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Sökning: L773:1741 7015 OR L773:1741 7015 > Performance of four...

LIBRIS Formathandbok  (Information om MARC21)
FältnamnIndikatorerMetadata
00005736naa a2200589 4500
001oai:DiVA.org:umu-85996
003SwePub
008140214s2014 | |||||||||||000 ||eng|
0247 a https://urn.kb.se/resolve?urn=urn:nbn:se:umu:diva-859962 URI
0247 a https://doi.org/10.1186/1741-7015-12-202 DOI
040 a (SwePub)umu
041 a engb eng
042 9 SwePub
072 7a ref2 swepub-contenttype
072 7a art2 swepub-publicationtype
1001 a Desai, Nikita4 aut
2451 0a Performance of four computer-coded verbal autopsy methods for cause of death assignment compared with physician coding on 24,000 deaths in low- and middle-income countries
264 c 2014-02-04
264 1b BioMed Central,c 2014
338 a electronic2 rdacarrier
520 a BACKGROUND: Physician-coded verbal autopsy (PCVA) is the most widely used method to determine causes of death (CODs) in countries where medical certification of death is uncommon. Computer-coded verbal autopsy (CCVA) methods have been proposed as a faster and cheaper alternative to PCVA, though they have not been widely compared to PCVA or to each other.METHODS: We compared the performance of open-source random forest, open-source tariff method, InterVA-4, and the King-Lu method to PCVA on five datasets comprising over 24,000 verbal autopsies from low- and middle-income countries. Metrics to assess performance were positive predictive value and partial chance-corrected concordance at the individual level, and cause-specific mortality fraction accuracy and cause-specific mortality fraction error at the population level.RESULTS: The positive predictive value for the most probable COD predicted by the four CCVA methods averaged about 43% to 44% across the datasets. The average positive predictive value improved for the top three most probable CODs, with greater improvements for open-source random forest (69%) and open-source tariff method (68%) than for InterVA-4 (62%). The average partial chance-corrected concordance for the most probable COD predicted by the open-source random forest, open-source tariff method and InterVA-4 were 41%, 40% and 41%, respectively, with better results for the top three most probable CODs. Performance generally improved with larger datasets. At the population level, the King-Lu method had the highest average cause-specific mortality fraction accuracy across all five datasets (91%), followed by InterVA-4 (72% across three datasets), open-source random forest (71%) and open-source tariff method (54%).CONCLUSIONS: On an individual level, no single method was able to replicate the physician assignment of COD more than about half the time. At the population level, the King-Lu method was the best method to estimate cause-specific mortality fractions, though it does not assign individual CODs. Future testing should focus on combining different computer-coded verbal autopsy tools, paired with PCVA strengths. This includes using open-source tools applied to larger and varied datasets (especially those including a random sample of deaths drawn from the population), so as to establish the performance for age- and sex-specific CODs.
650 7a MEDICIN OCH HÄLSOVETENSKAPx Hälsovetenskapx Folkhälsovetenskap, global hälsa, socialmedicin och epidemiologi0 (SwePub)303022 hsv//swe
650 7a MEDICAL AND HEALTH SCIENCESx Health Sciencesx Public Health, Global Health, Social Medicine and Epidemiology0 (SwePub)303022 hsv//eng
653 a Causes of death
653 a Computer-coded verbal autopsy (CCVA)
653 a InterVA-4
653 a King-Lu
653 a Physician-certified verbal autopsy (PCVA)
653 a Random forest
653 a Tariff method
653 a Validation
653 a Verbal autopsy
7001 a Aleksandrowicz, Lukasz4 aut
7001 a Miasnikof, Pierre4 aut
7001 a Lu, Ying4 aut
7001 a Leitao, Jordana4 aut
7001 a Byass, Peteru Umeå universitet,Epidemiologi och global hälsa,WHO Collaborating Centre for Verbal Autopsy, Umeå Centre for Global Health Research, Umeå University, Umeå4 aut0 (Swepub:umu)peby0002
7001 a Tollman, Stephenu Umeå universitet,Epidemiologi och global hälsa,Medical Research Council/Wits University Rural Public Health and Health Transitions Research Unit (Agincourt), School of Public Health, Faculty of Health Sciences, University of the Witwatersrand, Johannesburg, South Africa and International Network for the Demographic Evaluation of Populations and Their Health (INDEPTH) Network, Accra, Ghana4 aut0 (Swepub:umu)stto0004
7001 a Mee, Paulu Umeå universitet,Epidemiologi och global hälsa,Medical Research Council/Wits University Rural Public Health and Health Transitions Research Unit (Agincourt), School of Public Health, Faculty of Health Sciences, University of the Witwatersrand, Johannesburg, South Africa,Umeå Centre for Global Health Research4 aut0 (Swepub:umu)pame0007
7001 a Alam, Dewan4 aut
7001 a Rathi, Suresh Kumar4 aut
7001 a Singh, Abhishek4 aut
7001 a Kumar, Rajesh4 aut
7001 a Ram, Faujdar4 aut
7001 a Jha, Prabhat4 aut
7102 a Umeå universitetb Epidemiologi och global hälsa4 org
7730 t BMC Medicined : BioMed Centralg 12:1, s. 20-q 12:1<20-x 1741-7015
8564 u https://doi.org/10.1186/1741-7015-12-20y Fulltext
8564 u https://umu.diva-portal.org/smash/get/diva2:696495/FULLTEXT01.pdfx primaryx Raw objecty fulltext:print
8564 u https://bmcmedicine.biomedcentral.com/track/pdf/10.1186/1741-7015-12-20
8564 8u https://urn.kb.se/resolve?urn=urn:nbn:se:umu:diva-85996
8564 8u https://doi.org/10.1186/1741-7015-12-20

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