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Correcting for Verbal Autopsy Misclassification Bias in Cause-Specific Mortality Estimates.
Fiksel, Jacob; Gilbert, Brian; Wilson, Emily; Kalter, Henry; Kante, Almamy; Akum, Aveika; Blau, Dianna; Bassat, Quique; Macicame, Ivalda; Samo Gudo, Eduardo; Black, Robert; Zeger, Scott; Amouzou, Agbessi; Datta, Abhirup.
Afiliação
  • Fiksel J; Department of Biostatistics, Epidemiology and Informatics, University of Pennsylvania, Philadelphia, Pennsylvania.
  • Gilbert B; Department of Biostatistics, Johns Hopkins University, Baltimore, Maryland.
  • Wilson E; Department of International Health, Johns Hopkins University, Baltimore, Maryland.
  • Kalter H; Department of International Health, Johns Hopkins University, Baltimore, Maryland.
  • Kante A; Department of International Health, Johns Hopkins University, Baltimore, Maryland.
  • Akum A; Department of International Health, Johns Hopkins University, Baltimore, Maryland.
  • Blau D; Center for Global Health, Centers for Disease Control and Prevention, Atlanta, Georgia.
  • Bassat Q; ISGlobal, Hospital Clínic - Universitat de Barcelona, Barcelona, Spain.
  • Macicame I; Centro de Investigação em Saúde de Manhiça (CISM), Maputo, Mozambique.
  • Samo Gudo E; ICREA, Barcelona, Spain.
  • Black R; Pediatrics Department, Hospital Sant Joan de Déu, Universitat de Barcelona, Barcelona, Spain.
  • Zeger S; Consorcio de Investigación Biomédica en Red de Epidemiología y Salud Pública (CIBERESP), Madrid, Spain.
  • Amouzou A; Instituto Nacional de Saúde (INS), Maputo, Mozambique.
  • Datta A; Instituto Nacional de Saúde (INS), Maputo, Mozambique.
Am J Trop Med Hyg ; 108(5_Suppl): 66-77, 2023 05 02.
Article em En | MEDLINE | ID: mdl-37037438
Verbal autopsies (VAs) are extensively used to determine cause of death (COD) in many low- and middle-income countries. However, COD determination from VA can be inaccurate. Computer coded verbal autopsy (CCVA) algorithms used for this task are imperfect and misclassify COD for a large proportion of deaths. If not accounted for, this misclassification leads to biased estimates of cause-specific mortality fractions (CSMFs), a critical piece in health-policy making. Recent work has demonstrated that the knowledge of the CCVA misclassification rates can be used to calibrate raw VA-based CSMF estimates to account for the misclassification bias. In this manuscript, we review the current practices and issues with raw COD predictions from CCVA algorithms and provide a complete primer on how to use the VA calibration approach with the calibratedVA software to correct for verbal autopsy misclassification bias in cause-specific mortality estimates. We use calibratedVA to obtain CSMFs for child (1-59 months) and neonatal deaths using VA data from the Countrywide Mortality Surveillance for Action project in Mozambique.
Assuntos

Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Algoritmos / Software Tipo de estudo: Prognostic_studies Limite: Child / Humans / Newborn País/Região como assunto: Africa Idioma: En Revista: Am J Trop Med Hyg Ano de publicação: 2023 Tipo de documento: Article

Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Algoritmos / Software Tipo de estudo: Prognostic_studies Limite: Child / Humans / Newborn País/Região como assunto: Africa Idioma: En Revista: Am J Trop Med Hyg Ano de publicação: 2023 Tipo de documento: Article