Emerging multimorbidity patterns and its linkages with selected health outcomes among working-age group population


latent class analysis • multimorbidity • self-rated health • quality of life • primary care


Background. The study aims to identify recurrent multimorbidity pattern among individuals in the age-group 15-64 years. Further, the study examines the association of these identified patterns with sociodemographic and selected health outcomes.

Methods. The study utilized data on 2912 individuals in the age-group 15-64 years collected under the burden of diseases study among patients attending public health care settings of Odisha. A latent class analysis was used to identify commonly occurring disease clusters.

Results. The findings suggested that 2.4% of the individuals were multimorbid. Two latent disease clusters were identified, low co-morbidity and Hypertension-Diabetes-Arthritis. Findings highlighted that age, belonging to a non-aboriginal ethnicity and urban area increased the risk of being in the ‘Hypertension-Diabetes-Arthritis’ group. Furthermore, 50% of the individual in the ‘Hypertension-Diabetes-Arthritis’ group reported poor quality of life, whereas 30% reported poor self-rated health compared to only 11% by their counterparts. Additionally, the mean health score reported by the individuals in the ‘Hypertension-Diabetes-Arthritis’ group was 39.9 compared to 46.9 by their counterparts.

Conclusions. The study findings hint towards increasing burden of multimorbidity among the working age population, which depicts a shift in causation of diseases as a result of which preventive measures also need to be taken much prior. 



1. The Academy of Medical Sciences. Multimorbidity: a priority for global health research [Internet]. The Academy of Medical Sciences. 2015. Available from: https://acmedsci.ac.uk/file-download/82222577
2. Rosbach M, Andersen JS. Patient-experienced burden of treatment in patients with multimorbidity – A systematic review of qualitative data. PLoS One. 2017;12(6):1–18.
3. Gill A, Kuluski K, Jaakkimainen L, Naganathan G, Upshur R, Wodchis WP. “Where do we go from here?” Health system frustrations expressed by patients with multimorbidity, their caregivers and family physicians. Healthc Policy. 2014;9(4):73–89.
4. O’Brien R, Wyke S, Watt GGCM, Guthrie B, Mercer SW. The ‘Everyday Work’ of Living with Multimorbidity in Socioeconomically Deprived Areas of Scotland. J Comorbidity. 2014;4(1):1–10.
5. Garin N, Koyanagi A, Chatterji S, Tyrovolas S, Olaya B, Leonardi M, et al. Global Multimorbidity Patterns: A Cross-Sectional, Population-Based, Multi-Country Study. Journals Gerontol - Ser A Biol Sci Med Sci. 2016;71(2):205–14.
6. Afshar S, Roderick PJ, Kowal P, Dimitrov BD, Hill AG. Multimorbidity and the inequalities of global ageing: A cross-sectional study of 28 countries using the World Health Surveys. BMC Public Health. 2015;15(1):1–10.
7. Pati S, Swain S, Hussain MA, Van Den Akker M, Metsemakers J, Knottnerus JA, et al. Prevalence and outcomes of multimorbidity in South Asia: A systematic review. BMJ Open. 2015;5(10).
8. Singh K, Patel SA, Biswas S, Shivashankar R, Kondal D, Ajay VS, et al. Multimorbidity in South Asian adults: prevalence, risk factors and mortality. J Public Health (Bangkok) [Internet]. 2019 Mar 1 [cited 2019 Sep 15];41(1):80–9. Available from: https://academic.oup.com/jpubhealth/article/41/1/80/4840708
9. Uttamacharya, Kshipra J. Multiple Chronic Diseases and Their Linkages with Functional health and Subjective Wellbeing among adults in the low-middle income countries: An Analysis of SAGE Wave1 Data, 2007/10. Munich Pers RePEc Arch. 2013;(54914).
10. Banjare P, Pradhan J. Socio-Economic Inequalities in the Prevalence of Multi- Morbidity among the Rural Elderly in Bargarh District of Odisha ( India ). 2014;9(6).
11. Pati S, Swain S, Metsemakers J, Knottnerus JA, Van Den Akker M. Pattern and severity of multimorbidity among patients attending primary care settings in Odisha, India. PLoS One. 2017;12(9).
12. Barnett K, Mercer SW, Norbury M, Watt G, Wyke S, Guthrie B. Epidemiology of multimorbidity and implications for health care , research , and medical education : a cross-sectional study. Lancet [Internet]. 380(9836):37–43. Available from: http://dx.doi.org/10.1016/S0140-6736(12)60240-2
13. Ward BW, Schiller JS. Prevalence of multiple chronic conditions among US adults: Estimates from the national health interview survey, 2010. Prev Chronic Dis. 2013;10(4):1–15.
14. Larsen FB, Pedersen MH, Friis K, Gluèmer C, Lasgaard M. A Latent class analysis of multimorbidity and the relationship to socio-demographic factors and health-related quality of life. A national population-based study of 162,283 Danish Adults. PLoS One. 2017;12(1):1–17.
15. Prados-Torres A, Calderón-Larrañaga A, Hancco-Saavedra J, Poblador-Plou B, Van Den Akker M. Multimorbidity patterns: A systematic review. J Clin Epidemiol. 2014;67(3):254–66.
16. Pati S, Mahapatra P, Dwivedi R, Athe R, Sahoo KC, Samal M, et al. Multimorbidity and Its Outcomes Among Patients Attending Psychiatric Care Settings: An Observational Study From Odisha, India. Front Public Heal. 2021;8(April).
17. Park B, Lee HA, Park H. Use of latent class analysis to identify multimorbidity patterns and associated factors in Korean adults aged 50 years and older. bioRxiv. 2019;1–13.
18. Linzer DA, Lewis JB. poLCA: An R Package for Polytomous Variable Latent Class Analysis. J Statstical Softw. 2014;42(10):1–18.
19. Yap KH, Warren N, Allotey P, Reidpath DD. Chronic disease profiles of subjective memory complaints: a latent class analysis of older people in a rural Malaysian community. Aging Ment Heal [Internet]. 2020;24(5):709–16. Available from: https://doi.org/10.1080/13607863.2018.1550632
20. Talukdar B. Prevalence of Multimorbidity (Chronic NCDS) and Associated Determinants Among Elderly in India. Demogr India [Internet]. 2017;69–76. Available from: http://demographyindia.in/application/views/article/8-2017.pdf
21. Pati S, Swain S, Knottnerus JA, Metsemakers JFM, Van Den Akker M. Health related quality of life in multimorbidity: A primary-care based study from Odisha, India. Health Qual Life Outcomes. 2019;17(1):1–12.
22. Kshatri JS, Palo SK, Bhoi T, Barik SR, Pati S. Prevalence and Patterns of Multimorbidity Among Rural Elderly: Findings of the AHSETS Study. Front Public Heal. 2020;8(November):1–9.
23. Bajpai V. The Challenges Confronting Public Hospitals in India, Their Origins, and Possible Solutions. Adv Public Heal. 2014;2014:1–27.
24. Rohini C, Jeemon P. Prevalence and patterns of multi-morbidity in the productive age group of 30-69 years: A cross-sectional study in Pathanamthitta District, Kerala. Wellcome Open Res. 2020;5:1–19.
25. Mini GK, Thankappan KR. Pattern, correlates and implications of non-communicable disease multimorbidity among older adults in selected Indian states: A cross-sectional study. BMJ Open. 2017;7(3).
26. Kirchberger I, Meisinger C, Heier M, Zimmermann AK, Thorand B, Autenrieth CS, et al. Patterns of multimorbidity in the aged population. results from the KORA-Age study. PLoS One. 2012;7(1):1–7.
27. Freitag M, Glynn L, Muth C, Valderas JM. Prevalence , Determinants and Patterns of Multimorbidity in Primary Care : A Systematic Review of Observational Studies. 2014;9(7):3–11.
28. Zemedikun DT, Gray LJ, Khunti K, Davies MJ, Dhalwani NN. Patterns of Multimorbidity in Middle-Aged and Older Adults: An Analysis of the UK Biobank Data. Mayo Clin Proc. 2018 Jul;93(7):857–66.
29. Mission NH. National Programme for prevention & Control of Cancer, Diabetes, Cardiovascular Diseases & stroke (NPCDCS) [Internet]. Government of India. 2019. Available from: http://www.nhm.gov.in/index1.php?lang=1&level=2&sublinkid=1048&lid=604