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Enregistrement W4393182200 · doi:10.1007/s40258-024-00879-z

The Hidden Toll of Psychological Distress in Australian Adults and Its Impact on Health-Related Quality of Life Measured as Health State Utilities

2024· article· en· W4393182200 sur OpenAlexaff
Muhammad Iftikhar ul Husnain, Mohammad Hajizadeh, Hasnat Ahmad, Rasheda Khanam

Notice bibliographique

RevueApplied Health Economics and Health Policy · 2024
Typearticle
Langueen
DomainePsychology
ThématiqueMental Health Treatment and Access
Établissements canadiensDalhousie University
Organismes subventionnairesUniversity of Southern Queensland
Mots-clésMental healthMarital statusQuality of life (healthcare)Public healthPopulationDemographyHealth economicsPopulation healthConfoundingDistressPsychological interventionMedicineGerontologyPsychologyEnvironmental healthClinical psychologyPsychiatry

Résumé

récupéré en direct d'OpenAlex

BACKGROUND: Psychological distress (PD) is a major health problem that affects all aspects of health-related quality of life including physical, mental and social health, leading to a substantial human and economic burden. Studies have revealed a concerning rise in the prevalence of PD and various mental health conditions among Australians, particularly in female individuals. There is a scarcity of studies that estimate health state utilities (HSUs), which reflect the overall health-related quality of life in individuals with PD. No such studies have been conducted in Australia thus far. OBJECTIVE: We aimed to evaluate the age-specific, sex-specific and PD category-specific HSUs (disutilities) in Australian adults with PD to inform healthcare decision making in the management of PD. METHODS: Data on age, sex, SF-36/SF6D responses, Kessler psychological distress (K10) scale scores and other characteristics of N = 15,139 participants (n = 8149 female individuals) aged >15 years were derived from the latest wave (21) of the nationally representative Household, Income and Labor Dynamics in Australia survey. Participants were grouped into the severity categories of no (K10 score: 10-19), mild (K10: 20-24), moderate (K10: 25-29) and severe PD (K10: 30-50). Both crude and adjusted HSUs were calculated from participants' SF-36 profiles, considering potential confounders such as smoking, marital status, remoteness, education and income levels. The calculations were based on the SF-6D algorithm and aligned with Australian population norms. Additionally, the HSUs were stratified by age, sex and PD categories. Disutilities of PD, representing the mean difference between HSUs of people with PD and those without, were also calculated for each group. RESULTS: The average age of individuals was 46.130 years (46% male), and 31% experienced PD in the last 4 weeks. Overall, individuals with PD had significantly lower mean HSUs than those likely to be no PD, 0.637 (95% confidence interval [CI] 0.636, 0.640) vs 0.776 (95% CI 0.775, 0.777) i.e. disutility: -0.139 [95% CI -0.139, -0.138]). Mean disutilities of -0.108 (95% CI -0.110, -0.104), -0.140 (95% CI -0.142, -0.138), and -0.188 (95% CI -0.190, -0.187) were observed for mild PD, moderate PD and severe PD, respectively. Disutilities of PD also differed by age and sex groups. For instance, female individuals had up to 0.049 points lower mean HSUs than male individuals across the three classifications of PD. There was a clear decline in health-related quality of life with increasing age, demonstrated by lower mean HSUs in older population age groups, that ranged from 0.818 (95% CI 0.817, 0.818) for the 15-24 years age group with no PD to 0.496 (95% CI 0.491, 0.500) for the 65+ years age group with severe PD). Across all ages and genders, respondents were more likely to report issues in certain dimensions, notably vitality, and these responses did not uniformly align with ageing. CONCLUSIONS: The burden of PD in Australia is substantial, with a significant impact on female individuals and older individuals. Implementing age-specific and sex-specific healthcare interventions to address PD among Australian adults may greatly alleviate this burden. The PD state-specific HSUs calculated in our study can serve as valuable inputs for future health economic evaluations of PD in Australia and similar populations.

Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.

Comment cette classification a été obtenuedéplier

Prédiction distillée sur la base complète

Imitation des enseignants

Ni 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.

score de la tête « metaresearch » (Codex)0,004
score de la tête « metaresearch » (Gemma)0,000
Version: codex-gemma-dda1882f352aStatut de validation: machine_predicted_unvalidated
Catégories candidatesMéta-épidémiologie (sens strict)
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Observationnel · Signal consensuel: aucune
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,946
Score d'incertitude au seuil1,000

Scores Codex et Gemma par catégorie

CatégorieCodexGemma
Métarecherche0,0040,000
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0010,000
Bibliométrie0,0000,000
Études des sciences et des technologies0,0010,000
Communication savante0,0000,000
Science ouverte0,0000,000
Intégrité de la recherche0,0000,001
Charge utile insuffisante (le modèle a refusé de juger)0,0000,000

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.

Tête enseignante Opus0,150
Tête enseignante GPT0,499
Écart entre enseignants0,349 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_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 découle

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.

Devis d'étudeObservationnel
Domainenon disponible
GenreEmpirique

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations2
Publié2024
Routes d'admission1
Résumé présentoui

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