Study protocol for the use of time series forecasting and risk analyses to investigate the effect of the COVID-19 pandemic on hospital admissions associated with new-onset disability and frailty in a national, linked electronic health data setting

Thomas, Seth; Machuel, Perrine; Foubert, Josephine; Nafilyan, Vahe; Bannister, Neil; Colvin, Helen; Routen, Ash; Morriss, Richard; Khunti, Kamlesh; Farooqi, Azhar; Armstrong, Natalie; Gray, Laura J; Gordon, Adam

Abstract

INTRODUCTION: Older people were at particular risk of morbidity and mortality during COVID-19. Consequently, they experienced formal (externally imposed) and informal (self-imposed) periods of social isolation and quarantine. This is hypothesised to have led to physical deconditioning, new-onset disability and frailty. Disability and frailty are not routinely collated at population level but are associated with increased risk of falls and fractures, which result in hospital admissions. First, we will examine incidence of falls and fractures during COVID-19 (January 2020-March 2022), focusing on differences between incidence over time against expected rates based on historical data, to determine whether there is evidence of new-onset disability and frailty. Second, we will examine whether those with reported SARS-CoV-2 were at higher risk of falls and fractures. METHODS AND ANALYSIS: This study uses the Office for National Statistics (ONS) Public Health Data Asset, a linked population-level dataset combining administrative health records with sociodemographic data of the 2011 Census and National Immunisation Management System COVID-19 vaccination data for England. Administrative hospital records will be extracted based on specific fracture-centric International Classification of Diseases-10 codes in years preceding COVID-19 (2011-2020). Historical episode frequency will be used to predict expected admissions during pandemic years using time series modelling, if COVID-19 had not occurred. Those predicted admission figures will be compared with actual admissions to assess changes in hospital admissions due to public health measures comprising the pandemic response. Hospital admissions in prepandemic years will be stratified by age and geographical characteristics and averaged, then compared with pandemic year admissions to assess more granular changes. Risk modelling will assess risk of experiencing a fall, fracture or frail fall and fracture, if they have reported a positive case of COVID-19. The combination of these techniques will provide insight into changes in hospital admissions from the COVID-19 pandemic. ETHICS AND DISSEMINATION: This study has approval from the National Statistician's Data Ethics Advisory Committee (NSDEC(20)12). Results will be made available to other researchers via academic publication and shared via the ONS website.

Citation

Thomas, S., Machuel, P., Foubert, J., Nafilyan, V., Bannister, N., Colvin, H., Routen, A., Morriss, R., Khunti, K., Farooqi, A., Armstrong, N., Gray, L. J., & Gordon, A. (2023). Study protocol for the use of time series forecasting and risk analyses to investigate the effect of the COVID-19 pandemic on hospital admissions associated with new-onset disability and frailty in a national, linked electronic health data setting. BMJ Open, 13(5), Article e067786. https://doi.org/10.1136/bmjopen-2022-067786

• Journal Article Type: Article
• Acceptance Date: Mar 23, 2023
• Online Publication Date: May 19, 2023
• Publication Date: May 19, 2023
• Deposit Date: Mar 29, 2023
• Publicly Available Date: May 19, 2023
• Journal: BMJ Open
• Electronic ISSN: 2044-6055
• Publisher: BMJ Publishing Group
• Peer Reviewed: Peer Reviewed
• Volume: 13
• Issue: 5
• Article Number: e067786
• DOI: https://doi.org/10.1136/bmjopen-2022-067786
• Keywords: COVID-19, EPIDEMIOLOGY, GERIATRIC MEDICINE
• Public URL: https://nottingham-repository.worktribe.com/output/19001681
• Publisher URL: https://bmjopen.bmj.com/content/13/5/e067786