Predictors of depression in a sample of 1,021 primary care patients with osteoarthritis.
SourceArthritis and Rheumatism, 57, 3, (2007), pp. 415-422
Article / Letter to editor
Display more detailsDisplay less details
Centre for Quality of Care Research
Arthritis and Rheumatism
SubjectEBP 4: Quality of Care
OBJECTIVE: Although there is a strong relationship between depression, chronic pain, and physical activity, there are few findings regarding the prevalence and predictors of depression in patients with osteoarthritis (OA). The goal of the present study was to assess the prevalence and severity of depression in a large sample of patients with OA and to reveal predictors of depression. METHODS: Patients were approached consecutively in 75 general practices. Of 1,250 distributed questionnaires, 1,021 were returned and analyzed. Besides sociodemographic data, medication and comorbidities, depression, and arthritis were assessed using the Patient Health Questionnaire (PHQ-9) and the Arthritis Impact Measurement Scale. A stepwise multiple linear regression analysis with the PHQ-9 score as the dependent variable was performed. RESULTS: On the PHQ-9, 19.76% of men and 19.16% of women achieved a score of >or=15, indicating at least a moderately severe depression. Significant sex differences could not be revealed. The strongest predictor for depression severity was perceived pain (beta = 0.243, P < 0.001) and few social contacts (beta = 0.218, P < 0.001). Further predictors were physical limitation of the lower body (beta = 0.157, P < 0.001) and upper body (beta = 0.163, P < 0.001), age (beta = -0.168, P < 0.001), and body mass index (beta = 0.080, P = 0.020). CONCLUSION: These findings suggest an increased prevalence of depression among patients with OA and emphasize the need for recognition and appropriate treatment. Most of the revealed predictors are influenceable and should be potential targets in a comprehensive treatment of OA to interrupt the vicious circle of pain, physical limitation, and depression.
Upload full text
Use your RU credentials (u/z-number and password) tolog in with SURFconextto upload a file for processing by the repository team.