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Background: Hospital readmission rates are very high in patients with chronic obstructive pulmonary disease (COPD). Continuity of care (CoC) with general practitioners (GPs) and ambulatory specialists can impact readmission rates. This study aimed to identify shared patient networks of ambulatory care physicians and to examine the effect of provider connectedness on CoC and hospital readmissions. Methods: A retrospective observational study was conducted in claims data from the years 2016 to 2018 in patients with COPD (aged 40 years or older; hospital stay in 2017). Linkages between GPs, pneumologists, and cardiologists were determined on the basis of shared patients. Multilevel regression models were used to analyze the impact of provider connectedness, operationalized by several social network characteristics, on continuity of care (sequential continuity [SECON] index) and hospital readmission rates. Results: A total of 7294 patients linked to 3673 GPs were available for analysis. Closeness centrality (β=- 0.029) and the external-internal (EI)-index (β =0.037) impacted on the SECON index. The EI-index (odds ratio [OR]=1.25) and degree centrality (OR=1.257) impacted 30-day readmission. Network density (OR=0.811) and the SECON index (OR=1.121) affected the likelihood of a 90-day readmission. None of the predictors had a significant impact on 180-day and 365-day readmissions. Conclusions: Ambulatory care providers’ connectedness showed some effects on hospital readmissions and CoC in patients with COPD up to 90 days after hospital discharge, but the additional predictive power is limited.

Additional file 1.

Additional file 1 Table S1. List of ICD 10-GM Codes Used to Identify Hospitalizations for Ambulatory Care–Sensitive Conditions.

Introduction: Door-to-CT scan time (DCT) and door-to-needle time (DNT) are important process measures in acute ischemic stroke (AIS) patients undergoing intravenous thrombolysis (IVT). We examined the impact of a telemedical prenotification by emergency medical service (EMS) (called the “Stroke Angel” program) on DCT and DNT and IVT rate compared to standard of care. Patients and Methods: Two prospective observational studies including AIS patients admitted via EMS from 2011 to 2013 (cohort I; n = 496) and from January 1, 2015 to May 31, 2018 (cohort II; n = 349) were conducted. After cohort I, the 4-Item Stroke Scale and a digital thrombolysis protocol were added. Multivariable logistic and linear regression analysis was performed. Results: In cohort I, DCT was lower in the intervention group (13 vs. 26 min using standard of care; p < 0.001), but no significant difference in median DNT (35 vs. 39 min; p = 0.24) was observed. In cohort II, a reduction of DCT (8 vs. 15 min; p < 0.001) and DNT (25 vs. 29 min p = 0.003) was observed in the intervention group. Compared to standard of care, the likelihood of DCT ≤10 min or DNT ≤20 min in the intervention group was 2.7 (adjusted odds ratio [aOR] 2.7; 95% CI: 2.1–3.5) and 1.8 (aOR 1.8; 95% CI: 1.1–2.9), respectively. In cohort II, IVT rate was higher (aOR 1.4; 95% CI: 1.1–1.9) in the intervention group. Conclusion: Although the positive effects of Stroke Angel in AIS provided a rationale for implementation in routine care, larger studies of practice implementation will be needed. Using Stroke Angel in the prehospital management of AIS impacts on important process measures of IVT delivery.

In addition to the Robert Koch Institute’s health surveys, analyses of secondary data are essential to successfully developing a regular and comprehensive description of the progression of diabetes as part of the Robert Koch Institute’s diabetes surveillance. Mainly, this is due to the large sample size and the fact that secondary data are routinely collected, which allows for highly stratified analyses in short time intervals. The fragmented availability of data means that various sources of secondary data are required in order to provide data for the indicators in the four fields of action for diabetes surveillance. Thus, a milestone in the project was to check the suitability of different data sources for their usability and to carry out analyses. Against this backdrop, co-operation projects were specifically funded in the context of diabetes surveillance. This article presents the results that were achieved in co-operation projects between 2016 and 2018 that focused on a range of topics: from evaluating the usability of secondary data to statistically modelling the development of epidemiological indices. Moreover, based on the data of the around 70 million people covered by statutory health insurance, an initial estimate was calculated for the documented prevalence of type 2 diabetes for the years 2010 and 2011. To comparably integrate these prevalences over the years in diabetes surveillance, a reference definition was established with external expertise.

Contains fulltext : 87906.pdf (Publisher’s version ) (Closed access) OBJECTIVE: To compare the mortality rate of patients with type 2 diabetes who were enrolled in the German diabetes disease management program (DMP) with the mortality rate of those who were not enrolled. STUDY DESIGN: This observational study was part of the ELSID study (Evaluation of a Large Scale Implementation of disease management programs) in Germany. METHODS: Participants had type 2 diabetes and were either enrolled or not enrolled in the DMP. The DMP provides systems-based, multifaceted, and patient-centered interventions. To reduce imbalances between the groups, a matched sample was created using sex, age, retirement status, federal state, pharmacy-based cost groups, and diagnostic-cost groups as matching criteria. Cox proportional hazards regression model and the Kaplan-Meier method were used to assess overall mortality. The observation period was 3 years beginning on January 1, 2006. RESULTS: A total of 11,079 patients were included in the analysis. As of January 1, 2006, 2300 patients were enrolled in the DMP and 8779 were receiving routine care. There were 1927 matched pairs of patients in the DMP group and the non-DMP group. The overall mortality rate was 11.3% in the DMP and 14.4% in the non-DMP group (log-rank test P

Contains fulltext : 69171.pdf (Publisher’s version ) (Open Access) BACKGROUND: Due to technological progress and improvements in medical care and health policy the average age of patients in primary care is continuously growing. In equal measure, an increasing proportion of mostly elderly primary care patients presents with multiple coexisting medical conditions. To properly assess the current situation of co- and multimorbidity, valid scientific data based on an appropriate data structure are indispensable. CONTENT (CONTinuous morbidity registration Epidemiologic NeTwork) is an ambitious project in Germany to establish a system for adequate record keeping and analysis in primary care based on episodes of care. An episode is defined as health problem from its first presentation by a patient to a doctor until the completion of the last encounter for it. The study aims to describe co- and multimorbidity as well as health care utilization based on episodes of care for the study population of the first participating general practices. METHODS: The analyses were based on a total of 39,699 patients in a yearly contact group (YCG) out of 17 general practices in Germany for which data entry based on episodes of care using the International Classification of Primary Care (ICPC) was performed between 1.1.2006 and 31.12.2006. In order to model the relationship between the explanatory variables (age, gender, number of chronic conditions) and the response variables of interest (number of different prescriptions, number of referrals, number of encounters) that were applied to measure health care utilization, we used multiple linear regression. RESULTS: In comparison to gender, patients' age had a manifestly stronger impact on the number of different prescriptions, the number of referrals and number of encounters. In comparison to age (beta = 0.043, p < 0.0001), multimorbidity measured by the number of patients' chronic conditions (beta = 0.51, p < 0.0001) had a manifestly stronger impact the number of encounters for the observation period. Moreover, we could observe that the number of patients' chronic conditions had a significant impact on the number of different prescriptions (beta = 0.226, p < 0.0001) as well as on the number of referrals (beta = 0.3, p < 0.0001). CONCLUSION: Documentation in primary care on the basis of episodes of care facilitates an insight to concurrently existing health problems and related medical procedures. Therefore, the resulting data provide a basis to obtain co- and multimorbidity patterns and corresponding health care utilization issues in order to understand the particular complex needs caused by multimorbidity.