
doi: 10.1111/tmi.13678
pmid: 34498340
AbstractObjectivesTo systematically review current practices, strengths and limitations of existing VA approaches to increase understanding of health system stakeholders and researchers.MethodsThe review was conducted and reported based on the Preferred Reporting Items for Systematic Reviews and Meta‐Analysis (PRISMA) guidelines, in which articles were systematically obtained from the PubMed and SCOPUS online databases. The search was limited to English language journal articles published between 2010 and 2020. The review identified 5602 articles and after thorough scrutiny, 25 articles related to VA approaches were included.Results(1) InterVA and Tariff are widely used VA models; (2) Bayes rule is the most common and successful algorithm; (3) the lack of standardised datasets and metrics to evaluate models creates bias in determining VA model performance; (4) performance of the models trained using in‐hospital data cannot be replicated in community death; (5) the performance of models among physicians and computer‐coded algorithms differs with variation in settings.ConclusionThe physician‐certified verbal autopsy (PCVA) approaches are more effective in determining community CoD while computerised coding of verbal autopsy (CCVA) models perform well when the underlying CoD are reliably established using hospital data where data are trained in a similar environment to the target population. Our study recommends the use of hybrid models that combine strengths from various models and using an open standards dataset that includes death from different settings.
Interviews as Topic, United States Department of Veterans Affairs, Cause of Death, Humans, Models, Theoretical, United States
Interviews as Topic, United States Department of Veterans Affairs, Cause of Death, Humans, Models, Theoretical, United States
| selected citations These citations are derived from selected sources. This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically). | 7 | |
| popularity This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network. | Top 10% | |
| influence This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically). | Average | |
| impulse This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network. | Top 10% |
