ArticleInternational journal of epidemiology2020
Using statutory health insurance data to evaluate non-response in a cross-sectional study on depression among patients with diabetes in Germany.
Article in International journal of epidemiology, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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Who cites it
7 citing papers in PubMed.
- Linked survey and statutory health insurance data evaluating healthcare utilization patterns and associated factors of persons with diabetes in Germany - latent class analysis.Scientific reports · 2025Article
- Prospective health care costs and lost work days associated with diabetes-related distress and depression symptoms among 1488 individuals with diabetes.Scientific reports · 2024Article
- Linking health survey data with health insurance data: methodology, challenges, opportunities and recommendations for public health research. An experience from the HISlink project in Belgium.Archives of public health = Archives belges de sante publique · 2023Article
- PHQ-9, CES-D, health insurance data-who is identified with depression? A Population-based study in persons with diabetes.Diabetology & metabolic syndrome · 2023Article
- Diabetes-related research priorities of people with type 1 and type 2 diabetes: a cross-sectional study in Germany.Scientific reports · 2022Article
- Article
- Factors Associated with Survey Non-Response in a Cross-Sectional Survey of Persons with an Axial Spondyloarthritis or Osteoarthritis Claims Diagnosis.International journal of environmental research and public health · 2020Article
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14 authors.
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Abstract
backgroundLow 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.
methodsA 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).
resultsResponders 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.
conclusionWe 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.
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