dc.contributor.author |
Hunter, Michael L.
|
|
dc.contributor.author |
Knuiman, Matthew W.
|
|
dc.contributor.author |
Musk, Bill (A.W.)
|
|
dc.contributor.author |
Hui, Jennie
|
|
dc.contributor.author |
Murray, Kevin
|
|
dc.contributor.author |
Beilby, John P.
|
|
dc.contributor.author |
Hillman, David R.
|
|
dc.contributor.author |
Hung, Joseph
|
|
dc.contributor.author |
Newton, Robert U.
|
|
dc.contributor.author |
Bucks, Romola S.
|
|
dc.contributor.author |
Straker, Leon
|
|
dc.contributor.author |
Walsh, John P.
|
|
dc.contributor.author |
Zhu, Kun
|
|
dc.contributor.author |
Bruce, David G.
|
|
dc.contributor.author |
Eikelboom, Robert H.
|
|
dc.contributor.author |
Davis, Timothy M.E.
|
|
dc.contributor.author |
Mackey, David A.
|
|
dc.contributor.author |
James, Alan L.
|
|
dc.date.accessioned |
2022-02-09T09:55:44Z |
|
dc.date.available |
2022-02-09T09:55:44Z |
|
dc.date.issued |
2021-08-11 |
|
dc.description |
Additional file 1. Supplementary File 1. BHAS Phase 1 Questionnaire
2010–2015. |
en_ZA |
dc.description |
Additional file 2. Supplementary File 2. Statistical Supplement. Latent
Class Analysis method. |
en_ZA |
dc.description |
Additional file 3. Supplementary Table S1. Mean, median and
maximum number of conditions by gender and demographic groups. |
en_ZA |
dc.description |
Additional file 4. Supplementary Table S2. Relationship between
number of conditions and participant characteristics. |
en_ZA |
dc.description |
Additional file 5 Supplementary Table S3. All triplets of conditions with
prevalence > 1.5% and observed/expected (O/E) ratio > 1.5 (and p value<
0.0001). |
en_ZA |
dc.description |
Additional file 6. Supplementary Table S4. Morbidity patterns and
number of conditions. Number of participants with each number of
conditions and percentage within each morbidity pattern. |
en_ZA |
dc.description.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 depressionanxiety
(> 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. |
en_ZA |
dc.description.department |
Speech-Language Pathology and Audiology |
en_ZA |
dc.description.librarian |
am2022 |
en_ZA |
dc.description.sponsorship |
Data collection and research assistant salary costs in the Busselton Healthy
Ageing Study were supported by grants from the Government of Western
Australia (Department of Jobs, Tourism, Science and Innovation), the
Commonwealth Government (Department of Health), the City of Busselton
and from private donations to the Busselton Population Medical Research
Institute. |
en_ZA |
dc.description.uri |
https://bmcpublichealth.biomedcentral.com |
en_ZA |
dc.identifier.citation |
Hunter, M.L., Knuiman, M.W., Musk, B.A.W. et al. 2021, 'Prevalence and patterns of multimorbidity
in Australian baby boomers : the Busselton
healthy ageing study', BMC Public Health, vol. 21, art. 1539, pp. 1-12. |
en_ZA |
dc.identifier.issn |
1471-2458 (online) |
|
dc.identifier.other |
10.1186/s12889-021-11578-y |
|
dc.identifier.uri |
http://hdl.handle.net/2263/83708 |
|
dc.language.iso |
en |
en_ZA |
dc.publisher |
BioMed Central |
en_ZA |
dc.rights |
© The Author(s). 2021 Open Access. This article is licensed under a Creative Commons Attribution 4.0 International License. |
en_ZA |
dc.subject |
Multimorbidity |
en_ZA |
dc.subject |
Ageing |
en_ZA |
dc.subject |
Co-morbidities |
en_ZA |
dc.subject |
Middle-aged |
en_ZA |
dc.subject |
Chronic disease |
en_ZA |
dc.subject |
Busselton healthy ageing study (BHAS) |
en_ZA |
dc.title |
Prevalence and patterns of multimorbidity in Australian baby boomers : the Busselton healthy ageing study |
en_ZA |
dc.type |
Article |
en_ZA |