Factors associated with COVID‐19 related hospitalisation, critical care admission and mortality using linked primary and secondary care data

Abstract Background It is important that population cohorts at increased risk of hospitalisation and death following a COVID‐19 infection are identified and protected. Objectives We identified risk factors associated with increased risk of hospitalisation, intensive care unit (ICU) admission and mortality in inner North East London (NEL) during the first UK COVID‐19 wave. Methods Multivariate logistic regression analysis on linked primary and secondary care data from people aged 16 or older with confirmed COVID‐19 infection between 01/02/2020 and 30/06/2020 determined odds ratios (OR), 95% confidence intervals (CI) and P‐values for the association between demographic, deprivation and clinical factors with COVID‐19 hospitalisation, ICU admission and mortality. Results Over the study period, 1781 people were diagnosed with COVID‐19, of whom 1195 (67%) were hospitalised, 152 (9%) admitted to ICU and 400 (23%) died. Results confirm previously identified risk factors: being male, or of Black or Asian ethnicity, or aged over 50. Obesity, type 2 diabetes and chronic kidney disease (CKD) increased the risk of hospitalisation. Obesity increased the risk of being admitted to ICU. Underlying CKD, stroke and dementia increased the risk of death. Having learning disabilities was strongly associated with increased risk of death (OR = 4.75, 95% CI = [1.91, 11.84], P = .001). Having three or four co‐morbidities increased the risk of hospitalisation (OR = 2.34, 95% CI = [1.55, 3.54], P < .001; OR = 2.40, 95% CI = [1.55, 3.73], P < .001 respectively) and death (OR = 2.61, 95% CI = [1.59, 4.28], P < .001; OR = 4.07, 95% CI = [2.48, 6.69], P < .001 respectively). Conclusions We confirm that age, sex, ethnicity, obesity, CKD and diabetes are important determinants of risk of COVID‐19 hospitalisation or death. For the first time, we also identify people with learning disabilities and multi‐morbidity as additional patient cohorts that need to be actively protected during COVID‐19 waves.


| INTRODUC TI ON
The COVID-19 pandemic caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) continues to spread worldwide with over 48 million cases, and over 1.22 million deaths reported worldwide as 5 November 2020. 1 In the UK, the first two reported cases were on 31 January 2020 and the first reported COVID-19-related death was on 6 March 2020. As of 5 November 2020, 1.1 million people tested positive for COVID-19, and of those, 47 742 died within 28 days of a positive test. 2 Existing studies suggest that people of Asian or Black ethnicity, those over the age of 70 and males have a significantly higher risk of COVID-19-related mortality. 3,4 Also, areas with higher levels of deprivation, higher proportions of people from Asian or Black ethnic backgrounds or higher proportions of people with pre-existing health conditions tend to be more adversely affected by COVID- 19. [5][6][7] The Chinese Center for Disease Control and Prevention reported, in a study of 44 672 individuals with 1023 deaths, that cardiovascular disease, hypertension, diabetes, respiratory disease and cancers were associated with an increased risk of death from COVID-19. 8 A UK cross-sectional survey of 16 749 people who were hospitalised with COVID-19 showed the risk of death was higher in people with underlying cardiac, pulmonary and kidney disease, as well as cancer, dementia and obesity. 9 Obesity was also associated with higher risk of COVID-19 hospitalisation in New York. 10 North East London (NEL) has a diverse population, with high levels of deprivation and inequality. Residents in Newham, Hackney, Tower Hamlets and Barking and Dagenham are comparatively younger 11 and around half of all residents are from Black, Asian and Minority Ethnic (BAME) communities, ranging from around 16% in Havering to 68% in Newham. 12 In terms of deprivation, Barking and Dagenham, Newham, Tower Hamlets and Hackney are among the most deprived areas in England, and inner NEL wards tend to be more deprived but pockets of deprivation persist in outer boroughs also. 13 The difference between the lowest and highest healthy life expectancy across the boroughs is 9.3 and 8.0 years for men and women, respectively. 14 Boroughs in NEL have been disproportionally affected by the COVID-19 epidemic, with Newham (203.4) and Hackney (186. 8) having the second and third highest COVID-19 age-standardised mortality rate per 100 000 population in the UK (Figure 1). 6 The first case of COVID-19 in the region was reported on 19 February 2020, and the first COVID-19 associated death was documented on 6 March 2020. 2 An estimated 970 000 people live in the NEL areas of City and Hackney, Newham and Tower Hamlets, 11 and, as of 4 November 2020, there have been 11 886 confirmed cases of COVID-19 in the area. 2 On 23 March 2020, the UK Government imposed strict social distancing measures ("lockdown"), to protect the public, slow down the virus spread, reduce the associated morbidity and mortality, and prevent excess demand on the National Health Service (NHS).
In early June 2020, measures were gradually eased with the partial reopening of primary schools and secondary schools before nonessential businesses reopened on 4 July 2020. Following a period of slow growth in cases during July and August, cases started to rise again in September 2020. In response, the UK Government introduced a Tiered system of local lockdowns; however, on 5 November 2020, a second national lockdown was imposed following a sustained increase in cases and a rise in hospitalisations. 2,15 Analysing data on the local population is integral to the work of the NEL Integrated Health and Care System to ensure the development of equitable and appropriate epidemic response plans for managing future waves of COVID-19 as well as supporting the system and individuals recovering from COVID-19. Preliminary findings of this work have already been used to support system planning; in particular, information on cohorts at greater risk of infection and hospitalisation has informed the public health response locally.
In this paper, we determined which population cohorts are at increased risk of hospitalisation, ICU admission and death following a diagnosis of COVID-19 in the NEL areas of City of London and Hackney, Newham and Tower Hamlets. To achieve this, we used local health care data on demographic characteristics and clinical presentations of 1781 people (registered with a GP practice in the areas) with a confirmed diagnosis of COVID-19 between 1 February 2020 and 30 June 2020.
Our overall objective was to focus on an ethnically diverse area with high levels of deprivation. We feel this is crucial for understanding the impact of demographic and health factors on COVID-19-related hospitalisation and mortality as well as identifying the common clinical conditions experienced by people living in an urban setting who are at risk of adverse outcomes related to COVID-19. Our findings are broad and translatable to other settings.

| Data sources and observation period
Unique patient identifiers were used to obtain service use and mortality outcome data from the Secondary Uses Service (SUS) hospital inpatient data. 16 The SUS dataset is a comprehensive repository for anonymous patient-level healthcare data in England. It is primarily used for reporting and analysis of secondary care data to support COVID-19, COVID-19 mortality risk factors, regression analysis, risk factors for COVID-19 hospitalisation healthcare planning, commissioning, public health, clinical audit and governance, benchmarking, performance improvement, medical research and national policy development.

| Descriptive statistics results
Over the study period, 1781 people aged 16 or older (registered with a GP practice in Newham, Tower Hamlets and City and Hackney) were identified as being COVID-19 positive across the primary and secondary care datasets.

| Regression analysis results
The associations between patient-level factors and risk of COVID-  Table 2 and Figure 2).
The effect of deprivation was small and not statistically significant for all outcome measures (Table 2); however, this is likely due to the majority of people (92%) living in the top 40% most deprived areas.

| Multi-morbidity
Our results indicate that people with multi-morbidity are at increased risk of being hospitalised or dying following COVID-19 infection (

| Clinical factors associated with all outcomes
A range of clinical factors and conditions were determined to be associated with COVID-19-related hospitalisation ( Table 2 and Figure 2)), admission to ICU (Table 2 and Figure 3) and deaths (

| CON CLUS IONS
We used local linked primary and secondary data to examine the key risk factors for COVID-19-related hospitalisation, ICU admission and mortality in an ethnically diverse inner-city area with high levels of TA B L E 2 Results of multivariate logistic regression analysis that estimates the association between demographic and socioeconomic factors as well as obesity, smoking status and 17 individual clinical factors and the three outcome variables (hospitalisation, ICU admission and death following COVID-19 infection)   Our work has important policy implications for existing and emerging public health initiatives to address wider determinants of health, and the work has been shared and discussed with scientific advisory bodies within the UK.

E TH I C S A PPROVA L
This study is considered a retrospective service evaluation and as such is exempt, and ethics approval is waived by the UCL Research Ethics Committee.

CO N FLI C T O F I NTE R E S T
We declare no competing interests.

PATI E NT CO N S E NT S TATE M E NT
This study is considered a retrospective service evaluation and as such has patient consent waived by the UCL Research Ethics Committee.

PATI E NT A N D PU B LI C I N VO LV E M E NT S TATE M E NT
Patients or the public were not involved in the design, or conduct, or report, or dissemination plans of this research.

PE R M I SS I O N TO R E PRO D U CE M ATE R I A L FRO M OTH E R S O U RCE S
None of the material in this study was used from other sources, and hence, no permissions were required.

PE E R R E V I E W
The peer review history for this article is available at https://publo ns.com/publo n/10.1111/irv.12864.

DATA AVA I L A B I L I T Y S TAT E M E N T
The datasets used and analysed during this study and the numerical codes used to generate the outcomes of this paper are available from the corresponding author on reasonable request.