Use of latent class analysis to identify multimorbidity patterns and associated factors in Korean adults aged 50 years and older

Autoři: Bomi Park aff001;  Hye Ah Lee aff003;  Hyesook Park aff001
Působiště autorů: Department of Preventive Medicine, College of Medicine, Ewha Womans University, Seoul, Korea aff001;  National Cancer Control Institute, National Cancer Center, Goyang, Korea aff002;  Clinical Trial Center, Mokdong Hospital, Ewha Womans University, Seoul, Korea aff003
Vyšlo v časopise: PLoS ONE 14(11)
Kategorie: Research Article
doi: 10.1371/journal.pone.0216259



Multimorbidity associated with significant disease and economic burdens is common among the aged. We identified chronic disease multimorbidity patterns in Koreans 50 years of age or older, and explored whether such patterns were associated with particular sociodemographic factors and health-related quality-of-life.


The multimorbidity patterns of 10 chronic diseases (hypertension, dyslipidemia, stroke, osteoarthritis, tuberculosis, asthma, allergic rhinitis, depression, diabetes mellitus, and thyroid disease) were identified via latent class analysis of data on 8,370 Korean adults aged 50+ years who participated in the sixth Korean National Health and Nutrition Examination Survey (2013–2015). The associations between multimorbidity patterns, and sociodemographic factors and health-related quality of life, were subjected to regression analysis.


Three patterns of multimorbidity were identified: 1) a relatively healthy group (60.4% of the population); 2) a ‘cardiometabolic conditions’ group (27.8%); and, 3) an ‘arthritis, asthma, allergic rhinitis, depression, and thyroid disease’ group (11.8%). The female (compared to male) gender was associated with an increased likelihood of membership of the cardiometabolic conditions group (odds ratio [OR] = 1.32, 95% confidence interval [CI] = 1.15–1.51) and (to a much greater extent) the arthritis, asthma, allergy, depression, and thyroid disease group (OR = 4.32, 95% CI = 3.30–5.66). Low socioeconomic status was associated with membership of the two multimorbidity classes. Membership of the arthritis, asthma, allergy, depression, and thyroid disease group was associated with a significantly poorer health-related quality-of-life than was membership of the other two groups.


The co-occurrence of chronic diseases was not attributable to chance. Multimorbidity patterns were associated with sociodemographic factors and quality-of-life. Our results suggest that targeted, integrated public health and clinical strategies dealing with chronic diseases should be based on an understanding of multimorbidity patterns; this would improve the quality-of-life of vulnerable multimorbid adults.

Klíčová slova:

Allergic rhinitis – Allergies – Arthritis – Asthma – Quality of life – Socioeconomic aspects of health – Thyroid


1. Fortin M, Bravo G, Hudon C, Vanasse A, Lapointe L. Prevalence of multimorbidity among adults seen in family practice. Ann Fam Med. 2005;3: 223–228. doi: 10.1370/afm.272 15928225

2. van den Akker M, Buntinx F, Knottnerus JA. Comorbidity or multimorbidity. Eur J Gen Pract. 1996;2: 65–70.

3. Valderas JM, Starfield B, Sibbald B, Salisbury C, Roland M. Defining comorbidity: implications for understanding health and health services. Ann Fam Med. 2009;7: 357–363. doi: 10.1370/afm.983 19597174

4. Parekh AK, Goodman RA, Gordon C, Koh HK. Managing multiple chronic conditions: a strategic framework for improving health outcomes and quality of life. Public Health Rep. 2011;126: 460–471. doi: 10.1177/003335491112600403 21800741

5. 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. 2012;380: 37–43. doi: 10.1016/S0140-6736(12)60240-2 22579043

6. Park B, Ock M, Lee HA, Lee S, Han H, Jo MW, et al. Multimorbidity and health-related quality of life in Koreans aged 50 or older using KNHANES 2013–2014. Health Qual Life Outcomes. 2018;16: 186. doi: 10.1186/s12955-018-1016-6 30219061

7. Fortin M, Lapointe L, Hudon C, Vanasse A, Ntetu AL, Maltais D. Multimorbidity and quality of life in primary care: a systematic review. Health Qual Life Outcomes. 2004;2: 51–51. doi: 10.1186/1477-7525-2-51 15380021

8. Salisbury C, Johnson L, Purdy S, Valderas JM, Montgomery AA. Epidemiology and impact of multimorbidity in primary care: a retrospective cohort study. Br J Gen Pract. 2011;61: e12–21. doi: 10.3399/bjgp11X548929 21401985

9. Wallace E, Salisbury C, Guthrie B, Lewis C, Fahey T, Smith SM. Managing patients with multimorbidity in primary care. BMJ. 2015;350: h176. doi: 10.1136/bmj.h176 25646760

10. Lehnert T, Heider D, Leicht H, Heinrich S, Corrieri S, Luppa M, et al. Review: health care utilization and costs of elderly persons with multiple chronic conditions. Med Care Res Rev. 2011;68: 387–420. doi: 10.1177/1077558711399580 21813576

11. Fortin M, Bravo G, Hudon C, Lapointe L, Almirall J, Dubois MF, et al. Relationship between multimorbidity and health-related quality of life of patients in primary care. Qual Life Res. 2006;15: 83–91. doi: 10.1007/s11136-005-8661-z 16411033

12. Menotti A, Mulder I, Nissinen A, Giampaoli S, Feskens EJ, Kromhout D. Prevalence of morbidity and multimorbidity in elderly male populations and their impact on 10-year all-cause mortality: the FINE study (Finland, Italy, Netherlands, Elderly). J Clin Epidemiol. 2001;54: 680–686. doi: 10.1016/s0895-4356(00)00368-1 11438408

13. Librero J, Peiro S, Ordinana R. Chronic comorbidity and outcomes of hospital care: length of stay, mortality, and readmission at 30 and 365 days. J Clin Epidemiol. 1999;52: 171–179. doi: 10.1016/s0895-4356(98)00160-7 10210233

14. Wolff JL, Starfield B, Anderson G. Prevalence, expenditures, and complications of multiple chronic conditions in the elderly. Arch Intern Med. 2002;162: 2269–2276. doi: 10.1001/archinte.162.20.2269 12418941

15. McPhail SM. Multimorbidity in chronic disease: impact on health care resources and costs. Risk Manag Healthc Pol. 2016;9: 143–156.

16. Starfield B. Threads and yarns: weaving the tapestry of comorbidity. Ann Fam Med. 2006;4: 101–103. doi: 10.1370/afm.524 16569711

17. Islam MM, Valderas JM, Yen L, Dawda P, Jowsey T, McRae IS. Multimorbidity and comorbidity of chronic diseases among the senior Australians: prevalence and patterns. PLoS One. 2014;9: e83783. doi: 10.1371/journal.pone.0083783 24421905

18. Kessler R. Comorbidity. Amsterdam, NY: Elsevier Science Ltd; 2001.

19. Simoes D, Araujo FA, Severo M, Monjardino T, Cruz I, Carmona L, et al. Patterns and consequences of multimorbidity in the general population: there is no chronic disease management without rheumatic disease management. Arthritis Care Res (Hoboken). 2017;69: 12–20.

20. García-Olmos L, Salvador CH, Alberquilla Á, Lora D, Carmona M, García-Sagredo P, et al. Comorbidity patterns in patients with chronic diseases in general practice. PLoS One. 2012;7: e32141. doi: 10.1371/journal.pone.0032141 22359665

21. Schafer I, von Leitner EC, Schon G, Koller D, Hansen H, Kolonko T, et al. Multimorbidity patterns in the elderly: a new approach of disease clustering identifies complex interrelations between chronic conditions. PLoS One. 2010;5: e15941. doi: 10.1371/journal.pone.0015941 21209965

22. Kuwornu JP, Lix LM, Shooshtari S. Multimorbidity disease clusters in Aboriginal and non-Aboriginal Caucasian populations in Canada. Chronic Dis Inj Can. 2014;34: 218–225. 25408181

23. Cornell J, Pugh J, Williams J, Kazis L, Lee A, Parchman M, et al. Multimorbidity clusters: clustering binary data from multimorbidity clusters: clustering binary data from a large administrative medical database. Appl Multivar Res. 2009;12: 163–182.

24. Poblador-Plou B, van den Akker M, Vos R, Calderón-Larrañaga A, Metsemakers J, Prados-Torres A. Similar multimorbidity patterns in primary care patients from two European regions: results of a factor analysis. PLoS One. 2014;9: e100375. doi: 10.1371/journal.pone.0100375 24956475

25. Prados-Torres A, Poblador-Plou B, Calderon-Larranaga A, Gimeno-Feliu LA, Gonzalez-Rubio F, Poncel-Falco A, et al. Multimorbidity patterns in primary care: interactions among chronic diseases using factor analysis. PLoS One. 2012;7: e32190. doi: 10.1371/journal.pone.0032190 22393389

26. Kongsted A, Nielsen AM. Latent class analysis in health research. J Physiother. 2017;63: 55–58. doi: 10.1016/j.jphys.2016.05.018 27914733

27. Vermunt J, Magindson J. Latent class cluster analysis. In: Hagenaars J, McCutcheon A, editors. Applied latent class analysis. Cambridge: Cambridge University Press; 2002. pp. 89–106.

28. Kweon S, Kim Y, Jang MJ, Kim Y, Kim K, Choi S, et al. Data resource profile: the Korea national health and nutrition examination survey (KNHANES). Int J Epidemiol. 2014;43: 69–77. doi: 10.1093/ije/dyt228 24585853

29. Prados-Torres A, Calderon-Larranaga A, Hancco-Saavedra J, Poblador-Plou B, van den Akker M. Multimorbidity patterns: a systematic review. J Clin Epidemiol. 2014;67: 254–266. doi: 10.1016/j.jclinepi.2013.09.021 24472295

30. Hussain MA, Katzenellenbogen JM, Sanfilippo FM, Murray K, Thompson SC. Complexity in disease management: a linked data analysis of multimorbidity in aboriginal and non-Aboriginal patients hospitalised with atherothrombotic disease in Western Australia. PLoS One. 2018;13: e0201496. doi: 10.1371/journal.pone.0201496 30106971

31. Marengoni A, Rizzuto D, Wang HX, Winblad B, Fratiglioni L. Patterns of chronic multimorbidity in the elderly population. J Am Geriatr Soc. 2009;57: 225–230. doi: 10.1111/j.1532-5415.2008.02109.x 19207138

32. OECD. Terms of Reference OECD Project on the Distribution of Household Incomes. 2018. 20 August 2018. Available from:

33. Lee YK, Nam HS, Chuang LH, Kim KY, Yang HK, Kwon IS, et al. South Korean time trade-off values for EQ-5D health states: modeling with observed values for 101 health states. Value Health. 2009;12: 1187–1193. doi: 10.1111/j.1524-4733.2009.00579.x 19659703

34. Kim MH, Cho YS, Uhm WS, Kim S, Bae SC. Cross-cultural adaptation and validation of the Korean version of the EQ-5D in patients with rheumatic diseases. Qual Life Res. 2005;14: 1401–1406. doi: 10.1007/s11136-004-5681-z 16047514

35. Akaike H. A new look at the statistical model identification. IEEE Trans Autom. 1974;19: 716–723.

36. Schwarz G. Estimating the dimension of a model. Ann Statist. 1978;6: 461–464.

37. Nylund KL, Asparouhov T, Muthén BO. Deciding on the number of classes in latent class analysis and growth mixture modeling: a monte carlo simulation study. Struct Equ Model Multidiscip J. 2007;14: 535–569.

38. Masyn K. Latent class analysis and finite mixture modeling. In: Little T, editor. The Oxford handbook of quantitative methods in psychology. New York: Oxford University Press; 2013. pp. 551–611.

39. Nagin D. Group-based modeling of development. Cambridge, MA: Harvard University Press; 2005.

40. Nylund-Gibson K. & Choi AY. Ten Frequently Asked Questions About Latent Class Analysis. American Psychological Association. 2018;4: 440–461

41. Asparouhov T & Muthén BO. Auxiliary variables in mixture modeling: Three-step approaches using Mplus. Structural Equation Modeling, 2014;21: 329–341.

42. Holden L, Scuffham PA, Hilton MF, Muspratt A, Ng SK, Whiteford HA. Patterns of multimorbidity in working Australians. Popul Health Metr. 2011;9: 15. doi: 10.1186/1478-7954-9-15 21635787

43. 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: e30556. doi: 10.1371/journal.pone.0030556 22291986

44. Whitson HE, Johnson KS, Sloane R, Cigolle CT, Pieper CF, Landerman L, et al. Identifying patterns of multimorbidity in older americans: application of latent class analysis. J Am Geriatr Soc. 2016;64: 1668–1673. doi: 10.1111/jgs.14201 27309908

45. Anderson KM, Odell PM, Wilson PW, Kannel WB. Cardiovascular disease risk profiles. Am Heart J. 1991;121: 293–298. doi: 10.1016/0002-8703(91)90861-b 1985385

46. Jackson R, Lawes CM, Bennett DA, Milne RJ, Rodgers A. Treatment with drugs to lower blood pressure and blood cholesterol based on an individual's absolute cardiovascular risk. Lancet. 2005;365: 434–441. doi: 10.1016/S0140-6736(05)17833-7 15680460

47. Cooper R, Cutler J, Desvigne-Nickens P, Fortmann SP, Friedman L, Havlik R, et al. Trends and disparities in coronary heart disease, stroke, and other cardiovascular diseases in the United States: findings of the national conference on cardiovascular disease prevention. Circulation. 2000;102: 3137–3147. doi: 10.1161/01.cir.102.25.3137 11120707

48. Go AS, Mozaffarian D, Roger VL, Benjamin EJ, Berry JD, Blaha MJ, et al. Heart disease and stroke statistics—2014 update: a report from the American Heart Association. Circulation. 2014;129: e28–e292. doi: 10.1161/01.cir.0000441139.02102.80 24352519

49. Larsen FB, Pedersen MH, Friis K, Glumer 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: e0169426. doi: 10.1371/journal.pone.0169426 28056050

50. Feldman AZ, Shrestha RT, Hennessey JV. Neuropsychiatric manifestations of thyroid disease. Endocrinol Metab Clin North Am. 2013; 42:453–76. doi: 10.1016/j.ecl.2013.05.005 24011880

51. Bingyan Z, Dong W. Impact of thyroid hormones on asthma in older adults. J Int Med Res. 2019;47(9): 4114–4125. doi: 10.1177/0300060519856465 31280621

52. Przybyłowski J, Piestrak J, Kowalski D. Triiodothyronine (T3) and thyroxine (T4) levels in patients with bronchial asthma. Wiad Lek. 1989;42: 20–24. 2781800

53. Hossain F, Hong Y, Jin Y, Choi J, Hong Y. Physiological and Pathological Role of Circadian Hormones in Osteoarthritis: Dose-Dependent or Time-Dependent? J. Clin. Med. 2019;8(9), 1415

54. Loza E, Jover JA, Rodriguez L, Carmona L. Multimorbidity: prevalence, effect on quality of life and daily functioning, and variation of this effect when one condition is a rheumatic disease. Semin Arthritis Rheum. 2009;38:312–9. doi: 10.1016/j.semarthrit.2008.01.004 18336872

55. Freund T, Kunz CU, Ose D, Szecsenyi J, Peters-Klimm F. Patterns of multimorbidity in primary care patients at high risk of future hospitalization. Popul Health Manag. 2012;15: 119–124. doi: 10.1089/pop.2011.0026 22313440

56. Wong A, Boshuizen HC, Schellevis FG, Kommer GJ, Polder JJ. Longitudinal administrative data can be used to examine multimorbidity, provided false discoveries are controlled for. J Clin Epidemiol. 2011;64: 1109–1117. doi: 10.1016/j.jclinepi.2010.12.011 21454049

57. van den Bussche H, Koller D, Kolonko T, Hansen H, Wegscheider K, Glaeske G, et al. Which chronic diseases and disease combinations are specific to multimorbidity in the elderly? Results of a claims data based cross-sectional study in Germany. BMC Public Health. 2011;11: 101. doi: 10.1186/1471-2458-11-101 21320345

58. John R, Kerby DS, Hennessy CH. Patterns and impact of comorbidity and multimorbidity among community-resident American Indian elders. Gerontologist. 2003;43: 649–660. doi: 10.1093/geront/43.5.649 14570961

59. Newcomer SR, Steiner JF, Bayliss EA. Identifying subgroups of complex patients with cluster analysis. Am J Manag Care. 2011;17: e324–e332. 21851140

60. Schüz B, Wurm S, Warner LM, Tesch-Römer C. Health and subjective well-being in later adulthood: different health states—different needs? Appl Psychol Health Well Being. 2009;1: 23–45.

61. Pugh MJ, Finley EP, Copeland LA, Wang CP, Noel PH, Amuan ME, et al. Complex comorbidity clusters in OEF/OIF veterans: the polytrauma clinical triad and beyond. Med Care. 2014;52: 172–181. doi: 10.1097/MLR.0000000000000059 24374417

62. Swartz JA. Chronic medical conditions among jail detainees in residential psychiatric treatment: a latent class analysis. J Urban Health. 2011;88: 700–717. doi: 10.1007/s11524-011-9554-9 21394659

63. Hagenaars J, McCutcheon A. Applied latent class analysis. Cambridge, New York: Cambridge University Press; 2009.

64. Magidson J, Vermunt J. Latent class models for clustering: a comparison with K-means. Can J Market Res. 2002;20: 37–44.

65. Swartz JA, Ducheny K, Holloway T, Stokes L, Willis S, Kuhns LM. A Latent Class Analysis of Chronic Health Conditions Among HIV-Positive Transgender Women of Color. AIDS Behav. 2019 May 29. [Epub ahead of print]

66. Hesketh KR, Fagg J, Muniz-Terrera G, Bedford H, Law C, Hope S. Cooccurrence and clustering of health conditions at age 11: cross-sectional findings from the Millennium Cohort Study. BMJ Open 2016;6:e012919 doi: 10.1136/bmjopen-2016-012919 27881529

Článek vyšel v časopise


2019 Číslo 11