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

Population Health Metrics

Thomas N Williams¹  Anthony Etyang²  Eric Maitha³  Benjamin Tsofa³  Maureen Mutinda³  Barbara Mambo³  Charles Ondieki³  Lena Matata²  Gideon Nyutu²  George Mochamah²  Carolyne Ndila²  Evasius Bauni² 

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

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

Others  :  806618
DOI  :  10.1186/1478-7954-9-49

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

【 摘 要 】

Background

The 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.

Methods

Between 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.

Results

HCOD, 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.

Conclusion

Both 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】

   
2011 Bauni et al; licensee BioMed Central Ltd.

【 图 表 】

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