Validating the QCOVID risk prediction algorithm for risk of mortality from COVID-19 in the adult population in Wales, UK

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Jane Lyons
Vahé Nafilyan
Ashley Akbari
Gareth Davies
Rowena Griffiths
Ewen Harrison
Julia Hippisley-Cox
Joe Hollinghurst
Kamlesh Khunti
Laura North
Aziz Sheikh
Fatemeh Torabi
Ronan Lyons


COVID-19 risk prediction algorithms can be used to identify at-risk individuals from short-term serious adverse COVID-19 outcomes such as hospitalisation and death. It is important to validate these algorithms in different and diverse populations to help guide risk management decisions and target vaccination and treatment programs to the most vulnerable individuals in society.

To validate externally the QCOVID risk prediction algorithm that predicts mortality outcomes from COVID-19 in the adult population of Wales, UK.

We conducted a retrospective cohort study using routinely collected individual-level data held in the Secure Anonymised Information Linkage (SAIL) Databank. The cohort included individuals aged between 19 and 100 years, living in Wales on 24th January 2020, registered with a SAIL-providing general practice, and followed-up to death or study end (28th July 2020). Demographic, primary and secondary healthcare, and dispensing data were used to derive all the predictor variables used to develop the published QCOVID algorithm. Mortality data were used to define time to confirmed or suspected COVID-19 death. Performance metrics, including R2 values (explained variation), Brier scores, and measures of discrimination and calibration were calculated for two periods (24th January–30th April 2020 and 1st May–28th July 2020) to assess algorithm performance.

1,956,760 individuals were included. 1,192 (0.06%) and 610 (0.03%) COVID-19 deaths occurred in the first and second time periods, respectively. The algorithms fitted the Welsh data and population well, explaining 68.8% (95% CI: 66.9-70.4) of the variation in time to death, Harrell’s C statistic: 0.929 (95% CI: 0.921-0.937) and D statistic: 3.036 (95% CI: 2.913-3.159) for males in the first period. Similar results were found for females and in the second time period for both sexes.

The QCOVID algorithm developed in England can be used for public health risk management for the adult Welsh population.


The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection was first identified in Wuhan, China [1]. On the 24th January 2020, the UK recorded its first case of SARS-CoV-2 and as of 22nd August 2021, there have been 6,492,906 confirmed cases with 131,640 COVID-19-related deaths in the UK [2, 3]. Research has shown that increased age, being male, certain minority ethnic groups, and having pre-existing conditions such as diabetes, cardiovascular disease, and obesity are associated with serious adverse COVID-19 outcomes, including hospitalisation and death [49].

To protect the most vulnerable, and to minimise the burden on the National Health Service (NHS) and its staff, it is important to identify those at greatest risk of serious adverse COVID-19 outcomes [10, 11]. COVID-19 risk prediction algorithms can be used to identify and prioritise at-risk individuals for targeting vaccination and treatments as well as to inform risk management decisions and policy as the pandemic evolves [12].

The New and Emerging Respiratory Virus Threats Advisory Group (NERVTAG)’s effort to develop a population risk assessment framework led to the development and validation of the QCOVID tool, a population-based prediction algorithm to predict the risk of being admitted to hospital or dying from COVID-19 across an adult population [3, 13, 14]. The algorithm was initially developed and validated on a cohort of six million primary care patients from 1,205 English practices contributing to the QResearch database, which allows linkage at the individual-level to general practitioner (GP) primary care data, death records, hospital admissions data and COVID-19 test results. Predictive demographic, clinical, and pharmaceutical variables (Box 1) were based on the clinical vulnerability group criteria used to identify those advised to shield at the start of the pandemic, and risk factors associated with adverse outcomes for respiratory diseases [15, 16].

Box 1: List of predictor variables for the QCOVID risk equations


  • Age in years on 24th January 2020
  • Biological sex at birth
  • Townsend Deprivation Score
  • Ethnicity
  • What is your housing category - care home, homeless or neither?


  • Body Mass Index

Conditions on current shielding patient list

  • Have you had chemotherapy in the last 12 months?
  • Have you had radiotherapy in the last 6 months?
  • Have you had a bone marrow or stem cell transplant in the last 6 months?
  • Have you had a solid organ transplant (lung, liver, stomach, pancreas, spleen, heart or thymus)?
  • Do you have sickle cell disease or severe combined immune deficiency syndromes?
  • Do you have cystic fibrosis, bronchiectasis or alveolitis?
  • Have you a cancer of the blood or bone marrow such as leukaemia, myelodysplastic syndromes, lymphoma or myeloma and are at any stage of treatment?
  • Do you have lung or oral cancer?
  • Do you have congenital heart disease or have you had surgery for it in the past?

Conditions moderately associated with increased risk of complications as per current NHS guidance

  • Do you have a learning disability or Down’s Syndrome?
  • Chronic Kidney Disease (CKD) stage
  • Do you have asthma?
  • Do you have diabetes?
  • Do you have Parkinson’s disease?
  • Do you have cerebral palsy?
  • Do you have epilepsy?
  • Do you have rheumatoid arthritis or Systemic lupus erythematosus?
  • Do you have dementia?
  • Do you have chronic obstructive pulmonary disease (COPD)?
  • Do you have motor neurone disease, multiple sclerosis, myasthenia, or Huntington’s chorea?
  • Do you have coronary heart disease?
  • Do you have heart failure?

Other medical conditions that investigators hypothesized to confer elevated risk

  • Do you have peripheral vascular disease?
  • Do you have severe mental illness?
  • Have you had a prior fracture of hip, wrist, spine or humerus?
  • Do you have atrial fibrillation?
  • Do you have cirrhosis of the liver?
  • Do you have pulmonary hypertension or pulmonary fibrosis?
  • Have you had a thrombosis or pulmonary embolus?
  • Have you had a stroke or transient ischaemic attack?

Concurrent medications

  • Have you been prescribed immunosuppressants four or more times in the previous 6 months?
  • Have you been prescribed anti-leukotriene or long acting beta2-agonists (LABA) four or more times in the previous 6 months?
  • Have you been prescribed oral prednisolone containing preparations prescribed four or more times in the previous 6 months?

Replication of results in diverse populations is an important component of scientific research and is especially important for validation of prediction algorithms generated using routine data where the results may be used to plan clinical management of individual patients. It was decided to replicate and compare the performance of the algorithm in each of the four nations in the UK to ensure validity and contribute to the application of the algorithm in managing responses to the outbreak. A recent published study validated the QCOVID predictive algorithm in estimating the risk of mortality from COVID-19 in 35 million adult residents of England by the Office for National Statistics using linked Census 2011 data [17]. The aim of our study was to externally validate the QCOVID risk prediction algorithm to estimate mortality outcomes from COVID-19 in adults in Wales, UK. This paper replicates the English validation study and follows the RECORD and TRIPOD reporting guidelines [18, 19].


Study design and data sources

This study used routinely collected anonymised health and demographic data held in the Secure Anonymised Information Linkage (SAIL) Databank to create a retrospective population-based individual-level linked e-cohort. The SAIL Databank is a Trusted Research Environment (TRE), which hosts linkable anonymised individual and household-level health, demographic, administrative and environmental data for the population of Wales [20, 21].

Following the emergence of the SARS-CoV-2 infection and the subsequent COVID-19 pandemic, two population-level cohorts (known as C16 and C20) were created to support rapid analysis, provide evidence in understanding the evolving pandemic, and evaluate national interventions attempting to reduce the spread of infection [22]. The C20 contains all individuals alive and living in Wales from 1st January 2020 and followed up until death, emigration/break in Welsh residency, or cohort end date (currently 30th June 2021). This cohort is updated on a monthly basis to extend the available follow-up time. The C16 acts as a contextual comparative cohort and contains all individuals alive and living in Wales on 1st January 2016 and followed up until death, emigration/break in Welsh residency, or 31st December 2019.

For this study, we used the C20 to create a cohort of all individuals aged 19–100 years, living in Wales and registered with a SAIL providing general practice on 24th January 2020. The 24th January 2020 was chosen as the cohort entry as this is the date of the first confirmed COVID-19 case in the UK. Individuals were followed up until death or study end date (28th July 2020), with the study divided into two time periods, 24th January 2020–30th April 2020 and 1st May 2020–28thJuly 2020, to match the English validation study [17]. Individuals who had died prior to 1st May 2020 were excluded from the second time period analysis.

Predictor variables

To validate the QCOVID algorithm, the C20 cohort was linked to the Welsh Longitudinal General Practice (WLGP), Patient Episode Database for Wales (PEDW), Wales Dispensing DataSet (WDDS), and Office for National Statistics (ONS) Census 2011 (CENW) data [23] to derive the pre-existing conditions and demographic characteristics that were used to develop the QCOVID algorithm (Box 1).

The C20 cohort was used to define age, sex, and Townsend score. Townsend score is a measure of deprivation, based on the area of residence, and a higher score implies a higher level of deprivation. The CENW is linked to derive ethnicity (i.e. Bangladeshi, Black African, Black Caribbean, Chinese, Indian, Pakistani, Mixed, Other, and White) [24]. The ethnicity variable had a category corresponding to ‘not recorded/unknown’. This category was used whenever the corresponding value was missing.

The majority of pre-existing conditions were identified in the WLGP primary care data source using Read codes version 2 (CTV2). Where no timeframe was stated, a lookback period from 1st January 1998 to 24th January 2020 was used. For body mass index (BMI), the latest BMI measurement within 5 years to 24th January 2020 was used. BMI records outside this time period as well as BMIs <15 and >47 were set to missing. If an individual had multiple BMI records on the latest date, the highest BMI was included. Predicted values using all QCOVID predictor variables with age interactions from linear regression models, were used to impute any missing BMI values. Recorded BMI is dependent on the condition of interest and healthcare utilisation activity of the individual, therefore, it is possible to have individuals with no BMI recorded when using routinely collected healthcare data. For diabetes, if the latest health record had defined an individual with both type 1 and type 2 diabetes, type 2 took precedence [3]. For the housing covariate, if the latest record defined an individual being homeless and living in a care home, then living in a care home took precedence. For the learning disabilities covariate, if the latest record identified an individual as having learning disabilities and Down’s syndrome, then Down’s syndrome was prioritised.

Office of Population Censuses and Surveys (OPCS) Classification of Interventions and Procedures version 4 (OPCS-4) coded conditions in the inpatient (PEDW) data were used to identify chemotherapy status, Chronic Kidney Disease (CKD) stages, congenital heart disease surgery, bone marrow or stem cell transplant, radiotherapy, and solid organ transplant.

DMD (Dictionary of Medical Devices) coded prescriptions in the WDDS were used to identify individuals who had been dispensed immunosuppressants, anti-leukotriene or long acting beta2-agonists (LABA), or oral prednisolone at least four or more times within 6-months prior to 24th January 2020.

Outcome of interest – death involving COVID-19

We utilised a combination of data held in ONS Annual District Death Extract (ADDE) and Annual District Death Daily (ADDD), Welsh Demographic Service Dataset (WDSD) and Consolidated Death Data Source (CDDS) to identify all deaths, inclusive of in-hospital and out of hospital deaths, of Welsh residents. Deaths involving COVID-19 (confirmed or suspected) were identified using the tenth revision of the International Classification of Diseases (ICD-10) codes U07.1 or U07.2, or from text fields containing the causes of death within the data sources. Time to death from COVID-19 was calculated separately in the first period (24th January 2020–30th April 2020) and the second period (1st May 2020–28th July 2020).

Algorithm validation

The QCOVID risk equations (version 1) reported in the original study were fitted for males and females separately [3, 14]. The original paper utilised the Fine-Gray sub-distribution hazard model which is commonly used to estimate incidence of outcomes where competing risks exist. It relates covariates to the cumulative incidence function (CIF) of the outcome of interest [25, 26]. The following modifications for the Welsh adult population were required due to data issues. At the time of analysis, Systemic Anti-Cancer Therapy (SACT) data were not available, therefore, anyone receiving chemotherapy within 12-months of 24th January 2020 was assigned the chemotherapy group B (middle severity group) coefficients from the original study [27]. Due to low cohort numbers and subsequent outcome numbers for some ethnic groups, we collapsed ethnic groups to ensure ethnic minority populations or groups were not excluded from our study. Black Caribbean individuals were assigned Black African coefficients, Chinese individuals were assigned the coefficients for the Other ethnic group, and, all White ethnic groups were assigned the White British coefficients.

Performance metrics, including measures of discrimination and calibration, were calculated to validate the predicted risk of death from COVID-19 using the QCOVID algorithm at 97 days for the first period and 88 days for the second period [2830]. We calculated R2 values, D statistic, Harrell’s C statistic and Brier scores with corresponding 95% confidence intervals for the total cohort by sex and over the two time periods. The performance measurements were also calculated by age bands, ethnicity and Townsend deprivation quintiles. The R2 values refer to the proportion of variation in survival time explained by the model while the Brier score measures predictive accuracy. The D statistic and Harrell’s C statistic are discrimination measures that quantify the separation in survival between patients with different levels of predicted risks, and the extent to which people with higher risk scores have earlier events, respectively. To measure calibration, we compared the mean observed and predicted risks within each twentieths of predicted risk (20 groups) for the two time periods. Observed risks were derived in each of the 20 groups using non-parametric estimates of the cumulative incidences.


Overall, there were 1,956,760 individuals aged 19-100 years included in the final analysis for Wales. Of these, 967,975 (49.5%) were male with a mean age of 50.8 (SD 18.7) and the majority of individuals were from White ethnic backgrounds (1,741,527, 89.0%) (Table 1). In comparison with the English validation cohort and original cohort (Supplementary Table 1), these distributions of demographic characteristics were similar except for ethnicity with a lower proportion of individuals from ethnic minority backgrounds in Wales, but also a higher proportion (6.5%) of individuals missing this information (Table 1). The Welsh cohort had similar prevalence of pre-existing conditions when compared to the English validation cohort and original cohort. However, the proportion of people with higher BMI, CKD, respiratory cancer, venous thromboembolism (VTE), coronary heart disease (CHD) and osteoporotic fractures was slightly higher in the Welsh data and slightly lower for immunosuppressant use, dementia, or a serious mental illness compared to the English validation cohort. The proportions of people with missing BMI values, pulmonary hypertension and VTE were slightly higher in the Welsh data compared to original cohort.

Overall cohort COVID-19 deaths in first period (24 th Jan–30 th Apr 2020) COVID-19 deaths in second period (1 st May–28 th Jul 2020)
N % N % N %
Overall 1,956,760 1192 610
Male 967,975 49.47 674 56.54 299 49.02
Female 988,785 50.53 518 43.46 311 50.98
Age, years 50.8 18.7 79.4 11.8 81.0 11.1
Age group, years
19-29 318,681 16.29 * *
30-39 313,802 16.04 * *
40-49 304,363 15.55 16 1.34 *
50-59 353,539 18.07 61 5.12 28 4.59
60-69 291,042 14.87 132 11.07 49 8.03
70-79 240,840 12.31 305 25.59 136 22.30
80-89 111,631 5.70 429 35.99 250 40.98
≥90 22,862 1.17 242 20.30 138 22.62
Bangladeshi 7,011 0.36 * *
Black^ 8,312 0.42 * *
Indian 8,885 0.45 * *
Mixed 27,582 1.41 * *
Other^ 27,786 1.42 * *
Pakistani 7,688 0.39 * 0 0.00
White 1,741,527 89.00 1113 93.37 579 94.92
Not recorded 127,969 6.54 52 4.36 19 3.11
Townsend deprivation quintile
1 (most affluent) 335,459 17.14 156 13.09 98 16.07
2 413,486 21.13 221 18.54 129 21.15
3 559,024 28.57 369 30.96 179 29.34
4 453,474 23.17 304 25.50 141 23.11
5 (most deprived) 195,317 9.98 142 11.91 63 10.33
Neither homeless nor care home 1,940,224 99.15 987 82.80 476 78.03
Care home or nursing home 16,536 0.85 205 17.20 134 21.97
Body-mass index, kg/m2
<18.5 21,944 1.12 53 4.45 33 5.41
18.5 to <25 316,569 16.18 277 23.34 161 26.39
25 to <30 375,501 19.19 300 25.17 154 25.25
≥30 403,871 20.64 294 24.66 114 18.69
Not recorded 838,875 42.87 268 22.48 148 24.26
Chronic kidney disease
No Chronic Kidney disease 1,874,451 95.79 869 72.90 412 67.54
Stage 3 72,669 3.71 252 21.14 165 27.05
Stage 4 3,928 0.20 30 2.52 20 3.28
Stage 5 5,712 0.29 41 3.44 13 2.13
Learning disability
No learning disability 1,928,040 98.53 1163 97.57 587 96.23
Learning disability 28,486 1.46 29 2.43 23 3.77
Down Syndrome 234 0.01 0 0.00 0 0.00
Chemotherapy 0.00
No chemotherapy in past 12-months 1,949,761 99.64 1167 97.90 597 97.87
Chemotherapy in past 12-months 6,999 0.36 25 2.10 13 2.13
Cancer and immunosuppression
Blood cancer 10,547 0.54 38 3.19 14 2.30
Respiratory cancer 5,691 0.29 20 1.68 10 1.64
Radiotherapy in past 6-months 1,827 0.09 * *
Bone marrow transplant in past 6-months 56 0.00 0 0 0 0.00
Solid organ transplant 806 0.04 * *
Prescribed immunosuppressant medication by GP 2,884 0.15 * *
Prescribed leukotriene or LABA 38,658 1.98 59 4.95 42 6.89
Prescribed regular prednisolone 15,819 0.81 61 5.12 28 4.59
Other comorbidities
Diabetes 161,227 8.24 359 30.12 178 29.18
COPD 66,937 3.42 209 17.53 100 16.39
Asthma 290,490 14.85 186 15.60 109 17.87
Rare pulmonary diseases 9,471 0.48 26 2.18 12 1.97
Pulmonary hypertension or pulmonary fibrosis 3,741 0.19 17 1.43 14 2.30
Coronary heart disease 89,686 4.58 239 20.05 137 22.46
Stroke 55,336 2.83 233 19.55 121 19.84
Atrial fibrillation 62,712 3.20 253 21.22 140 22.95
Congestive cardiac failure 30,937 1.58 151 12.67 99 16.23
Venous thromboembolism 43,708 2.23 111 9.31 54 8.85
Peripheral vascular disease 18,639 0.95 77 6.46 36 5.90
Congenital heart disease 17,071 0.87 30 2.52 12 1.97
Dementia 18,840 0.96 304 25.50 160 26.23
Parkinson’s disease 5,717 0.29 40 3.36 32 5.25
Epilepsy 26,112 1.33 31 2.60 19 3.11
Rare neurological conditions 5,789 0.30 * *
Cerebral palsy 1,318 0.07 0 0.00 0 0.00
Severe mental illness 282,709 14.45 209 17.53 109 17.87
Osteoporotic fracture 73,679 3.77 154 12.92 96 15.74
Rheumatoid arthritis or SLE 22,485 1.15 35 2.94 16 2.62
Cirrhosis of the liver 7,210 0.37 17 1.43 *
Sickle cell disease 1,094 0.06 0 0 0 0.00
Table 1: Demographic and clinical characteristics for the total cohort and those who died with COVID-19 in the two time periods. Data are n (%) or mean (SD). * represents values which have been suppressed due to small numbers <10. ^represents collapsing of categories to suppress small numbers.

In total, there were 1,192 (0.06%) COVID-19 deaths during the first period and 610 (0.03%) in the second period, which was similar to the English validation (0.08% and 0.04%, respectively) [16]. In general, individuals who died from COVID-19 during the first period were more likely to be male (674, 56.5%), aged 70 years and older (976, 81.9%), with diabetes, CKD, obesity, and cardio-pulmonary diseases being the pre-existing conditions with the highest proportions of death (Table 1). Individuals who died from COVID-19 during the second period had similar characteristics to the first period, however, with a slight change to the sex ratio (56.5% of deaths in first period were in males compared to 51.0% deaths in the second period were in females).

The performance metrics calculated to validate the predicted risk of death from COVID-19 using the QCOVID algorithm are presented in Table 2 [3, 14]. The metrics have been provided for both sexes and time periods. In the first time-period for males, the algorithm explained 68.8% (95% CI: 66.9–70.4) of the variation in time to death, the Harrell’s C statistic was 0.929 (95% CI: 0.921–0.937), the D statistic was 3.036 (95% CI: 2.913–3.159) and Brier score was 0.0007. Similar results were found for females and in the second time period. Similar results were also found in the English validation, the D statistics was 3.761 (3.732–3.789), Harrell’s C statistic was 0.935 (95% CI: 0.933–0.937) and Brier score was 0.0013 in males in the first period, with similar results found in females and in the second time period [17]. Performance metrics by age band, ethnicity and Townsend deprivation quintile can be found in the Appendices (Supplementary Tables 2–5).

First period (24 th January 2020–30 th April 2020) Second period (1 st May 2020–28 th July 2020)
COVID-19 death in females COVID-19 deaths in males COVID-19 death in females COVID-19 deaths in males
R-squared statistic 0.691 (0.671–0.710) 0.688 (0.669–0.704) 0.721 (0.698–0.742) 0.711 (0.686–0.733)
D statistic 3.062 (2.922–3.202) 3.036 (2.913–3.159) 3.293 (3.113–3.472) 3.207 (3.024–3.390)
Harrell’s C statistic 0.930 (0.920–0.940) 0.929 (0.921–0.937) 0.950 (0.942–0.959) 0.933 (0.921–0.945)
Brier score 0.0005 0.0007 0.0003 0.0003
Table 2: Performance of the risk models to predict risk of COVID-19 death by sex and time period for total cohort. Data are estimated (95% CI).

The Harrell’s C statistic varied across the age bands and time periods (Figures 1, 2), with acceptable discrimination (>0.7) in both time periods for males and females, and across age groups. The oldest group (90+ years old) yielded poorer discrimination for both males and females as well as the youngest male group in the first time period. In the second time period, it was not possible to plot the Harrell’s C statistic for the youngest age groups for females (19-39 and 40-44 years) or for 19-39 years in males due to low numbers. Whilst the Harrell’s C statistic was slightly lower in Wales compared to England across sex and age groups, the pattern of reduced discrimination for certain age groups was similar.

Figure 1: The concordance index by sex and age group in the first time period (24th January–30th April 2020). Bars represent 95% CI.

Figure 2: The concordance index by sex and age group in the second time period (1st May–28th July 2020). Bars represent 95% CI.

The calibration plots in Figure 3 showed that the predicted and observed risks of COVID-19 related death were similar for both males and females in the first time period, demonstrating the QCOVID equations were well calibrated. However, there was slight under-prediction in the highest risk category for COVID-19 death which was also demonstrated in the English validation and original cohorts [3, 17]. Predicted and observed risks of COVID-19 related death in the second time period can be found in Supplementary Figure 1.

Figure 3: Predicted and observed risk of COVID-19-related death in the first time period (24th January–30th April 2020).

Figure 4 demonstrates that the sensitivity at different absolute risk thresholds for COVID-19-related deaths was higher for females in the top 13 centiles compared to males in the first period and was higher in females than males across the second period. 60.2% and 65.4% of deaths occurred in those in the top 5% for predicted absolute risk of death from COVID-19 in the first time period for males and females respectively; 64.9% and 72.0% of deaths occurred in those in the top 5% for predicted absolute risk of death from COVID-19 in the second time period for males and females, respectively (Supplementary Table 6).

Figure 4: Sensitivity for COVID-19-related death in the first (24th January–30th April 2020) and second (1st May–28th July 2020) time periods.


The results from this validation of the QCOVID risk prediction algorithm show that the models fit the Welsh population data well and yielded similar results, but with less precision (predictably, given the smaller population size) compared to the English validation and original study. This study used individual-level linked data on the adult population of Wales, registered with a SAIL providing general practice, which is independent of the original and validation study populations [22]. Use of SAIL Databank allowed linkage across primary and secondary health care data with mortality outcome data to allow replication of the original and English validation studies and inclusive of all predictor variables [3, 17].

The risk models from the original QCOVID and English validation paper were based on GP data largely from England [3, 17]. Age standardised death rates in Wales pre-pandemic were about 6% higher than in England [31]. Some differences in prediction accuracy are expected and this is consistent with the higher observed to predicted mortality numbers at the higher end of risk in Figure 3 [32]. The predicted and observed risks of COVID-19-related death were similar across most of the predicted risk distribution, demonstrating the models were well calibrated, (60.2-72.0% of deaths occurred in the top 5% for predicted absolute risk of death), apart from the highest 20th of risk where the risk of death was higher in Wales, as shown in Figure 3. This is similar to the English validation study, which demonstrated 65.9-77.2% of death occurred in individuals in the top 5% for predicted absolute risk of death [17].

The overall Harrell’s C statistic was >0.9 for males and females for both time periods, demonstrating good overall discrimination of the models. Lower and more varied Harrell’s C statistics across the age bands are likely due to a smaller population and more deaths occurring in the first period during the first peak of the UK pandemic [33].

Despite the predictive model performance metrics indicating that the algorithm performed well on the Welsh data, there are a number of limitations. The Welsh cohort was restricted to individuals registered to a SAIL providing general practice, therefore, results are based on 80% population coverage (330/412, of all general practices in Wales). This restriction was necessary due to the amount of predictor variables that required primary care GP data. Whilst we were able to calculate all predictor variables required, 42.8% of our cohort did not have a BMI recorded in the previous five years, therefore, missing observations were imputed. Also, this study was designed to replicate the English validation study and therefore focussed on COVID-19-related deaths, COVID-19-related hospital admissions will be presented in a subsequent paper. Additionally, as highlighted in the English validation study, testing for COVID-19 was limited in the early stages of the pandemic and therefore some of the early deaths might not be recorded as being COVID-19-related. As this study period covers the start of the pandemic, outcomes relate to the COVID-19 Wild type triggered wave and does not include subsequent Alpha and Delta variant waves. Finally, it was not possible to calculate performance metrics for some age groups and ethnic groups. Due to low numbers of some ethnic groups and consequent death we collapsed some ethnic groups to ensure privacy protection whilst including them in our study. We combined Black African and Black Caribbean groups, and Chinese and Other groups. This analysis was carried out on a smaller and less ethnically diverse population compared to the original studies [3, 17].


This validation of the QCOVID algorithm indicates that the risk prediction models are applicable on a population independent of the original study, which has not been reported before. Our validation is based on Welsh primary care registered patients, for whom the QCOVID algorithm was not modelled on, whereas the original study was based on English primary care registered patients. The Welsh validation offers evidence that the QCOVID algorithm can be used for public health risk management and also could be applied to other populations. This study covered the first wave of the pandemic in Wales/the UK; however, with the emergence of new variants of concern, subsequent new waves of infection and changes in presentation in symptoms of SARS-CoV-2 it is important to adapt these algorithms over longer periods and assess their predictive ability in the context of the evolving pandemic. Further work will include applying an updated algorithm to assess the predictive risk of COVID-19 death and hospitalisation over a longer period of time. We will also assess the impact of the national vaccination program to see how changes in immunity level have impacted adverse COVID-19 outcomes.


This study makes use of anonymised data held in the SAIL Databank. This work uses data provided by patients and collected by the NHS as part of their care and support and the Understanding Patient Data initiative. We would also like to acknowledge all data providers who make anonymised data available for research. We wish to acknowledge the collaborative partnership that enabled acquisition and access to the de-identified data, and sharing of necessary methodological documentation and scripts which led to this output. This is a collaboration between colleagues at University of Oxford, University of Edinburgh, University of Nottingham, Office for National Statistics, London School of Hygiene and Tropical Medicine, University College London, Office of the Chief Medical Officer, Department of Health and Social Care, NHS Digital, University of Leicester, University of Cambridge, NHS England, Queen Mary University of London, University of Liverpool, Queen’s University Belfast, Association of Local Authority Medical Advisors, Imperial College London, and Swansea University Health Data Research UK. Swansea University Health Data Research UK team is under the direction of the Welsh Government Technical Advisory Cell (TAC) and includes the following groups and organizations: the SAIL Databank, Administrative Data Research (ADR) Wales, Digital Health and Care Wales (DHCW), Public Health Wales, NHS Shared Services Partnership (NWSSP) and the Welsh Ambulance Service Trust (WAST). All research conducted has been completed under the permission and approval of the SAIL independent Information Governance Review Panel (IGRP) project number 0911. KK is supported by the National Institute for Health Research (NIHR) Applied Research Collaboration East Midlands (ARC EM) and the NIHR Leicester Biomedical Research Centre (BRC).

This work was supported by the Con-COV team funded by the Medical Research Council (grant number: MR/V028367/1). This work was supported by Health Data Research UK, which receives its funding from HDR UK Ltd (HDR-9006) and the Medical Research Council (MR/ S027750/1). HDR UK Ltd is funded by the UK Medical Research Council, Engineering and Physical Sciences Research Council, Economic and Social Research Council, Department of Health and Social Care (England), Chief Scientist Office of the Scottish Government Health and Social Care Directorates, Health and Social Care Research and Development Division (Welsh Government), Public Health Agency (Northern Ireland), British Heart Foundation (BHF) and the Wellcome Trust. This work was supported by the ADR Wales programme of work. The ADR Wales programme of work is aligned to the priority themes as identified in the Welsh Government’s national strategy: Prosperity for All. ADR Wales brings together data science experts at Swansea University Medical School, staff from the Wales Institute of Social and Economic Research, Data and Methods (WISERD) at Cardiff University and specialist teams within the Welsh Government to develop new evidence which supports Prosperity for All by using the SAIL Databank at Swansea University, to link and analyse anonymized data. ADR Wales is part of the Economic and Social Research Council (part of UK Research and Innovation) funded ADR UK (grant ES/S007393/1). This work was supported by the Wales COVID-19 Evidence Centre, funded by Health and Care Research Wales.

Conflicts of interest

AS is a member of the Scottish Government’s COVID-19 Chief Medical Officer’s Advisory Group and its Standing Committee on Pandemics; he is also a member of NERVTAG’s Risk Stratification Subgroup. KK is member of NERVTAG subgroup and member of the Scientific Advisory Group for Emergencies (SAGE). JHC reports grants from National Institute for Health Research (NIHR) Biomedical Research Centre, Oxford, grants from John Fell Oxford University Press Research Fund, grants from Cancer Research UK (CR-UK) grant number C5255/A18085, through the Cancer Research UK Oxford Centre, grants from the Oxford Wellcome Institutional Strategic Support Fund (204826/Z/16/Z) and other research councils, during the conduct of the study. JHC is an unpaid director of QResearch, a not-for-profit organisation which is a partnership between the University of Oxford and EMIS Health who supply the QResearch database used for this work. JHC is a founder and shareholder of ClinRisk ltd and was its medical director until 31st May 2019. ClinRisk Ltd produces open and closed source software to implement clinical risk algorithms (outside this work) into clinical computer systems. JHC is chair of the NERVTAG risk stratification subgroup and a member of SAGE COVID-19 groups and the NHS group advising on prioritisation of use of monoclonal antibodies in COVID-19 infection. RAL is a member of the Welsh Government COVID-19 Technical Advisory Group.

Ethics statement

The data used in this study are available in the SAIL Databank at Swansea University, Swansea, UK, but as restrictions apply they are not publicly available. All proposals to use SAIL data are subject to review by an independent Information Governance Review Panel (IGRP). Before any data can be accessed, approval must be given by the IGRP. The IGRP contains a multidisciplinary professional group, including members of the public, and it gives careful consideration to each project to ensure proper and appropriate use of SAIL data. When access has been granted, it is gained through a privacy protecting safe haven and remote access system referred to as the SAIL Gateway. SAIL has established an application process to be followed by anyone who would like to access data via SAIL at Participant consent was not required for this study as all data is anonymised and further encrypted.


SAIL Secure Anonymised Information Linkage
NERVTAG New and Emerging Respiratory Virus Threats Advisory Group
RECORD REporting of studies Conducted using Observational Routinely-collected health Data
WLGP Welsh Longitudinal General Practice
PEDW Patient Episode Database for Wales
WDDS Wales Dispensing DataSet
CENW Census 2011 data
BMI Body Mass Index
ICD-10 International Classification of Diseases, Tenth Revision
OPCS-4 OPCS Classification of Interventions and Procedures version 4
CKD Chronic Kidney Disease
ONS Office for National Statistics
ADDE Annual District Death Extract
ADDD Annual District Death Daily
WDSD Welsh Demographic Service Dataset
CDDS Consolidated Death Data Source
DMD Dictionary of Medicines and Devices
SACT Systemic Anti-Cancer Therapy


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Article Details

How to Cite
Lyons, J., Nafilyan, V. ., Akbari, A., Davies, G. ., Griffiths, R. ., Harrison, E. ., Hippisley-Cox, J. ., Hollinghurst, J., Khunti, K. ., North, L., Sheikh, A. ., Torabi, F. . and Lyons, R. (2022) “Validating the QCOVID risk prediction algorithm for risk of mortality from COVID-19 in the adult population in Wales, UK”, International Journal of Population Data Science, 5(4). doi: 10.23889/ijpds.v5i4.1697.

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