Environmental Risk Assessment Reports - Chapter 19

CHAPTER 19 Using Statistics in Health and Environmental Risk Assessments Michael E. Ginevan CONTENTS I. Introduction.................................................................................................390 II. Statistical Thinking and Regulatory Guidance ..........................................391 A. Risk Assessment...........................................................................391 1. The Hazard Index........................................................391 2. Assessment of Chemical Cancer Risk ........................392 B. Risk Assessment of Radionuclides...............................................393 C. Evaluation of Exposure ................................................................394 D. Data Quality Objectives (DQOs) .................................................394 1. The Data Quality Assessment Process........................395 III. Evaluation of the Utility of Environmental Sampling for Health and Environmental Risk Assessments......................................395 A. Graphical Methods ............................................................................. B. Distributional Fitting and Other Hypothesis Testing...................400 C. Nondetects.....................................................................................402 D. Sample Support.............................................................................402 E. Does Contamination Exceed Background?..................................403 IV. Estimation of Relevant Exposure: Data Use and Mental Models.............404 V. Finding Out What is Important: A Checklist.............................................408 VI. Tools............................................................................................................409 References...................................................................................................411 389 © 2001 by CRC Press LLC 390 A PRACTICAL GUIDE TO ENVIRONMENTAL RISK ASSESSMENT REPORTS I. INTRODUCTION This chapter reviews the role that statistical thinking and methodology should play in the conduct of health and environmental risk assessments. What do I mean when I refer to “statistics?” The Random House Unabridged Dictionary defines statistics as “the science that deals with the collection, classification, analysis, and interpre-tation of numerical facts or data, and that, by use of mathematical theories of probability, imposes order and regularity on aggregates of more or less disparate elements.” This has a simple translation: statistics finds ways of coping with uncer-tain, incomplete, and otherwise not wholly satisfactory data. Therefore, if you know the answer exactly, you don’t need statistics . . . . How might this methodology apply to planning, generating, and evaluating risk assessment reports? This question can best be approached by considering the four components of the risk assessment process, described in Chapters 2 and 3. The first step of the assessment is “hazard identification,” which reviews the inventory or materials present in the environment and uses information from toxicology or epi-demiology studies to determine which of these might pose a risk to human health and/or the environment. Statistical principles play important roles in epidemiology and both environmental and laboratory toxicology studies, but the form of these studies, and the role of statistics in them is so diverse that a meaningful discussion is beyond the scope of this chapter. Many hazards are quite well characterized (e.g., there is little debate that high levels of environmental lead are hazardous in a variety of contexts), so the identification can be taken as a given. However, in some cases the hazard identification of a material may rest on one or two studies that are of dubious validity. If the risk assessment is driven by such materials (we will discuss how to determine the factors that are of greatest importance to the estimation of risk) it is often worthwhile to reconsider the underlying literature to determine how valid the studies underlying the hazard identification actually are. The next step, toxicity assessment, requires development of a dose-response function. A dose-response function provides the risk coefficients used to translate exposure into risk. In essence it answers the question, “Given that substance X is bad, how rapidly do its effects increase with increasing dose?” Many such coeffi-cients are specified by regulatory agencies and will not be readily open to reevalu-ation. However, in our discussion we will consider how a dose-response function is developed. We will also treat the problem that arises because many “approved” dose-response coefficients are either 95% statistical upper bounds, or incorporate “safety factors” of between 100 and 10,000. That is, if one is assessing the risk of one material, an upper bound or safety factor estimate is arguably appropriate because such assessments should err on the side of safety. However, when, as is the case for hazardous waste sites, many risk coefficients are used, many materials are relevant to determining overall site risk. It has been observed that if one sums 95% upper bounds for 10 dose-response coefficients, the probability of all of the coefficients being at or above their 95% upper bound is 0.0510, or about 1 x 10-l3. As it turns out, this calculation, though correct, is not entirely relevant to the question of the conservatism inherent in a sum of upper bounds. We will discuss some approaches to getting a better answer to this problem. © 2001 by CRC Press LLC USING STATISTICS IN HEALTH AND ENVIRONMENTAL RISK ASSESSMENTS 391 The “exposure assessment” step frames the question of what actual exposures are likely to be. Estimation of exposure is what drives (or should drive) environmental sampling efforts and subsequent exposure assessment modeling. Both areas have substantial statistical content and will be treated in some detail. Important topics include the pattern of environmental sampling, and why many “engineering judge-ment” or “compliance monitoring” samples may be nearly useless in terms of assessing actual exposures; the importance of having a model of human (or animal) behavior as the basis for estimating actual exposure; and the necessity of under-standing the origin of your environmental contamination numbers. The final step, risk characterization, is the product of the estimated exposure and the risk coefficients adopted. In practice both quantities may have substantial uncer-tainties. We will examine the source of such uncertainties, and the use of analytic and Monte Carlo methods for obtaining an overview of the uncertainties in the final risk estimates. II. STATISTICAL THINKING AND REGULATORY GUIDANCE There is a lot of good (and some not so good) statistical advice to be found in regulatory guidance documents. This section will review three pertinent areas: risk assessment (U.S. EPA, 1989), data quality objectives (U.S. EPA, 1993), and data quality assessment (U.S. EPA 1996). A. Risk Assessment The Risk Assessment Guidance for Superfund (RAGS) document codifies many of the standard procedures used in HHRA. This describes three distinct subprocesses: risk assessment of nonradioactive, noncarcinogenic, chemical toxicants using a quantity referred to as the Hazard Index (HI); risk assessment of chemical carcino-gens using q1* values (also termed “slope factors” or “cancer potency factors”); and, risk assessment of radioactive materials (radionuclides). 1. The Hazard Index The HI is given by: N HI = åDi ÷ RfDi (1) i = 1 where Di = dose received from the ith toxicant; RfDi = reference dose from the ith toxicant. The origin of the RfD deserves some consideration. It is generally taken from a single animal or, rarely, human study. The starting point is the dose at which no biological response was observed (the no observed effect level or NOEL), the lowest dose level at which an effect was observed (the lowest observed effect level or © 2001 by CRC Press LLC 392 A PRACTICAL GUIDE TO ENVIRONMENTAL RISK ASSESSMENT REPORTS LOEL), or either the dose predicted to yield a response in 10% of the individuals (the ED10) or a 95% lower bound on this dose (the LED10). Once a starting dose has been determined, various safety factors of 10 are applied. That is, the value is usually divided by 10 to reflect uncertainties in animal to human extrapolation, and a second factor of 10 to reflect interindividual human variability. Additional factors of 10 may be invoked if the starting dose is an LOEL, rather than an NOEL, if the study from which the dose number was derived was a subchronic, as opposed to a chronic, bioassay, and if the person developing the RfD had reservations about the quality of the study from which data originated. Thus most RfDs are 100 to 1000fold below a dose which caused no or minimal effect, and reflect substantial regulatory conservatism. The site may be considered safe if the HI is less than 1. Actually, following the approach in RAGS, many HIs must usually be defined for the same site. For example, there may be HIs of chronic (long-term or lifetime) exposure and subchronic expo-sure (shorter term than chronic; usually weeks or months); inhalation HIs, ingestion HIs, and HIs for developmental toxicants; or HIs broken out by mode of action of the toxicants involved (e.g., all liver toxicants). It should be stressed that, despite this variety, the HI is not a quantitative measure of risk. A quantitative measure of risk is the RfD, which may be loosely defined as a dose at which we are quite sure nothing bad will happen. Three elements are lacking from the HI: a quantitative description of the degree of conservatism inherent in a given RfD, a definition of what bad is, and some notion how rapidly things get worse as the RfD is exceeded (a slope factor). For example an HI of 5 might mean that an exposed individual would suffer a small chance of a small depression in cholinesterase activity (an event of dubious clinical significance), or it might mean that an exposed individual could experience acute liver toxicity and possibly death. Likewise, while HI values less than 1 may be taken as safe, it does not follow that a site with an HI of 0.3 is safer than a site with an HI of 0.5. From a statistical perspective there is not much to say. The HI is intended as a screening index, not a quantitative statement of risk. Moreover, the diversity of the origin of the RfDs, and the arbitrary degrees of conservatism inherent in their derivation, makes it futile to discuss “distributional” properties of the HI. One can, however, make some quantitative statements. First, if one has a report with a single HI for all toxicants at a site, it is almost certainly too large, and its derivation contrary to regulatory guidance. That is, as noted above, RAGS clearly states that HIs should be calculated separately for toxicants with different modes of action and differing exposure scenarios. A second area of concern, which also applies to cancer risk assessment, is the accuracy of the exposure numbers used to derive the HI. These statistical issues will be discussed in detail in subsequent sections. 2. Assessment of Chemical Cancer Risk At first look, the determination of cancer risk for chemical carcinogens, CRC, looks much like the HI calculation: N CRC = åDi ´ q1*i (2) i = 1 © 2001 by CRC Press LLC USING STATISTICS IN HEALTH AND ENVIRONMENTAL RISK ASSESSMENTS 393 where Di = the dose or exposure from the ith carcinogen of interest; q1*i = the cancer potency for that carcinogen. However, this is an actual quantitative expression of risk, with units given in lifetime cancers per exposed individual. Thus, any calculation of this type has a common endpoint. An important feature of this calculation is that each q1*i i is an upper bound on the risk calculated on the basis of some model (usually the linearized multistage model of carcinogenesis). The derivation of these upper bounds deserves discussion. The starting point is usually an animal study, where 3 to 4 groups of animals are exposed to different doses of a carcinogen, and a separate control group of animals is left unexposed. The cancer response in these groups is fit with a dose-response model and the resulting dose-response model is used to develop a linear 95% upper bound on dose-response, referred to as the cancer potency factor, or q1* value. Thus, one statistical issue is that Equation (2) involves the summing of possibly many upper bounds, which seems to many to be excessively conservative. One approach to determining the conservatism inherent in Equation (2) involves Monte Carlo simulation methods. These methods first assume that the estimate of cancer potency, q1* follows a log-normal probability density (Putzrath and Ginevan, l99l). The logarithmic mean (µ) is calculated as: µ = 1n(qmle) (3) where q = the maximum likelihood or “best” estimate of q *. The logarithmic standard deviation (s) can also be estimated as: s = [ 1n (q1*) – µ ] ÷ Z0.95 (4) where Z = 1.645 (the normal score associated with an upper 95% bound on q ). After µ and s have been determined for each carcinogen of interest, a large number (500 – 1000) of realizations are generated of Equation (2) using randomly generated q1s, and the 95th percentile of this empirical distribution can be deter-mined. Use of this approach can show that the supposed conservatism is less than one might think, in that the result of Equation (2) using q1*s is rarely more than twice as large as the 95th percentile of the Monte Carlo empirical distribution. Still, Monte Carlo calculations like those described may be worthwhile when the number of carcinogens considered in Equation (2) is large. Differences of a factor of 5 or more are possible when the number of carcinogens is greater than 20. A more important aspect of Equation (2) is that Di is the lifetime average daily dose for the carcinogen in question. Thus Di must be a dose estimate derived from very long-term average exposure. This brings us again to the importance of exposure estimation to the entire risk assessment process. B. Risk Assessment of Radionuclides The situation for radiation is somewhat different from the situation for chemical carcinogens. First, there is an extensive literature on the epidemiology of humans © 2001 by CRC Press LLC ... - tailieumienphi.vn