Abstract
BackgroundVerbal autopsy (VA) is recognized as the only feasible alternative to comprehensive medical certification of deaths in settings with no or unreliable vital registration systems. However, a barrier to its use by national registration systems has been the amount of time and cost needed for data collection. Therefore, a short VA instrument (VAI) is needed. In this paper we describe a shortened version of the VAI developed for the Population Health Metrics Research Consortium (PHMRC) Gold Standard Verbal Autopsy Validation Study using a systematic approach.MethodsWe used data from the PHMRC validation study. Using the Tariff 2.0 method, we first established a rank order of individual questions in the PHMRC VAI according to their importance in predicting causes of death. Second, we reduced the size of the instrument by dropping questions in reverse order of their importance. We assessed the predictive performance of the instrument as questions were removed at the individual level by calculating chance-corrected concordance and at the population level with cause-specific mortality fraction (CSMF) accuracy. Finally, the optimum size of the shortened instrument was determined using a first derivative analysis of the decline in performance as the size of the VA instrument decreased for adults, children, and neonates.ResultsThe full PHMRC VAI had 183, 127, and 149 questions for adult, child, and neonatal deaths, respectively. The shortened instrument developed had 109, 69, and 67 questions, respectively, representing a decrease in the total number of questions of 40-55 %. The shortened instrument, with text, showed non-significant declines in CSMF accuracy from the full instrument with text of 0.4 %, 0.0 %, and 0.6 % for the adult, child, and neonatal modules, respectively.ConclusionsWe developed a shortened VAI using a systematic approach, and assessed its performance when administered using hand-held electronic tablets and analyzed using Tariff 2.0. The length of a VA questionnaire was shortened by almost 50 % without a significant drop in performance. The shortened VAI developed reduces the burden of time and resources required for data collection and analysis of cause of death data in civil registration systems.Electronic supplementary materialThe online version of this article (doi:10.1186/s12916-015-0528-8) contains supplementary material, which is available to authorized users.
Highlights
Verbal autopsy (VA) is recognized as the only feasible alternative to comprehensive medical certification of deaths in settings with no or unreliable vital registration systems
Electronic systems for data collection need to replace paper-based systems. We address these needs in this paper and describe a shortened version of the VA instrument (VAI) developed for the Population Health Metrics Research Consortium Gold Standard Verbal Autopsy Validation Study (PHMRC study) [16]
The shortening of an instrument may lead to a decrease in performance and some loss of specificity, at least for rare diseases, we have demonstrated by formal statistical methods applied to validation datasets, where the true Cause of death (COD) is known, that it is possible to reduce the length of a VA questionnaire by 40 % or more without a significant drop in performance
Summary
Verbal autopsy (VA) is recognized as the only feasible alternative to comprehensive medical certification of deaths in settings with no or unreliable vital registration systems. VAs have been incorporated into official data collection systems already in place in countries such as India [5], Brazil [6], Bangladesh [7], and Sri Lanka [8], as well as through the collection of VA samples during national censuses as in Mozambique [9], doubts have remained about the ability of VAs to provide accurate and timely information about the COD in populations This can be attributed, in large part, to the initial reliance on physician certification of verbal autopsies (PCVA) in demographic and health surveillance research sites. PCVA is time-consuming and expensive, and it is difficult to maintain the quality of cause assignment on a large scale over long periods of time
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