scholarly journals New prescription of antihyperglycemic agents among patients with diabetes in Germany: Moderate concordance between health insurance data and self‐reports

2020 ◽  
Author(s):  
Manuela Brüne ◽  
Silke Andrich ◽  
Burkhard Haastert ◽  
Matthias Kaltheuner ◽  
Andrea Icks
Keyword(s):  
Health Insurance ◽  
Health Insurance Data ◽  
Antihyperglycemic Agents ◽  
Self Reports ◽  
Patients With Diabetes ◽  
Insurance Data

scholarly journals Using statutory health insurance data to evaluate non-response in a cross-sectional study on depression among patients with diabetes in Germany

2020 ◽  
Vol 49 (2) ◽  
pp. 629-637 ◽  
Author(s):  
Ute Linnenkamp ◽  
Veronika Gontscharuk ◽  
Manuela Brüne ◽  
Nadezda Chernyak ◽  
Tatjana Kvitkina ◽  
...  
Keyword(s):  
Health Insurance ◽  
Cross Sectional Study ◽  
Response Analysis ◽  
Cross Sectional Survey ◽  
Health Insurance Data ◽  
Cross Sectional ◽  
Depression Diagnosis ◽  
Medication Utilization ◽  
Patients With Diabetes ◽  
Insurance Data

Abstract Background Low response rates do not indicate poor representativeness of study populations if non-response occurs completely at random. A non-response analysis can help to investigate whether non-response is a potential source for bias within a study. Methods A cross-sectional survey among a random sample of a health insurance population with diabetes (n = 3642, 58.9% male, mean age 65.7 years), assessing depression in diabetes, was conducted in 2013 in Germany. Health insurance data were available for responders and non-responders to assess non-response bias. The response rate was 51.1%. Odds ratios (ORs) for responses to the survey were calculated using logistic regression taking into consideration the depression diagnosis as well as age, sex, antihyperglycaemic medication, medication utilization, hospital admission and other comorbidities (from health insurance data). Results Responders and non-responders did not differ in the depression diagnosis [OR 0.99, confidence interval (CI) 0.82–1.2]. Regardless of age and sex, treatment with insulin only (OR 1.73, CI 1.36–2.21), treatment with oral antihyperglycaemic drugs (OAD) only (OR 1.77, CI 1.49–2.09), treatment with both insulin and OAD (OR 1.91, CI 1.51–2.43) and higher general medication utilization (1.29, 1.10–1.51) were associated with responding to the survey. Conclusion We found differences in age, sex, diabetes treatment and medication utilization between responders and non-responders, which might bias the results. However, responders and non-responders did not differ in their depression status, which is the focus of the DiaDec study. Our analysis may serve as an example for conducting non-response analyses using health insurance data.

A study on the health effects of residents in Namhae and Hadong - Centered on health insurance data

2020 ◽  
Vol 19 (3) ◽  
pp. 268-277
Author(s):  
YoonDeok Han ◽  
◽  
Sunghyeon Jung ◽  
Kwang-tae Ha ◽  
Seung-Mi Kwon ◽  
...  
Keyword(s):  
Health Insurance ◽  
Health Effects ◽  
Health Insurance Data ◽  
Insurance Data

A Novel Approach for Labelling Health Insurance Data for Cardiovascular Disease Risk Classification

2016 ◽  
Author(s):  
César L. C. Mattos ◽  
Amauri H. Souza Júnior ◽  
Ajalmar R. Rocha Neto ◽  
Guilherme A. Barreto ◽  
Ronaldo F. Ramos ◽  
...  
Keyword(s):  
Cardiovascular Disease ◽  
Health Insurance ◽  
Disease Risk ◽  
Cardiovascular Disease Risk ◽  
Risk Classification ◽  
Health Insurance Data ◽  
Novel Approach ◽  
Insurance Data

Can telehealth reduce health care expenditure? A lesson from German health insurance data

2019 ◽  
Vol 34 (4) ◽  
pp. 1121-1132
Author(s):  
Andree Ehlert ◽  
Dirk Oberschachtsiek
Keyword(s):  
Health Care ◽  
Health Insurance ◽  
Health Care Expenditure ◽  
Health Insurance Data ◽  
German Health ◽  
Insurance Data
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