Whether HSE Is Genuine or Not - Assignment Example

?Whether HSE is genuine or not in the evidence of difference in the potential health hazard between workers at the two operations Introduction and Background Crystalline silica is a basic component of soil, sand, granite, and many other mineralsi. The Control of Substances Hazardous to Health Regulations (COSHH) offers permissible exposure limits for crystalline silica for the purpose of achieving adequate control of worker exposureii. Exposure to respiratory particles of crystalline silica is associated with several human diseases such as cancer and lung diseasesiiiiv. The disease risk is related to both the total dose and duration of silica exposurev. Silicosis, a nodular pulmonary fibrosis, is the disease most associated with exposure to respirable crystalline silicavi. Studies have shown that exposure to crystalline silica can lead to physiological changes, disease and deathviiviiiix. There is a reliable link between cumulative silica dust exposure and increased mortality from lung cancerx. Calvert et al. found a relationship between crystalline silica exposure and rheumatoid arthritisxi. Meijer et al. showed significant association between exposure to concrete dust and a small lung infectionxii. There are a number of factors that influence the development of silicosis and these include size of particles, and concentration of silica particles in the air duration of exposurexiiixiv. RSAxv noted that chronic silicosis is mainly the result of long term exposure and that accelerated silicosis can develop after five to ten years of exposure. Morfeldxvi concluded that no other non-malignant health effect due to RCS is as specific and so clearly linked to RCS as silicosis. Research Questions 1. Is there any difference in the health of the workers in the two operations? 2. What associations exist, if any, between length of service and recorded health effect? These research questions will be answered through a hypothesis testing. As Dythamxvii noted, hypothesis testing is the cornerstone of scientific analysis. Tests are carried out to determine whether a stated hypothesis is correct. The hypothesis is rejected or accepted based on the P-values observed. Usually, the null hypothesis (the hypothesis that nothing is going on) is the one that is accepted or rejected based on the calculated probabilities. Most research will accept or reject a hypothesis at 95% level of confidence. Thus if the calculated p-values from a hypothesis test is less than 5% (or 0.05), we reject the null hypothesis and accept the alternative hypothesis. For this study, the alternative hypotheses for which the null hypotheses were tested were: H1: There is a difference in the health of workers in brick and tile operations. H2: There is a statistically significant association between length of service and recorded health effect. Study Methodology Primary data was collected from a sample of 65 workers randomly selected for blood testing, 38 from brick operations and 27 from tile operations. The study collected data on their identity, the sectors in which they worked, the length of service of each employee, their ages, and health. Following Dythamxviii, the first hypothesis was tested using independent samples t-test since the data was unpaired and the dependent variable was a continuous variable. Minitab 16 was used to perform a two-sample t-test to assess whether there were any differences in the health of workers in the two operations. The existence of association between length of service and recorded health effect was assessed using the Pearson’s product-moment correlation. Descriptive Results Descriptive results are presented and show the number of observations (N), mean, standard deviation, minimum values, first quartile (Q1), median, third quartile (Q3), maximum values, skewness and kurtosis. The normality tests are also shown together with the descriptive results. These are graphically presented for the three main variables of the study namely age (years), length of service (years), and % of damaged cells. Figure 1: Descriptive Results for % Damaged Cells in the Tile Operation Summary descriptive results for % damaged cells in the tile operation are shown in Figure 1. The mean % damaged cells in the operation was 1.3296 (95% confidence intervals of 0.9013 and 1.7579). The standard deviation is 1.0827 (95% confidence intervals of 0.8526 and 1.4837). Using a significance level of 0.05, the Anderson-Darling normality test (A-Squared = 1.88, P-Value = 0.005) indicates that the % damaged cells do not follow a normal distribution. The box plot also confirms that the data is not normally distributed as there are also outlier variables in the distribution. Figure 2: Descriptive Results for % Damaged Cells in the Brick Operation Summary descriptive results for % damaged cells in the brick operation are shown in Figure 2. The mean % damaged cells in the operation was 1.5354 (95% confidence intervals of 1.1711 and 1.8997). The standard deviation is 1.1084 (95% confidence intervals of 0.9036 and 1.4340). Using a significance level of 0.05, the Anderson-Darling normality test (A-Squared = 0.90, P-Value = 0.019) indicates that the % damaged cells do not follow a normal distribution. The box plot also confirms that the data is not normally distributed. Figure 3: Descriptive Results for Age (Years) in the Tile Operation Summary descriptive results for age of participants in the tile operation are shown in Figure 3. The mean age in the operation was 41.704 (95% confidence intervals of 36.585 and 46.823). The standard deviation is 12.940 (95% confidence intervals of 10.191 and 17.734). Using a significance level of 0.05, the Anderson-Darling normality test (A-Squared = 0.33, P-Value = 0.491) indicates that the age in the tile operation follows a normal distribution. The box plot also confirms that the data is normally distributed. Summary descriptive results for age of participants in the brick operation are shown in Figure 4. The mean age in the operation was 38.079 (95% confidence intervals of 33.558 and 42.600). The standard deviation is 13.753 (95% confidence intervals of 11.213 and 17.793). Using a significance level of 0.05, the Anderson-Darling normality test (A-Squared = 1.11, P-Value = 0.006) indicates that the age in the brick operation do not follow a normal distribution and the box plot results also confirm this data is not normally distributed. Figure 4: Descriptive Results for Age (Years) in the Brick Operation Figure 5: Descriptive Results for Length of Service (Years) in the Tile Operation Summary descriptive results for the length of service (years) of participants in the tile operation are shown in Figure 5. The mean length of service in the operation was 9.8288 (95% confidence intervals of 6.8827 and 12.7748). The standard deviation is 7.4473 (95% confidence intervals of 5.8649 and 10.2060). Using a significance level of 0.05, the Anderson-Darling normality test (A-Squared = 0.70, P-Value = 0.059) indicates that the length of service in the tile operation follows a normal distribution. The box plot does not confirm this. Summary descriptive results for the length of service (years) of participants in the brick operation are shown in Figure 6. The mean length of service in the operation was 8.4027 (95% confidence intervals of 5.9967 and 10.8087). The standard deviation is 7.3199 (95% confidence intervals of 5.9677 and 9.4701). Using a significance level of 0.05, the Anderson-Darling normality test (A-Squared = 1.28, P-Value = 0.005) indicates that the length of service in the brick operation do not follow a normal distribution and the box plot confirms this. Figure 6: Descriptive Results for Length of Service (Years) in the Brick Operation Hypothesis Testing The first hypothesis test is on whether there is a difference in the health of the workers in the two operations. The null hypothesis is that there is no difference in the recorded health effects across the two operations. This hypothesis test could only be performed with Minitab 16 using Mann-Whitney test. This statistic was used because the data was not normally distributed and therefore a non-parametric test of difference was called for. The test statistic W of 13.20.5 and p-values of 0.3796 and 0.3793 with adjusted ties shows that the null hypothesis is accepted at the 5% significant level. The same was the case for differences in age and length of service. The second hypothesis test was on whether there was an association between length of service and recorded health effect. The scatter-plot in Figure 7 shows roughly the relationship that exists between length of service and % damaged cells. The results show a steeper slope in the tile operation than in the brick operation though both operations show positive association. Figure 7: Association between % damaged Cells and Length of Service Since the data had failed normality tests, non-parametric test of association was used. The study chose the Spearman’s rank correlation to test the association. This was done by first ranking the data on % damaged cells and length of service for the brick and tile operations. Then a usual Pearson’s correlation analysis was run on the ranked data. The results showed a significant positive association between length of service and % damaged cells in the tile operation, r = 0.577, p-value = 0.002. There was a weak association between these variables in the brick operation, r = 0.307, p-value = 0.061. Age did not have any significant association with % damaged cells and was therefore not reported. With a significant association found for % damaged cells and length of service in the tile operation, the researcher opted to perform a further test to confirm if there was a causal relationship between these two variables in the tile operation. The dependent variable was suggested as the % damaged cells and the predictor variable was suggested as the length of service. It was hypothesised therefore that the length of service in the tile operations affects the % of damaged cells of workers. In order to test this, the null hypothesis that length of service in the tile operation did not affect % of damaged cells was tested using a simple regression analysis. But since the data was not normally distributed, a log transformation of the data was done. After transformation using natural logarithms of both % damaged cells and length of service, the normality tests confirmed that the data was now normally distributed (see Figure 8 and Figure 9). Figure 8: Summary for Ln (Length of Service) for Tile Operation Figure 9: Summary for Ln (% Damaged Cells) for Tile Operation The regression results showed that length of service had a positive and significant impact on % of damaged cells, ?=0.476, p-value = 0.002. The adjusted R2 shows that the model explained 28.9% of the variance in % of damaged cells. The analysis of variance results further show that the model was fit to explain the relationship, F=11.57, p-value = 0.002. Conclusion The study concludes that the brick and tile operations are not different as far as the % of damaged cells in workers is concerned. This suggests that the HSE does not have to be worried about a particular operation over the other in terms of the effects of RCS. Thus any measures to reduce health risks associated with RCS do not need to target a specific operation and leave out the other. Both must be targeted. The study also concludes that there is a significant link between length of service and recorded health effects in the tile operation. This is a cause of concern for HSE officials as the workers who have worked longer in the tile operation recorded higher levels of % of cell damages suggesting higher health risks and this could lead to silicosis. Measures should therefore be taken to reduce the exposure in the tile operation or to cap the number of years one can work in the tile operation. The study further concludes that length of service in the tile operation has a positive and significant effect on the % of damaged cells. This therefore suggests that measures need to be taken to reduce the exposure or the number of years a worker can work in the tile industry. References BRIDGE I. Crystalline Silica: A review of the dose response relationship and environmental risk. Air Quality and Climate Change, 43/1 (2009), 17-23. CALVERT G.M., RICE F.L., BOIANO J.M., and SANDERSON W.T., Occupational silica exposure and risk of various diseases: an analysis using death certificates from 27 states of the United States, Occupational and Environmental Medicine, 60/2 (2003), 122 -129 CHEN W., YANG J., CHEN J., and BRUCH J., Exposures to silica mixed dust and cohort mortality study in tin mines: exposure-response analysis and risk assessment of lung cancer, American Journal Industrial Medicine, 49 (2006), 67 DYTHAM C, Choosing and using statistics: a biologist’s guide ( Blackwell science: oxward). 1999. GOLDSMITH D., “Relationship of exposures to crystalline silica & health effects: An Epidemiologist view of the controversy”, (2006). , accessed 21 April 2013 HSE, Methods for Determination of Hazardous Substances, 2005. MCDONALD J.C., MCDONALD A.D., HUGHES J.M., RANDO R.J., and WEILL H., Mortality from lung and kidney disease in a cohort of North American industrial sand workers. Annals Occupational Hygiene, 49 (2005), 367-373 MEIJER, E., KROMHOUT, H. and HEEDERIK, D. Respiratory effects of exposure to low levels of concrete dust containing crystalline silica. American Journal of Industrial Medicine, 40 (2001), 133–140. MORFELD, P. Respirable Crystalline Silica: Rationale For Classification According to the CLP* Regulation and within the Framework of the Globally Harmonised System (GHS) of Classification and Labelling of Chemicals. 2010. NIOSH, Health Effects of Occupational Exposure to Respirable Crystalline Silica.. Department Of Health And Human Services, 2002. Office of Environmental Health Hazard Assessment (OEHHA), Adoption of Chronic Reference Exposure Levels for Silica (crystalline, respirable), 2005, , accessed 21 April 2013 OHS, Crystalline Silica at the Work Site, Chemical Hazards, 2009. Republic of South Africa Department of Labour (RSA), Silica Exposure and its effect on the physiology of workers, 2006. ROSNER D., and Markowitz G, Deadly Dust: Silicosis and the On-Going Struggle to Protect Workers Health (The University of Michigan Press, 2006). STEENLAND K., One agent, many diseases: exposure-response data and comparative risks of different outcomes following silica exposure, American Journal Industrial , 48 (2005), 16 -23 Appendices Mann-Whitney Test and CI: % damaged cells_brick, % damaged cells_tile N Median % damaged cells_brick 38 1.370 % damaged cells_tile 27 1.100 Point estimate for ETA1-ETA2 is 0.200 95.0 Percent CI for ETA1-ETA2 is (-0.300,0.807) W = 1320.5 Test of ETA1 = ETA2 vs ETA1 not = ETA2 is significant at 0.3796 The test is significant at 0.3793 (adjusted for ties) Mann-Whitney Test and CI: length of servic, length of servic N Median length of service (years)_brick 38 6.437 length of service (years)_tile 27 8.287 Point estimate for ETA1-ETA2 is -1.156 95.0 Percent CI for ETA1-ETA2 is (-4.863,1.715) W = 1188.5 Test of ETA1 = ETA2 vs ETA1 not = ETA2 is significant at 0.3869 The test is significant at 0.3864 (adjusted for ties) Mann-Whitney Test and CI: age (years)_tile, age (years)_brick N Median age (years)_tile 27 42.00 age (years)_brick 38 34.50 Point estimate for ETA1-ETA2 is 4.00 95.0 Percent CI for ETA1-ETA2 is (-3.00,12.00) W = 987.5 Test of ETA1 = ETA2 vs ETA1 not = ETA2 is significant at 0.2013 The test is significant at 0.2010 (adjusted for ties) Correlations: Rtimebrick, Rcellbrick Pearson correlation of Rtimebrick and Rcellbrick = 0.307 P-Value = 0.061 Correlations: Rtimetile, Rcelltile Pearson correlation of Rtimetile and Rcelltile = 0.577 P-Value = 0.002 Regression Analysis: LnCell_Tile versus LnTime_Tile The regression equation is LnCell_Tile = - 0.920 + 0.476 LnTime_Tile Predictor Coef SE Coef T P Constant -0.9196 0.2999 -3.07 0.005 LnTime_Tile 0.4759 0.1399 3.40 0.002 S = 0.673743 R-Sq = 31.6% R-Sq(adj) = 28.9% Analysis of Variance Source DF SS MS F P Regression 1 5.2509 5.2509 11.57 0.002 Residual Error 25 11.3482 0.4539 Total 26 16.5992 Unusual Observations Obs LnTime_Tile LnCell_Tile Fit SE Fit Residual St Resid 8 0.00 0.588 -0.920 0.300 1.507 2.50R R denotes an observation with a large standardized residual. 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