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Background: Most deaths in China occur at home, making it difficult to collect reliable cause of death (CoD) information. Verbal autopsy (VA) was applied using the SmartVA tool to a sample of home deaths in China to explore its feasibility as a means of improving the quality of CoD data. Methods: The study was carried out in 22 districts in 9 provinces, located in north-east, central, and western areas of China during 2017 and 2018. Trained interviewers selected suitable respondents in each household to collect information using the Population Health Metrics Research Consortium (PHMRC) shortened and validated electronic VA questionnaire on tablets. The CoD was diagnosed from the interview data using the SmartVA-Analyze 2.0 software (Tariff 2.0). Results: Non-communicable diseases (NCDs) dominated the leading causes of death in all age groups and for both sexes. After redistribution of undetermined causes, stroke (24%), ischemic heart diseases (IHD) (21%), chronic respiratory diseases (11%), and lung cancer (6%) were the leading causes of death. The cause fractions for level-one cause categories and ranking of specific causes were similar between SmartVA and results from the Global Burden of Disease (GBD) study. Conclusion: Evidence from this large pilot study suggests that SmartVA is a feasible and plausible tool and could be a valuable tool to improve the quality and standardization of CoD information across China. [ABSTRACT FROM AUTHOR]

Background: There is increasing interest in using verbal autopsy to produce nationally representative populationlevel estimates of causes of death. However, the burden of processing a large quantity of surveys collected with paper and pencil has been a barrier to scaling up verbal autopsy surveillance. Direct electronic data capture has been used in other large-scale surveys and can be used in verbal autopsy as well, to reduce time and cost of going from collected data to actionable information. Methods: We collected verbal autopsy interviews using paper and pencil and using electronic tablets at two sites, and measured the cost and time required to process the surveys for analysis. From these cost and time data, we extrapolated costs associated with conducting large-scale surveillance with verbal autopsy. Results: We found that the median time between data collection and data entry for surveys collected on paper and pencil was approximately 3 months. For surveys collected on electronic tablets, this was less than 2 days. For small-scale surveys, we found that the upfront costs of purchasing electronic tablets was the primary cost and resulted in a higher total cost. For large-scale surveys, the costs associated with data entry exceeded the cost of the tablets, so electronic data capture provides both a quicker and cheaper method of data collection. Conclusions: As countries increase verbal autopsy surveillance, it is important to consider the best way to design sustainable systems for data collection. Electronic data capture has the potential to greatly reduce the time and costs associated with data collection. For long-term, large-scale surveillance required by national vital statistical systems, electronic data capture reduces costs and allows data to be available sooner. [ABSTRACT FROM AUTHOR]

Background: More countries are using verbal autopsy as a part of routine mortality surveillance. The length of time required to complete a verbal autopsy interview is a key logistical consideration for planning large-scale surveillance. Methods: We use the PHMRC shortened questionnaire to conduct verbal autopsy interviews at three sites and collect data on the length of time required to complete the interview. This instrument uses a novel checklist of keywords to capture relevant information from the open response. The open response section is timed separately from the section consisting of closed questions. Results: We found the median time to complete the entire interview was approximately 25 minutes and did not vary substantially by age-specific module. The median time for the open response section was approximately 4 minutes and 60% of interviewees mentioned at least one keyword within the open response section. Conclusions: The length of time required to complete the interview was short enough for large-scale routine use. The open-response section did not add a substantial amount of time and provided useful information which can be used to increase the accuracy of the predictions of the cause of death. The novel checklist approach further reduces the burden of transcribing and translating a large amount of free text. This makes the PHMRC instrument ideal for national mortality surveillance. [ABSTRACT FROM AUTHOR]

Background: 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. 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.Methods: We 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.Results: The 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.Conclusions: We 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. [ABSTRACT FROM AUTHOR]

Objective: Population-based information on causes of death (CoD) by age, sex, and area is critical for countries with limited resources to identify and address key public health issues. This study analysed the demographic surveillance and verbal autopsy (VA) data to estimate age- and sex-specific mortality rates and cause-specific mortality fractions in two well-defined rural populations within the demographic surveillance system in Abhoynagar and Mirsarai subdistricts, located in different climatic zones. Design: During 2004-2010, the sample demographic surveillance system registered 1,384 deaths in Abhoynagar and 1,847 deaths in Mirsarai. Trained interviewers interviewed the main caretaker of the deceased with standard VA questionnaires to record signs and symptoms of diseases or conditions that led to death and health care experiences before death. The computer-automated InterVA-4 method was used to analyse VAs to determine probable CoD. Results: Age- and sex-specific death rates revealed a higher neonatal mortality rate in Abhoynagar than Mirsarai, and death rates and sex ratios of male to female death rates were higher in the ages after infancy. Communicable diseases (CDs) accounted for 16.7% of all deaths in Abhoynagar and 21.2% in Mirsarai -- the difference was due mostly to more deaths from acute respiratory infections, pneumonia, and tuberculosis in Mirsarai. Noncommunicable diseases (NCDs) accounted for 56.2 and 55.3% of deaths in each subdistrict, respectively, with leading causes being stroke (16.5-19.3%), neoplasms (13.2% each), cardiac diseases (8.9-11.6%), chronic obstructive pulmonary diseases (5.1-6.3%), diseases of the digestive system (3.1-4.1%), and diabetes (2.8-3.5%), together accounting for 49.2-51.2% points of the NCD deaths in the two subdistricts. Injury and other external causes accounted for another 7.5-7.7% deaths, with self-harm being higher among females in Abhoynagar. Conclusions: The computer-automated coding of VA to determine CoD reconfirmed that NCDs were the leading CoD with some differences between the sites. Incorporating VA into the national sample vital registration system can help policy makers to identify the leading CoDs for public health planning. [ABSTRACT FROM AUTHOR]

Background: Deaths in developing countries often occur outside health facilities, making it extremely difficult to gather reliable cause of death (COD) information. Automated COD assignment using a verbal autopsy instrument (VAI) has been proposed as a reliable and cost-effective alternative to traditional physician-certified verbal autopsy, but its performance is still being evaluated. The purpose of this study was to compare the similarity of diagnosis by Medical Assistants (MA) in the Matlab Health and Demographic Surveillance System (HDSS) with the SmartVA Analyze 1.2 (Tariff 2.0) diagnosis. Methods: This study took place between January 2011 and April 2014 in Matlab, Bangladesh. MA with 3 years of medical training assigned COD to Matlab residents by reviewing the information collected using the Population Health Metrics Research Consortium (PHMRC) long-form VAI. Smart VA Analyze 1.2 automatically assigned COD using the same questionnaire. COD agreement and cause-specific mortality fractions (CSMFs) were compared for MA and Tariff. Results: Of the 4969 verbal autopsy cases reviewed, 4328 were adults, 296 were children, and 345 were neonates. Cohen's kappa was 0.38 (0.36, 0.40) for adults, 0.43 (0.38, 0.49) for children, and 0.27 (0.22, 0.33) for neonates. For adults, the top two COD for MA were stroke (29.6%) and ischemic heart diseases (IHD) (14.2%) and for Tariff these were stroke (32.0%) and IHD (14.0%). For children, the top two COD for MA were drowning (33.5%) and pneumonia (13.2%) and for Tariff these were also drowning (36.8%) and pneumonia (12.4%). For neonates, the top two COD for MA were birth asphyxia (41.2%) and meningitis/sepsis (22.3%) and for Tariff these were birth asphyxia (37.0%) and preterm delivery (30.9%). Conclusion: The CSMFs for Tariff and MA showed very close agreement across all age categories but some differences were observed for neonate preterm delivery and meningitis/sepsis. Given the known advantages of automated methods over physician certified verbal autopsy, the SmartVA software, incorporating the shortened VAI questionnaire and Tariff 2.0, could serve as a cost-effective alternative to Matlab MA to routinely collect and analyze verbal autopsy data in a HDSS to generate essential population level COD data for planning. [ABSTRACT FROM AUTHOR]