TY - JOUR
T1 - Using verbal autopsy to measure causes of death
T2 - The comparative performance of existing methods
AU - Murray, Christopher J.L.
AU - Lozano, Rafael
AU - Flaxman, Abraham D.
AU - Serina, Peter
AU - Phillips, David
AU - Stewart, Andrea
AU - James, Spencer L.
AU - Vahdatpour, Alireza
AU - Atkinson, Charles
AU - Freeman, Michael K.
AU - Ohno, Summer L.
AU - Black, Robert
AU - Ali, Said M.
AU - Baqui, Abdullah H.
AU - Dandona, Lalit
AU - Dantzer, Emily
AU - Darmstadt, Gary L.
AU - Das, Vinita
AU - Dhingra, Usha
AU - Dutta, Arup
AU - Fawzi, Wafaie
AU - Gómez, Sara
AU - Hernández, Bernardo
AU - Joshi, Rohina
AU - Kalter, Henry D.
AU - Kumar, Aarti
AU - Kumar, Vishwajeet
AU - Lucero, Marilla
AU - Mehta, Saurabh
AU - Neal, Bruce
AU - Praveen, Devarsetty
AU - Premji, Zul
AU - Ramírez-Villalobos, Dolores
AU - Remolador, Hazel
AU - Riley, Ian
AU - Romero, Minerva
AU - Said, Mwanaidi
AU - Sanvictores, Diozele
AU - Sazawal, Sunil
AU - Tallo, Veronica
AU - Lopez, Alan D.
N1 - Funding Information:
The work was funded by a grant from the Bill & Melinda Gates Foundation through the Grand Challenges in Global Health Initiative. The funders had no role in study design, data collection and analysis, interpretation of data, decision to publish, or preparation of the manuscript. The corresponding author had full access to all data analyzed and had final responsibility for the decision to submit this original research paper for publication.
PY - 2014/1/9
Y1 - 2014/1/9
N2 - Background: Monitoring progress with disease and injury reduction in many populations will require widespread use of verbal autopsy (VA). Multiple methods have been developed for assigning cause of death from a VA but their application is restricted by uncertainty about their reliability.Methods: We investigated the validity of five automated VA methods for assigning cause of death: InterVA-4, Random Forest (RF), Simplified Symptom Pattern (SSP), Tariff method (Tariff), and King-Lu (KL), in addition to physician review of VA forms (PCVA), based on 12,535 cases from diverse populations for which the true cause of death had been reliably established. For adults, children, neonates and stillbirths, performance was assessed separately for individuals using sensitivity, specificity, Kappa, and chance-corrected concordance (CCC) and for populations using cause specific mortality fraction (CSMF) accuracy, with and without additional diagnostic information from prior contact with health services. A total of 500 train-test splits were used to ensure that results are robust to variation in the underlying cause of death distribution.Results: Three automated diagnostic methods, Tariff, SSP, and RF, but not InterVA-4, performed better than physician review in all age groups, study sites, and for the majority of causes of death studied. For adults, CSMF accuracy ranged from 0.764 to 0.770, compared with 0.680 for PCVA and 0.625 for InterVA; CCC varied from 49.2% to 54.1%, compared with 42.2% for PCVA, and 23.8% for InterVA. For children, CSMF accuracy was 0.783 for Tariff, 0.678 for PCVA, and 0.520 for InterVA; CCC was 52.5% for Tariff, 44.5% for PCVA, and 30.3% for InterVA. For neonates, CSMF accuracy was 0.817 for Tariff, 0.719 for PCVA, and 0.629 for InterVA; CCC varied from 47.3% to 50.3% for the three automated methods, 29.3% for PCVA, and 19.4% for InterVA. The method with the highest sensitivity for a specific cause varied by cause.Conclusions: Physician review of verbal autopsy questionnaires is less accurate than automated methods in determining both individual and population causes of death. Overall, Tariff performs as well or better than other methods and should be widely applied in routine mortality surveillance systems with poor cause of death certification practices.
AB - Background: Monitoring progress with disease and injury reduction in many populations will require widespread use of verbal autopsy (VA). Multiple methods have been developed for assigning cause of death from a VA but their application is restricted by uncertainty about their reliability.Methods: We investigated the validity of five automated VA methods for assigning cause of death: InterVA-4, Random Forest (RF), Simplified Symptom Pattern (SSP), Tariff method (Tariff), and King-Lu (KL), in addition to physician review of VA forms (PCVA), based on 12,535 cases from diverse populations for which the true cause of death had been reliably established. For adults, children, neonates and stillbirths, performance was assessed separately for individuals using sensitivity, specificity, Kappa, and chance-corrected concordance (CCC) and for populations using cause specific mortality fraction (CSMF) accuracy, with and without additional diagnostic information from prior contact with health services. A total of 500 train-test splits were used to ensure that results are robust to variation in the underlying cause of death distribution.Results: Three automated diagnostic methods, Tariff, SSP, and RF, but not InterVA-4, performed better than physician review in all age groups, study sites, and for the majority of causes of death studied. For adults, CSMF accuracy ranged from 0.764 to 0.770, compared with 0.680 for PCVA and 0.625 for InterVA; CCC varied from 49.2% to 54.1%, compared with 42.2% for PCVA, and 23.8% for InterVA. For children, CSMF accuracy was 0.783 for Tariff, 0.678 for PCVA, and 0.520 for InterVA; CCC was 52.5% for Tariff, 44.5% for PCVA, and 30.3% for InterVA. For neonates, CSMF accuracy was 0.817 for Tariff, 0.719 for PCVA, and 0.629 for InterVA; CCC varied from 47.3% to 50.3% for the three automated methods, 29.3% for PCVA, and 19.4% for InterVA. The method with the highest sensitivity for a specific cause varied by cause.Conclusions: Physician review of verbal autopsy questionnaires is less accurate than automated methods in determining both individual and population causes of death. Overall, Tariff performs as well or better than other methods and should be widely applied in routine mortality surveillance systems with poor cause of death certification practices.
KW - Cause of death
KW - InterVA
KW - King-Lu
KW - Random forests
KW - Symptom pattern
KW - Tariff
KW - VA
KW - Validation
KW - Verbal autopsy
UR - http://www.scopus.com/inward/record.url?scp=84891916965&partnerID=8YFLogxK
U2 - 10.1186/1741-7015-12-5
DO - 10.1186/1741-7015-12-5
M3 - Article
C2 - 24405531
AN - SCOPUS:84891916965
SN - 1741-7015
VL - 12
JO - BMC Medicine
JF - BMC Medicine
IS - 1
M1 - 5
ER -