Evidence map›Paper›PMID 34121112›Full record

ArticleJournal of public health (Oxford, England)2022

Creating an e-cohort of individuals with lived experience of homelessness and subsequent mortality in Wales, UK.

Jiao Song, Charlotte N B Grey, Alisha R Davies

Abstract read
In one paragraph

Article in Journal of public health (Oxford, England), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.

0numbers the graph read from it
0cells of the map it votes in
6citing papers in PubMed
–field-weighted citation impact
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

6 citing papers in PubMed.

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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

3 authors.

Jiao SongResearch & Evaluation Division, Knowledge Directorate, Public Health Wales, Cardiff, CF10 4BZ, UK.
Charlotte N B GreyResearch & Evaluation Division, Knowledge Directorate, Public Health Wales, Cardiff, CF10 4BZ, UK.
Alisha R DaviesResearch & Evaluation Division, Knowledge Directorate, Public Health Wales, Cardiff, CF10 4BZ, UK.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundHomelessness is an extreme form of social exclusion, with homeless people experiencing considerable social and health inequities. Estimates of morbidity and mortality amongst homeless populations is limited due to the lack of recording of housing status across health datasets. The aim of this study is to: (i) identify a homelessness e-cohort by linking routine health data in Wales, and (ii) explore whether a period of reported past homelessness, places this population at greater risk of morbidity and mortality.

methodsHomelessness identified through linkage across primary, secondary care and substance misuse datasets in the Secure Anonymised Information Linkage (SAIL) Databank. Mortality was examined through linkage to the Office for National Statistics mortality data.

resultsE-cohort of 15 472 individuals with lived experience of homelessness identified. Of those, 21 individuals died between February and July 2020 involving coronavirus disease of 2019 (COVID-19). Those with lived experience of homelessness had increased mortality from many causes including accidents, liver diseases and suicides.

conclusionLinking multiple routine datasets provides a more comprehensive dataset of a marginalized population, including individuals who are not included in government homeless statistics. Application of the cohort demonstrated that individuals with lived experience of homelessness have increased mortality involving COVID-19 and other causes. The underlying reasons, health needs and causes of death warrant further exploration.

Indexed as

COVID-19Ill-Housed PersonsSuicideHumansSocial ProblemsWalesCOVID-19homelessnesshousingmortalityrecord linkage

Identifiers

PMID34121112
PMCPMC9715293

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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.