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
BACKGROUND: 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.
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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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".