• Libby Holden Libby Holden
  • Paul Scuffham Paul Scuffham
  • Michael Hilton Michael Hilton
  • Alexander Muspratt Alexander Muspratt
  • Shu Kay Angus Ng Shu Kay Angus Ng
  • Harvey Whiteford Harvey Whiteford

Background Multimorbidity is becoming more prevalent. Previously-used methods of assessing multimorbidity relied on counting the number of health conditions, often in relation to an index condition (comorbidity), or grouping conditions based on body or organ systems. Recent refinements in statistical approaches have resulted in improved methods to capture patterns of multimorbidity, allowing for the identification of nonrandomly occurring clusters of multimorbid health conditions. This paper aims to identify nonrandom clusters of multimorbidity. Methods The Australian Work Outcomes Research Cost-benefit (WORC) study cross-sectiol screening dataset (approximately 78,000 working Australians) was used to explore patterns of multimorbidity. Exploratory factor alysis was used to identify nonrandomly occurring clusters of multimorbid health conditions. Results Six clinically-meaningful groups of multimorbid health conditions were identified. These were: factor 1: arthritis, osteoporosis, other chronic pain, bladder problems, and irritable bowel; factor 2: asthma, chronic obstructive pulmory disease, and allergies; factor 3: back/neck pain, migraine, other chronic pain, and arthritis; factor 4: high blood pressure, high cholesterol, obesity, diabetes, and fatigue; factor 5: cardiovascular disease, diabetes, fatigue, high blood pressure, high cholesterol, and arthritis; and factor 6: irritable bowel, ulcer, heartburn, and other chronic pain. These clusters do not fall neatly into organ or body systems, and some conditions appear in more than one cluster. Conclusions Considerably more research is needed with large population-based datasets and a comprehensive set of reliable health diagnoses to better understand the complex ture and composition of multimorbid health conditions.