Gender Differences in Disease Burden, Symptom Burden, and Quality of Life Among People Living With Heart Failure and Multimorbidity: Cross‐Sectional Study
Bibliographic record
Abstract
AIM: Heart failure is a leading cause of hospitalisation and often coexists with seven comorbid conditions on average. This study aimed to examine the gender differences in disease burden, symptom burden, and quality of life among older adults with heart failure and multimorbidity. DESIGN: Cross-sectional study. METHODS: This study utilised a baseline survey from an ongoing cohort study in 2022-2023. Adults aged ≥ 50 years with heart failure and more than one chronic condition were recruited from a university-affiliated hospital using an electronic patient portal. Disease burden was measured using a modified Disease Burden Impact Scale. The Edmonton Symptom Assessment Scale and EuroQoL-5D-5L assessed symptom burden and quality of life. Gender differences in baseline outcomes were examined using Pearson's Chi-square tests, Welch's t-tests, and multiple linear regressions. RESULTS: Among 353 participants who completed the baseline survey, the mean (±SD) age was 70 (±9.5) years, and 50.1% were women (mean age: 67 ± 9 vs. men: 72 ± 10). In adjusted models, women had 4.9 points higher disease burden (p = 0.003) and reported higher symptom scores of pain (p = 0.018), tiredness (p = 0.021), nausea (p = 0.007), and loss of appetite compared to men (p = 0.036). Women had significantly more moderate/severe problems in usual activities and pain/discomfort and 0.07 points lower EuroQoL index than men (p = 0.010). CONCLUSIONS: There were gender differences in disease/symptom burdens and quality of life. Women living with heart failure and multimorbidity had higher burdens but lower quality of life. IMPACT: Identifying gender differences among people with heart failure and multimorbidity can be the first step to explaining health disparities. Research should take more inclusive and equitable approaches to address these differences. Healthcare providers, including nurses, should implement targeted strategies for effective multimorbidity management by considering these differences and disparities in clinical settings. REPORTING METHOD: STROBE checklist, cross-sectional. PATIENT OR PUBLIC CONTRIBUTION: No patient or public contribution.
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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 | 0.002 | 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".