No abstract

COVID-19-associated deaths were reported in the United States (1). Understanding the demographic and clinical characteristics of decedents could inform medical and public health interventions focused on preventing COVID-19-associated mortality. This report describes decedents with laboratory-confirmed infection with SARS-CoV-2, the virus that causes COVID-19, using data from 1) the standardized CDC case-report form (case-based surveillance) (https://www.cdc.gov/coronavirus/2019-ncov/php/ reporting-pui.html) and 2) supplementary data (supplemental surveillance), such as underlying medical conditions and location of death, obtained through collaboration between CDC and 16 public health jurisdictions (15 states and New York City). Case-based surveillanceDemographic and clinical data about COVID-19 cases are reported to CDC from 50 states, the District of Columbia, New York City, and U.S. territories using a standardized case-report form (case-based surveillance) or in aggregate. Data on 52,166 deaths from 47 jurisdictions among persons with laboratoryconfirmed COVID-19 were reported individually to CDC via case-based surveillance during February 12-May 18, 2020. Among the 52,166 decedents, 55.4% were male, 79.6% were aged ≥65 years, 13.8% were Hispanic/Latino (Hispanic), 21.0% were black, 40.3% were white, 3.9% were Asian, 0.3% were American Indian/Alaska Native (AI/AN), 0.1% were Native Hawaiian or other Pacific Islander (NHPI), 2.6% were multiracial or other race, and race/ethnicity was unknown for 18.0%. (Table 1). Median decedent age was 78 years (interquartile range (IQR) = 67-87 years). Because information about underlying medical conditions was missing for the majority of these decedents (30,725; 58.9%), data regarding medical conditions were not analyzed further using the case-based surveillance data set. Because most decedents reported to the supplementary data program were also reported to case-based surveillance, no statistical comparisons of the decedent characteristics between the data sets were made. * Underlying medical conditions include cardiovascular disease (congenital heart disease, coronary artery disease, congestive heart failure, hypertension, cerebrovascular accident/stroke, valvular heart disease, conduction disorders or dysrhythmias, other cardiovascular disease); diabetes mellitus; chronic lung disease (chronic obstructive pulmonary disease/emphysema, asthma, tuberculosis, other chronic lung diseases); immunosuppression (cancer, human immunodeficiency virus (HIV) infection, identified as being immunosuppressed); chronic kidney disease (chronic kidney disease, end-stage renal disease, other kidney diseases); neurologic conditions (dementia, seizure disorder, other neurologic conditions); chronic liver disease (cirrhosis, alcoholic hepatitis, chronic liver disease, end-stage liver disease, hepatitis B, hepatitis C, nonalcoholic steatohepatitis, other chronic liver diseases); obesity (body mass index ≥30 kg/m 2 ). Information was collected from decedent medical records or death certificates. ...

Infection is associated with proximity to virus-infected animals and their excretions and secretions.

More than three-quarters of exposed HCWs reported at least 1 unprotected encounter with a patient who had monkeypox. One asymptomatic HCW showed laboratory evidence of recent orthopoxvirus infection, which was possibly attributable to either recent infection or smallpox vaccination. Transmission of monkeypox likely is a rare event in the health care setting.

In January 2015, an outbreak of undiagnosed human immunodeficiency virus (HIV) infections among persons who inject drugs (PWID) was recognized in rural Indiana. By September 2016, 205 persons in this community of approximately 4400 had received a diagnosis of HIV infection. We report results of new approaches to analyzing epidemiologic and laboratory data to understand transmission during this outbreak. HIV genetic distances were calculated using the polymerase region. Networks were generated using data about reported high-risk contacts, viral genetic similarity, and their most parsimonious combinations. Sample collection dates and recency assay results were used to infer dates of infection. Epidemiologic and laboratory data each generated large and dense networks. Integration of these data revealed subgroups with epidemiologic and genetic commonalities, one of which appeared to contain the earliest infections. Predicted infection dates suggest that transmission began in 2011, underwent explosive growth in mid-2014, and slowed after the declaration of a public health emergency. Results from this phylodynamic analysis suggest that the majority of infections had likely already occurred when the investigation began and that early transmission may have been associated with sexual activity and injection drug use. Early and sustained efforts are needed to detect infections and prevent or interrupt rapid transmission within networks of uninfected PWID.