A cross-sectional study was conducted on 83 female electronics factory workers. The respondents comprised 50 exposed workers who use lead alloy solder and 33 unexposed workers. The objective of this study was to assess the lead exposure of these workers. Breathing zone were sampled using air sampling pumps. Dust samples were collected by wipe method. Venous blood collected and blood pressure were measured. All lead analyses were carried out with Graphite Furnace Atomic Absorption Spectrophotometer. The mean air lead for exposed workers (57 0. ± 0.93 μg/m³) was significantly higher than the unexposed workers (0.0067 ± 0.0045μg/m³) (p
Introduction: Primary health care providers face a wide range of stressors and are at high risk of developing occu-pational burnout, which may cause ineffectiveness and reduce the productivity of the health care system. Methods: A cross-sectional study was conducted among the primary health care provider working in health facilities under Tuaran Area Health Office. A total of 199 of 604 providers randomly selected as respondents for this study. Self-ad-ministered questionnaire and the Maslach Burnout Inventory are used for data collection. Descriptive statistics were used to determine the prevalence and Chi-square test was used to determine the association of risk factors. Results: Prevalence of occupational burnout is 10.1% with high level of overall burnout (n = 20), 60.8% are having low to moderate level of overall burnout (n = 121) and 29.1% has no burnout (n = 58). A significant relationship was observed between burnout, high workload, out-of-scope workload and distance between home and workplace (p≤0.01). However, no significant relationship was observed between burnout and age, gender, marital status, finan-cial status, education level, experience and income. Conclusion: This study shows that distribution of workload as well as the job scope may affect burnout. Further study can be conducted to identify home-workplace distance re-lation to burnout. With the identification of these factors, a counter measures and intervention can be implemented.
This study compares the mean blood lead concentration and its association with the mean neurobehavioral scores between 2 groups of workers. The exposed group was made up of 50 male workers from 2 battery manufacturÂing factories and the comparative group was made up of 40 male adminisÂtrative workers from a local university. The neurobehavioral test was carried out by using a modified World Health Organization Neurobehavioral Core Test Battery (NCTS). The NCTS consists of 7 tests, which are made up of the Benton Visual Retention Test, Digit Symbol, Digit Span, Pursuit Aiming Test, Reaction Time, Santa Ana Manual Dexterity Test and Trail Making Test. Blood samples were collected by venous puncture method. Blood lead concentrations were determined by the Graphite Furnace Atomic Absorption Spectrophotometer (GFAAS). The mean blood lead concentration of the exposed group (38.5 μg/dL) is higher than the comparative group (5.6 μg/dL). Results show significant difference in the mean blood lead concentration between the 2 groups (p<0.001). There are also significant differences in the mean scores of each NCTS test such as Benton Visual Retention Test (p = 0.001), Digit Span Test (p< 0.001), Digit Symbol Test(p< 0.001), Pursuit Aiming Test (p< 0.001), Reaction Time Test (p< 0.001), Santa Ana Manual Dexterity Test (p< 0.001), Trail Making Test (p<0.001) (p< 0.001) and the overall NCTS test (p<0.001) between the 2 groups. There are significant inverse correlation between blood lead concentrations with each and overall NCTS scores when the two groups are combined. There are significant inverse correlations between blood lead concentrations with educational years and income for all respondents. Statistical tests show that blood lead, age, years of formal education, total income, years of work, and ethnicity contributes to the overall NCTS scores. The GLM model shows that 56.9% of the mean NCTS scores are influenced by the variability in the contributing factors mentioned before.
This is a study of the incidence of dental fluorosis and the urine fluoride concentration among school children. About 84 Malay students with the age range of 16 to 17 years from a National Secondary School in the district of Kuala Lipis, Pahang was selected as respondents. The selection was based on the exposure to fluoride in drinking water supply systems. Fifty two respondents were selected from the fluoridated water supply area while 32 others were selected from the non-fluoridated area (comparative group). The objectives of this study were to determine the relationship between urine fluoride concentrations with the incidence of dental fluorosis and to compare the difference in these two variables between the 2 groups of respondents. The urine fluoride concentration was determined using a fluoride-ion specific electrode. Dental fluorosis was examined through a physical examination using the Tooth Surface Index of Fluorosis (TSIF). There was no significant difference in the mean urine fluoride concentration (mg/L) (t=0.186, p=0.853), mean urine fluoride concentration (mg/g creatinine) (t=0.069, p=0.945) and dental fluorisis (TSIF mean score) (t=0.288, p=0.774) between the two groups. There was a significant direct correlation between the urine fluoride concentrations (mg/L) (r= 0.425, p<0.00l) and the urine fluoride concentraÂtions (mg/g creatinine) (r=0.252, p=0.021) with dental fluorisis (TSIF mean score). Multiple regression statistics, indicated that dental fluorosis was significantly related to urine fluoride concentrations (b=0.0.61, p=0.028) and the number of glass of their favourite drink consumed daily (b=0.071, p=0.003). In conclusion, the urine fluoride concentrations, which represent the degree of exposure to fluoride, were found to be related to dental fluorosis, which is the biological indicator for excessive exposure to fluoride. There is no difference on the degree and the effects of exposure to fluoride between the two groups of respondents although they consumed water from two different water supply systems. Thus, the exposure to fluoride is not only through the drinking water supply, but also by other sources such as the intake of carbonated drinks and fruit juice.