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Open Access 01-12-2021 | Affective Disorder | Research article

Prevalence and patterns of multimorbidity in Australian baby boomers: the Busselton healthy ageing study

Authors: Michael L. Hunter, Matthew W. Knuiman, Bill (A.W.) Musk, Jennie Hui, Kevin Murray, John P. Beilby, David R. Hillman, Joseph Hung, Robert U. Newton, Romola S. Bucks, Leon Straker, John P. Walsh, Kun Zhu, David G. Bruce, Robert H. Eikelboom, Timothy M. E. Davis, David A. Mackey, Alan L. James

Published in: BMC Public Health | Issue 1/2021

Abstract

Background and objective

Chronic medical conditions accumulate within individuals with age. However, knowledge concerning the trends, patterns and determinants of multimorbidity remains limited. This study assessed the prevalence and patterns of multimorbidity using extensive individual phenotyping in a general population of Australian middle-aged adults.

Methods

Participants (n = 5029, 55% female), born between 1946 and 1964 and attending the cross-sectional phase of the Busselton Healthy Ageing Study (BHAS) between 2010 and 2015, were studied. Prevalence of 21 chronic conditions was estimated using clinical measurement, validated instrument scores and/or self-reported doctor-diagnosis. Non-random patterns of multimorbidity were explored using observed/expected (O/E) prevalence ratios and latent class analysis (LCA). Variables associated with numbers of conditions and class of multimorbidity were investigated.

Results

The individual prevalence of 21 chronic conditions ranged from 2 to 54% and multimorbidity was common with 73% of the cohort having 2 or more chronic conditions. (mean ± SD 2.75 ± 1.84, median = 2.00, range 0–13). The prevalence of multimorbidity increased with age, obesity, physical inactivity, tobacco smoking and family history of asthma, diabetes, myocardial infarct or cancer. There were 13 pairs and 27 triplets of conditions identified with a prevalence > 1.5% and O/E > 1.5. Of the triplets, arthritis (> 50%), bowel disease (> 33%) and depression-anxiety (> 33%) were observed most commonly. LCA modelling identified 4 statistically and clinically distinct classes of multimorbidity labelled as: 1) “Healthy” (70%) with average of 1.95 conditions; 2) “Respiratory and Atopy” (11%, 3.65 conditions); 3) “Non-cardiometabolic” (14%, 4.77 conditions), and 4) “Cardiometabolic” (5%, 6.32 conditions). Predictors of multimorbidity class membership differed between classes and differed from predictors of number of co-occurring conditions.

Conclusion

Multimorbidity is common among middle-aged adults from a general population. Some conditions associated with ageing such as arthritis, bowel disease and depression-anxiety co-occur in clinically distinct patterns and at higher prevalence than expected by chance. These findings may inform further studies into shared biological and environmental causes of co-occurring conditions of ageing. Recognition of distinct patterns of multimorbidity may aid in a holistic approach to care management in individuals presenting with multiple chronic conditions, while also guiding health resource allocation in ageing populations.

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Title: Prevalence and patterns of multimorbidity in Australian baby boomers: the Busselton healthy ageing study
Authors: Michael L. Hunter
Matthew W. Knuiman
Bill (A.W.) Musk
Jennie Hui
Kevin Murray
John P. Beilby
David R. Hillman
Joseph Hung
Robert U. Newton
Romola S. Bucks
Leon Straker
John P. Walsh
Kun Zhu
David G. Bruce
Robert H. Eikelboom
Timothy M. E. Davis
David A. Mackey
Alan L. James
Publication date: 01-12-2021
Publisher: BioMed Central
Keywords: Affective Disorder
Bronchial Asthma
Arthritis
Affective Disorder
Published in: BMC Public Health / Issue 1/2021
Electronic ISSN: 1471-2458
DOI: https://doi.org/10.1186/s12889-021-11578-y

At a glance: The STEP trials

Springer Medicine

Prevalence and patterns of multimorbidity in Australian baby boomers: the Busselton healthy ageing study

Abstract

Background and objective

Methods

Results

Conclusion

At a glance: The STEP trials

Springer Medicine

Abstract

Background and objective

Methods

Results

Conclusion

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