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Abstract Background Older adults with multimorbidity represent a growing segment of the population. Metrics to assess quality, safety and effectiveness of care can support policy makers and healthcare providers in addressing patient needs. However, there is a lack of valid measures of quality of care for this population. In the MULTIqual project, 24 general practitioner (GP)-reported and 14 patient-reported quality indicators for the healthcare of older adults with multimorbidity were developed in Germany in a systematic approach. This study aimed to select, validate and pilot core sets of these indicators. Methods In a cross-sectional observational study, we collected data in general practices (n = 35) and patients aged 65 years and older with three or more chronic conditions (n = 346). One-dimensional core sets for both perspectives were selected by stepwise backward selection based on corrected item-total correlations. We established structural validity, discriminative capacity, feasibility and patient-professional agreement for the selected indicators. Multilevel multivariable linear regression models adjusted for random effects at practice level were calculated to examine construct validity. Results Twelve GP-reported and seven patient-reported indicators were selected, with item-total correlations ranging from 0.332 to 0.576. Fulfilment rates ranged from 24.6 to 89.0%. Between 0 and 12.7% of the values were missing. Seventeen indicators had agreement rates between patients and professionals of 24.1% to 75.9% and one had 90.7% positive and 5.1% negative agreement. Patients who were born abroad (− 1.04, 95% CI = − 2.00/ − 0.08, p = 0.033) and had higher health-related quality of life (− 1.37, 95% CI = − 2.39/ − 0.36, p = 0.008), fewer contacts with their GP (0.14, 95% CI = 0.04/0.23, p = 0.007) and lower willingness to use their GPs as coordinators of their care (0.13, 95% CI = 0.06/0.20, p

Abstract Objectives The aims of our study were to describe the effect of the COVID-19 pandemic and lockdown on primary care in Germany regarding the number of consultations, the prevalence of specific reasons for consultation presented by the patients, and the frequency of specific services performed by the GP. Methods We conducted a longitudinal observational study based on standardised GP interviews in a quota sampling design comparing the time before the COVID-19 pandemic (12 June 2015 to 27 April 2017) with the time during lockdown (21 April to 14 July 2020). The sample included GPs in urban and rural areas 120 km around Hamburg, Germany, and was stratified by region type and administrative districts. Differences in the consultation numbers were analysed by multivariate linear regressions in mixed models adjusted for random effects on the levels of the administrative districts and GP practices. Results One hundred ten GPs participated in the follow-up, corresponding to 52.1% of the baseline. Primary care practices in 32 of the 37 selected administrative districts (86.5%) could be represented in both assessments. At baseline, GPs reported 199.6 ± 96.9 consultations per week, which was significantly reduced during COVID-19 lockdown by 49.0% to 101.8 ± 67.6 consultations per week (p

Abstract Background In most countries, the general practitioner (GP) is the first point of contact in the healthcare system and coordinator of healthcare. However, in Germany it is possible to consult an outpatient specialist even without referral. Coordination by a GP might thus reduce health expenditures and inequalities in the healthcare system. The study describes the patients’ willingness/commitment to use the GP as coordinator of healthcare and identifies regional and patient-related factors associated with the aforementioned commitment to the GP. Methods Cross-sectional observational study using a standardised telephone patient survey in northern Germany. All counties and independent cities within a radius of 120 km around Hamburg were divided into three regional categories (urban areas, environs, rural areas) and stratified proportionally to the population size. Patients who had consulted the GP within the previous three months, and had been patients of the practice for at least three years were randomly selected from medical records of primary care practices in these districts and recruited for the study. Multivariate linear regression models adjusted for random effects at the level of federal states, administrative districts and practices were used as statistical analysis methods. Results Eight hundred eleven patients (25.1%) from 186 practices and 34 administrative districts were interviewed. The patient commitment to a GP attained an average of 20 out of 24 possible points. Significant differences were found by sex (male vs. female: + 1.14 points, p

Abstract Background Among other factors, the patients’ consultation reasons and GPs’ spectrum of services determine the process and outcome of the medical treatment. So far, however, there has been little information on differences in reasons for consultation and GPs’ services between urban and rural areas. Our study’s goal was thus to investigate these factors in relation to the regional location of GPs’ practices. Methods We conducted a cross-sectional observational study based on standardised GP interviews in a quota sampling design. All counties and independent cities within a radius of 120 km around Hamburg were divided into three regional categories (urban area, environs, rural area) and stratified proportionally to the population size. Differences in the number of reasons for consultation and services were analysed by multivariate linear regressions in mixed models adjusted for random effects on the levels of the German federal states and administrative districts. Differences in individual consultation reasons and services were identified by logistic regression via stepwise forward and backward selection. Results Primary care practices in 34 of the 37 selected administrative districts (91.9%) were represented in the dataset. In total, 211 GPs were personally interviewed. On average, GPs saw 344 patients per month with a slightly higher number of patients in rural areas. They reported 59.1 ± 15.4 different reasons for consultation and 30.3 ± 3.9 different services. There was no statistically significant regional variation in the number of different consultation reasons, but there was a broader service spectrum by rural GPs (ß = − 1.42; 95% confidence interval − 2.75/− 0.08; p = 0.038) which was statistically explained by a higher level of medical training. Additionally, there were differences in the frequency of individual consultation reasons and services between rural and urban areas. Conclusion GPs in rural areas performed more frequently services usually provided by medical specialists in urban areas. This might be caused by a low availability of specialists in rural areas. The association between medical training and service spectrum might imply that GPs compensate the specific needs of their patients by completing advanced medical training before or after setting up a medical practice. Trial registration The study was registered in ClinicalTrials.gov ( NCT02558322 ).

Objectives The aim of our study was to explore patient types in general practitioner (GP) practices and to quantify the regional differences of the frequencies of these patient types in northern Germany.Design and setting We conducted a mixed-methods study based on focus groups and standardised interviews with GPs. All counties and independent cities within a radius of 120 km around Hamburg were assigned one of three regional categories (urban areas, environs, rural areas). The focus groups were analysed using qualitative content analysis. Relative frequencies of consultations by patient types and differences between the regions were calculated. Logistic regression analyses were used to identify differences among regions.Participants Nine focus groups with 65 GPs (67.7% male). From the 280 initially recruited GPs 211 (65.4% male) could be personally interviewed.Results Four themes with 27 patient types were derived from the focus groups: patients classified by morbidity, sociodemographic characteristics, special care needs and patient behaviour. Five patient characteristics were significantly more prevalent in urban areas than rural areas: patients with migration background and culturally different disease concepts (OR 1.23; 95% CI 1.06 to 1.42), privately insured patients (OR 1.17; 95% CI 1.05 to 1.31), educationally disadvantaged patients with low health literacy (OR 1.11; 95% CI 1.04 to 1.19), patients with psychiatric disorders (OR 1.07; 95% CI 1.02 to 1.12) and senior citizens living on their own without caregivers (OR 1.05; 95% CI 1.05 to 1.31). Three patient types were significantly less prevalent in urban areas: minors accompanied by their parents (OR 0.71; 95% CI 0.61 to 0.83), patients with poor therapy adherence (OR 0.87, 95% CI 0.80 to 0.95) and patients with dementia (OR 0.90; 95% CI 0.82 to 0.99).Conclusions GPs could compensate the specific needs of their patients with medical training aligned with the requirements of their region. Urban GPs need skills treating patients with psychiatric, social and cultural problems, rural GPs regarding the care for children or noncompliant patients.Trial registration number NCT02558322

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 well-understood. 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. Registration: ISRCTN89818205

Obesity and the accompanying increased morbidity and mortality risk is highly prevalent among older adults. As obese elderly might benefit from intentional weight reduction, it is necessary to determine associated and potentially modifiable factors on senior obesity. This cross-sectional study focuses on multi-morbid patients which make up the majority in primary care. It reports on the prevalence of senior obesity and its associations with lifestyle behaviors.A total of 3,189 non-demented, multi-morbid participants aged 65-85 years were recruited in primary care within the German MultiCare-study. Physical activity, smoking, alcohol consumption and quantity and quality of nutritional intake were classified as relevant lifestyle factors. Body Mass Index (BMI, general obesity) and waist circumference (WC, abdominal obesity) were used as outcome measures and regression analyses were conducted.About one third of all patients were classified as obese according to BMI. The prevalence of abdominal obesity was 73.5%. Adjusted for socio-demographic variables and objective and subjective disease burden, participants with low physical activity had a 1.6 kg/m2 higher BMI as well as a higher WC (4.9 cm, p

OBJECTIVE: Multimorbidity is a common problem in the elderly that is significantly associated with higher mortality, increased disability and functional decline. Information about interactions of chronic diseases can help to facilitate diagnosis, amend prevention and enhance the patients' quality of life. The aim of this study was to increase the knowledge of specific processes of multimorbidity in an unselected elderly population by identifying patterns of statistically significantly associated comorbidity. METHODS: Multimorbidity patterns were identified by exploratory tetrachoric factor analysis based on claims data of 63,104 males and 86,176 females in the age group 65+. Analyses were based on 46 diagnosis groups incorporating all ICD-10 diagnoses of chronic diseases with a prevalence ≥ 1%. Both genders were analyzed separately. Persons were assigned to multimorbidity patterns if they had at least three diagnosis groups with a factor loading of 0.25 on the corresponding pattern. RESULTS: Three multimorbidity patterns were found: 1) cardiovascular/metabolic disorders [prevalence female: 30%; male: 39%], 2) anxiety/depression/somatoform disorders and pain [34%; 22%], and 3) neuropsychiatric disorders [6%; 0.8%]. The sampling adequacy was meritorious (Kaiser-Meyer-Olkin measure: 0.85 and 0.84, respectively) and the factors explained a large part of the variance (cumulative percent: 78% and 75%, respectively). The patterns were largely age-dependent and overlapped in a sizeable part of the population. Altogether 50% of female and 48% of male persons were assigned to at least one of the three multimorbidity patterns. CONCLUSION: This study shows that statistically significant co-occurrence of chronic diseases can be subsumed in three prevalent multimorbidity patterns if accounting for the fact that different multimorbidity patterns share some diagnosis groups, influence each other and overlap in a large part of the population. In recognizing the full complexity of multimorbidity we might improve our ability to predict needs and achieve possible benefits for elderly patients who suffer from multimorbidity.