Prevalence of Long COVID symptoms in Bangladesh: A Prospective Inception Cohort Study of COVID-19 survivors
Bibliographic record
Abstract
Abstract Objective The objective of this study was to identify the prevalence of ‘Long COVID’ symptoms (LCS) in a large cohort of survivors and identify any potential associated risk factors. Design A prospective survey was undertaken of an inception cohort of confirmed COVID-19 survivors (Aged 18 to 87 years). Participants and Setting 14392 participants were recruited from 24 testing facilities across Bangladesh between June, and November 2020. All participants had a previously confirmed positive COVID-19 diagnosis, and reported persistent symptoms and difficulties in performing daily activities. Main Outcome Measures Participants who consented, were contacted by face-to-face interview, and were interviewed regarding LCS, and restriction of activities of daily living using Post COVID-19 functional scale. Cardio-respiratory parameters were also measured. Results Among 2198 participants, the prevalence of LCS at 12 weeks was 16.1%. Overall, eight LCS were identified and in descending order of prominence are: fatigue, pain, dyspnea, cough, anosmia, appetite loss, headache, and chest pain. COVID survivors experienced between 1 to 5 LCS with an overall duration period of 21.8 ± 5.2 weeks. SEM predicted the length of LCS to be related to younger age, female gender, rural residence, prior functional limitation and smoking. Conclusion In this cohort of survivors, at 31 weeks post diagnosis, the prevalence of LCS was 16.1%. The risk factors identified for presence and longer length of LCS warrant further research and consideration to support public health initiatives.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".