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

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Hart, John D. ; de Andre, Paulo Afonso ; Saldiva de Andre, Carmen Diva ; Adair, Tim ; Barroso, Lucia Pereira ; Valongueiro, Sandra ; Bierrenbach, Ana Luiza ; de Carvalho, Patricia Ismael ; de Cerqueira Antunes, Maria Bernadete ; de Oliveira, Conceicao Maria ; Amador Pereira, Luiz Alberto ; Minto, Catia Martinez ; da Silva Bezerra, Tania Maria ; Costa, Sergio Parente ; de Azevedo, Barbara Araujo ; Alves de Lima, Jose Ricardo ; de Meira Mota, Denise Souza ; Ramos, Ana Maria de Oliveira ; de Souza, Maria de Fatima Marinho ; Ferraz da Silva, Luiz Fernando ; Franca, Elisabeth Barboza ; McLaughlin, Deirdre ; Riley, Ian D. ; Nascimento Saldiva, Paulo Hilario

• Tipo de documento: Artigo Científico
• Fonte: LANCET REGIONAL HEALTH-AMERICAS; v. 5, p. 8-pg., 2022-01-01.

Resumo

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 Sao 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 Sao 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. Copyright (c) 2021 The Author(s). Published by Elsevier Ltd. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/) (AU)

• Processo FAPESP: 13/21728-2 - Uso de modernas técnicas de autópsia na investigação de doenças humanas (MODAU)
• Beneficiário: Paulo Hilário Nascimento Saldiva
• Modalidade de apoio: Auxílio à Pesquisa - Temático