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10-year multimorbidity patterns among people with and without rheumatic and musculoskeletal diseases: an observational cohort study using linked electronic health records from Wales, UK

Farideh Jalali-najafabadi Orcid Logo, Rowena Bailey, Jane Lyons, Ashley Akbari Orcid Logo, Thamer Ba Dhafari, Narges Azadbakht, Jim Rafferty Orcid Logo, Alan Watkins Orcid Logo, Glen Philip Martin Orcid Logo, John Bowes, Ronan Lyons Orcid Logo, Anne Barton Orcid Logo, Niels Peek

BMJ Open, Volume: 14, Issue: 6, Start page: e079169

Swansea University Authors: Rowena Bailey, Jane Lyons, Ashley Akbari Orcid Logo, Jim Rafferty Orcid Logo, Alan Watkins Orcid Logo, Ronan Lyons Orcid Logo

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DOI (Published version): 10.1136/bmjopen-2023-079169

Abstract

Objectives To compare the patterns of multimorbidity between people with and without rheumatic and musculoskeletal diseases (RMDs) and to describe how these patterns change by age and sex over time, between 2010 and 2019.Participants 103 426 people with RMDs and 2.9 million comparators registered in...

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Published in: BMJ Open
ISSN: 2044-6055 2044-6055
Published: BMJ 2024
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URI: https://cronfa.swan.ac.uk/Record/cronfa66849
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Abstract: Objectives To compare the patterns of multimorbidity between people with and without rheumatic and musculoskeletal diseases (RMDs) and to describe how these patterns change by age and sex over time, between 2010 and 2019.Participants 103 426 people with RMDs and 2.9 million comparators registered in 395 Wales general practices (GPs). Each patient with an RMD aged 0–100 years between January 2010 and December 2019 registered in Clinical Practice Research Welsh practices was matched with up to five comparators without an RMD, based on age, gender and GP code.Primary outcome measures The prevalence of 29 Elixhauser-defined comorbidities in people with RMDs and comparators categorised by age, gender and GP practices. Conditional logistic regression models were fitted to calculate differences (OR, 95% CI) in associations with comorbidities between cohorts.Results The most prevalent comorbidities were cardiovascular risk factors, hypertension and diabetes. Having an RMD diagnosis was associated with a significantly higher odds for many conditions including deficiency anaemia (OR 1.39, 95% CI (1.32 to 1.46)), hypothyroidism (OR 1.34, 95% CI (1.19 to 1.50)), pulmonary circulation disorders (OR 1.39, 95% CI 1.12 to 1.73) diabetes (OR 1.17, 95% CI (1.11 to 1.23)) and fluid and electrolyte disorders (OR 1.27, 95% CI (1.17 to 1.38)). RMDs have a higher proportion of multimorbidity (two or more conditions in addition to the RMD) compared with non-RMD group (81% and 73%, respectively in 2019) and the mean number of comorbidities was higher in women from the age of 25 and 50 in men than in non-RMDs group.Conclusion People with RMDs are approximately 1.5 times as likely to have multimorbidity as the general population and provide a high-risk group for targeted intervention studies. The individuals with RMDs experience a greater load of coexisting health conditions, which tend to manifest at earlier ages. This phenomenon is particularly pronounced among women. Additionally, there is an under-reporting of comorbidities in individuals with RMDs.
College: Faculty of Medicine, Health and Life Sciences
Funders: The datasets used in this study were supported by grants cofunded by Medical Research Council (MRC) and National Institute for Health Research (NIHR) (grant number: MR/S027750/1); and supported by Health Data Research UK (grant number: HDR- 9006), which receives its funding from the UK Medical Research Council, Engineering and Physical Sciences Research Council, Economic and Social Research Council, Department of Health and Social Care (England), Chief Scientist Office of the Scottish Government Health and Social Care Directorates, Health and Social Care Research and Development Division (Welsh Government), Public Health Agency (Northern Ireland), British Heart Foundation and the Wellcome Trust. This work was supported by the ADR Wales programme of work. The ADR Wales programme of work is aligned to the priority themes as identified in the Welsh Government’s national strategy: Prosperity for All. ADR Wales brings together data science experts at Swansea University Medical School, staff from the Wales Institute of Social and Economic Research, Data and Methods (WISERD) at Cardiff University and specialist teams within the Welsh Government to develop new evidence which supports Prosperity for All by using the SAIL Databank at Swansea University, to link and analyse anonymised data. ADR Wales is part of the Economic and Social Research Council (part of UK Research and Innovation) funded ADR UK (grant ES/S007393/1). This research was partially funded by the NIHR’s Manchester Biomedical Research Centre. The views expressed are those of the author(s) and not necessarily those of the NHS, the National Institute for Health research or the Department of Health and Social Care. The work is supported by the Centre for Genetics and Genomics Versus Arthritis (UK grant number 21754). AB is an NIHR Senior Investigator. FJ- n’s research is supported by an MRC/University of Manchester Skills Development Fellowship (grant number MR/R016615).
Issue: 6
Start Page: e079169

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