The OpenSAFELY Collaborative et al.
BMC Medicine(2024) 22:288
https://doi.org/10.1186/s12916-024-03499-5

BMC Medicine

RESEARCH ARTICLE

Open AccessConsistency, completeness and external
validity of ethnicity recording in NHS primarycare records: a cohort study in 25 million
patients’ records at source using OpenSAFELYThe OpenSAFELY Collaborative, Colm D. Andrews1*, Rohini Mathur2,4, Jon Massey1, Robin Park1, Helen J. Curtis1,
Lisa Hopcroft1, Amir Mehrkar1, Seb Bacon1, George Hickman1, Rebecca Smith1, David Evans1, Tom Ward1,
Simon Davy1, Peter Inglesby1, Iain Dillingham1, Steven Maude1, Thomas O’Dwyer1, Ben F. C. Butler‑Cole1,
Lucy Bridges1, Chris Bates3, John Parry3, Frank Hester3, Sam Harper3, Jonathan Cockburn3, Ben Goldacre1,
Brian MacKenna1, Laurie A. Tomlinson2, Alex J. Walker1 and William J. Hulme1

Abstract
Background Ethnicity is known to be an important correlate of health outcomes, particularly during the COVID19 pandemic, where some ethnic groups were shown to be at higher risk of infection and adverse outcomes. The
recording of patients’ ethnic groups in primary care can support research and efforts to achieve equity in service
provision and outcomes; however, the coding of ethnicity is known to present complex challenges. We therefore set
out to describe ethnicity coding in detail with a view to supporting the use of this data in a wide range of settings,
as part of wider efforts to robustly describe and define methods of using administrative data.
Methods We describe the completeness and consistency of primary care ethnicity recording in the OpenSAFELY-TPP
database, containing linked primary care and hospital records in > 25 million patients in England. We also compared
the ethnic breakdown in OpenSAFELY-TPP with that of the 2021 UK census.
Results 78.2% of patients registered in OpenSAFELY-TPP on 1 January 2022 had their ethnicity recorded in pri‑mary care records, rising to 92.5% when supplemented with hospital data. The completeness of ethnicity recording
was higher for women than for men. The rate of primary care ethnicity recording ranged from 77% in the South Eastof England to 82.2% in the West Midlands. Ethnicity recording rates were higher in patients with chronic or other seri‑
ous health conditions. For each of the five broad ethnicity groups, primary care recorded ethnicity was within 2.9 per‑centage points of the population rate as recorded in the 2021 Census for England as a whole. For patients with mul‑
tiple ethnicity records, 98.7% of the latest recorded ethnicities matched the most frequently coded ethnicity. Patients
whose latest recorded ethnicity was categorised as Other were most likely to have a discordant ethnicity recording(32.2%).
Conclusions Primary care ethnicity data in OpenSAFELY is present for over three quarters of all patients, and com‑bined with data from other sources can achieve a high level of completeness. The overall distribution of ethnicities*Correspondence:Colm D. Andrews
colm.andrews@phc.ox.ac.uk
Full list of author information is available at the end of the article© The Author(s) 2024. Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which
permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to theoriginal author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images orother third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line
to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory
regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of thislicence, visit http://creativecommons.org/licenses/by/4.0/. The Creative Commons Public Domain Dedication waiver (http://creativecom‑mons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data.

The OpenSAFELY Collaborative et al. BMC Medicine(2024) 22:288

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across all English OpenSAFELY-TPP practices was similar to the 2021 Census, with some regional variation. This report
identifies the best available codelist for use in OpenSAFELY and similar electronic health record data.
Keywords Primary care health sciences, Electronic health records, Ethnicity, Data curation

Background
Ethnicity is known to be an important determinant ofhealth inequalities, particularly during the COVID-19
outbreak where a complex interplay of social and biological factors resulted in increased exposure, reduced
protection and increased severity of illness in particularethnic groups [1, 2]. The UK has a diverse ethnic population (The 2021 Office for National Statistics (ONS) Census estimated 9.6% Asian, 4.2% Black, 3.0% Mixed, 81.0%
White, 2.2% Other [3]), which can make health research
conducted in the UK generalisable to countries. Complete and consistent recording of patients’ ethnic groupin primary care can support efforts to achieve equity inservice provision and reduces bias in research [4, 5]. Ethnicity recording for new patients registering with generalpractice across the UK has improved following Quality
and Outcomes Framework (QOF) financial incentivisation between 2006/07 and 2011/12 [6, 7]. As a result,ethnicity is now being captured for the majority of the
population in routine electronic healthcare records andis comparable to the general population [6]. The uptake
and utilisation of healthcare services still varies acrossethnic groups, and the recently established NHS Raceand Health Observatory have led calls for a dedicateddrive by NHS England and NHS Digital to emphasise theimportance of collecting and reporting ethnicity data [8].
OpenSAFELY is a secure health analytics platform
created by our team on behalf of NHS England. OpenSAFELY provides a secure software interface allowing
analysis of pseudonymised primary care patient records
from England in near real-time within highly secure data
environments.
In primary care data, patient ethnicity is recorded via
clinical codes, similar to how any other clinical condition
or event is recorded. In OpenSAFELY-TPP, both ClinicalTerms Version 3 (CTV3 (Read)) codes and Systematised
Nomenclature of Medicine Clinical Terms (SNOMED
CT) codes are used. SNOMED CT is an NHS standard,
widely used across England.
Ethnicity is also recorded in secondary care, when
patients attend emergency care, inpatient or outpatient
services, independently of ethnicity in the primary carerecord. This is available via NHS England’s Secondary
Uses Service (SUS) [9]. It is common practice in OpenSAFELY to supplement primary care ethnicity, where
missing, with ethnicity data from SUS [10, 11]. Throughout this paper, we refer to ethnicity rather than race asrecommended by the ONS: ‘The word “race” places people into categories based on physical characteristics,
whilst ethnicity is self-defined and includes aspects suchas culture, heritage, religion and identity’. However, we
recognise that the distinction between and use of theseterms may differ in different settings.
In this paper, we study the completeness, consistency
and representativeness of routinely collected ethnicity
data in primary care.

Methods
Study design

Retrospective cohort study across 25 million patients registered with English general practices in OpenSAFELY-TPP.
Data sources

This study uses data from the OpenSAFELY-TPP database, covering around 40% of the English population.
The database includes primary care records of patientsin practices using the TPP SystmOne patient information
system and is linked to other NHS data sources, including
in-patient hospital records from NHS England’s Secondary Use Service (SUS), where ethnicity is also recorded
independently of ethnicity in the primary care record.
All data were linked, stored and analysed securely
within the OpenSAFELY platform https://​opens​afely.​org/. Data include pseudonymized data such as coded
diagnoses, medications and physiological parameters. No
free text data are included. All code is shared openly forreview and re-use under MIT open licence (opensafely/
ethnicity-short-data-report at notebook). Detailed pseudonymised patient data is potentially re-identifiable andtherefore not shared.
Study population

Patients were included in the study if they were registered at an English general practice using TPP on 1 January 2022.
Ethnicity ascertainment

In primary care data, there is no categorical ‘ethnicity’variable to record this information. Rather, ethnicity isrecorded using clinical codes—entered by a clinician oradministrator with a location and date—like any otherclinical or administrative event, with specific codes relating to each ethnic group [12–14]. This means ethnicity

The OpenSAFELY Collaborative et al. BMC Medicine(2024) 22:288

can be recorded by the practice in multiple, potentiallyconflicting, ways over time.
We created a new codelist, SNOMED:2022 [13], by
identifying relevant ethnicity SNOMED CT codes andensuring completeness by comparing the codelist to
the following: another OpenSAFELY created codelist(CTV3:2020) [13], a combined ethnicity codelist from
SARS-CoV2 COVID19 Vaccination Uptake Reporting
Codes published by Primary Care Information Services(PRIMIS) [12, 15] and a codelist from General Practice
Extraction Service (GPES) Data for Pandemic Planningand Research (GDPPR) [16]. Codes which relate to religion rather than ethnicity (e.g. ‘Muslim—ethnic category 2001 census’) and codes which do not specify a
specific ethnicity (e.g. ‘Ethnic group not recorded’) were
excluded. In total, 258 relevant ethnicity codes were identified. We then created a codelist categorisation basedon the 2001 UK Census categories, which are the NHS
standard for ethnicity [17], and cross referenced it against
the CTV3, PRIMIS and GDPPR codelists. The ‘Gypsy or
Irish Traveller’ and ‘Arab’ groups were not specificallylisted in 2001 however we categorised them as `White`and `Other` respectively as per the 2011 Census grouping [18]).

Table 1 2001 ONS Census ethnicity groupings
5-level group:
Asian or Asian British
Black or Black British,
Mixed,White
Chinese or other ethnic groups
16-level group:
IndianPakistani
Bangladeshi
Any other Asian background
Caribbean, African
Any other Black background
White and Black
Caribbean
White and Black African
White and Asian
Any other Mixed background
BritishIrish
Any other White background
ChineseAny other

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The codelist categorisation consists of two ethnicity
groupings based on the 2001 census (Table 1): all analyses
used the 5-group categorisation unless otherwise stated.
If a SNOMED:2022 ethnicity code appeared in the primary care record on multiple dates, the latest entry wasused unless otherwise stated.
In OpenSAFELY, the function ethnicity_from_sus
combines SUS ethnicity data from admitted patient carestatistics (APCS), emergency care (EC) and outpatient
attendance (OPA) and selects the most frequently usedethnicity code for each patient. In hospital records from
SUS, recorded ethnicity is categorised as one of the 16
categories on the 2001 UK census. This accords with the16-level grouping described above.
Subgroups

We looked at the completeness of ethnicity coding in thewhole population and across each of the following demographic and clinical subgroups:
Age

Patient age was calculated as of 1 January 2022 andgrouped into 5-year bands, to match the ONS age bands.

The OpenSAFELY Collaborative et al. BMC Medicine(2024) 22:288

Sex

We used categories ‘male’ and ‘female’, matching the ONS
recorded categories; patients with any other/unknown
sex were excluded.
Deprivation

Overall deprivation was measured by the 2019 Indexof Multiple Deprivation (IMD) [19] derived from thepatient’s postcode at lower super output area level. IMD
was divided by quintile, with 1 representing the mostdeprived areas and 5 representing least deprived areas.
Where a patient’s postcode cannot be determined the
IMD is recorded as unknown.
RegionRegion was defined as the Nomenclature of Territorial
Units for Statistics (NUTS 1) region derived from thepatient’s practice postcode.
As the rate of ethnicity recording would be expected
to be lower in patients with fewer clinical interactions,and therefore fewer opportunities for ethnicity to berecorded, completeness was also compared in the clinicalsubgroups of dementia, diabetes, hypertension and learning disability which are more likely to require additional
clinical interactions. Clinical subgroups were defined as
the presence or absence of relevant SNOMED CT codesin the GP records for dementia [20], diabetes [21], hypertension [22] and learning disabilities [23] as of 1 January
2022.
Statistical methods
Completeness and distribution of ethnicity recording

The proportion of patients with either (i) primary careethnicity recorded (that is, the presence of any code in
the SNOMED:2022 codelist in the patient record) or (ii)primary care ethnicity supplemented, where missing,with ethnicity data from secondary care [24] was calculated. Completeness was reported overall and withinclinical and demographic subgroups.
Amongst those patients where ethnicity was recorded,
the proportion of patients within each of the 5 groups
was calculated, within each clinical and demographic
subgroup. We also calculated the distribution of complete ethnicity recording across practices with at least
1000 registered patients.
Consistency of ethnicity recording within patients over time

Discrepancies may arise due to errors whilst entering the data or if a patient self-reports a different ethnic
group from their previously recorded ethnic group. We
calculated the proportion of patients with any ethnicity recorded which did not match their ‘latest’ recordedgrouped ethnicity for each of the five ethnic groups.

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We also calculated the proportion of patients whose
latest recorded ethnicity did not match their most frequently recorded ethnicity for each of the five ethnic
groups.
Consistency of ethnicity recording across data sources(primary care versus secondary care)

We calculated the proportion of patients whose latest
recorded ethnicity in primary care matched their ethnicity as recorded in secondary care for each of the five ethnic groups, where both primary and secondary care arerecorded.
External validation against the 2021 UK census population

The UK Census collects individual and household-level
demographic data every 10 years for the whole UK population. Data on ethnicity were obtained from the 2021
UK Census for England. The most recent census acrossthe UK was undertaken on 27 March 2021. Ethnic breakdowns for the population of England were obtained via
NOMIS [25].
The ethnic breakdown of the census population wascompared with our OpenSAFELY-TPP population andthe relative difference was calculated using the ONS
value as the baseline proportion and OpenSAFELY as thecomparator. In the 2021 UK Census, the Chinese ethnic
group was included in the Asian ethnic group, whereas
in the 2001 census, it was included in the Other ethnic
group [26]. In order to provide a suitable comparison
with primary care data, we regrouped the 2021 census
data as per the 2001 groups. As an additional analysis, we
also compared the primary care data with the census datausing the 2021 census categories.

Results
Completeness of ethnicity data

19,618,135 of the 25,102,210 patients (78.2%) registeredin OpenSAFELY-TPP on 1 January 2022 had a recorded
ethnicity, rising to 92.5% when supplemented with secondary care data (Fig. 1, Additional file 1: Table S1).
Primary care ethnicity recording completeness waslowest for patients aged over 80 years (80.1%) and under
30, whereas ethnicity recording was highest in those over
80 when supplemented with secondary care data (97.1%).
Women had a higher proportion of recorded ethnicities
than men (79.8% and 76.5% respectively, 94% and 91.1%
when supplemented with secondary care data). The completeness of primary care ethnicity recording ranged
from 77% in the South East of England to 82.2% in theWest Midlands. IMD was within 1.2 percentage points
for known values (77.7% in the least deprived group 5 to
78.9% in group 3) and was lowest for the unknown group(71.6%). Primary care ethnicity recording was at least 4

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Fig. 1 Bar plot showing proportion of registered TPP population with a recorded ethnicity by clinical and demographic subgroups, basedon primary care records (solid bars) and when supplemented with secondary care data (pale bars)

percentage points higher in all of the clinical subgroups
compared to the general population.
Distribution of ethnicity

Using ethnicity recorded in primary care only, 6.8% of
the population were recorded as Asian, 2.3% Black, 1.5%
Mixed, 65.6% White and 1.9% Other, and ethnicity was
not recorded for 21.8%. When supplementing with hospital-recorded ethnicity data, corresponding percentages
were 7.8% Asian, 2.6% Black, 1.9% Mixed, 77.9% White,
2.3% Other and 7.5% not recorded, representing a percentage point increase ranging from 0.3% in the Black
group to 12.3% in the White group.

Older patients tended to have a higher rate of recorded
White ethnicity (e.g. 76.3% in the 80 + group vs 50.0% in
the 0–19 group), whereas younger patients had a higher
rate of recording for Asian, Black, Mixed and Other
groups. The higher proportion of women with recorded
ethnicity was reversed in the Asian group where men(7.0% and 8.0% with secondary care data) had a higher
proportion of recording than women (6.6% and 7.6% withsecondary care data). The proportion of ethnicity reporting was lower for patients with dementia, hypertension
or learning disabilities in every ethnic group other thanWhite (Fig. 2/Additional file 1: Table S2). The breakdown by 16 group ethnicity is shown in Additional file 1:

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Fig. 2 Bar plot showing proportion of registered TPP population with a recorded ethnicity by clinical and demographic subgroups, basedon primary care records (solid bars) and when supplemented with secondary care data (pale bars)

Table S3. There was considerable variation in the completeness of ethnicity recording across practices with atleast 1000 registered patients (Fig. 3).
Consistency of ethnicity recording within patients

3.1% [260, 611] of the 19,618,135 patients with a recorded
ethnicity had at least one ethnicity record that was discordant with the latest recorded ethnicity (Table 3).
Patients whose latest recorded ethnicity was categorisedas Mixed were most likely to have a discordant ethnicity recording (32.2%, 118,560), of whom 17.0% (62,565)

also had a recorded ethnicity of White. 5.7% (33,205) of
the 583,770 patients with the latest recorded ethnicity ofBlack also had a recorded ethnicity of White (Table 2).
Overall, for 19,364,120 (98.7%) of patients, their latest
recorded ethnicity in primary care matched their mostfrequently recorded ethnicity in primary care (Table 3).
16,390,425 (99.5%) patients with the most recent ethnicity ‘White’ had matching most frequently recorded ethnicity. Other was the least concordant group, just 81.6%(399,440) of patients with the most recent ethnicity‘Mixed’ had matching most frequently recorded ethnicity.

Fig. 3 Boxplot showing the 5th, 25th, 50th, 75th and 95th percentiles of completeness of ethnicity recording across practices with at least 1000
registered patients

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Table 2 Count of patients with at least one recording of each ethnicity (proportion of latest ethnicity)
Latest ethnicity

Any recorded ethnicity
Asian

Black

Mixed

White

Other

Any discordant ethnicity

Asian: 1,708,430

1,708,430 (100.0)

8640 (0.5)

25,955 (1.5)

42,760 (2.5)

41,175 (2.4)

109,060 (6.4)

Black: 583,770

6680 (1.1)

583,770 (100.0)

41,245 (7.1)

33,205 (5.7)

11,495 (2.0)

85,075 (14.6)

Mixed: 367,980

18,400 (5.0)

32,990 (9.0)

367,980 (100.0)

62,565 (17.0)

15,920 (4.3)

118,560 (32.2)

White: 16,468,610

31,635 (0.2)

25,115 (0.2)

62,030 (0.4)

16,468,610 (100.0)

81,875 (0.5)

189,020 (1.1)

Other: 489,350

32,875 (6.7)

9430 (1.9)

16,795 (3.4)

60,865 (12.4)

489,350 (100.0)

109,545 (22.4)

Table 3 Count of patients with any recorded discordant ethnicity and a discordant ‘most frequently recorded’ ethnicity in primarycare, according to latest ethnicity
Latest ethnicity

Total patients

Any discordant ethnicity

Discordant withmost frequentethnicityAsian

1,708,430

109,060 (6.4)

12,685 (0.7)

Black583,770

85,075 (14.6)

14,480 (2.5)

Mixed367,980

118,560 (32.2)

58,760 (16.0)

White

16,468,610

189,020 (1.1)

78,185 (0.5)

Other489,350

109,545 (22.4)

89,915 (18.4)

Overall

19,618,135

611,260 (3.1)

254,025 (1.3)

0.9% (5450) of patients with latest ethnicity ‘Black’ had
the most frequently recorded ethnicity ‘White’ (Additional file 1: Table S4).
Consistency of ethnicity recording across data sources(primary care versus secondary care)

Of the 19.6 million total patients with a primary care ethnicity record, 12.9 million (66.0%) also had a secondary
care ethnicity record. The proportion of patients withno secondary care coded ethnicity ranged from 31.9%
in the White group to 58.6% in the Other group (Additional file 1: Table S5). SNOMED:2022 and secondary
care coded ethnicity matched for 93.5% of patients withboth coded ethnicities, ranging from 34.8% in the Mixedgroup to 96.9% in the White group (Fig. 4, Additional
file 1: Table S6).
Comparison with the 2021 UK census population

The proportion of patients in each ethnicity group basedon primary care records as of January 2022 was within 2.9
percentage points of the 2021 Census estimate (amended
to the 2001 grouping) for the same ethnicity group acrossEngland as a whole (Asian: 8.7% primary care, 8.8%
Census, relative difference (RD) − 1.5; Black: 3.0%, 4.2%,
RD − 29.4; Mixed: 1.9%, 3.0% RD − 36.5; White: 84.0%,
81.0% RD 3.6; Other: 2.5%, 2.9%, RD − 15.1). When supplemented secondary care data, this increased to 3.2%
(Fig. 5, Additional file 1: Table S7). In primary carerecords, the White population was underrepresented in
all regions other than the North West (7.1% percentage
points higher than Census estimates), South East (2.8%)
and South West (0.6%) and was most severely underestimated in the West Midlands (− 12.5%). The Asian population was overrepresented in all regions other than the
North West (− 3.6%) and South East (− 1.6%) (Fig. 6,
Additional file 1: Table S8). We also compared the primary care data to the 2021 Census estimates using 2021
rather than 2001 ethnicity groups (Additional file 1: Figs.
S1 and S2 and Additional file 1: Table S9).

Discussion
SummaryThis study reported ethnicity recording quality in around25 million patients registered with a general practice inEngland and available for analysis in the OpenSAFELYTPP database. Over three quarters of all patients had atleast one ethnicity record in primary care data. Whensupplemented with hospital records, ethnicity recording was 92.5% complete, which is consistent with previously reported England-wide primary care data sources[27, 28]. 98.7% of patients’ latest and most frequently
recorded ethnicity matched. As the latest recorded ethnicity is computationally more efficient within OpenSAFELY, we recommend the use of the latest recorded
ethnicity. The reported concordance of primary andsecondary care records of 93.5% is consistent with thoseThe OpenSAFELY Collaborative et al. BMC Medicine(2024) 22:288

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Fig. 4 Sankey plot comparing the categorisation of ethnicity in primary care and secondary careFig. 5 Bar plot showing the proportion of 2021 Census and primary care populations per ethnicity grouped into 5 groups (excluding those
without a recorded ethnicity (21.8% SNOMED:2020 and 7.5% supplemented with ethnicity data from secondary care)). Data labels indicate
the percentage point difference between 2021 Census and TPP populations

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Fig. 6 Bar plot showing the proportion of 2021 Census and TPP populations in each ethnicity group by region (excluding those without a recorded
ethnicity (21.8% in primary care and 7.5% supplemented with ethnicity data from secondary care)). Data labels indicate percentage point difference
between 2021 Census and TPP populations

previously reported [29]. Despite regional variations, theoverall ethnicity breakdown across all English OpenSAFELY-TPP practices was similar to the 2021 Census;
however, larger relative differences were observed, inparticular for the Mixed and Black groups. Therefore,
relative to the size of certain ethnic groups, discrepantethnicity recording practices may be a concern.
Strengths and weaknesses

This study provides a breakdown of primary care coding
in OpenSAFELY-TPP by key clinical and demographic
characteristics. The key strengths of this study are the useof large Electronic Health Record (EHR) datasets representing roughly 40% of the population of England registered with a GP, which enabled us to assess the qualityof ethnicity data against a variety of important clinicalcharacteristics.
Practices may utilise differing strategies for collecting
ethnicity information from patients. Typically ethnicity is self-reported by the patient at registration or during consultation [30] but may not always be self-reported
and may reflect an assumption made by the person entering the data. OpenSAFELY-TPP was missing ethnicity for

21.8% of patients, and the missingness of ethnicity data in
EHRs may not be random [6].
This study focussed on the 5 Group ethnicity of the
SNOMED:2022 codelists categorisation. However, there
can be important variations in clinical care within thesebroad categories, as seen in COVID vaccine uptake [31,32]. More detailed categorisations, alternative coding
systems and codelists have been further explored in the
OpenSAFELY-TPP Ethnicity short data report.
It is common for OpenSAFELY-TPP studies to supplement the primary care recorded ethnicity, where missing, with ethnicity data from secondary care [10, 11, 33].
The representativeness of the CTV3:2020 coded ethnicity
supplemented with SUS data has been reported previously [33]. However, secondary care data is only available for people attending hospital within the time periodthat data were available (currently April 2019 onwards
in OpenSAFELY). The population who still have no ethnicity record after supplementation are likely very different to the wider population, for example having a much
lower chance of having been admitted to hospital, orinteracting with healthcare services generally.
This study represents a snapshot of ethnicity recording
as of 1 January 2022 and does not provide insights into

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temporal trends in ethnicity recording. Trends in ethnicity recording over time are difficult to investigate due
to loss of record date during transfer of clinical records
when patients register with a new practice (Additional
file 1: Fig. S4). Therefore, we are unable to assess theimpact of QOF financial incentives being rescinded in
2011/12.
The most up-to-date formal estimates of England’s
population by ethnic group currently available are fromthe 2021 Census. Accuracy of the 2021 Census ethnicity
estimates may vary by region. The 2021 census response
rate was not even between regions, ranging from 95% in
London to 98% in the South East, South West and Eastof England [34]. The 2021 census used multiple imputation to account for missing ethnicity [35]; the percentage
of eligible persons who had an ethnicity value imputedor edited was not even between regions. Imputation rate
was highest in London (2.0%) and lowest in the North
East (1.0%) [34].
There are limitations in comparing the GP-registeredpopulation with the census population as differences
naturally arise. For example, patients registered with a
GP may have left the country some years ago and hence
not be counted in the census; certain populations are lesslikely to be registered with a GP (such as Gypsy, Romaand Traveller communities [36] and migrants [37, 38]);
not everyone responds to the census but some may beregistered with a GP; and regional differences occur, forexample due to students moving to cities during termtime. We looked at the GP-registered population in January 2022, whereas the census was taken in March 2021;
therefore, some small changes in population also may
have occurred during this time.
Findings in context

Over 20 studies have been conducted using the OpenSAFELY framework. It is important to understand thedata issues with using ethnicity in OpenSAFELY. Whilst
ethnicity data has been shown to be more complete for
the CTV3:2020 codelist than the SNOMED:2022 codelist [13], the CTV3:2020 codelist included codes such as‘XaJSe: Muslim—ethnic category 2001 census’ which
relate to religion rather than ethnicity and were, therefore, excluded from the SNOMED:2022 codelist. The
common practice of supplementing CTV3:2020 codedethnicity with either secondary care data or the PRIMIS
codelists could lead to inconsistent classification as bothsecondary care data and PRIMIS codelists follow the2001 census categories.
Recording ethnicity is not straightforward. Indeed,
despite often being used as a key variable to describe
health, the idea of ‘ethnicity’ has been disputed [39]. Ethnicity is a complex mixture of social constructs, genetic

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make-up and cultural identity [40]. Self-identified ethnicity is not a fixed concept and evolving socio-cultural
trends could contribute to changes in a person’s selfidentified ethnic group, particularly for those with mixed
heritage [41]. It is therefore perhaps not surprising to
see lower levels of concordance between latest ethnicity
and most common ethnicity in those with latest ethnicity
coded as ‘mixed’. It is not clear to what extent this would
explain all the discordance we identified or whether
other factors such as data error are involved. Our findings agree with previous literature, both from the US andUK [5, 41], which suggest that the consistency of ethnicity information tends to be highest for white populations,and lowest for Mixed or Other racial/ethnic groups [42].
The 2001 census categories are the NHS standard for
ethnicity [17], but we have not been able to find any
explanation for the continued use of the 2001 census categories as the standard.
Due to the significant differences experienced by ethnic groups in terms of health outcomes, accurate ethnicity coding to the most granular code possible is crucial.
Although we have focussed on codelist categorisations
based on the 2001 census categories, ethnicity can beextracted for each of the component codes (Additional
file 1: Table S8), so researchers have the option to use
custom categorisations as required.
We believe that the SNOMED:2022 codelist and codelist categorisation provides a more consistent representation of ethnicity as defined by the 2001 census categories
than the CTV3:2020 codelist and should be the preferred
codelist and categorisation for primary care ethnicity.
Policy implications and interpretation

This paper is principally to inform interpretation of thenumerous current and future analyses completed andpublished using OpenSAFELY-TPP and similar UK electronic healthcare databases. The practice of supplementing primary care ethnicity with secondary care ethnicity
from SUS can, depending on the study design, introduce
bias and should be used with caution. For example,
patients who have more clinical interactions are more
likely to have a recorded ethnicity and therefore patientswith a recorded ethnicity in secondary care data may
tend to be sicker than the general population. Ethnicity recording has been found to be more complete forpatients who died in hospital compared with those discharged [5].

Conclusions
This report describes the completeness and consistencyof primary care ethnicity in OpenSAFELY-TPP and suggests the adoption of the SNOMED:2022 codelist andcodelist categorisation as the best standard method.

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Abbreviations
APCS	Admitted patient care statistics
CTV3	Clinical Terms Version 3
EC	Emergency care
EHR	Electronic health record
GDPPR	General Practice Extraction Service Data for Pandemic Plan‑ning and Research
GP	General practitioner
GPES	General Practice Extraction Service
IMD	Index of Multiple Deprivation
NUTS 1	Nomenclature of Territorial Units for Statistics
ONS	Office for National Statistics
OPA	Outpatient attendance
PRIMIS	Primary Care Information Services
QOF	Quality and Outcomes Framework
SNOMED CT	Systematised Nomenclature of Medicine Clinical Terms
SUS	Secondary Uses Service

Supplementary Information
The online version contains supplementary material available at https://​doi.​
org/​10.​1186/​s12916-​024-​03499-5.
Additional file 1: Fig. S1. Bar plot showing the proportion of 2021 Census
and TPP populations (amended to 2021 grouping) per ethnicity grouped
into 5 groups (excluding those without a recorded ethnicity). Annotatedwith percentage point difference between 2021 Census and TPP popula‑tions. Fig S2. Bar plot showing the proportion of 2021 Census and TPP
populations (amended to 2021 grouping) per ethnicity grouped into 5groups per NUTS − 1 region (excluding those without a recorded ethnic‑ity). Annotated with percentage point difference between 2021 Census
and TPP populations. Fig. S3. Recording of ethnicity over time for latest
and first recorded ethnicity. Unknown dates of recording may be storedas ‘1900 − 01 − 01’. Table S1. Count of patients with a recorded ethnicity
in OpenSAFELY-TPP (proportion of registered TPP population) by clinicaland demographic subgroups. All counts are rounded to the nearest 5.
Table S2. Count of patients with a recorded ethnicity in OpenSAFELY TPP
by ethnicity group (proportion of registered TPP population) and clinicaland demographic subgroups. All counts are rounded to the nearest 5.
Table S3. Count of patients with a recorded ethnicity in OpenSAFELY TPP
by ethnicity group (proportion of registered TPP population) and clinicaland demographic subgroups. All counts are rounded to the nearest 5.
Table S4. Count of patients’ most frequently recorded ethnicity (proportionof latest ethnicity). Table S6. Count of patients with a recorded ethnicity in
Secondary Care by ethnicity group excluding Unknown ethnicites (pro‑portion of Primary Care population). All counts are rounded to the nearest
5. Table S7. Count of patients with a recorded ethnicity in OpenSAFELY
TPP by ethnicity group (proportion of registered TPP population) and 2021
ONS Census counts [amended to 2001 grouping] (proportion of 2021
ONS Census population). All counts are rounded to the nearest 5. Table S8.
Count of patients with a recorded ethnicity in OpenSAFELY TPP [amended
to the 2021 ethnicity grouping] (proportion of registered TPP population)and 2021 ONS Census counts (proportion of 2021 ONS Census popula‑tion). All counts are rounded to the nearest 5. Table S9. Count of individual
ethnicity code use.
Acknowledgements
We are very grateful for all the support received from the TPP Technical
Operations team throughout this work and for generous assistance from theinformation governance and database teams at NHS England and the NHS
England Transformation Directorate.
Guarantor
BG is guarantor.
Software and reproducibility
Data management was performed using Python 3.8, with analysis car‑ried out using Python and R. Code for data management and analysis, as

Page 11 of 13

well as codelists are archived online https://​github.​com/​opens​afely/​ethni​city-​short-​data-​report/.
Patient and public involvement
This analysis relies on the use of large volumes of patient data. Ensuring
patient, professional and public trust is therefore of critical importance.
Maintaining trust requires being transparent about the way OpenSAFELY
works, and ensuring patient and public voices are represented in the design
and use of the platform. Between February and July 2022, we ran a 6-monthpilot of Patient and Public Involvement and Engagement activity designed to
be aligned with the principles set out in the Consensus Statement on Public
Involvement and Engagement with Data-Intensive Health Research [43]. Our
engagement focused on the broader OpenSAFELY platform and comprised
three sets of activities: explain and engage, involve and iterate and participate
and promote. To engage and explain, we have developed a public website
at opensafely.org that provides a detailed description of the OpenSAFELY
platform in language suitable for a lay audience and are co-developing anaccompanying explainer video. To involve and iterate, we have created the
OpenSAFELY ‘Digital Critical Friends’ Group, comprised of approximately
12 members representative in terms of ethnicity, gender and educational
background; this group has met every 2 weeks to engage with and review
the OpenSAFELY website, governance process, principles for researchers andFAQs. To participate and promote, we are conducting a systematic review of
the key enablers of public trust in data-intensive research and have partici‑
pated in the stakeholder group overseeing NHS England’s ‘data stewardshippublic dialogue’.
Authors’ contributions
Conceptualisation: CDA, BM, RP, RM, JM and WJH. Data curation: CDA, RP, RM
and JM. Formal analysis: CDA, RP, RM, JM and WJH. Funding acquisition: BG.
Methodology: CDA, BM, RP, RM, JM and WJH. Project administration: CDA, RP,
RM and JM. Resources: CDA, RM, JM, RP, HJC, LH, LAT and BG. Software: CDA,
RM, JM, RP, HJC, LH, AM, SB, GH, RS, DE, TW, SD, PI, ID, SM, TO’D, BFCBC, LB, CB,
JP, FH, SH, JC, BG, BM, AJW and WJH. Supervision: AJW, LAT and WJH. Valida‑tion: CDA, BM, RP, RM, JM and WJH. Visualisation: CDA, RP, BM, BG, AJW and
WJH. Writing—original draft: CDA. Writing—review and editing: CDA, AJW,
BM, HJC and WJH.
Authors’ Twitter handles
Colm D Andrews-@colmresearcher.
Funding
The OpenSAFELY platform is principally funded by grants from:NHS England [2023–2025];
The Wellcome Trust (222,097/Z/20/Z) [2020–2024];
MRC (MR/V015737/1) [2020–2021].
Additional contributions to OpenSAFELY have been funded by grants from:
MRC via the National Core Study programme, Longitudinal Health and Well‑being strand (MC_PC_20030, MC_PC_20059) [2020–2022] and the Data andConnectivity strand (MC_PC_20029, MC_PC_20058) [2020–2022];
NIHR and MRC via the CONVALESCENCE programme (COV-LT-0009, MC_
PC_20051) [2021–2024];
NHS England via the Primary Care Medicines Analytics Unit [2021–2024].
The views expressed are those of the authors and not necessarily those of
the NIHR, NHS England, UK Health Security Agency (UKHSA), the Department
of Health and Social Care or other funders. Funders had no role in the study
design, collection, analysis and interpretation of data; in the writing of thereport and in the decision to submit the article for publication.
Availability of data and materials
Access to the underlying identifiable and potentially re-identifiable pseu‑
donymised electronic health record data is tightly governed by various
legislative and regulatory frameworks, and restricted by best practice. The data
in OpenSAFELY is drawn from General Practice data across England where
TPP is the Data Processor. TPP developers (CB, JC, JP, FH and SH) initiate anautomated process to create pseudonymised records in the core OpenSAFELY
database, which are copies of key structured data tables in the identifiable
records. These are linked onto key external data resources that have also
been pseudonymised via SHA-512 one-way hashing of NHS numbers usinga shared salt. Bennett Institute for Applied Data Science developers and PIs(BG, CEM, SB, AJW, KW, WJH, HJC, DE, PI, SD, GH, BBC, RMS, ID, KB, EJW and CTR)

The OpenSAFELY Collaborative et al. BMC Medicine(2024) 22:288

holding contracts with NHS England have access to the OpenSAFELY pseu‑
donymised data tables as needed to develop the OpenSAFELY tools. These
tools in turn enable researchers with OpenSAFELY Data Access Agreements towrite and execute code for data management and data analysis without directaccess to the underlying raw pseudonymised patient data and to review theoutputs of this code. All code for the full data management pipeline—from
raw data to completed results for this analysis—and for the OpenSAFELY
platform as a whole is available for review at github.com/OpenSAFELY.

Declarations
Ethics approval and consent to participate
NHS England is the data controller; TPP is the data processor; and the researchers
on OpenSAFELY are acting with the approval of NHS England. This implementa‑tion of OpenSAFELY is hosted within the TPP environment which is accredited to
the ISO 27001 information security standard and is NHS IG Toolkit compliant [44,45]; patient data has been pseudonymised for analysis and linkage using industry
standard cryptographic hashing techniques; all pseudonymised datasets transmit‑
ted for linkage onto OpenSAFELY are encrypted; access to the platform is via a
virtual private network (VPN) connection, restricted to a small group of research‑ers; the researchers hold contracts with NHS England and only access the platform
to initiate database queries and statistical models; all database activity is logged;
only aggregate statistical outputs leave the platform environment following bestpractice for anonymisation of results such as statistical disclosure control for low
cell counts [46]. The OpenSAFELY research platform adheres to the obligations of
the UK General Data Protection Regulation (GDPR) and the Data Protection Act
2018. In March 2020, the Secretary of State for Health and Social Care used powers
under the UK Health Service (Control of Patient Information) Regulations 2002
(COPI) to require organisations to process confidential patient information for the
purposes of protecting public health, providing healthcare services to the public
and monitoring and managing the COVID-19 outbreak and incidents of exposure;
this sets aside the requirement for patient consent [47]. Taken together, theseprovide the legal bases to link patient datasets on the OpenSAFELY platform. GP
practices, from which the primary care data are obtained, are required to share rel‑evant health information to support the public health response to the pandemic
and have been informed of the OpenSAFELY analytics platform.
This study was approved by the Health Research Authority (REC reference 20/
LO/0651) and by the LSHTM Ethics Board (reference 21863).
Consent for publication
Not applicable.
Competing interests
All authors declare the following: BG has received research funding from theBennett Foundation, the Laura and John Arnold Foundation, the NHS National
Institute for Health Research (NIHR), the NIHR School of Primary Care Research,
NHS England, the NIHR Oxford Biomedical Research Centre, the Mohn-West‑lake Foundation, NIHR Applied Research Collaboration Oxford and Thames Val‑ley, the Wellcome Trust, the Good Thinking Foundation, Health Data Research
UK, the Health Foundation, the World Health Organisation, UKRI MRC, Asthma
UK, the British Lung Foundation, and the Longitudinal Health and Wellbeing
strand of the National Core Studies programme; he is a Non-Executive Director
at NHS Digital; he also receives personal income from speaking and writing forlay audiences on the misuse of science. BMK is also employed by NHS Englandworking on medicines policy and clinical lead for primary care medicines data.
Author details
Nuffield Department of Primary Care Health Sciences, Bennett Institutefor Applied Data Science, Oxford University, Oxford OX2 6GG, UK. 2 London
School of Hygiene and Tropical Medicine, Keppel Street, London WC1E 7HT,
UK. 3 TPP, TPP House, 129 Low Lane, Horsforth, Leeds LS18 5PX, UK. 4 Wolfson
Institute for Population Health, University of London, London, Queen Mary E1
2AT, UK.

1

Received: 31 January 2024 Accepted: 24 June 2024

Page 12 of 13

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