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Identifying possible bladder cancer ocupational carcinogens via a case-control study and JEM Richardson, Kathryn Jane 2004

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Identifying Possible Bladder Cancer Occupational Carcinogens via a Case-Control Study and JEM by Kathryn Jane Richardson B.S.c, University of Warwick, 2000  A THESIS SUBMITTED IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE OF  Master of Science in THE FACULTY OF GRADUATE STUDIES (Department of Statistics)  We accept this thesis as conforming to the required standard  The University of British Columbia August 2004 © Kathryn Jane Richardson, 2004  THE UNIVERSITY OF BRITISH COLUMBIA  FACULTY OF GRADUATE STUDIES  Library Authorization  B  In presenting this thesis in partial fulfillment of the requirements for an advanced degree at the University of British Columbia, I agree that the Library shall make it freely available for reference and study. 1 further agree that permission for extensive copying of this thesis for scholarly purposes may be granted by the head of my department or by his or her representatives. It is understood that copying or publication of this thesis for financial gain shall not be allowed without my written permission.  mPHftVN  ^TA  igiCH/V £ P S  2S/O  cs^  Name of Author (please print)  Title of Thesis:  /CxznSTi  •f—rTLx.Q'W Degree: Department of  S / 3lOO Lj-  Date (dd/mm/yyyy)  f^SfrJG,  f^OSSJiS  L.EL  CAk / V ^ / Q  ZS^r m f U S g  ST7TT) sTTl  Year:  gLQ  Q  ^TPTrxJE^Y^  The University of British Columbia Vancouver, BC Canada  grad.ubc.ca/forms/?formlD=THS  page 1 of 1  last updated: 20-Jul-04  Abstract A significant proportion of cancer development is attributable to exposures to certain chemicals in the workplace. However, examining these occupational exposures is often a difficult and challenging task. In this thesis we use the relatively new approach of applying an extensive JEM (Job Exposure Matrix) to estimate the occupational exposures of 1,062 bladder cancer cases and 8,057 matched other cancer controls. The subjects are all male, and were at least 20 years old and resident in British Columbia when diagnosed with cancer between 1983 and 1990. A self-administered questionnaire provided the occupational histories and confounding information on the study subjects. The cumulative exposure (expected work-years of considerable exposure) to each of 11,132 occupational agents was estimated. The bladder cancer cases were matched to cancer controls of other cancer sites (excluding lung) on age at diagnosis and year of diagnosis. The analysis was performed via conditional logistic regression and the following confounders were taken into account: ethnicity, who completed the questionnaire, smoking duration, and alcohol drinking status. Of the 5,699 agents with at least 3 bladder cancer cases exposed, a significantly increased (5% level) odds-ratio was seen for ever exposure to 646 of them. Of the 3,450 agents with at least 9 bladder cancer cases exposed, 350 exhibited a significantly (5% level) increasing dose-response relationship. After adjusting for multiplicity, a subset of 30 agents was selected that demonstrated sufficient evidence of bladder carcinogeneity. Principal components analysis was performed on the cumulative exposures of these selected agents and 10 independent groups of agents were identified. The groups were mainly distinguished by job. The cumulative exposures to these 30 agents were mainly due to employment in logging, ship and boat building, and construction industries, and in occupations involving motor vehicles (e.g. gasoline service station attendant, mechanic, and truck driver). The selected 30 agents seem to mainly be of petroleum or mineral oil base.  Contents 11  Abstract Contents  iii  List of Tables  vii  List of Figures  ix  Acknowledgements  x  1 Introduction  1  2 Methodology Review  3  2.1 Study Methods  3  2.1.1  Animal Experiments  3  2.1.2  Cohort Studies  4  2.1.3  Case-Control Studies  6  2.1.4  Proportionate Mortality Studies  7  2.1.5  Summary of Study Designs  8  2.2 Further Issues with Case-Control Studies  8  2.2.1  Choice of Cases and Controls  2.2.2  Bias  10  2.2.3  Confounding  11  2.2.4  Matching  11  2.2.5  Analysis of Matched Case-Control Studies  12  2.3 Assessing Exposure 2.3.1  Cancer Biology  9  14 14  2.3.2  Measuring Exposure  2.3.3  Workplace Records  2.3.4  Assessing Current Workplace  16  2.3.5  Self-Administered Questionnaires  16  2.3.6  Personal Interviews  16  2.3.7  Job Exposure Matrix (JEM)  16  3 Risk Factors for Bladder Cancer  15 ,  15  18  3.1 Non-Occupational Bladder Cancer Risk Factors  18  3.2  Occupations Associated with Bladder Cancer  19  3.3  Occupational Bladder Carcinogens  20  4 Data Resources  23  4.1 The Study Area - British Columbia, Canada  23  4.2  BCCA Data  24  4.2.1  Data Editing  26  4.2.2  Analysis Inclusion Criteria  26  4.3 NIOSH JEM  4.4  27  4.3.1  Field Guidelines  28  4.3.2  Sampling Methodology  28  BCCA Canadian to US Job Translations  5 Exposure Assessment  29 31  5.1  Cumulative Exposure  31  5.2  Calculating Cumulative Exposure  33  5.2.1  Providing Adequate Job Information  33  5.2.2  Canadian Jobs and Latency  34  5.2.3  Coding the Canadian Jobs According to the CSOC/CSIC  34  5.2.4  Translating Canadian Jobs to US Equivalents  35  5.2.5  US Industries Studied by NIOSH  36  5.2.6  US Job-Translations on the JEM  36  5.2.7  Applying the JEM  37  5.2.8  Calculating Cumulative Exposure  37  5.2.9 6  Final Cumulative Exposure Estimates  Statistical Approach  38 39  6.1 Matching  39  6.2  Possible Confounder Variables  40  6.2.1  41  Characteristics of Cases and Controls  6.3 Developing the Base Model 6.3.1  The Base Model  43  6.3.2  Consequences of the Missing Data Exclusions on the Base Model  43  6.4 Testing the Agents Individually Agents with Exposed Cases  45  6.4.2  Ever/never  45  6.4.3  Dose-Response  45  6.4.4  Multiple Comparisons  46 48  6.5.1  Principal Component Analysis  48  6.5.2  Grouping of Agents and Analysis Approach  49  7 Results  51  7.1 Individual Agent Results  7.3  44  6.4.1  6.5 Testing the Agents in Groups: Principal Components Analysis  7.2  42  51  7.1.1  Ever/never  51  7.1.2  Dose-Response  52  Selecting Significant Associations  52  7.2.1  53  Linear Exposure  Grouped Agent Results: Principal Components Analysis  54  7.3.1  Component Groups - Dose-Response  54  7.3.2  Component Groups - Any Exposure  54  7.3.3  Component Groups - All Exposure  55  7.4 Properties of the Selected Agents 7.4.1  Discussion of the Selected Agents  7.5 IARC and Siemiatycki Results Comparison  55 56 59  8 Discussion  61  8.1 Summary of Findings  61  8.2  Bias and Confounding  62  8.2.1  Comparability of Source Populations for Cases and Controls  62  8.2.2  Selection Bias  63  8.2.3  Information Bias  63  8.2.4  Confounding by Non-Occupational Variables  64  8.2.5  Confounding by Occupational Exposure  64  8.3 Evaluation of Methods  v  64  8.3.1  Ever/never 0.5  65  8.3.2  Siemiatycki Comparison  65  8.4 Future Directions 9 Bibliography 10 Tables and Figures  67 6 8  72  Appendix A Appendix B  155  List of Tables 2.1 Two by Two Contingency Table for Cohort Studies  72  2.2 Two by Two Contingency Table for Case-Control Studies  72  3.1 Occupations Associated with Bladder Cancer  73  3.2  Chemicals Associated with Bladder Cancer  74  3.3  Siemiatycki Chemicals Associated with Bladder Cancer  75  3.4 Siemiatycki Exposure Coding  75  5.1 Distribution of Missing Job Code, and Start and End Year Data  77  5.2  78  Cancer Site Distribution of Jobs in the Armed Forces  5.3 Distribution of Patients' Canadian Industries  79  5.4 Distribution of Patients' US Industry Translations  80  5.5 Distribution of Patients' Canadian Occupations  81  5.6  Distribution of Patients' US Occupation Translations  82  5.7 Distribution of Patients' Industries and those on the JEM  83  5.8 Distribution of Patients' Occupations and those on the JEM  84  5.9 Location of Canadian Jobs Found on the JEM  85  6.1 Characterisitics of Cases and Controls  86  6.2  Odds Ratios (OR) for Potentially Important 0 Confounding Variables  87  6.3 Log Likelihood for Various Base Models  87  6.4  88  Characterisitics of Cases and Controls Before and After Exclusions  6.5 Distribution of Control Cancer Sites Before and After Exclusions 6.6  89  Odds Ratios (OR) for Potentially Important" Confounding Variables Before and After Exclusions 90  6.7 Distribution of Bladder Cases Exposed Across the 8,986 Agents  90  7.1 Distribution of p-values for Ever Exposure of 5,699 Agents  91  7.2  Agents Significant After Adjusting for Multiplicity Using the Hochberg and Benjamini Procedure from 5,699  91  7.3 Distribution of p-values Across 3,450 Agents Tested For Dose-Response  92  7.4 Number of Agents With Significant Ever Exposure and Ordinal Trend Results  92  7.5 Selected 30 Agents with Significant Associations  93  7.6  Results for Linear Fit of Transformed" Cumulative Exposure for Top 30 Agents  98  7.7  Component Scores 0 for PCA of the 30 Selected Agents  99  7.8  Dose-Response Results for Component Groups  7.9  0  Multivariate Ordinal Results for Component Groups  100 100  7.10 Results for Ever Exposure to Any of the Members of each Component Group  100  7.11 Multivariate Any Results 0 for Component Groups  101  7.12 Results for Ever Exposure to All of the Members of each Component Group  101  7.13 Multivariate All Results 0 for Component Groups  101  7.14 Properties of the Selected 30 Agents  103  7.15 Chemicals Associated with Bladder Cancer with Study Odds Ratios  104  7.16 Siemiatycki Chemicals Associated with Bladder Cancer with Study Odds Ratios  106  A.l Odds-Ratios of Ever Exposure and Dose-Response Results for 3,450 Agents 0 B.l NIOSH Agent Name Abbreviations  108 156  List of Figures 5.1 Calculating Cumulative Exposure Analysis Design  76  7.1 Histograms of Positive Cumulative Exposures for Top 30 Agents  94  7.2 Histograms of Transformed Positive Cumulative Exposures for Top 30 Agents  96  7.3 Proportion of Cumulative Exposure Due to Employment in Each US Job for the Top 30 Agents 102  Acknowledgements I would first like to thank Dr. Nhu Le from the British Columbia Cancer Agency. Nhu provided the idea for this thesis and much appreciated support and encouragement as my supervisor throughout its duration. I would also like to thank my second reader, Dr. Paul Gustafson, for his useful a'dvice and comments. In addition I would like to thank my colleagues at the BC Cancer Agency for their support and help, in particular, Amy MacArthur, Sandy Shen, Barbara Lang, and Melissa Friesen. Finally, a huge thank-you goes to my boyfriend, Andy Parker, for all his support and patience throughout my entire master's degree.  K A T H R Y N J A N E RICHARDSON  The University of British Columbia August 2004  Chapter 1  Introduction Occupational exposures to certain hazards have long been recognised as possible health concerns. During the 18th century concerns arose after physicians noted debilitating and fatal conditions occurring preferentially among workers in certain types of jobs. Percival Pott provided the first unambiguous evidence of chemical carcinogenesis from occupational exposure in 1775. He identified soot as the cause of scrotal cancer in London chimney sweeps based on clinical observations. Subsequently, associations between coal tar product exposures and cancer development have been seen in many studies. Cancer development is not fully understood and a better appreciation of the factors involved is clearly important. The future risk to the general population of particular occupational exposures can be projected, and public health policies can be steered towards minimising this risk through for example, workplace regulations. Occupational exposures are particularly important as many workers are exposed similarly at the same time and over a significant portion of their lifetime. Large groups of workers with similar exposures makes studying the exposures simpler, and it is also relatively straightforward to minimise and regulate exposures to protect the workers. It is difficult to estimate the percentage of cancers attributable to occupational exposure. Estimates range from 1% to around 40% (Siemiatycki, 1991). The proportion of male bladder cancers in the United States attributed to occupational exposures is estimated as 10% by Doll and Peto (1981), and around 21% to 25% in white males by Silverman et al. (1989). Also, about half of all known carcinogens are primarily industrial chemicals (Tomatis, 1990). The main approaches to identifying occupational carcinogens are introduced in section 2. Animal studies are scientifically valid, however, the results cannot always be applied to humans. Epidemiological studies are more commonly used to investigate the complex effects of occupational exposure to cancer development in humans. However these studies are difficult to implement when the disease is rare and has  many complex and interrelated causes. The most widely used approach is that of a case-control study as introduced in section 2.1.3 and discussed further in section 2.2. Exposure to a carcinogen may contribute to the initiation of tumour development, or it may hasten the onset of a tumour. Nevertheless, exposure to a carcinogen does not usually make cancer inevitable. Carcinogenesis and cancer biology are introduced in section 2.3. As cancer has a long induction period between exposure and manifestation, exposures need to be considered over most of a subjects lifetime. Various methods to estimate these lifetime occupational exposures are also described in section 2.3. Current research into bladder cancer carcinogens is discussed in section 3. Many animal studies have been undertaken, but conclusive evidence in humans is rare and further research is required. The approach taken in this thesis to identify possible bladder cancer occupational carcinogens is via a case-control study of bladder cancer cases and cancer controls identified in a BC Cancer Agency (BCCA) study. The approach taken could be repeated for other cancer sites of interest; however, bladder is the cancer site of interest here. A Job Exposure Matrix (JEM) developed in the US was thought most appropriate to estimate the lifetime occupational exposures to a range of agents of the cases and controls. Conditional logistic regression was implemented to estimate the risk of exposure to the occupational agents. Principal component analysis was undertaken to find groups of agents that act synergistically to increase the risk of bladder cancer. The BCCA study used for the case-control data is described in section 4.2 and the JEM used is introduced in section 4.3. The exposure assessment calculations are described in section 5. The statistical approach taken is detailed further in section 6, and the overall results are given in section 7. Finally, a discussion of the whole procedure is presented in section 8.  Chapter 2  Methodology Review The goal of this thesis is to identify possible occupational carcinogens for bladder cancer. There are many methods to try to accomplish this. The main study methods are discussed next in section 2.1. This thesis looks at this problem from the perspective of an epidemiological case-control study. Some important issues surrounding case-control studies are discussed in section 2.2. The ideas of carcinogenic exposure are discussed in section 2.3.  2.1  Study Methods  Many types of study can be conducted to try and identify possible occupational carcinogens. Ideally an experiment is performed to see if particular exposures give rise to cancer in humans where only the exposure varies between subjects. Firstly, this type of experiment would be unethical in humans. Secondly, it would be near impossible to keep all other factors constant among subjects. Thirdly, chronic diseases have complex etiology and a long study period would be required post exposure before the disease manifested itself. Similar experiments can be undertaken in animals, as described in section 2.1.1, but the results cannot always be extended to humans. These limitations confine most etiologic research to non-experimental epidemiologic varieties. Epidemiological studies are designed to reduce variation from extraneous factors other than those under study. The non-experimental epidemiological studies of cohort, case-control, and proportional mortality are described in sections 2.1.2, 2.1.3 and 2.1.4 respectively.  2.1.1  Animal Experiments  Controlled scientific experiments can be carried out on small animals such as rats and mice to investigate whether certain exposures lead to cancer development. Variation from other factors between animals can  be kept minimal by the investigator. In this way hypotheses about particular potential carcinogens can be tested directly. Humans are genetically very similar to rodents; however, the results from animal experiments cannot be applied to humans with complete confidence. The animal studies are designed to test for carcinogenicity of the substance in that particular animal, not to emulate the human experience. The doses administered, routes of exposure, lifestyle maintained, and induction periods in animal studies are unrealistic compared to the human experience (Siemiatycki, 1991). The sensitivity of detecting human carcinogens from animal experiments is quite high, however at the expense of the specificity. Most identified human carcinogens show carcinogenic activity in animal experiments, and there is often correlation between the target organs affected and carcinogenic potency (Siemiatycki, 1991). However, for most carcinogens found in animal experiments, equivalent associations have not been seen in human studies. Ashby and Tennant (1988) found a relatively low correlation between those carcinogens found from experiments on rats and those on mice, suggesting weak ability to extrapolate from rodents to humans. Many studies into potential carcinogens are performed via animal experiments, as the experiments are relatively quick and inexpensive to perform, and can be easily controlled. Experimental animal data on carcinogenicity exists for many substances, while human study data exists for relatively few. From the point of view of deciding public health policies, the animal data cannot be ignored. The International Agency for Research on Cancer (IARC) is the worlds leading authority on assessing evidence for carcinogenicity of substances in humans. The IARC recommends that when there is limited evidence of carcinogenicity in humans and sufficient evidence of carcinogenicity in experimental animals, the agent (mixture) be classified as probably carcinogenic to humans (IARC, 1987).  2.1.2  Cohort Studies  In a cohort study design subjects free of disease are selected into groups, or cohorts, according to their exposure to a suspected cause of the disease. The cohorts and then followed over time and the rate of disease compared within each group. Cohort studies, among all of the epidemiologic study designs, are the most accepted by the scientific community, as they mimic the scientific trial by observing disease in different exposure groups (Checkoway, 1989). The cohorts are selected independently to disease outcome, and the sequence of events follows naturally from suspected cause to effect. The simplest type of cohort study occurs when a group exposed to the suspect hazard, and a group not exposed to the hazard are studied for the same period of time. The amount of diseased subjects is  observed in each group. Table 2.1 displays the results for this simple cohort design. Suppose Aj subjects develop the disease in the exposed group and Ao subjects develop the disease in the non-exposed group. Also, B\ subjects are not diseased at the end of the study in the exposed group and Bq subjects are not diseased in the non-exposed group. So the original exposed cohort consists of subjects Ai + B\, and the not exposed cohort consists of subjects Aq + Bq. The overall measure of risk in the cohort study is calculated as the relative risk (RR). The relative risk is simply the ratio of the probability of disease in the exposed to the probability of disease in the non-exposed. Using the notation in table 2.1, the relative risk is calculated as: -  AMo + BQ) A0(A 1+B1)  The relative risk will equal 1 when there is no difference in the risk of developing the disease between the exposed and the non-exposed. The relative risk will be greater than one when the risk of developing the disease is greater for those exposed. When the non-exposed are at greater risk, the relative risk will lie between 0 and 1. The relative risk cannot be negative as it is a ratio of probabilities. Other factors affecting the outcome and exposure, called confounders, can be incorporated in the analysis and the resulting relative risk adjusted for these. Also the analysis and thus relative risk can account for the usual situation where subjects are followed for differing periods of time by comparing rates per person-years of follow-up rather than rates per person. Rates can also be compared to those in the general population, to see if there is any increased risk in the particular cohort (Checkoway, 1989). However, the members of occupational cohorts may differ from the general population in more respects than just the exposure. Cohort studies can either have a prospective or retrospective design. The intuitive design is prospective where the cohorts are selected and then follow-up proceeds into the future. In this way, the exposure estimates and estimates of other potential confounders can be accurately measured directly. Diseases are often rare in a population, meaning it would take time to see enough diseased cases develop in a cohort. This is even truer for chronic diseases, such as cancers. Either a lengthy study period or a large cohort is required due to their long induction and latent periods. The prospective cohort study then becomes a costly and timely exercise (Checkoway, 1989). A common approach to minimise the cost and time commitments in a prospective cohort study is to perform a retrospective (historical) cohort study. Here a cohort is enumerated as starting some time in the past, and follow-up is observed until the present time. The difficulty now comes in estimating exposures and confounding factors for cohort members retrospectively in time (Checkoway, 1989). This information may not be available or complete, and will certainly be less accurate than in the prospective cohort study.  Issues surrounding estimating exposure retrospectively are discussed in section 2.3.  2.1.3  Case-Control Studies  A case-control study is not as intuitively appealing as a cohort study, but is more efficient.  A cohort  study requires obtaining exposure data on a large number of subjects of which only a small proportion typically develop the disease (Checkoway, 1989). A case-control study gains efficiency by sampling only a small proportion of those that do not develop the disease. For a case-control study, one identifies a representative group of subjects with the disease, the cases, and a comparable group of subjects, but that are not diagnosed with the disease, the controls. The exposure histories and confounding factors are then sought as in a retrospective cohort study, but this time for the case and control groups. If the cases were significantly more exposed to a particular hazard than the controls, given that all other confounding factors are taken into account, one could infer that the hazard was associated with the occurrence of the disease. Table 2.2 depicts the results of the simplest type of case-control study. Of the cases, a j denotes the number exposed to a particular substance, and bi denotes the amount of controls exposed to the particular substance. So the total cases sampled is ai + ao, and the total controls sampled is 6i -I- bo. As the proportion of diseased to non-diseased subjects is a feature of the case-control design rather than representing the true proportion, the formula used to calculate the relative risk in the cohort study does not produce the true relative risk here. The denominators used in the relative risk formula are unknown; therefore a quantity called the odds-ratio (OR) is calculated instead. The odds-ratio is approximately equal to the relative risk when the disease is rare in the general population. This is true for cancer (Siemiatycki, 1991), so the odds-ratio is considered equal to the relative risk in this thesis. Again, we are interested in a departure from one in the odds-ratio. The odds-ratio is calculated as the ratio of the odds that a case was exposed to the odds that a control was exposed. Using the notation in table 2.2, it is calculated as follows:  Q R _ a i/ Q o _ aifto bi/b 0 a0bi The case and control groups can be considered as being drawn from the same hypothetical population as the cohort study groups were drawn (Rothman & Greenland, 1998). However, most of the diseased are sampled as cases for the case-control study, whilst only a small subset of the non-diseased are sampled as controls. When this truly happens and a case-control study is selected from cohort at end-point, it is referred to as a nested case-control study. The nested case-control.study then benefits from being able to estimate exposures and confounders more accurately (Siemiatycki, 1991). Case-control studies can provide as valid results as cohort studies, although controls have to be  carefully selected, and attention directed to avoid possible biases (Checkoway, 1989). The control group should form a representative sample from the population in which the cases were drawn. The control group must also be sampled independently of exposure status (Rothman & Greenland, 1998). A population-based disease registry provides a good source of possible cases when the registry contains accurate and up to date basic information about all possible patients with the disease in the population (Checkoway, 1989). More details about case and control selection are described in section 2.2.1. Case-control studies are susceptible to more biases than cohort studies (Rothman & Greenland, 1998). Selection bias may occur when controls are selected differentially to cases. For example, the controls may suffer from non-response bias. Recall bias may occur due to cases being more willing to provide better quality data than controls (Siemiatycki, 1991). Further discussion of possible biases in case-control studies can be found in section 2.2.2.  2.1.4  Proportionate Mortality Studies  Proportionate mortality studies compare the proportional distributions of causes of death in a subgroup to those in a reference population. Death certificates often contain information on cause of death and main occupation, or a workplace may keep records of the death certificates of its former employees. This method has the advantages of being a relatively quick and inexpensive approach to gauging information about disease excess in certain subgroup populations. It can also be useful when the full information required for a cohort or case-control study is incomplete (Checkoway, 1989). However, there are many limitations to this type of crude analysis (Siemiatycki, 1991). Information on cause of death may not be complete or accurate. The deaths recorded for a particular subgroup may not be representative of all deaths in that subgroup. Only limited information is available on exposure history and possible confounder variables. The proportional mortality ratio (PMR) of the ratio of deaths in the subgroup due to the disease of interest used as the measure of the effect is influenced by the proportions of deaths due to other diseases in the subgroup. Thus, if lung cancer was particularly prominent in a worker population, then mortality due to bladder cancer may look proportionately low when in fact there was a greater than expected number of absolute deaths due to bladder cancer. Finally, when interested in disease incidence, the proportionate mortality studies investigating associations with mortality will not always be indicative of incidence associations.  2.1.5  Summary of Study Designs  Epidemiological research applies directly to human beings. Animal experiments may be valid at testing hypotheses about animal carcinogens, but these results may not transfer to humans. Observing apparent clusters of excess disease in subgroups has motivated epidemiological research. Occupational Epidemiology study designs are similar in that they attempt to study a group's occupational and disease experience over time and sample from it (Checkoway, 1989). Proportionate mortality studies are quick and simple, but the conclusions regarding cancer incidence associations with occupational hazards may be imprecise and misleading. A more formal approach of a case-control or cohort study is required. Cohort and case-control studies mainly differ in whom they compare; cohort studies compare the exposed to the non-exposed, whilst case-control studies compare the diseased to the non-diseased. The cohort groups are followed from carcinogenic exposure to manifestation of the disease, whilst retrospective information about possible exposure is sought for case and control groups. Cohort studies are thus more appealing for hypothesis testing, and case-control studies for hypothesis generating (Siemiatycki, 1991). Cohort studies are advantageous when investigating specific associations as a workforce cohort is generally exposed to a narrow range of occupational agents. Case-control studies can evaluate a range of different exposures in a range of different occupations and industries (Breslow & Day, 1980). Cohort studies can also be prohibitively expensive and time consuming for studying a rare chronic disease, such as bladder cancer. However, case-control studies are susceptible to biases such as selection and recall bias, and care must be taken when planning a study. Occupational exposure assessment is also more difficult, and possible approaches are described in section 2.3. A case-control study is an appropriate design for investigating associations between occupational exposures and cancer, as required in this predominantly hypothesis generating thesis. More details about performing a case-control study are discussed in the next section.  2.2  Further Issues with Case-Control Studies  As the choice of cases and controls for a case-control study is important this is discussed further in section 2.2.1. As it is also important to be aware of possible sources of bias, these are described in section 2.2.2. Confounders are a special form of bias discussed in section 2.2.3. Possible methods of controlling confounders are introduced as section 2.2.4 discusses matching and section 2.2.5 discusses the analysis method of conditional logistic regression.  2.2.1  Choice of Cases and Controls  Firstly, a clear source population for the cases needs defining.  The source population need not be the  whole population, and can be restricted to improve information quality, control for possible confounders, and facilitate valid selection of controls (Checkoway, 1989). The group of cases should be a homogeneous etiological entity (Breslow & Day, 1980). To be sure all cases have the particular disease, the diagnosis should also be histologically confirmed (dos Santos Silva, 1999). In a registry-based study, the case group usually consists of all incident cases appearing in the registry during a specified period of time (Checkoway, 1989). The controls should then be selected from the source population; the same population that gave rise to the cases. The controls should be selected independently from exposure status. This is to prevent the controls being unrepresentative of the source population with respect to exposure. A control should be at risk of becoming a case. So, the specified period of time when a subject is eligible to become a case should be the time during which a subject is eligible to become a control (Rothman & Greenland, 1998). There are various sources of possible controls: population, neighbour, hospital, or other disease controls. Controls can be sampled from the associated subset of the general population. This is the ideal situation; enabling the study results to be generalised to the subset of the general population. However, the response rate may be low, making the results liable to selection bias. Also, those that respond may not provide as accurate information as they might, introducing possible recall bias. An extension of this method is to sample controls from the neighbourhood where the cases live or friends of the cases. These people will be more similar to the cases, may be easier to contact and more willing to participate. However, they may be too similar to the cases, in that their exposure status is related to that of the cases (Rothman & Greenland, 1998). When identifying hospital-based cases, hospital controls have several advantages. They are generally easy to contact and are less likely to be lost to follow-up. They are in a similar position to the cases, have more time and may be more willing to help, thus reducing recall bias. However, care must be taken to avoid selection bias. Many hospitalised patients' exposures will not be representative of the source population's exposures. This bias can be minimised by restricting controls to those with a diagnosis not thought related to the exposure. Also, choosing controls with different diagnoses will tend to balance out the effect of any introduced by a specific disease (Rothman & Greenland, 1998). The considerations when choosing controls with other diseases are similar to those for hospitalisedcontrols. This type of control is often chosen in registry-based studies, and in particular with cancers.  2.2.2  Bias  The purpose of the analysis of a case-control study is to quantify the associated risk each factor under study has with the disease (Breslow & Day, 1980). The observed associations may however be affected by bias, confounding and random variation. These problems hinder internal validity and the first aim is to minimise these effects so the true associations can be estimated. There are two main types of bias: selection bias and information bias, which are discussed next. Confounding is an issue much related to bias and is discussed in the next section. Random variation is an artefact of any study and its effects can be minimised by increasing the study size. Selection bias can be introduced when the cases or controls do not form a representative sample from the source population (Rothman & Greenland, 1998). A common source of this systematic error is non-response bias. When a considerable proportion of the control group chooses not to participate in the study, this proportion may be different in characteristics to those who do choose to participate (Gordis, 2000). The Healthy Worker Effect is also a common selection bias, particularly to occupational cohort studies (Checkoway, 1989). If there is a difference in the proportion of workers between study groups, then the groups may additionally differ due to the Healthy Worker Effect. Workers are known to be generally healthier than the rest of the population, especially those who remain in employment. To minimise selection bias, the case and control groups should be chosen appropriately (Checkoway, 1989). Attempts should be made to increase response rates, e.g. by providing incentives for compliance, not making compliance too time consuming, etc. If there is non-response in a study, it should be investigated to see if there are significant differences between the characteristics of responders and non-responders. Information bias consists of misclassification of subjects in two ways: differential or non-differential misclassification. Non-differential information bias occurs when the cases and controls are equally likely to be misclassified according to their exposure or disease status. This will tend to bias the effect estimate towards the null (Checkoway, 1989). Differential information bias is of greater concern. Here, the likelihood of misclassification differs between the cases and controls. The bias of the effect estimate can then occur in either direction from the truth (Checkoway, 1989). Recall bias is an example of differential information bias common in case-control studies. Case subjects will generally be willing to answer study questions to the best of their knowledge and take time thinking through their exposure histories. Control subjects are generally less likely to do so, particularly if they are population controls, as they have less interest in providing the most accurate information to the study to improve the research in that area. In this way the controls will be subject to more misclassification bias than the cases.  Information bias is minimised by ensuring as accurate data recall amongst cases and controls as possible (Rothman & Greenland, 1998). It is also important to investigate the magnitude of the effect of this bias in the study, such as by questionnaire validation.  2.2.3  Confounding  A confounder is a factor associated with exposure and with the disease, but it is not a step in the causal pathway from exposure to disease (Checkoway, 1989). Common confounders are age, gender, ethnicity and smoking habits. Distortion caused by confounding factors can lead to an overestimation or underestimation of the true effect of the exposure under study (Rothman & Greenland, 1998). Failure to control for confounders can lead to a biased estimate. Mistakenly controlling non-confounders can reduce the precision of an estimate. Misclassification of confounders can also reduce the ability to control for confounding (Checkoway, 1989). Confounders can be controlled in the study design, in the analysis, or both (Checkoway, 1989). The source population, from which the cases and controls were sampled, can be restricted, thus reducing any possible confounding. However, this will reduce the sample size of cases and controls. Confounders can be controlled via matching the cases and controls on the main confounders. This will help to optimise the efficiency of the analysis and improve the precision of the effect estimates. Further details about matching are discussed in section 2.2.4. The analysis can also simultaneously control for potential confounders by including the potential confounder variables in the logistic regression model. Confounders are identified by previous studies, biological knowledge of the disease, and known features of the study design. In order to assess potential confounders accurately, reliable information is required on them from the cases and control subjects.  2.2.4  Matching  Matching is a method used to attempt to control for the most important confounders. Individual matching involves pairing one or more controls to each case with respect to levels of the matching factors. Frequency matching involves selecting a set of controls to each group of cases within a stratum of matching factor values (Rothman & Greenland, 1998). Matching attempts to make the distribution of the matching factors for the control group more similar to that of the case group. In this way, the method improves statistical efficiency and increases the precision of the effect estimates (Checkoway, 1989).  However matching is costly; information is lost, in that subjects are excluded from the analysis that did not match. Also, the effect estimate of the matched factors can no longer be estimated as their relationship with disease has been distorted. After matching, the factors matched upon may still be confounders or may introduce selection bias that also needs controlling for in the analysis (Rothman and Greenland, 1998). Care should be taken not to overmatch so the cases are too similar to the controls apart from on the exposure under study. Matching on a variable associated with exposure but not disease harms statistical efficiency.  Matching on an intermediate between exposure and disease may harm the study validity and  result in an effect estimate biased towards the null. A third type of overmatching using convenient controls may harm cost efficiency or introduce bias (Rothman and Greenland, 1998).  2.2.5  Analysis of Matched Case-Control Studies  Logistic regression is the usual technique used to analyse case-control studies and allow for confounders. However, when the data is matched, then prior information is known about the distribution of patients across strata, and conditional logistic regression must be used to account for this. Breslow and Day (1980) show that when using logistic regression for a simple matched study design where each case is matched to one control and there is one covariate, the effect estimate is biased by 100%. For a subject's covariate vector x, the logistic regression model for the probability distribution of the binary dependent variable Y, is:  P(y = lb) =  ea+0'x  where a is the intercept parameter and thefl's are the covariate parameters. So if Y is the indicator variable representing a case with value 1 and a control with value 0, then the above model estimates the probability that a subject with covariate vector x is a case. The parameter coefficients are estimated via maximum likelihood estimation. Now a stratum-specific logistic regression model can be specified for a matched case-control study with K strata. Let nut denote the number of cases and no^ denote the number of controls in stratum k, k = 1 , 2 , . . . , K. Thus the conditional logistic regression model is: p  pCtk+P'x (y = n*) = l + e a k + 0 , x  (2.1)  where ak denotes the stratification variable for the fcth stratum. The conditional likelihood for thefcth stratum reflects the probability of the observed data relative to the probability of all possible configurations of the data amongst the cases and controls within that stratum. The number of possible combinations of the cases among all nk = n n + nofc subjects, is denoted by Cfc and  given by the expression:  Cfc  nifc!(n fc +n lf c )!  The subscript j denotes any one of the Ck assignments and i indexes the observed data and ij indexes the observed data for the jth assignment. Subjects 1 to n n correspond to the cases and n\k + 1 to rifc correspond to the controls. So, the conditional likelihood for stratum k can be written as: k[P>  E - ^ m ^ i n * : , , , \ y i j = i)nr/=„„ + 1  \Vii=on  This conditional likelihood can be simplified by applying Bayes theorem and substituting in the conditional logistic model (2.1):  T-T™!* P{yi=\\xi)P(xi ) T-rn/c Pfai=0|xi)P(:Ci) lli=l P(yi = 1) » «lli=n + lP(y;=0) P(yt=0 ) '•'•1=1 i-(y i = i) = » i i , tlki P(Vi.=0|x3i.)P(s3..) k(P) =v-^c* mfnnnlikt /''(j/.,-! p7— nk 2^j = lilli,=l ""j'P(v P(yi,=l) llij=n is=0) ij=l) llij=nlflfcc++ll . P(y P(yij =o) J  l> / a\  h(P)  n?=i p(yi = ii xi)p{Xi) nr=n lfc + i nvi = 01*0^*0 E ^ i t n ^ i p{vi, - i i ^ ) ^ ( ^ ) nr;=„ lfc + i ^ = oi^)^^.)} nr=i p(xi)n?±\ p ^ =  =  PC-r TT"fc 1 n1=1 ^V^t; l i in=i f1c l+e„ek+fi'i: iJ lli = nra>: lf c + l 1+e«fc+/3'a nf c  \pCfc rrrrn P/V . \ n TT Z^j = l\lli J = l ^K-Ljij) lUj = l 1+e"k+f>'*ji ,  j  1 llij=nlf c+l  1  J  1 rr nf c pct-'i n n i f c pQfc+/ 3'3;i n n f c e _ 11»=1 ^ W llt = l llt=Wlfc + l 1+ec.k+P'* j 1 ' ~ m n t p(t -sI rr nif c pOk+f' 1).; n n t . i e 2_J = 1 1.11^=1 ^K-Ljljl LUj=l lli^mt+l at+P'vjij J h(P) ^ J—jTiifc^ eak+p'x ji j J"|«ifc eak+0'xi rrUk  HP) =  ^ ^  p'xi  w  (2.2)  The likelihood function is then the product of the tk(P) in (2-2) over all K strata: K  K  rrnifc p/3'xi  m =ib=i nfc=i m =2_j=inllij=i  e  (2-3)  The conditional likelihood estimators for the f3 parameters are those values that maximise the likelihood in equation (2.3). Statistical software packages, such as SAS, are able to estimate these parameters for matched conditional logistic regression models.  2.3  Assessing Exposure  To perform a case-control study, exposure measurements to the suspected cause of disease are required for each subject. Firstly, the biological meaning of exposure is described and how it may affect cancer development in section 2.3.1. There are many different variables that attempt to model this biological exposure as explained in section 2.3.2. Clear and unambiguous evidence for exposure to potential carcinogens is rare. There are then many approaches to collect the surrogate exposure information which are described in the following sections: workplace records, assessing the current workplace, self-administered questionnaires, personal interviews and expert estimation, or via a JEM.  2.3.1  Cancer Biology  Cancer is essentially the term for a group of diseases characterised by a malignant growth of cells. The first stage of cancer is initiation. This is when a critical gene is irreversibly mutated. Gene mutations may be caused by ageing, exposure to chemicals, radiation, hormones or other factors within the body and the environment. A promoter is then required to encourage the cells to reproduce and grow. The promoter acts after the initiator and acts regularly over a certain period of time. After this a tumour may be visible and invasion and metastases to new body sites can occur. The time between cancer occurrence and detection is called the latent period (Rothman & Greenland, 1998). It is uncertain what causes cancer initiation and promotion, and when in a given subject (Siemiatycki, 1991). Also, different factors can make a subject more susceptible to the effects of carcinogens, e.g. age, gender, genes, health status, and diet. The initiator may be a carcinogen over the biologically effective dose, or a genetic pre-disposition. Therefore people exposed to an initiating carcinogen at a higher dose over a longer time than others will be more likely to develop cancer. Also those exposed to a promoting carcinogen regularly at a higher dose over a longer time than others will be more likely to develop cancer. Duration of exposure is an important factor when analysing the effects of a carcinogen. For a subject with cancer, it is difficult to know what contributed to the initiation and promotion of cancer and when. As cancer is a chronic disease, there is also a large time period during which these exposures could potentially have happened. Thus, much of the subject's lifetime exposure needs to be estimated (Siemiatycki, 1991). An exception is the latency period, but this period varies between subjects. An approach called exposure lagging can be used to allow for the latency period (Rothman & Greenland, 1998). As exposures close to cancer diagnosis are unlikely to have contributed to cancer development, only exposures preceding a certain cut-off time before cancer diagnosis are estimated. As the latency period is  unknown, analysis can be performed for a range of cut-off values.  2.3.2  Measuring Exposure  fdeally the exposure to each possible carcinogen, in terms of frequency and dose, over each subject's lifetime can be measured. The dose is the actual amount of substance that reaches the biological target (Checkoway, 1989). Given the same exposure to a carcinogen, the dose may vary between people as it depends upon many factors such as inhalation rate, health status, genetics, age, gender, etc. If the dose and exposure are related, then the exposure can be measured as a surrogate. Exposure measurement of carcinogens is comprised of concentration, duration and frequency (Checkoway, 1989). The concentration of a carcinogen is important, as lower concentrations are less likely to contribute to cancer development. The frequency of exposure to promoting carcinogens is also important, as occasional exposure is less likely to encourage cancer growth. Exposure to a promoting carcinogen over a longer period of time is also more likely to result in cancer growth. Note that these measurements may vary over time. Subjects may be exposed to carcinogens at a differing concentration and frequency as their specific job tasks change. Also, legislation or technical improvements may change the concentration or frequency of exposures over time. A time measure of exposure can be constructed, such as cumulative exposure, which aggregates the concentrations over time, ft is also useful to collect data on the nature of the exposures, e.g. the route of contact, and any behaviour that protects against the exposure. Accurate measurements of exposure, especially past exposures, are difficult to attain. Occupational exposures account for a considerable proportion of lifetime exposures and the exposures can be adequately measured. The main methods of estimating occupational exposures are by workplace records, assessing the current workplace, self-administered questionnaires, interviewing the subject, and job exposure matrices (JEMs).  2.3.3  Workplace Records  fdeally consistent exposure information to all possible carcinogens is available for each study subject at all their past workplaces. Many workplaces maintain exposure records, but collection methods vary between workplaces (Checkoway, 1989). If recent workplace exposures can be estimated, it will still be difficult to find data on early exposures.  2.3.4  Assessing Current Workplace  Given all employers gave consent, current exposures can be consistently measured at all subjects' workplaces. This would be a timely and costly undertaking. Also, earlier workplaces in a subject's job history may no longer exist, and if they do, practices and thus exposures may have altered considerably since the subject was employed there (Siemiatycki, 1991).  2.3.5  Self-Administered Questionnaires  Study subjects can be asked to complete questionnaires about their exposure history. Subjects will have difficulty remembering exposures many years ago, and may not have understood their exposures well enough to describe them accurately. Questionnaire responses from cases and controls are also liable to recall bias (Siemiatycki, 1991). Cases may be more willing to accurately recall past exposures than controls, as they are more interested in furthering knowledge into their particular cancer. Also, sometimes information can only be obtained from proxy respondents (e.g. spouses, family members), who will have even less recall ability.  2.3.6  Personal Interviews  The subjects themselves may have little knowledge of their lifetime occupational exposures, so some other approach is required. One method is to interview subjects and ask them to recall any known exposures or describe their occupations and workplaces in detail (Siemiatycki, 1991). Interviewing subjects may improve their recall ability. The interviewer can also ask additional questions to find more accurate details about important exposures. However, interview bias may be introduced, as interviewers may have pre-conceptions that influence the subject's responses.  Many interviewers are usually required, which introduces extra  variability between the interviewers' results. The exposure data can be improved upon by using the knowledge of technical experts (e.g. chemists, engineers, hygienists) and also workplace records. The technical experts may be able to estimate exposures backward in time given current estimates, although the exposure estimates will be lacking in precision.  2.3.7  Job Exposure Matrix (JEM)  Another approach used to assess exposures is to ascertain the occupational histories of the subjects, either via a self-administered questionnaire or interview, and then use a Job Exposure Matrix (JEM) to code the data. A JEM is typically a matrix containing all possible occupations as one dimension, and all possible occupational agents as the other dimension. One can then look up a particular agent for each subject's  job and find, depending on the JEM, either an indication, yes or no, whether the subject should have been exposed to the particular agent in that job, or more precisely, an estimate of the probability that they were exposed. Often the exposure is only measured above some pre-defined concentration or frequency. A problem with JEMs is that they are very costly and timely to complete accurately. Often a study will use a pre-developed JEM, which may not be relevant to the particular group of subjects in the study (Siemiatycki, 1991). A downfall to the JEM is that it can only distinguish between exposures up to the level of the job code (Siemiatycki, 1991). Individuals with the same job are assumed to have the same exposure and no further allowance is made for within job variability. Exposures in a certain job vary between workers depending on the particular work habits of the worker, the workers specific tasks, and the company the worker is employed with. However, exposures will be much more similar within the same industry and occupation, than between different industries and occupations. Occupational exposures vary across time as employment practices and safety regulations change and technology improves. A time dimension can be added, but JEMs commonly only represent one economy type at one particular moment in time.  Chapter 3  Risk Factors for Bladder Cancer Bladder cancer affects the inner lining of the bladder and develops slowly. As it grows, it may spread to other organs near the bladder. Carcinogenic chemicals are absorbed by the blood and filtered into the urine, which accumulates in the bladder. There were 4,841 new cases (3,636 male, 1,205 female) of bladder cancer diagnosed during 2000 in Canada. There were 1,082 male deaths and 437 female deaths due to bladder cancer. It was the fourth most common cancer in men in terms of incidence (5% of cancers), and the ninth most common in terms of mortality (3% of cancer mortality). It was the thirteenth most common cancer in terms of incidence (2% of cancers) and mortality (1% of cancer mortality) for women (National Cancer fnstitute of Canada, 2004). Internationally, the highest incidences of bladder cancer occur in Western Europe and North America (Schottenfeld and Fraumeni, 1996). Non-occupational bladder cancer risk factors are introduced in section 3.1. Section 3.2 describes the occupations with consistent elevated risks of bladder cancer and section 3.3 describes the specific occupational chemicals associated with bladder cancer.  3.1  Non-Occupational Bladder Cancer Risk Factors  The risk of developing bladder is increased in males, those of Caucasian ethnicity and risk increases with age. About three times as many males develop bladder cancer as females in Canada. The disease is more prevalent in Caucasians than other ethnic groups, with the incidence rate for male Caucasians being at least double the incidence rate for any other male ethnic group. Risk increases with age, with about two-thirds of bladder cancer cases occurring among persons aged 65 years and older (Schottenfeld and Fraumeni, 1996). Lifestyle factors that have associations with increased risk of bladder cancer include smoking, diets high in saturated fat, coffee drinking, artificial sweeteners, drinking water quality and use of hair dyes.  Cigarette and tobacco smoking increase the risk of bladder cancer. Smokers have around two to three times the risk of non-smokers. Risk also increases for increased intensity and duration of smoking (Schottenfeld and Fraumeni, 1996). Cessation of cigarette smoking has been associated with a 30% to 60% reduction in bladder cancer risk in many studies (IARC, 1986). Increased bladder cancer risks have been associated with diets high in saturated fat. A possible association has been suggested between coffee drinking and bladder cancer risk in case-control studies, but the findings are inconsistent across studies. Artificial sweeteners were suggested as potential human bladder carcinogens based on animal experiments, but epidemiological studies on humans have not substantiated the relation. Most studies that have evaluated alcohol consumption as a risk factor for bladder cancer have not supported a positive association. Epidemiological studies seem to support the association between chlorination by-product levels in drinking water sources and bladder cancer risk (Schottenfeld and Fraumeni, 1996). Use of hair dyes may be associated with bladder cancer risk as animal experiments indicate that some compounds in hair dyes are mutagens and people who dye their hair appear to excrete compounds in their urine. Results from several epidemiological studies, however, have not supported this association (Hartge et al, 1982). Family or personal history of bladder cancer increases the risk of developing bladder cancer. Repeated or chronic bladder infections, or bladder stones, slightly increase the risk of developing bladder cancer. Changes occur in the bladder as a result of repeated or persistent infection. The excessive use of drugs containing phenacetin and the use of Cyclophosphamide and Chlornaphazine have also been linked to risk of bladder cancer in many case-control studies (Schottenfeld and Fraumeni, 1996).  3.2  Occupations Associated with Bladder Cancer  The associations between certain occupations and bladder cancer risk are unclear. Studying large populations and recording personal occupational exposures accurately is difficult.  Some epidemiological studies have  consistently found associations with bladder cancer in certain occupations. However, these are often based on small samples and observed relative risks have typically been less than two. Table 3.1 shows the main occupations associated with increased risk of bladder cancer. This table was adapted from Schottenfeld and Fraumeni (1996) to include the IARC monograph occupations classified as having an association with bladder cancer and also lists some of the agents suspected of increasing the risk of bladder cancer within each occupation. The IARC monographs are a series of independent assessments of animal and human studies into  the carcinogenic risks posed to humans by a variety of agents, mixtures and exposure circumstances. For many agents there only exists evidence of carcinogenicity from animal studies, for some there are inconsistent epidemiological studies and for many there is no evidence. Since its inception in 1969 to 2004, the IARC monographs have reviewed more than 885 agents. The IARC classifies each agent according to its potential for human carcinogenicity into one of four categories: 1 - definitely carcinogenic, 2A - probably carcinogenic, 2B - possibly carcinogenic, 3 - not classifiable, or 4 - probably not carcinogenic. Chemicals classified as IARC group 3 are not necessarily non-carcinogenic, for this classification is where the available studies are of insufficient quality, consistency or statistical power to permit a conclusion. Further studies are required to aid the agent's IARC classification. Bladder cancer has well-established relationships with certain occupational exposures in human studies. These include aromatic amine manufacturing as 2-naphthylamine, benzidine, and 4-aminobiphenyl are considered definitely carcinogenic to humans by IARC, the manufacture of certain dyes as auramine and magenta are considered definitely carcinogenic to humans, rubber industries due to aromatic amines particularly 2-naphthylamine, painters as paints contain aromatic amines as well as other carcinogenic agents, and aluminium and coke production and coal gasification due to coal tar pitches considered definitely carcinogenic to humans by IARC (IARC, 1987). There are also some occupations with a consistent excess risk of bladder cancer for which causative agents are only hypothesised. Dry cleaning solvent-exposed workers are potentially exposed to many chemicals and risks associated with bladder cancer have been seen in many human studies. Boot and shoe workers have an increased risk of bladder cancer and leather workers have also been associated with risk of bladder cancer, but the causal agents are uncertain between leather dust, dyes, benzene and solvents. Hairdressers and barbers have consistently seen an association with bladder cancer possibly due to hair dyes. Other occupations exposed to petroleum products including polycyclic aromatic hydrocarbons (PAHs) have seen excess bladder cancer incidence such as petroleum refinery workers and truck drivers. Other occupations for which evidence is limited are machinists, metal workers, chemical workers, textile workers, carpenters, construction workers, miners, mechanics, gas station attendants, medical workers, photographic workers, pulp and paper workers, and welders (Schottenfeld and Fraumeni, 1996).  3.3  Occupational Bladder Carcinogens  Studies into the association of occupation and bladder cancer often only suggest possible agents that may be contributing to the increased risk within the occupation. However, epidemiological studies aimed at  examining the relation between the particular agent exposures and bladder cancer are uncommon. Much of the evidence on chemical carcinogens is again based on animal studies. Table 3.2 summarises the agents considered related to bladder cancer risk in humans and includes their IARC classification (if any), whether the evidence is from animal or human studies, and the results (if any) from a study by Siemiatycki, 1991. The agents the IARC classified as definitely carcinogenic to humans and have asssociations with bladder cancer are the aromatic amines 2-napthlamine, 4-aminobiphenyl and benzidine, magenta, arsenic, coal tar pitches, mineral oils, and drugs Cyclophosphamide and Chlornaphazin. There are a group of chemicals for which there is little evidence of bladder carcinogenicity in humans, but evidence in animal studies.  Bladder tumours have been seen in animals exposed to 1,3-  dichloropropene, 2-(2-formylhydrazino)-4-(5-nitro-2-furyl)thiazole, 2-nitroanisole, 4-chloro-ortho- phenylenediamine, benz(a)anthracene, CI basic red 9, citrus red no.  2, disperse blue 1, n-[4-(5-nitro-2-furyl)-2-  thiazolyl]acetamide, niridazole, nitrilotriacetic acid, n-nitrosodi-n-butylamine, oil orange SS, ortho- aminoazotoluene, para-cresidine, para-dimethylaminobenzene, ponceau 3R, and sodium ortho-phenylphenate. The human bladder carcinogenicity of these chemicals requires further research. Table 3.3 summarises the results of agents showing an association with blader cancer risk from a study by Siemiatycki in 1991. Siemiatycki carried out an extensive study of occupational carcinogens for all major cancer sites. The case-control study consisted of males aged 35 to 70, resident in the Montreal metropolitan area and diagnosed with a histological confirmed cancer between September 1979 and June 1985. Analysis was performed with bladder cancer patients (n = 484) and with cancer controls (n = 1,879) and with population controls (n = 533) separately. In addition, the analysis was repeated for French Canadians only (around 60% of the cases and controls). The occupational history of study subjects was obtained through personal interviews and occupational exposures assessed by experts in occupational hygiene, epidemiology, engineering and chemistry. Experts assessed each subject's exposure by the concentration, frequency and confidence it occurred during the period the subject was employed. Each criterion was assessed according to a three-point scale as shown in table 3.4. Odds-ratios and 90% confidence intervals were then calculated for 'any exposure' and 'substantial exposure' to each agent individually. The non-occupational confounders accounted for in the analysis were age (less than 55, 55 or older), family income (tertiles), cigarette index (none, 1-800, 800+ cigarette-years), coffee index (0-50, 51+ cup-years), and the respondent to the questionnaires and interviews (proxy, self). The subject was considered to have 'any exposure' if there was reasonable confidence (level 2 or 3) the exposure existed prior to 5 years before diagnosis. The subject was considered to have 'substantial exposure' if they had 'any exposure', concentration x frequency > 3, and at least 5 years of  exposure accumulated up to 5 years before diagnosis. So, the exposure was probable or definite, was above a background level, and occurred for at least 5% of the working time (i.e. no exposure criterion was coded as level 1) for at least 5 years. The odds-ratios shown in table 3.3 for those chemicals tested by Siemiatycki are the most significant out of all 4 possible configurations (cancer or population controls, all ethnicities or French Canadians) with at least 4 exposed cases. Siemiatycki found significant increases (at a 10% level) of risk of bladder cancer for 'substantial exposure' to cadmium compounds, carbon tetrachloride, diesel engine emissions, engine emissions, formaldehyde, ammonia, asphalt (bitumen), chlorine, fabric dust, laboratory products, natural gas combustion products, photographic products, polyester fibres, and polyethylene. Significant results at a 5% level with odds-ratios above 2 were found for 'substantial exposure' to carbon tetrachloride (French, cancer controls), diesel engine emissions (French, population controls) and natural gas combustion products (all, cancer controls), and for 'any exposure' to acrylic fibres (all, population controls), ionizing radiation (French, cancer controls), calcium carbonate (French, cancer controls), and titanium dioxide and titanium compounds (all, cancer controls).  Chapter 4  D a t a Resources The BC Cancer Agency (BCCA) has collected occupational and lifestyle data on male cancer patients resident in British Columbia (BC) and diagnosed between 1983 and 1990. The study area of British Columbia, Canada is described in section 4.1 to give an overview of the study subjects and their occupational exposures. The BCCA study is described in more detail in section 4.2. The National Institute for Occupational Safety and Health (NIOSH) has developed a Job Exposure Matrix (JEM) that gives exposure estimates to many agents in various US jobs. Using the translation system developed by BCCA, the Canadian job codes from the BCCA questionnaire results can be translated to US job codes. These translations can then be used to estimate the lifetime exposure to each agent for each patient, and in turn these exposures can be used in the analysis of the case-control study. The NIOSH JEM is described in section 4.3 and the translations introduced in section 4.4.  4.1  The Study Area - British Columbia, Canada  The province of British Columbia (BC) on the West Coast of Canada has seen rapid popularisation over the last century. In 1931 the population of BC was estimated to be a mere 0.7 million. By 1986, it had grown to 3 million. At the beginning of the 20th century more than half of the population was under the age of thirty and men outnumbered women nearly two to one. The percentage of males and females living in BC has been roughly equal since the 1960s. The population has also been ageing since the post World War If baby boom. Most of the population of BC during the early to mid 1900s descended from European origin. More recently the population has become more multi-cultural, including a significant increase in the proportion of Asian immigrants. In the 1986 census of Canada, 60% of British Columbians reported a single ethnic origin; nearly half of these had British ancestry, nearly 30% had other European ancestry, and 13%  were Asian (Schrier and Ip, 1994). Over the last century BC's economy has been highly dependent on resource-based industries such as logging, the railway, mining, fishing and agriculture. Manufacturing activities were based on the processing of natural resources, such as canning salmon, or producing lumber and paper. Resource-based industries continued to employ the largest share of the labour population until the early 1990s.  4.2  BCCA Data  The Health and The Workplace study of the BC Cancer Agency (BCCA) provided the patient data for this project. The Health and The Workplace study was initiated in 1983 to provide a population-based occupational study of male cancer patients resident in BC. Only male cancer patients were included in this study, as during the last century men were much more likely to have occupational exposure to carcinogens than women. Women also tended to spend longer periods of time working in households, where exposures were too variable to be studied. The study consisted of a self-administered questionnaire mailed to all males aged 20 years and older diagnosed with cancer ascertained by BCCA during the period 1983 to 1990. The questionnaire requested detailed job descriptions for up to 10 jobs held for at least a year. It also requested information on sociodemographic factors and lifestyle factors such as drinking and smoking habits. Questions regarding lifetime consumption of alcohol (wine, spirits, and beer) were initially omitted and then added during the fist year of the study. Questionnaires were sent to all new cases for the first 2 years. Subsequently, once 1000 completed questionnaires were returned for a given cancer site, data collection was ceased for that site. More information about the study can be found in Band et al. (1999). The BCCA sent in total 25,726 questionnaires to eligible male cancer cases, of which 15,463 were returned, giving a response rate of 60.1%. Patients aged over 80 were the only age group with a response rate below 50%. Also, liver, stomach, pancreas and unknown primary cancer sites had response rates below 50%. Response bias was addressed in the article by Band et al. (1999) and was thought to be minimal. Non-responders were not significantly different to the responders in education or smoking status. However, responders were more likely to be employed in the Managerial and Administrative occupational group than non-responders, and this was the only occupational group that was significantly different between the two groups (p < 0.001). These patients have low exposure to agents according to the NIOSH JEM, so perhaps the general cancer population has slightly greater occupational exposure than those responders included in the study.  The questionnaire was originally sent to non-cancer regional controls, but the response rate was low. This would have introduced selection bias, with the non-cancer controls that responded being more interested in health research, and therefore healthier. So, internal cancer controls of different sites to the case site were used as controls. This also minimises the recall bias, as the controls should be equally willing to provide complete responses in the questionnaire as the cases. This changes the interpretation of the results however, and instead of identifying possible occupational carcinogens for a particular cancer site per-se, carcinogens identified are those more likely to contribute to cancer in that site rather than in other sites of the body. The primary tumour site and diagnosis date information was available for all 15,463 patients through the BCCA Registry. Histological confirmation of diagnosis was obtained for all patients. The primary tumour site was coded by the 9th revision of the International Classification of Diseases (WHO) and grouped into 3-digit categories for analysis. Misclassification of case or control status is very unlikely. The job descriptions given in the questionnaire were coded manually into an industry and an occupation code according to the 1980 Canadian Standard Industrial Classification (CSIC) and the 1980 Standard Occupational Classification (CSOC), respectively (Statistics Canada, 1981). The CSIC consists of 853 possible codes and the CSOC consists of 503 possible codes. The questionnaire also asked for the location, start year, end year and duration of each job, and included a tick box to indicate if the job was part-time or seasonal. If there was no indication in the tick box that the job was part-time or seasonal, the job was assumed to be full-time. If the duration of the job was not given, this was taken as the end year minus the start year. Patients could provide information for a maximum of 10 jobs. So, it was assumed that if a patient reported 10 jobs they had no further jobs. Only 661 patients, or 4.7% of those with jobs, recorded 10 jobs. This would not have too much effect on exposure estimates, as any additional jobs may be of short duration or occur close to the diagnosis date. The BCCA questionnaire was found to be highly valid. For 81 patients who indicated working in one of two large companies in BC, personnel records from these companies were searched to check the starting year and duration of employment of these patients. The interclass correlation between the company records and questionnaires was 0.996 (95% confidence interval, 0.993 to 0.997) for starting year (excluding 2 patients with missing start date information) and 0.971 (95% confidence interval, 0.954 to 0.981) for duration of employment (excluding 4 patients with missing duration of employment and 3 patients that were not reported as being employed by the company records). A list of all employees employed for at least 3 years was also available from one of the companies. From the list of all questionnaire respondents, there were no further patients employed in that company than those that had reported so in the questionnaire. The questionnaire was also found to be very reliable by comparing the responses of 87 patients who  filled out the questionnaire on two occasions. Here the kappa statistic was 0.94 for smoking (ever/never) and the interclass correlation for age started smoking and number of cigarettes smoked per day were 0.92 and 0.81, respectively. The interclass correlation for the number of years worked in the most common occupations recorded (construction, farming, clerical, and sales) were 0.92, 0.93, and 0.96, respectively; missing information ranged from 2% (construction) to 14% (clerical and sales). When all occupational information was recorded (80% of the pairs), the interclass correlation was 0.92 for work start year and 0.89 r  for total years worked (Band et al., 1999). There were other factors thought related to cancer development that the questionnaire did not inquire about, such as coffee drinking, diet, drinking water, use of hair dyes, history of cancer in the family, genetic pre-dispositions, and overall health status. It would be difficult to ask questions about such factors without making the questionnaire very long. This should also not make much difference to the analysis as information was sought on the most important risk factors. The estimation of occupational exposures given all the occupational information from the patients' questionnaires is detailed in section 5. Firstly, the questionnaire data is edited and prepared for analysis as described in section 4.2.1 and the inclusion criteria for analysis is stipulated in section 4.2.2.  4.2.1  Data Editing  The questionnaire data was edited for errors and inconsistencies before analysis. The protocol for coding the initial questionnaire responses included entering the start year at age 12 if the patient was raised or worked on a farm from birth. The questionnaire data still included jobs before age 12, however, and 290 patients had jobs before they were 12 and 230 patients had jobs before they were 11. The patients with childhood jobs were reasonably randomly distributed across the cancer sites. Many of these patients reported working or living on farms during their childhood. Data is included for those childhood jobs given so as to provide as accurate a summary of personal lifetime exposure as possible. Five patients had their first job coded as starting before they were born, so the birth dates were assumed correct as they came from two sources (the questionnaire and the BCCA patient record), and the job dates were adjusted so the job started when the patient was born.  4.2.2  Analysis Inclusion Criteria  The criteria for the BCCA questionnaire mailing were BC males over 20 years old diagnosed with cancer between 1983-1990. The inclusion criteria for the analysis were that the questionnaire was completed and the primary  cancer site was known. There was only one patient who did not complete the questionnaire. Patients who completed only part of the questionnaire are discussed in section 5.2.1. When the primary cancer site was unknown in a patient, the tumour will have a true site, but it was just undetectable at the time. Patients with unknown primary cancer sites were excluded, as they could not serve as controls for other cancer sites. Some of the primary unknown cancer sites could truly be the same site as the cancer case site. This would result in additional misclassification errors amongst the cases and controls. Therefore, 708 patients with an unknown primary cancer site were excluded. This resulted in a total of 709 patients failing to meet the inclusion criteria, leaving 14,754 patients for analysis. Age criteria were also considered; such as excluding older patients due to questionnaire recall inability, a low questionnaire response rate and it being unlikely their cancer was due to occupational exposure. The latent period of cancer is not known precisely, so some cancers in old age could be due to occupational exposure up to retirement, and also many patients worked after retirement. There are few old patients, and matching of cases to controls on age later in the analysis will decrease their proportion further. The exclusion of younger patients was considered, as their cancers are also unlikely to be due to occupational exposures. Many younger patients were brought up on farms and thus exposed to many agents. All patients aged 20 and above were included so the study results could be generalised to all BC male cancer patients aged 20 and over.  4.3  NIOSH JEM  The JEM developed by the National Institute of Occupational Safety and Health (NIOSH) in the US was chosen for the estimation of exposure probabilities for the BCCA data. A JEM based in North America was desired so the occupational exposures approximately represented those in BC and Canada. The NIOSH JEM constructed from the National Occupational Exposure Survey (NOES) covered a broad range of agents and estimated exposure probabilities from actual measurements taken in a representative sample of US workplaces. Some jobs were excluded from the JEM, however, and the JEM does not measure the changes in exposure over time. Prom 1981 to 1983, NIOSH conducted the National Occupational Exposure Survey (NOES) to develop estimates of the number of workers potentially exposed to 12,945 chemical, physical, and biological agents in selected industries. Of the 12,945 agents, 9,557 (74%) have corresponding Chemical Abstracts Service (CAS) codes, 4,952 (38%) have corresponding Registry of Toxic Effects of Chemical Substances (RTECS) codes, allowing the substances to be compared across different studies. Jobs were classified ac-  cording to the US 1980 Census of Population Industrial Classification (USCENfND), and the US 1980 Census of Population Occupational Classification (USCENOCC). The US 1980 Census of Population consists of 231 Industrial Classifications, and 503 Occupational Classifications. The NOES survey involved visits to 4,490 establishments in 121 industry groups (52%) employing approximately 1,800,000 workers in 377 occupational categories (75%). The field guidelines and sampling methodology are discussed next, and further details can be found in NIOSH (1988) and NIOSH (1989) respectively.  4.3.1  Field Guidelines  Specifically trained surveyors collected exposure data via walk-through inspections of each facility. Exposure to an agent was only recorded if the agent had been observed in sufficient proximity to an employee so that one or more physical phases of the agent were likely to enter or contact the body of the employee, fn addition, an employee was classified as exposed to an agent if the exposure occurred for at least 30 minutes per week (on an annual average) or once per week for 90% of the weeks of work year. Thus, the JEM does not measure the level of exposure, but exposure above a certain concentration and frequency. This JEM exposure level can be thought of 'considerable exposure' and throughout this thesis shall be referred simply as 'exposure'. The presence of engineering controls over potential exposure was also recorded. The amount of employees exposed for more than 4 hours a day or at least 90% of the working year was recorded and defined as full-time exposure. The exposures were classified into trade name or actual agents. Approximately 70% of the data collected were from trade name products, and ingredients were determined for 85% of these.  4.3.2  Sampling Methodology  The target establishments were those within an industry on a list of target USCENIND codes, located in the United States, and reporting 8 or more employees at the time of the survey. Businesses with less than 8 employees were considered too numerous and transient to survey accurately. To construct a sample of the target establishments a two-stage systematic selection procedure was employed involving stratification by number of employees, SfC and geographical location. The first stage of the sampling procedure identified establishments from 604 geographical combinations of contiguous counties within metropolitan or urbanised areas. These were stratified by employee concentration by USCENfND code and geography into 98 strata. The second stage involved systematically selecting the 4,894 facilities to be surveyed from the strata by selecting independently across different sizes of facilities, where the number of employees defined the size. A total of 4,490 facilities co-operated with the study and were ultimately surveyed for the NOES JEM.  A downfall of the JEM is that the list of target USCENIND codes excluded 110 (48%) industries. Many of the employees in these industries were thought by NIOSH to have little agent exposure, so were not surveyed, e.g. finance, insurance, and real estate. Some industries were thought to be so large and heterogeneous that they warranted surveys of their own, e.g. mining. While others, such as private households, were not surveyed as they were thought to be difficult to survey accurately. Agricultural production, railroad transportation, federal, state and municipal government industries were also excluded from the NOES survey. The final NIOSH NOES JEM gives, for each job (industry and occupation code) and agent, the ratio of the expected number of employees considerably exposed nationally, to the amount employed in that job nationally. The estimation of the number of employees exposed nationally, given the survey results involved weighting each survey facility according to the probability of including a facility like it in the sample. The weightings were determined by ratio estimation, with ratio factors determined using outside sources such as the Bureau of the Census publication County Business Patterns (CBP), or the Dun Master Inventory (DMI). The amount employed in each job nationally was estimated via Duns Marketing Index (Dun and Bradstreet, 1980). The NIOSH JEM is essentially a 3-dimensional array with the USCENIND codes on one axis, the USCENOCC codes on another axis, and the agent codes on the final axis. The elements of the array are the exposure probabilities, however, only exposure probabilities greater than zero are recorded. Therefore, when a job-agent exposure estimate is missing from the NIOSH JEM, it is difficult to distinguish between the situations: the job-agent was surveyed with no exposure, there were no employees in that industry and occupation in the US, or the industry was not studied by NIOSH.  4.4  BCCA Canadian to US Job Translations  To translate the Canadian job codes to US equivalents, the BCCA translations were used (Svirchev, 1993). Experts in occupational coding designed a system to translate the 853 CSIC codes to 231 USCENIND equivalents, and to translate the 503 CSOC codes to 499 USCENOCC equivalents. The occupational categories are generally similar for the CSOC and USCENOCC, apart from for fabrication, processing, assembly, and machine operating occupations. Here, the CSOC classifies the occupations according to the product produced, whereas the USCENOCC classifies according to the equipment used (Svirchev, 1993). The CSIC also has more specific categories than the broader USCENIND categories. For example, the USCENIND defines all hospitals in one category: 831 Hospitals. Whereas the CSIC includes  eight categories distinguishing between the type of hospital: 8619 Other Specialty Hospitals, 8617 Children's (Paediatric) Hospitals, 8616 Nursing Stations and Outpost Hospitals, 8615 Addiction Hospitals, 8614 Mental (Psychiatric) Hospitals, 8613 Extended Care Hospitals, 8611 General Hospitals, and 8612 Rehabilitation Hospitals. Industry titles corresponding to each CSIC code were matched to US industry title equivalents. The matching industry titles were verified and those equivalents that did not correspond well or appeared infrequently were excluded. This resulted in, a group of USCENIND codes relating to each CSIC code, and similarly a group of USCENOCC codes relating to each CSOC code. Many translations are not one-to-one relationships. The relationship between the Canadian and US codes is often many-to-many. For example CSOC 2181 Mathematicians, Statisticians, Actuaries, translates to three USCENOCC codes: 066 Actuaries, 067 Statisticians, and 068 Mathematical Scientists n.e.c.. However, USCENOCC 068 Mathematical Scientists n.e.c. translates back to two CSOC codes: 2181 Mathematicians, Statisticians, Actuaries, and 2189 Occupations in Mathematics, Statistics, Systems Analysis, and related fields n.e.c.. Here, n.e.c. denotes Not Elsewhere Classified.  All translations for a particular job were  considered equal and no indication was given of which translation was more likely or closer to the 'truth'. Translations of the patients' jobs are described further in section 5.2.4.  Chapter 5  Exposure Assessment In order to analyse the agents for associations with bladder cancer incidence, a measure of exposure to many separate agents for each patient is required. For each US job, the NIOSH JEM gives the probability of a person employed in that job being considerably exposed to many agents. Considerable exposure is exposure that occurs for at least 30 minutes per working week or at least once per week for 90% of the weeks of the working year (see section 4.3.1). The BCCA questionnaire data includes the duration and type of jobs held by each patient in the study. As all jobs held by the study subjects are coded according to the Canadian job codes, and the JEM is coded by US job codes, each Canadian job needs translating into US equivalents first. The JEM probabilities in conjunction with the duration of each job provide a measure of cumulative exposure. The cumulative exposure to a given agent for a patient is estimated as the aggregation across all jobs of the product of that job's exposure probability estimate and the duration of that job. This gives an expected number of work-years with considerable exposure to each agent. The cumulative exposure estimate will give a higher weighting to the agent exposure probabilities in a patient's main job. Firstly the cumulative exposure definition is explained in more detail in section 5.1. The process of actually calculating the cumulative exposure index is then described in section 5.2.  5.1  Cumulative Exposure  For each agent, the cumulative exposure for a patient is defined as the aggregation over the patient's jobs of the product of the probability of considerable exposure and the job duration. Let i denote the ith patient (i — 1 , . . . , 15463), j denote the jth job (j = 1,..., 10) and k denote the fcth agent (k = 1 , . . . , 12945). So,  the cumulative exposure, Eik, to agent k for patient i, is estimated as: 10  ^ ^ ^ijk^ij  Eik  3= 1  where tij is the duration (in job-years) of job j, and e ^ is the exposure probability estimate for job j. A job-year is defined as one year in a full-time job. If a job is part-time, it is considered half as much work time as a full-time job and thus the duration in years is divided by two. So t^ is calculated as:  tij  —  dij  if PT=0  dij/2  if PT=1  <  where dij is the duration of job j, which is divided by two if the job was indicated to be seasonal or part-time (PT = 1 ) . If the duration of the job is not given, then the job duration is approximated by the end year minus the start year. The exposure probability estimates, e^fc, now need calculating. Before using the JEM to look up the exposure probabilities, each Canadian job needs to be translated to US equivalents. Each Canadian job consists of a CSIC industry (Xij) and CSOC occupation (yij) code. As discussed in section 4.4, the Canadian and US job codes do not have a one-to-one relationship. The relationship is often many-to-many: for each CSfC, there can be many USCENIND equivalents and for each USCENOCC, there may be many CSOC equivalents. Using the BCCA translation rules, let giND denote the function that translates Xij into Sij different US industry codes, and gocc denote the function that translates yij into Ty US occupation codes. Every possible permutation of the translated industry and occupation codes is considered equal for each Canadian job. For example, if one Canadian job translates to 2 US industry codes and 3 US occupation codes, then there are 2 x 3 = 6 possible permutations of US industry-occupation combinations. The set of all possible combinations of g/ND(%ij),  gocciVij) is then the translation of Canadian job j for person i to US equivalents.  The term 'job-translation' will be used to refer to each of these possible US industry-occupation combinations in this thesis. Each of the possible job-translations will not always be of equal value. Some of the US job-translations will be closer to the true meaning of the Canadian job than others. Some of these job-translations may not even exist in practice in the US. Estimating differing weights for each job-translation possibility, however, is very difficult. The amount employed in each industry and occupation combination in the US could be estimated. Given the amount employed in a US job, however, the proportion that corresponds with each of many Canadian job equivalents could not be estimated, as the relationship between US and Canadian job codes is often many-to-many. Also the proportions employed in each group would change over time.  Therefore, each job-translation is weighted equally, so that the JEM probabilities are averaged over all translations. So, e^fc is calculated as: _ lCf=l ijk —  e  Ylt=1 fjEM,k{giND,s(Xjj)goCC,t(yij)} C T  J- ij where x^ and y^ are the CSIC and CSOC codes respectively, for patient i's j t h job. The function that gives the sth translation of CSIC code Xij is denoted by grND,s(xij), and the function that gives the tth translation of CSOC code y^ is given by  gocc,t{Vij)-  The NIOSH JEM matrix function that gives the  exposure probability estimate to agent k for each US industry and occupation code combination given, is denoted by f  5.2  J EM,k-  Calculating Cumulative Exposure  Figure 5.1 depicts the flowchart displaying the approach used to calculate the cumulative exposure estimates for each eligible patient. Initially 14,754 patients met the inclusion criteria with completed questionnaire data and a known cancer diagnosis. Some further patients were excluded from the study as described below. The analysis was designed so any major cancer site could be chosen as the basis for the case series and potential occupational carcinogens could be analysed for that site. To enable analysis of possible carcinogens for any cancer site, exposure estimates were calculated for all eligible cancer cases and controls.  5.2.1  Providing Adequate Job Information  To calculate the cumulative exposure estimates for each patient, much information was required from the questionnaire. Each patient needed to adequately describe each occupation they had and report the industry, so it could be coded into a CSIC and CSOC code. Also, for each job they needed to provide the start year and end year, and indicate whether the job was part-time or seasonal. The start and end year was required to see if the job occurred within 5 years of diagnosis. If any of this information was missing or unclear then the exposure estimate could not be calculated. Only 705 (4.8%) patients did not complete all the necessary data, so these patients were excluded from the remainder of the study. Patients were excluded if any of the job information (industry, occupation, duration or start and end year) was missing or unclear. The job end date, start date and codes are the most important pieces of information for calculating the cumulative exposure. Table 5.1 shows the distribution of patients and the extent to which they completed the job codes and job end and start dates. The exclusions only form a small proportion (4.8%) of the patients. However, they should form a random sample from the patients so they do not affect the later case-control analysis. The consequences of  excluding these 705 patients from the study did not make a considerable difference to the types of matched cases and controls in the later bladder cancer analysis as described in section 6.3.2.  5.2.2  Canadian Jobs and Latency  The job history data was adjusted to allow for a 5-year latency period. All jobs starting less than 5 years before the patient's year of diagnosis were deleted. All jobs with end dates less than 5 years before diagnosis were reduced so they ended 5 years prior to diagnosis. The durations of the jobs were then adjusted accordingly, that is, exposure in the 5 year period preceding diagnosis was not considered. Originally 26 patients had no jobs recorded. After the deletion of jobs within 5 years of diagnosis, 100 patients had no jobs. There are now 14,049 patients eligible for analysis, for which exposure estimates needed calculating. The 100 patients that reported no jobs are estimated to be unexposed to all agents. The remaining patients reported 63,638 jobs that started more than 5 years before their diagnosis; this is an average and standard deviation of 4.6 and 2.5 jobs per patient respectively. These patients contributed 483,138 work-years or an average and standard deviation of 34.6 and 12.2 work-years per patient reporting jobs.  5.2.3  Coding the Canadian Jobs According to the C S O C / C S I C  Some Canadian occupations were not included in the CSOC classifications, and thus could not be translated to US equivalents. The two occupations excluded from the CSOC coding were occupations in the armed forces and students. There were 2,927 jobs (4.6%) reported in the armed forces (commissioned officers and other ranks). From the dates given, many of these jobs were during World War, II, and some were during World War L Some patients were also employed in the armed forces for their entire working life, as 31 patients reported no other jobs than those in the armed forces. These exposures occurred in different countries, in different wars and at different times and are thus very difficult to estimate, and therefore were assumed to be zero in this study. Also, some men may not have considered this work a job and may not have reported it. The 2,927 jobs in the armed forces belong to 2,667 patients. Employment in the armed forces contributes 16,685.5 work-years (3.5% of all work-years reported). This is an average and standard deviation of 6.3 and 6.1 work-years per patient respectively. The distribution of work-years in the armed forces per patient is right-skewed with a median of 5 work-years. Assuming no exposure for armed forces occupations means the cumulative exposures for these 2,667 patients may be slightly underestimated. Table 5.2 shows the distribution of cancer sites for the work-years in occupations in the armed forces compared to the work-  years in other occupations. The number of patients reporting any employment in the armed forces, and the work-years employed in the armed forces seem to form the same distribution across cancer sites as the other occupations. Additionally, 2 patients were coded as full-time students for 3 and 4 years and are assumed to have no exposure as teachers were by NIOSH. Therefore, of the 14,049 patients included for analysis, 100 reported no jobs before 5 years prior to diagnosis, and 31 reported only armed forces jobs. So, 131 are estimated as not exposed to all agents, and exposures need calculating for the remaining 13,918 patients.  5.2.4  Translating Canadian Jobs to US Equivalents  Using the BCCA translations described in section 4.4, each Canadian industry given was translated into an average of 1.2 US industry translations and each occupation was translated into an average of 2.7 US occupation translations. This resulted in 214,189 possible US job-translations for the 60,709 Canadian jobs (an average and standard deviation of 3.5 and 4.8 US job-translations per Canadian job respectively). This large number of job-translations per Canadian job was partly due to considering each permutation of translated industry and occupation code. The number of job-translations may have been reduced if the BCCA translations were performed on the Canadian job industry and occupation pairs rather than the two independently. Table 5.3 shows a summary of the proportion of jobs, work-years, and patients employed in each CSIC major group (2-digit code). The construction industry employed the largest proportion, 10.9% of the 60,709 Canadian jobs, had the largest proportion, 29.3%, of patients ever employed within it, and contributed the largest proportion, 10%, of work-years. Agriculture and manufacturing industries were also large employers of the 13,918 patients. To assess the translation accuracy, table 5.4 shows the number of job-translations and work-years contributed by each USCENStC translation grouping. The US industry equivalent for Canadian job CSIC code Xij is considered to be the average of the Sij USCENSIC equivalent translations. The work-years contributed by CStC code Xij, is the job's work-years, Uj. Therefore, each USCENSIC translation equivalently contributes % / S i j of work-years. The US industry translations seem adequate, as the proportion of work-years in each major industrial grouping remains approximately equal before and after translation. Table 5.5 shows the proportions of jobs and work-years in each major CSOC group (2-digit code) and table 5.6 shows the proportion of translations in approximately equivalent USCENSOC groupings. Most patients were employed in occupational groups managerial and administrative, sales, farming, product  fabricating, assembling, repairing and construction. Of the 60,709 jobs, 39% were in these occupations, and 41% of the work-years were in these occupations. Again, the US translations look adequate, as the proportion of work-years in each major occupational grouping is approximately equal before and after translation, although the US major occupational group definitions are not as consistent with the Canadian ones as the major industrial groupings were.  5.2.5  US Industries Studied by NIOSH  Many US industries were not included in the JEM, as discussed in section 4.3.2. This resulted in 100,444 (47%) of the job-translations not on the JEM because the US industry was not studied by NIOSH. Agent exposures will be underestimated in some jobs, but many of these jobs should be truly non-exposed.  5.2.6  US Job-Translations on the J E M  For 25% of US job-translations the industry was studied by NIOSH, but the industry and occupation combination was not found on the JEM for any agents. It is difficult to distinguish between the possibilities that 1) NIOSH studied the job and found no exposure to all agents, 2) there were no employees in that industry and occupation in the US, and 3) NIOSH did not study the job, as they believed it would not have considerable exposure to any agents. Zero exposure could be assumed for cases 1 and 3. However, for case 2 the job-translation is not valid, so it should be excluded from the analysis and thus the average. As these different possibilities were not detectable, and cases 1 and 3 are more likely, it was assumed that the exposure was zero for all agents. Again exposure may be underestimated for some jobs. For a Canadian job, with any US translations of case 3, which are not valid, then the exposure estimate will be underestimated by averaging over too many translations. Of the 214,189 job-translations, 60,356 (28%) could be found on the NIOSH JEM. These 29,306 Canadian jobs belong to 10,420 patients, leaving 3,629 (26%) patients estimated as unexposed to all agents. Table 5.7 shows the distribution of jobs and work-years in each major CSIC grouping for all Canadian jobs and those with any exposure estimated by the JEM. The construction industry had the greatest proportion, 96.4% of jobs with exposures on the JEM. The industries of fishing and trapping and finance, insurance and real estate had no jobs on the JEM, as they were not studied by NIOSH. The agriculture, mining and government services industries also had a very small proportion of jobs exposed. Table 5.7 also shows the work-years contributed in each industry and the equivalent amount of work-years accounted for on the JEM. A lesser proportion of work-years were accounted for on the JEM, due to many translations having zero exposure. Overall, 32.9% of work-years were accounted for on the JEM, or equivalently, were  considered exposed on the JEM, whilst 48.3% of jobs were exposed. Table 5.8 shows the same variables for the Canadian major occupational groupings. No jobs in teaching, religion or fishing and trapping occupations were considered exposed. A small proportion of those jobs in social science, farming, sales, services, and mining occupations were considered exposed. A large proportion of those jobs in materials processing, machining, and construction were considered exposed. As the JEM is assumed representative of jobs located in North America, the locations of the Canadian jobs with exposures estimated from the JEM should be examined. Table 5.9 shows the locations of the 29,306 Canadian jobs with any JEM exposures estimates, and the 153,618.6 work-years accounted for on the JEM. Of the 29,306 jobs at least 66.8% were in BC, and at least 71.4% of the work-years accounted for were in BC. At least 87.4% of the jobs and 88.2% of the work-years were in Canada. A maximum of 8.4% of the jobs and 7.9% of the work-years were outside Canada, and 4.2% of the jobs and 3.9% of the work-years had unknown locations. Therefore, a very small proportion of the exposures were estimated for jobs outside Canada, and many may still have been located in North America. The few work-years employed outside North America may have different levels of exposure, but it should make little difference to the results.  5.2.7  Applying the J E M  Firstly a subset of the NIOSH JEM was created to enable easier electronic data handling. The JEM subset was restricted to the probabilities of exposure in males only. The exposure probabilities were calculated as the ratio of the NIOSH estimate of those exposed in that job nationally, to the amount employed in that job nationally according to Dun's Marketing Index (Dun and Bradstreet, 1980). Sometimes this was slightly larger than 1 when NIOSH observed more employees than actually recorded as employed in Dun's Marketing Index. A maximum of 1 was set for these proportions so they represented true probabilities. In addition, the JEM was restricted to only those US job-translations found in the study. Therefore, the NIOSH male JEM subset consisted of 405,183 industry-occupation-agent combinations, with 12,688 agents. The JEM subset was then applied to the 60,356 US job-translations. This resulted in over 9 million person-job-translation-agents.  5.2.8  Calculating Cumulative Exposure  The person-job-translation-agents data were then compiled into the required cumulative agent estimates. Firstly, for each person-job-agent, the probability estimates for each US job translation were averaged. This resulted in a probability of considerable exposure to each agent for each Canadian job. Next, each probability estimate associated with each job was multiplied by its duration and divided by 2 if the job was part-time.  These estimates were then aggregated across jobs for each patient and agent to give the final cumulative exposure estimates to each agent.  5.2.9  Final Cumulative Exposure Estimates  The process resulted in over 4 million person-agent exposure estimates, Eik, greater than zero. All remaining person-agent exposures were estimated to be zero. 10,420 patients (74%) had cumulative exposures greater than zero for some agents. The 4 million person-agent estimates consisted of 11,882 different agents. Therefore none of the patients in the study were estimated as being considerably exposed to 1,091 of the NfOSH agents. The exposure estimates to each agent for each patient tended to be quite small. The average cumulative exposure for an exposed patient to each of the 11,882 agents was calculated. The average and standard deviation of these average cumulative exposures were 0.36 and 0.61 respectively. Similarly, the average and standard deviation of the maximum cumulative exposure for an exposed patient of all 11,882 agents was 5.13 and 8.58 respectively, with the overall maximum exposure being 67.60 for a patient exposed to continuous noise. The histograms of cumulative exposure across all patients for each agent are generally very right-skewed with the majority of patients having a cumulative exposure of zero or near zero. Taking the natural logarithm of the positive cumulative exposures tends to make them normally distributed.  Chapter 6  Statistical Approach Chapter 5 described how the cumulative exposure to each agent for each patient was estimated. Before testing for associations between cumulative exposure and bladder cancer incidence, possible confounders must be considered. The most important confounders of age at diagnosis and year of diagnosis are first used as matching variables. They and additional factors are taken into account in the conditional logistic regression analysis. The matching as discussed in section 6.1 identifies the case and control patient groups. Possible confounders considered are discussed in section 6.2 and the characteristics of the cases and controls are outlined in section 6.2.1. A conditional logistic regression base model is developed to account for the most important confounders and is described in section 6.3. The consequences of the subjects excluded due to missing occupational data previously in section 5.2.1 on this base model are investigated in section 6.3.2. Finally each agent is tested independently whilst simultaneously adjusting for the important confounders as described in section 6.4. In addition, the agents are grouped into components that may act synergistically on bladder cancer development via principal component analysis as outlined in section 6.5.  6.1  Matching  The analysis of potential occupational carcinogens involves matching bladder cases to cancer controls on exact age at diagnosis and year of diagnosis. Age is a well-known important risk factor for all cancers; bladder cancer risk increases with age. Age is also associated with occupational exposures; on average older patients will have had greater exposure to carcinogens than younger patients and they may have also been out of the workforce for longer. In addition age is associated with most risk factors, such as smoking habits, alcohol drinking habits, level of education, etc. Matching on age to make the control comparison group more similar to the case group in age distribution was considered the best method to deal with these problems.  There are also differences between patients diagnosed in different years due to questionnaire collection ceasing in later years for some cancer sites. The questionnaire questions also differed in early years. Again, matching on year of diagnosis was used to allow for these. Frequency matching was used to maximise the number of cases and controls used in the analysis. Patients with lung cancer were excluded from the control series for the bladder cancer analysis, as lung cancer is too strongly associated with cigarette smoking. After removing the 2,808 lung cancers and those 705 patients with missing occupational data (see section 5.2.1) there are 1,066 possible bladder cases and 10,175 eligible controls. Matching on age and year of diagnosis leads to 1,062 bladder cancers and 8,057 controls. These matched subjects were all diagnosed between 1983 and 1987, as all bladder cancers were diagnosed during this period. Also matching restricted the age range to 21 to 95. Further characteristics of the cases and controls are discussed in section 6.2.1.  6.2  Possible Confounder Variables  When analysing each occupational exposure for its association with bladder cancer, there are many other factors that could potentially act as confounding variables and thus need controlling for in the analysis in addition to those already matched upon. Information was sought on potential confounders in the BCCA questionnaire. This included information on who completed the questionnaire, ethnic origin, marital status, years of formal education, smoking habits and alcohol consumption habits of the patient. Caucasian men are known to be at greater risk of developing bladder cancer than men of other ethnicity (see section 3.1). Therefore a simple ethnicity variable (Caucasian/Non-Caucasian) was considered in the analysis. Smoking is also a known risk factor for bladder cancer. Therefore, many variables attempting to model lifetime exposure to smoking, in particular cigarette smoking, were considered in the analysis. The continuous variables were categorised, as there may not be a linear relationship between the variable and bladder cancer, with sensible cut-offs chosen. Most variables also have an unknown category for when the patient did not provide an answer. Variables considered were ever smoker versus never smoker (cigarettes, pipes, or cigars), cigarettes smoked per day (0, 1-19, 20-29, 30+), years smoked cigarettes (0, 1-29, 30-44, 45+), cigarette pack-years defined as the packs smoked per day multiplied by the years smoked, where 1 pack contains 20 cigarettes (0, 1-24, 25-49, 50+), and whether the patient quit smoking before diagnosis and if so, the number of years they have quit for (non-smoker, current smoker, 1-4, 5-9, 10+). The responses to some of these smoking variables were quite varied, particularly pack-years. These variables are not as  reliable or valid as possible as people may be unwilling or unable to provide true answers. Also, smoking habits differ across a patient's lifetime and this was not reflected in the questionnaire. Although alcohol consumption is not thought associated with bladder cancer, it is associated with many other cancer sites. The occurrence of malignant tumours of the oral cavity, pharynx, larynx, oesophagus and liver is causally related to the consumption of alcoholic beverages (IARC, 1987). Knowledge of whether the patient drinks alcohol or not, was the only alcohol variable considered. An alcohol score variable was recorded as the aggregation across beer, wine and spirits of the units drank per week multiplied by the years drank. Much of this information was unknown, especially for the controls, leading to much rnisclassification and some differential rnisclassification. The reported units per week are also liable to much error and fluctuated greatly across a patient's lifetime. An important variable in the analysis of questionnaire data is who completed the questionnaire. Proxy responders are known to not complete the questionnaire as well as the patient themselves and also tend to leave more questions unanswered. Therefore the simple variable, person completing the questionnaire (patient, proxy) was considered in the analysis. Educational level is related to occupation, age and income and associated with life-style confounders like smoking, diet, and access to healthcare. The education variable (< 8 years, 8-11 years, high school graduate, post secondary education) was considered in the analysis. Marital status is also related to age, occupation and life-style confounders and so the marital status variable (single, married or common-law, widowed, separated or divorced) was also considered in the analysis. Naturally age and year of diagnosis are adjusted for in the analysis by performing conditional logistic regression conditional upon the matched distributions of these variables.  6.2.1  Characteristics of Cases and Controls  Table 6.1 shows the characteristics of the cases and controls on the main possible confounding variables. The bladder cases are still slightly older than the controls after matching. Whereas the cases are almost uniformly distributed across year of diagnosis, the controls are more concentrated across earlier years, which is to be expected due to many cancer sites ceasing questionnaire collection before the end of the study. The average amount of work-years contributed by each case and control is relatively similar. Also, 0.3% of both cases and controls had no jobs. The bladder patients are more likely to be of Caucasian ethnicity and to have smoked. If they smoked cigarettes, bladder cancer patients were more likely to still be smokers at diagnosis. They also smoked slightly more cigarettes per day and for more years than the controls, tf the cases had quit by diagnosis, they had  quit for a shorter period of time than the controls. The patient himself was more likely to have completed the questionnaire if he was a bladder case than if he was a control. Cases and controls are relatively similar on marital status and education and whether they drink alcohol.  6.3  Developing the Base Model  A base model is required to model the probability of a subject being a case whilst taking into account the most important confounders and the matched variables. As described in section 2.2.5, conditional logistic regression is the most appropriate model for this situation. A parsimonious model that still explains the data is necessary so the model is more stable and more easily generalised. Backwards stepwise selection was the regression modelling technique implemented here. Maximum likelihood estimation was used to estimate the model parameters using the SAS procedure PHREG. The Wald statistic was used to test the hypothesis that a model parameter was zero and this was rejected when the resulting p-value was less than 0.20. The Wald statistic for a parameter is the square of the parameter estimate divided by its standard error. This is asymptotically distributed as a chi-square distribution (Hosmer and Lemeshow, 2000). However, the disadvantage of the Wald statistic is that for large parameter estimates, the estimated standard error is inflated, resulting in failure to reject the null hypothesis when the null hypothesis is false (Menard, 2002). For categorical variables, the p-value testing the global null hypothesis that the coefficient for each dummy variable was zero was considered. Stepwise selection strategies are common in regression modelling. It is possible for forward selection methods to exclude variables such as those involved in suppressor effects.  A suppressor effect is when  one variable may appear to have.a statistically significant effect only when another variable is controlled. Backwards selection methods may not miss these variables, as they are all included in the initial model. However, the method is sensitive to the choice of initial model. Research has shown that the choice of alpha level of 0.05 is too stringent, often excluding important variables from the model. Choosing a value for alpha in the range from 0.15 to 0.20 is highly recommended (Hosmer and Lemeshow, 2000). All the possible confounding variables (ethnic origin, marital status, education, who completed the questionnaire, smoking status, cigarettes per day, years smoked cigarettes, cigarette pack-year, years quit smoking, and alcohol status) were entered into the conditional logistic regression model where year of diagnosis and age at diagnosis were the strata variables. The variables were deleted from the model with the largest p-value in the following order: education, marital status, cigarette pack-year, cigarettes per day, smoking status, then years quit smoking.  6.3.1  The Base Model  The method described above resulted in a base model including the variables; who completed the questionnaire, years of smoking cigarettes, ethnic group and alcohol status. Table 6.2 shows the resulting odds-ratios for these variables in the base model. A feature of the questionnaire responses was that questionnaires completed by proxies were significantly less likely to be bladder cases than controls after taking into account age, year of diagnosis, ethnicity, and smoking and drinking habits. This difference is due to the prognosis for bladder cancer being much better than for other cancers in the control group. Thus bladder cancer patients are more willing or able to answer the questionnaire than other cancer patients. Patients reporting their ethnicity as non-Caucasian were less likely, although not significantly at a 5% level, to develop bladder cancer than other control cancers when taking all other important confounders into account. This is as expected as Caucasians are at greater risk of bladder cancer than other ethnic groups. Alcohol drinking was associated with a decreased risk of bladder cancer here (but ever drinking alone is not significant, p = 0.29), as alcohol drinking is a risk factor for other cancers serving as controls. There was a definite dose-response relationship between smoking and risk of bladder cancer. The risk was significantly increased with each increased category of cigarette smoking duration. Other models were tested and the previous base model was best in terms of log likelihood. Table 6.3 shows the log likelihood for the base model and some related models. The least significant variable in the base model was ethnicity. Adding ethnicity to model 1 excluding it was significant at the 20% level. Replacing the cigarette year variable with any other smoking variable did not improve the log likelihood. No remaining variables were significant (all had p-values > 0.3) when added to base model. When the more complex alcohol score variable was added to the model, or replaced the simple alcohol status variable, it was not significant at the 20% level. Many interaction terms were added to the base model such as cigarette years and alcohol status, and smoking status and alcohol status, but none were significant at the 20% level.  6.3.2  Consequences of the Missing Data Exclusions on the Base Model  tn section 5.2.1 705 subjects were excluded from analysis due to missing occupational information. If these had not been deleted, matching on age and year of diagnosis would result in 1,125 cases and 8,492 controls. Their characteristics on the possible confounding variables are compared with the cases and controls after exclusions in table 6.4. The distributions across all variables for cases and controls matched from all subjects  and after exclusions are very similar. The only differences are that the cases and controls after exclusions are slightly younger and a marginally greater proportion reported jobs (99.7%, rather than 99.3%). A decrease in the average age is expected, as the older patients may be more susceptible to recall difficulties.  The  proportion of questionnaires completed by the patient increases after exclusions as much of the missing occupational data was from proxy questionnaires. The proportion of unknown responses in the variables is also slightly decreased after exclusions, as patients who did not complete their occupational histories often also did not complete the lifestyle factor questions. Table 6.5 shows the distribution of cancer sites across the controls before and after exclusions. The distribution of cancer sites comprising the control group remains very similar after excluding subjects with missing occupational information. Using the same method as in the previous section, the cases and controls without exclusions resulted in the same variables in the base model. Table 6.6 shows base model comparison of the significant confounding variables. The odds-ratios differ only slightly before and after exclusions. The risk for non-Caucasian patients is just significantly decreased with all subjects included, than with only those with complete occupational data. The alcohol score variable is actually significant (p-value = 0.17) when added to the base model before exclusions. However, the alcohol score is not a reliable variable as discussed in section 6.2. Also, the Pearson correlation between alcohol score and alcohol status is quite high at 0.63. Replacing the alcohol status variable by the alcohol score variable does not improve the log-likelihood. Hence, the base model resulting from the cases and controls was considered approximately the same regardless of missing occupational data exclusions.  6.4  Testing the Agents Individually  Now the 9,119 cases and controls are identified, the cumulative exposure estimates from section 5 for these patients can be used to analyse each agent's relation with bladder cancer risk whilst taking into account the important confounders via the base model. Firstly, those agents the cases and controls are exposed to are summarised in section 6.4.1. Each agent is tested separately using conditional logistic regression and the base model, but there are many possible ways to analyse the cumulative exposure variable. First section 6.4.2 introduces an indicator ever/never exposure variable. As it is unlikely that the continuous cumulative exposure variable has a linear relationship with bladder cancer risk, the cumulative exposure is split into tertiles according to the exposed  controls and a dose-response analysis is performed as described in section 6.4.3. Another consideration is that when testing many agents, there are bound to be significant results by chance alone. This can be taken into account with multiple testing techniques, where various methods are described in section 6.4.4.  6.4.1  Agents with Exposed Cases  There needs to be sufficient bladder cases exposed to an agent to enable analysis of the exposure-disease relationship and to have confidence in the results. Here exposed means that the cumulative exposure estimate is greater than zero. Of all 11,882 agents that any patients included in the study were exposed to (see section 5.2.9), only 8,986 agents had at least one bladder cancer case exposed. Table 6.7 shows the distribution of bladder cases exposed to the 8,986 agents. On average 40 bladder cases were exposed to each agent with a standard deviation of 91 and a median of 5. Of all 8,986 agents, each patient was exposed to an average and standard deviation number of agents of 440 and 390 respectively. The median number of agents exposed to was 340. The 5,699 agents with at least 3 bladder cases exposed are considered for analysis. The 3,450 with at least 9 bladder cases exposed are considered for the dose-response analysis.  6.4.2  Ever/never  Any exposure versus no exposure is a simple indicator variable to analyse and interpret. However, in this study a cumulative exposure above zero does not mean the subject was ever exposed. All exposures in the NIOSH JEM were given a probability of exposure, and the majority of occupation-agent combinations had a low exposure probability estimate or were based on small numbers. The ever/never of the cumulative exposure indicates whether the patient ever had a probability above zero of being exposed (across all job translations) to the agent. Alternatively, the ever/never variable indicates whether NIOSH studied any of the patient's US job-translations and found any employees exposed to that agent in their sample. The ever/never analysis is restricted to only those 5,699 chemicals with at least three bladder cancer cases ever exposed to that chemical to ensure sufficient numbers for analysis.  6.4.3  Dose-Response  Each agent can be tested in the conditional logistic regression base model using the continuous cumulative exposure variable. However, this assumes the association between cumulative exposure and bladder cancer risk is linear. As this relationship is unlikely to be linear, the cumulative exposure is categorised instead.  A dose-response relationship can be investigated by categorising the cumulative exposure according to biological risk levels, e.g. low, medium and high. However, there are no biological cut-off values that will apply to all agents separately or as a group. Instead the groups are devised based on the cumulative exposure distributions. When the groups are divided according to the controls' cumulative exposures, the null hypothesis is that if there were no association with bladder cancer risk then the cases should separate equally into the groups and no differences between the cases and controls could be detected. So, the unexposed constitute one group and the exposed are divided into tertiles according to the cumulative exposures of the controls. Thus four groups are created: unexposed (reference group), low exposure (lower 33% of exposed controls), medium exposure (mid 33% of exposed controls), and high exposure (top 33% of exposed controls). If there is a truly increasing dose-response relationship between the agent and bladder cancer then the low, medium and high exposure groups should have an odds-ratio significantly greater than 1 and with the risk increasing across the groups. An agent is considered a carcinogen here if the true dose-response relationship with cumulative exposure is increasing or has a threshold so the risk remains relatively flat until the threshold where it increases significantly. Additionally, an ordinal test was performed to test for a linearly increasing risk across the four exposure groups. This involved assigning labels of 0, 1, 2 and 3 to the reference, low, medium and high exposure groups respectively and tested the hypothesis that the slope amongst them in the conditional logistic regression model was zero. Assigning group medians as the ordinal score was considered, but as the scale of cumulative exposure is not linear with risk then the simple 0, 1, 2, 3 scoring was preferred. The dose-response analysis is restricted to only those chemicals with at least nine bladder cancer cases ever being exposed to that chemical to ensure sufficient numbers for the low, medium and high exposure groups for analysis.  6.4.4  Multiple Comparisons  The p-value resulting from testing the association of one chemical exposure with the incidence of bladder cancer, is the type I error, the probability of a false positive. However, when multiple chemicals are tested and multiple p-values result, many positive results are expected by chance alone. If multiple chemicals are tested, but interest lies in looking at the results of only one, then this is not a concern. However, if a list of possible bladder cancer carcinogens is required, as is the case here, then the multiple testing must be taken into account. The most conservative way to allow for multiple comparisons is to make a Bonferroni style adjustment to control the Family-Wise Error Rate (FWER). The FWER is the probability of at least one false positive  from all chemicals tested. The Bonferroni adjustment involves multiplying each p-value by the number of chemicals tested and comparing this to the desired FWER, usually 5%. Effectively chemical exposures are declared significant when their p-values are extremely small. This method lacks power and there will many true bladder carcinogens that do not get detected. Hochberg (1988) provides strong control (under all configurations of the true and false hypotheses) of the FWER, but with greater power. If m hypotheses H\, Hi, • • •, H m are tested with corresponding p-values Pi, P2, • • •, P m , then the p-values are ordered  < P( 2 ) < • • • < P( m ) and H^ denotes the null hypothesis  corresponding to P^y Hochberg's step-up procedure controls the FWER at a rate of a as follows: let k be the largest i for which P ^ = then reject all H^ i = 1,2,...  m_|f 1_i;  ,k.  Control of the FWER is a conservative requirement that is often not necessary. Alternatively, the False Discovery Rate (FDR) can be controlled allowing more power than the FWER controlling procedures. The FDR is the expected rate of false positives among the rejected hypotheses. Whereas the Hochberg procedure guarantees that the probability of at least one false positive is less than a, the Benjamini and Hochberg (1995) controls the rate of false discoveries at an expected value of a%. Thus, in reality the true rate of false discoveries could be much larger (or smaller) than a. Although the Benjamini and Hochberg procedure has greater power than the Hochberg procedure, the overall type I error could be much higher than a. Note that when the FWER is controlled at rate a, the expected rate of false positives (FDR) is less than a%. Benjamini and Hochberg control the FDR at a rate of a when the hypotheses are independent as follows: let k be the largest i for which  =  then reject all H^ i — 1, 2 , . . . , k. Controlling for the FDR essentially declares the same or, more usually, a greater number of hypothesises significant than controlling the FWER. The FDR is intuitively appealing here as it looks at the error rate in the list of chemicals selected, and has greater power than other procedures, so the Benjamini and Hochberg procedure is favoured in this thesis. Although the hypotheses to be tested here are not all independent, the Hochberg and Benjamini and Hochberg procedures should provide a guideline to the control of the multiplicity problem.  6.5  Testing the Agents in Groups: Principal Components Analysis  NIOSH provided exposure estimates for many agents, many of which are related to each other or have the same exposure probabilities across all jobs. Firstly, an attempt was made to construct natural groups from the agents. This organisation task has not been completed by NfOSH for their agents. The CAS (Chemical Abstracts Service) Registry is the largest substance identification system in existence. The registry contains records for more than 23 million organic and inorganic substances each assigned a unique CAS registry number, yet there is no defined grouping structure to the CAS registry numbers. Grouping the NIOSH agents would be difficult because agents with similar names could have different compositions or functions, or ones with similar functions or make-up could have different names. To distinguish between all possible different carcinogenic effects of chemicals, the groups would have to be quite small so that each agent is almost considered separately anyway. The process would require experts, be very complex, costly, and time consuming. Given that grouping based on the agent names would be complex; grouping based on the cumulative exposure distributions across subjects can be undertaken. One approach to group the agents in this way is via principal component analysis. The ideas of principal component analysis are discussed in section 6.5.1. The components extracted can then be used in the conditional logistic regression model and the analysis approach taken is described in section 6.5.2.  6.5.1  Principal Component Analysis  The aim of principal component analysis (PCA) is to reduce the dimensionality of a data set which consists of a large number of interrelated variables, while retaining as much as possible of the variation present in the data set. This is achieved by transforming to a new set of variables, the principal components (PCs), which are uncorrelated, and which are ordered so that the first few retain most of the variation present in all the original variables (Jolliffe, 1986). The terms component and factor are often used interchangeably in PCA. The first PC is found by seeking a linear combination of the original variables that extracts the maximum variation from the data. This variation is removed and the second PC is sought explaining the maximum variation remaining in the data, and so on. Rotation methods serve to make the resulting PCs more interpretable. Varimax rotation is an orthogonal rotation method that minimises the number of variables that have high component loadings on each PC so that each PC has variables with either large or small loadings. The component loadings produced are the correlation coefficients between the variables and  PCs. Variables highly correlated with a PC are the defining constituents of that PC. A common rule is that a component loading is considered "weak" is less than 0.4 and "strong" if greater than 0.6. The percent of variation explained by a PC is the average of the squared component loadings across all variables. The total variation explained by a PC is its eigenvalue. The important PCs to extract are those that combined account for most of the variation in the data. The Kaiser rule (Kaiser, 1960) is the most commonly used method to decide which PCs to extract. This criterion recommends extracting those PCs with eigenvalues of at least one. The variable scores are standardised across subjects and combined according to the linear combination of variables described by the component to calculate the component score for each subject. These components scores can then be used in place of the original variable scores in other analysis such as logistic regression. The multicollinearity problems no longer exists for multiple regression as the components are independent of each other and the dimensionality of the regression has been reduced considerably. It is important to include all variables relevant to uncovering the latent structure in PCA and exclude irrelevant variables. PCA does not require multivariate normality apart from for significance testing. Including more variables into the PCA is not a good idea when there is a possibility of suboptimal factor solutions ("bloated factors"). Too many similar variables will mask the true underlying factors. To avoid suboptimization, PCA should start with a small set of the most defensible variables that represent the range of each component. For algebraic reasons it is essential that there are more subjects than variables. There should be at least twice as many subjects as variables (Kline, 1994).  6.5.2  Grouping of Agents and Analysis Approach  There were many more possible agents (11,132) than subjects (9,119) to include in a PCA to uncover the latent structure of the data and many of the agents were not thought to be possible bladder carcinogens. The agents with the greatest potential of being bladder carcinogens were instead identified via the individual testing described in section 6.4 and principal components analysis performed on the continuous cumulative exposures of this subset. The cumulative exposure across these selected agents may be correlated as some patients were exposed to more than one agent at a time with some agents always occurring together in certain jobs. PCA with varimax rotation was performed.  Those PCs with eigenvalues greater than one were  extracted. Agents were assigned to the component with which they had the greatest component loading and a component loading was considered "weak" if less than 0.4 and "strong" if greater than 0.6. Component scores were also calculated for each patient, and these each have a zero mean and unit standard deviation  across patients. Component cumulative exposures were created using a weighted average of the cumulative exposures of those agents assigned to each component using the component loading as the weighting. The dose-response and ordinal analysis could then be repeated comparing components rather than the agents individually. Also, an ever/never style analysis was performed where a dichotomous variable for each component indicated whether the patient was ever exposed to any of the agents assigned to that component, versus the patient was never exposed to the assigned agents. Similarly, a dichotomous variable for each component indicated whether the patient was ever exposed to all of the agents assigned to that component, versus the patient was never exposed to at least one of the assigned agents. The analyses of ordinal dose-response, ever exposed to any, and ever exposed to all, were also each combined in a multivariate conditional logistic regression. The correlations amongst the newly created variables were checked for no significant multicollinearity.  Chapter 7  Results The results are reported in the order described in chapter 6. Firstly section 7.1 reports individual agent results. The agents selected that exhibit a significant association with bladder cancer risk are described in section 7.2. Section 7.3 then reports the results of the principal component analysis on the selected agents. The selected agents and their properties are discussed further in section 7.4. Finally, section 7.5 also provides a comparison of the results from this study for those IARC classified carcinogens with bladder cancer associations and those possible bladder carcinogens identified by Siemiatycki (1991).  7.1  Individual Agent Results  Section 7.1.1 reports the ever versus never exposed results for the 5,699 agents with at least 3 bladder cases exposed. Section 7.1.2 reports the dose response results for those 3,450 agents with at least 9 bladder cases exposed. Both the ever/never and dose-response results for those 3,450 agents with at least 9 bladder cases exposed are listed in the appendix in table A.l. Note that to save space, only the NIOSH agent codes are given in the table, so the associated NIOSH agent names can be found at the following website: www.cdc.gov/noes/srch-noes.html.  7.1.1  Ever/never  Table 7.1 summarises the results for the 5,699 agents with at least 3 bladder cases exposed with the ever versus never exposure as the exposure variable tested. A significantly (at the 5% level) increased odds-ratio was seen for 646 agents, of which 163 have odds-ratios above 2. Table 7.2 lists the 7 agents that remain significant at a 5% level after adjusting for multiplicity using the Benjamini and Hochberg procedure. The top 2 agents (2, 5- pyrrolidinedione, l-(2-((2-((2-((2-aminoethyl)amino)ethyl)amino)ethyl) amino)ethyl)-,  monopolyisobutenyl derivs, and natural gas, liquified) with the smallest p-values are also significant at the 5% level for the Hochberg multiple testing procedure.  7.1.2  Dose-Response  The cumulative exposure estimates for the 3,450 agents with at least 9 bladder cases exposed are divided into tertiles according to the controls. To give an idea of the values of these cut-off values, the average and standard deviation of the cumulative exposure estimate for the 33rd percentile was 0.05 and 0.14 respectively, and the average and standard deviation for the 67th percentile was 0.25 and 0.48 respectively. Table 7.3 shows the distribution of p-values for the dose-response variables. The p-values and oddsratios associated with the ordinal test of a linear dose-response trend (by assigning scores of 0, 1, 2 and 3 to the non-exposed, low, medium, and high cumulative exposures respectively) axe included in the table. Of the 3,450 agents, 350 had a significant (5% level) linear increasing dose-response relationship; 22 and 2 of which were significant at the 5% level after adjusting for multiplicity using the Hochberg and Benjamini and Hochberg multiple testing procedures respectively. The results for the top 22 significant agents are shown later in table 7.5. The top 2 significant agents are 1, 2-ethanediamine, reaction products with chlorinated isobutylene homopolymer, and natural gas, liquified. Table 7.3 also shows the distribution of p-values for the odds-ratio for the low, medium and high exposure groups. There were 377 agents with a significantly (5% level) increased risk for the low exposure group, 290 agents with a significantly (5% level) increased risk for the medium exposure group, and 215 agents with a significantly (5% level) increased risk for the high exposure group. None of the p-values were significant (5% level) after adjusting for multiple testing using the Hochberg or Hochberg and Benjamini procedures when looking at the results for each exposure group separately. Table 7.4 compares the results from the ever versus never exposure and dose-response analysis. Significantly (5% level) increased odds-ratios for both the ever versus never exposure variable and ordinal variable were seen for 307 agents. Also, 107 agents had significantly increased odds-ratios for both the ever/never and ordinal variable at a 1% level.  7.2  Selecting Significant Associations  It is useful to select a small subset of agents that exhibit sufficient evidence indicating possible bladder carcinogenic properties that warrant further research. Many of the agents tested exhibited some positive relationship with bladder cancer risk. To provide a shorter selected list of agents with less chance of false  positives, those agents with a significantly increased ever exposure risk or a significantly increasing linear doseresponse relationship after separately adjusting for multiplicity via the Hochberg and Benjamini procedure were selected. In order to have adequate numbers for dose-response analysis, the selection process was restricted to those 3,450 agents with at least 9 cases exposed. This enabled greater power in the ever/never analysis meaning that now 25 and 4 agents were significant at the 5% level after adjusting for multiplicity using the Hochberg and Benjamini and Hochberg multiple testing procedures respectively. This selection procedure resulted in 30 selected agents, as listed in table 7.5. All of the agents had an ever/never p-value less than 0.2% and ordinal p-value less than 0.8%. Of the agents selected 20 had ever/never odds-ratios greater than 1.3. Only 9 agents did not have all three dose-response levels with oddsratios significantly greater than one at a 20% level, whereas 22 agents did not have all three dose-response levels with odds-ratios significantly greater than one at a 5% level.  7.2.1  Linear Exposure  As mentioned previously in section 5.2.9, the distribution of the positive cumulative exposures for most agents is highly right-skewed. Figure 7.1 shows the histograms of the positive cumulative exposures for each of the 30 selected agents. However, a logarithm transformation results in normally distributed positive cumulative exposures for most agents. A linear conditional logistic regression fit through the original cumulative exposures would be highly dependent on extreme observations. A linear regression through the transformed cumulative exposures would be a much more robust fit. However, a logarithmic transformation leaves the question of what to do about the zeros, the non-exposed patients. The results of a linear regression fit would vary depending on the score assigned to the non-exposed patients. The Box-Cox transformation (Box and Cox, 1964) is used instead as it tends to a logarithmic transformation as A tends to zero. The box-cox transformation is as follows: x(X) = <  if A / 0 log(X) if A = 0  A value of A of 1/100 or 0.01 was chosen as sufficiently small to transform the distributions of the original positive cumulative exposures to be approximately normal. Figure 7.2 shows the histograms of the transformed positive cumulative exposures for the 30 selected agents. Table 7.6 shows the results of fitting a straight line through these transformed cumulative exposures for the top 30 agents. As expected, all 30 agents have a very significant increasing linear trend, all with a p-value less than 0.0014. If all 3,450 agents were tested, then the top 4 agents (sulfonic acids, petroleum, magnesium salts; natural gas liquefied; phosphorodithioic acid, mixed O, 0-bis(sec-Bu and 1,3-dimethylbutyl) esters, zinc salts; 1, 2-ethanediamine,  reaction products with chlorinated isobutylene homopolymer) would remain significant after adjusting for multiplicity using the Hochberg procedure.  7.3  Grouped Agent Results: Principal Components Analysis  The exposures to the chosen 30 significant agents may not be independent. For example, some agents may always occur together, so if a patient was exposed to one agent then they were also exposed to the partnering ones. It would then be difficult to distinguish which agent is truly associated with the bladder cancer risk. Also, the analysis of the chosen 30 agents has been only univariate thus far. A multivariate analysis could be performed to allow the effects of an agent's exposure to be jointly adjusted for all the effects of the other agent exposures. The patients' cumulative exposures to many of the selected agents are highly correlated with each other (12 of the agents have a Pearson correlation coefficient greater than 0.9 with at least one other agent), and hence multicollinearity is a potential problem. Therefore, principal components analysis was performed to examine the relationships between the agents. Performing principal components analysis on the 30 agents resulted in 10 components with an eigenvalue greater than one. Table 7.7 lists the component loadings for each agent and identifies those loadings greater than 0.4. The largest component accounts for 26.3% of the total variance, and the first 3 components combined account for more than 50% of the total variance. The component scores were used to create a cumulative exposure variable for each component by using the component scores of those agents associated with a component as the weights and computing a weighted average of the agents' cumulative exposure. The following sections provide the results of a dose-response analysis, an 'any exposure' analysis and an 'all exposure' analysis.  7.3.1  Component Groups - Dose-Response  Table 7.8 shows the results of the dose-response analysis on the component groups. As expected all components show a significantly linearly increasing dose-response relationship. Table 7.9 shows that components 2, 4 and 9 remain significant (5% level) after backwards selection when all 10 component ordinal variables are entered into a multivariate conditional logistic regression model.  7.3.2  Component Groups - Any Exposure  Table 7.10 shows the results of the 'any exposure' analysis on the component groups where 'any exposure' is defined as cumulative exposure greater than zero for any members of the component. As expected all com-  ponents show a significantly increased risk if a patient was ever exposed to any members of the component. Table 7.11 shows that components 3, 6 and 10 are significant (5% level) from backwards selection when all 10 component 'any exposure' variables are entered into a multivariate conditional logistic regression model.  7.3.3  Component Groups - All Exposure  Table 7.12 shows the results of the 'all exposure' analysis on the component groups where 'all exposure' is defined as cumulative exposure greater than zero to all of the members of the component. As expected all. the components show a significantly increasing risk when a patient is exposed to-all members of the component. The odds-ratio for the 25 patients ever exposed to all 5 agents in component 2 is large at 3.11. Table 7.13 shows that components 1 and 2 remain significant (5% level) after backwards selection when all 10 component ordinal variables are entered into a multivariate conditional logistic regression model.  7.4  Properties of the Selected Agents  The cumulative exposures of the agents were derived from a JEM that distinguished exposure probabilities according to job type. So it was suspected that the selected 30 agents would be grouped in some way according to jobs. If workers in a job are exposed to a particular agent that is a bladder carcinogen, but they are always exposed to other agents alongside the carcinogen, then it would be difficult to distinguish between the agents. This effect can be seen to some extent in the selected agents. Figure 7.3 shows a breakdown of the total cumulative exposure to each agent contributed by all study patients according to US job (industry and occupation pair). As an example, 85% of the total cumulative exposure to X2307 (alkenes, C15-18 alpha-, reaction products with sulfurized dodecylphenol calcium salt, sulfurized) experienced by the 9,119 patients was due to employment in a timber cutting or logging occupation in the logging industry. All agents comprising the first principal component have a substantial proportion of their cumulative exposure due to employment in this job. In fact, timber cutting or logging occupations in the logging industry accounted for the largest proportion (32%) of the total cumulative exposure to all 30 agents. Interestingly, all cumulative exposure to Y1006 (natural gas, liquified) was due to employment in gasoline service station related occupations. Furthermore, a proportion of the participants of the NIOSH NOES study employed in this occupational group were exposed to 28 of the agents selected, all but 73075 (SN, tin - MF unknown) and 90590 (clay, nec). It must be noted that these jobs are the US classifications, and there may be more than one possible Canadian job translation equivalent. However, the gasoline service station related occupations only translate to one Canadian equivalent; Gasoline Service Stations - Service  Station Attendants. As expected, many of the agents seem to form principal component groupings according to the distribution of the US job equivalents they occurred in. The cumulative exposures across all 9,119 patients for X2305 (2,5-pyrrolidinedione, l-(2-((2-((2-((2-aminoethyl)amino)ethyl)amino)ethyl)amino)ethyl)-, monopolyisobutenyl dervis., reaction pr) and X2308 (sulfonic acids, petroleum, magnesium salts) comprising the fourth principal component have a correlation of 0.999. Their similarity can also be seen across their job distributions. Table 7.14 lists the agents in order of the principal components as per table 7.7. The US job that contributes most to the cumulative exposure of that agent and what percentage it contributes (as seen in figure 7.3) is given. Additionally, it provides the JEM proportion of employees exposed to that agent in the listed job. For example, just over 50% of people in the NIOSH NOES study employed in timber cutting and logging occupations were exposed to each of the agents comprising the first principal component. However, a very small proportion (1-3%) of people employed in gasoline service station related occupations were exposed to each of the agents comprising the second principal component. Table 7.14 also lists the number of bladder cancer cases exposed to each agent and the CAS (Chemical Abstracts Service) number for each NIOSH agent if applicable. The CAS number was used to identify an IARC classification, which is also provided if available. An IARC classification could not be found for most agents mainly due to the complexity of the agents involved. There is very little information available on many of these complex chemicals.  7.4.1  Discussion of the Selected Agents  First Principal Component. Most (52%) of the cumulative exposure to agents in this component was due to employment in timber cutting and logging occupations.  Just over half of the NIOSH NOES study  participants employed in timber cutting and logging occupations were exposed to each of these agents. X2298 (phenol, dodecyl-, sulfurized, carbonates, calcium salts, overbased) has not been classified by IARC. It is an ingredient (1-5%) in "Energol CLO 50M" (diesel engine oil). X2293 (sulfonic acids, petroleum, calcium salts, overbased) is not classified by IARC, but is 100% of the ingredients of "Syndustrial P Compressor Oil (All Grades)". X2295 (phosphorodithioic acid, O, 0-bis(2-ethylhexyl) ester, zinc salt) is not classified by IARC, but comprises 0.5-1% of "CHAMPION SUPER GRADE 5W20 (4229)" (petroleum based lubricating oil): X2689 (1, 2-ethanediamine, reaction products with chlorinated isobuylene homopolymer) is not classified by IARC, but comprises 40-50% of "VANLUBE 869" (industrial antioxidant). Here 60713 (products of combustion - gasoline (leaded)) is considered the same IARC classification, possibly carcinogenic, as gasoline engine exhaust (IARC monographs Vol.: 46 (1989) (p. 41)). X5263 (products of combustion - jet fuel  and gasoline, unleaded) has not been classified by IARC. However, as gasoline engine exhaust is classified as possibly carcinogenic and jet fuel is classified as not classifiable, then X5263 may have carcinogenic properties. The agents comprising the first principal component seem to be petroleum or mineral oil based, and occur frequently in the logging industry. Second Principal Component. Most (52%) of the cumulative exposure to agents in this component was due to employment in gasoline service station related occupations. Very few (less than 4%) of the NIOSH NOES study participants employed in gasoline service station related occupations were exposed to these agents. Few bladder cancer cases were exposed to these agents, with the maximum being 35 cases exposed to X1401 (2-butenedioic acid (E)-, polymer with 1,3-butadiene and ethenylbenzene). This agent has not been classified by IARC, but ethenylbenzene is classified as possibly carcinogenic, and 1,3-butadiene is classified as probably carcinogenic. Thus, agent X1401 may have possible carcinogenic properties. 83048 (nonylphenoxyethanol) is not classified by TARC, however it is an alkylphenol ethoxylate. Alkylphenol ethoxylates are used in industrial detergents (such as those used for wool washing and metal finishing), in some industrial processes, and in some liquid clothes detergents (Warhurst, 1995). Alkylphenols are an environmental concern as they do not break down in the environment and accumulate in rivers, fish and birds, and they have oestrogenic properties. The effects of nonylphenol on cultured human breast cells (Soto et al., 1991) also led to health concerns in humans. Subsequently, many European countries have brought in controls on alkylphenols, and Switzerland has banned the use of all alkylphenol ethoxylates. X4267 (ether, tert - butyl methyl) is classified as not classifiable by TARC. Methyl tert-butyl ether is a volatile synthetic chemical that has been used widely since the 1980s in proportions up to 15% as a component of gasolines for its octane-enhancing and air pollution-reducing properties. In service stations where fuels containing > 10% methyl tert-butyl ether are delivered, the average concentration to which attendants are exposed is about 0.5 ppm (2 mg/m3) (IARC monographs Vol.: 73 (1999) (p. 339)). No epidemiological studies have directly addressed the relationship between methyl tert-butyl ether exposure and human cancer risk. However, inhalation of methyl tert-butyl ether resulted in increased incidence of renal tubular tumours in male rats. Perhaps the relationship seen between the cumulative exposure to agents in the second component and bladder cancer risk in this study is due to the possible carcinogenic effects of X4267 (ether, tert - butyl methyl). Alternatively, some other chemical exposures involved in the gasoline service station attendant job could make these employees at greater risk of bladder cancer. Third Principal Component. Much (27%) of the total cumulative exposure to this component comes from employment as a plumber pipefitter and streamfitter apprentice in the construction industry. Agent  90320 (asphalt) is classified by IARC as not classifiable (IARC monographs Supplement 7 (1987) (p. 133)). There have been no epidemiological studies looking directly at the association with asphalt exposure and human cancer risk. However, a cohort study of US roofers indicated an increased risk for cancer of the lung and suggests increased risks for bladder cancer. The asphalt group is on the priority list of agents to consider in future IARC monographs due to several ongoing epidemiological studies. For example, Randem, et al. (2003) recently found increased lung cancer incidence rates in a cohort of male Norwegian asphalt workers. Siemiatycki (1991) also found a significantly increased risk of bladder cancer for substantial exposure to asphalt. The relationship seen between the cumulative exposure to agents in the third component and bladder cancer risk in this study could be due to the possible carcinogenic effects of 90320 (asphalt). Fourth Principal Component. Much (25%) of the total cumulative exposure to this component is due to employment as a carpenter in the ship and boat building industry. A further 23% of the total cumulative exposure is due to employment as a miscellaneous electronic equipment repairer in the pulp paper and paperboard mill industry, and 18% of the total cumulative exposure to this component is due to employment as a heavy truck driver in the trucking service industry. Fifth Principal Component. X1075 (phosphorodithioic acid, 0-(2-ethylhexyl) O-isobutyl ester, zinc salt) is not classified by IARC, but is <10% of the ingredients of "multi-purpose lubricant (dri-side)". Agent 36955 (hexane) is not classified by IARC, but is chemical made from crude oil and often used to produce solvents. Sixth Principal Component. M1150 (cyclohexylamine, n - ethyl -) is not classified by IARC, although cyclohexylamine is not classifiable (IARC monographs Supplement 7: (1987) (p. 178)). The classified IARC group is for cyclamates, which are artificial sweeteners. The IARC states that the evidence that the risk of bladder cancer is increased among users of artificial sweeteners is inconsistent. Exposure to M1150 could have associations with increased risk of bladder cancer. M0984 (ethanol, 2-(2-(2-butoxyethoxy) ethoxy)-) has not been classified by IARC, but it is a triethylene glycol ether. Some monoethylene glycol ethers are nominated for IARC review, so it is possible that M0984 could have some carcinogenic effects. Seventh Principal Component. Agent 90590 (clay, nec) has not been classified by IARC, however Siemiatycki (1991) found a significantly increased risk of bladder cancer for ever exposure to clay dust. Perhaps inhalation of the dust of agent 90590 has an effect on bladder cancer development here. Agent T1475 (solvent refined heavy paraffinic distillate (petroleum)) has been classified by IARC. It is either classified as definitely carcinogenic or not classifiable depending upon whether it is untreated and mildly treated mineral oil or a highly-refined mineral oil respectively (IARC monographs Vol.: 33 (1984) (p. 87)).  Eighth Principal Component. Cumulative exposure to T1909 (nonylphenol ethylene oxide adduct) was mostly due (64%) to employment as a lathe and turning machine set-up operator in the ship and boat building industry. It has not been classified by IARC, but it is also an alkylphenol ethoxylate as agent 83048 is from the second component. Ninth Principal Component. Agent 92500 (oil, hydraulic) is a mineral oil and depending upon whether it is untreated and mildly treated or highly-refined, then it is classified as definitely carcinogenic or not classifiable respectively (IARC monographs Vol.: 33 (1984) (p. 87)). Agent P0620 (impact noise) is probably an example of an agent that always occurs alongside the possible carcinogen (here, hydraulic oil). Tenth Principal Component. Most (51%) of the total cumulative exposure to X1894 (2-propenoic acid, 2-me-, C12 ester, poly w/ C16 2me2propenoate, iso-CIO 2me2propenoate, me 2me2propenoate, C18 2me2propenoate, C14 2me2propenoate) is due to employment as a power plant operator in a hospital. All of the NfOSH NOES subjects employed in this job were exposed to agent X1894. A further 23% of the total cumulative exposure is due to employment as a knitting, looping, taping, and weaving machine operator in the apparel and accessories (except knit) industry.  7.5  IARC and Siemiatycki Results Comparison  It would be useful to see how the results compare for those agents that are already considered bladder carcinogens. Table 7.15 shows table 3.2 from section 3.3 updated with corresponding agent results where possible. The chemicals listed are those IARC has classified that include bladder as one the cancer sites affected by the chemical. Most chemicals were translated into a NIOSH equivalent via the CAS number. When there were multiple CAS numbers for the chemical, or multiple NfOSH equivalents, then all are provided. Often there were no NIOSH equivalent chemicals. Often if there was a NIOSH equivalent chemical then it had very few cases exposed possibly because the use of the chemical had been restricted. The results for the IARC classified definitely carcinogenic chemicals do not seem to support that classification, although the odds-ratios are not significant and the numbers are small. It could be the case that these chemicals are more carcinogenic for other cancer sites so are not showing a result for bladder. However, the results for the fARC classified possibly carcinogenic chemicals are much more consistent. Lead is the only possibly carcinogenic classified agent to show consistent results. Table 7.16 shows the Siemiatycki potential bladder carcinogens from table 3.3 updated with corresponding agent results where possible. Finding equivalent NIOSH results was more difficult here as often Siemiatycki would group chemicals in broad categories. There were then no such NfOSH categories to com-  pare with. The results in this study were consistent with Siemiatycki's for titanium dioxide, engine emissions, diesel engine emissions, calcium carbonate, formaldehyde, and asphalt. Other agents showed results similar to Siemiatycki's, but were not statistically significant, often due to small numbers.  Chapter 8  Discussion Identifying occupational carcinogens is a difficult task. Cancer is a chronic disease, so a long time period may elapse between occupational carcinogen exposure and cancer symptoms. In order to observe enough study subjects that develop cancer, estimation of the occupational exposures and other confounders is often imprecise or may involve many assumptions. Despite some concerns over the validity of epidemiological studies, it is clear that formal attempts at examining the human experience are required when extrapolating the results of animal experiments is difficult. Whilst many assumptions were made in this study, and the methodology and JEM applied not perfectly precise, we have been as rigorous as possible to provide as valid results as we could given the difficult problem and data available. In testing so many chemical exposures we were bound to find many positive associations by chance alone, however allowance for multiplicity was made in the calculations. Section 8.1 provides a summary of the study's findings, fn section 8.2 issues of bias and confounding are discussed and the validity of the study methodology is discussed in section 8.3. Further research directions are outlined in section 8.4.  8.1  Summary of Findings  Many of the patients in our study were potentially exposed to many of the NfOSH agents investigated. There were 5,699 agents for which at least 3 bladder cancer cases had potentially been exposed, of which 3,450 had at least 9 bladder cancer cases exposed. Positive associations with bladder cancer were seen in many agents. A significantly (5% level) increased odds-ratio was seen for ever exposure to 646 agents. A significantly (5% level) increasing linear dose-response was seen in 350 agents. A subset of 30 agents was selected as exhibiting sufficient results to indicate possible bladder carcino-  genie properties requiring further research. The agent exposures were correlated, mainly amongst jobs, and 10 independent groups of agents were identified. Most of the selected agents seemed to have some petroleum or mineral oil base. IARC has classified occupational exposures in petroleum refining (IARC monographs Vol. 45; 1989) and diesel engine exhaust (IARC monographs Vol. 46; 1989) as probably carcinogenic, marine diesel fuel (IARC monographs Vol. 45; 1989), gasoline engine exhaust (IARC monographs Vol. 46; 1989), heavy residual fuel oils (IARC monographs Vol. 45; 1989), and gasoline (IARC monographs Vol. 45; 1989) as possibly carcinogenic. Excess bladder cancer risk has also been observed frequently among truck and motor vehicle drivers (Silverman, 1989). This cancer risk is thought to be partly due to exposure to the polycyclic aromatic hydrocarbons (PAHs) contained in exhaust emissions. A significant (5% level) odds-ratio of 1.92 for ever exposure to the PAH benz(a)anthracene was found in this study. Only a small number of bladder cancer cases were ever exposed to the other PAHs. Most of the cumulative exposures to the 30 agents occurred due to employment in timber cutting and logging occupations, ship and boat building and construction industries, and occupations involving motor vehicles (gasoline service station attendant, mechanic, truck driver, motor vehicle production). Significantly increased bladder cancer risks were seen for these occupations and industries when an analysis was performed on the same BCCA occupational data as in this study, but when looking at ever or usual employment in an occupation or industry (Band et al, 2004). We hope to have provided some further insight into what particular chemicals within these occupations may contribute to the increased bladder cancer risk.  8.2  Bias and Confounding  Issues of bias and confounding are discussed further in the following sections.  8.2.1  Comparability of Source Populations for Cases and Controls  The bladder cases were obtained from a well-defined source population of males resident in BC aged over 20 when diagnosed with cancer between 1983 and 1990 and ascertained by BCCA. BCCA receives information from every newly diagnosed cancer case in the province. So the bladder cases are approximately an exhaustive group of eligible subjects diagnosed with bladder cancer within the study period. The controls came from the same source population and are almost an exhaustive group of those patients diagnosed with cancer within the study period, apart from the primary unknown sites and the lung cancer patients are excluded from analysis. Thus the controls represent those in the source population that would have been cases if their "primary cancer were diagnosed as bladder cancer.  8.2.2  Selection Bias  Non-response bias was minimal as the response rate was quite high (64.7% of bladder cancers responded and 64.1% of possible control cancer sites responded) and there were no major differences between nonresponders and responders apart from responders were more likely to have managerial or administrative as their usual occupation. The cases and controls may not represent the source population fully with respect to occupational exposure, as the source population may be slightly more exposed to potential occupational carcinogens than the cases and controls. This difference should not be large and the results should still extend to the entire source population. Patients with missing occupational data were excluded from analysis, but they were a small subgroup and did not differ from the remaining patients substantially. The source population contains all males aged over 20; so those that worked for many years in few jobs, those that changed jobs frequently, those with few work-years, and those non-workers. Healthy worker bias is unlikely here as both the cases and controls had the same proportion (0.3%) of non-workers, both had reported a similar amount of jobs (the cases reported an average and standard deviation of 4.6 and 2.5 jobs and the controls reported an average and standard deviation of 4.9 and 2.6 jobs), and both had a similar duration of work-years for each patient.  8.2.3  Information Bias  Little recall bias was expected as all responders from the source population used in the analysis responded to the questionnaire as if they were a case. The information gained on the cases and controls would have contained some error, but very little, if any of this would have been differential. Misclassification of case and control status is very unlikely as diagnoses were classified using the ICD-9 codes and all were histologically confirmed. The patients with a primary unknown cancer site were also removed from the analysis to avoid this misclassification. The questionnaire was considered valid and reliable. However, the questionnaire data will contain much misclassification. Data is often unknown for some patients, some patients could have accidentally answered incorrectly or have been unwilling to answer truthfully. However the degree of the misclassifications should not differ between cases and controls. A variable that may have suffered from differential misclassification was the alcohol score variable, so this variable was excluded from the analysis. A greater proportion of controls received the early questionnaire that omitted questions regarding alcohol drinking habits so the controls were likely to have greater misclassification on the alcohol score variable than the cases. Misclassification of occupational exposure is expected due to the difficulty in approximating the true  exposure. Again, this misclassification should not be differential. Patients with missing occupational data were excluded from analysis, however the occupational data given by the remaining patients could be with error. The larger errors occur in approximating the occupational exposure from the occupational histories. The JEM used to estimate exposures in different US jobs has limitations, for example, it was only applicable to one period in time, the jobs were located in the US, there was no allowance for exposure variability within a job, and many industries were excluded from the JEM. The calculation method will have incorporated error also; all job translations were considered equally adequate, the average was considered an appropriate method to combine exposures across job-translations, a part-time job was considered half as any work-hours as a full-time job, exposures missing from the JEM were considered zero, and the exposures were considered to have equal weight across a persons lifetime (e.g. here childhood and early exposures are as important as late exposures or exposures after retirement, when it could be the case that early exposures are much more important than late exposures).  8.2.4  Confounding by Non-Occupational Variables  Non-occupational confounders were taken into account in the matching and conditional logistic regression base model. The confounders were consistent with current knowledge of bladder cancer and the questionnaire design. Information was not sought for some other possible bladder cancer non-occupation confounders, but they may not have had much effect on the base model beyond the main confounders identified.  8.2.5  Confounding by Occupational Exposure  Occupational exposure to known bladder cancer carcinogens may confound the effect of other occupational exposures. Different occupational exposures may cluster together within a job and it would be hard to distinguish which agents actually contributed to the exposure-disease association. These problems are addressed in part by the principal component analysis, which considers groups of agents that group together according to the cumulative exposure estimates.  8.3  Evaluation of Methods  The methodology used in this study had many limitations. The greatest possibly being the use and applicability of the NIOSH JEM. As discussed in chapters 4 and 5, some important industries were excluded from the NIOSH NOES study. The NIOSH JEM only covered a short period in time, whilst we were estimating exposures over patients' lifetimes, during which time many workplace conditions have changed. Assump-  \  tions were made that the NIOSH JEM probabilities represented the lifetime probabilities of exposure for the BCCA patients. Assumptions that the US jobs were comparable to Canadian jobs seem fair and the great majority of the BCCA patients' jobs occurred within Canada. The results of this study indicate another limitation of the JEM, in that it only differentiates up to the level of the job. No allowances are made for exposure variability or different working habits within a job. Also, in this study we were unable to distinguish between different concentrations and frequencies of exposure, as was the case in Siemiatycki (1991) study. However, using a JEM with probability assessments enabled us to put a much more precise estimate on the actual exposure of a patient rather than that obtained through interviewing the patient. There were also many possible ways of analysing the results. Two additional methods, which were not considered as relevant as the ever/never variable and dose-response analysis, are discussed in the next two sections.  8.3.1  Ever/never 0.5  As mentioned before in section 6.4.2, the ever/never variable used in the analysis does not indicate definite exposure to an agent. In an attempt to find a variable that more truly measures ever versus never exposure, a possible cut-off value for the cumulative exposure estimates was hypothesised. A cut-off value of 0.5, so ever exposure represented those patients expected to be exposed for at least half a work-year (or a part-time work-year) versus those expected to be exposed for less, was a natural cut-off value. However, very few patients had exposures that high. Of the 2,772,021 exposed bladder cases and controls and agent pairs, 622,560 (22%) had a cumulative exposure greater than 0.5. Of all 8,986 agents, only 2,237 (25%) had at least three bladder cases with cumulative exposure above 0.5. The value of 0.5 was chosen as an attempt to capture those patients with likely exposure in a job (i.e. probability of exposure greater than 0.5) and those with a large amount of expected exposed years. However, if a patient's true chance of exposure in their Canadian job was at least 50%, they did not necessarily have a cumulative exposure greater than 0.5. This is because the probability of exposure calculated for the Canadian job was the average over all those for the US translation combinations and some of these may have had zero exposures. Therefore the results for the analysis on cumulative exposures above 0.5 versus those below 0.5 were not provided in this thesis, and instead the results from the simple ever/never variable were presented.  8.3.2  Siemiatycki Comparison  The study undertaken by Siemiatycki (1991) was described in section 3.3. Exposure variables comparable to his 'any exposure' and 'substantial exposure' were considered. His definition of 'any exposure' was exposure  that was at least probable, at least at a background level and occurred at least 1% of the time. This was not easily comparable to the calculated cumulative exposure as NIOSH measured exposures occurring at least 1.4% of the time at any concentration and the confidence was measured by the JEM probability. Siemiatycki's definition of 'substantial exposure' was exposure that was at least probable, above a background level and occurred at least 5% of the time for at least 5 years. The 'any exposure' and 'substantial exposure' classified exposures were different in terms of the frequency of exposure, and these differences were not detectable from the NIOSH JEM. This thesis could have involved recording the NIOSH JEM data in categories similar to that of Siemiatycki, e.g. by labelling confidence of an exposure as those JEM probabilities less than 0.5 as 'possible', those 0.5 - 1 as 'probable', and those equal to 1 as 'definite'. However, this throws away information, and the majority of JEM estimates would be labelled 'possible' and those 'definite' would be unreliable, as they would be based on small samples. Also, combining the confidence codes of each US job-translation possibilities for each Canadian job would be difficult. Siemiatycki's 'any exposure' and.'substantial exposure' definitions constitute cut-off values consistent across all agents tested. A consistent cut-off value for cumulative exposure across all agents in the study was considered inappropriate as the agents have different exposure distributions. To apply to many agents, a very low cut-off value with little practical meaning would be required. As there is no biological method to define a cut-off value for each agent's cumulative exposure, cut-offs based on the individual distribution of exposure to each agent were considered. For example, this could involve labelling the top 10% exposures for each agent as being 'exposed' and the remaining 90% as not. The exposure distributions of Siemiatycki's study subjects should be very similar to those experienced by the BCCA subjects as they are both male Canadian subjects exposed over roughly the same period in history. A suggestion was that the proportions used for Siemiatycki's definitions of 'any exposure' and 'substantial exposure' could be transferred to each agent in the current study. For example, 'substantial exposure' could refer to the top 10% of exposures and 'any exposure' to the top 30%. However, the proportions vary across chemicals and it would be very difficult to find equivalent proportions for each of the thousands of chemicals studied by NIOSH, so an average was considered. Depending on which chemicals were averaged over and what Siemiatycki design configuration was used (population or cancer controls, French Canadian population or all ethnicities); the proportion of 'any exposure' and 'substantial exposure' ranged from 1.5 2.9% and 4.8 - 8.2% respectively. However, applying any of the cut-off proportions in the ranges calculated to all agents in the study will result in some agents having patients classified as exposed when they have zero cumulative exposure.  The clearest distinction in the cumulative exposure estimates could be made between those with a value of zero (no exposure) and those with a value above zero (exposed). Given these two groups were different, they were separated in the analysis of the ever/never variable and the dose-response analysis.  8.4  Future Directions  An immediate step to take is to assess the impact of any measurement error in the cumulative exposure variables. The cumulative exposure assessments were composed of the duration of employment, obtained from questionnaire responses, which are known to often contain error, and the JEM probabilities of exposure, which more importantly were often based on small numbers and certainly contained a margin of error. The positive cumulative exposures were normally distributed after taking a Box-Cox transformation.  So, a  multiplicative measurement error model could be fit to the data and Bayesian methods used to assess the implications of different levels of measurement error (Gustafson, Le and Vallee, 2002). As most of the uncertainty in the results is due to the JEM used, a more comprehensive one could be applied to the data in the future if one became available that contained more information on the exposures, allowed for changes in time, gave some measure of error or variability, and studied exposures in more industries. Alternatively, industry specific JEMs created in Canada, or ideally within the BCCA, could be applied in conjunction with the current NIOSH JEM to improve some of the estimates or provide estimates for the industries that were not studied. Other improvements beyond the JEM include conducting pairwise (industry and occupation code combined) translations of the Canadian jobs that could include weightings as to which translations are more likely. Further information could be sought via questionnaires on the patients, such as family history of cancer, body weight, stress levels, fitness levels, diet, etc. Finally, the 30 agents identified in this study warrant further research. Perhaps animal studies could be undertaken on exposure to the chemicals, or specific cohort studies conducted to examine the relationships seen.  Chapter 9  Bibliography 1. Ashby J, Tennant RW. Chemical structure, Salmonella mutagenicity and extent of carcinogenicity as indicators of genotoxic carcinogenesis among 222 chemicals tested in rodents by US NCI/NTP. Mutation Research. 1988; 204:17-115. 2. Band PR, Le ND, MacArthur AC, Fang R, Gallagher RP. Identification of occupational cancer risks in British Columbia: A Population-based Case-Control Study of Bladder Cancer. Unpublished. 3. Band PR, Spinelli JJ, Threlfall WJ, Fang R, Le ND, and Gallagher RP. Identification of occupational cancer rpisks in British Columbia, Part I: Methodology, descriptive results, and analysis of cancer risks by cigarette smoking categories of 15,463 incident cancer cases. Journal of occupational and environmental medicine. 1999; 41:224-232. 4. Benjamini Y, Hochberg Y. Controlling the false discovery rate: a practical and powerful approach to multiple testing. Journal of the Royal Statistical Society. Series B. 1995; Vol. 57: 289-300. 5. Box GEP, Cox DR. An analysis of transformation. Journal of the Royal Statistical Society. 1964; Vol. 26: 211-243. 6. Breslow NE, Day NE. Statistical Methods in Cancer Research: Volume 1 - The analysis of case-control studies. International Agency for Research on Cancer; 1980. 7. Canadian Cancer Statistics 2004. Toronto, Canada: National Cancer Institute of Canada; 2004. 8. Census 1980. Bureau of the Census alphabetical index to industries and occupations, 1980. Washington, DC: U.S. Department of Commerce, Bureau of the Census; 1980.  9. Checkoway H, Pearce N, Crawford-Brown DJ. Research Methods in Occupational Epidemiology. Oxford: Oxford University Press; 1989. 10. Doll R, Peto R. The causes of cancer. Quantitative estimates of avoidable risks of cancer in the United States today. Oxford: Oxford University Press; 1981. 11. Dos Santos Silva 1. Cancer Epidemiology: Principles and Methods.  International Agency for Research  on Cancer; 1999. 12. Dun's Marketing Index. Dun and Bradstreet, Inc.; 1980. 13. Gordis L. Epidemiology: Second Edition. W.B. Saunders Company; 2000. 14. Gustafson P, Le ND, Vallee M. A Bayesian approach to case-control studies with errors in covariables. Biostatistics. 2002; Vol 3: 229-243. 15. Hartge P, Hoover R, Altman R, et al. Use of hair dyes and risk of bladder cancer. Cancer Research. 1982; 42:4784-4787. 16. Hochberg Y. A sharper Bonferroni procedure for multiple tests of significance, Biometrika. 1988; Vol. 75: 800-802. 17. Hosmer DW, Lemeshow S. Applied Logistic Regression: Second Edition. Wiley; 2000. 18. IARC (fnternational Agency for Research on Cancer). Evaluation of the Carcinogenic Risk of Chemicals to Humans: Diesel and Gasoline Engine Exhausts and Some Nitroarenes, Vol. 46- IARC, Lyon; 1989. 19. IARC (International Agency for Research on Cancer). Evaluation of the Carcinogenic Risk of Chemicals to Humans: Occupational Exposures in Petroleum Refining; Crude Oil and Major Petroleum Fuels, Vol. 45. IARC, Lyon; 1989. 20. IARC (International Agency for Research on Cancer). Evaluation of the Carcinogenic Risk of Chemicals to Humans: Some Chemicals that Cause Tumours of the Kidney or Urinary Bladder in Rodents and Some Other Substances, Vol. 73. IARC, Lyon; 1999. 21. IARC (International Agency for Research on Cancer). 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Cincinnati, OH: U.S. Department of Health and Human Services, Centers for Disease Control, National Institute for Occupational Safety and Health, DHHS (NIOSH); 1989. Publication No. 89-102. 36. Siemiatycki J. Risk Factors for Cancer in the Workplace. CRC Press; 1991. 37. Silverman DT, Levin LI, Hoover RN, et al. Occupational risks of bladder cancer in the United States: I. White Men. Journal of the National Cancer Institute. 1989; 81:1472-1480. 38. Soto AM, Justicia H, Wray JW, and Sonnenschein, C. p-Nonylphenol, an estrogenic xenobiotic released from 'modified' polystyrene. Environmental Health Perspectives. 1991; 92:167-173.  39. Standard Occupational Classification  1980. Ottawa, Ontario, Canada: Statistics Canada; 1981.  40. Standard Industrial Classification 1980. Ottawa, Ontario, Canada: Statistics Canada; 1981. 41. Svirchev LM, Kan D, Lai AM, Ng V, Moody JM, Band PR. 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Geneva: WHO; 1977.  Chapter 10  Tables and Figures Table 2.1: Two by Two Contingency Table for Cohort Studies Diseased Not Diseased Total Sample Exposed Si A1+B1 Not Exposed Aa Bo A0 + B0  Table 2.2: Two by Two Contingency Table for Case-Control Studies Diseased Not Diseased Exposed <Zl bi Not Exposed o0 bo Total Sample ai + ao h+bo  Occupation or Industry Aluminium production Aromatic amine manufacturing workers  IARC" 1 N/A  Boot and shoe manufacture and repair Leather workers Coal gasification Coke production Drivers of trucks and other motor vehicles Dry cleaning solvent-exposed workers  1 3 1 1 N/A  Suspected agents Pitch violates, coal-tar pitch volatiles, aromatic amines 2-naphthylamine, benzidine, 4-aminobiphenyl. Possibly: MDA (4,4-methylene-dianiline), MBOCA (4,4-methylenebis(2-chloroaniline), 4-cholor-o-toluidine (4-COT). Leather dust, dyes, benzene and other solvents  Leather dust, dyes, solvents Coal tar, coal-tar fumes, individual PAHs Coal-tar fumes, polynuclear aromatic hydrocarbons (PAHs) Motor exhaust (polycyclic aromatic hydrocarbons, nitroPAHs) 2B Benzene, naphtha, gasoline, stoddard solvent (mineral or white spirits), carbon tetrachloride, trichloroethylene, tetra-' chloroethylene, chlorofluorocarbon solvents, chlorinated solvents, amyl acetate, bleaching agents, acetic acid, aqueous ammonia, oxalic acid, hydrogen peroxide and dilute hydrogen fluoride solutions Dyestuffs workers and dye N/A 3 aromatic amines (2-naphthylamine, benzidine, 1users naphthylamine), o-toluidine, 4,4-methylene bis(2methylaniline). Auramine manufacture 1 2-naphthylamine, auramine, other chemicals Magenta manufacture 1 Magenta, ortho-toluidine, 4,4-methylene bis(2-methylaniline), ortho-nitrotoluene Hairdresser or barber 2A Some compounds in hair dyes, aromatic amines, aminophenols, hydrogen peroxide, aminoanthraquinones, azo dyes, lead acetate, volatile solvents, propellants, aerosols, formaldehyde, methacrylates Painters 1 Paints (benzidine, polychlorinated biphenyls, formaldehyde, asbestos) and solvents (benzene, dioxane, methylene chloride). Petroleum refining 2A Aliphatic hydrocarbons, aromatic hydrocarbons, hydrogen sulfide, polycyclic aromatic compounds Printing processes 2B Carbon black, titanium dioxide, azo, anthraquinone and triarylmethane dyes, and phthalocyanines Rubber industry 1 Aromatic amines, solvents, 2-naphthylamine, phenyl-bnaphthylamine (PBNA). Textile manufacturing 2B Textile-related dusts, dyes, optical brighteners, organic solvents and fixatives, benzidine, formaldehyde, flame retardants (including organophosphorus and organobromine compounds) a IARC classification where 1 is definitely carcinogenic, 2A is probably carcinogenic, 2B is possibly carcinogenic, and 3 is not classifiable  Siemiatycki ORs IARC Any Substantial Class" Evidence Chemical Name 1,3-Dichloropropene 2B Animal 2-(2-Formylhydrazino)-4(5N2F)T 6 2B Animal 2-Naphthylamine 1 Human 2-Nitroanisole 2B Animal 2B Human 3,3'-Dichlorobenzidine 2B Human 3,3'-Dimethoxybenzidine 4,4'-Methylenebis(2-chloroaniline) 2A Animal Adriamycin 2A Animal 4-Aminobiphenyl (xenylamine) 1 Human 4-chloro-ortho- phenylenediamine 2B Animal, Arsenic 1 Human Human 2B Auramine 2A Animal Benz(a)anthracene Benzidine 1 Human 2A Animal Benzidine based dyes Carbon black 2.2* 2B Human 1.8 Human 2B Chlordane 2B Human Chloroform (in drinking water) t t CI Basic Red 9 2B Animal 2B Animal Citrus Red No. 2 1 Human Coal tar pitches 0.9 2.3 Cyclophosphamide 1 Human Diesel engine emissions 1.4 2.3** 2A Human Disperse Blue 1 2B Animal Engine emissions 1.2* 1.3* 2A Human 1.1 2B Human Gasoline 0.9 2B Human Lead 1 Human Magenta Mineral oils6 1.2 2.2 1 Human Animal 2B N- [4- (5-Ni tro-2-Furyl) 2TZ] A b Niridazole 2B Animal Animal 2B Nitrilotriacetic Acid Human 1 N,N-Bis(2-CE)-2-NL6 (Chlornaphazine) N-Nitrosodi-n-butylamine 2B Animal 2B Animal Oil Orange SS ortho-Aminoazotoluene 2B Animal 2A Human para-Chloro-ortho-Toluidine 2B Animal para-Cresidine para-Dimethylaminobenzene 2B Animal Phenacetin 2A Human Animal 2B Ponceau 3R Animal 2B Sodium ortho-phenylphenate Tetrachloroethylene 2A Human t t Trichloroethylene 0.6 0.7 2A Human a IARC classification where 1 is definitely carcinogenic, 2A is probably carcinogenic, 2B is possibly carcinogenic, and 3 is not classifiable b 2-(2-FormyIhydrazino)-4(5N2F)T = 2-(2-Formylhydrazino)-4-(5-Nitro-2-Furyl) Thiazole, N-[4-(5Nitro-2-Furyl)2TZ]A = N-[4-(5-Nitro-2-Furyl)-2-Thiazolyl] Acetamide. Mineral oils = Minerail oils, untreated or mildly treated. N,N-Bis(2-CE)-2-NL = N,N-Bis(2-Chloroethyl)-2-naphthylamine f Less than 4 cases exposed * Significant at p=0.10, one-sided, with at least 4 exposed cases ** Significant at p=0.05, one-sided, with at least 5 exposed cases  IARC Siemiatycki ORs Chemical Name Any Substantial Class'1 Evidence Acrylic fibres 3.9** 3.3 Aliphatic aldehydes 1.4* 1.6 Ammonia 1.2 2.1* Animal Asphalt (bitumen) 2.2* 3 0.9 Cadmium compounds 1.6 4.9* 1 Human Calcium carbonate 1.6 1.9** Human 2B Carbon black 2.2* 1.8 Human 2B Carbon tetrachloride 2.5** 1.6 2.7* Chlorine 1 Clay dust 2.2* 1.8 Creosote 2.6* 2.6 2A Human Diesel engine emissions 1.4 2.3** 2A Human Engine emissions 1.2* 1.3* 1 3.7* Fabric dust 2A Human 1.7* Formaldehyde 1.2 Hydrogen cyanide 3.4* 0 Ionizing radiation 44** 0 1 Human Laboratory products 5.5* 1.5 Lead chromate 1.8* 2.2 2B Human Human 1.1 2B Lead compounds 1.3* Natural gas comb, products 1.6* 3.8** Photographic products 2.5 2.9* Polyester fibers 1.4 2.5* Polyethylene 2.5* 13 3 Animal Titanium compounds ^ 7** 2.2 Titanium dioxide 4.5 3 Animal ^ y** a IARC classification where 1 is definitely carcinogenic, 2A is probably carcinogenic, 2B is possibly carcinogenic, and 3 is not classifiable * Significant at p=0.10, one-sided, with at least 4 exposed cases ** Significant at p=0.05, one-sided, with at least 5 exposed cases  Code 1 2 3  Confidence Possible Exposure Probable Exposure Definite Exposure  Table 3.4: Siemiatycki Exposure Coding Frequency Concentration Low: background level Low: 1-5% of working time Medium: intermediate situations Medium:5-30% of working time High: agent in concentrated form High: >30% of working time  Figure 5.1: Calculating Cumulative Exposure Analysis Design  Table 5.1: Distribution of Missing Job Code, and Start and End Year Data Start and Job Codes Total End Years All Missing Some Missing All Complete All Missing 18 7 111 136 Some Missing 0 27 389 416 All Complete 17 136 14,049 14,202 Total  35  170  14,549  14,754  Table 5.2: Cancer Site Distribution of Jobs in the Armed Forces Armed Forces Employment Total Work-Years Armed Forces Other IDC-9 a Ever % % Primary tumour site % Never 140-149 78 2.9 482 4.2 441.0 2.6 17,224.5 Oral cavity and pharynx Esophagus 150 31 1.2 145 1.3 145.0 0.9 6,122.5 Stomach 151 111 4.2 459 4.0 663.5 4.0 19,646.0 8.4 7.7 1,299.0 7.8 39,553.5 224 881 153 Colon 849 1,169.0 7.0 37,396.0 154 202 7.6 7.5 Rectum 0.4 1,313.5 4 0.1 40 17.0 0.1 Liver 155 1.2 110 1.0 203.0 4,619.0 27 1.0 Pancreas 157 244 2.1 398.0 2.4 10,482.0 2.5 Larynx 161 68 162 618 23.2 2,195 19.3 4,061.0 24.3 96,771.5 Lung 136 1.2 103.0 0.6 3,782.0 0.7 171 18 Soft tissue sarcoma 172 3.3 557 4.9 595.0 3.6 17,006.5 Melanoma skin 89 924 8.1 1,996.5 12.0 41,110.0 288 10.8 Non-melanoma skin 173 1,161 10.2 1,761.5 10.6 55,460.0 294 11.0 Prostate 185 213 1.9 69.0 0.4 2,797.5 0.5 186 13 Testis 8.2 848 7.5 1,468.5 8.8 37,678.5 Bladder 188 218 92 3.4 461 4.1 524.0 3.1 18,360.5 Kidney 189 1.5 288 2.5 309.0 1.9 8,119.5 Brain 191 39 104 0.9 15.0 0.1 1,781.0 4 0.1 Hodgkin's disease 201 202 128 4.8 626 5.5 821.5 4.9 23,159.0 Non-Hodgkin's lymphoma Multiple myeloma 203 24 0.9 104 0.9 140.0 0.8 4,558.0 Leukemia 204-208 33 1.2 205 1.8 153.0 0.9 6,994.0 2.4 350 3.1 333.0 2.0 12,517.5 64 Other sites -  2,667 100.0 11,382 Total a IDC-9, International Classification of Diseases, 9th Revision  100.0  16,685.5  100.0  466,452.5  % 3.7 1.3 4.2 8.5 8.0 0.3 1.0 2.2 20.7 0.8 3.6 8.8 11.9 0.6 8.1 3.9 1.7 0.4 5.0 1.0 1.5 2.7 100.0  Industrial Group Agriculture Fishing, Trapping Logging, Forestry Mining, Quarrying, Oil Well Manufacturing Food, beverage, tobacco Rubber, plastic, leather, textile, clothing Wood, furniture, paper, printing Other Construction Transportation Communication, Utility Wholesale Retail Finance, Insurance, Real Estate Services Business Government Education, Health Other  CDN Code" Jobs 01-02 v 4,845 03 652 04-05 2,945 06-09 2,802  % 8.0 1.1 4.9 4.6  WorkYears 46,067.5 5,317.0 17,216.0 14,093.5  % 9.9 1.1 3.7 3.0  Ever Industry 3,557 513 1,759 1,545  % 25.6 3.7 12.6 11.1  10-12 15-24  1,694 419  2.8 0.7  12,449.5 3,023.0  2.7 0.6  1,100 276  7.9 2.0  25-28  5,616  9.3  43,103.0  9.2  3,377  24.3  29-39 40-44 45-47 48-49 50-59 60-69 70-76  5,969 6,591 5,690 1,707 3,345 5,270 2,101  9.8 10.9 9.4 2.8 5.5 8.7 3.5  40,885.0 46,339.0 46,492.0 16,247.0 24,543.0 39,676.5 16,657.5  8.8 9.9 10.0 3.5 5.3 8.5 3.6  3,988 4,084 3,249 1,035 2,108 3,327 1,306  28.7 29.3 23.3 7.4 15.1 23.9 9.4  77 81-84 85-86 91-99  1,297 3,900 2,455 3,411  2.1 6.4 4.0 5.6  10,399.5 35,600.5 23,524.0 24,812.0  2.2 7.6 5.0 5.3  827 2,633 1,528 2,404  5.9 18.9 11.0 17.3  Total 60,709 a Canadian 1980 Standard Industrial Classification  100.0  466,445.5  100.0  Industrial Group Agriculture Fishing, Hunting, Trapping Forestry Mining Manufacturing Food, beverage, tobacco Rubber, plastic, leather, textile, clothing Wood, furniture, paper, printing Other  US Code" 010-020 031 030 040-050  Job-translations 19,827 800 419 10,053  % 9.3 0.4 0.2 4.7  Work-Years* 46,138.1 5,317.0 1,192.0 14,093.5  % 9.9 1.1 0.3 3.0  6,360 2,213  3.0 1.0  12,370.5 3,115.9  2.7 0.7  21,999  10.3  59,109.2  12.7  100-130 132-150, 210-220 160-170, 230-240 180-200, 250-390 060 400-430 440-470 500-570 580-690 700-712  25,968  12.1  40,656.8  8.7  17,943 14,574 6,165 13,526 24,212 3,984  8.4 6.8 2.9 6.3 11.3 1.9  45,884.8 50,119.9 13,233.3 20,865.0 37,350.0 16,667.5  9.8 10.7 2.8 4.5 8.0 3.6  721-760 900-932 812-892 761-791 800-802  12,099 10,876 3,104 3,920 16,147  5.6 5.1 1.4 1.8 7.5  18,578.4 33,993.3 9,567.5 3,814.0 34,378.9  4.0 7.3 2.1 0.8 7.4  Total 214,189 a US 1980 Census of the Population Industrial Classification b Work-years contributed  100.0  466,445.5  100.0  Construction Transportation Communications, Utilities Wholesale Retail Finance, Insurance, Real Estate Services Business, Repair Public Administration Professional Personal Services Entertainment, Recreation  Occupational Group Managerial and Administrative Natural Sciences, Engineering, Mathematics Social Sciences Religion Teaching Medicine and Health Artistic, Literary, Recreational Clerical Sales Services Farming, Horticultural, Animal Husbandry Fishing, Trapping Forestry, Logging Mining, Quarrying Materials Processing Machining Product Fabricating, Assembling, Repairing Construction Transport Equipment Operating Material Handling Other Crafts and Equipment Operating Not Elsewhere Classified  CDN Code" 11 21  Jobs 5,923 2,005  9.8 3.3  WorkYears 54,044 14,034  % 11.6 3.0  Ever Occupation 3,099 959  % 22.3 6.9  23 25 27 31 33 41 51 61 71  298 179 965 735 686 3,770 5,470 3,672 4,929  0.5 0.3 1.6 1.2 1.1 6.2 9.0 6.0 8.1  2,759 1,966 8,511 9,208 5,534 25,223 42,231 27,392 46,544  0.6 0.4 1.8 2.0 1.2 5.4 9.1 5.9 10.0  191 81 589 454 423 2,257 3,076 2,344 3,583  1.4 0.6 4.2 3.3 3.0 16.2 22.1 16.8 25.7  73 75 77 81-82 83 85  523 2,229 1,568 4,629 2,314 6,020  0.9 3.7 2.6 7.6 3.8 9.9  4,631 13,028 7,213 32,819 16,709 43,966  1.0 2.8 1.5 7.0 3.6 9.4  442 1,454 927 3,187 1,326 3,224  3.2 10.4 6.7 22.9 9.5 23.2  87 91  6,560 4,998  10.8 8.2  48,779 37,180  10.5 8.0  3,580 2,937  25.7 21.1  93 95  1,715 1,207  2.8 2.0  12,019 10,902  2.6 2.3  1,316 734  9.5 5.3  99  314  0.5  1,761  0.4  290  2.1  %  Total 60,709 100.0 ° Canadian 1980 Standard Occupational Classification  466,446  100.0  US Codea 003-037  Jobtranslations 11,991  % 5.6  Work-Years 6 54,260  % 11.6  043-083, 213-235 166-175, 178-179 176-177 113-163 084-106, 203-208 164-165, 183-199 303-389 243-285 403-469 473-489 497-499 494-496 613-617 633-699 703-779 503-549, 783-799 553-599 803-834 843-859 863-889  9,446  4.4  21,355  4.6  485  0.2  2,479  0.5  179 12,196 572  0.1 5.7 0.3  1,966 8,686 7,092  0.4 1.9 1.5  1,528  0.7  5,029  1.1  11,375 25,168 10,733 18,337 523 2,188 2,182 11,542 16,495 31,144  5.3 11.8 5.0 8.6 0.2 1.0 1.0 5.4 7.7 14.5  26,286 37,630 27,276 45,275 4,631 10,232 2,609 24,696 30,883 43,120  5.6 8.1 5.8 9.7 1.0 2.2 0.6 5.3 6.6 9.2  16,531 9,749 9,006 12,819  7.7 4.6 4.2 6.0  39,790 36,252 17,203 19,697  8.5 7.8 3.7 4.2  100.0 Total 214,189 a US 1980 Census of the Population Occupational Classification 6 Work-years contributed  466,446  100.0  Occupational Group Executive, Administrative, Managerial Natural Sciences, Engineering, Mathematics Social Sciences Religion Teaching Medicine and Health Artistic, Literary, Recreational Administrative Support, Clerical Sales Services Farming Fishing, Trapping Forestry, Logging Extractive occupations Precision production Machine Operators Fabricators, Assemblers, Mechanics, Repairers Construction Transport Equipment Operating Material Moving Handlers, Equipment Cleaners, Helpers, Laborers  Industry Group Agriculture Fishing, Trapping Logging, Forestry Mining, Quarrying, Oil Well Manufacturing Food, beverage, tobacco Rubber, plastic, leather, textile, clothing Wood, furniture, paper, printing Other Construction Transportation Communication, Utility Wholesale Retail Finance, Insurance, Real Estate Services Business Government Education, Health Other  CDN Code" 01-02 03 04-05 06-09  JEM 56 0 2,167 151  Jobs All 4,845 652 2,945 2,802  % 1.2 0.0 73.6 5.4  Work-Years JEM0 All 328.7 46,067.5 0.0 5,317.0 11,128.9 17,216.0 528.2 14,093.5  % 0.7 0.0 64.6 3.7  10-12 15-24  1,311 321  1,694 419  77.4 76.6  7,205.2 1,427.6  12,449.5 3,023.0  57.9 47.2  25-28  5,065  5,616  90.2  29,067.3  43,103.0  67.4  29-39 40-44 45-47 48-49 50-59 60-69 70-76  4,972 6,351 3,904 1,119 962 1,541 0  5,969 6,591 5,690 1,707 3,345 5,270 2,101  83.3 96.4 68.6 65.6 28.8 29.2 0.0  23,774.5 32,702.7 24,258.6 6,049.9 2,522.0 6,373.7 0.0  40,885.0 46,339.0 46,492.0 16,247.0 24,543.0 39,676.5 16,657.5  58.1 70.6 52.2 37.2 10.3 16.1 0.0  77 81-84 85-86 91-99  204 24 547 611  1,297 3,900 2,455 3,411  15.7 0.6 22.3 17.9  564.1 109.2 4,170.3 3,407.9  10,399.5 35,600.5 23,524.0 24,812.0  5.4 0.3 17.7 13.7  60,709  48.3  153,618.6  466,445.5  32.9  Total 29,306 a Canadian 1980 Standard Industrial Classification 6 Work-years contributed by the JEM  Occupation Group Managerial and Administrative Natural Sciences, Engineering, Mathematics Social Sciences Religion Teaching Medicine and Health Artistic, Literary, Recreational Clerical Sales Services Farming, Horticultural, Animal Husbandry Fishing, Trapping Forestry, Logging Mining, Quarrying Materials Processing Machining Product Fabricating, Assembling, Repairing Construction Transport Equipment Operating Material Handling Other Crafts and Equipment Operating Not Elsewhere Classified  CDN Code a 11 21  JEM 1,237 580  Jobs All 5,923 2,005  % 20.9 28.9  Work-Years JEM 6 All 8,765.2 54,043.5 2,676.0 14,034.0  % 16.2 19.1  23 25 27 31. 33 41 51 61 71  11 0 0 307 288 1,114 854 609 94  298 179 965 735 686 3,770 5,470 3,672 4,929  3.7 0.0 0.0 41.8 42.0 29.5 15.6 16.6 1.9  21.4 0.0 0.0 2,901.7 1,602.7 4,258.5 3,002.0 2,702.3 233.1  2,758.5 1,965.5 8,510.5 9,207.5 5,534.0 25,223.0 42,231.0 27,391.5 46,543.5  0.8 0.0 0.0 31.5 29.0 16.9 7.1 9.9 0.5  73 75 77 81-82 83 85  0 1,726 141 4,054 2,066 4,753  523 2,229 1,568 4,629 2,314 6,020  0.0 77.4 9.0 87.6 89.3 79.0  0.0 9,784.8 354.8 25,009.9 9,377.9 20,351.6  4,630.5 13,027.5 7,213.0 32,818.5 16,708.5 43,965.5  0.0 75.1 4.9 76.2 56.1 46.3  87 91  5,915 3,280  6,560 4,998  90.2 65.6  29,485.2 20,650.8  48,779.0 37,179.5  60.4 55.5  93 95  1,331 760  1,715 1,207  77.6 63.0  6,177.4 5,460.4  12,019.0 10,901.5  51.4 50.1  99  186  314  59.2  802.9  1,760.5  45.6  48.3  153,618.6  466,445.5  32.9  Total 29,306 60,709 a Canadian 1980 Standard Occupational Classification 6 Work-years contributed by the JEM  Table 5.9: Location of Canadian Jobs Found on the JEM Location Jobs % Work Years" % Alberta 1,723 5.9 7,036.0 4.6 71.4 19,562 109,677.8 British Columbia 66.8 3,528.0 2.3 Manitoba 819 2.8 0.2 82 251.3 New Brunswick 0.3 91.2 0.1 0.1 Newfoundland 29 74.1 0.0 Northwest Territories 34 0.1 0.1 211.3 Nova Scotia 60 0.2 3.8 1,454 5.0 5,807.1 Ontario 14 25.5 0.0 0.0 Prince Edward Island 1,983.0 1.3 Quebec 436 1.5 Saskatchewan 884 3.0 3,717.5 2.4 0.1 64 0.2 185.8 Yukon Territories Canada Canada + BC Canada + elsewhere Outside of Canada  191 252 124 2,346  0.7 0.9 0.4 8.0  1,159.4 1,805.9 949.2 11,141.1  0.8 1.2 0.6 7.3  Unknown  1,232  4.2  5,974.5  3.9  Total 29,306 100.0 a Work-years contributed on the JEM  153,618.6  100.0  Table 6.1: Characterisitics of Cases and Controls Controls (n = 8057) Cases (n — 1062) Mean Mean Patients % Patients Characteristic % (± SD) (± SD) 67.0 (11.4) 65.9 (10.9) Age at diagnosis, years Employment duration, work-years 0. 36.7 (11.0) 35.9 (11.2) No jobs reported 0 3 0.3 24 0.3 Year of diagnosis 1983 222 20.9 2600 32.3 1801 22.4 20.2 215 1984 1475 18.3 221 20.8 1985 1088 13.5 20.3 216 1986 1093 13.6 17.7 188 1987 Ethnic origin Caucasian 1027 96.7 7665 95.1 350 4.3 Non-Caucasian 31 2.9 4 0.4 42 0.5 Unknown Marital status 42 4.0 385 4.8 Single Married or common-law 889 83.7 6706 83.2 6.2 492 6.1 66 Widowed 5.4 403 5.0 57 Separated or divorced Unknown 8 0.8 71 0.9 Education 11.1 894 11.1 118 <8 years 480 45.2 3583 44.5 8-11 years High school graduate 119 11.2 884 11.0 Post secondary education 298 28.1 2305 28.6 Unknown 47 4.4 391 4.9 10.0 (2.3) 10.0 (2.3) Years Tobacco smoking Never smoker 117 11.0 1,444 17.9 Ever smoker 34 3.2 323 4.0 Pipe and cigar only Cigarette only 909 85.6 6,268 77.8 Unknown 2 0.2 22 0.3 Cigarette smoking only Current smoker 314 34.5 1,894 30.2 4,118 65.7 564 Former smoker 62.0 3.4 256 4.1 Unknown 31 Cigarette smoking only Cigarettes/day 21.3 (12.7) 20.9 (12.5) Years/smoked 36.5 (14.9) 33.5 (15.0) 33.6 (29.0) 27.9 (28.3) Pack-years 16.8 (11.9) 18.3 (12.6) Years quit (former smokers) Alcohol consumption Never 113 10.6 842 10.5 5882 73.0 76.4 Ever 811 1333 16.5 13.0 Unknown 138 Person completing questionnaire Patient 888 83.6 6357 78.9 1490 18.5 14.1 Other 150 210 2.6 24 Unknown 2.3 a Prior to 5 years before diagnosis  Table 6.2: Odds Ratios (OR) for Potentially Important 11 Confounding Variables No. of Cases OR 95% Confidence Interval Confounding Variable Respondent to questionnaire Patient 888 1.00 0.53 - 0.78 0.65 150 Proxy Unknown 24 0.92 0.59 - 1.42 Ethnic origin Caucasian 1,027 1.00 0.71 0.48 - 1.05 Non-Caucasian 31 0.22 - 1.79 4 0.63 Unknown Alcohol consumption status Never drinker 113 1.00 0.88 0.70 - 1.11 Ever drinker 811 0.87- 1.67 1.20 138 Unknown Cigarette smoking duration, years 0 151 1.00 1.13 - 1.75 262 1.41 1-29 1.56 - 2.40 1.93 30-44 338 1.89 - 2.95 2.36 300 45+ 11 1.16 0.60 - 2.23 Unknown a p-value < 20% -  -  -  -  Table 6.3: Log Likelihood for Various Base Models Degrees of Deviance from Freedom -2LL a Base Model Model Variables ~ Base Who completed questionnaire, ethnicity, 10 5,433 alcohol status, cigarette years 1 Who completed questionnaire, alcohol 8 5,437 4.00 status, cigarette years 2 Who completed questionnaire, ethnicity, 8 5,468 NA alcohol status, smoking status Who completed questionnaire, ethnicity, 10 5,448 NA 3 alcohol status, cigarette pack-years 4 Who completed questionnaire, ethnicity, 11 5,440 NA alcohol status, years quitsmoking a LL = Log likelihood  p-value -  0.14 NA NA NA  All Subjects Cases Controls (n = 1125) (n = 8492) No. (%) No. (%) 8 (0.7) 62 (0.7)  Characteristic No jobs reported 0 Year of diagnosis 1983 240 (21.3) 1984 229 (20.4) 1985 228 (20.3) 1986 231 (20.5) 1987 197 (17.5) Ethnic origin Caucasian 1,088 (96.7) Non-Caucasian 32 (2.8) Unknown 5 (0.4) Marital status Single 45 (4.0) Married or common-law 933 (82.9) Widowed 74 (6.6) Separated or divorced 65 (5.8) Unknown 8 (0.7) Education 123 (10.9) <8 years 8-11 years 508 (45.2) High school graduate 129 (11.5) Post secondary education 312 (27.7) Unknown 53 (4.7) Tobacco smoking Never smoker 123 (10.9) Ever smoker 35 (3.1) Pipe and cigar only Cigarette only 965 (85.8) Unknown 2 (0.2) Cigarette smoking only Current smoker 332 (34.4) Former smoker 600 (62.2) Unknown 33 (3.4) Alcohol consumption Never 119 (10.6) Ever 858 (76.3) 148 (13.2) Unknown Person completing questionnaire Patient 934 (83.0) Other 164 (14.6) 27 (2.4) Unknown a Prior to 5 years before diagnosis  2,715 1,907 1,534 1,181 1,155  (32.0) (22.5) (18.1) (13.9) (13.6)  Complete Occupational Data Cases Controls (n = 1062) (n = 8057) No. (%) No. (%) 3 (0.3) 24 (0.3) 222 215 221 216 188  (20.9) (20.2) (20.8) (20.3) (17.7)  2,600 1,801 1,475 1,088 1,093  (32.3) (22.4) (18.3) (13.5) (13.6)  8,073 (95.1) 370 (4.4) 49 (0.6)  1,027 (96.7) 31 (2.9) 4 (0.4)  7,665 (95.1) 350 (4.3) 42 (0.5)  415 (4.9) 7,014 (82.6) 532 (6.3) 448 (5.3) 83 (1.0)  42 (4.0) 889 (83.7) 66 (6.2) 57 (5.4) 8 (0.8)  385 (4.8) 6,706 (83.2) 492 (6.1) 403 (5.0) 71 (0.9)  972 (11.4) 3,781 (44.5) 926 (10.9) 2,384 (28.1) 429 (5.1)  118 (11.1) 480 (45.2) 119 (11.2) 298 (28.1) 47 (4.4)  894 (11.1) 3,583 (44.5) 884 (11.0) 2,305 (28.6) 391 (4.9)  1,503 (17.7)  117 (11.0)  1,444 (17.9)  342 (4.0) 6,621 (78.0) 26 (0.3)  34 (3.2) 909 (85.6) 2 (0.2)  323 (4.0) 6,268 (77.8) 22 (0.3)  2,020 (30.5) 4,326 (65.3) 275 (4.2)  314 (34.5) 564 (62.0) 31 (3.4)  1,894 (30.2) 4,118 (65.7) 256 (4.1)  881 (10.4) 6,201 (73.0) 1,410 (16.6)  113 (10.6) 811 (76.4) 138 (13.0)  842 (10.5) 5,882 (73.0) 1333 (16.5)  6,644 (78.2) 1,630 (19.2) 218 (2.6)  888 (83.6) 150 (14.1) 24 (2.3)  6357 (78.9) 1490 (18.5) 210 (2.6)  Characteristic Age at diagnosis, years Employment duration, work-years" Years of Education Cigarette smoking only Cigarettes/day Years smoked Pack-years Alcohol score Former smokers only Years quit a Prior to 5 years before diagnosis  Table 6.4: Continued All Subjects Cases Controls (n = 1125) (n = 8492) Mean (SD) Mean (SD) 66.0 (10.9) 67.3 (11.4) 36.4 (11.3) 35.6 (11.6) 9.9 (2.3) 10.0 (2.3)  Complete Occupational Data Cases Controls (n = 8057) (n = 1062) Mean (SD) Mean (SD) 67.0 (11.4) 65.9 (10.9) 36.7 (11.0) 35.9 (11.2) 10.0 (2.3) 10.0 (2.3)  21.2 (12.6) 36.6 (14.9) 33.5 (29.1) 416.7 (678.9)  20.9 (12.5) 33.6 (15.0) 28.1 (28.4) 422.7 (640.1)  21.3 (12.7) 36.5 (14.9) 33.6 (29.0) 411.7 (662.0)  20.9 (12.5) 33.5 (15.0) 27.9 (28.3) 415.5 (613.2)  16.1 (12.2)  17.6 (12.9)  16.8 (11.9)  18.3 (12.6)  Table 6.5: Distribution of Control Cancer Sites Before and After Exclusions Complete All Subjects Occupational Data Patients Primary tumour site IDC-9 a Patients % % Oral cavity and pharynx 140-149 524 6.2 479 5.9 Esophagus 150 176 2.1 159 2.0 Stomach 151 353 4.2 330 4.1 1,044 13.0 13.0 Colon 153 1,101 841 10.4 154 892 10.5 Rectum 36 0.4 Liver 39 0.5 155 1.6 129 1.6 Pancreas 157 138 304 284 3.5 Larynx 3.6 161 Soft tissue sarcoma 171 113 1.3 106 1.3 460 5.7 Melanoma skin 172 479 5.6 1,121 13.2 1,091 13.5 Non-melanoma skin 173 17.4 1,415 17.6 185 1,479 Prostate 1.1 86 1.1 Testis 186 91 4.0 320 4.0 189 336 Kidney Brain 191 159 1.9 149 1.8 0.7 56 0.7 57 Hodgkin's disease 201 Non-Hodgkin's lymphoma 202 438 5.2 416 5.2 Multiple myeloma 203 123 1.4 116 1.4 Leukemia 204-208 211 2.5 201 2.5 4.2 339 4.2 Other sites 358 -  8,492 Total 100.0 a IDC-9, International Classification of Diseases, 9th Revision  8,057  100.0  Table 6.6: Odds Ratios (OR) for Potentially Important" Confounding Variables Before and After Exclusions All Subjects Complete Occupational Data Confounding Variable Cases OR 95% CP Cases OR 95% CI 6 Respondent to questionnaire Patient 934 1.00 888 1.00 0.53 - 0.78 Proxy 164 0.64 0.53 - 0.77 150 0.65 24 0.92 0.59 - 1.42 Unknown 27 0.99 0.65 - 1.50 Ethnic origin Caucasian 1088 1.00 1027 1.00 0.48 - 1.05 Non-Caucasian 32 31 0.71 0.68 0.47 - 1.00 4 0.63 0.22 - 1.79 Unknown 0.66 0.26 - 1.69 5 Alcohol consumption status Never drinker 119 1.00 113 1.00 0.70 - 1.11 Ever drinker 811 0.88 0.88 0.70 - 1.10 858 138 1.20 0.87- 1.67 Unknown 148 1.16 0.85 - 1.59 Smoking duration, years 0 159 1.00 151 1.00 262 1.41 1.13 - 1.75 1-29 277 1.43 1.15 - 1.77 30-44 338 1.93 1.56 - 2.40 355 1.93 1.56 - 2.38 1.89 - 2.95 322 300 2.36 45+ 2.35 1.90 - 2.92 11 0.60 - 2.23 12 Unknown 1.08 0.58 - 2.02 1.16 a p-value < 20% b CI = Confidence Interval -  -  -  -  -  -  -  -  Table 6.7: Distribution of Bladder Cases Exposed Across the 8,986 Agents Cumulative Cumulative Cases Exposed Agents Frequency Percentage Percentage 201+ 539 539 6.0 6.0 512 5.7 11.7 101-200 1,051 1,282 2,333 14.3 26.0 21-100 36.6 10-20 955 3,288 10.6 162 38.4 3,450 9 1.8 41.5 275 3.1 8 3,725 45.2 3.8 7 340 4,065 4,384 48.8 319 3.5 6 552 54.9 4,936 6.1 5 4 332 3.7 58.6 5,268 63.4 4.8 431 5,699 3 74.9 2 6,728 1,029 11.5 100.0 1 25.1 8,986 2,258  Table 7.1: Distribution of p-values for Ever Exposure of 5,699 Agents Ever Exposed OR < 1 1 < OR < 2 OR > 2 p-value (p) Agents Agents Agents % % % p < 0.5% 1 0.0 128 2.2 24 0.4 14 0.2 0.5% < p < 1% 56 1.0 32 4 2.2 0.6 1% < p < 2.5% 0.1 128 93 1.6 2.5% < p < 5% 171 3.0 18 0.3 241 4.2 63 1.1 1.1 5% < p < 10% 63 63 121 2.1 8.2 1.1 10% < p < 20% 470 25 0.4 2,676 p > 20% 1,308 23.0 47.0 Total  1,515  26.6  3,870  67.9  314  5.5  Table 7.2: Agents Significant After Adjusting for Multiplicity Using the Hochberg and Benjamini Procedure from 5,699 Ever Exposure Cases O R 95% CI CAS Agent Name 2,5-PYRROLIDINEDIONE, 12AE MPIB D a 67762-72-5 361 1.39 1.21 - 1.60 3.11 1.92 - 5.04 25 NATURAL GAS, LIQUIFIED 68784-31-6 3 35 1.38 1.20 - 1.60 PHOSPHORODITHIOIC ACID, MOOB E ZS° 25 2.89 1.79 - 4.67 1, 2-ETHANEDIAMINE, RP W C IB HP° 68891-84-9 1.38 1.19 - 1.60 301 ALKENES, C15-18 ALPHA-, RPW SDP CS S° 72275-86-6 1.48 1.23 - 1.77 176 ETHANOL, 2-(2-(2-BE)E)-a 143-22-6 PHENOL, DODECYL-, SULFURIZED, CCSO a 68784-26-9 390 1.34 1.16 - 1.53 ° See appendix table B.l for agent name abbreviations  Table 7.3: Distribution of p-values Across Low Exposure OR < 1 OR > p-value (p) Agents Agents % p < 0.5% 67 0.5% < p < 1% 47 1% < p < 2.5% 1 0.0 126 2.5% < p < 5% 2 0.1 137 5% < p < 10% 12 0.3 223 2.1 10% < p < 20% 73 353 32.1 p > 20% 1,108 1,301  3,450 Agents Tested For Dose-Response Medium Exposure 1 OR < 1 OR > 1 Agents Agents % % % 1.9 59 1.7 42 1.2 1.4 2 69 2.0 0.1 3.7 0.2 120 3.5 8 4.0 24 193 5.6 0.7 6.5 2.4 287 8.3 10.2 83 1,558 45.2 1,005 29.1 37.7  Total  62.0  p-value (p) p < 0.5% 0.5% < p < 1% 1% < p < 2.5% 2.5% < p < 5% 5% < p < 10% 10% < p < 20% p > 20% Total  1,196  34.7  2,140  High Exposure OR < 1 OR > 1 Agents Agents % % 36 1.0 25 0.7 3 0.1 68 2.0 0.4 13 86 2.5 1.2 42 5.1 177 2.3 7.9 81 273 1,014 29.4 1,632 47.3 1,153  33.4  2,236  64.8  1,122  32.5  2,227  64.6  Ordinal Trend Test OR < 1 OR > 1 Agents Agents % % 86 2.5 1 0.0 . 38 1.1 5 0.1 106 3.1 0.2 120 3.5 7 6.6 29 226 0.8 401 11.6 50 1.4 1621 47.0 760 22.0 852  24.7  2,598  75.3  Table 7.4: Number of Agents With Significant Ever Exposure and Ordinal Trend Results Ordinal Trend Test Ever Exposure OR < 1 OR > 1 OR p-value p < 1% 1% < p < 5% p > 5% p < 1% 1% < p < 5% p > 5% <1 <1% 1 0 0 0 0 0 0 0 5 1 0 0 1% - 5% 119 7 614 0 0 0 > 5% >1  Total  <1% 1% - 5% > 5%  Total 1 6 740  0 0 0  0 0 0  0 0 224  107 15 2  74 111 41  20 186 1,923  201 312 2,190  1  12  839  124  226  2,248  3,450  Table 7.5: Selected 30 Agents with Significant Associations Cases NIOSH Agent Name X9078 1 - Propene, 2 - Methyl - , Sulfurized 397 1, 2-Ethanediamine, RP W C IB HP a 25 X2689 206 X2305 2,5-Pyrrolidinedione, 12AE MPIB D R P a 361 X2303 2,5-Pyrrolidinedione, 12AE MPIB D a 2-Butenedioic Acid (E)-, PW 1,3-B EB Q 35 X1401 48 XI894 2-Propenoic Acid, 2M CEPWC2° 301 Alkenes, C15-18 Alpha-, RPW SDP CS S° X2307 Asphalt 499 90320 Clay, NEC 375 90590 Cyclohexylamine, N - Ethyl 28 M1150 M0984 Ethanol, 2-(2-(2-BE)E)-" 176 Ether, Tert - Buty Methyl 32 X4267 Heptane 457 36060 Hexane 477 36955 Impact Noise 545 P0620 25 Natural Gas, Liquified Y1006 Nonylphenol Ethylene OA" 80 T1909 Nonylphenoxyethanol 27 83048 OFW Steel 221 S2599 51 92500 Oil, Hydraulic 390 Phenol, Dodecyl-, Sulfurized, CCSO a X2298 335 X2306 Phosphorodithioic Acid, MOOB E ZS a 450 X2295 Phosphorodithioic Acid, OOB(2E)E ZSQ 161 X1075 Phosphorodithioic Acid, OOZS a 617 60713 POC - Gasoline (leaded)" 557 X5263 POC - Jet Fuel & Gasoline, ULD" SN, Tin - MF Unknown 420 73075 535 Solvent RD HVY PF DIST (Petroleum)" T1475 Sulfonic Acids, Petroleum, CSO° 375 X2293 Sulfonic Acids, Petroleum, MS" 208 X2308 " See appendix table B.l for agent name abbreviations b p-value  Ever Exposure OR 95% CI 1.27 1.11-1.46 2.89 1.79-4.67 1.38 1.17-1.64 1.39 1.21-1.60 2.18 1.47-3.22 1.86 1.34-2.60 1.38 1.19-1.60 1.29 1.13-1.47 1.29 1.13-1.48 2.29 1.48-3.54 1.48 1.23-1.77 1.96 1.31-2.93 1.30 1.14-1.49 1.30 1.14-1.48 1.30 1.14-1.48 3.11 1.92-5.04 1.63 1.26-2.11 2.49 1.59-3.90 1.37 1.16-1.61 1.74 1.26-2.39 1.34 1.16-1.53 1.38 1.20-1.60 1.30 1.14-1.49 1.42 1.17-1.71 1.26 1.10-1.44 1.28 1.12-1.46 1.30 1.13-1.48 1.24 1.08-1.41 1.30 1.13-1.49 1.40 1.18-1.66  Low OR Pb 0.34 1.11 0.16 1.93 0.17 1.22 0.00 1.42 0.20 1.62 0.12 1.63 0.00 1.40 0.00 1.40 0.01 1.33 0.02 2.31 0.00 1.57 0.65 1.19 0.00 1.35 0.00 1.44 0.08 1.18 0.00 2.70 0.52 1.17 0.08 2.08 0.00 1.45 0.56 0.80 0.01 1.33 0.00 1.40 0.03 1.25 0.23 1.22 0.15 1.15 0.03 1.24 0.06 1.22 0.09 1.18 0.03 1.26 0.12 1.25  Dose-Response Medium High pfc p6 OR OR 0.01 1.30 0.00 1.39 0.00 4.00 0.01 2.95 0.06 1.32 0.00 1.62 0.01 1.33 0.00 1.42 0.02 2.25 0.00 2.68 0.00 2.20 0.06 1.73 0.01 1.36 0.00 1.39 0.00 1.34 0.24 1.13 0.02 1.28 0.03 1.27 0.95 1.03 0.00 3.59 0.06 1.34 0.01 1.52 0.01 2.56 0.01 2.40 0.00 1.33 0.05 1.22 0.02 1.25 0.06 1.21 0.00 1.34 0.00 1.36 0.00 4.12 0.28 2.46 0.00 2.01 0.01 1.76 0.02 2.61 0.01 2.81 0.02 1.37 0.08 1.28 0.00 2.15 0.00 2.30 0.03 1.27 0.00 1.41 0.00 1.41 0.01 1.34 0.01 1.31 0.00 1.35 0.00 1.59 0.02 1.44 0.00 1.36 0.01 1.27 0.02 1.24 0.00 1.37 0.00 1.38 0.01 1.30 0.00 1.45 0.38 1.09 0.02 1.30 0.01 1.34 0.04 1.34 0.00 1.61  Trend Test 0.0001 <.0001 <.0001 <.0001 0.0001 0.0002 0.0001 0.0018 0.0020 0.0035 0.0002 0.0003 0.0008 0.0071 0.0001 <.0001 <.0001 0.0001 0.0022 <.0001 0.0004 0.0001 0.0003 0.0003 0.0002 0.0003 0.0002 0.0002 0.0006 <.0001  Figure 7.1: Histograms of Positive Cumulative E x p o s u r e s for Top 30 A g e n t s  36060 U)  36955  o  c o £ S ra CL  co  0  I 8 -I ro Q. o -J  10  50  30  30  20  40  Cumulative Exposure  73075  83048  90320  (0  O o J o ra CO CL  o o o  to  Q_  i—i—i—i—r~ 5 10  -1  20  0.00  30  —  10  0.20  0.10  r~  20  Cumulative Exposure  Cumulative Exposure  Cumulative Exposure  90590  92500  M0984  ra Q.  o o o  <o  r i—i—i—i i i i  —I—I—I—I 0  5  10  0 2 4 6 8  20  to CL  oo -l _ o (O "T" ~I  12  10  15  Cumulative Exposure  Cumulative Exposure  Cumulative Exposure  M1150  P0620  S2599  O o o 01 CD Q.  ra  CL —i—r 0.0  1.0  o o o co  01 CL "i—r~  "I—I—I—I 2.0  0  3.0  co  n—i  15  5  o o o  T n—r 1 2 3  0  25  Cumulative Exposure  Cumulative Exposure  Cumulative Exposure  T1475  T1909  X1075  m o c o .2 oco -I ra CL —i—i—i—r~ 1 1 0  10  35  25  Cumulative Exposure  O -i oo _^ co  uj = o§ C .D S 16 CL  15  0 5  O o o CD •<t _ Q. o - Cb_  Cumulative Exposure  0  ra  i r "i—i—i—i r I  1 T" "I 1  U> O —I  0.  o o o co  Q.  "1  o  60713  5 10  20  Cumulative Exposure  30  0  o oo co  co  CL 1—I—I—I—I—I 1 2  3  4  5 6  Cumulative Exposure  0.0  T  T  1  0.5  1.0  1.5  Cumulative Exposure  ra  Q.  oo o (D  o o o  to —  —I— T" ~I 0.00  0.10  I I I I I I I I  r~  15  10  0.20  0 5  15  25  35  Cumulative Exposure  Cumulative Exposure  Cumulative Exposure  X2295  X2298  X2303  o o o  o o o  tO -J  <0  ra  CO CL  i—r 1—i—i—r 15  0 5  ra  X2293  X1894  X1401  Q.  l  35  25  i i r ~1  0 5  15  25  i  35  5  r  15  1 I  —  Cumulative Exposure  Cumulative Exposure  Cumulative Exposure  X2305  X2306  X2307  oo _ to -  .sra  CL  °o _1 (O -  oo _  to _  CL  ~i—i—r ~1  10  25  15  I  r i—i—i—i—r  i  35  0 5  15  25  Cumulative Exposure  Cumulative Exposure  Cumulative Exposure  X2308  X2689  X4267  °o _n o to -  W O —I  I  35  25  ra  35  oo _ to -  CL  ~l  1 1  6  8  T" 0.4  T  r  0.0  10  0.2  i—r -I—i—i—i—i—i o.oo 0.10 0.20 0.30  n 0.6  Cumulative Exposure  Cumulative Exposure  Cumulative Exposure  X5263  X9078  Y1006  o o  (oD -J  <0  o o  (O J  ra  CL  CL  0  "I 10  1 20  1 30  1 40  Cumulative Exposure  °o _-i o to -  i 0  i  5 10  i  —I—I—I—I—I  n1 1 20  Cumulative Exposure  30  0.00  0.10  0.20  0.30  Cumulative Exposure  Figure 7.2: Histograms of Transformed Positive Cumulative E x p o s u r e s for Top 30 A g e n t s  36060 <2  oo -•o- -  c 15  .2  1L_  -4  0  2  o o ®  J=bi_ i—i—r ~i—i—i—i  Q_  I 1 1 1 1 1 1 -8  60713  36955  4 .  0  2  ra Q_  oo -oo i—i—i—i—i—i—i -8  4  £L  - d l  ^  -4  0  2  4  Transformed Cumulative Exposure  Transformed Cumulative Exposure  83048  90320  Transformed Cumulative Exposure  73075 w c  o  o CO i  3 -r  15  J J  CL  i  r  i  i i  0  2  4  —  —i—i —  1  -5  r  -4  Tn  -3  to c® ra CL  n  oo CD _ LL_ I—I—I—I—I—I—I  o -  -8  -2  -4  0  2  4  Transformed Cumulative Exposure  Transformed Cumulative Exposure  Transformed Cumulative Exposure  90590  92500  M0984  oo --  o  i i -8  r  I I I 0  -4  2  CO Q.  LL  n  r  -6  4  CO  in  QL  CL  "i—r i -4-2  0  I—I—I—I—I—I—I  2  -10  -6  -2  2  Transformed Cumulative Exposure  Transformed Cumulative Exposure  Transformed Cumulative Exposure  M1150  P0620  S2599  o  Tt  c  o  ffl  m=,  CM -6  - 4 - 2  0  15  o o co  CL  c  o o.  15  i—i—r -8  2  1—I  -4  0  2  o  o H CN r JZl -8  4  -6  0 1 -4-2  0  2  Transformed Cumulative Exposure  Transformed Cumulative Exposure  Transformed Cumulative Exposure  T1475  T1909  X1075  o  ° H i -8  On. I I I  r -4  0  2  4  Transformed Cumulative Exposure  c  a>  15  CL  c .9! 15  o  •<t i  r -4  1  0  1  2  Transformed Cumulative Exposure  CL  -  om I -10  I  n  I I -6  -2  I  0  Transformed Cumulative Exposure  X1401 in CO in o -1  X2293  X1894 «  -7  r d i i  r  -5  T -3  Li  ca> o nco _  ro CL  15  i  I  0.  r  i . o - i—r n—r T—I—I—I 10  -4  -2  -6  2 4  Transformed Cumulative Exposure  Transformed Cumulative Exposure  Transformed Cumulative Exposure  X2295  X2298  X2303  oo CO i  "L n l  i r  -10  - 6  -2  o 0) o  15  CL  i  2 4  r  th_ "I—I—I -4  CO co o o CO CO CL o  0 2 4  Oh. 1—I  -ri-rff "i—i—r -10  2 4  - 6  Transformed Cumulative Exposure  Transformed Cumulative Exposure  Transformed Cumulative Exposure  X2305  X2306  X2307  as  CL  -r-mT  th^ "1—i—I—I—I—I  -10  -6  -2  o° H co _  IK 1—i  i i i r  2  -10  w c0) 15 CL  2 4  -6  o oCO  -m-H"  o  Ox  i i i i r -10  -6  \  I  2 4  -2  Transformed Cumulative Exposure  Transformed Cumulative Exposure  Transformed Cumulative Exposure  X2308  X2689  X4267  oo -CM -  -HTTT  I—I—I—T -10  -6  i—i -2  .Si in I 15 Q.  2  i i r  -7  -5  1—i—  m c2 15 0.  o _ CD _ oCO _ o -  r -8  -3  i , "1  T -6  -4  -2  Transformed Cumulative Exposure  Transformed Cumulative Exposure  Transformed Cumulative Exposure  X5263  X9078  Y1006  o ° H CO  10  CL 1—i—r -4  n 0  2 4  Transformed Cumulative Exposure  oo CO -J  o _  -rl~l I—I—r  -10  -6  o -2  2 4  Transformed Cumulative Exposure  -5  T  -4  H~L -3  T  -2  -1  Transformed Cumulative Exposure  Table 7.6: Results for Linear Fit of Transformed" Cumulative Exposure for Top 30 Agents Transformed 0 Cumulative Exposure 95% CI NIOSH Agent Name Cases p-value OR 36060 HEPTANE 457 0.0001 1.003 1.001 - 1.004 1.003 1.001 - 1.004 HEXANE 477 0.0001 36955 1.002 1.001 - 1.004 POC - GASOLINE (LEADED)6 617 0.0007 60713 420 0.0002 1.003 1.001 - 1.004 73075 SN, TIN - MF UNKNOWN 1.010 1.005 - 1.014 NONYLPHENOXYETHANOL 27 <.0001 83048 0.0002 1.003 1.001 - 1.004 ASPHALT 499 90320 1.003 1.001 - 1.004 CLAY, NEC 375 0.0003 90590 OIL, HYDRAULIC 51 0.0006 1.006 1.002 - 1.009 92500 1.004 1.002 - 1.006 M0984 ETHANOL, 2-(2-(2-BE)E)-6 176 <.0001 0.0002 1.009 1.004 - 1.013 M1150 CYCLOHEXYLAMINE, N - ETHYL 28 1.003 1.001 - 1.004 P0620 IMPACT NOISE 545 0.0001 OFW STEEL 221 0.0002 1.003 1.002 - 1.005 S2599 1.002 1.001 - 1.004 SOLVENT RD HVY PF DIST (PETROLEUM) 6 0.0014 T1475 535 NONYLPHENOL ETHYLENE OA 6 0.0002 1.005 1.002 - 1.008 T1909 80 1.004 1.002 - 1.006 0.0002 161 X1075 PHOSPHORODITHIOIC ACID, OOZS6 1.008 1.004 - 1.012 X1401 2-BUTENEDIOIC ACID (E)-, PW 1,3-B EB 6 ' 35 <.0001 1.006 1.003 - 1.010 X1894 2-PROPENOIC ACID, 2M CEPWC2 6 0.0002 48 0.0002 1.003 1.001 - 1.004 X2293 SULFONIC ACIDS, PETROLEUM, CSO 6 375 1.003 1.001 - 1.004 X2295 PHOSPHORODITHIOIC ACID, OOB(2E)E ZS6 450 0.0001 PHENOL, DODECYL-, SULFURIZED, CCSO 6 1.003 1.002 - 1.004 X2298 390 <.0001 1.003 1.002 - 1.005 X2303 2,5-PYRROLIDINEDIONE, 12AE MPIB D 6 361 <.0001 2,5-PYRROLIDINEDIONE, 12AE MPIB D RPh 1.003 1.002 - 1.005 X2305 206 0.0001 1.003 1.002 - 1.005 X2306 <.0001 PHOSPHORODITHIOIC ACID, MOOB E ZS6 335 X2307 <.0001 1.003 1.002 - 1.005 ALKENES, C15-18 ALPHA-, RPW SDP CS S b 301 1.004 1.002 - 1.005 X2308 SULFONIC ACIDS, PETROLEUM, MS 6 208 <.0001 1, 2-ETHANEDIAMINE, RP W C IB HP 6 1.011 1.006 - 1.016 X2689 25 <.0001 1.007 1.003 - 1.011 X4267 ETHER, TERT - BUTYL METHYL 32 0.0010 1.002 1.001 - 1.004 POC - JET FUEL & GASOLINE, ULD6 557 0.0002 X5263 1.002 1.001 - 1.004 X9078 1 - PRQPENE, 2 - METHYL - , SULFURIZED 397 0.0006 1.012 1.007- 1.017 NATURAL GAS, LIQUIFIED <.0001 Y1006 25 a Box-Cox transformation with A = 0.01 6 See appendix table B.l for agent name abbreviations  Table 7.7: Component Scores" for PCA of the 30 Selected Agents Component Agent Name 1 2 3 4 5 6 ALKENES, C15-18 ALPHA-, RPW SDP CS S 6 99 -2 -3 -5 -3 1 98 2,5-PYRROLIDINEDIONE, 12AE MPIB D 6 . 0 -3 10 -2 1 98 PHOSPHORODITHIOIC ACID, MOOB E ZS6 -1 -3 10 1 -3 98 -1 -2 PHENOL, DODECYL-, SULFURIZED, CCSO 6 -5 1 2 97 -1 1 SULFONIC ACIDS, PETROLEUM, CSO 6 9 -4 1 96 -2 PHOSPHORODITHIOIC ACID, OOB(2E)E ZS6 3 10 -4 1 1 - PROPENE, 2 - METHYL - , SULFURIZED 76 0 56 -6 -2 0 67 17 15 POC - GASOLINE (LEADED)6 -3 20 -3 POC - JET FUEL & GASOLINE, ULD6 65 18 54 -6 -4 11 NATURAL GAS, LIQUIFIED 1 96 -1 2 4 -10 NONYLPHENOXYETHANOL 2 94 0 2 22 0 2 94 2-BUTENEDIOIC ACID (E)-, PW 1,3-B EB 6 0 2 22 1 1 92 -1 2 1, 2-ETHANEDIAMINE, RP W C IB HP 6 3 -12 ETHER, TERT - BUTYL METHYL 2 74 2 1 55 -3 ASPHALT 12 1 90 2 -2 -5 -2 HEXANE -1 82 2 1 2 -2 SN, TIN - MF UNKNOWN 61 -3 3 16 6 2,5-PYRROLIDINEDIONE, 12AE MPIB D RP 6 7 4 2 99 1 0 7 4 2 99 SULFONIC ACIDS, PETROLEUM, MS 6 1 0 OFW STEEL 0 2 4 -1 75 4 HEPTANE 0 -4 8 0 70 13 4 43 PHOSPHORODITHIOIC ACID, OOZS6 9 8 52 -10 CYCLOHEXYLAMINE, N - ETHYL 1 22 5 1 -6 85 ETHANOL, 2-(2-(2-BUTOXYETHOXY)ETHOXY)0 1 -2 0 46 15 CLAY, NEC -3 1 11 1 -2 -3 65 SOLVENT RD HVY PF DIST (PETROLEUM) 6 -1 0 -3 12 3 NONYLPHENOL ETHYLENE OA 6 1 2 4 2 0 2 IMPACT NOISE 1 0 15 2 11 0 OIL, HYDRAULIC 0 3 -5 -1 -13 5 2-PROPENOIC ACID, 2M CEPWC2 6 0 2 1 1 7 -4  7 1 0 0 1 -1 -1 3 13 5 0 1 1 0 2 4 6 1 0 0 -9 11 1 4 -4 95 65 2 7 -7 2  8 -1 0 -1 8 0 0 -4 -4 -5 2 1 1 1 0 -3 -8 19 2 2 -4 0 42 0 1 -1 9 91 -17 23 -5  9 -2 -2 -2 -2 -4 -2 -2 30 15 2 0 0 3 -2 2 17 -10 1 1 -4 5 0 -4 5 1 -1 1 77 61 0  10 0 0 0 0 1 0 9 -11 -4 0 1 1 0 2 0 2 -2 0 0 -5 12 -4 3 -4 -2 10 -4 -20 27 92  Percentage of Variance 26.30 15.40 8.49 6.75 4.29 3.69 3.42 3.35 4.87 4.09 Cumulative Percentage 26.30 41.70 50.19 56.94 61.82 66.11 70.20 73.89 77.31 80.66 a Component scores are multiplied by 100 and rounded to the nearest integer, component scores greater than 0.4 are highlighted 6 See appendix table B.l for agent name abbreviations  Component 1 2 3 4 5 6 7 8 9 10  Table 7.8: Dose-Response Results for Component Groups Dose-Response Low Medium High Cases P-value OR P-value OR P-value OR 0.65 1.05 <.01 1.32 <.01 1.39 666 1.87 2.70 0.51 0.73 0.03 <.01 40 1.32 <.01 1.40 637 <.01 1.37 <.01 1.32 1.59 0.07 1.30 0.05 <.01 208 1.24 1.31 501 0.03 <.01 1.38 <.01 1.49 1.57 0.05 1.35 <.01 177 <.01 1.37 1.34 <.01 585 <.01 0.03 1.23 0.52 1.17 1.76 <.01 2.01 80 <.01 1.14 1.40 1.35 546 0.19 <.01 <.01 0.12 2.20 1.63 0.06 1.73 <.01 48  Ordinal P-value OR <.0001 1.13 <.0001 1.36 <.0001 1.12 <.0001 1.17 0.0002 1.12 0.0002 1.17 0.0004 1.11 <.0001 1.28 <.0001 1.12 0.0002 1.32  Table 7.9: Multivariate Ordinal Results" for Component Groups Ordinal Component Cases OR 95% CI 2 40 1.25 1.07- 1.47 4 1.11 1.02-1.20 208 9 1.10 1.04-1.17 546 a p-value < 5%  Table 7.10: Results for Ever Exposure to Any of the Members of each Component Group Any Exposure Component Cases p-value OR 95% CI 1 666 0.0014 1.25 1.09 - 1.44 2 1.78 1.24 - 2.55 40 0.0017 637 1.36 1.19 - 1.56 3 <.0001 4 1.40 1.18 - 1.66 208 <.0001 501 1.31 1.15 - 1.50 5 <.0001 177 1.47 1.23 - 1.76 6 <.0001 585 1.31 1.15 - 1.50 7 <.0001 0.0002 80 1.63 1.26 - 2.11 8 1.30 1.14 - 1.48 9 546 0.0001 48 0.0002 1.86 1.34 - 2.60 10  Table 7.11: Multivariate Any Results" for Component Groups Any Component Cases OR 95% CI 3 637 1.26 1.09 - 1.45 6 177 1.25 1.03 - 1.52 1.54 1.10 - 2.17 10 48 " p-value < 5%  Table 7.12: Results for Ever Exposure to All of the Members of each Component Group All Exposed Component Cases p-value OR 95% CI 1 223 <.0001 1.42 1.21 - 1.67 2 25 <.0001 3.11 1.92 - 5.04 1.21 1.04 - 1.40 295 3 0.0113 4 0.0002 206 1.38 1.17 - 1.64 1.47 1.15 - 1.87 5 89 0.0020 6 27 <.0001 2.49 1.59 - 3.90 0.0052 7 325 1.23 1.06 - 1.41 0.0002 1.63 1.26 - 2.11 8 80 1.74 1.26 - 2.41 9 50 0.0008 0.0002 1.86 1.34 - 2.60 10 48  Table 7.13: Multivariate All Results" for Component Groups All Exposed Component Cases OR 95% CI 1 223 1.32 1.12 - 1.57 2 25 2.47 1.49 - 4.08 " p-value < 5%  Figure 7.3: Proportion of Cumulative Exposure Due to Employment in Each US Job for the Top 30 Agents  • Gasoline service stations - related occupations Q Motor vehicles and equipment - Supervisors production IZ Gasoline service stations - Automobile mechanics H Hospitals - Power plant operators CD Construction - Insulation workers a Automotive repair shops - Automobile mechanics 13 Ship and boat building - Carpenters Q Pulp paper & paperboard mills - Misc. electronic equipment repairers • Automotive repair shops - Bus truck & stationary engine mechanics • Construction - Plumbers pipefitters and steamfitters • Other  • Timber cutting and logging occupations • Ship & boat building - Lathe & turning machine set-up op. H Construction - Electrical power installers & repairers Ea Trucking service - Truck drivers heavy E3 Automotive repair shops - Automobile body repairers • Construction - Plumber pipefitter & steamfitter apprentices CI Apparel & accessories - Knitting looping taping & weaving mach. op. ffl Meat products - Bus truck & stationary engine mechanics • Water transportation - Marine engineers Q Sawmills planing mills and millwork - Stationary engineers  Table 7.14: Properties of the Selected 30 Agents d CAS IARC" Cases P C b Most Common US Job Agent Name % c JEM% e ALKENES, C15-18 ALPHA-, RPW SDP CS S 72275-86-6 301 1 Timber cutting & logging 85 52 67762-72-5 361 1 Timber cutting & logging 52 2,5-PYRROLIDINEDIONE, 12AE MPIB D e 79 68784-31-6 1 Timber cutting & logging 52 335 80 PHOSPHORODITHIOIC ACID, MOOB E ZS e 68784-26-9 390 1 Timber cutting & logging 73 52 PHENOL, DODECYL-, SULFURIZED, CCSO e 68783-96-0 375 1 Timber cutting & logging 67 54 SULFONIC ACIDS, PETROLEUM, CSO e 450 1 Timber cutting & logging 54 60 PHOSPHORODITHIOIC ACID, OOB(2E)E ZS e 4259-15-8 68511-50-2 397 1 Timber cutting & logging 44 54 1 - PROPENE, 2 - METHYL - , SULFURIZED 2B 617 1 Timber cutting & logging 64 23 POC - GASOLINE (LEADED) 6 557 1 Timber cutting & logging 28 56 POC - JET FUEL & GASOLINE, ULD e NATURAL GAS, LIQUIFIED 25 2 Gasoline service station related 100 1 NONYLPHENOXYETHANOL 27986-36-3 27 2 Gasoline service stations related 74 1 24938-12-3 35 2 Gasoline service station related 1 66 2-BUTENEDIOIC ACID (E)-, PW 1,3-B EB e 68891-84-9 25 2 Gasoline service station related 94 3 1, 2-ETHANEDIAMINE, RP W C IB HP e 1634-04-4 32 2 Gasoline service station related 1 3 48 ETHER, TERT - BUTYL METHYL ASPHALT 8052-42-4 3 499 3 Con.-Plumber pipe & steam fitter ap. 27 100 110-54-3 477 3 Con.-Plumber pipe & steam fitter ap. 29 100 HEXANE 7440-31-5 420 3 Con.-Plumbers pipe & steam fitter 16 40 SN, TIN - MF UNKNOWN 2,5-PYRROLIDINEDIONE, 12AE MPIB D RP e 72269-41-1 206 4 Ship & boat building - Carpenter 26 18 61789-87-5 208 4 Ship & boat building - Carpenter 25 18 SULFONIC ACIDS, PETROLEUM, MS e 221 5 Automotive repair-body repairers 33 8 OFW STEEL 142-82-5 457 5 Automotive repair-Bus truck & sta- 20 95 HEPTANE tionary engine mechanic 26566-95-0 161 5 Trucking service - Truck drivers heavy 41 2 PHOSPHORODITHIOIC ACID, OOZSe CYCLOHEXYLAMINE, N - ETHYL 5459-93-8 3 28 6 Gasoline service stations-mechanic 59 23 Con.-Electrical power installer 143-22-6 176 6 100 53 ETHANOL, 2-(2-(2-BE)E)-e 375 7 Construction-Insulation worker 39 32 CLAY, NEC Timber cutting & logging 52 535 7 37 1/3 SOLVENT RD HVY PF DIST (PETROLEUM) e 64741-88-4 NONYLPHENOL ETHYLENE OA e 26027-38-3 80 8 Ship & boat building - Lathe & turn- 64 100 ing machine set-up op. IMPACT NOISE 545 9 Construction - Carpenters 13 17 51 9 Motor vehicles & equipment - Super- 78 100 1/3 OIL, HYDRAULIC visor production 2-PROPENOIC ACID, 2M CEPWC2 6 66057-34-9 48 10 Hospitals - Power plant operator 51 100 ° IARC classification where 1 is definitely carcinogenic, 2B is possibly carcinogenic, and 3 is not classifiable. b Principal component number c Percentage of total cumulative exposure of 9119 patients due to employment in the US Job in column 6. d Proportion of NIOSH NOES study participants employed in the US job in column 6 exposed to the agent. e See appendix table B.l for agent name abbreviations  Chemical Name 4-Aminobiphenyl (xenylamine) N,N-Bis(2-CE)-2-NL6 2-Naphthylamine Benzidine Cyclophosphamide Magenta Arsenic Coal tar pitches Mineral oils6 4,4'-Methylenebis(2-chloroaniline) Adriamycin para-Chloro-ortho-Toluidine Phenacetin Benzidine based dyes  IARC Q 1 1 1 1 1 1 1 1 1 2A 2A 2A 2A 2A  Benz(a)anthracene Trichloroethylene Tetrachloroethylene Diesel engine emissions Engine emissions  2A 2A 2A 2A 2A  N-[4-(5-Nitro-2-Furyl)2TZ]A 6 N-Nitrosodi-n-butylamine Oil Orange SS para-Cresidine 2-(2-Formylhydrazino)-4(5N2F)T 6 2-Nitroanisole 3,3'-Dichlorobenzidine 4-chloro-ortho-phenylenediamine Niridazole Citrus Red No. 2 Auramine  2B 2B 2B 2B 2B 2B 2B 2B 2B 2B 2B  CAS 92-67-1 494-03-1 91-59-8 92-87-5 50-18-0 632-99-5 7440-38-2 65996-93-2 8002-05-9 101-14-4 23214-92-8 95-69-2 62-44-2 62-44-2 1937-37-7 2602-46-2 16071-86-6 56-55-3 79-01-6 127-18-4  531-82-8 924-16-3 2646-17-5 120-71-8 3570-75-0 91-23-6 91-94-1 95-83-0 61-57-4 6358-53-8 492-80-8  NIOSH Name Naphthylamine, betaBenzidine Oxazaphosphorine, 2-(bis6 CI Basic Violet 14, MHC6 Arsenic Pitch, Coal tar Petroleum Aniline, 4,4'-methylenebis6 Adriamycin Toludine, 4-chloro-, orthoAcetophenetidide, para Phenacetin, powder C.I. Direct black 38, DS 6 C.I. Direct blue 6, TS 6 C.I. Direct brown 95, DS 6 Benz (a) anthracene Ethylene, trichloro Tetrachloroethylene POC - Diesel fuels POC - Gasoline (leaded) POC - Gasoline (lead CU) 6  Cases  Dose-Response Medium High  Ever  Low  Ordinal  0.69 0.80 1.04 1.02  0.54 0.78 1.10 1.22  0.49 0.76 1.03 0.98  1.07 0.86 1.01 0.89  0.43 0.40 0.84 0.70  1.36  1.55  1.14  1.46  0.39  1.92* 1.21** 1.25** 1.18* 1.26** 1.11  2.65* 1.10 1.24* 1.14 1.15 1.54  1.38 1.33** 1.32** 1.17 1.27* 0.72  1.69 1.20 1.19 1.25* 1.36** 1.06  0.12 <.01 <.01 0.01 <.01 0.91  + + +  12 23 89 152  + + + 4  +  12  + +  13 345 470 605 617 40  0.78  Dose-Response CAS NIOSH Name Cases Ever Low Medium High Ordinal 12789-03-6 Chlordane + 2475-45-8 Anthraquinone, 1,4,5,8-ta-6 + 8006-61-9 Gasoline, natural + 97-56-3 C.I. Solvent yellow 3 + 60-11-7 C.I. Solvent yellow 2 + 3564-09-8 Ponceau-3R + 2.44 4 569-61-9 C.I. Basic red 9, MHC 6 119-90-4 Benzidene, 3, 3' dimethoxy 1.69 8 542-75-6 Propene, 1 , 3 - dichloro 12 1.59 1.63 1.58 1.57 0.20 67-66-3 Chloroform 27 0.99 0.69 1.26 1.06 0.81 139-13-9 Nitrilotriacetic Acid 31 1.14 0.71 0.37 0.70 0.55 132-27-4 Biphenol, sodium salt, 2 114 1.02 0.96 0.92 0.88 1.18 7439-92-1 PB, lead - MF Unk 396 1.25** 1.18 1.30* 1.28* <.01 7439-92-1 PB, lead powder - MF Unk 14 7439-92-1 PB, lead - pure + 7439-92-1 PB, lead fume - MF Unk + 0.32 1333-86-4 Carbon black 554 1.08 1.03 1.10 2B 1.10 Carbon black 1333-86-4 Carbon lampblack, powder + a IARC classification where 1 is definitely carcinogenic, 2A is probably carcinogenic, and 2B is possibly carcinogenic. 6 2-(2-Formylhydrazino)-4(5N2F)T = 2-(2-Formylhydrazino)-4-(5-Nitro-2-Furyl) Thiazole, N-[4-(5-Nitro-2-Furyl)2TZ]A = N-[4-(5-Nitro-2-Furyl)-2Thiazolyl] Acetamide. Mineral oils = Mineral oils, untreated or mildly treated. Oxazaphosphorine, 2-(bis = Oxazaphosphorine, 2-(bis(2-chloroethyl) amino)tetrahydro-, 2-oxide, 2H-1, 3, 2-. MHC = monohydrochloride, DS = disodium salt, TS = tetrasodium salt, CU = content unknown, ta = tetraamino. N,N-bis(2-CE)-2-NL = N,N-bis(2-Chloroethyl)-2-naphthylamine (Chlornaphazine). + Less than 3 cases exposed * Significant at a 5% alpha level ** Significant at a 1% alpha level  Chemical Name Chlordane Disperse Blue 1 Gasoline ortho-Aminoazotoluene para-Dimethylaminobenzene Ponceau 3R CI Basic Red 9 3,3'-Dimethoxybenzidine 1,3-Dichloropropene Chloroform (in drinking water) Nitrilotriacetic Acid Sodium ortho-phenylphenate Lead  IARC" 2B 2B 2B 2B 2B 2B 2B 2B 2B 2B 2B 2B 2B  Siemiatycki Chemical Natural gas comb, products Carbon tetrachloride Diesel engine emissions Laboratory products Cadmium compounds Fabric dust Photographic products Chlorine Polyester fibers Asphalt (bitumen) Ammonia Formaldehyde Engine emissions (leaded or unleaded) ionizing radiation Acrylic fibres Calcium carbonate  Any 1.6* 1.6 1.4 1.5 1.6 1 2.5 1 1.4 0.9 1.2 1.2 1.2*  Sub" 3.8** 2.5** 2.3** 5.5* 4.9* 3.7* 2.9* 2.7* 2.5* 2.2* 2.1* 1.7* 1.3*  44** 3.9** 1.9**  0 3.3 1.6  Titanium dioxide Titanium compounds Hydrogen cyanide Creosote Polyethylene  ^ y** 7** 3.4* 2.6* 2.5*  4.5 2.2 0 2.6 13  Carbon black  2.2*  1.8  Clay dust Lead chromate  2.2* 1.8*  1.8 2.2  CAS 56-23-5  7782-50-5 80595-68-2 8052-42-4 7664-41-7 50-00-0  NIOSH Name  Cases  Ever  Low  Medium  High  Ordinal  Carbon tetrachloride POC - Diesel fuels  229 605  1.11 1.18f  1.20 1.14  1.12 1.17  1.01 1.25f  0.46 0.01  Fabric dust-synthetic  + 0.98 0.98 1.29ft 1.06 1.15| 1.26ft 1.11 0.57f  1.04 0.90 1.40 ft 1.15 1.18 1.15 1.54 0.49  0.93 1.00 1.13 1.09 1.07 1.27f 0.72 0.98  0.98 1.05 1.34ft 0.94 1.18 1.36ft 1.06 0.27f  0.80 0.98 <.01 0.90 0.08 <.01 0.91 0.02  Chlorine Polyester fibers (MF Unk.) Asphalt Ammonia Formaldehyde POC - Gasoline (Leaded) POC - Gasoline (Lead CU) Ionizing Radiation Acrylic fibers (MF Unk.) Calcium carbonate, powder Carbonic acid, calcium salt Marble, dust Limestone Limestone, powder Titanium oxide (Tf02)  136 12 499 336 489 617 40 18 652 38 470 290 654  1.16f 1.35 1.22ft 1.21f 1.20ft  1.19 1.76f 1.29ft 1.24 1.19  1.11 1.34 1.12 1.1 1.22f  1.18 0.97 1.24f 1.28f 1.20  0.09 0.34 0.02 0.02 0.02  9002-88-4 9002-88-4 9002-88-4 1333-86-4 1333-86-4  Hydrogen cyanide Creosote Polyethylene wax Ethylene, polymers Polyethylene, fiber Carbon black Carbon lampblack, powder  13 130 194 470 11 554  1.23 1.08 1.09 1.11 1.67 1.08  1.14 0.98 1.01 1.11 3.08f 1.1  1.21 1.17 1.13 1.06 0.81 1.03  1.34 1.12 1.13 1.14 1.3 1.1  0.48 0.34 0.25 0.15 0.40 0.32  7758-97-6 7758-97-6  Lead chromate Chromic acid, lead(2+) salt  98 24  1.12 1.03  0.99 1.37  1.15 0.79  1.22 0.98  0.23 0.90  471-34-1 471-34-1 471-34-1 1317-65-3 1317-65-3 13463-67-7 74-90-8  + +  +  1.4* 1.6 Aliphatic aldehydes Lead compounds 1.3* 1.1 a Odds-ratio for substantial exposure. -I- Less than 3 cases exposed, f Significant at a 5% alpha level, ff Significant at a 1% alpha level ** Significant at p=0.05, one-sided, with at least 5 exposed cases. * Significant at p=0.10, one-sided, with at least 4 exposed  Appendix A  NIOSH 01038 01568 01600 02740 02820 02900 03298 03530 03540 03570 03800 04280 04530 04580 04605 04620 04980 05250 05270 06063 06145 06163 06175 06580 07310 07325 07485 07545 08625 08640 08650 08655 09070 10210 11280 11360 11590  Cases 107 407 205 280 559 17 119 10 29 238 164 13 14 15 63 255 494 336 308 14 481 105 238 27 288 24 108 23 19 168 71 234 285 75 169 165 45  Table A.l: Low Ever 0.88 0.83 1.05 1.13 1.18 1.08 1.24** 1.24 1.25** 1.24* 1.12 0.81 1.04 1.09 1.85 2.13 0.79 1.03 1.12 1.16 1.24 1.06 1.55 1.30 1.01 1.06 0.82 0.66 1.13 0.98 1.37** 1.13 1.24* 1.14 1.15 1.06 1.05 1.05 0.39 0.89 1.11 1.13 1.04 1.03 1.24 1.13 1.41 1.74 1.23** 1.13 0.84 0.96 1.12 1.26 0.78 0.80 1.02 1.36 0.83 1.05 1.07 1.03 1.10 0.99 1.16 1.39** 1.02 0.99 1.11 1.09 1.17 1.09 1.34 1.45  Odds-Ratios of Ever Exposure and Dose-Response Results for 3,450 Agents" NIOSH Cases Ever Low Medium Medium High Ordinal 0.86 0.93 0.35 11600 25 0.95 1.57 0.48 1.12 0.92 11610 114 1.03 1.25 1.12 0.91 0.93 0.95 0.74 1.16 0.89 0.86 11770 15 0.02 1.14 1.02 0.75 12783 27 1.36** 1.13 214 1.16 1.10 1.13 1.19 <.01 12845 1.33** 1.16 0.72 324 1.03 1.13 1.88 0.35 12960 0.97 0.83 1.05 1.21 1.03 0.47 12963 165 1.41 1.22 1.27 0.90 1.86 0.18 13100 61 1.12 1.25 0.94 0.93 1.36 0.59 13410 61 1.22 1.15* 1.14 1.22* 0.15 13480 459 0.99 1.27* 1.13 0.91 13850 435 1.18* 0.95 0.99 514 1.21** 1.20 1.20 0.98 1.35 0.49 13980 1.22 441 1.13 1.12 1.06 0.78 14380 0.95 1.12 0.94 1.25 0.86 0.60 14400 210 0.95 1.01 0.91 1.13 0.97 0.83 0.61 14410 181 0.42 1.27** 1.10 1.29 0.95 1.09 14720 223 0.95 0.66 0.95 1.05 1.13 0.20 14730 16 164 1.10 1.24 1.10 0.94 0.90 15570 1.09 0.62 652 1.16* 1.19 1.11 1.10 1.01 15705 1.14 1.25* 1.05 1.39 0.86 0.93 15720 317 1.24** 1.27* 1.15 1.14 0.17 15743 457 1.06 0.72 1.05 1.02 1.07 1.04 15746 171 1.09 0.34 464 1.16* 1.10 1.21 1.13 1.03 15755 1.17* 1.19 1.06 1.49 15765 378 0.89 0.20 1.04 1.04 1.09 1.28* 1.28* <.01 15800 103 1.21** 1.09 1.29** 1.41 0.68 0.78 17366 476 1.21** 1.16 502 1.15 1.14 0.96 0.63 17367 0.16 1.65 23 1.29 0.76 0.86 0.40 17370 29 0.95 0.60 1.12 1.19 0.50 0.66 17385 1.17* 1.20 1.01 0.17 17460 271 1.00 1.31* 1.11 1.20 1.12 1.09 1.09 0.59 17490 229 372 1.19* 1.18 1.28* 1.22 1.08 0.23 17525 0.92 0.57 437 1.15* 1.13 1.10 1.16 17683 1.17* 1.43** 0.96 0.68 1.30 0.75 17695 248 0.98 1.04 0.93 1.08 1.16 0.26 18040 136 0.94 0.41 1.07 0.85 1.40 1.16 18045 29 1.42* 1.10 1.80* 1.36 1.23 0.15 18190 55  High 0.88 0.72 1.10 1.68 1.24 0.81 1.03 1.49 1.17 1.09 1.15 1.21* 1.21 1.18 0.97 1.44** 1.19 0.97 1.18 1.11 1.30** 1.18 1.18 1.26* 0.98 1.25* 1.31** 2.18* 1.11 1.29* 1.01 1.11 1.24* 1.12 0.98 0.97 1.50  Ordinal 0.47 0.54 0.87 0.30 0.06 0.50 0.92 0.14 0.53 0.11 0.08 0.01 0.06 0.09 0.88 <.01 0.92 0.63 0.09 0.23 <.01 0.28 0.03 0.03 0.90 <.01 <.01 0.03 0.88 0.04 0.46 0.05 0.03 0.26 0.80 0.70 0.01  NIOSH 18260 18500 19130 19360 19380 19395 19425 19430 19540 19680 19710 19767 19770 19935 19985 20115 20155 20245 20265 20340 20380 20810 20850 20900 21190 21560 21660 22734 23180 23275 23360 24003 24006 24095 24130 24235 24425  Cases 119 27 15 88 32 363 10 71 58 387 81 94 142 22 187 495 16 13 560 117 193 237 16 117 87 66 364 38 10 34 9 207 12 349 47 26 11  Ever 1.28* 0.99 0.84 0.99 0.93 1.16* 1.18 1.05 0.95 1.12 1.03 0.92 1.05 1.02 1.18 1.21** 1.10 1.19 1.23** 1.07 1.26** 1.16 1.16 0.97 1.11 1.03 1.16* 1.11 0.67 1.56* 1.57 1.17 0.73 1.11 0.92 1.27 1.18  Low 1.33 0.69 0.40 1.01 0.96 1.27* 2.42* 1.22 1.18 1.13 1.04 1.18 1.00 0.68 1.31* 1.25* 1.73 0.98 1.29** 1.04 1.29 1.13 1.18 1.01 0.97 0.98 1.26* 1.32 0.30 1.08 1.77 0.97 1.33 1.18 0.99 1.31 1.04  Medium 1.23 1.26 0.84 0.83 1.28 1.24* 0.00 0.98 0.90 1.22* 1.14 0.83 1.13 1.08 1.07 1.15 1.02 1.31 1.14 1.10 1.13 1.20 1.58 0.97 1.37 1.05 0.93 1.02 1.94 1.67 2.32 1.39* 0.38 1.14 0.95 1.53 0.69  High 1.28 1.06 1.20 1.13 0.53 0.97 1.22 0.93 0.78 1.00 0.93 0.78 1.02 1.32 1.17 1.24* 0.60 1.33 1.25* 1.06 1.36* 1.16 0.73 0.94 0.99 1.05 1.29* 0.98 0.38 1.93* 0.52 1.14 0.51 1.02 0.81 0.93 1.78  Ordinal 0.04 0.81 0.90 0.95 047 0.30 0.94 0.97 0.43 0.32 0.93 0.22 0.63 0.61 0.14 0.01 0.78 0.49 0.01 0.57 0.01 0.08 0.81 0.72 0.41 0.79 0.05 0.80 0.35 0.01 0.46 0.05 0.16 0.39 0.51 0.44 0.44  NIOSH 24615 24680 24930 25145 25210 25544 25820 26075 26095 26130 26335 26560 26615 26880 26940 27590 27615 27760 27780 28510 29010 29325 29930 31350 31470 31490 31500 31830 31900 31970 32220 32385 32500 32550 32590 32925 32940  Cases 418 14 73 390 259 545 168 207 23 101 67 237 590 10 56 179 288 22 130 36 206 29 565 248 418 186 497 300 18 136 153 619 50 115 342 52 61  Ever 1.15* 1.13 1.05 1.08 1.24** 1.23** 1.00 1.11 1.09 1.01 0.99 1.17 1.19* 1.39 1.30 1.16 1.16* 1.26 1.07 1.12 1.18 0.93 1.16* 1.01 1.25** 1.15 1.11 1.11 1.25 1.02 1.05 1.23** 0.97 0.87 1.23** 0.95 0.96  Low 1.22* 0.98 1.17 1.06 1.33* 1.15 0.93 0.95 0.99 1.08 1.07 1.06 1.24* 1.23 1.63* 1.27 1.09 2.09* 1.08 1.32 1.13 1.24 1.20 1.06 1.17 1.26 1.19 1.22 1.41 1.23 0.97 1.33** 1.53 0.81 1.17 0.99 0.89  Medium 1.22* 1.42 1.04 1.09 1.02 1.17 1.05 1.18 0.90 0.77 0.83 1.20 1.13 1.86 1.08 1.03 1.18 1.28 1.17 1.21 1.18 1.03 1.27** 0.98 1.32** 1.12 1.17 1.06 1.02 0.98 1.16 1.17 0.76 0.81 1.29* 1.17 1.26  High 1.01 0.96 0.94 1.11 1.35* 1.37** 1.01 1.21 1.39 1.17 1.03 1.26 1.20 0.97 1.19 1.18 1.22 0.49 0.98 0.83 1.23 0.49 1.03 0.97 1.26* 1.08 0.98 1.06 1.33 0.87 1.03 1.19 0.66 0.97 1.23 0.77 0.82  Ordinal 0.26 0.71 0.97 0.23 0.01 <.01 0.90 0.10 0.54 0.85 0.88 0.03 0.04 0.43 0.23 0.19 0.03 0.99 0.64 0.87 0.05 0.34 0.21 0.87 <.01 0.27 0.54 0.38 0.46 0.69 0.55 0.05 0.29 0.35 <.01 0.58 0.72  NIOSH 33115 33160 33165 33230 33235 33307 33350 33370 33415 33565 33595 33635 33640 33675 33720 33850 33940 34120 34370 34715 35085 35120 35260 35455 35505 35755 35925 35927 36060 36330 36340 36710 36955 37330 37510 37630 38110  Cases 325 62 25 39 243 273 146 20 18 223 203 58 489 277 201 10 107 218 201 142 511 10 51 31 213 13 49 128 457 29 82 19 477 12 450 394 62  Ever 1.14 1.04 0.91 0.82 1.09 1.18* 1.08 0.86 1.03 1.02 1.27** 0.83 1.15* 1.16 1.00 1.15 0.89 1.16 1.10 0.97 1.15* 1.31 0.81 1.18 1.15 1.07 0.96 1.13 1.30** 1.29 1.16 0.76 1.30** 1.25 1.17* 1.08 1.04  Low 1.35** 0.78 0.92 0.60 1.23 1.24 0.98 0.56 0.88 0.87 1.39* 0.79 1.18 1.16 0.93 0.72 1.01 1.09 1.13 1.17 1.09 0.45 0.85 1.14 1.24 1.07 0.82 1.30 1.35** 1.57 1.32 1.17 1.44** 1.80 1.23* 1.07 1.52*  Medium 1.10 1.07 1.06 0.63 1.13 1.20 1.12 0.28 1.19 1.09 1.26 0.84 1.07 1.14 0.91 1.41 0.86 1.13 1.09 0.95 1.26* 1.30 0.79 1.65 1.12 0.90 1.03 1.03 1.22 1.19 0.85 0.56 1.25* 0.82 1.18 1.01 1.00  High 0.97 1.29 0.76 1.26 0.91 1.09 1.13 1.62 0.99 1.09 1.14 0.85 1.18 1.18 1.16 1.28 0.81 1.26 1.09 0.79 1.11 2.12 0.79 0.78 1.09 1.22 1.01 1.04 1.33** 1.13 1.32 0.51 1.21 0.92 1.12 1.15 0.62  Ordinal 0.56 0.40 0.62 0.69 0.83 0.12 0.33 0.85 0.86 0.48 0.04 0.26 0.08 0.08 0.59 0.56 0.20 0.05 0.34 0.31 0.06 0.21 0.18 0.62 0.23 0.77 0.94 0.54 <.01 0.40 0.28 0.12 <.01 0.85 0.08 0.24 0.44  NIOSH 38530 38550 38575 38580 38585 38605 38620 38670 38950 40030 40297 40370 40380 40410 40430 40910 40984 40987 41775 42355 42490 42685 43040 43320 43360 43410 43660 44000 44030 44440 44870 45655 45850 45930 46240 46470 46935  Cases 13 126 23 446 161 327 243 37 14 54 337 123 52 193 274 28 132 664 516 44 396 10 30 274 179 189 100 453 93 22 38 18 92 584 130 11 13  Ever 1.23 1.24* 1.02 1.12 1.20 1.13 1.29** 1.03 1.35 0.93 1.21* 1.27* 1.27 1.24* 1.05 0.97 1.31** 1.24** 1.26** 1.45* 1.25** 0.91 0.87 1.19* 1.20* 1.13 1.12 1.18* 1.03 0.88 0.98 0.77 1.15 1.16* 1.04 1.65 1.30  Low 1.14 1.24 0.99 1.20 1.29 1.22 1.30* 0.80 1.06 1.14 1.19 1.32 1.01 1.21 1.01 1.11 1.65** 1.27** 1.31** 1.82* 1.18 0.81 1.18 1.03 1.19 1.33* 1.00 1.23* 1.02 0.76 1.19 0.64 1.08 1.19 1.12 0.52 0.29  Medium 1.21 1.26 1.35 1.12 1.11 1.04 1.25 1.72* 2.13 0.87 1.21 1.44* 1.56 1.35* 1.15 0.57 1.22 1.40** 1.19 1.72 1.30* 0.78 0.65 1.42** 1.24 1.09 1.28 1.13 0.87 1.26 0.97 0.82 1.21 1.17 0.96 2.42 2.18  High 1.34 1.22 0.61 1.06 1.19 1.13 1.32* 0.52 0.89 0.74 1.21 1.05 1.23 1.17 1.00 1.27 1.05 1.07 1.28** 0.90 1.28* 1.17 0.82 1.12 1.17 0.96 1.05 1.17 1.19 0.66 0.80 0.85 1.16 1.12 1.04 1.65 1.51  Ordinal 0.48 0.07 0.89 0.30 0.11 0.20 <.01 0.94  0.38 0.35 0.02 0.08 0.11 0.03 0.56 0.98 0.13 0.08 <.01 0.20 <.01 0.93 0.35 0.02 0.07 0.59 0.34 0.05 0.63 0.56 0.66 0.42 0.24 0.12 0.85 0.09 0.19  NIOSH 46970 47030 47270 47700 48320 48535 48625 48910 49600 50195 50420 50440 50470 50480 50510 50742 50795 50865 50870 50888 50890 50910 51090 51100 51118 51705 51910 52132 52136 52138 52141 52142 52145 52190 52480 53615 54160  Cases 595 128 537 103 309 395 282 307 241 170 397 13 12 34 53 176 9 258 136 135 57 308 17 104 171 102 24 335 17 30 605 38 117 477 137 21 141  Ever 1.20** 1.10 1.18* 1.02 1.08 1.26** 1.14 1.14 1.18* 1.11 1.20** 0.76 1.34 0.98 1.18 1.04 1.14 1.00 1.27* 1.31** 0.91 1.12 1.01 1.18 1.14 1.24 0.99 1.15 1.15 1.96** 1.27** 1.08 1.18 1.15* 0.95 1.19 1.28*  Low 1.23* 1.47* 1.20 1.01 1.19 1.44** 1.15 1.18 1.27 1.49** 1.24* 1.37 2.67* 0.71 1.15 0.90 0.39 0.95 1.49* 1.49* 1.00 1.12 1.14 1.30 1.44** 1.28 1.34 1.12 1.28 2.29** 1.29** 0.99 1.05 1.22* 1.00 1.28 1.59**  Medium 1.26* 0.88 1.22* 0.92 1.13 0.95 1.16 1.22 1.21 1.01 1.20 0.33 0.64 1.11 1.12 1.14 1.36 1.00 0.98 1.06 1.03 1.13 1.10 0.98 0.95 1.18 0.96 1.19 1.23 1.54 1.24* 1.04 1.21 1.20 0.90 1.18 1.17  High 1.10 0.97 1.14 1.15 0.93 1.39** 1.10 1.02 1.06 0.81 1.16 0.58 0.71 1.12 1.27 1.09 1.68 1.04 1.34 1.40* 0.70 1.11 0.77 1.26 1.03 1.25 0.71 1.13 0.95 1.86 1.27* 1.18 1.30 1.03 0.95 1.10 1.06  Ordinal 0.08 0.93 0.05 0.70 0.81 <.01 0.15 0.25 0.15 0.81 0.04 0.18 0.98 0.80 0.27 0.42 0.42 0.83 0.05 0.02 0.31 0.17 0.87 0.21 0.57 0.09 0.64 0.08 0.76 <.01 <.01 0.59 0.08 0.25 0.55 0.58 0.15  NIOSH 54185 54243 54480 54790 55460 56240 57210 57280 57340 57740 58520 59115 59185 59210 59230 59450 59465 60122 60125 60297 60315 60350 60360 60370 60400 60410 60420 60440 60490 60540 60570 60711 60712 60713 60714 60717 60721  Cases 380 126 110 470 463 76 66 22 194 10 513 129 58 109 113 61 161 145 29 33 26 374 208 29 21 35 119 519 52 204 18 87 180 617 40 68 184  Ever 1.14 1.18 1.14 1.25** 1.16* 1.19 1.18 1.37 1.15 2.08 1.13 0.99 1.29 1.28* 1.20 1.22 1.14 1.18 1.56* 1.04 0.90 1.12 1.12 2.01** 0.85 1.42 1.28* 1.17* 1.03 1.18 1.22 1.01 1.38** 1.26** 1.11 0.99 1.21*  Low 1.03 1.10 0.96 1.24* 1.16 1.01 1.38 1.57 1.07 2.54 1.11 1.07 1.23 1.43* 1.37 1.42 1.29 1.54** 2.11* 1.15 0.75 1.14 1.00 2.55** 1.38 2.03* 1.10 1.26* 1.41 1.24 1.77 1.25 1.30 1.15 1.54 1.07 1.25  Medium 1.37** 1.13 1.20 1.32** 1.14 1.68** 0.88 1.69 1.27 2.15 1.17 0.98 1.38 1.29 1.06 0.82 1.01 0.82 1.61 0.89 0.78 1.21 1.23 2.62** 0.35 1.41 1.44* 1.17 0.75 1.01 1.17 0.73 1.49** 1.27* 0.72 1.00 1.15  High 1.02 1.31 1.24 1.19 1.18 0.91 1.31 0.89 1.10 1.62 1.10 0.93 1.27 1.12 1.16 1.38 1.11 1.16 0.96 1.08 1.14 1.01 1.13 0.92 0.86 0.90 1.31 1.08 0.95 1.29 0.65 1.03 1.34 1.36** 1.06 0.88 1.21  Ordinal 0.11 0.08 0.15 <.01 0.05 0.26 0.30 0.39 0.14 0.11 0.13 0.76 0.11 0.11 0.22 0.24 0.35 0.38 0.19 0.91 0.87 0.29 0.14 0.02 0.30 0.34 0.02 0.18 0.76 0.06 0.93 0.82 <.01 <.01 0.91 0.72 0.07  NIOSH 62000 62460 63265 63525 63550 65080 66495 66950 67220 67405 67410 67537 67680 67915 67918 68208 68295 68508 68509 68512 68657 68695 68730 68765 68766 68768 68770 68820 68850 68870 68880 68900 68905 68950 69000 69055 69070  Cases 45 34 71 603 189 20 518 61 37 23 30 300 46 314 10 12 16 32 65 110 584 469 204 298 188 12 185 13 601 28 515 100 204 39 434 292 599  Ever 0.90 1.18 1.11 1.11 1.09 1.11 1.22** 1.46* 0.75 0.93 0.94 1.26** 0.93 1.21** 1.90 1.16 0.85 1.36 0.99 0.93 1.16* 1.23** . 1.12 1.12 1.20* 1.70 1.00 0.80 1.18* 0.90 1.16* 1.22 1.15 1.09 1.14 1.03 1.12  Low 1.25 1.43 1.22 1.15 1.10 1.11 1.32** 1.60* 0.67 0.99 0.45 1.35** 1.21 1.35** 2.36 1.67 1.03 1.69 0.97 0.93 1.25* 1.21 1.20 1.00 1.40* 0.00 1.23 0.81 1.29** 0.91 1.24* 1.37 1.24 1.27 1.20 0.99 1.11  Medium 1.00 0.90 1.25 1.13 1.18 0.65 1.05 1.06 0.85 1.51 0.70 1.15 0.88 1.12 2.64 0.55 1.16 1.10 0.96 0.95 1.06 1.22* 0.95 1.20 1.05 1.30 0.86 0.53 1.18 0.52 1.14 1.60** 1.07 1.34 1.16 1.10 1.16  High Ordinal 0.49* 0.17 1.25 0.49 0.88 0.75 1.06 0.33 0.99 0.48 1.56 0.52 1.31** <.01 1.69* 0.02 0.74 0.16 0.34 0.41 1.62 0.55 1.27* 0.01 0.70 0.34 1.17 0.06 0.61 0.27 1.29 0.82 0.41 0.32 1.24 0.27 1.04 1.00 0.92 0.54 1.17 0.12 1.27* <.01 1.21 0.22 1.17 0.07 1.14 0.18 4.83** <.01 0.90 0.47 1.10 0.61 1.06 0.28 1.30 0.86 1.11 0.14 0.68 - 0.50 1.12 0.24 0.66 0.92 1.05 0.23 1.00 0.74 1.08 0.21  NIOSH 69090 69120 69220 69230 69270 69330 69375 69445 69460 69470 69715 69730 69740 69855 70130 70131 70845 70860 70865 70870 70995 71025 71055 71058 71095 71640 71695 71900 72200 73075 73255 73300 73390 73470 73525 73730 73790  Cases 203 11 434 481 110 11 29 118 49 168 253 20 490 620 310 242 431 200 10 379 117 11 536 20 25 20 292 30 13 420 271 573 45 39 168 135 345  Ever 0.97 0.49* 1.17* 1.21** 1.20 0.65 0.91 1.02 1.14 0.98 1.15 0.83 1.21** 1.21** 1.04 1.03 1.18* 1.23* 1.96 1.06 1.13 1.14 1.13 0.69 1.00 1.30 1.17* 0.90 0.87 1.30** 1.16 1.21** 1.60** 1.01 1.16 1.20 1.21**  Low Medium 0.92 1.20 0.56 0.26 0.97 1.39** 1.24* 1.35** 1.34 1.18 0.58 1.13 0.62 1.18 0.83 1.13 1.22 1.27 1.00 1.01 1.14 1.23 0.93 1.18 1.26* 1.16 1.22* 1.26* 1.04 0.94 1.11 0.85 1.12 -1.24* 1.38* 1.19 1.97 1.93 1.03 1.08 0.97 1.27 0.95 1.33 1.18 1.09 1.02 0.70 1.23 0.72 1.68 0.96 1.04 1.11 1.02 0.68 0.24 1.14 1.22 1.30* 1.15 1.27* 1.28** 1.15 2.00* 1.27 1.49 0.46 1.28 1.23 1.28 1.08 1.10 1.33**  High 0.79 0.67 1.16 1.06 1.09 0.31 0.95 1.10 0.96 0.94 1.09 0.36 1.22* 1.14 1.14 1.12 1.18 1.12 1.97 1.08 1.16 1.15 1.12 0.40 1.07 1.22 1.37** 0.99 1.12 1.38** 1.07 1.20 1.56 1.10 1.00 1.23 1.20  Ordinal 0.49 0.05 <.01 0.06 0.25 0.15 0.87 0.53 0.62 0.77 0.13 0.26 0.02 0.05 0.46 0.73 0.02 0.09 0.08 0.35 0.18 0.66 0.17 0.06 0.92 0.48 0.01 0.61 0.99 <.01 0.12 0.04 0.02 0.79 0.32 0.12 <.01  NIOSH 74010 74175 74430 74635 74655 74795 74980 74990 75158 76165 76210 76355 76445 76510 76720 77115 77150 77190 77215 77220 77265 80004 80017 80032 80037 80041 80047 80048 80049 80051 80053 80056 80058 80059 80061 80064 80069  Cases 204 78 14 118 40 342 103 295 94 128 10 209 90 180 624 413 252 402 353 45 106 13 605 215 328 237 164 147 62 438 382 318 31 150 342 68 386  Ever 1.27** 1.18 1.42 1.15 1.32 1.11 1.03 1.28** 1.03 1.08 0.85 1.13 1.01 1.11 1.21** 1.21** 1.06 1.16* 1.16* 1.12 1.12 1.08 1.18* 1.07 1.13 1.19* 1.11 1.06 0.91 1.19* 1.17* 1.12 1.14 1.23* 1.16* 1.22 1.05  Low 1.54** 1.13 1.38 0.96 0.80 1.24* 0.99 1.23 1.01 1.14 1.24 1.24 0.90 1.29 1.23* 1.22 0.95 1.22 1.20 1.29 1.27 1.57 1.14 1.30* 1.26* 1.23 1.16 1.14 0.85 1.20 1.20 1.00 1.37 1.16 1.17 1.17 1.04  Medium 1.31 1.29 1.01 1.19 1.61 1.16 0.92 1.23 0.98 1.02 0.52 0.92 1.22 0.95 1.21* 1.29* 1.12 1.23* 1.16 1.24 0.80 0.52 1.17 0.82 1.00 1.20 1.04 1.10 0.99 1.32** 1.16 1.22 0.79 1.41* 1.17 1.09 1.08  High 0.99 1.10 1.96 1.29 1.54 0.94 1.18 1.39** 1.10 1.10 0.76 1.23 0.92 1.09 1.18 1.14 1.10 1.04 1.10 0.84 1.31 1.06 1.25* 1.08 1.13 1.15 1.15 0.93 0.89 1.04 1.14 1.13 1.32 1.13 1.14 1.44 1.04  Ordinal 0.12 0.26 0.19 0.10 0.05 0.64 0.58 <.01 0.72 0.52 0.48  0.21 0.92 0.51 0.04 0.03 0.32 0.16 0.12 0.84 0.32 0.94 0.01 0.82 0.24 0.07 0.30 0.89 0.55 0.08 0.07 0.09 0.55 0.06 0.08 0.11 0.51  NIOSH 80071 80073 80076 80079 80083 80090 80092 80094 80105 80109 80123 80133 80140 80142 80143 80144 80145 80148 80153 80157 80158 80164 80165 80169 80175 80177 80181 80182 80194 80197 80199 80200 80201 80202 80203 80206 80214  Cases 115 90 394 417 296 50 220 74 78 550 88 15 197 64 17 365 11 9 89 34 187 366 292 34 100 12 15 10 15 37 18 45 38 88 23 23 296  Ever 1.01 1.00 1.15 1.10 1.06 1.17 1.18 1.02 1.40* 1.18* 1.26 0.65 1.15 1.01 2.16** 1.15 0.63 0.67 1.11 1.10 1.04 1.15 1.16* 0.85 1.10 0.90 1.54 1.20 0.93 0.76 1.00 0.95 0.78 0.92 1.00 1.13 1.07  Low 1.24 0.72 1.18 1.09 1.05 1.72* 1.40** 1.07 1.21 1.15 1.08 0.77 1.22 0.88 2.65* 1.37** 0.70 0.93 1.29 0.85 1.09 1.17 1.31* 0.52 0.90 0.74 0.81 0.93 1.17 0.64 0.73 0.88 1.14 0.92 0.74 1.32 1.17  Medium 0.81 1.09 1.26* 1.14 1.10 0.77 1.12 0.75 1.98** 1.15 1.41 0.71 1.12 1.68* 2.04 1.05 0.39 0.61 0.77 1.17 0.92 1.25* 1.03 0.69 1.04 0.24 2.31 2.21 0.50 0.77 1.51 1.31 0.31* 0.85 0.87 1.30 1.09  High 0.99 1.16 1.01 1.06 1.02 1.02 1.03 1.14 1.09 1.24* 1.31 0.49 1.12 0.52* 1.70 1.02 0.76 0.49 1.27 1.28 1.13 1.03 1.14 1.38 1.36 1.61 1.62 0.69 1.16 0.87 0.69 0.70 0.91 0.98 1.37 0.76 0.96  Ordinal 0.74 0.57 0.22 0.27 0.58 0.78 0.30 0.83 0.03 0.01 0.05 0.10 0.20 0.71 0.03 0.45 0.19 0.19 0.45 0.42 0.61 0.18 0.17 0.96 0.16 0.88 0.08 0.69 0.81 0.25 0.98 0.62 0.13 0.59 0.68 0.89 0.76  NIOSH 80215 80216 80218 80219 80220 80223 80224 80231 80235 80237 80243 80244 80248 80249 80251 80257 80258 80260 80261 80265 80268 80270 80273 80276 80282 80283 80285 80286 80287 80288 80293 80295 80298 80299 80300 80301 80305  Cases 42 442 45 69 64 434 305 361 188 43 455 277 357 468 125 50 195 12 51 64 124 22 17 11 26 232 46 133 97 274 165 13 466 332 20 13 57  Ever 1.70** 1.14 0.95 1.14 1.24 1.10 1.08 1.08 1.08 1.23 1.21** 1.09 1.12 1.17* 1.09 0.82 1.06 1.42 1.13 1.15 1.05 1.11 1.21 1.24 1.42 1.03 1.47* 1.12 1.07 1.13 1.09 1.59 1.20** 1.17* 0.98 0.89 1.34  Low 0.84 1.23* 0.79 1.25 1.07 1.25* . 1.01 1.24* 0.85 1.36 1.34** 1.17 1.30* 1.08 1.21 1.14 1.07 1.08 1.57 0.95 1.07 1.09 1.21 1.35 1.65 0.98 2.05** 1.20 1.23 1.29* 1.25 2.55* 1.24* 1.12 0.48 1.17 1.69*  Medium 2.75** 1.08 1.09 1.05 1.50 1.09 1.41** 1.00 1.11 1.09 1.13 1.00 1.06 1.16 1.12 0.50* 1.14 1.49 1.03 1.47 1.12 1.16 1.57 1.71 1.51 1.06 0.86 1.11 0.94 1.01 1.00 0.95 1.15 1.15 1.42 0.87 1.22  High 1.43 1.12 0.94 1.11 1.17 0.98 0.83 1.00 1.25 1.21 1.16 1.12 1.00 1.25* 0.93 0.81 0.97 1.67 0.84 1.04 0.97 1.10 0.94 0.64 0.99 1.04 1.54 1.03 1.04 1.10 1.03 1.34 1.21 1.26* 0.96 0.62 1.04  Ordinal <.01 0.17 0.87 0.49 0.13 0.67 0.62 0.80 0.12 0.34 0.05 0.35 0.56 0.01 0.78 0.11 0.67 0.22 0.97 0.33 0.77 0.67 0.62 0.75 0.28 0.67 0.08 0.47 0.77 0.33 0.63 0.35 0.02 0.02 0.82 0.51 0.26  NIOSH 80310 80314 80323 80331 80332 80341 80343 80346 80347 80349 80350 80354 80358 80365 80368 80369 80371 80372 80381 80389 80390 80393 80417 80419 80421 80439 80441 80447 80452 80461 80487 80488 80496 80507 80517 80527 80530  Cases 11 19 16 9 140 143 436 50 75 245 60 236 17 118 228 45 14 14 15 28 321 45 24 16 11 134 526 125 20 141 18 32 167 157 66 17 313  Ever 0.71 1.01 0.70 0.97 1.02 1.14 1.08 1.30 1.35* 1.05 1.33 1.12 0.79 1.08 1.08 0.82 0.85 0.99 0.74 0.83 1.11 1.18 1.28 0.64 1.10 1.13 1.17* 1.03 1.02 1.05 0.94 0.98 1.03 1.05 0.98 0.62 1.16*  Low 0.81 0.51 0.79 0.25 1.23 1.18 1.00 1.48 1.15 1.11 1.18 1.13 0.32 0.98 1.02 0.55 1.56 1.00 0.18 0.92 1.00 1.15 0.92 1.06 0.93 1.27 1.29** 1.05 0.86 1.20 0.75 0.90 1.11 0.93 1.24 0.94 1.22  Medium 0.90 1.03 0.96 2.84* 0.97 1.09 1.13 1.28 1.74** 1.07 1.53 1.16 1.06 1.22 1.29* 1.00 0.35 0.70 1.07 0.24* 1.14 1.34 1.48 0.47 2.38 0.93 1.06 0.93 1.05 1.01 0.97 0.80 1.02 1.06 0.98 0.22* 1.26*  High 0.49 1.43 0.36 0.33 0.88 1.14 1.11 1.13 1.14 0.97 1.28 1.08 0.94 1.06 0.94 0.92 0.68 1.26 0.88 1.37 1.18 1.06 1.44 0.38 0.46 1.19 1.15 1.11 1.14 0.95 1.11 1.24 0.97 1.16 0.72 0.72 1.02  Ordinal 0.24 0.56 0.12 0.96 0.70 0.27 0.17 0.24 0.04 0.85 0.06 0.24 0.65 0.41 0.47 0.48 0.32 0.91 0.56 0.63 0.08 0.39 0.20 0.04 1.00 0.33 0.13 0.71 0.82 0.99 1.00 0.86 0.94 0.37 0-45 0.06 0.19  NIOSH 80531 80538 80542 80545 80549 80563 80564 80570 80573 80574 80579 80585 80587 80588 80589 80595 80596 80602 80611 80612 80625 80675 80680 80685 80705 80720 80725 80780 80785 80790 80828 80836 80900 80945 81005 81040 81080  Cases 38 9 333 19 17 9 26 202 16 14 27 9 80 101 216 39 17 284 291 134 378 290 71 94 413 12 94 23 99 21 47 137 23 21 20 248 42  Ever 1.12 1.37 1.20* 0.93 1.21 1.45 0.81 1.15 1.17 0.78 0.99 0.65 1.07 1.08 1.08 0.83 0.84 0.99 1.14 1.10 1.18* 1.21* 1.10 1.12 1.14 1.36 1.15 1.27 1.23 1.27 1.00 1.09 1.26 0.88 1.07 1.08 0.95  Low 1.05 0.48 1.28* 0.77 0.67 1.94 0.66 1.17 0.79 0.70 0.95 0.42 1.02 1.20 1.19 0.86 1.46 1.14 1.11 1.17 1.23 1.31* 0.99 1.51* 1.09 2.26 1.04 1.74 1.59** 2.01 0.96 1.48** 1.01 0.80 1.37 1.17 0.61  Medium 1.30 2.60* 1.14 0.29 1.71 1.49 0.69 1.06 1.79 0.78 0.60 0.43 1.30 1.17 0.91 0.79 0.79 1.08 1.19 1.13 1.12 1.22 1.16 0.75 1.05 0.69 1.26 0.91 0.98 1.12 0.85 1.11 1.28 1.02 0.96 1.07 1.06  High 0.98 0.60 1.17 1.79 1.15 0.99 1.09 1.22 1.04 0.87 1.45 1.10 0.88 0.88 1.15 0.83 0.28 0.77* 1.12 1.01 1.19 1.09 1.16 1.14 1.27* 1.05 1.14 1.21 1.08 0.75 1.20 0.69 1.47 0.83 0.84 1.00 1.24  Ordinal 0.61 0.38 0.05 0.70 0.37 0.51 0.56 0.12 0.51 0.50 0.77 0.44 0.77 0.85 0.43 0.30 0.14  0.24 0.11 0.54 0.05 0.08 0.39 0.67 0.02 0.72 0.24 0.51 0.35 0.81 0.81 0.59 0.24 0.64 0.93 0.63 0.72  NIOSH 81085 81115 81120 81125 81135 81350 81355 81390 81440 81455 81460 81510 81515 81560 81650 81651 81663 81664 81667 81668 81671 81675 81676 81679 81680 81683 81684 81692 81695 81696 81698 81700 81702 81710 81711 81713 81715  Cases 11 17 46 114 9 252 11 242 13 18 279 140 225 10 302 74 497 66 22 387 128 33 162 103 31 83 9 10 12 17 24 11 15 11 21 80 66  Ever 0.76 1.06 1.08 1.02 1.09 1.15 0.85 0.97 0.57 0.62 1.19* 1.10 1.06 1.03 1.14 0.99 1.18* 1.08 0.88 1.18* 1.30* 1.06 1.13 1.09 0.93 0.95 0.91 1.24 0.84 0.73 1.16 0.76 1.07 0.99 1.29 1.21 1.08  Low 0.43 1.82 1.16 0.96 2.82* 1.13 0.47 1.11 0.53 0.84 1.35* 1.06 1.14 1.03 1.25 0.81 1.18 1.28 0.80 1.26* 1.43* 1.02 1.02 0.94 1.01 0.93 0.91 1.71 0.23 0.99 1.65 0.69 1.12 0.29 1.86 1.43 1.22  Medium 0.64 0.60 1.61 1.18 0.38 1.09 1.15 1.03 0.46 0.41 1.10 1.10 0.86 1.28 1.15 1.13 1.21* 1.02 0.62 1.18 1.48* 0.74 1.07 1.04 0.74 1.08 0.92 1.87 1.27 0.69 1.23 0.92 1.45 1.46 1.22 1.30 0.93  High 1.21 0.83 0.56 0.92 0.27 1.25 0.91 0.77 0.69 0.60 1.12 1.16 1.18 0.80 1.02 1.03 1.13 0.93 1.21 1.09 1.00 1.44 1.31 1.29 1.04 0.83 0.90 0.00 0.93 0.53 0.59 0.70 0.64 1.18 0.90 0.88 1.07  Ordinal 0.73 0.73 0.85 0.88 0.40 0.06 0.80 0.20 0.10 0.05 0.13 0.28 0.43 0.97 0.33 0.84 0.05 0.91 0.82 0.11 0.09 0.57 0.08 0.24 0.76 0.58 0.80 0.93 0.87 0.15 0.95 0.44 0.98 0.70 0.66 0.47 0.75  NIOSH 81720 81721 81724 81731 81736 81741 81751 81753 81754 81755 81763 81767 81770 81777 81779 81787 81800 81806 81811 81815 81821 81826 81830 81836 81843 81851 81853 81855 81857 81873 81876 81877 81879 81882 81884 81885 81886  Cases 32 105 16 24 22 101 169 13 172 105 9 25 10 13 422 27 28 108 54 357 54 10 16 259 24 557 19 30 24 24 24 33 295 119 47 44 13  Ever 1.14 1.39** 0.79 1.32 1.12 1.04 1.23* 1.21 1.19 1.19 1.16 0.85 1.36 0.98 1.13 1.46 1.36 0.97 0.97 1.09 0.98 0.83 0.96 1.10 0.93 1.20** 1.66 0.76 1.03 0.87 1.03 1.34 1.13 1.18 0.80 1.07 0.97  Low 1.21 1.50* 0.97 1.33 1.90* 0.99 1.32 1.15 1.27 1.38 0.90 0.79 0.84 1.28 1.08 1.13 1.48 0.81 0.98 0.97 0.98 1.40 1.77 1.25 1.24 1.26* 1.92 0.83 0.75 1.04 1.37 1.53 1.06 1.38 0.82 1.21 0.68  Medium 1.43 1.19 0.78 2.01 0.86 1.16 1.32 1.29 1.05 1.10 1.93 0.71 2.43 0.51 1.21 1.48 1.62 0.99 1.17 1.19 1.17 0.55 0.66 0.91 0.59 1.13 1.65 0.98 0.95 0.81 0.79 1.26 1.20 1.10 0.87 0.96 1.05  High 0.77 1.49* 0.61 0.80 0.63 0.98 1.05 1.20 1.26 1.07 0.84 1.05 0.50 1.08 1.10 1.78 0.95 1.10 0.83 1.09 0.87 0.49 0.40 1.13 0.93 1.20* 1.41 0.47 1.39 0.78 0.98 1.19 1.14 1.07 0.73 1.05 1.17  Ordinal 0.78 <.01 0.29 0.43 0.74 0.75 0.11 0.56 0.08 0.34 0.70 0.62 0.47 0.85 0.11 0.05 0.31 0.84 0.70 0.17 0.80 0.32 0.34 0.45 0.58 0.04 0.11 0.11 0.57 0.44 0.90 0.26 0.09 0.31 0.16 0.81 0.88  NIOSH 81887 81891 81894 81905 81908 81914 81915 81921 81922 81931 81935 81945 81949 81953 81957 81963 81964 81971 81974 81975 81986 81987 81990 81991 81992 81993 81999 82001 82002 82006 82009 82013 82030 82035 82037 82056 82057  Cases 205 13 36 13 21 14 252 144 30 17 15 20 14 37 12 10 103 147 19 29 13 233 396 338 275 233 103 12 41 12 204 12 23 12 282 34 51  Ever 1.03 0.95 1.09 0.97 0.92 0.67 1.08 1.03 1.16 1.91* 1.04 0.97 0.92 0.94 0.89 0.88 1.09 1.22* 0.82 1.32 0.93 0.99 1.14 1.20* 1.24** 1.17 0.98 1.23 1.12 1.70 1.11 0.79 1.12 1.32 1.19* 1.03 1.26  Low 1.02 0.27 1.37 1.24 0.94 0.77 1.07 1.14 0.82 1.10 0.74 0.98 0.75 0.92 0.81 0.74 1.06 1.41* 0.73 1.49 1.26 0.96 1.19 1.23 1.24 1.28 1.00 0.95 1.13 0.40 1.20 0.63 0.56 1.25 1.11 0.92 1.16  Medium 1.11 1.27 1.02 0.91 0.80 0.56 1.02 0.90 1.52 2.09 0.97 0.61 0.79 0.99 1.07 0.53 1.15 1.15 0.44 1.32 0.61 0.93 1.25* 1.25* 1.00 1.08 1.01 1.94 1.32 2.69* 1.13 0.86 1.60 1.87 1.15 0.88 1.23  High 0.96 1.20 0.92 0.72 1.03 0.70 1.16 1.05 1.13 2.48* 1.42 1.28 1.40 0.90 0.83 1.40 1.07 1.11 1.24 1.17 0.93 1.07 0.99 1.12 1.47** 1.14 0.93 0.66 0.88 2.20 1.02 0.87 1.22 0.92 1.31* 1.31 1.40  Ordinal 0.88 0.78 0.93 0.70 0.77 0.19 0.25 0.91 0.37 <.01 0.61 0.94 0.97 0.72 0.74 0.96 0.48 0.16 0.71 0.29 0.67 0.90 0.28 0.05 <.01 0.16 0.78 0.63 0.68 0.04 0.43 0.57 0.38 0.51 0.01 0.66 0.12  NIOSH 82065 82078 82082 82097 82100 82101 82113 82118 82120 82127 82134 82135 82136 82156 82164 82177 82181 82184 82187 82206 82207 82208 82210 82214 82224 82233 82253 82254 82256 82272 82274 82276 82786 82789 82792 82795 82798  Cases 28 10 10 28 186 9 218 15 18 380 110 23 11 29 11 25 140 130 18 20 12 46 42 48 16 12 176 194 21 34 14 36 153 422 36 58 26  Ever 1.40 0.88 0.72 0.80 1.12 1.12 1.17 1.38 0.64 1.07 1.05 0.83 1.15 0.87 0.66 0.74 1.15 1.09 0.64 1.24 1.12 1.28 1.02 1.22 1.20 1.20 1.05 1.24* 0.97 1.13 1.45 1.07 1.13 1.10 1.30 0.99 1.02  Low 0.42 1.96 0.42 1.01 1.11 2.12 1.18 1.40 0.67 1.18 0.84 0.99 1.06 1.10 0.37 1.09 1.48** 0.94 0.50 0.36 0.53 1.09 0.78 1.01 0.88 0.64 0.95 1.05 1.56 1.32 1.85 1.17 1.13 1.12 1.39 1.00 1.20  Medium 1.47 0.00 0.67 1.10 1.22 0.81 1.40** 1.20 0.54 0.98 1.06 0.64 1.76 0.79 0.91 0.50 1.08 1.11 0.87 1.69 1.91 1.77* 1.44 1.09 1.70 1.60 1.12 1.42** 0.78 1.80* 0.66 0.90 1.31 1.20 1.42 0.89 1.01  High 2.44** 0.73 1.10 0.28* 1.03 0.38 0.95 1.59 0.69 1.04 1.25 0.85 0.67 0.71 0.72 0.67 0.89 1.22 0.54 1.75 0.73 0.95 0.82 1.57 0.95 1.30 1.08 1.24 0.58 0.28* 2.42 1.12 0.97 0.98 1.08 1.04 0.85  Ordinal <.01 0.35 0.60 0.10 0.31 0.66 0.22 0.27 0.10 0.70 0.31 0.37 0.82 0.33 0.34 0.10 0.76 0.22 0.10 0.13 0.64 0.21 0.92 0.11 0.55 0.43 0.46 0.01 0.44  0.81 0.21 0.78 0.37 0.46 0.29 0.96 0.85  NIOSH 82806 82807 82815 82819 82834 82840 82841 82849 82859 82861 82869 82871 82880 82886 82889 82897 82905 82907 82917 82920 82924 82927 82934 82942 82946 82948 82949 82951 82953 82955 82960 82963 82967 82978 82994 82995 82998  Cases 49 91 13 69 164 74 178 226 9 187 10 36 276 33 19 38 397 40 19 163 100 74 81 115 130 49 18 27 20 275 25 169 9 112 9 305 143  Ever 0.84 1.08 1.08 0.94 1.17 1.03 1.14 1.21* 1.53 1.24* 1.07 0.93 1.13 1.16 1.04 0.83 1.19* 1.19 1.07 1.07 1.03 1.01 0.86 0.96 1.14 0.95 1.42 0.97 1.03 1.16 1.73* 1.29** 1.26 1.16 0.91 1.14 1.08  Low 0.98 1.05 1.61 0.98 1.26 1.01 1.18 1.37* 2.81* 1.28 0.58 0.95 1.05 1.89* 0.82 0.58 1.26* 0.88 1.55 1.19 1.28 0.98 0.75 0.68 1.37* 1.07 2.08 1.38 1.05 1.13 2.93** 1.26 1.93 1.24 0.89 1.21 1.31  Medium 0.93 0.84 0.83 1.11 1.39* 0.95 1.13 1.28 0.00 1.32* 1.27 1.03 1.13 0.64 1.17 1.31 1.24* 1.45 0.86 1.01 1.13 1.20 1.11 1.02 1.14 1.21 0.99 0.52 0.46 1.06 1.05 1.48** 1.14 1.23 0.57 1.11 1.08  High 0.63 1.36 0.84 0.72 0.91 1.13 1.11 0.98 1.61 1.12 1.44 0.81 1.20 0.92 1.12 0.64 1.07 1.27 0.80 1.02 0.67 0.89 0.72 1.17 0.88 0.58 1.17 1.07 1.58 1.29* 1.42 1.13 0.81 1.00 1.33 1.09 0.84  Ordinal 0.15 0.31 0.90 0.45 0.34 0.74 0.23 0.20 0.54 0.05 0.57 0.64  0.08 0.98 0.76 0.38 0.10 0.22 0.82 0.71 0.53 0.99 0.24 0.66 0.67 0.42 0.43 0.74 0.66 0.03 0.11 0.02 0.84 0.37 0.95 0.20 0.93  NIOSH 83002 83007 83017 83019 83024 83030 83032 83033 83038 83046 83048 83062 83065 83066 83079 83085 83102 83104 83105 83110 83111 83115 83124 83128 83138 83140 83142 83150 83151 83152 83162 83166 83170 83177 83180 83181 83182  Cases 104 99 128 140 205 63 243 60 117 280 27 14 10 90 23 128 409 26 52 261 156 16 25 10 19 19 274 9 24 14 14 133 43 98 11 33 156  Ever 1.11 1.13 1.10 1.14 1.18* 1.38* 1.16 1.19 1.22 1.05 2.49** 1.06 1.12 1.00 1.34 1.09 1.15* 1.17 1.34 1.25** 1.11 1.41 1.27 1.69 0.84 1.09 1.20* 1.84 1.31 0.96 1.79 1.10 1.65** 1.03 0.77 1.07 1.06  Low 1.18 1.08 0.99 1.10 1.33* 1.32 1.03 0.93 1.37 1.08 2.08 0.48 0.65 1.18 1.02 1.03 1.16 0.80 1.32 1.29* 0.89 0.80 1.11 1.04 1.18 1.20 1.19 1.48 1.56 1.34 1.54 1.28 1.74 1.24 0.27 0.83 1.23  Medium 1.07 1.05 1.20 1.32 1.15 1.67* 1.18 1.52 1.04 1.04 2.61* 0.97 1.65 1.01 1.09 1.16 1.18 1.57 1.18 1.26 1.24 0.55 1.22 2.44 0.58 0.95 1.29* 1.12 1.14 0.21 2.35 1.13 1.48 1.08 0.81 1.34 1.05  High 1.09 1.26 1.10 0.99 1.08 1.15 1.25 1.13 1.25 1.04 2.81** 1.68 1.11 0.80 1.92 1.06 1.10 1.13 1.51 1.21 1.19 2.81** 1.46 1.53 0.69 1.13 1.11 2.89 1.29 1.33 1.37 0.87 1.71 0.76 1.11 1.03 0.90  Ordinal 0.46 0.23 0.30 0.33 0.19 0.06 0.04 0.19 0.12 0.62 <.01 0.43 0.61 0.57 0.11 0.44 0.11 0.38 0.06 0.01 0.13 0.05 0.24 0.14 0.30 0.79 0.06 0.08 0.34 0.91 0.09 0.88 <.01 0.68 0.74  0.64 1.00  NIOSH Cases 83184 121 83185 50 12 83186 11 83189 83190 123 83193 15 83194 10 83196 13 83197 85 19 83198 54 83199 37 83200 39 83201 83204 101 83205 15 83207 10 409 83208 87 83209 83213 9 83217 39 83218 23 83224 150 83233 10 9 83248 83252 30 83258 45 11 83262 83265 ' 108 83271 218 83275 118 129 83276 83277 169 122 83278 22 83279 83280 269 152 83290 304 83293  Ever 1.11 1.13 0.86 1.90 1.34** 2.04* 1.61 0.98 1.03 1.00 1.03 1.06 1.21 1.29* 1.20 1.38 1.23** 0.89 1.61 0.93 1.45 1.06 1.73 1.16 0.79 0.85 1.16 1.16 1.20* 1.11 1.13 1.09 1.04 1.11 1.08 1.11 1.20*  Low 1.10 0.97 0.26 2.08 1.40 2.81* 1.52 1.56 1.00 0.80 1.67* 0.91 1.18 1.56* 1.22 1.54 1.24* 1.00 0.52 0.95 2.02 1.24 1.97 0.66 0.86 1.02 0.96 1.05 1.22 1.04 1.14 1.29 1.16 0.84 1.19 1.21 1.34**  Medium 1.25 1.61* 1.23 1.57 1.62** 1.97 3.03* 0.90 1.01 1.23 0.62 1.18 1.31 1.13 1.76 1.11 1.19 0.76 2.46 0.92 1.67 1.16 2.14 2.56 0.43* 0.67 0.67 1.10 1.22 1.29 1.16 0.87 0.94 1.36 1.13 1.13 1.03  High 0.98 0.81 1.00 2.06 0.99 1.47 0.43 0.46 1.07 0.95 0.88 1.09 1.14 1.20 0.53 1.52 1.25* 0.90 1.83 0.92 0.72 0.79 1.06 0.39 1.12 0.86 1.85 1.31 1.16 1.02 1.08 1.10 1.02 1.15 0.91 0.99 1.25  Ordinal 0.49 0.58 0.93 0.09 0.05 0.07 0.38 0.52 0.77 0.94 0.55 0.66 0.35 0.10 0.77 0.40 <.01 0.30 0.12 0.68 0.42 0.80 0.24 0.77 0.38 0.27 0.43 0.12 0.06 0.37 0.34 0.63 0.93 0.54 0.87 0.55 0.04  NIOSH 83299 83302 83307 83308 83323 83331 83332 83335 83351 83353 83354 83355 83364 83365 83369 83376 83379 83383 83404 83413 83433 83434 83435 83436 83440 83441 83444 83446 83447 83449 83451 83453 83461 83475 83477 83480 83495  Cases 99 10 199 14 24 152 146 12 136 19 16 81 22 35 28 15 18 463 12 74 79 272 17 34 119 87 142 16 103 175 97 235 42 208 53 38 155  Ever 1.01 2.22* 1.14 1.07 1.23 1.11 1.36** 1.10 1.10 1.26 0.59* 1.26 0.84 1.05 0.80 1.00 1.13 1.12 1.10 1.30 1.14 1.17* 1.08 1.25 1.31* 1.27 1.28* 0.85 1.32* 1.12 0.98 1.09 1.11 1.11 1.22 1.48* 1.28*  Medium Low 0.99 1.02 4.47** 0.66 1.25 0.99 1.54 0.63 1.22 0.83 1.18 1.03 1.42* 1.48* 0.53 1.30 .1.22 0.93 0.66 1.39 0.83 0.30* 1.31 1.25 0.77 1.10 1.02 1.15 1.01 1.10 0.72 1.24 1.04 1.23 1.24* 1.08 0.70 1.46 1.02 1.71** 1.29 0.97 1.14 1.11 0.80 1.60 1.52 0.91 1.42* 1.20 1.54* 1.18 1.50** 1.09 0.96 0.76 1.28 1.30 1.20 1.03 0.92 0.96 1.02 1.19 1.19 0.83 1.15 1.10 1.09 1.38 1.91* 1.16 1.22 1.20  High 1.03 0.86 1.18 1.05 1.73 1.12 1.17 1.56 1.15 1.67 0.65 1.20 0.62 0.97 0.28* 1.01 1.11 1.03 1.20 1.15 1.18 1.25 0.88 1.27 1.32 1.06 1.24 0.84 1.37 1.15 1.05 1.07 1.29 1.08 1.20 1.41 1.42*  Ordinal 0.87 0.03 0.08 0.65 0.26 0.38 0.02 0.45 0.22 0.19 0.05 0.11 0.41 0.85 0.10 0.90 0.72 0.22 0.61 0.05 0.41 0.04 0.90 0.38 0.01 0.23 0.06 0.62 0.02 0.27 0.97 0.27 0.53 0.34 0.20 0.10 <.01  NIOSH 83496 83497 83506 83508 83509 83512 83513 83514 83515 83517 83551 83553 83554 83562 83571 83572 83573 83574 83581 83587 83589 83596 83598 83600 83609 83626 83628 83629 83639 83641 83643 83646 83649 83660 83664 83665 83669  Cases 16 40 10 308 388 279 35 177 23 15 9 163 304 136 47 30 20 27 119 30 40 23 9 9 68 243 34 52 106 13 68 80 19 62 17 188 154  Ever 1.01 1.33 1.36 1.23** 1.18* 1.10 0.92 1.06 1.19 0.89 0.95 1.04 1.19* 1.33** 1.23 1.19 1.16 1.46 1.12 1.09 0.93 1.13 0.84 0.58 1.26 1.10 1.07 1.29 1.24 1.28 0.97 1.20 1.01 1.05 0.93 0.96 1.12  Low 0.74 1.42 0.92 1.23 1.14 1.02 1.08 1.00 0.66 1.16 1.08 0.84 1.17 1.53** 1.50 1.07 0.74 1.50 1.29 1.39 0.62 1.47 0.63 1.09 1.67* 1.13 0.70 1.45 1.21 1.61 1.01 1.21 0.51 1.00 1.41 1.13 1.11  Medium 1.25 1.24 1.17 1.26* 1.27* 1.19 0.95 1.34* 1.74 0.84 1.16 1.03 1.38** 1.25 1.15 1.00 1.96 1.49 1.07 1.05 1.53 0.59 0.57 0.00 1.01 1.11 1.05 1.27 1.29 1.32 1.04 0.82 1.03 1.28 0.78 0.94 1.28  High 1.18 1.33 1.95 1.22 1.13 1.10 0.73 0.84 1.27 0.70 0.61 1.24 1.03 1.20 1.03 1.52 0.95 1.41 0.99 0.84 0.67 1.31 1.31 0.56 1.09 1.06 1.43 1.13 1.22 0.87 0.88 1.60* 1.43 0.88 0.64 0.81 0.99  Ordinal 0.74 0.16 0.25 0.01 0.04 0.17 0.47 0.81 0.27 0.52 0.74 0.24 0.08 0.03 0.46 0.28 0.46 0.12 0.61 0.98 0.73 0.74 0.90 0.08 0.35 0.35 0.38 0.23 0.08 0.70 0.73 0.08 0.56 0.85 0-48 0.25 0.38  NIOSH 83676 83678 83681 83685 83705 83718 83726 83731 83732 83734 83736 83739 83741 83748 83758 83760 83765 83770 83786 83788 83800 83818 83820 83823 83830 83831 83835 83844 83849 83866 83871 83872 83889 83904 83906 83911 83919  Cases 9 155 104 189 18 21 12 13 14 51 356 19 70 159 138 28 14 78 90 108 18 142 172 25 16 127 22 50 15 10 168 79 36 175 15 19 13  Ever 1.02 1.06 1.27* 1.21* 1.02 1.08 0.42** 1.08 0.69 1.23 1.22** 1.10 1.26 1.17 1.07 0.92 1.53 1.03 1.19 1.28* 0.76 1.06 1.22* 1.12 1.11 1.10 0.96 1.13 0.85 0.90 1.22* 1.04 1.09 1.12 0.93 0.95 0.96  Low 0.61 1.00 1.50* 1.11 1.62 0.70 0.52 1.57 0.80 1.74* 1.15 0.54 0.96 1.11 1.00 0.87 0.36 0.92 1.17 1.16 0.65 1.23 1.29 1.24 1.30 1.19 0.80 1.53 0.41 0.80 1.56** 0.94 1.27 1.39* 1.01 1.25 0.39  Medium 1.56 1.11 1.29 1.28 0.66 1.02 0.35* 0.91 0.30 1.15 1.35** 0.86 1.78** 1.16 1.01 0.80 3.21** 0.99 1.22 1.32 0.65 1.06 1.32 1.16 0.87 1.09 1.49 1.26 0.98 1.36 1.03 0.81 0.97 1.01 0.84 0.78 1.43  High 1.00 1.08 1.01 1.25 0.83 1.48 0.38 0.78 0.97 0.80 1.16 1.85 1.05 1.23 1.19 1.08 1.08 1.18 1.18 1.37 1.07 0.91 1.05 0.97 1.14 1.03 0.55 0.67 1.11 0.54 1.07 1.33 1.03 0.97 0.94 0.79 1.15  Ordinal 0.81 0.47 0.20 0.03 0.71 0.46 <.01 0.89 0.29 0.75 0.01 0.29 0.09 0.09 0.35 0.83 0.13 0.57 0.19 0.02 0.48 0.98 0.11 0.76 0.79 0.54 0.74 0.90 0.90 0.69 0.25 0.44 0.83 0.71 0.79 0.61 0.77  NIOSH 83937 83946 83951 83952 83967 83987 84001 84030 84031 84035 84037 84048 84063 84077 84081 84086 84090 84093 84097 84100 84105 84116 84118 84133 84153 84154 84160 84180 84183 84192 84195 84203 84204 84233 84235 84238 84240  Cases 68 258 14 29 12 49 139 141 9 12 11 17 18 69 9 14 25 98 15 56 14 206 12 9 28 351 23 9 69 10 28 46 352 125 41 45 21  Ever 1.18 1.17* 1.41 0.85 0.78 1.30 1.31** 1.19 1.26 1.07 0.48* 1.11 0.92 1.27 1.95 0.85 1.28 1.25 0.98 0.95 1.30 1.13 0.69 0.96 1.26 1.16* 1.04 0.59 1.28 0.83 0.90 1.15 1.12 1.21 1.15 1.14 1.30  Low 1.31 1.05 2.71* 1.10 0.61 1.75* 1.29 1.05 0.40 1.78 0.24* 1.29 1.36 1.25 1.76 0.68 1.81 1.50* 0.83 0.96 1.38 1.27 0.54 0.95 1.18 0.98 0.77 0.64 1.19 0.91 0.54 1.01 1.15 1.26 1.41 1.76* 0.77  Medium 1.34 1.15 1.34 0.72 0.94 0.80 1.24 1.33 1.94 0.80 0.74 1.61 0.59 1.07 2.04 1.02 1.61 1.02 1.02 1.09 0.00 1.07 0.49 0.72 1.86 1.30* 1.52 0.33 1.39 0.66 0.74 1.17 1.07 1.10 1.01 0.95 1.59  High 0.90 1.32* 0.31 0.73 0.80 1.20 1.39* 1.18 1.55 0.73 0.52 0.41 0.82 1.49 2.11 0.81 0.45 1.21 1.07 0.86 2.46* 1.06 1.07 1.19 0.82 1.21 0.85 0.87 1.25 0.93 1.45 1.27 1.15 1.25 1.00 0.69 1.55  Ordinal 0:48 0.01 0.93 0.28 0.53 0.33 <.01 0.08 0.32 0.78 0.06 0.90 0.51 0.07 0.09 0.64 0.84 0.18 0.96 0.67 0.22 0.35 0.43 0.99 0.44 0.01 0.85 0.21 0.09 0.62 0.78 0.31 0.15 0.10 0.71 0.81 0.16  NIOSH 84269 84274 84287 84295 84296 84313 84314 84318 84326 84330 84335 84341 84349 84352 84364 84370 84376 84381 84383 84386 84407 84414 84425 84426 84427 84428 84443 84445 84446 84447 84458 84462 84463 84468 84470 84472 84473  Cases 30 22 28 196 9 11 9 122 10 24 65 83 33 21 32 9 264 24 139 135 13 10 182 37 94 34 74 • 11 9 12 17 15 9 73 13 45 9  Ever 1.43 1.27 1.91** 1.16 0.91 0.84 1.25 1.18 1.15 1.40 1.32 1.29* 0.77 1.22 1.27 1.51 1.13 1.04 1.27* 1.04 0.93 0.86 1.22* 1.02 1.15 1.11 1.04 1.23 2.20* 0.96 1.06 2.01* 1.10 0.83 1.54 0.94 1.92  Medium Low 1.33 1.47 1.39 0.98 2.64** 1.75 1.01 1.30 2.32 0.00 0.87 0.66 1.95 1.33 1.37 1.13 1.26 0.69 0.98 1.90 1.21 1.56 1.20 1.56* 0.44* 1.18 0.94 1.78 2.06* 0.83 2.37 0.48 1.19 1.20 1.22 1.11 1.24 1.26 1.29 1.06 0.65 1.38 0.74 0.73 1.04 1.25 0.74 0.96 0.89 1.50* 1.17 1.29 1.20 1.08 1.34 0.90 1.74 2.96* 0.29 1.33 1.14 0.42 2.73* 1.38 1.46 0.66 0.79 0.90 1.20 3.16** 0.98 1.08 3.72** 1.35  High 1.50 1.40 1.38 1.17 0.49 1.01 0.47 1.04 1.52 1.33 1.21 1.10 0.71 0.97 0.96 1.68 1.01 0.82 1.30 0.80 0.71 1.14 1.37* 1.36 1.08 0.86 0.81 1.51 1.61 1.02 1.54 2.09 1.25 0.81 0.27 0.77 0.61  Ordinal 0.09 0.37 0.04 0.18 0.29 0.76 0.94 0.35 0.64 0.30 0.07 0.18 0.08 0.75 0.22 0.16 0.31 0.96 0.03 0.68 0.54 0.82 <.01 0.52 0.19 0.79 0.78 0.40 0.06 0.73 0.65 0.05 0.62 0.21 0.38 0.57 0.48  NIOSH 84475 84477 84478 84479 84480 84494 84495 84499 84505 84508 84513 84515 84526 84535 84537 84544 84549 84563 84566 84569 84613 84620 84628 84646 .84662 84674 84696 84705 84716 84718 84736 84743 84745 84754 84755 84758 84765  Cases 14 133 11 14 14 141 73 59 35 37 85 9 194 12 256 39 70 318 10 16 16 130 18 20 150 136 217 10 210 13 25 138 26 10 97 38 12  Ever 1.52 1.21 1.37 0.68 0.84 1.08 1.31* 0.98 1.01 1.13 1.03 1.10 1.08 0.91 1.19* 1.05 1.34* 1.15 1.60 0.70 1.30 1.13 0.78 1.11 1.26* 1.10 1.16 0.91 1.13 1.32 1.32 1.15 0.82 1.28 1.08 1.12 0.93  Low 1.36 1.44* 1.60 0.69 1.94 1.19 1.34 0.99 0.96 1.11 0.78 1.48 1.14 0.89 1.07 1.01 1.36 1.14 2.20 0.68 1.55 1.08 0.89 1.29 1.32 1.15 1.27 0.30 1.17 1.57 0.67 1.03 0.77 1.29 0.95 0.92 0.87  Medium 1.97 1.24 1.09 0.46 0.69 1.09 1.20 0.98 0.86 1.44 1.11 0.97 1.02 0.81 1.22 0.79 1.42 1.18 1.09 0.83 1.15 1.25 0.28 0.40 1.08 1.10 1.17 1.51 1.03 1.40 1.65 1.20 0.58 1.70 1.07 1.25 1.07  High 1.30 0.96 1.38 0.88 0.15 0.94 1.38 0.97 1.21 0.84 1.19 0.99 1.08 1.06 1.29* 1.40 1.23 1.11 1.74 0.58 1.23 1.05 1.15 1.53 1.38* 1.05 1.06 0.84 1.21 0.85 1.59 1.23 1.08 0.85 1.22 1.18 0.85  Ordinal 0.21 0.33 0.46 0.26 0.12 0.79 0.06 0.87 0.81 0.71 0.47 0.93 0.51 0.85 0.01 0.55 0.07 0.11 0.27 0.20 0.47 0.32 0.48  0.59 0.02 0.47 0.22 0.99 0.15 0.56 0.10 0.11 0.55 0.62 0.34 0.44 0.83  NIOSH 84772 84789 84805 84809 84830 84832 90310 90320 90340 90410 90590 90620 90800 90820 90870 90880 90883 90885 90900 90980 91095 91110 91115 91120 91150 91190 92150 92255 92290 92310 92320 92355 92470 92500 92630 92650 92685  Cases 124 46 15 45 72 23 337 499 471 10 375 39 38 215 291 552 148 507 156 228 559 83 15 45 13 15 54 56 35 12 15 133 44 51 54 24 42  Ever 1.08 1.34 0.93 1.23 1.27 1.02 1.20* 1.29** 1.26** 1.35 1.29** 1.42 1.35 1.13 1.24** 1.23** 1.20 1.27** 1.08 1.19* 1.25** 1.31* 1.20 0.83 0.76 0.97 0.93 0.98 1.23 1.06 0.82 1.14 1.08 1.74** 0.97 1.30 1.05  Low 1.08 1.16 0.68 1.59 1.51 0.93 1.34** 1.40** 1.33** 0.00 1.33** 1.50 1.33 1.14 1.13 1.15 1.14 1.26* 1.08 1.19 1.40** 1.08 0.97 0.59 0.54 1.32 0.99 0.97 1.18 1.23 0.73 1.41* 1.04 0.80 0.99 1.99* 1.47  Medium 1.09 2.15* 1.02 0.73 1.00 1.40 1.07 1.13 1.21 1.78 1.27* 1.27 1.46 1.06 1.46** 1.23* 1.10 1.28* 1.09 1.19 1.17 1.27 1.09 0.98 1.57 0.68 1.08 1.19 1.41 0.94 1.02 0.99 0.97 2.30** 1.16 0.86 1.30  High 1.08 1.12 1.12 1.38 1.30 0.70 1.18 1.34** 1.24* 2.66 1.28* 1.49 1.28 1.20 1.13 1.31** 1.37* 1.27* 1.06 1.20 1.19 1.60* 1.59 0.92 0.31 0.98 0.75 0.76 1.11 1.01 0.72 1.03 1.21 2.15** 0.84 0.92 0.47  Ordinal 0.50 0.11 0.98 0.34 0.17 0.95 0.08 <.01 <.01 0.10 <.01 0.08 0.14 0.14 <.01 <.01 0.04 <.01 0.49 0.05 0.04 0.01 0.38 0.47 0.31 0.78 0.45 0.71 0.33 0.94 0.50 0.55 0.57 <.01 0.73 0.73 0.50  NIOSH 92740 92780 92850 92910 92930 92960 92980 94140 94220 A1021 A1049 A1053 A1065 A1070 A1073 A1075 A1082 A1091 A1112 A1165 A1167 A1179 A1200 A1204 A1214 A1216 A1220 A1221 A1242 A1259 A1262 A1278 A1279 A1324 A1328 A1329 A1337  Cases 117 248 82 15 12 10 9 313 481 97 22 10 184 10 169 33 19 17 149 24 18 131 79 223 67 17 87 17 58 20 10 9 93 19 15 23 12  Ever 0.92 1.19* 1.41** 1.39 1.56 1.32 0.87 1.03 1.21** 1.12 0.65 1.29 1.08 0.93 1.21* 1.28 0.89 1.39 1.10 1.26 1.20 1.13 1.39* 1.10 1.12 0.87 1.14 1.57 1.28 1.05 0.60 1.22 1.10 1.29 1.42 1.23 1.44  Low 1.05 1.22 1.54* 1.59 1.26 0.91 0.81 1.03 1.18 0.83 0.72 0.88 1.00 1.24 1.35* 1.31 0.39 0.64 1.35* 1.46 0.96 1.34 1.65* 1.08 1.08 0.94 1.33 2.12 1.21 1.99* 1.25 0.77 1.05 1.17 1.97 1.05 2.09  Medium 0.83 1.15 1.65* 1.35 2.02 0.78 0.30 0.97 1.15 1.62** 1.15 1.48 1.09 0.00 1.16 1.23 1.66 1.15 0.98 0.62 1.62 1.17 0.96 1.01 1.09 0.77 1.15 1.23 1.48 0.49 0.00 2.57 1.43* 1.50 1.30 0.63 2.07  High 0.88 1.22 1.04 1.22 1.41 2.14 1.51 1.08 1.30** 0.92 0.16* 1.46 1.15 1.74 1.15 1.28 0.76 2.75* 0.97 1.70 1.11 0.89 1.54* 1.21 1.19 0.90 0.92 1.39 1.15 0.72 0.51 0.46 0.83 1.20 0.88 2.07* 0.49  Ordinal 0.31 0.04 0.05 0.37 0.19 0.25 0.96 0.64 <.01 0.30 0.02 0.38 0.29 0.97 0.12 0.26 0.86 0.04 0.82 0.28 0.45 0.68 0.03 0.18 0.39 0.60 0.66 0.22 0.14 0.55 0.06 0.68 0.66 0.36 0.56 0.17 0.72  NIOSH A1339 A1346 A1353 A1355 A1356 A1357 A1358 A1359 A1360 A1458 A1463 A1466 A1481 A1491 A1515 A1604 A1642 A1667 A1728 A1771 A1827 A1874 B0043 B0044 B0045 B0105 L0035 L0112 M0006 M0073 M0125 M0126 M0130 M0132 M0155 M0156 M0218  Cases 93 207 66 44 22 15 81 17 13 15 65 31 11 12 41 79 32 11 29 27 25 18 9 175 13 317 20 20 95 153 85 114 145 59 111 97 265  Ever 1.21 1.10 1.23 0.86 1.06 0.80 1.17 1.29 1.03 1.10 0.98 1.27 1.19 1.69 1.28 1.27 0.98 1.29 1.24 1.54* 1.25 0.90 0.64 1.14 1.92* 1.10 0.95 0.98 0.88 1.11 1.05 1.12 1.10 0.91 0.92 0.94 1.14  Low 1.21 1.11 1.26 0.91 1.33 0.49 1.21 1.43 1.59 0.65 1.04 0.93 1.19 2.82* 0.92 1.20 1.01 0.71 1.03 1.94* 0.96 0.72 0.44 1.20 2.65* 1.24* 0.54 0.97 0.78 1.40* 1.06 0.97 1.01 1.10 1.06 0.90 1.20  Medium 1.50* 1.17 1.14 1.02 1.56 1.63 1.20 1.17 0.47 0.91 0.85 1.23 0.36 0.80 1.78* 1.83** 0.81 1.09 1.63 1.59 1.74 0.50 0.65 1.01 1.38 1.15 0.98 0.68 0.91 1.00 0.90 1.21 1.26 0.68 0.79 0.88 1.09  High 0.93 1.01 1.29 0.66 0.39 0.42 1.09 1.25 1.02 1.79 1.02 1.72 1.94 1.55 1.12 0.85 1.12 2.05 1.05 1.00 1.04 1.55 0.82 1.21 1.69 0.91 1.37 1.36 0.96 0.95 1.20 1.18 1.02 0.95 0.91 1.03 1.13  Ordinal 0.26 0.44 0.18 0.27 0.69 0.42 0.34 0.44 0.82 0.39 0.85 0.11 0.42 0.28 0.15 0.19 0.98 0.23 0.33 0.18 0.33 0.92 0.34 0.18 0.12 0.81 0.73 0.88 0.48 0.80 0.59 0.21 0.37 0.43 0.36 0.75 0.19  NIOSH M0238 M0239 M0244 M0256 M0259 M0260 M0262 M0264 M0321 M0327 M0347 M0377 M0386 M0421 M0430 M0451 M0461 M0462 M0478 M0527 M0529 M0538 M0539 M0577 M0578 M0579 M0599 M0600 M0602 M0603 M0609 M0626 M0627 M0628 M0644 M0645 M0646  Cases 15 98 25 147 176 513 209 23 16 . 29 321 59 71 149 115 20 21 55 16 209 109 74 85 176 94 306 106 423 334 467 138 18 11 454 71 90 21  Ever 0.64 1.29* 1.04 0.97 1.14 1.16* 1.05 1.04 0.84 1.27 1.13 1.20 0.98 1.14 1.17 1.22 0.98 0.93 0.95 1.27** 1.20 1.31* 1.23 1.12 1.12 1.15 1.03 1.11 1.13 1.16* 1.06 1.43 1.10 1.25** 1.04 1.33* 1.13  Low 0.77 1.32 1.46 0.90 1.31 1.15 0.90 0.95 0.38 1.12 1.11 1.33 0.89 1.27 1.10 0.54 1.31 0.98 1.23 1.46** 1.46* 1.65* 1.32 1.26 1.18 1.16 1.32 1.08 1.27* 1.19 1.06 1.69 1.01 1.27* 1.29 1.63** 1.02  Medium 0.93 1.27 0.44 0.90 1.02 1.27* 1.14 0.53 1.19 1.83 1.27* 1.38 1.12 0.79 1.14 1.61 0.78 0.79 0.72 1.27 1.08 1.12 0.75 0.88 0.79 1.24 0.93 1.12 1.05 1.25* 1.01 0.84 1.15 1.20 0.89 1.28 1.02  High 0.24 1.28 1.29 1.12 1.10 1.08 1.10 1.68 0.86 0.97 1.02 0.85 0.92 1.37* 1.26 1.56 0.86 1.02 0.89 1.08 1.07 1.09 1.56* 1.19 1.38 1.06 0.83 1.12 1.07 1.03 1.12 1.83 1.13 1.28* 0.90 1.04 1.37  Ordinal 0.06 0.05 0.96 0.88 0.33 0.09 0.34 0.52 0.73 0.35 0.20 0.50 0.92 0.16 0.13 0.20 0.70 0.68 0.71 0.06 0.32 0.23 0.08 0.33 0.26 0.13 0.61 0.15 0.35 0.18 0.52 0.20 0.75 <.01 0.80 0.16 0.51  NIOSH M0647 M0648 M0650 M0651 M0652 M0653 M0661 M0662 M0674 M0675 M0679 M0680 M0682 M0683 M0689 M0692 M0698 M0699 M0700 M0701 M0708 M0716 M0717 M0720 M0725 M0745 M0747 M0749 M0752 M0756 M0760 M0773 M0774 M0779 M0783 M0785 M0787  Cases 554 15 41 368 19 363 512 149 100 9 56 80 415 59 35 107 39 18 464 398 58 20 16 12 176 235 366 24 29 71 288 331 39 27 22 21 40  Ever 1.08 1.17 1.03 1.20* 0.86 1.13 1.22** 1.08 1.30* 0.86 1.19 1.04 1.20** 1.11 1.25 1.27* 1.11 1.50 1.17* 1.25** 1.04 1.12 1.54 0.81 1.13 1.24** 1.12 0.73 1.33 1.01 1.18* 1.23** 1.13 1.03 0.85 0.99 1.07  Low 1.10 0.87 1.23 1.15 0.96 1.18 1.16 1.03 1.52* 0.59 1.27 0.95 1.25* 1.19 1.16 1.51* 1.66* 2.21 1.34** 1.26* 0.85 0.80 2.45* 1.10 0.96 1.50** 1.15 0.87 0.90 0.99 1.34* 1.31* 1.20 1.02 1.02 0.57 0.70  Medium 1.03 0.99 0.75 1.29* 0.66 1.20 1.20 1.08 1.18 1.71 0.91 0.93 1.20 1.29 1.35 1.54* 0.95 1.84 1.10 1.44** 1.35 1.34 1.00 0.20 1.35* 0.98 1.11 0.61 1.17 0.82 1.04 1.08 0.74 1.15 0.72 1.43 1.24  High 1.10 1.68 1.08 1.15 0.97 1.01 1.30** 1.12 1.19 0.28 1.34 1.22 1.16 0.83 1.22 0.79 0.73 0.48 1.07 1.07 0.93 1.24 1.27 1.15 1.06 1.25 1.11 0.72 2.05* 1.21 1.17 1.29* 1.49 0.92 0.82 1.14 1.25  Ordinal 0.32 0.36 0.99 0.02 0.57 0.30 <.01 0.38 0.10 0.59 0.27 0.54 0.03 0.80 0.26 0.29 0.77 0.51 0.26 0.02 0.73 0.49 0.35 0.56 0.18 0.06 0.17 0.15 0.05 0.73 0.11 0.01 0.39 0.96 0.42 0.69 0.41  NIOSH M0789 M0794 M0799 M0812 M0826 M0833 M0850 M0863 M0867 M0870 M0873 M0877 M0879 M0881 M0888 M0892 M0894 M0899 M0900 M0905 M0909 M0912 M0913 M0916 M0918 M0920 M0926 M0927 M0928 M0930 M0937 M0939 M0947 M0950 M0951 M0952 M0959  Cases 15 166 245 71 32 131 190 10 16 94 92 9 10 386 78 17 31 37 161 14 26 328 654 225 16 31 323 19 15 624 9 88 39 47 132 10 16  Ever 1.20 1.38** 1.10 1.10 1.32 1.10 1.04 0.89 0.92 1.21 1.08 1.29 1.43 1.14 1.26 1.18 1.00 1.17 1.09 0.74 1.03 1.08 1.20** 1.10 1.20 1.30 1.11 0.91 0.95 1.23** 0.91 1.05 1.34 1.20 1.12 0.64 1.15  Low 1.61 1.18 0.98 1.12 1.07 1.05 1.27 1.86 1.00 1.00 0.96 0.44 0.89 1.14 1.13 0.98 0.47 1.08 1.06 0.68 1.07 1.10 1.19 1.07 1.61 1.74 1.16 0.48 1.05 1.33** 1.19 1.46* 1.02 0.98 0.92 0.56 1.03  Medium 0.00 1.51** 1.25 0.72 1.74 1.16 0.95 0.24 1.15 1.48* 1.09 1.47 1.36 1.17 1.40 0.46 1.19 0.96 0.94 0.63 1.26 1.03 1.22* 1.14 1.16 1.01 1.15 0.90 0.62 • 1.15 0.60 0.78 1.81* 1.32 1.53** 0.58 0.82  High Ordinal 2.10 0.43 1.44* <.01 1.07 0.20 1.48 0.31 1.16 0.17 1.11 0.32 0.92 0.79 0.76 0.42 0.64 0.60 1.16 0.10 1.18 0.38 0.24 2.08 2.17 0.19 1.12 0.11 1.29 0.07 2.03 0.27 1.37 0.47 1.46 0.28 0.21 1.28 0.90 0.41 0.76 0.93 1.12 0.30 0.02 1.20 1.10 0.26 0.79 0.83 1.10 0.45 1.03 0.34 1.30 0.89 1.26 0.95 1.23* 0.03 0.95 0.72 0.71 0.91 1.19 0.10 1.32 0.19 0.91 0.33 0.80 0.28 1.57 . 0.45  NIOSH M0960 M0961 M0969 M0983 M0984 M0985 M1000 M1002 M1010 M1023 M1026 M1027 M1028 M1030 M1042 M1047 M1051 M1055 M1102 M1105 M1112 M1114 M1128 M1130 M1137 M1142 M1145 M1150 M1152 M1155 M1164 M1173 M1174 M1176 M1183 M1184 M1187  Cases 83 17 235 11 176 101 744 77 12 46 29 90 21 261 88 207 16 187 24 104 160 48 59 21 393 24 93 28 53 23 43 35 229 46 206 214 184  Ever 1.08 1.12 1.11 1.15 1.48** 1.03 1.20* 1.41** 0.92 0.89 0.95 1.23 1.15 1.06 1.04 1.15 0.77 1.00 1.21 1.24 1.14 0.99 0.88 1.07 1.26** 1.51 1.14 2.29** 0.90 1.12 0.92 0.93 1.10 1.19 1.03 1.18 1.15  Low 1.07 1.17 1.14 2.04 1.57** 0.94 1.22* 1.95** 1.17 0.90 0.58 1.21 0.68 1.08 0.86 1.13 0.17 1.21 0.83 1.33 1.05 0.91 0.98 0.89 1.30* 1.34 1.05 2.31* 0.95 1.55 1.05 1.01 1.13 1.17 1.15 1.15 1.00  Medium 1.08 1.60 1.17 0.91 1.34 1.10 1.15 1.33 1.22 1.12 1.11 1.41 1.63 1.09 1.22 0.99 1.02 0.94 0.99 1.32 1.28 1.07 1.04 0.79 1.21 1.93 1.24 3.59** 1.05 0.68 0.79 1.19 1.09 1.42 1.12 1.35* 1.42**  High 1.10 0.61 1.01 0.60 1.52** 1.06 1.21* 0.97 0.42 0.66 1.16 1.06 1.13 1.02 1.02 1.34* 1.00 0.85 1.85 1.06 1.09 0.98 0.65 1.54 1.26* 1.37 1.12 1.03 0.75 1.14 0.92 0.57 1.08 0.97 0.82 1.04 1.05  Ordinal 0.52 0.93 0.41 0.83 <.01 0.67 0.06 0.15 0.52 0.34 0.83 0.17 0.43 0.56 0.62 0.05 0.67 0.45 0.16 0.17 0.19 1.00 0.20 0.50 <.01 0.10 0.28 <.01 0.39 0.88 0.56 0.45 0.36 0.43 0.65 0.13 0.12  NIOSH M1190 M1199 M1202 M1203 M1205 M1207 M1211 M1217 M1218 M1226 M1232 M1289 M1300 M1309 M1312 M1320 M1327 M1341 M1342 M1348 M1351 M1381 M1392 M1407 M1410 M1419 M1422 M1423 M1428 M1429 M1431 M1432 M1433 M1436 M1438 M1439 M1441  Cases 66 26 118 74 14 89 235 82 125 245 19 80 96 174 225 11 100 67 212 79 12 64 20 44 12 19 20 93 9 49 54 15 22 173 79 28 9  Ever 1.26 1.22 1.11 1.01 1.50 1.12 1.22* 1.03 1.10 1.18* 0.97 1.16 1.11 1.08 1.11 0.72 0.99 1.06 1.05 0.94 0.96 1.10 1.13 1.25 1.36 1.28 1.03 0.98 1.61 1.23 1.36* 1.07 1.22 1.20* 1.29 1.28 1.08  Low 1.11 0.99 1.38 1.01 0.31 1.19 1.43** 1.51* 1.24 1.08 0.76 1.32 1.64** 1.22 1.21 1.81 0.91 1.38 1.09 1.03 0.82 0.67 0.90 1.08 1.55 1.40 0.67 1.08 2.82 1.36 1.48 0.46 1.04 1.35* 1.25 1.62 1.52  Medium 1.49 1.10 1.01 1.10 1.93 0.99 1.15 0.99 1.14 1.14 0.16 1.04 0.82 0.95 0.98 0.19 0.90 0.62 1.00 1.04 0.71 1.22 1.21 1.14 1.14 0.84 0.44 0.72 1.39 1.07 1.42 1.38 1.60 1.16 1.77** 1.49 0.69  High 1.21 1.59 0.97 0.93 2.36 1.16 1.09 0.63 0.94 1.30* 1.97* 1.10 0.90 1.07 1.12 0.19 1.16 1.22 1.05 0.77 1.33 1.37 1.26 1.57 1.46 1.58 1.93* 1.14 0.61 1.26 1.19 1.34 1.01 1.09 0.89 0.77 1.08  Ordinal 0.10 0.23 0.76 0.99 0.04 0.44 0.13 0.35 0.69 0.02 0.52 0.43 0.89 0.62 0.36 0.06 0.74 0.83 0.69 0.44 0.90 0.18 0.51 0.10 0.39 0.33 0.39 0.97 0.59 0.28 0.11 0.51 0.45 0.17 0.16 0.60 0.97  NIOSH Ml 444 M1448 M1450 M1456 M1462 M1463 M1467 M1469 M1475 M1485 M1492 M1495 M1515 M1517 M1519 M1525 M1527 M1528 M1529 M1531 M1532 M1540 M1541 M1542 M1545 M1548 M1566 M1569 M1577 M1596 M1598 M1604 M1608 M1609 M1633 M1634 Ml 643  Cases 17 13 31 20 11 560 19 16 79 9 36 26 119 25 72 470 11 133 182 13 186 16 23 436 23 9 22 249 270 61 13 17 54 114 17 60 9  Ever 0.84 1.02 0.98 1.31 0.83 1.20** 1.13 0.63 1.03 1.09 0.84 0.93 1.06 0.86 1.31* 1.22** 1.77 0.99 1.19 2.25* 1.07 0.98 0.78 1.24** 1.20 1.01 1.04 1.17* 1.13 0.99 0.96 1.03 1.07 1.05 0.66 1.08 3.20**  Low 1.62 1.89 0.99 2.23* 0.35 1.28** 0.76 0.49 0.82 1.14 0.89 1.13 0.85 0.90 1.49 1.29** 0.89 0.89 1.38* 2.78* 1.01 0.63 0.76 1.35** 1.31 0.66 0.79 1.19 1.16 0.92 0.84 0.87 0.93 1.16 0.61 0.85 1.08  Medium 0.33 1.24 1.29 0.83 1.18 1.12 1.14 0.84 1.23 0.62 0.53 0.69 1.13 0.91 1.25 1.12 3.04* 1.08 1.07 2.75 1.12 1.06 0.70 1.13 0.98 0.93 1.25 1.00 1.02 1.29 0.47 0.82 1.09 1.01 0.48 1.34 3.48  High 0.55 0.18 0.63 1.00 1.18 1.20 1.44 0.54 1.04 1.65 1.11 0.92 1.17 0.75 1.18 1.24* 1.51 0.97 1.11 1.09 1.09 1.21 0.89 1.24* 1.30 1.51 1.14 1.33* 1.20 0.79 1.56 1.40 1.19 1.00 0.92 1.05 5.20**  Ordinal 0.19 0.40 0.71 0.70 0.98 0.04 0.41 0.12 0.61 0.68 0.50 0.62 0.34 0-44 0.13 0.02 0.08 0.99 0.21 0.07 0.39 0.81 0.38 0.01 0.48 0.70 0.70 0.04 0.13 0.84 0.80 0.69 0.52 0.85 0.23 0.48 <.01  NIOSH M1650 M1659 M1662 M1673 M1677 M1687 M1702 M1710 M1711 M1717 Ml 720 M1721 M1726 M1737 Ml 743 Ml 765 Ml 766 M1772 M1800 M1806 M1812 M1813 M1818 M1821 M1832 M1833 M1839 M1842 M1844 M1850 M1851 M1859 M1866 M1872 M1874 Ml 884 M1910  Cases 60 10 29 10 18 58 52 27 207 34 83 10 61 124 18 40 83 127 34 167 24 218 18 63 11 30 43 18 42 49 18 23 27 29 9 54 14  Ever 1.37* 1.13 1.02 1.22 0.80 1.24 1.14 1.20 1.04 1.01 1.04 1.51 1.30 1.26* 1.03 1.14 1.16 1.15 0.96 1.05 1.12 1.21* 1.68 1.02 0.70 1.24 0.96 0.80 1.40 0.99 1.49 1.00 1.73* 1.13 0.83 1.20 0.74  Low 1.50 1.07 0.97 1.36 0.98 1.59 1.30 0.83 1.13 0.89 1.01 1.55 1.86** 1.17 1.14 0.78 1.16 1.10 1.16 1.20 0.82 1.28 1.15 0.78 0.79 1.27 0.99 0.98 2.03** 0.89 1.71 0.95 1.26 0.83 0.38 1.35 1.01  Medium 1.13 0.96 0.99 1.44 0.96 1.25 1.14 1.35 0.92 1.37 0.93 1.51 0.98 0.96 0.48 1.07 1.13 1.37 0.65 1.03 0.98 1.02 1.85 1.23 0.56 1.21 0.64 0.96 0.85 1.13 0.82 1.00 1.26 1.41 2.74 1.34 0.38  High 1.50 1.37 1.09 0.59 0.48 0.92 0.99 1.40 1.09 0.78 1.16 1.47 1.00 1.64** 1.48 1.56 1.19 0.99 1.08 0.93 1.54 1.35* 2.00 1.01 0.75 1.24 1.26 0.48 1.38 0.97 1.84 1.03 2.68** 1.15 .0.52 0.94 0.96  Ordinal 0.05 0.66 0.85 0.78 0.23 0.47 0.62 0.25 0.69 0.95 0.64 0.30 0.42 <.01 0.75 0.19 0.26 0.28 0.79 1.00 0.38 0.03 0.03 0.72 0.29 0.35 0.95 0.23 0.20 0.95 0.15 0.97 <.01 0.43 0.77 0.48 0.34  NIOSH M1913 M1915 M1920 M1922 M1931 M1936 M1937 M1941 M1951 M1956 M1957 M1959 M1962 M1966 M1992 M1993 M2025 M2038 M2040 M2073 M2074 M2075 M2078 M2089 M2090 M2098 M2100 M2101 M2105 M2109 M2111 M2113 M2125 M2126 M2127 M2128 M2129  Cases 62 123 387 84 47 83 111 14 278 38 15 24 15 23 80 90 9 16 12 13 48 11 23 17 14 86 31 181 21 104 22 39 14 20 15 30 71  Ever 0.88 1.03 1.22** 1.27 1.17 1.26 1.37** 0.87 1.14 1.05 1.14 1.38 1.55 1.28 0.98 1.12 2.15* 1.41 1.13 0.75 0.96 0.99 1.11 1.24 0.63 1.04 1.07 1.01 0.97 0.99 1.39 1.26 1.32 1.20 1.30 1.00 1.13  Low 0.96 1.24 1.18 1.54* 1.65* 1.27 1.41 1.03 1.16 1.02 1.83 1.20 1.76 1.39 1.04 0.94 2.60 2.44* 0.51 0.72 1.22 0.54 1.32 0.99 0.85 0.91 1.42 0.93 1.69 0.95 1.67 1.16 1.34 1.29 1.76 1.20 1.27  Medium 0.93 0.98 1.33** 1.17 0.69 1.61* 1.08 0.00 1.18 1.08 0.26 1.98 2.01 1.32 0.76 1.18 3.50* 0.60 0.71 0.56 0.75 0.57 1.04 0.40 0.42 1.00 0.53 1.21 0.80 1.04 1.21 1.40 1.15 0.73 1.06 0.61 1.25  High 0.75 0.88 1.16 1.07 1.10 0.90 1.65** 1.34 1.07 1.05 1.17 1.12 0.89 1.16 1.12 1.24 0.00 1.02 2.16 0.95 0.92 1.81 0.99 2.61** 0.62 1.21 1.22 0.90 0.51 0.97 1.34 1.22 1.46 1.56 0.94 1.16 0.86  Ordinal 0.28 0.76 0.01 0.23 0.75 0.21 <.01 0.81 0.20 0.79 0.98 0.21 0.29 0.40 0.98 0.21 0.21 0.68 0.26 0.46  0.58 0.58 0.84 0.14 0.10 0.49 0.88 1.00 0.37 0.93 .0.27 0.21 0.37 0.42 0.68 0.96 0.71  NIOSH M2130 M2131 M2135 M2138 M2140 M2141 M2142 M2148 M2152 M2153 M2154 M2161 M2162 M2163 M2167 M2171 M2175 M2181 M2187 M2193 M2194 M2196 M2198 M2201 M2203 M2206 M2208 M2209 M2210 M2211 M2212 M2216 M2218 M2221 M2223 M2225 M2226  Cases 19 18 143 11 12 19 37 171 84 78 85 12 16 14 9 20 12 78 19 9 128 33 37 28 19 10 45 40 17 14 11 „12 15 55 114 13 15  Ever 1.16 1.06 1.01 1.13 1.35 1.34 1.03 1.05 1.15 0.97 1.25 0.87 2.27** 0.96 0.72 1.30 1.13 1.11 0.86 0.85 1.26* 0.74 1.05 1.12 1.07 1.17 1.11 1.25 1.06 0.92 1.08 1.13 0.90 1.06 1.06 0.88 1.05  Low 0.54 1.25 1.38* 2.09 0.68 1.60 1.12 1.22 1.21 0.99 1.46 0.23 4.18** 0.23 0.89 1.54 0.32 0.99 0.50 0.55 1.12 0.84 1.13 0.62 0.68 0.84 0.98 1.52 2.10* 1.58 0.74 0.51 1.06 1.17 1.01 0.68 0.79  Medium 1.67 1.56 0.84 0.62 2.02 0.84 1.15 1.18 1.10 0.82 1.24 0.91 2.02 0.84 0.41 0.73 0.81 1.05 0.69 0.76 1.20 0.96 0.73 0.81 0.86 1.09 1.18 1.06 0.39 0.42 0.94 0.71 1.88 1.03 0.92 1.02 1.57  High 1.32 0.37 0.84 0.39 1.20 1.58 0.82 0.74 1.16 1.06 1.03 1.40 0.47 1.68 0.92 1.68 2.16 1.29 1.43 1.32 1.46* 0.44* 1.30 1.93* 1.67 1.75 1.19 1.17 0.69 0.61 1.54 2.16 0.00 0.98 1.23 0.92 0.84  Ordinal 0.32 0.75 0.40 0.63 0.29 0.30 0.89 0.62 0.33 0.87 0.27 0.85 0.15 0.54 0.38 0.29 0.27 0.26 0.93 0.95 0.01 0.06 0.71 0.17 0.41 0.44 0.44 0.38 0.53 0.38 0.59 0.26 0.29 0.88 0.40 0.79 0.85  NIOSH M2227 M2233 M2235 M2238 M2241 M2244 M2248 M2255 M2259 M2263 M2266 M2268 M2271 M2274 M2278 M2280 M2284 M2286 M2287 M2301 M2307 M2309 M2310 M2325 M2326 M2327 M2347 M2379 M2386 M2395 M2398 M2441 M2452 M2498 M2509 M2524 M2544  Cases 14 25 130 50 12 11 13 152 29 145 18 25 11 79 135 405 13 58 43 90 12 154 16 79 11 113 12 46 29 27 13 10 25 14 115 11 15  Ever 1.13 1.07 1.01 1.20 0.97 1.10 0.85 1.14 1.33 1.10 1.04 1.18 1.35 1.04 1.20 1.16* 1.52 1.16 1.17 1.26 1.05 1.15 1.04 0.98 1.31 1.04 0.98 1.25 1.16 1.00 1.01 0.70 2.21** 1.16 1.08 1.22 0.96  Low 0.76 1.23 1.19 1.85** 0.29 1.20 0.67 1.39* 1.43 0.95 1.40 1.68 0.38 1.04 1.22 1.15 2.65* 1.13 1.40 1.00 1.59 0.96 1.16 0.95 0.35 1.15 1.23 1.00 0.64 0.89 1.13 0.67 2.37** 1.29 1.06 1.43 0.57  Medium 1.66 0.78 0.92 0.93 0.75 0.98 0.98 1.16 0.80 1.18 0.96 0.77 1.45 1.13 1.22 1.26* 0.34 1.24 0.84 1.35 0.28 1.24 0.39 0.91 2.27 0.90 1.22 1.46 1.33 1.38 0.86 1.00 2.41 0.27 1.05 0.99 1.43  High 0.94 1.20 0.91 0.86 1.72 1.10 0.89 0.89 1.80 1.16 0.70 1.16 2.16 0.95 1.16 1.08 1.65 1.11 1.26 1.43 1.16 1.24 1.58 1.06 1.21 1.06 0.50 1.34 1.50 0.82 1.04 0.42 1.79 1.93 1.13 1.22 1.15  Ordinal 0.65 0.79 0.69 0.86 0.56 0.83 0.70 0.68 0.14 0.23 0.76 0.70 0.14 0.88 0.12 0.09 0.38 0.37 0.48 0.03 0.90 0.07 0.73 0.99 0.28 0.87 0.67 0.13 0.22 0.95 0.98 0.26 <.01 0.46 0.44 0.65 0.79  NIOSH M2553 M2566 M2571 M2576 M2589 M2606 M2609 M2629 M2632 M2634 M2637 M2648 M2651 M2653 M2673 M2677 M2690 M2698 M2703 M2704 M2709 M2710 M2716 M2725 M2761 M2766 M2776 M2779 M2848 M2878 M2880 M2891 M2894 M2900 M2954 M2955 M2958  Cases 100 112 123 13 17 12 134 14 10 10 265 52 22 11 9 19 24 11 18 15 135 176 116 36 22 40 31 60 14 74 251 20 152 87 40 92 27  Ever 1.11 1.09 1.20 1.24 1.23 0.58 1.32** 1.37 0.78 0.92 1.09 0.96 0.98 1.00 0.78 0.86 1.06 1.55 0.70 1.39 1.17 1.22* 1.02 1.15 1.01 1.37 0.93 1.15 0.87 1.03 1.09 1.35 1.19 1.31* 1.20 1.09 1.14  Low 1.38 1.00 1.53** 1.67 0.57 0.56 1.18 0.92 0.85 0.70 1.30* 0.99 0.54 1.16 0.72 1.06 1.07 1.93 0.72 0.93 1.00 1.39* 1.06 0.85 1.22 1.54 1.56 0.89 0.62 0.92 1.04 2.15* 1.19 1.61* 0.88 1.14 1.58  Medium 1.19, 1.07 0.96 1.03 1.24 0.77 1.57** 2.35* 0.71 1.67 0.91 1.17 1.19 0.50 0.26 0.94 1.53 1.30 0.59 1.87 1.35 1.07 0.81 1.26 0.72 1.49 0.67 1.34 0.69 0.83 1.11 0.66 1.18 1.14 1.69* 1.12 0.49  High 0.77 1.19 1.12 • 1.10 2.09 0.42 1.17 0.88 0.78 0.55 1.05 0.78 1.20 1.39 1.39 0.56 0.62 1.37 0.78 1.85 1.14 1.21 1.16 1.33 1.08 1.06 0.56 1.23 1.33 1.33 1.12 1.09 1.19 1.16 0.99 1.00 1.39  Ordinal 0.98 0.32 0.30 0.67 0.14 0.08 0.01 0.35 0.48  0.77 0.72 0.61 0.72 0.90 0.76 0.37  0.94  0.33 0.21 0.13 0.11 0.09 0.76 0.29 0.95 0.20 0.22 0.23 0.97 0.49 0.25 0.63 0.10 0.14 0.29 0.65 0.65  NIOSH M2972 M2975 M2989 M2994 M2997 M3004 M3019 M3032 M3033 M3050 M3057 M3058 M3091 M3095 M3098 M3107 M3114 M3116 M3118 M3125 M3135 M3162 M3187 M3190 M3191 M3192 M3195 M3207 M3210 M3213 M3220 M3225 M3232 M3249 M3252 M3258 M3275  Cases 143 14 80 69 29 14 12 22 77 12 73 49 44 76 14 54 10 11 219 31 9 12 140 64 507 139 12 10 17 21 158 46 12 58 63 17 19  Ever 0.94 1.05 1.04 0.98 0.88 1.12 0.971.25 1.09 1.16 1.19 1.08 1.22 1.10 0.85 0.89 1.18 1.51 1.10 1.03 1.06 1.13 1.12 1.30 1.25** 1.15 1.05 0.53 1.13 0.90 1.14 1.03 0.88 1.20 0.91 0.86 1.09  Low 1.01 1.51 0.98 1.16 0.81 0.46 0.65 1.65 0.84 1.12 1.70** 0.96 1.42 0.96 1.51 1.00 1.46 0.86 0.90 0.96 0.34 0.51 0.95 1.52 1.22* 1.38* 1.59 0.71 1.71 0.95 1.11 0.61 0.76 1.64* 0.99 0.80 0.82  Medium 0.86 1.14 1.18 0.80 1.14 1.13 1.00 1.31 1.28 1.29 0.91 1.23 0.93 1.34 0.72 0.93 1.51 1.42 1.37* 1.62 1.59 0.71 1.19 1.39 1.28* 1.03 0.28 0.42 1.12 0.72 0.96 1.11 0.43 0.83 0.96 1.09 1.42  High 0.96 0.47 0.98 0.92 0.67 1.88 1.33 0.65 1.16 1.09 1.00 1.05 1.37 1.00 0.35 0.73 0.42 2.31 1.05 0.46 1.25 2.16 1.22 0.97 1.25* 1.03 1.16 0.49 0.59 1.04 1.34* 1.42 1.44 1.17 0.80 0.69 1.18  Ordinal 0.52 0.70 0.78 0.63 0.47 0.30 0.79 0.75 0.32 0.68 0.64 0.60 0.29 0.48 0.23 0.29 0.99 0.11 0.17 0.84 0.60 0.26 0.13 0.26 <.01 0.51 0.90 0.06 0.87 0.72 0.10 0.33 0.98 0.49 0.40 0.53 0.57  NIOSH M3288 M3289 M3295 M3297 M3299 M3304 M3305 M3308 M3309 M3374 M3389 M3390 . M3397 M3405 M3417 M3821 M3823 M3829 M3832 M3835 M3841 M3846 M3847 M3848 M3849 M3850 M3855 M3856 M3860 M3862 M3864 M3866 M3873 M3881 M3886 M3891 M3895  Cases 25 39 56 35 18 194 25 16 22 27 19 86 52 20 78 250 64 48 82 68 118 144 18 12 27 26 60 57 34 16 46 31 189 210 102 15 164  Ever 1.28 1.36 1.26 1.17 1.03 1.19 1.16 0.89 1.02 0.99 1.05 1.27 0.96 1.19 0.87 1.19* 1.02 1.21 1.33* 1.19 1.08 1.07 0.72 1.17 0.99 1.06 1.08 1.22 1.29 1.41 1.04 1.04 1.16 1.18* 1.16 1.30 1.23*  Low 1.42 1.03 1.46 1.00 1.13 1.48** 0.70 0.83 0.70 1.10 0.89 1.26 0.99 0.51 1.12 1.23 0.99 1.49 1.13 1.56* 1.35 1.47** 1.45 0.65 1.14 1.20 1.09 1.53 0.69 1.41 1.09 1.07 1.13 1.24 0.96 2.62* 1.29  Medium 0.57 1.80* 1.55 1.14 1.41 1.09 1.49 0.35 1.76 1.01 0.85 1.25 1.17 1.66 0.99 1.15 1.03 1.18 1.34 0.93 0.75 1.03 0.47 2.70* 0.68 0.94 0.95 1.01 1.34 0.86 0.93 0.76 1.04 1.05 1.59** 1.07 1.16  High 1.94* 1.28 0.79 1.37 0.60 0.99 1.28 1.45 0.58 0.85 1.46 1.30 0.78 1.46 0.51* 1.20 1.05 0.93 1.51* 1.00 1.14 0.74 0.32 0.36 1.12 1.06 1.20 1.11 1.82* 1.72 1.09 1.29 1.29 1.27 0.95 0.24 1.23  Ordinal 0.18 0.09 0.44 0.29 0.80 0.38 0.33 0.98 1.00 0.81 0.62 0.07 0.61 0.24 0.06 0.05 0.84 0.59 0.01 0.61 0.72 0.52 0.04 0.64 0.94 0.87 0.55 0.42 0.05 0.21 0.82 0.73 0.07 0.06 0.24 0.75 0.06  NIOSH M3898 M3899 M3901 M3914 M3939 M3981 M3983 M3984 M3985 M3986 M4011 M4016 M4022 M4037 M4039 M4051 M4052 M4056 M4058 M4063 M4077 M4088 M4090 M4097 M4102 M4115 M4117 M4132 M4134 M4188 M4210 M4214 M4215 M4219 M4220 M4232 M4235  Cases 217 184 34 9 18 333 9 17 17 17 33 379 90 15 105 9 11 66 146 200 9 16 22 34 12 15 30 27 10 83 9 57 11 17 14 10 20  Ever 1.16 1.25* 0.99 0.84 1.05 1.11 1.31 1.08 1.10 1.06 0.75 1.19* 1.06 1.06 1.23 1.50 2.72** 1.13 1.22* 1.06 0.83 1.03 1.18 1.11 0.88 1.08 1.04 1.11 0.73 1.12 0.52 1.06 1.17 0.98 0.96 0.84 1.24  Low 1.05 1.45** 0.97 0.97 2.01 1.18 2.62 1.27 1.71 1.32 0.92 1.25* 0.97 1.45 1.16 2.50 2.57 0.97 1.21 1.14 0.59 0.52 0.94 0.87 0.74 0.90 0.65 1.08 0.70 0.97 0.54 1.35 0.90 0.73 0.65 0.83 0.94  Medium 1.18 1.19 1.67 1.19 0.81 1.12 1.16 0.84 0.65 0.38 0.70 1.22 1.01 0.77 1.41 1.45 3.49* 1.15 1.11 1.10 1.45 1.38 0.95 1.19 1.09 0.82 1.01 1.68 0.22 1.10 0.92 0.85 1.02 1.06 0.93 0.41 1.80  High 1.24 1.13 0.46 0.46 0.52 1.03 0.38 1.14 1.04 1.50 0.63 1.12 1.20 0.99 1.13 0.52 2.09 1.28 1.32 0.95 0.50 1.26 1.60 1.28 0.81 1.61 1.46 0.53 1.24 1.30 0.15 0.95 1.63 1.22 1.32 1.46 0.94  Ordinal 0.05 0.08 0.60 0.47 0.48  0.40 0.94 0.87 0.98 0.78 0.09 0.06 0.45 0.96 0.10 0.72 0.02 0.25 0.05 0.82 0.58 0.59 0.31 0.40 0.75 0.55 0.43 0.93 0.58 0.22 0.05 0.92 0.45 0.79 0.82 0.85 0.42  NIOSH M4242 M4250 M4258 M4279 M4292 M4308 M4318 M4320 M4331 M4338 M4353 M4358 M4370 M4376 M4385 M4392 M4401 M4404 M4408 M4410 M4438 M4444 M4448 M4487 M4491 M4513 M4514 M4517 M4554 M4558 M4575 M4598 M4618 M4624 M4636 M4649 M4663  Cases 152 95 9 31 130 67 342 16 266 17 42 16 14 254 20 95 68 186 12 11 29 9 58 14 12 11 19 14 23 16 16 22 9 46 11 42 9  Ever 1.02 1.17 1.27 1.33 1.17 0.99 1.19* 1.14 1.22* 1.08 1.12 1.30 0.87 1.09 1.00 0.96 0.89 1.05 1.13 0.80 1.55* 1.62 1.14 1.32 0.95 1.14 1.26 1.00 1.03 0.99 1.10 1.73* 0.80 1.06 0.83 1.45* 0.72  Low 1.22 1.55* 2.48 1.72 0.99 1.16 1.13 1.46 1.26 0.75 0.70 0.95 1.03 1.01 1.28 1.13 0.98 1.11 0.51 0.85 2.79** 2.58 1.48 1.57 0.81 0.95 0.39 1.27 0.82 1.23 2.01 3.19** 0.35 0.79 0.90 1.58 0.31  Medium 0.98 0.92 1.15 1.06 1.33 0.79 1.25* 0.84 1.23 1.22 1.20 0.86 0.00 0.97 0.86 0.89 1.07 0.96 0.71 1.17 0.98 1.54 0.86 1.38 1.00 1.33 1.53 0.72 1.53 0.76 0.83 0.49 2.74 1.49 0.69 1.77* 2.61  High 0.89 1.03 0.00 1.16 1.17 0.93 1.20 1.11 1.18 1.29 1.47 2.22 1.34 1.27* 0.89 0.86 0.64 1.08 2.16 0.42 1.09 0.62 1.10 0.95 1.03 1.15 1.96 1.04 0.75 0.96 0.55 1.62 0.52 0.95 0.90 1.03 0.47  Ordinal 0.70 0.59 0.76 0.37 0.10 0.66 0.02 0.81 0.03 0.58 0.21 0.16 0.81 0.12 0.81 0.46 0.21 0.66 0.26 0.38 0.30 0.56 0.66 0.56 0.96 0.66 0.11 0.90 0.97 0.83 0.64 0.15 0.72 0.63 0.60 0.13 0.55  NIOSH M4673 M4692 M4718 M4719 M4743 M4755 M4756 M4839 M4884 M4891 M4896 M4897 M4905 M4946 M4982 M4987 M4999 M5090 M5222 P0120 P0310 P0410 P0412 P0418 P0420 P0430 P0431 P0432 P0450 P0610 P0620 P0640 P0651 P0652 P0710 P0720 P2000  Cases 18 29 22 173 38 53 12 106 40 11 11 222 39 11 177 24 52 64 282 14 148 102 18 36 25 141 79 16 56 705 545 81 630 601 493 163 718  Ever 1.14 1.28 1.18 1.30** 1.03 1.27 1.58 1.05 1.19 1.47 0.52* 1.06 1.10 0.98 1.06 1.23 0.96 0.95 1.16 0.81 1.10 1.11 0.57* 1.17 0.83 1.04 1.01 0.75 0.83 1.18* 1.30** 1.11 1.15* 1.18* 1.12 1.05 1.19*  Low 0.57 0.78 1.25 1.00 1.23 1.50 2.26 0.96 1.05 1.51 1.04 1.11 0.71 1.05 1.22 0.80 0.99 1.00 1.20 0.32 1.12 0.91 0.49 0.69 1.04 1.04 1.04 0.12* 0.68 1.19 1.18 1.54* 1.12 1.16 1.14 0.86 1.24*  Medium 1.51 0.84 0.45 1.40* 1.15 1.25 1.86 1.10 1.81* 1.16 0.30 0.96 1.28 1.35 0.97 1.29 1.17 0.96 1.11 0.89 1.19 1.27 0.98 1.29 0.76 1.01 0.93 0.74 1.13 1.07 1.36** 0.59 1.13 1.14 1.01 1.20 1.18  High 1.36 2.17** 1.91 1.48** 0.75 1.07 0.72 1.07 0.84 1.77 0.25 1.12 1.29 0.54 0.97 1.62 0.78 0.90 1.16 1.25 0.99 1.15 0.27* 1.55 0.69 1.07 1.04 1.54 0.65 1.28** 1.34** 1.21 1.21* 1.24* 1.19 1.07 1.15  Ordinal 0.37 0.05 0.32 <.01 0.79 0.29 0.45 0.60 0.49 0.26 0.01 0.49 0.34 0.77 0.95 0.19 0.61 0.66 0.11 0.92 0.49 0.25 0.02 0.16 0.27 0.68 0.98 1.00 0.23 0.02 <.01 0.76 0.03 0.02 0.11 0.40 0.11  NIOSH P2001 P2002 P2003 P2004 P2005 P2006 P2007 P2008 P2009 P2011 P2013 S0001 S0002 S0005 S0006 S0008 S0009 S0012 S0019 S0020 S0022 S0024 S0026 S0028 S0030 S0037 S0042 S0045 S0049 S0050 S0051 S0056 S0057 S0058 S1030 S1031 S2002  Cases 641 700 727 693 735 643 332 420 223 100 56 150 35 102 9 86 40 46 136 44 68 66 37 81 71 66 13 13 22 239 136 14 31 12 16 10 111  Ever 1.24** 1.19* 1.20* 1.17* 1.19* 1.15* 1.04 1.07 1.03 0.96 0.94 1.17 1.28 1.11 0.82 1.20 1.41 1.28 1.19 1.28 1.02 1.33* 1.21 1.08 1.35* 1.01 1.14 1.14 1.05 1.27** 1.11 1.14 1.27 0.98 1.33 1.38 1.18  Low 1.21* 1.14 1.13 1.15 1.15 1.18 1.02 1.22* 0.95 1.34 1.01 1.41* 1.21 1.00 1.21 1.09 1.58 1.63 1.09 1.13 0.94 1.70* 1.46 0.95 1.70* 0.97 1.69 1.69 1.39 1.21 1.19 1.30 1.66 1.26 0.95 0.90 1.22  Medium 1.24* 1.20 1.10 1.13 1.19 1.21* 1.03 0.94 1.10 0.77 1.07 0.86 1.71 0.98 0.26 1.38 1.06 1.29 1.20 1.57 0.92 1.38 1.10 1.06 1.31 0.98 1.36 1.36 0.43 1.42** 0.99 0.79 1.06 1.03 1.33 2.01 1.19  High 1.27* 1.23* 1.37** 1.22* 1.24* 1.07 1.07 1.04 1.03 0.81 0.78 1.23 0.94 1.34 1.05 1.14 1.58 0.93 1.27 1.14 1.19 0.95 1.05 1.25 1.05 1.07 0.48 0.48 1.28 1.19 1.13 1.28 1.04 0.68 1.81 1.53 1.11  Ordinal <.01 0.02 <.01 0.04 0.02 0.22 0.52 0.81 0.64 0.27 0.52 0.23 0.30 0.19 0.57 0.15 0.08 0.45 0.06 0.17 0.68 0.26 0.53 0.34 0.17 0.85 0.86 0.86 0.96 <.01 0.40 0.68 0.52 0.72 0.19 0.26 0.23  NIOSH S2003 S2004 S2009 S2010 S2031 S2035 S2042 S2044 S2045 S2046 S2049 S2059 S2063 S2065 S2068 S2069 S2075 S2077 S2080 S2084 S2085 S2088 S2090 S2091 S2092 S2093 S2094 S2095 S2099 S2100 S2101 S2105 S2106 S2113 S2114 S2120 S2123  Cases 229 153 22 97 25 17 42 90 107 9 18 9 31 60 270 78 9 204 19 58 21 18 24 14 218 17 58 426 78 14 269 29 32 64 56 93 55  Ever 1.19* 1.13 0.89 0.99 1.39 1.22 1.28 1.22 1.12 1.81 1.29 1.13 1.33 1.11 1.20* 1.21 1.26 1.13 1.21 1.04 0.89 1.12 1.00 0.85 1.11 1.05 1.24 1.21** 1.25 0.70 1.16 1.26 1.24 0.93 0.98 1.18 1.23  Low 1.00 1.02 1.04 0.92 0.83 1.07 1.94** 1.17 1.09 2.90* 1.38 0.85 1.72 0.81 1.10 1.28 2.07 1.15 1.07 1.53 1.06 1.74 0.78 0.92 1.11 1.03 1.30 1.26* 1.15 1.28 1.14 1.73 1.70 0.96 0.97 1.53* 1.29  Medium 1.42** 0.98 0.56 1.03 1.81 1.13 0.94 1.33 0.90 0.00 • 1.54 2.01 1.06 1.37 1.24 1.19 0.35 1.16 1.00 0.61 0.76 1.10 1.13 0.95 1.10 0.91 1.33 1.14 1.15 0.14 1.14 0.89 0.90 1.01 1.24 0.82 1.15  High 1.14 1.39* 1.02 1.02 1.69 1.53 0.88 1.16 1.34 1.86 0.92 0.79 1.16 1.15 1.27* 1.16 1.68 1.09 1.58 0.99 0.86 0.62 1.12 0.70 1.11 1.19 1.06 1.24* 1.46 0.77 1.20 1.10 1.08 0.81 0.82 1.20 1.24  Ordinal 0.03 0.07 0.64 0.94 0.07 0.37 0.68 0.13 0.21 0.30 0.50 0.76 0.37 0.32 0.01 0.22 0.62 0.24 0.34 0.75 0.56 0.80 0.81 0.52 0.27 0.81 0.28 0.01 0.06 0.14 0.06 0.58 0.60 0.49 0.74 0.40 0.23  NIOSH S2124 S2126 S2129 S2133 S2134 S2139 S2140 S2156 S2167 S2184 S2186 S2199 S2205 S2206 S2207 S2209 S2210 S2213 S2215 S2226 S2227 S2228 S2257 S2258 S2259 S2260 S2261 S2262 S2263 S2265 S2269 S2271 S2312 S2315 S2316 S2317 S2318  Cases 29 15 9 32 146 92 19 39 28 58 44 58 58 88 67 214 81 12 35 12 12 12 119 29 86 135 35 12 83 32 16 15 64 17 25 14 17  Ever 1.17 1.29 0.86 1.31 1.07 1.21 1.18 1.12 1.23 1.18 1.21 0.98 1.30 1.21 1.22 1.20* 1.32* 1.34 1.26 1.34 1.19 1.34 1.04 1.45 1.00 1.11 1.10 1.34 0.94 0.99 1.34 1.22 1.03 1.07 1.07 1.38 1.12  Low 1.81 0.73 1.17 1.38 0.94 1.43 1.20 0.85 1.73 1.37 1.17 0.93 1.66* 1.48* 1.29 1.18 1.46 1.34 1.64 1.34 1.26 1.34 1.14 1.50 0.67 0.94 1.33 1.34 0.88 0.82 1.01 0.99 1.29 1.09 1.01 2.09 1.99  Medium 1.11 1.38 0.57 1.11 1.27 1.18 0.68 1.32 0.65 1.05 1.40 1.12 1.06 1.03 1.39 1.26 1.46 0.85 0.77 0.85 0.72 0.85 0.91 1.37 1.25 1.17 1.04 0.85 0.98 1.05 1.23 1.38 1.00 1.43 1.29 0.64 0.45  High 0.67 1.85 0.84 1.48 0.98 1.02 1.58 1.28 1.32 1.11 1.04 0.90 1.18 1.08 0.96 1.17 1.03 2.20 1.34 2.20 1.99 2.20 1.06 1.51 1.07 1.20 0.91 2.20 0.97 1.10 2.01 1.40 0.79 0.65 0.85 1.70 0.89  Ordinal 0.93 0.20 0.58 0.19 0.52 0.35 0.43 0.33 0.54 0.44 0.36 0.87 0.24 0.36 0.33 0.05 0.12 0.28 0.38 0.28 0.47 0.28 0.85 0.10 0.58 0.18 0.92 0.28 0.75 0.86 0.16 0.39 0.69 0.99 0.86 0.44 0.83  NIOSH S2323 S2325 S2326 S2393 S2394 S2395 S2396 S2397 S2399 S2400 S2401 S2404 S2405 S2410 S2414 S2421 S2425 S2427 S2430 S2431 S2434 S2436 S2437 S2438 S2439 S2442 S2443 S2447 S2453 S2459 S2460 S2465 S2467 S2471 S2472 S2473 S2474  Cases 15 22 106 169 54 209 131 50 31 12 21 163 31 12 86 57 12 35 24 322 9 33 45 75 113 16 9 14 112 69 95 59 71 339 14 33 45  Ever 1.10 1.25 1.14 1.13 1.33 1.20* 1.09 1.25 1.18 0.90 0.79 1.10 1.33 0.69 1.08 1.15 0.89 1.00 1.16 1.15 1.70 1.27 1.43* 1.06 0.98 1.50 1.70 1.06 1.27* 1.15 0.96 1.16 1.16 1.19* 1.80 1.27 1.43*  Low 1.21 0.67 1.03 1.09 1.46 1.03 0.98 1.51 1.60 0.91 1.03 1.15 1.72 0.75 0.94 1.40 1.04 1.49 1.30 1.19 1.28 1.71 1.54 1.16 0.92 0.62 1.28 1.10 1.47* 1.22 0.93 1.33 1.52* 1.22 1.54 1.71 1.54  Medium 0.64 1.57 1.09 1.16 1.25 1.33* 0.97 1.07 0.87 1.08 0.63 1.11 1.06 0.94 0.95 1.14 0.58 0.98 1.41 1.01 0.48 0.98 1.74* 1.02 1.10 1.36 0.48 0.65 0.94 1.20 0.97 1.38 1.12. 1.18 1.26 0.98 1.74*  High 1.31 1.49 1.31 1.12 1.26 1.24 1.32 1.16 1.04 0.70 0.72 1.04 1.16 0.44 1.35 0.87 1.10 0.57 0.74 1.25* 3.61* 1.08 1.00 0.99 0.91 2.38* 3.61* 1.44 1.40 1.00 0.97 0.72 0.84 1.17 2.51* 1.08 1.00  Ordinal 0.75 0.18 0.14 0.22 0.13 0.02 0.20 0.34 0.75 0.68 0.25 0.46 0.37 0.19 0.27 0.76 0.73 0.44 0.79 0.06 0.06 0.51 0.12 0.85 0.82 0.04 0.06 0.74 0.05 0.49 0.78 0.76 0.81 0.04 0.04 0.51 0.12  NIOSH S2475 S2479 S2482 S2483 S2484 S2487 S2489 S2490 S2494 S2499 S2501 S2503 S2507 S2511 S2515 S2517 S2524 S2531 S2532 S2540 S2541 S2544 S2545 S2547 S2552 S2555 S2558 S2561 S2569 S2581 S2582 S2583 S2584 S2586 S2591 S2594 S2596  Cases 45 12 68 37 58 52 234 45 9 9 29 17 114 56 14 9 63 38 17 115 12 66 109 19 31 42 224 33 31 120 18 116 62 44 12 249 12  Ever 1.29 1.05 0.99 1.03 1.39* 1.28 1.12 1.43* 1.70 1.70 1.19 1.12 1.01 0.98 0.86 0.84 1.23 1.15 1.32 1.02 1.28 1.01 1.15 0.91 1.33 1.00 1.11 1.27 1.32 1.28* 0.91 1.28* 1.59** 1.32 1.34 1.17* 1.34  Low Medium 1.67* 1.24 2.01 0.53 0.95 0.75 1.31 0.68 1.23 1.55 1.69* 1.22 1.12 1.19 1.54 1.74* 1.28 0.48 1.28 0.48 1.73 0.83 1.15 0.69 1.02 0.99 1.29 0.95 0.48 0.76 0.56 1.01 1.43 1.05 1.58 1.00 1.61 1.27 0.94 1.13 1.63 0.64 0.97 0.98 1.19 0.96 0.63 0.84 " 1.72 1.06 0.63 1.04 0.97 1.32* 1.71 0.98 1.70 . 1.10 1.24 1.36 0.65 0.92 1.52* 1.31 1.75* 1.52 1.07 1.60 1.34 0.85 1.09 1.31* 1.34 0.85  High 0.95 0.71 1.25 1.08 1.41 0.90 1.06 1.00 3.61* 3.61* 1.00 1.76 1.01 0.72 1.46 0.97 1.20 0.83 1.12 0.99 2.20 1.07 1.30 1.23 1.16 1.31 1.06 1.08 1.10 1.24 1.11 1.00 1.50 1.40 2.20 1.12 2.20  Ordinal 0.44 0.65 0.77 0.95 0.03 0.47 0.25 0.12 0.06 0.06 0.79 0.52 0.97 0.46 0.92 0.81 . 0.28 0.92 0.47 0.80 0.41 0.85 0.19 0.97 0.37 0.53 0.18 0.51 0.40 0.04 0.94 0.10 <.01 0.08 0.28 0.06 0.28  NIOSH S2597 S2598 S2599 S2601 S2603 S2604 S2627 S2630 T0016 T0021 T0027 TOO 38 T0052 T0055 T0059 T0062 T0084 T0118 T0166 T0176 TO 180 TO 183 T0189 T0202 T0204 T0245 T0262 T0263 T0265 T0269 T0345 T0362 T0375 T0379 T0420 T0430 T0453  Cases 99 278 221 13 41 13 257 56 31 50 56 70 11 9 32 46 91 142 23 299 590 16 17 9 61 34 148 18 17 240 125 45 10 34 47 21 13  Ever 1.07 1.19* 1.37** 1.29 1.02 0.75 1.14 0.98 1.08 1.14 1.22 1.33* 1.87 1.45 1.20 1.31 1.30* 1.09 1.14 1.12 1.25** 1.23 1.09 0.94 1.23 1.27 1.30** 0.94 1.24 1.18* 1.09 0.96 1.06 1.25 1.29 0.95 1.02  Low 1.13 1.11 1.45** 1.26 0.85 0.61 1.20 0.99 0.90 1.05 1.16 1.31 0.48 1.67 1.77* 1.39 1.17 0.97 0.69 1.18 1.32** 1.39 1.36 0.25 1.23 1.62 1.29 0.66 1.41 1.24 0.90 0.53 0.93 0.81 1.13 0.43 1.04  Medium 1.00 1.27* 1.37* 0.77 1.38 0.66 1.14 1.17 1.42 1.51 1.24 1.36 3.50** 1.22 0.82 L20 1.50* 1.11 1.20 1.15 1.25* 1.40 0.91 2.13 1.43 1.04 1.29 1.38 1.09 1.05 1.40* 1.34 1.12 1.57 1.39 1.39 1.51  High 1.06 1.18 1.28 2.14 0.83 0.96 1.09 0.86 0.95 0.88 1.26 1.32 1.67 1.49 1.04 1.35 1.24 1.20 1.47 1.03 1.17 0.91 1.01 0.88 1.05 1.11 1.31 0.69 1.16 1.25 1.00 0.98 1.13 1.33 1.37 1.00 0.50  Ordinal 0.69 0.03 <.01 0.29 0.95 0.50 0.20 0.79 0.70 0.55 0.19 0.06 0.04 0.39 0.74 0.15 0.04 0.23 0.33 0.34 0.02 0.66 0.91 0.85 0.26 0.45 0.02 0.84  0.56 0.06 0.35 0.84 0.82 0.16 0.10 0.86 0.83  NIOSH T0481 T0482 T0508 T0532 T0535 T0550 T0624 T0641 T0670 T0763 TO 79 5 T0798 T0819 T0837 T0861 T0890 T0892 T0902 T0962 T0981 T0995 T1017 T1055 T1063 T1074 T1076 T1120 T1124 T1150 T1153 T1155 T1185 T1186 T1187 T1188 T1192 T1194  Cases 42 10 92 12 89 104 147 16 30 18 23 10 19 11 18 9 276 12 225 69 9 16 14 130 12 130 12 27 25 269 191 364 209 641 569 19 190  Ever 1.39 0.86 1.37** 1.50 1.04 1.17 1.22* 0.81 0.80 1.95* 1.36 0.91 1.20 0.81 1.49 1.03 1.01 0.56 1.16 1.27 0.94 1.03 1.07 1.17 1.05 1.14 0.80 1.05 1.12 1.20* 1.13 1.28** 1.14 1.22** 1.23** 1.07 1.13  Low 1.07 0.83 1.70** 0.85 1.10 1.59** 1.53** 0.82 0.98 1.42 2.53** 0.62 1.34 1.03 1.89 1.50 0.90 0.14 1.30* 1.47 0.25 1.47 0.54 0.89 1.26 1.08 1.86 1.11 0.69 1.15 1.32* 1.29* 1.29* 1.26* 1.16 1.06 1.09  Medium 1.43 0.57 1.21 1.37 1.03 0.88 0.97 0.93 0.76 2.58* 0.81 2.19 1.35 0.54 1.78 0.00 1.15 1.18 1.09 0.98 2.13 0.70 1.34 1.51** 0.25 1.29 0.34 0.69 1.15 1.08 1.12 1.25* 1.32* 1.13 1.21 0.68 1.11  High 1.69 1.14 1.16 2.22 1.01 1.03 1.17 0.65 0.66 1.90 0.75 0.51 0.93 0.79 0.98 1.48 0.99 0.40 1.11 1.31 0.88 0.94 1.24 1.11 1.68 1.04 0.41 1.32 1.52 1.35* 0.96 1.30* 0.82 1.27** 1.32** 1.63 1.17  Ordinal 0.03 0.82 0.08 0.11 0.84 0.63 0.19 0.42 0.18 0.02 0.84 0.79 0.64 O.46  0.35 0.99 0.71 0.12 0.19 0.16 0.85 0.86 0.60 0.09 0.75 0.27 0.16 0.72 0.32 0.01 0.55 <.01 0.64 0.01 <.01 0.60 0.17  NIOSH T1214 T1269 T1270 T1271 T1272 T1274 T1293 T1307 T1341 T1364 T1366 T1378 T1379 T1460 T1473 T1474 T1475 T1486 T1488 T1492 T1493 T1500 T1505 T1516 T1523 T1525 T1531 T1542 T1554 T1557 T1558 T1575 T1577 T1583 T1585 T1587 T1595  Cases 43 503 482 411 430 12 10 29 125 9 27 81 10 141 9 165 535 42 21 65 65 73 9 11 69 36 73 495 170 423 171 253 47 73 101 30 23  Ever 1.24 1.18* 1.22** 1.13 1.16* 1.07 0.73 0.88 1.10 1.40 1.11 1.14 0.90 1.11 0.73 0.97 1.24** 1.25 1.21 1.32* 1.30 1.11 1.27 2.38* 0.89 1.03 1.32* 1.13 0.99 1.10 1.21* 1.18* 1.10 1.35* 1.01 1.10 0.89  Low 1.26 1.38** 1.27* 1.21 1.15 0.83 0.42 0.80 1.37 0.82 1.57 1.25 1.42 1.12 0.71 0.90 1.18 1.18 1.03 1.37 1.27 0.90 0.83 2.42 1.07 0.81 1.32 1.16 0.92 1.12 1.46** 1.21 1.03 1.47 1.02 1.19 0.71  Medium 1.29 1.00 1.16 1.12 1.14 1.36 1.27 1.07 1.03 2.98* 1.14 1.18 0.00 0.92 0.84 1.17 1.09 1.28 1.66 1.49 1.53 1.52* 0.87 3.25* 0.85 1.34 1.45 1.10 1.32* 1.13 1.17 1.08 1.28 1.06 1.02 0.72 0.68  High 1.18 1.16 1.22* 1.06 1.19 1.03 0.48 0.76 0.90 0.51 0.64 1.00 1.21 1.28 0.65 0.85 1.45** 1.29 0.92 1.12 1.11 0.92 2.09 1.43 0.74 0.94 1.18 1.12 0.75 1.04 1.01 1.24 0.97 1.50 0.99 1.40 1.29  Ordinal 0.27 0.16 0.02 0.29 0.04 0.77 0.41 0.55 0.93 0.43 0.86 0.50 0.72 0.23 0.39 0.68 <.01 0.21 0.49 0.11 0.12 0.46 0.30 0.04 0.20 0.79 0.08 0.15 0.68 0.37 0.26 0.05 0.63 0.04 0.99 0.56 0.96  NIOSH T1624 T1628 T1649 T1650 T1651 T1652 T1676 T1706 T1720 T1722 T1734 T1764 T1768 T1792 T1799 T1816 T1833 T1854 T1857 T1867 T1870 T1872 T1873 T1876 T1880 T1887 T1890 T1891 T1892 T1909 T1912 T1941 T1947 T1949 T1956 T1966 T1998  Cases 66 290 10 257 247 30 97 14 44 34 37 408 552 15 194 11 216 151 183 102 227 104 14 134 230 295 21 21 10 80 229 23 13 17 304 16 16  Ever 1.04 1.12 1.02 1.09 1.10 1.41 1.06 1.22 1.09 0.86 1.11 1.19* 1.17* 1.22 1.09 1.62 1.13 1.18 1.09 0.94 1.14 1.07 1.08 1.01 1.13 1.06 0.86 0.84 0.72 1.63** 1.22* 0.98 1.18 0.76 1.00 0.92 1.30  Low 1.38 1.18 0.00 1.19 1.03 0.66 1.06 1.33 1.00 1.21 0.98 1.23* 1.21* 0.75 1.01 1.91 1.22 1.42* 0.99 1.03 1.05 1.01 1.61 0.97 1.12 0.97 1.47 1.22 0.69 1.17 1.24 1.31 0.61 0.87 1.05 0.77 0.53  Medium 0.86 1.04 1.61 1.16 1.15 1.99* 1.10 1.16 1.19 0.44 1.07 1.19 1.13 1.68 1.13 0.46 1.10 1.25 0.95 1.03 1.30* 1.13 1.01 0.87 1.06 1.14 0.39 0.45 1.07 1.76** 1.23 0.67 0.87 0.15 1.01 1.19 1.14  High 0.86 1.13 1.56 0.93 1.12 1.67 1.01 1.18 1.09 0.98 1.26 1.15 1.17 1.28 1.13 2.50 1.06 0.85 1.34* 0.77 1.07 1.09 0.64 1.21 1.21 1.07 0.86 0.87 0.41 2.01** 1.18 1.02 1.92 1.18 0.94 0.78 2.26*  Ordinal 0.72 0.23 0.46 0.68 0.20 0.03 0.71 0.58 0.58 0.36 0.46 0.04 0.06 0.37 0.25 0.15 0.32 0.55 0.13 0.35 0.14 0.49 0.78 0.59 0.11 0.32 0.32 0.35 0.29 <.01 0.04 0.79 0.29 0.47 0.73 0.76 0.10  NIOSH T2003 T2051 T2060 T2072 T2076 T2080 T2086 T2096 T2097 T2099 X0001 X0029 X0063 X0074 X0089 X0093 X0105 X0108 X0145 X0150 X0158 X0167 X0182 XI009 X1017 X1021 X1028 X1031 X1036 X1038 X1041 XI049 X1056 X1067 X1068 X1075 X1079  Cases 16 65 18 18 109 50 25 .13 19 9 61 55 57 192 46 78 9 22 64 11 91 14 45 53 23 103 31 427 117 440 16 17 28 16 257 161 321  Ever 1.03 1.06 1.04 1.03 1.10 1.20 1.02 0.87 1.01 0.93 0.98 1.05 1.15 1.04 1.04 1.14 1.66 0.99 0.92 0.89 1.25 0.76 1.09 1.38* 1.12 1.07 1.32 1.15* 1.10 1.25** 1.78* 1.03 1.05 1.11 1.25** 1.42** 1.20*  Low 2.49** 0.96 0.57 0.99 0.97 1.03 0.75 0.77 0.34 1.26 0.94 1.20 1.71* 0.87 1.03 1.25 1.13 0.81 0.97 0.51 1.34 0.91 0.93 1.25 1.35 0.87 1.71 1.08 1.03 1.17 1.75 1.59 0.82 1.58 1.34* 1.22 1.10  Medium 0.19 0.79 1.08 0.84 0.99 1.32 1.46 1.24 1.34 0.00 0.99 0.99 0.90 1.49** 1.18 1.19 2.42 1.43 0.96 1.35 1.00 1.03 1.17 1.34 1.15 1.15 1.05 1.20 0.92 1.29* 1.39 1.37 0.94 0.40 1.16 1.59** 1.21  High 0.52 1.40 1.41 1.23 1.34 1.31 0.84 0.60 1.29 1.40 1.02 0.98 0.82 0.77 0.90 0.98 1.51 0.88 0.84 0.75 1.40 0.41 1.18 1.54 0.90 1.15 1.14 1.17 1.33 1.27* 2.17 0.28 1.39 1.36 1.26 1.44* 1.29*  Ordinal 0.32 0.39 0.54 0.81 0.19 0.19 0.89 0.59 0.56 0.86 0.99 0.93 0.96 0.86 0.94 0.53 0.18 0.97 0.47 0.82 0.08 0.21 0.49 0.03 0.86 0.36 0.39 0.04 0.22 <.01 0.04 0.53 0.52 0.83 0.02 <.01 <.01  NIOSH X1080 X1100 X1102 X1103 X1105 X1106 X1107 X1108 X1109 X1112 X1114 X1118 X1120 X1121 X1133 X1134 X1135 X1139 XI140 X1141 X1146 X1156 X1162 X1165 XI166 X1167 X1170 X1172 X1173 X1174 X1175 X1184 X1185 X1187 X1197 XI209 X1217  Cases 177 12 12 18 56 20 14 266 20 67 9 183 9 12 43 28 14 10 47 25 131 33 15 149 18 45 45 28 111 18 39 51 156 85 63 11 42  Ever 1.19 1.40 1.44 1.19 1.32 0.99 0.82 1.19* 1.26 0.91 0.86 1.10 0.82 1.13 1.25 1.25 0.73 0.67 1.08 1.22 0.99 1.22 1.27 1.14 1.49 1.27 1.07 1.22 1.08 1.25 1.29 1.18 1.15 0.96 0.90 0.67 1.23  Low 0.97 0.35 0.35 0.69 1.13 0.65 0.00 1.20 2.09* 0.97 0.47 1.19 0.89 0.51 0.90 0.79 0.75 0.59 1.22 0.83 1.01 1.96* 1.64 1.29 1.89 1.41 0.69 0.75 1.00 0.99 1.50 1.16 1.10 1.08 0.98 0.88 1.13  Medium 1.51** 2.02 1.30 1.30 1.52 1.62 0.98 1.24 1.29 0.97 1.55 0.89 1.09 0.71 1.82* 1.70 0.17 0.38 0.86 1.08 0.83 0.71 0.25 0.92 1.78 1.09 1.28 1.38 1.07 1.27 1.48 1.42 1.15 0.78 0.87 0.86 1.78*  High 1.08 1.83 2.77* 1.70 1.28 0.74 1.40 1.14 0.34 0.81 0.66 1.22 0.50 2.16 1.27 1.27 1.24 1.07 1.15 1.72 1.13 1.04 1.94 1.20 0.98 1.30 1.26 1.58 1.16 1.52 0.89 1.01 1.21 0.96 0.84 0.20 0.80  Ordinal 0.06 0.13 0.06 0.24 0.08 0.98 0.88 0.06 0.85 0.41 0.81 0.28 0.49 0.26 0.11 0.19 0.49 0.41 0.71 0.17 0.91 0.76 0.39 0.30 0.35 0.24 0.37 0.16 0.38 0.28 0.42 0.42 0.12 0.60 0.37 0.12 0.40  NIOSH X1219 X1222 X1224 X1226 X1228 X1230 X1231 X1239 XI240 , X1255 X1256 X1257 X1258 X1266 X1267 X1268 X1280 X1281 X1302 X1303 XI304 XI306 XI308 X1312 X1314 X1322 X1329 X1333 X1334 X1335 X1336 X1340 XI342 XI343 X1350 X1351 X1354  Cases 250 50 26 11 121 10 352 15 31 35 12 15 10 9 19 61 37 18 14 52 12 23 14 285 15 176 61 24 15 104 14 13 10 15 19 12 18  Ever 1.19* 0.96 1.23 1.71 1.07 1.05 1.22** 1.34 0.73 0.94 1.00 1.18 0.73 1.32 1.13 1.21 1.08 1.07 1.60 0.95 1.07 0.94 1.08 1.18* 1.26 1.13 1.24 1.10 1.38 1.12 1.13 1.15 0.91 0.95 0.94 1.10 1.03  Low 1.24 0.97 0.71 2.41 1.05 0.67 1.09 0.77 0.86 0.85 0.53 1.48 . 0.26 0.43 0.58 0.89 1.21 0.61 0.66 1.00 1.37 1.08 1.16 1.10 1.38 1.00 1.40 0.45 1.50 1.09 0.55 0,54 0.62 0.36 1.57 0.51 0.50  Medium 1.08 0.96 1.43 2.57 0.95 1.82 1.36** 1.48 0.63 0.72 0.97 0.62 0.54 1.31 1.26 1.56* 0.78 1.16 1.42 1.17 0.00 1.03 1.75 1.30* 1.42 1.24 1.29 1.40 0.88 1.27 1.53 1.57 2.19 1.03 0.45 0.69 1.67  High 1.25 0.93 1.55 0.00 1.21 1.01 1.21 1.85 0.68 1.23 1.45 1.55 1.39 2.65 1.50 1.20 1.23 1.56 2.77* 0.76 1.96 0.69 0.48 1.13 0.99 1.14 1.01 1.38 1.75 1.00 1.24 1.35 0.51 1.47 0.84 2.04 0.84  Ordinal 0.05 0.76 0.17 0.49 0.39 0.73 <.01 0.16 0.09 0.96 0.67 0.57 0.76 0.18 0.35 0.12 0.67 0.40 0.03 0.56 0.70 0.61 0.89 0.04 0.60 0.15 0.34 0.35 0.26 0.44 0.48 0.43 0.79 0.64 0.50 0.31 0.74  NIOSH X1355 X1357 X1367 X1376 X1379 X1380 XI394 X1395 X1396 X1401 X1402 X1411 XI424 X1425 X1442 X1447 XI448 X1449 X1450 X1454 X1456 X1457 X1458 X1459 X1460 X1463 X1464 X1468 X1471 X1475 X1484 X1485 X1486 X1488 X1490 X1496 X1497  Cases Ever 9 1.06 0.85 15 199 1.09 27 1.25 157 1.25* 92 1.01 127 1.36** 221 1.23* 177 1.35** 35 2.18** 67 . 1.00 1.19 56 9 2.83* 1.21* 218 127 1.09 151 1.01 1.82 14 102 1.03 1.33 15 24 1.38 1.10 170 94 1.29* . 41 1.12 1.04 17 87 1.26 1.19 50 1.14 18 1.51 25 1.13 39 1.12 10 1.37 17 1.14 90 1.17 101 1.01 15 1.38 10 1.39* 83 34 1.20.  Low 1.64 0.66 1.18 0.73 1.36* 0.91 1.38 0.94 1.50** 1.62 1.19 1.16 1.10 1.26 1.13 0.98 2.00 0.87 2.45* 1.81 1.25 1.41 0.87 1.13 1.50* 1.17 0.43 1.36 0.70 1.68 2.48* 1.22 1.04 0.45 2.89* 1.61* 0.70  Medium 0.36 0.67 1.02 1.57 1.08 1.19 1.60** 1.51** 1.49** 2.68** 0.81 1.04 4.48* 1.19 1.11 1.20 1.46 1.04 1.40 1.09 1.04 1.26 1.00 0.41 1.07 1.26 2.04* 1.51 0.96 0.58 0.60 1.12 1.13 1.59 0.80 1.43 1.27  High 1.16 1.27 1.06 1.43 1.32 0.94 1.11 1.23 1.07 2.25* 0.93 1.36 2.70 1.18 1.04 0.84 2.04 1.16 0.27 1.24 1.03 1.22 1.55 1.52 1.19 1.16 0.88 1.68 1.76* 1.12 1.14 1.07 1.32 0.86 0.41 1.13 1.63  Ordinal 0.94 0.87 0.51 0.17 0.04 0.93 0.02 <.01 0.02 <.01 0.70 0.22 <.01 0.06 0.52 0.84 0.07 0.52 0.98 0.33 0.56 0.08 0.25 0.73 0.18 0.32 0.49 0.07 0.13 0.96 0.66 0.43 0.11 0.81 0.92 0.06 0.13  NIOSH XI500 X1503 X1505 X1506 X1507 X1508 X1509 X1511 X1512 X1513 X1516 X1550 X1551 X1569 X1573 X1576 X1579 X1580 X1585 XI586 X1588 X1590 X1594 X1595 X1597 XI598 X1613 X1636 X1638 X1639 X1641 X1642 XI643 XI646 X1650 X1651 X1652  Cases 105 108 115 86 10 297 370 34 287 117 41 101 111 59 242 186 249 151 83 106 19 24 15 14 9 89 19 11 23 326 17 26 12 74 23 118 166  Ever 1.31* 1.26* 1.03 1.06 0.95 1.24** 1.25** 0.97 1.13 1.03 1.15 1.26* 1.15 1.21 1.16 1.15 1.10 1.12 1.29* 1.30* 1.20 1.34 0.78 1.02 2.08 1.00 0.95 0.83 1.27 1.10 1.23 1.08 1.29 1.12 1.35 1.11 1.17  Low 1.33 1.49* 0.98 0.95 1.28 1.05 1.19 1.23 1.28* 1.31 0.67 1.53* 0.99 0.97 1.24 0.90 1.12 0.93 1.56* 1.55* 0.44 1.10 0.45 0.43 2.80 1.01 0.51 0.76 0.70 1.12 0.56 1.12 0.32 0.98 0.68 0.83 1.26  Medium 1.23 1.13 1.18 0.89 0.34 1.26 1.26* 1.15 1.16 0.78 1.79* 0.98 1.23 1.39 1.01 1.19 1.10 1.18 1.20 1.28 1.98 1.37 0.89 0.88 0.57 0.75 0.95 1.04 1.03 1.16 1.46 0.93 1.25 1.46 1.49 1.37 1.25  High 1.38 1.15 0.93 1.31 1.11 1.41** 1.31* 0.53 0.96 1.02 0.89 1.29 1.23 1.28 1.23 1.36* 1.08 1.24 1.10 1.09 1.12 1.54 1.03 1.88 3.32 1.23 1.34 0.69 2.02* 1.03 1.88 1.22 2.34 0.91 1.90 1.13 1.01  Ordinal 0.03 0.15 0.86 0.38 0.81 <.01 <.01 0.47 0.51 0.88 0.34 0.12 0.14 0.14 0.10 0.02 0.33 0.12 0.18 0.12 0.33 0.16 0.67 0.43 0.08 0.74 0.78 0.58 0.10 0.36 0.17 0.69 0.13 0.51 0.07 0.18 0.28  NIOSH X1653 X1654 X1655 X1656 X1657 X1658 X1667 X1673 X1674 X1680 X1688 X1696 X1698 X1699 XI700 X1712 X1718 X1752 X1783 X1791 XI794 X1808 X1814 X1827 X1829 X1830 X1833 X1835 X1836 X1837 X1841 X1842 X1850 X1867 X1868 X1869 X1872  Cases 28 166 26 170 343 34 9 12 18 14 13 13 131 12 32 15 87 128 13 9 15 176 10 63 14 13 13 24 116 203 129 46 45 21 10 34 410  Ever 1.27 1.16 1.20 1.34** 1.12 1.22 0.99 1.13 1.26 1.65 1.86* 0.97 1.11 1.05 1.15 1.19 1.29* 1.26* 0.58 1.17 0.65 1.18 0.76 0.93 1.06 1.08 0.66 1.05 1.23 1.18 1.16' 1.30 1.29 1.34 1.02 0.95 1.25**  Low 1.36 1.25 0.90 1.52** 1.19 0.86 0.88 0.51 1.51 0.88 4.50** 0.46 1.24 1.03 1.03 0.70 1.29 1.23 0.43 0.90 0.65 1.43** 1.24 0.91 1.52 1.57 1.21 1.34 1.33 1.28 0.96 1.67* 1.67* 0.73 2.53* 1.25 1.22  Medium 1.22 1.24 1.28 1.48** 0.99 1.58 0.00 0.71 0.94 1.67 0.38 1.23 1.12 0.77 1.52 1.60 1.18 1.20 0.62 1.63 0.25 0.99 0.50 1.14 0.98 0.91 0.41 1.39 1.03 0.98 1.44* 1.09 1.24 1.66 0.62 0.60 1.23*  High 1.24 1.00 1.39 1.04 1.20 1.27 2.10 2.16 1.28 2.78* 1.23 1.20 0.99 1.40 0.89 1.30 1.39 1.37 0.67 1.04 1.10 1.13 0.56 0.81 0.67 0.78 0.44 0.47 1.33 1.27 1.07 1.12 0.95 1.71 0.24 0.98 1.30*  Ordinal 0.34 0.32 0.28 0.03 0.14 0.21 0.63 0.26 0.50 0.03 0.40 0.79 0.57 0.76 0.57 0.39 0.05 0.02 0.12 0.62 0.28 0.25 0.27 0.54 0.78 0.89 0.07 0.70 0.08 0.08 0.15 0.32 0.44 0.11 0.35 0.62 <.01  NIOSH X1877 XI893 XI894 X1895 X1897 XI899 X1902 X1906 X1909 X1910 X1918 X1922 X1923 X1925 X1930 X1936 XI948 X1957 XI966 X1970 X1977 X1980 X1981 XI984 X1986 XI988 X1992 X1994 X1995 X1998 XI999 X2001 X2003 X2006 X2007 X2011 X2013  Cases 250 44 48 13 356 60 24 11 122 13 24 11 17 55 20 16 21 17 17 18 9 38 12 25 32 12 29 22 13 22 19 23 9 65 59 33 14  Ever Low 1.14 1.15 1.06 0.95 1.86** 1.63 1.06 2.10 1.26** 1.18 1.14 1.43 1.72* 2.45* 0.62 1.00 1.44* 1.13 0.90 0.56 1.72* 2.81** 1.04 1.91 0.96 1.00 1.25 1.17 1.60 2.03 0.71 0.85 1.25 1.57 1.23 0.57 1.13 1.82 1.15 1.05 1.19 0.90 1.35 1.03 1.14 1.36 0.71 1.37 1.02 1.25 1.42 1.83 1.30 0.97 1.05 .1.10 1.60 1.60 1.21 0.95 1.01 0.74 1.21 0.54 1.70 1.28 1.06 1.05 1.29 1.45 0.77 0.80 1.34 1.28  Medium 1.11 1.24 1.73 0.62 1.18 0.98 1.48 1.03 0.86 0.22 1.03 0.71 1.69 1.50 2.05 0.50 1.13 1.46 1.02 0.96 2.31 1.80* 1.75 1.96* 1.76 1.89 1.20 0.60 1.90 1.00 1.05 1.12 0.48 0.98 1.55 1.11 1.44  High 1.16 0.96 2.20** 0.69 1.41** 1.02 1.27 1.29 1.08 1.93 1.27 0.59 0.32 1.16 0.95 1.53 1.09 1.88 0.43 1.40 0.48 1.27 0.29 1.36 0.97 0.63 1.74 1.41 1.26 1.61 1.19 1.92 3.61* 1.16 0.90 0.37* 1.29  Ordinal 0.13 0.77 <.01 0.66 <.01 0.67 0.12 0.77 0.61 0.68 0.17 0.64 0.57 0.19 0.24 0.92 0.55 0.17 0.70 0.50 0.72 0.09 0.91 0.10 0.33 0.58 0.11 0.71 0.21 0.26 0.78 0.14 0.06 0.59 0.29 0.10 0.37  NIOSH X2014 X2015 X2016 X2017 X2019 X2020 X2022 X2023 X2027 X2028 X2029 X2031 X2035 X2062 X2063 X2065 X2066 X2083 X2143 X2145 X2180 X2192 X2202 X2204 X2283 X2293 X2295 X2297 X2298 X2303 X2305 X2306 X2307 X2308 X2309 X2310 X2311  Cases 131 12 226 9 49 153 90 20 50 10 127 38 18 103 27 76 105 10 80 40 27 32 12 94 11 375 450 262 390 361 206 335 301 208 31 154 155  Ever 1.19 0.91 1.06 1.16 1.07 1.06 1.01 1.60 1.22 1.72 1.30* 1.44* 1.49 1.06 1.08 1.22 1.08 1.52 1.26 1.42 1.27 1.22 1.11 1.28* 1.08 1.30** 1.30** 1.25** 1.34** 1.39** 1.38** 1.38** 1.38** 1.40** 1.23 1.04 1.08  Low 1.18 0.49 1.18 0.90 0.92 1.00 0.90 1.84 0.98 1.39 1.35 1.61 1.89 0.79 0.88 1.33 0.99 1.11 1.04 1.04 0.95 1.01 1.04 1.59* 0.79 1.26* 1.25* 1.14 1.33** 1.42** 1.22 1.40** 1.40** 1.25 1.06 1.01 1.12  Medium 1.11 1.15 0.93 1.91 1.22 1.26 1.10 1.94 1.35 1.11 1.19 0.97 1.78 1.40 1.39 1.30 1.10 2.51 1.57* 1.80* 1.06 1.30 0.75 1.08 1.78 1.34** 1.35** 1.25 1.41** 1.42** 1.32 1.41** 1.39** 1.34* 1.46 0.92 1.05  High 1.28 1.06 1.08 0.54 1.07 0.92 1.03 1.16 1.29 2.75 1.37 1.71 0.98 1.00 0.95 1.03 1.16 1.30 1.24 1.45 1.77 1.35 1.61 1.16 0.66 1.30* 1.31** 1.37** 1.27* 1.33* 1.62** 1.34* 1.36* 1.61** 1.17 1.18 1.08  Ordinal 0.09 1.00 0.67 0.75 0.61 0.66 0.80 0.17 0.16 0.07 0.02 0.06 0.35 0.44 0.71 0.29 0.37 0.24 0.06 0.04 0.12 0.25 0.59 0.18 0.86 <.01 <.01 <.01 <.01 <.01 <.01 <.01 <.01 <.01 0.30 0.50 0.49  NIOSH X2312 X2314 X2315 X2316 X2317 X2318 X2319 X2320 X2325 X2327 X2328 X2329 X2330 X2331 X2332 X2333 X2335 X2336 X2342 X2346 X2351 X2352 X2354 X2361 X2363 X2365 X2373 X2377 X2378 X2379 X2380 X2381 X2384 X2386 X2393 X2394 X2395  Cases 18 24 9 14 33 23 23 33 18 10 20 15 15 15 15 15 86 48 12 15 16 16 12 100 98 23 18 64 9 12 85 30 175 11 55 21 11  Ever 0.96 0.92 1.88 0.94 1.19 1.02 1.20 0.95 1.06 0.67 1.09 1.20 1.20 1.20 1.20 1.20 0.99 1.06 0.80 0.93 1.13 1.19 0.81 1.22 1.29* 1.27 1.01 1.11 0.91 1.17 1.29* 1.07 1.14 0.83 1.26 1.06 1.11  Low 0.38 0.86 2.19 0.45 1.01 0.73 0.73 0.92 0.93 0.63 0.52 1.08 1.08 1.08 1.08 1.08 1.07 0.96 1.32 0.60 0.98 0.89 1.29 1.28 1.48* 1.73 0.38 1.07 0.55 1.72 1.56* 1.02 1.23 0.84 1.53 0.87 0.31  Medium 1.08 0.95 3.34* 1.04 1.34 1.12 1.30 1.20 0.73 0.83 1.16 0.46 0.46 0.46 0.46 0.46 0.73 1.37 0.63 1.02 0.60 1.33 0.78 1.11 1.16 0.34 1.11 1.09 0.85 0.30 1.23 1.35 1.04 0.63 1.00 1.17 1.52  High 1.30 0.95 0.00 1.28 1.21 1.18 1.48 0.72 1.63 0.55 1.55 2.22 2.22 2.22 2.22 2.22 1.16 0.82 0.40 1.16 1.94 1.35 0.39 1.29 1.24 1.72 1.49 1.15 1.41 1.39 1.08 0.86 1.13 1.05 1.25 1.11 1.50  Ordinal 0.71 0.77 0.33 0.80 0.34 0.71 0.26 0.68 0.57 0.25 0.36 0.27 0.27 0.27 0.27 0.27 0.97 0.82 0.23 0.95 0.39 0.42 0.25 0.11 0.09 0.39 0.52 0.46 0.89 0.81 0.19 0.84 0.28 0.66 0.25 0.73 0.42  NIOSH Cases X2396 12 X2398 98 10 X2400 X2401 . 20 X2403 68 X2404 10 X2405 48 77 X2417 X2418 18 64 X2423 X2424 31 X2436 13 X2441 11 X2449 179 X2463 18 X2467 13 X2468 10 X2470 96 111 X2475 X2480 118 X2482 14 X2496 53 24 X2501 X2513 118 X2514 118 X2517 56 218 X2518 X2521 9 X2522 9 136 X2523 X2529 46 X2532 10 X2533 13 X2534 97 X2537 21 62 X2538 X2541 22  Ever 0.84 1.31* 1.02 1.06 1.02 1.02 1.07 1.32* 1.03 1.06 1.21 0.77 0.88 1.05 1.06 1.02 1.02 1.31* 1.06 1.11 1.32 0.91 1.37 1.11 1.11 1.30 1.23* 1.05 1.04 1.15 0.97 0.94 1.55 1.24 0.86 1.24 1.18  Low 1.14 1.54* 2.15 0.46 0.97 2.15 1.65* 1.06 0.39 0.88 0.67 1.91 0.48 1.17 1.57 0.90 2.15 1.46 1.00 1.06 1.57 1.00 0.94 1.28 1.28 1.54 1.16 1.24 1.13 1.25 0.88 0.58 2.96* 1.08 1.01 1.31 0.70  Medium 0.46 1.31 0.93 1.30 0.96 0.93 1.09 1.67* 1.13 0.96 1.58 0.16 1.61 1.11 0.98 1.12 0.93 1.25 0.76 1.26 1.38 1.00 1.43 1.07 1.07 1.13 1.27 0.78 0.85 1.29 1.24 0.52 1.78 1.34 0.85 0.97 1.56  High 0.91 1.08 0.24 1.49 1.13 0.24 0.52 1.31 1.49 1.30 1.45 0.46 0.51 0.90 0.64 1.05 0.24 1.23 1.42* 1.03 0.95 0.72 1.80 0.98 0.98 1.24 1.26 1.08 1.18 0.89 0.79 1.76 0.31 1.33 0.76 1.47 1.24  Ordinal 0.54 0.12 0.42 0.39 0.73 0.42 0.42 0.03 0.49 0.40 0.15 0.11 0.72 0.98 0.71 0.88 0.42 0.06 0.27 0.38 0.56 0.34 0.09 0.66 0.66 0.19 0.01 0.96 0.91 0.46 0.79 0.71 0.69 0.05 O.46 0.13 0.34  NIOSH X2548 X2549 X2555 X2560 X2562 X2570 X2572 X2582 X2598 X2604 X2608 X2629 X2644 X2652 X2656 X2657 X2661 X2672 X2674 X2676 X2686 X2689 X2701 X2703 X2712 X2730 X2732 X2744 X2754 X2769 X2777 X2789 X2793 X2797 X2803 X2805 X2807  Cases 71 54 52 31 23 21 19 9 45 26 17 65 9 44 123 25 84 18 50 146 45 25 37 14 23 68 45 67 14 15 106 32 364 47 19 21 9  Ever 1.18 0.92 0.94 1.30 1.08 1.15 1.31 1.05 1.20 1.08 1.15 1.24 1.07 1.10 1.17 1.45 1.11 1.39 1.17 1.13 1.23 2.89** 1.29 1.17 1.37 1.20 1.29 1.00 1.04 1.10 1.03 1.22 1.20* 1.30 1.13 0.91 0.85  Low 1.31 0.98 1.00 1.66 0.67 0.68 0.57 0.97 1.54 1.45 0.56 1.32 0.85 1.02 1.04 1.59 0.86 1.28 1.00 1.16 1.59 1.93 1.10 0.65 1.44 1.41 1.67* 1.19 0.96 1.28 0.93 1.01 1.06 1.14 1.03 0.80 0.57  Medium 1.06 1.07 0.93 1.02 2.19* 1.62 1.52 1.04 1.09 1.41 1.10 1.19 1.11 0.90 1.39* 2.02* 1.26 0.91 1.30 0.99 1.21 2.95** 1.47 1.04 1.27 1.18 1.24 0.81 0.95 1.58 0.80 1.30 1.36** 1.72* 1.45 1.13 0.88  High 1.15 0.73 0.87 1.16 0.45 1.12 1.98 1.16 0.96 0.37 1.76 1.23 1.35 1.38 1.08 0.83 1.21 1.89 1.20 1.23 0.90 4.00** 1.29 2.07 1.43 1.01 0.95 0.93 1.24 0.49 1.33 1.35 1.18 1.04 1.02 0.82 1.08  Ordinal 0.35 0.39 0.60 0.41 0.83 0.44 0.09 0.84 0.61 0.65 0.25 0.18 0.70 0.42 0.16 0.28 0.23 0.15 0.28 0.21 0.59 <.01 0.16 0.27 0.21 0.44 0.44 0.70 0.79 0.89 0.41 0.25 <.01 0.16 0.66 0.72 0.85  NIOSH X2812 X2821 X2822 X2823 X2826 X2836 X2838 X2839 X2842 X2844 X2847 X2851 X2852 X2861 X2862 X2863 X2864 X2865 X2866 X2867 X2869 X2871 X2873 X2874 X2876 X2878 X2881 X2887 X2897 X2900 X2905 X2925 X2938 X2951 X2954 X2955 X2963  Cases 62 68 68 14 12 9 91 28 25 11 9 9 173 23 18 177 56 59 64 39 21 131 9 9 18 25 56 85 11 15 12 64 91 23 103 53 39  Ever 1.05 1.36* 1.00 0.94 1.13 1.39 1.37** 1.28 1.40 1.67 1.16 0.85 1.27** 1.19 1.08 1.26* 1.25 0.99 1.10 1.31 1.12 1.10 0.95 0.89 1.61 1.28 1.19 1.27 1.12 0.95 1.13 1.31 1.23 1.08 1.39** 0.96 1.00  Low Medium 0.99 1.06 0.95 1.49 0.96 0.86 0.62 0.83 0.71 0.51 0.00 1.71 0.98 1.63* 1.32 1.30 1.97 0.68 3.08* 0.81 0.90 1.91 0.81 0.33 1.55** 1.01 1.30 0.83 1.59 0.81 1.50** 1.11 1.44 1.55 0.97 0.98 1.07 1.08 1.16 1.76 0.67 ' 1.58 1.04 1.18 0.83 1.14 0.98 0.57 1.92 0.87 1.39 0.91 1.04 1.16 1.14 1.63* 1.54 0.50 0.66 0.69 0.51 0.71 1.50 1.29 1.37 1.28 1.70 0.72 1.60** 1.54* 0.96 1.22 0.67 1.21  High 1.10 1.62* 1.17 1.36 2.16 3.27* 1.51* 1.22 1.57 1.30 0.54 1.33 1.23 1.40 0.72 1.15 0.79 1.02 1.15 1.10 1.10 1.09 0.85 1.14 1.99 1.56 1.36 1.03 1.46 1.54 2.16 1.10 1.01 0.81 1.02 0.78 1.15  Ordinal 0.67 <.01 0.79 0.83 0.26 0.07 0.02 0.32 0.06 0.40 0.75 0.87 0.07 0.31 0.78 0.09 0.45 1.00 0.49 0.19 0.50 0.46 0.92 0.98 0.10 0.26 0.22 0.29 0.45 0.78 0.26 0.18 0.21 0.7 7 0.06 0.64 0.65  NIOSH X2965 X2966 X2973 X2974 X2981 X2982 X2984 X2986 X2987 X2988 X3000 X3003 X3007 X3008 X3009 X3012 X3014 X3017 X3019 X3020 X3025 X3027 X3032 X3033 X3038 X3039 X3045 X3048 X3051 X3054 X3064 X3069 X3077 X3082 X3088 X3089 X3093  Cases 11 10 57 89 61 80 68 9 16 13 61 124 125 12 140 52 539 296 252 169 53 262 186 45 230 39 81 21 20 42 304 103 13 178 37 53 13  Ever 0.80 1.03 0.97 1.32* 0.96 1.25 1.37* 2.02 0.90 1.84 1.16 1.12 0.99 1.13 1.10 0.96 1.16* 1.25** 1.18* 1.09 0.96 1.04 1.16 0.98 1.20* 1.07 1.19 0.99 1.04 0.97 1.27** 1.18 1.00 1.08 1.19 1.12 0.63  Low 0.42 0.36 0.99 1.55* 1.00 1.44 1.25 4.34** 0.53 2.34 0.90 0.93 0.98 0.56 1.20 0.99 1.14 1.27* 1.26 1.29 0.99 1.03 0.99 0.85 1.32* 1.49 1.27 0.75 0.98 1.01 1.19 1.56** 0.71 1.24 1.13 1.46 1.09  Medium 1.13 1.07 1.08 1.21 • 0.93 1.34 1.85** 1.78 1.32 2.77* 1.44 1.30 1.01 0.60 1.04 1.17 1.15 1.37** 1.23 0.87 1.17 1.10 1.24 0.90 1.10 0.45 0.98 1.17 1.40 0.78 1.31* 0.84 1.25 0.85 1.26 0.96 0.16  High 0.86 1.63 0.87 1.17 0.94 0.98 0.96 0.00 0.84 0.38 1.16 1.13 0.97 2.16 1.05 0.78 1.20* 1.11 1.07 1.10 0.80 1.01 1.25 1.18 1.19 1.28 1.35 1.04 0.82 1.14 1.32* 1.12 1.06 1.11 1.17 0.94 0.59  Ordinal 0.69 0.57 0.74 0.11 0.72 0.27 0.07 0.57 0.87 0.25 0.23 0.19 0.89 0.29 0.53 0.61 0.03 0.02 0.13 0.61 0.65 0.66 0.05 0.84 0.07 0.92 0.18 0.89 0.96 0.98 <.01 0.46 0.86 0.62 0.37 0.88 0.08  NIOSH X3100 X3103 X3104 X3105 X3106 X3108 X3111 X3117 X3120 X3130 X3142 X3148 X3152 X3160 X3167 X3204 X3205 X3211 X3220 X3231 X3232 X3235 X3239 X3241 X3243 X3256 X3262 X3264 X3265 X3269 X3272 X3275 X3278 X3286 X3287 X3289 X3290  Cases 39 180 45 130 10 57 11 47 69 28 41 11 14 20 64 28 16 140 20 184 15 64 16 100 11 96 104 21 290 12 14 38 15 167 14 18 11  Ever 1.71** 1.02 1.26 1.08 0.99 1.12 1.04 1.00 1.03 0.99 1.14 1.91 0.87 1.05 1.24 1.67* 0.96 1.34** 1.01 1.17 1.06 1.03 1.17 1.06 0.99 1.05 1.14 1.07 1.21* 1.38 0.85 1.35 0.94 1.19 1.18 0.86 1.07  Low 1.52 0.96 1.38 0.98 1.84 1.45 1.49 0.92 1.00 1.60 1.07 1.99 1.30 1.26 1.17 2.30* 1.06 1.68** 1.47 0.85 1.22 0.72 1.04 0.85 1.00 1.09 1.18 0.94 1.24 1.31 1.02 1.13 1.12 1.13 0.96 0.81 0.80  Medium 1.86 1.18 0.74 1.17 0.63 0.63 1.25 1.02 0.99 0.64 1.39 3.03* 0.88 0.98 1.33 1.76 1.62 1.26 0.82 1.53** 1.39 1.37 2.26 0.87 0.94 1.21 1.03 0.97 1.10 1.26 0.53 1.72 0.76 1.15 0.46 0.64 0.91  High 1.78 0.92 1.70* 1.12 0.33 1.27 0.48 1.06 1.10 0.77 0.96 0.58 0.38 0.91 1.21 1.04 0.49 1.12 0.77 1.12 0.48 0.99 0.83 1.44* 1.01 0.85 1.21 1.31 1.28* 1.54 1.01 1.23 0.95 1.28 2.30* 1.12 1.56  Ordinal <.01 0.93 0.13 0.34 0.44 0.63 0.69 0.90 0.74 0.50 0.55 0.17 0.33 0.97 0.16 0.13 0.59 0.06 0.69 0.04 0.85 0.64 0.68 0.21 0.97 0.96 0.26 0.64 0.02 0.31 0.61 0.11 0.77 0.05 0.27 0.75 0.59  NIOSH X3373 X3396 X3401 X3542 X3558 X3559 X3569 X3570 X3635 X3644 X3647 X3672 X3691 X3693 X3695 X3700 X3712 X3716 X3720 X3722 X3743 X3755 X3761 X3764 X3765 X3767 X3782 X3812 X3835 X3842 X3845 X3870 X3881 X3912 X3941 X3950 X3955  Cases 11 15 21 20 12 64 12 11 104 46 11 373 245 25 42 10 251 11 - 20 10 16 11 245 18 9 191 357 40 16 176 9 142 109 12 145 16 36  Ever 1.55 0.73 1.08 0.63 0.87 0.91 1.68 1.51 1.33* 1.46* 2.00* 1.21** 1.12 1.55 1.39 0.61 1.15 0.89 1.04 0.88 1.14 1.50 1.22* 0.82 1.19 1.19* 1.09 1.11 1.10 1.09 1.50 1.10 1.20 1.59 1.08 0.80 0.86  Low 0.90 1.02 1.13 1.21 0.45 0.91 0.36 0.36 1.57* 1.63 1.79 1.11 1.12 0.57 1.76* 0.80 1.18 1.36 0.78 0.95 1.78 2.03 1.14 0.57 1.60 1.19 1.19 1.01 1.11 1.05 1.40 1.32 1.42* 1.63 1.26 0.17 1.09  Medium 1.60 0.76 0.94 0.42 1.80 1.22 2.15 1.61 1.22 1.55 1.20 1.21 1.06 1.84 1.06 0.59 1.12 1.27 1.00 1.09 0.00 1.45 1.12 1.48 1.12 1.28 1.27* 0.90 0.82 1.07 2.35 0.98 1.08 1.58 0.93 1.14 0.96  High 2.16 0.43 1.15 0.37 0.42 0.63 2.82* 2.81* 1.18 1.21 2.82* 1.31** 1.18 2.25* 1.32 0.39 1.15 0.00 1.34 0.58 1.92 0.88 1.40** 0.48 0.85 1.11 0.81 1.41 1.35 1.16 0.60 0.94 1.11 1.57 1.04 1.02 0.54  Ordinal 0.11 0.15 0.77 0.02 0.66 0.33 0.02 0.05 0.08 0.08 0.03 <.01 0.16 0.01 0.17 0.10 0.13 0.30 0.62 0.60 0.58 0.51 <.01 0.41 0.88 0.10 0.92 0.39 0.66 0.27 0.44 0.89 0.27 0.20 0.73 0.81 0.19  NIOSH X3989 X3991 X3992 X4004 X4016 X4021 X4025 X4033 X4041 X4042 X4045 X4046 X4057 X4063 X4067 X4073 X4096 X4097 X4101 X4103 X4104 X4107 X4113 X4119 X4131 X4134 X4140 X4178 X4207 X4230 X4231 X4233 X4237 X4242 X4255 X4263 X4267  Cases 29 18 33 31 23 32 21 11 17 111 64 18 34 18 15 11 30 75 96 13 16 9 54 13 68 9 9 15 16 12 30 9 79 158 31 27 32  Ever 1.35 1.07 1.06 0.93 1.12 0.94 1.18 1.99* 1.14 1.25* 1.43* 0.79 1.37 1.33 1.37 1.12 1.08 1.05 0.99 1.72 1.50 0.62 0.93 0.99 1.31 1.07 1.96 1.08 1.67 3.52** 0.96 0.75 0.99 1.00 0.97 1.10 1.96**  Low 1.16 1.69 1.37 0.87 0.41 1.61 1.39 0.49 1.36 1.38 1.45 0.14 1.12 1.82 2.05 0.35 0.81 1.07 1.03 1.73 1.54 0.47 0.98 0.64 1.25 0.55 3.19* 1.43 1.09 2.71 1.00 0.85 1.06 0.89 0.85 1.24 1.19  Medium 1.44 1.39 0.81 0.72 1.42 0.79 1.27 3.50** 0.94 1.34 1.62* 0.92 2.14** 1.18 0.45 1.82 1.01 1.16 1.23 1.60 1.24 0.61 1.06 2.19* 1.62* 2.22 1.64 0.82 0.97 4.22** 1.05 0.79 0.92 0.92 1.30 1.24 2.40*  High 1.44 0.30 1.01 1.22 1.59 0.36* 0.95 2.01 1.10 1.03 1.22 1.25 0.89 0.92 1.79 1.11 1.43 0.92 0.73 1.81 1.77 0.75 0.77 0.23 1.06 0.47 1.25 0.99 3.09** 3.50 0.83 0.65 0.98 1.19 0.72 0.78 2.56**  Ordinal 0.14 0.54 1.00 0.95 0.23 0.15 0.72 0.02 0.77 0.17 0.04 0.90 0.17 0.60 0.38 0.55 0.43 0.88 0.60 0.11 0.15 0.27 0.49 0.81 0.11 0.80 0.25 0.99 0.02 <.01 0.73 0.40 0.84  0.55 0.83 0.92 <.01  NIOSH X4268 X4282 X4297 X4330 X4393 X4425 X4471 X4521 X4540 X4542 X4548 X4599 X4622 X4668 X4697 X4699 X4731 X4753 X4779 X4785 X4794 X4849 X4891 X4906 X4918 X4922 X4987 X5001 X5029 X5034 X5037 X5044 X5054 X5065 X5067 X5068 X5071  Cases 10 29 11 11 91 9 14 11 24 83 35 17 9 35 67 9 78 20 109 36 9 16 9 10 95 12 75 9 289 21 21 36 33 9 62 10 54  Ever 1.88 1.40 1.45 1.81 1.01 1.58 0.99 0.92 1.11 0.98 1.25 1.02 1.27 1.27 0.99 3.02** 1.09 1.41 1.18 1.25 2.65* 1.34 1.52 1.08 1.09 1.08 1.05 1.65 1.14 1.01 0.92 0.95 1.23 1.82 1.38* 1.60 1.37*  Low 0.53 1.88* 0.86 1.13 1.05 0.69 0.44 1.54 1.32 0.97 1.56 1.43 0.91 1.66 1.05 2.53 1.15 0.70 1.40 1.65 3.29 2.19* 0.48 1.02 0.94 2.06 0.87 1.20 1.27* 1.19 1.27 1.06 1.13 2.16 1.36 1.93 1.27  Medium 3.24* 1.17 1.25. 3.21* 1.03 3.74* 2.15* 0.97 0.60 0.94 1.67 0.51 1.99 0.76 1.14 2.91 1.18 1.34 1.21 0.70 2.39 0.52 3.14* 1.61 1.10 0.66 0.90 1.84 1.02 0.53 0.55 0.70 1.08 1.03 1.66* 1.52 1.67*  High 1.56 0.98 2.16 0.96 0.95 1.28 0.41 0.27 1.49 1.01 0.52 1.15 0.85 1.37 0.82 3.66 0.95 2.13* 0.91 1.41 2.39 1.26 0.64 0.63 1.23 0.78 1.40 1.83 1.09 1.32 0.96 1.08 1.45 2.47 1.13 1.44 1.15  Ordinal 0.05 0.42 0.14 0.13 0.95 0.15 0.96 0.41 0.58 0.89 0.79 0.90 0.57 0.36 0.71 <.01 0.75 0.06 0.52 0.37 0.04 0.61 0.25 0.97 0.30 0.77 0.32 0.16 0.30 0.91 0.61 0.80 0.23 0.14 0.06 0.27 0.07  NIOSH X5075 X5085 X5093 X5094 X5115 X5131 X5135 X5145 X5161 X5184 X5185 X5189 X5192 X5206 X5213 X5235 X5258 X5262 X5263 X5299 X5311 X5318 X5404 X5408 X5417 X5421 X5427 X5447 X5459 X5467 X5495 X5502 X5503 X5504 X5505 X5507 X5509  Cases 9 11 12 16 38 25 12 46 49 10 90 9 13 9 28 11 11 631 557 255 58 11 149 31 397 9 9 164 22 29 34 182 250 20 32 10 52  Ever 0.94 1.99 2.97** 1.21 1.28 1.06 1.43 1.18 1.30 3.00** 1.07 1.67 0.85 0.93 1.16 1.07 0.97 1.21** 1.28** 0.97 1.20 1.26 1.24* 1.11 1.14 0.96 1.19 1.18 1.08 1.04 1.40 1.24* 1.07 1.03 0.84 1.03 1.27  Low 0.61 1.12 3.00* 1.08 0.89 1.00 2.39 1.26 1.66* 5.62** 1.32 0.53 0.58 0.64 0.63 2.06 1.77 1.15 1.24* 0.90 1.46 1.80 1.12 1.16 1.07 1.38 1.29 1.14 0.71 0.72 1.63 1.21 1.10 0.58 0.81 0.67 1.19  Medium 0.97 2.37 4.35* 1.13 1.70 1.01 0.69 1.50 1.18 0.90 1.07 2.66 1.03 1.31 2.32** 0.71 0.51 1.11 1.24* 1.06 1.06 1.75 1.32 1.15 1.30** 1.11 0.82 1.28 1.32 1.00 1.40 1.22 1.09 0.90 0.96 1.46 1.45  High 1.24 2.50 1.67 1.42 1.22 1.18 1.34 0.84 1.03 2.86 0.76 1.83 0.93 0.85 0.65 0.59 0.60 1.36** 1.37** 0.95 1.08 0.00 1.27 1.04 1.07 0.35 1.42 1.11 1.25 1.40 1.18 1.30 1.02 1.60 0.75 0.91 1.16  Ordinal 0.91 0.03 0.01 0.43 0.15 0.72 0.50 0.58 0.36 0.03 0.80 0.10 0.75 0.91 0.53 0.66 0.51 <.01 <.01 0.81 0.44 0.96 0.03 0.69 0.08 0.59 0.63 0.12 0.51 0.51 0.21 0.02 0.58 0.44 0.36 0.86 0.18  NIOSH X5515 X5516 X5518 X5520 X5521 X5582 X5629 X5638 X5654 X5658 X5673 X5683 X5686 X5689 X5690 X5691 X5697 X5701 X5704 X5710 X5711 X5714 X5752 X5757 X5758 X5808 X5811 X5822 X5824 X5825 X5849 . X5864 X5873 X5876 X5886 X5890 X5891  Cases 89 171 12 137 21 9 11 98 14 24 38 60 501 19 126 9 386 9 13 14 88 18 12 79 239 17 12 80 15 12 470 83 88 288 204 11 15  Ever 1.21 1.24* 1.72 1.10 0.92 1.00 1.01 1.12 0.87 1.06 1.13 0.99 1.12 0.96 1.13 1.09 1.20** 1.16 1.22 0.85 0.93 1.22 1.53 1.16 1.17 1.11 1.07 1.31* 0.98 1.07 1.17* 1.12 1.03 1.18* 0.96 0.79 1.03  Low 1.30 1.32 2.09 1.44* 0.83 0.74 1.65 0.99 0.38 0.76 0.80 1.00 1.21* 1.29 0.89 0.59 1.12 0.69 1.21 0.97 0.98 0.38 0.36 0.95 0.95 1.28 1.85 1.40 1.48 1.77 1.23* 1.01 1.12 1.33* 1.05 0.73 1.12  Medium 1.21 1.16 1.79 1.14 0.45 2.60 1.17 1.15 0.85 1.10 0.95 1.52* 1.01 0.59 1.37 2.53 1.31** 1.47 1.40 0.71 0.78 1.77 3.12** 1.32 1.34* 0.53 0.49 1.12 0.63 0.95 1.16 0.87 0.77 1.11 0.93 0.76 0.88  High 1.12 1.23 1.15 0.76 1.60 0.00 0.27 1.22 1.44 1.27 1.63 0.44* 1.15 0.98 1.13 0.39 1.17 1.42 1.01 0.89 1.04 1.66 1.20 1.19 1.21 1.60 0.84 1.45 0.76 0.54 1.13 1.47* 1.16 1.12 0.91 0.88 1.07  Ordinal 0.23 0.05 0.21 0.81  0.86  0.80  0.55 0.23 0.88 0.59 0.20 0.47 0.20 0.72 0.16 0.86 0.01 0.51 0.61 0.58 0.66 0.17 0.12 0.19 0.02 0.62 0.76 0.05 0.61  0.71 0.08 0.17 0.81 0.12  O.46  0.55 0.95  NIOSH X5892 X5898 X5899 X5906 X5907 X5911 X5915 X5918 X5928 X5930 X5935 X5940 X5941 X5942 X5943 X5945 X5946 X5950 X5974 X5976 X5983 X5986 X5991 X5992 X5997 X6034 X6115 X6125 X6186 X6191 X6205 X6222 X6238 X6246 X6265 X6289 X6293  Cases 9 16 11 233 21 158 25 30 30 25 16 13 55 17 16 10 22 424 18 182 138 55 12 50 39 14 83 9 360 139 34 141 182 133 10 66 293  Ever 0.97 0.85 1.76 1.20* 1.10 1.07 1.29 1.33 1.11 1.33 1.10 0.92 0.90 1.16 1.34 1.15 1.09 1.24** 1.02 1.14 1.17 1.00 1.00 1.02 1.03 1.08 1.29* 1.45 1.17* 1.10 0.85 1.04 1.19 1.10 0.95 1.37* 1.17*  Low 0.24 0.97 1.70 1.16 0.87 1.07 1.04 1.35 1.35 1.09 0.65 0.40 0.98 0.65 1.01 2.03 0.73 1.34** 0.88 0.93 1.09 0.66 1.22 0.69 1.40 0.26 1.56* 0.45 1.15 1.20 0.78 0.92 1.00 1.39* 0.45 1.61* 1.12  Medium 1.41 0.47 2.94* 1.22 1.29 0.99 1.85 1.39 0.84 1.63 0.95 1.07 1.09 1.12 1.38 0.00 1.00 1.19 0.85 1.25 1.29 0.78 0.76 1.10 0.99 1.32 1.20 2.39 1.17 1.04 0.94 1.07 1.39* 0.94 2.26 1.00 1.23  High 1.63 1.12 0.55 1.22 1.17 1.15 0.89 1.22 1.19 1.17 1.76 1.33 0.67 1.72 1.78 1.29 1.60 1.19 1.41 1.23 1.13 1.57* 1.01 1.29 0.72 1.61 1.10 1.55 1.18 1.06 0.82 1.12 1.17 0.97 0.33 1.46 1.16  Ordinal 0.56 0.64  0.24 0.03 0.58 0.44 0.32 0.24 0.73 0.23 0.39 0.81 0.30 0.31 0.19 0.96 0.39 0.02 0.72 0.06 0.14 0.32 0.92 0.47 0.68 0.38 0.18 0.20 0.04 0.53 0.44 0.49 0.04 0.84 0.91 0.06 0.05  NIOSH X6352 X6353 X6358 X6360 X6362 X6363 X6366 X6367 X6368 X6371 X6372 X6373 X6374 X6376 X6377 X6379 X6382 X6386 X6388 X6391 X6393 X6396 X6407 X6423 X6430 X6432 X6434 X6449 X6456 X6463 X6468 X6472 X6475 X6492 X6493 X6494 X6499  Cases 28 81 9 46 20 40 29 40 51 9 17 15 14 24 19 9 188 18 18 22 86 22 60 17 399 44 13 16 38 124 12 12 106 12 448 34 623  Ever 1.24 1.01 1.57 1.04 1.08 1.10 1.20 1.21 1.26 2.06 1.05 1.11 1.03 1.19 1.19 1.81 1.09 1.12 1.15 1.29 1.26 1.19 0.95 0.67 1.25** 1.22 1.03 1.22 0.86 1.05 0.81 0.82 1.11 1.13 1.20** 1.15. 1.21**  Low 0.41 0.99 2.03 1.06 0.50 0.82 0.90 1.17 1.22 1.72 0.79 0.65 0.65 0.85 0.59 2.04 1.09 0.58 0.54 0.71 1.51* 0.68 0.98 0.88 1.33** 1.11 1.58 1.78 0.98 0.98 0.23 1.13 1.52** 0.77 1.25* 0.70 1.15  Medium 1.50 0.80 0.52 1.09 1.15 1.27 1.13 1.36 1.18 1.20 1.74 0.85 0.85 1.45 1.31 0.75 1.18 1.26 1.08 1.27 1.14 1.11 0.90 0.52 1.22 1.19 0.25 0.58 0.88 0.92 1.07 0.60 0.96 1.78 1.20 2.22** 1.18  High Ordinal 1.82 0.07 1.22 0.72 2.23 0.25 0.92 0.95 1.60 0.35 0.37 1.26 0.21 1.59 0.36 1.08 0.14 1.40 3.82* 0.03 0.65 0.89 1.96 0.35 0.54 1.71 0.37 1.19 0.24 1.66 0.13 2.43 0.50 1.00 0.37 1.53 0.22 1.90 0.11 1.89 1.11 0.21 0.20 1.81 0.94 0.67 0.10 0.63 0.02 1.19 1.36 0.20 1.22 0.91 0.62 1.42 0.72 0.30 1.22 0.43 1.02 0.81 0.71 0.41 0.81 0.80 0.90 0.66 0.04 1.15 0.61 0.53 1.29** <.01  NIOSH X6502 X6505 X6507 X6517 X6518 X6523 X6524 X6525 X6526 X6527 X6528 X6529 X6536 X6537 X6538 X6539 X6540 X6542 X6543 X6551 X6557 X6559 X6564 X6567 X6569 X6572 X6582 X6583 X6586 X6588 X6596 X6597 X6599 X6602 X6603 X6619 X6620  Cases 14 9 28 16 11 16 13 16 13 9 18 24 13 17 11 25 14 16 13 18 15 9 14 12 11 68 14 9 12 26 •88 34 45 45 353 388 235  Ever 1.14 1.07 1.09 1.29 1.05 1.09 1.24 1.09 1.25 1.09 1.12 1.11 1.14 1.25 1.39 0.81 1.17 1.09 1.24 1.29 1.30 1.07 1.21 1.09 1.45 1.27 1.18 1.49 0.81 1.14 1.07 1.43 1.12 1.12 1.16* 1.10 1.11  Low 1.04 0.58 0.94 1.14 1.83 1.40 1.21 1.40 0.95 0.68 1.54 1.22 0.79 0.58 0.76 1.08 1.07 1.33 1.18 0.40 1.05 0.53 0.85 1.11 0.86 1.24 0.93 1.28 0.87 0.86 0.93 1.51 1.38 1.28 1.20 1.06 1.10  Medium 1.43 2.45 1.21 0.96 0.26 0.68 1.48 0.68 1.93 1.00 0.48 1.08 1.46 1.62 1.10 1.15 1.34 0.91 0.70 1.99 1.69 2.55 1.76 1.46 1.25 1.32 1.26 3.37* 0.86 1.51 0.87 1.56 1.45 0.92 1.13 1.14 1.05  High 1.00 0.40 1.13 1.82 0.95 1.25 1.01 1.25 0.98 1.74 1.45 0.99 1.16 1.71 2.37 0.27* 1.13 1.00 2.10 1.61 1.20 0.42 1.15 0.73 2.16 1.25 1.40 0.58 0.70 1.02 1.40 1.20 0.56 1.19 1.17 1.10 1.17  Ordinal 0.69 0.87 0.61 0.25 0.74 0.86 0.57 0.86 0.46 0.53 0.74 0.79 0.56 0.17 0.14 0.11 0.60 0.93 0.32 0.13 0.36 0.81 0.44 0.95 0.14 0.11 0.45 0.42 0.46 0.51 0.27 0.14 0.94 0.57 0.07 0.19 0.19  NIOSH X6624 X6628 X6629 X6648 X6652 X6658 X6663 X6678 X6693 X6695 X6732 X6749 X6756 X6757 X6765 X6766 X6767 X6769 X6773 X6777 X6785 X6786 X6791 X6800 X6801 X6804 X6806 X6819 X6858 X6859 X6864 X6874 X6880 X6881 X6891 X6895 X6898  Cases 179 106 470 41 15 10 28 99 551 12 15 14 39 10 12 17 14 58 24 14 38 80 22 432 9 288 13 52 9 26 11 24 20 89 15 9 52  Ever 1.13 1.21 1.11 1.30 0.99 0.75 1.06 1.29* 1.21** 0.98 1.09 1.03 1.13 1.01 1.00 0.94 1.26 1.07 1.08 0.93 1.35 1.13 1.40 1.17* 0.87 1.12 1.30 1.34 1.31 1.03 1.67 0.68 0.88 1.12 0.73 1.33 0.96  Low 1.09 1.48* 1.11 1.06 1.28 0.75 1.05 1.23 1.34** 0.90 0.59 0.59 1.12 2.18 0.54 1.54 1.40 1.00 0.65 1.01 1.76 1.25 1.90 1.14 2.34 1.11 1.40 1.13 1.68 0.80 1.84 0.94 0.64 1.09 1.27 1.78 0.99  Medium 0.99 1.11 1.06 1.77* 0.53 0.23 1.25 1.29 1.19 1.00 1.04 1.12 1.44 0.46 0.55 0.55 0.98 1.11 1.50 0.90 1.34 1.06 0.96 1.07 0.56 0.99 1.02 1.27 1.46 1.58 1.51 0.60 0.52 0.91 0.49 1.34 1.17  High Ordinal 1.29 0.12 0.34 1.03 1.14 0.15 1.09 - 0.15 1.21 0.95 0.62 1.26 0.86 0.89 0.03 1.35 0.12 1.10 0.98 1.05 - 0.37 1.87 0.57 1.45 0.84 0.71 0.55 0.65 0.46 2.36 0.61 0.39 0.46 1.45 1.11 0.58 1.14 0.52 0.76 0.88 0.97 0.34 1.06 0.53 1.29 0.33 <.01 1.31** 0.00 0.15 0.09 1.25* 1.58 0.40 1.62 0.03 0.74 0.81 0.72 0.99 1.65 0.18 0.05 0.51 0.93 1.50 0.24 1.35 0.48 0.12 0.87 0.70 0.61 0.78  NIOSH X6903 X6904 X6905 X6911 X6912 X6913 X6921 X6953 X6966 X6967 X6970 X6990 X6998 X7020 X7030 X7040 X7044 X7080 X7092 X7100 X7108 X7123 X7128 X7153 X7157 X7161 X7163 X7203 X7204 X7211 X7233 X7255 X7312 X7313 X7314 X7315 X7316  Cases 13 9 14 177 83 33 56 25 19 32 366 51 12 149 13 13 9 329 10 12 34 22 58 10 25 9 12 316 156 47 12 234 139 543 41 52 179  Ever Low 0.93 0.56 1.78 1.33 1.03 0.59 1.14 1.22 1.56* 1.29* 0.90 0.81 1.18 1.15 1.19 1.76 1.29 0.82 0.83 1.07 1.12 1.11 0.74 1.18 1.18 1.61 1.21 1.17 2.03* 2.86* 0.76 1.13 2.02 1.35 1.20* 1.35** 1.05 0.70 1.07 0.51 1.20 1.05 0.92 0.74 1.03 0.99 1.99 1.89 1.60 1.39 1.25 2.46 1.72 1.08 1.21** 1.23 1.12 0.92 0.89 0.76 1.12 1.09 1.14 1.15 1.21 1.31 1.21** 1.18 0.97 0.72 1.23 1.67* 1.10 1.17  Medium 0.67 1.34 1.12 1.09 1.20 0.96 1.04 0.46 2.51** 0.42 1.23 1.53 1.37 1.19 2.75* 0.58 3.46* 1.12 1.50 1.57 1.07 0.93 1.16 2.78 1.22 0.75 0.60 1.24 1.26 0.97 1.17 1.11 1.17 1.20 0.92 1.08 1.08  High 1.62 0.87 1.45 1.10 1.10 0.90 1.36 1.32 0.58 0.71 1.01 1.23 0.36 1.27 0.48 0.52 1.32 1.13 0.91 1.16 1.46 1.09 0.99 1.26 1.33 0.75 0.79 1.18 1.19 0.94 1.08 1.16 1.14 1.24* 1.25 0.96 1.04  Ordinal 0.74 0.70 0.57 0.28 0.18 0.64 0.22 0.66 0.44 0.19 0.27 0.17 0.99 0.05 0.18 0.21 0.09 0.09 0.83 0.61 0.24 0.93 0.86 0.11 0.24 0.98 0.80 0.02 0.12 0.64 0.76 0.15 0.15 <.01 0.75 0.54 0.50  NIOSH X7320 X7327 X7328 X7348 X7385 X7390 X7393 X7397 X7407 X7411 X7422 X7432 X7433 X7440 X7442 X7443 X7445 X7456 X7465 X7480 X7486 X7507 X7514 X7515 X7516 X7540 X7544 X7556 X7578 X7579 X7592 X7610 X7611 X7620 X7621 X7639 X7643  Cases 48 214 147 58 39 9 9 147 19 11 16 99 29 9 318 220 308 13 230 37 52 411 18 41 252 18 14 16 11 15 14 380 168 336 172 85 361  Ever 1.09 1.13 1.22* 1.16 1.30 0.84 0.84 1.09 1.07 2.34* 0.89 1.10 1.06 0.87 1.16* 1.20* 1.14 0.74 1.04 1.33 0.96 1.23** 1.06 1.12 1.17 0.74 1.00 0.95 1.41 1.38 1.03 1.11 1.27* 1.24** 1.03 1.24 1.22**  Low 1.19 1.34* 1.22 1.09 1.06 0.39 0.39 0.99 1.97 2.55 0.78 1.07 1.54 0.77 1.22 1.17 1.15 0.55 1.16 0.84 0.99 1.13 1.01 1.10 1.22 1.62 0.95 0.91 2.32 1.02 0.59 1.21 1.67** 1.12 0.96 1.49* 1.45**  Medium 1.07 1.17 1.27 1.41 1.37 2.74 2.74 1.35* 0.83 2.29 1.02 1.14 0.11* 1.34 1.19 1.35* 1.01 1.04 0.99 1.71 1.17 1.33** 1.58 1.25 1.05 0.13* 1.23 0.49 0.78 2.54* 1.12 1.13 1.33 1.34** 1.09 1.15 1.08  High 1.03 0.89 1.17 0.97 1.45 0.52 0.52 0.94 0.57 2.24 0.88 1.08 1.46 0.56 1.09 1.09 1.27* 0.62 0.96 1.37 0.78 1.22* 0.62 0.99 1.22 0.47 0.85 1.48 1.14 0.93 1.45 1.00 0.83 1.26* 1.02 1.06 1.14  Ordinal 0.72 0.66 0.08 0.44 0.11 0.78 0.78 0.50 0.58 0.03 0.74 0.44 0.88 0.64 0.13 0.07 0.05 0.36 0.95 0.08 0.61 <.01 1.00 0.62 0.08 0.05 0.96 0.88 0.62 0.34 0.57 0.47 0.34 <.01 0.72 0.30 0.08  NIOSH X7648 X7651 X7669 X7885 X7911 X7917 X7937 X7942 X7949 X7972 X7988 X7989 X7990 X8158 X8176 X8178 X8187 X8191 X8193 X8195 X8196 X8203 X8206 X8232 X8237 X8241 X8247 X8248 X8251 X8256 X8257 X8258 X8261 X8264 X8267 X8272 X8286  Cases 18 50 459 56 19 25 47 181 16 10 24 38 11 127 13 21 10 21 19 13 13 57 11 13 15 62 26 21 14 83 26 68 12 35 13 76 201  Ever 1.12 1.23 1.10 1.18 1.07 1.22 1.14 1.15 1.31 1.38 0.80 0.95 1.07 1.07 0.72 1.22 1.01 0.85 1.15 0.91 0.92 1.03 1.34 0.95 0.97 1.06 0.78 0.85 1.12 1.04 0.80 1.00 1.08 0.95 1.02 1.31* 1.13  Low 0.76 1.31 1.06 0.90 0.77 1.61 1.53 1.20 0.78 1.45 0.72 0.53 1.01 1.26 1.19 0.99 2.18 1.34 1.50 1.73 1.85 0.86 0.88 1.47 0.90 1.33 1.11 0.60 1.94 1.05 0.86 1.11 2.26 1.42 1.89 1.14 1.25  Medium 0.86 0.99 1.09 1.38 1.10 1.45 1.03 1.14 1.45 1.96 0.58 0.79 0.35 0.89 0.36 1.81 0.46 0.60 0.72 0.66 0.64 1.22 1.67 0.40 0.81 0.86 0.81 1.07 0.80 1.15 1.08 1.08 0.43 0.71 0.46 1.30 1.00  High 1.83 1.40 1.15 1.25 1.37 0.60 0.88 1.11 1.66 0.76 1.12 1.56 1.78 1.06 0.51 0.95 0.65 0.54 1.17 0.44 0.45 1.00 1.37 0.98 1.19 0.98 0.40 0.88 0.72 0.94 0.41 0.82 0.82 0.73 0.69 1.52 1.13  Ordinal 0.35 0.20 0.13 0.18 0.52 0.82 0.87 0.21 0.20 0.55 0.54 0.52 0.60 0.76 0.13 0.46 0.55 0.20 0.77 0.34 0.36 0.75 0.34 0.66 0.96 0.97 0.09 0.65 0.82 0.86 0.18 0.70 0.71 0.40 0.57 0.02 0.31  NIOSH X8287 X8299 X8309 X8317 X8319 X8321 X8322 X8335 X8345 X8350 X8532 X8533 X8536 X8545 X8553 X8559 X8563 X8564 X8569 X8570 X8571 X8573 X8574 X8577 X8582 X8583 X8585 X8586 X8593 X8594 X8596 X8645 X8646 X8647 X8648 X8651 X8652  Cases 151 73 184 95 ' 22 9 10 75 134 81 309 388 164 9 397 62 9 20 72 87 423 89 146 9 44 213 18 12 60 13 14 173 71 21 20 50 45  Ever 1.22* 1.03 1.09 0.96 1.26 1.25 0.76 1.37* 1.08 1.04 1.18* 1.09 1.11 1.35 1.11 0.91 1.26 1.46 1.04 1.17 1.17* 1.17 1.13 1.10 0.79 1.06 2.03** 1.13 0.95 1.14 1.12 1.17 0.98 1.07 1.00 1.13 0.91  Low 1.50** 1.17 1.16 1.15 1.39 0.76 0.26 1.26 1.02 0.93 1.13 1.17 1.19 1.89 1.12 0.96 1.65 1.45 0.98 1.03 1.05 1.16 1.02 1.26 1.03 0.98 3.59** 0.51 1.01 1.06 1.00 1.39* 0.96 0.83 0.93 0.81 1.04  Medium 1.21 0.94 1.25 1.09 1.29 1.74 0.60 1.26 1.03 1.12 1.24 1.12 0.88 0.95 0.96 1.07 2.17 1.30 0.79 1.33 1.32** 1.35 1.32 0.73 0.81 1.10 0.70 0.71 0.87 1.30 1.40 1.08 0.95 1.18 0.95 1.18 0.66  High 0.93 0.96 0.88 0.66 1.10 1.34 1.43 1.60* 1.20 1.06 1.18 1.00 1.26 1.23 1.23* 0.72 0.00 1.62 1.34 1.17 1.13 0.98 1.06 1.36 0.56 1.10 1.98 2.16 0.94 1.05 0.93 1.03 1.04 1.21 1.13 1.44 1.07  Ordinal 0.36 0.95 0.70 0.30 0.45 0.43 0.86 0.01 0.32 0.65 0.03 0.55 0.25 0.60 0.10 0.34 0.96 0.15 0.48 0.18 0.03 0.32 0.23 0.80 0.07 0.39 0.06 0.26 0.65 0.70 0.76 0.36 0.98 0.62 0.91 0.19 0.63  NIOSH X8654 X8656 X8658 X8708 X8711 X8712 X8713 X8716 X8741 X8742 X8745 X8749 X8750 X8752 X8793 X8798 X8833 X8843 X8859 X8862 X8864 X8865 X8866 X8867 X8868 X8923 X8962 X8963 X8964 X8975 X8978 X8981 X8982 X8987 X8988 X8993 X8996  Cases 73 28 283 14 9 59 60 11 11 90 12 82 15 11 52 14 11 11 10 230 156 41 24 149 372 14 25 84 73 24 .93 175 27 334 44 15 294  Ever Low 1.00 0.94 1.24 1.25 1.20* 1.06 1.32 2.03 0.45* 0.67 0.99 0.98 0.95 0.96 1.31 1.16 1.10 1.20 1.22 1.30 1.13 0.51 1.00 0.99 1.59 1.51 1.05 1.20 0.92 0.99 1.13 1.63 1.12 0.93 0.92 1.07 0.75 0.26 1.21* 1.31* 1.11 1.35* 1.13 1.19 0.84 0.71 1.29* 1.47* 1.25** 1.37** 1.38 0.83 1.37 1.35 1.09 1.18 1.04 1.13 0.78 0.20* 0.97 0.90 1.06 0.84 1.00 1.29 1.25** 1.20 0.84 1.20 0.89 1.63 1.19* 1.25  Medium 1.26 1.00 1.41** 1.11 0.40 1.11 0.96 1.15 0.98 1.49* 0.71 1.05 2.13 0.85 1.13 0.92 1.32 1.19 0.62 1.13 0.95 1.03 0.93 1.10 1.10 1.56 1.74 1.00 1.14 1.04 0.89 1.23 0.97 1.20 1.83* 2.75* 1.08  High 0.80 1.48 1.13 0.93 0.19 0.92 0.91 1.70 1.10 0.88 2.16 0.96 1.20 1.11 0.71 0.91 1.12 1.11 1.28 1.17 1.02 1.16 0.90 1.30 1.28* 1.80 0.95 1.08 0.84 1.09 1.10 1.10 0.78 1.35** 0.90 1.34 1.23  Ordinal 0.84 0.28 0.02 0.67 0.02 0.90 0.67 0.35 0.83 0.33 0.26 0.95 0.18 0.92 0.40 0.96 0.69 0.78 0.77 0.08 0.69 0.55 0.57 0.03 <.01 0.16 0.28 0.61 0.88 0.75 0.98 0.29 0.72 <.01 0.31 0.08 0.04  NIOSH X9001 X9011 X9012 X9014 X9015 X9018 X9019 X9021 X9022 X9023 X9024 X9025 X9027 X9028 X9029 X9030 X9031 X9032 X9035 X9036 X9045 X9052 X9053 X9059 X9062 X9071 X9073 X9075 X9076 X9078 X9079 X9081 X9082 X9085 X9088 X9092 X9093  Cases 52 13 24 13 20 224 16 87 99 199 374 61 72 152 160 191 50 68 71 16 20 71 71 69 2638 9 14 69 397 291 43 31 9 148 235 34  Ever 1.19 1.49 1.22 0.99 1.28 0.94 1.20 1.21 1.05 1.10 1.11 0.93 0.98 1.11 1.06 1.21* 1.36 0.98 0.99 1.15 1.10 0.99 0.98 1.01 1.22 1.15 0.66 1.25 1.00 1.27** 1.22** 1.07 1.43 1.06 1.02 1.14 1.11  Low 1.24 1.02 1.52 0.87 0.63 1.10 0.71 1.22 0.96 1.12 1.19 1.06 1.05 1.33* 0.82 1.23 0.90 1.05 1.20 1.73 1.86 1.22 1.22 1.19 0.66 1.05 0.66 1.33 0.97 1.11 1.20 1.16 0.75 1.69 0.92 1.27 0.83  Medium 1.23 1.55 0.69 1.02 1.91 0.97 2.15* 1.05 1.03 1.05 1.24* 1.00 1.01 0.95 1.31 1.17 1.95** 1.08 0.78 0.66 0.52 0.78 0.78 0.86 2.27** 1.26 0.29 1.08 1.03 1.30* 1.28* 1.06 1.67 1.00 1.11 1.03 1.21  High 1.09 1.86 1.54 1.04 1.26 0.76* 0.68 1.34 1.17 1.13 0.92 0.71 0.86 1.01 1.06 1.22 1.26 0.82 0.93 1.05 1.05 0.93 0.90 0.92 0.92 1.13 0.96 1.33 0.99 1.39** 1.17 0.99 1.84 0.61 1.04 1.12 1.29  Ordinal 0.38 0.14 0.40 0.98 0.22 0.12 0.56 0.12 0.47 0.30 0.58 0.33 0.68 0.70 0.31 0.05 0.04 0.65 0.64 0.91 0.97 0.65 0.58 0.74  0.31 0.44 0.36 0.49 1.00 <.01 0.02 0.85 0.03 0.77 0.67 0.25 0.40  A  NIOSH X9100 X9101 X9102 X9106 X9107 X9126 X9133 X9143 X9153 X9181 X9182 X9188 X9191 X9196 X9197 X9199 X9200 X9201 X9202 X9203 X9216 X9217 X9218 X9244 X9248 X9249 X9252 X9256 X9258 X9259 X9260 X9266 X9270 X9271 X9273 X9275 X9277  Cases 11 9 12 11 12 358 9 10 132 274 12 153 9 12 15 15 84 12 33 25 25 351 38 23 132 276 13 239 371 158 249 18 39 153 137 118 9  Ever 1.05 1.32 1.07 1.05 1.07 1.11 0.86 2.30* 1.04 1.16 1.17 1.11 2.96** 1.13 1.00 1.13 1.11 0.98 0.98 1.41 1.32 1.20** 1.06 1.35 1.21 1.15 0.90 1.24** 1.13 1.13 1.02 1.03 1.07 1.15 1.13 1.11 1.70  Low 1.83 0.75 1.80 1.83 1.87 0.92 1.14 2.11 0.90 1.23 1.08 1.01 1.22 0.51 0.63 0.47 1.37 0.27 1.40 1.57 1.00 1.30* 0.82 2.00 1.37 1.33* 0.47 1.20 1.18 1.09 1.10 1.64 1.00 1.19 1.22 1.04 1.28  Medium 0.26 4.12** 0.22 0.26 0.91 1.26* 0.79 3.91* 1.12 1.20 1.62 1.17 5.07** 0.71 1.17 1.32 0.84 0.81 0.77 1.58 1.48 1.18 1.04 1.35 1.25 0.92 1.29 1.16 1.03 1.01 0.98 1.28 1.03 1.23 1.14 1.29 0.48  High 0.95 0.00 1.30 0.95 0.54 1.15 0.63 0.73 1.11 1.06 0.72 1.13 1.33 2.16 1.20 1.58 1.16 1.69 0.81 1.00 1.47 1.13 1.35 0.80 1.02 1.20 0.87 1.34* 1.19 1.28 0.99 0.29 1.20 1.03 1.05 1.02 3.61*  Ordinal 0.74 0.68 0.93 0.74 0.69 0.05 0.53 0.08 0.46 0.17 0.73 0.24 0.01 0.26 0.76 0.35 0.60 0.54 0.56 0.27 0.16 0.06 0.45 0.58 0.25 0.18 0.91 <.01 0.11 0.14 0.98 0.46 0.59 0.29 0.39 0.37 0.06  NIOSH X9279 X9284 X9285 X9286 X9287 X9288 X9289 X9290 X9291 X9293 X9296 X9298 X9300 X9305 X9316 X9321 X9322 X9323 X9326 X9327 X9328 X9332 X9333 X9336 X9343 X9347 X9350 X9352 X9363 X9364 X9365 X9374 X9375 X9380 X9383 X9385 X9390  Cases 9 182 22 29 43 286 20 55 21 164 573 20 122 146 248 64 9 72 72 18 226 18 262 95 60 46 24 217 53 157 110 11 179 45 33 24 9  Ever 1.70 1.17 0.81 1.11 0.93 1.12 1.26 1.35 1.06 1.13 1.26** 0.77 1.16 1.23* 1.09 1.02 0.94 0.99 0.99 1.01 1.11 1.01 1.31** 1.04 1.17 1.13 1.24 1.12 1.15 1.08 1.11 1.37 1.10 1.38 1.22 1.41 0.75  Low 1.28 1.17 1.10 0.96 1.05 1.22 0.87 1.72* 1.29 0.96 1.25* 0.89 1.26 1.45* 1.01 1.12 0.90 0.85 0.85 1.53 1.11 0.52 1.19 1.22 1.52 1.15 1.08 1.28 1.00 0.93 1.28 0.90 0.96 1.18 1.01 1.46 0.52  Medium 0.48 1.22 0.56 1.55 0.89 1.14 1.34 1.30 0.98 1.34* 1.31** 0.85 1.05 1.11 1.21 0.90 1.20 1.25 1.25 1.07 1.13 1.41 1.50** 1.03 1.09 1.34 1.18 1.04 1.36 1.26 0.97 1.64 1.19 1.36 1.28 1.12 0.68  High 3.61* 1.11 0.80 0.81 0.86 1.00 1.59 1.02 0.86 1.09 1.21* 0.61 1.17 1.15 1.06 1.04 0.68 0.86 0.86 0.47 1.09 1.11 1.25 0.88 0.94 0.90 1.43 1.05 1.07 1.03 1.07 1.74 1.14 1.64 1.37 1.57 1.09  Ordinal 0.06 0.14 0.30 0.72 0.58 0.42 0.21 0.23 0.93 0.16 <.01 0.24  0.25 0.14 0.25 0.98 0.80 0.93 0.93 0.52 0.25 0.71 <.01 0.86 0.64 0.67 0.28 0.45 0.37 0.39 0.60 0.23 0.19 0.04 0.23 0.16 0.65  NIOSH X9391 X9394 X9397 X9398 X9399 X9401 X9402 X9403 X9407 X9411 X9412 X9414 X9417 X9423 X9424 X9435 X9437 X9444 X9446 X9447 X9448 X9454 X9455 X9462 X9464 X9465 X9466 X9472 X9474 X9475 X9481 X9489 X9502 X9503 X9504 X9515 X9516  Cases 128 13 72 15 21 14 13 50 9 106 171 18 44 63 12 13 388 20 48 17 17 136 118 74 170 17 29 70 18 12 15 14 23 9 60 68 50  Ever 1.10 0.99 1.04 1.44 0.68 1.22 1.11 1.20 1.26 1.14 1.17 1.23 1.31 0.88 1.13 1.12 1.21** 1.28 1.17 0.81 0.81 1.07 1.17 1.08 1.12 0.89 1.26 1.27 0.66 1.10 1.24 1.15 1.06 1.17 1.27 1.20 1.23  Low 1.01 1.75 0.91 1.73 0.89 1.80 1.60 0.96 1.65 1.24 1.40* 0.75 1.36 0.99 0.51 0.52 1.18 0.81 0.86 0.74 0.74 0.83 1.11 1.10 1.06 0.90 0.96 1.24 0.60 0.29 1.55 0.49 0.96 0.88 1.52 1.41 1.50  Medium 1.35 0.00 1.02 0.33 0.58 1.44 1.07 1.34 2.17 1.16 1.18 1.34 1.14 0.95 0.71 1.57 1.27* 1.46 1.66* 0.93 0.93 1.27 1.19 1.04 1.06 1.04 1.40 1.05 1.04 1.14 1.32 0.93 0.93 2.23 1.50 1.18 1.23  High 0.95 0.94 1.21 2.10 0.60 0.46 0.56 1.27 0.00 1.02 0.95 1.68 1.44 0.71 2.16 1.23 1.17 1.70 0.96 0.76 0.76 1.09 1.21 1.10 1.23 0.79 1.46 1.54* 0.27 2.00 0.95 2.11 1.33 0.48 0.80 1.01 0.95  Ordinal 0.47 0.58 0.50 0.21 0.08 0.95 0.83 0.18 0.96 0.44 0.42 0.23 0.13 0.22  0.26 0.50 0.02 0.16 0.32 0.46 0.46 0.32 0.14 0.59 0.16 0.65 0.17 0.06 0.08 0.34 0.68 0.23 0.63 0.75 0.40 0.44 0.50  NIOSH X9518 X9521 X9531 X9536 X9541 X9542 X9547 X9564 X9566 X9571 X9572 X9574 X9576 X9578 X9582 X9588 X9593 X9597 X9601 X9602 X9604 X9605 X9606 X9607 X9610 X9620 X9623 X9628 X9638 X9641 X9643 X9646 X9649 X9652 X9656 X9666 X9685  Cases 45 15 149 12 31 22 10 10 38 12 14 14 39 74 18 11 77 49 14 45 16 25 109 86 16 48 21 138 99 16 20 60 9 81 47 14 96  Ever 1.21 0.60 1.06 0.74 1.39 1.17 0.84 1.33 1.08 1.33 1.16 1.11 0.92 1.04 1.13 1.08 0.93 1.23 1.17 1.12 1.35 0.88 1.14 1.01 0.81 1.33 1.16 1.08 1.20 1.14 1.26 1.32 0.97 1.17 1.27 1.09 1.35*  Low 0.98 0.66 1.49** 0.24 1.40 0.80 1.12 0.00 1.11 1.06 1.03 0.25 0.97 0.95 0.63 0.58 0.94 1.58 0.65 1.15 1.83 1.01 1.04 0.86 0.36 1.50 1.13 0.92 1.08 2.41* 0.59 1.41 0.69 0.86 1.41 1.81 1.48  Medium 1.51 0.78 1.02 0.82 1.42 1.57 0.55 1.78 1.15 1.44 1.24 0.71 0.87 0.77 1.11 1.13 0.78 1.54 1.04 1.01 1.09 0.64 1.17 1.04 1.69 1.30 1.23 1.16 1.13 0.44 1.99 1.62* 0.65 1.63** 1.16 0.63 1.19  High 1.23 0.30 0.67* 1.05 1.36 1.13 0.79 2.64 0.97 1.49 1.21 2.39* 0.93 1.38 1.58 1.54 1.05 0.63 2.07 1.21 1.15 0.99 1.21 1.12 0.71 1.19 1.12 1.17 1.39 0.72 1.60 0.91 1.51 0.98 1.25 1.03 1.39  Ordinal 0.20 0.05 0.36 0.63 0.14 0.42 0.50 0.11 0.80 0.33 0.57 0.19 0.63 0.43 0.37 0.53 0.69 0.74 0.27 0.51 0.50 0.58 0.19 0.69 0.67 0.17 0.58 0.26 0.07 0.69 0.14 0.20 0.79 0.22 0.24 0.97 0.03  NIOSH X9686 X9687 X9689 X9692 X9701 X9719 X9722 X9730 X9731 X9773 X9777 X9786 X9788 X9791 X9792 X9795 X9797 X9800 X9801 X9845 X9854 X9857 X9875 X9878 X9879 X9880 X9881 X9891 X9893 X9894 X9895 X9898 X9902 X9903 X9912 X9914 X9916  Cases 34 30 18 348 9 214 201 28 10 12 12 73 12 65 32 18 35 32 251 55 39 10 15 179 100 170 156 10 193 175 160 70 73 43 25 26 146  Ever 1.50* 1.10 1.10 1.15 0.94 1.07 1.31** 0.97 1.01 1.13 1.13 0.99 0.87 1.12 1.23 1.49 0.88 1.07 1.15 1.31 0.92 0.60 1.12 1.05 1.01 1.27* 1.14 0.75 1.33** 1.38** 1.40** 1.54** 1.13 0.77 1.41 1.05 1.14  Low 2.11* 1.00 0.54 1.10 0.88 1.03 1.35* 0.94 2.18 0.51 0.51 1.05 1.40 1.07 1.60 1.89 0.79 1.60 1.23 1.58 0.52 0.39 0.51 1.04 1.01 1.08 0.98 0.75 1.29 1.47** 1.42* 1.97** 1.28 0.84 1.57 1.02 1.07  Medium 0.89 1.57 1.42 1.20 0.33 1.09 1.46** 1.34 0.46 0.71 0.71 0.90 0.66 1.08 1.05 1.78 1.11 0.82 1.03 1.07 1.08 0.61 1.53 0.94 0.93 1.59** 1.15 1.07 1.58** 1.62** 1.61** 1.57* 0.85 0.95 1.58 0.86 1.27  High 1.44 0.75 1.37 1.14 1.63 1.10 1.14 0.65 0.65 2.16 2.16 1.02 0.46 1.21 1.00 0.98 0.76 0.79 1.19 1.29 1.22 0.81 1.24 1.17 1.07 1.11 1.28 0.43 1.13 1.06 1.18 1.09 1.27 0.53 1.00 1.26 1.10  Ordinal 0.14 0.81 0.42 0.07 0.88 0.37 0.01 0.70 0.55 0.26 0.26 0.91 0.34 0.38 0.59 0.35 0.51 0.76 0.14 0.17 0.78 0.24 0.49 0.43 0.90 0.02 0.07 0.34 <.01 0.01 <.01 0.03 0.41 0.07 0.27 0.71 0.19  NIOSH X9918 X9920 X9921 X9922 X9923 X9925 X9926 X9927 X9928 X9929 X9930 X9933 X9934 X9936 X9937 X9940 X9941 X9944 X9945 X9946 X9947 X9948 X9949 X9950 X9952 X9953 X9956 X9958 X9962 X9971 X9975 X9977 X9978 X9984  xxxxx Y0005 Y1000  Cases 93 173 12 78 11 20 11 14 12 13 11 10 84 116 43 17 157 19 9 26 261 99 203 27 36 23 19 92 9 49 135 102 12 74 15 28 407  Ever 1.10 1.31** 1.51 1.24 2.41* 1.25 1.13 1.13 1.07 1.11 1.14 1.11 1.07 0.99 1.04 0.64 1.08 0.99 1.63 0.88 1.10 1.02 1.07 1.24 1.29 1.04 1.04 1.31* 0.97 1.26 1.09 1.31* 1.08 1.01 0.68 1.57* 1.15*  Low 1.26 1.06 2.29 1.19 3.20* 2.14* 0.97 1.01 1.36 0.76 0.95 0.36 1.22 1.00 0.56 0.72 1.02 0.99 2.64 1.19 1.03 1.04 1.01 1.01 1.11 0.65 0.47 1.29 1.37 1.62* 1.23 1.48* 2.09 1.24 0.67 2.53** 1.12  Medium 1.04 1.26 1.32 1.23 0.73 1.06 1.28 1.34 0.84 1.42 1.33 1.74 1.11 0.82 0.85 0.54 1.22 0.98 1.06 0.64 1.14 0.98 1.02 1.24 1.83* 1.23 1.14 1.11 1.52 1.15 1.04 1.30 0.53 0.83 1.03 0.85 1.27*  High 1.02 1.61** 1.09 1.30 3.04 0.69 1.15 1.00 1.00 1.18 1.15 1.12 0.89 1.14 1.76* 0.68 1.00 0.99 1.39 0.84 1.13 1.04 1.18 1.45 0.94 1.28 1.56 1.52* 0.00 0.99 0.98 1.14 0.72 0.97 0.36 1.39 1.08  Ordinal 0.65 <.01 0.43 0.10 0.03 0.95 0.67 0.71 0.99 0.60 0.66 0.56 0.93 0.91 0.20 0.11 0.48 0.96 0.38 0.41 0.17 0.87 0.28 0.24 0.25 0.56 0.41 0.02 0.49 0.44 0.74 0.08 0.70 0.82 0.13 0.18 0.09  NIOSH Y1006 Y1012 Y1013 Y1014 Y1016 Y1018 Y1019 Y1020 Y1022 Y1023 Y1024 Y1026 Y1028 Y1030 Y1032 Y1034 Y1036 Y1037 Y1038 Y1040 Y1041 Y1042 Y1043 Y1044 Y1045 Y1046 Y1047 Y1049 Y1050 Y1051 Y1053 Y1054 Y1055 Y1056 Y1057 Y1058 Y1059  Cases 25 124 236 489 479 66 237 609 215 63 280 234 22 317 72 275 15 300 191 510 29 461 203 157 26 234 260 17 269 444 149 179 52 154 27 26 475  Ever 3.11** 1.03 1.27** 1.22** 1.19* 1.05 1.26** 1.23** 1.27** 0.94 1.15 1.28** 0.92 1.23** 0.98 1.18* 1.15 1.17* 1.16 1.17* 1.37 1.26** 1.28** 1.12 1.01 1.27** 1.21* 1.01 1.16 1.21** 1.12 1.09 0.96 1.16 1.45 1.06 1.18*  Low 2.70** 0.90 1.21 1.20 1.37** 1.13 1.20 1.20 1.12 0.96 1.38** 1.31* 1.41 1.25 1.19 1.25 0.64 1.22 1.23 1.05 0.70 1.33** 1.66** 1.12 1.45 1.19 1.18 0.53 0.92 1.21 1.01 0.90 0.99 0.91 0.80 0.91 1.26*  Medium 2.46 1.18 1.52** 1.24* 1.13 1.09 1.56** 1.21* 1.12 1.07 1.01 1.39** 0.82 1.16 0.79 1.18 1.47 1.20 1.22 1.15 1.79 1.15 0.99 1.24 1.02 1.12 1.23 1.07 1.39** 1.29** 1.20 1.04 1.17 1.32 1.70 1.44 1.11  High 4.12** 1.02 1.09 1.23* 1.08 0.93 1.05 1.28** 1.58** 0.82 1.05 1.15 0.59 1.28* 0.92 1.11 1.41 1.08 1.04 1.32** 1.63 1.29* 1.19 1.00 0.58 1.51** 1.21 1.47 1.17 1.14 1.15 1.34* 0.78 1.24 1.89 0.81 1.17  Ordinal <.01 0.63 0.01 <.01 0.20 0.92 0.02 <.01 <.01 0.57 0.42 0.01 0.38 0.01 0.60 0.10 0.40 0.13 0.23 <.01 0.05 <.01 0.08 0.39 0.56 <.01 0.02 0.57 0.02 0.02 0.19 0.09 0.61 0.05 0.03 0.88 0.06  Low Medium High Low Medium High Ordinal NIOSH Cases Ever Ordinal N I O S H Cases Ever Y1061 119 1.03 1.02 1.21 0.86 0.97 Z0267 11 0.69 0.38 0.66 1.08 0.52 0.57 2.03* 1.02 1.06 0.97 Z0475 1.39 1.39* 1.08 33 0.39 Y1062 127 1.15 0.94 0.43 1.68* 2.20* 1.71 1.05 0.12 0.99 1.31 Z0477 26 Y1064 47 1.08 Z0482 1.24 1.12 0.91 0.82 1.39* 1.10 0.15 138 1.09 1.12 0.86 Y1066 148 1.22 0.57 1.16 1.09 0.95 Z0483 218 1.25** 1.37* 1.21 0.05 Y1067 146 1.09 1.20 1.25 1.53 2.74* 0.06 1.15 1.00 0.12 11 0.36 Z0495 183 1.47 Y1068 1.24 1.21 0.03 94 1.16 0.85 1.41 232 1.21* 1.15 1.25 Z0496 0.10 Y1069 <.01 42 1.01 1.09 0.81 1.20 0.87 1.32** 1.34* 1.28 1.36* Z0547 195 Y1070 1.22 0.02 1.03 1.14 1.07 0.88 Z0583 60 0.91 202 1.28** 1.36* 1.25 Y1071 1.04 1.11 1.37 1.16 0.82 0.97 1.00 Z0599 16 0.80 232 1.02 1.06 Y1072 1.41 0.17 0.92 1.18 0.33 Z0660 0.71 0.50 0.06 1.09 13 Y1074 169 1.07 0.77 0.55 Z0673 12 0.60 0.89 0.39 0.58 0.09 34 0.78 Y1079 0.70 0.04 0.95 0.99 1.17 0.78 1.01 1.07 0.51 Z0701 53 0.57 124 1.11 1.25 Y1080 1.02 0.12 1.19 0.59 1.66 0.24 1.24 Z0920 19 1.31 1.17 1.26 171 Y1081 0.05 424 1.21** 1.16 1.19 1.34* 1.53* 1.23 1.26 Z0927 1.28* 0.01 96 Y1083 0.91 0.99 1.04 0.71 1.22 1.13 0.68 0.55 Z0947 53 0.34 62 1.00 Y1085 1.12 14 0.52 1.23 1.04 0.39 Z1037 0.90 1.01 0.90 1.21 1.45 Y1086 75 1.26 1.40 0.82 1.24 1.06 1.17 0.16 Z1043 13 1.05 0.49 193 1.16 Y1087 2.29 0.87 1.22 0.65 2.17 1.65 0.28 Z1061 16 1.06 0.18 12 1.32 Y1090 24 1.30 1.99* 0.86 0.92 0.67 1.20 0.95 Z1121 0.73 1.30 Y1092 9 1.05 0.57 1.54 1.20 0.33 Z1122 17 1.23 1.46 1.88 0.17 1.39 15 1.39 Y1096 1.14 Z3004 1.39 0.83 1.79 1.66 0.99 1.46* 0.11 23 0.08 118 1.17 Y1098 1.25 0.78 9 0.85 0.83 0.97 0.77 1.40 0.61 Z3140 0.66 30 1.07 ZOOOO a Column Headings: NIOSH = NIOSH agent code. See NIOSH NOES website www.cdc.gov/noes/srch-noes.html to search for NIOSH agent names and NIOSH agent codes. Cases = number of bladder cancer cases exposed. Ever = odds ratio for ever exposure. Low/Medium/High = odds ratio for low/medium/high cumulative exposure relative to the non-exposed where groups are divided by tertiles of the controls. Ordinal = p-value for fitting a line through the non-exposed, low, medium, and high exposure groups by assigning scores of 0, 1, 2, and 3 respectively. Odds Ratios in italics represent a decreasing linear fit. * Significant at a 5% alpha level ** Significant at a 1% alpha level  Appendix B  Table B.l: NIOSH Agent Name Abbreviations NIOSH Agent Name Agent Name Abbreviation 1, 2-ETHANEDI AMINE, REACTION PRODUCTS WITH CHLORINATED ISOBUTY1, 2-ETHANEDIAMINE, RP W C IB HP LENE HOMOPOLYMER 2,5-PYRROLIDINEDIONE, l-(2-((2-((2-((2-AMINOETHYL)AMINO)ETHYL)AMINO) 2,5-PYRROLIDINEDIONE, 12AE MPIB D RP ETHYL)AMINO)ETHYL)-, MONOPOLYISOBUTENYL DERIVS., REACTION PR 2,5-PYRROLIDINEDIONE, l-(2-((2-((2-((2-AMINOETHYL)AMINO)ETHYL)AMINO) 2,5-PYRROLIDINEDIONE, 12AE MPIB D ETHYL)AMINO)ETHYL)-, MONOPOLYISOBUTENYL DERIVS. 2-BUTENEDIOIC ACID (E)-, POLYMER WITH 1,3-BUTADIENE AND ETHENYLBEN2-BUTENEDIOIC ACID (E)-, PW 1,3-B EB ZENE 2-PROPENOIC ACID, 2-ME-, C12 ESTER, POLY W/ C16 2ME2PROPENOATE, ISO2-PROPENOIC ACID, 2M CEPWC2 C10 2ME2PROPENOATE, ME 2ME2PROPENOATE, C18 2ME2PROPENOATE, C14 2ME2PROPENOATE ALANINE, 3-(P-(BIS(2-CE)A)P-, LALANINE, 3-(P-(BIS(2-CHLOROETHYL)AMINO)PHENYL-, L- ' ALKENES, C15-18 ALPHA-, RPW SDP CS S ALKENES, C15-18 ALPHA-, REACTION PRODUCTS WITH SULFURIZED DODECYLPHENOL CALCIUM SALT, SULFURIZED BUTYRIC ACID, 4-(P-(B(2-CE)A)P)BUTYRIC ACID, 4-(P-(BIS(2-CHLOROETHYL)AMINO)PHENYL)ETHANOL, 2-(2-(2-BE)E)ETHANOL, 2-(2-(2-BUTOXYETHOXY) ETHOXY)ETHYLAMINE, 2-(P-(l, 2-D-l-B)P)-N,N-D-,(Z)- ETHYLAMINE, 2-(P-(l, 2-DIPHENYL-l-BUTENYL)PHENOXY)-N,N-DIMETHYL-,(Z)NICKEL CHLORIDE (NICL2) , HH NICKEL CHLORIDE (NICL2) , HEXAHYDRATE N,N-BIS(2-CE)-2-NL (CHLORNAPHAZINE) N,N-BIS(2-CHLOROETHYL)-2-NAPTHYLAMINE (CHLORNAPHAZINE) NONYLPHENOL ETHYLENE OA NONYLPHENOL ETHYLENE OXIDE ADDUCT PHENOL, DODECYL-, SULFURIZED, CCSO PHENOL, DODECYL-, SULFURIZED, CARBONATES, CALCIUM SALTS, OVERBASED PHOSPHORODITHIOIC ACID, MOOB E ZS PHOSPHORODITHIOIC ACID, MIXED 0, 0-BIS(SEC-BU AND 1,3-DIMETHYLBUTYL) ESTERS, ZINC SALTS PHOSPHORODITHIOIC ACID, 0, O-BIS(2-ETHYLHEXYL) ESTER, ZINC SALT PHOSPHORODITHIOIC ACID, OOB(2E)E ZS PHOSPHORODITHIOIC ACID, O-(2-ETHYLHEXYL) O-ISOBUTYL ESTER, ZINC SALT PHOSPHORODITHIOIC ACID, OOZS PLUTONIUM, RADIOACTIVE ELEMENT (NATURALLY OCCURING) PLUTONIUM, RADIOACTIVE E (NO) POC - GASOLINE (LEADED) PRODUCTS OF COMBUSTION - GASOLINE (LEADED) POC - JET FUEL & GASOLINE, ULD PRODUCTS OF COMBUSTION - JET FUEL AND GASOLINE, UNLEADED PURINE, 6-((1-M-4-N-5-YL)THIO)PURINE, 6-((l-METHYL-4-NITROIMIDAZOL-5-YL)THIO)SOLVENT RD HVY PF DIST (PETROLEUM) SOLVENT REFINED HEAVY PARAFFINIC DISTILLATE (PETROLEUM) SULFONIC ACIDS, PETROLEUM, CSO SULFONIC ACIDS, PETROLEUM, CALCIUM SALTS, OVERBASED SULFONIC ACIDS, PETROLEUM, MS SULFONIC ACIDS, PETROLEUM, MAGNESIUM SALTS SULFURIC ACID, AMMONIUM N(2+) S(2:2:l) SULFURIC ACID, AMMONIUM NICKEL(2+) SALT (2:2:1) SULFURIC ACID, NICKEL(2+) SALT(1:1) , HH SULFURIC ACID, NICKEL(2+) SALT (1:1) , HEXAHYDRATE UREA, N-(2-CE)-N'-(4-MC)-N-NITROSOUREA, N-(2-CHLOROETHYL)-N'-(4-METHYLCYCLOHEXYL)-N-NITROSO-  

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