title: Deriving causes of child mortality by re-analyzing national verbal autopsy data applying a standardized computer algorithm in Uganda, Rwanda and Ghana.
abstract: BACKGROUND
  To accelerate progress toward the Millennium Development Goal 4, reliable information on causes of child mortality is critical. With more national verbal autopsy (VA) studies becoming available, how to improve consistency of national VA derived child causes of death should be considered for the purpose of global comparison. We aimed to adapt a standardized computer algorithm to re-analyze national child VA studies conducted in Uganda, Rwanda and Ghana recently, and compare our results with those derived from physician review to explore issues surrounding the application of the standardized algorithm in place of physician review.
  
  
  RESULTS
  We adapted the standardized computer algorithm considering the disease profile in Uganda, Rwanda and Ghana. We then derived cause-specific mortality fractions applying the adapted algorithm and compared the results with those ascertained by physician review by examining the individual- and population-level agreement. Our results showed that the leading causes of child mortality in Uganda, Rwanda and Ghana were pneumonia (16.5-21.1%) and malaria (16.8-25.6%) among children below five years and intrapartum-related complications (6.4-10.7%) and preterm birth complications (4.5-6.3%) among neonates. The individual level agreement was poor to substantial across causes (kappa statistics: -0.03 to 0.83), with moderate to substantial agreement observed for injury, congenital malformation, preterm birth complications, malaria and measles. At the population level, despite fairly different cause-specific mortality fractions, the ranking of the leading causes was largely similar.
  
  
  CONCLUSIONS
  The standardized computer algorithm produced internally consistent distribution of causes of child mortality. The results were also qualitatively comparable to those based on physician review from the perspective of public health policy. The standardized computer algorithm has the advantage of requiring minimal resources from the health care system and represents a promising way to re-analyze national or sub-national VA studies in place of physician review for the purpose of global comparison.
authors.affiliation: Department of Population, Family, and Reproductive Health, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA ; Institute of International Programs, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA ; Joint first authors.
authors.forename: Li
authors.initials: L
authors.lastname: Liu
authors.affiliation: Department of Population, Family, and Reproductive Health, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA ; Joint first authors.
authors.forename: Mengying
authors.initials: M
authors.lastname: Li
authors.affiliation: MEASURE Evaluation, Carolina Population Center, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA.
authors.forename: Stirling
authors.initials: S
authors.lastname: Cummings
authors.affiliation: Institute of International Programs, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA.
authors.forename: Robert E
authors.initials: RE
authors.lastname: Black