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’ o...
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| Format: | Article |
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BMJ Publishing Group
2020-09-01
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| Series: | BMJ Open |
| Online Access: | https://bmjopen.bmj.com/content/10/9/e041370.full |
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| author | Adrian Pratt Richard Wood Charlie Kenward Sam Creavin Jennifer A Cooper |
| author_facet | Adrian Pratt Richard Wood Charlie Kenward Sam Creavin Jennifer A Cooper |
| author_sort | Adrian Pratt |
| collection | DOAJ |
| 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. |
| format | Article |
| id | doaj-art-5894cc8e113241d9bb5293a94da66ff2 |
| institution | DOAJ |
| issn | 2044-6055 |
| language | English |
| publishDate | 2020-09-01 |
| publisher | BMJ Publishing Group |
| record_format | Article |
| series | BMJ Open |
| spelling | doaj-art-5894cc8e113241d9bb5293a94da66ff22025-08-20T02:44:12ZengBMJ Publishing GroupBMJ Open2044-60552020-09-0110910.1136/bmjopen-2020-041370Population Health Management to identify and characterise ongoing health need for high-risk individuals shielded from COVID-19: a cross-sectional cohort studyAdrian Pratt0Richard Wood1Charlie Kenward2Sam Creavin3Jennifer A Cooper4Modelling and Analytics Team, NHS Bristol, North Somerset and South Gloucestershire CCG, Bristol, UKHealth Data Research UK South-West of England Partnership, Bristol, UK5 North Somerset and South Gloucestershire Integrated Care Board, Bristol, UKclinical lecturer in general practice and general practitionerDepartment of Modelling and Analytics, NHS Bristol, North Somerset and South Gloucestershire Clinical Commissioning Group, Bristol, UKObjectives 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.https://bmjopen.bmj.com/content/10/9/e041370.full |
| spellingShingle | Adrian Pratt Richard Wood Charlie Kenward Sam Creavin Jennifer A Cooper Population Health Management to identify and characterise ongoing health need for high-risk individuals shielded from COVID-19: a cross-sectional cohort study BMJ Open |
| 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 |
| url | https://bmjopen.bmj.com/content/10/9/e041370.full |
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