A comparative analysis of health surveillance strategies for administrative video display terminal employees.

BACKGROUND: The objective of this study was to develop a strategy to optimize medical health surveillance protocols for administrative employees using video display terminals (VDTs). A total of 2453 medical examinations were analysed for VDT users in various sectors. From these data, using Bayesian statistics we inferred which factors were most relevant to medical diagnosis of the main disorders affecting VDT users. This information was used to build an influence diagram to evaluate the time and monetary costs associated with each diagnostic test and define an optimal protocol strategy based on occupational risks. RESULTS: Musculoskeletal and ophthalmological diseases were identified as the most frequent disorders among VDT users. The Bayesian network inferred age, sleep quality, activity level, smoking and the consumption of alcohol as risk factors. The blood count was the most costly test (5.23 USD/employee) and the second most costly test in time terms (4 min/employee), yet is a diagnostic test that has little influence on the medical decision regarding an employee's capacity to perform their job. CONCLUSIONS: Current occupational health surveillance protocols for VDT users may lead to expenditure that is 54% greater than necessary. For many employees and employers, failure to perform a wide range of medical tests for occupational health surveillance purposes is subjectively perceived as a threat to health. Awareness needs to be raised of the appropriate role of different health areas, so as to optimize diagnostic efficiency on the basis of greater flexibility.

A Bayesian assessment of occupational health surveillance in workers exposed to silica in the energy and construction industry.

Medical records generated during occupational health surveillance processes have large amounts of unexploited information that can help to reduce silica-related health risks and many occupational diseases. The methodology applied in this study consists in analyzing through machine learning techniques a database with 70,000 medical examinations from workers in the energy and construction industry in Spain. First, a general unsupervised Bayesian model is built and node force analysis is used to identify the factors with the greatest impact on the worker's health surveillance process. Second, a predictive Bayesian model is created and mutual information is employed to assess the more relevant factors affecting the medical capability of workers exposed to silica dust. The lung auscultation and the breathing exploration are the two factors that influence the most the medical capability of silica-exposed employees. Probabilistic inference shows a remarkable gender effect, where women present more resilience towards occupational diseases than men showing a higher proportion of normal results in certain key factors, such as body mass index (♀49.73%, ♂25.17%) or spirometry (♀53.73%, ♂48.91%). Finally, environmental conditions demonstrate to have a major influence on spatial variability of occupational diseases. The design of health prevention programs based on geographical variations can be crucial to the attainment of an ongoing and sustained healthier workforce with a reduction in the number of chronic workplace illnesses.

Intercomparison exercise for gases emitted by a cement industry in Spain: a functional data approach.

This paper describes the results of an intercomparison exercise referring to the measurement of atmospheric pollutants emitted by cement plants. The research was conducted in 2008 in Catalonia, Spain. Thirteen Spanish companies accredited to make pollutant measurements and with suitably approved equipment and trained staff participated in the research. The aim of the research was to evaluate the technical competence of the accredited companies in conducting tests in situ. The results obtained from the application of the methodology described in international standards--on the basis of conventional statistical analyses--were compared with those obtained using functional data analysis. On comparing the two methods, it was concluded that the functional method, although more complex, has advantages over the conventional statistical method. This is because the functional approach compares curves rather than mean values. Furthermore, it does not presuppose a normal distribution of the data.

Uncertainty determination methodology, sampling maps generation and trend studies with biomass thermogravimetric analysis.

This paper investigates a method for the determination of the maximum sampling error and confidence intervals of thermal properties obtained from thermogravimetric analysis (TG analysis) for several lignocellulosic materials (ground olive stone, almond shell, pine pellets and oak pellets), completing previous work of the same authors. A comparison has been made between results of TG analysis and prompt analysis. Levels of uncertainty and errors were obtained, demonstrating that properties evaluated by TG analysis were representative of the overall fuel composition, and no correlation between prompt and TG analysis exists. Additionally, a study of trends and time correlations is indicated. These results are particularly interesting for biomass energy applications.

Biomass thermogravimetric analysis: uncertainty determination methodology and sampling maps generation.

The objective of this study was to develop a methodology for the determination of the maximum sampling error and confidence intervals of thermal properties obtained from thermogravimetric analysis (TG), including moisture, volatile matter, fixed carbon and ash content. The sampling procedure of the TG analysis was of particular interest and was conducted with care. The results of the present study were compared to those of a prompt analysis, and a correlation between the mean values and maximum sampling errors of the methods were not observed. In general, low and acceptable levels of uncertainty and error were obtained, demonstrating that the properties evaluated by TG analysis were representative of the overall fuel composition. The accurate determination of the thermal properties of biomass with precise confidence intervals is of particular interest in energetic biomass applications.

Heterogenic solid biofuel sampling methodology and uncertainty associated with prompt analysis.

Accurate determination of the properties of biomass is of particular interest in studies on biomass combustion or cofiring. The aim of this paper is to develop a methodology for prompt analysis of heterogeneous solid fuels with an acceptable degree of accuracy. Special care must be taken with the sampling procedure to achieve an acceptable degree of error and low statistical uncertainty. A sampling and error determination methodology for prompt analysis is presented and validated. Two approaches for the propagation of errors are also given and some comparisons are made in order to determine which may be better in this context. Results show in general low, acceptable levels of uncertainty, demonstrating that the samples obtained in the process are representative of the overall fuel composition.