Factors Associated with Survey Non-Response in a Cross-Sectional Survey of Persons with an Axial Spondyloarthritis or Osteoarthritis Claims Diagnosis

Johanna Callhoff, Hannes Jacobs, Katinka Albrecht, Joachim Saam, Angela Zink and Falk Hoffmann
Additional contact information
Johanna Callhoff: Epidemiology, German Rheumatism Research Centre, 10117 Berlin, Germany
Hannes Jacobs: Department of Health Services Research, Carl von Ossietzky University Oldenburg, 26129 Oldenburg, Germany
Katinka Albrecht: Epidemiology, German Rheumatism Research Centre, 10117 Berlin, Germany
Joachim Saam: Department Medicine and Health Services Research, BARMER Institute for Health System Research, BARMER, 73525 Schwäbisch-Gmünd, Germany
Angela Zink: Epidemiology, German Rheumatism Research Centre, 10117 Berlin, Germany
Falk Hoffmann: Department of Health Services Research, Carl von Ossietzky University Oldenburg, 26129 Oldenburg, Germany

IJERPH, 2020, vol. 17, issue 24, 1-13

Abstract: Non-response in surveys can lead to bias, which is often difficult to investigate. The aim of this analysis was to compare factors available from claims data associated with survey non-response and to compare them among two samples. A stratified sample of 4471 persons with a diagnosis of axial spondyloarthritis (axSpA) and a sample of 8995 persons with an osteoarthritis (OA) diagnosis from a German statutory health insurance were randomly selected and sent a postal survey. The association of age, sex, medical prescriptions, specialist physician contact, influenza vaccination, hospitalization, and Elixhauser comorbidity index with the survey response was assessed. Multiple logistic regression models were used with response as the outcome. A total of 47% of the axSpA sample and 40% of the OA sample responded to the survey. In both samples, the response was highest in the 70–79-year-olds. Women in all age groups responded more often, except for the 70–79-year-olds. Rheumatologist/orthopedist contact, physical therapy prescription, and influenza vaccination were more frequent among responders. In the logistic regression models, rheumatologist/orthopedist treatment, influenza vaccination, and physical therapy were associated with a higher odds ratio for response in both samples. The prescription of biologic drugs was associated with higher response in axSpA. A high Elixhauser comorbidity index and opioid use were not relevantly associated with response. Being reimbursed for long-term care was associated with lower response—this was only significant in the OA sample. The number of quarters with a diagnosis in the survey year was associated with higher response. Similar factors were associated with non-response in the two samples. The results can help other investigators to plan sample sizes of their surveys in similar settings.

Keywords: survey non-response; claims data; health services research; osteoarthritis; spondyloarthritis; non-response bias (search for similar items in EconPapers)
JEL-codes: I I1 I3 Q Q5 (search for similar items in EconPapers)
Date: 2020
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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