METHODS: We conducted a series medical examinations among the forestry, construction and automobile industry workers in Malaysia adopting the compulsory medical examination procedure used by Wakayama Medical University for Japanese vibratory tools workers. We matched the duration of vibration exposure and compared our results against the Japanese workers. We also compared the results of the Malaysian tree fellers against a group of symptomatic Japanese tree fellers diagnosed with HAVS.
RESULTS: Malaysian subjects reported a similar prevalence of finger tingling, numbness and dullness (Malaysian=25.0%, Japanese=21.5%, p=0.444) but had a lower finger skin temperature (FST) and higher vibrotactile perception threshold (VPT) values as compared with the Japanese workers. No white finger was reported in Malaysian subjects. The FST and VPT of the Malaysian tree fellers were at least as bad as the Japanese tree fellers despite a shorter duration (mean difference=20.12 years, 95%CI=14.50, 25.40) of vibration exposure.
CONCLUSIONS: Although the vascular disorder does not manifest clinically in the tropical environment, the severity of HAVS can be as bad as in the temperate environment with predominantly neurological disorder. Hence, it is essential to formulate national legislation for the control of the occupational vibration exposure.
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.