Population norms for the AQoL derived from the 2007 Australian National Survey of Mental Health and Wellbeing

Hawthorne, Graeme; Korn, Sam; Richardson, Jeffrey Ralph James

doi:10.1111/1753-6405.12004

Cited by 113 publications

(138 citation statements)

References 30 publications

Supporting

Mentioning

123

Contrasting

Order By: Relevance

“…Then, five new variables, four dimension scores and one overall instrument score, which ranged from −0.04 to 1, were created. A score of 1.00 indicated the best quality of life equal to perfect health, and 0.00 indicated quality of life equal to death, and negative values (0 to −0.04) indicated quality of life worse than death [23]. …”

Section: Methodsmentioning

confidence: 99%

Multimorbidity and health-related quality of life (HRQoL) in a nationally representative population sample: implications of count versus cluster method for defining multimorbidity on HRQoL

Wang

Palmer

Cocker

et al. 2017

Health Qual Life Outcomes

View full text Add to dashboard Cite

BackgroundNo universally accepted definition of multimorbidity (MM) exists, and implications of different definitions have not been explored. This study examined the performance of the count and cluster definitions of multimorbidity on the sociodemographic profile and health-related quality of life (HRQoL) in a general population.MethodsData were derived from the nationally representative 2007 Australian National Survey of Mental Health and Wellbeing (n = 8841). The HRQoL scores were measured using the Assessment of Quality of Life (AQoL-4D) instrument. The simple count (2+ & 3+ conditions) and hierarchical cluster methods were used to define/identify clusters of multimorbidity. Linear regression was used to assess the associations between HRQoL and multimorbidity as defined by the different methods.ResultsThe assessment of multimorbidity, which was defined using the count method, resulting in the prevalence of 26% (MM2+) and 10.1% (MM3+). Statistically significant clusters identified through hierarchical cluster analysis included heart or circulatory conditions (CVD)/arthritis (cluster-1, 9%) and major depressive disorder (MDD)/anxiety (cluster-2, 4%). A sensitivity analysis suggested that the stability of the clusters resulted from hierarchical clustering. The sociodemographic profiles were similar between MM2+, MM3+ and cluster-1, but were different from cluster-2. HRQoL was negatively associated with MM2+ (β: −0.18, SE: −0.01, p < 0.001), MM3+ (β: −0.23, SE: −0.02, p < 0.001), cluster-1 (β: −0.10, SE: 0.01, p < 0.001) and cluster-2 (β: −0.36, SE: 0.01, p < 0.001).ConclusionsOur findings confirm the existence of an inverse relationship between multimorbidity and HRQoL in the Australian population and indicate that the hierarchical clustering approach is validated when the outcome of interest is HRQoL from this head-to-head comparison. Moreover, a simple count fails to identify if there are specific conditions of interest that are driving poorer HRQoL. Researchers should exercise caution when selecting a definition of multimorbidity because it may significantly influence the study outcomes.

show abstract

Section: Methodsmentioning

confidence: 99%

Multimorbidity and health-related quality of life (HRQoL) in a nationally representative population sample: implications of count versus cluster method for defining multimorbidity on HRQoL

Wang

Palmer

Cocker

et al. 2017

Health Qual Life Outcomes

View full text Add to dashboard Cite

show abstract

“…The AQOL is designed to be self-administered taking an average of five to seven minutes to complete. Population norms are available, which allow the results to be interpreted relative to the age-matched average Australian population [3,4]. The published minimum important difference (MID) is 0.06 utilities [3].…”

Section: Methodsmentioning

confidence: 99%

“…The Assessment of Quality of Life (AQoL) [2] is one generic instrument that has been developed for and validated in the Australian population [3,4] and has been demonstrated to be a sensitive measure of HRQoL in community dwelling older adults [5]. …”

Section: Introductionmentioning

confidence: 99%

Relationship between health-related quality of life, and acute care re-admissions and survival in older adults with chronic illness

Hutchinson

Rasekaba

Graco

et al. 2013

Health and Quality of Life Outcomes

Self Cite

View full text Add to dashboard Cite

BackgroundAustralia’s ageing population means that there is increasing emphasis on developing innovative models of health care delivery for older adults. The assessment of the most appropriate mix of services and measurement of their impact on patient outcomes is challenging. The aim of this evaluation was to describe the health related quality of life (HRQoL) of older adults with complex needs and to explore the relationship between HRQoL, readmission to acute care and survival.MethodsThe study was conducted in metropolitan Melbourne, Australia; participants were recruited from a cohort of older adults enrolled in a multidisciplinary case management service. HRQoL was measured at enrolment into the case-management service using The Assessment of Quality of Life (AQoL) instrument. In 2007–2009, participating service clinicians approached their patients and asked for consent to study participation. Administrative databases were used to obtain data on comorbidities (Charlson Comorbidity Index) at enrolment, and follow-up data on acute care readmissions over 12 months and five year mortality. HRQoL was compared to aged-matched norms using Welch’s approximate t-tests. Univariate and multivariate logistic regression models were used to explore which patient factors were predictive of readmissions and mortality.ResultsThere were 210 study participants, mean age 78 years, 67% were female. Participants reported significantly worse HRQoL than age-matched population norms with a mean AQOL of 0.30 (SD 0.27). Seventy-eight (38%) participants were readmitted over 12-months and 5-year mortality was 65 (31%). Multivariate regression found that an AQOL utility score <0.37 (OR 1.95, 95%CI, 1.03 – 3.70), and a Charlson Comorbidity Index ≥6 (OR 4.89, 95%CI 2.37 – 10.09) were predictive of readmission. Multivariate analysis demonstrated that age ≥80 years (OR 7.15, 95%CI, 1.83 – 28.02), and Charlson Comorbidity Index ≥6 (OR 6.00, 95%CI, 2.82 – 12.79) were predictive of death.ConclusionThis study confirms that the AQoL instrument is a robust measure of HRQoL in older community-dwelling adults with chronic illness. Lower self-reported HRQoL was associated with an increased risk of readmission independently of comorbidity and kind of service provided, but was not an independent predictor of five-year mortality.

show abstract

“…The AQoL utility score was significantly lower than the published population norms (Table 3). 11 Functional outcomes of sleep questionnaire The FOSQ was completed by 162 participants. Our tetraplegic population scored significantly worse than the normal sample in the activity level and intimate and sexual activity domains, but there was no difference in the total score and other three domains (Table 3).…”

Section: Assessment Of Quality Of Lifementioning

confidence: 99%

Subjective sleep disturbances and quality of life in chronic tetraplegia

et al. 2015

View full text Add to dashboard Cite

Study design: This is a cross-sectional survey. Objectives: The objective of this study was to evaluate the subjective sleep disturbances and quality of life in chronic tetraplegia. Setting: This study was conducted in a community sample from Victoria, Australia. Methods: People with tetraplegia were mailed a survey battery including the following: demographic questions; Karolinska Sleepiness Scale (KSS); Basic Nordic Sleepiness Questionnaire; Functional Outcomes of Sleep Questionnaire (FOSQ); Multivariate Apnoea Prediction Index and Assessment of Quality of Life (AQoL) Questionnaire. Scores were compared with the best available normative data. Results: A total of 163 of 424 (38%) surveys were returned (77% male; 39% sensory and motor complete; mean age±s.d. = 46±14 years; mean years since injury = 11±8 years). The AQoL health utility score (0.31±0.29) was significantly lower than published population norms. FOSQ total (17.55±2.57) and KSS (3.93±2.27) scores were no different from the best available population data. People with tetraplegia reported worse sleep habits, symptoms and quality than a normal population, as indicated on 17 of 21 questions on the Basic Nordic Sleep Questionnaire. Multivariate analysis found that greater injury severity (coefficient (95% CI) = 0.14 (0.10, 0.18)), increasing age (−0.004 (−0.008, − 0.001)) and worse sleep symptoms (−0.005 (−0.009, − 0.0003)) were all significantly associated with reduced quality of life. Conclusion: People with chronic tetraplegia experience more subjective sleep problems and worse quality of life than their able-bodied counterparts. Quality of life is related to injury severity, age and sleep symptoms. Treating the sleep disorders experienced by people living with tetraplegia has the potential to improve their health and well-being.

show abstract

Population norms for the AQoL derived from the 2007 Australian National Survey of Mental Health and Wellbeing

Cited by 113 publications

References 30 publications

Multimorbidity and health-related quality of life (HRQoL) in a nationally representative population sample: implications of count versus cluster method for defining multimorbidity on HRQoL

Multimorbidity and health-related quality of life (HRQoL) in a nationally representative population sample: implications of count versus cluster method for defining multimorbidity on HRQoL

Relationship between health-related quality of life, and acute care re-admissions and survival in older adults with chronic illness

Subjective sleep disturbances and quality of life in chronic tetraplegia

Contact Info

Product

Resources

About