Abstract
While pain is very common in older adults, the associated impact on daily life, including usage of medication and healthcare, varies considerably and often pain remains inadequately treated. It is not clear what is associated with this variation. Latent class analysis (LCA) is a model-based approach to identifying underlying subgroups in a population. In this study LCA was used to examine biopsychosocial risk classes of adults aged 50years and older, who were often troubled by pain, from The Irish Longitudinal Study on Ageing (TILDA), (n=2,896), and the associations with future medication and healthcare use. Four biopsychosocial risk classes (Low Biopsychosocial Risk, Physical Health Risk, Mental Health Risk, High Biopsychosocial Risk) were identified, with the 'High Biopsychosocial Risk' class accounting for 24% of older adults with pain. This class were much more likely to use medication and healthcare services when followed up across three waves of the TILDA study. In contrast, the Physical Health Risk and the Mental Health Risk classes reported lower usage of medication and healthcare at waves 2 and 3. Amongst the higher risk classes of older adults who are troubled by pain, there is considerable consumption of medication and healthcare services evident. Given our ageing population and significant number of adults in this high risk class, there is a need to optimize current pain management approaches among older adults. Intensive non-pharmacological approaches to pain management in older adults, tailored to individual biopsychosocial risk indicators for each individual class, may be worth exploring. While pain is very common in older adults, the usage of medication and healthcare varies considerably and often pain remains inadequately treated. Given our ageing population and the significant number of older adults reporting high biopsychosocial risk (24%), there is a need to optimize current pain management approaches. Intensive non-pharmacological approaches to pain management in older adults, tailored to individual biopsychosocial risk indicators for each individual class, may be worth exploring.
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