Skip to main navigation menu Skip to main content Skip to site footer

Original article

Vol. 149 No. 2526 (2019)

Patterns of multimorbidity in internal medicine patients in Swiss university hospitals: a multicentre cohort study

DOI
https://doi.org/10.4414/smw.2019.20094
Cite this as:
Swiss Med Wkly. 2019;149:w20094
Published
30.06.2019

Abstract

AIMS OF THE STUDY

Despite the high prevalence of multimorbidity, we lack detailed descriptive data on the most prevalent combinations of chronic comorbidities in Switzerland. We aimed to describe and quantify the most prevalent combinations of comorbidities in internal medicine multimorbid inpatients.

METHODS

We conducted a multicentre retrospective cohort study including all consecutive adults (n = 42,739) discharged from the general internal medicine department of three Swiss tertiary teaching hospitals in 2010–2011. We used the Chronic Condition Indicator and the Clinical Classification Software to classify International Classification of Diseases diagnosis codes into chronic or acute diseases, into body system categories and into categories of chronic comorbidities. We defined multimorbidity as ≥2 chronic diseases. We described the most prevalent combinations of comorbidities and their prevalence.

RESULTS

Seventy-nine percent (n = 33,871) of the patients were multimorbid, with a median of four chronic diseases. Chronic heart disease, chronic kidney disease, solid malignancy and substance-related disorders were the most prevalent comorbidities, with a prevalence of more than 10% for each. All these comorbidities were frequently found in combination with chronic obstructive pulmonary disease and bronchiectasis, pulmonary heart disease, and peripheral and visceral atherosclerosis. Chronic heart disease was identified in 80% of the most prevalent combinations. Half of the combinations occurred more often than it would have been expected if they were independent.

CONCLUSIONS

The vast majority of patients fulfilled the criteria for multimorbidity. Chronic heart disease, chronic kidney disease, solid malignancy and substance-related disorders were each present in at least one tenth of the patients. This in-depth description of the most frequent comorbidities and of their frequent associations in a multicentre population may advise healthcare providers to improve preventive care and develop appropriate guidelines for multimorbid patients.

References

  1. Johnston MC, Crilly M, Black C, Prescott GJ, Mercer SW. Defining and measuring multimorbidity: a systematic review of systematic reviews. Eur J Public Health. 2019;29(1):182–9.
  2. Aubert CE, Streit S, Da Costa BR, Collet TH, Cornuz J, Gaspoz JM, et al. Polypharmacy and specific comorbidities in university primary care settings. Eur J Intern Med. 2016;35:35–42. doi:.https://doi.org/10.1016/j.ejim.2016.05.022
  3. 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(9836):37–43. doi:.https://doi.org/10.1016/S0140-6736(12)60240-2
  4. Rizza A, Kaplan V, Senn O, Rosemann T, Bhend H, Tandjung R ; FIRE study group. Age- and gender-related prevalence of multimorbidity in primary care: the Swiss FIRE project. BMC Fam Pract. 2012;13(1):113. doi:.https://doi.org/10.1186/1471-2296-13-113
  5. Schneider F, Kaplan V, Rodak R, Battegay E, Holzer B. Prevalence of multimorbidity in medical inpatients. Swiss Med Wkly. 2012;142:w13533. doi:.https://doi.org/10.4414/smw.2012.13533
  6. Gijsen R, Hoeymans N, Schellevis FG, Ruwaard D, Satariano WA, van den Bos GA. Causes and consequences of comorbidity: a review. J Clin Epidemiol. 2001;54(7):661–74. doi:.https://doi.org/10.1016/S0895-4356(00)00363-2
  7. Bähler C, Huber CA, Brüngger B, Reich O. Multimorbidity, health care utilization and costs in an elderly community-dwelling population: a claims data based observational study. BMC Health Serv Res. 2015;15(1):23. doi:.https://doi.org/10.1186/s12913-015-0698-2
  8. Marengoni A, Angleman S, Melis R, Mangialasche F, Karp A, Garmen A, et al. Aging with multimorbidity: a systematic review of the literature. Ageing Res Rev. 2011;10(4):430–9. doi:.https://doi.org/10.1016/j.arr.2011.03.003
  9. Déruaz-Luyet A, N’Goran AA, Senn N, Bodenmann P, Pasquier J, Widmer D, et al. Multimorbidity and patterns of chronic conditions in a primary care population in Switzerland: a cross-sectional study. BMJ Open. 2017;7(6):e013664. doi:.https://doi.org/10.1136/bmjopen-2016-013664
  10. Pache B, Vollenweider P, Waeber G, Marques-Vidal P. Prevalence of measured and reported multimorbidity in a representative sample of the Swiss population. BMC Public Health. 2015;15(1):164. doi:.https://doi.org/10.1186/s12889-015-1515-x
  11. Rizza A, Kaplan V, Senn O, Rosemann T, Bhend H, Tandjung R ; FIRE study group. Age- and gender-related prevalence of multimorbidity in primary care: the Swiss FIRE project. BMC Fam Pract. 2012;13(1):113. doi:.https://doi.org/10.1186/1471-2296-13-113
  12. Streit S, da Costa BR, Bauer DC, Collet TH, Weiler S, Zimmerli L, et al. Multimorbidity and quality of preventive care in Swiss university primary care cohorts. PLoS One. 2014;9(4):e96142. doi:.https://doi.org/10.1371/journal.pone.0096142
  13. 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(2):119–24. doi:.https://doi.org/10.1089/pop.2011.0026
  14. 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(2):e32141. doi:.https://doi.org/10.1371/journal.pone.0032141
  15. Cornell JE, Pugh JA, Williams JW, Jr, Kazis L, Lee AFS, Parchman ML, et al.. Multimorbidity clusters: clustering binary data from multimorbidity clusters: clustering binary data from a large administrative medical database. App Multiv Res. 2008;12(3):163. doi:.https://doi.org/10.22329/amr.v12i3.658
  16. Goldstein G, Luther JF, Jacoby AM, Haas GL, Gordon AJ. A Taxonomy of medical comorbidity for veterans who are homeless. J Health Care Poor Underserved. 2008;19(3):991–1005. doi:.https://doi.org/10.1353/hpu.0.0040
  17. John R, Kerby DS, Hagan Hennessy C. Patterns and impact of comorbidity and multimorbidity among community-resident American Indian elders. Gerontologist. 2003;43(5):649–60. doi:.https://doi.org/10.1093/geront/43.5.649
  18. 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):e30556. doi:.https://doi.org/10.1371/journal.pone.0030556
  19. Newcomer SR, Steiner JF, Bayliss EA. Identifying subgroups of complex patients with cluster analysis. Am J Manag Care. 2011;17(8):e324–32.
  20. Marengoni A, Rizzuto D, Wang HX, Winblad B, Fratiglioni L. Patterns of chronic multimorbidity in the elderly population. J Am Geriatr Soc. 2009;57(2):225–30. doi:.https://doi.org/10.1111/j.1532-5415.2008.02109.x
  21. Schäfer I, von Leitner EC, Schön 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(12):e15941. doi:.https://doi.org/10.1371/journal.pone.0015941
  22. 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(1):101. doi:.https://doi.org/10.1186/1471-2458-11-101
  23. Holden L, Scuffham PA, Hilton MF, Muspratt A, Ng SK, Whiteford HA. Patterns of multimorbidity in working Australians. Popul Health Metr. 2011;9(1):15. doi:.https://doi.org/10.1186/1478-7954-9-15
  24. Déruaz-Luyet A, N’Goran AA, Pasquier J, Burnand B, Bodenmann P, Zechmann S, et al. Multimorbidity: can general practitioners identify the health conditions most important to their patients? Results from a national cross-sectional study in Switzerland. BMC Fam Pract. 2018;19(1):66. doi:.https://doi.org/10.1186/s12875-018-0757-y
  25. Prados-Torres A, Poblador-Plou B, Calderón-Larrañaga A, Gimeno-Feliu LA, González-Rubio F, Poncel-Falcó A, et al. Multimorbidity patterns in primary care: interactions among chronic diseases using factor analysis. PLoS One. 2012;7(2):e32190. doi:.. Correction in: PLoS One. 2013;8(12): 6e3c77093262. doi:https://doi.org/10.1371/journal.pone.0032190
  26. von Elm E, Altman DG, Egger M, Pocock SJ, Gøtzsche PC, Vandenbroucke JP ; STROBE Initiative. The Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) Statement: guidelines for reporting observational studies. Int J Surg. 2014;12(12):1495–9. doi:.https://doi.org/10.1016/j.ijsu.2014.07.013
  27. Violan C, Foguet-Boreu Q, Flores-Mateo G, Salisbury C, Blom J, Freitag M, et al. Prevalence, determinants and patterns of multimorbidity in primary care: a systematic review of observational studies. PLoS One. 2014;9(7):e102149. doi:.https://doi.org/10.1371/journal.pone.0102149
  28. Elixhauser A, Steiner C, Harris DR, Coffey RM. Comorbidity measures for use with administrative data. Med Care. 1998;36(1):8–27. doi:.https://doi.org/10.1097/00005650-199801000-00004
  29. Charlson ME, Pompei P, Ales KL, MacKenzie CR. A new method of classifying prognostic comorbidity in longitudinal studies: development and validation. J Chronic Dis. 1987;40(5):373–83. doi:.https://doi.org/10.1016/0021-9681(87)90171-8
  30. Deyo RA, Cherkin DC, Ciol MA. Adapting a clinical comorbidity index for use with ICD-9-CM administrative databases. J Clin Epidemiol. 1992;45(6):613–9. doi:.https://doi.org/10.1016/0895-4356(92)90133-8
  31. van Walraven C, Austin PC, Jennings A, Quan H, Forster AJ. A modification of the Elixhauser comorbidity measures into a point system for hospital death using administrative data. Med Care. 2009;47(6):626–33. doi:.https://doi.org/10.1097/MLR.0b013e31819432e5
  32. Quan H, Sundararajan V, Halfon P, Fong A, Burnand B, Luthi JC, et al. Coding algorithms for defining comorbidities in ICD-9-CM and ICD-10 administrative data. Med Care. 2005;43(11):1130–9. doi:.https://doi.org/10.1097/01.mlr.0000182534.19832.83
  33. Agency for Healthcare Research and Quality (AHRQ), Healthcare Cost and Utilization Project (HCUP). Chronic Condition Indicator (CCI) for ICD-9-CM. https://www.hcup-us.ahrq.gov/toolssoftware/chronic/chronic.jsp. Accessed April 4 2019.
  34. Agency for Healthcare Research and Quality (AHRQ). Clinical Classifications Software (CCS) for ICD-9-CM. https://www.hcup-us.ahrq.gov/toolssoftware/ccs/ccs.jsp. Accessed April 4 2019.
  35. Clerencia-Sierra M, Calderón-Larrañaga A, Martínez-Velilla N, Vergara-Mitxeltorena I, Aldaz-Herce P, Poblador-Plou B, et al. Multimorbidity Patterns in Hospitalized Older Patients: Associations among Chronic Diseases and Geriatric Syndromes. PLoS One. 2015;10(7):e0132909. doi:.https://doi.org/10.1371/journal.pone.0132909
  36. 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(10):1109–17. doi:.https://doi.org/10.1016/j.jclinepi.2010.12.011
  37. Friedman B, Jiang HJ, Elixhauser A, Segal A. Hospital inpatient costs for adults with multiple chronic conditions. Med Care Res Rev. 2006;63(3):327–46. doi:.https://doi.org/10.1177/1077558706287042
  38. Schellevis FG, van der Velden J, van de Lisdonk E, van Eijk JT, van Weel C. Comorbidity of chronic diseases in general practice. J Clin Epidemiol. 1993;46(5):469–73. doi:.https://doi.org/10.1016/0895-4356(93)90024-U
  39. Fortin M, Bravo G, Hudon C, Vanasse A, Lapointe L. Prevalence of multimorbidity among adults seen in family practice. Ann Fam Med. 2005;3(3):223–8. doi:.https://doi.org/10.1370/afm.272
  40. Marengoni A, Bonometti F, Nobili A, Tettamanti M, Salerno F, Corrao S, et al.; Italian Society of Internal Medicine (SIMI) Investigators. In-hospital death and adverse clinical events in elderly patients according to disease clustering: the REPOSI study. Rejuvenation Res. 2010;13(4):469–77. doi:.https://doi.org/10.1089/rej.2009.1002
  41. 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):e0183966. doi:.https://doi.org/10.1371/journal.pone.0183966
  42. 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. doi:.https://doi.org/10.1016/j.jclinepi.2013.09.021
  43. Jadad AR, To MJ, Emara M, Jones J. Consideration of multiple chronic diseases in randomized controlled trials. JAMA. 2011;306(24):2670–2. doi:.https://doi.org/10.1001/jama.2011.1886
  44. Diederichs C, Berger K, Bartels DB. The measurement of multiple chronic diseases--a systematic review on existing multimorbidity indices. J Gerontol A Biol Sci Med Sci. 2011;66A(3):301–11. doi:.https://doi.org/10.1093/gerona/glq208
  45. 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):e007235. doi:.https://doi.org/10.1136/bmjopen-2014-007235
  46. Lochner KA, Cox CS. Prevalence of multiple chronic conditions among Medicare beneficiaries, United States, 2010. Prev Chronic Dis. 2013;10:120137. doi:.https://doi.org/10.5888/pcd10.120137

Most read articles by the same author(s)