Clustering of SARS-CoV-2 in Households in New York City: A Building-Level Analysis, March-December 2020.

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.

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.

Successful, Easy to Access, Online Publication of COVID-19 Data During the Pandemic, New York City, 2020.

Making public health data easier to access, understand, and use makes it more likely that the data will be influential. Throughout the COVID-19 pandemic, the New York City (NYC) Department of Health and Mental Hygiene's Web-based data communication became a cornerstone of NYC's response and allowed the public, journalists, and researchers to access and understand the data in a way that supported the pandemic response and brought attention to the deeply unequal patterns of COVID-19's morbidity and mortality in NYC. (Am J Public Health. 2021;111(S3):S193-S196. https://doi.org/10.2105/AJPH.2021.306446).

Persistent disparities in SARS-CoV-2 test percent positivity by neighborhood in New York City, March 1-July 25, 2020.

PURPOSE: To examine neighborhood-level disparities in SARS-CoV-2 molecular test percent positivity in New York City (NYC) by demographics and socioeconomic status over time to better understand COVID-19 inequities. METHODS: Across 177 neighborhoods, we calculated the Spearman correlation of neighborhood characteristics with SARS-CoV-2 molecular test percent positivity during March 1-July 25, 2020 by five periods defined by trend in case counts: increasing, declining, and three plateau periods to account for differential testing capacity and reopening status. RESULTS: Percent positivity was positively correlated with neighborhood racial and ethnic characteristics and socioeconomic status, including the proportion of the population who were Latino and Black non-Latino, uninsured, Medicaid enrollees, transportation workers, or had low educational attainment. Correlations were generally consistent over time despite increasing testing rates. Neighborhoods with high proportions of these correlates had median percent positivity values of 62.6%, 28.7%, 6.4%, 2.8%, and 2.2% in the five periods, respectively, compared with 40.6%, 11.7%, 1.7%, 0.9%, and 1.0% in neighborhoods with low proportions of these correlates. CONCLUSIONS: Disparities in SARS-CoV-2 molecular test percent positivity persisted in disadvantaged neighborhoods during multiple phases of the first few months of the COVID-19 epidemic in NYC. Mitigation of the COVID-19 burden is still urgently needed in disproportionately affected communities.

Rapid Emergence and Epidemiologic Characteristics of the SARS-CoV-2 B.1.526 Variant - New York City, New York, January 1-April 5, 2021.

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 laboratory-diagnosed 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.