Evidence map›Paper›PMID 32979308›Full record

ArticleThe Lancet. Respiratory medicine2020

COVID-19 among people experiencing homelessness in England: a modelling study.

Dan Lewer, Isobel Braithwaite, Miriam Bullock, Max T Eyre, Peter J White, Robert W Aldridge, Alistair Story, Andrew C Hayward

Open access · hybridAbstract read
In one paragraph

Article in The Lancet. Respiratory medicine, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 51 papers, 5 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
51citing papers in PubMed, 5 pooled it
24.5field-weighted citation impact, top 1% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

51 citing papers in PubMed, 5 syntheses or guidelines pooled it, 121 citations in OpenAlex.

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4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

8 authors at 4 institutions in 1 country.

Dan LewerUCL Collaborative Centre for Inclusion Health, University College London, London, UK. Electronic address: d.lewer@ucl.ac.uk.
Isobel BraithwaiteUCL Public Health Data Science Research Group, Institute of Health Informatics, University College London, London, UK.
Miriam BullockUCL Collaborative Centre for Inclusion Health, University College London, London, UK.
Max T EyreCentre for Health Informatics, Computing, and Statistics, Lancaster University Medical School, Lancaster, UK.
Peter J WhiteMRC Centre for Global Infectious Disease Analysis, Department of Infectious Disease Epidemiology, Imperial College London, London, UK; Modelling and Economics Unit, National Infection Service, Public Health England, London, UK.
Robert W AldridgeUCL Public Health Data Science Research Group, Institute of Health Informatics, University College London, London, UK.
Alistair StoryUCL Collaborative Centre for Inclusion Health, University College London, London, UK; Find and Treat, University College London Hospitals NHS Foundation Trust, London, UK.
Andrew C HaywardUCL Collaborative Centre for Inclusion Health, University College London, London, UK.
University College London · GBImperial College London · GBLancaster University · GBUniversity College London Hospitals NHS Foundation Trust · GB

Funding

Medical Research Council MR/R015600/1Wellcome TrustWellcome Trust 206602
6 · The paper itself

Abstract

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.

Indexed as

AdultCOVID-19EnglandFemaleHospitalizationHumansIll-Housed PersonsIncidenceMaleMarkov ChainsMiddle AgedPandemicsSARS-CoV-2

Identifiers

PMID32979308
PMCPMC7511167
OpenAlexW3087103640

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.