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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...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BMJ Publishing Group
2020
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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 |
Sumario: | 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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