Comorbidity and health-related quality of life among Australian adults with psychological distress: a detailed longitudinal study
Bibliographic record
Abstract
Abstract Aim This observational study explores how clinically relevant comorbidities affect health-related quality of life (HRQoL) in individuals with psychological distress (PD), focusing on the number, types, and patterns of comorbidities to improve patient care and outcomes. Subject and methods We utilized unit record data for individuals with PD from the Household, Income, and Labor Dynamics in Australia (HILDA) survey. HRQoL, expressed as the health state utility score (HSU), was assessed via the Short-Form Six-Dimension (SF-6D) health survey derived from the 36-Item Short Form Survey (SF-36) and calculated using an Australian scoring algorithm. Multimorbidity was defined as the presence of two or more chronic conditions. A linear mixed model (LMM) was used to assess the impact of comorbidities on HRQoL in individuals with PD, and additional LMM regressions were performed to examine differences based on comorbidity type and pattern. Results The final sample included 26,991 observations (mean age 40.75 years; 58.25% female). Among individuals with PD, 31.36% had at least one comorbidity, with cardiovascular disease the most common (14.09%). The most prevalent pattern was ‘cardiovascular + musculoskeletal’ (9.43%). Higher numbers of comorbidities significantly worsened HRQoL, from −0.01 (95% CI −0.03, 0.01) for one comorbidity to −0.06 (95% CI −0.08, −0.03) for five comorbidities. Cancer had the greatest impact (−0.02; 95% CI −0.03, −0.02), while patterns involving cardiovascular and cancer or metabolic with multiple conditions reduced HSU by −0.03 (95% CI −0.05, −0.01) to −0.05 (95% CI −0.08, −0.02). Conclusions The types and patterns of comorbidities significantly impact HRQoL, even with a consistent comorbidity count. Early detection and treatment of these conditions can enhance HRQoL in individuals with PD.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".