Prevalence of Diabetes Mellitus and Hypertension Among COVID‐19 Patients: A Cross‐Sectional Study Exploring Associations With Sociodemographic and Biological Factors in Rajshahi Division, Bangladesh
Bibliographic record
Abstract
ABSTRACT Background and Aims Diabetes mellitus (DM) and hypertension (HT) are major global health concerns, with a rising prevalence worsened by the COVID‐19 pandemic. The interplay between these chronic conditions and COVID‐19 presents a unique public health challenge, particularly in low‐ and middle‐income countries such as Bangladesh. This study aimed to (1) determine the prevalence of DM and HT among individuals affected by COVID‐19 pandemic in Rajshahi Division, Bangladesh, and (2) explore associations with sociodemographic and biological factors. Methods This cross‐sectional study was conducted in the Rajshahi Division of Bangladesh between April and August 2021. Data was collected from 390 COVID‐19‐positive patients using a structured questionnaire and physical measurements, focusing on sociodemographic and biological factors associated with DM and HT. Logistic regression analyses were performed to identify significant risk factors. Results Among participants, 12.05% had DM, 15.89% had HT, and 7.95% had both. Older age ( > 50 years) was significantly associated with higher prevalence of both conditions ( p < 0.001). Females were more likely to have DM (16.6% vs. 9.2%), HT (19.2% vs. 13.8%), and both (10.6% vs. 6.3%) than males ( p < 0.05). Private or self‐employed individuals had a significantly higher risk of both DM and HT (RRR: 7.66, 95% CI: 4.26–12.37, p < 0.001). Obesity (BMI ≥ 25) was linked to increased prevalence of DM (13.2%), HT (21.0%), and both (10.2%). Elevated SBP and DBP were strongly associated with comorbidity ( p < 0.001), and tobacco use increased the odds of DM (AOR: 2.45, 95% CI: 1.13–5.29, p = 0.02). Conclusion Targeted interventions, such as community‐based education, improved chronic disease management, and culturally tailored strategies, are recommended to address these conditions effectively. Strengthening healthcare infrastructure and conducting longitudinal research to explore causal pathways will be critical in mitigating the impact of these comorbidities and improving pandemic preparedness in resource‐limited settings.
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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 | 0.026 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| 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".