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.
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: Paul Roddy; Sara L Thomas; Benjamin Jeffs; Pascoal Nascimento Folo; Pedro Pablo Palma; Bengi Moco Henrique; Luis Villa; Fernando Paixao Damiao Machado; Oscar Bernal; Steven M Jones; James E Strong; Heinz Feldmann; Matthias Borchert Journal: J Infect Dis Date: 2010-06-15 Impact factor: 5.226
Authors: Shaun K Morris; Diego G Bassani; Rajesh Kumar; Shally Awasthi; Vinod K Paul; Prabhat Jha Journal: PLoS One Date: 2010-03-08 Impact factor: 3.240
Authors: Hooman Khademi; Arash Etemadi; Farin Kamangar; Mehdi Nouraie; Ramin Shakeri; Behrooz Abaie; Akram Pourshams; Mohammad Bagheri; Afshin Hooshyar; Farhad Islami; Christian C Abnet; Paul Pharoah; Paul Brennan; Paolo Boffetta; Sanford M Dawsey; Reza Malekzadeh Journal: PLoS One Date: 2010-06-17 Impact factor: 3.240
Authors: B Lopman; A Cook; J Smith; G Chawira; M Urassa; Y Kumogola; R Isingo; C Ihekweazu; J Ruwende; M Ndege; S Gregson; B Zaba; T Boerma Journal: J Epidemiol Community Health Date: 2009-10-23 Impact factor: 3.710
Authors: Hafizur R Chowdhury; Sandra C Thompson; Mohammed Ali; Nurul Alam; Mohammed Yunus; Peter K Streatfield Journal: Popul Health Metr Date: 2010-08-17