| Summary: | Eleanor Boyle,1 Lars Folkestad,2– 5 Erik Frafjord,6 Bart W Koes,1,7 Soren Thorgaard Skou,1,8 Jan Hartvigsen1,6 1Center for Muscle and Joint Health, Department of Sports Science and Clinical Biomechanics, University of Southern Denmark, Odense, Denmark; 2Department of Endocrinology and Metabolism, Odense University Hospital, Odense, Denmark; 3Department of Internal Medicine, Hospital of South West Denmark, Esbjerg, Denmark; 4Department of Clinical Research, University of Southern Denmark, Odense, Denmark; 5Open Patient Exploratory Network, University of Southern Denmark, Odense, Denmark; 6Chiropractic Knowledge Hub, Odense, Denmark; 7Department of General Practice, Erasmus MC, Rotterdam, the Netherlands; 8The Research Unit PROgrez, Department of Physiotherapy and Occupational Therapy, Næstved-Slagelse-Ringsted Hospitals, Slagelse, DenmarkCorrespondence: Jan HartvigsenDepartment of Sports Science and Clinical Biomechanics, University of Southern Denmark, Campusvej 55, Odense M, Odense, 5230, DenmarkTel +45 65504522Email jhartvigsen@health.sdu.dkPurpose: To conduct a non-responder analysis on a musculoskeletal (MSK) electronic questionnaire.Methods: Individuals aged 18 years and older, diagnosed with diabetes mellitus (DM), and attended an ambulatory DM clinic formed the sample frame. They were invited to complete an electronic musculoskeletal (MSK) conditions and symptoms questionnaire booklet using a secured electronic email system. Individuals whose secured email box was not active at the time were discarded. Using the Central Person Registry number, a unique number assigned to all Danish residents, we linked the sample frame to different registries to learn more about non-responders. Non-responders were either individuals who did not respond to a single question and those who responded “No” to the first question about willing to participate. We calculated descriptive statistics for each characteristic. Univariate logistic regression models were conducted to determine the relationship between each characteristic and non-responder status.Results: The response rate was 36% (n = 3812). Individuals with type 2 DM (OR 2.0 (95% CI 1.8– 2.2)), secondary DM (1.9 (1.3– 2.8)) or unspecified DM (2.1 (1.8– 2.4)) were more likely to be non-responders than individuals with Type 1 DM. Also, individuals aged 70– 79 (1.3 (1.1– 1.6)) and 80 years and older (5.9 (4.5– 7.7)) were more likely to be non-responders than 18– 29 years old individuals. However, individuals aged 40– 49 (1.5 (1.2– 1.8)), 50– 59 (1.5 (1.3– 1.8)) or 60– 69 (1.4 (1.1– 1.6)) were more likely to be responders than 18– 29 years old individuals. Individuals with Charlson Comorbidity Index (CCI) of 1 (2.0 (1.3.2.9) or CCI of 2 (1.7 (1.1– 2.5) were more likely to be responders than individuals with a CCI of 0. Lastly, individuals who were currently outside of the workforce (1.6 (2.4– 2.9) or had unknown/missing socioeconomic status (3.9 (2.8– 5.3) were more likely to be non-responders than individuals who were working.Conclusion: Although we did find a non-response bias, this cohort will be an important source to determine the prevalence and consequences of MSK conditions in a secondary care DM population.Keywords: diabetes, survey, non-participation analysis, musculoskeletal conditions
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