1
COVID-19 Infection, Admission and Death Amongst People with Rare Autoimmune Rheumatic
Disease in England. Results from the RECORDER Project.
Authors:
Megan Rutter
1,2
, Peter C. Lanyon
1,2, 3,4
, Matthew J. Grainge
1
, Richard Hubbar d
1,4
, Emily
Peach
1
, Mary Bythell
3
, Peter Stilwell
3
, Jeanette Aston
3
, Sarah Stevens
3
, Fiona A. Pearce
1,2,3, 4
Affiliations:
1
Department of Population and Lifespan Sciences, School of Medicine, University of Nottingham,
Nottingham, UK
2
Department of Rheumatology, Nottingham University Hospitals NHS Trust, Nottingham, UK
3
National Congenital Anomaly and Rare D isease Regist ration Service, National Disease Registration
Service, Public Health England, UK
4
National Institute for Health Research (NIHR) Nottingham Biomedical Research Centre,
Nottingham, UK
Corresponding author:
Megan Rutter, Clinical Sciences Building, City Hospital Campus, University of
Nottingham, NG5 1PB. megan.rutter@nottingham.ac.uk
ORCID iD 0002-1522-9620
Acknowledgements:
We would like to thank Chetan Mukhtyar, Reem Al-Jayyousi, Bridget Griffiths,
Richard Watts, Mithun Chakravorty, Cattleya Godsave, Julie Battista, Robin Glover, Matthew Bell and
Kay Randall for their help confirming diagnoses in hospital medical notes.
We would also like to thank Charlotte Eversfield for the thorough quality assurance of the data
included in this study.
This work uses data that has been provided by patients, the NHS and other health care organisations
as part of patient care and support. The data is collated, maintained and quality assured by the
National Congenital Anomaly and Rar e Disease Registration Service, which is part of Public Health
England (PHE).
Competing Interest Statement:
FP and PCL are recipients of a grant from Vifor pharma. Vifor pharma had no influence on the design,
conduct or interpretation of this study.
At the time of her contribution to this study, EP was employed by the University of Nottingham. She
has gone on to work for Astra Zeneca,
who
had no influence on the design, conduct or
interpretation of this study.
Funding Statement:
MR is funded by Vasculitis UK (patient charity) and the British Society for Rheumatology, and would
like to thank them for their help and support.
FP is funded by an NIHR A dvanc ed Fello w ship.
This project is a HDR-U K Better Care theme project.
FP and PCL are recipients of a grant from Vifor pharma. Vifor pharma had no influence on the design,
conduct or interpretation of this study.
Data availability statement:
. CC-BY-ND 4.0 International licenseIt is made available under a
is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. (which was not certified by peer review)
The copyright holder for this preprintthis version posted September 4, 2021. ; https://doi.org/10.1101/2021.08.17.21260846doi: medRxiv preprint
NOTE: This preprint reports new research that has not been certified by peer review and should not be used to guide clinical practice.
2
Due to legal and ethical considerations, supporting data cannot be made openly available. However,
NCARDRS data a re available to all who have a legal basi s to access them. Fur ther details about the
data and conditions for access are available by application to the National Disease Registration
Service (https://www.gov.uk/guidance/the-national-congenital-anomaly-and-rare-disease-
registration-service-ncardrs). Information on how to access this data can also be obtained from the
University of Nottingham data repository: DOI: 10.17639/nott.7131
. CC-BY-ND 4.0 International licenseIt is made available under a
is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. (which was not certified by peer review)
The copyright holder for this preprintthis version posted September 4, 2021. ; https://doi.org/10.1101/2021.08.17.21260846doi: medRxiv preprint
3
Abstract
Objectives:
To calculate the rates of COVID-19 infection and COVID-19-related death among people with rare
autoimmune rheumatic diseases (RAIRD) during the first wave of the COV ID-19 pandemic in England
compared to the general population.
Methods:
We used Hospital Episode Statistics to identify all people alive 01 March 2020 with ICD-10 codes for
RAIRD from the whole population of England. We used linked national health records (demographic,
death certificate, admissions and PCR testing data) to calculate rates of COVID-19 infection and
death up to 31 July 2020. Our primary definition of COVID-19-related death was mention of COVID-
19 on the death certificate. General population data from Public Health England and the Office for
National Statistics were used for comparison. We also describe COVID-19-related hospital
admissions and all-cause deaths.
Results:
We identified a cohort of 168,680 people with RAIRD, of whom 1874 (1.11%) had a positive COVID-
19 PCR test. The age-standardised infection rate was 1.54 (95% CI 1.50-1.59) times higher than in the
general population. 713 (0.42%) people with RAIRD died with COVID-19 on their death certificate
and the age-sex-standardised mortalit y rate for COVID-19-related death was 2.41
(2.30 – 2.53) times
higher than in the general population. There was no evidence of an increase in deaths from other
cause s in the RAIRD population.
Conclusions:
During the first wave of COVID-19 in England, people with RAIRD had a 54% increased risk of COVID-
19 infection and more than twice the risk of COVID-19-related death compared to the general
population. These increases were seen despite shielding policies.
Keywords:
COVID-19, coronavirus, mortality, rare autoimmune rheumatic diseases,
epidemiology, shielding, infection
Key Messages:
1.
People with RAIRD were at increased risk of COVID-19 infection during the first wave.
2.
Compared to the general population, they had over twice the risk of COVID-19-related
death.
3.
These increased risks were seen despite shielding policies in place in England.
. CC-BY-ND 4.0 International licenseIt is made available under a
is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. (which was not certified by peer review)
The copyright holder for this preprintthis version posted September 4, 2021. ; https://doi.org/10.1101/2021.08.17.21260846doi: medRxiv preprint
4
Introduction
Our previous work has shown that people with rare autoimmune rheumatic diseases (RAIRD) were
at increased risk of all-cause mortality during the first wave of the COVID-19 pandemic (March-April
2020), when compared to the general population in England(1). However, this study did not examine
whether the increased mortality was due to COVID-19 infection itself, or due to indirect effects of
the pandemic.
This study uses linked national health records for the whole population of England to calculate the
rates of laboratory confirmed COVID-19 infection and COVID-19-related death among people with
RAIRD from 01 March to 31 July 2020, the first wave of the COVID-19 pandemic, and compares these
rates to that in the general population. We describe COVID-19-related hospital and ICU admission,
underlying causes of death by category and COVID-19-related mortality stratified by RAIRD
diagnosis.
Methods
Background
The Registration of Complex Rare Diseases Exemplars in Rheumatology (RECORDER) project is a
collaboration between the U niversity of Nottingham, Nottingham University Hospitals NHS Trust and
the National Congenital Anomaly and Rare Diseas e Registration Service (NCARDRS). NCARDRS, based
within Public Health England (PH E), registers people with rare conditions in order to support high
quality clinical practice and research, provide epidemiology data and empower patients(2). It has
unique access to linked national datasets of electronic health records at patient-identifiable level for
the whole population of England.
This study uses Hosp ital Episode St atistics (HES, which contains every episode of admitted patient
care in NHS hospitals in England), COVID-19 polymerase chain reaction (PCR) test results and Office
for National Statistics (ONS) death certificate data.
Data validation
Our previous work validating HES ICD-10 codes for RAIRD has shown high accuracy, with a positive
predictive value was 84.7%(1). Prevalence estimates based on our findings for ANCA-associated
vasculitis, systemic lupus erythematosus and scleroderma, are similar to reported population
estimates(3-5).
Study cohort
People with a diagnostic code for RAIRD in HES from 2003 onwards, resident in England, and alive 01
March 2020 were included in the study, using the same cohort as our preliminary all-cause mortality
study(1). A data flow diagram is shown in Supplementary Figure 1. Vital status data from the NH S
Personal Demographics Service were used to confirm whether people were alive, or to confirm date
of death(6).
RAIRD diagnoses
Participants were grouped by RAIRD diagnosis , based on their most recent diagnostic code. Where
the most recent code was non-specific, for example “Renal tubulo-interstitial disorder in systemic
connective tissue disorder”, earlier, more specific diagnostic codes were used where available,
following the algorithm in Appendix 1.
Death certificate data
. CC-BY-ND 4.0 International licenseIt is made available under a
is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. (which was not certified by peer review)
The copyright holder for this preprintthis version posted September 4, 2021. ; https://doi.org/10.1101/2021.08.17.21260846doi: medRxiv preprint
5
Death certificate and underlying cause of death data (free text and ICD-10 coded) provided by the
ONS were utilised. Using internationally agreed rules, ONS assigns underlying cause of death, usually
based on the lowest completed line of Part 1 of a death certificate(7). Our data were examined for
ICD-10 codes s pecific to COVID-19 (U07.1, U07.2). The free text was manually checked for keywords
(“
cov”
, “
virus”
or “
19”
) which confirmed that no deaths related to COVID-19 (including misspellings)
had been omitted. Whilst COVID-19-specific ICD-10 codes were not introduced until 25 March
2020(8), all deaths with a free text mention of COVID-19 occurring before that date had been
captured retrospectively by the ONS coding system. We classified underlying all-cause death by
category.
COVID-19 status
A population-level dataset of COVID-19 PCR test results, held in the Second Generation Surveillance
System in PHE, was used. Po sitive te sts am ongst the RAIR D cohort were extract ed , al ong with the
date the laboratory reported the result. Demographics are described by pillar 1 (in-hospital) or pillar
2 (community) testing.
Hospital and intensive care unit admissions
HES admitted patient car e (APC) data on hospital and intensive care unit (IC U) ad mission s with an
ICD-10 diagnostic code for COVID-19 were extracted. Duration and number of admissions, and basic
and advanced respiratory support days on ICU are described.
Mortality rate calculation
We report two measures of COVID-19-related deaths. Our primary definition is death with any
mention of COVID-19 on the death certificate as used by the ONS(9). This was chosen due to the
limited availability of COVID-19 PCR testing in the community during the first wave. Our secondary
definition is death within 28 days of a positive COVID-19 PCR test, as used by PHE(10).
The crude all-cause mortality rate from 01 March to 31 July 2020 was calculated, with the cohort of
patients identified as having RAIRD used as the denominator population, along with the crude
mortality rates for the two measures of COVID-19-related death.
Age-sex-standardised mortality r ates per 100,000 in the population were calculated, standardised to
the 2019 mid-year estima te for the Engla nd populati on using 5-year age bands. Age-standa rdised
mortality rates (ASMRs) standardised to the 2013 European Standard Population (ESP) were also
calcul ated . As the ESP i s not disaggrega te d by sex and assumes equal number s of males and female s,
and identical age distribut ions within sexes, this population was not used to calculate age-sex
standardised rates.
The ONS provided data for all-cause deaths, and deaths with any mention of COVID-19 on t he death
certificate, over the same time period in England, split by sex and age band (11, 12). PHE provided
comparable data for deaths within 28 days of a positive COVID-19 test (available to the public on
request). These data were used to calculate the crude, age-standardised and age-specific mortality
rates as a comparator. Publicly available data from the government Coronavirus das hboard(13) were
used to compare infection rates in the general population. The 2019 mid-year estimate for the
population of England was us ed as the denominator.
COVID-19 infection rate calculation
Laboratory confirmed COVID-19 infection rate from 01 March to 31 July 2020 was calculated, with
the cohort of patients identified as having RAIRD us ed as the denominator population. Infection rate
was age-standardised to the mid-year 2019 England population.
. CC-BY-ND 4.0 International licenseIt is made available under a
is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. (which was not certified by peer review)
The copyright holder for this preprintthis version posted September 4, 2021. ; https://doi.org/10.1101/2021.08.17.21260846doi: medRxiv preprint