The Democratic Republic of the Congo armed conflict 1998-2004: Assessing excess mortality based on factual and cournter-factual projection scenarios

  • Richard Kapend University of Portsmouth
  • Jakub Bijak, Professor University of Southampton - Department of Social Statistics and Demography
  • Andrew Hinde, Dr University of Southampton - Southampton Statistical Sciences Research Institute

Abstract

To document the scale and scope of the 1998–2004 armed conflicts in the Democrat­ic Republic of the Congo (DRC), the current study combined four different data sources: the 1984 DRC Population Census, the 1995 and 2001 DRC Multiple Indicator Cluster Surveys and the 2007 DRC Demographic and Health Survey, to reconstruct missing demographic estimates and assess the level of excess mortality associated with the conflict, going from 1998 to 2007. Findings from this study do not corrobo­rate previous estimates on the same armed conflict and for the same period: these range from excess mortality of 5.4 million population according to Coghlan et al. (2009), to 0.2 million according to Lambert and Lohlé-Tart (2008).

The cohort component projection method as used in this study is a cost-effective approach as it allows the analysis of a complex issue, that is excess mortality associat­ed with an armed conflict, with relatively modest resources. This study highlights that the choice of baseline rates is a key factor in determining the level of excess mortality when data points are scarce. This study produced a range of plausible estimates of excess mortality between 1 and 1.9 million population rather than a single best estimate. The range of excess mortality produced in this study is narrower and less extreme when compared to previous studies on the same conflict. As a further contribution to the debate in this field, the current study advocates producing a range of plausible estimates rather than a single best estimate of excess mortality. This is justified by the uncertainties associated with the scarcity of the data, the statistical modelling and the overall analysis process.

Published
2020-10-28
Section
Articles