Literature DB >> 8482998

Non-response bias in studies of diabetic complications: the Rochester Diabetic Neuropathy Study.

L J Melton1, P J Dyck, J L Karnes, P C O'Brien, F J Service.   

Abstract

Non-response can bias studies of disease conditions but its influence has rarely been evaluated due to limitations of available data on the non-respondents. Because of a detailed medical record review for eligibility, we were able to compare clinical as well as demographic characteristics of respondents and non-respondents in a population-based study of diabetic complications among Rochester, Minnesota residents. Non-respondents were older, less well educated, more likely to be widowed and more often retired. They were much more likely to have cardiovascular disease at baseline, but the prevalence of retinopathy, nephropathy and diabetic neuropathy was similar for respondents and non-respondents, who were also comparable with regard to type of diabetes and diabetic therapy. While these findings indicate that data from the Rochester Diabetic Neuropathy Study can probably be generalized to diabetic residents generally, they reemphasize the potential for non-response bias in epidemiologic studies of clinical conditions, especially cardiovascular disease.

Entities:  

Mesh:

Year:  1993        PMID: 8482998     DOI: 10.1016/0895-4356(93)90148-t

Source DB:  PubMed          Journal:  J Clin Epidemiol        ISSN: 0895-4356            Impact factor:   6.437


  13 in total

1.  Bias.

Authors:  Miguel Delgado-Rodríguez; Javier Llorca
Journal:  J Epidemiol Community Health       Date:  2004-08       Impact factor: 3.710

2.  The relations between psychosocial factors at work and health status among workers in home care organizations.

Authors:  Hege R Eriksen; Camilla Ihlebaek; Jeroen P Jansen; Alex Burdorf
Journal:  Int J Behav Med       Date:  2006

3.  Cohort Profile: The Diabetes Study of Northern California (DISTANCE)--objectives and design of a survey follow-up study of social health disparities in a managed care population.

Authors:  Howard H Moffet; Nancy Adler; Dean Schillinger; Ameena T Ahmed; Barbara Laraia; Joe V Selby; Romain Neugebauer; Jennifer Y Liu; Melissa M Parker; Margaret Warton; Andrew J Karter
Journal:  Int J Epidemiol       Date:  2008-03-07       Impact factor: 7.196

4.  Investigation of relative risk estimates from studies of the same population with contrasting response rates and designs.

Authors:  Nicole M Mealing; Emily Banks; Louisa R Jorm; David G Steel; Mark S Clements; Kris D Rogers
Journal:  BMC Med Res Methodol       Date:  2010-04-01       Impact factor: 4.615

Review 5.  The Rochester Epidemiology Project: exploiting the capabilities for population-based research in rheumatic diseases.

Authors:  Hilal Maradit Kremers; Elena Myasoedova; Cynthia S Crowson; Guergana Savova; Sherine E Gabriel; Eric L Matteson
Journal:  Rheumatology (Oxford)       Date:  2010-07-13       Impact factor: 7.580

6.  Income non-reporting: implications for health inequalities research.

Authors:  G Turrell
Journal:  J Epidemiol Community Health       Date:  2000-03       Impact factor: 3.710

7.  Investigating Respondents and Nonrespondents of a Postal Breast Cancer Questionnaire Survey Regarding Differences in Age, Medical Conditions, and Therapy.

Authors:  Anna L Frobeen; Christoph Kowalski; Verena Weiß; Holger Pfaff
Journal:  Breast Care (Basel)       Date:  2016-04-27       Impact factor: 2.860

8.  Issues of recruitment and maintaining high response rates in a longitudinal study of older hospital patients in England--pathways through care study.

Authors:  B A Gregson; M Smith; J Lecouturier; N Rousseau; H Rodgers; J Bond
Journal:  J Epidemiol Community Health       Date:  1997-10       Impact factor: 3.710

9.  Analysis of factors influencing telephone call response rate in an epidemiological study.

Authors:  Jorge Matías-Guiu; Pedro Jesús Serrano-Castro; José Ángel Mauri-Llerda; Francisco José Hernández-Ramos; Juan Carlos Sánchez-Alvarez; Marisa Sanz
Journal:  ScientificWorldJournal       Date:  2014-10-21

10.  An electronic health record driven algorithm to identify incident antidepressant medication users.

Authors:  William V Bobo; Jyotishman Pathak; Hilal Maradit Kremers; Barbara P Yawn; Scott M Brue; Cynthia J Stoppel; Paul E Croarkin; Jennifer St Sauver; Mark A Frye; Walter A Rocca
Journal:  J Am Med Inform Assoc       Date:  2014-04-29       Impact factor: 4.497

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