Sex-specific prevalence, inequality and associated predictors of hypertension, diabetes, and comorbidity among Bangladeshi adults: results from a nationwide cross-sectional demographic and health survey
Bibliographic record
Abstract
OBJECTIVES: To determine the sex-specific prevalence, inequality and factors associated with healthcare utilisation for diabetes mellitus (DM), hypertension and comorbidity among the adult population of Bangladesh. STUDY DESIGN: This study analysed cross-sectional nationwide Bangladesh Demographic and Health Survey data from 2011. Comorbidity was defined as the coexistence of both DM and hypertension. Several socioeconomic and demographic factors such as age, sex, education, geographic location, administrative division, employment status, education and wealth index were considered as major explanatory variables. Inequality in prevalence and healthcare utilisation was measured using the 'Lorenz curve'. Adjusted multiple logistic regression models were performed to observe the effects of different factors and reported as adjusted ORs (AORs) with 95% CIs. A p value of <0.05 was adopted as the level of statistical significance. SETTING: The study was conducted in Bangladesh. PARTICIPANTS: A total of 7521 adult participants with availability of biomarkers information were included. RESULTS: The mean age of the study participants was 51.4 years (SD ±13.0). The prevalence of hypertension, diabetes and comorbidity were 29.7%, 11.0% and 4.5% respectively. Socioeconomic inequality was observed in the utilisation of healthcare services. A higher prevalence of hypertension and comorbidity was significantly associated with individuals aged >70 years (AOR 7.0, 95% CI 5.0 to 9.9; AOR 6.7, 95% CI 3.0 to 14.9). The risk of having hypertension, diabetes and comorbidity were significantly higher among more educated, unemployed as well as among individuals from Khulna division. CONCLUSIONS: The study revealed a rising prevalence of hypertension, diabetes and comorbidity with inequality in service utilisation. A joint effort involving public, private and non-governmental organisations is necessary to ensure improved accessibility in service utilisation and to reduce the disease burden.
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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 | 0.001 | 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 | 0.000 | 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".