ANNEX 2: Assessment of the 7 points agreed by WATCH as meriting attention (cover paper, paragraph 9, bullet points) by Andy Darnton, HSE

Size: px
Start display at page:

Download "ANNEX 2: Assessment of the 7 points agreed by WATCH as meriting attention (cover paper, paragraph 9, bullet points) by Andy Darnton, HSE"

Transcription

1 ANNEX 2: Assessment of the 7 points agreed by WATCH as meriting attention (cover paper, paragraph 9, bullet points) by Andy Darnton, HSE The 7 issues to be addressed outlined in paragraph 9 of the cover paper have been grouped together and discussed under three headings as follows: 1. Low-dose extrapolations of the H&D model and associated uncertainties i. Determine extrapolated risks at 0.1, 0.01, and f/ml.years; ii. Determine confidence limits for the above extrapolations; and iii. Outline justification for limiting extrapolations to crocidolite & amosite. 2. Adjusting the H&D predictions to allow for life expectancy and age of first exposure iv. Determine age-related adjustment factors to age 0; v. Determine age-related adjustment factors for children with life expectancy to age 90; 3. Exposure ranges consistent with a given level of risk vi. Determine exposure levels for "acceptable" risk; vii. Determine exposure levels for risks that would be double those for spontaneous mesothelioma.

2 1. Low-dose extrapolations of the H&D model and associated uncertainties The summary statement agreed following the October 2008 WATCH meeting (Annex 1 here) included combined lifetime risk estimates for mesothelioma and lung-cancer for cumulative exposures to each of the three main asbestos types of 10, 1 and 0.1 f/ml.yr assumed to be accrued from age 30 over 5 years. These were included to illustrate the sorts of estimates that can be produced using the model. Consideration of the risk at these three particular exposure levels arose from the papers prepared for the October 2008 meeting. The intention of these papers was to illustrate the uncertainties in predictions at various points progressing down the exposure scale in order to appropriately inform the discussion about what could be reliably said about the risk at different points. Additional extrapolations have now been done, progressing further down the exposure scale beyond 0.1 f/ml.yr in orders of magnitude to f/ml.yr in order to illustrate the predictions of the model and how these differ from a simple linear extrapolation. These extrapolations are shown in Tables 1-3. For each of the three asbestos types extrapolations have been produced for mesothelioma and lung cancer separately and combined and using both the H&D models and a simple linear extrapolation in order to show the contribution of mesothelioma and lung cancer to the extrapolated total risk values at different exposures. Lifetime risks were calculated from the H&D models using the methods set out on p584 in the original paper.

3 Table 1. Lifetime risk extrapolations for crocidolite exposure for 5 years from age 30 Lifetime risk per million: Cumulative exposure (f/ml.yr) Crocidolite H&D best estimate (minimum, maximum) Linear Mesothelioma Lung cancer Total (24000, 58000) (8000, 26000) (32000, 84000) (2300, 13000) (200, 2600) (2500, 16000) * 1200 (230, 3300) (5, 260) (240, 3600) (23, 840) (0.13, 26) (23, 870) (2.3, 210) (0.0032, 2.6) (2.3, 210) (0.23, 53) ( , 0.26) (0.23, 53) (0.023, 13) ( , 0.026) (0.023, 13) (0.0023, 3.3) ( , ) (0.0023, 3.3) *Lower bound of exposure in WATCH October 2008 summary statement

4 Table 2. Lifetime risk extrapolations for amosite exposure for 5 years from age 30 Cumulative exposure (f/ml.yr) Amosite Lifetime risk per million: H&D best estimate (minimum, maximum) Linear Mesothelioma Lung cancer Total (1600, 14000) (8000, 26000) (9600, 40000) (150, 3100) (200, 2600) (350, 5700) * 160 (15, 770) (5, 260) (20, 1000) (1.5, 190) (0.13, 26) (1.7, 220) (0.15, 49) (0.0032, 2.6) (0.16, 51) (0.015, 12) ( , 0.26) (0.015, 13) (0.0015, 3.1) ( , 0.026) (0.0015, 3.1) ( , 0.77) ( , ) ( , 0.78) *Lower bound of exposure in WATCH October 2008 summary statement

5 Table 3. Lifetime risk extrapolations for chrysotile exposure for 5 years from age 30 Cumulative exposure (f/ml.yr) Chrysotile Lifetime risk per million: H&D best estimate (minimum, maximum) Linear Mesothelioma Lung cancer Total (63, 700) (170, 2700) (230, 3400) (6.3, 180) 7 15 (4.2, 270) (10, 450) * 8.2 (0.63, 44) (0.11, 27) (0.73, 71) (0.063, 11) (0.0027, 2.7) (0.065, 14) (0.0063, 2.8) ( , 0.27) (0.0063, 3.1) ( , 0.7) ( , 0.027) ( , 0.73) ( , 0.18) ( , ) ( , 0.18) ( , 0.044) ( , ) ( , 0.045) *Lower bound of exposure in WATCH October 2008 summary statement

6 Summary of main features of Tables 1-3 Crocidolite The mesothelioma risk dominates the total risk across the exposure range. At high exposures the mesothelioma risk predicted by H&D is similar to that based on the linear model but at low exposures the H&D predictions are much higher relative to the linear model. For lung cancer the opposite effect is seen: the H&D predictions are much lower than those of the linear model at low exposures. At the lowest exposures tabulated many orders of magnitude below the data range the mesothelioma risk based on H&D is over 50 times higher than that based on the linear extrapolation, whereas the lung cancer risk based on H&D is about 1/180th that based on the linear extrapolation. (Note, here the exposure has reduced a factor of 100,000 below the lower level considered by WATCH in October 2008 (ie 0.1 f/ml.yr), whereas the risk estimated by the H&D best model has reduced by a factor of about 5500.) For the combined risk, the predictions of the linear model are close to the bottom end of the H&D uncertainty range. Amosite Lung cancer risks are the same as those for crocidolite but mesothelioma risks are lower. Therefore, lung cancer dominates the total risk at high exposures, though mesothelioma still dominates at low exposures. At the lowest exposures the mesothelioma risk based on H&D is about 40 times higher than that based on the linear extrapolation, whereas the lung cancer risk based on H&D is again about 1/180th that based on the linear extrapolation. For the combined risk at lower exposures, the H&D predictions are still higher than the linear model but the predictions are closer than for crocidolite. Chrysotile Risks are generally much lower than for the amphiboles and soon become insignificant progressing below 0.1 f/ml.yr, whether based on the H&D model or the linear extrapolation. At high exposures H&D predicts similar risks for mesothelioma and lung cancer whereas the linear model predicts fewer mesotheliomas and more lung cancers. However, the combined risk at high exposures is similar for both the H&D and linear models. At lower exposures mesothelioma dominates based on H&D whereas lung cancer dominates based on the linear model. Basis for uncertainty ranges The uncertainty ranges of the H&D predictions encompass variation because of the different possible shapes of the non-linear dose-response curves. Thus any discussion about the width of the uncertainty intervals inevitably involves consideration of these non-linear curves and the evidence underpinning them. In order to facilitate discussion, these issues are summarised below.

7 It is important to note that the analysis in the H&D paper took in to account the statistical uncertainty in the mortality outcomes and not that of the exposure estimates. WATCH has previously considered whether any evidence based adjustment to cohort level average exposures should be made to improve the model, but concluded that there was no convincing empirical basis for doing this. In general, ignoring uncertainty in the independent variable in regression analyses may tend to lead to an underestimation of the magnitude of the slope of any fitted line. WATCH may wish to keep these issues in mind when considering the following section. Mathematical form of the H&D model mesothelioma A natural approach for exploring dose-response relationships is to fit statistical models with particular mathematical forms, use evidence to select the best model, and then produce predictions with associated confidence intervals based on that particular model. Another approach is to simply assume a given mathematical form for the model (perhaps because there is evidence from other studies), and fit this to the available data, again producing predictions and confidence intervals. Indeed, this was the starting point for the H&D analysis: a linear relationship between risk and cumulative exposure underpins the summaries produced in Tables 1 and 2 of H&D 2000 giving the mesothelioma and excess lung cancer risk per unit of cumulative exposure. Here, the resulting uncertainty ranges are derived from the statistical variation in the observed mortality outcomes from each of the studies. In the H&D analysis, further consideration of the epidemiological evidence led to a different approach in which a range of plausible models with different mathematical forms were considered. All these models can be described with the same basic equation, in which risk is proportional to cumulative exposure raised to some power which is estimated from the data. But the value of the power parameter (or slope) determines the mathematical form: the extent to which the models are non-linear (a power of 1 representing the linear case). In fact, the final mesothelioma model has two terms, the first representing pleural mesothelioma, and the second, peritoneal mesothelioma as follows: Total mesothelioma risk, P M = A pl.x r + A pt.x t Here, A pl and A pt are constants and r and t are the slope parameters for pleural and peritoneal mesothelioma respectively. In the best fitting model both terms are non-linear with r=0.75 and t=2.1, with values of A pl and A pt dependent on fibre type. (Note, A pt =0 for chrysotile which implies that there is no associated peritoneal risk for this fibre type). It is unclear why this should be the case. However, the limitations in the underlying data to which the models were fitted meant that the basis for preferring a model with particular values of the slope parameters r and t was not clear-cut and relied upon a degree of judgment. Confidence intervals for predictions based on our preferred ( best ) model are

8 therefore unlikely to capture the full extent of the uncertainty. The uncertainty ranges for the H&D predictions (eg as presented in Table 11 of H&D) therefore take into account the range of predictions from all the plausible models that we argued could be consistent with the observed data. This includes models with values of r and t both lower and higher than 0.75 and 2.1 respectively ie models with different degrees of non-linearity. Thus any consideration of the extent of the uncertainty of the predictions necessarily involves a consideration of the extent of the non-linearity. The evidence led to the fitting of 3 sets of models with slope parameters as follows: Best slope model: P M =A pl.x A pt.x 2.1 High slope model: P M =A pl.x + A pt.x 2.5 Low slope model: P M =A pl.x A pt.x 1.7 The shape of each of these models for either crocidolite or amosite exposure (with confidence intervals) is illustrated in the following graph (not drawn to scale). Because the value of A pl is much bigger than A pt, the pleural term accounts for most of the combined risk unless the exposure X is large. For low-dose extrapolations, the low slope model (with r=0.6) predicts the highest risks, and the high slope model (r=1 ie linear in this region) predicts the lowest risks. Note, since for chrysotile there is no peritoneal term the rapid increase at high exposures does not occur, though the shape for low dose extrapolations is as illustrated. 5 Comparison of dose response shapes, H&D high-slope, best, and lowslope models (illustrative purposes only) Combined pleural and peritoneal mesothelioma risk Low slope: P M =A pl X 0.6 +A pt X 1.7 Best slope: P M =A pl X A pt X 2.1 High slope: P M =A pl X+A pt X Cumulative exposure, f/ml.yr As illustrated in the following chart, the H&D maximum and minimum models are then defined by the maximum and minimum predictions from any of these models over the whole exposure range, taking account of the confidence

9 intervals of the parameters A pl and A pt. The H&D best model is given by the best slope model throughout the exposure range. Combined pleural and peritoneal mesothelioma risk Comparison of dose response shapes, H&D max, best, and min models (illustrative purposes only) H&D max model H&D best model Cumulative exposure, f/ml.yr H&D min model

10 Mathematical form of the H&D model lung cancer The H&D model for lung cancer is somewhat simpler than for mesothelioma: Excess lung cancer risk, P L = A L.X r The constant A L is common for both crocidolite and amosite, but is lower for chrysotile for each of three models with slope parameters as follows: Linear (low slope): P L = A L.X Best slope: P L = A L.X 1.3 High slope: P L = A L.X 1.6 The same approach used for mesothelioma of considering the highest and lowest predictions across the whole exposure range was used to define the H&D maximum and minimum models. Again these extremes set the upper and lower uncertainty limits of the predictions. Since the slope, r, is greater than or equal to 1 in all of these models, in contrast to mesothelioma, the upper bound of the uncertainty range in the lowest dose extrapolations is defined by the linear model, and the lower bound by the non-linear high-slope model as illustrated in the following graph. 5 Comparison of dose response shapes, H&D high-slope, best, and lowslope lung cancer models (illustrative purposes only) 4 Excess lung cancer 3 2 Best-slope: X^1.3 Low-slope (linear) High-slope: X^ Cumulative exposure, f/ml.yr

11 Summary of the evidence for non-linear risk models for mesothelioma The evidence is set out in the H&D 2000 but set out again here in summary form: Separate regressions for pleural and peritoneal mesothelioma suggest risk is proportional to a power of cumulative exposure (ie pleural risk, P r X r ; peritoneal risk, P t X t ). Best fitting values of these powers were r=0.75 and t=2.1, but the limited range of data points led to wide confidence intervals such that linear models (r or t=1) are also consistent with the data. However, at least one of the pleural or peritoneal outcomes must be non-linear since regressions of peritoneal risk (P t ) vs pleural risk (P r ) from a wider group of amphibole exposed cohorts (included those that lack the quantitative exposure data required for the dose-response modelling) suggest the peritoneal risk is proportional to at least the square or even the cube of the pleural risk (P t P r k, with k>2). The power, k, above equates to the ratio t/r in the initial regressions, so the range of plausible values of k can be fed into regressions which include terms for both pleural and peritoneal mesothelioma to further confirm that the slopes r and t are likely to be in the region of 0.75 and 2.1 respectively. Nevertheless there is considerable uncertainty associated with the slope parameters. A lower bound for the uncertainty range of r=0.6 was chosen on the grounds that an analysis by Berry etal. gave a value of 0.5 but with a likelihood of this being biased downwards. An upper bound of r=1 was chosen on the grounds that a linear relationship represents a natural assumption (effect is proportional to cause) in the absence of evidence to the contrary. Regressions were then carried out with r constrained to 0.6, 0.75 and 1.0 in order to obtain the corresponding values of t (1.7, 2.1 and 2.5 respectively) and the estimates and confidence intervals for the constants A pl and A pt within the model: P M =A pl.x r +A pt.x t. Possible biological basis for a convex (r<1) dose-response for pleural mesothelioma Low-dose extrapolations of the mesothelioma model are dominated by the pleural mesothelioma term (A pl.x 0.75 ). The shape of the resulting doseresponse curve is unusual: it is the opposite of an S-shaped dose-response curve which would tend to be consistent with threshold-like behaviour. This raises the question of whether the H&D model is biologically plausible. It is possible to envisage a non-linear relationship between cumulative exposure and pleural mesothelioma risk if the risk is dependent on some function of the concentration of fibres within the target tissue. For example, one possible explanation risk being proportion to a power of cumulative exposure less than 1 is as follows:

12 Asbestos fibres produce foci of inflammation in or near the pleura (perhaps at lymph nodes which get clogged up with fibres). These foci become the centres of approximate spheres of "inflammatory product". The volume of the inflammatory product is proportional to number of fibres in the area. Pleural cells are more susceptible to the carcinogenic influence of the inflammatory product than other cells in the area. The diameter of the sphere of inflammatory product is large in relation to the thickness of the pleura. The susceptible pleural cells thus effectively form a 2-D slice through the spherical volume of inflammatory product. Thus, cumulative exposure should be proportional to the total number of cells within the volume of inflammatory product produced, but risk will be proportional to the number of susceptible pleural cells in the slice through these spheres. If the radius of a sphere of inflammatory product is r, then the total volume of cells will be proportional to r cubed. But the volume of susceptible cells will be proportional r squared (the slice of pleural cells through the sphere). If so, then pleural mesothelioma risk should be approximately proportional to cumulative exposure to the power of 2/3. Presumably this relationship would cease to hold at some point down the exposure scale since the number of fibres would be insufficient to initiate the inflammatory process in the first place. However, this point could presumably still be at very low exposures such that in practical terms no threshold would apply. There are a number of assumptions within this argument, including the notion of spheres of inflammatory product. It also begs the questions why the power function for peritoneal mesothelioma should be very different, i.e. 2.1 rather than 0.75.

13 2. Adjusting the H&D mesothelioma predictions to allow for life expectancy and age of first exposure The key question at the root of this is how to get from the risk metric in the H&D analysis to an estimate of lifetime risk LR that is relevant to today s population. The metric for mesothelioma is just the observed number of mesotheliomas as a percentage of expected all cause mortality (adjusted to age 30): P M = 100.O m /E ac. If mesothelioma and all cause mortality follow the same pattern indefinitely within cohorts, then P M is effectively equal to the LR. After an initial follow-up period O m /E ac will be a good estimate of the LR regardless of the amount of subsequent follow-up. In the H&D 2000 paper we argued that this is not likely to be the case. If the mesothelioma risk eventually levels off and then decreases at very long times since exposure, then this means that at some point all cause mortality will accumulate more quickly than mesothelioma. If true, O m /E ac will tend to overestimate the lifetime risk. We argued that for exposure starting at age 30, the mesothelioma risk is unlikely to continue beyond age 80, so that all of the mesotheliomas resulting from such exposure will have occurred by this point. For an exposed population of 30 year old men the mesothelioma risk P M can be estimated from the model. The predicted number of mesotheliomas can then be calculated from the expected number of all cause deaths under the assumption that the ratio O m /E ac is constant up to age 80 after which O m is level (since all the mesotheliomas have occurred). The average lifetable for the 1970s predicts that 70% of men aged 30 will die before age 80. The estimated LR is therefore 0.7 x P M. With life expectancy as it was in 1970, if the mesothelioma risk does in fact continue to increase beyond age 80 in line with the all cause mortality, the predicted LR would need to be increased by about 40% (= 1 / 0.7). In reality, P M is only approximately constant up to age 80. Even if mesothelioma rates continue beyond age 80 according to the pattern of the HEI model (where the rate increases in proportion to the cube of time since exposure), the ratio of mesothelioma to all cause mortality gradually reduces at long follow up times, as shown in the following graph.

14 Cumulative expected all cause and cumulative predicted mesothelioma mortality by time since first exposure Number of deaths Time since first exposure (yrs) (1) Cumulative expected all cause mortality (2) Cumulative predicted mesothelioma mortality Ratio (2)/(1) *Cumulative all cause mortality for a cohort of 100,000 men based on the 1970s lifetable. Pattern of predicted mesothelioma following a 5 year exposure based on the HEI model in which rate (t-10) 3 (t-10-d) 3, where t is time since start of exposure and D is the duration of exposure in years. Summing the total predicted mesothelioma deaths to age 90 and the total all cause deaths to age 90 based on the 1970s average lifetable suggest that if the mesothelioma risk continues to increase indefinitely according to the HEI model, we would be need to increased the H&D LR estimate by about 20% (not 40%). We can also use this approach based on the HEI model to look at the effects of life expectancy by comparing the results when applying a more up-to-date lifetable for all cause mortality. Here the number of observed mesotheliomas is increased because of increased survival to ages when the mesothelioma risk is expressed. The approach can also be extended to derive adjustment factors for exposure starting at ages other than 30 years, adopting different assmptions for how long the mesothelioma risk continues following exposure. The following table summarises various adjustment factors under different assumptions about life expectancy and the length of time the mesothelioma risk continues following the start of exposure

15 Table 4. Adjustment factors for the H&D mesothelioma predicted LR Life Age at first exposure expectancy Number of years mesothelioma risk continues following start of exposure Up to 60* Up to 90** Today Up to 60* Today Up to 90** * Risk truncated at age 80 or age+60 if age<20 years ** Risk truncated at age 90 This analysis suggests that some adjustment may be warranted when applying the H&D model to predict risks resulting from exposure at younger ages. However, it should be noted that assumption that mesothelioma risk continues for 90 years from exposure (rows 2 and 4 in Table 4) is likely to represent an extreme worst-case scenario. In fact the limited evidence available about the risk at very long follow up times suggests that the risk may eventually reduce substantially. If so this would imply that the adjustment factors in rows 1 and 3 are more appropriate. In considering whether adjustment factors should be used, WATCH may also wish to consider that these factors may be counterbalanced by the fact that airborne concentrations are likely to be estimated as being substantially higher based on modern measurement methods than those used in the original epidemiological studies. Thus, whereas ignoring the age at first exposure will tend to lead to an underestimation of the risk, ignoring the fibre measurement differences will tend to lead to an overestimation.

16 3. Determine exposure ranges consistent with a given levels of risk Acceptable risk is subjective notion. Nevertheless, in the H&D paper we referred to LRs below 1 in 100,000 as insignificant on the grounds that they would be equivalent to annual risk well below 1 per million and are also well below the risk of mesothelioma in the absence of asbestos exposure (ie so called spontaneous or idiopathic cases). The H&D models have therefore been used to derive the range of exposures to each of the three kinds of asbestos that are consistent with LR of 1 in 100,000 (Table 5). There is considerable uncertainty in the evidence about the extent of the lifetime risk of spontaneous mesothelioma. However, the evidence points to annual incidence rate of 1-2 per million, or between about 60 and 120 cases per year (half in men and half in women). The equivalent lifetime risk is therefore in the range 1 in 5000 to 1 in 10,000. In order to illustrate the range of exposures consistent with levels of risk of this order, the H&D model has been used to estimate exposures for the mid-point of this range, ie 1 in Table 5: Cumulative exposures accrued over 5 years from age 30 that are consistent with insignificant LR or of a similar order as the plausible spontaneous risk level. Lifetime risk Cumulative exposure (f/ml.yr): H&D best estimate (maximum, minimum) Linear Crocidolite Amosite Chrysotile 1 per 100,000 (insignificant) ( , ) ( , 0.064) (0.0084, 1.6) per 7500 (spontaneous) ( , 0.056) (0.0051, 0.84) (0.60, 21) 19 Table 5 shows that a wide range of cumulative exposures could be consistent with both these levels of LR. However, in each case the linear extrapolation suggests that the exposure would have to be considerably higher than suggested by the H&D model in order to achieve LRs of these levels.

17

The risks of mesothelioma and lung cancer in relation to relatively lowlevel exposures to different forms of asbestos

The risks of mesothelioma and lung cancer in relation to relatively lowlevel exposures to different forms of asbestos WATCH/2008/7 Annex 1 The risks of mesothelioma and lung cancer in relation to relatively lowlevel exposures to different forms of asbestos What statements can reliably be made about risk at different exposure

More information

Asbestos : Revising the overall summary analysis of cohorts Approach 2

Asbestos : Revising the overall summary analysis of cohorts Approach 2 WATCH/2008/5 Annex 1 Asbestos : Revising the overall summary analysis of cohorts Approach 2 Introduction and summary This annex sets out the results of work on Approach 2 as set out in The risks of lung

More information

(1) Comparison of studies with different follow-up periods

(1) Comparison of studies with different follow-up periods (1) Comparison of studies with different follow-up periods Is the absolute potency of amphiboles and relative potency of chrysotile underestimated because of studies with substantially incomplete follow-up?

More information

Executive summary. Background

Executive summary. Background Executive summary Health Council of the Netherlands. Asbestos: Risks of environmental and occupational exposure. The Hague: Health Council of the Netherlands, 2010; publication no. 2010/10. Background

More information

The quantitative risks of mesothelioma and lung cancer in relation to asbestos exposure a comparison of risk models based on asbestos exposed cohorts

The quantitative risks of mesothelioma and lung cancer in relation to asbestos exposure a comparison of risk models based on asbestos exposed cohorts WATCH/2007/8 Annex 3 The quantitative risks of mesothelioma and lung cancer in relation to asbestos exposure a comparison of risk models based on asbestos exposed cohorts Introduction and aims 1. In 2000,

More information

EFFECT OF CHILDREN'S AGE AND LIFE EXPECTATION ON MESOTHELIOMA RISK 1

EFFECT OF CHILDREN'S AGE AND LIFE EXPECTATION ON MESOTHELIOMA RISK 1 EFFECT OF CHILDREN'S AGE AND LIFE EXPECTATION ON MESOTHELIOMA RISK 1 Robin Howie 2, Robin Howie Associates, Edinburgh It is generally accepted that the major risk from "low" level exposures to asbestos

More information

MESOTHELIOMA MORTALITY IN GREAT BRITAIN: ESTIMATING THE FUTURE BURDEN

MESOTHELIOMA MORTALITY IN GREAT BRITAIN: ESTIMATING THE FUTURE BURDEN MESOTHELIOMA MORTALITY IN GREAT BRITAIN: ESTIMATING THE FUTURE BURDEN December 2003 Mesothelioma mortality in Great Britain: estimating the future burden Summary Mesothelioma deaths in Great Britain continue

More information

Lung cancer and asbestos

Lung cancer and asbestos Lung cancer and asbestos Bureau Veritas Training Bill Sanderson For the benefit of business and people To begin with.. There are known knowns, that is there are things we know that we know. There are known

More information

Asbestos & Cancer: An Update. Suresh H. Moolgavkar, M.D., Ph.D.

Asbestos & Cancer: An Update. Suresh H. Moolgavkar, M.D., Ph.D. Asbestos & Cancer: An Update Suresh H. Moolgavkar, M.D., Ph.D. Fiber Type and Cancer Epidemiological data clearly indicate that not all fiber types have the same potency as carcinogens. With respect to

More information

Influence of Fiber Type, Size, and Number in Human Disease: Conclusions from Fiber Burden Analysis

Influence of Fiber Type, Size, and Number in Human Disease: Conclusions from Fiber Burden Analysis Influence of Fiber Type, Size, and Number in Human Disease: Conclusions from Fiber Burden Analysis Andrew Churg, MD Department of Pathology University of British Columbia Vancouver, BC, Canada Techniques,

More information

Current Usage and Health Significance of the Modern Use of Chrysotile Products: Review of Recently Published Evidence

Current Usage and Health Significance of the Modern Use of Chrysotile Products: Review of Recently Published Evidence Current Usage and Health Significance of the Modern Use of Chrysotile Products: Review of Recently Published Evidence John Hoskins Health & Safety Consultant, Haslemere, Surrey, UK ASBESTOS SERPENTINE

More information

THE TIMES OF OCCURRENCE OF MESOTHELIOMAS IN RATS FOLLOWING INOCULATION WITH ASBESTOS

THE TIMES OF OCCURRENCE OF MESOTHELIOMAS IN RATS FOLLOWING INOCULATION WITH ASBESTOS 582 THE APPLICATION OF A MATHEMATICAL MODEL DESCRIBING THE TIMES OF OCCURRENCE OF MESOTHELIOMAS IN RATS FOLLOWING INOCULATION WITH ASBESTOS G. BERRY AND J. C. WAGNER From the Medical Research Council's

More information

Changing Trends in Mesothelioma Incidence. Hans Weill, M.D. Professor of Medicine Emeritus Tulane University Medical Center

Changing Trends in Mesothelioma Incidence. Hans Weill, M.D. Professor of Medicine Emeritus Tulane University Medical Center Changing Trends in Mesothelioma Incidence Hans Weill, M.D. Professor of Medicine Emeritus Tulane University Medical Center International Conference on Chrysotile Montreal, May 23, 2006 Global Mesothelioma

More information

December 19, 2005. Honorable Arlen Specter Chairman Committee on the Judiciary United States Senate Washington, DC 20510. Dear Mr.

December 19, 2005. Honorable Arlen Specter Chairman Committee on the Judiciary United States Senate Washington, DC 20510. Dear Mr. CONGRESSIONAL BUDGET OFFICE U.S. Congress Washington, DC 20515 Douglas Holtz-Eakin, Director December 19, 2005 Honorable Arlen Specter Chairman Committee on the Judiciary United States Senate Washington,

More information

Scientific Update on Safe Use of Asbestos. Robert P. Nolan, PhD International Environmental Research Foundation New York, New York www.ierfinc.

Scientific Update on Safe Use of Asbestos. Robert P. Nolan, PhD International Environmental Research Foundation New York, New York www.ierfinc. Scientific Update on Safe Use of Asbestos Robert P. Nolan, PhD International Environmental Research Foundation New York, New York www.ierfinc.org When We Talk about Asbestos What Do We Mean? Anthophyllite

More information

Mesothelioma mortality in Great Britain 1968-2009. Summary 2. Overall scale of disease including trends 3. Region 6. Occupation 7

Mesothelioma mortality in Great Britain 1968-2009. Summary 2. Overall scale of disease including trends 3. Region 6. Occupation 7 Health and Safety Executive Mesothelioma Mesothelioma mortality in Great Britain 1968-2009 Contents Summary 2 Overall scale of disease including trends 3 Region 6 Occupation 7 Estimation of the future

More information

PROTOCOL TO ASSESS ASBESTOS-RELATED RISK

PROTOCOL TO ASSESS ASBESTOS-RELATED RISK Contract No. DTRS57-01-C-10044. DRAFT PROTOCOL TO ASSESS ASBESTOS-RELATED RISK Prepared for: Mark Raney Volpe Center U.S. Department of Transportation 55 Broadway Kendall Square Cambridge MA 02142 and

More information

Review of Eliminating occupational cancer in Europe and globally by J. Takala

Review of Eliminating occupational cancer in Europe and globally by J. Takala Review of Eliminating occupational cancer in Europe and globally by J. Takala There primary concerns of this manuscript are outlined below. More detail discussion of these points is presented on the following

More information

PATTERNS OF MORTALITY IN ASBESTOS FACTORY WORKERS IN LONDON*

PATTERNS OF MORTALITY IN ASBESTOS FACTORY WORKERS IN LONDON* PATTERNS OF MORTALITY IN ASBESTOS FACTORY WORKERS IN LONDON* M. L. Newhouse TUC Centenary Institute of Occupational Health London School of Hygiene and Tropical Medicine London WCIE 7HT. England G. Berry

More information

RR876. Mesothelioma mortality in Great Britain. The revised risk and two-stage clonal expansion models

RR876. Mesothelioma mortality in Great Britain. The revised risk and two-stage clonal expansion models Health and Safety Executive Mesothelioma mortality in Great Britain The revised risk and two-stage clonal expansion models Prepared by the Health and Safety Laboratory for the Health and Safety Executive

More information

NHS Barking and Dagenham Briefing on disease linked to Asbestos in Barking & Dagenham

NHS Barking and Dagenham Briefing on disease linked to Asbestos in Barking & Dagenham APPENDIX 1 NHS Barking and Dagenham Briefing on disease linked to Asbestos in Barking & Dagenham 1. Background 1.1. Asbestos Asbestos is a general name given to several naturally occurring fibrous minerals

More information

STATEMENT ON ESTIMATING THE MORTALITY BURDEN OF PARTICULATE AIR POLLUTION AT THE LOCAL LEVEL

STATEMENT ON ESTIMATING THE MORTALITY BURDEN OF PARTICULATE AIR POLLUTION AT THE LOCAL LEVEL COMMITTEE ON THE MEDICAL EFFECTS OF AIR POLLUTANTS STATEMENT ON ESTIMATING THE MORTALITY BURDEN OF PARTICULATE AIR POLLUTION AT THE LOCAL LEVEL SUMMARY 1. COMEAP's report 1 on the effects of long-term

More information

The Burden of Occupational Lung Cancer Paul A. Demers, PhD

The Burden of Occupational Lung Cancer Paul A. Demers, PhD The Burden of Occupational Lung Cancer Paul A. Demers, PhD February 24 th, 2014 Measuring the Impact (burden) of Occupational Cancer Number or proportion of cancer deaths Number or proportion of new cancers

More information

Mathematical Modeling of Risk Acceptability Criteria

Mathematical Modeling of Risk Acceptability Criteria Mathematical Modeling of Risk Acceptability Criteria Jim Rasmuson Andrey Korchevskiy Eric Rasmuson Chemistry & Industrial Hygiene, Inc. 10201 W. 43 rd Ave., Wheat Ridge, CO Jim@c-ih.com 303-420-8242 Objectives

More information

Increasing mesothelioma deaths amongst school staff and former pupils

Increasing mesothelioma deaths amongst school staff and former pupils Increasing mesothelioma amongst school staff and former pupils The 2003 to 2012 mesothelioma statistics for the Education Sector show an ever increasing rise in the number of school teachers dying from

More information

Regulatory Risk Assessment

Regulatory Risk Assessment Regulatory Risk Assessment Richard B. Belzer PhD Regulatory Checkbook Mt. Vernon, VA Belzer@RegulatoryCheckbook.Org What is the Purpose of Risk Assessment? For regulatory analysis [A] risk assessment should

More information

Asbestos: health effects and risk. Peter Franklin Senior Scientific Officer, EHD Senior Research Fellow, UWA

Asbestos: health effects and risk. Peter Franklin Senior Scientific Officer, EHD Senior Research Fellow, UWA Asbestos: health effects and risk Peter Franklin Senior Scientific Officer, EHD Senior Research Fellow, UWA What is asbestos Naturally occurring mineral that has crystallised to form long thin fibres and

More information

Size of a study. Chapter 15

Size of a study. Chapter 15 Size of a study 15.1 Introduction It is important to ensure at the design stage that the proposed number of subjects to be recruited into any study will be appropriate to answer the main objective(s) of

More information

Table 2.4. Summary of design and findings from mesothelioma case-control studies

Table 2.4. Summary of design and findings from mesothelioma case-control studies categories Agudo et al. (2000) Barcelona and Cadiz, Spain 32 cases (77% males) of histologically con rmed malignant pleural mesothelioma identified from hospital in the region between //993 and 2/3/996.

More information

Emerging evidence that the ban on asbestos use is reducing the occurrence of pleural mesothelioma in Sweden

Emerging evidence that the ban on asbestos use is reducing the occurrence of pleural mesothelioma in Sweden 596500SJP0010.1177/1403494815596500B. Järvholm and A. BurdorfAsbestos ban reduces mesothelioma incidence research-article2015 Scandinavian Journal of Public Health, 1 7 Original Article Emerging evidence

More information

Executive Summary All invited experts at the meeting agreed that:

Executive Summary All invited experts at the meeting agreed that: Meeting Notes - GCSA meeting on the Classification and Regulation of Chrysotile Asbestos 10:30-12:30, Monday 7 th March 2011 Government Office for Science, 1 Victoria Street, London SW1H 0ET Attendees

More information

Asbestos in Soil and Made Ground

Asbestos in Soil and Made Ground Asbestos in Soil and Made Ground Professor Paul NATHANAIL CGeol SiLC University of Nottingham & Land Quality Management Ltd SiLC Annual Forum, Royal Society of Chemistry, Burlington House, 30 April 2013

More information

Science-Based Facts Relevant Health Issues 2015. For environmental occupational health safe and responsible use

Science-Based Facts Relevant Health Issues 2015. For environmental occupational health safe and responsible use Science-Based Facts Relevant Health Issues 2015 For environmental occupational health safe and responsible use SCIENCE-BASED FACTS AND RELEVANT HEALTH ISSUES 2015 ON THE DIFFERENT ASBESTOS FIBER TYPES:

More information

Mesothelioma Trends as Predictors of the Asbestos- Related Lung Cancer Burden

Mesothelioma Trends as Predictors of the Asbestos- Related Lung Cancer Burden Mesothelioma Trends as Predictors of the Asbestos- Related Lung Cancer Burden Valerie McCormack UICC World Cancer Congress Montreal August 2012 Outline Background Estimating the lung cancer mortality burden

More information

Tips for surviving the analysis of survival data. Philip Twumasi-Ankrah, PhD

Tips for surviving the analysis of survival data. Philip Twumasi-Ankrah, PhD Tips for surviving the analysis of survival data Philip Twumasi-Ankrah, PhD Big picture In medical research and many other areas of research, we often confront continuous, ordinal or dichotomous outcomes

More information

Table of Contents. I. Executive Summary... 1. A. Summary of our Findings... 1. II. Background... 2. III. Methodology... 4. IV. Key Data Sources...

Table of Contents. I. Executive Summary... 1. A. Summary of our Findings... 1. II. Background... 2. III. Methodology... 4. IV. Key Data Sources... Table of Contents I. Executive Summary... 1 A. Summary of our Findings... 1 II. Background... 2 III. Methodology... 4 IV. Key Data Sources... 6 A. WCIS Data... 6 B. SEER Data... 8 V. Discussion of NIOSH

More information

Update of the scientific evidence on asbestos and cancer. Kurt Straif, MD MPH PhD. The IARC Monographs

Update of the scientific evidence on asbestos and cancer. Kurt Straif, MD MPH PhD. The IARC Monographs Update of the scientific evidence on asbestos and cancer Kurt Straif, MD MPH PhD International Agency for Research on Cancer Lyon, France World Health Organisation Asturias, 17 March 2011 The IARC Monographs

More information

April 2011. Asbestos in the university and higher education sector

April 2011. Asbestos in the university and higher education sector April 2011 Asbestos in the university and higher education sector Contents Introduction... 1 Executive summary... 1 Background to asbestos... 2 Closer look: Research report results, and their impact on

More information

The American Cancer Society Cancer Prevention Study I: 12-Year Followup

The American Cancer Society Cancer Prevention Study I: 12-Year Followup Chapter 3 The American Cancer Society Cancer Prevention Study I: 12-Year Followup of 1 Million Men and Women David M. Burns, Thomas G. Shanks, Won Choi, Michael J. Thun, Clark W. Heath, Jr., and Lawrence

More information

Asbestos and Mesothelioma a briefing document for the Metropolitan Police

Asbestos and Mesothelioma a briefing document for the Metropolitan Police Asbestos and Mesothelioma a briefing document for the Metropolitan Police Prepared by Professor John Cherrie, Heriot Watt University, Edinburgh, UK. Introduction The purpose of this document is to provide

More information

Comparison of Mesothelioma Deaths between the Education Sector and Other Occupations.

Comparison of Mesothelioma Deaths between the Education Sector and Other Occupations. Comparison of Mesothelioma between the Education Sector and Other Occupations. The incidence of teachers mesothelioma deaths is significantly greater than some other occupations. Asbestos exposure normally

More information

Estimates of the impact of extending the scope of the Mesothelioma payment scheme. December 2013

Estimates of the impact of extending the scope of the Mesothelioma payment scheme. December 2013 Estimates of the impact of extending the scope of the Mesothelioma payment scheme December 2013 Contents Introduction... 6 Background... 7 Estimated volumes and costs if the scheme started on particular

More information

Investigating Area Under a Curve

Investigating Area Under a Curve Mathematics Investigating Area Under a Curve About this Lesson This lesson is an introduction to areas bounded by functions and the x-axis on a given interval. Since the functions in the beginning of the

More information

Actuarial projections for mesothelioma: an epidemiological perspective Mark Clements, Geoffrey Berry and Jill Shi

Actuarial projections for mesothelioma: an epidemiological perspective Mark Clements, Geoffrey Berry and Jill Shi Actuarial projections for mesothelioma: an epidemiological perspective Mark Clements, Geoffrey Berry and Jill Shi 27 slides to go Who are we? I am an epidemiologist/biostatistician from the Australian

More information

Incorrect Analyses of Radiation and Mesothelioma in the U.S. Transuranium and Uranium Registries Joey Zhou, Ph.D.

Incorrect Analyses of Radiation and Mesothelioma in the U.S. Transuranium and Uranium Registries Joey Zhou, Ph.D. Incorrect Analyses of Radiation and Mesothelioma in the U.S. Transuranium and Uranium Registries Joey Zhou, Ph.D. At the Annual Meeting of the Health Physics Society July 15, 2014 in Baltimore A recently

More information

SENSITIVITY ANALYSIS AND INFERENCE. Lecture 12

SENSITIVITY ANALYSIS AND INFERENCE. Lecture 12 This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike License. Your use of this material constitutes acceptance of that license and the conditions of use of materials on this

More information

Measures of disease frequency

Measures of disease frequency Measures of disease frequency Madhukar Pai, MD, PhD McGill University, Montreal Email: madhukar.pai@mcgill.ca 1 Overview Big picture Measures of Disease frequency Measures of Association (i.e. Effect)

More information

SPONTANEOUS MESOTHELIOMA DATA: AN INTERPRETATION. Robin Howie, Robin Howie Associates, Edinburgh.

SPONTANEOUS MESOTHELIOMA DATA: AN INTERPRETATION. Robin Howie, Robin Howie Associates, Edinburgh. SPONTANEOUS MESOTHELIOMA DATA: AN INTERPRETATION Robin Howie, Robin Howie Associates, Edinburgh. SPONTANEOUS MESOTHELIOMA MESOTHELIOMA DEATHS HSE (2003a) estimated there are about 26 spontaneous deaths

More information

Contents. 3 Survival Distributions: Force of Mortality 37 Exercises Solutions... 51

Contents. 3 Survival Distributions: Force of Mortality 37 Exercises Solutions... 51 Contents 1 Probability Review 1 1.1 Functions and moments...................................... 1 1.2 Probability distributions...................................... 2 1.2.1 Bernoulli distribution...................................

More information

Asbestos Health Risks. Dr Andrew Pengilley Acting Chief Health Officer

Asbestos Health Risks. Dr Andrew Pengilley Acting Chief Health Officer Asbestos Health Risks Dr Andrew Pengilley Acting Chief Health Officer Asbestos Asbestos is a name given to several different fibrous minerals Three main commercial types are Chrysotile (white asbestos)

More information

Pathologist s Discussion of Plaintiffs Latest Theories

Pathologist s Discussion of Plaintiffs Latest Theories Pathologist s Discussion of Plaintiffs Latest Theories Mary Beth Beasley, MD Mt. Sinai Medical Center Annenberg Building 15th Floor Room 50 1468 Madison Avenue New York, NY 10029 (212) 241-5307 mbbeasleymd@yahoo.com

More information

Asbestos. General information

Asbestos. General information Asbestos General information Key Points Fire Non flammable and non combustible under normal conditions Chemically inert under normal conditions. Resistant to most solvents, acids and alkalis In the event

More information

Assessing Forecasting Error: The Prediction Interval. David Gerbing School of Business Administration Portland State University

Assessing Forecasting Error: The Prediction Interval. David Gerbing School of Business Administration Portland State University Assessing Forecasting Error: The Prediction Interval David Gerbing School of Business Administration Portland State University November 27, 2015 Contents 1 Prediction Intervals 1 1.1 Modeling Error..................................

More information

The Science of Chemical Safety Essential Toxicology - 4. Hazard and Risk. John Duffus & Howard Worth. IUPAC Educators Resource Material IUPAC

The Science of Chemical Safety Essential Toxicology - 4. Hazard and Risk. John Duffus & Howard Worth. IUPAC Educators Resource Material IUPAC The Science of Chemical Safety Essential Toxicology - 4 Hazard and Risk John Duffus & Howard Worth IUPAC Educators Resource Material IUPAC Hazard and Risk - 1 Hazard is the potential of a substance to

More information

Credit Card Market Study Interim Report: Annex 4 Switching Analysis

Credit Card Market Study Interim Report: Annex 4 Switching Analysis MS14/6.2: Annex 4 Market Study Interim Report: Annex 4 November 2015 This annex describes data analysis we carried out to improve our understanding of switching and shopping around behaviour in the UK

More information

Mesothelioma in Australia: Incidence (1982 to 2013) and Mortality (1997 to 2012)

Mesothelioma in Australia: Incidence (1982 to 2013) and Mortality (1997 to 2012) Mesothelioma in Australia: Incidence (1982 to 213) and Mortality (1997 to 212) 215 Disclaimer The information provided in this document can only assist you in the most general way. This document does not

More information

A possible formula to determine the percentage of candidates who should receive the new GCSE grade 9 in each subject. Tom Benton

A possible formula to determine the percentage of candidates who should receive the new GCSE grade 9 in each subject. Tom Benton A possible formula to determine the percentage of candidates who should receive the new GCSE grade 9 in each subject Tom Benton ARD, Research Division Cambridge Assessment Research Report 15 th April 2016

More information

Canada Pension Plan Retirement, Survivor and Disability Beneficiaries Mortality Study

Canada Pension Plan Retirement, Survivor and Disability Beneficiaries Mortality Study f Canada Pension Plan Retirement, Survivor and Disability Beneficiaries Mortality Study Actuarial Study No. 16 June 2015 Office of the Chief Actuary Office of the Chief Actuary Office of the Superintendent

More information

NISG Asbestos. Caroline Kirton

NISG Asbestos. Caroline Kirton NISG Asbestos Caroline Kirton 1 The Control of Asbestos Regulations 2012, Regulation 10 requires every employer to ensure that adequate information, instruction and training is given to their employees

More information

Estimating the Global Burden of Asbestos-related Diseases Using YPLL

Estimating the Global Burden of Asbestos-related Diseases Using YPLL Ramazzini Days Carpi, Italy October, 2012 Estimating the Global Burden of Asbestos-related Diseases Using YPLL Ken Takahashi 1, Ying Jiang 1, Mehrnoosh Movahed 1, Giang Vinh Le 2,1, Eun-Kee Park 3,1, Rokho

More information

Work-related Ill Health in Railway Operatives

Work-related Ill Health in Railway Operatives Health and Safety Executive Work-related Ill Health in Railway Operatives Contents Background 2 Key facts 2 Supporting Data 3 Background notes and Caveats 5 This document is available from www.hse.gov.uk/statistics/

More information

Malignant Mesothelioma Among Employees of a Connecticut Factory that Manufactured Friction Materials Using Chrysotile Asbestos

Malignant Mesothelioma Among Employees of a Connecticut Factory that Manufactured Friction Materials Using Chrysotile Asbestos Ann. Occup. Hyg., Vol. 54, No. 6, pp. 692 696, 2010 Ó The Author 2010. Published by Oxford University Press on behalf of the British Occupational Hygiene Society doi:10.1093/annhyg/meq046 Malignant Mesothelioma

More information

The CRM for ordinal and multivariate outcomes. Elizabeth Garrett-Mayer, PhD Emily Van Meter

The CRM for ordinal and multivariate outcomes. Elizabeth Garrett-Mayer, PhD Emily Van Meter The CRM for ordinal and multivariate outcomes Elizabeth Garrett-Mayer, PhD Emily Van Meter Hollings Cancer Center Medical University of South Carolina Outline Part 1: Ordinal toxicity model Part 2: Efficacy

More information

CREATING A CORPORATE BOND SPOT YIELD CURVE FOR PENSION DISCOUNTING DEPARTMENT OF THE TREASURY OFFICE OF ECONOMIC POLICY WHITE PAPER FEBRUARY 7, 2005

CREATING A CORPORATE BOND SPOT YIELD CURVE FOR PENSION DISCOUNTING DEPARTMENT OF THE TREASURY OFFICE OF ECONOMIC POLICY WHITE PAPER FEBRUARY 7, 2005 CREATING A CORPORATE BOND SPOT YIELD CURVE FOR PENSION DISCOUNTING I. Introduction DEPARTMENT OF THE TREASURY OFFICE OF ECONOMIC POLICY WHITE PAPER FEBRUARY 7, 2005 Plan sponsors, plan participants and

More information

Age to Age Factor Selection under Changing Development Chris G. Gross, ACAS, MAAA

Age to Age Factor Selection under Changing Development Chris G. Gross, ACAS, MAAA Age to Age Factor Selection under Changing Development Chris G. Gross, ACAS, MAAA Introduction A common question faced by many actuaries when selecting loss development factors is whether to base the selected

More information

- Compensation issues

- Compensation issues Charité- Universitätsmedizin Berlin Institut für Arbeitsmedizin Prof. Dr. med. X. Baur Prevention, recognition and compensation of asbestosinduced diseases (AD) - Which diseases are asbestos-related? -

More information

The Public Health Significance of Asbestos Exposures from Large Scale Fires

The Public Health Significance of Asbestos Exposures from Large Scale Fires HPA-CHaPD-003 The Public Health Significance of Asbestos Exposures from Large Scale Fires K R Smith and P J Saunders ABSTRACT Large scale fires involving asbestos containing materials are a relatively

More information

Appendix E: Graphing Data

Appendix E: Graphing Data You will often make scatter diagrams and line graphs to illustrate the data that you collect. Scatter diagrams are often used to show the relationship between two variables. For example, in an absorbance

More information

Physics Lab Report Guidelines

Physics Lab Report Guidelines Physics Lab Report Guidelines Summary The following is an outline of the requirements for a physics lab report. A. Experimental Description 1. Provide a statement of the physical theory or principle observed

More information

Comment of Consumer Reports Regarding Draft Guidance for Industry: Arsenic In Apple Juice Action Level Docket No. FDA-2012-D-0322

Comment of Consumer Reports Regarding Draft Guidance for Industry: Arsenic In Apple Juice Action Level Docket No. FDA-2012-D-0322 November 12, 2013 Division of Dockets Management (HFA-305) Food and Drug Administration 5630 Fishers Lane Room 1061 Rockville, MD 20852. Comment of Consumer Reports Regarding Draft Guidance for Industry:

More information

The expected burden of mesothelioma mortality in Great Britain from 2002 to 2050

The expected burden of mesothelioma mortality in Great Britain from 2002 to 2050 British Journal of Cancer (25) 92, 587 593 & 25 Cancer Research UK All rights reserved 7 92/5 $3. www.bjcancer.com The expected burden of mesothelioma mortality in Great Britain from 22 to 25 JT Hodgson*,1,

More information

I. CENSUS SURVIVAL METHOD

I. CENSUS SURVIVAL METHOD I. CENSUS SURVIVAL METHOD Census survival methods are the oldest and most widely applicable methods of estimating adult mortality. These methods assume that mortality levels can be estimated from the survival

More information

Mortality Assessment Technology: A New Tool for Life Insurance Underwriting

Mortality Assessment Technology: A New Tool for Life Insurance Underwriting Mortality Assessment Technology: A New Tool for Life Insurance Underwriting Guizhou Hu, MD, PhD BioSignia, Inc, Durham, North Carolina Abstract The ability to more accurately predict chronic disease morbidity

More information

Asbestos exposure during Canterbury rebuild unlikely to cause significant health problems for house occupants report finds

Asbestos exposure during Canterbury rebuild unlikely to cause significant health problems for house occupants report finds News release from the Royal Society of New Zealand and the Office of the Prime Minister s Chief Science Advisor 15 April 2015 Asbestos exposure during Canterbury rebuild unlikely to cause significant health

More information

Lecture 18 Linear Regression

Lecture 18 Linear Regression Lecture 18 Statistics Unit Andrew Nunekpeku / Charles Jackson Fall 2011 Outline 1 1 Situation - used to model quantitative dependent variable using linear function of quantitative predictor(s). Situation

More information

E-1. Appendix E CONCENTRATION-RESPONSE RELATIONSHIPS FOR MODEL SENSITIVITY ANALYSIS IN RISK ASSESSMENT

E-1. Appendix E CONCENTRATION-RESPONSE RELATIONSHIPS FOR MODEL SENSITIVITY ANALYSIS IN RISK ASSESSMENT E-1 Appendix E CONCENTRATION-RESPONSE RELATIONSHIPS FOR MODEL SENSITIVITY ANALYSIS IN RISK ASSESSMENT The interpretation of specific concentration-response relationships is understood to be one of the

More information

Asbestos - Frequently Asked Questions

Asbestos - Frequently Asked Questions Asbestos - Frequently Asked Questions 1. What is asbestos? Asbestos is the name given to a group of fibrous minerals which occur naturally in the earth. These are grouped into two mineral types known as

More information

Disability Benefits Suspended or Terminated

Disability Benefits Suspended or Terminated Disability enefits Suspended or Terminated ecause of Work by ody Schimmel and David C. Stapleton* We use a new variable in the Social Security Administration s Ticket Research File to produce statistics

More information

A New Way To Assess Damages For Loss Of Future Earnings

A New Way To Assess Damages For Loss Of Future Earnings A New Way To Assess Damages For Loss Of Future Earnings Richard Lewis, Robert McNabb and Victoria Wass describe research which reveals claimants to have been under-compensated by tort This article summarises

More information

Efficient Curve Fitting Techniques

Efficient Curve Fitting Techniques 15/11/11 Life Conference and Exhibition 11 Stuart Carroll, Christopher Hursey Efficient Curve Fitting Techniques - November 1 The Actuarial Profession www.actuaries.org.uk Agenda Background Outline of

More information

F. Farrokhyar, MPhil, PhD, PDoc

F. Farrokhyar, MPhil, PhD, PDoc Learning objectives Descriptive Statistics F. Farrokhyar, MPhil, PhD, PDoc To recognize different types of variables To learn how to appropriately explore your data How to display data using graphs How

More information

Public and Private Sector Earnings - March 2014

Public and Private Sector Earnings - March 2014 Public and Private Sector Earnings - March 2014 Coverage: UK Date: 10 March 2014 Geographical Area: Region Theme: Labour Market Theme: Government Key Points Average pay levels vary between the public and

More information

Schools Value-added Information System Technical Manual

Schools Value-added Information System Technical Manual Schools Value-added Information System Technical Manual Quality Assurance & School-based Support Division Education Bureau 2015 Contents Unit 1 Overview... 1 Unit 2 The Concept of VA... 2 Unit 3 Control

More information

Exposure-risk relationship for aluminium silicate fibres Page - 1 -

Exposure-risk relationship for aluminium silicate fibres Page - 1 - Exposure-risk relationship for aluminium silicate fibres Page - 1 - As of: May 2010 ERR (exposure-risk relationship) for aluminium silicate fibres Tolerable risk (4:1000): 100000 fibres/m 3 Acceptable

More information

Malignant Mesothelioma

Malignant Mesothelioma Malignant Malignant mesothelioma is a tumour originating from mesothelial cells. 85 95% of mesotheliomas are caused by asbestos exposure. It occurs much more commonly in the chest (malignant pleural mesothelioma)

More information

Malignant Mesothelioma

Malignant Mesothelioma Malignant mesothelioma is a tumour originating from mesothelial cells. 85 95% of mesotheliomas are caused by asbestos exposure. It occurs much more commonly in the chest (malignant pleural mesothelioma)

More information

Worldwide mesothelioma mortality trends

Worldwide mesothelioma mortality trends Worldwide mesothelioma mortality trends Harvard Symposium 24 th July 2009 Julian Peto London School of Hygiene and Tropical Medicine and Institute of Cancer Research Asbestos-related diseases Asbestosis

More information

Sir William Osler: Listen to the patient; the patient tells you everything.

Sir William Osler: Listen to the patient; the patient tells you everything. Sir William Osler: Listen to the patient; the patient tells you everything. Jean-Martin Charcot: The patient is a liar. Epidemiology of Mesothelioma Jeffrey H. Mandel, MD, MPH Division of Environmental

More information

Chapter 3. The effect of fibre heterogeneity on the forcelength relationship for a muscle. 3.1 Introduction

Chapter 3. The effect of fibre heterogeneity on the forcelength relationship for a muscle. 3.1 Introduction The effect of fibre heterogeneity on the forcelength relationship for a muscle 3.1 Introduction Muscle modelling has been used to predict the changes torque generated by muscles about a joint in response

More information

Pricing the Critical Illness Risk: The Continuous Challenge.

Pricing the Critical Illness Risk: The Continuous Challenge. Pricing the Critical Illness Risk: The Continuous Challenge. To be presented at the 6 th Global Conference of Actuaries, New Delhi 18 19 February 2004 Andres Webersinke, ACTUARY (DAV), FASSA, FASI 9 RAFFLES

More information

Testimony of. Laura Welch, M.D. Medical Director Center to Protect Workers Rights November 17, 2005

Testimony of. Laura Welch, M.D. Medical Director Center to Protect Workers Rights November 17, 2005 Testimony of Laura Welch, M.D. Medical Director Center to Protect Workers Rights November 17, 2005 Testimony of Laura Welch, MD Medical Director, Center to Protect Workers Rights On Asbestos Related Diseases

More information

Comments on the Draft Report by the California Council on Science and Technology Health Impacts of Radio Frequency from Smart Meters

Comments on the Draft Report by the California Council on Science and Technology Health Impacts of Radio Frequency from Smart Meters Comments on the Draft Report by the California Council on Science and Technology Health Impacts of Radio Frequency from Smart Meters by Daniel Hirsch 1 31 January 2011 Abstract The draft report by the

More information

Cancer Survival Analysis

Cancer Survival Analysis Cancer Survival Analysis 2 Analysis of cancer survival data and related outcomes is necessary to assess cancer treatment programs and to monitor the progress of regional and national cancer control programs.

More information

June 20, 2002. 2002.06.20: Wagner Testimony on Workplace Exposure to Asbestos. This is an archive page. The links are no longer being updated.

June 20, 2002. 2002.06.20: Wagner Testimony on Workplace Exposure to Asbestos. This is an archive page. The links are no longer being updated. Page 1 of 6 skip navigational links This is an archive page. The links are no longer being updated. Statement by Gregory R. Wagner, M.D. Director, Division of Respiratory Disease Studies National Institute

More information

Statistical Intervals. Chapter 7 Stat 4570/5570 Material from Devore s book (Ed 8), and Cengage

Statistical Intervals. Chapter 7 Stat 4570/5570 Material from Devore s book (Ed 8), and Cengage 7 Statistical Intervals Chapter 7 Stat 4570/5570 Material from Devore s book (Ed 8), and Cengage Confidence Intervals The CLT tells us that as the sample size n increases, the sample mean X is close to

More information

This is the author s version of a work that was submitted/accepted for publication in the following source:

This is the author s version of a work that was submitted/accepted for publication in the following source: This is the author s version of a work that was submitted/accepted for publication in the following source: Stickley, Amanda P. (2012) Long term exposure to asbestos satisfies test for causation. Queensland

More information

CALCULATIONS & STATISTICS

CALCULATIONS & STATISTICS CALCULATIONS & STATISTICS CALCULATION OF SCORES Conversion of 1-5 scale to 0-100 scores When you look at your report, you will notice that the scores are reported on a 0-100 scale, even though respondents

More information

Lessons learned from the Western Australian experience with mesothelioma

Lessons learned from the Western Australian experience with mesothelioma Lessons learned from the Western Australian experience with mesothelioma Alison Reid, Western Australian Institute for Medical Research In partnership with Nick de Klerk, Nola Olsen, Jan Sleith, Geoffrey

More information