Methodology: The study was performed by analyzing the urine samples of the participants for methylhippuric acid, the established biomarker of xylene with the aid of high-performance liquid chromatography.
Results and Conclusion: The work hours per week of the occupationally exposed participants were statistically analyzed with that of the excretory values of the metabolites of xylene, and the P value was found to be highly significant. Various side effects of xylene including respiratory, dermatological, neurological and gastrointestinal symptoms were observed among the study groups.
METHODS: 41 medical personnel performing 79 procedures were monitored for their eye lens exposure using the NanoDot™ optically-stimulated luminescence dosimeters (OSLD) taped to the outer canthus of their eyes. The air-kerma area product (KAP), fluoroscopy time (FT) and number of procedure runs were also recorded.
RESULTS: KAP, FT and number of runs were strongly correlated. However, only weak to moderate correlations were observed between these parameters with the measured eye lens doses. The average median equivalent eye lens dose was 0.052 mSv (ranging from 0.0155 to 0.672 mSv). The eye lens doses of primary operators were found to be significantly higher than their assistants due to the closer proximity to the patient and X-ray tube. The left eye lens of the operators received the highest amount of radiation due to their habitual positioning towards the radiation source.
CONCLUSION: KAP and FT were not useful in predicting the equivalent eye lens dose exposure in interventional radiological procedures. Direct in vivo measurements were needed to provide a better estimate of the eye lens doses received by medical personnel during these procedures. This study highlights the importance of using direct measurement, such as OSLDs, instead of just indirect factors to monitor dose in the eye lens in radiological procedures.
METHODS AND ANALYSIS: This is a 3-year project in which a survey of 100 000 workers from all 13 states in Malaysia will be conducted using a web-based screening tool that is comprised of two parts: occupational disease screening tool and hazard identification, risk assessment and risk control method. Data will be collected using a multistage stratified sampling method from 500 companies, including seven critical industrial sectors. The independent variables will be sociodemographic characteristics, comorbidities, previous medical history, high-risk behaviour and workplace profile. The dependent variable will be the types of occupational diseases (noise-induced hearing loss, respiratory, musculoskeletal, neurotoxic, skin and mental disorders). Subsequently, suggestions of referral for medium and high-risk workers to occupational health clinics will be attained. The approved occupational health service clinics/providers will make a confirmatory diagnosis of each case as deemed necessary. Subsequently, a walk-through survey to identify workplace hazards and recommend workplace improvement measures to prevent these occupational diseases will be achieved. Both descriptive and inferential statistics will be used in this study. Simple and adjusted binary regression will be used to find the determinants of occupational diseases.
ETHICS AND DISSEMINATION: This study has been approved by the MARA University of Technology Research Ethics Board. Informed, written consent will be obtained from all study participants. Findings will be disseminated to the Department of Occupational Health and Safety, involved industries, and through peer-reviewed publications.
METHODOLOGY: The test was conducted for two different road conditions, tarmac and dirt roads. HAV exposure was measured using a Brüel & Kjær Type 3649 vibration analyzer, which is capable of recording HAV exposures from steering wheels. The data was analyzed using I-kaz Vibro to determine the HAV values in relation to varying speeds of a truck and to determine the degree of data scattering for HAV data signals.
RESULTS: Based on the results obtained, HAV experienced by drivers can be determined using the daily vibration exposure A(8), I-kaz Vibro coefficient (Ƶ(v)(∞)), and the I-kaz Vibro display. The I-kaz Vibro displays also showed greater scatterings, indicating that the values of Ƶ(v)(∞) and A(8) were increasing. Prediction of HAV exposure was done using the developed regression model and graphical representations of Ƶ(v)(∞). The results of the regression model showed that Ƶ(v)(∞) increased when the vehicle speed and HAV exposure increased.
DISCUSSION: For model validation, predicted and measured noise exposures were compared, and high coefficient of correlation (R(2)) values were obtained, indicating that good agreement was obtained between them. By using the developed regression model, we can easily predict HAV exposure from steering wheels for HAV exposure monitoring.