Long-term changes in health-related quality of life among Australian adults with psychological distress: a 16-year perspective
Notice bibliographique
Résumé
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.
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Comment cette classification a été obtenuedéplier
Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,001 | 0,001 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,000 |
| Bibliométrie | 0,000 | 0,001 |
| Études des sciences et des technologies | 0,000 | 0,001 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».