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 ...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.展开更多
文摘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.
