Health-related quality of life and treatment satisfaction of patients with cardiovascular disease in Ethiopia
Bibliographic record
Abstract
Purpose Cardiovascular disease is the most prevalent health problem associated with poorer health-related quality of life (HRQoL). We aimed to assess HRQoL and treatment satisfaction of cardiovascular disease patients in Ethiopia. Methods A cross-sectional survey was conducted among adults attending the outpatient cardiac clinic at Tikur Anbessa Specialized Hospital from July to September 2021. Patients were recruited consecutively during follow-up visits. Treatment Satisfaction Questionnaire for Medication and European Quality of life questionnaires were used to evaluate treatment satisfaction and HRQoL, respectively. Kruskal-Wallis and Mann-Whitney U-tests were used to compare utility weights between patient subgroups. Utility values were computed using disutility weights of the Ethiopian general population derived using a hybrid regression model. Tobit regression modeling was used to explore factors associated with poor HRQoL. Statistical significance was determined at p < 0.05. Results A total of 357 patients participated in the study with a mean age of 49.3 ± 17.8 years. The most frequently reported health problems were pain/discomfort (75.4%), followed by mobility (73.4%). The median (interquartile range) European Quality questionnaires five dimensions with five levels utility (EQ-5D-5L) and European Quality of life Visual Analog Scale scores were 0.84 (0.55–0.92) and 70.0 (50.0–85.0), respectively. The highest and lowest mean (standard deviation) treatment satisfaction scores were for the convenience and safety satisfaction dimensions: 87.7 (17.9) and 53.1 (33.5), respectively. Unemployment, older age, previous hospital admission, non-adherence to lifestyle modification, and presence of three or more cardiovascular disease factors were significantly negatively associated with HRQoL. Conclusions Overall, the study found that cardiovascular disease had a profound negative effect on HRQoL and patient treatment satisfaction. We suggest that interventions to enhance HRQoL and treatment satisfactions should focus on modifiable associated factors including lifestyle changes and controlling disease progression.
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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 | 0.002 |
| 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 | 0.001 | 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".