Long COVID burden and risk factors in 10 UK longitudinal studies and electronic health records

Abstract

The frequency of, and risk factors for, long COVID are unclear among community-based individuals with a history of COVID-19. To elucidate the burden and possible causes of long COVID in the community, we coordinated analyses of survey data from 6907 individuals with self-reported COVID-19 from 10 UK longitudinal study (LS) samples and 1.1 million individuals with COVID-19 diagnostic codes in electronic healthcare records (EHR) collected by spring 2021. Proportions of presumed COVID-19 cases in LS reporting any symptoms for 12+ weeks ranged from 7.8% and 17% (with 1.2 to 4.8% reporting debilitating symptoms). Increasing age, female sex, white ethnicity, poor pre-pandemic general and mental health, overweight/obesity, and asthma were associated with prolonged symptoms in both LS and EHR data, but findings for other factors, such as cardio-metabolic parameters, were inconclusive.

Fig. 1. Trends in long COVID frequency among COVID-19 cases by age, in four age-homogeneous LS (left) and EHRs (right).

Left—in four longitudinal studies (MCS N = 1055; NS N = 848; BCS70 N = 889; NCDS N = 709) where participants are of near-identical ages, proportions reporting symptom length of four or more weeks in COVID-19 cases were ascertained from questionnaire responses. Right–in OpenSAFELY (N = 4189), proportions represent individuals within 10-year age categories (with estimates grouped at the mid-point of each category) who have long COVID codes in GP records, hence the proportions are substantially lower than in the corresponding cohort data. Data are presented as percentages and 95% confidence intervals (CIs) as appropriate. Trend lines and 95% CIs shading represent absolute differences in long COVID frequencies with increasing age, estimated by linear meta-regression of data from the four cohorts and from 18- to 70-year-olds in OpenSAFELY (data from older individuals were not modelled; refer to results text for further explanation).

Fig. 2. Risk factors associated with long COVID from meta-analyses of LS findings alongside corresponding analyses from EHRs.

The reference category for ‘Diabetes’, ‘Hypertension’, ‘High Cholesterol’, and ‘Asthma’ is the absence of condition. All associations were adjusted for age and sex, except where redundant. In all instances where it was possible to derive results from both meta-analyses of longitudinal studies (N up to = 6907) and analysis of EHRs (N up to = 4189), the corresponding results are plotted side-by-side for comparison. Estimates from fixed effects meta-analyses of longitudinal study data and EHR analyses are presented as odds ratios (OR) and 95% confidence intervals (CIs). The outcome used for longitudinal study analyses presented here was symptoms lasting for 4+ weeks, and the outcome in EHRs was any reporting of a long COVID read code in GP records (regardless of duration of symptoms). Full study-level results, heterogeneity statistics and random-effect estimates for the longitudinal study meta-analyses are presented in Supplementary Figs. 3 and 4. The equivalent meta-analyses of longitudinal study data where symptom duration of 12+ weeks was instead used as the outcome are depicted in Supplementary Figs. 5 and 6. Index of multiple deprivation quintile 1 represents individuals from the most deprived area, and quintile 5 represents individuals from the least deprived area. ‘Poor overall health’ represents the self-rated health exposure in the LS meta-analysis, and comorbidities in OpenSAFELY. The outcome ‘Overweight and obesity’ represents combined BMI categories over 25 in the LS, and solely individuals with BMI 30–34.9 in OpenSAFELY.