P-209 Data Analytics Applied to Occupational Health: Occupational Risk Analysis of United States Companies

Introduction Data science is an interdisciplinary field that integrates different disciplines: statistics, programming, mathematics, computing and logic, among others. Its purpose is the search, collection, extraction, and analysis of data that can be structured or unstructured to carry out decision making. Methods A public database “Severely Injured Workers” located in the Kaggle® web community was used to provide information for this paper. Injuries were coded using the Occupational Health and Illness Classification System. Data covered 22 000 incidents from 1st January 2015 to 28 February 2017. Two data analysis tools were used: Microsoft Excel® and Public Tableau®. Results Data analytics permitted the identification of companies with the highest and lowest rates of occupational accidents and the severity of the injuries, among other variables. Discussion The data analysis allowed us to understand the patterns of occupational hazards and risks in different industries throughout the United States of America. Conclusion Data analysis allows a new and innovative approach to data interpretation by using not only predictive models in the detection of accidents, but in occupational diseases too. It allows the identification of occupational hazards and risks in different industries and companies. It also facilitates the analysis and interpretation of large amounts of data, to make informed decisions, with the ultimate aim of reducing the morbidity and mortality in workers.“