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1.
Thorax ; 77(11): 1113-1120, 2022 11.
Article in English | MEDLINE | ID: covidwho-1533077

ABSTRACT

INTRODUCTION: We aimed to examine the profile of, and outcomes for, all people hospitalised with COVID-19 across the first and second waves of the pandemic in England. METHODS: This was an exploratory retrospective analysis of observational data from the Hospital Episode Statistics data set for England. All patients aged ≥18 years in England with a diagnosis of COVID-19 who had a hospital stay that was completed between 1 March 2020 and 31 March 2021 were included. In-hospital mortality was the primary outcome of interest. The second wave was identified as starting on 1 September 2020. Multilevel logistic regression modelling was used to investigate the relationship between mortality and demographic, comorbidity and temporal covariates. RESULTS: Over the 13 months, 374 244 unique patients had a diagnosis of COVID-19 during a hospital stay, of whom 93 701 (25%) died in hospital. Adjusted mortality rates fell from 40%-50% in March 2020 to 11% in August 2020 before rising to 21% in January 2021 and declining steadily to March 2021. Improvements in mortality rates were less apparent in older and comorbid patients. Although mortality rates fell for all ethnic groups from the first to the second wave, declines were less pronounced for Bangladeshi, Indian, Pakistani, other Asian and black African ethnic groups. CONCLUSIONS: There was a substantial decline in adjusted mortality rates during the early part of the first wave which was largely maintained during the second wave. The underlying reasons for consistently higher mortality risk in some ethnic groups merits further study.


Subject(s)
COVID-19 , Pandemics , Adolescent , Adult , Aged , England/epidemiology , Hospital Mortality , Hospitals , Humans , Retrospective Studies
2.
Lancet Reg Health Eur ; 5: 100104, 2021 Jun.
Article in English | MEDLINE | ID: covidwho-1220950

ABSTRACT

BACKGROUND: Previous research by our team identified factors associated with in-hospital mortality in patients with a diagnosis of COVID-19 in England between March and May 2020. The aim of the current paper was to investigate the changing role of demographics and co-morbidity, with a particular focus on ethnicity, as risk factors for in-hospital mortality over an extended period. METHODS: This was a retrospective observational study using the Hospital Episode Statistics administrative dataset. All patients aged ≥ 18 years in England with a diagnosis of COVID-19 who had a hospital stay that was completed (discharged alive or died) between 1st March and 30th September 2020 were included. In-hospital mortality was the primary outcome of interest. Multilevel logistic regression was used to model the relationship between in-hospital mortality with adjustment for the covariates: age, sex, deprivation, ethnicity, date of discharge and a number of comorbidities. FINDINGS: Compared to patients in March-May (n = 93,379), patients in June-September (n = 24,059) were younger, more likely to be female and of Asian ethnicity, but less likely to be of Black ethnicity. In-hospital mortality rates, adjusted for covariates, declined from 33-34% in March to 11-12% in September. Compared to the March-May period, Bangladeshi, Indian and Other Asian ethnicity patients had a lower relative odds of death (compared to White ethnicity patients) during June-September. For Pakistani patients, the decline in-hospital mortality rates was more modest across the same time periods with the relative odds of death increasing slightly (odds ratio (95% confidence interval)) 1.24 (1.10 to 1.40) and 1.35 (1.08 to 1.69) respectively. From March-May to June-September the relative odds of death in patients with a diagnosis of metastatic carcinoma increased (1.90 (1.73 to 2.08) vs 3.01 (2.55 to 3.54)) but decreased for male patients (1.44 (1.39 to 1.49) vs 1.27 (1.17 to 1.38)) and patients with obesity (1.42 (1.34 to 1.52) vs 0.97 (0.83 to 1.14)) and diabetes without complications (1.14 (1.10 to 1.19) vs 0.95 (0.87 to 1.05)). INTERPRETATION: In-hospital mortality rates for patients with a diagnosis of COVID-19 have fallen substantially and there is evidence that the relative importance of some covariates has changed since the start of the pandemic. These patterns should continue to be tracked as new variants of the virus emerge, vaccination programmes are rolled out and hospital pressures fluctuate.

3.
EClinicalMedicine ; 35: 100859, 2021 May.
Article in English | MEDLINE | ID: covidwho-1202394

ABSTRACT

BACKGROUND: A key first step in optimising COVID-19 patient outcomes during future case-surges is to learn from the experience within individual hospitals during the early stages of the pandemic. The aim of this study was to investigate the extent of variation in COVID-19 outcomes between National Health Service (NHS) hospital trusts and regions in England using data from March-July 2020. METHODS: This was a retrospective observational study using the Hospital Episode Statistics administrative dataset. Patients aged ≥ 18 years who had a diagnosis of COVID-19 during a hospital stay in England that was completed between March 1st and July 31st, 2020 were included. In-hospital mortality was the primary outcome of interest. In secondary analysis, critical care admission, length of stay and mortality within 30 days of discharge were also investigated. Multilevel logistic regression was used to adjust for covariates. FINDINGS: There were 86,356 patients with a confirmed diagnosis of COVID-19 included in the study, of whom 22,944 (26.6%) died in hospital with COVID-19 as the primary cause of death. After adjusting for covariates, the extent of the variation in-hospital mortality rates between hospital trusts and regions was relatively modest. Trusts with the largest baseline number of beds and a greater proportion of patients admitted to critical care had the lowest in-hospital mortality rates. INTERPRETATION: There is little evidence of clustering of deaths within hospital trusts. There may be opportunities to learn from the experience of individual trusts to help prepare hospitals for future case-surges.

4.
Lancet Respir Med ; 9(4): 397-406, 2021 04.
Article in English | MEDLINE | ID: covidwho-1180129

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. FUNDING: None.


Subject(s)
COVID-19/mortality , Hospital Mortality/trends , Minority Groups/statistics & numerical data , Pandemics/statistics & numerical data , Patient Acceptance of Health Care/statistics & numerical data , Adolescent , Adult , Age Factors , Aged , Aged, 80 and over , COVID-19/diagnosis , Comorbidity , Datasets as Topic , Electronic Health Records/statistics & numerical data , England/epidemiology , Female , Humans , Length of Stay/statistics & numerical data , Male , Middle Aged , Retrospective Studies , Risk Factors , Severity of Illness Index , Sex Factors , Young Adult
5.
Crit Care ; 25(1): 40, 2021 01 28.
Article in English | MEDLINE | ID: covidwho-1054831

ABSTRACT

The current coronavirus pandemic has impacted heavily on ICUs worldwide. Although many hospitals and healthcare systems had plans in place to manage multiple casualties as a result of major natural disasters or accidents, there was insufficient preparation for the sudden, massive influx of severely ill patients with COVID-19. As a result, systems and staff were placed under immense pressure as everyone tried to optimize patient management. As the pandemic continues, we must apply what we have learned about our response, both good and bad, to improve organization and thus patient care in the future.


Subject(s)
COVID-19/therapy , Critical Care/organization & administration , Health Services Research , Intensive Care Units/organization & administration , COVID-19/epidemiology , Humans
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