Long-term changes in health-related quality of life among Australian adults with psychological distress: a 16-year perspective
Bibliographic record
Abstract
BACKGROUND: Human and economic burden of psychological distress (PD) are well known. However, how PD and its various classifications impact health-related quality of life (HRQoL) and its various domains such as physical function (PF), role physical (RP), mental health (MH) and role emotional (RE) is poorly understood. OBJECTIVES: To measure the longitudinal decline in HRQoL related to PD and its various dimensions, and to examine sociodemographic factors associated with HRQoL. METHODS: Data on SF-36 profiles, Kessler Psychological Distress (K10) scale scores and other sociodemographic characteristics of individuals with PD aged ≥ 15 were sourced from 8 waves (7, 11, 13, 15, 17, 19, 21, n = 41,545) of the Household, Income, and Labour Dynamics in Australia (HILDA) survey spanning the years 2007 to 2021. PD severity was classified as no (K10 score: 10-19), mild (K10: 20-24), moderate (K10: 25-29) and severe PD (K10: 30-50). HRQoL was measured as health state utilities (HSUs) via the SF-6D algorithm aligned with Australian population norms. Five separate linear mixed models were estimated, each with HSUs, PF, RP, MH, or RE as the outcome variable. RESULTS: The mean age of the respondents was 44.88 years (53% female), and the most common age group was 24-44 years (34%). HRQoL declined over time; however, the time coefficients became insignificant after accounting for other sources of variation, including age, sex, English proficiency, Indigenous status, region of residence, marital status, education, employment, physical activity, body mass index (BMI), club membership, smoking, drinking, and income. The adverse impact of PD on HSUs intensified with increasing severity, ranging from - 0.086 in mild PD to -0.177 in severe PD. HRQoL differed across age (from - 0.016 in 25-44 years to -0.059 in 65 + years) and sex groups (disutility difference - 0.012). Different domains of HRQoL were affected by PD disproportionally with the highest and lowest effect recorded in the domain of MH (-0.364) and PF (-0.114), respectively in the category of severe PD. Factors such as education, physical activity, being employed, drinking, and income were positively associated with PD while BMI level and smoking negatively affected HRQoL. CONCLUSIONS: Effective HRQoL management in individuals with PD requires tailored interventions that consider disease severity, age, and sex. The insights on the association of time and other sociodemographic determinants with HRQoL have potential applications in PD-related cost-effective analyses of health interventions.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| 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".