METHODS: We searched 3 major databases, i.e., PubMed, Embase and Lippincott Williams & Wilkins Journals@Ovid, for studies published up until 1May 2013 without language restrictions. All study designs were included in this review. The studies were identified and retrieved by two independent authors.
RESULTS: Of 118 titles scanned, 14 duplicates were removed, and a total of 13 abstracts from all three databases were identified for full-text retrieval. From the full text, eight articles met the inclusion criteria for this systematic review. These articles showed acceptable quality based on our scoring system. Most of the studies indicated that temporary threshold shifts were much lower when subjects were exposed to a noise level of 85 dBA or lower.
CONCLUSIONS: There were more threshold shifts in subjects adopting 90 dBA compared with 85 dBA. These temporary threshold shifts may progress to permanent shifts over time. Action curtailing noise exposure among employees would be taken earlier on adoption of 85 dBA as the permissible exposure limit, and hence prevalence of noise-induced hearing loss may be reduced.
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.
METHODS: Unmatched case control and comparative studies were carried out among fertilizer factory workers in Sarawak with the aim of determining contributing factors for hearing impairment. Respondents consisted of 49 cases that were diagnosed from 2005 to 2008 with 98 controls from the same work places. Chi-square test and Mann-Whitney test were used in a univariate analysis to determine the association between hearing impairment and the contributing risks being studied.
RESULTS: The results of the univariate analysis showed that hearing impairment was significantly (p<0.05) associated with older age, lower education level, high smoking dose, high occupational daily noise dose, longer duration of service, infrequent used of hearing protection device (HPD), and low perception of sound on HPD usage. Multivariate logistic regression of hearing impairment after controlling for age found the following five variables: occupational daily noise dose ≥50% (OR 3.48, 95% CI 1.36-8.89), ≥15 years of services (OR 2.92, 95% CI 1.16-7.33), infrequent use of HPD (OR 2.79, 95% CI 1.15-6.77), low perception of sound on HPD (POR 2.77, 95% CI 1.09-6.97), and smoking more than 20 packs per year (OR 4.71, 95% CI 1.13-19.68).
DISCUSSION: In conclusion, high occupational noise exposure level, longer duration of service, low perception of sound on HPD, infrequent used of HPD, and smoking more than 20 packs per year were the contributing factors to hearing impairment, and appropriate intervention measures should be proposed and taken into considerations.