Patient factors and temporal trends associated with COVID-19 in-hospital mortality in England: an observational study using administrative data

Abstract

Background Analysis of the effect of COVID-19 on the complete hospital population in England has been lacking. Our aim was to provide a comprehensive account of all hospitalised patients with COVID-19 in England during the early phase of the pandemic and to identify the factors that influenced mortality as the pandemic evolved. Methods This was a retrospective exploratory analysis using the Hospital Episode Statistics administrative dataset. All patients aged 18 years or older in England who completed a hospital stay (were discharged alive or died) between March 1 and May 31, 2020, and had a diagnosis of COVID-19 on admission or during their stay were included. In-hospital death was the primary outcome of interest. Multilevel logistic regression was used to model the relationship between death and several covariates: age, sex, deprivation (Index of Multiple Deprivation), ethnicity, frailty (Hospital Frailty Risk Score), presence of comorbidities (Charlson Comorbidity Index items), and date of discharge (whether alive or deceased). Findings 91 541 adult patients with COVID-19 were discharged during the study period, among which 28 200 (30•8%) in-hospital deaths occurred. The final multilevel logistic regression model accounted for age, deprivation score, and date of discharge as continuous variables, and sex, ethnicity, and Charlson Comorbidity Index items as categorical variables. In this model, significant predictors of in-hospital death included older age (modelled using restricted cubic splines), male sex (1•457 [1•408-1•509]), greater deprivation (1•002 [1•001-1•003]), Asian (1•211 [1•128-1•299]) or mixed ethnicity (1•317 [1•080-1•605]; vs White ethnicity), and most of the assessed comorbidities, including moderate or severe liver disease (5•433 [4•618-6•392]). Later date of discharge was associated with a lower odds of death (0•977 [0•976-0•978]); adjusted in-hospital mortality improved significantly in a broadly linear fashion, from 52•2% in the first week of March to 16•8% in the last week of May. Interpretation Reductions in the adjusted probability of in-hospital mortality for COVID-19 patients over time might reflect the impact of changes in hospital strategy and clinical processes. The reasons for the observed improvements in mortality should be thoroughly investigated to inform the response to future outbreaks. The higher mortality rate reported for certain ethnic minority groups in community-based studies compared with our hospital-based analysis might partly reflect differential infection rates in those at greatest risk, propensity to become severely ill once infected, and health-seeking behaviours.

Variability in COVID-19 in-hospital mortality rates between national health service trusts and regions in England: A national observational study for the Getting It Right First Time Programme

“…In descriptive analysis, data were categorised as detailed above and summarised in terms of frequency and percentage. Based on previous work by our team [6], a multilevel (mixed-effects) logistic regression model of patient-related and temporal factors associated with in-hospital mortality was constructed for the current dataset using the 'melogit' command in Stata. A two-level intercept only model was constructed, allowing adjustment for clustering of patients within hospital trusts.…”

“…Age: Categorised as 18À39 years, 40À49 years, 50À59 years, 60À69 years, 70À79 years and 80 years for exploratory analysis and treated as continuous for modelling. The age bands were selected based on previous work in this field by our team and other researchers [10,6].…”

“…The Getting Right First Time programme in England aims to drive clinical improvement by understanding variation in practice and outcomes [5]. Our previous work using the Hospital Episodes Statistics (HES) dataset has provided an understanding of the patient factors that affected COVID-19 related morality rates and the reduction in adjusted probability of in-hospital deaths as the pandemic progressed [6]. Using the same comprehensive dataset, we aimed to investigate the nature of variation in COVID-19 in-hospital mortality rates regionally and between NHS hospital trusts.…”

Patient Characteristics and Clinical Course of COVID-19 Patients Treated at a German Tertiary Center during the First and Second Waves in the Year 2020

“…However, markedly diverse inpatient mortality rates due to COVID-19 have been reported across different countries [ 38 ]. The extreme straining of many healthcare systems surely has contributed to high mortality rates in some countries especially during the early phases of the pandemic [ 16 , 39 , 40 , 41 ]. Ticinesi et al reported that while COVID-19 patients treated with COVID-19 at an Italian hospital hub during the first weeks of the pandemic were younger and had fewer comorbidities than those admitted between late March and early June 2020, mortality decreased from 27% to 22% [ 42 ].…”

“…A multivariate logistic regression model was used to determine whether presentation during the first versus second wave was an independent predictor for in-hospital mortality. An a priori decision was made to include the variables age ≥ 60 years, male sex, and ACCI in the model since these variables have been previously shown to be important risk factors for mortality in COVID-19 patients [ 16 ]. A log-rank test was used to compare the proportion of patients discharged alive during the first 120 days of hospitalization.…”