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ObjectivePatients with multimorbidity may carry a large symptom burden. Symptoms are often what drive patients to seek healthcare and they also assist doctors with diagnosis. We examined whether symptom burden is additive in people with multimorbidity compared with people with a single morbidity.DesignThis is a longitudinal cohort study drawing on questionnaire and Danish national registry data. Multimorbidity was defined as having diagnoses from at least two out of ten morbidity groups. Associations between morbidity groups and symptom burden were estimated with multivariable models.ParticipantsIn 2012, 47 452 participants from the Danish Symptom Cohort answered a questionnaire about symptoms (36 symptoms in total), including whether symptoms were affecting their daily activities (impairment score) and their worries about present symptoms (worry score) (the highest score among the 36 symptoms on a 0–4 scale).Main outcome measureThe primary outcome was symptom burden.ResultsParticipants without morbidity reported 4.77 symptoms (out of 36 possible). Participants with one, two or three morbidities reported more symptoms than patients without morbidity (0.95 (CI 0.86 to 1.03), 1.87 (CI 1.73 to 2.01) and 2.89 (CI 2.66 to 3.12), respectively). Furthermore, they reported a higher impairment score (0.36 (0.32 to 0.39), 0.65 (0.60 to 0.70) and 1.06 (0.98 to 1.14)) and a higher worry score (0.34 (0.31 to 0.37), 0.62 (0.57 to 0.66) and 1.02 (0.94 to 1.10)) than participants without morbidity. In 45 possible combinations of multimorbidity (participants with two morbidities), interaction effects were additive in 37, 41 and 36 combinations for the number of symptoms, impairment score and worry score, respectively.ConclusionParticipants without morbidity reported a substantial number of symptoms. Having a single morbidity or multimorbidity resulted in approximately one extra symptom for each extra morbidity. In most combinations of multimorbidity, symptom burden was additive.

Initiation of statin treatment is suggested to increase the international normalised ratio (INR) among warfarin users. However, available data is limited and conflicting. We conducted a register-based cohort study to evaluate the drug-drug interaction between warfarin and statins. By linking data on INR measurements and filled prescriptions, we identified warfarin users 2000-2015 initiating simvastatin (n = 1363), atorvastatin (n = 165) or rosuvastatin (n = 23). Simvastatin initiation led to an increase in mean INR from 2.40 to 2.71, with INRs peaking after 4 weeks, corresponding to a mean change of 0.32 (95%CI 0.25-0.38). High-dose and low-dose simvastatin led to comparable changes (mean change 0.33 vs 0.29). Initiation of atorvastatin and rosuvastatin lead to INR increases of 0.27 (95%CI 0.12-0.42) and 0.30 (95%CI -0.09-0.69). In conclusion, initiation of simvastatin, atorvastatin or rosuvastatin among warfarin users led to a minor increase in INR. The magnitude of this change is for most patients likely of limited clinical relevance.

Objective is to explore how multimorbidity is defined in the scientific literature, with a focus on the roles of diseases, risk factors, and symptoms in the definitions.DESIGN: Systematic review.METHODS: MEDLINE (PubMed), Embase, and The Cochrane Library were searched for relevant publications up until October 2013. One author extracted the information. Ambiguities were resolved, and consensus reached with one co-author. Outcome measures were: cut-off point for the number of conditions included in the definitions of multimorbidity; setting; data sources; number, kind, duration, and severity of diagnoses, risk factors, and symptoms. We reviewed 163 articles. In 61 articles (37%), the cut-off point for multimorbidity was two or more conditions (diseases, risk factors, or symptoms). The most frequently used setting was the general population (68 articles, 42%), and primary care (41 articles, 25%). Sources of data were primarily self-reports (56 articles, 42%). Out of the 163 articles selected, 115 had individually constructed multimorbidity definitions, and in these articles diseases occurred in all definitions, with diabetes as the most frequent. Risk factors occurred in 98 (85%) and symptoms in 71 (62%) of the definitions. The severity of conditions was used in 26 (23%) of the definitions, but in different ways. The definition of multimorbidity is heterogeneous and risk factors are more often included than symptoms. The severity of conditions is seldom included. Since the number of people living with multimorbidity is increasing there is a need to develop a concept of multimorbidity that is more useful in daily clinical work. Key points The increasing number of multimorbidity patients challenges the healthcare system. The concept of multimorbidity needs further discussion in order to be implemented in daily clinical practice. Many definitions of multimorbidity exist and most often a cut-off point of two or more is applied to a range of 4-147 different conditions. Diseases are included in all definitions of multimorbidity. Risk factors are often included in existing definitions, whereas symptoms and the severity of the conditions are less frequently included.

Objective: The aims of this study were to (1) quantify the development and composition of multimorbidity (MM) during 16 years following the diagnosis of type 2 diabetes and (2) evaluate whether the effectiveness of structured personal diabetes care differed between patients with and without MM. Research design and methods: One thousand three hundred eighty-one patients with newly diagnosed type 2 diabetes were randomized to receive either structured personal diabetes care or routine diabetes care. Patients were followed up for 19 years in Danish nationwide registries for the occurrence of outcomes. We analyzed the prevalence and degree of MM based on 10 well-defined disease groups. The effect of structured personal care in diabetes patients with and without MM was analyzed with Cox regression models. Results: The proportion of patients with MM increased from 31.6% at diabetes diagnosis to 80.4% after 16 years. The proportion of cardiovascular and gastrointestinal diseases in surviving patients decreased, while, for example, musculoskeletal, eye, and neurological diseases increased. The effect of the intervention was not different between type 2 diabetes patients with or without coexisting chronic disease. Conclusions: In general, the proportion of patients with MM increased after diabetes diagnosis, but the composition of chronic disease changed during the 16 years. We found cardiovascular and musculoskeletal disease to be the most prevalent disease groups during all 16 years of follow-up. The post hoc analysis of the intervention showed that its effectiveness was not different among patients who developed MM compared to those who continued to have diabetes alone.

Aims To explore the effect of structured personal care on diabetes symptoms and self-rated health over 14 years after diabetes diagnosis while patients are gradually diagnosed with other chronic conditions (multimorbidity). Methods Post hoc analysis of the Danish randomized controlled trial Diabetes Care in General Practice including 1381 patients newly diagnosed with type 2 diabetes. The effect of structured personal care compared with routine care on diabetes symptoms and self-rated health was analysed 6 and 14 years after diagnosis with a generalized multilevel Rasch model. Results Structured personal care reduced the overall likelihood of reporting diabetes symptoms at the end of the intervention (OR 0.79; 95% CI: 0.64–0.97), but this effect was not explained by glycaemic control or multimorbidity. There was no effect of the intervention on diabetes symptoms after 14 years or on self-rated health after 6 years or 14 years. Conclusions Structured personal care had a beneficial effect on diabetes symptoms 6 years after diagnosis, but not on self-rated health at either follow up point. To optimally manage patients over time it is important to supplement clinical information by information provided by the patients.

BACKGROUND: Many register studies make use of information about permanent nursing home residents. Statistics Denmark (StatD) identifies nursing home residents by two different indirect methods, one based on reports from the municipalities regarding home care in taken place in a nursing home, and the other based on an algorithm created by StatD. The aim of the present study was to validate StatD's nursing home register using dedicated administrative municipality records on individual nursing home residents as gold standard.METHODS: In total, ten Danish municipalities were selected. Within each Danish Region, we randomly selected one municipality reporting to Stat D (Method 1) and one not reporting where instead an algorithm created by StatD was used to discover nursing home residents (Method 2). Method 1 means that municipalities reported to Stat D whether home care has taken place in a nursing home or in a private home. Method 2 is based on an algorithm created by Stat D for the municipalities where Method 1 is not applicable. Our gold standard was the information from the local administrative system in all ten selected municipalities. Each municipality provided a list with all individuals > 65 years living in a nursing home on January 1st, 2013 as well as the central personal number. This was compared to the list of individuals >65 living in nursing home facilities in the same ten municipalities on January 1st, 2013 retrieved from StatD.RESULTS: According to the data received directly from the municipalities, which was used as our gold Standard 3821 individuals were identified as nursing home residents. The StatD register identified 6,141 individuals as residents. Additionally, 556 of the individuals identified by the municipalities were not identified in the StatD register. Overall sensitivity for the ten municipalities in the StatD nursing home register was 0.85 (95% CI 0.84-0.87) and the PPV was 0.53 (95% CI 0.52-0.54). The municipalities for which nursing home status was based on the StatD algorithm (method 2) had a sensitivity of 0.84 (95% CI 0.82-0.86) and PPV of 0.48 (95% CI 0.46-0.50). Both slightly lower than the reporting municipalities (method 1) where the sensitivity was 0.87(95% CI 0.85-0.88) and the PPV was 0.57 (95% CI 0.56-0.59). Additionally, the sensitivity and PPV of the Stat D register varied heavily among the ten municipalities from 0.51 (95% CI 0.43-0.59) to 0.96 (95% CI 0.95-0.98) and PPV correspondingly, from 0.14 (95% CI: 0.11-0.17) to 0.73 (95% CI 0.69-0.77).CONCLUSIONS: The overall PPV of StatD nursing home register was low and differences between municipalities existed. Even in countries with extensive nation-wide registers, validating studies should be conducted for outcomes based on these registers.

Background: Knowledge about prevalent and deadly combinations of multimorbidity is needed. Objective: To determine the nationwide prevalence of multimorbidity and estimate mortality for the most prevalent combinations of one to five diagnosis groups. Furthermore, to assess the excess mortality of the combination of two groups compared to the product of mortality associated with the single groups. Design: A prospective cohort study using Danish registries and including 3.986.209 people aged ≥18 years on 1 January, 2000. Multimorbidity was defined as having diagnoses from at least 2 of 10 diagnosis groups: lung, musculoskeletal, endocrine, mental, cancer, neurological, gastrointestinal, cardiovascular, kidney, and sensory organs. Logistic regression (odds ratios, ORs) and ratio of ORs (ROR) were used to study mortality and excess mortality. Results: Prevalence of multimorbidity was 7.1% in the Danish population. The most prevalent combination was the musculoskeletal–cardiovascular (0.4%), which had double the mortality (OR, 2.03) compared to persons not belonging to any of the diagnosis groups but showed no excess mortality (ROR, 0.97). The neurological–cancer combination had the highest mortality (OR, 6.35), was less prevalent (0.07%), and had no excess mortality (ROR, 0.94). Cardiovascular–lung was moderately prevalent (0.2%), had high mortality (OR, 5.75), and had excess mortality (ROR, 1.18). Endocrine–kidney had high excess mortality (ROR, 1.81) and cancer–mental had low excess mortality (ROR, 0.66). Mortality increased with the number of groups. Conclusions: All combinations had increased mortality risk with some of them having up to a six-fold increased risk. Mortality increased with the number of diagnosis groups. Most combinations did not increase mortality above that expected, that is, were additive rather than synergistic.