Environmental Risk Assessment Reports - Chapter 19 potx

Evaluation of the Utility of Environmental Sampling for Health and Environmental Risk Assessments .... That is, if one is assessing the risk of onematerial, an upper bound or safety fact

Trang 1

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

LA4111 ch19 new Page 389 Wednesday, December 27, 2000 2:54 PM

Trang 2

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

as “the science that deals with the collection, classification, analysis, and tation of numerical facts or data, and that, by use of mathematical theories ofprobability, imposes order and regularity on aggregates of more or less disparateelements.” This has a simple translation: statistics finds ways of coping with uncer-tain, incomplete, and otherwise not wholly satisfactory data Therefore, if you knowthe answer exactly, you don’t need statistics

interpre-How might this methodology apply to planning, generating, and evaluating riskassessment reports? This question can best be approached by considering the fourcomponents of the risk assessment process, described in Chapters 2 and 3 The firststep of the assessment is “hazard identification,” which reviews the inventory ormaterials present in the environment and uses information from toxicology or epi-demiology studies to determine which of these might pose a risk to human healthand/or the environment Statistical principles play important roles in epidemiologyand both environmental and laboratory toxicology studies, but the form of thesestudies, 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 casesthe hazard identification of a material may rest on one or two studies that are ofdubious validity If the risk assessment is driven by such materials (we will discusshow to determine the factors that are of greatest importance to the estimation ofrisk) it is often worthwhile to reconsider the underlying literature to determine howvalid the studies underlying the hazard identification actually are

The next step, toxicity assessment, requires development of a dose-responsefunction A dose-response function provides the risk coefficients used to translateexposure into risk In essence it answers the question, “Given that substance X isbad, 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 isdeveloped We will also treat the problem that arises because many “approved” dose-response coefficients are either 95% statistical upper bounds, or incorporate “safetyfactors” of between 100 and 10,000 That is, if one is assessing the risk of onematerial, an upper bound or safety factor estimate is arguably appropriate becausesuch assessments should err on the side of safety However, when, as is the case forhazardous 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% upperbounds for 10 dose-response coefficients, the probability of all of the coefficients

out, this calculation, though correct, is not entirely relevant to the question of theconservatism inherent in a sum of upper bounds We will discuss some approaches

to getting a better answer to this problem

Trang 3

USING STATISTICS IN HEALTH AND ENVIRONMENTAL RISK ASSESSMENTS 391

The “exposure assessment” step frames the question of what actual exposuresare likely to be Estimation of exposure is what drives (or should drive) environmentalsampling efforts and subsequent exposure assessment modeling Both areas havesubstantial statistical content and will be treated in some detail Important topicsinclude the pattern of environmental sampling, and why many “engineering judge-ment” or “compliance monitoring” samples may be nearly useless in terms ofassessing 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 andthe risk coefficients adopted In practice both quantities may have substantial uncer-tainties We will examine the source of such uncertainties, and the use of analyticand Monte Carlo methods for obtaining an overview of the uncertainties in the finalrisk estimates

II STATISTICAL THINKING AND REGULATORY GUIDANCE

There is a lot of good (and some not so good) statistical advice to be found inregulatory guidance documents This section will review three pertinent areas: riskassessment (U.S EPA, 1989), data quality objectives (U.S EPA, 1993), and dataquality assessment (U.S EPA 1996)

A Risk Assessment

The Risk Assessment Guidance for Superfund (RAGS) document codifies many ofthe standard procedures used in HHRA This describes three distinct subprocesses:risk assessment of nonradioactive, noncarcinogenic, chemical toxicants using aquantity referred to as the Hazard Index (HI); risk assessment of chemical carcino-

risk assessment of radioactive materials (radionuclides)

The HI is given by:

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 nobiological response was observed (the no observed effect level or NOEL), the lowestdose level at which an effect was observed (the lowest observed effect level or

Trang 4

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 dosehas been determined, various safety factors of 10 are applied That is, the value isusually 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 thestudy from which the dose number was derived was a subchronic, as opposed to achronic, bioassay, and if the person developing the RfD had reservations about thequality of the study from which data originated Thus most RfDs are 100 to 1000foldbelow a dose which caused no or minimal effect, and reflect substantial regulatoryconservatism

The site may be considered safe if the HI is less than 1 Actually, following theapproach 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, ingestionHIs, and HIs for developmental toxicants; or HIs broken out by mode of action ofthe toxicants involved (e.g., all liver toxicants) It should be stressed that, despitethis variety, the HI is not a quantitative measure of risk A quantitative measure ofrisk is the RfD, which may be loosely defined as a dose at which we are quite surenothing bad will happen Three elements are lacking from the HI: a quantitativedescription of the degree of conservatism inherent in a given RfD, a definition ofwhat 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 individualwould 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 couldexperience acute liver toxicity and possibly death Likewise, while HI values lessthan 1 may be taken as safe, it does not follow that a site with an HI of 0.3 is saferthan a site with an HI of 0.5

From a statistical perspective there is not much to say The HI is intended as ascreening index, not a quantitative statement of risk Moreover, the diversity of theorigin of the RfDs, and the arbitrary degrees of conservatism inherent in theirderivation, 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 differingexposure scenarios A second area of concern, which also applies to cancer riskassessment, is the accuracy of the exposure numbers used to derive the HI Thesestatistical issues will be discussed in detail in subsequent sections

At first look, the determination of cancer risk for chemical carcinogens, CRC, looksmuch like the HI calculation:

Trang 5

potency for that carcinogen

However, this is an actual quantitative expression of risk, with units given inlifetime cancers per exposed individual Thus, any calculation of this type has a

upper bound on the risk calculated on the basis of some model (usually the linearizedmultistage model of carcinogenesis)

The derivation of these upper bounds deserves discussion The starting point isusually an animal study, where 3 to 4 groups of animals are exposed to differentdoses 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 theresulting dose-response model is used to develop a linear 95% upper bound on dose-

Thus, one statistical issue is that Equation (2) involves the summing of possiblymany upper bounds, which seems to many to be excessively conservative Oneapproach to determining the conservatism inherent in Equation (2) involves MonteCarlo simulation methods These methods first assume that the estimate of cancer

The logarithmic mean (µ) is calculated as:

number (500 – 1000) of realizations are generated of Equation (2) using randomly

deter-mined Use of this approach can show that the supposed conservatism is less 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 ormore are possible when the number of carcinogens is greater than 20

very long-term average exposure This brings us again to the importance of exposureestimation to the entire risk assessment process

B Risk Assessment of Radionuclides

The situation for radiation is somewhat different from the situation for chemicalcarcinogens First, there is an extensive literature on the epidemiology of humans

Trang 6

exposed to radiation Thus cancer risk coefficients are well known and relativelyprecise Second, the physical means by which radiation damages cells are wellknown, and precise dosimetric calculations are nearly always possible Finally,radiation is relatively easy to measure in the environment, and actual concentrationscan be determined unambiguously It should also be mentioned that, because of thesuperior database, radiation cancer risk coefficients are usually based on best esti-mates rather than upper bounds

C Evaluation of Exposure

A general theme running through the RAGS document is that exposure assessmentsand, thus, doses should be based on values which are conservative, but not tooconservative Yet the question of uncertainty is treated in a way which would besurprising to most statisticians: “Highly quantitative statistical uncertainty analysis

is usually not practical or necessary for Superfund site risk assessments for a number

of reasons, not the least of which are the resource requirements to collect and analyzesite data in such a way that results can be presented as valid probability distributions.”

It seems clear that this is not so, and given that cleanup costs are often in the tens

of millions dollars it is hard to see why resources to do the job right would not beforthcoming

Moreover, the two U.S EPA documents which outline the DQO and Data QualityAssessment process, give careful guidance and recommend many good statisticaltools which can be used in assessing data needs and data quality, and which aredirectly relevant to the issue of assessing environmental contamination, and hencethe potential for exposure to human beings or other biota This contrast is interestingbecause the DQO and Data Quality Assessment documents are much more recentthan the RAGS document, and reflect the evolving position of U.S EPA in the area

of desirable levels of statistical sophistication In terms of regulatory risk assessment,

we are moving from a qualitative to a quantitative world and from simple istic models to more sophisticated probabilistic ones

determin-D Data Quality Objectives (DQOs)

The DQO process as defined by U.S EPA is useful for any data collection, not justthe collection of data for Superfund sites (see Chapter 11) This process has sevensteps:

l State the problem:

What sort of environmental contamination is it that you want to characterize? One might be interested in gas phase contaminants (e.g., radon, volatile organics), par- ticulates (e.g., asbestos), or soil contamination One might be concerned with exposure from inhalation (e.g., radon, asbestos), dermal contact (e.g., pesticides), or soil ingestion (metals) Likewise the exact exposure scenario will affect data needs.

2 Identify the decision:

What sort of question needs to be answered? Do you want to know about term average exposures (carcinogens), short-term maxima (neurotoxicants), or LA4111 ch19 new Page 394 Wednesday, December 27, 2000 2:54 PM

Trang 7

long-USING STATISTICS IN HEALTH AND ENVIRONMENTAL RISK ASSESSMENTS 395

episodic exposure resulting from particular human activities? What is the exact form of the question you want answered?

3 Identify the inputs to the decision:

How will you use the data? A hypothesis testing exercise might have different data requirements from a modeling study.

4 Define the study boundaries:

Where and when should the data apply? Are you interested in current risks, or a particular site, or risks that may evolve over time (e.g., groundwater)?

5 Develop a decision rule:

You want to be able to say that given these data the exposure of interest is: a quantity, acceptable, unacceptable; or to precisely define the extent of remediation required.

6 Specify limits on decision errors:

How precise do exposure estimates need to be? What is the “loss” of calling an acceptable exposure unacceptable or vice versa If you are trying to infer dose- response, will your study lose an unacceptable amount of power because of impre- cise exposure data?

7 Optimize the design for obtaining data:

Define the most resource-effective sampling and analysis design for generating the data needed to satisfy the DQOs of the project.

The purpose of this seven-step process is to identify the characteristics of thedata required, and to arrive at a strategy for collection It should be noted that theinteraction between Step 7 and Steps l – 6 is iterative That is, if one defines DQOsthat exceed one’s resources, one must rethink the question to identify DQOs withmore reasonable resource requirements In the extreme case, one might be forced

to abandon a particular study because meaningful data cannot be collected forreasonable cost

This process assumes that you already have environmental contamination data andwant to determine whether or not this data is adequate to the task at hand, i.e.,assessing exposures and thus risks of a particular site or activity It is nearly thesame as for defining a data collection effort, except here one must identify DQOsthat can be met by the data at hand That is, the whole process of data qualityassessment is aimed at defining whether or not a set of data meets a particular set

of DQOs, or alternatively defining what set of DQOs a given data set will support

III EVALUATION OF THE UTILITY OF ENVIRONMENTAL SAMPLING FOR HEALTH AND ENVIRONMENTAL RISK ASSESSMENTS

As noted above, exposure assessment is the factor which most often drives theuncertainty in a risk assessment, and environmental monitoring data are the factorswhich most commonly drive the exposure assessment Following the DQO process,

we need to state the problem, which is to characterize the risk a given site or activity

Trang 8

might pose to human health and the environment The ultimate decision (DQO, Step2) we want to make is whether environmental contamination poses an unacceptablerisk We must also specify the model we will use to determine whether or notunacceptable risks are present, because the form of the model will determine theinputs required (DQO, Step 3) We must also specify where and when the decisionapplies That is, what is the extent of the area of interest, and what time frame appliesthe decision of interest (DQO, Step 4)? Having defined the parameters of ourdecision, we must then determine what overall scale will determine whether risksare unacceptable (DQO, Step 5), and how sure we want to be about our decision(DQO, Step 6) Finally, armed with a clear description of what we want to accom-plish, we can either plan our environmental sampling efforts, or evaluate the data athand (DQO/DQA Step 7) This, of course, is not how things usually happen, but it

is good to have an ideal as a yardstick

Perhaps the most frequent flaw in environmental sampling efforts is the tution of “compliance sampling” for “characterization sampling.” Compliance sam-pling is embodied by the “engineering-judgement sample” also described as the

substi-“sample-the-dirty-spots strategy.” This approach focuses sampling efforts on those

disciplines like industrial hygiene where the goal is worker protection Here, if onesamples all high exposure areas and these are found to be in compliance, exposuresfrom the process may be assumed to be acceptably low For a well-defined process,this strategy is excellent, but for most environmental contamination problems thepurpose of the sampling effort is to determine the nature and extent of contamination.Thus, it is a characterization problem, not a compliance monitoring problem

A Graphical Methods

Figure 1 shows a pseudo 3-D ball and stick plot of contamination for “bad stuff” at

a hypothetical hazardous waste site There are four quadrants, each with 150 potentialsamples Quadrant 1 is uncontaminated, quadrant 2 is lightly contaminated, quadrant

3 is moderately contaminated, and quadrant 4 is heavily contaminated What wouldhappen if we followed a compliance monitoring approach and sampled almostexclusively in quadrant 4? Clearly the site is heavily contaminated, but is this thecorrect answer? An evenly distributed sample would give a better overview of theextent of contamination and would allow a more reasonable risk assessment Plotslike Figure 1 can give a very good idea of the distributions of the samples taken at

a site, and can indicate whether a given sample is unbiased and representative withrespect to defining environmental contamination

One should also be interested in the distribution of contamination in the sampleused to characterize the site The box and whisker plot is a graphical aid that isuseful in this context (see Figure 2) The line in the center in Figure 2 marks the50th percentile or median of the data The upper and lower “hinges” appear at the25th and 75th percentiles of the data The “whiskers” connect the upper and lowerhinges to the largest and smallest data point within 1.5 times the distance betweenthe hinges, termed the interquartile range (IQ) from its respective hinge Outsidepoints are between the hinge plus (upper) or minus (lower) 1.5 times the IQ and 3

Trang 9

times the IQ Far outside points are beyond the hinges plus or minus 3 times the

IQ Outside points are atypical of the data and may represent statistical outliers.Figure 3 shows box plots for the log-transformed data from 4 quadrants of ourhypothetical site It is easy to see that, as one goes from quadrant 1 to quadrant 4,each data for each quadrant has a reasonably symmetrical distribution and a medianabout 10fold greater than the median for the preceding quadrant

While box plots are a simple way to convey the central tendency and form of aset of data, one can use even simpler graphics See, for example, the dot plot inFigure 4 This plot was generated by sorting the data into “bins” of specified width(here about 0.2) and plotting the points in a bin as a stack of dots (hence the namedot plot) Dot plots give a general idea of the shape and spread of a set of data andthey are very simple to interpret

Aside from the spatial structure of the data and its general shape and centraltendency, we are often interested in the temporal structure of a data set Pesticiderisk studies, for example, frequently involve 5 or 6 sets of data collected on the day

of pesticide application and at several time periods postapplication Figure 5 trates a temporal set of environmental measurements In Figure 5 we see a set oflog-transformed pesticide residue measurements plotted against the time since appli-cation The plot shows clearly that residue measurements diminish over time and

illus-Figure 1 This hypothetical hazardous waste site has four areas Areas 1 and 2 have little

contamination, while 3 has moderate contamination and 4 is heavily contaminated LA4111 ch19 new Page 397 Wednesday, December 27, 2000 2:54 PM

Trang 10

that the rate of decline in the logarithm of residue levels is well-approximated by astraight line If we fit a linear regression to such data, the equation is of the form:

If we rearrange (5) we get

Figure 2 A sample box plot The median is the 50% point of the data; the upper hinge (UH)

is the 75% point of the data The lower hinge (LH) is the 25% point of the data; the upper whisker extends from the UH to the largest data value less than the UH plus 1.5 times the difference between the UH and the LH hinges [the interquartile range (IQ)]; the lower whisker extends from the LH to the smallest data value greater than the LH minus 1.5 times the LQ; outside points are either between the UH plus 1.5 IQ and UH plus 3 IQ or LH minus 1.5 IQ and LH minus 3 IQ Far outside points are beyond UH plus 3 IQ or LH minus 3 IQ.

Trang 11

Figure 3 A box plot of contamination levels in areas 1-4 Note that median contamination

level increases about an order of magnitude as one moves from area to area

Figure 4 An example dot-plot.

Tiêu đề	Using Statistics In Health And Environmental Risk Assessments
Tác giả	Michael E. Ginevan
Thể loại	Chương
Năm xuất bản	2001

Định dạng
Số trang	23
Dung lượng	848,67 KB