Potential COVID-19 test fraud detection: Findings from a pilot study comparing conventional and statistical approaches.

Autor:	Bosnjak M; Trier University, Department for Psychological Research Methods, Trier, Germany.; Robert Koch Institute, Department of Epidemiology and Health Monitoring, Berlin, Germany., Dahm S; Robert Koch Institute, Department of Epidemiology and Health Monitoring, Berlin, Germany., Kuhnert R; Robert Koch Institute, Department of Epidemiology and Health Monitoring, Berlin, Germany., Weihrauch D; City of Cologne, Health Authority, Infectious and Environmental Hygiene, Cologne, Germany., Rosario AS; Robert Koch Institute, Department of Epidemiology and Health Monitoring, Berlin, Germany., Hurraß J; City of Cologne, Health Authority, Infectious and Environmental Hygiene, Cologne, Germany., Schmich P; Robert Koch Institute, Department of Epidemiology and Health Monitoring, Berlin, Germany., Wieler LH; Digital Global Public Health at the Hasso-Plattner-Institute (HPI), University of Potsdam, Potsdam, Germany.; Robert Koch Institute, Department of Epidemiology and Health Monitoring, Berlin, Germany.
Jazyk:	angličtina
Zdroj:	Journal of health monitoring [J Health Monit] 2024 Jun 19; Vol. 9 (2), pp. e12100. Date of Electronic Publication: 2024 Jun 19 (Print Publication: 2024).
DOI:	10.25646/12100
Abstrakt:	Background: Some COVID-19 testing centres have reported manipulated test numbers for antigen tests/rapid tests. This study compares statistical approaches with traditional fraud detection methods. The extent of agreement between traditional and statistical methods was analysed, as well as the extent to which statistical approaches can identify additional cases of potential fraud. Methods: Outlier detection marking a high number of tests, modeling of the positivity rate (Poisson Regression), deviation from distributional assumptions regarding the first digit (Benford's Law) and the last digit of the number of reported tests. The basis of the analyses were billing data (April 2021 to August 2022) from 907 testing centres in a German city. Results: The positive agreement between the conventional and statistical approaches ('sensitivity') was between 8.6% and 24.7%, the negative agreement ('specificity') was between 91.3% and 94.6%. The proportion of potentially fraudulent testing centres additionally identified by statistical approaches was between 7.0% and 8.7%. The combination of at least two statistical methods resulted in an optimal detection rate of test centres with previously undetected initial suspicion. Conclusions: The statistical approaches were more effective and systematic in identifying potentially fraudulent testing centres than the conventional methods. Testing centres should be urged to map paradata (e.g. timestamps of testing) in future pandemics. Competing Interests: Conflicts of interest The authors state that there is no conflict of interest. (© Robert Koch Institute. All rights reserved unless explicitly granted.)
Databáze:	MEDLINE
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