Prevalence and factors associated with depression and anxiety among COVID-19 survivors in Dhaka city
Bibliographic record
Abstract
Abstract Background Coronavirus disease 2019 (COVID-19) is a global public health concern. Evidence shows that depression and anxiety are common among patients with COVID-19 after recovery. About one-third of the total COVID-19 cases in Bangladesh have been reported in Dhaka city. Therefore, the study aimed to assess the prevalence of depression and anxiety and associated factors among COVID-19 survivors in Dhaka city.Methods A cross-sectional study was conducted among a total of 384 COVID-19 survivors aged 18 years or older. Data collection was done through face-to-face and telephone interviews using a semi-structured questionnaire. Patient Health Questionnaire (PHQ-9) and Generalized Anxiety Disorder (GAD-7) scales were used to assess depression and anxiety, respectively. Binary logistic regression analysis was performed to identify factors associated with depression and anxiety.Results The overall prevalence of depression and anxiety was 26.0% and 23.2%, respectively. Respondents aged ≥ 60 years were 2.85 and 3.59 times more likely to have depression and anxiety, respectively than those aged 18–39 years. Hospitalized COVID-19 patients had a 1.98 and 2.28 times higher chance of having depression and anxiety, respectively than non-hospitalized COVID-19 patients. COVID-19 patients with comorbidities were at a 3.48 and 2.87 times higher risk of depression and anxiety, respectively compared to those without comorbidities.Conclusions The study reported a high prevalence of depression and anxiety among COVID-19 survivors in Dhaka city. The findings suggest the need for appropriate interventions to reduce mental health complications in COVID-19 survivors.
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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 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".