Sensitivity and specificity of algorithms for the identification of nonspecific low back pain in medico-administrative databases

Abstract

Abstract Identifying nonspecific low back pain (LBP) in medico-administrative databases is a major challenge because of the number and heterogeneity of existing diagnostic codes and the absence of standard definitions to use as reference. The objective of this study was to evaluate the sensitivity and specificity of algorithms for the identification of nonspecific LBP from medico-administrative data using self-report information as the reference standard. Self-report data came from the PROspective Québec Study on Work and Health, a 24-year prospective cohort study of white-collar workers. All diagnostic codes that could be associated with nonspecific LBP were identified from the International Classification of Diseases, Ninth and Tenth Revisions (ICD-9 and ICD-10) in physician and hospital claims. Seven algorithms for identifying nonspecific LBP were built and compared with self-report information. Sensitivity analyses were also conducted using more stringent definitions of LBP. There were 5980 study participants with (n = 2847) and without (n = 3133) LBP included in the analyses. An algorithm that included at least 1 diagnostic code for nonspecific LBP was best to identify cases of LBP in medico-administrative data with sensitivity varying between 8.9% (95% confidence interval [CI] 7.9-10.0) for a 1-year window and 21.5% (95% CI 20.0-23.0) for a 3-year window. Specificity varied from 97.1% (95% CI 96.5-97.7) for a 1-year window to 90.4% (95% CI 89.4-91.5) for a 3-year window. The low sensitivity we found reveals that the identification of nonspecific cases of LBP in administrative data is limited, possibly due to the lack of traditional medical consultation.