International Section of the ISSA for Electricity, Gas and Water
The mission of the Section for Electricity, Gas and Water is the protection of workers against electrical accidents and occupational diseases due to electricity and ionizing radiation.
Optimize the collection and use of OSH data
One of the 17 Sustainable Development Goals (SDG) for 2030 is to promote inclusive and sustainable economic growth, employment and decent work for all. A major challenge is the creation of quality jobs. An important measure of quality is the protection offered to workers to prevent occupational accidents and diseases. In the first instance, to do so, there is a requirement to collect, collate and analyse data on occupational accidents and diseases.
In this regard, the member institutions of the International Social Security Association (ISSA), in strategic partnership with other relevant national agencies, employers, workers’ associations, and enterprises specializing in the development of Big data software and analytic tools, have a key global role to play in contributing to the realization of SDG No. 8.
The International Labour Office’s campaign for the 2017 World Day for Safety and Health at Work focuses on the critical need for countries to improve their capacity to collect and utilize reliable occupational safety and health (OSH) data.
The International Social Security Association (ISSA) supports this initiative through its global membership of social security institutions, and particularly employment injury schemes, that are engaged in providing insurance against work-related risks.
The ISSA Guidelines on the Prevention of Occupational Risks define the need for an employment injury scheme to have an adequate and reliable reporting system for occupational accidents and suspected cases of occupational diseases. The reporting and collection of such data in a dedicated database provides an indispensable platform to permit data analysis and continuous data collection.
Every year occupational accidents and diseases cause 2.3 million fatalities. Detailed knowledge about work injuries is critical for the success of OSH-strategies and prevention campaigns and consequently for reducing the immense human toll and huge economic losses they lead to. Social security organizations play a key role in the collection and analysis of OSH data and contribute significantly to national prevention programmes. The ISSA is committed to further strengthen this important contribution of social security to safety, health and well-being at work in line with the global goals established by the UN.
Hans-Horst Konkolewsky, Secretary General of the International Social Security Association
In practical terms, insurers of occupational accidents and diseases maintain records of the accidents and diseases for which they provide compensation. These records may contain information on insured persons, the cause of accidents, the names of companies and the economic sectors in which accidents and diseases occur. On this basis, the frequency rates of fatal and non-fatal occupational injuries, for instance by sex and civil status, can then be easily generated.
Analysis enables the responsible institution to conduct targeted prevention activities based on identified occupational risks. In turn, this contributes to the evaluation of prevention activities by comparing longitudinal data from interventions.
To strengthen data collection of national accident statistics and their analysis, institutional cooperation with competent state authorities (e.g. labour inspectorates) and social partners should be considered. In the Republic of Korea, the Korea Occupational Safety and health Agency (KOSHA) produces its occupational accidents statistics with the help of the Korea Workers’ Compensation and Welfare Service (WCWS) and the Ministry of Employment and Labour (MOEL). Such joint action can also help address under-reporting of occupational accidents and disease.
In the Republic of Korea two processes support raw data collection and the subsequent development of statistical evidence: one process involves occupational accidents reported under the Korean Compensation Act and the other is derived from the data of deaths and of accidents necessitating at least a three-day leave period that have been reported to the MOEL. Statistics are categorized by industry, size, area, date, gender, age, working period, original cause material, and so forth, for further analysis. The MOEL publishes statistics annually.
Of importance, access to data enables social security institutions to calculate the risk-related contributions to be paid by insured companies. This is done by linking each employer’s insurance contribution to the probability of incidents (occupational accidents and occupational diseases) in their workplace. This probability calculation takes into account the frequency, severity and cost of insurance cases within the sector of economic activity in which the employer operates.
The German Social Accident Insurance (DGUV) collects comprehensive data with regard to the frequency, cause and impact of occupational accidents and diseases, which provide an important basis for risk prevention and rehabilitation strategies and programmes. These very detailed work injury statistics enable DGUV to successfully target prevention efforts to risk sectors, professions and activities, and have resulted in a continuous reduction in injury rates as well as the necessary levels of contributions by insured companies, which in 2016 reached an all-time low.
Joachim Breuer, President of the International Social Security Association and Director-General, German Social Accident Insurance (Deutsche Gesetzliche Unfallversicherung – DGUV)
Motivating employers and enterprises to proactively engage in the field of prevention is essential. To support this aim, incentive schemes, such as risk-related contributions and financial and even non-financial incentives, offer a degree of leverage. The idea behind these schemes is that insurance premiums paid by an employer or enterprise should be linked directly to the assessed safety and health performance. Applying a bonus-malus system, those employers or enterprises with lower than average accident and disease rates will pay lower premiums while the premiums of those with higher than average rates will be higher.
Analysis of collated data permits to identify the major causes of occupational accidents and exposures to health risks. Once correctly identified, targeted measures can be introduced, such as targeted prevention campaigns or health screening and long-term follow up (in case of risk exposure to substances that cause occupational diseases).
As regards occupational health risks, some institutions also maintain a comprehensive database on the work history, workplace exposures and the results of medical check-ups of exposed insured persons, which can be used for preventive and compensation purposes.
INAIL’s approach to data collection, collation, analysis and application
The Italian National Social Insurance Institute (INAIL) is a valuable partner for safety and health authorities. INAIL disposes of detailed data on insured workers, including:
- Disability data reporting on the potential remaining capabilities and abilities of people living with assessed disabilities, to be used to facilitate their reintegration into the working environment;
- Statistics regarding the economic sector and the profession of the workers assessed as having been involved in occupational accidents or diagnosed with occupational diseases;
- Longitudinal data containing national and regional statistics relevant to occupational accidents and diseases, covering the period from 1951;
- Eurostat data for European Union member States, to illustrate how work accidents and fatal accidents are being compensated (except commuting accidents).
INAIL manages the National Information System for prevention at the workplace, which combines data from INAIL, the Ministry of Labour, the Ministry of Health, and the Ministry of Home Affairs, as well as from the autonomous Italian Provinces.
INAIL compiles the data into Statistical datasets, which are supplemented with metadata and additional aggregated data and managerial data, e.g. on INAIL’s offices and Regional Operational Centres. The data is published regularly and is publicly accessible.
The data is used for the preparation of national occupational risk prevention and health promotion programmes, in particular for targeting risk areas and for setting priorities, as well as for the evaluation of their impact. The huge volume of information contained in the databases allows INAIL to carry out effective prevention action. This is done in terms of information, training, assistance and advice, promotion of a “prevention culture” and also financial support, which is provided to companies which invest in safety, through discounted premiums.
In the case of the Republic of Korea, KOSHA has one of the largest data sets available, enabling its use in a number of ways. KOSHA uses the data to produce applied statistics and this directs choices for prevention activities such as the establishment of prevention strategies, targeting high-risk industries, or anticipating future trends. In addition to the information gleaned from annual accident statistics, the Korean Occupational Safety and Health Research Institute (OSHRI) analyses data taken from a Working Conditions Survey, which consists of 130 items such as family current situation, workforce, working time, working environment, working pattern, work speed, task organization, training and education, communication, exposure to violence, discrimination, health conditions, occupational satisfaction, academic ability, income, etc. The data is used to look at the future OSH environment, define new polices and initiate new research on OSH in order to improve working conditions.
Data is all about targeting and making best use of existing resources. Data should therefore be connected to the knowledge of accident prevention and serve the workers that are exposed to occupational risks.
Jong-Kyu Kwon, Director, Korea Occupational Safety and Health Agency
When OSH data is paired with other available economic and demographic data, the merged datasets can help monitor additional programme elements, including using business and occupations tax credits for the purchase of equipment; site inspections by the health, labour or hygiene inspectors; and workers’ compensation claims. On this basis, more accurate forecasting of accidents and illnesses can be generated.
Big Data is being used to predict where the next accident is likely to take place. As one example, the German statutory accident insurance (BGETEM) uses data mining as a strategic instrument of prevention. Researchers at BGETEM argue that by using different prediction models and comparing real accident data with data from other sources, it is possible for the BGETEM to identify companies at risk of accidents.
The results produced from data mining support Technical Supervisory Officials and Prevention Advisors from BGETEM to work closer with companies and strengthen prevention activities. Specifically, data mining allows a more precise selection of the companies to be inspected according to a company’s probability of future accident risk. This is done with a view to preventing a future accident.
Such an approach to occupational accident and disease prevention is likely to become mainstream. With “Big data” companies running cognitive computing algorithms, and with technological advancements in creating faster, smaller and cheaper processors and improved storage hardware, it is already possible for software to run algorithms in a faster and more reliable way than before. Some commercial products are already helping physicians to identify, evaluate and compare treatment options. Such software can read and understand thousands of medical records in a very short time frame and offer support to each unique patient case. Such technology can also used to forecast health outcomes with enormous impacts on future health conditions.
The use of cognitive computing is already underway in the insurance sector and OSH professionals can equally make important use of systems that can cross-analyse variables such as working conditions, education, place of residence, social and family support mechanisms, amongst others, to improve the effectiveness of evidence-based prevention, compensation and rehabilitation measures.
In all likelihood these developments will have a future bearing on the design and roles of social security and health systems.