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...
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| Veröffentlicht in: | International journal of epidemiology 2020-04, Vol.49 (2), p.629-637 |
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| creator | Linnenkamp, Ute Gontscharuk, Veronika Brüne, Manuela Chernyak, Nadezda Kvitkina, Tatjana Arend, Werner Fiege, Annett Schmitz-Losem, Imke Kruse, Johannes Evers, Silvia M A A Hiligsmann, Mickaël Hoffmann, Barbara Andrich, Silke Icks, Andrea |
| description | 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. |
| doi_str_mv | 10.1093/ije/dyz278 |
| format | Article |
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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.</description><identifier>ISSN: 0300-5771</identifier><identifier>EISSN: 1464-3685</identifier><identifier>DOI: 10.1093/ije/dyz278</identifier><identifier>PMID: 31990354</identifier><language>eng</language><publisher>England: Oxford University Press</publisher><subject>Aged ; Aged, 80 and over ; Cross-Sectional Studies ; Depression - epidemiology ; Diabetes Mellitus - drug therapy ; Diabetes Mellitus - epidemiology ; Female ; Germany - epidemiology ; Humans ; Insurance, Health ; Male ; Methods ; Middle Aged ; Surveys and Questionnaires - statistics & numerical data</subject><ispartof>International journal of epidemiology, 2020-04, Vol.49 (2), p.629-637</ispartof><rights>The Author(s) 2020. Published by Oxford University Press on behalf of the International Epidemiological Association. 2020</rights><rights>The Author(s) 2020. Published by Oxford University Press on behalf of the International Epidemiological Association.</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c338t-38ce0145c6295282da07fc0494086a0795d51a5403f2b5793a51777c87747c9b3</citedby><cites>FETCH-LOGICAL-c338t-38ce0145c6295282da07fc0494086a0795d51a5403f2b5793a51777c87747c9b3</cites><orcidid>0000-0003-0956-0015</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><link.rule.ids>230,314,780,784,885,1584,27924,27925</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/31990354$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>Linnenkamp, Ute</creatorcontrib><creatorcontrib>Gontscharuk, Veronika</creatorcontrib><creatorcontrib>Brüne, Manuela</creatorcontrib><creatorcontrib>Chernyak, Nadezda</creatorcontrib><creatorcontrib>Kvitkina, Tatjana</creatorcontrib><creatorcontrib>Arend, Werner</creatorcontrib><creatorcontrib>Fiege, Annett</creatorcontrib><creatorcontrib>Schmitz-Losem, Imke</creatorcontrib><creatorcontrib>Kruse, Johannes</creatorcontrib><creatorcontrib>Evers, Silvia M A A</creatorcontrib><creatorcontrib>Hiligsmann, Mickaël</creatorcontrib><creatorcontrib>Hoffmann, Barbara</creatorcontrib><creatorcontrib>Andrich, Silke</creatorcontrib><creatorcontrib>Icks, Andrea</creatorcontrib><title>Using statutory health insurance data to evaluate non-response in a cross-sectional study on depression among patients with diabetes in Germany</title><title>International journal of epidemiology</title><addtitle>Int J Epidemiol</addtitle><description>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.</description><subject>Aged</subject><subject>Aged, 80 and over</subject><subject>Cross-Sectional Studies</subject><subject>Depression - epidemiology</subject><subject>Diabetes Mellitus - drug therapy</subject><subject>Diabetes Mellitus - epidemiology</subject><subject>Female</subject><subject>Germany - epidemiology</subject><subject>Humans</subject><subject>Insurance, Health</subject><subject>Male</subject><subject>Methods</subject><subject>Middle Aged</subject><subject>Surveys and Questionnaires - statistics & numerical data</subject><issn>0300-5771</issn><issn>1464-3685</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2020</creationdate><recordtype>article</recordtype><sourceid>TOX</sourceid><sourceid>EIF</sourceid><recordid>eNp9kUFPHCEYhkmjqavtpT-g4eLFZCoMMAyXJo1Ra2LixT1PvmW-3cXMwgQYm_FP9C-LbmvaiycIPDwvX15CvnD2jTMjzt0DnvfzU63bD2TBZSMr0bTqgCyYYKxSWvMjcpzSA2NcSmk-kiPBjWFCyQX5vUzOb2jKkKcc4ky3CEPeUufTFMFbpD1koDlQfIRhgozUB19FTGPwCQtHgdoYUqoS2uyCh6HYpn6mwdMex0KmckphF0rOCNmhz4n-ciWkd7DCjOnFco1xB37-RA7XMCT8_Gc9Icury_uLn9Xt3fXNxY_bygrR5kq0Fss0yja1UXVb98D02jJpJGubsjeqVxyUZGJdr5Q2AhTXWttWa6mtWYkT8n3vHafVDntbPhVh6MbodhDnLoDr_r_xbtttwmOn66ZRQhfB2V7wOnzE9dtbzrqXWrpSS7evpcBf_017Q__2UIDTPRCm8T3RM40RmnU</recordid><startdate>20200401</startdate><enddate>20200401</enddate><creator>Linnenkamp, Ute</creator><creator>Gontscharuk, Veronika</creator><creator>Brüne, Manuela</creator><creator>Chernyak, Nadezda</creator><creator>Kvitkina, Tatjana</creator><creator>Arend, Werner</creator><creator>Fiege, Annett</creator><creator>Schmitz-Losem, Imke</creator><creator>Kruse, Johannes</creator><creator>Evers, Silvia M A A</creator><creator>Hiligsmann, Mickaël</creator><creator>Hoffmann, Barbara</creator><creator>Andrich, Silke</creator><creator>Icks, Andrea</creator><general>Oxford University Press</general><scope>TOX</scope><scope>CGR</scope><scope>CUY</scope><scope>CVF</scope><scope>ECM</scope><scope>EIF</scope><scope>NPM</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>5PM</scope><orcidid>https://orcid.org/0000-0003-0956-0015</orcidid></search><sort><creationdate>20200401</creationdate><title>Using statutory health insurance data to evaluate non-response in a cross-sectional study on depression among patients with diabetes in Germany</title><author>Linnenkamp, Ute ; Gontscharuk, Veronika ; Brüne, Manuela ; Chernyak, Nadezda ; Kvitkina, Tatjana ; Arend, Werner ; Fiege, Annett ; Schmitz-Losem, Imke ; Kruse, Johannes ; Evers, Silvia M A A ; Hiligsmann, Mickaël ; Hoffmann, Barbara ; Andrich, Silke ; Icks, Andrea</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c338t-38ce0145c6295282da07fc0494086a0795d51a5403f2b5793a51777c87747c9b3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2020</creationdate><topic>Aged</topic><topic>Aged, 80 and over</topic><topic>Cross-Sectional Studies</topic><topic>Depression - epidemiology</topic><topic>Diabetes Mellitus - drug therapy</topic><topic>Diabetes Mellitus - epidemiology</topic><topic>Female</topic><topic>Germany - epidemiology</topic><topic>Humans</topic><topic>Insurance, Health</topic><topic>Male</topic><topic>Methods</topic><topic>Middle Aged</topic><topic>Surveys and Questionnaires - statistics & numerical data</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Linnenkamp, Ute</creatorcontrib><creatorcontrib>Gontscharuk, Veronika</creatorcontrib><creatorcontrib>Brüne, Manuela</creatorcontrib><creatorcontrib>Chernyak, Nadezda</creatorcontrib><creatorcontrib>Kvitkina, Tatjana</creatorcontrib><creatorcontrib>Arend, Werner</creatorcontrib><creatorcontrib>Fiege, Annett</creatorcontrib><creatorcontrib>Schmitz-Losem, Imke</creatorcontrib><creatorcontrib>Kruse, Johannes</creatorcontrib><creatorcontrib>Evers, Silvia M A A</creatorcontrib><creatorcontrib>Hiligsmann, Mickaël</creatorcontrib><creatorcontrib>Hoffmann, Barbara</creatorcontrib><creatorcontrib>Andrich, Silke</creatorcontrib><creatorcontrib>Icks, Andrea</creatorcontrib><collection>Access via Oxford University Press (Open Access Collection)</collection><collection>Medline</collection><collection>MEDLINE</collection><collection>MEDLINE (Ovid)</collection><collection>MEDLINE</collection><collection>MEDLINE</collection><collection>PubMed</collection><collection>CrossRef</collection><collection>PubMed Central (Full Participant titles)</collection><jtitle>International journal of epidemiology</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Linnenkamp, Ute</au><au>Gontscharuk, Veronika</au><au>Brüne, Manuela</au><au>Chernyak, Nadezda</au><au>Kvitkina, Tatjana</au><au>Arend, Werner</au><au>Fiege, Annett</au><au>Schmitz-Losem, Imke</au><au>Kruse, Johannes</au><au>Evers, Silvia M A A</au><au>Hiligsmann, Mickaël</au><au>Hoffmann, Barbara</au><au>Andrich, Silke</au><au>Icks, Andrea</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Using statutory health insurance data to evaluate non-response in a cross-sectional study on depression among patients with diabetes in Germany</atitle><jtitle>International journal of epidemiology</jtitle><addtitle>Int J Epidemiol</addtitle><date>2020-04-01</date><risdate>2020</risdate><volume>49</volume><issue>2</issue><spage>629</spage><epage>637</epage><pages>629-637</pages><issn>0300-5771</issn><eissn>1464-3685</eissn><abstract>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.</abstract><cop>England</cop><pub>Oxford University Press</pub><pmid>31990354</pmid><doi>10.1093/ije/dyz278</doi><tpages>9</tpages><orcidid>https://orcid.org/0000-0003-0956-0015</orcidid><oa>free_for_read</oa></addata></record> |
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| source | MEDLINE; Oxford University Press Journals All Titles (1996-Current); EZB-FREE-00999 freely available EZB journals; Alma/SFX Local Collection |
| subjects | Aged Aged, 80 and over Cross-Sectional Studies Depression - epidemiology Diabetes Mellitus - drug therapy Diabetes Mellitus - epidemiology Female Germany - epidemiology Humans Insurance, Health Male Methods Middle Aged Surveys and Questionnaires - statistics & numerical data |
| title | 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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