Two-year epidemiology of post-COVID-19 conditions in Bangladesh: a cohort study of post-COVID-19 from 12,925 SARS-CoV-2 cases between July and December 2021–2023 in Bangladesh
Bibliographic record
Abstract
BACKGROUND: Post-COVID-19 conditions (PCCs), also known as long COVID, is persistently debilitating disorders that need investigation on their incidence, morbidity, and case-fatality rate. PURPOSE: The objectives of this cohort study were to determine the incidence, characteristics, case-fatality, morbidity, and recovery of post-COVID-19 symptoms throughout a two-year period of observation. METHODS: This was a population-based cohort study of post-COVID-19 cases among 12,925 SARS-CoV-2 positive individuals in eight administrative districts of Bangladesh between July and December 2021-2023. PCC was diagnosed according to WHO clinical diagnostic criteria, and the screening procedure was completed through a household screening process. RESULTS: The incidence of PCC was 3.6%, the case-fatality rate was 1.92%, and the recovery rate was 9.0%. The significant predictors of PCC morbidity were geographical distribution, vaccination, comorbidities, and a longer duration of symptoms or multiple symptoms (p < 0.05). CONCLUSION: Nearly 465 out of 522 people suffering from PCC are persistent and have a significant disability. However, the rate of recovery was 9.0%. It is necessary to investigate approaches to improve the recovery of PCC in Bangladesh.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 source (direct Gemma or distilled Codex), 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".