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.