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

Abstract

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.

Self-monitoring of Blood Pressure in Patients With Hypertension-Related Multi-morbidity: Systematic Review and Individual Patient Data Meta-analysis

“…[1][2][3][4] Increasing multimorbidity is associated with reduced quality of life. 5,6 Due to the complexities of studying individuals with multiple conditions, few studies have examined interventions specifically designed to improve outcomes in patients with multi-morbidity. 7 Optimal management of blood pressure (BP) represents the most effective way to prevent stroke and cardiovascular disease.…”

Age-specific trends in health-related quality of life among US adults: findings from National Health and Nutrition Examination Survey, 2001–2016

“…Health-related quality of life (HRQoL) encompasses one's physical health, mental health, and social well-being [1]. Better HRQoL is associated with lower utilization of outpatient services and hospitalization rates [2,3], and is an important population health indicator due to its relationship with multimorbidity in adults [4] and mortality in older adults [5,6].…”

Effects of chronic diseases on health‐related quality of life and self‐rated health among three adult age groups

“…Chronic diseases might cause various negative consequences at the individual, health‐care system, and societal level (Marengoni et al, ; Picco et al, ; Wang, Palmer, Cocker, & Sanderson, ). As the ultimate goal of health care for patients with chronic diseases is to improve their health‐related quality of life (HRQoL) and general health status, it is necessary to examine the effect of individual chronic diseases on HRQoL and general health status.…”

“…Chronic diseases might cause various negative consequences at the individual, health-care system, and societal level (Marengoni et al, 2011;Picco et al, 2016;Wang, Palmer, Cocker, & Sanderson, 2017).…”