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.

Multimorbidity is a phenomenon with high burden and high prevalence in the elderly. Our previous research has shown that multimorbidity can be divided into the multimorbidity patterns of 1) anxiety, depression, somatoform disorders (ADS) and pain, and 2) cardiovascular and metabolic disorders. However, it is not yet known, how these patterns are influenced by patient characteristics. The objective of this paper is to analyze the association of socio-demographic variables, and especially socio-economic status with multimorbidity in general and with each multimorbidity pattern. The MultiCare Cohort Study is a multicentre, prospective, observational cohort study of 3.189 multimorbid patients aged 65+ randomly selected from 158 GP practices. Data were collected in GP interviews and comprehensive patient interviews. Missing values have been imputed by hot deck imputation based on Gower distance in morbidity and other variables. The association of patient characteristics with the number of chronic conditions is analysed by multilevel mixed-effects linear regression analyses. Multimorbidity in general is associated with age (+0.07 chronic conditions per year), gender (-0.27 conditions for female), education (-0.26 conditions for medium and -0.29 conditions for high level vs. low level) and income (-0.27 conditions per logarithmic unit). The pattern of cardiovascular and metabolic disorders shows comparable associations with a higher coefficient for gender (-1.29 conditions for female), while multimorbidity within the pattern of ADS and pain correlates with gender (+0.79 conditions for female), but not with age or socioeconomic status. Our study confirms that the morbidity load of multimorbid patients is associated with age, gender and the socioeconomic status of the patients, but there were no effects of living arrangements and marital status. We could also show that the influence of patient characteristics is dependent on the multimorbidity pattern concerned, i.e. there seem to be at least two types of elderly multimorbid patients. First, there are patients with mainly cardiovascular and metabolic disorders, who are more often male, have an older age and a lower socio-economic status. Second, there are patients mainly with ADS and pain-related morbidity, who are more often female and equally distributed across age and socio-economic groups. ISRCTN89818205

Not only single, but also multiple, chronic conditions are becoming the normal situation rather than the exception in the older generation. While many studies show a correlation between multimorbidity and various health outcomes, the long-term effect on care dependency remains unclear. The objective of this study is to follow up a cohort of older adults for 5 years to estimate the impact of multimorbidity on long-term care dependency.

In order to estimate the future demands for health services, the analysis of current utilization patterns of the elderly is crucial. The aim of this study is to analyze ambulatory medical care utilization by elderly patients in relation to age, gender, number of chronic conditions, patterns of multimorbidity, and nursing dependency in Germany.

Multimorbidity is highly prevalent in the elderly and relates to many adverse outcomes, such as higher mortality, increased disability and functional decline. Many studies tried to reduce the heterogeneity of multimorbidity by identifying multimorbidity clusters or disease combinations, however, the internal structure of multimorbidity clusters and the linking between disease combinations and clusters are still unknown. The aim of this study was to depict which diseases were associated with each other on person-level within the clusters and which ones were responsible for overlapping multimorbidity clusters. The study analyses insurance claims data of the Gmünder ErsatzKasse from 2006 with 43,632 female and 54,987 male patients who were 65 years and older. The analyses are based on multimorbidity clusters from a previous study and combinations of three diseases ("triads") identified by observed/expected ratios ≥ 2 and prevalence rates ≥ 1%. In order to visualise a "disease network", an edgelist was extracted from these triads, which was analysed by network analysis and graphically linked to multimorbidity clusters. We found 57 relevant triads consisting of 31 chronic conditions with 200 disease associations ("edges") in females and 51 triads of 29 diseases with 174 edges in males. In the disease network, the cluster of cardiovascular and metabolic disorders comprised 12 of these conditions in females and 14 in males. The cluster of anxiety, depression, somatoform disorders, and pain consisted of 15 conditions in females and 12 in males. We were able to show which diseases were associated with each other in our data set, to which clusters the diseases were assigned, and which diseases were responsible for overlapping clusters. The disease with the highest number of associations, and the most important mediator between diseases, was chronic low back pain. In females, depression was also associated with many other diseases. We found a multitude of associations between disorders of the metabolic syndrome of which hypertension was the most central disease. The most prominent bridges were between the metabolic syndrome and musculoskeletal disorders. Guideline developers might find our approach useful as a basis for discussing which comorbidity should be addressed.

Multimorbidity is a common phenomenon in primary care. Until now, no clinical guidelines for multimorbidity exist. For the development of these guidelines, it is necessary to know whether or not patients are aware of their diseases and to what extent they agree with their doctor. The objectives of this paper are to analyze the agreement of self-reported and general practitioner-reported chronic conditions among multimorbid patients in primary care, and to discover which patient characteristics are associated with positive agreement. The MultiCare Cohort Study is a multicenter, prospective, observational cohort study of 3,189 multimorbid patients, ages 65 to 85. Data was collected in personal interviews with patients and GPs. The prevalence proportions for 32 diagnosis groups, kappa coefficients and proportions of specific agreement were calculated in order to examine the agreement of patient self-reported and general practitioner-reported chronic conditions. Logistic regression models were calculated to analyze which patient characteristics can be associated with positive agreement. We identified four chronic conditions with good agreement (e.g. diabetes mellitus κ = 0.80;PA = 0,87), seven with moderate agreement (e.g. cerebral ischemia/chronic stroke κ = 0.55;PA = 0.60), seventeen with fair agreement (e.g. cardiac insufficiency κ = 0.24;PA = 0.36) and four with poor agreement (e.g. gynecological problems κ = 0.05;PA = 0.10). Factors associated with positive agreement concerning different chronic diseases were sex, age, education, income, disease count, depression, EQ VAS score and nursing care dependency. For example: Women had higher odds ratios for positive agreement with their GP regarding osteoporosis (OR = 7.16). The odds ratios for positive agreement increase with increasing multimorbidity in almost all of the observed chronic conditions (OR = 1.22-2.41). For multimorbidity research, the knowledge of diseases with high disagreement levels between the patients’ perceived illnesses and their physicians’ reports is important. The analysis shows that different patient characteristics have an impact on the agreement. Findings from this study should be included in the development of clinical guidelines for multimorbidity aiming to optimize health care. Further research is needed to identify more reasons for disagreement and their consequences in health care. ISRCTN89818205