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

Abstract

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.

Linking health facility data from young adults aged 18-24 years to longitudinal demographic data: Experience from The Kilifi Health and Demographic Surveillance System

“…The epidemiological transition is evident in the clinic visit data, where there has been a partial shift away from infectious diseases and undernutrition, with sexually transmitted infections, reproductive health problems and injuries becoming relatively more prominent 13 . The findings confirm previous work by Bauni and colleagues, who have shown that injuries and pregnancy-related conditions are now major causes of death among young men and women, respectively, living within the KHDSS area 25 . Although injury and HIV were not among the main reasons for visiting the health facility, other studies of the same community have shown injury and HIV as the main cause of death for young men and women, respectively 25 .…”

“…The findings confirm previous work by Bauni and colleagues, who have shown that injuries and pregnancy-related conditions are now major causes of death among young men and women, respectively, living within the KHDSS area 25 . Although injury and HIV were not among the main reasons for visiting the health facility, other studies of the same community have shown injury and HIV as the main cause of death for young men and women, respectively 25 . Furthermore, Etyang and colleagues have reported that injuries were the most common cause of admission to Kilifi County Hospital among men, while infectious and parasitic diseases, and pregnancy-related complications were the most common among women 26 .…”

Causes of death among persons of all ages within the Kilifi Health and Demographic Surveillance System, Kenya, determined from verbal autopsies interpreted using the InterVA-4 model

“…The approach has been used to determine CODs among people of all ages in a wide range of settings (2, 7, 9–16); however, in Kenya, although VAs have been widely used as a tool for documenting CODs (7, 12–14, 17–21) , no study has yet been published in which the WHO-compliant InterVA-4 has been applied across deaths of all ages. The InterVA-4 model uses posterior probabilities for CODs, given an a priori distribution of CODs in the population and conditional probabilities for circumstances leading to death.…”

Returning home to die or leaving home to seek health care? Location of death of urban and rural residents in Burkina Faso and Senegal

“…But the illness history is not taken into account in the inter-VA-4 software [31]. Previous research has shown ‘a moderate agreement’ between causes of death derived from the inter-VA software and diagnosed by physicians [44,45]. Furthermore, in this study, inconsistency between the two methods of cause of death interpretation may be limited by the use of broad groups of causes of death.…”