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0012 A holistic approach to calculating a multimorbidity score: the usefulness of multi-correspondence analysis
  1. Monica Ubalde-Lopez1,2,
  2. David Gimeno1,3,
  3. George Delclos1,4,
  4. Eva Calvo-Bonacho5,
  5. Fernando G Benavides1,2
  1. 1CISAL-Center for Research in Occupational Health, Universitat Pompeu Fabra, Barcelona, Spain
  2. 2CIBERESP, CIBER in Epidemiology and Public Health, Madrid, Spain
  3. 3Southwest Center for Occupational and Environmental Health, Division of Epidemiology, Human Genetics, and Environmental Sciences, The University of Texas School of Public Health, San Antonio Campus, Texas, USA
  4. 4Southwest Center for Occupational and Environmental Health, Division of Epidemiology, Human Genetics, and Environmental Sciences, The University of Texas Health Science Center at Houston, Texas, USA
  5. 5Ibermutuamur, (Mutua de Accidentes de Trabajo Y Enfermedades Profesionales de La Seguridad Social 274), Madrid, Spain
  6. 6IMIM-Institut Hospital Del Mar d’Investigacions Mèdiques. Parc de Salut Mar, Barcelona, Spain

Abstract

Objectives Most frequently, multimorbidity measures available in the literature are heavily dependent on one outcome. We propose a method to construct a global multimorbidity score that incorporates chronic and non-chronic health conditions as well as health-related behaviours and symptoms, regardless of any specific outcome.

Method Cross-sectional study of 373 905 Spanish workers who underwent a standardised medical evaluation in 2006. By applying an algorithm based on the results of a multi-correspondence analysis we computed a multimorbitidy score separated by sex.The score distribution was described by age groups and occupational social class for both sexes.

Results Two dimensions were generated by the multi-correspondence analysis that explained around 80% of the total variability in both sexes. The main dimension was related to cardiovascular chronic conditions and personal habits, whereas the second dimension included symptoms, in addition to sleep disturbances in women. As compared to women, men showed a higher prevalence of multimorbidity (78% vs 17%), higher scores [mean 14 (SD 11.9) versus mean 9 (SD 9.5)], and a rising trend with age. No differences were found by occupational social class.

Conclusions Multimorbidity can reflect clustering of health-related conditions, providing information on its burden and distribution in a specific population By calculating a multimorbidity score that considers both health-related conditions and symptoms, we provide a more holistic approach to multimorbidity, applicable to any database.

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