Detection and characterization of the SARS-CoV-2 lineage B.1.526 in New York.

Wide-scale SARS-CoV-2 genome sequencing is critical to tracking viral evolution during the ongoing pandemic. We develop the software tool, Variant Database (VDB), for quickly examining the changing landscape of spike mutations. Using VDB, we detect an emerging lineage of SARS-CoV-2 in the New York region that shares mutations with previously reported variants. The most common sets of spike mutations in this lineage (now designated as B.1.526) are L5F, T95I, D253G, E484K or S477N, D614G, and A701V. This lineage was first sequenced in late November 2020. Phylodynamic inference confirmed the rapid growth of the B.1.526 lineage. In concert with other variants, like B.1.1.7, the rise of B.1.526 appears to have extended the duration of the second wave of COVID-19 cases in NYC in early 2021. Pseudovirus neutralization experiments demonstrated that B.1.526 spike mutations adversely affect the neutralization titer of convalescent and vaccinee plasma, supporting the public health relevance of this lineage.

Detection and characterization of the SARS-CoV-2 lineage B.1.526 in New York.

Wide-scale SARS-CoV-2 genome sequencing is critical to tracking viral evolution during the ongoing pandemic. Variants first detected in the United Kingdom, South Africa, and Brazil have spread to multiple countries. We developed the software tool, Variant Database (VDB), for quickly examining the changing landscape of spike mutations. Using VDB, we detected an emerging lineage of SARS-CoV-2 in the New York region that shares mutations with previously reported variants. The most common sets of spike mutations in this lineage (now designated as B.1.526) are L5F, T95I, D253G, E484K or S477N, D614G, and A701V. This lineage was first sequenced in late November 2020 when it represented <1% of sequenced coronavirus genomes that were collected in New York City (NYC). By February 2021, genomes from this lineage accounted for ~32% of 3288 sequenced genomes from NYC specimens. Phylodynamic inference confirmed the rapid growth of the B.1.526 lineage in NYC, notably the sub-clade defined by the spike mutation E484K, which has outpaced the growth of other variants in NYC. Pseudovirus neutralization experiments demonstrated that B.1.526 spike mutations adversely affect the neutralization titer of convalescent and vaccinee plasma, indicating the public health importance of this lineage.

Poorly Controlled Diabetes in New York City: Mapping High-Density Neighborhoods.

OBJECTIVE: To identify geographic areas in New York City (NYC) for implementing programming focused on reducing the burden attributed to poor glycemic control and improving the health of New Yorkers. DESIGN: We geocoded addresses of NYC residents in the NYC Hemoglobin A1c (HbA1C) Registry with high (>9%) HbA1c test values from 2011 to 2013 on an NYC base map. The ArcGIS point density spatial analysis tool was applied to create a map of NYC residents with diabetes in poor glycemic control. SETTING: The setting for HbA1c testing was medical facilities within NYC. PARTICIPANTS: The study population included NYC residents (excluding undomiciled persons and addresses corresponding to prisons, hospitals, or nursing homes) 18 years or older who underwent HbA1c testing from 2011 to 2013. MAIN OUTCOME MEASURES: A map depicting point density of NYC residents with poor glycemic control was developed each year from 2011 to 2013 (2011: n = 70 359; 2012: n = 75 643; 2013: n = 78 694). RESULTS: Particularly, high densities of persons in poor glycemic control were identified in Flatbush, East Harlem, Washington Heights/Inwood, and the South Bronx. The 2 highest-density gradients (out of 9) covered approximately 1.7% of the total habitable area in NYC, while accounting for more than 1 in 10 (10.5%) persons in poor glycemic control. The 3 highest-density gradients covered 4.1% of NYC's habitable area and accounted for more than 1 in 5 (21.9%) persons in poor glycemic control. CONCLUSION: The point density analysis highlighted several defined geographic areas representing a meaningful proportion of the population in poor glycemic control. This analysis could be used to raise community awareness and guide potential programming focused on reducing the burden of poor glycemic control such as the placement of diabetes self-management education classes, community health workers, and farmers' markets. Given the geographic breadth of NYC and limited resources, focused efforts on these defined areas would reach a sizeable number of the at-risk population.