Maternal Mortality Rate—A Reliable Indicator?

Gregor Pollach; Kai Jung; Felix Namboya; Christian Pietruck

doi:10.4236/ijcm.2015.65044

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International Journal of Clinical Medici... > Vol.6 No.5, May 2015

Maternal Mortality Rate—A Reliable Indicator? ()

Gregor Pollach, Kai Jung, Felix Namboya, Christian Pietruck

Department of Anaesthesia and Intensive Care, College of Medicine, University of Malawi, Blantyre, Malawi.

DOI: 10.4236/ijcm.2015.65044 PDF HTML XML 2,105 Downloads 2,524 Views Citations

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

can be calculated through the following equation:

observed and expected values were compared. To confirm statistical significance examination we used the Chi-square test. Results: The chi-square value for MMR was 21.08 for the 2012 report and 19.97 for the 2014 report. 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.

Keywords

Maternal Mortality Rate, Benford’s Law, Forensic Auditing, Unicef Data, Flawed Data

Share and Cite:

Pollach, G. , Jung, K. , Namboya, F. and Pietruck, C. (2015) Maternal Mortality Rate—A Reliable Indicator?. International Journal of Clinical Medicine, 6, 342-346. doi: 10.4236/ijcm.2015.65044.

Conflicts of Interest

The authors declare no conflicts of interest.

References

[1]	Benford, F. (1938) The Law of Anomalous Numbers. Proceedings of the American Philosophical Society, 78, 551-572.
[2]	Müller, H.C. (2011) Greece Was Lying About Its Budget Numbers. Forbes Magazine, 18, 10-12.
[3]	Durtschi, C., Hillison, W. and Pacini, C. (2004) The Effective Use of BenfordsLaw to Assist in Detecting Fraudin Accounting Data. Journal of Forensic Accounting, 5, 17-34.
[4]	Hill, T.P. (1995) The Significant-Digit Phenomenon. The American Mathematical Monthly, 102, 322-327. http://dx.doi.org/10.2307/2974952
[5]	Nigrini, M.J. (1996) Taxpayers Compliance Application of Benford’s Law. Journal of the American Taxation Association, 18, 72-92.
[6]	Smith, S.W. (1999) Chapter 34, Explaining Benfords Law. In: The Scientist and Engineers Guide to Digital Signal Processing, California Technical Publishing, San Diego, 701-723.
[7]	Fewster, R.M. (2009) A Simple Explanation of Benford’s Law. The American Statistician, 63, 26-32. http://dx.doi.org/10.1198/tast.2009.0005
[8]	Suh, I., Headrick, T.C. and Minaburo, S. (2011) An Effective and Efficient Analytic Technique: A Bootstrap Regression Procedure and Benford’s Law. Journal of Forensic & Investigative Accounting, 3, 25-45.
[9]	Unicef: The State of The World’s Children; Unicef Reports 2012. www.unicef.com
[10]	Unicef: The State of The World’s Children; Unicef Reports 2014. www.unicef.com
[11]	Hennekens, C.H. and Buring, J.E. (1987) Epidemiology in Medicine, Little, Brown and Company. Boston/Toronto.
[12]	Dawn, G. (2012) Statistics. Churchill Livingston.
[13]	Newcomb, S. (1881) Note of the Frequency of Use of the Different Digits in Natural Numbers. American Journal of Mathematics, 4, 39-40. http://dx.doi.org/10.2307/2369148
[14]	Unicef Homepage. www.unicef.com
[15]	Varian, H.R. (1972) Benford’s Law. The American Statistician, 26, 65-66.