Dataset Information

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

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.

SUBMITTER: Linnenkamp U

PROVIDER: S-EPMC7266537 | biostudies-literature | 2020 Apr

REPOSITORIES: biostudies-literature

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Publications

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

Linnenkamp Ute U Gontscharuk Veronika V Brüne Manuela M Chernyak Nadezda N Kvitkina Tatjana T Arend Werner W Fiege Annett A Schmitz-Losem Imke I Kruse Johannes J Evers Silvia M A A SMAA Hiligsmann Mickaël M Hoffmann Barbara B Andrich Silke S Icks Andrea A

International journal of epidemiology 20200401 2

<h4>Background</h4>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.<h4>Methods</h4>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 d ...[more]

PMID: 31990354

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Dataset Information

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

Background

Methods

Results

Conclusion

Publications

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

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