OBJECTIVE: Administrative health data are frequently used for large population-based studies. However, the validity of these data for identifying neurologic conditions is uncertain. METHODS: This article systematically reviews the literature to assess the validity of administrative data for identifying patients with neurologic conditions. Two reviewers independently assessed for eligibility all abstracts and full-text articles identified through a systematic search of Medline and Embase. Study data were abstracted on a standardized abstraction form to identify ICD code-based case definitions and corresponding sensitivity, specificity, positive predictive values (PPVs), and negative predictive values (NPVs). RESULTS: Thirty full-text articles met the eligibility criteria. These included 8 studies for Alzheimer disease/dementia (sensitivity: 8-86.5, specificity: 56.3-100, PPV: 60-97.9, NPV: 68.0-98.9), 2 for brain tumor (sensitivity: 54.0-100, specificity: 97.0-99.0, PPV: 91.0-98.0), 4 for epilepsy (sensitivity: 98.8, specificity: 69.6, PPV: 62.0-100, NPV: 89.5-99.1), 4 for motor neuron disease (sensitivity: 78.9-93.0, specificity: 99.0-99.9, PPV: 38.0-90.0, NPV: 99), 2 for multiple sclerosis (sensitivity: 85-92.4, specificity: 55.9-92.6, PPV: 74.5-92.7, NPV: 70.8-91.9), 4 for Parkinson disease/parkinsonism (sensitivity: 18.7-100, specificity: 0-99.9, PPV: 38.6-81.0, NPV: 46.0), 3 for spinal cord injury (sensitivity: 0.9-90.6, specificity: 31.9-100, PPV: 27.3-100), and 3 for traumatic brain injury (sensitivity: 45.9-78.0 specificity: 97.8, PPV: 23.7-98.0, NPV: 99.2). No studies met eligibility criteria for cerebral palsy, dystonia, Huntington disease, hydrocephalus, muscular dystrophy, spina bifida, or Tourette syndrome. CONCLUSIONS: To ensure the accurate interpretation of population-based studies with use of administrative health data, the accuracy of case definitions for neurologic conditions needs to be taken into consideration.
OBJECTIVE: Administrative health data are frequently used for large population-based studies. However, the validity of these data for identifying neurologic conditions is uncertain. METHODS: This article systematically reviews the literature to assess the validity of administrative data for identifying patients with neurologic conditions. Two reviewers independently assessed for eligibility all abstracts and full-text articles identified through a systematic search of Medline and Embase. Study data were abstracted on a standardized abstraction form to identify ICD code-based case definitions and corresponding sensitivity, specificity, positive predictive values (PPVs), and negative predictive values (NPVs). RESULTS: Thirty full-text articles met the eligibility criteria. These included 8 studies for Alzheimer disease/dementia (sensitivity: 8-86.5, specificity: 56.3-100, PPV: 60-97.9, NPV: 68.0-98.9), 2 for brain tumor (sensitivity: 54.0-100, specificity: 97.0-99.0, PPV: 91.0-98.0), 4 for epilepsy (sensitivity: 98.8, specificity: 69.6, PPV: 62.0-100, NPV: 89.5-99.1), 4 for motor neuron disease (sensitivity: 78.9-93.0, specificity: 99.0-99.9, PPV: 38.0-90.0, NPV: 99), 2 for multiple sclerosis (sensitivity: 85-92.4, specificity: 55.9-92.6, PPV: 74.5-92.7, NPV: 70.8-91.9), 4 for Parkinson disease/parkinsonism (sensitivity: 18.7-100, specificity: 0-99.9, PPV: 38.6-81.0, NPV: 46.0), 3 for spinal cord injury (sensitivity: 0.9-90.6, specificity: 31.9-100, PPV: 27.3-100), and 3 for traumatic brain injury (sensitivity: 45.9-78.0 specificity: 97.8, PPV: 23.7-98.0, NPV: 99.2). No studies met eligibility criteria for cerebral palsy, dystonia, Huntington disease, hydrocephalus, muscular dystrophy, spina bifida, or Tourette syndrome. CONCLUSIONS: To ensure the accurate interpretation of population-based studies with use of administrative health data, the accuracy of case definitions for neurologic conditions needs to be taken into consideration.
Authors: Brahmajee K Nallamothu; Hitinder S Gurm; Henry H Ting; Philip P Goodney; Mary A M Rogers; Jeptha P Curtis; Justin B Dimick; Eric R Bates; Harlan M Krumholz; John D Birkmeyer Journal: JAMA Date: 2011-09-28 Impact factor: 56.272
Authors: E S Fisher; F S Whaley; W M Krushat; D J Malenka; C Fleming; J A Baron; D C Hsia Journal: Am J Public Health Date: 1992-02 Impact factor: 9.308
Authors: Christian Hoppe; Patrick Obermeier; Susann Muehlhans; Maren Alchikh; Lea Seeber; Franziska Tief; Katharina Karsch; Xi Chen; Sindy Boettcher; Sabine Diedrich; Tim Conrad; Bron Kisler; Barbara Rath Journal: Drug Saf Date: 2016-10 Impact factor: 5.606
Authors: Benjamin R Kummer; Iván Diaz; Xian Wu; Ashley E Aaroe; Monica L Chen; Costantino Iadecola; Hooman Kamel; Babak B Navi Journal: Ann Neurol Date: 2019-08-29 Impact factor: 10.422
Authors: Nicole Rosendale; Elan L Guterman; John P Betjemann; S Andrew Josephson; Vanja C Douglas Journal: Neurology Date: 2019-05-24 Impact factor: 9.910
Authors: Jacob K Greenberg; Travis R Ladner; Margaret A Olsen; Chevis N Shannon; Jingxia Liu; Chester K Yarbrough; Jay F Piccirillo; John C Wellons; Matthew D Smyth; Tae Sung Park; David D Limbrick Journal: Neurosurgery Date: 2015-08 Impact factor: 4.654
Authors: Victoria L Tang; Rebecca Sudore; Irena Stijacic Cenzer; W John Boscardin; Alex Smith; Christine Ritchie; Margaret Wallhagen; Emily Finlayson; Laura Petrillo; Kenneth Covinsky Journal: J Gen Intern Med Date: 2016-09-07 Impact factor: 5.128