Usability of German hospital administrative claims data for healthcare research: General assessment and use case of multiple myeloma in Munich university hospital in 2015-2017.

OBJECTIVES: To assess the usability of German hospital administrative claims data (GHACD) to determine inpatient management patterns, healthcare resource utilization, and quality-of-care in patients with multiple myeloma (PwMM). METHODS: Based on German tertiary hospital's claims data (2015-2017), PwMM aged >18 years were included if they had an International Classification of Diseases, Tenth Revision, code of C90.0 or received anti-MM therapy. Subgroup analysis was performed on stem cell transplantation (SCT) patients. RESULTS: Of 230 PwMM, 59.1% were men; 56.1% were aged ≥65 years. Hypertension and infections were present in 50% and 67.0%, respectively. Seventy percent of PwMM received combination therapy. Innovative drugs such as bortezomib and lenalidomide were given to 36.1% and 10.9% of the patients, respectively. Mean number of admissions and mean hospitalization length/patient were 3.69 (standard deviation (SD) 2.71 (1-16)) and 12.52 (SD 9.55 (1-68.5)) days, respectively. In-hospital mortality was recorded in 12.2%. Seventy-two percent of SCT patients (n = 88) were aged ≤65 years, 22.7% required second transplantation, and 89.8% received platelet transfusion at a mean of 1.42(SD 0.63 (1-3)). CONCLUSION: GHACD provided relevant information essential for healthcare studies about PwMM from routine care settings. Data fundamental for quality-of-care assessment were also captured.

Population-based assessment of health, healthcare utilisation, and specific needs of Syrian migrants in Germany: what is the best sampling method?

BACKGROUND: Studies elucidating health-related information and special needs of Syrian migrants living in Germany are urgently required. However, data is scarce and finding appropriate sampling strategies to obtain representative results is challenging. In order to increase survey response in hard-to-reach populations, new methods were developed. One of them is respondent-driven sampling (RDS), a network sampling technique. We aimed to assess if respondent-driven sampling is a better approach to recruit Syrian migrants for health research than classical random sampling via the population registry. METHODS: A cross-sectional study was conducted in Munich between April and June 2017 inviting adults (18+ years) born in Syria to answer an online questionnaire asking for sociodemographic and health-related information. Recruitment of participants was done using a) random sampling via the population registry (PR) and b) RDS. The two study populations recruited via respondent-driven sampling and the population registry were compared to a sample drawn from the population registry with respect to gender and citizenship. In addition, the two study populations were compared to each other regarding self-reported health status, healthcare utilisation, lifestyle factors, social network size, and acculturation. RESULTS: Of 374 persons randomly drawn from the population registry, 49 individuals answered the questionnaire completely (response: 13.1%) while via RDS 195 participants were recruited by 16 seeds. More persons possessed German citizenship in the total sample (20.5, 95% CI: 16.6 to 24.8%) and in the PR study population (28.6, 95% CI: 16.6 to 43.3%) than in the study population (0.5, 95% CI: 0.1 to 1.5%). Participants recruited via the population registry were older, smoked less, reported more often to hold a university degree, and indicated a higher prevalence of chronic diseases, more frequent healthcare utilisation, higher scores of acculturation as well as a larger social network compared to the study population obtained via RDS. CONCLUSIONS: Response was very low in the PR sample. The number of participants recruited via RDS was larger and led to a study population with substantially different characteristics. Our study thus indicates that RDS is a useful way to gain access to specific subgroups that are hard to reach via traditional random sampling.