Trends in Laboratory-Confirmed SARS-CoV-2 Reinfections and Associated Hospitalizations and Deaths Among Adults Aged ≥18 Years - 18 U.S. Jurisdictions, September 2021-December 2022.

Although reinfections with SARS-CoV-2 have occurred in the United States with increasing frequency, U.S. epidemiologic trends in reinfections and associated severe outcomes have not been characterized. Weekly counts of SARS-CoV-2 reinfections, total infections, and associated hospitalizations and deaths reported by 18 U.S. jurisdictions during September 5, 2021-December 31, 2022, were analyzed overall, by age group, and by five periods of SARS-CoV-2 variant predominance (Delta and Omicron [BA.1, BA.2, BA.4/BA.5, and BQ.1/BQ.1.1]). Among reported reinfections, weekly trends in the median intervals between infections and frequencies of predominant variants during previous infections were calculated. As a percentage of all infections, reinfections increased substantially from the Delta (2.7%) to the Omicron BQ.1/BQ.1.1 (28.8%) periods; during the same periods, increases in the percentages of reinfections among COVID-19-associated hospitalizations (from 1.9% [Delta] to 17.0% [Omicron BQ.1/BQ.1.1]) and deaths (from 1.2% [Delta] to 12.3% [Omicron BQ.1/BQ.1.1]) were also substantial. Percentages of all COVID-19 cases, hospitalizations, and deaths that were reinfections were consistently higher across variant periods among adults aged 18-49 years compared with those among adults aged ≥50 years. The median interval between infections ranged from 269 to 411 days by week, with a steep decline at the start of the BA.4/BA.5 period, when >50% of reinfections occurred among persons previously infected during the Alpha variant period or later. To prevent severe COVID-19 outcomes, including those following reinfection, CDC recommends staying up to date with COVID-19 vaccination and receiving timely antiviral treatments, when eligible.

Built by epidemiologists for epidemiologists: an internal COVID-19 dashboard for real-time situational awareness in New York City.

Objective: New York City (NYC) experienced a large first wave of coronavirus disease 2019 (COVID-19) in the spring of 2020, but the Health Department lacked tools to easily visualize and analyze incoming surveillance data to inform response activities. To streamline ongoing surveillance, a group of infectious disease epidemiologists built an interactive dashboard using open-source software to monitor demographic, spatial, and temporal trends in COVID-19 epidemiology in NYC in near real-time for internal use by other surveillance and epidemiology experts. Materials and methods: Existing surveillance databases and systems were leveraged to create daily analytic datasets of COVID-19 case and testing information, aggregated by week and key demographics. The dashboard was developed iteratively using R, and includes interactive graphs, tables, and maps summarizing recent COVID-19 epidemiologic trends. Additional data and interactive features were incorporated to provide further information on the spread of COVID-19 in NYC. Results: The dashboard allows key staff to quickly review situational data, identify concerning trends, and easily maintain granular situational awareness of COVID-19 epidemiology in NYC. Discussion: The dashboard is used to inform weekly surveillance summaries and alleviated the burden of manual report production on infectious disease epidemiologists. The system was built by and for epidemiologists, which is critical to its utility and functionality. Interactivity allows users to understand broad and granular data, and flexibility in dashboard development means new metrics and visualizations can be developed as needed. Conclusions: Additional investment and development of public health informatics tools, along with standardized frameworks for local health jurisdictions to analyze and visualize data in emergencies, are warranted.

COVID-19 Outbreak - New York City, February 29-June 1, 2020.

New York City (NYC) was an epicenter of the coronavirus disease 2019 (COVID-19) outbreak in the United States during spring 2020 (1). During March-May 2020, approximately 203,000 laboratory-confirmed COVID-19 cases were reported to the NYC Department of Health and Mental Hygiene (DOHMH). To obtain more complete data, DOHMH used supplementary information sources and relied on direct data importation and matching of patient identifiers for data on hospitalization status, the occurrence of death, race/ethnicity, and presence of underlying medical conditions. The highest rates of cases, hospitalizations, and deaths were concentrated in communities of color, high-poverty areas, and among persons aged ≥75 years or with underlying conditions. The crude fatality rate was 9.2% overall and 32.1% among hospitalized patients. Using these data to prevent additional infections among NYC residents during subsequent waves of the pandemic, particularly among those at highest risk for hospitalization and death, is critical. Mitigating COVID-19 transmission among vulnerable groups at high risk for hospitalization and death is an urgent priority. Similar to NYC, other jurisdictions might find the use of supplementary information sources valuable in their efforts to prevent COVID-19 infections.

Characteristics of Persons Who Died with COVID-19 - United States, February 12-May 18, 2020.

During January 1, 2020-May 18, 2020, approximately 1.3 million cases of coronavirus disease 2019 (COVID-19) and 83,000 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).

Fecal metagenomic profiles in subgroups of patients with myalgic encephalomyelitis/chronic fatigue syndrome.

BACKGROUND: Myalgic encephalomyelitis/chronic fatigue syndrome (ME/CFS) is characterized by unexplained persistent fatigue, commonly accompanied by cognitive dysfunction, sleeping disturbances, orthostatic intolerance, fever, lymphadenopathy, and irritable bowel syndrome (IBS). The extent to which the gastrointestinal microbiome and peripheral inflammation are associated with ME/CFS remains unclear. We pursued rigorous clinical characterization, fecal bacterial metagenomics, and plasma immune molecule analyses in 50 ME/CFS patients and 50 healthy controls frequency-matched for age, sex, race/ethnicity, geographic site, and season of sampling. RESULTS: Topological analysis revealed associations between IBS co-morbidity, body mass index, fecal bacterial composition, and bacterial metabolic pathways but not plasma immune molecules. IBS co-morbidity was the strongest driving factor in the separation of topological networks based on bacterial profiles and metabolic pathways. Predictive selection models based on bacterial profiles supported findings from topological analyses indicating that ME/CFS subgroups, defined by IBS status, could be distinguished from control subjects with high predictive accuracy. Bacterial taxa predictive of ME/CFS patients with IBS were distinct from taxa associated with ME/CFS patients without IBS. Increased abundance of unclassified Alistipes and decreased Faecalibacterium emerged as the top biomarkers of ME/CFS with IBS; while increased unclassified Bacteroides abundance and decreased Bacteroides vulgatus were the top biomarkers of ME/CFS without IBS. Despite findings of differences in bacterial taxa and metabolic pathways defining ME/CFS subgroups, decreased metabolic pathways associated with unsaturated fatty acid biosynthesis and increased atrazine degradation pathways were independent of IBS co-morbidity. Increased vitamin B6 biosynthesis/salvage and pyrimidine ribonucleoside degradation were the top metabolic pathways in ME/CFS without IBS as well as in the total ME/CFS cohort. In ME/CFS subgroups, symptom severity measures including pain, fatigue, and reduced motivation were correlated with the abundance of distinct bacterial taxa and metabolic pathways. CONCLUSIONS: Independent of IBS, ME/CFS is associated with dysbiosis and distinct bacterial metabolic disturbances that may influence disease severity. However, our findings indicate that dysbiotic features that are uniquely ME/CFS-associated may be masked by disturbances arising from the high prevalence of IBS co-morbidity in ME/CFS. These insights may enable more accurate diagnosis and lead to insights that inform the development of specific therapeutic strategies in ME/CFS subgroups.

Distinct plasma immune signatures in ME/CFS are present early in the course of illness.

Myalgic encephalomyelitis/chronic fatigue syndrome (ME/CFS) is an unexplained incapacitating illness that may affect up to 4 million people in the United States alone. There are no validated laboratory tests for diagnosis or management despite global efforts to find biomarkers of disease. We considered the possibility that inability to identify such biomarkers reflected variations in diagnostic criteria and laboratory methods as well as the timing of sample collection during the course of the illness. Accordingly, we leveraged two large, multicenter cohort studies of ME/CFS to assess the relationship of immune signatures with diagnosis, illness duration, and other clinical variables. Controls were frequency-matched on key variables known to affect immune status, including season of sampling and geographic site, in addition to age and sex. We report here distinct alterations in plasma immune signatures early in the course of ME/CFS (n = 52) relative to healthy controls (n = 348) that are not present in subjects with longer duration of illness (n = 246). Analyses based on disease duration revealed that early ME/CFS cases had a prominent activation of both pro- and anti-inflammatory cytokines as well as dissociation of intercytokine regulatory networks. We found a stronger correlation of cytokine alterations with illness duration than with measures of illness severity, suggesting that the immunopathology of ME/CFS is not static. These findings have critical implications for discovery of interventional strategies and early diagnosis of ME/CFS.