title: Multiple-bias analysis as a technique to address systematic error in measures of abortion-related mortality.
abstract: BACKGROUND
  The UN Millennium Development Goals (MDGs) and Sustainable Development Goals (SDGs) have brought heightened global attention to the measurement of maternal mortality. It is imperative that new and novel approaches be used to measure maternal mortality and to better understand existing data. In this paper we present one approach: an epidemiologic framework for identifying the identification and quantification of systematic error (multiple-bias analysis), outline the necessary steps for investigators interested in conducting multiple-bias analyses in their own data, and suggest approaches for reporting such analyses in the literature.
  
  
  METHODS
  To conceptualize the systematic error present in studies of abortion-related deaths, we propose a bias framework. We posit that selection bias and misclassification are present in both verbal autopsy studies and facility-based studies. The multiple-bias analysis framework provides a relatively simple, quantitative strategy for assessing systematic error and resulting bias in any epidemiologic study.
  
  
  RESULTS
  In our worked example of multiple-bias analysis on a study reporting 20.6 % of maternal deaths to be abortion related, after adjustment for selection bias, misclassification, and random error, the median increased, on average, to 0.308, approximately 20 % greater than the reported proportion of abortion-related deaths.
  
  
  CONCLUSIONS
  Reporting results of multiple-bias analyses in estimates of abortion-related mortality, predictors of unsafe abortion, and other reproductive health questions that suffer from similar biases would not only improve reporting practices in the field, but might also provide a more accurate understanding of the range of potential impact of policies and programs that target the underlying causes of unsafe abortion and abortion-related mortality.
authors.affiliation: Ibis Reproductive Health, 1330 Broadway St, Suite 1100, Oakland, CA 94612 USA.
authors.forename: Caitlin
authors.initials: C
authors.lastname: Gerdts
authors.affiliation: Division of Epidemiology, University of California, Berkeley, School of Public Health, Berkeley, CA USA.
authors.forename: Jennifer
authors.initials: J
authors.lastname: Ahern