We present population-based data highlighting a disproportionate burden of MIS-C among Black and Hispanic children in NYC. It is unclear whether this finding represents a phenomenon distinct from the increased burden of COVID-19 in Black and Hispanic communities, because we also observed a disproportionate burden of COVID-19 hospitalizations among Black and Hispanic children. This analysis is limited by missing race/ethnicity data for most confirmed, nonhospitalized, and nonfatal COVID-19 cases in NYC, which prohibits evaluating the excess burden of MIS-C and COVID-19 hospitalizations among children of color. Furthermore, some patients meeting the MIS-C criteria may have been misclassified or not reported. Larger studies are needed to explore the relationship between MIS-C and race/ethnicity and to elucidate the impact of structural racism in perpetuating health disparities. 6 Although MIS-C is uncommon, clinicians should be aware of the potential enhanced risk of this emerging syndrome among Black and Hispanic children.

On May 5, 2021, this report was posted as an MMWR Early Release on the MMWR website (https://www.cdc.gov/mmwr).Recent studies have documented the emergence and rapid growth of B.1.526, a novel variant of interest (VOI) of SARS-CoV-2, the virus that causes COVID-19, in the New York City (NYC) area after its identification in NYC in November 2020 (1-3). Two predominant subclades within the B.1.526 lineage have been identified, one containing the E484K mutation in the receptor-binding domain (1,2), which attenuates in vitro neutralization by multiple SARS-CoV-2 antibodies and is present in variants of concern (VOCs) first identified in South Africa (B.1.351) (4) and Brazil (P.1).* The NYC Department of Health and Mental Hygiene (DOHMH) analyzed laboratory and epidemiologic data to characterize cases of B.1.526 infection, including illness severity, transmission to close contacts, rates of possible reinfection, and laboratorydiagnosed breakthrough infections among vaccinated persons. Preliminary data suggest that the B.1.526 variant does not lead to more severe disease and is not associated with increased risk for infection after vaccination (breakthrough infection) or reinfection. Because relatively few specimens were sequenced over the study period, the statistical power might have been insufficient to detect modest differences in rates of uncommon outcomes such as breakthrough infection or reinfection. Collection of timely viral genomic data for a larger proportion of citywide cases and rapid integration with population-based surveillance data would enable improved understanding of the impact of emerging SARS-CoV-2 variants and specific mutations to help guide public health intervention efforts.SARS-CoV-2 specimens were sequenced at the Public Health Laboratory (PHL) or the Pandemic Response Laboratory (PRL). During January 1-April 5, 2021, PHL received specimens primarily from NYC residents at nine COVID Express laboratories. All nucleic acid amplification test (NAAT)positive SARS-CoV-2 specimens with a cycle threshold (Ct) value <32 underwent whole genome sequencing (WGS) (Scott Hughes, PhD, NYC PHL, personal communication, April 2021). At PRL, specimens collected at approximately 190 outpatient facilities were randomly selected, and those with a Ct value ≤30 were sequenced (5,6). Characteristics of persons *

No abstract

Depression is responsible for a large burden of disability in the USA. We estimated the prevalence of depression in the New York City (NYC) adult population in 2013-14 and examined associations with demographics, health behaviors, and employment status. Data from the 2013-14 New York City Health and Nutrition Examination Survey, a population-based examination study, were analyzed, and 1459 participants met the inclusion criteria for this analysis. We defined current symptomatic depression by a Patient Health Questionnaire (PHQ-9) score ≥ 10. Overall, 8.3% of NYC adults had current symptomatic depression. New Yorkers with current symptomatic depression were significantly more likely to be female, Latino, and unemployed yet not looking for work; they were also significantly more likely to have less than a high school education and to live in a high-poverty neighborhood. Socioeconomic inequalities in mental health persist in NYC and highlight the need for better diagnosis and treatment.

Objectives: To identify the proportion of coronavirus disease 2019 (COVID-19) cases that occurred within households or buildings in New York City (NYC) beginning in March 2020 during the first stay-at-home order to determine transmission attributable to these settings and inform targeted prevention strategies. Design: The residential addresses of cases were geocoded (converting descriptive addresses to latitude and longitude coordinates) and used to identify clusters of cases residing in unique buildings based on building identification number (BIN), a unique building identifier. Household clusters were defined as 2 or more cases within 2 weeks of onset or diagnosis date in the same BIN with the same unit number, last name, or in a single-family home. Building clusters were defined as 3 or more cases with onset date or diagnosis date within 2 weeks in the same BIN who do not reside in the same household. Setting: NYC from March to December 2020. Participants: NYC residents with a positive SARS-CoV-2 nucleic acid amplification or antigen test result with a specimen collected during March 1, 2020, to December 31, 2020. Main Outcome Measure: The proportion of NYC COVID-19 cases in a household or building cluster. Results: The BIN analysis identified 65 343 building and household clusters: 17 139 (26%) building clusters and 48 204 (74%) household clusters. A substantial proportion of NYC COVID-19 cases (43%) were potentially attributable to household transmission in the first 9 months of the pandemic. Conclusions: Geocoded address matching assisted in identifying COVID-19 household clusters. Close contact transmission within a household or building cluster was found in 43% of noncongregate cases with a valid residential NYC address. The BIN analysis should be utilized to identify disease clustering for improved surveillance.