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"Temporary agency work has been associated with a heightened risk of unwanted occupational health and safety outcomes, such as accidents. This study provides information about accidents at work during temporary agency work, analyzed based on the Finnish national occupational accident statistics database, to inform their prevention. The analyses focus on the trends, circumstances, sequence of events, and consequences of the accidents. Data from a case of a temporary work agency was analyzed to understand practical accident prevention measures. The results showed that accident frequency remained high in temporary agency work during the last decade. Accident prevention in temporary agency work requires consideration of accident risks typical of different industries and tasks. Accidents related to handling objects, movement, and carrying by hand were common during temporary agency work. Several prevention measures were suggested, with carefulness being the most mentioned measure. The understanding of accident prevention measures should be developed towards a more comprehensive approach."

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"The utilisation of data mining methods has become common in many fields. In occupational accident analysis, however, these methods are still rarely exploited. This study applies methods of data mining (decision tree and association rules) to the Finnish national occupational accidents and diseases statistics database to analyse factors related to slipping, stumbling, and falling (SSF) accidents at work from 2006 to 2007. SSF accidents at work constitute a large proportion (22%) of all accidents at work in Finland. In addition, they are more likely to result in longer periods of incapacity for work than other workplace accidents. The most important factor influencing whether or not an accident at work is related to SSF is the specific physical activity of movement. In addition, the risk of SSF accidents at work seems to depend on the occupation and the age of the worker. The results were in line with previous research. Hence the application of data mining methods was considered successful. The results did not reveal anything unexpected though. Nevertheless, because of the capability to illustrate a large dataset and relationships between variables easily, data mining methods were seen as a useful supplementary method in analysing occupational accident data."