title: Predictive value of ICD-9-CM codes used in vaccine safety research.
abstract: OBJECTIVE
  To assess how well selected ICD-9-CM diagnosis codes predict adverse events; to model bias and power loss when vaccine safety analyses rely on unverified codes.
  
  
  METHODS
  We extracted chart verification data for ICD-9-CM diagnosis codes from six Vaccine Safety Datalink (VSD) publications and modeled biases and power losses using positive predictive value (PPV) estimates and ranges of code sensitivity.
  
  
  RESULTS
  Positive predictive values were high for type 1 diabetes (80%) in children, relative to WHO criteria, and intussusception (81%) in young children, relative to a standard published case definition. PPVs were moderate (65%) for inpatient and emergency department childhood seizures and low (21%) for outpatient childhood seizures, both relative to physician investigator judgment. Codes for incident central nervous system demyelinating disease in adults had high PPV for inpatient codes (80%) and low PPV for outpatient codes (42%) relative to physicians' diagnoses. Modeled biases were modest, but large increases in frequencies of adverse events are required to achieve adequate power if unverified ICD-9-CM codes are used, especially when vaccine associations are weak.
  
  
  CONCLUSIONS
  ICD-9-CM codes for type 1 diabetes in children, intussusception in young children, childhood seizures in inpatient and emergency care settings, and inpatient demyelinating disease in adults were sufficiently predictive for vaccine safety analyses to rely on unverified diagnosis codes. Adverse event misclassification should be accounted for in statistical power calculations.
authors.affiliation: Center for Health Research, Kaiser Permanente Northwest, Portland, OR 97227-1110, USA. john.mullooly@kpchr.org
authors.forename: J P
authors.initials: JP
authors.lastname: Mullooly
authors.forename: J G
authors.initials: JG
authors.lastname: Donahue
authors.forename: F
authors.initials: F
authors.lastname: DeStefano
authors.forename: J
authors.initials: J
authors.lastname: Baggs
authors.forename: E
authors.initials: E
authors.lastname: Eriksen
mesh_terms: D015982:Bias; D035843:Biomedical Research; D002648:Child; D002675:Child, Preschool; D019992:Databases as Topic; D064420:Drug-Related Side Effects and Adverse Reactions; D005544:Forecasting; D006801:Humans; D007223:Infant; D007231:Infant, Newborn; D038801:International Classification of Diseases; D011237:Predictive Value of Tests; D014612:Vaccines / Q000009:adverse effects*
chemical_list: D014612:Vaccines