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Original research
Multimorbidity patterns in low-middle and high income regions: a multiregion latent class analysis using ATHLOS harmonised cohorts
  1. Ivet Bayes-Marin1,2,3,
  2. Albert Sanchez-Niubo1,2,
  3. Laia Egea-Cortés4,
  4. Hai Nguyen5,
  5. Matthew Prina5,
  6. Daniel Fernández2,6,
  7. Josep Maria Haro1,2,3,
  8. Beatriz Olaya1,2
  1. 1Research, Innovation and Teaching Unit, Parc Sanitari Sant Joan de Déu, Sant Boi de Llobregat, Spain
  2. 2Centro de Investigación Biomédica en Red de Salud Mental, CIBERSAM, Madrid, Spain
  3. 3Department of Medicine, Universitat de Barcelona, Barcelona, Spain
  4. 4Center of Epidemiological Studies of HIV/AIDS and STI of Catalonia (CEEISCAT), Health Department, Generalitat de Catalunya, Badalona, Spain
  5. 5Health Service and Population Research Department, Institute of Psychiatry, Psychology and Neuroscience, King’s College London, London, United Kingdom
  6. 6Serra Húnter fellow. Department of Statistics and Operations Research, Polytechnic University of Catalonia, Barcelona, Spain
  1. Correspondence to Dr Albert Sanchez-Niubo; albert.sanchez{at}


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.

  • multimorbidity
  • non-communicable diseases (NCDs)
  • low- and middle-income countries (LMICs)
  • high-income country (HICs)
  • latent class analysis (LCA)

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  • Contributors IB-M participated in the database management, drafted the paper, carried out the statistical analyses and worked on the interpretation of data; AS-N participated in the study design, database management, statistical support and critical revision of the paper; LE-C participated in the interpretation of data and critical revision of the paper; HN participated in critical revision of the paper; AMP participated in the study design and critical revision of the paper; DF participated in the study design, database management, statistical support and critical revision of the paper; JMH participated in the study design, acquisition of data, interpretation of data and critical revision of the paper; BO participated in the acquisition of data, study design, database management and critical revision of the paper. All authors gave final approval of the version to be published and agreed to be accountable for all aspects of the work in ensuring that questions related to the accuracy or integrity of any part of the work are appropriately investigated and resolved.

  • Funding This work was supported by the 5-year Ageing Trajectories of Health: Longitudinal Opportunities and Synergies (ATHLOS) project and the Centro de InvestigaciónBiomédica 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 RetosInvestigación 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 AgenciaEstatal 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 CientíficsiTecnòlegs' (grant number SLT006/17/00066), with the support of the Health Department from the Generalitat de Catalunya.

  • Competing interests None declared.

  • Patient consent for publication Not required.

  • Ethics approval The study protocol was approved by the Committee on the Ethics of Clinical Research, CEIC FundacióSant Joan de Déu (Protocol No: PIC-22–15). All data were anonymised and EHR confidentially was respected in accordance with national and international law.

  • Provenance and peer review Not commissioned; externally peer reviewed.

  • Data availability statement Data are available upon reasonable request. Data may be obtained from a third party and are not publicly available. The original studies data are available on their respective websites: the Study on Global Ageing and Adult Health—SAGE (, the English Longitudinal Study of Ageing—ELSA (, and the Survey of Health, Ageing and Retirement in Europe—SHARE ( R codes for harmonising the included variables, as well as the STATA codes for the performed analysis are available on

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