tetano
Editor, Senior Moderator
J Public Health Manag Pract
. 2023 Mar 17.
doi: 10.1097/PHH.0000000000001728. Online ahead of print.
Clustering of SARS-CoV-2 in Households in New York City: A Building-Level Analysis, March-December 2020
Catherine Gulley[SUP] 1 [/SUP], Kelsey L Kepler, Stephanie Ngai, HaeNa Waechter, Robert Fitzhenry, Corinne N Thompson, Anne Fine, Vasudha Reddy
Affiliations
Abstract
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.
. 2023 Mar 17.
doi: 10.1097/PHH.0000000000001728. Online ahead of print.
Clustering of SARS-CoV-2 in Households in New York City: A Building-Level Analysis, March-December 2020
Catherine Gulley[SUP] 1 [/SUP], Kelsey L Kepler, Stephanie Ngai, HaeNa Waechter, Robert Fitzhenry, Corinne N Thompson, Anne Fine, Vasudha Reddy
Affiliations
- PMID: 36943404
- DOI: 10.1097/PHH.0000000000001728
Abstract
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.