Health-related quality of life of patients with type 2 diabetes mellitus at a tertiary care hospital in Ethiopia
Bibliographic record
Abstract
BACKGROUND: Type 2 diabetes mellitus (T2DM) and its treatment impact patients' physical health as well as emotional and social wellbeing. This study aimed to assess health-related quality of life (HRQoL) and associated factors among patients with T2DM at a tertiary care hospital in Ethiopia. METHODS: A face-to-face cross-sectional survey was conducted among patients with T2DM at Tikur Anbessa Specialized Hospital in Addis Ababa, Ethiopia. We collected data using a validated Amharic version of the 5-level EuroQoL-5 dimensions (EQ-5D-5L) questionnaire. Descriptive statistics were used to present patient characteristics. Kruskal-Wallis and Mann-Whitney U tests were performed to explore differences in the median scores of EQ-5D-5L utility and visual analog scale (EQ-VAS). Multivariable Tobit regression models were used to identify predictors of HRQoL. Utility scores were calculated using disutility weights of the Ethiopian general population. Statistical significance was determined at p < 0.05. RESULTS: A total of 360 patients with T2DM participated. Mean (SD) age was 64.43(10.61) years. Reported health problems were mostly in the pain/discomfort (67.3%) dimension followed by mobility (60.5%), whereas the usual activities domain (34.1%) was the least health problem being reported. The median (IQR) EQ-5D-5L utility and EQ-VAS scores were 0.95 (0.88-0.96) and 80 (75.0-85.0), respectively. In multivariable Tobit regression models older age, having poor glycemic control, longer duration of diabetes, insulin usage, obesity, and having diabetes-related complications were significant negative predictors of HRQoL. CONCLUSIONS: Overall, patients with T2DM had lower HRQoL than the general population, which was attributed to being older age, longer duration of diabetes, insulin use, obesity, inadequate glycemic control, and diabetes-related complications. The utility index we generated can be used in future economic evaluations to inform decisions about alternative interventions and resource allocation.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.000 | 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 teacher head, 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".