Abstract Background Verbal autopsy (VA) has emerged as an increasingly popular technique to assign cause of death in parts of the world where the majority of deaths occur without proper medical certification. The purpose of this study was to examine the key characteristics of studies that have attempted to validate VA cause of death against an established cause of death. Methods A systematic review was conducted by searching the MEDLINE, EMBASE, Cochrane-library, and Scopus electronic databases. Included studies contained 1) a VA component, 2) a validation component, and 3) original analysis or re-analysis. Characteristics of VA studies were extracted. A total of 527 studies were assessed, and 481 studies screened to give 66 studies selected for data extraction. Results Sixty-six studies were included from multiple countries. Ten studies used an existing database. Sixteen studies used the World Health Organization VA questionnaire and 5 studies used the Population Health Metrics Research Consortium VA questionnaire. Physician certification was used in 36 studies and computer coded methods were used in 14 studies. Thirty-seven studies used high level comparator data with detailed laboratory investigations. Conclusion Most studies found VA to be an effective cause of death assignment method and compared VA cause of death to a high-quality established cause of death. Nonetheless, there were inconsistencies in the methodologies of the validation studies, and many used poor quality comparison cause of death data. Future VA validation studies should adhere to consistent methodological criteria so that policymakers can easily interpret the findings to select the most appropriate VA method. Prospero Registration CRD42020186886. 

/pdf/validation-studies-of-verbal-autopsy-methods-a-systematic-198i1d4w.pdf

Validation studies of verbal autopsy methods: a systematic review

This paper describes the lessons from scaling up a verbal autopsy (VA) intervention to improve data about causes of death according to a nine-domain framework: governance, design, operations, human resources, financing, infrastructure, logistics, information technologies and data quality assurance. We use experiences from China, Myanmar, Papua New Guinea, Philippines and Solomon Islands to explore how VA has been successfully implemented in different contexts, to guide other countries in their VA implementation. The governance structure for VA implementation comprised a multidisciplinary team of technical experts, implementers and staff at different levels within ministries. A staged approach to VA implementation involved scoping and mapping of death registration processes, followed by pretest and pilot phases which allowed for redesign before a phased scale-up. Existing health workforce in countries were trained to conduct the VA interviews as part of their routine role. Costs included training and compensation for the VA interviewers, information technology (IT) infrastructure costs, advocacy and dissemination, which were borne by the funding agency in early stages of implementation. The complexity of the necessary infrastructure, logistics and IT support required for VA increased with scale-up. Quality assurance was built into the different phases of the implementation. VA as a source of cause of death data for community deaths will be needed for some time. With the right technical and political support, countries can scale up this intervention to ensure ongoing collection of quality and timely information on community deaths for use in health planning and better monitoring of national and global health goals.

https://gh.bmj.com/content/bmjgh/6/11/e006760.full.pdf

Integrating community-based verbal autopsy into civil registration and vital statistics: lessons learnt from five countries.

Approximately 30% of deaths in Shanghai either occur at home or are not medically attended. The recorded cause of death (COD) in these cases may not be reliable. We applied the Smart Verbal Autopsy (VA) tool to assign the COD for a representative sample of home deaths certified by 16 community health centers (CHCs) from three districts in Shanghai, from December 2017 to June 2018. The results were compared with diagnoses from routine practice to ascertain the added value of using SmartVA. Overall, cause-specific mortality fraction (CSMF) accuracy improved from 0.93 (93%) to 0.96 after the application of SmartVA. A comparison with a “gold standard (GS)” diagnoses obtained from a parallel medical record review investigation found that 86.3% of the initial diagnoses made by the CHCs were assigned the correct COD, increasing to 90.5% after the application of SmartVA. We conclude that routine application of SmartVA is not indicated for general use in CHCs, although the tool did improve diagnostic accuracy for residual causes, such as other or ill-defined cancers and non-communicable diseases.

/pdf/assessing-the-diagnostic-accuracy-of-physicians-for-home-310fgdrr.pdf

Assessing the Diagnostic Accuracy of Physicians for Home Death Certification in Shanghai: Application of SmartVA

Engagement With Death Registration and Cause-of-Death Reporting to Strengthen Suicide Statistics

Little is known about the proportion and causes of out-of-hospital deaths in Mongolia. In this study, we aimed to determine the proportion and causes of out-of-hospital deaths in Mongolia during a six-month observation period before the COVID-19 pandemic.In a retrospective study, the Mongolian National Death Registry was screened for all deaths occurring from 01 to 06/2020. The proportion and causes of out-of-hospital deaths, causes of out-of-hospital deaths likely treatable by emergency/critical care interventions, as well as sex, regional and seasonal differences in the proportion and causes of out-of-hospital deaths were determined. The primary endpoint was the proportion and causes of out-of-hospital death in children and adults. Descriptive statistical methods, the Fisher's Exact, multirow Chi2-or Mann-Whitney-U-rank sum tests were used for data analysis.Five-thousand-five-hundred-fifty-three of 7762 deaths (71.5%) occurred outside of a hospital. The proportion of out-of-hospital deaths was lower in children than adults (39.3% vs. 74.8%, p < 0.001). Trauma, chronic neurological diseases, lower respiratory tract infections, congenital birth defects, and neonatal disorders were the causes of out-of-hospital deaths resulting in most years of life lost in children. In adults, chronic heart diseases, trauma, liver cancer, poisonings, and self-harm caused the highest burden of premature mortality. The proportion of out-of-hospital deaths did not differ between females and males (70.5% vs. 72.2%, p = 0.09). The proportion (all, p < 0.001; adults, p < 0.001; children, p < 0.001) and causes (adults, p < 0.001; children, p < 0.001) of out-of-hospital deaths differed between Mongolian regions and Ulaanbaatar. The proportion of out-of-hospital deaths was higher during winter than spring/summer months (72.3% vs. 69.9%, p = 0.03). An expert panel estimated that 49.3% of out-of-hospital deaths were likely treatable by emergency/critical care interventions.With regional and seasonal variations, about 75% of Mongolian adults and 40% of Mongolian children died outside of a hospital. Heart diseases, trauma, cancer, and poisonings resulted in most years of life lost. About half of the causes of out-of-hospital deaths could be treated by emergency/critical care interventions.Institutional funding. 

Out-of-hospital deaths in Mongolia: a nationwide cohort study on the proportion, causes, and potential impact of emergency and critical care services

Background: Setting public health policies and effectively monitoring the impact of health interventions requires accurate, timely and complete cause of death (CoD) data for populations. In Sri Lanka, almost half of all deaths occur outside hospitals, with questionable diagnostic accuracy, thus limiting their information content for policy. Objectives: To ascertain whether SmartVA is applicable in improving the specificity of cause of death data for out-of-hospital deaths in Sri Lanka, and hence enhance the value of these routinely collected data for informing public policy debates. Methods: SmartVA was applied to 2610 VAs collected between January 2017 and March 2019 in 22 health-unit-areas clustered in six districts. Around 350 community-health-workers and 50 supervisory-staffs were trained. The resulting distribution of Cause-Specific-Mortality-Fractions (CSMFs) was compared to data from the Registrar-General's-Department (RGD) for out-of-hospital deaths for the same areas, and to the Global-Burden-of-Disease (GBD) estimates for Sri Lanka. Results: Using SmartVA, for only 15% of deaths could a specific-cause not be assigned, compared with around 40% of out-of-hospital deaths currently assigned garbage codes with "very high" or "high" severity. Stroke (M: 31.6%, F: 35.4%), Ischaemic Heart Disease (M: 13.5%, F: 13.0%) and Chronic Respiratory Diseases (M: 15.4%, F: 10.8%) were identified as the three leading causes of home deaths, consistent with the ranking of GBD-Study for Sri Lanka for all deaths, but with a notably higher CSMF for stroke. Conclusions: SmartVA showed greater diagnostic specificity, applicability, acceptability in the Sri Lankan context. Policy formulation in Sri Lanka would benefit substantially with national-wide implementation of VAs.

/pdf/improving-the-policy-utility-of-cause-of-death-statistics-in-3ojp14z33b.pdf

Improving the Policy Utility of Cause of Death Statistics in Sri Lanka: An Empirical Investigation of Causes of Out-of-Hospital Deaths Using Automated Verbal Autopsy Methods.

Handle everyday research tasks with reliable, citation-backed results

Your personal Research Agent to handle research tasks with citation-backed results

Popular Tasks used by Researchers

How can I help with your research?

Meet SciSpace

Get more enhanced response by uploading the PDFs you want me to reference.

No relevant PDFs in your library

SciSpace is the AI research assistant for academics. Run systematic literature reviews on 280M+ papers, and write papers with cited sources. Trusted by 1M+ students, PhDs & researchers.

SciSpace | AI for Research

Analyze PDFs

Code & Manuscripts

Funding & Grants

Literature & Patents

Medical & Clinical Data

Systematic Review

Visualize & Present

Web & Data

Build a Google Scholar-like website for your research.

Build a website

Create charts and images for your research

Create a Chart

Write a paper for submission to a journal

Draft a manuscript

Patent Search

Design eye-catching scientific posters in minutes.

Scientific Poster Generation

Systematic Literature Review

One task is running at the moment. Your messages will be shown right after.

Drag and drop or click here to browse

Loved by <highlight>1 million+</highlight> researchers

Extract a list of specific topics and their sources from unstructured text

Topics

Compare and analyze relevant papers that matches with your search

Papers

Get insights from PDFs and bookmarked papers from your library

My library

Recent searches

Try searching for:

Catch AI-generated content in scholarly and non-scholarly content

{ai} Detector

Ai Writer

Get PDF Summaries, highlighted text explanations 

Chat with PDF

Effortlessly create in-text citations and bibliographies in APA and 2,500 other formats

Citation generator

Get explanations, summaries, and answers on academic papers

Ease up your research workflow with {scispace}'s cohort of exciting AI tools

Elevate your academic writing skills and convey your ideas the way you want

Paraphraser

Explore our range of reading and writing tools

Your file is being prepared and should be ready in a few minutes. If it's a large file, it might take a bit longer. You can close this window, and we'll email you the file when it's done.

You have reached a maximum limit of <strong>{limit}</strong> columns in the table. Remove at least <strong>1</strong> column to add or create another one.

Vindya Kumarapeli

Author Tools

Chat about Author

Papers

Improving the Policy Utility of Cause of Death Statistics in Sri Lanka: An Empirical Investigation of Causes of Out-of-Hospital Deaths Using Automated Verbal Autopsy Methods.