Incidence of post-acute COVID-19 symptoms across healthcare settings in seven countries, 2024, Xie+

Incidence of post-acute COVID-19 symptoms across healthcare settings in seven countries: an international retrospective cohort study using routinely-collected data
Junqing Xie; Kim López-Güell; Daniel Dedman; Talita Duarte-Salles; Raivo Kolde; Raúl López-Blasco; Álvaro Martínez; Gregoire Mercier; Alicia Abellan; Johnmary T. Arinze; Zara Cuccu; Antonella Delmestri; Dominique Delseny; Sara Khalid; Chungsoo Kim; Ji-woo Kim; Kristin Kostka; Cora Loste; Lourdes Mateu; Miguel A. Mayer; Jaime Meléndez-Cardiel; Núria Mercadé-Besora; Mees Mosseveld; Akihito Nishimura; Hedvig M.E. Nordeng; Jessie O. Oyinlola; Laura Pérez-Crespo; Marta Pineda-Moncusí; Juan Manuel Ramírez-Anguita; Nhung T.H. Trinh; Anneli Uusküla; Bernardo Valdivieso; Theresa Burkard; Edward Burn; Martí Català; Daniel Prieto-Alhambra; Roger Paredes; Annika M. Jödicke

BACKGROUND
The World Health Organisation (WHO) has identified a range of symptomatic manifestations to aid in the clinical diagnosis of post-COVID conditions, herein referred to as post-acute COVID-19 symptoms. We conducted an international network cohort study to estimate the burden of these symptoms in North American, European, and Asian populations.

METHODS
A federated analysis was conducted including 10 databases from the United Kingdom, Netherlands, Norway, Estonia, Spain, France, South Korea, and the United States, between September 1st 2020 and latest data availability (which varied from December 31st 2021 to February 28th 2023), covering primary and secondary care, nationwide registries, and claims data, all mapped to the Observational Medical Outcomes Partnership Common Data Model (OMOP CDM). We defined two cohorts for the main analyses: a SARS-CoV-2 infection cohort [positive polymerase chain reaction (PCR) or rapid lateral flow test (LFT) result or clinical COVID-19 diagnosis] and a general population cohort. Individuals with less than 365 days of prior history or 120 days of follow-up were excluded. We estimated incidence rates (IRs) of the 25 WHO-proposed post-acute COVID-19 symptoms, considering symptoms that occurred ≥90 and ≤365 days after index date, excluding individuals with the respective symptoms 180 days prior to the index event. Stratified analyses were conducted by age and sex. Incidence rate ratios (IRRs) were calculated comparing rates in the infected cohort versus the general population. Results from the different databases were combined using random-effects meta-analyses.

FINDINGS
3,019,408 individuals were included in the infection cohort. 1,585,160 of them were female and 1,434,248 of them male. 929,351,505 individuals were included in the general population group. 461,195,036 of them were female and 466,022,004 of them male. The 1-year IR of any post-acute COVID-19 symptom in the COVID-19 infection cohort varied significantly across databases, from 4.4 (95% CI 3.8–5.1) per 100 person-years to 103.9 (95% CI 103.2–104.7). The five most common symptoms were joint pain (from 1.6 (95% CI 1.3–1.9) to 14.3 (95% CI 14.1–14.6)), abdominal pain (from 0.3 (95% CI 0.1–0.5) to 9.9 (95% CI 9.7–10.1)), gastrointestinal issues (from 0.6 (95% CI 0.4–0.9) to 13.3 (95% CI 13.1–13.6)), cough (from 0.3 (95% CI 0.2–0.5) to 9.1 (95% CI 8.9–9.3)), and anxiety (from 0.8 (95% CI 0.6–1.2) to 11.4 (95% CI 11.2–11.6)); whereas muscle spasms (from 0.01 (95% CI 0.008–0.2) to 1.7 (95% CI 1.6–1.8)), pins and needles (from 0.05 (95% CI 0.03–0.0.9) to 1.5 (95% CI 1.4–1.6)), memory issues (from 0.03 (95% CI 0.02–0.06) to 0.8 (95% CI 0.7–0.8)), cognitive dysfunction (from 0.007 (95% CI 0.004–0.01) to 0.6 (95% CI 0.4–0.8)), and altered smell and/or taste (from 0.04 (95% CI 0.03–0.04) to 0.7 (95% CI 0.6–0.8)) were least common. Incidence rates of any post-acute COVID-19 symptoms generally increased with age, with certain symptoms peaking in middle-aged adults (anxiety, depressive disorders, headache, altered smell and taste) and others in pre-school children (gastrointestinal issues and cough). Females had higher incidence rates for most symptoms. Based on the random-effects model, the infected cohort had a higher incidence of any post-acute COVID-19 symptom than the general population, with a meta-analytic incidence rate ratio (meta-IRR) of 1.4 (1–2). A similar pattern was seen for all individual symptoms. The highest meta-IRRs were depressive disorder, 2.6 (1.7–3.9); anxiety, 2.3 (1.4–3.8); allergy, 2.1 (1.7–2.8) and sleep disorders, 2.1 (1.5–2.6). The meta-IRR for altered smell and/or taste was 1.9 (1.3–2.8).

INTERPRETATION
Post-acute COVID-19 symptoms, as listed by the WHO, were commonly observed following COVID-19 infection. However, even after standardising research methods, there was significant heterogeneity in the incidence rates from different healthcare settings and geographical locations. This is the first international study of the epidemiology of post-acute COVID-19 symptoms using the WHO-listed symptoms. Its findings contibute to understand the epidemiology of this condition from a multinational approach.

LIMITATIONS
of this study include the lack of consensus of the post-acute COVID-19 definition, as well as the difficulty to capture the impact on daily life of the post-acute COVID-19 symptoms in the available datasets.

FUNDING
This work has been funded by the European Health Data Evidence Network (EHDEN) through an Evidence Generation Fund Grant and by the National Institute for Health and Care Research (NIHR) Oxford Biomedical Research Centre (BRC).


Link | PDF (Lancet: eClinicalMedicine) [Open Access]

 

To get valid data out, first you have to enter valid data in. Until the problem of not entering valid data, and entering invalid data, is fixed, health care records are basically no better than Internet surveys. Barring a comprehensive study that does it all. There is indeed no code for PEM. Which the authors clearly paid little attention as they used an incorrect similar version with post-exertional fatigue instead. This is a problem with the systems. Systems that refuse to self-correct, appear incapable of it.

This is equivalent to searching for rape statistics in counties that don't record any such reports, creating cycles of underreporting, which then lead to justification for not bothering to record ("it's so rare anyway"). You can't conclude that there is little of a type of data when you know that this type of data is not correctly recorded. But you have to pay attention outside the bubble of health care to know this, which almost none do, and instead assume that the invalid data they have in their systems must be more accurate than the flood of heterogenous data from people reporting things that never find their way onto health care records.

Despite this they find that it's common. The underreporting is anywhere from 5-10x. All by choice. All to feed the psychosomatic gods and their capricious ways.