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Making the invisible visible: using national surveillance data to identify people experiencing homelessness in England with COVID-19.

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.

SUBMITTER: Capelastegui F

PROVIDER: S-EPMC10063863 | biostudies-literature | 2023 Feb

REPOSITORIES: biostudies-literature

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Making the invisible visible: using national surveillance data to identify people experiencing homelessness in England with COVID-19.

Capelastegui Fernando F Flannagan Joe J Augarde Elizabeth E Tessier Elise E Chudasama Dimple D Dabrera Gavin G Lamagni Theresa T Campos-Matos Ines I

Epidemiology and infection 20230228

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 ...[more]

PMID: 36852580

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Project description:BackgroundPeople experiencing homelessness are vulnerable to COVID-19 due to the risk of transmission in shared accommodation and the high prevalence of comorbidities. In England, as in some other countries, preventive policies have been implemented to protect this population. We aimed to estimate the avoided deaths and health-care use among people experiencing homelessness during the so-called first wave of COVID-19 in England-ie, the peak of infections occurring between February and May, 2020-and the potential impact of COVID-19 on this population in the future.MethodsWe used a discrete-time Markov chain model of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection that included compartments for susceptible, exposed, infectious, and removed individuals, to explore the impact of the pandemic on 46 565 individuals experiencing homelessness: 35 817 living in 1065 hostels for homeless people, 3616 sleeping in 143 night shelters, and 7132 sleeping outside. We ran the model under scenarios varying the incidence of infection in the general population and the availability of prevention measures: specialist hotel accommodation, infection control in homeless settings, and mixing with the general population. We divided our scenarios into first wave scenarios (covering Feb 1-May 31, 2020) and future scenarios (covering June 1, 2020-Jan 31, 2021). For each scenario, we ran the model 200 times and reported the median and 95% prediction interval (2·5% and 97·5% quantiles) of the total number of cases, the number of deaths, the number hospital admissions, and the number of intensive care unit (ICU) admissions.FindingsUp to May 31, 2020, we calibrated the model to 4% of the homeless population acquiring SARS-CoV-2, and estimated that 24 deaths (95% prediction interval 16-34) occurred. In this first wave of SARS-CoV-2 infections in England, we estimated that the preventive measures imposed might have avoided 21 092 infections (19 777-22 147), 266 deaths (226-301), 1164 hospital admissions (1079-1254), and 338 ICU admissions (305-374) among the homeless population. If preventive measures are continued, we projected a small number of additional cases between June 1, 2020, and Jan 31, 2021, with 1754 infections (1543-1960), 31 deaths (21-45), 122 hospital admissions (100-148), and 35 ICU admissions (23-47) with a second wave in the general population. However, if preventive measures are lifted, outbreaks in homeless settings might lead to larger numbers of infections and deaths, even with low incidence in the general population. In a scenario with no second wave and relaxed measures in homeless settings in England, we projected 12 151 infections (10 718-13 349), 184 deaths (151-217), 733 hospital admissions (635-822), and 213 ICU admissions (178-251) between June 1, 2020, and Jan 31, 2021.InterpretationOutbreaks of SARS-CoV-2 in homeless settings can lead to a high attack rate among people experiencing homelessness, even if incidence remains low in the general population. Avoidance of deaths depends on prevention of transmission within settings such as hostels and night shelters.FundingNational Institute for Health Research, Wellcome, and Medical Research Council.

Project description:Backgroundolder people experiencing homelessness can have mental and physical indicators of aging several decades earlier than the general population and experience premature mortality due to age-related chronic conditions. Digital interventions could positively impact the health and well-being of homeless people. However, increased reliance on digital delivery may also perpetuate digital inequalities for socially excluded groups. The potential triple disadvantage of being older, homeless, and digitally excluded creates a uniquely problematic situation warranting further research. Few studies have synthesized available literature on digital interventions for older people experiencing homelessness.ObjectiveThis scoping review examined the use, range, and nature of digital interventions available to older people experiencing homelessness and organizations supporting them.MethodsThe scoping review followed Arksey and O'Malley's proposed methodology, PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews) guidelines, and recent Joanna Briggs Institute guidelines. We searched 14 databases. Gray literature sources were searched to supplement the electronic database search. A narrative synthesis approach was conducted on the included articles, and common themes were identified inductively through thematic analysis.ResultsA total of 19,915 records were identified through database and gray literature searching. We identified 10 articles reporting on digital interventions that had a clearly defined a participant age group of >50 years or a mean participant age of >50 years. A total of 9 of 10 studies were published in the United States. The study design included descriptive studies, uncontrolled pilot studies, and pilot randomized controlled trials. No studies aimed to deliver an intervention exclusively to older people experiencing homelessness or organizations that supported them. Four types of intervention were identified: telecare for people experiencing homelessness, distributing technology to enable digital inclusion, text message reminders, and interventions delivered digitally. Interventions delivered digitally included smoking cessation support, vocational training, physical activity promotion, and cognitive behavioral therapy. Overall, the included studies demonstrated evidence for the acceptability and feasibility of digital interventions for older people experiencing homelessness, and all 10 studies reported some improvements in digital inclusion or enhanced engagement among participants. However, several barriers to digital interventions were identified, particularly aspects related to digital inclusion, such as infrastructure, digital literacy, and age. Proposed facilitators for digital interventions included organizational and peer support.ConclusionsOur findings highlight a paucity of evaluated digital interventions targeted at older people experiencing homelessness. However, the included studies demonstrated evidence of the acceptability and feasibility of digital interventions for older people experiencing homelessness. Further research on digital interventions that provide services and support older people experiencing homelessness is required. Future interventions must address the barriers older people experiencing homelessness face when accessing digital technology with the input of those with lived experience of homelessness.Trial registrationOSF Registries OSF.IO/7QGTY; https://doi.org/10.17605/OSF.IO/7QGTY.

Dataset Information

Making the invisible visible: using national surveillance data to identify people experiencing homelessness in England with COVID-19.

Publications

Making the invisible visible: using national surveillance data to identify people experiencing homelessness in England with COVID-19.

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