| Title:
|
UK prevalence of underlying conditions which increase the risk of severe COVID-19 disease: a point prevalence study using electronic health records |
| Abstract:
|
Background This study aimed to describe the population at risk of severe COVID-19 due to underlying health conditions across the United Kingdom in 2019. Methods We used anonymised electronic health records from the Clinical Practice Research Datalink GOLD to describe the point prevalence on 5 March 2019 of the at-risk population following national guidance. Prevalence for any risk condition and for each individual condition is given overall and stratified by age and region. We repeated the analysis on 5 March 2014 for full regional representation and to describe prevalence of underlying health conditions in pregnancy. We additionally described the population of cancer survivors and assessed the value of linked secondary care records for ascertaining COVID-19 at-risk status. Findings On 5 March 2019 24.4% of the UK population were at risk due to a record of at least one underlying health condition including 8.3% of school-aged children 19.6% of working-aged adults and 66.2% of individuals aged 70 years or more. 7.1% of the population had multimorbidity. The size of the at-risk population was stable over time comparing 2014 to 2019 despite increases in chronic liver disease and diabetes and decreases in chronic kidney disease and current asthma. Separately 1.6% of the population had a new diagnosis of cancer in the past five years. Interpretation The population at risk of severe COVID-19 (aged []70 years or with an underlying health condition) comprises 18.5 million individuals in the UK including a considerable proportion of school-aged and working-aged individuals. |
| Published:
|
2020-08-26 |
| DOI:
|
10.1101/2020.08.24.20179192 |
| DOI_URL:
|
http://doi.org/10.1101/2020.08.24.20179192 |
| Author Name:
|
Walker J L |
| Author link:
|
https://covid19-data.nist.gov/pid/rest/local/author/walker_j_l |
| Author Name:
|
Grint D J |
| Author link:
|
https://covid19-data.nist.gov/pid/rest/local/author/grint_d_j |
| Author Name:
|
Strongman H |
| Author link:
|
https://covid19-data.nist.gov/pid/rest/local/author/strongman_h |
| Author Name:
|
Eggo R M |
| Author link:
|
https://covid19-data.nist.gov/pid/rest/local/author/eggo_r_m |
| Author Name:
|
Peppa M |
| Author link:
|
https://covid19-data.nist.gov/pid/rest/local/author/peppa_m |
| Author Name:
|
Minassian C |
| Author link:
|
https://covid19-data.nist.gov/pid/rest/local/author/minassian_c |
| Author Name:
|
Mansfield K E |
| Author link:
|
https://covid19-data.nist.gov/pid/rest/local/author/mansfield_k_e |
| Author Name:
|
Rentsch C T |
| Author link:
|
https://covid19-data.nist.gov/pid/rest/local/author/rentsch_c_t |
| Author Name:
|
Douglas I J |
| Author link:
|
https://covid19-data.nist.gov/pid/rest/local/author/douglas_i_j |
| Author Name:
|
Mathur R |
| Author link:
|
https://covid19-data.nist.gov/pid/rest/local/author/mathur_r |
| Author Name:
|
Wong A |
| Author link:
|
https://covid19-data.nist.gov/pid/rest/local/author/wong_a |
| Author Name:
|
Quint J K |
| Author link:
|
https://covid19-data.nist.gov/pid/rest/local/author/quint_j_k |
| Author Name:
|
Andrews N |
| Author link:
|
https://covid19-data.nist.gov/pid/rest/local/author/andrews_n |
| Author Name:
|
Lopez Bernal J |
| Author link:
|
https://covid19-data.nist.gov/pid/rest/local/author/lopez_bernal_j |
| Author Name:
|
Scott J A |
| Author link:
|
https://covid19-data.nist.gov/pid/rest/local/author/scott_j_a |
| Author Name:
|
Ramsay M |
| Author link:
|
https://covid19-data.nist.gov/pid/rest/local/author/ramsay_m |
| Author Name:
|
Smeeth L |
| Author link:
|
https://covid19-data.nist.gov/pid/rest/local/author/smeeth_l |
| Author Name:
|
McDonald H I |
| Author link:
|
https://covid19-data.nist.gov/pid/rest/local/author/mcdonald_h_i |
| sha:
|
d0a7e3616204c158f02afa071b3b6c3b188d00ed |
| license:
|
medrxiv |
| source_x:
|
MedRxiv; WHO |
| source_x_url:
|
https://www.who.int/ |
| url:
|
https://doi.org/10.1101/2020.08.24.20179192
http://medrxiv.org/cgi/content/short/2020.08.24.20179192v1?rss=1 |
| has_full_text:
|
TRUE |
| Keywords Extracted from Text Content:
|
children
COVID-19
kidney
medRxiv preprint
cancer
working-aged
UK
liver
7·0
COVID-19
coronavirus
bone marrow
Patient
protein kinase inhibitors
CPRD GOLD-registered
UK MRC
OCPS-4
cancers
leukaemia
79·4
medRxiv preprint
sickle cell
≥65
JKQ
myeloma
AZ
under-ascertain
left CPRD
bone marrow cancer
≥70
MR/S003975/1
Bill
liver
https://doi.org/10.1101/2020.08.24.20179192 doi
Supplementary Table 3
PARP
NHS
cancer
non-pregnant women
lymphoma
terminations
children
steroids
nerves
line
stem cell
neurone
kidney
brain
lung
https://doi.org/10.17037/DATA.00001833
NA,
UK
grants
People
medRxiv preprint 19
emphysema
heart
HPRU
women
OPCS-4
Bayer, grants
people
men
blood
antenatal
medRxiv
SCID
Cancer survivors
Lung
GBD-based
7·5
PHE
lung cancer
working-aged
individuals
cerebral palsy
Agebased
COVID-
organ
GSK
patients
Brazil
Vaccine
990,939
heart
kidney |
| Extracted Text Content in Record:
|
First 5000 Characters:This study aimed to describe the population at risk of severe COVID-19 due to underlying health conditions across the United Kingdom in 2019.
We used anonymised electronic health records from the Clinical Practice Research Datalink GOLD to describe the point prevalence on 5 March 2019 of the at-risk population following national guidance. Prevalence for any risk condition and for each individual condition is given overall and stratified by age and region. We repeated the analysis on 5 March 2014 for full regional representation and to describe prevalence of underlying health conditions in pregnancy. We additionally described the population of cancer survivors, and assessed the value of linked secondary care records for ascertaining COVID-19 at-risk status.
On 5 March 2019, 24·4% of the UK population were at risk due to a record of at least one underlying health condition, including 8·3% of school-aged children, 19·6% of working-aged adults, and 66·2% of individuals aged 70 years or more. 7·1% of the population had multimorbidity. The size of the atrisk population was stable over time comparing 2014 to 2019, despite increases in chronic liver disease and diabetes and decreases in chronic kidney disease and current asthma. Separately, 1·6% of the population had a new diagnosis of cancer in the past five years.
Interpretation : medRxiv preprint
Evidence before this study We searched Pubmed for peer-reviewed articles, preprints, and research reports on the size and distribution of the population at risk of severe COVID. We used the terms (1) risk factor or comorbidity or similar (2) COVID or SARS or similar and (3) prevalence to search for studies aiming to quantify the COVID-19 at-risk UK population published in the previous year to 19 July 2020, with no language restrictions. We found one study which modelled prevalence of risk factors based on the Global Burden of Disease (which included the UK) and one study which estimated that 8.4 million individuals aged ≥30 years in the UK were at risk based on prevalence of a subset of relevant conditions in England. There were no studies which described the complete COVID-19 at-risk population across the UK.
We used a large, nationally-representative dataset based on electronic health records to estimate prevalence of increased risk of severe COVID-19 across the United Kingdom, including all conditions in national guidance. We stratified by age, sex and region to enable regionally-tailored prediction of COVID-19-related healthcare burden and interventions to reduce transmission of infection, and planning and modelling of vaccination of the at-risk population. We also quantified the value of linked secondary care records to supplement primary care records.
Individuals at moderate or high risk of severe COVID-19 according to current national guidance (aged ≥70 years, or with a specified underlying health condition) comprise 18·5 million individuals in the United Kingdom, rather than the 8.43 million previously estimated.
The 8·3% of school-aged children and 19·6% of working-aged adults considered at-risk according to national guidance emphasises the need to consider younger at-risk individuals in shielding policies and when re-opening schools and workplaces, but also supports prioritising vaccination based on age and condition-specific mortality risk, rather than targeting all individuals with underlying conditions, who form a large population even among younger age groups.
Among individuals aged ≥70 years, 66·2% had at least one underlying health condition, suggesting an age-targeted approach to vaccination may efficiently target individuals at risk of severe COVID-19.
These national estimates broadly support the use of Global Burden of Disease modelled estimates and age-targeted vaccination strategies in other countries.
People with underlying health conditions account for the majority of COVID-19-related hospital and intensive care admissions, and are at increased risk of death from COVID-19 compared to the general population of the same age. 1-4 Prevalence of many conditions increases with age, which is also an independent risk factor for COVID-19 mortality. 3, 4 Characterising the population at risk of severe COVID-19 is vital for effective policy and planning in response to the COVID-19 pandemic. 5 Age-and region-specific prevalence of at-risk groups are key to predicting mortality and managing pressure on hospital inpatient and intensive care services across the country. Numbers of school-aged children and working-aged adults at risk are important for re-opening local schools and workplaces. Vaccination planning requires at-risk population size for vaccine numbers, and age and regional distribution for modelling impact on regional transmission, since vaccine response typically decreases with older age. 6 Modelling based on the Global Burden of Disease (GBD) study suggests that approximately one in five individuals worldwide have a health condition that increases |
| PDF JSON Files:
|
document_parses/pdf_json/d0a7e3616204c158f02afa071b3b6c3b188d00ed.json |
| G_ID:
|
uk_prevalence_of_underlying_conditions_which_increase_the_risk_of_severe_covid_19 |
