ABSTRACT
Background:
Long COVID is the
patient-coined term for the persistent symptoms of COVID-19 illness for weeks, months or years following the acute
infection. There is a large burden of
long COVID globally from
self-reported data, but the
epidemiology, causes and
treatments remain poorly understood.
Primary care is used to help identify and treat
patients with
long COVID and therefore
Electronic Health Records (EHRs) of past COVID-19
patients could be used to help fill these
knowledge gaps. We aimed to describe the
incidence and differences in demographic and clinical characteristics in recorded
long COVID in
primary care records in
England.
Methods:
With the approval of NHS
England we used routine clinical data from over 19 million
adults in
England linked to
SARS-COV-2 test result, hospitalisation and
vaccination data to describe
trends in the recording of 16 clinical
codes related to
long COVID between November 2020 and January 2023. Using OpenSAFELY, we calculated rates per 100,000
person-years and plotted how these changed over
time. We compared crude and adjusted (for age,
sex, 9 NHS regions of
England, and the dominant variant circulating) rates of recorded
long COVID in
patient records between different key demographic and
vaccination characteristics using negative
binomial models.
Findings:
We identified a total of 55,465 people recorded to have
long COVID over the study period, which included 20,025
diagnoses codes and 35,440
codes for further assessment. The
incidence of new
long COVID records increased steadily over 2021, and declined over 2022. The overall rate per 100,000
person-years was 177.5 cases in
women (95% CI 175.5-179) and 100.5 in
men (99.5-102). The majority of those with a
long COVID record did not have a recorded positive
SARS-COV-2 test 12 or more weeks before the
long COVID record.
Interpretation:
In this descriptive study, EHR recorded
long COVID was very low between 2020 and 2023, and incident
records of
long COVID declined over 2022. Using EHR diagnostic or
referral codes unfortunately has major limitations in identifying and ascertaining true cases and timing of
long COVID.
Funding:
This
research was supported by the National Institute for
Health and Care
Research (NIHR) (OpenPROMPT COV-LT2-0073).