Long COVID prevalence and risk factors in adults residing in middle- and high-income countries: secondary analysis of the multinational Anti-Coronavirus Therapies (ACT) trials
Bibliographic record
Abstract
BACKGROUND: During the recent COVID-19 pandemic, reports of long-term persistence or recurrence of symptoms after SARS-CoV-2 infection emerged, which are now collectively referred to as 'long COVID'. Most descriptions of long COVID originate from patients residing in high-income countries. We set out to characterise long COVID in a large-scale clinical trial that was conducted in low-middle, high-middle and high-income countries. METHODS: The Anti-Coronavirus Therapies trials enrolled 6528 adult patients with symptomatic COVID-19 in Argentina, Brazil, Canada, Colombia, Ecuador, Egypt, India, Nepal, Pakistan, Philippines, Russia, Saudi Arabia, South Africa and the United Arab Emirates. Long COVID was defined as the presence of patient-reported symptoms at 180 days after enrolment. Multivariable logistic regression was used to evaluate associations of baseline characteristics with long COVID. RESULTS: Of 4697 included participants, 1181 (25.1%) reported long COVID symptoms. The most frequently reported symptoms were sleeping disorders (n=601; 12.8%), joint pain (n=461; 9.8%), fatigue (n=410; 8.7%) and headaches (n=382; 8.1%). Long COVID prevalence was higher in participants from lower middle-income compared with high-income countries (29.8% (850/2854) vs 14.4% (102/706); adjusted OR (aOR) 1.53 (1.10 to 2.14); p=0.012). Prevalence also varied between participants of different ethnic backgrounds and was highest (36.1% (775/2145)) for patients of Arab/North African ethnicity. Patients requiring inpatient admission were at increased risk of long COVID (aOR: 2.04 (1.63 to 2.54); p<0.001). Other independent predictors of long COVID were male sex, older age and hypertension. Vaccination, prior lung disease, smoking and diabetes mellitus conferred protective effects. CONCLUSION: Symptoms of long COVID are reported in a quarter of cases of symptomatic COVID-19 in this study and were significantly more prevalent in participants from countries with lower income status and in patients of Arab/North African ethnicity. Research to further assess the health burden posed by long COVID in low- and middle-income countries is urgently needed.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".