title: Child mortality estimation: a comparison of UN IGME and IHME estimates of levels and trends in under-five mortality rates and deaths.
abstract: BACKGROUND
  Millennium Development Goal 4 calls for a reduction in the under-five mortality rate (U5MR) by two-thirds between 1990 and 2015. In 2011, estimates were published by the United Nations Inter-agency Group for Child Mortality Estimation (UN IGME) and the Institute for Health Metrics and Evaluation (IHME). The difference in the U5MR estimates produced by the two research groups was more than 10% and corresponded to more than ten deaths per 1,000 live births for 10% of all countries in 1990 and 20% of all countries in 2010, which can lead to conflicting conclusions with respect to countries' progress. To understand what caused the differences in estimates, we summarised differences in underlying data and modelling approaches used by the two groups, and analysed their effects.
  
  
  RESULTS
  UN IGME and IHME estimation approaches differ with respect to the construction of databases and the pre-processing of data, trend fitting procedures, inclusion and exclusion of data series, and additional adjustment procedures. Large differences in U5MR estimates between the UN IGME and the IHME exist in countries with conflicts or civil unrest, countries with high HIV prevalence, and countries where the underlying data used to derive the estimates were different, especially if the exclusion of data series differed between the two research groups. A decomposition of the differences showed that differences in estimates due to using different data (inclusion of data series and pre-processing of data) are on average larger than the differences due to using different trend fitting methods.
  
  
  CONCLUSIONS
  Substantial country-specific differences between UN IGME and IHME estimates for U5MR and the number of under-five deaths exist because of various differences in data and modelling assumptions used. Often differences are illustrative of the lack of reliable data and likely to decrease as more data become available. Improved transparency on methods and data used will help to improve understanding about the drivers of the differences. Please see later in the article for the Editors' Summary.
authors.affiliation: Department of Statistics and Applied Probability and Saw Swee Hock School of Public Health, National University of Singapore, Singapore. alkema@nus.edu.sg
authors.forename: Leontine
authors.initials: L
authors.lastname: Alkema
authors.forename: Danzhen
authors.initials: D
authors.lastname: You
mesh_terms: D002648:Child; D046688:Child Mortality / Q000639:trends*; D019992:Databases as Topic / Q000706:statistics & numerical data; D003643:Death; D019049:Developed Countries / Q000706:statistics & numerical data; D003906:Developing Countries / Q000706:statistics & numerical data; D004190:Disasters; D015658:HIV Infections / Q000453:epidemiology; D006801:Humans; D015233:Models, Statistical; D015995:Prevalence; D012042:Registries / Q000706:statistics & numerical data; D013223:Statistics as Topic / Q000639:trends*; D014480:United Nations / Q000706:statistics & numerical data*