Double Trouble: Prevalence and Factors Associated with Tuberculosis and Diabetes Comorbidity in Bangladesh
Bibliographic record
Abstract
Background Diabetes among tuberculosis patients increases the risk of tuberculosis treatment failure, death, and development of multidrug-resistant tuberculosis. Yet, there is no data is available in Bangladesh on the prevalence of diabetes among tuberculosis patients. The objective of the current study was to estimate prevalence and identify factors associated with tuberculosis-diabetes co-morbidity among TB patients enrolled in the Directly Observed Treatment, Short course program. Methods A community based cross-sectional quantitative study was conducted among 1910 tuberculosis patients living in six urban and eleven rural areas among whom Oral Glucose Tolerance Test (those who fasted) and Random Blood Sugar test (those who did not fast) were performed. Besides glucose levels, data on socio-demographic information, family history of diabetes and anthropometric measurements (height and weight) were also collected. Result Among the 1910 TB patients who participated in screening for diabetes, 245 (12.8%) were found to have diabetes and 296 (15.5%) to have pre-diabetes. Out of those who had diabetes, 34.7% were newly diagnosed through the current study and 65.3% already knew their status. Among those who were found to have prediabetes, 27 (9.1%) had impaired Fasting Blood Glucose (FBG), 230 (77.7%) had Impaired Glucose Tolerance (IGT), and 39 (13.2%) had both Impaired FBG and IGT. Older age, higher BMI, higher education (secondary level and above), being married, participation in less active work, and family history of diabetes are associated with higher prevalence of diabetes. Conclusion We observed a higher prevalence of diabetes and pre-diabetes in TB patients than reported previously in Bangladesh among the general population which may challenge TB and diabetes control in Bangladesh. Diabetes diagnosis, treatment and care should be integrated in the National TB Program.
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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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".