Pain significantly restricts the quality of life and well-being of older adults. With our increasingly ageing population, it is important to examine whether differing classes of biopsychosocial risk factors can predict the development of pain in older adults. Latent class analysis provides a model-based approach to identifying underlying subgroups in a population, based on some measured characteristics. In this study, latent class analysis was used to identify biopsychosocial risk classes in people aged 50 years and older, from The Irish Longitudinal Study on Ageing, who reported not often being troubled by pain at wave 1 and completed the 2-year follow-up at wave 2 (n = 4458). Four classes were identified based on 11 potential risk factors at wave 1. These classes were characterised as “Low Risk,” “Physical Health Risk,” “Mental Health Risk,” and “High Risk.” The Low-Risk class accounted for over half the sample (51.2%), whereas the High-Risk class represented 7.8% of the sample. At follow-up (wave 2), 797 (17.9%) participants reported being troubled by pain. Associations between the biopsychosocial risk classes and developing pain were examined using logistic regression, adjusting for sociodemographic variables. The High-Risk class was more likely to develop pain compared with the Low-Risk class (adjusted OR = 3.16, 95% CI = 2.40-4.16). These results add to existing data in other populations supporting the role of a range of biopsychosocial risk factors that increase the risk of developing pain. These findings have important implications for the identification, and potential moderation, of these risk factors.
Differing classes of biopsychosocial risk factors were identified in older adults, using latent class analysis. These classes were associated with pain development in later life.
aDepartment of Mathematics and Statistics, University of Limerick, Limerick, Ireland
bSports Spine Centre, Aspetar Orthopaedic and Sports Medicine Hospital, Doha, Qatar
cSchool of Allied Health, University of Limerick, Limerick, Ireland
dHealth Research Institute, University of Limerick, Limerick, Ireland
eSydney School of Public Health, University of Sydney, Australia
fGraduate Entry Medical School, University of Limerick, Limerick, Ireland
Corresponding author. Address: A2015, Department of Mathematics and Statistics, University of Limerick, Castletroy, Limerick, Ireland. Tel.: +353-61-233736. E-mail address: firstname.lastname@example.org (A. O'Neill).
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Received November 15, 2017
Received in revised form April 05, 2018
Accepted April 16, 2018