activity Reporting System (QARS) database for travelers with inbound international or domestic air travel, a positive SARS-CoV-2 lab result, and a surveillance categorization of SARS-CoV-2 infection reported during January 2020 to December 2021. Travelers were categorized as infectious during travel if they had arrival dates from 2 days before to 10 days after symptom onset or a positive viral test. Results. We identified 80 715 persons meeting our inclusion criteria; 67 445 persons (83.6%) had at least 1 symptom reported. Of 67 445 symptomatic passengers, 43 884 (65.1%) reported an initial symptom onset date after their flight arrival date. The number of infectious travelers mirrored the overall number of US SARS-CoV-2 cases. Conclusions. Most travelers in the study were asymptomatic during travel, and therefore unknowingly traveled while infectious. During periods of high community transmission, it is important for travelers to stay up to date with COVID-19 vaccinations and consider wearing a high-quality mask to decrease the risk of transmission. (Am J Public Health. 2023;113(8):904-908. https://doi.org/10.2105/AJPH.2023.307325).

Underlying Medical Conditions and Severe Illness Among 540,667 Adults Hospitalized With COVID-19, March 2020-March 2021.

INTRODUCTION: Severe COVID-19 illness in adults has been linked to underlying medical conditions. This study identified frequent underlying conditions and their attributable risk of severe COVID-19 illness. METHODS: We used data from more than 800 US hospitals in the Premier Healthcare Database Special COVID-19 Release (PHD-SR) to describe hospitalized patients aged 18 years or older with COVID-19 from March 2020 through March 2021. We used multivariable generalized linear models to estimate adjusted risk of intensive care unit admission, invasive mechanical ventilation, and death associated with frequent conditions and total number of conditions. RESULTS: Among 4,899,447 hospitalized adults in PHD-SR, 540,667 (11.0%) were patients with COVID-19, of whom 94.9% had at least 1 underlying medical condition. Essential hypertension (50.4%), disorders of lipid metabolism (49.4%), and obesity (33.0%) were the most common. The strongest risk factors for death were obesity (adjusted risk ratio [aRR] = 1.30; 95% CI, 1.27-1.33), anxiety and fear-related disorders (aRR = 1.28; 95% CI, 1.25-1.31), and diabetes with complication (aRR = 1.26; 95% CI, 1.24-1.28), as well as the total number of conditions, with aRRs of death ranging from 1.53 (95% CI, 1.41-1.67) for patients with 1 condition to 3.82 (95% CI, 3.45-4.23) for patients with more than 10 conditions (compared with patients with no conditions). CONCLUSION: Certain underlying conditions and the number of conditions were associated with severe COVID-19 illness. Hypertension and disorders of lipid metabolism were the most frequent, whereas obesity, diabetes with complication, and anxiety disorders were the strongest risk factors for severe COVID-19 illness. Careful evaluation and management of underlying conditions among patients with COVID-19 can help stratify risk for severe illness.

Underlying Medical Conditions Associated With Severe COVID-19 Illness Among Children.

Importance: Information on underlying conditions and severe COVID-19 illness among children is limited. Objective: To examine the risk of severe COVID-19 illness among children associated with underlying medical conditions and medical complexity. Design, Setting, and Participants: This cross-sectional study included patients aged 18 years and younger with International Statistical Classification of Diseases, Tenth Revision, Clinical Modification code U07.1 (COVID-19) or B97.29 (other coronavirus) during an emergency department or inpatient encounter from March 2020 through January 2021. Data were collected from the Premier Healthcare Database Special COVID-19 Release, which included data from more than 800 US hospitals. Multivariable generalized linear models, controlling for patient and hospital characteristics, were used to estimate adjusted risk of severe COVID-19 illness associated with underlying medical conditions and medical complexity. Exposures: Underlying medical conditions and medical complexity (ie, presence of complex or noncomplex chronic disease). Main Outcomes and Measures: Hospitalization and severe illness when hospitalized (ie, combined outcome of intensive care unit admission, invasive mechanical ventilation, or death). Results: Among 43 465 patients with COVID-19 aged 18 years or younger, the median (interquartile range) age was 12 (4-16) years, 22 943 (52.8%) were female patients, and 12 491 (28.7%) had underlying medical conditions. The most common diagnosed conditions were asthma (4416 [10.2%]), neurodevelopmental disorders (1690 [3.9%]), anxiety and fear-related disorders (1374 [3.2%]), depressive disorders (1209 [2.8%]), and obesity (1071 [2.5%]). The strongest risk factors for hospitalization were type 1 diabetes (adjusted risk ratio [aRR], 4.60; 95% CI, 3.91-5.42) and obesity (aRR, 3.07; 95% CI, 2.66-3.54), and the strongest risk factors for severe COVID-19 illness were type 1 diabetes (aRR, 2.38; 95% CI, 2.06-2.76) and cardiac and circulatory congenital anomalies (aRR, 1.72; 95% CI, 1.48-1.99). Prematurity was a risk factor for severe COVID-19 illness among children younger than 2 years (aRR, 1.83; 95% CI, 1.47-2.29). Chronic and complex chronic disease were risk factors for hospitalization, with aRRs of 2.91 (95% CI, 2.63-3.23) and 7.86 (95% CI, 6.91-8.95), respectively, as well as for severe COVID-19 illness, with aRRs of 1.95 (95% CI, 1.69-2.26) and 2.86 (95% CI, 2.47-3.32), respectively. Conclusions and Relevance: This cross-sectional study found a higher risk of severe COVID-19 illness among children with medical complexity and certain underlying conditions, such as type 1 diabetes, cardiac and circulatory congenital anomalies, and obesity. Health care practitioners could consider the potential need for close observation and cautious clinical management of children with these conditions and COVID-19.

Risk of Clinical Severity by Age and Race/Ethnicity Among Adults Hospitalized for COVID-19-United States, March-September 2020.

BACKGROUND: Older adults and people from certain racial and ethnic groups are disproportionately represented in coronavirus disease 2019 (COVID-19) hospitalizations and deaths. METHODS: Using data from the Premier Healthcare Database on 181 813 hospitalized adults diagnosed with COVID-19 during March-September 2020, we applied multivariable log-binomial regression to assess the associations between age and race/ethnicity and COVID-19 clinical severity (intensive care unit [ICU] admission, invasive mechanical ventilation [IMV], and death) and to determine whether the impact of age on clinical severity differs by race/ethnicity. RESULTS: Overall, 84 497 (47%) patients were admitted to the ICU, 29 078 (16%) received IMV, and 27 864 (15%) died in the hospital. Increased age was strongly associated with clinical severity when controlling for underlying medical conditions and other covariates; the strength of this association differed by race/ethnicity. Compared with non-Hispanic White patients, risk of death was lower among non-Hispanic Black patients (adjusted risk ratio, 0.96; 95% CI, 0.92-0.99) and higher among Hispanic/Latino patients (risk ratio [RR], 1.15; 95% CI, 1.09-1.20), non-Hispanic Asian patients (RR, 1.16; 95% CI, 1.09-1.23), and patients of other racial and ethnic groups (RR, 1.13; 95% CI, 1.06-1.21). Risk of ICU admission and risk of IMV were elevated among some racial and ethnic groups. CONCLUSIONS: These results indicate that age is a driver of poor outcomes among hospitalized persons with COVID-19. Additionally, clinical severity may be elevated among patients of some racial and ethnic minority groups. Public health strategies to reduce severe acute respiratory syndrome coronavirus 2 infection rates among older adults and racial and ethnic minorities are essential to reduce poor outcomes.

Antibiotic Multidrug Resistance of Escherichia coli Causing Device- and Procedure-related Infections in the United States Reported to the National Healthcare Safety Network, 2013-2017.

BACKGROUND: Escherichia coli is one of the most common causes of healthcare-associated infections (HAIs); multidrug resistance reduces available options for antibiotic treatment. We examined factors associated with the spread of multidrug-resistant E. coli phenotypes responsible for device- and procedure-related HAIs from acute care hospitals, long-term acute care hospitals, and inpatient rehabilitation facilities, using isolate and antimicrobial susceptibility data reported to the National Healthcare Safety Network during 2013-2017. METHODS: We used multivariable logistic regression to examine associations between co-resistant phenotypes, patient and healthcare facility characteristics, and time. We also examined the geographic distribution of co-resistant phenotypes each year by state and by hospital referral region to identify hot spots. RESULTS: A total of 96 672 E. coli isolates were included. Patient median age was 62 years, and 60% were female; more than half (54%) were reported from catheter-associated urinary tract infections. From 2013 to 2017, 35% of the isolates were nonsusceptible to fluoroquinolones (FQs), 17% to extended-spectrum cephalosporins (ESCs), and 13% to both ESCs and FQs. The proportion of isolates co-resistant to ESCs and FQs was higher in 2017 (14%) than in 2013 (11%) (P < .0001); overall prevalence and increases were heterogeneously distributed across healthcare referral regions. Co-resistance to FQs and ESCs was independently associated with male sex, central line-associated bloodstream infections, long-term acute care hospitals, and the 2016-2017 (vs 2013-2014) reporting period. CONCLUSIONS: Multidrug resistance among E. coli causing device- and procedure-related HAIs has increased in the United States. FQ and ESC co-resistant strains appear to be spreading heterogeneously across hospital referral regions.

Characteristics of Hospitalized COVID-19 Patients Discharged and Experiencing Same-Hospital Readmission - United States, March-August 2020.

Coronavirus disease 2019 (COVID-19) is a complex clinical illness with potential complications that might require ongoing clinical care (1-3). Few studies have investigated discharge patterns and hospital readmissions among large groups of patients after an initial COVID-19 hospitalization (4-7). Using electronic health record and administrative data from the Premier Healthcare Database,* CDC assessed patterns of hospital discharge, readmission, and demographic and clinical characteristics associated with hospital readmission after a patient's initial COVID-19 hospitalization (index hospitalization). Among 126,137 unique patients with an index COVID-19 admission during March-July 2020, 15% died during the index hospitalization. Among the 106,543 (85%) surviving patients, 9% (9,504) were readmitted to the same hospital within 2 months of discharge through August 2020. More than a single readmission occurred among 1.6% of patients discharged after the index hospitalization. Readmissions occurred more often among patients discharged to a skilled nursing facility (SNF) (15%) or those needing home health care (12%) than among patients discharged to home or self-care (7%). The odds of hospital readmission increased with age among persons aged ≥65 years, presence of certain chronic conditions, hospitalization within the 3 months preceding the index hospitalization, and if discharge from the index hospitalization was to a SNF or to home with health care assistance. These results support recent analyses that found chronic conditions to be significantly associated with hospital readmission (6,7) and could be explained by the complications of underlying conditions in the presence of COVID-19 (8), COVID-19 sequelae (3), or indirect effects of the COVID-19 pandemic (9). Understanding the frequency of, and risk factors for, readmission can inform clinical practice, discharge disposition decisions, and public health priorities such as health care planning to ensure availability of resources needed for acute and follow-up care of COVID-19 patients. With the recent increases in cases nationwide, hospital planning can account for these increasing numbers along with the potential for at least 9% of patients to be readmitted, requiring additional beds and resources.

Facility-Wide Testing for SARS-CoV-2 in Nursing Homes - Seven U.S. Jurisdictions, March-June 2020.

Undetected infection with SARS-CoV-2, the virus that causes coronavirus disease 2019 (COVID-19) contributes to transmission in nursing homes, settings where large outbreaks with high resident mortality have occurred (1,2). Facility-wide testing of residents and health care personnel (HCP) can identify asymptomatic and presymptomatic infections and facilitate infection prevention and control interventions (3-5). Seven state or local health departments conducted initial facility-wide testing of residents and staff members in 288 nursing homes during March 24-June 14, 2020. Two of the seven health departments conducted testing in 195 nursing homes as part of facility-wide testing all nursing homes in their state, which were in low-incidence areas (i.e., the median preceding 14-day cumulative incidence in the surrounding county for each jurisdiction was 19 and 38 cases per 100,000 persons); 125 of the 195 nursing homes had not reported any COVID-19 cases before the testing. Ninety-five of 22,977 (0.4%) persons tested in 29 (23%) of these 125 facilities had positive SARS-CoV-2 test results. The other five health departments targeted facility-wide testing to 93 nursing homes, where 13,443 persons were tested, and 1,619 (12%) had positive SARS-CoV-2 test results. In regression analyses among 88 of these nursing homes with a documented case before facility-wide testing occurred, each additional day between identification of the first case and completion of facility-wide testing was associated with identification of 1.3 additional cases. Among 62 facilities that could differentiate results by resident and HCP status, an estimated 1.3 HCP cases were identified for every three resident cases. Performing facility-wide testing immediately after identification of a case commonly identifies additional unrecognized cases and, therefore, might maximize the benefits of infection prevention and control interventions. In contrast, facility-wide testing in low-incidence areas without a case has a lower proportion of test positivity; strategies are needed to further optimize testing in these settings.

Computer-based Prompt's impact on postural variability and sit-stand desk usage behavior; a cluster randomized control trial.

Sit-to-stand workstations have been deployed in office environments to reduce sedentary behavior and improve worker's health. However, efforts to initiate and sustain long-term usage of sit-stand workstations has been a challenge, with primarily anecdotal evidence suggesting many employees cease using their sit-stand workstations once the newness diminishes. To objectively determine sit-stand workstation usage and what impact computer-based prompts would have on sit-stand desk use and sustainability, 200 office workers (118 control and 82 treatment) in two different geographic locations were continuously monitored over a 4 ½ month period, which consisted of a 6-week baseline and a 3-month experimental period. During the 3-month experimental period, computer-based prompts elicited a 229% increase in daily standing transitions which was sustained over the entire 3 months with 40% of the participants adhering to a pre-determined sit to stand schedule. These findings indicate that the use of computer-based prompts can be used to motivate employees to change their behavior regarding the use of sit-to-stand workstations.