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.
Highlights
In epidemiology and health services research, surveys are an important data collection method.Survey non-response is unavoidable and may lead to great challenges in the conduct, analysis, and interpretation of a survey [1,2]
In the 18–39, 40–49, and 50–59 year age groups, women had a higher Odds ratios (OR) for response than men, while in the 70–79 years group, women had a lower OR for response (0.73; 95% confidence intervals (CI) 0.56; 0.97)
Factors associated with survey non-response were analyzed in two claims data samples of individuals with axial spondyloarthritis (axSpA) or OA diagnoses
Summary
In epidemiology and health services research, surveys are an important data collection method. Survey non-response is unavoidable and may lead to great challenges in the conduct, analysis, and interpretation of a survey [1,2]. It can threaten the validity of a study in the presence of non-response bias [3]. While there are reasons for non-response that are not connected to the research question of. Res. Public Health 2020, 17, 9186; doi:10.3390/ijerph17249186 www.mdpi.com/journal/ijerph
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