Journal of Safety Research

Journal of Safety Research

Volume 82, September 2022, Pages 28-37
Journal of Safety Research

Finding statistically significant high accident counts in exploration of occupational accident data

https://doi.org/10.1016/j.jsr.2022.04.003Get rights and content
Under a Creative Commons license
open access

Abstract

Introduction

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.

Keywords

Occupational accident
Silent signals
Workplace
Prevention

Cited by (0)

Tuula Räsänen is a researcher at the Finnish Institute of Occupational Health. She has over 30 years’ experience of occupational safety research. She completed her PhD in 2007 at Tampere University of Technology about the management of occupational safety and health information in Finnish production companies.

Arto Reiman is a research team leader at the University of Oulu in Finland. His research interests include ergonomics & human factors and occupational safety, and how they can be included in the design and development processes to improve well-being and productivity at work.

Kai Puolamäki is Associate Professor of computer science and atmospheric sciences in the Department of Computer Science and Institute for Atmospheric and Earth System Research (INAR) at the University of Helsinki. He completed his PhD in 2001 in theoretical physics. His primary interests lie in the areas of exploratory data analysis, machine learning, and related algorithms. He has a website at http://www.iki.fi/kaip/.

Rafael Savvides is a doctoral student at the Exploratory Data Analysis group at the University of Helsinki, Finland. His research interests include visual and interactive data exploration.

Emilia Oikarinen is university lecturer at Department of Computer Science at University of Helsinki, Finland. Her research interests broadly span artificial intelligence research, ranging from knowledge representation, reasoning, and optimization to explorative data analysis with applications in a wide variety of domains.

Eero Lantto M.Soc.Sci, is a researcher at the Finnish Institute of Occupational Health, working in the Occupational Safety unit. Before FIOH Eero gained experience at Eurofound on work and employment related research topics. During the two and a half years Eero has spent at FIOH he has participated in various research and development projects in different industries.