Applying Benford’s Law to Monitor Death Registration Data: A Management Tool for the COVID-19 Pandemic

Morillas-Jurado, Francisco Gabriel; Caballer-Tarazona, María; Caballer-Tarazona, Vicent

doi:10.3390/math10010046

Cited by 10 publications

(5 citation statements)

References 36 publications

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“…A surveillance system's overall evaluation should consider its attributes, such as its adaptability, sensitivity, acceptance, simplicity, representativeness, stability, timeliness, and PPV but are commonly evaluated after an epidemic. Benford's law was an effective method for evaluating the accuracy or reliability of the data produced by various nations or even distinct regions within one nation [16]. The authors tested the reliability of COVID-19 death-case reporting in nations with authoritarian regimes using Benford's law, and their results are presented.…”

Section: Resultsmentioning

confidence: 99%

“…For the field of epidemic control, a reliable epidemiological surveillance system is essential. Providing high-quality data so that decisions can be made using evidence is one of its responsibilities [16]. In this study, Benford's law analysis was used to evaluate the first significant digit distribution of daily confirmed cases and COVID-19 related deaths in Indonesia.…”

Section: Introductionmentioning

confidence: 99%

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Benford’s law analysis to evaluate the quality data of COVID-19 epidemiological surveillance in Indonesia

Sinaga,

Sudharma

2024

IJPHS

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Countries worldwide, including Indonesia, grappled with the unprecedented challenges brought about by the coronavirus disease (COVID-19) pandemic. Surveillance data vividly illustrates the profound effect of the COVID-19 pandemic in Indonesia. Both daily cases and deaths were raised, revealing the rapid transmission of the virus within communities. A quantitative study using a statistical approach was accomplished with secondary data to evaluate the quality of COVID-19 epidemiological surveillance data in Indonesia during the period between March 2020 to January 2021. The data was sourced from the World Health Organization (WHO) website using data reports on COVID-19 confirmed cases and deaths. A rapid tool called the first digit law or the fulfillment of Benford’s law was used to suggest good data quality for epidemiological surveillance. Data analysis used the Chi-squared test and the log-likelihood ratio test. Also, it displayed the difference in mean absolute deviation (MAD) to identify the proximity of the data and Benford’s law distribution. The results showed that both confirmed, and death case distributions were statistically non-conformity with Benford’s law distribution. In terms of quality data regarding the COVID-19 pandemic, the epidemiological surveillance system falls short of Benford’s law assumption. Benford's law has been acknowledged as an initial analysis that can expeditiously assess the performance of a surveillance system. The next phase of this study would be to conduct a complete evaluation suitably, especially in post-pandemic COVID-19.

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Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Benford’s law analysis to evaluate the quality data of COVID-19 epidemiological surveillance in Indonesia

Sinaga,

Sudharma

2024

IJPHS

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“…Although the death-related data of cancer patients are governed at the national level, inhomogeneous events such as pandemics could also cause a data discrepancy. In Spain, owing to the huge casualties caused by the first wave of the coronavirus pandemic (February–August 2020), the government proposed national-level objectives and homogenous criteria to maintain reliable and current information [ 23 ].…”

Section: Discussionmentioning

confidence: 99%

Examining Final-Administered Medication as a Measure of Data Quality: A Comparative Analysis of Death Data with the Central Cancer Registry in Republic of Korea

Tak

Han

Park

et al. 2023

Cancers

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Death is a crucial outcome in retrospective cohort studies, serving as a criterion for analyzing mortality in a database. This study aimed to assess the quality of extracted death data and investigate the potential of the final-administered medication as a variable to quantify accuracy for the validation dataset. Electronic health records from both an in-hospital and the Korean Central Cancer Registry were used for this study. The gold standard was established by examining the differences between the dates of in-hospital deaths and cancer-registered deaths. Cosine similarity was employed to quantify the final-administered medication similarities between the gold standard and other cohorts. The gold standard was determined as patients who died in the hospital after 2006 and whose final hospital visit/discharge date and death date differed by 0 or 1 day. For all three criteria—(a) cancer stage, (b) cancer type, and (c) type of final visit—there was a positive correlation between mortality rates and the similarities of the final-administered medication. This study introduces a measure that can provide additional accurate information regarding death and differentiates the reliability of the dataset.

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“…In Spain, owing to the huge casualties caused by the rst wave the COVID-19 pandemic (February-August 2020), the government proposed national-level objective and homogenous criteria to maintain reliable and current information. [11] Comparison between Gold Standard and Control Groups Through comparison between the control group and the gold standard, we investigated how effective the gold standard was as an indicator of death information accuracy. First, when compared by seer stage, Figure 5 shows that the higher the mortality rate, the higher the cosine similarity with the gold standard.…”

Section: The Negativity Of Hand Written Input Compared To Automated I...mentioning

confidence: 99%

Assessing death data quality through skepticism about death data accuracy

Tak

Han

Park

et al. 2022

Preprint

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Background Death is a crucial measure in electronic medical record (EMR) studies, where it has significance as a criterion for analyzing mortality in the database. This study aimed to assess extracted death data quality and investigate the potential of a final administered medication variable as an indicator to quantify the accuracy of a newly extracted control group’s death data. Methods Data were collected through Asan Biomedical Research Environment, which comprised data from both the Asan Medical Center and The Korean Central Cancer Registry. The gold standard was established by examining differences according to death information sources through a chart review. Cosine similarity was employed to quantify the final administered medication similarities between the gold standard and other cohorts using the Anatomical Therapeutic Chemical classification system code. Results The gold standard was determined as patients who died in hospital after 2006, when the final hospital visit/discharge date and death date differed by 0 or 1. For all three criteria, a) Seer Stage, b) cancer type c) type of final visit; as the mortality rate increased, the final administered medication cosine similarity with the golden standard increased. Conclusion This study introduced an indicator that can provide additional accurate death information and differentiate reliability. In the future, variables other than EMR could be used to further determine death information quality, in addition to the final administered medication.

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Applying Benford’s Law to Monitor Death Registration Data: A Management Tool for the COVID-19 Pandemic

Cited by 10 publications

References 36 publications

Benford’s law analysis to evaluate the quality data of COVID-19 epidemiological surveillance in Indonesia

Benford’s law analysis to evaluate the quality data of COVID-19 epidemiological surveillance in Indonesia

Examining Final-Administered Medication as a Measure of Data Quality: A Comparative Analysis of Death Data with the Central Cancer Registry in Republic of Korea

Assessing death data quality through skepticism about death data accuracy

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