Annabelle Rose Machin
Investigating comorbid mood disorders in people with inflammatory rheumatological conditions: a mixed methods study
Machin, Annabelle Rose
Authors
Contributors
Carolyn Chew-Graham
Supervisor
Samantha Hider
Supervisor
Abstract
Background
Mood problems in people with rheumatoid arthritis (RA) are under-recognised and treated, contributing to increased mortality. Comorbid depression is associated with increased disease activity and reduced quality of life (QoL), though the impact of anxiety is unclear. Understanding patients' preferences regarding case-finding for mood problems and determining the impact of anxiety in RA, could support the development of an intervention to improve the management of comorbid mood problems. There is a lack of literature reporting the prevalence of mood problems in other inflammatory rheumatological conditions (IRCs).
Methods
Using mixed methods, I have conducted a qualitative study, to explore RA patients' perspectives of comorbid mood problems and a systematic review, to determine the impact of anxiety in RA. Through a cohort study, using Read codes from primary care data, I have established the incidence and prevalence of mood problems in different IRCs. I have also analysed patients' responses to case-finding questions for mood problems within questionnaires, to determine the proportion with IRCs who self-report mood symptoms.
Results
People with RA feel able to disclose mood symptoms within a nurse-led review. Anxiety is associated with worse QoL and increased disease activity in RA. Self-reported symptoms of mood problems are common in different IRCs, though they are less frequently recorded in primary care records, suggesting potential under-recognition of mood problems in people with IRCs.
Conclusions
Comorbid mood problems in people with IRCs are common, yet under-recognised, and can negatively impact on outcomes, supporting the requirement for case-finding within a nurse-led review.
Thesis Type | Thesis |
---|---|
Publicly Available Date | May 26, 2023 |
Award Date | 2020-03 |
Files
MachinPhD2020.pdf
(11.9 Mb)
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