Validating physician-certified verbal autopsy and probabilistic modeling (InterVA) approaches to verbal autopsy interpretation using hospital causes of adult deaths

Population Health Metrics

Research

Gideon Nyutu¹  George Mochamah¹  Carolyne Ndila¹  Lena Matata¹  Evasius Bauni¹  Anthony Etyang¹  Benjamin Tsofa²  Thomas N Williams³  Barbara Mambo⁴  Maureen Mutinda⁴  Eric Maitha⁴  Charles Ondieki⁴ 

[1] Department of Epidemiology and Demography, KEMRI-Wellcome Trust Research Programme, PO Box 230, 80108, Kilifi, Kenya;Department of Epidemiology and Demography, KEMRI-Wellcome Trust Research Programme, PO Box 230, 80108, Kilifi, Kenya;Kilifi District Hospital, PO Box 9, 80108, Kilifi, Kenya;Department of Epidemiology and Demography, KEMRI-Wellcome Trust Research Programme, PO Box 230, 80108, Kilifi, Kenya;Nuffield Department of Medicine, John Radcliffe Hospital, OX39DS, Oxford, UK;Department of Paediatrics, John Radcliffe Hospital, OX39DS, Oxford, UK;INDEPTH Network of Demographic Surveillance Sites, Accra, Ghana;Kilifi District Hospital, PO Box 9, 80108, Kilifi, Kenya;

关键词: verbal autopsy;    InterVA;    validation;    cause-specific mortality fraction;    kappa;    ROC;   

DOI  :  10.1186/1478-7954-9-49

 received in 2011-02-11, accepted in 2011-08-05,  发布年份 2011

来源: Springer

【 摘 要 】

BackgroundThe most common method for determining cause of death is certification by physicians based either on available medical records, or where such data are not available, through verbal autopsy (VA). The physician-certification approach is costly and inconvenient; however, recent work shows the potential of a computer-based probabilistic model (InterVA) to interpret verbal autopsy data in a more convenient, consistent, and rapid way. In this study we validate separately both physician-certified verbal autopsy (PCVA) and the InterVA probabilistic model against hospital cause of death (HCOD) in adults dying in a district hospital on the coast of Kenya.MethodsBetween March 2007 and June 2010, VA interviews were conducted for 145 adult deaths that occurred at Kilifi District Hospital. The VA data were reviewed by a physician and the cause of death established. A range of indicators (including age, gender, physical signs and symptoms, pregnancy status, medical history, and the circumstances of death) from the VA forms were included in the InterVA for interpretation. Cause-specific mortality fractions (CSMF), Cohen's kappa (κ) statistic, receiver operating characteristic (ROC) curves, sensitivity, specificity, and positive predictive values were applied to compare agreement between PCVA, InterVA, and HCOD.ResultsHCOD, InterVA, and PCVA yielded the same top five underlying causes of adult deaths. The InterVA overestimated tuberculosis as a cause of death compared to the HCOD. On the other hand, PCVA overestimated diabetes. Overall, CSMF for the five major cause groups by the InterVA, PCVA, and HCOD were 70%, 65%, and 60%, respectively. PCVA versus HCOD yielded a higher kappa value (κ = 0.52, 95% confidence interval [CI]: 0.48, 0.54) than the InterVA versus HCOD which yielded a kappa (κ) value of 0.32 (95% CI: 0.30, 0.38). Overall, (κ) agreement across the three methods was 0.41 (95% CI: 0.37, 0.48). The areas under the ROC curves were 0.82 for InterVA and 0.88 for PCVA. The observed sensitivities and specificities across the five major causes of death varied from 43% to 100% and 87% to 99%, respectively, for the InterVA/PCVA against the HCOD.ConclusionBoth the InterVA and PCVA compared well with the HCOD at a population level and determined the top five underlying causes of death in the rural community of Kilifi. We hope that our study, albeit small, provides new and useful data that will stimulate further definitive work on methods of interpreting VA data.

【 授权许可】

CC BY   
© Bauni et al; licensee BioMed Central Ltd. 2011