Study design and population

The cohorts were derived from de-identified linked data from the SAIL Databank. We created two population-based cohorts derived from multiple demographic and healthcare data sources (figure 1).

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Figure 1

Data linkage of multiple demographic and healthcare data sources used in the creation of two population-wide cohorts: C20 and C16. ADDD, Annual District Death Daily; ADDE, Annual District Death Extract; CAPD, Postponed Admitted Procedures; CARE, Care homes Index; CCDS, Critical Care Data Set; CDDS, Consolidated Death Data Source; CVSP, COVID-19 Shielded People list; CVST, KCL Zoe Symptom Tracker App; EDDD, Emergency Department Dataset Daily; EDDS, Emergency Department Data Set; ICNARC, Intensive Care National Audit & Research Centre; LIMS, Laboratory Information Management System; LSOA, Lower Layer Super Output Area; OPDW, Out Patient Dataset for Wales; PEDW, Patient Episode; PLASC, Pupil Level Annual School Census; SAIL, Secure Anonymised Information Linkage; SQL, Structured Query Language; WDSD, Wash Demographic Service Dataset; WIMD, Welsh Index of Multiple Deprivation; WLGP, Welsh Longitudinal General Practise; WRRS, Wales Results Reporting Service.

• The C20 cohort consists of all people alive and known to the NHS in Wales from the 1 January 2020 with follow-up until 31 May 2020. We include people who moved into or were born in Wales after 1 January 2020. Follow-up data will be added prospectively and the C20 cohort will be updated on a monthly basis in line with a full month of coverage of available data. Linkage to other data sources is also available beyond the cohort end date where the frequency and quality of each data source allow its use. Some datasets are analysed daily.

• The C16 cohort includes all individuals living in Wales and known to the NHS on the 1 January 2016 with follow-up to 31 December 2019. C16 is designed to provide counterfactual and contextual comparative data on population health service utilisation and mortality rates.

Membership of both cohorts is based on the inclusion of a person’s residence in Wales, registered to a Welsh General Practice (GP), a free to use NHS system at the point of primary care registration in the UK (figure 2). This is recorded within the Wales Demographic Service Dataset (WDSD). People are censored by study endpoints or migration out of Wales.

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Figure 2

Flow diagram of the C20 cohort inclusion criteria. WDSD, Wales Demographic Service Dataset.

Data sources

Baseline populations are created using the weekly updated WDSD, the monthly updated Office for National Statistics (ONS) mortality registry data known as the Annual District Death Extract, two new COVID-19 daily data sources: the Consolidated Death Data Source created by NHS Wales Informatics Service (NWIS) and the Annual District Death Daily from ONS.

Anonymised linkage fields

Linkage fields are used to anonymously link between data sources in the SAIL Databank. SAIL uses a multiple encryption system in which a trusted third party, the NWIS, uniquely matches identities to an Anonymised Linkage Field (ALF) and residences to a Residential Anonymised Linkage Field (RALF) before uploading data to SAIL.2 3 14

Demographic data

The cohorts include the following variables: ALF, age, sex, date of death, date of movement out of Wales, RALF and Care Home Anonymised Linkage Fields (CHALFs) for older people at cohort inception. The CHALF was derived from a data extract from Care Inspectorate Wales in 2020 for all adult care home settings.8 Geographical variables associated with the RALF and CHALF include Lower layer Super Output Area (LSOA) 2011 boundaries that are small statistical areas containing around 1500 people. LSOA 2011 has been mapped to the Welsh Index of Multiple Deprivation (WIMD) version 2019 to derive deprivation quintiles; Welsh health board of residence; and urban/rurality categories.15 16 Using the Welsh Government’s Pupil-Level Annual School Census (PLASC), the school population can also be linked to the cohorts for analyses by the school network.17

In addition, permission has been granted to embed occupation and role categories from electronic staff records of all NHS health boards and trusts, local authority social care workers and education staff. For healthcare workers, the electronic staff records system used in all health boards and trusts (111 000) are categorised by whether roles involve direct patient care or not and by occupational groups: Additional Professional Scientific and Technical; Additional Clinical Service; Administrative and Clerical; Allied Health Professionals; Estates and Ancillary; Healthcare Scientist; Medical and Dental, Nursing and Midwifery Registered; and Students. Social care workers are registered (https://socialcare.wales/) and grouped into social workers, child home workers and domiciliary care workers (estimated 32 000). Educational staff records (estimated 70 000) include categories for teachers, support and administrative staff. This information is collected from the annual School Workforce Annual Census held by the Welsh Government.18 Data on care home staff are collected as part of Public Health Wales (PHW) testing of all staff and residents and made available in SAIL through the standard Laboratory Information Management System dataset. Permission has also been granted to link 2011 ONS census fields on ethnicity, occupation, housing tenure, over-crowding and socioeconomic status (SES). Ethnicity codes are derived from multiple health and social data sources mapped to Census 2011 groupings.19

Health data

All hospital admissions, outpatient and emergency department attendances treated in NHS hospitals as well as GP data on all diagnoses and treatments from SAIL providing practices (80% population coverage) are available for cohort participants.20 As of the beginning of 2020, we have added:

• Daily GP respiratory and COVID-19 codes for 100% of Welsh GP practices.

• Daily COVID-19 antigen test results.

• Bi-weekly data on participants reporting symptoms through the King's College London/ZOE symptom tracking app.21

• Weekly critical care data from the Intensive Care National Audit and Research Centre.22 23

• Bi-weekly COVID-19 viral genomic variant call format data and viral lineage assignments from PHW.24

• Monthly community dispensing data from pharmacies providing NHS issued prescriptions, backdated to 2016.25

Exposure variables and potential confounding factors

A number of exposure variables will be used to contextualise the study primary outcomes, including age, sex, SES and clinical risk groups.

SES will be derived from WIMD with quintile 1 including the 20% most deprived areas, and also at individual/household level from the 2011 Census using the following codes: approximated social grade (SCGPUK11), highest level of qualification (HLQPUK11) and National Statistics Socioeconomic Classification.26

Clinical risk groups have been derived from those used in scientific papers:

• Predictors of influenza and COVID-19 outcomes.27 28

• Published phenotype (disease conditions) libraries including the 308 phenotypes created by the Cardiovascular disease research using linked bespoke studies and electronic health records (CALIBER) study.29

• Commonly used comorbidity indices (Charlson and Elixhauser).30 31

• Frailty indices (electronic Frailty Index for GP data and Hospital Frailty Risk Score).32–34

  In order to compare and combine results with other studies, we will replicate the 19 clinical groups included in a similar study in Scotland.35

Microbiological testing data will be de-duplicated and used to generate case-data for standard case definitions, agreed at UK level where possible.

Body mass index will be categorised as <20, 20–24, 25–29, 30–39, ≥40 kg/m²; and smoking status categorised into four groups: current smoker, non-smoker, ex-smoker and not recorded for patients with no data on smoking, replicated from a study carried out in Scotland.35

Statistical analysis

We will describe baseline characteristics for exposures and outcomes of interest using means, medians, proportions, ORs and rate ratios with appropriate measures of dispersion. We will report on the prevalence of missing data by variable and use two-tailed hypothesis tests with a 5% significance level.

Non-independence of observations measured over time or within associated clusters, for example, GP or households will be taken into account using random effects. We will use causal frameworks where causal relationships are implied.36 Hypotheses being tested will be stated in advance and plans will be drawn up for each research project. Analyses will primarily be conducted in the R statistical programming language.37 38

Analyses

We will test associations for demographic, socioeconomic and clinical risk factors for COVID-19 infection and associated morbidity and mortality. COVID-19 infection will be defined in a number of ways: (a) positive SARS-CoV-2 laboratory antigen test; (b) clinical diagnosis of COVID-19 infection in GP records, intensive care, or hospital discharge records; (c) ONS mortality records listing COVID-19 as the underlying or contributory cause; and (d) positive serology report (when available).

Planned analyses include:

• Incidence of COVID-19 over time and by geography and demographic groups.

• Influence of area deprivation and individual SES metrics on infection and outcomes.

• Impact of COVID-19 on short-term (<6 months) and longer-term population outcomes such as changes in health service utilisation and excess, overall and disease-specific mortality.

• Description of the extent of clustering of cases within all available residential, educational, occupational and geographic units, thus providing signatures of spatial spread at defined levels, and between levels, thought to have played a crucial role in transmission.

We will investigate the relationship between health (physical and mental), socioeconomic and environmental factors, such as self-rated health, limiting long-term illness, housing tenure, over-crowding, education status, and occupation on infection risk and outcome.

Changes in healthcare utilisation will be assessed by measuring differences preinfection and postinfection for: NHS111 telephone calls, GP consultations, emergency department attendances, hospital admissions and length of stay, and intensive care admissions.

Analytical techniques will include descriptive statistics, univariate and multivariate generalised linear mixed models, survival analyses and the use of self-controlled case series for temporary risk factors.39 Relationships between variables will be clarified before specific analyses.

Cohort characteristics

The C16 and C20 cohorts have been constructed from patients registered with all GPs in Wales (table 1).

The power to detect relevant outcomes will be assessed as the pandemic evolves. There are plans to collaborate with researchers across the UK and collating data from similar cohorts,35 to maximise power, support the evaluation of natural experiments in policy and its timing around disease control, and exit strategies from the lockdown on physical restrictions that commenced on 23 March 2020.

The individual datasets that comprise the cohorts are held on the globally accessible SAIL Databank available to accredited researchers.

Proposed future developments

As the pandemic evolves so will policies and practices to control the epidemic and mitigate negative consequences. As these develop, we plan to use the cohorts as a platform for their evaluation, by linking dates and presence of interventions as data become available. There are subtle differences in the timing and approaches to controlling the epidemic in diverse settings and in exiting lockdown across the four UK nations. This provides opportunities for collaborative and timely evaluation of natural experiments of policies and approaches across the UK, which would refine evidence-based exit strategies. We are also keen to contribute to international initiatives.

Patient and public involvement

This study is based on an extension of the developing Wales Multimorbidity Cohort (WMC). CD and JD are members of the public who were involved in the design of the WMC and C20/C16 studies. Additional members of the public are in the process of being recruited to the research steering committee to represent the views of health, social care and educational staff.