title: Diagnostic accuracy of physician review, expert algorithms and data-derived algorithms in adult verbal autopsies.
abstract: BACKGROUND
  The verbal autopsy (VA) is used to collect information on cause-specific mortality from bereaved relatives. A cause of death may be assigned by physician review of the questionnaires, or by an algorithm. We compared the diagnostic accuracy of physician review, an expert algorithm, and data-derived algorithms.
  
  
  METHODS
  Data were drawn from a multicentre validation study of 796 adult deaths that occurred in hospitals in Tanzania, Ethiopia, and Ghana. A 'gold standard' cause of death was assigned using hospital records and death certificates. The VA interviews were carried out by trained fieldworkers 1-21 months after the subject's death. A cause of death was assigned by physician review and an expert algorithm. Data-derived algorithms that most accurately estimated the cause-specific mortality fraction (CSMF) for each cause of death were identified using logistic regression.
  
  
  RESULTS
  The most common causes of death were tuberculosis/AIDS (CSMF = 18.6%), malaria (CSMF = 10.7%), meningitis (CSMF = 8.3%), and cardiovascular disorders (CSMF = 8.2%). The CSMF obtained using physician review was within +/-20% of the gold standard value for 12 causes of death including the four common causes. The CSMF obtained using the expert algorithm was within +/-20% of the gold standard for eight causes of death, including tuberculosis/AIDS, malaria, and meningitis. The CSMF obtained using the data-derived algorithms was within +/-20% of the gold standard for seven causes of death, including tuberculosis/ AIDS, meningitis, and cardiovascular disorders. All three methods yielded a specificity of at least 80% for all causes of death, and a sensitivity of at least 80% for deaths due to injuries and rabies.
  
  
  CONCLUSIONS
  For those settings where physician review is not feasible, expert and data-derived algorithms provide an alternative approach for assigning many causes of death. We recommend that the algorithms proposed herein are validated further.
authors.affiliation: Department of Infectious and Tropical Diseases, London School of Hygiene and Tropical Medicine, UK.
authors.forename: M A
authors.initials: MA
authors.lastname: Quigley
authors.forename: D
authors.initials: D
authors.lastname: Chandramohan
authors.forename: L C
authors.initials: LC
authors.lastname: Rodrigues
mesh_terms: D000163:Acquired Immunodeficiency Syndrome / Q000175:diagnosis / Q000401:mortality; D000328:Adult; D000465:Algorithms; D001344:Autopsy / Q000592:standards*; D002318:Cardiovascular Diseases / Q000175:diagnosis / Q000401:mortality; D002423:Cause of Death; D005002:Ethiopia / Q000453:epidemiology; D005869:Ghana / Q000453:epidemiology; D006801:Humans; D008288:Malaria / Q000175:diagnosis / Q000401:mortality; D008581:Meningitis / Q000175:diagnosis / Q000401:mortality; D008875:Middle Aged; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D013636:Tanzania / Q000453:epidemiology; D014376:Tuberculosis / Q000175:diagnosis / Q000401:mortality; D014947:Wounds and Injuries / Q000401:mortality