Diabetes, Hypertension, and Comorbidity among Bangladeshi Adults: Associated Factors and Socio-Economic Inequalities
Bibliographic record
Abstract
Diabetes, hypertension, and comorbidity are still crucial public health challenges that Bangladeshis face. Nonetheless, very few studies have been conducted to examine the associated factors, especially the socioeconomic inequalities in diabetes, hypertension, and comorbidity in Bangladesh. This study explored the prevalence of, factors connected with, and socioeconomic inequalities in diabetes, hypertension, and comorbidity among Bangladeshi adults. We used the Bangladesh Demographic and Health Survey (BDHS) data set of 2017−2018. A total of 12,136 (weighted) Bangladeshi adults with a mean age of 39.5 years (±16.2) participated in this study. Multilevel (mixed-effect) logistic regression analysis was employed to ascertain the determinants of diabetes, hypertension, and comorbidity, where clusters were considered as a level-2 factor. The concentration curve (CC) and concentration index (CIX) were utilized to investigate the inequalities in diabetes, hypertension, and comorbidity. The weighted prevalence of diabetes, hypertension, and comorbidity was 10.04%, 25.70%, and 4.47%, respectively. Age, body mass index, physical activity, household wealth status, and diverse administrative divisions were significantly associated with diabetes, hypertension, and comorbidity among the participants. Moreover, participants’ smoking statuses were associated with hypertension. Women were more prone to hypertension and comorbidity than men. Diabetes (CIX: 0.251, p < 0.001), hypertension (CIX: 0.071, p < 0.001), and comorbidity (CIX: 0.340, p < 0.001) were higher among high household wealth groups. A pro-wealth disparity in diabetes, hypertension, and comorbidity was found. These inequalities in diabetes, hypertension, and comorbidity emphasize the necessity of designing intervention schemes geared towards addressing the rising burden of these diseases.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| 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".