Maternal Mortality Rate—A Reliable Indicator?

Abstract

Introduction: Through forensic auditing a new way to monitor medical data was opened. Forensic auditing uses Benford's law, which explains the frequency distribution in naturally occurring data sets. We applied this law on data for Maternal Mortality. This is an extremely important number in policy-making for sustainable project implementation. Methodology: The law states that the probability of a leading occurring number Chi-square was higher than the cut off value, which leads to the rejection the null hypothesis. The rejection of the null hypothesis means that the numbers observed in the publication are not following Benford's law. Explanations can reach from errors, operational discrepancies and psychological challenges to manipulations in the struggle for international funding. Conclusion: Knowledge on this mathematical relation is not used widely in medicine, despite being a very valuable and quick tool to identify datasets in need of close scrutiny.

Data science for assessing possible tax income manipulation: The case of Italy

“…In fact, BL1 is also applied outside the financial audit realm; e.g. see Fu et al (2007) for image forensics, Ausloos et al (2015) for birth rate anomalies, Pollach et al (2015) for maternal mortality rates, or elsewhere in the natural sciences (Sambridge, et al 2010), and on religious activities (Mir 2012). We notice that the literature is huge: see Alali and Romero (2013), for approximately the last decade, Costa et al (2013), or Beebe (2016, which contains a rather exhaustive list of references.…”