Validation of SmartVA using conventional autopsy: A study of adult deaths in Brazil.

Autor:	Hart JD; Melbourne School of Population and Global Health, The University of Melbourne, Carlton, Victoria, Australia., de André PA; University of São Paulo, School of Medicine, São Paulo, São Paulo, Brazil., de André CDS; University of São Paulo, Institute of Mathematics and Statistics, São Paulo, São Paulo, Brazil., Adair T; Melbourne School of Population and Global Health, The University of Melbourne, Carlton, Victoria, Australia., Barroso LP; University of São Paulo, Institute of Mathematics and Statistics, São Paulo, São Paulo, Brazil., Valongueiro S; Federal University of Pernambuco, Recife, Pernambuco, Brazil., Bierrenbach AL; Sírio-Libanês Hospital, São Paulo, São Paulo, Brazil.; Vital Strategies, São Paulo, São Paulo, Brazil., de Carvalho PI; Executive Secretary of Health Surveillance of the State of Pernambuco, Brazil., Antunes MBC; University of Pernambuco, Recife, Pernambuco, Brazil., de Oliveira CM; Executive Secretary of the Municipal Health Surveillance of Recife, Pernambuco, Brazil., Pereira LAA; University of São Paulo, School of Medicine, São Paulo, São Paulo, Brazil., Minto CM; São Paulo Health State Secretary, São Paulo, São Paulo, Brazil., Bezerra TMDS; Secretary of State for Health, Recife, Pernambuco, Brazil., Costa SP; Health Surveillance, Municipal Department of Health, Olinda, Pernambuco, Brazil., de Azevedo BA; Secretary of State for Health, Recife, Pernambuco, Brazil., de Lima JRA; Recife Autopsy Service, Recife, Brazil., Mota DSM; Recife Autopsy Service, Recife, Brazil., Ramos AMO; Federal University of Rio Grande do Norte, Health Sciences Center, Natal, Rio Grande do Norte, Brazil.; Natal Autopsy Service, Natal, Rio Grande do Norte, Brazil., de Souza MFM; Vital Strategies, São Paulo, São Paulo, Brazil., da Silva LFF; University of São Paulo, School of Medicine, São Paulo, São Paulo, Brazil.; São Paulo Autopsy Service, University of São Paulo, Sao Paulo, Brazil., França EB; Federal University of Minas Gerais, School of Medicine, Belo Horizonte, Minas Gerais, Brazil., McLaughlin D; Melbourne School of Population and Global Health, The University of Melbourne, Carlton, Victoria, Australia., Riley ID; Melbourne School of Population and Global Health, The University of Melbourne, Carlton, Victoria, Australia., Saldiva PHN; University of São Paulo, School of Medicine, São Paulo, São Paulo, Brazil.
Jazyk:	angličtina
Zdroj:	Lancet regional health. Americas [Lancet Reg Health Am] 2021 Oct 31; Vol. 5, pp. 100081. Date of Electronic Publication: 2021 Oct 31 (Print Publication: 2022).
DOI:	10.1016/j.lana.2021.100081
Abstrakt:	Background: Accurate cause of death data are essential to guide health policy. However, mortality surveillance is limited in many low-income countries. In such settings, verbal autopsy (VA) is increasingly used to provide population-level cause of death data. VAs are now widely interpreted using the automated algorithms SmartVA and InterVA. Here we use conventional autopsy as the gold standard to validate SmartVA methodology. Methods: This study included adult deaths from natural causes in São Paulo and Recife for which conventional autopsy was indicated. VA was conducted with a relative of the deceased using an amended version of the SmartVA instrument to suit the local context. Causes of death from VA were produced using the SmartVA-Analyze program. Physician coded verbal autopsy (PCVA), conducted on the same questionnaires, and Global Burden of Disease Study data were used as additional comparators. Cause of death data were grouped into 10 broad causes for the validation due to the real-world utility of VA lying in identifying broad population cause of death patterns. Findings: The study included 2,060 deaths in São Paulo and 1,079 in Recife. The cause specific mortality fractions (CSMFs) estimated using SmartVA were broadly similar to conventional autopsy for: cardiovascular diseases (46.8% vs 54.0%, respectively), cancers (10.6% vs 11.4%), infections (7.0% vs 10.4%) and chronic respiratory disease (4.1% vs 3.7%), causes accounting for 76.1% of the autopsy dataset. The SmartVA CSMF estimates were lower than autopsy for "Other NCDs" (7.8% vs 14.6%) and higher for diabetes (13.0% vs 6.6%). CSMF accuracy of SmartVA compared to autopsy was 84.5%. CSMF accuracy for PCVA was 93.0%. Interpretation: The results suggest that SmartVA can, with reasonable accuracy, predict the broad cause of death groups important to assess a population's epidemiological transition. VA remains a useful tool for understanding causes of death where medical certification is not possible. Competing Interests: The authors declare no conflicts of interest. (© 2021 The Author(s).)
Databáze:	MEDLINE
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