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

OBJECTIVES: To 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. DESIGN: Individuals at ‘high risk...

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Autores principales: Kenward, Charlie, Pratt, Adrian, Creavin, Sam, Wood, Richard, Cooper, Jennifer A
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BMJ Publishing Group 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7523155/
https://www.ncbi.nlm.nih.gov/pubmed/32988953
http://dx.doi.org/10.1136/bmjopen-2020-041370
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author Kenward, Charlie
Pratt, Adrian
Creavin, Sam
Wood, Richard
Cooper, Jennifer A
author_facet Kenward, Charlie
Pratt, Adrian
Creavin, Sam
Wood, Richard
Cooper, Jennifer A
author_sort Kenward, Charlie
collection PubMed
description OBJECTIVES: To 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. DESIGN: Individuals 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. SETTING: A 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. PARTICIPANTS: 1 013 940 individuals from 78 contributing general practices. RESULTS: Compared 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. CONCLUSIONS: PHM 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.
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spelling pubmed-75231552020-09-30 Population Health Management to identify and characterise ongoing health need for high-risk individuals shielded from COVID-19: a cross-sectional cohort study Kenward, Charlie Pratt, Adrian Creavin, Sam Wood, Richard Cooper, Jennifer A BMJ Open Epidemiology OBJECTIVES: To 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. DESIGN: Individuals 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. SETTING: A 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. PARTICIPANTS: 1 013 940 individuals from 78 contributing general practices. RESULTS: Compared 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. CONCLUSIONS: PHM 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. BMJ Publishing Group 2020-09-28 /pmc/articles/PMC7523155/ /pubmed/32988953 http://dx.doi.org/10.1136/bmjopen-2020-041370 Text en © Author(s) (or their employer(s)) 2020. Re-use permitted under CC BY. Published by BMJ. https://creativecommons.org/licenses/by/4.0/ https://creativecommons.org/licenses/by/4.0/This is an open access article distributed in accordance with the Creative Commons Attribution 4.0 Unported (CC BY 4.0) license, which permits others to copy, redistribute, remix, transform and build upon this work for any purpose, provided the original work is properly cited, a link to the licence is given, and indication of whether changes were made. See: https://creativecommons.org/licenses/by/4.0/.
spellingShingle Epidemiology
Kenward, Charlie
Pratt, Adrian
Creavin, Sam
Wood, Richard
Cooper, Jennifer A
Population Health Management to identify and characterise ongoing health need for high-risk individuals shielded from COVID-19: a cross-sectional cohort study
title Population Health Management to identify and characterise ongoing health need for high-risk individuals shielded from COVID-19: a cross-sectional cohort study
title_full Population Health Management to identify and characterise ongoing health need for high-risk individuals shielded from COVID-19: a cross-sectional cohort study
title_fullStr Population Health Management to identify and characterise ongoing health need for high-risk individuals shielded from COVID-19: a cross-sectional cohort study
title_full_unstemmed Population Health Management to identify and characterise ongoing health need for high-risk individuals shielded from COVID-19: a cross-sectional cohort study
title_short Population Health Management to identify and characterise ongoing health need for high-risk individuals shielded from COVID-19: a cross-sectional cohort study
title_sort population health management to identify and characterise ongoing health need for high-risk individuals shielded from covid-19: a cross-sectional cohort study
topic Epidemiology
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7523155/
https://www.ncbi.nlm.nih.gov/pubmed/32988953
http://dx.doi.org/10.1136/bmjopen-2020-041370
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