Evidence map›Paper›PMID 32988953›Full record

ArticleBMJ open2020

Population Health Management to identify and characterise ongoing health need for high-risk individuals shielded from COVID-19: a cross-sectional cohort study.

Charlie Kenward, Adrian Pratt, Sam Creavin, Richard Wood, Jennifer A Cooper

Open access · goldAbstract read
In one paragraph

Article in BMJ open, 2020. 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
0.8field-weighted citation impact, top 20% 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

6 citing papers in PubMed, 9 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

5 authors at 2 institutions in 1 country.

Charlie KenwardNHS Bristol, North Somerset and South Gloucestershire Clinical Commissioning Group, Bristol, UK charlie.kenward@nhs.net.
Adrian PrattDepartment of Modelling and Analytics, NHS Bristol, North Somerset and South Gloucestershire Clinical Commissioning Group, Bristol, UK.
Sam CreavinDepartment of Population Health Sciences, University of Bristol, Bristol, UK.
Richard WoodDepartment of Modelling and Analytics, NHS Bristol, North Somerset and South Gloucestershire Clinical Commissioning Group, Bristol, UK.ORCID 0000-0002-3476-395X
Jennifer A CooperDepartment of Modelling and Analytics, NHS Bristol, North Somerset and South Gloucestershire Clinical Commissioning Group, Bristol, UK.ORCID 0000-0001-9364-7353
Bristol Clinical Commissioning Group · GBUniversity of Bristol · GB

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

objectivesTo use Population Health Management (PHM) methods to identify and characterise individuals at high-risk of severe COVID-19 for which shielding is required, for the purposes of managing ongoing health needs and mitigating potential shielding-induced harm.

designIndividuals at 'high risk' of COVID-19 were identified using the published national 'Shielded Patient List' criteria. Individual-level information, including current chronic conditions, historical healthcare utilisation and demographic and socioeconomic status, was used for descriptive analyses of this group using PHM methods. Segmentation used k-prototypes cluster analysis.

settingA major healthcare system in the South West of England, for which linked primary, secondary, community and mental health data are available in a system-wide dataset. The study was performed at a time considered to be relatively early in the COVID-19 pandemic in the UK.

participants1 013 940 individuals from 78 contributing general practices.

resultsCompared with the groups considered at 'low' and 'moderate' risk (ie, eligible for the annual influenza vaccination), individuals at high risk were older (median age: 68 years (IQR: 55-77 years), cf 30 years (18-44 years) and 63 years (38-73 years), respectively), with more primary care/community contacts in the previous year (median contacts: 5 (2-10), cf 0 (0-2) and 2 (0-5)) and had a higher burden of comorbidity (median Charlson Score: 4 (3-6), cf 0 (0-0) and 2 (1-4)). Geospatial analyses revealed that 3.3% of rural and semi-rural residents were in the high-risk group compared with 2.91% of urban and inner-city residents (p<0.001). Segmentation uncovered six distinct clusters comprising the high-risk population, with key differentiation based on age and the presence of cancer, respiratory, and mental health conditions.

conclusionsPHM methods are useful in characterising the needs of individuals requiring shielding. Segmentation of the high-risk population identified groups with distinct characteristics that may benefit from a more tailored response from health and care providers and policy-makers.

Indexed as

Coronavirus InfectionsPandemicsPneumonia, ViralPopulation Health ManagementRisk ManagementAgedBetacoronavirusCOVID-19Cross-Sectional StudiesDemographyEnglandFemaleGeneral PracticeHealth Information SystemsHumansMaleepidemiologyhealth informaticshealth services administration & managementprimary carepublic healthrisk management

Identifiers

PMID32988953
PMCPMC7523155
OpenAlexW3091714385

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.