Effects of sleep disturbance on dyspnoea and impaired lung function following hospital admission due to COVID-19 in the UK: a prospective multicentre cohort study
Jackson C, Stewart ID, Plekhanova T, Cunningham PS, Hazel AL, Al-Sheklly B, Aul R, Bolton CE, Chalder T, Chalmers JD, Chaudhuri N, Docherty AB, Donaldson G, Edwardson CL, Elneima O, Greening NJ, Hanley NA, Harris VC, Harrison EM, Ho LP, Houchen-Wolloff L, Howard LS, Jolley CJ, Jones MG, Leavy OC, Lewis KE, Lone NI, Marks M, McAuley HJC, McNarry MA, Patel BV, Piper-Hanley K, Poinasamy K, Raman B, Richardson M, Rivera-Ortega P, Rowland-Jones SL, Rowlands AV, Saunders RM, Scott JT, Sereno M, Shah AM, Shikotra A, Singapuri A, Stanel SC, Thorpe M, Wootton DG, Yates T, Gisli Jenkins R, Singh SJ, Man WD, Brightling CE, Wain LV, Porter JC, Thompson AAR, Horsley A, Molyneaux PL, Evans RA, Jones SE, Rutter MK, Blaikley JF and
Sleep disturbance is common following hospital admission both for COVID-19 and other causes. The clinical associations of this for recovery after hospital admission are poorly understood despite sleep disturbance contributing to morbidity in other scenarios. We aimed to investigate the prevalence and nature of sleep disturbance after discharge following hospital admission for COVID-19 and to assess whether this was associated with dyspnoea.
Examining the effect of interventions in emergency care for older people using a system dynamics decision support tool
England T, Brailsford S, Evenden D, Street A, Maynou L, Mason SM, Preston L, Burton C, Van Oppen J and Conroy S
Rising demand for Emergency and Urgent Care is a major international issue and outcomes for older people remain sub-optimal. Embarking upon large-scale service development is costly in terms of time, energy and resources with no guarantee of improved outcomes; computer simulation modelling offers an alternative, low risk and lower cost approach to explore possible interventions.
The dynamics of frailty development and progression in older adults in primary care in England (2006-2017): a retrospective cohort profile
Fogg C, Fraser SDS, Roderick P, de Lusignan S, Clegg A, Brailsford S, Barkham A, Patel HP, Windle V, Harris S, Zhu S, England T, Evenden D, Lambert F, Walsh B and
Frailty is a common condition in older adults and has a major impact on patient outcomes and service use. Information on the prevalence in middle-aged adults and the patterns of progression of frailty at an individual and population level is scarce. To address this, a cohort was defined from a large primary care database in England to describe the epidemiology of frailty and understand the dynamics of frailty within individuals and across the population. This article describes the structure of the dataset, cohort characteristics and planned analyses.
Computer simulation of dementia care demand heterogeneity using hybrid simulation methods: improving population-level modelling with individual patient decline trajectories
Evenden D, Brailsford S, Kipps C, Roderick P, Walsh B and
The aim of the study was to model dementia prevalence and outcomes within an ageing population using a novel hybrid simulation model that simultaneously takes population-level and patient-level perspectives to better inform dementia care service planning, taking into account severity progression variability.
ADCOMS sensitivity versus baseline diagnosis and progression phenotypes
Evenden D, Prosser A, Michopoulou S, Kipps C and
The Alzheimer's Disease COMposite Score (ADCOMS) is more sensitive in clinical trials than conventional measures when assessing pre-dementia. This study compares ADCOMS trajectories using clustered progression characteristics to better understand different patterns of decline.
Hybrid simulation modelling for dementia care services planning
Evenden D, Evenden DC, Brailsford SC, Kipps CM, Roderick PJ and Walsh B
Hybrid simulation modelling for dementia care services planning
Evenden D, Evenden DC, Brailsford SC, Kipps CM, Roderick PJ and Walsh B
Dementia is an increasing problem in today’s ageing society, and meeting future demand for care is a major concern for policy-makers and planners. This paper presents a novel hybrid simulation model that simultaneously takes population-level and patient-level perspectives to calculate the numbers of patients at different stages of disease severity over time, and their associated care costs. System Dynamics is used at population level to capture ageing, dementia onset, and all-cause mortality, whereas disease progression is modelled at individual patient level using Agent-Based methods. This enables the model to account for variability between patients in the rate of cognitive decline, dementia-related mortality and response to treatment interventions. Using epidemiological data from the medical literature, disease progression is modelled via a longitudinal clustering method to identify progression type, followed by mixed-effects regression to reflect each individual’s rate of cognitive decline. Results are presented for population data from the south of England, and show that the currently available interventions have only modest effects at population level.
