Does variation in general practitioner (GP) practice matter for the length of sick leave? A multilevel analysis based on Norwegian GP-patient data
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In Norway, as in many countries, the national insurance system is under economic stress from demographic change impacting on the pensions versus contributions balance, and an increasing number of disability and sickness benefit claimants. The general practitioner (GP) is responsible for assessing work capacity and issuing certificates for sick leave based on an evaluation of the patient. Although many studies have analyzed certified sickness absence and predictive factors, no studies assess its variation between patients, GPs or geographical areas within a multilevel framework. Using a rich Norwegian matched patient-GP data set and employing a multilevel random intercept model, the study attempts to disentangle patient, GP and municipality-level variation in the certified sickness absence length for Norwegian workers in 2003. We find that most observed patient and GP characteristics are significantly associated with the length of sick leave (LSL) and medical diagnosis is an important observed factor explaining certified sickness durations. However, 98% of the unexplained variation in the LSL is attributed to patient factors rather than influenced by variation in GP practice or differences in municipality-level characteristics. Our findings indicate that GPs practice variation does not matter much for the patients' LSL. Our results are compatible with a high degree of patient involvement in current general practice. Based on this understanding one may infer that GPs play an advocate role for their patients in Norway, where the patients' own wishes are important when decisions are made.
CitationAakvik A, Holmås TH, Islam M. Kamrul. Does variation in general practitioner (GP) practice matter for the length of sick leave? A multilevel analysis based on Norwegian GP-patient data. Social Science and Medicine. 2010;70(10):1590-1598
SubjectNorwayGeneral practitioners (GPs)Length of sick leaveMultilevel regression modelsMatched GP-patient data
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