EPA/600/R-05/093F 

 September 2006 

A Framework for Assessing Health Risks of Environmental 

Exposures to Children 

National Center for Environmental Assessment 

Office of Research and Development 

U.S. Environmental Protection Agency 

Washington, DC 
DISCLAIMER 

This document has been reviewed in accordance with U.S. Environmental Protection 

Agency policy and approved for publication. Mention of trade names or commercial products 

does not constitute endorsement or recommendation for use. 

Preferred Citation: 

U.S. Environmental Protection Agency (EPA). (2006) A framework for assessing health risks of 

environmental exposures to children. National Center for Environmental Assessment, 

Washington, DC; EPA/600/R-05/093F. Available from the National Technical Information 

Service, Springfield, VA, and online at http://www.epa.gov/ncea. 

ii 
CONTENTS

LIST OF TABLES......................................................................................................................... vi 

LIST OF FIGURES ...................................................................................................................... vii 

LIST OF ABBREVIATIONS AND ACRONYMS .................................................................... viii 

PREFACE...................................................................................................................................... ix 

AUTHORS, CONTRIBUTORS, AND REVIEWERS ................................................................ xii 

1. EXECUTIVE SUMMARY .................................................................................................... 1-1 

2. INTRODUCTION AND PURPOSE ...................................................................................... 2-1 

3. LIFESTAGE-SPECIFIC PROBLEM FORMULATION....................................................... 3-1 

3.1. PLANNING AND SCOPING ....................................................................................... 3-1 

3.2. CONCEPTUAL MODEL.............................................................................................. 3-5 

3.2.1. Exposure Considerations ................................................................................... 3-5 

3.2.2. Outcome Considerations.................................................................................... 3-8 

3.2.3. Integrating Exposure and Outcome Considerations .......................................... 3-9 

3.3. ANALYSIS PLAN ...................................................................................................... 3-10 

4. LIFESTAGE-SPECIFIC ANALYSIS.................................................................................... 4-1 

4.1. LIFESTAGE-SPECIFIC HAZARD CHARACTERIZATION..................................... 4-2 

4.1.1. Introduction........................................................................................................ 4-2

4.1.2. Qualitative Evaluation of Individual Studies..................................................... 4-3 

4.1.2.1. Study Purpose .................................................................................... 4-5 

4.1.2.2. Study Design....................................................................................... 4-5 

4.1.2.3. Identifying Critical Windows of Exposures ....................................... 4-6 

4.1.2.4. Outcomes Related to Developmental Lifestage Exposure.................. 4-6 

4.1.2.5. Toxicokinetic Data.............................................................................. 4-7 

4.1.2.6. Toxicodynamic Data........................................................................... 4-7 

4.1.2.7. Mode of Action Information............................................................... 4-8 

4.1.2.8. Qualitative Evaluation of Dose-Response ......................................... 4-8 

4.1.2.9. Variability Analysis ............................................................................ 4-9 

4.1.2.10. Uncertainty Analysis....................................................................... 4-10 

4.1.3. Summarization of the Hazard Database........................................................... 4-12 

4.1.3.1. Evaluation of the Weight-of-Evidence of the Hazard

 Database........................................................................................... 4-13 

4.1.3.1.1. Temporality........................................................................... 4-14 

4.1.3.1.2. Strength of the association.................................................... 4-15 

4.1.3.1.2.1. Variability analysis .......................................... 4-16 

4.1.3.1.2.2. Uncertainty analysis......................................... 4-16 

4.1.3.1.3. Qualitative dose-response relationship ................................. 4-17 

4.1.3.1.4. Experimental evidence.......................................................... 4-18 

4.1.3.1.5. Reproducibility ..................................................................... 4-18 

4.1.3.1.6. Biological plausibility........................................................... 4-19 

4.1.3.1.7. Alternative or multiple explanations..................................... 4-20 

iii 
CONTENTS (continued) 

4.1.3.1.8. Specificity ............................................................................. 4-20 

4.1.3.1.9. Coherence ............................................................................. 4-21 

4.1.4. Iteration with Dose-Response and Exposure Characterization........................ 4-23 

4.1.5. Lifestage-Specific Hazard Characterization Narrative .................................... 4-24 

4.2. LIFESTAGE-SPECIFIC DOSE-RESPONSE CHARACTERIZATION.................... 4-25 

4.2.1. Introduction...................................................................................................... 4-25 

4.2.2. Mode of Action Conceptualization.................................................................. 4-28 

4.2.2.1. Summarizing the Available Dose-Response Data ............................ 4-28 

4.2.2.2. Mechanistic Data and Mode of Action (MOA) ................................ 4-30 

4.2.2.3. Selection of Dose Metric Informed by MOA ................................... 4-30 

4.2.3. Analysis in the Range of Observation and Dose-Response Models................ 4-32 

4.2.4. Extrapolations and Risk Derivation from a Lifestage Approach..................... 4-38 

4.2.4.1. Duration and Route Adjustments ..................................................... 4-38 

4.2.4.2. Interspecies and Intraspecies Adjustments ....................................... 4-39 

4.2.4.3. Low-Dose Extrapolation................................................................... 4-43 

4.2.4.4. Reference and Risk Value Derivation............................................... 4-44 

4.2.5. Variability Analysis ......................................................................................... 4-45 

4.2.6. Sensitivity Analysis ......................................................................................... 4-46 

4.2.7. Uncertainty Analysis........................................................................................ 4-46 

4.2.8. Iteration with Hazard and Exposure Characterization..................................... 4-47 

4.2.9. Lifestage-Specific Dose-Response Characterization Narrative....................... 4-48 

4.3. LIFESTAGE-SPECIFIC EXPOSURE CHARACTERIZATION............................... 4-48 

4.3.1. Introduction......................................................................................................... 4-48 

4.3.2. Evaluation of Available Exposure Data.............................................................. 4-49 

4.3.2.1. Chemical Properties, Environmental Sources, Fate, and Transport .... 4-54 

4.3.2.2. Environmental Media Concentrations ................................................. 4-55 

4.3.2.3. Lifestage-Specific Exposure Measurement Data................................. 4-55 

4.3.2.4. Lifestage-Specific Exposure Factors ................................................... 4-57 

4.3.2.5. Cumulative Evaluation of Environmental Stressors............................ 4-59 

4.3.3. Lifestage-Specific Exposure Analysis ............................................................... 4-60 

4.3.3.1. Exposure Measurement and Estimation Approach.............................. 4-61 

4.3.3.2. Analysis Level or Tiered Assessment.................................................. 4-62 

4.3.3.2.1. Screening assessment......................................................... 4-63 

4.3.3.2.2. Refined assessment ............................................................ 4-64 

4.3.3.2.3. Supplemental data collection ............................................. 4-65 

4.3.4. Variability Analysis ............................................................................................ 4-66 

4.3.5. Sensitivity Analysis ............................................................................................ 4-67 

4.3.6. Uncertainty Analysis........................................................................................... 4-67 

4.3.7. Iteration with Hazard and Dose-Response Characterization .............................. 4-68 

4.3.8. Lifestage-Specific Exposure Characterization Narrative.................................... 4-69 

iv 
CONTENTS (continued) 

5. LIFESTAGE-SPECIFIC RISK CHARACTERIZATION ..................................................... 5-1 

5.1. LIFESTAGE-SPECIFIC RISK CHARACTERIZATION SUMMARY....................... 5-3 

5.1.1. Key Information from the Analysis Phase......................................................... 5-3 

5.1.2. Scientific Assumptions ...................................................................................... 5-4 

5.1.3. Risk Drivers ....................................................................................................... 5-4 

5.1.4. Strengths and Weaknesses ................................................................................. 5-4 

5.1.4.1. Variability ........................................................................................... 5-5 

5.1.4.2. Sensitivity ........................................................................................... 5-5 

5.1.4.3. Uncertainty.......................................................................................... 5-6 

5.1.5. Key Conclusions ................................................................................................ 5-6 

5.1.6. Alternative Risk Estimates Considered ............................................................. 5-7 

5.1.7. Research Needs.................................................................................................. 5-7 

5.2. RISK CONTEXT........................................................................................................... 5-8 

6. SUMMARY AND IDENTIFICATION OF GAPS IN APPROACHES FOR 

CHILDREN’S HEALTH RISK ASSESSMENT.................................................................... 6-1 

GLOSSARY ............................................................................................................................... G-1 

REFERENCES ............................................................................................................................R-1 

v 
LIST OF TABLES 

3-1. Developmental lifestages and age groups for exposure assessments .............................. 3-7 

3-2. Lifestage-specific database inventory sheet................................................................... 3-11 

4-1. Examples of lifestage-specific questions for evaluation of individual studies 

within hazard characterization ....................................................................................... 4-11 

4-2. Examples of lifestage-specific questions for evaluation of the WOE of the 

 hazard database .............................................................................................................. 4-21 

4-3. Examples of lifestage-specific questions for MOA conceptualization.......................... 4-31 

4-4. Examples of lifestage-specific questions for analysis in the range of observation........ 4-38 

4-5. Examples of lifestage-specific questions for extrapolations and risk derivation........... 4-45 

4-6. Examples of lifestage-specific questions for dose-response variability, 

sensitivity, and uncertainty analyses.............................................................................. 4-47 

4-7. Examples of lifestage-specific questions for scenario development ............................. 4-53 

4-8. Examples of lifestage-specific questions for evaluation of the available 

 exposure data ................................................................................................................. 4-59 

4-9. Examples of lifestage-specific questions for exposure analysis level or tiered 

 assessment ..................................................................................................................... 4-65 

4-10. Examples of lifestage-specific questions for exposure variability, sensitivity, 

and uncertainty analyses ................................................................................................ 4-68 

vi 
LIST OF FIGURES 

2-1. Flow diagram for a lifestage-specific risk assessment framework .................................. 2-2 

2-2. Lifestages of outcomes after developmental exposure .................................................... 2-4

3-1. Flow diagrams for lifestage-specific problem formulation ............................................. 3-2 

4-1. Exposure to risk continuum ............................................................................................. 4-1 

4-2. Flow diagram for lifestage-specific analysis ................................................................... 4-2 

4-3. Flow diagram for lifestage-specific hazard characterization........................................... 4-4 

4-4. Conceptual view of a WOE evaluation.......................................................................... 4-14 

4-5. Flow diagram for lifestage-specific dose-response characterization............................. 4-26 

4-6. Use of BBDR modeling................................................................................................. 4-37

4-7. Interspecies and intraspecies adjustments with lifestage considerations....................... 4-41 

4-8. Flow diagram for lifestage-specific exposure characterization ..................................... 4-50 

4-9. Exposure routes during developmental lifestages.......................................................... 4-52 

5-1. Flow diagram for lifestage-specific risk characterization................................................ 5-1 

vii 
LIST OF ABBREVIATIONS AND ACRONYMS 

ADAF Age-dependent adjustment factors 

AUC Area under the curve 

BBDR Biologically based dose-response 

BMD Benchmark dose 

BMDL Benchmark dose lower confidence level 

CAA Clean Air Act 

CatReg Categorical regression 

Cmax Maximum concentration 

CHAD Consolidated Human Activity Database 

CSF Cancer slope factor 

CYP Cytochrome P450 

DAF Dosimetric adjustment factor 

EPA U. S. Environmental Protection Agency 

FIFRA Federal Insecticide, Fungicide, and Rodenticide Act 

FQPA Food Quality Protection Act 

GLP Good laboratory practice 

HEC Human equivalent concentration 

HED Human equivalent dose 

HEDS Human Exposure Database System 

LOAEL Lowest-observed-adverse-effect level 

MOA Mode of action 

MOE Margin of exposure 

NHEXAS National Human Exposure Assessment Survey 

NOAEL No-observed-adverse-effect level 

PBTK Physiologically based toxicokinetic 

POD Point of departure 

QRE Quantitative risk estimation 

RfC Reference concentration 

RfD Reference dose 

RfV Reference value 

SAR Structure-activity relationship 

TD Toxicodynamic 

TK Toxicokinetic 

TSCA Toxic Substances Control Act 

UF Uncertainty factor 

Vmax Maximum velocity 

WOE Weight-of-evidence 

viii 
PREFACE 

The mission of the U.S. Environmental Protection Agency (EPA) is to protect human 

health and the environment. In the early 1990s, the National Research Council (NRC) released a 

watershed report, Pesticides in the Diets of Infants and Children, regarding evaluation of risk to 

environmental exposures (NRC, 1993). Increased emphasis on protecting children from 

environmental exposures has evolved since this report due to mounting scientific evidence to 

support the vulnerability of the developing fetus and child. Legislative and administrative 

mandates have been enacted since this NRC report. In 1995, the EPA Administrator issued 

Policy on Evaluating Health Risks to Children (U.S. EPA, 1995a), which states that EPA will 

consider risks to infants and children consistently and explicitly as a part of risk assessments 

generated during its decision-making process, including the setting of standards to protect public 

health and the environment. Subsequent provisions in the Food Quality Protection Act (FQPA) 

(U.S. 104th Congress, 1996a) and the Safe Drinking Water Act (SDWA) Amendments (U.S. 

104th Congress, 1996b) underscored this policy by focusing on the evaluation of children’s 

exposures and toxicities in the context of risk assessment. Evaluation of environmental risks to 

children is an implicit consideration in human health risk assessment in other EPA legislative 

mandates (Clean Air Act [CAA] [U.S. 101st Congress, 1990]; Comprehensive Environmental 

Response, Compensation, and Liability Act [CERCLA] [U.S. 96th Congress, 1980], Toxic 

Substances Control Act [TSCA] [ U.S. 94th Congress, 1976], Federal Insecticide, Fungicide, and 

Rodenticide Act [FIFRA] [U.S.104th Congress, 1996c]). In 1997, Presidential Executive Order 

13045, Protection of Children from Environmental Health Risks and Safety Risks (Executive 

Order, 1997), gave further emphasis to the need for establishing potential risks from 

environmental exposures during childhood. The EPA subsequently published Strategy for 

Research on Environmental Risks to Children in 2000 (U.S. EPA, 2000f). 

EPA risk assessment guidelines relevant to children’s health issues have been published 

(U.S. EPA, 1991, 1996, 1998b, 2002a, 2005a,b,e), and other guidelines, policies, and 

recommendations are under development (U.S. EPA, 2002c). Implementation of the FQPA and 

the SDWA amendments required additional development of guidance and policy for protecting 

children’s health. In response, the application of the FQPA 10-fold safety factor was discussed 

in the Determination of the Appropriate FQPA Safety Factors(s) in Tolerance Assessment (U.S. 

EPA, 2002d). Thus, there are a number of guidelines and policies related to children’s health, 

ix 
but no single, comprehensive document that can serve as a resource of information on children’s 

health risk assessment. 

In 1999, a draft report that collected information on current EPA guidance and practices 

was developed for the Office of Children’s Health Protection (ICF Consulting, 1999). This 

report was a compendium of information on child-related risk assessment policy and 

methodology guidance at the time. This Framework document builds on that report and others 

referred to above by updating the information and linking to reference documents and other 

published information that can be used as a resource for those interested in children’s health risk 

assessment. 

Another major effort sponsored by EPA and others that serves as background for this 

document was a workshop held in Stowe, VT, July 30–August 2, 2001, organized by the 

International Life Sciences Institute (ILSI). The report of that workshop (ILSI, 2003) and 

subsequent publications (Daston et al., 2004; Ginsberg et al., 2004c; Landrigan et al., 2004; 

Morford et al., 2004; Olin and Sonawane, 2003) proposed a framework for children’s exposures 

and health risk assessment and laid out a number of issues of concern. The Framework 

presented in this document builds on the efforts of the experts and participants at that workshop. 

Parallel activities have been or are being developed at other agencies such as the U.S. 

Food and Drug Administration (FDA), which regulates pharmaceuticals, medical devices, 

biologics, food, animal feed and drugs, cosmetics, radiation-emitting devices, and combination 

products. For example, under the Best Pharmaceuticals for Children Act (U.S. FDA, 2002), an 

amendment to Section 11 of the Food and Drug Modernization Act (U.S. FDA, 1997), FDA’s 

Office of Pediatric Therapeutics coordinates and facilitates all activities affecting the pediatric 

population, the practice of pediatrics, or pediatric issues within the FDA. Assessment of risks 

and benefits to children is conducted in compliance with the Pediatric Research Equity Act (U.S. 

108th Congress, 2003), which requires that all applications for new active ingredients indications, 

dosage forms, dosing regimens, and routes of administration contain a pediatric assessment 

unless a waiver or deferral has been granted. Although the draft guidance document Guidance 

for Industry - How to Comply with the Pediatric Research Equity Act (U.S. FDA, 2005) may 

apply specifically to pharmaceutical testing and regulation, there can be significant overlap with 

assessments conducted to determine risk to children from environmental exposures. For 

example, Guidance to Industry – Nonclinical Safety Evaluation of Pediatric Drug Products (U.S. 

x 
FDA, 2006) addresses considerations on the evaluation of pharmaceuticals in juveniles, one of 

the lifestages discussed in this Framework. 

Additionally, the International Programme for Chemical Safety of the World Health 

Organization recently developed a draft Environmental Health Criteria document entitled 

Principles for Evaluating Health Risks Associated with Chemical Exposures to Children. This 

EHC draft document serves as useful background information for using this EPA Framework. 

Finally, EPA’s Risk Assessment Forum has been working for several years to harmonize 

approaches to cancer and noncancer risk assessment (U.S. EPA, 1997c, 1998c). Efforts to 

develop a framework for a harmonized approach to human health risk assessment are underway, 

and the intent is for this Framework on health risks from environmental exposures to children to 

be incorporated into the overall framework. 

xi 
AUTHORS, CONTRIBUTORS, AND REVIEWERS 

AUTHORS 

Stan Barone Jr., National Center for Environmental Assessment, Office of Research and 

Development, U.S. Environmental Protection Agency, Washington, DC. 

Rebecca C. Brown, National Center for Environmental Assessment, Office of Research and 

Development, U.S. Environmental Protection Agency, Washington, DC. 

Susan Y. Euling, National Center for Environmental Assessment, Office of Research and 

Development, U.S. Environmental Protection Agency, Washington, DC. 

Elaine Cohen Hubal, National Center for Computational Toxicology, Office of Research and 

Development, U.S. Environmental Protection Agency, Research Triangle Park, NC. 

Carole A. Kimmel, formerly of National Center for Environmental Assessment, Office of 

Research and Development, U.S. Environmental Protection Agency, Washington, DC. 

Susan Makris, National Center for Environmental Assessment, Office of Research and 

Development, U.S. Environmental Protection Agency, Washington, DC. 

Hisham El-Masri, National Health and Environmental Effects Research Laboratory, Office of 

Research and Development, U.S. Environmental Protection Agency, Research Triangle Park, 

NC. 

Jacqueline Moya, National Center for Environmental Assessment, Office of Research and 

Development, U.S. Environmental Protection Agency, Washington, DC. 

Sherry G. Selevan, formerly of National Center for Environmental Assessment, Office of 

Research and Development, U.S. Environmental Protection Agency, Washington, DC. 

Babasaheb R. Sonawane, National Center for Environmental Assessment, Office of Research 

and Development, U.S. Environmental Protection Agency, Washington, DC. 

Tracey Thomas, formerly of American Association for the Advancement of Science Fellow, 

National Center for Environmental Assessment, Office of Research and Development, U.S. 

Environmental Protection Agency, Washington, DC. 

Chad Thompson, National Center for Environmental Assessment, Office of Research and 

Development, U.S. Environmental Protection Agency, Washington, DC. 

INVITED INTERNAL AGENCY PANEL REVIEWERS 

Framework for Children’s Health Risk Assessment (CHRA)1 and Harmonization of Human 

Health Risk Assessment Colloquium, October 5-6, 2004, Arlington, VA 

Hugh Barton, ORD, NCCT 

Jerry Blancato, ORD, NCCT 

Vicki Dellarco, OPPTS, OPP, HED 

1 Framework for Children’s Health Risk Assessment was the previous title of this document. 

xii 
AUTHORS, CONTRIBUTORS, AND REVIEWERS (continued) 

Elizabeth Doyle, OW, OST, HECD

Brenda Foos Perkovich, OA, OCHP 

Greg Miller, OA, NCEE, OPEI 

Deirdre Murphy, OAR, OAQPS, ESD 

Marian Olsen, Region 2 

Jennifer Seed, OPPTS, OPPT, RAD 

Linda Sheldon, ORD, NERL, HEASD 

Daniel Stralka, Region 9 

Vanessa Vu, OA, SAB 

ADDITIONAL INTERNAL AGENCY REVIEWERS 

Thomas Bateson, ORD, NCEA 

Ila Cote, ORD, NCEA

Michael Firestone, OA, OCHP 

Matt Heberling, ORD, NCEA 

Sarah Levinson, Region 1 

Kelly Maguire, OA, NCEE, OPEI 

Lanelle Wiggins, OA, NCEE, OPEI 

Tracey Woodruff, OA, NCEE, OPEI 

William Wood, ORD, NCEA 

INTERNAL AGENCY REVIEW PROJECT OFFICER 

Marilyn Brower, formerly RAF 

EXTERNAL PEER PANEL REVIEWERS 

External Peer Review Workshop for Draft Framework for Assessing Health Risks of 

Environmental Exposures to Children, June 6–7, 2006, Washington, DC 

James V. Bruckner, College of Pharmacy, University of Georgia, Athens, GA 

Gary Ginsberg, Connecticut Department of Public Health, Hartford, CT 

Lynn R. Goldman,

2

 Johns Hopkins Bloomberg School of Public Health, Baltimore, MD 

Melanie Marty, California Environmental Protection Agency, Oakland, CA 

P. Barry Ryan, Rollins School of Public Health, Emory University, Atlanta, GA 

Robin Whyatt, Columbia University School of Public Health, New York, NY 

PUBLIC COMMENTERS 

Richard A. Becker, American Chemistry Council, Arlington, VA 

Shannon Cunniff, Department of Defense, Arlington, VA 

Maxene R. Dwyer and Allison Jenkins, Tetra Tech EM Inc., Houston, TX 

B. Sachau, Florham Park, NJ 

Scott Slaughter, Center for Regulatory Effectiveness, Washington, DC 

2

 Chair of the external peer review panel. 

xiii 
AUTHORS, CONTRIBUTORS, AND REVIEWERS (continued) 

EXTERNAL AGENCY REVIEW PROJECT OFFICER 

Stan Barone Jr., ORD, NCEA 

ACKNOWLEDGEMENTS 

Jonathan Francis, formerly ORD, NCEA 

Elizabeth Fryer, ECFlex, Contractor to NCEA-Cin

Terri Konoza, ORD, NCEA 

Lana Wood, ECFlex, Contractor to NCEA-Cin 

Bette Zwayer, ORD, NCEA-Cin 

xiv 
1. EXECUTIVE SUMMARY 

The purpose of this document is to provide an overarching framework for a more 

complete assessment of children’s exposure to environmental agents and the resulting potential 

health risks within the U.S. Environmental Protection Agency’s (EPA’s) risk assessment 

paradigm. This Framework examines the impact of potential exposures during developmental 

lifestages and subsequent lifestages, while emphasizing the iterative nature of the analysis phase 

with a multidisciplinary team. In addition to outlining the risk assessment process from a 

lifestage perspective, the document points to published sources for more detailed information. 

Guidance, policies, and other relevant materials are referenced in the document and linked 

electronically (when copyright allows) to the actual reference documents for easy access. In 

addition, many terms are included in a glossary at the end of this document. This Framework is 

a conceptual overview of the considerations for evaluation of early-life exposures and 

subsequent outcomes and does not constitute EPA guidance defined as a step-by-step process or 

standard operating procedure. 

The term “children” as used in this document is shorthand to include the stages of 

development from conception through adolescence. EPA is concerned about health risks that 

result from exposure to all lifestages; however, this document focuses on preconceptional 

exposure and exposure throughout development to adulthood. Developmental exposure is used 

throughout this document to define developmental lifestage exposures (preconception through 

adolescence). Health risks may be identified during the same lifestage as when the exposure 

occurred, or they may not become apparent until much later in life. 

Lifestages are defined in this document as temporal stages of life that have distinct 

anatomical, physiological, and behavioral or functional characteristics that contribute to potential 

differences in vulnerability to environmental exposures. A lifestage approach to risk assessment 

considers the relevant periods of exposure in developmental lifestages and subsequent outcomes 

that may not be expressed until later lifestages. This approach explicitly considers existing data 

as well as data gaps for both exposure and health outcomes at various lifestages. 

Information on mode(s) of action (MOA) that may inform lifestages is another main 

emphasis of this approach. Risk assessment using a lifestage approach is a shift in perspective 

1-1 
from the current methodology that focuses primarily on adults, and then, secondarily, looks for 

information that may suggest greater susceptibility from exposures at other lifestages. 

The added value of using a lifestage approach to risk assessment is a more 

comprehensive evaluation of the potential for vulnerability of the population at various 

lifestages. Children may be more or less vulnerable than adults, but without data on exposure 

and response and without systematic evaluation of these data, determining which lifestage may 

be more vulnerable is challenging. The approach outlined here encourages evaluation of the 

potential for toxicity and any adverse health outcomes during all developmental lifestages, based 

on knowledge of external exposure, critical windows of development for different organ systems, 

MOAs, anatomy, physiology, and behavior that can affect external exposure and internal dose 

metrics (units of measurement for dose). The use of MOA information is integral to this 

Framework and is employed in a consistent manner to the Guidelines for Carcinogen Risk 

Assessment (U.S. EPA, 2005a) and the Supplemental Guidance for Assessing Susceptibility from 

Early-Life Exposure to Carcinogens (2005b). The MOA information is extended to the 

evaluation of all outcomes. 

It is important to consider whether anything known about developmental lifestages would 

indicate particular vulnerability and incorporate that information into an assessment. This 

framework addresses the difficult issue of integrating toxicity data and exposure information, 

which is especially challenging when data are limited for particular lifestages (e.g., pregnancy 

and early childhood development). 

The conceptual framework used in this document follows the basic components 

developed for other areas of risk assessment (U.S. EPA, 1997a, 1998a, 2003a) and includes 

problem formulation, analysis, and risk characterization as the three major phases in the process. 

Within this structure, questions for consideration in the process of scoping the problem to be 

addressed, reviewing the toxicity and exposure data, and characterizing the risks are posed as a 

way of prompting and refining the assessment process. Gaps in guidance needed for various 

aspects of assessing risk from children’s exposure are also discussed. In particular, guidance is 

lacking for lifestage-specific evaluation of several system- and disease-specific areas, related 

biomarkers and outcomes, MOA(s), dose-response assessment, and exposure assessment. Also, 

guidance on the use of specific developmental or latent outcomes for application to risk 

assessments for various timing (exposure windows) and durations of exposure has not been 

1-2 
defined even though this issue is considered in many of the risk assessments currently being 

generated across EPA. Implementation of this Framework will necessitate development of 

guidance for children’s health risk assessment. 

1-3 
2. INTRODUCTION AND PURPOSE 

The purpose of this document is to provide an overarching framework for a more 

complete assessment of children’s exposure to environmental agents and the resulting potential 

health risks. The term “children” as used in this document is shorthand to include the stages of 

development from conception through adolescence. EPA is concerned about health risks that 

result from exposure to all lifestages; however, this document focuses on exposures during 

preconception through adolescence. Developmental exposure, as used in this document, means 

developmental lifestage exposures (preconception through adolescence). Health risks may be 

identified during the same lifestage as when the exposure occurred, or they may not become 

apparent until much later in life. 

The major encompassing question to be addressed by using this document is, What is the 

potential risk of environmental exposure during developmental lifestages? This Framework 

outlines the phases for assessing the risks of exposure to environmental agents during childhood, 

singly or in combination. This information can be used in various situations, depending on the 

problem to be addressed. For example, if an overall assessment of health risks is needed, the 

information on risks from developmental lifestage exposures can be incorporated into the overall 

assessment. If, on the other hand, the major concern is about health risks to children as a result 

of environmental exposure, the information derived from this process could be used directly to 

assess risk, set standards, and mitigate exposures. 

In addition to outlining the process of assessing health risks as a result of environmental 

exposure during childhood, this framework uses existing sources for more detailed information 

which are referenced and linked to the actual reference documents (when copyright allows). 

These sources include guidelines, guidance documents, policies, and other relevant published 

materials that currently exist. This document incorporates this information while focusing on 

inherent and acquired susceptibility at different lifestages (e.g., children and adults), as well as 

the potential for greater exposure of environmental agents to children than adults. 

The outline of this document follows the basic framework developed for other areas of 

risk assessment (U.S. EPA, 1997a, 1998a, 2003a) and includes problem formulation, analysis, 

and risk characterization as the three major phases in the process, each with a focus on lifestages 

(Figure 2-1, adapted from Daston et al., 2004; Olin and Sonawane, 2003). Each phase of the 

2-1 
Figure 2-1. Flow diagram for a lifestage-specific risk assessment framework. This diagram 

presents the framework for lifestage-specific risk assessment used in this document. It is based on 

a number of documents on children’s health risk assessment, including the ILSI workshop (Daston 

et al., 2004; Olin and Sonawane, 2003). It includes three phases also identified in Guidelines for 

Ecological Risk Assessment (U.S. EPA, 1998a) and Framework for Cumulative Risk Assessment 

(U.S. EPA, 2003a). 

2-2 
process raises questions to consider when assessing potential health risks to children from 

environmental exposure. Assessing potential health risks to children as a result of their 

environmental exposure to toxicants includes considering risk from exposure before conception, 

during the prenatal period, and through childhood and adolescence (Figure 2-2). Lifestages are 

defined in this document as periods of life with distinct anatomical, physiological, and 

behavioral or functional characteristics that contribute to potential differences in vulnerability to 

environmental exposures. Preconception is any time before conception; the prenatal stage 

includes the embryonic and fetal stages from conception to birth; infancy is the period from birth 

through the first birthday; child encompasses all early postnatal lifestages from birth until 

adolescence, which occurs approximately between 12 and 21 years of age (with difference 

between genders). The continuum between the reproductive-age adult and aged adult begins at 

approximately 21 years of age and reaches aged adulthood at approximately 65 years. Broad 

exposure interval categories (e.g., child) are shown in Figure 2-2 for illustration, and divisions 

between lifestages are not precise (e.g., there is some reproductive age overlap between the 

adolescent and the adult periods) (U.S. EPA, 2005c, 2002a, Table 3-1). The lifestages from 

conception through adolescence comprise the period of development; adverse outcomes may 

occur during that same lifestage or later in life. Neither the outcomes nor the risks from these 

developmental exposures will necessarily be the same for all lifestages. Rather, the outcomes 

will depend on the underlying developmental processes that determine susceptibility at the time 

of exposure. A lifestage approach for evaluating potential risks to children is a hypothesisdriven approach that takes into account all relevant periods of exposure explicitly considering 

where data do and do not exist for exposure and health outcomes. It focuses attention on 

considerations of early-life exposure and potential outcomes, which may be latent in their 

manifestation. This is predicated on considerations of MOA(s) for all lifestages of exposures. 

MOA is defined in this Framework as the sequence of key events and processes, starting with 

interaction of a toxic agent with a cell, proceeding through functional and anatomical changes, 

and resulting in the adverse health outcomes. “A key event is an empirically observable 

precursor step that is itself a necessary element of the MOA or is a biologically based marker for 

such an element” (U.S. EPA, 2005a,b). Both toxicokinetic (TK) and toxicodynamic (TD) steps 

are part of the mechanism and MOA leading to the toxic response (Andersen et al., 2000; 

Clewell et al., 2002a). As stated in the latest cancer guidelines, “MOA is contrasted with 

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Figure 2-2. Lifestages of outcomes after developmental exposure. Panel A: In this figure, A illustrates 

the developmental lifestages of exposure considered in this document (shown in the shaded boxes on the 

left) and lifestages of potential outcomes considered in this document (shown in the shaded boxes on the 

right). The exposure to risk continuum is discussed across the top of the figure, and expanded upon in 

Figure 4-1. Panel B: Exposure during the preconception and prenatal stages may result in outcomes 

occurring in any lifestage beginning prenatally. 

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Panel C: Exposure during infancy and childhood may result in outcomes occurring in any lifestage 

beginning in infancy. Panel D: Exposure during the adolescent stage may result in outcomes occurring in 

any lifestage beginning in adolescence. 

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mechanism of action, which implies a more detailed understanding and description of events, 

often at the molecular level” (U.S. EPA, 2005a,b). Risk assessments may require a more refined 

definition of exposure intervals (e.g., bins) than the lifestages shown in Figure 2-2 because of 

rapid changes during development, even within a lifestage. For example, gestational exposure is 

typically evaluated for each trimester; however, specific periods of vulnerability (also known as 

critical windows) for particular outcomes might be much shorter period of time as discussed in a 

series of publications that resulted from an EPA-sponsored workshop (Selevan et al., 2000). 

This report synthesizes the information currently available at EPA on assessing health 

risks as a result of children’s exposures and is based in part on existing risk assessment 

guidelines, guidance, and science policies. Also, areas and topics are identified where further 

guidance and research is needed. Within this structure, questions to be considered in the process 

of reviewing data are posed as a way of prompting the data evaluation. This Framework is not a 

guideline or science policy paper but rather describes an overall vision of the structure, process, 

and the components considered important for assessing risks as a result of children’s exposure. 

This document intends to provide documentation of the approaches for assessing risk to children. 

It is not intended to be prescriptive or to define a step-by-step process or standard operating 

procedure. 

The primary intended users of this approach are risk assessors involved in hazard, doseresponse, and exposure characterization. The central focus of this Framework is the prenatal 

stage, infancy, childhood, and adolescence, thus extending and expanding the approach in 

Guidelines for Developmental Toxicity Risk Assessment (U.S. EPA, 1991), which only focuses 

on prenatal outcomes. The Framework also takes a child-protective approach to assessing risk 

(Landrigan et al., 2004) by putting the child, rather than an environmental agent, at the focus of 

the evaluation. Children are not a unique population but rather childhood is a series of lifestages 

through which all individuals pass; therefore, a child-protective approach is inherently public 

health-oriented. 

The added value of using a lifestage approach to assess risks to children from 

environmental exposure is a comprehensive evaluation of the potential for vulnerability of 

various lifestages. In contrast, assessments that use only available chemical-specific data, which 

are often limited to data from adults, do not necessarily account for the lack of data at other 

lifestages. The approach outlined here encourages evaluation of the potential for toxicity during 

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all developmental lifestages, based on what is known about critical windows of development for 

different organ systems and differences in anatomy, physiology, and behavior that can impact 

external exposure and internal dose metrics. In developing an assessment, the lack of data for 

certain lifestages is not meant to imply susceptibility and/or greater uncertainty in the assessment 

of risk from childhood exposure. Rather, the intent is to consider whether anything is known 

about lifestages that would indicate particular vulnerability during that stage and incorporate that 

information into the assessment. This document also addresses the difficult issue of integrating 

animal toxicity or adverse health outcome data and exposure information relevant for assessing 

risks to humans. This integration is especially challenging because of data limitations for 

particular periods during pregnancy and early childhood development. One result of using this 

framework will be more transparent and scientifically justifiable risk characterizations, while 

documenting data gaps and identifying priority data needs for children’s risk. 

The approach outlined here encourages evaluation of the potential for toxicity during all 

developmental lifestages, based on what is known about critical windows of development for 

different organ systems, MOAs, anatomy, physiology, and behavior that can affect external 

exposure and internal dose metrics. 

Because of the complex issues to be considered for assessing risks from children’s 

exposures, it is impossible for any one person to be an expert in all areas of this process. Thus, 

consultation and collaboration with appropriate experts in hazard, dose-response, and exposure 

assessment is recommended in all phases of the process. 

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3. LIFESTAGE-SPECIFIC PROBLEM FORMULATION 

Problem formulation is a systematic planning phase that defines the problem to be 

addressed in the assessment. The purpose of a problem formulation phase is to aid in efficiency 

and transparency of the assessment. A general discussion of problem formulation can be found 

in the Framework for Cumulative Risk Assessment (U.S. EPA, 2003a). The major components 

of problem formulation are no different whether applied to broad assessment (e.g., National 

Ambient Air Quality Standards, U.S. EPA, 2005d) of all lifestages of exposure or to a narrow 

assessment of specific lifestages of exposure (e.g., Superfund site). However, some of the 

specific considerations are different in a risk assessment for developmental exposures. 

The lifestage-specific problem formulation phase establishes the context of the risk 

assessment and feeds into the lifestage-specific analysis phase (Chapter 4) and ultimately to 

lifestage-specific risk characterization (Chapter 5). A planning and scoping step (Section 3.1) 

initially characterizes exposures and outcomes during all developmental lifestages. The problem 

formulation results in two products. First, a conceptual model (Section 3.2) is developed which 

considers exposures (e.g., sources, receptors, stressors, pathways, individual characteristics) and 

outcomes. Second, an analysis plan (Section 3.3) is developed, where preliminary consideration 

of study methods, dose-response models, data gaps, and uncertainty and variability is used to 

inform hazard characterization, dose-response characterization, and exposure characterization 

(Figure 3-1). 

These products are then used in the lifestage-specific analysis (Chapter 4), which 

comprises hazard characterization (Section 4.1), dose-response characterization (Section 4.2), 

and exposure characterization (Section 4.3). Iteration between each of the three analysis steps 

may lead to further refinement of the conceptual model and analysis plan. 

3.1. PLANNING AND SCOPING 

In the planning and scoping step, the assessment goals, breadth, and focus are 

established, and regulatory and policy factors are identified. This step includes defining and 

identifying the purpose, scope, participants/stakeholders, approaches, resources, and relevant 

past assessments available. 

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Figure 3-1. Flow diagram for lifestage-specific problem formulation. Problem formulation 

includes a planning and scoping step that initially characterizes exposures and outcomes during all 

developmental lifestages, and the development of two products: a conceptual model and an 

analysis plan. 

Source: Adapted from U.S. EPA, 2003a, Figure 1-3. 

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A clear purpose of the assessment is defined in order to guide the lifestage-specific risk 

assessment strategy. Risk assessments are often conducted within the context of a regulatory 

requirement (e.g., CAA, U.S. 101st Congress, 1990; FQPA, U.S. 104th Congress, 1996a; SDWA, 

b), a community need, a health concern, or some other driving force (U.S. EPA, 2003a), and they 

require varying levels of scope or depth (U.S. EPA, 2005a, Section 1.2.2). For example, there 

may be judicial and societal considerations that may influence the timing and breadth of the 

assessment (e.g., consent agreement on soil contamination for a site-specific cleanup). These 

factors may influence the risk management options, management goals, key participants, data 

sources, selection of assessment outcomes, or the schedule for developing the assessment. The 

risk management and assessment planning teams need to develop dialogue on the regulatory 

basis for the risk assessment and determine what kind of information is needed to satisfy such 

requirements. 

The scope sets the parameters of the assessment, allowing for decisions to include or 

exclude various elements. Screening level analyses of hazard and exposure may help refine the 

scope of the assessment. The scope can be narrow (e.g., at a site where soil screening levels are 

developed with lifestage-specific data) or broad (e.g., national rule-making, tolerance setting), 

depending upon the problem. Age-specific information on factors related to exposure and 

response are considered in the analysis plan (Section 3.3). 

Choosing the appropriate participants for problem formulation will depend on the 

problem being addressed. The participants who have information, expertise, or a stake in the 

assessment process and conclusion(s) of the assessment are identified in this planning and 

scoping step. Stakeholders are broadly defined as the interested parties who are concerned with 

the decisions made about how a risk may be avoided, mitigated, or eliminated, and as those who 

may be affected by regulatory decisions. This process can include specialized expertise and a 

basic understanding of critical windows of exposure and optimum timing for evaluating 

outcomes. The risk assessment team (which may include epidemiologists, toxicologists, public 

health specialists, child behavior specialists, exposure assessors, chemists, and other technical 

experts) and the risk management team (which may include economists, policy analysts, 

engineers, and public health specialists) work together, informed by stakeholder input (which 

may include parents, pediatricians, community groups, non-governmental organizations, etc.) to 

develop the rationale, scope, and relevant outputs for the risk assessment and characterization 

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(U.S. EPA, 2001a). The conceptual model and analysis plan, including the possible outputs of 

the assessment, may require negotiation among the members of the risk assessment team. The 

Framework for Cumulative Risk Assessment (U.S. EPA, 2003a, p. 21) provides guidelines for 

stakeholder involvement, which are based on the recommendations in Science and Judgment in 

Risk Assessment (NRC, 1994) and by the Presidential/Congressional Commission on Risk 

Assessment and Risk Management (1997a,b). 

Methods used for risk assessment of health outcomes can have an impact on the 

economic evaluation in benefits analysis (Griffiths et al., 2002; U.S. EPA, 2000a, 2003b, 2005e) 

(Section 5.1.7). Bringing economists into the discussion at the problem formulation stage will 

help clarify the approaches needed for data evaluation and quantification that may be most useful 

for assessing benefits. Another key consideration here is the selection of outcomes for which 

economic valuation will be considered in the assessment, because this includes dialogue between 

risk assessors and economists. 

Identifying available resources to achieve assessment goals within the time frame of the 

assessment involves a qualitative screening evaluation of resources, which may or may not 

identify whether children have a greater potential for higher exposures or greater intrinsic 

susceptibility. The evaluation includes a preliminary examination of the quality and quantity of 

the available data on exposure and outcomes. More detailed evaluations (refined assessment) 

may or may not be necessary or may not be possible, depending on the available data. Where 

adequate data exist (particularly on potential critical windows of exposure, level of exposure, 

individual and community characteristics, optimum timing of outcome evaluation, and the 

magnitude of concerns about the public health outcome), a more detailed approach can be 

employed to address important questions for the exposure and health effects characterization. 

These include identifying past assessments that relate to the purpose and scope of the assessment 

and that may assist the process with existing tools, methods, or models. 

• Why is the risk assessment being done? What are the needs of the assessment? Is 

there a regulatory driver(s)? 

• What is the public health concern? Is there a specific concern for developmental 

lifestage exposure? 

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• What is the risk question(s) being asked, and is it lifestage-specific? Will the 

assessment consider exposure at all lifestages or exposure at specific 

developmental lifestages? 

• Which lifestage(s) (age bin[s]) is likely to have the greatest external exposure, the 

greatest internal dose, and the greatest inherent vulnerability? 

• Have other risk assessments included consideration of health risks from children’s 

exposures on this chemical (e.g., EPA, other federal agencies, other 

organizations)? 

3.2. CONCEPTUAL MODEL 

Within the conceptual model, the risk assessment team develops preliminary hypotheses 

about why adverse effects have occurred or may occur in the future. A conceptual model is 

developed, keeping in mind the relationships among the individual characteristics, exposures, 

and outcomes. The relationships are informed by the initial identification of lifestage-specific 

exposure scenarios, the lifestage of exposure, the optimum times for evaluation of outcomes that 

will be addressed and the identified characteristics and toxicologic outcomes of the chemical(s) 

that may contribute to latent effects from early exposure and children’s risk. 

A qualitative characterization of hazard and exposure for specific lifestages results in the 

accumulation of the information needed to develop a conceptual model that aids the segue from 

the problem formulation stage to the analysis phase. The conceptual model is the starting point 

for the lifestage-specific analysis phase (Chapter 4) and can be presented as a diagram, a flow 

chart, or a narrative description of the predicted key relationships (U.S. EPA, 2002b). 

The following provides an approach to a preliminary evaluation of the available exposure 

data (Section 3.2.1), outcome data (Section 3.2.2), and the integration of the two (Section 3.2.3) 

to help define the conceptual model and aid in the development of a problem-driven analysis 

plan with a focus on lifestages. 

3.2.1. Exposure Considerations 

The exposure considerations include performing a preliminary examination of the data to 

determine the lifestages likely to be exposed, given the chemical properties and uses of the 

environmental agent(s) in the defined scope of the assessment. The preliminary examination 

involves a qualitative characterization of the sources, pathways of exposures (including exposure 

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media and routes), exposure scenarios (lifestages, time frames, locations, and activities), and 

pattern of exposures (magnitude and duration) to parents or children, as appropriate, including 

the potential for dietary, drinking water, soil and air exposures, and other exposure media (e.g., 

pharmaceuticals) (U.S. EPA, 1992, 2002c). 

An issue to consider is whether all lifestages are at the same risk from a given exposure 

or whether a specific developmental lifestage is more vulnerable because of higher exposures or 

intrinsic susceptibility. This includes a qualitative understanding of lifestage-specific activity 

patterns to identify potentially highly exposed lifestages. Currently, EPA’s Guidance on 

Selecting Age Groups for Monitoring and Assessing Childhood Exposures to Environmental 

Contaminants (U.S. EPA, 2005e) is to be used as a starting point for identifying and selecting 

age bins for analysis (see Table 3-1). This guidance includes expert analysis of existing generic 

exposure data. This guidance provides a detailed discussion of how these age groups were 

developed and how to implement them in an assessment. In brief, the recommended age groups 

are based on the current understanding of differences in behavior and physiology that may 

impact exposures to children. Information on critical windows of susceptibility also is factored 

into these age bin considerations for potential vulnerability at different lifestages. 

Typically, the conceptual model will consider human exposure in the context of the 

source-to-effects paradigm (U.S. EPA, 2003b, Figure 1-3). When formulating an exposure 

assessment, it is useful to qualitatively evaluate this model from the “effects” back to the 

“source.” In this way, potentially important time periods of exposure, exposure pathways, and 

vulnerable individuals or populations can be identified. However, as the risk assessment 

becomes more complex, some limitations in the source-to-effect model become apparent. 

Exposure assessments using a source-to-effect model are based on the characteristics of the 

specific source of the exposure (e.g., geographical location, release rate, point source) and not 

the characteristics of the lifestage being exposed. As a result, only individuals or populations 

with exposure to this specific source are included in the model. Yet, exposure may result from 

multiple independent sources, all of which could contribute toward total exposure to a chemical 

or mixture of chemicals. In this case, a person-oriented exposure assessment better characterizes 

the person and lifestage of interest along with the applicable sources than a population-oriented 

exposure assessment. 

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Table 3-1. Developmental lifestages and age groups for exposure 

assessments. 

a The age groupings from birth to adulthood are from U.S. EPA (2005e). These standard age 

groups were developed based on the results of a peer involvement workshop (U.S. EPA, 2000b) 

focused on developmental changes in behavior and physiology impacting exposures to children. 

Below are some questions that are useful in framing the examination of exposure 

considerations. 

• What data are available that characterize children’s exposure? 

• Will the risk assessment consider all possible sources, media, pathways, and 

routes of exposure (aggregate and cumulative), or is it confined to specific 

scenarios (e.g., children living near a specific Superfund site and potentially 

exposed via air, soil, and groundwater)? 

• Is it suspected that individuals in developmental lifestages are actually being 

exposed to the compound? 

• What are the potential exposure sources, media (e.g., breast milk, indoor air), 

pathways, and routes of exposure? 

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• What are the human lifestage behaviors (e.g., mouthing, crawling), activities (e.g., 

bathing, sleeping), and locations (e.g., indoors, outdoors, daycare) that may 

impact exposure? 

• What other individual or community characteristics may be present that could put 

children at higher risk of exposure and thus make them more vulnerable (e.g., preexisting diseases or disorders, belonging to a farm worker family, socio-economic 

status, poor nutrition, sanitation conditions, cultural practices)? 

• What are elements of the physical environment that may impact exposure (e.g., 

altitude, climate, urban vs. rural)? 

3.2.2. Outcome Considerations 

In this screening approach, a preliminary identification of toxic effects is performed, 

including TK and TD profiles, including to what degree these data support a hypothesized 

MOA(s). Evaluating critical windows of susceptibility and number of critical effects that have 

been observed relevant to the problem or scenario of concern for the risk assessment can be used 

to qualitatively assess the database. This qualitative assessment assures that the risk assessment 

team is appropriately staffed and has the essential resources to meet the timetables established in 

the analysis plan. 

Below are some questions that are useful in framing the examination of hazard and doseresponse considerations. 

• What toxicology, epidemiology, or other data are available that examine outcomes 

following exposure to the chemical(s) of interest? 

• Are there any suspected MOAs and other factors to be considered for relevant child 

health outcomes? 

• Are there TK (e.g., metabolic activation/conjugation) or TD (e.g., MOA) 

considerations during certain developmental lifestages that may make the chemical 

more or less toxic? 

• What do we know about the properties of the chemical being evaluated that may be 

important for considering lifestage-specific risk? 

• Does the chemical cause known organ-specific toxicity? How might these organs 

be differentially susceptible during development? 

• What is known about critical windows of exposure (e.g., developmental windows of 

susceptibility) for humans? For the experimental animal species and strain? 

• What is known about critical windows of effect (e.g., latent expression of 

developmental toxicity) for the experimental animal species and strain? 

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• Are there any toxicologic outcomes noted in animal or human studies that are 

signals of possible increased susceptibility of developmental lifestages (e.g., 

carcinogenicity, neurotoxicity, immunotoxicity, endocrine disruption)? 

• What are the background rates for outcomes of concern in the general population? 

• What dose metrics (AUC or Cmax) are being considered for the lifestage-specific 

assessment? 

3.2.3. Integrating Exposure and Outcome Considerations 

The concepts of timing and dosimetry are incorporated as unifying factors for both 

exposure and hazard components of the analysis. In a child-centered approach, multiple 

stressors may need to be considered for a particular outcome of interest due to convergence on a 

common MOA, as well as possible confounding, effect modification, or bias present in some 

studies. Additional stressors may have an impact on behavior. For example, a person with 

asthma may be less active or spend less time outside where an exposure may occur. Dialogue 

between experts such as exposure scientists, health scientists, epidemiologists, and toxicologists 

will ensure that the critical windows of exposure and critical effects are sufficiently identified, at 

least at a qualitative level, for the development of a conceptual model and an adequate analysis 

plan (Section 3.3). Below are some questions that are useful when integrating exposure and 

response considerations. 

• How do chemical sources, fate, and transport influence target outcomes for various 

lifestages? 

• How do magnitude, patterns, and pathways of exposure influence target outcomes 

for various lifestages? 

• How does lifestage-specific dosimetry impact the temporal resolution required for 

exposure assessment? 

• Based on the transport and fate of the chemical under evaluation, do the available 

exposure and hazard data address the compound(s) to which children may actually 

be exposed? 

• Can exposure to multiple stressors during a critical window of development lead to 

modification of a health outcome of interest (e.g., additivity, synergism, 

antagonism)? 

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3.3. ANALYSIS PLAN 

The analysis plan identifies the methods, models, critical data gaps, major variabilities 

and uncertainties, and key assumptions to be considered as the problem-driven assessment 

moves forward to a more in-depth lifestage-specific analysis (Chapter 4). The analysis plan is a 

working outline that provides the rationale for the resources (expertise, time, and finances) 

required to complete the assessment. Examination of the most vulnerable age groups and key 

risk drivers relevant to the problem identified will help conscribe the assessment and shape the 

decision points and decision tree in the analysis plan. 

A database inventory may be useful for identifying data gaps (Table 3-2). This table 

presents an example of a database inventory method. After assessing the available information 

on lifestages of exposure, the assessor can note whether there are the various types of 

information for each lifestage. For example, are there human studies assessing outcomes after in 

utero exposure? In many instances, few of these fields will have data. Input from the relevant 

risk managers may be needed on the scope of the conceptual model and analysis plan, 

particularly with respect to the questions the assessment is meant to answer. This exercise can 

facilitate identification of strengths and weaknesses in the database, especially with regard to a 

lifestage-specific assessment. Many of these boxes will be blank for most chemicals; these data 

gaps do not necessarily represent research needs, but the data gaps may be useful in identifying 

where more information would be helpful and communicate this need to conduct research. For 

example, if the problem formulation suggests that infants have a potentially high risk due to 

biological susceptibility or probability of increased exposure, then absence of data for that 

lifestage may affect the relevancy of the risk assessment to address the identified problem or 

question of the assessment. 

Planning and scoping (Section 3.1), the conceptual model (Section 3.2), and the analysis 

plan (Section 3.3) are then used in the lifestage-specific analysis (Chapter 4), which comprises 

hazard characterization (Section 4.1), dose-response characterization (Section 4.2), and exposure 

characterization (Section 4.3). Further scoping may be considered in each of the three analysis 

steps, thus leading to further refinement of the conceptual model and analysis plan. 

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Table 3-2. Lifestage-specific database inventory sheet. Types of information 

are described in the left-hand column, and lifestages of exposure are shown in the 

top row. 

• Does the analysis plan focus on what are likely to be the most vulnerable age 

groups? 

• Does the analysis plan focus on the key risk drivers? 

• What decision points are needed in the analysis plan for the specific problem 

identified? 

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4. LIFESTAGE-SPECIFIC ANALYSIS 

The analysis phase of risk assessment includes hazard characterization (Section 4.1), 

dose-response characterization (Section 4.2), and exposure characterization (Section 4.3), where 

data are analyzed, both qualitatively and quantitatively. Iterations among all three steps provide 

communication among the risk assessment team members and refine the focus on the key 

assessment questions identified in the problem formulation phase (Chapter 3). These iterations 

are performed to enhance, but not effectively delay, the final assessment. 

Focusing on data with outcomes after exposure during developmental lifestages of 

greatest susceptibility (i.e., critical windows) is key to the lifestage-specific evaluation of hazard, 

dose-response, and exposure data. These data may identify critical windows of exposure and 

data gaps for particular lifestages of exposure. MOA information based on TK and TD data may 

inform the lifestage-specific analysis (Figure 4-1). 

Figure 4-1. Exposure to risk continuum. This figure identifies the major elements in Figure 

2-2a. This includes specific elements of TK and TD that may be lifestage-specific. This TK and 

TD information (MOA) can lead to increased characterization of the altered structural and 

functional outcomes 

Source: Adapted from: Schulte, 1989. 

The next three Sections (4.1, 4.2, and 4.3) discuss the three steps of the analysis phase 

and provide information to guide the assessor through the process (Figure 4-2). In order to link 

exposures and outcomes appropriately, an iterative process among all steps of the analysis is 

suggested for a robust risk characterization, the final phase in the risk assessment process 

(Chapter 5). 

4-1 
Figure 4-2. Flow diagram for lifestage-specific analysis. Following the problem formulation 

stage, the three steps of the analysis phase include hazard characterization (Section 4.1), doseresponse characterization (Section 4.2), and exposure characterization (Section 4.3). This is 

followed by the risk characterization (Chapter 5) and risk communications/management phases. 

4.1. LIFESTAGE-SPECIFIC HAZARD CHARACTERIZATION 

4.1.1. Introduction 

Hazard characterization is the analysis step in which the data are evaluated for potential 

adverse health effects. Hazard characterization begins with the identification of the human and 

animal toxicology studies to be included in the database. It includes the identification of any 

outcomes associated with exposure at specific doses. The primary purpose of a lifestage-specific 

hazard characterization is to develop a detailed description of the potential for health outcomes 

after exposure to the agent of interest during preconception or developmental lifestages. This 

begins with a description of each of the available studies (Section 4.1.2), considering critical 

windows of exposure and susceptibility, TK, TD, MOA, and dose-response information as well 

as the variability and uncertainty present in each study. The database is then synthesized from 

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the individual study evaluations, and the quality and quantity (i.e., the comprehensiveness) are 

characterized using a weight-of-evidence (WOE) evaluation (Section 4.1.3.1). This includes 

information about differences and similarities in experimental animal species versus humans 

regarding lifestage-specific TK and TD, the extent of the database for different lifestages, and 

lifestage-specific susceptibilities. The results of the hazard characterization are iterated with the 

dose-response and exposure analyses (Section 4.1.4) if indicated by the conclusions from 

summarizing the hazard database. 

Finally, the lifestage-specific hazard characterization is summarized including a scientific 

rationale for the identification of relevant outcomes and susceptible lifestages based upon the 

data (Section 4.1.5). The identified outcomes and susceptible lifestages are further evaluated in 

the subsequent dose-response characterization step (Section 4.2). This information feeds into the 

comprehensive lifestage-specific risk characterization (Chapter 5). 

Throughout the hazard characterization, relevance of the information to the overall goals 

of the assessment is considered. It may be appropriate to refine the conceptual model (Section 

3.2) or analysis plan (Section 3.3) after thoroughly evaluating the available hazard data. For 

example, a conceptual model may focus on an exposure to a chemical or chemical class that 

results in thyroid tumors. Thyroid hormone is critical to development of the nervous system 

(Farwell et al., 2006; Pals et al., 2006; Ramos and Weiss, 2006; Santisteban and Bernal, 2005) 

and immune system (Bossowski et al., 2003; Lam et al., 2005). If development of these organ 

systems were not considered in the conceptual model for analysis of the chemical(s), then the 

conceptual model will need to be refined to consider the relevant critical windows of 

development. 

Figure 4-3 illustrates a detailed approach to characterizing hazard from environmental 

exposures during development. More specific information on hazard characterization for 

developmental lifestage exposures can be found in the existing EPA risk assessment guidelines 

for developmental toxicity (U.S. EPA, 1991), reproductive toxicity (U.S. EPA, 1996), 

neurotoxicity (U.S. EPA, 1998b), and cancer (U.S. EPA, 2005b). 

4.1.2. Qualitative Evaluation of Individual Studies 

The objectives and scope of the risk assessment, defined in the problem formulation 

phase (Chapter 3), provide focus and a plan for identifying and examining all the relevant 

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Figure 4-3. Flow diagram for lifestage-specific hazard characterization. The steps in hazard 

characterization include the evaluation of individual studies (Section 4.1.2), summarization of the 

hazard database (Section 4.1.3), an evaluation of the weight-of-evidence (Section 4.1.3.1), 

potential iteration with the other analysis steps (Section 4.1.4), and the hazard characterization 

narrative (Section 4.1.5). The dashed lines indicate where iterations may occur with other parts of 

the risk assessment process. 

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published human and experimental animal studies. A thorough qualitative evaluation of each 

study includes a complete description of the findings, an assessment of the study conduct and 

data quality, and a determination of sufficiency of data. To focus on risk from exposure to 

children, the evaluation process considers lifestage-specific information (pertaining to both the 

lifestage at which exposures occur and outcomes are observed) and issues within the overall 

context of the risk assessment. To assess study quality, the adequacy of the methods and results 

are characterized. In addition, it can be helpful to establish criteria for confidence in the 

evaluation and interpretation of the study findings that can be used later in the WOE evaluation 

(Section 4.1.3.1). The description of individual studies will contribute to the overall 

determination of the adequacy, strength, and completeness of the database for the 

characterization of hazard across lifestages. The following subsections describe topics to 

consider during the qualitative evaluation of each study, and example questions are addressed in 

Table 4-1. 

4.1.2.1. Study Purpose 

Describing the purpose of each study may provide information to evaluate the study as it 

relates to lifestages. For example, the study may be conducted in response to general risk 

evaluation issues, to explore an aspect of basic toxicology or biology, or to investigate a specific 

public health concern. The purpose of the study can range from hypothesis generation to 

hypothesis testing. 

4.1.2.2. Study Design 

A clear, concise description of the study design includes the number of subjects in each 

exposure group; descriptions of the study participants (e.g., gender, age); route, timing, and 

duration of exposure; and outcomes assessed. The timing of exposure and outcome assessment 

is important in relation to identifying and characterizing lifestage-specific risk. All of these are 

related to statistical power, which is further discussed in the WOE evaluation (Section 4.1.3.1). 

It is helpful to highlight strengths and weaknesses in the study design, particularly in relation to 

lifestage-specific assessments and how they may illuminate questions identified in the problem 

formulation (Chapter 3). For example, statistical power is a limitation that is often not discussed 

when studies are concluded to be “negative.” 

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4.1.2.3. Identifying Critical Windows of Exposure 

An evaluation of the exposures (or dosing/treatment to experimental animals) to the study 

participants involves characterizing the timing and duration of the exposures (e.g., exposure 

during preconception and critical windows of pre- or postnatal development) that have occurred 

across the lifestages of the study individuals. The timing and the duration of exposure to test 

substance in experimental animal studies could be informed by data on the critical windows of 

development of organ systems. 

A useful source of information is the proceedings of a workshop on critical windows of 

exposure for children (Selevan et al., 2000), which addresses the respiratory and immune 

systems (Dietert et al., 2000; Holladay and Smialowicz, 2000; Peden, 2000; Pinkerton and Joad, 

2000), the reproductive system (Lemasters et al., 2000; Pryor et al., 2000), the nervous system 

(Adams et al., 2000; Rice and Barone, 2000), the cardiovascular and endocrine systems (Barr et 

al., 2000; Hoet et al., 2000; Osmond and Barker, 2000; Sadler, 2000), and cancer/neoplasms 

(Anderson et al., 2000; Olshan et al., 2000). The WHO draft document, Principles for 

Evaluating Health Risks in Children Associated with Exposure to Chemicals (WHO, 2006) also 

reviews critical windows of development by organ systems. 

4.1.2.4. Outcomes Related to Developmental Lifestage Exposure 

A description of study findings, including the relationship of the outcome (both the 

outcome itself and timing of the outcome assessment) to the time of exposure, is a primary goal 

of hazard characterization. This includes an explicit consideration of outcomes at various 

lifestages due to exposure occurring during developmental lifestage(s). Developmental lifestage 

exposures may result in early or latent effects (Selevan et al., 2000; WHO, 2006). The 

evaluation of each study includes whether and how study outcomes address questions raised 

during the problem formulation phase (Chapter 3). For example, if the problem formulation 

specifically identifies a potential for risk after exposure to pregnant women in a residential 

setting, then it is important to carefully evaluate any available human and experimental animal 

data that examines outcomes following gestational exposures. Toxicities resulting from 

alteration of precursor events may be expected to be different depending on lifestage. Alteration 

of a precursor event in a mature animal or adult human may not have any significant health 

consequence, where the same precursor event alteration in a developing organism may have 

significant health consequences. 

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4.1.2.5. Toxicokinetic Data 

All available lifestage-specific TK data are included and described in order to determine 

the relevance and impact of the TK data in evaluating the study and to determine the impact of 

exposure on response across lifestages. TK data can be used to verify that indirect exposure of 

the fetus or neonate (e.g., via maternal circulation or breast milk) occurred without relying on 

observable outcomes. In some situations, internal dose can be measured, providing greater 

confidence in derivation of the dose metrics (Section 4.2.2.3). If TK data are available across 

lifestages, this information can aid in highlighting key lifestages for the assessment. For 

example, immaturity of specific metabolic enzymes or renal capabilities (e.g., elimination) can 

result in a more or less toxic response in the young. Therefore, information on the 

developmental profiles of enzymes or organ systems can help identify particularly susceptible 

age groups. 

Studies may find increased susceptibility of immature individuals but lack TK data to 

assist in the interpretation of these findings. In that case, default assumptions are generally 

applied. Three typical examples are (1) internal dose is equivalent to dose at the portal of entry, 

(2) the dose to the fetus is equivalent to the dose administered to the maternal animal, or (3) the 

internal dose to the immature individual is equivalent to that of adults. However, these default 

assumptions may not be health protective; therefore, the availability and use of TK data will 

likely decrease uncertainty in the risk assessment. 

4.1.2.6. Toxicodynamic Data 

TD data include information about the steps between the toxicant’s first interaction with 

the target organ and the subsequent toxic outcome. Describing TD data for specific lifestages 

may provide corroborative evidence of potentially susceptible lifestages for a given chemical. 

For example, if TD information for a chemical suggests effects on the nervous system via 

decreasing luteinizing hormone and disruption of the hypothalamic-pituitary-gonadal axis, then 

greater concern would be warranted in cases when there are lifestage-specific TK data. This TK 

data may demonstrate that the chemical is found in the brain only during a developmental 

lifestage when the blood-brain barrier is not fully formed. 

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4.1.2.7. Mode of Action Information 

Consideration of MOA information (key TK and/or TD steps) can be useful in 

• understanding the susceptibility differences among various lifestages, 

• determining the most appropriate experimental animal model for relevance to 

humans, 

• determining when human exposure or outcome data during lifestages are limited or 

not available, 

• predicting types of effects that might be seen during particular lifestages, and 

• predicting potential susceptible lifestages. 

For example, if a chemical has an anti-androgen MOA, in utero and peri-pubertal intervals might 

be sensitive exposure windows for male reproductive outcomes. Further, differences in 

androgen activity by lifestage can explain some of the observed differences in susceptibility; for 

example, for the pesticide vinclozolin (Anway et al., 2006; Euling and Kimmel, 2001). It is also 

possible that the MOA for a given chemical differs among lifestages; this is one possible 

explanation for differences in outcomes after exposures during developmental lifestages versus 

adulthood. For example, diethylstilbestrol (DES) produces reproductive, developmental, and 

carcinogenic outcomes after in utero exposure which are not observed following adult exposure 

(Herbst, 1987; Mericskay et al., 2005; Robboy et al., 1982). Also, organophosphorous pesticides 

inhibit cholinesterase throughout one’s lifespan, but certain of these pesticide’s inhibitory effects 

on neuronal differentiation and migration, which are attributed to an alternative, noncholinergic 

MOA, occur only during in utero and early postnatal neurological development (Campbell et al., 

1997; Chakraborti et al., 1993; Dam et al., 1998; Young et al., 2005). However, chemicals with 

more than one MOA, such as methoxychlor, have been described (Chapin et al., 1997; Gaido et 

al., 2000; Gray et al., 1999a). Therefore, it is possible that the activity of the different MOAs 

may vary across lifestages. 

4.1.2.8. Qualitative Evaluation of Dose-Response 

A detailed qualitative evaluation of the lifestage-specific dose-response profile is useful, 

but not always available, when interpreting the outcome for individual studies. A wellcharacterized dose-response relationship helps support the judgment of whether an outcome is 

due to exposure during a specific lifestage. The shape of the dose-response curve may or may 

not be monotonic in nature. 

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These dose-response data are carried forward into the WOE evaluation (Section 

4.1.3.1.3) because determining the relationship between adverse responses and exposures is 

achieved through consideration of the results in context of the other studies in the database and 

may highlight the importance of borderline or suggestive findings in individual studies and, 

ultimately, refine the interpretation of the data. For example, a prenatal developmental toxicity 

study in rats may identify a treatment-related malformation (e.g., spina bifida) that occurs with a 

demonstrable dose-response relationship; in a two-generation reproduction study, the 

interpretation of incidences of spina bifida that are observed in litters from treated groups may 

take on greater weight in the overall hazard characterization even in spite of the lack of 

significant incidence or a clear dose-response. 

4.1.2.9. Variability Analysis 

There are a number of sources of variability, both intrinsic and extrinsic, in human and 

animal toxicologic data. Intrinsic, or biological, variability includes heterogeneity across 

lifestages and is expressed to some degree in each parameter being measured. Examples of 

intrinsic variables in both human and experimental animal studies include age, gender, and 

genetic factors. On the other hand, the sources of extrinsic variability are external to the study 

individuals and can often be attributed to methodological considerations, to errors in study 

design, or to variations in implementation. Examples of extrinsic variables for experimental 

animal studies include handling techniques, ambient temperature, and noise. For epidemiologic 

studies, examples include variations in recruitment or data collection procedures. 

Variability can be adequately characterized by the appropriate statistical treatment of 

individual study data. For example in developmental toxicologic studies, all pups in one litter 

are used as the unit of measure (n=1) to address issues of between-litter variability in response. 

High levels of variability may affect the ability to identify associations and make the 

interpretation of study data difficult. A detailed consideration of variability with appropriate 

analyses contributes to a determination of the adequacy, strength, and reliability of a study and 

its conclusions. Variability can be a source of uncertainty in the evaluation and interpretation of 

individual studies (Section 4.1.3.1.2). High variability can sometimes render a study 

uninterpretable within the context of the rest of the data or result in reduced confidence in the 

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veracity of the study findings, thereby decreasing the confidence placed in the study and its value 

for use in the WOE evaluation (Section 4.1.3.1). 

4.1.2.10. Uncertainty Analysis 

Uncertainty from a variety of sources in lifestage-specific data can affect the assessment 

of risk. Uncertainties can result from data gaps (i.e., missing information), inadequacies in the 

study protocol or methodologies, inaccuracies in the reporting of study findings, or inconclusive 

results. After a thorough consideration and description of the uncertainties for each study, any 

resulting assumptions, extrapolations, or speculative interpretations are described and utilized in 

the risk characterization (Chapter 5). Detailing data gaps helps provide an adequate 

characterization of the uncertainties of the risk from developmental lifestage exposure. For 

example, in laboratory animal studies, if the toxicologic evaluation characterizes adverse 

outcomes following exposures that traditionally occur throughout all developmental lifestages, 

then future study exposure methods may need to incorporate direct dosing techniques during 

specific lifestages (e.g., in pre-weaning or juvenile experimental animals) (Bruckner and Weil, 

1999; Zoetis and Walls, 2003). In particular, experimental animal studies of exposure during the 

juvenile period specifically are rare, although they are increasingly becoming more common as 

they gain greater prominence in regulatory hazard characterization (Hurtt et al., 2004; U.S. FDA, 

2006). Developmental (in utero) studies are more common but are not done for all chemicals 

and are limited because they do not involve direct dosing in postnatal life. One- and twogeneration reproduction studies are also not conducted for all chemicals and are often limited in 

having postnatal dosing only via nursing and involve a limited number of outcomes (e.g., 

reproductive outcomes). Developmental neurotoxicity, developmental immunotoxicity, and 

other organ system-specific developmental studies also are not commonly performed and have 

limitations regarding the exposure route and apical outcomes/organ systems assessed. Due to the 

iterative nature of the evaluation process and the consideration of information from multiple 

sources, data from other human or experimental animal studies, data on structure-activity 

relationships (SARs), or TK or TD information, may be used to address uncertainties identified 

in a given study. 

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Table 4-1. Examples of lifestage-specific questions for evaluation of 

individual studies within hazard characterization. 

Topic Lifestage-Specific Question(s) 

Study Purpose 

(Section 4.1.2.1) 

Was the purpose of the study to address a lifestage-specific hypothesis or public health 

concern? 

Study Design 

(Section 4.1.2.2) 

Did the study design and methods address specific lifestages of exposure and their 

outcomes? What lifestages were assessed? How was lifestage/age measured? 

What were the strengths and limitations of the study design in assessing lifestagespecific exposure and outcome? 

For human studies, how did the methods impact the validity and reliability to determine 

children’s exposure and outcome? Were lifestage factors (potential confounders) 

examined and accounted for, where appropriate? 

In experimental animal studies, was an appropriate route and matrix (e.g., vehicle, 

formulation, duration) of exposure employed across various lifestages? Were the dose 

range and levels appropriate across lifestages evaluated? 

Was the power of the study adequate to detect an effect after exposure during a specific 

lifestage? Were sample sizes, inclusion of both sexes, and animal litter numbers 

considered? 

Identifying 

Critical Windows 

of Exposure 

(Section 4.1.2.3) 

What is known about critical windows of exposure for the outcome and chemical? 

Were the routes of exposure relevant to the age-related exposure pathways for the age 

groups of interest? Did the exposure interval cover different lifestages, partially or 

completely? 

• What exposure/dose levels were assessed during the lifestage(s) of development? 

Were they the same across all the lifestage(s) identified in the study? 

• Were lifestage-specific behaviors discussed that could influence the exposure (e.g., 

maternal nurturing behaviors, offspring nursing or weaning activities, or 

exploratory/play behaviors in the immature individual)? If so, in what direction 

would the dose likely be affected? 

Was exposure verified for critical lifestages? 

• In animals, what was the route of exposure and was it the same throughout all 

lifestages? Did exposure occur across more than one developmental lifestage(s) in 

the study? 

• For humans, was there more likely to be exposure(s) from this source during 

certain lifestages than others? If so, would this be expected to affect the results of 

the study and was this accounted for in the study? Were other possible sources of 

exposure considered for various lifestages? 

Outcomes Related 

to Developmental 

Lifestage 

Exposure 

(Section 4.1.2.4) 

What was the timing of assessment of outcomes? Were outcomes dependent upon the 

exposures during critical stages of development? How were latent effects assessed? 

Were lifestage-specific outcomes assessed in the study (e.g., different outcomes during 

different developmental stages vs. adult stages)? 

What methods were used to assess lifestage-specific outcomes after developmental 

lifestage exposures? Were they appropriate? What were their limitations (e.g., were 

relevant lifestage-specific outcomes not assessed)? 

Were biological plausibility and internal consistency of findings considered for 

lifestage-specific data? 

Did the authors make lifestage-specific conclusions in the study, and what were their 

assumptions and interpretations? 

TK Data 

(Section 4.1.2.5) 

Are there lifestage-specific differences in absorption, distribution, metabolism 

(toxification or detoxification), or elimination assessed in the study? At varying doses? 

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TD Data 

(Section 4.1.2.6) 

Are there TD data for the specific lifestage(s) that relate to outcomes? 

MOA Information 

(Section 4.1.2.7) 

For the outcome(s) assessed in this study, what is known about the chemical’s MOA 

after exposure at different lifestages? Is there information suggesting similarities or 

differences in MOA for different lifestages of exposure? 

Have precursor events (e.g., biomarkers) been identified for a particular outcome? If so, 

were precursor events similar across lifestages? Are the toxicities resulting from 

precursor events expected to be different depending on lifestage of the outcome? 

Are outcomes related to the MOA relevant to the lifestages of concern in this study? Are 

there different MOAs suspected for different lifestages? 

If there are multiple outcomes described at differing lifestages, then are these consistent 

with one or more MOAs? 

Qualitative 

Evaluation of 

Dose-Response 

(Section 4.1.2.8) 

Are there lifestage-specific dose-response relationships assessed in the study? What are 

the similarities and differences in dose-response across lifestage of exposure? 

What is the shape of the dose-response curve for lifestage-specific toxicologic 

outcomes? 

Variability 

Analyses 

(Section 4.1.2.9) 

What was the variability in the control data for parameters of normal growth and 

development and other outcomes for the lifestage of interest? 

Was this variability in measures within expected ranges? If not, could this mask 

detection of an outcome? 

Uncertainty 

Analyses 

(Section 4.1.2.10) 

Are there any lifestage data gaps or uncertainty considerations (i.e., were some lifestages 

exposed and/or assessed, while others were not)? 

Were critical windows of exposure and associated outcomes adequately addressed? 

Were lifestage-specific studies conducted with appropriate quality laboratory practices 

and standards (e.g., Good Laboratory Practice) (U.S. FDA, 1978)? 

Did the conduct of the study result in uncertainties in findings that are particularly 

pertinent to lifestage-specific data interpretation? Were any inadequacies in the data 

lifestage specific? 

4.1.3. Summarization of the Hazard Database 

After summarizing the relevant studies for the lifestage-specific hazard database (Section 

4.1.2), the exposure-response array is assembled and then evaluated. Not all summarized studies 

judged may be useful to the risk assessment (NRC, 1994). Well-justified decisions to include or 

exclude a study are provided in the hazard narrative (Section 4.1.5). The adequacy of studies 

and characterization of the database are discussed in detail in A Review of the Reference Dose 

and Reference Concentration Processes (U.S. EPA, 2002a, Section 4.3). 

The overall hazard database includes detailed descriptions of all available studies relevant 

to and critical for evaluating the hazard to children, specifically those with developmental 

exposures, effects, or outcomes. The database may also include in vitro data, MOA or 

mechanistic studies, and toxicity data in adults to help profile the toxicologic response in 

children, or the database may provide support for assumptions made during the hazard 

characterization. A careful review of the studies’ exposure durations and lifestages may help to 

4-12 
determine the relative importance (weight) of the studies when estimating potential risks to 

children. Issues to consider include the pathways (including media and route), and whether they 

are relevant to children; the intervals of exposure, and whether they included critical lifestages; 

and issues suggestive of differential susceptibility of children or specific lifestages. 

A detailed characterization of the study outcomes is also important for the 

characterization of the database. Often, the structure and presentation of data summaries are 

driven by the outcome data. Common links are examined across studies. For example, for one 

chemical with detailed MOA information, the summary could focus on hazard in relation to that 

MOA and what the MOA may predict about potential critical windows. For other chemicals, the 

description might focus on specific developmental outcomes, target organs, or susceptible 

lifestages. The emphasis of the hazard summary is on the relationships (i.e., patterns) across 

observed outcomes, in relationship to lifestages and MOA. For some chemicals, only very 

limited human or experimental animal hazard information may be available. However, detailing 

the lack of information about an agent (i.e., data gaps and uncertainties) is crucial to an adequate 

characterization of risk to children from environmental exposures. 

4.1.3.1. Evaluation of the Weight-of-Evidence of the Hazard Database 

During the evaluation of the hazard database, the major strengths and weaknesses of the 

available relevant data are identified and are summarized in the WOE evaluation. The WOE 

evaluation includes expert judgment of the completeness of the database. For this Framework, 

key themes were adapted to meet the needs of evaluating human and toxicologic studies relevant 

to children’s health risk assessment. These key themes include temporality, strength of the 

association, qualitative dose-response relationship, experimental evidence, reproducibility, 

biological plausibility, alternative explanations, specificity, and coherence (Figure 4-4) (Hill, 

1965; Gray et al., 2001; Seed et al., 2005; U.S. EPA, 2000c, Chapter 4; Vineis and Kriebel, 

2006; Weed, 2005). Criteria for evaluating the key themes for the WOE may be developed 

during problem formulation (Chapter 3) to address specific assessment needs. The adequacy, 

strength, and completeness of the entire database are considered. The description of the database 

includes a qualitative exposure-response array, data gaps, uncertainties, and assumptions that are 

summarized in the hazard characterization narrative (Section 4.1.5). 

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Biological

Plausibility Reproducibility

Temporality

Strength of

Association

Alternative

Explanations

Experimental

Evidence

Dose-Response

Relationship

Weight ofEvidence 

Assessment 

of the

Hazard

Database

Specificity

Coherence

-

Biological 

Plausibility Reproducibility 

Temporality 

Strength of 

Association 

Alternative 

Explanations 

Experimental 

Evidence 

Dose-Response 

Relationship 

Weight-ofEvidence 

Assessment 

of the 

Hazard 

Database 

Specificity 

Coherence 

Figure 4-4. Conceptual view of a WOE evaluation. This figure illustrates the considerations

within a WOE evaluation of toxicity data. The relative weight of each consideration will vary for 

each assessment. 

Source: Adapted from Hill, 1965; Gray et al., 2001. 

The principles developed by Hill (1965) focused on evaluating human studies, while 

Gray et al. (2001) focused on evaluating animal toxicology studies (Figure 4-4). Further details 

about EPA’s WOE evaluation approach can be found in the Methods for Derivation of Inhalation 

Reference Concentrations and Application of Inhalation Dosimetry (U.S. EPA, 1994), 

Guidelines for Carcinogen Risk Assessment (U.S. EPA, 2005a), Supplemental Guidance for 

Assessing Susceptibility from Early-Life Exposure to Carcinogens (U.S. EPA, 2005b), A Review 

of the Reference Dose and Reference Concentration Processes (U.S. EPA, 2002a, Section 

4.3.2.1), and Determination of the Appropriate FQPA Safety Factor(s) in Tolerance Assessment 

(U.S. EPA, 2002d, Section III). The following subsections describe the key themes that can be 

considered in the WOE evaluation, and example questions are presented in Table 4-2. 

4.1.3.1.1. Temporality. Temporality is the basic premise that the exposure must occur prior to 

the outcome (U.S. EPA, 2002a, pp. 4-13 to 4-14). For developmental lifestage-specific data, 

temporality includes consideration of the relationship between the timing of exposure and 

outcome. Depending on what is known about potential critical windows of exposure (Section 

4.1.2.3), more or less credence may be given to the association with the observed outcome. 

When developmental lifestage-exposure data exist, the temporal relationship between the 

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exposure and outcome(s) may be assessed. Further, when there are data that provide an accurate 

characterization of the timing of the exposure and outcome, the latency time between the 

exposure to the outcome may be determined. For example, exposure to dibutyl phthalate (DBP) 

during late gestation leads to a number of male reproductive developmental effects (e.g., 

decreased anogenital distance, increased nipple retention, and hypospadias) that are observed at 

different stages of development in the rat (Barlow and Foster, 2003; Gray et al., 1999b; 

Mylchreest et al., 1999, 2000). 

4.1.3.1.2. Strength of the association. Greater weight is generally given to more rigorous 

studies as well as those with higher statistical power, and therefore, greater statistical precision. 

Strength of the association considers both rigor and statistical power. Rigor is the degree of 

proper design, conduct, and analysis of a study. It can be difficult to determine rigor because in 

some cases, study methods presented in published studies lack sufficient detail. Additionally, 

rigor is not simply equivalent to conduct under GLP regulations for nonclinical laboratory 

studies (U.S. FDA, 1978). Many older studies showing early-lifestage sensitivity to carcinogens 

were rigorously conducted, but before the GLP regulations were first published in 1978;3 

similarly, many rigorous studies in academic institutions do not follow GLP regulations. 

Statistical power is the ability of a study to detect effects of a relevant magnitude and relates to 

the sample size, the number of data points, the stratification of findings, and the background rates 

of the specific outcome(s). 

For the evaluation of human studies, the strength of an observed association may be 

affected by the presence of uncontrolled or unmeasured confounders, the prevalence of effect 

modifiers in the study population, or bias. A confounder is a variable that can cause or prevent 

the detection of a change in an outcome of interest and is not an intermediate variable on the 

causal pathway between exposure and outcome but is associated with the factor under 

investigation. A confounding factor can often be controlled for or accounted for in the statistical 

analysis. An effect modifier is a variable that modifies the outcome of interest by a greater 

(synergistic or additive) or lesser (antagonistic) effect. An effect modifier can sometimes be 

identified through stratification of the data. Many effect modifiers and confounders are 

3

 There are different GLP citations for U.S. FDA and U.S. EPA (including for FIFRA and TSCA). These have been 

updated several times over the years. 

4-15 
potentially lifestage specific, and whether and how these have been evaluated in the data analysis 

could affect the study outcomes or interpretation of study results. A lifestage-specific example 

of a confounder is maternal socio-economic status (SES), which can influence or bias the 

interpretation of the offspring’s cognitive development. For animal toxicology studies, a 

lifestage-specific example of an effect modifier is maternal health status, such as maternal or 

offspring nutrition, which can influence the development and maturation of the young (Cappon 

et al., 2005; Fleeman et al., 2005). Another example is the timing of heat exposure and effects 

on skeletal development in the rat (Cuff et al., 1993; Kimmel et al., 1993). 

4.1.3.1.2.1. Variability analysis. The sources of variability within individual studies (Section 

4.1.2.9) are also important factors for the interpretation of the dataset. They can contribute to 

overall uncertainties in the database, including those uncertainties that are applicable to the 

lifestage-specific hazard characterization. Variability of response across studies and possible 

reasons for the variability are assessed and considered when developing an exposure-response 

array. For example, in animal studies the response variable could vary among studies performed 

when using different strains of the same experimental animal species or when studies are 

performed in different decades, possibly due to genetic drift in laboratory animal populations 

(Hartl, 2001; White and Lee, 1998). 

4.1.3.1.2.2. Uncertainty analysis. In the evaluation of individual studies (Section 4.1.2), data 

gaps (missing information) may be identified that could impact the quality of the study, and these 

are considered in total when evaluating the database. In addition, when combining the data from 

all the studies, data gaps for the comprehensive database of information on the chemical can be 

assessed. For example, the combined studies may have assessed outcomes after exposure during 

all developmental stages except for the peri-pubertal period. If this were the case, then a data 

gap in coverage of this particular developmental lifestage of exposure would be noted. For any 

chemical assessment, there will be inevitable gaps in the available lifestage-specific information; 

it is the relative impact of missing or inadequate information to the overall goals of the 

assessment that are to be judged. In some cases, information gleaned from the toxicologic 

profiles of structurally-related chemicals or chemicals with a similar MOA can assist in 

interpreting the relative importance of a data insufficiency. Sometimes this information can 

4-16 
provide a way of bridging a data gap (Julien et al., 2004). When evaluating lifestage-specific 

uncertainties and data gaps, study design (e.g., measurements, exposure, and outcomes across 

lifestages) is addressed (U.S. EPA, 1991, Section 3.1.2.1; U.S. EPA, 1996, Section 3.3.1.5; U.S. 

EPA, 2002a, Section 4.3.1). The characterization of data gaps also includes a determination of 

whether required toxicologic studies (i.e., by statute or convention) are present (e.g., a rodent and 

a non-rodent prenatal developmental toxicity study, and a reproduction and fertility effects 

study). 

Uncertainties arising from the absence of any other relevant data identified are addressed. 

The potential qualitative and quantitative impact of these missing data on the risk assessment 

(e.g., on the point of departure [POD]) is considered and may be useful in determining the 

magnitude of a database uncertainty factor (UF) during dose-response characterization (U.S. 

EPA, 2002a) (Section 4.2.4.4). Additionally, information from the exposure characterization 

(Section 4.3) could be useful when identifying any remaining uncertainties in the hazard 

characterization. For example, if the exposure characterization identifies a high potential of 

exposure to nursing infants, specific TK data on milk partitioning may be deemed particularly 

important in the risk assessment, and absence of these data could be considered an important 

source of uncertainty. Finally, the level of confidence in the final risk estimates is based on a 

detailed description of the assumptions and interpretations of the uncertainties in the overall 

database. 

Sometimes, other types or sources of data can assist in satisfying an identified data gap or 

uncertainty. For example, if for a chemical being evaluated, there are no data relevant to the 

hazard characterization following exposure during a particular lifestage, data from a similar 

lifestage exposed for a different chemical that has been shown to produce the same active 

metabolite might be useful in informing the assessment and reducing uncertainties relevant to 

this data gap. 

4.1.3.1.3. Qualitative dose-response relationship. The dose-response relationship demonstrates 

a predictable change in an effect as a function of exposure/dose. Studies that directly relate the 

exposure/dose to the degree of the effect (i.e., increasing dose results in increasing effects) give 

stronger weight to the evidence (exposure-response array). For example, an association between 

increasing blood lead levels and a lower IQ in children has been reported (Canfield et al., 2003). 

4-17 
In some cases, the failure to observe a dose-response relationship may be due to the choice of 

dose levels or dose spacing in given studies, to a threshold effect, or to a more complex (e.g., a 

U- or J-shaped) dose-response relationship. Also, an observed dose-response relationship may, 

in fact, be related to a confounder, if that confounder has a direct response on the effect, and may 

be associated with the exposure at higher doses but not at lower doses. This is further discussed 

in the hazard characterization narrative (Section 4.1.5) and the dose-response characterization 

(Section 4.2). 

4.1.3.1.4. Experimental evidence. Experimental evidence is provided with hypothesis testing. 

This hypothesis testing includes manipulation of the exposure scenario with resulting alterations 

in the response or response rate of outcomes. Hill (1965) defined experimental evidence, as 

evidence that removal of the exposure or supplementation with an antidote leads to a reversal of 

the outcome. Experimental evidence or hypothesis testing would include manipulation of the 

exposure scenario with resulting alterations in the response or response rate of outcomes. For 

some agents, this concept can apply to a lifestage assessment. For example, cases of exogenous 

estrogen exposure in prepubertal boys can lead to gynecomastia (male breast development) that 

can be reversed after removal of the estrogenic agent (Edinin and Levitsky, 1982; Felner and 

White, 2000). However, for other agents, removal of the exposure after the critical window has 

passed may not result in a reversible effect. In addition, effects may not occur when the 

exposure occurs outside of a given critical window. If studies exist that demonstrate a particular 

exposure during a defined critical developmental window, then this could constitute 

experimental evidence of the importance of that critical window of exposure. For example, 

prenatal thalidomide exposure leads to altered limb bud development (Stevens and Fillmore, 

2000), and prenatal alcohol exposure can result in the irreversible outcomes related to fetal 

alcohol syndrome (e.g., facial dysmorphogenesis, cognitive deficits, Rubert et al., 2006; Yelin et 

al., 2005). If there is a hypothesized MOA, in vitro or transgenic animal models (e.g., knock-out, 

knock-in, conditional expressors) may add further weight and experimental evidence to a 

hypothesized association. 

4.1.3.1.5. Reproducibility. Reproducibility, also termed corroboration by Gray et al. (2001), 

means that specific effects are seen under varied conditions. In the case that a lifestage-specific 

4-18 
effect is consistently observed in similar studies, under varied conditions, in multiple 

laboratories, across species, and by various routes of exposure, stronger weight can be placed on 

the chemical’s association with the effect since it is less likely that biases or confounding factors 

are responsible for the results. However, inconsistent findings may be notable. For 

developmental toxic agents, exposure occurring during only one specific lifestage, but not all 

developmental exposures, may result in the outcome of concern. What may appear as a lack of 

reproducibility may actually be the result of disparate study designs examining different critical 

windows. Therefore, caution is warranted in dismissing seemingly inconsistent findings without 

careful consideration. 

4.1.3.1.6. Biological plausibility. Biological plausibility is the determination of whether an 

observed outcome could be attributed to the toxicologic insult, given the currently known 

science. Biological plausibility may be informed by such things as available information on the 

biologic mechanism of a toxic response or on TK and TD similarities and differences across 

species or strains or for various lifestages. Some differences in sensitivity between different 

rodent strains have been found (Spearow et al., 1999, 2001). A toxic response observed 

following developmental lifestage exposure may be different from the response after exposure to 

an adult, and the response may be explained by critical windows of susceptibility. Cross-species 

and cross-strain similarities or differences in developmental windows of exposures may impact 

comparison for the database as a whole. For example, certain prenatal stages in humans are 

comparable to certain postnatal stages in rodents. However, when intra- or interspecies lifestagespecific data are lacking, a default assumption that exposure during any lifestage in experimental 

animals causes similar effects in humans is often applied. Another default assumption is that a 

response observed in experimental animals is expected to occur in humans (U.S. EPA, 1991, 

1996, 1998b, 2005a,b). Defining these assumptions and the uncertainties that they address is a 

key part of the WOE evaluation and the identification of data needs. 

To move towards more quantitative interspecies comparisons will require a better 

understanding of developmental biology and ontogeny of different organ systems. Several 

relevant papers comparing organ and system development across species are available for 

reference (Hattis et al., 2004, 2005; Hurtt and Sandler, 2003a,b; Selevan et al., 2000). 

Comparison of specific physiological systems include the female (Beckman and Feuston, 2003) 

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and male (Marty et al., 2003) reproductive system, the cardiac system (Hew and Keller, 2003), 

the immune system (Holsapple et al., 2003), the central nervous system (Wood et al., 2003), the 

gastrointestinal system (Walthall et al., 2005), the renal system (Zoetis and Hurtt, 2003a), the 

respiratory system (Zoetis and Hurtt, 2003b), and osteogenesis (Zoetis et al., 2003). 

4.1.3.1.7. Alternative or multiple explanations. One must consider and clearly articulate other 

explanations for the observed outcome(s) after the exposure of interest. It is important to 

consider whether these explanations are consistent with the database. Reasons for null findings 

must also be examined. Alternative hypotheses may also explain similar findings. If other 

hypotheses can be ruled out, then more weight can be given to the principal hypothesis or 

alternative hypotheses defined in problem formulation (Chapter 3). For example, a nonmutagenic MOA could be considered as an alternate explanation to the primary hypothesis of a 

mutagenic MOA leading to childhood leukemia. In another example, decreased pup body 

weight in a two-generation reproduction study may be the result of direct toxicity to the pups at a 

susceptible lifestage, or alternatively, the toxicant may be interfering with lactation in the dams, 

thereby depriving the pups of nutrition needed for normal growth. These alternative 

explanations could have very different implications for judgments about children’s risk. This 

information is also discussed in the risk characterization when considering explanations for 

alternative risk estimates (Section 5.1.6). 

4.1.3.1.8. Specificity. Specificity, as discussed by Hill (1965), entails a single cause and effect 

relationship resulting from exposure to an environmental agent. It may be difficult to define 

such a relationship for developmental outcomes since the alteration of organizational events may 

be altered during development and thus may lead to multiple outcomes, depending on the critical 

window of exposure (Barker hypothesis, Lau and Rogers, 2004). Evaluating specificity of a 

particular MOA, with regard to both the timing of exposure and individual outcomes, presents a 

challenge in part because so much time elapses between the occurrence of exposure and latency 

of expression of an outcome (Section 4.1.3.1.1). Specificity is defined within the context of this 

document as a determination of the relationship between one exposure, the effect(s), and whether 

each effect is mediated through a single or alternative MOAs. Exposure during a critical window 

may lead to several adverse outcomes; alternatively, the MOA may be unique for developmental 

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lifestage exposures when compared to later lifestage exposures. Similarly, the effect of one 

agent may vary depending on differences in critical windows of development for the target tissue 

or organ. 

4.1.3.1.9. Coherence. Coherence summarizes all the principles discussed above and discusses 

the extent to which the data are similar in outcome and exposure/dose and whether they support 

each biologically plausible hypothesis or MOA. An observed association is given more weight 

when it is consistent across the database. This relates to both reproducibility (Section 4.1.3.1.5) 

and biologic plausibility (Section 4.1.3.1.6). An example of coherence is the observance of 

treatment-related increases in total resorptions at cesarean section in a prenatal developmental 

toxicity study and the corollary observation of reduced litter sizes at parturition in a twogeneration reproduction study. Relating the existing database to the larger toxicologic database 

about structurally related chemicals or chemicals with a similar MOA can be useful to address 

coherence and bridge some data gaps. For example, for one chemical with detailed MOA 

information, the summary could focus on hazard in relation to that MOA and what the MOA 

may predict about potential critical windows. For other chemicals, the description might focus 

on specific developmental outcomes, target organs, or susceptible lifestages. SARs with other 

chemicals or chemical classes may be explored to determine the extent to which these data can 

inform the assessment via an MOA discussion or to help reduce uncertainties. 

Table 4-2. Examples of lifestage-specific questions for evaluation of the 

WOE of the hazard database. 

Topic Lifestage-Specific Question(s) 

Temporality 

(Section 4.1.3.1.1) 

To what degree were the timing of exposures described, including the exposure level 

and the lifestage of exposure? 

Do time-course data exist following developmental lifestage exposures? 

Within the hazard database, are exposure intervals or timing of outcome assessments 

missing that are necessary in describing the relationship between the exposure and 

outcome timing? 

Strength of the 

Association 

(Section 4.1.3.1.2) 

How sufficient is the database for evaluating developmental lifestage exposure? 

Are the lifestage-specific data of adequate quality? Do the adequate quality studies 

comprise a database of adequate quantity? 

Did the relevant studies have sufficient statistical precision for confidence in the 

results? 

For human data, to what degree were confounding factors, effect modifiers, and other 

risk factors considered? Were the major demographic and other personal/community 

characteristics examined (e.g., age, sex, ethnic group, socioeconomic status, smoking 

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status, occupational exposure)? To what degree were biases considered? 

Variability Analysis 

What sources of variability have been identified in the lifestage-specific database? 

What effect does this variability have on the interpretation of the lifestage-specific 

database? 

Uncertainty Analysis 

What are the significant data gaps in the database with regard to children’s risk? 

• Which lifestages of exposure were assessed? Did exposure occur throughout all 

critical lifestages? Were relevant lifestage pathways of exposure and exposure 

intervals evaluated? Were there developmental stages during which exposure 

was intermittent or did not occur. What was the potential impact of any gaps in 

exposure? Were lifestage-appropriate biomarkers of exposure assessed? 

• Were all critical outcomes evaluated across lifestages? Have appropriate organ 

systems, tissues, and outcomes been adequately assessed for all lifestages of 

concern? Were lifestage-appropriate biomarkers of outcome assessed? 

• Does the extent of the database for risk from children’s exposure indicate the 

need for follow-up studies to better define uncertainties for the specific 

assessment question and issues? 

What are the resulting uncertainties in the database with regard to children’s risk? 

• Have any uncertainties in developmental exposure been identified? 

• Have any uncertainties in internal dose estimation been identified following 

developmental exposures (e.g., are there TK data that support the study design 

and the interpretation of the data for critical lifestages)? 

• Did the conduct of the studies in the database result in uncertainties in findings 

that are particularly pertinent to lifestage-specific data interpretation? Were 

some studies or data excluded on the basis of poor quality? 

Can information from the comparison of structurally related chemicals, or chemicals 

with a similar MOA with lifestage-specific data, be used to modify the impact of 

identified uncertainties or data gaps? 

Qualitative DoseResponse 

Relationship 

(Section 4.1.3.1.3) 

What is the nature of the dose-response relationship for developmental exposures and 

outcomes at all lifestages? What is the shape of the dose-response curve? 

Are there differences seen in dose-response curves for the same outcome between 

studies? Could confounding factors explain these differences? 

Are there differences in dose-response curves for specific lifestages? 

Experimental 

Evidence 

(Section 4.1.3.1.4) 

Has the hypothesized critical window of exposure been supported by additional 

epidemiologic data in humans or experimental evidence in animals? 

Do alterations or differences in exposure paradigms result in alterations in outcome? 

Reproducibility 

(Section 4.1.3.1.5) 

Were the findings examined for consistency within and across studies, laboratories, 

species, and strains? 

Could inconsistencies in findings be explained by differences in exposures during a 

critical window of development? 

Biological 

Plausibility 

(Section 4.1.3.1.6) 

If there are lifestage-specific findings, were they examined for biologic plausibility? 

Are there temporal differences between experimental animals and humans for the 

lifestages when exposures or specific outcomes occur (i.e., what are the comparable 

developmental events among the species and strains)? 

Are there any cross-species differences in developmental windows of exposures that 

impact comparison for the database as a whole? 

Was dosing/exposure during potential or known critical windows of exposure 

identified for both humans and experimental animals? Is the dosing route used for 

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animals relevant to human exposure? 

Is the dose-response relationship seen in experimental animals at doses that are 

relevant to exposure at developmental lifestages in humans (i.e., environmental 

levels)? 

Are there any interspecies similarities or differences of effects for comparable 

lifestages of development? 

Are there any intraspecies (e.g., cross-strain) concordance of effects at lifestages of 

development? If not, are there underlying biological reasons to explain these 

differences? 

What are the key toxicologic and epidemiologic studies that provide the basis for 

health concerns following children’s exposures? Do other valid studies support or 

contradict these findings? Are negative studies considered? 

What adverse outcomes at the lowest exposure levels were observed, and what is the 

basis for these observed outcomes? Have precursor events/biomarkers or the MOA 

been identified? 

Besides the developmental lifestage effects observed in the key studies, are there 

other health outcomes of concern? 

Have the appropriate studies been performed (within the database or elsewhere) to 

determine critical windows of exposure? If so, what are they? Did exposure intervals 

include known or suspected critical windows? 

Alternative or 

Multiple 

Explanations 

(Section 4.1.3.1.7) 

Should some data or studies be eliminated from consideration or inclusion in the 

WOE evaluation? 

To what degree were alternative explanations considered? 

Are studies with null findings considered? 

Are alternative hypotheses considered that might explain the observed lifestagespecific outcomes? Does an alternative hypothesis better explain the data than the 

primary hypothesis? 

Specificity 

(Section 4.1.3.1.8) 

Is there a specific outcome associated with a specific lifestage exposure? 

Are there multiple outcomes that manifest after developmental lifestage exposures? 

Can these be explained through a common MOA? 

Coherence 

(Section 4.1.3.1.9) 

Was a meta-analysis performed to combine epidemiologic or toxicologic studies? 

Do the data provide information about lifestage susceptibility? Is the relationship 

consistent across lifestages or specific to exposure during one or more lifestages? 

What types of human studies are available (e.g., case-control, cohort or human 

ecologic studies, or case reports or series)? 

Assuming relevant exposure routes considered for specified lifestages, were study 

results consistent? 

Could differences in pathways and intervals of exposure explain differences in study 

results for relevant lifestages? 

4.1.4. Iteration with Dose-Response and Exposure Characterization 

The information gathered in this hazard characterization step will subsequently be used in 

the dose-response characterization step (Section 4.2). For example, if there are data from the 

exposure-response array (e.g., no-observable-adverse-effect-levels [NOAELs], lowestobservable-adverse-effect-levels [LOAELs], benchmark doses [BMDs], BMD lower confidence 

limits [BMDLs]), or data supporting other quantitative approaches like quantitative risk 

4-23 
estimation (QRE), then this information is subsequently considered in dose-response 

characterization. 

For human studies, consideration of and coordination with the exposure characterization 

step (Section 4.3) is helpful at this point in the process and can provide important context for the 

evaluation of the hazard outcomes, characterization of uncertainties, and identification of further 

testing or research needs. 

4.1.5. Lifestage-Specific Hazard Characterization Narrative 

In this final step in the hazard characterization, a scientific rationale for the selection of 

outcomes is clearly and concisely summarized. Included are considerations of lifestage-specific 

outcomes, including susceptibility of individuals, the impact of interindividual variability on 

response, and remaining uncertainties in the hazard evaluation. Lifestage-relevant outcomes in 

the lower dose ranges are described for use in the quantitative dose-response characterization 

(Section 4.2). For example, different low-dose ranges (e.g., NOAEL; LOAEL) may have been 

identified for different outcomes or for different lifestages of exposure, depending upon the 

routes and durations of exposure. The hazard characterization information is also combined with 

the exposure characterization information (Section 4.3) to determine a risk characterization that 

includes components for describing lifestage-specific risks (Chapter 5). 

 The report A Review of the Reference Dose and Reference Concentration Processes 

recommends summarizing the extent of the database by describing it as a continuum from a 

minimal to a robust database (U.S. EPA, 2002a, pp. 4-19). These terms define the continuum of 

database characteristics, with minimal describing the least amount of information that would be 

sufficient to conduct a risk assessment, and robust including data that fully characterize the 

potential toxicity of a chemical or group of chemicals. The intent is for the assessors to 

characterize and justify the extent of the database in a narrative form, including variabilities, data 

gaps, and uncertainties (e.g., lifestage-specific exposures and outcomes, TK and TD data, the 

types of outcomes evaluated and lifestages of assessment of outcomes, reversibility of effect, and 

latency to response) that aid in determining the extent of the database. 

The lifestage-specific hazard characterization narrative includes thorough assessment of 

the overall variabilities (Section 4.1.3.1.2.1), uncertainties, and data gaps (Section 4.1.3.1.2.2) 

that have been identified in the database, both generally and specifically for evaluation across 

4-24 
lifestages. For example, well-justified decisions to include or exclude a given study from the 

database or exposure-response array are explicitly stated. The emphasis of the hazard summary 

is on the relationships (i.e., patterns) across observed outcomes in relationship to lifestages and 

MOA. Often, the structure and presentation of the summaries are driven by the outcome data. 

The database may also include in vitro, MOA, and exposure data and toxicity data in adults that 

help profile the toxicologic response in children or provide support for assumptions made during 

the hazard characterization. Following are some overarching questions to ask in the hazard 

characterization narrative: 

• What are the lifestage-specific outcomes from the whole database that were 

identified in the lower dose range(s) (not just a single “critical effect”)? What are 

the lifestage-specific outcomes relevant for use in quantitative dose-response 

characterization? 

• What are the most susceptible lifestages for exposure (e.g., women of 

childbearing age [preconception and fetuses], breast feeding infants, toddlers or 

older children) from the available data? Is there justification for the most 

susceptible lifestage(s) provided by the data to support the relevant outcomes of 

concern? 

4.2. LIFESTAGE-SPECIFIC DOSE-RESPONSE CHARACTERIZATION 

4.2.1. Introduction 

Ideally, the adverse health effects identified in the hazard characterization (Section 4.1) 

are linked to relevant environmental exposure predictions (Section 4.3) through a dose-response 

characterization. The nature and number of risk estimates is governed by the problem 

formulation (Chapter 3), hazard characterization (Section 4.1), and the available data. A 

lifestage-specific dose-response characterization (Figure 4-5) begins with the summary of the 

available data from the hazard characterization (Section 4.1) to conceptualize a MOA, to select 

dose-response models, and to apply extrapolations and derive risk values. Variability, 

sensitivity, and uncertainty are analyzed, and the results of the entire analysis are iterated, if 

necessary, with the hazard and exposure characterizations. The dose-response characterization 

culminates with a descriptive narrative of the data, models, estimates, and uncertainties applied 

in the dose-response estimate. 

Consideration of differences in both human and experimental animals for routes and 

durations of exposure, TK and TD processes, and outcomes helps inform the selection of the 

4-25 
Figure 4-5. Flow diagram for lifestage-specific dose-response characterization. In the 

lifestage-specific dose-response characterization, a selection is made for the dose-response 

relationships for lifestages of interest based on input from the hazard data (Section 4.1) and the 

exposure data (Section 4.3). Appropriate extrapolations and risk value derivations are performed 

and described in the dose-response characterization. 

4-26 
most appropriate dose-response data and models for a given assessment. In the past, different 

analytical approaches have been used, depending on whether the outcomes were cancer or 

noncancer effects. More recently, there has been a recognition within the scientific community 

that the traditional dichotomy of cancer versus noncancer dose-response characterization is 

problematic, and approaches for characterizing outcomes to have either threshold (i.e. nonlinear) 

or non-threshold (i.e., linear) responses based upon their MOA(s) have been proposed 

(Bogdanffy et al., 2001). This harmonized approach recognizes that both cancer and noncancer 

outcomes can appropriately be characterized as threshold or non-threshold when data are 

available to support this selection. 

Based on the problem formulation for a given risk assessment, an approach for carrying 

out a dose-response characterization is developed. As described in the problem formulation 

(Chapter 3), the scope and breadth of an assessment are established and generally fall into two 

categories, narrow and broad. The narrow or broad focus of the problem can restrict the doseresponse characterization to more defined approaches. Regardless of the breadth of the 

assessment, the exposure scenario, or the hypothesized MOA of the environmental agent, the 

lifestage approach can add to the overall soundness and confidence in the assessment. 

Dose-response values are typically categorized by route (oral, dermal, inhalation) and 

duration of exposure (acute, short-term, chronic). For instance, reference dose (RfD) and 

reference concentration (RfC) values can be calculated for various routes and durations of 

exposure (U.S. EPA, 2002a). Acute, short-term, and subchronic exposures are of particular 

concern because embryogenesis and prenatal, neonatal, and postnatal development provide 

ample opportunities for toxicant exposures to alter the regulation of development, which may 

lead to qualitatively different outcomes than equivalent exposures in adults. 

Perhaps less apparent, however, is the applicability of long-term, or chronic, risk values 

to children. Although reference values (RfVs) derived from adult data are thought to be healthprotective of sensitive populations (due to the application of intraspecies and database UFs), 

children may be chronically exposed to environmental toxicants. Chronic exposure is defined as 

exposure up to 10% of lifetime; therefore, seven years of exposure meets the EPA definition of 

chronic human exposure (U.S. EPA, 2002a). Thus if data suggest that a developmental lifestage 

is the most sensitive and sufficient data are available, an RfV could be derived from this 

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lifestage. In this derivation, the magnitude of the intraspecies and database UFs may be different 

than if the RfV were derived from adult data. 

Unit risk estimates such as cancer slope factor (CSF) and inhalation unit risk (IUR) are 

used to define the exposure concentration that yields a given level of risk during a lifetime (e.g., 

1 × 10-6). Although the latency of time to tumor may mask detection of cancer from exposures 

occurring in developmental lifestages, early exposures may indeed increase the risk of tumor 

development in later lifestages. In fact, there is evidence to support the notion that susceptibility 

to tumor development from exposure to mutagenic chemicals during earlier lifestages is greater 

relative to exposure in later lifestages (U.S. EPA, 2005b). Depending on the goals stated in the 

problem formulation of a risk assessment (Chapter 3), consideration of studies that have 

examined cancer in adult humans and experimental animals following early-life exposure may be 

warranted. 

4.2.2. Mode of Action Conceptualization 

Dose-response characterization can proceed along two paths, one in which the 

quantitative dose-response values are developed with little or no insight into the MOA of an 

environmental toxicant, or one in which the dose-response values are informed by MOA. In the 

latter case, the assessment uses a broader body of scientific literature to look for commonalities 

in responses across studies, similarities to other chemicals, and mechanistic data from a wide 

array of studies and fields of specialization. MOA information is increasingly recognized in the 

scientific community as a foundation from which to build a dose-response characterization 

(Andersen et al., 2000; Andersen and Dennison, 2001; Clewell et al., 2002a; Preston, 2004). In 

order to conceptualize an MOA, the following are summarized: the available dose-response 

model(s), the mechanistic data that relate the critical effect(s) of interest to a particular dose 

metric, and the data supporting the choice of a likely or hypothesized dose metric. The following 

subsections describe topics to consider during the MOA conceptualization and example 

questions are addressed in Table 4-3. 

4.2.2.1. Summarizing the Available Dose-Response Data 

Quantitative assessments identify and summarize dose-response data to characterize the 

potential risks from exposure scenarios identified during the problem formulation (Chapter 3). 

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This process also interfaces with the exposure characterization (Section 4.3), where source-todose modeling informs assessors about the relevant exposure scenarios (i.e., route and duration) 

and likely ranges of external exposure levels for various lifestages. Because low-dose 

extrapolation has inherent uncertainties regarding MOA over dose ranges (Slikker et al., 

2004a,b), the exposure characterization can help inform selection of the appropriate doseresponse model from which to obtain a POD. 

An exposure-response array can help identify critical outcomes (U.S. EPA, 2002a, 

Section 4.4.1) across dose ranges and aid in the conceptualization of the MOA. For instance, 

different effects at similar doses may originate through common mechanisms, and thus lend 

support to one or more MOAs. Alternatively, different effects across dose ranges may represent 

a gradient of effects operating through common mechanisms, and thus also lend support to one 

or more MOAs. It is also possible, of course, that different MOAs are operational across dose 

ranges, and an exposure-response array can be useful for defining the range of effects. Multiple 

responses can be described as a continuum of dose as well as continuum of lifestages when using 

this array. For instance, exposure-response arrays for various toxicity outcomes across 

developmental lifestages have been used to help inform outcome selection for dose-response. 

For example, exposure-response arrays have been used in the assessment of dibutyl phthalate 

(U.S. EPA, 2006a); where, using this approach, it becomes evident that adverse developmental 

effects occur at lower exposure levels than other adverse effects (e.g., hepatotoxicity). This 

approach is both important for dose-response characterization and is informative for risk 

characterization (Chapter 5). An alternative approach for summarizing the dose-response data is 

to use categorical regression (Section 4.2.3.1). This approach lumps different responses together 

by assigning key outcomes to severity categories–perhaps irrespective of MOA. 

In circumstances where data exist for multiple lifestages, it is possible that effects at 

earlier lifestages pose greater risk due to the potential for irreversible changes (e.g., 

developmental neurotoxicity) or changes that confer an increase in risk to subsequent exposures 

in later lifestages. For instance, it is hypothesized that acute lymphocytic leukemia (the most 

prevalent childhood leukemia) results from an early (perhaps prenatal) initiation event forming a 

fusion gene, followed by a subsequent key event in later childhood (Greaves, 2003). If this 

initiation event could be ascribed to a particular environmental exposure, then this event could 

potentially be an important precursor event to consider due to the increased risk for latent 

4-29 
adverse effects, such as leukemia. Therefore, detailed MOA considerations can inform the 

selection of lifestage data for dose-response characterization. However, in cases where doseresponse data do not exist for specific lifestages of concern (i.e., data gaps), MOA may be able to 

inform dose-response characterization for these lifestages by allowing for intraspecies (e.g., 

lifestage) extrapolation using biologically based modeling techniques (Section 4.2.3). Effects 

that are thought to share common key events in the proposed MOAs can give assessors 

confidence in choosing dose-response models that most closely relate to the underlying biology 

and adapt those models to other lifestages of interest. 

4.2.2.2. Mechanistic Data and Mode of Action 

The complexity of physiological development provides opportunity for toxic exposures to 

create TD effects that may or may not be relevant to all lifestages within a species. 

Developmental stages or age groupings (Table 3-1, U.S. EPA, 2005e) can be based on such 

metrics as growth rates/spurts, behavioral traits, organ systems, or perhaps functional 

development. It may be possible to plot these metrics for development throughout lifestages and 

across species. This comparison can aid in identification of organ systems (e.g., respiratory, 

cardiovascular, central and peripheral nervous systems, immune system) that might be at risk 

during comparable windows of exposure and can inform the decision of which effects and doseresponse data are most useful. Although matching comparable lifestages across species is a 

challenge (U.S. EPA, 2002a, Table 3-1), such efforts have the potential to decrease the 

interspecies TD differences that influence dose-response relationships across species 

(Section 4.2.4.2). 

4.2.2.3. Selection of Dose Metric Informed by Mode of Action 

When physiologically based toxicokinetic (PBTK) models are available for a chemical, it 

may be possible to convert the external/applied dose in a study to an internal target tissue dose 

(i.e., dose metric). This can be an internal measure of the chemical or its metabolite(s) but can 

also be measures of adduct formations, cofactor depletion, etc. In addition to identifying the 

chemical moiety (e.g., adduct) of the dose metric, it is equally important to identify the most 

appropriate measure of the dose metric; frequently these are the average daily doses under the 

concentration versus time curve (area under the curve, AUC), peak concentration (Cmax), or rate 

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of production. Selection of the appropriate dose metric for a given dose-response relationship is 

done in the context of what is known or hypothesized about the MOA, and thus is an inherently 

iterative process between dose-response characterization and hazard characterization (Section 

4.1). In practical terms, the measured substance may not be the toxic moiety at the target tissue 

but rather a surrogate such as blood concentration of the parent compound or one or more of its 

metabolites. Another important consideration to the selection of the dose metric is the outcome. 

For example, peak concentration may be more important for some outcomes compared to others 

(e.g., neurotoxicity vs. tumors). When choosing among potential dose metrics, often the 

appropriate choice can be identified as the one that demonstrates a consistent relationship with 

positive and negative responses observed at various dose levels and across exposure scenarios 

within a single species (U.S. EPA, 2006b). 

Clewell et al. (2002a) have proposed two criteria for dose metric determination: 

plausibility, defined as consistency with MOA and ability to simplify a complex dose-response 

relationship, and conservatism, defined as the selection of the dose metric that poses the highest 

risk or the lowest acceptable exposure level. It is the environmental exposure level to humans 

that is regulated as a result of risk assessment; thus, a potent dose metric is not synonymous with 

a potent external dose. Therefore, when there is insufficient data with which to determine the 

more appropriate dose metric, the one related to the most potent external exposure dose is often 

appropriate. More detailed information on dose metric selections is in Approaches for the 

Application of Physiologically Based Pharmacokinetic (PBPK) Models and Supporting Data in 

Risk Assessment (U.S. EPA, 2006b). 

Table 4-3. Examples of lifestage-specific questions for MOA 

conceptualization. 

Topic Lifestage-Specific Question(s) 

Summarizing the 

Available DoseResponse Data 

(Section 4.2.2.1.) 

What dose-response data/models are available for lifestages of interest (e.g., preconception, 

pregnancy, infancy, childhood)? 

Are the exposure scenarios in these studies the scenarios of interest? Can route and duration 

extrapolations be employed using modeling techniques (Section 4.2.4)? 

If data are available for a different lifestage than is of interest, are these amenable to 

extrapolation to lifestages for which there is little or no data (Section 4.2.4)? 

Can an exposure-response array inform the most relevant studies and outcomes? 

Mechanistic Data 

and MOA 

(Section 4.2.2.2.) 

Are TD effects known or hypothesized? 

What are the relative expression levels of the key players (e.g., receptors, metabolic enzymes, 

DNA repair enzymes) in the known or hypothesized MOA at the lifestages of interest? 

4-31 
If multiple outcomes are evident, are they likely linked by MOA? Do the outcomes share 

common mechanisms? Or, do the outcomes represent a gradient of the same MOA? 

Selection of Dose 

Metric Informed 

by MOA (Section 

4.2.2.3) 

What is the human lifestage exposure scenario of interest (route, duration, and pattern)? 

Are animal data available regarding the dose metric that is likely to be relevant to the human 

lifestage of interest? 

Is the selected type of dose metric appropriate for both the outcome and the exposure (e.g., 

duration) of interest? 

Are there models available which can convert the external/applied dose used in a study to an 

internal delivered dose (i.e., a dose metric)? 

4.2.3. Analysis in the Range of Observation and Dose-Response Models 

A number of models are typically employed in order to determine PODs, which are used 

for extrapolations in dose-response characterization and margin of exposure (MOE) analysis in 

risk characterization (Section 5.1.3). Data for dose-response characterization in the range of 

observation come in many forms: empirical PODs derived from either a NOAEL, a LOAEL, or 

sophisticated models incorporating mechanistic data. The nature and amount of data required for 

each type of dose-response characterization might represent a hierarchy, although the more 

sophisticated dose-response models still rely on the same experimental animal studies from 

which a NOAEL or LOAEL can be derived, either as a basis for curve fitting mathematical 

models or a starting point from which to calculate an internal target tissue dose using other 

modeling techniques. 

In Guidelines for Carcinogen Risk Assessment (U.S. EPA, 2005a), the EPA has adopted 

an approach that advocates the use of as much biologically informed dose-response data as 

possible, and suggests that “default” approaches be used only in instances where little data exists 

concerning an environmental toxicant of interest. PBTK modeling and BBDR modeling provide 

strong biological foundations for a chemical risk assessment; their application in risk assessment 

is discussed more thoroughly in Approaches for the Application of Physiologically Based 

Pharmacokinetic (PBPK) Models and Supporting Data in Risk Assessment (U.S. EPA, 2006b). 

Moreover, their use in conjunction with statistical modeling is perhaps the most rigorous and 

scientifically based approach to dose-response modeling (U.S. EPA, 1999a). 

The following brief descriptions summarize the types of analyses used in dose-response 

characterization. Example questions are addressed in Table 4-4, including those based on 

limited data sets and those requiring rich data sets for dose-response characterization. 

4-32 
Traditional approaches to dose-response modeling of a toxicant with an assumed 

nonlinear MOA have relied (and continue to rely) heavily on the use of empirical data points for 

determining PODs. Often these are NOAEL and LOAEL values derived from experimental 

dosing conditions in toxicologic studies. Two main disadvantages of using these single point 

estimate values are that they do not consider the shape of the dose-response curve, and they do 

not allow for estimation of risks at any exposure level of interest (Allen et al., 1998). Thus the 

use of NOAEL and LOAEL values alone represents the bottom tier of dose-response models and 

are used most often when limited data are available concerning the toxicant of interest. 

Empirical modeling approaches, sometimes called curve fitting or statistical modeling, 

represent an improvement over traditional NOAEL and LOAEL dose-response characterization 

techniques. In these approaches, statistical models are fit to empirical response data (e.g., 

tumors) or precursor events (e.g., signal transduction or changes in blood hormone level). In 

some instances, low-dose extrapolation beyond the observed response data can be informed by 

precursor data over the low-dose range (U.S. EPA, 2005a). In other instances, linear low-dose 

extrapolation may be employed for extrapolating from the range of observation down to, for 

instance, background levels, rates, or incidence. This form of statistical modeling has been used 

for noncancer outcomes to develop quantitative risk estimates (discussed at the end of this 

section). The draft Air Quality Criteria for Lead (U.S. EPA, 2006c) contains further discussions 

on implications for low-dose extrapolation using statistical modeling (i.e., linear and log linear 

models). 

Another form of statistical modeling for determining PODs is BMD analysis (Crump, 

1984).

4

 The BMD is defined as the dose at which a predetermined change in response incidence 

(e.g., 5% or 10% change in critical effect such as pup body weight or pup mortality) occurs; with 

the 95% lower confidence limit being the BMDL (Allen et al., 1994a,b; Faustman et al., 1994; 

Kavlock et al., 1995; Kimmel et al., 1995; U.S. EPA, 1995b). An advantage of this approach is 

that it attempts to fit statistical models to existing dose-response data, regardless of whether the 

MOA is linear or nonlinear, taking into account all of the data points in an individual doseresponse study (Brown and Strickland, 2003). Thus, unlike the NOAEL/LOAEL approach, the 

BMD is influenced by the shape of the dose-response curve for developmental outcomes (Allen 

et al., 1998). The selection of BMD may require studies with more dose groups and a higher 

4

 EPA has developed software for BMD analysis, available at http://cfpub.epa.gov/ncea/cfm/bmds.cfm. 

4-33 
number of subjects, and therefore, can be performed only when the scientific database for an 

environmental chemical is relatively large. Because the BMDL depends on the study design, 

more rigorous studies generally have narrower confidence limits (Barnes et al., 1995). 

Importantly, the BMD approach is less sensitive to dose spacing, and thus a BMD can be 

determined in the absence of a NOAEL as well as for any increase in response level (Allen et al., 

1998; Barnes et al., 1995). For further readings on choosing studies for BMD analysis, refer to 

the draft Benchmark Dose Technical Guidance Document (U.S. EPA, 2000d). 

Categorical regression5

 analysis is similar to BMD analysis, but whereas BMD analysis 

uses a single study, categorical regression combines studies. In this method, data are pooled 

from different studies (possibly with different exposure parameters and outcomes) that are 

“assigned” to the same severity category (Brown and Strickland, 2003). An advantage to this 

approach is that a small number of studies can essentially be combined into one larger study and 

can thus narrow the confidence limits (Brown and Strickland, 2003). This methodology may be 

particularly useful in a lifestage approach where it is likely that fewer studies have been 

performed on the specific lifestages of interest or critical windows of susceptibility. 

PBTK and BBDR modeling are perhaps the most amenable modeling techniques for 

using a lifestage approach as they are designed to mimic true biological processes and model 

whole organisms. Knowledge and understanding of TK differences during each lifestage 

(absorption, distribution, metabolism, and elimination), as well as anatomy and behaviors, are 

used in estimating delivered dose and may require modification of available adult models. 

Several reviews have described the variation in TK factors between adults and children 

(Besunder et al., 1988a,b; Bruckner, 2000; Clewell et al., 2002a,b, 2004; Hines and McCarver, 

2002; McCarver and Hines, 2002). 

Although the use of PBTK models for internal dose estimates is increasing, more effort is 

needed in developing such models for children’s dosimetric adjustments across lifestages and 

experimental animal species. In this regard, pharmacokinetic data from pediatric pharmacologic 

studies could be appropriately applied for some portions of certain risk assessments for 

developmental lifestage environmental exposures. For instance, general knowledge of 

differences between adults and children in metabolic clearance of CYP3A-specific 

pharmaceutical substrates could be used by adjusting for these differences in activities in a TK 

5

 EPA has developed CatReg software, available at http://cfpub.epa.gov/ncea/cfm/recordisplay.cfm?deid=18162. 

4-34 
model when the toxicant is thought to be metabolized by CYP3A (Ginsberg et al., 2004a,b). 

Ginsberg et al. (2002) compiled a database of 45 drugs for which TK data are available across 

lifestages.6 

PBTK models are particularly useful for conducting extrapolations (e.g., route-to-route, 

duration, interspecies, including lifestage extrapolations). Other advantages are that these 

models can mimic any exposure scenario (continuous or otherwise) and changes in the 

underlying biology (e.g., development). For instance, if children are likely to be exposed to an 

environmental toxicant for one hour per day for five days a week (followed by 48 hours of no 

exposure), these models can predict the levels of metabolites of interest under these conditions. 

Similarly, numerous small exposure doses from breast milk to nursing infants could be modeled 

to determine steady-state levels of a toxicant at one month and at three months after birth. 

However, PBTK models are not necessarily applicable for extrapolating from short-term 

exposure studies to longer-term predictions. This is because the key events leading to the 

observed responses are not likely to be impervious to the effects of time and repeated exposure. 

Many dose-response relationships may be dependent on temporal changes in TD processes due 

to developmental- and exposure-induced changes (e.g., cell proliferation rates, DNA repair 

processes, receptor tolerance and desensitization, and age-related changes in physiologic 

parameters). Thus, it is feasible to predict steady-state levels of a compound in the body over 

long periods of time, yet the response to these levels may differ between short- and long-term 

durations of exposure. These differences due to duration of exposure highlight the importance of 

having dose-response data for the exposure duration and the lifestages of interest. 

Application and review of PBTK models in risk assessment can be found in Ginsberg et 

al. (2004b), Pelekis et al. (2001), and U.S. EPA (2006b). There are some developmental 

lifestage PBTK models, some of which include infant exposure to chemicals such as dioxin in 

breast milk (Gentry et al., 2003; Lorber and Phillips, 2002), fetal exposure to ethylene glycol 

monomethyl ether (Gargas et al., 2000), and neonatal exposure to compounds such as lead 

(O’Flaherty, 1998) and perchlorate (Clewell et al., 2003; Clewell and Gearhart, 2002). Several 

pregnancy and lactation models have been reviewed (Corley et al., 2003). 

6 This database can be accessed at http://www2.clarku.edu/faculty/dhattis. 

4-35 
BBDR models represent the state of the art in dose-response characterization, where 

mechanistic TD data are modeled in such a way that responses can be predicted, even at low 

exposure levels. Usually, output from a PBTK model serves as the dose input to a BBDR model, 

relating that dose to a response outcome (Andersen and Dennison, 2001; Ashani and Pistinner, 

2004; Setzer et al., 2001). In addition to lifestage-specific TK data, the relationship between the 

internal dose metric and response may require lifestage-specific TD data. Currently, relatively 

few BBDR models are available due to the inherent complexity of integrating TK and TD data. 

Model transparency, quality criteria, and short shelf-life of some models beyond initial 

publication also limit BBDR model development (DeWoskin et al., 2001). The use of BBDR 

models is expected to increase as toxicologic studies move beyond more frank effects toward 

molecular precursor events (Andersen and Dennison, 2001; Faustman et al., 1999). 

In instances where the dose metric of a toxicant of interest is structurally related to 

another compound for which there exists a validated BBDR model, consideration of the 

application of this model to the toxicant being assessed may be warranted.7 As stated in 

Evaluation of BBDR Modeling for Developmental Toxicity: A Workshop Report, “the challenge 

is to define...application of a quantitative BBDR model...generalizable to other compounds in a 

similar class and perhaps to certain other classes of compounds” (Lau et al., 2000). For example, 

two chemicals might be hypothesized to affect similar TD processes (e.g., activation of a 

particular receptor), yet a BBDR model may exist for only one of the chemicals. If a PBTK 

model is available (or can be developed) for the chemical that does not have a corresponding 

BBDR model, it is conceivable that the existing BBDR model might be sufficient for analyzing 

both chemicals. 

The top line in Figure 4-6 represents a BBDR model for the dose-response of chemical A, 

where TKA, TDA, and RA represent the TK, TD, and response of interest related to chemical A, 

respectively. In this scenario, the TD of chemical B (TDB) is thought to be equivalent to that of 

chemical A (i.e., both have the same MOA from a TD perspective). If a PBTK model (but not a 

BBDR model) exists for chemical B (TKB), then the predicted internal target tissue dose of 

chemical B can be integrated into the existing BBDR model for chemical A. 

7

 Compounds with common TD effects may not necessarily be structurally related. 

4-36 
BBDR model exists for chemical A: {TKA→ [TDA = TDB] → RA} 

↑

PBTK model exists for chemical B: [TKB] 

Figure 4-6. Use of BBDR modeling. 

A probabilistic risk assessment approach has typically been used in exposure 

characterization and is increasingly being applied for dose-response characterization as data 

become available for physiological parameters such as genetic polymorphisms in TK and TD 

pathways (Beck et al., 2001; Pelekis et al., 2003). When readily measurable, inputs such as 

exposure dose and duration, intake rate, clearance, and body mass can be expressed as 

distributions and modeled in such a way as to estimate dose for a particular population, over a 

certain time frame, or at a specific location. Similarly, lifestage-specific parameters can be 

employed in order to estimate the variability in dose and response among lifestages (e.g., infants 

and children). 

In regard to noncancer outcomes, one limitation applicable to many of the 

aforementioned dose-response modeling approaches is that the analyses are based on toxicologic 

outcomes as opposed to public health outcomes. QRE is a broad-based method for relating 

human exposures to non-toxicologic outcomes. For example, exposure to 1,2-dibromo-3-

chloropropane can be linked to increases in infertility rates through mathematical modeling 

(Pease et al., 1991). In this regard, it is similar to BMD analysis, but whereas risk is typically 

defined by percent change (e.g., 1% or 5%) in a biological response (e.g., sperm count), QRE 

attempts to define risk (e.g., excess infertility cases) for all human exposure levels. The 

advantage of this approach over BMD is that a noncancer risk can be defined for any individual 

based on exposure level as is done for cancer assessments. Other examples of this type of 

analysis include associations among particulate matter and daily mortality and certain measures 

of morbidity (U.S. EPA, 2005d) and associations between acute ozone exposures and respiratory 

morbidity and mortality (U.S. EPA, 2005f). An inherent disadvantage to this approach is that 

acceptable levels of risk must be defined, whereas other approaches to noncancer dose-response 

modeling arguably rely less on value judgment. 

4-37 
Table 4-4. Examples of lifestage-specific questions for analysis in the range 

of observation. 

Topic 

Selection of DoseResponse Models 

(Section 4.2.3) 

Lifestage-Specific Question(s) 

What data were used to develop the dose-response curve? Are data available from the 

lifestage and exposure scenario of interest? Were there differences (e.g., in potency) in the 

dose-response curves for different lifestages? 

Was a model used to develop the dose-response curve and, if so, which one? What rationale 

supports this choice? For example, how was the benchmark response chosen? 

What modeling approaches are amenable to the available dose-response data? Is there 

sufficient data to support, for example, the use of biological modeling approaches? 

4.2.4. Extrapolations and Risk Derivation from a Lifestage Approach 

After PODs have been established from various dose-response studies or modeling 

techniques, low dose extrapolation is performed in order to derive dose-response values. Again, 

this may be done for assessments of narrow or broad scope (Section 3.1), and will have 

regulatory implications for various adjustments in order to extrapolate to the exposure scenarios 

and lifestages of interest. As described below, these adjustments may involve sophisticated 

approaches or default approaches that have developed over time. Despite the term default, many 

of these approaches are informed and supported by empirical evidence. For example, empirical 

analysis supports the use of body weight scaling (see below) to adjust for TK differences across 

species. 

However, the use of more sophisticated techniques does not necessarily result in 

refinements of final reference or risk values. For instance, a recent assessment of xylenes 

resulted in nearly identical RfC values using either default approaches starting from a NOAEL or 

sophisticated PBTK modeling (U.S. EPA, 2003c). Despite the fact that this may be a possible 

outcome, the use of sophisticated techniques, MOA information, and lifestage analyses certainly 

improve the confidence that dose-response values (i.e., RfVs and risk values) are health 

protective. The following subsections describe topics to consider for extrapolations and risk 

derivation, and example questions are addressed in Table 4-5. 

4.2.4.1. Duration and Route Adjustments 

Experimental animal studies almost always employ discontinuous exposure protocols and 

therefore use continuous dose adjustment. Although such adjustments are conservative from a 

risk evaluation standpoint (i.e., they shift the dose-response curve leftward), mathematical 

adjustments do not necessarily maintain the dose-response relationship (i.e., AUC) that likely 

4-38 
reflects the MOA by which a response is generated. An alternative to continuous dose 

adjustment is to use PBTK models to determine (in silico) an applied dose (continuous or 

otherwise) that results in the same AUC or Cmax which simulates that which could have been 

generated in the experimental animal under the original laboratory study conditions. This may 

require parameterization with lifestage- and species-specific data. Developmental windows of 

susceptibility are relatively short, thus the changing underlying biology during development 

suggests that Cmax may be a more relevant dose metric in young children than AUC. Since the 

minimal exposure period to elicit an increased risk is often not known, especially during a 

window of vulnerability, the choice of exposure period is a critical decision point that integrates 

TK, TD, and exposure information. 

For route-to-route extrapolation, default equivalent dose adjustments can be used. For 

example, standard mg/kg/day adjustments assume similar TK and TD processes between 

experimental animals and humans. However, such assumptions are tenuous because different 

cell types, enzymes, and proliferation rates exist across portals of entry. PBTK models can be 

used to predict target dose across routes by incorporating route-specific TK factors. A limitation, 

however, is that route extrapolations are not useful in instances where the critical effects are 

specific to the portal of entry. For more on route and duration adjustments, see Approaches for 

the Application of Physiologically Based Pharmacokinetic (PBPK) Models and Supporting Data 

in Risk Assessment (U.S. EPA, 2006b) and A Review of the Reference Dose and Reference 

Concentration Processes (U.S. EPA, 2002a). 

4.2.4.2. Interspecies and Intraspecies Adjustments 

The EPA RfC process describes the interspecies adjustment from experimental animals to 

human equivalent concentration (HEC) via dosimetric adjustment factors (DAFs) (U.S. EPA, 

2002a). For oral exposures, default interspecies extrapolation based on body weight (BW) 

scaling, either BW1

 or BW3⁄4, have been employed. In particular, BW3⁄4 scaling is typically 

thought to account for TK differences among species, and therefore, often reduces the 

interspecies UF from 10 to 3 (U.S. EPA, 2002a). Recent harmonization efforts at EPA advocate 

the adoption of BW3⁄4 scaling for RfD derivation in instances where there are limited data with 

which to perform an assessment (U.S. EPA, 2006d). This has been proposed in an effort to 

harmonize oral RfD methodology with RfC methodology. In addition, this effort also aims to 

4-39 
harmonize the use of BW3/4 scaling in the application of DAFs for oral cancer assessments (U.S. 

EPA, 2006a). 

For inhalation exposures, DAFs are applied on the basis of physicochemical, anatomical, 

and physiological parameters. These parameters include such factors as species-to-species ratios 

of surface area:ventilation rate, blood:gas partition coefficients, and regional deposition dose 

ratios for particulate matter. In the case of children, it is currently recommended that HECs and 

human equivalent doses (HEDs) be determined experimentally and theoretically (U.S. EPA, 

2002a). In the absence of DAFs, simple ventilation rate adjustments can be made for HECs. 

Finally, it is worth noting that DAFs are thought to be most appropriately applied for chronic 

exposures, where the dose metric is likely best represented by AUC; discussion of adjustments 

for acute exposures can be found elsewhere (U.S. EPA, 2002a). 

In addition to interspecies adjustments, BW3⁄4 scaling may also be useful for intraspecies 

adjustments based on lifestage (U.S. EPA, 2006d). Pharmaceutical data indicate that TK 

processes (e.g., chemical half life) in children may also scale to BW3⁄4, particularly in children 

over two months of age (Ginsberg et al., 2002, 2004a,b; Hattis et al., 2004). Under two months 

of age, however, the immaturity of such processes likely precludes scalability. 

When more data are available for carrying out an assessment, lifestage considerations can 

be incorporated using either intraspecies adjustments or interspecies extrapolation (Figure 4-7). 

Adjustments across human lifestages from adult to earlier developmental stages includes 

exposure, TK, and TD considerations (Barton, 2005), and this process can be qualitative or 

quantitative (Ginsberg et al., 2002). Qualitatively, adult:child ratios for TK processes 

representing various metabolic pathways can be used to predict the relative difference in TK 

processes between children and adults for a toxicant that is metabolized by the same pathway. 

For example, the mean half-lives of several pharmaceuticals metabolized by CYP3A can be 

compared in adults and children; this ratio could then be used to adjust the intraspecies UF for an 

environmental toxicant that is known to be metabolized by CYP3A. Quantitatively, adult PBTK 

models (if available) could be parameterized in order to predict the dose metric in children. The 

left panel in Figure 4-7 depicts the frequent case where adult animal toxicity data is used to 

extrapolate to humans. If sufficient data and models are available, a subsequent intraspecies (or 

lifestage) extrapolation could be performed. The right panel depicts a less-frequent (but 

preferred) case where toxicity data in a lifestage of interest is used for interspecies extrapolation 

4-40 
to the corresponding lifestage of interest in humans. Importantly, this approach requires a 

qualitative or quantitative evaluation of how homologous the animal lifestage is relative to the 

lifestage of interest in humans. In the former case, such TK changes might increase the 

intraspecies UF with respect to TK consideration; it has been shown, for example, that such 

differences between adults and infants can exceed 3.2-fold (Hattis et al., 2004). In the latter 

case, the intraspecies UF may be reduced due to the improved characterization of TK. The 

advantage to this approach is that assessors may have greater confidence in extrapolating within 

the human species; on the other hand, this approach requires that the underlying toxic response 

and MOA are concordant across lifestages. This assumption may add additional uncertainty to 

the dose-response characterization. 

a

Importantly, this approach requires a qualitative or quantitative evaluation of how homologous the animal lifestage 

is relative to the lifestage of interest in humans. 

 Figure 4-7. Interspecies and intraspecies adjustments with lifestage considerations. 

More often, however, the data needed for lifestage extrapolation will be available only in 

experimental animals and thus will often require both qualitative and quantitative adjustments 

(Barton, 2005) (Figure 4-7, right panel). Qualitative adjustments include determining the 

4-41 
developmental stages in experimental animals and humans that exhibit the same window of 

susceptibility related to the critical outcome of interest. This may require both empirical 

evidence and expert judgment. Several articles have examined the relative development of organ 

systems across species (reviewed in Hurtt and Sandler, 2003a,b; Selevan et al., 2000; WHO, 

2006). Quantitative adjustments are then needed to account for the TK differences that exist 

across species at the equivalent (with respect to the window of susceptibility) lifestages. For 

instance, rodents are born at an overall developmental stage roughly equivalent to the end of the 

second human trimester. Thus, if equivalent windows of susceptibility exist at these two 

different lifestages across species, then altogether different PBTK models and TK data would be 

needed to calculate the equivalent internal dose, i.e., a lactational model for the rodent and a 

pregnancy model for the human. 

An advantage of this approach is that the assessor starts with age-relevant developmental 

effects (e.g., two-generation reproduction studies) as opposed to assuming concordance of effects 

across lifestages. This will likely have the effect of reducing the interspecies UF due to TK 

adjustments and due to a general increase in confidence that TD differences (if they exist) have 

been minimized. One caveat is that human data (from controlled exposures or epidemiologic 

studies) with which to test the predictive capability of the model is often nonexistent. 

Additionally, if extrapolation requires the use of different model structures (e.g., perinatal 

exposure in rats and fetal exposure in humans), then each model, with its own inherent 

uncertainties, may add to the overall uncertainty in the extrapolation (U.S. EPA, 2006b). 

Because the majority of data concerning a chemical will pertain to nonhuman species, TK 

and TD data are important elements for lifestage-specific dose-response characterization. It is 

for this reason that PBTK and BBDR models have been emphasized for dose-response modeling 

under the lifestages approach. There are several examples where existing adult models have 

been adapted to developmental lifestages. Gentry et al. (2003) incorporated new tissue 

compartments and parameters into a previously published PBTK model for modeling 

isopropanol and acetone metabolism in adult humans and rats (Clewell et al., 2001). These 

additions include compartments for the uterus, mammary tissue, placenta, and fetus (Gentry et 

al., 2003), some of which are modeled to account for growth throughout gestation. Physiological 

parameter values were derived from numerous previous publications; currently, the EPA is 

developing relational databases for human and rodent physiological parameters so that these 

4-42 
values become more standardized and reduce some of the variability and uncertainty in PBTK 

models.8 Pelekis et al. (2003) demonstrated the use of a lifestage approach by applying 

probabilistic analysis to a previously published PBTK model. Briefly, this study modeled daily 

exposure of individuals to dichloromethane from birth to 70 years of age using age-specific 

physiological parameters, partition coefficients, and CYP2E1 age-specific metabolic data. This 

model does not take into account age-related differences in exposure, nor are TD factors 

addressed (Figure 4-7, left panel). Lifestage data have also been used in BBDR modeling. For 

instance, a BBDR model has been developed for modeling the developmental effects on fetuses 

following maternal exposure to 5-fluorouracil on gestational day 14 resulting in birth defects and 

birth weight in rats (Figure 4-7, right panel) (Lau et al., 2000). This model employs a PBTK 

component that describes the formation of the metabolite, relates the metabolite levels to 

deoxyribonucleotide pool perturbation, and relates this perturbation to low fetal birth weight 

(Shuey et al., 1994) and fetal malformation (Lau et al., 2001; Setzer et al., 2001; Shuey et al., 

1994). 

4.2.4.3. Low-Dose Extrapolation 

Ideally, extrapolation beyond the range of observation is informed by MOA. When 

MOA is not known, it is possible that the shape of the dose-response curve can be informative 

for low-dose extrapolation; however, Lutz et al. (2005) have demonstrated that these shapes can 

sometimes be misleading. For instance, the linearity of the dose-response curve often seen in 

epidemiologic studies may be due in part to interindividual genetic and life style differences as 

well as other issues related to epidemiologic studies such as difficulties in dose reconstruction. 

Conversely, Lutz et al. (2005) also demonstrated that animal bioassay studies that suggest a 

threshold effect may be misleading. For instance, in silico simulations of dose-response 

relationships can result in threshold (or J-shaped) relationships by chance; thus animal bioassays, 

often unrepeated, may suggest a relationship that does not exist in reality. Conolly et al. (2005), 

also using in silico methods, demonstrated that modeling of adaptive responses to DNA damage 

can result in both linear and threshold dose-response relationships depending upon model 

8

 Conversely, these databases can be used to incorporate variability in physiological parameters into probabilistic 

modeling techniques. 

4-43 
assumptions. Taken together, these studies highlight the importance of a strong understanding of 

MOA for choosing the most appropriate low-dose extrapolation approach. 

The Guidelines for Carcinogen Risk Assessment (U.S. EPA, 2005a) advocate an MOA 

approach to low-dose extrapolation of cancer outcomes, where low-dose linear extrapolation is 

performed when a carcinogen is thought to act through a linear MOA (e.g., mutagenesis) or 

when the MOA for a carcinogen is not understood. This is based, in part, on the concept of 

additivity (Crump et al., 1976), where any amount of a carcinogen adds to the underlying 

biological processes that are responsible for the background incidence of a particular cancer. 

Nonlinear extrapolation is used when the MOA can be demonstrated to result from a 

threshold (i.e., nonlinear) MOA and can be used for both cancer and noncancer outcomes. 

Although nonlinear extrapolation approaches are frequently used for noncancer outcomes, risk 

based approaches to noncancer outcome low-dose extrapolation, with potential relevance to costbenefit analysis, have been proposed (Clewell and Crump, 2005; Gaylor and Kodell, 2002). 

There may also be biological support for low-dose linear extrapolation for certain noncancer 

outcomes. For example, 1,2-dibromo-3-chloropropane is thought to reduce sperm count by 

interaction with DNA (Pease et al., 1991); thus, like for mutagens, there may be a scientific 

rationale for using low-dose linear extrapolation for this compound. 

4.2.4.4. Reference and Risk Value Derivation 

Lifestage extrapolations for RfV and risk value derivations can affect the magnitude of 

the UFs applied in the final risk value derivation. Current practices for RfC and RfD derivation 

and the application of UFs are outlined in A Review of the Reference Dose and Reference 

Concentration Processes (U.S. EPA, 2002a). New guidance on CSF derivation from early-life 

exposure to environmental agents can be found in the Supplemental Guidance for Assessing 

Susceptibility from Early-Life Exposure to Carcinogens (U.S. EPA, 2005b). In brief, the new 

guidance states that for toxicants acting through a mutagenic MOA where data concerning early 

life susceptibility are lacking, early life susceptibility should be assumed and the following agedependent adjustment factors (ADAFs) should be applied to the CSF: 

• 10-fold for exposure occurring before 2 years of age 

• 3-fold for exposure occurring between the ages of 2 and 16 

• no adjustment after 16 years of age 

4-44 
No such adjustments are advocated for toxicants with either an unknown or non-mutagenic 

MOA. These adjustments are based, in part, on analyses indicating an increased incidence of 

tumor formation from early-life exposure as compared to adult exposure. 

Historically, lifestage-related uncertainties have been folded into the database UF when 

the MOA is nonlinear. Lifestage-specific data gaps do not necessarily imply a greater database 

UF; rather, the method helps focus attention on the most critical data gaps deserving of 

additional uncertainty weighting. This additional weighting following uncertainty analysis 

(Section 4.2.7) would support prioritization of data needs. Indeed, the rationale for using the 

lifestage approach is to better characterize individual risk and thus decrease uncertainty in risk 

assessment. 

Table 4-5. Examples of lifestage-specific questions for extrapolations and 

risk derivation. 

Topic Lifestage-Specific Question(s) 

Duration and 

Route 

Adjustments 

(Section 4.2.4.1) 

Do default duration adjustments maintain the relationship between exposure and response? 

Are the effects specific to the portal of entry? 

Can existing models be used to extrapolate to the lifestage-specific exposure scenario of 

interest using PBTK models? 

Interspecies and 

Intraspecies 

Adjustments 

(Section 4.2.4.2) 

Should the same interspecies factors (e.g., DAFs) be applied in deriving HECs and human 

equivalent doses for all lifestages? 

Can developmental lifestage dose-response characterization be conducted based on adult 

animal or human data (Figure 4-7)? 

Can developmental lifestage dose-response characterization be conducted based on 

developmental lifestage animal data (Figure 4-7)? 

What data are available to perform extrapolations for developmental lifestages? 

Low-Dose 

Extrapolation 

(Section 4.2.4.3) 

Is the MOA known? 

Is the chemical a known mutagen? 

Do statistical modeling approaches result in reasonable results in the low-dose range? 

Are PBTK models available? Can, for instance, a BMD10 be based on internal dose metric 

rather than applied dose? 

Reference and 

Risk Value 

Derivation 

(Section 4.2.4.4) 

Is the toxicant of interest mutagenic? If so, is there sufficient data to argue against using an 

ADAF? 

Have inter- and intra-species TK and TD differences been addressed through modeling? 

Are there significant concerns about a missing lifestage? What impact will this have on the 

database UF? 

4.2.5. Variability Analysis 

Variability analysis evaluates the range of values for a parameter in a population. This is 

particularly useful when sensitivity analysis has identified a key parameter as having a 

significant impact on model output. When an outcome is predicted to be sensitive to certain 

4-45 
parameters, probabilistic approaches (e.g., Monte Carlo simulation) can be incorporated into 

models (U.S. EPA, 2006b). This type of analysis, for instance, allows assessors to predict upper 

and lower bounds on a dose metric level in an experimental species; thus multiple calculations of 

the relevant exposure concentration for humans could be calculated and perhaps used for 

subsequent risk derivation. 

Model evaluation may not be the final step in the dose-response process. Sensitive 

parameters provide red flags that are examined carefully for variability of these parameters 

within the population. Alternatively, the sensitivity might suggest the need for careful 

examination and consideration of susceptible lifestages. Example questions regarding doseresponse variability are in Table 4-6. 

4.2.6. Sensitivity Analysis 

Sensitivity analysis allows risk assessors to examine which parameters in a model are 

most important to the outcome of concern. This analysis is a key evaluation technique for PBTK 

models. This analysis can identify the key parameters that can be further examined for accuracy, 

either through available data or estimation. In addition, selection of sensitive parameters could 

help in identifying more susceptible lifestages. For instance, model sensitivity to ventilation rate 

provides a starting point for addressing lifestage differences. Example questions regarding doseresponse sensitivity are in Table 4-6. 

4.2.7. Uncertainty Analysis 

Uncertainty analysis can have both quantitative and qualitative components. Model 

uncertainty comprises that which is unknown about how well a model reflects the underlying 

biology. Models are approximations of biological processes, and therefore, have inherent 

shortfalls. Quantitative elements include model structure, choice of dose metric, and 

extrapolation procedures. Often these elements can be altered in order to compare model results. 

Results from this type of analysis, together with reasons supporting the various choices used in 

each model, can be expressed as subjective probabilities that each model is correct. Qualitative 

elements of uncertainty analysis include such things as choice of experimental animal species or 

the applicability of experimental animal species to the human lifestage of interest (Section 

4.2.4.2). These particular efforts enhance the scientific underpinnings of the dose-response 

4-46 
characterization and are explicitly carried forward in the dose-response narrative (Section 4.2.9) 

through to the risk characterization (Chapter 5). See the final Approaches for the Application of 

Physiologically Based Pharmacokinetic (PBPK) Models and Supporting Data in Risk 

Assessment (U.S. EPA, 2006b) for an in-depth treatment of PBTK model evaluation. Example 

questions regarding dose-response uncertainty are in Table 4-6. 

Table 4-6. Examples of lifestage-specific questions for dose-response 

variability, sensitivity, and uncertainty analyses. 

Topic Lifestage-Specific Question(s) 

Variability Analysis 

(Section 4.2.5) 

How has variability been incorporated into a dose-response model? Have “average” 

individuals for one or more age groups been modeled, or has population variability (across 

age groups) been incorporated using probabilistic approaches? 

Are the differences in model outcomes among different age groups or the entire population 

greater or lesser than the typical intraspecies UF of 10? 

Sensitivity Analysis 

(Section 4.2.6) 

What lifestage-specific parameters (inputs) have been included in the dose-response model? 

What parameters have the greatest influence on the dose-response model outcome? 

Are the parameters to which a model is most sensitive likely to vary across lifestages? What 

is the likely impact of such differences on model predictions? 

Uncertainty 

Analysis 

(Section 4.2.7) 

Can the outcomes of multiple dose-response models and/or multiple variations (e.g., 

structures or curve fits) of such models be compared? How much do these outcomes differ? 

Can variability and uncertainty in a parameter be distinguished from one another? Is the 

variability true variation or is it a large component uncertainty that can be reduced through 

more lifestage-specific data collection or research? 

4.2.8. Iteration with Hazard and Exposure Characterization 

During the dose-response characterization, situations may arise where information 

obtained can lead to iteration with hazard characterization (Section 4.1). For instance, it is 

conceivable that evaluation of a PBTK model could lead to the conclusion that the model 

inadequately predicts empirical data. While this could be due to deficiencies in the model, it 

could also suggest that the dose metric previously hypothesized to be associated with a response 

may not be correct and thus may require a re-evaluation of the MOA. Such a situation may arise 

when the dose-response relationship between exposure and response does not become clearer 

when based on an internal dose metric. 

Analysis of dose-response data could also warrant re-examination of the exposure 

characterization (Section 4.3). For example, data that indicate a sensitive dose-response 

relationship at environmentally relevant low-exposure levels, particularly in the context of 

precursor events, may suggest that certain exposure scenarios are more important than initially 

4-47 
thought and perhaps be an impetus for further characterization and refinement of exposure 

models employed for predicting external doses. 

4.2.9. Lifestage-Specific Dose-Response Characterization Narrative 

The dose-response narrative summarizes recommended estimates, data supporting those 

estimates, modeling approaches, a POD narrative, key default assumptions, uncertainty, 

sensitivity, and variability. The narrative also provides identification of susceptible lifestages 

and quantification of their susceptibility. A discussion of the strengths and limitations of the 

dose-response characterization are presented, highlighting significant issues in developing risk 

values, including alternative approaches considered equally plausible, and how these issues were 

resolved. Dose-response estimates may be accompanied by the descriptors used in the WOE 

evaluation (Section 4.1.3.1). For instance, a toxicant may be described as “likely to be 

carcinogenic to humans” when exposed by “oral route” (U.S. EPA, 2005a). In this regard, risk 

managers will be able to put each estimate into context. Questions to ask during the doseresponse characterization narrative include the following: 

• What were the results of variability, sensitivity, and uncertainty analyses? 

• Are there data needs that should be highlighted to direct future research (by 

various scientific bodies and processes)? 

• Are there lessons/implications for past, current, or future assessments? 

4.3. LIFESTAGE-SPECIFIC EXPOSURE CHARACTERIZATION 

4.3.1. Introduction 

Exposure characterization is the analysis step in which human interaction with the 

environmental agent of concern is evaluated. Exposure (sometimes referred to as potential dose) 

is the pattern of contact of an individual with a toxic agent. To characterize exposure, an 

assessor needs information on the concentrations of a pollutant in exposure media, the activities 

that result in contact, and the transfer rates from the exposure media to the individual. Exposure 

results in an internal dose when the agent is transferred into and taken up by the body. Clearly, 

not all exposures will result in a significant dose (e.g., contaminated hands may be washed 

before dermal absorption or oral transfer can occur). Yet, it is the dose at the target tissue that 

will ultimately cause health effects. The primary purpose of a lifestage-specific exposure 

4-48 
characterization is to get a detailed description of the potential for exposure during preconception 

or developmental lifestages. 

Exposure characterization (Figure 4-8) begins in the problem formulation phase 

(Chapter 3) with identification of potential sources, pathways, and scenarios. The resulting 

conceptual model (Section 3.2) is used to guide collection of available exposure data and other 

required information for exposure characterization. The assessor identifies and evaluates 

potentially significant exposure scenarios in order to conduct a lifestage-specific exposure 

characterization. Variability, sensitivity, and uncertainty analyses are conducted to determine 

impact of the available exposure data on the resulting analysis. The results of the exposure 

characterization are iterated with the hazard and dose-response characterizations if a critical 

window of susceptibility is identified that was not considered in the initial exposure 

characterization or if an important exposure period is identified that was not considered in the 

hazard or dose-response characterization. Finally, the assessor writes a summary of the exposure 

characterization, which includes a discussion of the confidence in the analysis results based on 

available data. This information feeds into the comprehensive lifestage-specific risk 

characterization (Chapter 5). 

Throughout the exposure characterization, the assessor keeps in mind the relevance of the 

information to the overall goals of the assessment. It may be appropriate to refine the conceptual 

model (Section 3.2) or analysis plan (Section 3.3) after more thoroughly evaluating the available 

exposure data. For example, a conceptual model may focus on exposure to a chemical that is 

transformed in the environment before there is potential for a child to contact the agent. If the 

final form of the compound relevant for exposure was not considered in the conceptual model, 

then the conceptual model will need to be refined to consider all relevant agents. 

4.3.2. Evaluation of Available Exposure Data 

The objectives and scope of the risk assessment, defined in the problem formulation 

phase (Chapter 3), provide focus for identifying all the relevant human exposure data and other 

required information. To characterize exposure for a broad (e.g., national-scale) risk assessment 

will require distributional exposure factor data for all relevant lifestages. A narrow (e.g., sitespecific) assessment will require measured or modeled environmental concentrations to estimate 

potentially significant exposures for all relevant lifestages. 

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Figure 4-8. Flow diagram for lifestage-specific exposure characterization. Using the 

lifestage-specific exposure information identified in problem formulation (Chapter 3), exposure is 

estimated using a tiered approach. The lifestage-specific exposure is characterized by discussing 

the variability and uncertainty in the results. Key sources of variability and uncertainty can be 

assessed using sensitivity analysis. Iteration with hazard characterization (Section 4.1) and doseresponse characterization (Section 4.2) (illustrated by dashed arrows) occurs throughout this 

process to ensure that critical windows of exposure are considered. 

4-50 
To focus on risk from exposure to children, the most appropriate data will be on sources 

and exposure media concentrations that have been identified in the locations where children 

spend time, which may change by developmental lifestage. For example, sources may be 

identified in 

• residence and workplace for pregnant and lactating women; 

• residence, daycare, and outdoor play areas for infants and toddlers; 

• residence, school, and locations of after-school activities for school-age children; and 

• residence, school, and locations of after-school activities and workplace for 

adolescents. 

For a given source, exposure media (e.g., water, soil/dust/sediments, food, and 

objects/surfaces) and exposure routes (i.e., inhalation, ingestion, dermal absorption, and indirect 

ingestion) define the pathway of exposure (U.S. EPA, 2002c, 2003a). Figure 4-9 highlights the 

stages of development and their relevant exposure routes. The result of evaluating the exposure 

data would be a table in which potential exposure routes are identified for each exposure medium 

(direct and indirect) (Hubal et al., 2000). 

Exposure media may also change with lifestage. For example, the fetus will be exposed 

to cord blood and amniotic fluid, the infant to breast milk, the teething child to many objects for 

mouthing, the school-age child to pesticides used in the classroom, and the adolescent to 

vocational or recreational hazards. 

For any given pathway, a set of associated exposure scenarios describes how an exposure 

takes place and is used to estimate distribution of exposure. An exposure scenario is defined by 

the combination of all the discussed details (Hubal et al., 2000). Example questions for refining 

life-stage specific scenarios to facilitate exposure analysis are presented in Table 4-7. 

Children may experience unique exposure patterns that are important to consider in 

relation to their kinetic development and critical windows for effects. Therefore, the assessor 

must carefully consider the temporal scale for estimating exposures and doses in children. 

Exposure estimates may be presented as 

• peak doses 

• exposures occurring over a very short period of time (e.g., minutes) 

• time weighted averages (e.g., TWA over 8 hours) 

• single day doses (representing the sum over 24 hours) 

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Figure 4-9. Exposure routes during developmental lifestages. The solid lines represent relevant 

exposure, while dotted lines represent exposures that are not relevant to the specific lifestage. During 

gestation, the majority of exposures (except for physical factors) occur transplacentally through exposure to 

the mother. After birth, exposures may either be directly to the child, with an additional route from the 

mother for those agents that may be present in human milk. 

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Table 4-7. Examples of lifestage-specific questions for scenario development. 

Sources of Exposure What are the sources of exposure to chemicals or agents that are of special concern for 

children? 

Where in the environment can the child come into contact with the chemical? In what 

quantities? If it is a consumer product, how is it used by children? 

Pathways of 

Exposure 

What are all the potential exposure media (e.g., breast milk)? 

What are all the potential exposure routes (hand-to-mouth ingestion)? 

What are the specific pathways that may be of concern for children (e.g., absorption from 

amniotic fluid, ingesting breast milk, ingestion of food eaten off contaminated floor)? 

How are parents and/or children being exposed, from the source to the absorbed dose, for all 

pathways of exposure? 

Lifestages of 

Exposure 

What are the potentially exposed lifestages? 

Are there any community factors that may put a subgroup of children at higher risk (e.g., 

ethnic, cultural, racial, or socioeconomic groups)? 

Are there any individual characteristics that may put an individual child at higher risk (e.g., 

health status, nutritional status, genetic susceptibility)? 

Exposure Patterns What is the relevant time frame of exposure (e.g., acute, short term, chronic, intermittent)? 

Locations of 

Exposure 

What are the potential locations of exposure (e.g., in utero, residence, school, outdoors, 

indoors)? 

Are there other relevant factors that may be relevant for identifying exposure scenarios for 

specific lifestages? Geographical location? Urban, rural? Near water bodies? Near parks? 

Near industrial sites? 

Activities and 

Behaviors 

What are the potential activities (e.g., mouthing, playing soccer, mowing lawns) at the 

lifestages of concern that may lead to exposure? 

What developmental stage-specific behaviors may lead to contact with the chemicals? How 

do the behaviors vary among children of various ages? 

• short-term average daily doses (e.g., averaged over a month or a year) 

• lifetime average daily doses 

A potential problem with the time integration of exposure estimates is that the pattern of 

exposure can be obscured. If the exposure pattern is relatively continuous and at a constant 

level, the time averaged doses will be close in magnitude to single-day dose estimates and will 

match actual human experience. However, when infrequent exposure events of high magnitude 

and short duration are averaged, they are equated with continuous, lower-level exposures that do 

not match human experience. 

The following subsections describe information required to characterize exposure. Data 

and other information used to assess exposure include chemical properties, environmental 

sources, fate and transport (Section 4.3.2.1), environmental media concentrations (Section 

4.3.2.2), lifestage-specific exposure measurement data (Section 4.3.2.3), lifestage-specific 

exposure factors (Section 4.3.2.4), and cumulative evaluation of environmental stressors (Section 

4.3.2.5). Although there have been several large human exposure studies conducted to collect 

4-53 
integrated data on environmental concentrations, personal exposure measurements, and timeactivity data (e.g., Human Exposure Measurements: National Human Exposure Assessment 

Survey [NHEXAS]

9

), these studies have focused on the adult lifestage. As such, these data may 

be useful for characterizing preconception exposures to the parent but less so for accurately 

characterizing exposures to pregnant women and to very young children. Nevertheless, these 

provide a significant data source that should be evaluated with respect to the utility for 

addressing the significant life-stage exposures of a given assessment. The Human Exposure 

Database System (HEDS)10 is an integrated database system that contains information related to 

many of these EPA human exposure research studies. Some additional life-stage specific 

resources are described in the following subsections and example questions for each section are 

presented in Table 4-8. 

4.3.2.1. Chemical Properties, Environmental Sources, Fate, and Transport 

An agent of concern may be released into the ambient environment from multiple sources 

(e.g., industrial, agricultural, mobile, household, and natural sources). Also, the agent of concern 

may be released directly into exposure media (e.g., via occupational activities, residential use of 

consumer products, and cooking activities) of direct concern for a lifestage-specific assessment. 

Once a chemical is released into the environment, it may be chemically modified 

or transported, in its original or transformed state, into an exposure medium of concern for 

children (e.g., outdoor air, residential water, food, and/or breast milk). Scientists and engineers 

can predict the environmental movement of a chemical using information on chemical properties 

(e.g., volatilization rate, water solubility, soil/water partitioning coefficients, chemical state, and 

bioavailability) and environmental conditions (e.g., soil characteristics, amount of rainfall, wind 

direction, and presence of water bodies). Information on the form, fate and transport of the 

agents in the residential environment is also required for exposure characterization and can be 

predicted based on properties of the chemicals and residential environment (e.g., size of rooms, 

surface types, air exchange). 

Information on these types of releases and associated fate and transport may be generally 

required for risk assessment and is not lifestage specific. However, information on chemical 

9

 The NHEXAS database is available online at http://www.epa.gov/heasd/edrb/nhexas.htm. 10 The HEDS database is available online at http://www.epa.gov/heds. 

4-54 
properties is required to identify potential lifestage concerns as well as particular scenarios and 

pathways that may be of particular concern for children. For example, if a chemical is lipid 

soluble, an infant’s ingestion of breast milk may be an important route of exposure to consider. 

As another example, if a chemical is highly volatile, the inhalation pathway will be of particular 

concern because on a body-mass basis, young children have higher ventilation rates than adults. 

4.3.2.2. Environmental Media Concentrations 

Exposure characterization requires information on contaminant concentrations in the 

exposure media in the environment where the individual spends time. Contaminant 

concentrations can be measured directly in the exposure medium of interest or predicted by using 

information on the release of the contaminant and subsequent fate and transport in the 

environment. Site-specific assessments will require measured and/or model information on 

concentrations of an agent in the relevant media (e.g., soil, water, indoor air). For broad (e.g., 

population-based) assessments, information may be available in the literature. 

The largest exposure study conducted to collect exposure media concentration data for 

children is A Pilot Study of Children’s Total Exposure to Persistent Pesticides and Other 

Persistent Organic Pollutants (CTEPP) (Morgan et al., 2006). In this study, concentrations of a 

wide range of environmental contaminants were measured in multiple media in the homes and 

daycares of children from ages 3 to 5 years. Exposure media concentration data was also 

collected for children in the Minnesota NHEXAS study (Adgate et al., 2004). Recently there has 

also been considerable research conducted to develop residential models for several 

environmental contaminants including pesticides (Stout and Mason, 2003) and pthalates (Xu and 

Little, 2006). These models use data collected in controlled laboratory settings and test house 

situations and may provide insight into potential pathways for lifestage-specific exposures. 

4.3.2.3. Lifestage-Specific Exposure Measurement Data 

Additional data may also be available that provide a more direct measure of exposure. 

Personal monitoring techniques, such as the collection of personal air or duplicate diet samples, 

are used to directly measure exposure to an individual during particular time intervals. In 

children especially, different factors might affect the child’s dose. It is important to give 

consideration to measurement techniques at the physical locations where the child spends his/her 

4-55 
time (e.g., home, school, daycare) as well as the child’s characteristics and behaviors. For 

example, the breathing zone of a child is closer to the floor than the breathing zone of an adult, 

and concentrations of chemicals that are heavier than air may be higher in areas closer to the 

ground. Some of these types of data are available in the CTEPP study (Morgan et al., 2006) and 

in other smaller studies that have been published in the literature (Adgate et al., 2004; Cohen 

Hubal et al., 2006; Liu et al., 2003; MacIntosh et al., 2001). 

For some environmental contaminants, biomarkers can serve as a useful measure of 

direct exposure aggregated over all sources and pathways. However, few studies using 

biomarkers have collected all the information required to accurately estimate exposure. The 

most significant source of biomonitoring information is the Third National Report on Human 

Exposure to Environmental Chemicals (CDC, 2005), collected as part of the National Health and 

Nutrition Examination Survey (NHANES) study.11 This study measured a wide range of 

chemicals in the blood and urine of a representative sample of the U.S. population. However, 

young children (under 6 years) are only monitored for a select group of chemicals in this study 

(lead, mercury, pthalates, and organophosphates). Other lifestage-specific biomonitoring data 

have been collected in studies conducted by the National Institute of Environmental Health 

Sciences (NIEHS)/EPA Centers for Children's Environmental Health and Disease Prevention 

Research (Kimmel et al., 2005). Several of these centers have collected data from a variety of 

biological media from both pregnant mothers and their infants. Additional information on 

collection and interpretation of biomonitoring data for lifestage-specific exposure 

characterization is presented by Barr et al. (2005). 

It is important to note that biomonitoring data may demonstrate exposure, although it 

may be difficult to translate into estimates of exposure. Biomonitoring may be useful for 

quantifying exposures at the population level, if the relationship between the substance found in 

the body and the amount of substance the child was in contact with can be established. 

Currently, there are significant research efforts associated with interpreting biomonitoring data 

for assessing human exposure to environmental agents (Albertini et al., 2006; NRC, 2006). 

11 Details on the NHANES study are available online at http://www.cdc.gov/nchs/nhanes.htm. 

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4.3.2.4. Lifestage-Specific Exposure Factors 

In addition to information on sources, exposure media concentrations, and human 

exposure measurements, exposure factor data (time-activity data; product use; and air, fluid, and 

dietary intake rates) are required to characterize exposure. Information is required on activities 

and behaviors that result in significant exposures (e.g., breast feeding, mouthing, sports, afterschool employment) for each lifestage. The most current version of Child-Specific Exposure 

Factors Handbook (U.S. EPA, 2002c) could be the starting point for identifying these values. 

The purposes of the Child-Specific Exposure Factors Handbook are to summarize key data on 

human behaviors and characteristics that affect children's exposure to environmental 

contaminants and to recommend values to use for these factors. Data contained in the handbook 

includes drinking water consumption; soil ingestion; inhalation rates; dermal factors including 

skin surface area and soil adherence factors; consumption of produce, fish, meats, dairy products, 

homegrown foods, and breast milk; activity patterns; body weight; and consumer products. Agespecific activity data are also available from the Consolidated Human Activity Database 

(CHAD).

12 

Within each lifestage there may be a series of critical developmental periods for which 

exposure could be characterized. These periods may be defined on the basis of exposures that 

can affect development (e.g., parental preconception exposures, U.S. EPA, 1991, 1996), or 

windows of potentially high exposure due to age-specific behaviors (e.g., crawling, teething), 

activities (e.g., types of sport/other activities, length of sport seasons, physical education 

requirements), and physiology. Behavior varies by developmental stage, and this may have a 

significant impact on exposure. 

EPA has recommended a standard set of age groups (Table 3-1) for exposure assessors to 

consider when assessing childhood exposure and potential dose to environmental contaminants 

and for purposes of designing exposure monitoring studies (U.S. EPA, 2005e). These age groups 

reflect a consideration of developmental changes in various behavioral, anatomical, and 

physiologic characteristics that impact exposure and potential dose. Data from the ChildSpecific Exposure Factors Handbook emphasize the value of independently assessing the 

relevant age group where sufficient data are available. In the case of vegetable intake, data 

indicate that biases are introduced when combining age groups, especially for the <1-year-olds 

12 The CHAD database is available online at http://www.epa.gov/chadnet1/. 

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because children 6 to <12 months eat three times as many vegetables than children 3 to <6 

months old. 

Guidance on Selecting Age Groups for Monitoring and Assessing Childhood Exposures 

to Environmental Contaminants (U.S EPA, 2005e) also recognizes that exposure factors data 

may not be available for many of the recommended age groupings or that a specific age group 

may not need to be the subject of a particular assessment; therefore, flexibility and professional 

judgment is essential in applying these generic age groupings. There may be instances where 

combining some of these age groups (e.g., combining the first three groups into one representing 

birth to < 6 months) could be considered when estimating exposure or potential dose, especially 

if little variation is expected. For example, there is little variation in ventilation rates for children 

between 11 and 18 years. Therefore, these age categories can be combined into one age group 

representing 11 to <18 years. In addition, there may be instances where it is not necessary to 

address every age group in Table 3-1 because the focus of a risk assessment may be on toxicity 

data that indicate a health effect for which only one or two of the age groups represent a critical 

window. 

Exposure factors and resulting effects during developmental stages may be a function of 

additional individual and population characteristics. These factors may be characteristics of the 

communities in which children live and include, for example, SES, family size, ethnicity, 

cultural setting, geographical location, and seasonal considerations (e.g., temperature, humidity, 

rainfall, sun exposure). Other factors specific to the individual child include genetic 

susceptibility, nutritional status, and health status. Mechanisms of vulnerabilities associated with 

individual and community characteristics include differences in susceptibility, differential 

exposure, differential preparedness, and differential ability to recover. These mechanisms are 

defined and discussed in the Framework for Cumulative Risk Assessment (U.S. EPA, 2003a, pp. 

39–42). Discussion on other risk factors, effect modifiers, and confounders is detailed in 

Guidelines for Developmental Toxicity Risk Assessment (U.S. EPA, 1991, Section 3.1.2.1.1.c, pp. 

24–25) and Guidelines for Reproductive Toxicity Risk Assessment (U.S. EPA, 1996, Section 

3.3.1.5.3, pp. 60–61). 

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4.3.2.5. Cumulative Evaluation of Environmental Stressors 

The focus of this section is the examination of vulnerability associated with differential 

exposure due to lifestage. It is difficult to separate consideration of vulnerability due to lifestage 

from consideration of vulnerability due to other key individual (e.g., ethnicity, dietary 

preferences) and community characteristics (e.g., social and physical home environment, 

religious/cultural practices) that may influence or modify exposures. In order to fully 

characterize risk to children, consideration could include environmental heath disparities (e.g., 

residential segregation) (Gee and Payne-Sturges, 2004) and the built environment (e.g., design 

and integrity of housing, land use and planning) (Cummins and Jackson, 2001). 

EPA is examining the full range of issues related to characterizing risks to children 

through a variety of initiatives, including development of Framework for Cumulative Risk 

Assessment (U.S. EPA, 2003a). As EPA develops further guidance for cumulative risk 

assessment, the full range of vulnerabilities will be considered more consistently in both hazard 

characterization (Section 4.1) and exposure characterization. A child-centered approach (Section 

3.2.3) to cumulative risk assessment may be useful in moving these issues forward (WHO, 2006, 

Chapter 5). 

Table 4-8. Examples of lifestage-specific questions for evaluation of the 

available exposure data. 

Topic Lifestage-Specific Question(s) 

Chemical 

Properties, 

Environmental 

Sources, Fate, and 

Transport 

(Section 4.3.2.1) 

What are the physical and chemical properties of the chemicals or agents? What is known 

about their fate and transport? 

What are the environmental conditions (e.g., wind direction, rainfall) that may affect the 

fate and transport of the chemical(s)? In the case of a release of an air pollutant, are there 

areas highly populated by children that are downwind from the release (e.g., schools, play 

grounds)? 

What are potential chemical sources (industrial, agricultural, occupational, residential, 

consumer product) of the compound? 

What are the release rates from the chemical source? What is known about the 

manufacturing processes that may lead to information about where the chemical can be 

found (e.g., children’s toys, play ground equipment, certain foods)? 

Are there data on the temporal and spatial patterns of compound release and transport 

relevant for specific lifestages? Is the release from the source continuous, periodic, or 

intermittent? 

What does the fate of the compound imply for exposure? Is the exposure to the released 

compound a byproduct created in the manufacturing process or a degradation product? If 

not, what are the compounds that should be assessed? 

Environmental 

Media 

Concentrations 

What are the concentrations of the chemicals in various media (e.g., air, water, food, breast 

milk, on surfaces, in consumer products) that the child may come into contact with during 

an exposure? 

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(Section 4.3.2.2) Are the ranges and distributions of environmental media concentration data relevant for 

children’s exposure? 

What bioavailability data are there for the chemical(s) from the various exposure media? 

How are the concentrations in environmental media changing over time? Are these 

intermittent? 

If environmental monitoring data are not available, are there models that can be used to 

predict the concentration at the exposure point? 

Lifestage-Specific 

Exposure 

Measurement 

Data 

(Section 4.3.2.3) 

Are relevant exposure measurements available for various lifestages (parents, infants, and 

children)? Are these direct or indirect measurements of exposure (e.g., personal air, 

handwipes, duplicate diet, biomarkers of exposure)? 

Are there biomonitoring data that demonstrate exposure potential? Is additional 

information available to use the biomonitoring data to estimate a population’s exposure? 

Are there lifestage-specific data in biological media (e.g., maternal cord blood, placenta, 

meconium)? Can these be used to estimate exposure or to indicate potentially critical 

windows of exposure? 

Lifestage-Specific 

Exposure Factors 

(Section 4.3.2.4) 

What are the child-specific exposure factors (U.S. EPA, 2002c) that characterize the 

exposure scenarios? 

What are the ranges or distributions of exposure factors for relevant lifestages? 

Are time-activity data available for all relevant lifestages? 

Are dietary data available for all relevant lifestages? How do differences in diet during 

specific lifestages impact exposure? 

Are product-use data available for all relevant lifestages (e.g., pregnant women, children)? 

Are the products used by children or in proximity of children? 

Are data available for other children’s exposure factors (e.g., contact rates for the 

individual with exposure media, contaminant transfer efficiency from the contaminated 

medium to the individual)? 

Do children’s physiological parameters influence exposure to the specific agent (e.g., body 

weight, uptake rates – inhalation, dermal absorption, gastrointestinal absorption)? If so, are 

there data available (Hattis, 2004)? 

Cumulative 

Evaluation of 

Environmental 

Stressors 

(Section 4.3.2.5) 

Are there data indicating potentially important co-exposures with chemicals that may 

interact to increase health risk for a sensitive lifestage? 

Are there data on relevant non-chemical stressors that may impact exposure and/or increase 

vulnerability of specific lifestages (e.g. SES, health status)? 

Are there any community factors that may put a subgroup of children at higher risk (e.g., 

ethnic, cultural, racial, or socioeconomic groups)? 

Are there any individual characteristics that may put an individual child at higher risk (e.g., 

health status, nutritional status, genetic susceptibility)? 

4.3.3. Lifestage-Specific Exposure Analysis 

Based on the data and information identified for exposure characterization (Section 

4.3.2), the scenarios developed during problem formulation (Chapter 3) could be refined to 

facilitate exposure analysis. Exposure estimates may be developed for all relevant lifestagespecific scenarios. At this point in the assessment, patterns of exposure will be characterized 

(intermittent, continuous, acute, or chronic) and exposure levels will be quantified. Because 

children may have higher exposures (Section 4.3.2.4) or because they may experience unique 

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exposure patterns (Section 4.3.2.5), exposures may be significant during critical windows, which 

can then affect the outcomes observed. 

The health effect of concern is considered when selecting the appropriate temporal scale 

for estimating exposure/dose. Depending upon the problem, it may be important to consider 

peak exposures as well as exposures that have been averaged over a specified period of time 

(U.S. EPA, 2005e). Assessments of agents with multiple sources or in multiple media may 

require additional analysis to estimate children’s exposure patterns. This would indicate that 

even for a screening-level analysis (Section 4.3.3.2.1), a large number of factors may need to be 

collected and tracked, along with their associated variabilities and uncertainties. Thus to 

efficiently and effectively assess children’s exposures, a person/population-oriented approach 

(Section 3.2.1) may be needed for all but the most basic assessments. 

To conduct the lifestage-specific exposure characterization, a calculation approach 

described in Section 4.3.3.1 is selected on the basis of available data and the risk assessment 

questions that were defined during the problem formulation phase (Chapter 3). Typically, an 

exposure characterization will begin with a screening-level assessment (Section 4.3.3.2.1) and 

then, if there appear to be significant exposures or an unacceptable level of uncertainty, a second, 

more refined level of analysis will be conducted (Section 4.3.3.2.2). This type of tiered level 

analysis is often used to facilitate efficient allocation of resources. Often, two or more 

calculation approaches will be used and the results compared in the exposure characterization 

narrative (Section 4.3.8). The following subsections describe each tier, and example questions 

are presented in Table 4-9. 

4.3.3.1. Exposure Measurement and Estimation Approach 

Three approaches may be used to calculate exposures: (1) the point-of-contact approach, 

(2) the scenario evaluation approach, and (3) the dose reconstruction approach. Each approach 

has advantages and disadvantages over another. 

The point-of-contact approach, sometimes referred to as the direct approach, involves 

measurements of chemical concentrations at the point where exposure occurs (at the interface 

between the person and the environment) and records of the length of contact with each 

chemical. This approach does not take into account an individual’s characteristics or behaviors. 

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The scenario evaluation approach, sometimes referred to as the indirect approach, utilizes 

data on chemical concentration, frequency, and duration of exposure as well as information on 

the exposed lifestage. Child-specific behaviors and physiologic characteristics may be assumed 

on the basis of exposure factor data (U.S. EPA, 2002c) or from exposure study databases (the 

Consolidated Human Activity Database [CHAD]; the Human Exposure Database System 

[HEDS]),13 or they can be obtained specifically for the assessment (e.g., by questionnaire, diary, 

videotaping). Chemical concentration may be determined by sampling and analysis or by use of 

fate and transport models (including simple dilution models). Models can be particularly helpful 

when resources for additional sampling are limited but some analytical data are available. 

Finally, the dose reconstruction approach allows exposure to be estimated from dose, 

which can be reconstructed through internal indicators (e.g., biomarkers, body burden, excretion 

levels) after the exposure has taken place. The use of biomarkers of exposure or effect may 

provide a more detailed analysis; however, only a few examples currently exist for applying this 

approach successfully. At the present time, much of biomarker data are difficult to interpret, 

either because the presence of a biomarker may not be unique (e.g., many stressors result in a 

change in the same biomarker) or there may not be adequate exposure pathway information to 

link the biomarker to the exposure. Currently, this approach is most successful for persistent 

compounds. 

4.3.3.2. Analysis Level or Tiered Assessment 

Typically, an exposure characterization will begin with a screening-level assessment and 

then, if there appears to be significant exposures or an unacceptable level of uncertainty, a 

second, more refined level of analysis will be conducted. Probabilistic techniques may be used 

at either level of analysis depending on the types of scenarios being evaluated. The major 

difference between the levels of assessment described below is related to the assumptions that 

are used. 

The first tier screening assessment (Section 4.3.3.2.1) is used to identify and prioritize 

potentially important exposures. After results of the screening assessment are compared with 

results of the hazard characterization (Section 4.1), a more refined assessment (Section 4.3.3.2.2) 

13 The CHAD database is available online at http://www.epa.gov/chadnet1; the HEDS database is available online at 

http://www.epa.gov/heds. 

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may be required, using more realistic estimates of exposure for selected scenarios to reduce the 

uncertainty. This second tier is generally more resource-intensive than the first tier and is used 

to refine estimates for exposure scenarios that were identified as potentially significant in the 

screening assessment. Finally, if a high level of uncertainty remains around estimates of 

exposure following a refined assessment, supplemental data collection may be needed. 

4.3.3.2.1. Screening assessment. The purpose of a screening tier is to identify probable 

pathways and scenarios and to rule out insignificant ones. Bounding values for exposure factors 

and conservative simplifying assumptions are used at this level of analysis. As a result, the 

output may have a high level of uncertainty. Historically, deterministic calculations were used in 

most screening-level exposure analyses. However, exposure assessments have become 

increasingly complex, and probabilistic techniques may be useful when, for example, exposure 

parameters have large variability or when multiple sources exist (U.S. EPA, 2001b). 

Based on the bounding assumptions used in this level of analysis and comparison with 

the hazard characterization (Section 4.1), a set of potentially significant exposure scenarios for 

relevant age groups will be identified. In the screening-level analysis, differences in exposure 

between children of different developmental stages are identified. For some specific exposure 

scenarios and compounds, combining or subdividing some of the age groups may be appropriate, 

for example, where variation in exposure factors and resulting exposures is insignificant (U.S. 

EPA, 2005e). 

Limited data may be an impediment in conducting accurate lifestage-specific 

assessments, and for making decisions regarding combining or eliminating age groups. When 

making an assessment and limited data is available, the assessor should use the recommended 

age groups (U.S. EPA, 2005e) as a starting point. Then, based on qualitative information, the 

assessor can determine if little variability is expected among some age groups, in which case the 

age bins can be combined. If data are not available to make this determination, then this can be 

described as an area of uncertainty and identified as an area for future research. A possible 

approach to estimating exposure factors and dose when data are not available uses age-dependent 

curve fitting to help fill in the data gaps. Any assumptions used in assessing exposure for a 

particular age bin should be discussed in the assessment. 

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Once screening-level estimates of exposure are developed for each scenario and each age 

group, the questions in Table 4-9 could be considered. In order to identify and understand the 

importance of parameters and uncertainties in these exposure estimates, a sensitivity analysis is 

generally conducted on the potentially significant scenarios. For a screening assessment to have 

value, the potential range of parameter values is considered when conducting the sensitivity 

analysis (e.g., some parameters can vary only between 0 and 1; others can vary by three orders of 

magnitude). In addition, the uncertainty associated with assumptions that are based on little or 

no data would need to be evaluated before any conclusions about the level of "conservatism" can 

be made. Methods for conducting a sensitivity analysis are discussed further in Section 4.3.5. 

4.3.3.2.2. Refined assessment. This tier of the analysis level provides more detail for 

potentially relevant scenarios and potentially vulnerable age groups. The goal of this tier is often 

to estimate the distribution of exposure for the relevant lifestages. Based on results of the 

sensitivity analysis conducted for the screening-level assessment, significant exposure factors 

and important assumptions are revisited to develop more realistic estimates of exposure. 

This more advanced analysis may include the application of sophisticated modeling tools 

to develop exposure estimates for use in regulatory decisions. A variety of modeling tools have 

been developed over the years to facilitate exposure assessment (Price et al., 2003, and 

references therein for review of available tools). Some of the types of models available include 

total source models (e.g., aggregate and cumulative models developed to meet requirements of 

FQPA); multi-route models of exposure (e.g., local waste site models, tap-water exposure 

models), models of exposures to specific sources or routes (e.g., dietary models, consumer 

product models), indoor air models, and occupational models. Few of these models are designed 

currently to specifically address lifestage exposures. As a result, data on the age bins used in the 

models and outputs produced by the models may not address the specific age groups of interest 

for a complete lifestage-specific assessment. This issue is discussed further in Guidance on 

Selecting Age Groups for Monitoring and Assessing Childhood Exposures to Environmental 

Contaminants (U.S. EPA, 2005e). 

Limitations of the data, model results, and associated uncertainties remaining in the 

refined tier are considered and addressed in this analysis. Available exposure data sets may not 

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allow modelers or risk assessors to directly extract data from the underlying sources to conduct 

lifestage-specific analyses. Potential approaches to address this issue include the following: 

• reorganizing the exposure input data set to conform to the age groupings; 

• using probabilistic sampling techniques to go beyond the categorical limits of the 

underlying database to utilize all the data, and then formatting the probabilistic model 

output into the desired age groupings to represent exposure doses; and 

• developing a weighting scheme for the underlying data set to align it with the desired 

age groupings. 

The exposure data may need to be statistically weighted so that equal weight is given to all ages 

within the group when estimating the group mean and variability statistics. 

4.3.3.2.3. Supplemental data collection. Based on results of the refined assessment and the 

associated sensitivity and uncertainty analyses, specific data needs may be identified. If the 

objectives of the risk assessment indicate that any specific uncertainties in the exposure 

characterization be addressed, collection of new data to address them may be needed and 

additional analyses conducted. 

Table 4-9. Examples of lifestage-specific questions for exposure analysis 

level or tiered assessment. 

Topic Lifestage-Specific Question(s) 

Screening 

Assessment 

(Section 4.3.3.2.1) 

Do these results address the questions posed in the problem definition phase of the risk 

assessment? 

What are the bounding assumptions used to identify relevant sources, pathways, and scenarios? 

What is the potential magnitude of exposures? 

How do potentially relevant scenarios and potentially vulnerable age groups compare with 

critical windows identified in the hazard characterization? 

How do these lifestages compare to the critical windows identified based on the TK and TD 

vulnerabilities (Section 4.1)? 

How do potential exposure levels compare with hazard levels (e.g., MOE)? 

Which exposure factors drive the results of the screening assessment and why? 

What is the potential variability of exposure factors (e.g., orders of magnitude vs. factor of 2 or 

3)? 

Is the available exposure information adequate? What criteria are used to determine 

adequacy? What are the significant exposure data needs that may require additional exposure 

data? 

Refined 

Assessment 

(Section 4.3.3.2.2) 

Were the exposure data adequate to sufficiently investigate and identify relevant differences 

across age groups? 

What is the central tendency of the distribution of the exposure when compared with the high4-65 
end exposures? 

How do potential exposure levels compare with dose-response characterization results? 

Which developmental stage of children (age bins) represent the highest exposures? 

Is the available exposure information adequate for a more refined assessment? What 

additional criteria are used to determine adequacy? What are the significant exposure data 

needs that may require additional exposure data? 

What are the available exposure assessment models? 

Are distributions available for exposures of interest (e.g., by media, source, pathway)? If not, 

do they need to be developed? Are there sufficient data for their development? 

How will variability and uncertainty be addressed? 

What are the time patterns of exposure? 

How will exposure monitoring data, PBTK modeling, and biomonitoring data be incorporated? 

What are the additional stressors and their cumulative impact? 

Supplemental Have any critical data needs been identified? 

Data Collection 

(Section 4.3.3.2.3) 

4.3.4. Variability Analysis 

Variability refers to the inherent lack of uniformity in a population that cannot be reduced 

with additional data but can be presented by providing ranges or distributions of the exposure. 

Differences among individuals in a population are referred to as inter-individual variability. 

Differences associated with an individual over time are referred to as intra-individual variability. 

Among children, inter-individual variability is due to rapid physiologic and behavioral 

changes. Even within a relatively narrow age group, variability may be large. For oral and 

dermal exposures, variability in exposure/dose is due to factors such as gross motor 

development, fine motor development, cognitive development, and social development. For 

inhalation exposures, relevant factors influencing variability in exposure/dose include, for 

example, activity level and breathing behavior (e.g., the transition from mouth to nasal 

breathing) (U.S. EPA, 2005e). Infants may be breast-fed or bottle-fed. Young children may 

have higher contact with surfaces than do older children and they explore their environment by 

mouthing objects. Physiologic characteristics affecting variability in exposure/dose include 

anatomical characteristics (e.g., body weight and proportion of body fat) and specific organ and 

physiologic systems. For example, infants have immature immune systems, and renal functions 

are less than those predicted by surface area (U.S. EPA, 2005e). 

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This variability affects the determination of upper percentiles of exposure and its 

associated risk. That is, given a high-quality/high-quantity set of data for each age group, there 

may still be significant variability for a particular exposure factor, set of factors, or exposure 

pathway. The better the data and the characterization of this variability, the better the basis for 

final selection of age groups for a specific assessment. Example questions are presented in 

Table 4-10. 

4.3.5. Sensitivity Analysis 

Sensitivity analysis is defined as the assessment of the impact of changes in input values 

on model outputs. Its main purpose in any exposure characterization is to determine which 

variables in the model equations and what pathways or scenarios most affect the exposure 

estimate. These techniques can also be used to assess key sources of variability and uncertainty 

for the purpose of prioritizing additional data collection or research. This is particularly relevant 

in children’s assessments because they are often based on limited data. Because the variables of 

particular interest are those that have an impact on lifestage-specific estimates, the sensitivity 

analysis may need to focus considerable attention on the impact of exposure factors related to 

children’s behavior. These factors affect the exposure patterns in space and time and are also 

typically the most uncertain. Example questions are presented in Table 4-10. 

4.3.6. Uncertainty Analysis 

Uncertainty is described as a lack of knowledge about factors affecting exposure or risk. 

Uncertainty in the exposure estimates may be a result of limited data for significant exposure 

factors for a particular age group. Uncertainty may also be due to assumptions made in 

development of the model. For example, soil ingestion studies in the literature have focused on 

children between 2 and 7 years of age, resulting in a lack of data for children less than 2 years of 

age. Uncertainties are acknowledged and characterized to the extent possible. 

Probabilistic assessments can be useful statistical tools for analyzing variability and 

uncertainty in risk assessments, given that adequate data are available. The Monte Carlo analysis 

can be used to better characterize variability and uncertainty across the population, and to 

compare one lifestage (e.g., infants) to another (e.g., adults). General issues to consider when 

applying these quantitative methods are described in EPA’s Guiding Principles for Monte Carlo 

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Analysis (U.S. EPA, 1997b). The EPA sponsored workshop in 1998 discussed issues regarding 

the selection of input distributions for probabilistic assessments (U.S. EPA, 1999b). 

Methodologies for selecting parametric distributions to be used in probabilistic assessments are 

described in Options for Developing Parametric Probability Distributions for Exposure Factors 

(U.S. EPA, 2000b). Example questions are presented in Table 4-10. 

Table 4-10. Examples of lifestage-specific questions for exposure variability, 

sensitivity, and uncertainty analyses.

 Topic Lifestage-Specific Question(s) 

Variability Analysis 

(Section 4.3.4.) 

If different approaches were used to estimate exposure for different lifestages or within a 

lifestage, what were the results? Can they be compared and, if so, how do they compare? 

Which approach is more appropriate? 

Does the lifestage-specific assessment capture the variability in the exposed groups? What 

are the ranges or distributions of exposure? 

What are the route, level, timing (i.e., lifestage), and duration of exposure used in the 

experimental animal studies as compared with expected human exposures? 

Are the available data from the same route of exposure as the expected human exposures? If 

not, are TK data available to extrapolate across routes of exposure? 

Are experimental animal data available from the same lifestages as the expected exposed 

human lifestage? If not, are TK data available to extrapolate across species and lifestages? 

What information was used to support duration adjustment and to calculate the human 

equivalent concentration or dose? 

How far does one need to extrapolate from the observed data to environmental exposures 

(i.e., MOE)? One, two or multiple orders of magnitude? What is the impact of such an 

extrapolation? 

Sensitivity Analysis 

(Section 4.3.5) 

What parameters have the greatest influence on the exposure model outputs? 

What is the adequacy of the data for the parameters that are identified in the sensitivity 

analysis as the most important parameters? 

Uncertainty 

Analysis 

(Section 4.3.6.) 

What are the uncertainties in the estimates, both within and across lifestages? 

What are the data limitations and how do they compare across lifestages? 

What data gaps exist, both within and across lifestages? How significant are these data 

gaps? How sensitive are the results to these data gaps? 

Is it feasible or desirable to collect more data pertaining to particular lifestages? Could the 

exposure estimates be refined if more data were available? 

4.3.7. Iteration with Hazard and Dose-Response Characterization 

Following exposure characterization, coordination, and communication with the hazard 

and dose-response assessors (Sections 4.1 and 4.2) may be useful. For example, if a screeninglevel analysis revealed that the 0–1 year age bin was more highly exposed due to nursing 

ingestion than was any other lifestage, an assessor may be prompted to re-evaluate hazard and 

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dose-response characterization to make sure that potential vulnerabilities during this age window 

are well understood or if further data needs could be identified. 

4.3.8. Lifestage-Specific Exposure Characterization Narrative 

The results of the exposure characterization are summarized in a narrative that includes a 

discussion of the results, analysis, and conclusions. The narrative includes a discussion of the 

key assumptions, limitations, and uncertainties associated with the exposure estimates and any 

potential bias in the results. Variability analysis (Section 4.3.4), sensitivity analysis (Section 

4.3.5), and uncertainty analysis (Section 4.3.6) are summarized. It is useful to also include a 

description of how the exposure characterization can be improved and uncertainties be reduced 

by additional research or collection of data. Through this narrative, the results of the exposure 

characterization are communicated in a clear and concise manner to the risk manager. These 

results include considerations of childhood variability and uncertainty within the exposure 

characterization. 

The focus of the exposure characterization is to identify age groups and address 

vulnerability resulting from differential exposure. It is impossible to completely separate 

consideration of exposure and potential dose from consideration of internal dosimetry and 

response; therefore hazard characterization (Section 4.1), dose-response characterization (Section 

4.2), and exposure characterization are intimately linked. For example, information on exposure 

scenarios of a compound to humans ensures that hazard information is relevant to the measured 

exposure. Also, understanding the dosimetry of an absorbed agent can inform the temporal 

resolution needed in the exposure data and characterization. Some questions to consider when 

summarizing the exposure characterization narrative include the following: 

• What is the basis for the exposure characterization (i.e., monitoring, modeling, or 

other analyses of exposure distributions)? 

• How was the central tendency estimate developed? What factors or methods were 

used in developing this estimate? 

• How was the high-end estimate developed? What factors or methods were used 

in developing this estimate? 

• How do the adverse health effects identified in the hazard characterization phase 

(Section 4.1) inform the identification of exposures of greatest relevance for the 

observed outcomes? 

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• How do patterns of exposure (continuous vs. intermittent) and half-life in the 

body influence the health outcome? What are the exposures during critical 

windows in development? 

• Are there particular developmental stages during which children are highly 

exposed? Do health outcomes vary during different developmental periods? 

How does this inform identification of the exposures of greatest biological 

significance for the observed outcomes? 

• How does information on dosimetry indicate the level of temporal resolution 

needed in exposure data and modeling? What dose metrics are being considered 

for child-related assessments? 

• How does the fate of the agent being evaluated affect exposure in children? Are 

children exposed to other agents with a similar MOA to the one being assessed? 

Is sufficient MOA information available to consider a cumulative exposure 

assessment? 

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5. LIFESTAGE-SPECIFIC RISK CHARACTERIZATION 

Risk characterization is the final phase of the risk assessment process (Figure 5-1). This 

final phase of the risk assessment utilizes the information from the problem formulation 

(Chapter 3) and analysis (Chapter 4) phases. After risk characterization is put into context 

(Section 5.2), the information is utilized in risk communication and risk management. 

Figure 5-1. Flow diagram for lifestage-specific risk characterization. 

The risk characterization describes the overall picture of health risks resulting from 

children’s exposures, in which the hazard characterization (Section 4.1), dose-response 

characterization (Section 4.2), and exposure characterization (Section 4.3) components of the 

analysis phase are integrated and summarized. Major non-technical conclusions are drawn that 

inform the risk managers, who will make risk decisions in context with the problem identified in 

problem formulation (Chapter 3). 

5-1 
During hazard characterization (Section 4.1), an assessor evaluates and describes the 

information on the capacity of an environmental agent’s exposure during developmental 

lifestages to cause outcomes at any lifestage in both laboratory animals and humans. The 

qualitative WOE evaluation is based both on the type and quality of data derived from humans 

and laboratory animals and on the integration of ancillary data (SAR, genetic toxicity, TK, TD, 

and MOA). 

The dose-response characterization (Section 4.2) focuses on quantitative relationships 

between exposure during developmental lifestages of concern and critical outcomes during 

lifestages of concern identified in the hazard characterization (Section 4.1). Methods for 

assessing dose-response relationships often depend on assumptions used in the absence of data. 

Thus, assumptions are clearly articulated in the risk characterization section. 

The exposure characterization (Section 4.3) describes the basis for values used in 

exposure scenarios. Exposure estimates are based on a combination of available data and 

assumptions. In exposure characterizations, the quality and representativeness of the available 

data are discussed. Then, in turn, the assumptions made, the general logic to develop these 

assumptions, and the effect that they may have on the results are also discussed. The major 

factors considered to contribute to the greatest uncertainty in the exposure characterization are 

described and linked to information from sensitivity analyses. Lack of exposure data or 

limitations of specific types of data are described. 

Detailed guidance on integration of these analysis steps into a risk characterization is 

provided in EPA’s Science Policy Handbook: Risk Characterization (U.S. EPA, 2000e). Other 

sources of information that provide guidance regarding children’s health risk assessment include 

the EPA guidelines for developmental toxicity (U.S. EPA, 1991), reproductive toxicity (U.S. 

EPA, 1996), neurotoxicity (U.S. EPA, 1998b), and cancer risk assessment (U.S. EPA, 2005a,b). 

In addition, the NRC report Science and Judgment in Risk Assessment (NRC, 1994) provides 

additional information about the risk characterization process. 

The issues to be addressed in risk characterization are provided in example questions, 

with an emphasis on lifestage-specific issues, to guide the assessor through this process. The 

information to answer these questions is derived from the analysis phase (Chapter 4) and used in 

the risk characterization. The questions that follow are a modification of those presented in 

EPA’s Science Policy Handbook: Risk Characterization (U.S. EPA, 2000e) and those developed 

5-2 
for risk characterization within the Guidelines for Reproductive Toxicity Risk Assessment (U.S. 

EPA, 1996). 

5.1. LIFESTAGE-SPECIFIC RISK CHARACTERIZATION SUMMARY 

A lifestage-specific risk characterization includes a concise description of the key 

qualitative and quantitative aspects of the analysis. This includes a discussion of the critical 

windows for duration and timing of exposure and outcome. Then, the assessor identifies and 

describes the assumptions, uncertainties, and significant data gaps that could affect the major 

conclusions. Finally, the summary includes a qualitative and quantitative justification for the 

application of lifestage-specific adjustments for duration-specific health values (e.g., use of 

lifestage-specific RfV for a specific duration of exposure) if the assessment warrants it. Three 

basic questions this Framework highlights are (U.S. EPA, 2000e, p.39) 

• Have the potential hazards to children been adequately characterized? 

• Were the potential hazards incorporated into dose-response characterization 

(Section 4.2)? 

• Have the exposures to children been adequately characterized? 

5.1.1. Key Information from the Analysis Phase 

The assessor reviews the narratives for the three analysis steps of the risk assessment 

(Chapter 4) in order to determine the key information relevant to children’s risk. In the narrative, 

the assessor identifies the key studies, summarizes the WOE, presents the justification for the 

calculated major risk estimates, and articulates the defaults and assumptions. The assessor 

considers how the key information from the analysis phase relates back to the purpose and scope 

of the assessment. The following are sample questions to ask when considering the key 

information from the analysis phase of the assessment: 

• What lifestages were assessed? Are there any highly exposed subgroups? 

• What are the most significant lifestage-specific exposure scenarios? What are the 

ranges of exposures? 

• What are the critical effects observed following developmental lifestage 

exposures? Do they differ qualitatively and/or quantitatively from adults who are 

exposed? 

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• How were the exposure scenarios and lifestage(s) accounted for in dose-response 

characterization (Section 4.2)? 

• What are the key studies for TK, TD, and MOA? Does available MOA 

information aid in the interpretation of the hazard data for different lifestages? 

What are the implications of the hypothesized MOAs for potential adverse effects 

and their relationship to risk? 

5.1.2. Scientific Assumptions 

During risk characterization, scientific assumptions and defaults used in the analysis 

phase (Chapter 4) are described. An example of an assumption is using a 3⁄4 body weight scaling 

for inhalation dosimetry in children (U.S. EPA, 2006d). It is important to transparently 

document these assumptions and rationale for decisions made in the assessment. 

• What are the major scientific assumptions related to children’s risks and how are 

they addressed? 

• Was SAR information or MOA information used to bridge chemical-specific data 

gaps for specific lifestages of concern? 

5.1.3. Risk Drivers 

The development of MOE or hazard quotients for critical effects that might occur during 

specified exposures scenarios for certain lifestages may provide worst case scenarios and provide 

some appreciation of relative risk for different adverse outcomes for different exposure 

scenarios. 

• What are the risk drivers, and what are the policy implications? 

• Are specific exposure scenarios during specific lifestages major risk drivers? 

• Are specific critical windows of exposures contributing to the critical outcomes 

that are the major risk drivers? 

5.1.4. Strengths and Weaknesses 

Characterizing the strengths and weaknesses of the database is central to a lifestagespecific risk characterization. In many cases, the information on outcomes following exposure 

during developmental lifestages will be very limited but substantial enough to invoke concern or 

consideration of the strengths of the database. Weaknesses in the database will influence the 

lifestage-characterization of the variability (Section 5.1.4.1), sensitivity (Section 5.1.4.2), and 

uncertainty (Section 5.1.4.3). Integration of the WOE evaluation (Section 4.1.3.1) with the 

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variability, sensitivity, and uncertainty analyses for dose-response (Sections 4.2.5, 4.2.6, and 

4.2.7) and exposure (Sections 4.3.4, 4.3.5, and 4.3.6) provide further characterization and 

integration of the strengths and weaknesses of the overall assessment. This summary strives for 

balance by describing the areas of confidence and uncertainty in the assessment. 

5.1.4.1. Variability 

Explicit acknowledgment of sources of variability is considered in the risk 

characterization phase. By summarizing the findings from the variability analyses conducted in 

the analysis phase (Sections 4.1.2.9, 4.1.3.1.2.1, 4.2.5, and 4.3.4), it may be possible to determine 

whether different approaches provide similar risk estimates. Answers to the following questions 

may be helpful to describe the overall variability of the assessment: 

• Does the assessment capture the variability in the exposed population? How is 

variability addressed? 

• Who is most at risk (e.g., physiologically, genetically, highly exposed)? 

• What is the relevance of experimental animal studies to humans at particular 

lifestages? 

• What are the limitations of the data available regarding variability? What data 

gaps related to variability exist? 

• Are there biological, behavioral, ethnic, racial, or socioeconomic factors that may 

affect variability in human exposure or response? 

5.1.4.2. Sensitivity 

The findings from the sensitivity analyses conducted in the analysis phase (Sections 4.2.6 

and 4.3.5) are summarized in the risk characterization phase in order to underscore the strengths 

and the weaknesses related to the derivations of health values and exposure values in the 

assessment. Answers to the following questions may help describe the overall sensitivity of the 

assessment: 

• What parameters have the greatest influence on the dose-response and exposure 

model outputs? 

• Are the parameters to which a model is most sensitive likely to vary across 

lifestages? What is the likely impact of such differences on model predictions on 

defining variability or uncertainty in the assessment? 

• What are the limitations of the data available regarding sensitivity? What data 

gaps related to sensitivity exist? 

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5.1.4.3. Uncertainty 

Uncertainty originating from various data sources can have an impact on risk analysis. 

Explicit acknowledgment of sources of uncertainty described in the analysis phase (Sections 

4.1.2.10, 4.1.3.1.2.2, 4.2.7, and 4.3.6) is considered when integrating the uncertainties in the risk 

characterization. This summary includes clear and concise statements about the limitations of 

the data from the analysis phase for this lifestage-specific assessment and may include discussion 

of uncertainties in other related assessments. Critical data gaps, defined by the impact they have 

on the risk assessment, are identified and described. These critical data gaps may require 

consideration and application of uncertainty factors (e.g., database UF). In addition, uncertainty 

or critical data gaps may suggest further studies that may provide new information or insight to 

reduce uncertainties in a future risk assessment. Answers to the following questions may prove 

helpful in describing the overall uncertainty of the assessment: 

• What are the uncertainties in the assessment for different lifestages of 

development? How are these uncertainties addressed? 

• How are the limitations of the available data related to uncertainty? What 

significant data gaps exist relevant to uncertainty? How do these impact the 

magnitude of uncertainty in the assessment? 

• What are the priority data-needs studies that could produce information that may 

reduce uncertainties in lifestage-specific risk assessment? 

• What are the degrees of confidence in the dose-response and exposure model(s) 

that are used to derive risk values? 

5.1.5. Key Conclusions 

A description of critical effects and the supporting evidence for these conclusions is 

included in this section. Attendant risk numbers or a range of risk values for the critical effects 

can illustrate some degree of certainty for the key conclusions. For outputs of this analysis to be 

most useful in benefits analysis (Chapter 3), the outcomes that are quantified are expressed as 

changes in adverse outcomes or precursor effect (e.g., change in incidence of illness or 

symptoms) that are readily understood by the public. Reliance on single point risk estimates for 

key conclusions may not be very useful for benefits analysis. 

• What are the major qualitative conclusions regarding risk from developmental 

exposure? What is the degree of confidence in the conclusions? 

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• What are the quantitative estimates of the risk from developmental exposure? 

How do risks compare across lifestages? What is the degree of confidence in the 

risk estimates? 

• Are there any broad risk implications for classes of compounds (e.g., SARrelated, same MOA)? Lifestages (e.g., in male fetuses, the period of sexual 

differentiation in utero is sensitive to exposure to anti-androgens)? 

5.1.6. Alternative Risk Estimates Considered 

Consideration of alternative hypotheses to explain lifestage-specific outcomes and the 

related exposures (Section 4.1.3.1.7) is part of transparency. Principles of parsimony (economy 

or simplicity of assumptions in logical formulation) should be considered in the presentation of 

alternatives and related to the lifestage-specific data that exist. The following examples are 

questions to consider regarding alternative risk estimates: 

• What are the results of different analysis approaches (i.e., modeling, monitoring, 

and probability distributions)? 

• Were adults considered to be more or less sensitive than other lifestages? 

• What is the relative difference in the final risk value when using adult versus 

developmental lifestages of exposure? What is the relative difference in the final 

risk value when using a default versus a data rich approach? 

• Are alternative hypotheses considered that might explain the observed lifestagespecific outcomes? Does an alternative hypothesis provide different risk 

estimates than the primary hypothesis? 

5.1.7. Research Needs 

The characterization of risk in many cases reveals lifestage-specific data gaps, but not all 

of these data gaps may translate into critical research needs. Research needs may be based upon 

qualitative or quantitative considerations in the database and the prioritization of research needs 

helps determine whether specific new data could potentially reduce uncertainty in the 

assessment. Questions to consider when assessing research needs for characterizing variability 

and uncertainty in risk estimates include the following: 

• What are the priority lifestage-specific research needs? Are these chemicalspecific, chemical class-specific, or basic research needs? 

• Can priorities be assigned if more than one lifestage-specific research need is 

identified? 

5-7 
• Can the impact of the research be estimated (e.g., reduction of uncertainty in the 

assessment)? 

• What are the key sources of variability, sensitivity, and uncertainty for the 

purpose of prioritizing additional data collection or research? 

5.2. RISK CONTEXT 

The risk characterization is anticipated to provide an answer to the problem formulation 

(Chapter 3), which may have included an initial screening of risk for prioritization and a 

preliminary estimation of risk). If the statement of the problem evolved during the analysis 

phase (Chapter 4), then this process is summarized in the risk characterization phase. 

The risk estimates in this lifestage-specific assessment are described in the context of 

other similar or related risk assessments. The science policy assumptions employed in this 

assessment are clearly articulated in order to compare with previous decisions. Discussion of 

alternative hypotheses, alternative MOAs, and alternative risk estimates can be included to 

provide context to other previous risk decisions. The risk context could include discussion of 

cumulative and multiple exposures and their potential impact on a common MOA(s). 

The risk context can also provide background for developing risk communication 

materials, which could include risk perception in light of related or prior risk decisions. 

Questions regarding risk context include the following: 

• Where appropriate, can this risk be compared with other risks characterized by EPA 

or by other federal or state agencies? Have these other previous assessments 

reached similar or significantly different conclusions? What are the limitations of 

making these comparisons? 

• What science policy (default) assumptions were employed in each of the three steps 

of the analysis phase? 

• What were the scientific assumptions in each of the three steps of the analysis phase 

that may have policy implications? 

• What alternative hypotheses were evaluated? What is the justification for the 

decision to choose one hypothesis over another? 

• Is there reason to be concerned about cumulative or multiple exposures to classes of 

agents with a similar mechanism or MOA? 

• Are there significant community concerns or common risks with which people may 

be familiar that may influence public perception of risk to children? 

• Is the risk characterization information presented in a way that could be used for 

benefit analysis? 

5-8 
• Is the risk characterization information presented in a way that could be used for 

benefit analysis? 

5-9 
6. SUMMARY AND IDENTIFICATION OF GAPS IN APPROACHES FOR 

CHILDREN’S HEALTH RISK ASSESSMENT 

This Framework summarizes the process for assessing health risks resulting from 

children’s exposure to environmental agents using a phased approach that includes problem 

formulation (Chapter 3), analysis (Chapter 4), and risk characterization (Chapter 5). It uses 

many EPA documents that have outlined similar risk assessment approaches (U.S. EPA, 1998a, 

2003a) and a workshop report that identified the need for and began the development of an 

approach to assessing children’s risk from environmental exposures (ILSI, 2003). 

This Framework is a conceptual overview of the considerations for evaluation of early 

life exposures and subsequent outcomes and does not constitute EPA guidance defined as a stepby-step process or standard operating procedure. This overview is accomplished by posing 

targeted questions to address each phase of the process and by referencing appropriate 

guidelines, guidance documents, and other relevant reports and literature. These references, 

including several EPA risk assessment guidelines related to health risks from children’s 

exposures, can be drawn upon for more detailed information. One of the most relevant 

references is the Guidelines for Developmental Toxicity Risk Assessment (U.S. EPA, 1991) that 

focuses primarily on the effects of prenatal exposures and, to a limited extent, on postnatal 

exposures and outcomes. Other EPA guidelines or guidance are focused on system- or diseasespecific issues that include the effects of developmental exposures, specifically reproductive 

toxicity (U.S. EPA, 1996), neurotoxicity (U.S. EPA, 1998b), and cancer (U.S. EPA, 2005a,b). 

Guidelines or guidance on the effects of developmental exposures on other systems (e.g., 

respiratory, immune, renal, hepatic, cardiovascular, and, to some extent, endocrine) or outcomes 

(e.g., biomarkers of exposure or effect, toxicogenomics data) are lacking. 

The relevance of specific developmental exposures on latent outcomes for application to risk 

assessments for various durations of exposure (i.e., acute, short term, and subchronic) is 

considered in many of the risk assessments currently being generated across EPA, although this 

issue has not been thoroughly explored to date. The document A Review of the Reference Dose 

and Reference Concentration Processes previously identified data needs and alternative 

approaches and strategies for developing testing guidelines; these have not yet been addressed 

and are reiterated below (U.S. EPA, 2002a, Section 5). In addition, there is a need for focused 

guidance on dose-response assessment after developmental exposures, despite the fact that a 

6-1 
good deal of research and methods development on BMD (U.S. EPA, 2000d) and biological 

modeling (Clewell et al., 2002a; Ginsberg et al., 2004b; Lau et al., 2000, 2001; Setzer et al., 

2001) has been done using developmental data in experimental animals and humans. With 

regard to exposure assessment, there is limited EPA guidance on approaches specific to children 

at different lifestages, with the exception of the interim document Child- Specific Exposure 

Factors Handbook (U.S. EPA, 2002c) and the Guidance on Selecting Age Groups for 

Monitoring and Assessing Childhood Exposures to Environmental Contaminants (U.S. EPA, 

2005e). Methods for both screening level and more detailed quantitative estimates of children’s 

exposures are needed. Data for the recommended age groups (U.S. EPA, 2005e) are limited or 

nonexistent for some exposure factor determinations. The Framework for Cumulative Risk 

Assessment (U.S. EPA, 2003a) addresses generic concepts and approaches to evaluate 

cumulative risk; however, there is no specific guidance on developmental lifestage risk from 

cumulative exposures. 

The integration of toxicity data and children’s exposure estimates is an area for which no 

guidance exists but is needed. This integration is important because one exposure can lead to 

multiple outcomes, particularly for developmental exposures. In addition, the characteristics for 

each age group of concern to environmental agents can differ significantly for exposure and 

susceptibility. Therefore, guidance is also needed on using information on biological processes 

underlying development, MOA information, chemical-specific mechanisms, and anatomical, 

physiological, and behavioral characteristics at different developmental lifestages to determine 

critical times for exposure and the corresponding outcomes of concern. 

At this time, significant research questions remain unanswered on the use of available 

exposure data to assess children’s risk, such as 

• How can biomonitoring data be interpreted to characterize exposure? How can 

available adult biomonitoring data be applied to children? 

• How can available data from children be interpreted across developmental stages 

for which there are limited data? 

• How can activity pattern data be used to classify children for exposure 

characterization? 

• What resources or approaches can one use to address risk methodology for 

extrapolating inhalation dose to developmental lifestages? 

6-2 
• Can guidance be developed on incorporating critical window of vulnerability to 

reduce uncertainty, specifically for the time frame over which exposure should be 

averaged? 

• How can risks be extrapolated to developmental exposure to non-genotoxic 

carcinogens? 

• How can developmental lifestage-specific MOAs influence latent expression of 

adverse outcomes? 

• Since TK and TD in children can rarely be studied, how can model variability in 

internal dose and sensitivity to toxicant action be better characterized? 

Many of these questions are actively being investigated. These efforts will likely 

contribute to future guidance and policy papers on specific issues related to children’s exposure 

and subsequent outcomes. 

6-3 
GLOSSARY 

Activity Pattern Data – Information on human activities used in exposure assessments. The 

information may include a description of the activity, frequency of activity, duration spent 

performing the activity, and the microenvironment in which the activity occurs. 

Adverse Effect – A biochemical change, functional impairment, or pathologic lesion that affects 

the performance of the whole organism or reduces an organism’s ability to respond to an 

additional environmental challenge. 

Age-Dependent Adjustment Factors (ADAF) – Adjustments to cancer slope factors that 

recognize the increased susceptibility to cancer from early life exposures to mutagens in the 

absence of chemical-specific data. 

Area Under the Curve (AUC) – The area of the time x concentration curve that helps to define 

the internal dose. 

Benchmark Dose (BMD) – A dose that produces a predetermined change in response rate of an 

adverse effect (called the benchmark response or BMR) compared to background. 

Benchmark Dose Lower Confidence Level (BMDL) – A statistical lower confidence limit on 

the dose at the BMD. 

Benefits Analysis – A method that develops monetary values comparing costs and benefits to 

inform the policy making process (U.S. EPA, 2000a). 

Bias – A trend in methodology or analysis that can lead to systematic deviations from the true 

data. 

Biologically Based Dose-Response (BBDR) Model – A predictive model that describes 

biological processes at the cellular and molecular level linking the target organ dose to the 

adverse effect. 

Biomarker – A biological molecule or biochemical indicator of exposure or biological changes 

resulting from exposures, or markers of risk or susceptibility. 

Biomonitoring – The assessment of human exposure to chemicals by the measurement of the 

chemicals or their metabolites (breakdown products) in human tissues or fluids such as blood or 

urine. Blood and urine levels reflect the amount of the chemical in the environment that actually 

gets into the body. 

Body Burden – The amount of a particular chemical, especially a potentially toxic chemical, 

stored in the body at a particular time as a result of exposure. Body burdens can be the result of 

long-term or short-term storage, e.g., the amount of a metal in bone, the amount of a lipophilic 

G-1 
substance such as PCB in adipose tissue, or the amount of carbon monoxide (as 

carboxyhemoglobin) in the blood. 

Bounding Estimate – An estimate of exposure, dose, or risk that is higher than that incurred by 

the person in the population with the highest exposure, dose, or risk. Bounding estimates are 

useful in developing statements that exposures, doses, or risks are "not greather than" the 

estimated value. 

Cancer – A disease of heritable, somatic mutations affecting cell growth and differentiation and 

characterized by an abnormal, uncontrolled growth of cells. 

Case-Control Study – An epidemiologic study that compares subjects with the disease of 

interest (cases) to subjects without the disease (controls). The groups are compared with respect 

to exposure history to ascertain whether they differ in the proportion exposed to the chemical(s) 

under investigation. 

Case Report – A description of a person in a population or study group identified as having a 

particular disease, health disorder, or condition under investigation, without a comparison made 

to a control. 

Child – Conception to maturation of all organ systems, approximately 21 years of age. 

Concentration – The ratio of the mass or volume of a solute to the mass or volume of the 

solution or solvent. 

Conceptual Model – A written description or a visual representation of actual or predicted 

relationships between humans or ecological entities and the chemicals or other stressors to which 

they may be exposed. 

Confounder (or Confounding Factor) – A condition or variable that is both a risk factor for 

disease and is associated with an exposure or outcome of interest. This association between the 

exposure of interest and the confounder may make it falsely appear that the exposure of interest 

is associated with the outcome. 

Critical Effect – The first adverse effect, or its known precursor, that occurs to the most 

sensitive species as the dose rate of an agent increases. 

Critical Window of Exposure – Developmental period when vulnerability to exposures is 

increased and can result in developmental effects. 

Cumulative Impact – The combination of aggregate exposures to multiple agents or stressors. 

Detoxification – Process of chemical modification that make a toxic molecule less toxic. 

Dose – The amount of a substance available for interaction with metabolic processes or 

biologically significant receptors after crossing the outer boundary of an organism. 

G-2 
• Absorbed Dose is the amount crossing a specific absorption barrier (e.g., the exchange 

boundaries of skin, lung, and digestive tract) through uptake processes. 

• Biologically Effective Dose is the amount of the chemical available for interaction by any 

particular organ or cell. 

• Internal Dose is a more general term denoting the amount absorbed without respect to 

specific absorption barriers or exchange boundaries. 

• Potential Dose is the amount ingested, inhaled, or applied to the skin. 

Dose Metric – The target tissue dose that is closely related to ensuing adverse response. Dose 

metrics reflect the biologically active form of the chemical, its level, and duration of exposure, 

and its intensity. Examples of units of measurement for dose are AUC, maximum concentration. 

Dose-Response Assessment – The determination of the relationship between the magnitude of 

administered, applied, or internal dose and a specific biological response. Response can be 

expressed as measured or observed incidence, percent response in groups of subjects (or 

populations), or the probability of occurrence of a response in a population. 

Dose-Response Curve – A graphical representation of the quantitative relationship between 

administered, applied, or internal dose of a chemical or agent, and a specific biological response 

to that chemical or agent. 

Dosimetric Adjustment Factor (DAF) – A multiplicative factor used to adjust observed 

experimental or epidemiological data to human equivalent concentration for assumed ambient 

scenario. 

Dosimetry – A process of measuring or estimating dose. 

Effect Modifier – A variable that modifies the outcome of interest by a greater (synergistic) or 

lesser (antagonistic) effect. An effect modifier can be identified through stratification of the 

data. 

Environmental Fate – The destiny of a chemical or biological pollutant after release into the 

environment. Environmental fate involves temporal and spatial considerations of transport, 

transfer, storage, and transformation. 

Epidemiology – The study of the distribution and determinants of health-related states or events 

in specified populations. 

Exposure – Contact made between a chemical, physical, or biological agent and the outer 

boundary of an organism. Exposure is quantified as the amount of an agent available at the 

exchange boundaries of the organism (e.g., skin, lungs, gut). 

• Acute Exposure is exposure by the oral, dermal, or inhalation route for 24 hours or less. 

• Chronic Exposure is repeated exposure by the oral, dermal, or inhalation route for more 

than approximately 10% of the life span in humans (more than approximately 90 days to 

2 years in typically used laboratory animal species). 

G-3 
• Intermittent Exposure is a repeated exposure in which there is no effect of one exposure 

on the effect of the next; this definition implies sufficient time for the chemical and its 

metabolites to subchronic clear the biological system before the subsequent exposure 

(i.e., non-cumulative toxicokinetics). 

• Longer-Term Exposure is repeated exposure by the oral, dermal, or inhalation route for 

more than 30 days, up to approximately 10% of the life span in humans (more than 30 

days up to approximately 90 days in typically used laboratory animal species). 

• Short-Term Exposure is multiple or continuous exposure to an agent for a short period of 

time, usually 1 week. 

Exposure Assessment – An identification and evaluation of the human population exposed to a 

toxic agent that describes its composition and size and the type, magnitude, frequency, route, and 

duration of exposure. 

Exposure Concentration – The concentration of a chemical in its transport or carrier medium at 

the point of contact. 

Exposure Factor – Variables that define how exposure to a chemical or agent takes place (e.g., 

concentration, intake, body weight). 

Exposure Media – Major environmental categories that surround or contact humans, animals, 

plants, and other organisms (surface water, ground water, soil, or air) and through which 

chemicals or pollutants move. 

Exposure Pathway – The physical course a chemical or pollutant takes from its source to the 

organism exposed. 

Exposure Route – The way a chemical or pollutant enters an organism after contact, e.g., by 

ingestion, inhalation, or dermal absorption. 

Exposure Scenario – A combination of facts, assumptions, and inferences that define a discrete 

situation where potential exposures may occur. These may include the source, the exposed 

population, the time frame of exposure, microenvironment(s), and activities. Scenarios are often 

created to aid exposure assessors in estimating exposure. 

Database (Extent of) – Minimal Database is a database in which no human data are available, 

and route-specific toxicity data are limited to dose-response data applicable to the duration in 

question with assessment of outcomes other than mortality. A study showing only effect levels 

for mortality or other extremely severe toxicity would not be sufficient to set a reference value. 

Robust Database is a database that includes extensive human and/or animal toxicology data that 

cover route-specific information on many health outcomes, durations of exposure, timing of 

exposure, lifestages, and susceptible subpopulations (see U.S. EPA, 2000b, pages 4-19). 

Hazard Assessment – The process of determining whether exposure to an agent can cause an 

increase in the incidence of a particular adverse health effect (e.g., cancer, birth defect) and 

whether the adverse health effect is likely to occur in humans. 

G-4 
Hazard Characterization – A description of the potential adverse health effects attributable to a 

specific environmental agent, the mechanisms by which agents exert their toxic effects, and the 

associated dose, route, duration, and timing of exposure. 

Human Equivalent Concentration (HEC) or Dose (HED) – The human concentration (for 

inhalation exposure) or dose (for other routes of exposure) of an agent that is believed to induce 

the same magnitude of toxic effect as the experimental animal species concentration or dose. 

This adjustment may incorporate toxicokinetic information on the particular agent, if available, 

or use a default procedure, such as assuming that daily oral doses experienced for a lifetime are 

proportional to body weight raised to the 0.75 power. 

Intake Rate – Rate of inhalation, ingestion, and dermal contact, depending on the route of 

exposure. For ingestion, the intake rate is simply the amount of food containing the contaminant 

of interest that an individual ingests during some specific time period (units of mass/time). For 

inhalation, the intake rate is the rate at which contaminated air is inhaled. Factors that affect 

dermal exposure are the amount of material that comes into contact with the skin and the rate at 

which the contaminant is absorbed. 

Key Event – A key event is an empirically observable precursor step that is itself a necessary 

element of the mode of action (U.S. EPA, 2005a,b). Toxicokinetic and toxicodynamic steps that 

lead to a toxic response can be considered as key event(s). 

Lifestage Approach – The comparison of exposure and effect data across different lifestages 

from conception to old age. This approach provides a temporal context in which to evaluate data 

for risk assessment. 

Longitudinal Study – An epidemiologic study comparing subject with an exposure of interest to 

those without the exposure. These two cohorts are then followed over time to determine the 

differences in the rates of disease between the exposure subjects. 

Low-Dose Extrapolation – An estimate of the response at a point below the range of the 

experimental data, generally through the use of a mathematical model. 

Lowest-Observed-Adverse-Effect Level (LOAEL) – The lowest exposure level at which there 

are biologically significant increases in frequency or severity of adverse effects among the 

exposed population when compared with an appropriate control group. 

Margin of Exposure (MOE) – The ratio of the point of departure (POD) over an exposure 

estimate (MOE = POD/Exposure). 

Mechanism of Action – The complete sequence of biological events (i.e., including 

toxicokinetic and toxicodynamic events) from exposure to the chemical to the ultimate cellular 

and molecular consequences of chemical exposure that are required in order to produce the toxic 

effect. However, events that are coincident but not required to produce the toxic outcome are not 

included. 

G-5 
Media – see Exposure Media. 

Meta-Analysis – Any systematic method that uses statistical analysis to integrate the data from a 

number of independent studies. 

Mode of Action – The sequence of key event(s) (i.e., toxicokinetics and toxicodynamics) after 

chemical exposure upon which the toxic outcome depend. 

Model – A mathematical function with parameters that can be adjusted so that the function 

closely describes a set of empirical data. A mechanistic model usually reflects observed or 

hypothesized biological or physical mechanisms and has model parameters with real world 

interpretation. In contrast, statistical or empirical models selected for particular numerical 

properties are fitted to data; model parameters may or may not have real world interpretation. 

When data quality is otherwise equivalent, extrapolation from mechanistic models (e.g., 

biologically based dose-response models) often carries higher confidence than extrapolation 

using empirical models (e.g., logistic model). 

No-Observed-Adverse-Effect Level (NOAEL) – The highest exposure level at which there are 

no biologically significant increases in the frequency or severity of adverse effect between the 

exposed population and its appropriate control; some effects may be produced at this level, but 

they are not considered adverse or precursors of adverse effects. 

Outcome – A clinical manifestation of biological effects that results from an exposure. 

Pathway – see Exposure Pathway. 

Person-Oriented Model – An approach in which the individual’s exposure-related 

characteristics are defined first and then used to determine the probability of the individuals’ 

being exposed to a specific source and the resulting dose. 

Physiologically based Toxicokinetic (PBTK) Model – A model that estimates the dose to a 

target tissue or organ by taking into account the rate of absorption into the body, distribution 

among target organs and tissues, metabolism, and excretion. (Also referred to as physiologically 

based pharmacokinetic model.) 

Point-of-Contact Approach – An approach to quantifying exposure by taking measurements of 

concentration over time at or near the point of contact between the chemical and an organism 

while the exposure is taking place. 

Point of Departure (POD) – The dose-response point that marks the beginning of a low-dose 

extrapolation. This point can be the lower bound on dose for an estimated incidence or a change 

in response level from a dose-response model (BMD) or a NOAEL or LOAEL for an observed 

incidence, or change in level of response. 

Portal of Entry – The point at which the contaminant enters the body (e.g., mouth, nose, skin). 

G-6 
Precursor Event – An early condition or state preceding the pathological onset of a disease. 

Reference Concentration (RfC) – An estimate (with uncertainty spanning perhaps an order of 

magnitude) of a continuous inhalation exposure to the human population (including sensitive 

subgroups) that is likely to be without an appreciable risk of deleterious effects during a lifetime. 

It can be derived from a NOAEL, a LOAEL, or a benchmark concentration, with uncertainty 

factors generally applied to reflect limitations of the data used. It is generally used in EPA's 

noncancer health assessments. 

Reference Dose (RfD) – An estimate (with uncertainty spanning perhaps an order of magnitude) 

of a daily oral exposure to the human population (including sensitive subgroups) that is likely to 

be without an appreciable risk of deleterious effects during a lifetime. It can be derived from a 

NOAEL, a LOAEL, or a benchmark dose, with uncertainty factors generally applied to reflect 

limitations of the data used. It is generally used in U.S. EPA’s noncancer health assessments. 

Reference Value (RfV) – An estimation of an exposure for (a given duration) to the human 

population (including susceptible subgroups) that is likely to be without an appreciable risk of 

adverse effects over a lifetime. It is derived from a BMDL, a NOAEL, a LOAEL, or another 

suitable POD, with uncertainty/variability factors applied to reflect limitations of the data used. 

Risk (in the context of human health) – The probability of adverse effects resulting from 

exposure to an environmental agent or mixture of agents. 

Risk Assessment (in the context of human health) – The evaluation of scientific information 

on the hazardous properties of environmental agents (hazard characterization), the dose-response 

relationship (dose-response assessment), and the extent of human exposure to those agents 

(exposure assessment). The product of the risk assessment is a statement regarding the 

probability that populations or individuals so exposed will be harmed and to what degree (risk 

characterization). 

Risk Characterization – The integration of information on hazard, exposure, and dose-response 

to provide an estimate of the likelihood that any of the identified adverse effects will occur in 

exposed people. 

Risk Management (in the context of human health) – A decision-making process that 

accounts for political, social, economic, and engineering implications together with risk-related 

information in order to develop, analyze, and compare management options and select the 

appropriate managerial response to a potential chronic health hazard. 

Route – see Exposure Route. 

Structure-Activity Relationship (SAR) approach to toxicology screening – This approach 

elucidates the relationship between features of chemical structure and biological activity. It is 

based on the premise that the biological fate and activity of a chemical (i.e., whether it is 

absorbed, metabolized, or bioaccumulated and whether it interacts at a molecular level to exert a 

response) is ultimately determined by chemical structure. 

G-7 
Scenario Evaluation Approach – An approach to quantifying exposure by measurement or 

estimation of both the amount of a substance contacted and the frequency/duration of contact and 

subsequently linking these together to estimate exposure or dose. 

Sensitivity Analysis – Refers to the variation in output of a model with respect to changes in the 

values of the model input(s). Sensitivity analysis can provide a quantitative ranking of the model 

inputs based on their relative contributions to model output variability and uncertainty (U.S. EPA 

2001b). 

Short-Term Exposure – Repeated exposure by the oral, dermal, or inhalation route for more 

than 24 hours, up to 30 days. 

Slope Factor – An upper bound, approximating a 95% confidence limit, on the increased cancer 

risk from a lifetime exposure to an agent. This estimate, usually expressed in units of proportion 

(of a population) affected per mg/kg/day, is generally reserved for use in the low-dose region of 

the dose-response relationship, i.e., for exposures corresponding to risks less than 1 in 100. 

Source – The origin of an agent for the purposes of an exposure assessment. 

Source-to-Dose Model – An approach where an environmental agent is followed from its source 

to the resulting dose. 

Stakeholder – An interested party who is concerned with the decisions made about how a risk 

may be mitigated, avoided, reduced, or eliminated, and the communities that may be impacted by 

regulatory decisions. 

Stressor – Any entity, stimulus, or condition that can modulate normal functions of the organism 

or induce an adverse response (e.g., agent, lack of food, drought). 

Superfund – Federal authority, established by the Comprehensive Environmental Response, 

Compensation, and Liability Act (CERCLA) (U.S. 96th Congress, 1980) to respond directly to 

releases or threatened releases of hazardous substances that may endanger health or welfare. 

Susceptibility – Increased likelihood of an adverse effect or an exposure, often discussed in 

terms of relationship to a factor, that can be used to describe a human subpopulation (e.g., 

lifestage, demographic feature, or genetic characteristic). 

Susceptible Subgroups – May refer to lifestages (e.g., children or the elderly), or to other 

segments of the population (e.g., asthmatics, the immune-compromised, or the highly exposed). 

The term is likely to be chemical-specific, and may not be consistently defined in all cases. 

Target Organ – The biological organ most adversely affected by exposure to a chemical, 

physical, or biological agent. 

Toxicity – Deleterious or adverse biological effects elicited by a chemical, physical, or 

biological agent. 

G-8 
Toxicodynamic (TD) – The determination and quantification of the sequence of events at the 

cellular and molecular levels leading to a toxic response to an environmental agent (sometimes 

referred to as pharmacodynamics, also MOA. 

Toxicokinetic (TK) – The determination and quantification of the time course of absorption, 

distribution, metabolism, and excretion of chemicals (sometimes referred to as 

pharmacokinetics). 

Toxification – Metabolic conversion of a potentially toxic substance to a product that is more 

toxic. 

Uncertainty – Uncertainty occurs because of a lack of knowledge. It is not the same as 

variability. For example, a risk assessor may be very certain that different people drink different 

amounts of water but may be uncertain about how much variability there is in water intakes 

within the population. Uncertainty can often be reduced by collecting more and better data, 

whereas variability is an inherent property of the population being evaluated. Variability can be 

better characterized with more data but it cannot be reduced or eliminated. Efforts to clearly 

distinguish between variability and uncertainty are important for both risk assessment and risk 

characterization. 

Uncertainty Factor (UF) – One of several, generally 10-fold, default factors used in 

operationally deriving the RfD and RfC from experimental data. The factors are intended to 

account for 

• variation in susceptibility among the members of the human population (i.e., 

interindividual or intraspecies variability) 

• uncertainty in extrapolating experimental animal data to humans (i.e., interspecies 

uncertainty); 

• uncertainty in extrapolating from data obtained in a study with less-than-lifetime 

exposure (i.e., extrapolating from subchronic to chronic exposure) 

• uncertainty in extrapolating from a LOAEL rather than from a NOAEL and 

• uncertainty associated with extrapolation when the database is incomplete. 

Variability – Variability refers to true heterogeneity or diversity. For example, among a 

population that drinks water from the same source and with the same contaminant concentration, 

the risks from consuming the water may vary. This may be due to differences in exposure (i.e., 

different people drinking different amounts of water and having different body weights, different 

exposure frequencies, and different exposure durations) as well as differences in response (e.g., 

genetic differences in resistance to a chemical dose). Those inherent differences are referred to 

as variability. Differences among individuals in a population are referred to as interindividual 

variability; differences for one individual over time is referred to as intraindividual variability. 

Vulnerability – A matrix of physical, chemical, biological, social, and cultural factors which 

result in certain communities and subpopulations being more susceptible to environmental 

toxins, being more exposed to toxins, or having compromised ability to cope with and/or 

recover from such exposure. Four types of vulnerability are considered with regard to a 

G-9 
lifestage approach: susceptibility or sensitivity, differential exposure, differential 

preparedness, and differential ability to recover (NEJAC, 2004). 

Weight-of-Evidence (WOE) – An approach requiring a critical evaluation of the entire body of 

available data for consistency and biological plausibility. Potentially relevant studies are judged 

for quality and studies of high quality given much more weight than those of lower quality (see 

U.S. EPA, 2000b, pages 4-11-12). 

G-10 
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