Validating hierarchical verbal autopsy expert algorithms in a large data set with known causes of death
Physician assessment historically has been the most common method of analyzing verbal autopsy (VA) data. Recently, the World Health Organization endorsed two automated methods, Tariff 2.0 and InterVA–4, which promise greater objectivity and lower cost. A disadvantage of the Tariff method is that i...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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Edinburgh University Global Health Society
2016-06-01
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| Series: | Journal of Global Health |
| Subjects: | |
| Online Access: | http://www.jogh.org/documents/issue201601/jogh-06-010601.pdf |
| _version_ | 1818055552229441536 |
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| author | Henry D Kalter Jamie Perin Robert E Black |
| author_facet | Henry D Kalter Jamie Perin Robert E Black |
| author_sort | Henry D Kalter |
| collection | DOAJ |
| description | Physician assessment historically has been the most common method of analyzing verbal autopsy (VA) data. Recently, the World Health Organization endorsed two automated methods,
Tariff 2.0 and InterVA–4, which promise greater objectivity and lower cost. A disadvantage of the Tariff method is that it requires a training data set from a prior validation study, while InterVA relies on clinically specified conditional probabilities. We undertook to validate the hierarchical expert algorithm analysis of VA data, an automated, intuitive, deterministic method that does not require a training data set. |
| first_indexed | 2024-12-10T12:14:45Z |
| format | Article |
| id | doaj.art-46da7514be0b4d02947ed9a435714606 |
| institution | Directory Open Access Journal |
| issn | 2047-2978 2047-2986 |
| language | English |
| last_indexed | 2024-12-10T12:14:45Z |
| publishDate | 2016-06-01 |
| publisher | Edinburgh University Global Health Society |
| record_format | Article |
| series | Journal of Global Health |
| spelling | doaj.art-46da7514be0b4d02947ed9a4357146062022-12-22T01:49:15ZengEdinburgh University Global Health SocietyJournal of Global Health2047-29782047-29862016-06-016110.7189/jogh.06.010601Validating hierarchical verbal autopsy expert algorithms in a large data set with known causes of deathHenry D Kalter0Jamie Perin1Robert E Black2Health Systems Program, Department of International Health, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USACenter for Child and Community Health Research, Department of Pediatrics, Johns Hopkins School of Medicine, Baltimore, MD, USA; Institute for International Programs, Department of International Health, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USAInstitute for International Programs, Department of International Health, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USAPhysician assessment historically has been the most common method of analyzing verbal autopsy (VA) data. Recently, the World Health Organization endorsed two automated methods, Tariff 2.0 and InterVA–4, which promise greater objectivity and lower cost. A disadvantage of the Tariff method is that it requires a training data set from a prior validation study, while InterVA relies on clinically specified conditional probabilities. We undertook to validate the hierarchical expert algorithm analysis of VA data, an automated, intuitive, deterministic method that does not require a training data set.http://www.jogh.org/documents/issue201601/jogh-06-010601.pdfVASAvalidation |
| spellingShingle | Henry D Kalter Jamie Perin Robert E Black Validating hierarchical verbal autopsy expert algorithms in a large data set with known causes of death Journal of Global Health VASA validation |
| title | Validating hierarchical verbal autopsy expert algorithms in a large data set with known causes of death |
| title_full | Validating hierarchical verbal autopsy expert algorithms in a large data set with known causes of death |
| title_fullStr | Validating hierarchical verbal autopsy expert algorithms in a large data set with known causes of death |
| title_full_unstemmed | Validating hierarchical verbal autopsy expert algorithms in a large data set with known causes of death |
| title_short | Validating hierarchical verbal autopsy expert algorithms in a large data set with known causes of death |
| title_sort | validating hierarchical verbal autopsy expert algorithms in a large data set with known causes of death |
| topic | VASA validation |
| url | http://www.jogh.org/documents/issue201601/jogh-06-010601.pdf |
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