New study maps out long COVID patterns in England, showing who is most at risk

By Vijay Kumar Malesu

In a recent study published in the journal EClinicalMedicine, researchers described the incidence and differences in demographic and clinical characteristics of recorded long coronavirus disease (COVID) in primary care records in England.

Study: Clinical coding of long COVID in primary care 2020–2023 in a cohort of 19 million adults: an OpenSAFELY analysis. Image Credit: p.ill.i / Shutterstock

Study: Clinical coding of long COVID in primary care 2020–2023 in a cohort of 19 million adults: an OpenSAFELY analysis. Image Credit: p.ill.i / Shutterstock

Background 

Some individuals experience prolonged symptoms for weeks or months following Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-COV-2) infection, known as long COVID. It includes cardiovascular disease, chronic fatigue syndrome, and dysautonomia, each with distinct pathophysiologies. The heterogeneity within long COVID contributes to inconsistent definitions and varied prevalence estimates. More research on the causes and consequences is necessary. Electronic health records (EHRs) offer a potential data source despite diagnostic accuracy and inconsistent coding challenges. In the United Kingdom (UK), diagnostic codes for long COVID have been available since November 2020. Further research is needed to understand the causes, consequences, and accurate prevalence of long COVID due to its heterogeneous nature and inconsistent definitions.

About the study 

The present study utilized a database of 19 million adults in England, managed by The Phoenix Partnership SystmOne (TPP SystmOne), covering 40% of General Practitioner (GP) practices. Data was accessed via the Open Secure Analytics For Electronic Health Records (OpenSAFELY) platform, which ensures data remains pseudonymized and excludes free text. Additional linked data included COVID-19 vaccination status from the National Immunisation Management System (NIMS), in-patient records from Hospital Episode Statistics (HES), and national testing records from the Second Generation Surveillance System (SGSS).

Participants aged 18-100, registered with a TPP SystmOne GP from 1 November 2020, were followed until the earliest of an EHR-long COVID record, end of registration, death, or 31 January 2023. Hospitalization with COVID-19 was also included as a control outcome, analyzing COVID-19 test results and hospitalizations over 12 weeks before follow-up ended.

Vaccination status was time-updated, categorized by the number of doses and type (messenger Ribonucleic Acid (mRNA) or non-mRNA). Other covariates, defined at baseline, included age, sex, National Health Service (NHS) region, index of multiple deprivation (IMD) quintiles, ethnicity, chronic comorbidities, and two "probable shielding" variables based on Systematized Nomenclature of Medicine (SNOMED) codes.

Crude long COVID rates per 100,000 person-years and negative binomial models adjusted for confounders were estimated. Monthly incidences and a Sankey diagram illustrated SARS-CoV-2 histories. Ethical approval was obtained from relevant committees, and the OpenSAFELY platform uses legal powers that bypass the need for patient consent.

Study results 

Between November 2020 and January 2023, data from 19,462,260 adults in England were analyzed, with a median follow-up time of 2.2 years. The cohort was evenly split between men and women, with 70% identified as white ethnicity. Most participants resided in the East Midlands (17%), East (23%), South West (14%), and Yorkshire and the Humber (14%), reflecting the regional use of SystmOne. Over a third had at least one chronic comorbidity. The study identified 55,465 individuals with long COVID, including 20,025 diagnosis codes and 35,440 referral codes. Long COVID cases rose throughout 2021, peaked in January 2022, and then declined over the next year. Referral codes increased over time, with most new records since mid-2022 being referrals to post-COVID assessment clinics.

Initially, long COVID records were only in unvaccinated individuals, but as vaccinations increased, more long COVID codes were recorded in vaccinated individuals. Weekly patterns revealed significant spikes on specific dates, primarily due to the "Signposting to Your COVID Recovery" SNOMED code. Long COVID records peaked with national SARS-CoV-2 infection rates but did not mirror the decline in early 2021 or the 2022 infection waves.

Crude rates of long COVID were highest among women, those aged 40-60, white individuals, those with comorbidities, and those at high risk of COVID-19 complications. Rates were lowest among those with three or more vaccine doses and those who received an mRNA vaccine as their first dose. Notably, long COVID rates were higher in less deprived areas, but this association did not hold when only diagnosis codes were analyzed. Exploratory analysis showed the lowest long COVID rates in individuals with three or more vaccine doses, although these results are not causal.

The study also examined pathways to a long COVID record, linking SARS-CoV-2 tests and COVID-19 hospitalization data. It was found that 59% of individuals with a long COVID record did not have a recorded positive test result ≥12 weeks before the long COVID record, and only 6.5% had been hospitalized with COVID-19. Those with a previous positive test were more likely to be female, older, from a more deprived area, vaccinated, and not hospitalized with COVID-19. These systematic differences highlight the complex nature of long COVID recording and its relation to prior SARS-CoV-2 testing and hospitalization.

Conclusions 

To summarize, the health records of over 19 million adults in England revealed low rates of GP-recorded long COVID diagnoses and referrals, with referral codes becoming more common in 2021 but new cases declining in 2022. Demographic differences exist between those receiving referrals versus diagnosis codes. Regional variations and increased referrals in less deprived areas were noted. The study highlights challenges in using EHR data for accurate long COVID identification, emphasizing that GPs may not record many self-reported cases.

Journal reference:

• Alasdair D. Henderson, Ben FC. Butler-Cole, John Tazare, et al. Clinical coding of long COVID in primary care 2020–2023 in a cohort of 19 million adults: an OpenSAFELY analysis, EClinicalMedicine (2024), DOI - 10.1016/j.eclinm.2024.102638, https://www.thelancet.com/journals/eclinm/article/PIIS2589-5370(24)00217-7/fulltext