International Review of Surveillance and Control of Workplace Exposures: NOHSAC Technical Report 5
3.6.1 Exposure databases and observations
Data entered into exposure databases is generally collected by hygienists or workplace inspectors operating under the auspices of an OHS service. For example, the German Berufsgenossenschaften (BG) MEGA, which is the chemical workplace exposure database of the Institute for Occupational Safety (BIA), uses data collected by the inspectorates of the BGs. The BGs are statutory accident institutions for insurance and prevention, and social insurance law requires such data collection. The inspectors conduct workplace measurements of chemical and biological agents, and in 1999, 31,000 measurements with 68,000 analyses were taken in 4,000 enterprises. Samples and data collected are analysed by the BIA, and reports that form the basis of recommendations and instructions to businesses are returned to the inspectors.
All results of workplace data collection and analyses are stored in the MEGA-database, with up to 150 pieces of information (describing type of workplace, working conditions, measured substances, sampling strategy, sampling duration, sampling and analytical method, etc) for each result. At the end of 1999, MEGA contained about 1,000,000 measurements of more than 420 substances taken in about 45,000 businesses since 1972. MEGA is used by BIA and the BGs for prevention through identification of hazards, assessing the efficiency of risk control measures and determination of the technical criteria of exposure limit values. It is also used for epidemiological purposes and investigations of occupational diseases.
The Health and Safety Executive (HSE) established the National Exposure Database (NEDB) in the UK in 1986, to provide detailed and comprehensive exposure data for the setting of new occupational exposure limits, to provide a data source for epidemiological studies and to facilitate dissemination of information on occupational exposures. The database focuses on occupational exposure to airborne substances and includes details of particular industries, processes and work activities to which the exposure relates, together with information on process conditions and provision of control measures in the form of local exhaust ventilation and personal respiratory protective equipment. The exposure data is gathered by HSE occupational hygiene inspectors.
Occupational hygienists within the Finnish Institute of Occupational Health (FIOH) have measured chemical, physical and microbiological agents at workplaces since 1950. More recent chemical measurements have been entered into a computerised database, along with limited background information that includes company or workplace information, the industry, year, work task, agent measured, concentration and measurer. Between 1994 and 1997, 11,386 measurements were collected.
The variability of working conditions requires large numbers of workplaces to be studied to allow generalisations on the whole labour force. Despite the size of databases such as the Finnish Register of Occupational Hygiene Measurements, in general, they are often unrepresentative of workplaces, and information about the measurement sites, measurement time, and so on is inadequate, and their use becomes limited in regard to exposure assessment and hazard control.
Job exposure matrices offer an approach to overcoming some of these limitations. These involve determining, for a range of occupations and industries, levels of exposure to various substances. The Finnish Job Exposure Matrix (FINJEM) has three dimensions; occupations, agents and periods. The agents cover chemical, physical, microbiological, ergonomic and psychosocial factors. Exposure to an agent in an occupation during a fixed time period is specified by the prevalence of exposure and level of exposure. Kauppinen reports:
“FINJEM was designed by a team of scientists experienced in assessing physical, chemical, ergonomic, and psychosocial exposures. Agents and agent-specific minimum criteria of exposure were first defined to improve the consistency of assessment work. Summarized data on Finnish industrial hygiene measurements, interview surveys and workforce surveys were next entered in the database. Fourteen experts from FIOH then assessed prevalences and levels of exposure based on the data and their own experience. Premises of estimates and bibliographic references were documented in the same database.” (p154)
Sabic et al point out that the application of a job exposure matrix to the job history of an individual worker allows an assessment of the exposure of the individual without having made any specific exposure measurements for that person. Thus it provides a cheap method for estimating exposure. However, it is prone to error in the estimations, as the individual may have experienced different exposures, or exposures at different levels, in the particular tasks in which they worked, compared to those recorded in the matrix. Sabic et al continue that the development of the matrix is extremely expensive, requiring measurements of thousands of occupation-industry-exposure combinations and that the matrix will gradually become out of date unless on-going measurements are made, but this can prove very costly.
In their proposal for the standardisation of core information for storage and exchange of exposure measurement information, Rajan et al identify that workplace exposure measurements may be influenced by many variables, including processes and chemicals used, influences by people in the work site, and the sampling equipment and the analytical techniques used. Further variables are introduced through the interpretation of data. They assert that failure to collect and store information relevant to measurements collected may result in wasted effort and wrong decisions, and that a lack of consensus on core information to record, accurate and standardised definitions for that core information, and effective coding systems with which to capture the core information, are the main reasons for failure to exploit the full potential of exposure measurements. Rajan et al propose and define a number of core data elements for inclusion in measurement databases while suggesting that the ways in which exposure measurements are collected, stored and used are heavily influenced by cultural, legal and industrial structures.
Observational surveys offer an alternative to the collection of exposure measurements. Observational surveys do not rely on the quantification of exposure through measurement of agents, but collect data with which to build a database that may be used to estimate the number of exposed workers. They do not, however, provide information on exposure levels and thus the subjectivity of data collectors may be introduced.
Examples of observational surveys are the NIOSH National Occupational Hazard Survey (NOHS), the National Occupational Exposure Survey (NOES) and the National Occupational Health Survey of Mining (NOHSM) conducted between 1972 and 1989. NOHS 1972–1974 and NOES 1981–1983 were conducted in establishments regulated by the OSH Act, and NOHSM 1984–1989 was conducted in mineral mines. These three national surveys yielded qualitative information on potential exposures to chemical, physical and biological agents. Information collection was based on whether the following two criteria were met: (1) the agent or trade-named product must have been observed in sufficient proximity to the worker such that it was likely to enter or contact the body, and (2) the duration of the potential exposure was at least 30 minutes per week on an annual average, or at least 30 minutes for 90 percent of the weeks of the work year. For each survey, data were collected using a standardised questionnaire and an observational walkthrough. The questionnaires were administered to management and elicited information on facility demographics; type of health, safety, and medical surveillance activities and resources; and use of exposure controls. Rantanen et al records that NOES covered about 12,000 different hazards in 532 industrial and 410 occupational classes, with the field work being carried out by 115 surveyors. However, the bulk of the data remains unpublished, and the NOES database has not been updated since July 1, 1990.
Thus databases of exposure measurements offer a very rich source of information for the assessment of trends in workplaces and industries, for the evaluation of the effectiveness of policy, and the evaluation and setting of exposure limits. However, they require the collection of large numbers of measurements by trained personnel (such as hygienists) with appropriate and standardised equipment and are thus costly.
Observational surveys are lower cost but yield only qualitative data and are potentially influenced by subjectivity of the surveyor.
Exposure measurement databases
- Based on uniform definitions and methodology (reproducibility).
- Systematic, based on a representative sample.
- Based on objective work of experts and visits to actual workplaces (reliability).
- Slow and very expensive to carry out.
- Requires systematic access to workplaces for measurement collection.
- International comparison requires uniformity of coding.
- Heavily influenced by cultural, legal and industrial structures.
Observational exposure assessment databases
- Based on uniform definitions and methodology.
- Systematic, based on a representative sample.
- Based on observations of experts and visits to actual workplaces (reliability).
- Provides national estimates of potential exposure by industry and occupation.
- Supports the setting of regulatory, policy and research priorities.
- Supports trend analysis.
- Supports discovery (e.g., new hazards, unrecognised groups at risk).
- Conceptual definitions may be difficult to apply in practice.
- Based on subjective observations.
- Validity may remain unknown.
- Exposure levels difficult to assess.
- Comparability poor across countries.
- Slow and very expensive to carry out.
- Requires extensive methods development for some hazards.
Boiano and Hull point out that some NIOSH hazard surveys may have been underutilised, due to difficulty accessing data, access restrictions, long elapsed time between data collection and publication of analyses, and limited promotion.