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Multimorbidity Patterns in the Elderly: A New Approach of Disease Clustering Identifies Complex Interrelations between Chronic Conditions

Author

Listed:
  • Ingmar Schäfer
  • Eike-Christin von Leitner
  • Gerhard Schön
  • Daniela Koller
  • Heike Hansen
  • Tina Kolonko
  • Hanna Kaduszkiewicz
  • Karl Wegscheider
  • Gerd Glaeske
  • Hendrik van den Bussche

Abstract

Objective: Multimorbidity is a common problem in the elderly that is significantly associated with higher mortality, increased disability and functional decline. Information about interactions of chronic diseases can help to facilitate diagnosis, amend prevention and enhance the patients' quality of life. The aim of this study was to increase the knowledge of specific processes of multimorbidity in an unselected elderly population by identifying patterns of statistically significantly associated comorbidity. Methods: Multimorbidity patterns were identified by exploratory tetrachoric factor analysis based on claims data of 63,104 males and 86,176 females in the age group 65+. Analyses were based on 46 diagnosis groups incorporating all ICD-10 diagnoses of chronic diseases with a prevalence ≥ 1%. Both genders were analyzed separately. Persons were assigned to multimorbidity patterns if they had at least three diagnosis groups with a factor loading of 0.25 on the corresponding pattern. Results: Three multimorbidity patterns were found: 1) cardiovascular/metabolic disorders [prevalence female: 30%; male: 39%], 2) anxiety/depression/somatoform disorders and pain [34%; 22%], and 3) neuropsychiatric disorders [6%; 0.8%]. The sampling adequacy was meritorious (Kaiser-Meyer-Olkin measure: 0.85 and 0.84, respectively) and the factors explained a large part of the variance (cumulative percent: 78% and 75%, respectively). The patterns were largely age-dependent and overlapped in a sizeable part of the population. Altogether 50% of female and 48% of male persons were assigned to at least one of the three multimorbidity patterns. Conclusion: This study shows that statistically significant co-occurrence of chronic diseases can be subsumed in three prevalent multimorbidity patterns if accounting for the fact that different multimorbidity patterns share some diagnosis groups, influence each other and overlap in a large part of the population. In recognizing the full complexity of multimorbidity we might improve our ability to predict needs and achieve possible benefits for elderly patients who suffer from multimorbidity.

Suggested Citation

  • Ingmar Schäfer & Eike-Christin von Leitner & Gerhard Schön & Daniela Koller & Heike Hansen & Tina Kolonko & Hanna Kaduszkiewicz & Karl Wegscheider & Gerd Glaeske & Hendrik van den Bussche, 2010. "Multimorbidity Patterns in the Elderly: A New Approach of Disease Clustering Identifies Complex Interrelations between Chronic Conditions," PLOS ONE, Public Library of Science, vol. 5(12), pages 1-10, December.
  • Handle: RePEc:plo:pone00:0015941
    DOI: 10.1371/journal.pone.0015941
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    Cited by:

    1. Bomi Park & Hye Ah Lee & Hyesook Park, 2019. "Use of latent class analysis to identify multimorbidity patterns and associated factors in Korean adults aged 50 years and older," PLOS ONE, Public Library of Science, vol. 14(11), pages 1-13, November.
    2. Ignatios Ioakeim-Skoufa & Mercedes Clerencia-Sierra & Aida Moreno-Juste & Carmen Elías de Molins Peña & Beatriz Poblador-Plou & Mercedes Aza-Pascual-Salcedo & Francisca González-Rubio & Alexandra Prad, 2022. "Multimorbidity Clusters in the Oldest Old: Results from the EpiChron Cohort," IJERPH, MDPI, vol. 19(16), pages 1-15, August.
    3. Gabriele Doblhammer & Gerard J van den Berg & Thomas Fritze, 2013. "Economic Conditions at the Time of Birth and Cognitive Abilities Late in Life: Evidence from Ten European Countries," PLOS ONE, Public Library of Science, vol. 8(9), pages 1-12, September.
    4. Keloharju, Matti & Knüpfer, Samuli & Tåg, Joacim, 2020. "CEO Health," Working Paper Series 1326, Research Institute of Industrial Economics, revised 30 May 2022.
    5. Ingmar Schäfer, 2012. "Does Multimorbidity Influence the Occurrence Rates of Chronic Conditions? A Claims Data Based Comparison of Expected and Observed Prevalence Rates," PLOS ONE, Public Library of Science, vol. 7(9), pages 1-8, September.
    6. Inge Kirchberger & Christa Meisinger & Margit Heier & Anja-Kerstin Zimmermann & Barbara Thorand & Christine S Autenrieth & Annette Peters & Karl-Heinz Ladwig & Angela Döring, 2012. "Patterns of Multimorbidity in the Aged Population. Results from the KORA-Age Study," PLOS ONE, Public Library of Science, vol. 7(1), pages 1-7, January.
    7. Miao Fu, 2022. "A Clustering Spatial Estimation of Marginal Economic Losses for Vegetation Due to the Emission of VOCs as a Precursor of Ozone," Sustainability, MDPI, vol. 14(6), pages 1-22, March.
    8. Concepció Violan & Quintí Foguet-Boreu & Gemma Flores-Mateo & Chris Salisbury & Jeanet Blom & Michael Freitag & Liam Glynn & Christiane Muth & Jose M Valderas, 2014. "Prevalence, Determinants and Patterns of Multimorbidity in Primary Care: A Systematic Review of Observational Studies," PLOS ONE, Public Library of Science, vol. 9(7), pages 1-9, July.
    9. Caroline A Jackson & Annette J Dobson & Leigh R Tooth & Gita D Mishra, 2016. "Lifestyle and Socioeconomic Determinants of Multimorbidity Patterns among Mid-Aged Women: A Longitudinal Study," PLOS ONE, Public Library of Science, vol. 11(6), pages 1-16, June.
    10. Kalpana Singh & Albara Alomari & Badriya Lenjawi, 2022. "Prevalence of Multimorbidity in the Middle East: A Systematic Review of Observational Studies," IJERPH, MDPI, vol. 19(24), pages 1-10, December.
    11. Ignatios Ioakeim-Skoufa & Beatriz Poblador-Plou & Jonás Carmona-Pírez & Jesús Díez-Manglano & Rokas Navickas & Luis Andrés Gimeno-Feliu & Francisca González-Rubio & Elena Jureviciene & Laimis Dambraus, 2020. "Multimorbidity Patterns in the General Population: Results from the EpiChron Cohort Study," IJERPH, MDPI, vol. 17(12), pages 1-15, June.

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