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Background: The elderly population deals with multimorbidity (three chronic conditions) and increasinged drug use with age. A comprehensive characterisation of the medication - including prescription and over-the-counter (OTC) drugs - of elderly patients in primary care is still insufficient., Objectives: This study aims to characterise the medication (prescription and OTC) of multimorbid elderly patients in primary care and living at home by identifying drug patterns to evaluate the relationship between drugs and drug groups and reveal associations with recently published multimorbidity clusters of the same cohort., Methods: MultiCare was a multicentre, prospective, observational cohort study of 3189 multimorbid patients aged 65 to 85 years in primary care in Germany. Patients and general practitioners were interviewed between 2008 and 2009. Drug patterns were identified using exploratory factor analysis. The relations between the drug patterns with the three multimorbidity clusters were analysed with Spearman-Rank-Correlation., Results: Patients (59.3% female) used in mean 7.7 drugs; in total 24,535 drugs (23.7% OTC) were detected. Five drug patterns for men (drugs for obstructive pulmonary diseases (D-OPD), drugs for coronary heart diseases and hypertension (D-CHD), drugs for osteoporosis (D-Osteo), drugs for heart failure and drugs for pain) and four drug patterns for women (D-Osteo, D-CHD, D-OPD and drugs for diuretics and gout) were detected. Significant associations between multimorbidity clusters and drug patterns were detectable (D-CHD and CMD: male: ρ  = 0.376, CI 0.322-0.430; female: ρ  = 0.301, CI 0.624-0.340)., Conclusion: The drug patterns demonstrate non-random relations in drug use in multimorbid elderly patients and systematic associations between drug patterns and multimorbidity clusters were found in primary care.

Background: Multimorbidity in elderly patients is a major challenge for physicians, because of a high prevalence of and associations with many adverse outcomes. However, the mechanisms of progressing multimorbidity are not wellunderstood. The aim of our study was to determine if the progression of multimorbidity is influenced by health behaviour and social support and to analyse if the patients' socio-economic status had an effect on these prognostic factors. Methods: The study was designed as prospective cohort study based on interviews of 158 GPs and 3189 patients randomly selected from GP records (response rate: 46.2%). Patients were aged 65-85 years at recruitment and observed in four waves of data collection (dropout rate: 41.5%). Statistical analyses of the 'hot deck' imputed data included multilevel mixed-effects linear regression allowing for random effects at the study centre and GP practice within study centre level. Results: Regarding cardiovascular and metabolic diseases, multimorbidity progressed more rapidly in patients who reported less physical activity (ß = -0.28; 95% confidence interval = -0.35 to -0.20), had more tobacco-related pack years (0.15; 0.07-0.22) and consumed less alcohol (-0.21; -0.31 to -0.12) at baseline. Multimorbidity related to psychiatric and pain-related disorders progressed more rapidly if the patients had less perceived social support (-0.31; -0.55 to -0.07) and reported less physical activity (-0.08; -0.15 to -0.02) at baseline. Education and income only slightly modified the effects of these variables. Conclusion: Depending on the multimorbidity cluster, different strategies should be used for slowing down the progression of multimorbidity. Changing lifestyle and increasing social support are beneficial for the entire group of elderly multimorbid patients - regardless of their socio-economic status. [ABSTRACT FROM AUTHOR]

Background: In primary care, patients with multiple chronic conditions are the rule rather than the exception. The Chronic Care Model (CCM) is an evidence-based framework for improving chronic illness care, but little is known about the extent to which it has been implemented in routine primary care. The aim of this study was to describe how multimorbid older patients assess the routine chronic care they receive in primary care practices in Germany, and to explore the extent to which factors at both the practice and patient level determine their views. Methods: This cross-sectional study used baseline data from an observational cohort study involving 158 general practitioners (GP) and 3189 multimorbid patients. Standardized questionnaires were employed to collect data, and the Patient Assessment of Chronic Illness Care (PACIC) questionnaire used to assess the quality of care received. Multilevel hierarchical modeling was used to identify any existing association between the dependent variable, PACIC, and independent variables at the patient level (socio-economic factors, weighted count of chronic conditions, instrumental activities of daily living, health-related quality of life, graded chronic pain, no. of contacts with GP, existence of a disease management program (DMP) disease, self-efficacy, and social support) and the practice level (age and sex of GP, years in current practice, size and type of practice). Results: The overall mean PACIC score was 2.4 (SD 0.8), with the mean subscale scores ranging from 2.0 (SD 1.0, subscale goal setting/tailoring) to 3.5 (SD 0.7, delivery system design). At the patient level, higher PACIC scores were associated with a DMP disease, more frequent GP contacts, higher social support, and higher autonomy of past occupation. At the practice level, solo practices were associated with higher PACIC values than other types of practice. Conclusions: This study shows that from the perspective of multimorbid patients receiving care in German primary care practices, the implementation of structured care and counseling could be improved, particularly by helping patients set specific goals, coordinating care, and arranging follow-up contacts. Studies evaluating chronic care should take into consideration that a patient's assessment is associated not only with practice-level factors, but also with individual, patient-level factors. [ABSTRACT FROM AUTHOR]

Background It is not well established how psychosocial factors like social support and depression affect health-related quality of life in multimorbid and elderly patients. We investigated whether depressive mood mediates the influence of social support on health-related quality of life. Methods Cross-sectional data of 3,189 multimorbid patients from the baseline assessment of the German MultiCare cohort study were used. Mediation was tested using the approach described by Baron and Kenny based on multiple linear regression, and controlling for socioeconomic variables and burden of multimorbidity. Results Mediation analyses confirmed that depressive mood mediates the influence of social support on health-related quality of life (Sobel's p < 0.001). Multiple linear regression showed that the influence of depressive mood (β = -0.341, p < 0.01) on health-related quality of life is greater than the influence of multimorbidity (β = -0.234, p < 0.01). Conclusion Social support influences health-related quality of life, but this association is strongly mediated by depressive mood. Depression should be taken into consideration in research on multimorbidity, and clinicians should be aware of its importance when caring for multimorbid patients. [ABSTRACT FROM AUTHOR]

Objective: We investigated the degree of comparability of the prevalence of chronic diseases and disease combinations in the elderly in two databases comparable with regard to diseases included, sex and age of the patients (65e85 years), and cutoff score for case definition. Study Design and Setting: One study is based on chart-supported interviews with the primary care physicians within a cohort study of 3,189 multimorbid elderly patients. The second study analyzed claims data from ambulatory care delivered to the multimorbid members of one German Health Insurance (n 5 70,031). Multimorbidity was defined by the presence of three or more chronic conditions from an identical list of 46 diseases. Results: The difference of the median number of chronic conditions was 1 (mean 6.7 vs. 5.7). The prevalences of individual conditions were approximately one-third lower in the claims data, but the relative rank order corresponded well between the two databases. These relatively small prevalence differences cumulate when combinations of chronic conditions are investigated, for example, the prevalence differences between the two databases increased to nearly 100% for triadic combinations and nearly 170% for quartets. Conclusion: The study shows that conclusions regarding the prevalence of combinations of diseases should be drawn with caution when based on a single database. [ABSTRACT FROM AUTHOR]