Evidence map›Paper›PMID 33780469›Full record

ArticlePloS one2021

Deriving household composition using population-scale electronic health record data-A reproducible methodology.

Rhodri D Johnson, Lucy J Griffiths, Joe P Hollinghurst, Ashley Akbari, Alexandra Lee, Daniel A Thompson, Ronan A Lyons, Richard Fry

Open access · goldAbstract read
In one paragraph

Article in PloS one, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 papers.

0numbers the graph read from it
0cells of the map it votes in
20citing papers in PubMed
2.9field-weighted citation impact, top 8% 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

20 citing papers in PubMed, 24 citations in OpenAlex.

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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

8 authors at 1 institution in 1 country.

Rhodri D JohnsonPopulation Data Science, Swansea University Medical School, Swansea, United Kingdom.ORCID 0000-0001-9636-0753
Lucy J GriffithsPopulation Data Science, Swansea University Medical School, Swansea, United Kingdom.
Joe P HollinghurstPopulation Data Science, Swansea University Medical School, Swansea, United Kingdom.
Ashley AkbariPopulation Data Science, Swansea University Medical School, Swansea, United Kingdom.ORCID 0000-0003-0814-0801
Alexandra LeePopulation Data Science, Swansea University Medical School, Swansea, United Kingdom.
Daniel A ThompsonPopulation Data Science, Swansea University Medical School, Swansea, United Kingdom.
Ronan A LyonsPopulation Data Science, Swansea University Medical School, Swansea, United Kingdom.
Richard FryPopulation Data Science, Swansea University Medical School, Swansea, United Kingdom.
Swansea University · GB

Funding

British Heart FoundationChief Scientist OfficeHCRW_ HRG-20-1755(P)HCRW_ SCG-19-1654Medical Research Council MR/V028367/1Wellcome Trust
6 · The paper itself

Abstract

backgroundPhysical housing and household composition have an important role in the lives of individuals and drive health and social outcomes, and inequalities. Most methods to understand housing composition are based on survey or census data, and there is currently no reproducible methodology for creating population-level household composition measures using linked administrative data.

methodsUsing existing, and more recent enhancements to the address-data linkage methods in the SAIL Databank using Residential Anonymised Linking Fields we linked individuals to properties using the anonymised Welsh Demographic Service data in the SAIL Databank. We defined households, household size, and household composition measures based on adult to child relationships, and age differences between residents to create relative age measures.

resultsTwo relative age-based algorithms were developed and returned similar results when applied to population and household-level data, describing household composition for 3.1 million individuals within 1.2 million households in Wales. Developed methods describe binary, and count level generational household composition measures.

conclusionsImproved residential anonymised linkage field methods in SAIL have led to improved property-level data linkage, allowing the design and application of household composition measures that assign individuals to shared residences and allow the description of household composition across Wales. The reproducible methods create longitudinal, household-level composition measures at a population-level using linked administrative data. Such measures are important to help understand more detail about an individual's home and area environment and how that may affect the health and wellbeing of the individual, other residents, and potentially into the wider community.

Indexed as

Electronic Health RecordsInformation Storage and RetrievalAdolescentAdultAgedAged, 80 and overChildDatabases, FactualFamily CharacteristicsFemaleHousingHumansMaleMiddle AgedWalesYoung Adult

Identifiers

PMID33780469
PMCPMC8007012
OpenAlexW3142774786

What OpenQuestion holds

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LicenceCC BY
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Registered trials

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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.