Diagnostic accuracy of physician review, expert algorithms and data-derived algorithms in adult verbal autopsies.
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 da...
| Main Authors: | , , |
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| Format: | Journal article |
| Language: | English |
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1999
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| _version_ | 1797102226462736384 |
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| author | Quigley, M Chandramohan, D Rodrigues, L |
| author_facet | Quigley, M Chandramohan, D Rodrigues, L |
| author_sort | Quigley, M |
| collection | OXFORD |
| description | 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. |
| first_indexed | 2024-03-07T06:02:58Z |
| format | Journal article |
| id | oxford-uuid:ecda0cb2-c460-4559-9390-3e54c863587a |
| institution | University of Oxford |
| language | English |
| last_indexed | 2024-03-07T06:02:58Z |
| publishDate | 1999 |
| record_format | dspace |
| spelling | oxford-uuid:ecda0cb2-c460-4559-9390-3e54c863587a2022-03-27T11:20:32ZDiagnostic accuracy of physician review, expert algorithms and data-derived algorithms in adult verbal autopsies.Journal articlehttp://purl.org/coar/resource_type/c_dcae04bcuuid:ecda0cb2-c460-4559-9390-3e54c863587aEnglishSymplectic Elements at Oxford1999Quigley, MChandramohan, DRodrigues, LBACKGROUND: 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. |
| spellingShingle | Quigley, M Chandramohan, D Rodrigues, L Diagnostic accuracy of physician review, expert algorithms and data-derived algorithms in adult verbal autopsies. |
| title | Diagnostic accuracy of physician review, expert algorithms and data-derived algorithms in adult verbal autopsies. |
| title_full | Diagnostic accuracy of physician review, expert algorithms and data-derived algorithms in adult verbal autopsies. |
| title_fullStr | Diagnostic accuracy of physician review, expert algorithms and data-derived algorithms in adult verbal autopsies. |
| title_full_unstemmed | Diagnostic accuracy of physician review, expert algorithms and data-derived algorithms in adult verbal autopsies. |
| title_short | Diagnostic accuracy of physician review, expert algorithms and data-derived algorithms in adult verbal autopsies. |
| title_sort | diagnostic accuracy of physician review expert algorithms and data derived algorithms in adult verbal autopsies |
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