Which XXXX and disease combinations are specific to multimorbidity in the elderly? Results of a claims data based cross-sectional study in Germany.

@entity1 @entity1643 @entity101 @entity6 @entity571 @entity10043 @entity102 @entity4977 @entity741 @entity210 @entity7391
BACKGROUND: 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 @entity102 and disease combinations are specific to multimorbidity in the elderly. METHODS: 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 @entity1 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. RESULTS: 62% of the sample was multimorbid. @entity1 in general and @entity1 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 ( @entity101 , @entity4977 , @entity1643 , @entity6 , @entity571 and @entity741 ) span the disease spectrum of 42% of the multimorbid sample. Gender differences were minor. Observed-to-expected ratios were highest when @entity10043 / @entity7391 metabolism disorders/ @entity210 and @entity571 were part of the multimorbidity patterns. CONCLUSIONS: The above list of dominating chronic conditions and their combinations could present a pragmatic start for the development of needed guidelines related to multimorbidity.
