Covid-19 Medical Risk Assessment – Alama (2022)

Last updated 4th January 2022

Covid-age was developed to assist health professionals when advising workers in the UK on their personal vulnerability to COVID-19, and implications for their employment. Vulnerability can be quantified as the probability of death should infection occur. It varies according to age, sex, ethnicity, the presence of other health conditions (comorbidities), and immunity from vaccination or previous infection. Covid-age summarises published evidence on the impacts of age, sex, ethnicity and comorbidities in the absence of vaccination or previous infection. It is intended for use as part of occupational health assessment of individuals’ fitness for work, and not in clinical treatment pathways.

WHAT DOES COVID-AGE MEAN?

An individual’s Covid-age is calculated by expressing the impacts of risk factors as added years of age that would carry a similar increase in vulnerability. The values for each applicable risk factor are then added to the individual’s true age. This gives an estimate of the age of a healthy white man, who in the absence of vaccination or previous infection, would be expected to have similar vulnerability.

HOW SHOULD COVID-AGE BE USED?

When assessing a worker’s occupational risk from COVID-19, their Covid-age can be set alongside their history of vaccination and/or infection to derive an overall estimate of personal vulnerability. That assessment can then be combined with knowledge about their job and the expected community prevalence of infection, to obtain an assessment of risk (for more detailed guidance, see section below on Management of occupational risks from COVID-19).

HOW DO I FIND A PERSON’S COVID-AGE?

The easy way is to enter risk factors into the online calculator below.

Alternatively, you can calculate Covid-age using Table 1 at the bottom of this page (see section below on Estimating Covid-age using tables).

Before using Covid-age, it is important to note the following caveats.

CAVEATS

Because each individual is different, Covid-age is not an exact measure of vulnerability. It is an estimate consistent with the current balance of scientific evidence.

Some of the categories of comorbidity are broad-ranging, and within them, vulnerability may vary. The tool gives an overall average estimate of vulnerability, which may be individually tailored through clinical judgement of a suitably qualified health professional.

Some potentially relevant health problems are not covered by the tool, because insufficient evidence is available on them. Using clinical judgement, it may in some cases be reasonable to apply added years that have been estimated for another similar condition. For example, there is evidence that inflammatory bowel diseases and inflammatory skin diseases carry similar vulnerability to inflammatory joint diseases.

Covid-age does not assess risks from Covid-19 in pregnancy. While there is evidence of increased vulnerability during pregnancy, it is not in a form that can be incorporated into the current risk model. Moreover, the disease can impact adversely on obstetric outcomes as well as maternal health. When assessing and managing occupational risks from Covid-19 during pregnancy, users should refer to https://www.rcog.org.uk/en/guidelines-research-services/guidelines/coronavirus-pregnancy/covid-19-virus-infection-and-pregnancy/

Covid-age is based on evidence relating to various comorbidities, but in most cases, their treatment has not been considered separately. The estimate of added years for a condition represents an average across the spectrum of treatment for that disease. This includes the effects of treatment with immunosuppressive medication, and it is not necessary to add further years of age in respect of such treatment.

(Video) Helping patients decide on return to work during COVID-19

The group ‘other immunosuppressive disorders’ refers to medical conditions that depress immune responses, and which are not included elsewhere in the calculator. It does not cover immunosuppressive medication. Any immunological effects of diseases that are listed elsewhere in the calculator (e.g. diabetes, splenectomy) are covered by those categories.

Calculations assume as a default that added years for different risk factors can be summed (i.e. that relative risks multiply). However, that assumption may not be accurate for all risk factors, especially when it leads to calculated Covid-ages greater than 85. The online calculator therefore presents Covid-ages in this upper range simply as >85. It does, however, show the years added for each risk factor so that clinicians can see the assumed impact of different conditions.

SOURCES OF EVIDENCE ON VULNERABILITY

Further details of the background evidence and methods can be found here: Methods at 211214

MANAGEMENT OF OCCUPATIONAL RISKS FROM COVID-19

CONTEXT

In managing occupational risks of Covid-19, employers must control exposure to the virus so far as is reasonably practicable, taking into account the possibility that some workers will be more vulnerable than others should they contract the disease.

Strategies may include changes to the way in which work is carried out, use of barriers and personal protective equipment (PPE), and in some cases, exclusion or redeployment of individuals who are more vulnerable. The need for selective exclusion/redeployment of vulnerable workers will depend on the likelihood of their contracting Covid-19 through their work, and on the extent of their personal vulnerability to severe illness should they get the disease.

The level of individual risk that is considered acceptable will depend on value judgements, which can differ from person to person, and it is therefore not possible to lay down hard and fast rules. However, we here suggest an approach that may be a useful starting point for decision-making.

The approach entails first using Covid-age to assess the individual’s vulnerability to COVID-19 in the absence of previous infection or vaccination. That assessment is then modified to account for any previous infection or vaccination. Finally, a matrix is applied to guide decisions according to the nature of the job and local prevalence of infection.

VULNERABILITY LEVELS

It may be convenient to stratify vulnerability into ranges. We have used a lower bound of Covid-age 85 to define a stratum of ‘very high vulnerability’. In the absence of vaccination or previous infection, this may correspond to infection fatality rates in excess of around one in twenty, and could be considered equivalent to being ‘clinically extremely vulnerable’.

To define a second stratum of ‘high vulnerability’, we suggest a lower bound at Covid-age 70 (which in those with no vaccination or previous infection has carried an infection fatality rate of around one in a hundred). This corresponds approximately to the threshold for the Government’s category of ‘clinically vulnerable’.

To account for differences in vulnerability below that level, we suggest a further cut-point at Covid-age 50, which distinguishes ‘moderate vulnerability’ from ‘low vulnerability’.

Covid-age will determine the stratum of vulnerability to which an individual is initially assigned, but in some cases clinical judgement may indicate that a different stratum is appropriate because of individual circumstances.

ACCOUNTING FOR SPECIFIC IMMUNITY

Specific immunity from previous infection and/or vaccination may reduce both the risk of becoming infected by SARS-CoV-2 and personal vulnerability should infection occur. The level of protection will vary according to time interval(s) since infection/vaccination, number of vaccinations, type(s) of vaccine administered, and the variant(s) of the virus to which the individual is subsequently exposed. It may also differ in the presence of comorbidities, and particularly those which impair immune responses.

(Video) COVID-19 Risk Assessment Guidelines for Performers

Currently available data do not allow detailed quantitative assessment of how reductions in risk differ according to these variables, or how they are apportioned between lower rates of infection and lower vulnerability once infection has occurred. It appears, however, that on average among people of working age, previous infection or vaccination will provide protection in excess of 80% against mortality from variants of SARS-CoV-2 that are currently dominant in the UK. Moreover, that protection will last for months or longer, and can be enhanced by further vaccination.

The table below shows relative risks corresponding to different levels of vaccine protection against death from Covid-19, and reductions in Covid-age that would give an equivalent reduction in risk.

Level of vaccine protection (%) against death from Covid-19Relative risk of death from Covid-19Reduction in Covid-age (years) that would give equivalent relative risk
97.50.02536
950.0529
900.122
850.1518
800.216
750.2513
700.312
650.3510
600.49
500.57
400.65
300.73

We suggest that to account for reduced risk from vaccination, it would be reasonable in many cases to shift a previously infected/vaccinated individual down by one stratum of vulnerability – for example, from ‘very high’ to ‘high’ or from ‘high’ to ‘moderate’.

An exception might be individuals on immune suppressant medication or with conditions affecting immunity such as HIV or cancer, who may not respond so well to vaccines. Particularly in such cases, clinical judgement should be applied when considering adjustments to assessed vulnerability.

ACCOUNTING FOR VIRAL PREVALENCE

A major determinant of risk is local prevalence of infection by SARS-CoV-2, Other things being equal, in occupations that do not involve selective contact with people more likely to be carrying the virus (as occurs for example in healthcare), risk of exposure can be expected to vary in proportion to the local prevalence of infection.

A MATRIX FOR RISK MANAGEMENT

Based on the above considerations, we set out below a matrix combining what we know about vulnerability, immunity, and viral prevalence to classify individuals according to the type of work that they might reasonably undertake. It should be viewed as a rough guide and tailored to account for special circumstances and the views of the worker.

Matrix guide for estimation of a worker’s overall risk pre-and post-vaccination

Overall risk is very high, avoid this activity
Overall risk is high, only undertake this activity if it is essential and cannot be avoided
Overall risk is moderate, avoid if the activity is unnecessary
Overall risk is low, no requirement for any additional adjustments or controls

70-84

Viral prevalence per weekα
Workplace riskCovid-age
Adjusted for immunity
1-9/​100,00010-99/​100,000100-999/​100,0001000+/​100,000
Very High
In rooms, wards or vehicles caring for Covid-positive patients where full PPE cannot be worn reliably.
85 & above
70-84
50-69
Under 50
High
In rooms, wards, accommodation buildings or vehicles in close proximity to people with suspected Covid-19.
85 & above
70-84
50-69
Under 50
Medium
High number of different face-to-face contacts. e.g. healthcare, care homes, social care, hairdressing, teaching, police, probation work, supermarket staff.
Public transport staff and passengers
85 & above
70-84
50-69
Under 50
Low
Good social distancing, ventilation and hygiene measures e.g. call centre work, office work, in-home utility and repair work.
Commuting by car, bicycle and walking.
85 & above
70-84
50-69
Under 50
Working from homeAll ages

αIndividual Government websites provide current viral prevalence rates, although this can also be accessed via: https://www.bbc.co.uk/news/uk-51768274

A guide to using this matrix can also be downloaded from

https://www.som.org.uk/sites/som.org.uk/files/COVID-19_return_to_work_in_the_roadmap_out_of_lockdown_March_2021.pdf

(Video) Deployed to a COVID ward | Let's talk about COVID19: What's your COVID Age?! | Inpatient Management

ESTIMATING COVID-AGE USING TABLES

Use these tables by starting with the person’s actual age and then adding or subtracting years for each risk factor that applies, using Table 1 below.

First find their actual age in the top line of the table, then follow the column down to find the estimated impact (i.e. years to add or subtract from their actual age) for each risk factor that applies to that person. For example:

  1. A healthy white woman, aged 40, has a Covid-age of (40-5) = 35 years
  2. A white man aged 45, BMI 36 with severe asthma, has a Covid-age of (45+13+11) = 69 years.
  3. An Asian woman aged 50 with Type 2 diabetes, unknown HbA1c, has a Covid-age of (50-5+5+20) = 70 years.

Whether a person’s Covid-age is obtained from the online calculator or using Table 1, minor modification may be warranted to account for the specific circumstances of the individual (e.g. the severity of relevant comorbidities). If you judge that to be appropriate, you should make the adjustment using your clinical judgement,

Table 1. Vulnerability from risk factors expressed as equivalence to added years of age

True age (years)20212223242526272829
Female sex-5-5-5-5-5-5-5-5-5-5
Ethnicity
Asian or Asian British5555555555
Black7777777777
Mixed 5555555555
Other non-white4444444444
Body mass index (Kg/m2)
30-34.97777766666
35-39.919191918181818171717
≥4025252424242323232222
Hypertension 12121212121212111111
Heart failure25252525252524242424
Other chronic heart disease20202020202019191919
Cerebrovascular disease17171716161616161616
Asthma
Mild1111111111
Severe 15151515151514141414
Other chronic respiratory disease 17171717171616161616
Diabetes
Type 1
HbA1≤58 mmol/mol in past year24242424242423232323
HbA1>58 mmol/mol in past year27272727272726262626
HbA1c unknown29292929292828282828
Type 2 and other
HbA1≤58 mmol/mol in past year21212121212020202020
HbA1>58 mmol/mol in past year23232323232222222222
HbA1c unknown22222222222121212121
Chronic kidney disease
Estimated GFR 30-60 mL/min42414039383737363534
Estimated GFR < 30 mL/min53525150504948474646
Non-haematological cancer
Diagnosed <1 year ago34333332323131303029
Diagnosed 1-4.9 years ago25252524242423232222
Diagnosed ≥5 years ago18181818171717161616
Haematological malignancy
Diagnosed <1 year ago33333232323231313131
Diagnosed 1-4.9 years ago32313131303030292929
Diagnosed ≥5 years ago21212121212020202020
Liver disease32313130302929282827
Chronic neurological disease other than stroke or dementia*23232222222222222222
Organ transplant25252424242424242424
Spleen diseases†14141313131313131313
Rheumatoid/lupus/psoriasis2222222222
Other immunosuppressive condition‡30302929282827272626
True age (years)30313233343536373839
Female sex-5-5-5-5-5-5-5-5-5-5
Ethnicity
Asian or Asian British5555555555
Black7777777777
Mixed 5555555555
Other non-white4444444444
Body mass index (Kg/m2)
30-34.96666666655
35-39.917161616161515151515
≥4022212121202019191918
Hypertension 11111111101010101010
Heart failure24232323222222222121
Other chronic heart disease19181818171717171616
Cerebrovascular disease16161616161616151515
Asthma
Mild1111111111
Severe 14141413131313131212
Other chronic respiratory disease 16151515151514141414
Diabetes
Type 1
HbA1≤58 mmol/mol in past year23232222222222212121
HbA1>58 mmol/mol in past year26262525252525252424
HbA1c unknown28282827272727272626
Type 2 and other
HbA1≤58 mmol/mol in past year20202019191919191919
HbA1>58 mmol/mol in past year22222221212121212121
HbA1c unknown21212121212121212020
Chronic kidney disease
Estimated GFR 30-60 mL/min33323231302928272626
Estimated GFR < 30 mL/min45444443424140393837
Non-haematological cancer
Diagnosed <1 year ago29282827272626252524
Diagnosed 1-4.9 years ago22212121202019191818
Diagnosed ≥5 years ago15151514141313121211
Haematological malignancy
Diagnosed <1 year ago30303030292929292828
Diagnosed 1-4.9 years ago28282827272726262525
Diagnosed ≥5 years ago20191919191818181817
Liver disease27262625252424232322
Chronic neurological disease other than stroke or dementia*22222121212121212120
Organ transplant23232323232322222222
Spleen diseases†13131312121212121212
Rheumatoid/lupus/psoriasis2222222222
Other immunosuppressive condition‡25252424232322222121
True age (years)40414243444546474849
Female sex-5-5-5-5-5-5-5-5-5-5
Ethnicity
Asian or Asian British5555555555
Black7777777777
Mixed 5555555555
Other non-white4444444444
Body mass index (Kg/m2)
30-34.95555555444
35-39.914141414131313131212
≥4018171717161616151514
Hypertension 9999988888
Heart failure21202020191919181818
Other chronic heart disease16151515141414131313
Cerebrovascular disease15151515151515141414
Asthma
Mild1111111111
Severe 12121211111111101010
Other chronic respiratory disease 14131313131313121212
Diabetes
Type 1
HbA1≤58 mmol/mol in past year21212020202020191918
HbA1>58 mmol/mol in past year24242423232323222222
HbA1c unknown26262525252524242423
Type 2 and other
HbA1≤58 mmol/mol in past year19181818181817171716
HbA1>58 mmol/mol in past year21202020202019191918
HbA1c unknown20202020202019191918
Chronic kidney disease
Estimated GFR 30-60 mL/min25242322212019191818
Estimated GFR < 30 mL/min36353534333332323130
Non-haematological cancer
Diagnosed <1 year ago24232322222121202019
Diagnosed 1-4.9 years ago18171716161615151413
Diagnosed ≥5 years ago111110101099988
Haematological malignancy
Diagnosed <1 year ago28282727272626262525
Diagnosed 1-4.9 years ago25242423232222222222
Diagnosed ≥5 years ago17171716161615151414
Liver disease22212120201919181717
Chronic neurological disease other than stroke or dementia*20202020202019191919
Organ transplant22222121212121202020
Spleen diseases†11111111111110101010
Rheumatoid/lupus/psoriasis2222222222
Other immunosuppressive condition‡20201919181717161615
True age (years)50515253545556575859
Female sex-5-5-5-5-5-5-5-5-5-5
Ethnicity
Asian or Asian British5555555555
Black7777777777
Mixed 5555555555
Other non-white4444444444
Body mass index (Kg/m2)
30-34.94443333333
35-39.9121111111010101099
≥4014141313121212111111
Hypertension 7777766655
Heart failure17171716161615151414
Other chronic heart disease1312121212111110109
Cerebrovascular disease14141413131313131212
Asthma
Mild1111111111
Severe 9999888777
Other chronic respiratory disease 1212111111111010109
Diabetes
Type 1
HbA1≤58 mmol/mol in past year18181717161616151514
HbA1>58 mmol/mol in past year21212020191919181817
HbA1c unknown23232222212120201919
Type 2 and other
HbA1≤58 mmol/mol in past year16161515141414131312
HbA1>58 mmol/mol in past year18181717161616151514
HbA1c unknown18181717161616151514
Chronic kidney disease
Estimated GFR 30-60 mL/min17161615141413131211
Estimated GFR < 30 mL/min30292828272626252423
Non-haematological cancer
Diagnosed <1 year ago19181817161615151414
Diagnosed 1-4.9 years ago1312111110109988
Diagnosed ≥5 years ago8777666554
Haematological malignancy
Diagnosed <1 year ago24242323222221212020
Diagnosed 1-4.9 years ago21212121202020191918
Diagnosed ≥5 years ago131212111110101099
Liver disease16151514141313121211
Chronic neurological disease other than stroke or dementia*18181818171717161616
Organ transplant19191918181817171616
Spleen diseases†9998888777
Rheumatoid/lupus/psoriasis2222222222
Other immunosuppressive condition‡15151414131313121211
True age (years)60616263646566676869
Female sex-5-5-5-5-5-5-5-5-5-5
Ethnicity
Asian or Asian British5555555555
Black7777777777
Mixed5555555555
Other non-white4444444444
Body mass index (Kg/m2)
30-34.93222222222
35-39.99888777665
≥401010109998877
Hypertension5544433322
Heart failure13131212111111101010
Other chronic heart disease9887766555
Cerebrovascular disease12121211111111111010
Asthma
Mild1111111111
Severe6655444433
Other chronic respiratory disease9988877777
Diabetes
Type 1
HbA1≤58 mmol/mol in past year14131312121111111010
HbA1>58 mmol/mol in past year17161615151414141313
HbA1c unknown18181717161615151414
Type 2 and other
HbA1≤58 mmol/mol in past year121111101099887
HbA1>58 mmol/mol in past year14131312121111111010
HbA1c unknown14131312121111111010
Chronic kidney disease
Estimated GFR 30-60 mL/min111099887766
Estimated GFR < 30 mL/min23222221202019191818
Non-haematological cancer
Diagnosed <1 year ago131312121111101099
Diagnosed 1-4.9 years ago8777666554
Diagnosed ≥5 years ago4332211110
Haematological malignancy
Diagnosed <1 year ago19191817171616151514
Diagnosed 1-4.9 years ago18171716161515141413
Diagnosed ≥5 years ago9888777766
Liver disease1110109988776
Chronic neurological disease other than stroke or dementia*16151515141414141313
Organ transplant15151414131312121111
Spleen diseases†6665555443
Rheumatoid/lupus/psoriasis2222222222
Other immunosuppressive condition‡11111010999887
True age (years)707172737475
Female sex-5-5-5-5-5-5
Ethnicity
Asian or Asian British555555
Black777777
Mixed 555555
Other non-white444444
Body mass index (Kg/m2)
30-34.9211111
35-39.9554433
≥40766555
Hypertension 211000
Heart failure999888
Other chronic heart disease444333
Cerebrovascular disease10109999
Asthma
Mild111111
Severe 332222
Other chronic respiratory disease 666666
Diabetes
Type 1
HbA1≤58 mmol/mol in past year1099888
HbA1>58 mmol/mol in past year131212121111
HbA1c unknown141313121212
Type 2 and other
HbA1≤58 mmol/mol in past year766655
HbA1>58 mmol/mol in past year999888
HbA1c unknown999887
Chronic kidney disease
Estimated GFR 30-60 mL/min554433
Estimated GFR < 30 mL/min171716161515
Non-haematological cancer
Diagnosed <1 year ago988877
Diagnosed 1-4.9 years ago433322
Diagnosed ≥5 years ago000000
Haematological malignancy
Diagnosed <1 year ago141313121211
Diagnosed 1-4.9 years ago131212111111
Diagnosed ≥5 years ago665555
Liver disease665544
Chronic neurological disease other than stroke or dementia*131312121212
Organ transplant10109988
Spleen diseases†322110
Rheumatoid/lupus/psoriasis222222
Other immunosuppressive condition‡776655

*Chronic neurological disease other than stroke or dementia includes motor neurone disease, myasthenia gravis, multiple sclerosis, Parkinson’s disease, cerebral palsy, quadriplegia, hemiplegia and progressive cerebellar disease.

†Spleen diseases include splenectomy, or spleen dysfunction (e.g. from sickle cell disease).

‡Other immunosuppressive condition includes HIV, conditions inducing permanent immunodeficiency (ever diagnosed), aplastic anaemia, and temporary immunodeficiency recorded within the past year.

PROJECT PARTICIPANTS

The work has been undertaken by the Joint Occupational Health COVID-19 Group:

Principal Authors:

Prof David Coggon, Southampton, Prof Peter Croft, Keele, Prof Paul Cullinan, Imperial College London, Dr Tony Williams, Working Fit Ltd

OpenSAFELY team:

(Video) COVID-19 briefing with Gov. Ige, Lt. Gov. Green and local medical leaders pt. 1

Our thanks to the OpenSAFELY team, and in particular to Prof Krishnan Bhaskharan, Professor of Statistical Epidemiology at the London School of Hygiene and Tropical Medicine, for providing the additional analyses used in the update of the Covid Age tables and Dr Elizabeth Williamson, Associate Professor of Medical Statistics at the London School of Hygiene and Tropical Medicine. These analyses are open access and can be found, with all other OpenSafely publications, athttps://opensafely.org/research/

Website calculator:

Our thanks to Dr David Hodkin for developing the calculator, and for RStudio for hosting the tool on their shinyapps.io cloud.

Excel calculator:

Our thanks to Dr Mark Glover and Lalji Varsani for developing the Excel calculators.

Strategy Group:

Prof Ewan MacDonald, Glasgow (Chair), Prof Raymond Agius, Manchester, Prof Mike Pearson, Liverpool, Dr Anne de Bono, Faculty of Occupational Medicine, Dr Will Ponsonby, Society of Occupational Medicine, Dr Blandina Blackburn, NHS Health at Work Network, Dr Alastair Leckie, NHS Lothian, Dr Drushca Lalloo, Glasgow, Dr Munna Roy, Glasgow, Dr Clare Rayner, Manchester

Working Group for Tables:

Dr Jacqui Bollman, Dr Pam Collins, Dr Andrew Dickson, Dr Emma McCollum, Dr Kerry McNeil, Dr Pam Mellors, Dr Peter Noone, Dr Chris Valentine, Dr Eugene Waclawski, Dr Tony Williams

Project Manager:

Dr Tony Williams, Working Fit Ltd

covid19@workingfit.com

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