Background:Multimorbidity (≥2 chronic conditions) escalates the risk of adverse health outcomes. However, its burden in people with osteoarthritis (OA) remains largely unknown.Objectives:To identify the clusters of patients with multimorbidity and associated factors in OA and non-OA populations and to estimate the risk of developing multimorbidity clusters after the index date (after diagnosis).Methods:The study used the Clinical Practice Research Datalink – a primary care database from the UK. Firstly, age, sex and practice matched OA and non-OA people aged 20+ were identified to explore patterns and associations of clusters of multimorbidity within each group. Non-OA controls were assigned with same index date as that of matched OA cases. Secondly, multimorbidity trajectories for 20 years after the index date were examined in people without any comorbidities at baseline in both OA and non-OA groups. Latent class analysis was used to identify clusters and latent class growth modelling was used for cluster trajectories. The associations between clusters and age, sex, body mass index (BMI), alcohol use, smoking habits at baseline were quantified through multinomial logistic regression.Results:In total, 47 long-term conditions were studied in 443,822 people (OA- 221922; non-OA- 221900), with a mean age of 62 years (standard deviation ± 13 years), and 58% being women. The prevalence of multimorbidity was 76.6% and 68.9% in the OA and non-OA groups, respectively. In the OA group five clusters were identified including relatively healthy (18%), ‘cardiovascular (CVD) and musculoskeletal (MSK)’ (12.3%), metabolic syndrome (28.2%), ‘pain and psychological (9.1%), and ‘musculoskeletal’ (32.4%). The non-OA group had similar patterns except that the ‘pain+ psychological’ cluster was replaced by ‘thyroid and psychological’. (Figure 1) Among people with OA, ‘CVD+MSK’ and metabolic syndrome clusters were strongly associated with obesity with a relative risk ratio (RRR) of 2.04 (95% CI 1.95-2.13) and 2.10 (95% CI 2.03-2.17), respectively. Women had four times higher risk of being in the ‘pain+ psychological’ cluster than men when compared to the gender ratio in the healthy cluster, (RRR 4.28; 95% CI 4.09-4.48). In the non-OA group, obesity was significantly associated with all the clusters.Figure 1: Posterior probability distribution of chronic conditions across the clusters in Osteoarthritis (OA, n=221922) and Non-Osteoarthritis (Non-OA, n=221900) group. COPD- Chronic Obstructive Pulmonary Disease; CVD- Cardiovascular; MSK- MusculoskeletalOA (n=24139) and non-OA (n=24144) groups had five and four multimorbidity trajectory clusters, respectively. Among the OA population, 2.7% had rapid onset of multimorbidity, 9.5% had gradual onset and 11.6% had slow onset, whereas among the non-OA population, there was no rapid onset cluster, 4.6% had gradual onset and 14.3% had slow onset of multimorbidity. (Figure 2)Figure 2: Clusters of multimorbidity trajectories after index date in OA (n=24139) and Non-OA (n=24144)Conclusion:Distinct identified groups in OA and non-OA suggests further research for possible biological linkage within each cluster. The rapid onset of multimorbidity in OA should be considered for chronic disease management.Supported by:Acknowledgments:We would like to thank the University of Nottingham, UK, Beijing Joint Care Foundation, China and Foundation for Research in Rheumatology (FOREUM) for supporting the study.Disclosure of Interests:Subhashisa Swain: None declared, Carol Coupland: None declared, Victoria Strauss: None declared, Christian Mallen Grant/research support from: My department has received financial grants from BMS for a cardiology trial., Chang-Fu Kuo: None declared, Aliya Sarmanova: None declared, Michael Doherty Grant/research support from: AstraZeneca funded the Nottingham Sons of Gout study, Consultant of: Advisory borads on gout for Grunenthal and Mallinckrodt, Weiya Zhang Consultant of: Grunenthal for advice on gout management, Speakers bureau: Bioiberica as an invited speaker for EULAR 2016 satellite symposium
Managing city-scale slow-onset disasters: Learning from Cape Town's 2015–2018 drought disaster planning
