The Impact of Long-Term Conditions and Comorbidity Patterns on COVID-19 Infection and Hospitalisation: A Cohort Study

Yun-Ting Huang; Andrew Steptoe; Riyaz S Patel; Esme Fuller Thomson; Dorina Cadar

doi:10.1159/000531848

The Impact of Long-Term Conditions and Comorbidity Patterns on COVID-19 Infection and Hospitalisation: A Cohort Study

Gerontology. 2023;69(10):1200-1210. doi: 10.1159/000531848. Epub 2023 Sep 11.

Authors

Yun-Ting Huang^{1

2}, Andrew Steptoe³, Riyaz S Patel^{4

5

6

7}, Esme Fuller Thomson^{8

9}, Dorina Cadar^{3

10

11}

Affiliations

¹ Department of Epidemiology and Public Health, University College London, London, UK.
² Centre for Epidemiology Versus Arthritis, Centre for Musculoskeletal Research, University of Manchester, Manchester, UK.
³ Department of Behavioural Science and Health, University College London, London, UK.
⁴ Institute of Cardiovascular Sciences, University College London, London, UK.
⁵ London Biomedical Research Centre, NIHR University College London, London, UK.
⁶ London and University College London Hospitals NHS Foundation Trust, London, UK.
⁷ UCL BHF Research Accelerator, University College London, London, UK.
⁸ Faculty of Medicine, University of Toronto, Toronto, Ontario, Canada.
⁹ Institute for Life Course and Aging, University of Toronto, Toronto, Ontario, Canada.
¹⁰ Centre for Dementia Studies, Department of Neuroscience, Brighton and Sussex Medical School, Sussex, Brighton, UK.
¹¹ Department of Primary Care and Public Health, Brighton and Sussex Medical School, Sussex, Brighton, UK.

Abstract

Introduction: Older adults are more vulnerable to COVID-19 infections; however, little is known about which comorbidity patterns are related to a higher risk of COVID-19 infection. This study investigated the role of long-term conditions or comorbidity patterns on COVID-19 infection and related hospitalisations.

Methods: This study included 4,428 individuals from Waves 8 (2016-2017) and 9 (2018-2019) of the English Longitudinal Study of Ageing (ELSA) who also participated in the ELSA COVID-19 Substudy in 2020. Comorbidity patterns were identified using an agglomerative hierarchical clustering method. The relationships between comorbidity patterns or long-term conditions and COVID-19-related outcomes were examined using multivariable logistic regression.

Results: Among a representative sample of community-dwelling older adults in England, those with cardiovascular disease (CVD) and complex comorbidities had an almost double risk of COVID-19 infection (OR = 1.87, 95% CI = 1.42-2.46) but not of COVID-19-related hospitalisation. A similar pattern was observed for the heterogeneous comorbidities cluster (OR = 1.56, 95% CI = 1.24-1.96). The individual investigations of long-term conditions with COVID-19 infection highlighted primary associations with CVD (OR = 1.46, 95% CI = 1.23-1.74), lung diseases (OR = 1.40, 95% CI = 1.17-1.69), psychiatric conditions (OR = 1.40, 95% CI = 1.16-1.68), retinopathy/eye diseases (OR = 1.39, 95% CI = 1.18-1.64), and arthritis (OR = 1.27, 95% CI = 1.09-1.48). In contrast, metabolic disorders and diagnosed diabetes were not associated with any COVID-19 outcomes.

Conclusion: This study provides novel insights into the comorbidity patterns that are more vulnerable to COVID-19 infections and hospitalisations, highlighting the vulnerability of those with CVD and other complex comorbidities. These findings facilitate crucial new evidence that should be considered for appropriate screening measures and tailored interventions for older adults in the ongoing global outbreak.

Keywords: COVID-19; Cardiovascular diseases; Complex comorbidities; Multimorbidity.

Publication types

Research Support, N.I.H., Extramural
Research Support, Non-U.S. Gov't

MeSH terms

Aged
COVID-19* / epidemiology
Cardiovascular Diseases* / epidemiology
Cohort Studies
Comorbidity
Hospitalization
Humans
Longitudinal Studies

Abstract

Publication types

MeSH terms

Grants and funding