Finding occupational accident patterns in the extractive industry using a systematic data mining approach
Highlights
► The study reveals accident patterns that are statistically significant and clear to interpret. ► The multivariate facet of the approach disclosed much more informative patterns. ► The association strength (statistical cohesion) of each pattern is objectively quantified. ► The patterns identified allowed proposing potential strategies for improving safety. ► The details of a pattern may reveal hidden factors, useful for risk control
Introduction
Since the establishment of the International Labour Organisation (ILO), in the early decades of the 1900 s, the collection of accident data and production of statistical analysis has always been a privileged source of accident information, from where to derive prevention and (international) resolutions concerning safety at work.
The aim of this work is to typify patterns of non-fatal accidents in the Portuguese mineral extractive industry, hereafter called simply Extractive Industry. This study continues and extends substantially a previous one by Jacinto & Guedes Soares in 2008 [1]. The novelty in 2008 was the ability to identify accident patterns, especially in the case of the so-called “typical accident”; such patterns were quite accurate and, above all, their relevance in terms of statistical association were clearly quantified at the level of each modality (or category) of the main variable, rather than simply associating the main variables themselves. This ability and the overall methodology applied at that time offered some novelty.
In the present work, the authors intend to go further and establish accident patterns, which are even more accurate and also more complete, as they now encompass multiple variables; or best said: include specific categories of multiple variables (i.e., modalities from the main categorical variables). The first study covered the triennium 2001–2003 of Economic Activity – Sector C (Mining & Quarrying; also referred to as Extractive Industry), whilst this one covers 2005–2007. Again, all the data was supplied fist-hand (raw data) to the authors directly by the competent authority, i.e., the Office of Strategy & Planning (GEP), which is the national agency responsible for collecting and coding all data on accidents at work.
The motivation for this second study was driven by the fact that availability of accident data is continuously increasing, partly because more countries are implementing the ESAW system (European Statistics of Accidents at Work), defined by the Eurostat in 2001 [2] and the 1998 ILO Resolution [3]. As stressed by Jørgensen in 2008 [4], who was for many years a leading member of the ESAW task-force, the statistical analysis of accidents at work constitutes an essential source of information to support the development of new prevention strategies. Hence, the higher is the availability of data, the higher is the need to explore new techniques and statistical tools for mining “hidden” details, which might help a better understanding of the phenomena; the main assumption is that understanding a phenomena is an essential condition to be able to control it.
The novelty in this work lies on two aspects: the multivariate facet of the findings (i.e., much more informative accident patterns) and, equally important, on the data mining approach developed to find such patterns. The referred technique not only permitted more information about patterns, but also enabled measuring (quantitatively) their cohesion.
Section snippets
Background review
Within the mining sector alone, there are abundant studies in the specialised literature concerning statistical analysis of accidents at work [5], [6], [7], [8], [9], [10], [11], [12], [13], [14], [15], [16], [17], [18], [19] some focus on accident causes – and sometimes causal patterns – whereas others report forecasting techniques for prediction of accident rates. However, the development of data-mining approaches to find causal patterns and to establish the statistical cohesion and
Study design:. Data
The introductory section already gave some information on the study design, namely in what concerns the Economic Activity under scrutiny (Sector “C” – Mining & Quarrying; or simply Extractive Industry), as well as the period covered (2005–2007). The source of data was also duly acknowledged, i.e., the governmental Office of Strategy & Planning (GEP), who keeps records of all accidents at work at national level.
As this point it is noteworthy mentioning that, in Portugal, the year 2005+ marked a
Characterisation of the sector (qualitative overview)
Before applying the data mining technique described earlier, this preliminary Section 4.1 gives a brief and generic characterisation of the entire sector under scrutiny. The aim of this initial step is twofold: (1) to show that data of this 3-years period can be used altogether, because there are no significant variations within the period, and (2) to demonstrate how the classical approaches of descriptive statistics can be used to derive a broad picture of the “typical accident” in this
Limitations and contributions of this work
The main limitations and contributions are briefly identified and commented in this section. One relevant limitation is that data mining approaches usually require large amounts of data, as was the case with this study. In turn, this also restricts the application field. In fact, the accident patterns found in this work are more likely useful for the authorities to define national strategies and/or prevention campaigns, as well as insurance companies, on even large corporate groups of
Concluding remarks
This work typifies occupational accident patterns. Such patterns have the ability of incorporating multiple variables and, simultaneously, quantifying the statistical strength of each association (each pattern). To attain this purpose, the paper describes the development of a data mining approach that allows multivariate analysis in the field of occupational accidents. In contrast with traditional univariate studies (analysing variables one at a time), or even with bivariate approaches
Acknowledgement
The authors are grateful to the Portuguese Office of Strategy & Planning GEP (Gabinete de Estratégia e Planeamento), of the Portuguese Ministry of Labour and Social Solidarity, for supplying the national data used in this work.
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