Heather E Foley1, John C Knight1,2, Michelle Ploughman3, Shabnam Asghari4, Rick Audas1. 1. Division of Community Health and Humanities, Faculty of Medicine, Memorial University of Newfoundland, St. John's, Newfoundland and Labrador, Canada. 2. Primary Health Care Research Unit, Faculty of Medicine, Memorial University of Newfoundland, St. John's, Newfoundland and Labrador, Canada. 3. Physical Medicine & Rehabilitation, Faculty of Medicine, Memorial University of Newfoundland, St. John's, Newfoundland and Labrador, Canada. 4. Discipline of Family Medicine, Faculty of Medicine, Memorial University of Newfoundland, St. John's, Newfoundland and Labrador, Canada.
Abstract
BACKGROUND: 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. AIM: 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. METHODS: 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. RESULTS: 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. CONCLUSIONS: 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.
BACKGROUND: 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. AIM: 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. METHODS: 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. RESULTS: 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. CONCLUSIONS: 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.
Authors: Crystal MacKay; Mayilee Canizares; Aileen M Davis; Elizabeth M Badley Journal: Arthritis Care Res (Hoboken) Date: 2010-02 Impact factor: 4.794
Authors: Bruce E Sands; Mei-Sheng Duh; Clorinda Cali; Anuli Ajene; Rhonda L Bohn; David Miller; J Alexander Cole; Suzanne F Cook; Alexander M Walker Journal: Pharmacoepidemiol Drug Saf Date: 2006-01 Impact factor: 2.890