Which chronic diseases and disease combinations are specific to multimorbidity in the elderly? Results of a claims data based cross-sectional study in Germany
1 Department of Primary Medical Care, Center of Psychosocial Medicine, University Medical Center Hamburg-Eppendorf, Martinistrasse 52, 20246 Hamburg, Germany
2 Division of Health Economics, Health Policy and Outcomes Research, Centre for Social Policy Research, University of Bremen, Parkallee 39, 28209 Bremen, Germany
3 Department of Medical Biometry and Epidemiology, Center for Experimental Medicine, University Medical Center Hamburg-Eppendorf, Martinistrasse 52, 20246 Hamburg, Germany
BMC Public Health 2011, 11:101 doi:10.1186/1471-2458-11-101Published: 14 February 2011
Growing interest in multimorbidity is observable in industrialized countries. For Germany, the increasing attention still goes still hand in hand with a small number of studies on multimorbidity. The authors report the first results of a cross-sectional study on a large sample of policy holders (n = 123,224) of a statutory health insurance company operating nationwide. This is the first comprehensive study addressing multimorbidity on the basis of German claims data. The main research question was to find out which chronic diseases and disease combinations are specific to multimorbidity in the elderly.
The study is based on the claims data of all insured policy holders aged 65 and older (n = 123,224). Adjustment for age and gender was performed for the German population in 2004. A person was defined as multimorbid if she/he had at least 3 diagnoses out of a list of 46 chronic conditions in three or more quarters within the one-year observation period. Prevalences and risk-ratios were calculated for the multimorbid and non-multimorbid samples in order to identify diagnoses more specific to multimorbidity and to detect excess prevalences of multimorbidity patterns.
62% of the sample was multimorbid. Women in general and patients receiving statutory nursing care due to disability are overrepresented in the multimorbid sample. Out of the possible 15,180 combinations of three chronic conditions, 15,024 (99%) were found in the database. Regardless of this wide variety of combinations, the most prevalent individual chronic conditions do also dominate the combinations: Triads of the six most prevalent individual chronic conditions (hypertension, lipid metabolism disorders, chronic low back pain, diabetes mellitus, osteoarthritis and chronic ischemic heart disease) span the disease spectrum of 42% of the multimorbid sample. Gender differences were minor. Observed-to-expected ratios were highest when purine/pyrimidine metabolism disorders/gout and osteoarthritis were part of the multimorbidity patterns.
The above list of dominating chronic conditions and their combinations could present a pragmatic start for the development of needed guidelines related to multimorbidity.