General and disease-specific pain trajectories as predictors of social and political outcomes in arthritis and cancer View Full Text


Ontology type: schema:ScholarlyArticle      Open Access: True


Article Info

DATE

2018-12

AUTHORS

Richard J. E. James, David A. Walsh, Eamonn Ferguson

ABSTRACT

BACKGROUND: While the heterogeniety of pain progression has been studied in chronic diseases, the extent to which patterns of pain progression among people in general as well as across different diseases affect social, civic and political engagement is unclear. We explore these issues for the first time. METHODS: Using data from the English Longitudinal Study of Ageing, latent class growth models were used to estimate trajectories of self-reported pain in the entire cohort, and within subsamples reporting diagnoses of arthritis and cancer. These were compared at baseline on physical health (e.g. body mass index, smoking) and over time on social, civic and political engagement. RESULTS: Very similar four-trajectory models fit the whole sample and arthritis subsamples, whereas a three-trajectory model fit the cancer subsample. All samples had a modal group experiencing minimal chronic pain and a group with high chronic pain that showed slight regression (more pronounced in cancer). Biometric indices were more predictive of the most painful trajectory in arthritis than cancer. In both samples the group experiencing the most pain at baseline reported impairments in social, civic and political engagement. CONCLUSIONS: The impact of pain differs between individuals and between diseases. Indicators of physical and psychological health differently predicted membership of the trajectories most affected by pain. These trajectories were associated with differences in engagement with social and civic life, which in turn were associated with poorer health and well-being. More... »

PAGES

51

Identifiers

URI

http://scigraph.springernature.com/pub.10.1186/s12916-018-1031-9

DOI

http://dx.doi.org/10.1186/s12916-018-1031-9

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1103188887

PUBMED

https://www.ncbi.nlm.nih.gov/pubmed/29628016


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