Räsänen, T., Reiman, A., Puolamäki, K., Savvides, R., Oikarinen, E., & Lantto, E. (2022). Finding statistically significant high accident counts in exploration of occupational accident data. Journal of Safety Research, 82, 28–37. https://doi.org/10.1016/j.jsr.2022.04.003
Finding statistically significant high accident counts in exploration of occupational accident data
|Author:||Räsänen, Tuula1; Reiman, Arto2; Puolamäki, Kai3,4;|
1Finnish Institute of Occupational Health, Finland
2Industrial Engineering and Management, University of Oulu, Finland
3Department of Computer Science, University of Helsinki, Finland
4Institute for Atmospheric and Earth System Research, University of Helsinki, Finland
|Online Access:||PDF Full Text (PDF, 0.5 MB)|
|Persistent link:|| http://urn.fi/urn:nbn:fi-fe2022092159801
|Publish Date:|| 2022-09-21
Finnish companies are legally required to insure their employees against occupational accidents. Insurance companies are then required to submit information about occupational accidents to the Finnish Workers’ Compensation Center (TVK), which then publishes occupational accident statistics in Finland together with Statistics Finland. Our objective is to detect silent signals, by which we mean patterns in the data such as increased occupational accident frequencies for which there is initially only weak evidence, making their detection challenging. Detecting such patterns as early as possible is important, since there is often a cost associated with both reacting and not reacting: not reacting when an increased accident frequency is noted may lead to further accidents that could have been prevented.
Method: In this work we use methods that allow us to detect silent signals in data sets and apply these methods in the analysis of real-world data sets related to important societal questions such as occupational accidents (using the national occupational accidents database).
Results: The traditional approach to determining whether an effect is random is statistical significance testing. Here we formulate the described exploration workflow of contingency tables into a principled statistical testing framework that allows the user to query the significance of high accident frequencies.
Conclusions: Our results show that we can use our iterative workflow to explore contingency tables and provide statistical guarantees for the observed frequencies. Practical Applications: Our method is useful in finding useful information from contingency tables constructed from accident databases, with statistical guarantees, even when we do not have a clear a priori hypothesis to test.
Journal of safety research
|Pages:||28 - 37|
|Type of Publication:||
A1 Journal article – refereed
|Field of Science:||
222 Other engineering and technologies
This study was supported by the Academy of Finland (decisions 326280 and 326339).
Supplementary data to this article can be found online at https://doi.org/10.1016/j.jsr.2022.04.003.
© 2022 The Author(s). This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).