Identifying cases of chronic pain using health administrative data: A validation study

• Main Authors: Foley, Heather E., Knight, John C., Ploughman, Michelle, Shabnam Asghari, Audas, Rick
• Format: Dataset
• Language: unknown
• Published: Taylor & Francis 2020
• Subjects: Medicine; Biotechnology; 69999 Biological Sciences not elsewhere classified; FOS Biological sciences; 80699 Information Systems not elsewhere classified; FOS Computer and information sciences; Cancer; Science Policy; Canada; Newfoundland
• Online Access: https://dx.doi.org/10.6084/m9.figshare.13020607; https://tandf.figshare.com/articles/dataset/Identifying_cases_of_chronic_pain_using_health_administrative_data_A_validation_study/13020607

Most prevalence estimates of chronic pain are derived from surveys and vary widely, both globally (2%–54%) and in Canada (6.5%–44%). Health administrative data are increasingly used for chronic disease surveillance, but their validity as a source to ascertain chronic pain cases is understudied. The aim of this study was to derive and validate an algorithm to identify cases of chronic pain as a single chronic disease using provincial health administrative data. A reference standard was developed and applied to the electronic medical records data of a Newfoundland and Labrador general population sample participating in the Canadian Primary Care Sentinel Surveillance Network. Chronic pain algorithms were created from the administrative data of patient populations with chronic pain, and their classification performance was compared to that of the reference standard via statistical tests of selection accuracy. The most performant algorithm for chronic pain case ascertainment from the Medical Care Plan Fee-for-Service Physicians Claims File was one anesthesiology encounter ever recording a chronic pain clinic procedure code OR five physician encounter dates recording any pain-related diagnostic code in 5 years with more than 183 days separating at least two encounters. The algorithm demonstrated 0.703 (95% confidence interval [CI], 0.685–0.722) sensitivity, 0.668 (95% CI, 0.657–0.678) specificity, and 0.408 (95% CI, 0.393–0.423) positive predictive value. The chronic pain algorithm selected 37.6% of a Newfoundland and Labrador provincial cohort. A health administrative data algorithm was derived and validated to identify chronic pain cases and estimate disease burden in residents attending fee-for-service physician encounters in Newfoundland and Labrador.