5 Risk Analysis, VoL 14, No.4, 1994 Human Interindividual Variability-A Major Source of Uncertainty in Assessing Risks for Noncancer Health Effects Dale Battis! and Ken Silver Received February 23, 1993; revised November 30, 1993 For noncancer effects, the degree of human interindividual variability plays a central role in de­ termining the risk that can be expected at low exposures. This discussion reviews available data on observations of interindividual variability in (a) breathing rates, based on observations in British coal miners; (b) systemic pharmacokinetic parameters, based on studies of a number of drugs; (c) susceptibility to neurological effects from fetal exposure to methyl mercury, based on observations of the incidence of effects in relation to hair mercury levels; and (d) chronic lung function changes in relation to long-term exposure to cigarette smoke. The quantitative ranges of predictions that follow from uncertainties in estimates of interindividual variability in susceptibility are illustrated. KEY WORDS: Interindividual variability; noncancer risk assessment; pharmacokinetics; pharmacodynamics; Monte Carlo simulation. 1. INTRODUCTION different subpopulations of patients can be mod­ eled based in part on direct observations).(I) • Assessments of the effects of ubiquitous ambient By this time the NOEUsafety factor approach, air pollutants such as ozone, carbon monoxide, which incorporates a IO-fold factor to account crudely lead, and acid particulates.(2,3) for possible interindividual differences within the human • Assessments of the effects on lung function of population, has been hallowed by long use and has an chronic lung-damaging agents such as smoking, almost unassailable position in the habits of many reg­ occupational exposure to coal dust, silica, etc.(4-6) ulatory toxicologists. Nevertheless, there are occasions • Assessments of reproductive and developmental where it is both feasible and desirable to develop pro­ effects, some of which appear likely to produce cedures for estimating noncancer risks more quantita­ some excess risk extending to very low dos­ tively. Quantification of the incidence and intensity of ages.(7) noncancer effects could contribute to decision-making, • Assessments of the incidence of high-dose mor­ in particular where actual or anticipated exposures are bidity and mortality that could be expected to high enough to produce effects in a directly observable result from large releases of acutely toxic agents, fraction of exposed people. Examples of this include the such as chlorine or hydrogen sulfide.(8) following. There is sometimes good reason to doubt the uni­ • The design of improved protocols for the use of versal expectation of population thresholds that is built pharmaceuticals (where, ideally, the incidence of into the NOEUUncertainty Factor schema. In particular both beneficial and adverse biological effects for cases there may be some finite fraction of individuals who, because of disease or other causes, are marginal I Center for Technology, Environment, and Development, Clark Uni­ versity, 950 Main Street, Worcester, Massachusetts, 01610. for biological functions affected by the chemical and 2 Boston University, School of Public Health, Boston, Massachusetts. who may be pushed beyond a functional threshold for 421 0272-4332194108OO-042IS07.00/1 C 1994 Society for Risk Analysis L 422 Hattis and Silver 1.5 • Uptake: Individual differences in the environ­ y =1.306 + O.1027x mental concentration needed to produce a given -r: R =0.981 1.4 ~ intake of toxicant into the body, e.g., due to dif­ i ferences in breathing rates, dietary habits, etc. '1S 1.3 • Pharmacokinetic: Individual differences in the >­ .: amount of uptake needed to produce a particular ! 1.2 concentration-time product of active agent in the II/) blood or at the site of action, e.g., due to differ­ oS 1.1 ences in metabolic activation or clearance. • Response: Individual differences in the dose at 1.0 the active site that produces a similar risk of re­ ·3 ·2 • 1 0 2 3 Z-Score sponse. Fig. 1. Fit of a lognormal distribution to the minute volumes of 62 The discussion below is organized around these major British coal miners--data of Jones et aL (0) categories of heterogeneity. an adverse effect by a small finite dose of the chemical. For example, for healthy workers there may indeed be 3. ILLUSTRATION OF EXPOSURE/UPTAKE a functional reserve capacity for oxygen delivery to the HETEROGENEITY-INTERINDMDUAL myocardium and, hence, a finite tolerance for a small DIFFERENCES IN BREATHING RATES IN impairment of oxygen delivering capacity for the blood BRITISH COAL MINERS due to carbon monoxide or agents that cause the con­ version of hemoglogin to methemoglogin. However, for a worker who has just begun to experience a myocardial Breathing rate is a good example of a parameter infarction, oxygen delivery to portions of the mycardium that directly affects a person's primary uptake of an air­ is known to be seriously compromised, and it is possible borne toxicant. Other things being equal, a worker who that a small difference in oxygen delivery capacity due breathes more air per unit time will take in more dust, to a modest blood carboxyhemoglobin or methemoglo­ etc., per unit time, for a given concentration of dust in bin concentration could prove the difference between life air. One of the best data sets on workers' breathing rates and death for portions of the heart muscle that are sud­ that we have seen is in a report by Jones et al. (9) covering denly forced to rely on collateral arterial vessels for ox­ 62 underground British coal miners. Breathing rates ygen supply. were measured over periods that were typically about 90 In the discussion below, we illustrate (i) how we min, and in most cases three replicate determinations have attempted to use available data to give us some were made. Data of this type allow us to illustrate three insight into the'magnitude of likely interindividual var­ different problems in analyzing individual variability in iability in susceptibility (heterogeneity) in the cases of a this type of directly measurable parameter: few specific noncancer risk assessments, (ii) how we have attempted to assess quantitatively the several un­ • How do we decide what statistical form to use certainties in our estimates of human heterogeneity, and for the population distribution of the parameter? (iii) the magnitude of the uncertainties in risk that flow • How do we decide how much of the observed from our uncertainties on the issue of human heteroge­ variation in a set of measurements is due to neity. "true" interindividual variability and how much is due to measurement error? • How do we determine the uncertainty in our es­ 2. HETEROGENEITY AT VARIOUS STEPS IN timate of the population variation of the parame­ THE PATHWAY TO ADVERSE EFFEcrS ter? That is, if we calculate a standard deviation, or a geometric standard deviation from individual A few classes of heterogeneity/interindividual var­ measurements, how often could we expect to be iability in susceptibility can be defined as components wrong by various amounts in relation to the stan­ along the pathway from environmental exposure through dard deviation we would calculate if we had the production of adverse effects. measurements on an infinite number of people? c Interindividual Variability in Noncancer Risks 423 40,--------------------------­ look lognormal, as long as you don't look too closely." Although phrased facetiously and deliberately over­ Y % 20.76 + 4.7Ox stated, these laws do represent regularities we have com­ R = 0.991 30 monly observed in our practical experience. In this case, we mean to imply that where one does not have a strong mechanistic reason to prefer one type of distribution, it can help illuminate both the facts and the associated un­ 20 certainties to compare observations with expectations under a range of distributional forms. I!I 10+-~~~~~~~~~~~~~ - 3 - 2 -1 0 2 3 3.2. Removing Estimated Measurement Error from Z·Score Estimates or True Population Heterogeneity Fig. 1.. Fit of a normal distribution to the minute volumes of 62 British coal miners-data of Jones et al. (9J The second issue-measurement (and, implicitly, other short term) variability-arises because as risk as­ sessors we are not simply interested in describing the distribution of a set of observations. We want to use the data to help make inferences about how much different 3.1. Analyses Using Alternative Statistical Forms the true delivered doses and risks might be facing dif­ ferent people in the same environment. For this purpose, Figure 1 shows a lognormal probability plot of dis­ because an agent such as coal dust is expected to pose tributional data of this type. 3 A true lognormal distri­ lung damaging hazards that depend primarily on doses bution would be expected to result from a situation in delivered over an extended period (at least months, and which many factors each contributed in small ways to more often years or decades), we need to estimate the variation in the measured parameter, and the factors all distribution of long-term doses that are expected to result acted multiplicatively. In practice, many parameters tend from differences in relatively stable characteristics of in­ to be reasonably well described by this type of plot. dividuals and their job requirements. The distribution of However, in this case, as can be seen in the comparison our observations (Fig. 1) represents the combined effect with Fig. 2, a normal distribution