Making the invisible visible: using national surveillance data to identify people experiencing homelessness in England with COVID-19. (28th February 2023)

• Record Type: Journal Article
• Authors: Capelastegui, Fernando; Flannagan, Joe; Augarde, Elizabeth; Tessier, Elise; Chudasama, Dimple; Dabrera, Gavin; Lamagni, Theresa; Campos-Matos, Ines
• Is Part Of: Epidemiology and infection. Volume 151(2023)
• Publication Date: 2023-02-28
• Subjects: COVID-19 -- England -- homelessness -- SARS-CoV-2; Communicable diseases -- Periodicals; Epidemiology -- Periodicals; 614.4
• DOI: 10.1017/S095026882300033X
• Languages: English
• ISSNs: 0950-2688

Abstract

Abstract: Persons experiencing homelessness (PEH) or rough sleeping are a vulnerable population, likely to be disproportionately affected by the coronavirus disease 2019 (COVID-19) pandemic. The impact of COVID-19 infection on this population is yet to be fully described in England. We present a novel method to identify COVID-19 cases in this population and describe its findings. A phenotype was developed and validated to identify PEH or rough sleeping in a national surveillance system. Confirmed COVID-19 cases in England from March 2020 to March 2022 were address-matched to known homelessness accommodations and shelters. Further cases were identified using address-based indicators, such as NHS pseudo postcodes. In total, 1835 cases were identified by the phenotype. Most were <39 years of age (66.8%) and male (62.8%). The proportion of cases was highest in London (29.8%). The proportion of cases of a minority ethnic background and deaths were disproportionality greater in this population, compared to all COVID-19 cases in England. This methodology provides an approach to track the impact of COVID-19 on a subset of this population and will be relevant to policy making. Future surveillance systems and studies may benefit from this approach to further investigate the impact of COVID-19 and other diseases on select populations.