Lifestyle and Comorbidity-Related Risk Factors of Severe and Critical COVID-19 Infection: A Comparative Study Among Survived COVID-19 Patients in Bangladesh
Bibliographic record
Abstract
Faroque Md Mohsin,1,2 Ridwana Nahrin,2 Tajrin Tahrin Tonmon,2 Maherun Nesa,3 Sharmin Ahmed Tithy,1 Shuvajit Saha,4 Mahmudul Mannan,5 Md Shahjalal,2 Mohammad Omar Faruque,6 Mohammad Delwer Hossain Hawlader2 1Directorate General of Health Services, Ministry of Health and Family Welfare, Dhaka, Bangladesh; 2Department of Public Health, North South University, Dhaka, Bangladesh; 3Department of Surgery, Sheikh Hasina National Institute of Burn & Plastic Surgery, Dhaka, Bangladesh; 4Department of Maternal and Child Health, Projahnmo Research Foundation, Dhaka, Bangladesh; 5Department of Health Research Methods, Evidence and Impact, McMaster University, Hamilton, Canada; 6Department of Botany, University of Chittagong, Chattogram, BangladeshCorrespondence: Mohammad Delwer Hossain HawladerDepartment of Public Health, North South University, Dhaka, 1229, BangladeshEmail mohammad.hawlader@northsouth.eduBackground: Severe COVID-19 infections have already taken more than 4 million lives worldwide. Factors, such as socio-demographics, comorbidities, lifestyles, environment, and so on, have been widely discussed to be associated with increased severity in many countries. The study aimed to determine the risk factors of severe–critical COVID-19 in Bangladesh.Methods: This was a comparative cross-sectional study among various types of COVID-19 patients (both hospitalized and non-hospitalized) confirmed by reverse transcription polymerase chain reaction (RT-PCR). We have selected 1500 COVID-19 positive patients using a convenient sampling technique and analyzed lifestyle and comorbidity-related data using IBM SPSS-23 statistical package software. Chi-square test and multinomial logistic regression were used to determine risk factors of life-threatening COVID-19 infection.Results: The mean age of the study participants was 43.23 (± 15.48) years. The study identified several lifestyle-related factors and common commodities as risk factors for severe–critical COVID-19. The patient’s age was one of the most important predictors, as people > 59 years were at higher risk (AOR=18.223). Among other lifestyle factors, active smoking (AOR=1.482), exposure to secondary smoking (AOR=1.728), sleep disturbance (AOR=2.208) and attachment with SLT/alcohol/substance abuse (AOR=1.804) were identified as significant predictors for severe–critical COVID-19. Patients those were overweight/obese (AOR=2.105), diabetic (AOR=4.286), hypertensive (AOR=3.363), CKD patients (AOR=8.317), asthma patients (AOR=2.152), CVD patients (AOR=7.747) were also at higher risk of severe–critical COVID-19 infection.Conclusion: This study has identified several vital lifestyles and comorbidity-related risk factors of severe–critical COVID-19. People who have these comorbidities should be under high protection, and risky lifestyles of the general population should modify through the proper educational campaign.Keywords: COVID-19, lifestyle, comorbidities, risk factor, 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 | 0.000 | 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 | 0.001 |
| 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".