OBJECTIVE: To assess the positive and negative predictive values of osteoarthritis (OA) diagnoses contained in an administrative database. METHODS: We identified all members (> or =18 years of age) of a Massachusetts health maintenance organization with documentation of at least one health care encounter associated with an OA diagnosis during the period 1994-1996. From this population, we randomly selected 350 subjects. In addition, we randomly selected 250 enrollees (proportionally by the age and sex of the 350 subjects) who did not have a health care encounter associated with an OA diagnosis. Trained nurse reviewers abstracted OA-related clinical, laboratory, and radiologic data from the medical records of both study groups (all but 1 chart was available for review). Pairs of physician reviewers evaluated the abstracted information for both groups of subjects and rated the evidence for the presence of OA according to 3 levels: definite, possible, and unlikely. RESULTS: Among the group of patients with an administrative diagnosis of OA, 215 (62%) were rated as having definite OA, 36 (10%) possible OA, and 98 (28%) unlikely OA, according to information contained in the medical record. The positive predictive value of an OA diagnosis was 62%. In those without an administrative OA diagnosis, 44 (18%) were assigned a rating of definite OA. The negative predictive value of the absence of an administrative OA diagnosis was 78%. CONCLUSION: Use of administrative data in epidemiologic and health services research on OA may lead to both case misclassification and under ascertainment.
OBJECTIVE: To assess the positive and negative predictive values of osteoarthritis (OA) diagnoses contained in an administrative database. METHODS: We identified all members (> or =18 years of age) of a Massachusetts health maintenance organization with documentation of at least one health care encounter associated with an OA diagnosis during the period 1994-1996. From this population, we randomly selected 350 subjects. In addition, we randomly selected 250 enrollees (proportionally by the age and sex of the 350 subjects) who did not have a health care encounter associated with an OA diagnosis. Trained nurse reviewers abstracted OA-related clinical, laboratory, and radiologic data from the medical records of both study groups (all but 1 chart was available for review). Pairs of physician reviewers evaluated the abstracted information for both groups of subjects and rated the evidence for the presence of OA according to 3 levels: definite, possible, and unlikely. RESULTS: Among the group of patients with an administrative diagnosis of OA, 215 (62%) were rated as having definite OA, 36 (10%) possible OA, and 98 (28%) unlikely OA, according to information contained in the medical record. The positive predictive value of an OA diagnosis was 62%. In those without an administrative OA diagnosis, 44 (18%) were assigned a rating of definite OA. The negative predictive value of the absence of an administrative OA diagnosis was 78%. CONCLUSION: Use of administrative data in epidemiologic and health services research on OA may lead to both case misclassification and under ascertainment.
Authors: Miriam G Cisternas; Louise Murphy; Jeffrey J Sacks; Daniel H Solomon; David J Pasta; Charles G Helmick Journal: Arthritis Care Res (Hoboken) Date: 2016-05 Impact factor: 4.794
Authors: Mara Meyer Epstein; Cassandra Saphirak; Yanhua Zhou; Candace LeBlanc; Alan G Rosmarin; Arlene Ash; Sonal Singh; Kimberly Fisher; Brenda M Birmann; Jerry H Gurwitz Journal: Pharmacoepidemiol Drug Saf Date: 2019-11-17 Impact factor: 2.890
Authors: Rodrigo Jimenez-Garcıa; Manuel Villanueva-Martınez; Cesar Fernandez-de-Las-Penas; Valentın Hernandez-Barrera; Antonio Rıos-Luna; Pilar Carrasco Garrido; Ana Lopez de Andres; Isabel Jimenez-Trujillo; Jesus San Roman Montero; Angel Gil-de-Miguel Journal: BMC Musculoskelet Disord Date: 2011-02-09 Impact factor: 2.362
Authors: Lisa M Lix; James Ayles; Sharon Bartholomew; Charmaine A Cooke; Joellyn Ellison; Valerie Emond; Naomi C Hamm; Heather Hannah; Sonia Jean; Shannon LeBlanc; Siobhan O'Donnell; J Michael Paterson; Catherine Pelletier; Karen A M Phillips; Rolf Puchtinger; Kim Reimer; Cynthia Robitaille; Mark Smith; Lawrence W Svenson; Karen Tu; Linda D VanTil; Sean Waits; Louise Pelletier Journal: Int J Popul Data Sci Date: 2018-10-05