Multimorbidity patterns in low-middle and high income regions: a multiregion latent class analysis using ATHLOS harmonised cohorts
Objectives: Our aim was to determine clusters of non-communicable diseases (NCDs) in a very large, population-based sample of middle-aged and older adults from low- and middle-income (LMICs) and high-income (HICs) regions. Additionally, we explored the associations with several covariates. Design: The total sample was 72¿140 people aged 50+ years from three population-based studies (English Longitudinal Study of Ageing, Survey of Health, Ageing and Retirement in Europe Study and Study on Global Ageing and Adult Health) included in the Ageing Trajectories of Health: Longitudinal Opportunities and Synergies (ATHLOS) project and representing eight regions with LMICs and HICs. Variables were previously harmonised using an ex-post strategy. Eight NCDs were used in latent class analysis. Multinomial models were made to calculate associations with covariates. All the analyses were stratified by age (50–64 and 65+ years¿old). Results Three clusters were identified: 'cardio-metabolic' (8.93% in participants aged 50–64 years and 27.22% in those aged 65+ years), 'respiratory-mental-articular' (3.91% and 5.27%) and 'healthy' (87.16% and 67.51%). In the younger group, Russia presented the highest prevalence of the 'cardio-metabolic' group (18.8%) and England the 'respiratory-mental-articular' (5.1%). In the older group, Russia had the highest proportion of both classes (48.3% and 9%). Both the younger and older African participants presented the highest proportion of the 'healthy' class. Older age, being woman, widowed and with low levels of education and income were related to an increased risk of multimorbidity. Physical activity was a protective factor in both age groups and smoking a risk factor for the 'respiratory-mental-articular'. Conclusion: Multimorbidity is common worldwide, especially in HICs and Russia. Health policies in each country addressing coordination and support are needed to face the complexity of a pattern of growing multimorbidity. ; This work was supported by the 5-year Ageing Trajectories of Health: Longitudinal Opportunities and Synergies (ATHLOS) project and the Centro de Investigación Biomédica en Red de Salud Mental (CIBERSAM). The ATHLOS project has received funding from the European Union's Horizon 2020 research and innovation programme under grant agreement No 635 316. DF's work has been supported by grant RTI2018-100927-J-I00 Retos Investigación from Ministerio de Ciencia e Innovación (MCI), by Marsden grant E2987-3648 (Royal Society of New Zealand), and by grant 2017 SGR 622 (GRBIO) from the Departament d'Economia i Coneixement de la Generalitat de Catalunya (Spain). This work, grant number RTI2018-100927-J-I00, is supported by the Ministerio de Ciencia e Innovación (MCI, Spain), by the Agencia Estatal de Investigación (AEI, Spain) and by the European Regional Development Fund FEDER (FEDER, UE). BO's work is supported by the PERIS programme 2016–2020 'Ajuts per a la Incorporació de Científics i Tecnòlegs' (grant number SLT006/17/00066), with the support of the Health Department from the Generalitat de Catalunya. ; Peer Reviewed ; Postprint (published version)