OAR Box 1112 

Prepped by Ryan Dugan 

Document Number: 

25) ll-C-lc 

Docket Number: 

A-83-22 

Printed 4/16/2008 9:12:06 AM Sheet 25 of 97 

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• 

DEVELOPMENT OF DECISION ANALYSIS METHODOLOGY 

FOR HEALTH RISK ASSESSMENT 

c 

c 

*(3£! 

An Illustrative Application to Alternative 

Air Quality Standards for Carbon Monoxide 

Final Report 

By: 

Anne E. Smith 

Peter C. McNamee, Project Leader 

Miley W. Merkhofer, Project Supervisor 

Project Officer: Thomas McCurdy 

Contract: 68-02-3575 

Prepared for: 

Environmental Protection Agency 

Research Triangle Park 

North Carolina 27711 

SRI Project 3432 

333 Ravenswood Ave. . Manic Park. CA 9^025 

859-6200 . TWX: 910-373-2046 : 486 
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CONTENTS 

LIST OF ILLUSTRATIONS vii 

LIST OF TABLES xi 

PREFACE xiii 

PART I—OVERVIEW OF RISK ASSESSMENT PROCEDURE 1 

I INTRODUCTION, RESEARCH SUMMARY, AND CONCLUSIONS 3 

Methodology Requirements 3 

Overview of the Decision-Analytic Risk Assessment Process . 4 

Summary and Results of the Illustrative Application of the 

Risk Assessment Process to Carbon Monoxide 9 

Methodological Conclusions 14 

Overall Characteristics of the Program 17 

REFERENCES 19 

PART II—EMISSIONS, ATMOSPHERIC DISPERSION, AND EXPOSURE MODELS . 21 

II EMISSIONS AND ATMOSPHERIC DISPERSION 23 

Emission and Atmospheric Dispersion Submodel 23 

Proposed Ambient Air Quality Standards Analyzed in this 

Report 28 

EPA Model of Implementation of Standards 29 

Summary 30 

REFERENCES 31 

III EXPOSURE MODEL 33 

Exposure Model for CO 33 

Data Supplied for This Study by EPA Model 37 

Summary 38 

REFERENCES 41 

PART III—HEALTH DOSE-RESPONSE MODELS 43 

IV HEALTH EFFECTS DOSE-RESPONSE MODELS 45 

The Modeling Approach 45 

Choice of Sensitive Groups 47 

Measures of Health Risk 50 

V ARTERIOSCLEROTIC HEART DISEASE 53 

Background and Modeling Issues 53 

Deterministic Model 55 

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V ARTERIOSCLEROTIC HEART DISEASE (Concluded) 

Probabilistic Phase 63 

Discussion 71 

Other Manifestations of ASHD 77 

Summary 78 

REFERENCES 79 

VI FETAL HEALTH EFFECTS 81 

Physiological Background 81 

Deterministic Model . 82 

Probabilistic Phase ' . . 88 

Discussion of Results 91 

Summary 93 

REFERENCES 95 

VII CHRONIC OBSTRUCTIVE PULMONARY DISEASE 97 

Background and Modeling Issues ... . 97 

Deterministic Model 98 

Probabilistic Model 105 

Summary 108 

REFERENCES 115 

VIII . CENTRAL NERVOUS SYSTEM 117 

Background and Modeling Issues 117 

Deterministic Model 119 

Probabilistic Model 130 

Discussion 133 

Summary 133 

REFERENCES 135 

PART IV—-SIGNIFICANCE OF RESULTS 137 

IX HEALTH RISKS FOR THE NATION 139 

Relation of the Health Risk and Exposure Models 139 

Estimates of Health Risks for Entire Nation 143 

Probabilistic Phase 148 

Summary 148 

REFERENCES 157 

X SIGNIFICANCE WEIGHTING 159 

Choice of Units 159 

Assignment of EDRAs 160 

Results 161 

Conclusion 164 

XI UNCERTAINTIES WITH HIGH VALUES OF RESOLUTION 167 

APPENDIX A 171 

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r r- -IJTT 

APPENDIX B 197 

Pitfalls of Encoding 197 

The Encoding Sessions 199 

Use of Encoded Distributions 202 

Summary of Other Inverviews 207 

REFERENCES 213 
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ILLUSTRATIONS 

1.1 Health Risk Assessment Cycle 5 

1.2 Major Components of Health Impact Outcome Calculator .. . 6 

2.1 Emissions and Atmospheric Dispersion Model 24 

3.1 Exposure Model 34 

5.1 Probability Distribution on Maximal Myocardial Oxygen 

Supply 56 

5.2 Group Means of Aronow's Results for Double Product at Onset 

of Angina 58 

5.3 Probability Distribution on MOC 59 

5.4 Relationships Between Lowered MOS and Higher Frequency of 

Anginal Pain 61 

5.5 Probability Tree for Angina Model 64 

5.6 Uncertainty in Measurement of Slope of Double Product at 

Onset of Angina 67 

5.7 Probability Distributions of Incidence of Anginal Pain for 

Los Angeles 68 

5.8 Probability Distributions on Fractional Change from As-Is 

Situation for Ischemic Episodes (Los Angeles) 70 

5.9 Example of a Sigmoidal Dose-Response Curve 74 

5.10 Health Dose-Response Curve with Superimposed Threshold . . 75 

6.1 Probability Distribution for Fetal Oxygen Tension in the 

Umbilical Artery 84 

6.2 Probability of Fetal Death as a Function of Oxygen Tension 

in Fetal Umbilical Artery 86 

6.3 Probability Tree Used with Fetal Death Model 89 

6.4 Probability Distribution on Fetal Deaths (>20 Week 

Gestation) per Year in Los Angeles 90 

7.1 Probability that Pa 2 Falls Below the Value Indicated 

During the Day for a Sensitive COPD Patient 101 

7.2 Probability that SaQ2 Falls below the Stated Value on a 

Given Day for Sensitive COPD Patients 102 

7.3 Probability of Acute Respiratory Failure as a Function of 

Sa02 10 4 

7.4 Probability of Death as a Function of Sa02, Given that 

Acute Respiratory Failure has Occurred 106 

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"I-* 1 lllf-fll 

7.5 Probability Tree Used for COPD Model 107 

7.6 Probability Distribution for Hospitalizations/Year for the 

Sensitive COPD Population in Los Angeles 109 

7.7 Probability Distribution for the Life Expectancy for a 

Sensitive COPD Patient Ill 

7.8 Probability Distributions on the Change in Life Expectancy 

from As-Is for a COPD Patient in Los Angeles 112 

8.1 Effects of Alcohol on Driving-Skill Tests . . .* 120 

8.2 Effects of COHb on Driving-Related Skills 121 

8.3 Comparison of Test Results with COHb Rescaled to Effective 

Alcohol Concentration 123 

8.4 Comparison of Three Basic EBAC Functions 124 

8.5 Probability of Fatal Accident as Function of Blood-Alcohol 

Content Relative to Probability at Blood-Alcohol Content of 

Zero 125 

8.6 Hypothetical Relationship Between Average COHb Levels while 

Driving and Fraction of the Driving Time when Fatal 

Accident is Possible 127 

8.7 Example of how DTFA is a Function of the Standard for 

Carbon Monoxide 128 

8.8 Derivation of Net Accident Risk Function from Physical and 

Human Elements 129 

8.9 Probability Tree Used for Traffic Accidents Model 131 

8.10 Probability Distributions for Traffic Deaths in 

Los Angeles 132 

9.1 Angina Attacks per Day per Patient as a Function of Average 

COHb 140 

9.2 Angina Attacks per Year per Million Adult Population as a 

Function of Average COHb 141 

9.3 Fetal Deaths per Thousand Pregnancies as a Function of 

Average COHb 144 

9.4 Number of Hospitalizations per Year of a Patient Suffering 

COPD as a Function of the Average Highest Daily Value of 

COHb Experienced 145 

9.5 Life Expectancy of a Patient Suffering Severe COPD as a 

Function of the Average Highest Daily Value of COHb 

Experienced 146 

9.6 Distributions for National Urban Deaths due to ASHD Under 

Various Proposed Standards 149 

9.7 Distributions for National Urban Fetal Deaths Under Various 

Proposed Standards 150 

9.8 Distributions for Total Days of Life Lost for National 

Urban COPD Sensitive Patients when the Standard is Changed 

from the Strictest Standard 151 

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9.9 Distributions for National Urban Deaths in Car Accidents 

Under Various Standards 152 

9.10 Distributions on Angina Attacks for National Urban ASHD 

Patients ' 153 

9.11 Distributions for Hospitalizations of National Urban COPD 

Patients 154 

10.1 Comparisons of Ranges of EDRAs Attributable to Each Health 

Effect 162 

10.2 Probability Distributions for the Change in EDRAs from Most 

Stringent Standard for Six Less Stringent Standards ... . 165 

11.1 Distributions for the National Health Impact per Year Under 

a Change from the Strictest to the Most Lenient Proposed 

Standard 168 

A.l Distributions on Changes in Ischemic Episodes from As-Is in 

Chicago 175 

A.2 Distributions on Changes in Ischemic Episodes from As-Is in 

Philadelphia 177 

A.3 Distributions on Changes in Ischemic Episodes from As-Is in 

St. Louis 179 

A.4 Probability Distributions on Fetal Deaths (>20 Weeks 

Gestation) per Year in Chicago 181 

A.5 Probability Distributions on Fetal Deaths (>20 Weeks 

Gestation) per Year in Philadelphia 183 

A.6 Probability Distributions on Fetal Deaths (>20 Weeks 

Gestation) per Year in St. Louis 185 

A.7 Probability Distributions on Changes in Life Expectancy for 

Sensitive COPD Patients in Chicago 190 

A.8 Probability Distributions on Changes in Life Expectancy for 

Sensitive COPD Patients in Philadelphia 192 

A.9 Probability Distributions on Changes in Life Expectancy for 

Sensitive COPD Patients in St. Louis 194 

B.l Encoded Distribution on Peak Hourly Heart Rate (Day, 

Subject Awake) 203 

B.2 Encoded Distribution on Peak Hourly Heart Rate (Night, 

Subject Asleep) 204 

B.3 Encoded Distribution on Peak Hourly Mean Systolic Blood 

Pressure, Given Heart Rate of 80 Beats/Minute and Subject 

Sleeping 205 

B.4 Encoded Distribution on Peak Hourly Mean Systolic Blood 

Pressure, Given Heart Rate of 100 Beats/Minute and Subject 

Awake 206 

B.5 Distribution on Double Product Implied by Encoded 

Distributions on Heart Rate and Blood Pressure 208 

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"*»>"• 

TABLES 

2.1 Estimated Coefficients for Microenvironment Concentration . 27 

2.2 Standards Analyzed in This Report 28 

2.3 Rollback Factors for Four Cities 29 

2.4 Estimated Background Concentrations 30 

3.1 Age/Occupation Categories 35 

3.2 Parameters Used in Coburn Equation 38 

3.3 COHb Levels: Los Angeles, CA 40 

3.4 COHb Levels Under the Strictest Proposed Standard 40 

4.1 Sensitive Groups and Associated Health Effects 48 

4.2 Ranking of Sensitive Groups 49 

5.1 Definition of Cohorts Used in ASHD Model 62 

5.2 Frequencies of Double Product for a Range of Values of u . 65 

5.3 Means of Total Annual Incidence of Anginal Pain 69 

5.4 Comparison of Annual Incidence of Anginal Pain 69 

5.5 Comparison of Predicted Cardiovascular Mortality 71 

5.6 CO Monitors Used to Test Implications of Hexter Model .. . 76 

5.7 Comparison of Epidemiological Results and of ASHD Model for 

Deaths in Los Angeles 76 

6.1 Distribution of Smokers Among Pregnant Women 87 

6.2 Annual Pregnancies by Cohort 88 

6.3 Expected Changes in Fetal Deaths (>20 Weeks Gestation) Per 

Year 91 

6.4 Ratio of Fetal Death Rates of Smokers to Nonsmokers ... . 92 

6.5 Extrapolation of Fetal Death Model to Other Fetal Effects . 93 

7.1 Description of COPD Cohorts 100 

7.2 Comparison of Frequency of Acute Respiratory Failure 

Incidence in Sensitive COPD Population Across Standards . . 110 

7.3 Comparison of Life Expectancies for COPD Sensitive 

Population Across Standards 113 

8.1 Fatal Accident Levels and Estimated COHb Exposure 

Patterns 130 

9.1 Possible Distributions of Urban Population by 

Representative City Types 147 

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9.2 National Extrapolation of Predicted Changes from Strictest 

Standard 155 

9.3 National Extrapolation of Predicted Percent Changes from 

Strictest Standard 155 

10.1 Possible EDRA Assignments 160 

10.2 Changes in EDRA Per Year at the National Level 161 

10.3 Percent Contribution of Each Health Effect to Total, by 

Standard 163 

A.l Comparison of Incidence of Anginal Pain—Chicago 172 

A.2 Comparison of Annual Incidence of Anginal Pain— 

Philadelphia 173 

A.3 Comparison of Annual Incidence of Anginal Pain—St. Louis . 174 

A.4 Comparison of Predicted Cardiovascular Mortality—Chicago . 176 

A.5 Comparison of Predicted Cardiovascular Mortality— 

Philadelphia 178 

A.6 Comparison of Predicted Cardiovascular Mortality— 

St. Louis 180 

A.7 Expected Changes in Fetal Deaths (>20 Weeks Gestation) Per 

Year—Chicago 182 

A.8 Expected Changes in Fetal Deaths (>20 Weeks Gestation) Per 

Year—Philadelphia 184 

A.9 Expected Changes in Fetal Deaths (>20 Weeks Gestation) Per 

Year—St. Louis 186 

A.10 Comparison of Changes in Acute Respiratory Failure for COPD 

Sensitives—Chicago 187 

A .H Comparison of Changes in Acute Respiratory Failure for COPD 

Sensitives—Philadelphia 188 

A.12 Comparison of Changes in Acute Respiratory Failure for COPD 

Sensitives—St. Louis 189 

A.13 Comparison of Changes in Life Expectancy for Each COPD 

Sensitive Patient—Chicago 191 

A.14 Comparison of Changes in Life Expectancy for Each COPD 

Sensitive Patient—Philadelphia 193 

A.15 Comparison of Changes in Life Expectancy for Each COPD 

Sensitive Patient—St. Louis 195 

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PREFACE 

The purpose of the research described in this report was to further 

develop a methodology to assess the health risk associated with alternative ambient air quality standards. This methodology was developed in 

the context of an illustrative application to ambient air quality standards for carbon monoxide, which was directed toward two goals. The primary goal of the analysis was to draw conclusions concerning the usefulness and applicability of the risk assessment methodology. The secondary 

goal of the analysis was to create a structurally sound and functioning 

risk model. The analysis is complete in the sense that it develops or 

links all the components necessary to an evaluation. It is preliminary 

in that many assumptions and simplifications of data requirements have 

been made. These simplifications can be relaxed readily once the scientific community has agreed on the basic structure of the models. 

It should be emphasized that, because of the preliminary nature of 

this work, the resulting health risk assessments for carbon monoxide 

should not be referred to outside the context of a preliminary test of 

the model. A full scale implementation of the model could very well give 

results that are quite different. 

Because the work described in this report represents a long and complex chain of reasoning and calculation, it has proved convenient to divide the report into the following described four parts. 

Part I: Overview of Risk Assessment Procedure—The overall structure of the work is specified and the important steps of analysis are reviewed. General conclusions on the methodology are presented. 

Part II; Emissions, Atmospheric Dispersion and Exposure Models—The 

major structural elements of these models are discussed as they apply to 

carbon monoxide. The computer model developed by the Environmental Protection Agency is analyzed in terms of this general structure. 

Part III; Health Dose-Response Models—Detailed health doseresponse models are developed for the most important health effects resulting from carbon monoxide. Important uncertainties in the inputs to 

these models are analyzed. 

Part IV: Significance of Results—A method is developed for extrapolating health effects from the four cities analyzed to a national scale. 

A significance weighting is performed to identify the most important 

health effects and the areas in which future research will prove most 

useful for the assessment. 

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PART I 

OVERVIEW OF RISK ASSESSMENT PROCEDURE 

The process of setting standards to protect the health of the general population is difficult because it involves obtaining reliable answers to many different types of questions, and these questions require 

input from many different fields of knowledge. In this first part of the 

report, a methodology is described for identifying, collecting, and organizing relevant information into a formal model for assessing the health 

risks of alternative standards. The methodology has several major advantages: (1) it analyzes each question in turn, allowing the judgments of 

experts to be focused on those parts of the analysis that fall within the 

experts' realm of experience, (2) it allows for explicit consideration of 

uncertainty where full understanding of processes has not yet been attained, and (3) it presents the decision maker with a tool to explore the 

consequences of different courses of action. 

Methodological conclusions are drawn from the experience gained in 

performing the illustrative application of the methodology. These conclusions should aid in the planning and execution of complete future assessments of health risks associated with alternative ambient air quality 

standards. 
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INTRODUCTION, RESEARCH SUMMARY, AND CONCLUSIONS 

The Ambient Standards Branch of the U.S. Environmental Protection 

Agency (EPA) Office of Air Quality Planning and Standards (OAQPS) is exploring the potential of quantitative methods to assess the health risks 

associated with alternative ambient air quality standards for noncarcinogenic pollutants. The Clean Air Act requires the EPA Administrator to set national primary air quality standards that in the Administrator's judgment "protect the public health" with an adequate "margin of 

safety." To be useful in this decision-making process, the risk assessment methodology must estimate the health effects resulting from each 

alternative standard and also the uncertainty in these estimates. 

Methodology Requirements 

Estimating the health consequences of alternative air quality standards is extremely difficult. A wide range of health effects are suspected of being associated with exposures to criteria pollutants, ranging 

from mild, temporary, and reversible physiological effects to serious 

disease and discomfort. The severity of these effects is known to be a 

complicated function of exposure conditions, individual characteristics, 

and environmental cofactors. All of these, in turn, are difficult to 

predict because of variations in pollutant concentrations over time and 

space, because of the activity patterns of people, and because of interpersonal differences in susceptibility. 

Not only is the relationship between air quality and human health 

complex, it is also highly uncertain and controversial. Scientific evidence for health effects is frequently ambiguous, disputed, or even contradictory. Often there is only animal study evidence or theoretical hypotheses on a particular health effect. Human information tends to be 

drawn from healthy individuals or populations. Synergistic, additive, 

subtractive, or antagonistic cofactors are rarely investigated. 

To be useful to the EPA, health risk assessment methodology must be 

capable of dealing with complexity and the paucity of scientific data. 

It should be open to review by the public and other interested parties, 

and its assumptions should be clear and explicit. It should provide an 

appropriate use of logic and mathematics and consider all relevant information and points of view. Furthermore, it should minimize arbitrariness, be flexible enough to handle different kinds of health effects, and 

be capable of incorporating methodological improvements and new information. 

Recent applications of decision analysis to air pollution control 

problems suggest the possibility of designing a health risk assessment 

methodology based on decision analysis. SRI International (SRI) has 
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therefore conducted research to extend decision analysis methods and 

techniques and to evaluate their usefulness as a basis for health risk 

assessment. A previous report proposed a conceptual framework designed 

to serve as a basis for a health risk assessment procedure. This report 

describes research performed by SRI to further develop that procedure 

through a pilot application to the criteria pollutant carbon monoxide. 

Overview of the Decision-Analytic Risk Assessment Process 

The proposed methodology is a process for constructing and analyzing 

models that describe available knowledge and uncertainty in the relationship between alternative standards and health effects. Defining the 

methodology as a process rather than as a specific set of modeling assumptions and analysis techniques is appropriate, given the realities of 

the EPA standards-setting problem. Because of the lack of hard scientific data, the risk assessment methodology must rely heavily on the 

judgments of experts. Procedures that enable experts to contribute to 

the assessment according to their specific areas of expertise and that 

enable uncertainties to be assessed and made explicit are central to the 

methodology. 

Figure 1.1 illustrates the six steps of the proposed process, organized into three major phases of analysis—a deterministic phase, a probabilistic phase, and an informational phase. The objectives of each 

phase and a brief description of the component steps are presented below. 

Deterministic Phase 

In the deterministic phase, a "health effects outcome calculator" is 

created and evaluated. The health effects outcome'calculator is a set of 

equations, implemented in computer code, representing the best available 

information for the relationships between alternative air quality standards and a measure of the seriousness of the resulting health effects. 

No attempt is made to account for judgmental uncertainty in this phase, 

but statistical uncertainties, such as changes in ambient concentrations 

because of the weather, are modeled. 

Step 1; Develop and Implement a Health Effects Outcome Calculator— 

The component models needed to form an outcome calculator are shown in 

Figure 1.2: an emissions and atmospheric dispersion model, an exposure 

model, health dose-response models, and a significance weighting model. 

The purpose of the emissions and atmospheric dispersion model is to 

estimate the ambient concentrations of the pollutant under alternative 

standards. An ambient air quality standard usually specifies a concentration level, averaging time, and number of instances in which concentrations are permitted to exceed the level over a given period of time. 

Given a specific standard and a set of input assumptions, the emissions 

and atmospheric dispersion model produces as output a series of frequency 

distributions describing concentrations for each geographic location and 

for various points throughout the time horizon under consideration. 
Initial 

Knowledge 

Base 

DETERMINISTIC PHASE 

Step 1 

Develop and Implement 

a Health Effects 

Outcome Calculator 

Step 2 

Conduct a Sensitivity 

Analysis of Health 

Effects Outcome 

Calculator 

i i 

Si 

PROBABILISTIC PHASE 

tep 3 

Encode Uncertainties 

Step 4 

Develop a Probability 

Distribution on the 

Health Impact Measure 

Information 

Gathering and 

Model Updating 

INFORMATIONAL PHASE 

Step 5 

Identify Uncertainties 

with High Values of 

Resolution 

Step 6 

Gather Further 

Information and Repeat 

Cycle as Necessary 

Health 

Risk 

Assessment 

Output 

FIGURE 1.1 HEALTH RISK ASSESSMENT CYCLE 
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Ambient 

Air Quality " •*** 

Standards 

Emissions and 

Atmospheric 

Dispersion 

Model 

Ambient 

Pollutant 

Concentrations 

Exposure 

Model 

Pollutant 

Dosage 

Conditions } 

Health 

Dose-Response 

Model 

Health 

Effects 

' 

Significance 

Weighting 

Mode il 

Measure 

of Health 

Impact o 

FIGURE 1.2 MAJOR COMPONENTS OF HEALTH 

IMPACT OUTCOME CALCULATOR 
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The exposure model estimates conditions of exposure to the pollutant 

for the population at risk. The model takes as input (1) ambient concentrations (or modeled data) and (2) assumptions on the number of individuals in various sensitive population groups and their activity patterns. 

The output of the exposure model must be in a form suitable for input for 

the dose-response models. In a detailed case, this would consist of exposure profiles representing the frequency and duration of exposures to 

various pollution levels by activity (for example, level of exercise), 

sensitive group category, and environmental factors that may influence 

health response. 

Health dose-response models represent current knowledge concerning 

the health effects of exposure to the pollutant. Application of the models converts the exposure profiles into numbers of health effects of various types. Summing over time and over geographic locations provides estimates of the total number of health effects of each type occurring 

under each alternative regulatory standard. 

The significance weighting model is designed to explore alternative 

means for aggregating health effects of different types to produce an 

overall summary statistic of health impact. To permit aggregation, all 

health effects must be expressed in some common unit of measurement 

(e.g., "equivalent days of restricted activity"). The significance 

weights are not meant to define a precise health equivalence, but rather 

to provide a means of explicitly incorporating value judgments and of enabling the decision maker to investigate the sensitivity of the estimated 

health impact to various uncertainties. 

Development of each of the component models in the health outcome 

calculator is accomplished through an iterative process involving analysts and experts. Experts provide basic scientific evidence and judgment concerning the important variables and their influences. Analysts 

structure the variables into mathematical relationships and verify the 

structures by reviewing the assumptions and implications of the relationships with the experts. For efficiency, all models should be implemented 

in computer code. Where models already exist (for example, atmospheric 

dispersion models) they can be used for initial Implementations of the 

outcome calculator provided that their input requirements and outputs can 

be made compatible with the other components of the health outcome calculator and provided their operating characteristics (e.g., flexibility, 

operating costs) are suitable to the needs of the risk assessment. 

Step 2: Conduct a Sensitivity Analysis of the Health Effects 

Outcome Calculator—Running the computer model representing the health 

outcome calculator for each alternative air quality standard with a set 

of nominal estimates for the value of all input variables yields a nominal estimate of the health impact under alternative standards. The outcome calculator can also be used to identify which uncertainties are most 

critical to the assessment and must be explicitly accounted for in the 

risk estimate. Sensitivity analysis is performed initially by varying 

each input variable in the outcome calculator across a range of values 

selected to approximate the range of uncertainty on that variable. For 

those variables whose variation produces the largest change in overall 
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health impact, the uncertainties should be explicitly quantified. Joint 

sensitivities in which two or more variables are simultaneously varied 

across their respective ranges of uncertainties are also conducted to 

avoid missing variables whose sensitivities are related to cross dependencies in the models. 

Probabilistic Phase 

The objective of the probabilistic phase is to assess uncertainty on 

the sensitive input variables and to measure the impact of such uncertainty on the estimates of health risk. 

Step 3: Encode Uncertainties—The variables for which uncertainty 

has been shown to be important in Step 2 are now assigned probability 

distributions. These probability distributions should be elicited from 

recognized experts using probability encoding techniques. Where differences in scientific opinion exist, experts should be chosen to encompass 

each credible point of view. 

Because most individuals have difficulty in thinking about uncertainty, the method of extracting the probability distributions is extremely important. The probability method proposed for the health risk 

assessment model is described in Reference 2. 

Differences in the distributions assessed from various experts can 

often be resolved by bringing the experts together and allowing them to 

share information. Nevertheless, in many situations it is impossible to 

get two or more experts to agree on an appropriate probability distribution. Rather than reconciling divergent distributions by assigning 

weights to each, multiplying, and adding, divergent distributions are 

grouped Into nominal, optimistic, and pessimistic sets. Whether the expert differences are significant and must be retained can then be determined through sensitivity analysis. 

Step 4: Develop a Probability Distribution on the Health Impact 

Measure—Having developed an outcome calculator and obtained probability 

distributions on the crucial input variables, it is straightforward to 

design a computer program that will generate the probability distribution 

over the measure of overall health impact for various settings of the 

standard. This probability distribution summarizes uncertainty in the 

health impact resulting from a given standard. 

Informational Phase 

In the informational phase, additional sensitivity analyses are conducted to evaluate the relative importance of reducing uncertainty in 

each area of the outcome calculator. Comparing the relative importance 

of eliminating each uncertainty with the feasibility of improving the 

corresponding assessment permits research alternatives and information 

gathering options to be prioritized. It also indicates which elements of 
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the outcome calculator can provide significant improvements in the assessment if they are expanded and refined. 

Step 5: Identify Uncertainties with High Values of Resolution—The 

first step in the informational phase is a stochastic sensitivity analysis. Each crucial uncertain variable is set equal to a trial value 

within its range of uncertainty. All other uncertain variables are assigned the appropriate conditional joint probability distributions. Running the computer program will then produce a probability distribution on 

the health impact measure, and the expected value of this distribution 

can be calculated. Sweeping the trial value from one end of its range to 

the other shows how much the distribution and its expected value change, 

depending on the actual value of the uncertain variable. If the change 

is small, there is evidence that the analysis may be simplified by eliminating consideration of uncertainty on that variable. 

Step 6: Gather Further Information and Repeat the Cycle as 

Necessary—Actual information-gathering programs will seldom completely 

resolve the uncertainty in a given variable. Selection of specific steps 

to be taken to improve information must therefore be based not only on 

calculations such as those described in Step 5, but also on the anticipated effectiveness of the gathered data at reducing uncertainty, the 

costs, and the time required to obtain the information. 

Several iterations of the risk assessment cycle may be necessary to 

complete the assessment. Following the first application of the three 

phases, the outcome calculator will be constructed of highly simplified 

or readily available models. A major purpose of this first implementation, the pilot model, is to identify necessary information and model improvements. As additional information is gathered, the cycle should be 

repeated, with elements of the analysis updated to account for the improved information. Some variables may be uncertain primarily because 

detailed models have not been constructed. Using a combination of deterministic and stochastic sensitivity analysis, the analyst can direct the 

development of the risk assessment model in an efficient way. The resulting improved implementation might be called a prototype model. It is 

a more detailed representation of the problem, which, although it will 

generally have some aspects that must be improved, demonstrates the overall appearance and performance of the final version. The final model results when sensitivity analysis shows that gathering additional readily 

available information or making model improvements will not significantly 

reduce uncertainty in health impacts. This is the most accurate representation of the problem that should be produced as part of the health 

risk assessment. 

Summary and Results of the Illustrative Application of the Risk 

Assessment Process to Carbon Monoxide 

Carbon monoxide (CO) is one of the seven criteria pollutants identified by EPA for regulation under the Clean Air Act. The primary source 

of CO as a pollutant is the burning of carbon based fuels under 
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fuel-rich, oxygen-poor conditions; in urban areas, most ambient CO comes 

from the emissions from transportation vehicles. 

Because of the greater affinity of blood for CO than for oxygen, CO 

tends to combine with blood hemoglobin and hence reduce the oxygen delivery to the tissues. Even at current ambient levels of CO, there is concern that certain sensitive groups with compromised oxygen supply (e.g., 

cardiovascular disease or chronic obstructive pulmonary disease patients) 

may suffer adverse effects. 

To evaluate and illustrate the health risk assessment process outlined above, the process was applied to the analysis of health risks associated with each of seven alternative ambient standards for CO. The 

standards selected for analysis are shown in Table 2.2. (Note: tables 

and figures appear in the chapters where they are discussed in detail.) 

Health effects under each standard were assessed for four cities 

(Chicago, IL; Los Angeles, CA; Philadelphia, PA; St. Louis, MO); the 

analysis was then extrapolated to obtain a preliminary estimate of the 

health risks for the entire nation. 

Any statement of results of this risk assessment must be prefaced 

with a strong disclaimer. The project was an exploratory one, looking 

toward the problems and successes of the methodology in dealing with a 

very complicated problem. In terms of an actual risk assessment, it is 

at best a pilot model, as described above. 

Step 1: Develop and Implement a Health Effects Outcome Calculator 

As noted above, implementation of an outcome calculator requires 

four major components (Figure 1.2). Much of the work described in this 

report involved developing or linking the component models for this 

calculator. 

Emissions and Atmospheric Dispersion and Exposure Models—An existing computer implementation developed by EPA was used as the basis for 

two components of the outcome calculator. The EPA implementation actually provides a greater level of detail than is required for this application. The model takes monitored CO data for a given city, population 

densities, and human activity patterns for that city and predicts the exposures to CO under a given standard. Because the primary effect of CO 

on humans is thought to be the result of the combination of CO with blood 

hemoglobin, important output of this model is carboxyhemoglobin (COHb) 

levels for each hour of the year for a large number of subsets of the 

population (cohorts). For the purposes of the outcome calculator this 

output was used to obtain frequency distributions for the occurrence of 

COHb levels in a much smaller number of cohorts. Tables 3.3 and 3.4 

illustrate the nature of the output produced. 

Health Dose-Response Models—Tables 4.1 and 4.2 show population 

groups identified as sensitive to elevated COHb levels. An initial application of the risk assessment cycle reduced the number of sensitive 

10 
P.21 

groups selected for formal analysis to four: arteriosclerotic heart disease patients (health risks are increased incidence of myocardial pain 

and deaths due to cardiovascular disease); human fetuses (health risks 

are fetal death, malformation, and retardation); chronic obstructive pulmonary disease patients (health risks are increased morbidity and decreased life expectancy); motor vehicle drivers (health risk is increased 

fatal accidents)• 

In the initial application of the assessment cycle, it was also 

realized that the construction of formal models of health dose response 

based on physiology was preferable to direct probabilistic encoding of 

dose-response curves from experts. The models developed are very simple, 

preliminary representations of existing medical knowledge; nevertheless, 

the health models have all received some measure of verification (and 

interest) from the health experts consulted. 

For the arteriosclerotic heart disease model and the fetal effects 

model, the physiological basis of the model is the comparison of oxygen 

needs (of the heart or fetus) and oxygen availability (which is lowered 

by CO exposure). The models take into account that both of these factors 

vary over time in an unpredictable manner. 

For chronic obstructive pulmonary disease, a different model logic 

was used. Patients suffering from this disease can suffer acute respiratory failure; one of the contributing factors to this failure is reduced 

blood oxygen, and CO exposure reduces blood oxygen. Results of acute 

respiratory failure can be hospitalization and even death. Care was 

taken in defining the structure of this model to avoid overestimating the 

effects of prolonged periods of high CO levels. 

For motor vehicle accidents, still another type of model was used. 

There is little direct evidence for the risks of CO exposure at ambient 

concentrations in motor vehicle driving. The model therefore uses alcohol [for which the risks are well known (Figure 8.7)] as a "reference 

substance." In addition, some care was taken to model the fact that much 

driving at high ambient CO concentrations takes place in congested traffic, where the risk of fatal accidents is reduced (Figures 8.8 and 8.10); 

this effect has been seen in an epidemiological study described in 

Chapter VIII. 

Basic input to all of the health dose-response models was obtained 

from physiological data, knowledge concerning physiological processes, 

encoded distributions representing scientific judgment of uncertainties, 

and available demographical and epidemiological data. 

Significance Weighting Model—Two calculations were performed by 

this model. First, national health risks were extrapolated from the 

analysis of four cities studied. A method for extrapolation using available data was outlined based on general relationships of health risks to 

the output of the exposure model (Figures 9.1-6). Unfortunately, the 

data necessary to perform the extrapolation were not readily available at 

the time this work was performed. A much simpler extrapolation was 

therefore used for illustration (Table 9.1). 

11 
P.22 

The second calculation in the significance weighting model was to 

combine the various health effects by using an overall measure of health 

impact. The measuring scale selected was "equivalent days of restricted 

acitivity." Nominal weights and the ranges used in sensitivity analysis 

are shown in Table 10.1. 

Step 2: Conduct a Sensitivity Analysis of the Health Effects 

Outcome Calculator 

It was not possible to perform a sensitivity analysis with the EPA 

computer implementation selected to serve as the Emissions and Atmospheric Dispersion and the Exposure Models, because only a limited amount 

of data was available from EPA's runs of the models. Extensive sensitivity tests on the other models were performed, however, and critical variables were identified. 

Step 3. Encoding Uncertainties 

In the preliminary application of the assessment cycle reported 

here, it was discovered that the critical uncertainties identified 

through sensitivity analysis did not always fall within the realm of 

expertise of the health experts who had previously been selected to provide input to the analysis. For example, critical variables concerned 

the variation of physiological parameters in daily life. Selected health 

experts, however, were often known primarily for research conducted under 

clinical or laboratory conditions. A mixture of formal encoding, informal encoding, physiological reasoning and calibration of the models to 

existing epidemiological data was used to arrive at simple preliminary 

probability distributions for critical uncertainties (Figures 5.5, 6.3, 

7.5, 8.11). Subsequent iterations of the cycle would develop more refined models and identify the experts who could best provide input to 

these models. 

Step 4: Develop a Probability Distribution on the Health Impact 

Measure 

Distributions were developed to describe uncertainties in each of 

the health effects areas and for each city analyzed (e.g., Figures 5.7-8 

and Tables 5.3-5). Distributions were then extrapolated to estimate the 

uncertainty on the magnitude of each health effect for the nation as a 

whole (Figures 9.6-11 and Tables 9.2-3) and for the national health impact measure (Figure 10.2 and Tables 10.2-3). 

A synopsis of the conclusions arrived at in this report are presented below; the derivation of these conclusions are presented in subsequent chapters. It should be emphasized, however, that these conclusions 

are based on exposure data provided by EPA. EPA has since discovered a 

significant error in the data base used in calculating the exposure data; 

this error has the effect of making the esimates reported below too 

large. 

12 
P.23 

Observing the magnitude and relative contributions to the measured 

health risk under alternative standards provides important insights. The 

most important group sensitive to changes in CO standards appears to be 

persons who suffer from arteriosclerotic heart disease. This large group 

suffers from restricted oxygen supply to the heart, and CO exposure further reduces this supply. The national health risk incurred in going 

from the strictest proposed standard (9 ppm CO, 8 hour averaging, 1 expected exceedance per year) to the most lenient proposed standard (35 

ppm, 1 hour, 1 expected exceedance) is estimated to be 15,000 deaths per 

year; this is about a 5% increase in the 330,000 deaths per year from 

this disease in urban areas. The increase in angina attacks per year in 

this group for the same change of standard is estimated to be 4%. 

The most sensitive group in terms of risk per sensitive individual 

appears to be that of patients with advanced chronic obstructive pulmonary diseases. For this group, the decrease in life expectancy from 

lowest to highest standard is about 7%. Although the size of this sensitive population is uncertain, it is far smaller than that of heart disease patients. Roughly 50,000 people in the urban United States are in 

this group, as compared with about 4 million urban victims of arteriosclerotic heart disease. 

Another significant health risk exposure occurs to fetuses. The 

sensitive population is so large that fetal effects may outweigh lung 

disease effects in national significance. Each year about 2 million 

pregnancies advance beyond the second trimester in the urban population. 

About 20,000 of these fetuses will die before birth. Another 100,000 

will probably be born with some physical or mental defect. Changes in CO 

will change ischemia during gestation, which may be responsible for some 

portion of these defects. An estimated 130 fetuses annually may survive 

under the strictest standard, who would not survive under the most leninent standard. Nonfatal outcomes were not modelled, but rough estimates 

indicate that the difference in CO between these two standards may affect 

thousands of children born each year. 

Step 5: Identify Uncertainties with High Values of Resolution 

Uncertainties on three parameters of the arteriosclerotic heart disease model were identified as having high values of resolution (Figure 

11.1). Given the current state of knowledge, other uncertainties were 

relatively unimportant. It should be noted, however, that this is predicated on very simple, preliminary versions of the health effects models. 

Step 6; Gather Futher Information and Repeat Cycle as Necessary 

The next iteration of the cycle should concentrate on obtaining improved Health Risk Models. In particular, an arteriosclerotic heart disease model would require more differentiation between patients than it 

has in this work; particular emphasis should be placed on cardiovascular 

mortality in this model. In addition, an elaboration of the fetal effects model to adequately include malformation and retardation (Table 

6.5) would be important. 

13 
P.24 

Methodological Conclusions 

The primary purpose of this project was to further develop methodology for estimating the health impact of establishing alternative ambient 

air quality standards. A number of general methodological conclusions 

can be drawn from the work performed in the illustrative application to 

carbon monoxide. These conclusions fall under four general headings: 

model building, probability encoding, model output, and overall characteristics of the process. 

Model Building 

Asking health experts to estimate directly a dose-response curve requires a leap from exposure data to health risks that may place an unreasonable demand on their information processing capability. Estimating 

the number of health effects to be expected under specific exposure conditions is difficult because it requires the expert simultaneously to 

consider a large number of uncertain and poorly understood cause-effect 

relationships. Decomposing the relationship into its logical components 

and working with experts to obtain the best information concerning each 

of those components was found to be more effective than asking experts to 

estimate dose-response curves directly. Experts seemed more satisfied 

with the quality of the data they were supplying when the problem was decomposed. In addition, it was found in several cases that some of the 

considerations necessary for estimating health effects were in areas that 

were outside a given expert's area of expertise. For example, an expert 

on cardiovascular effects of COHb may have difficulty in estimating the 

frequency with which physiological parameters reach certain levels in the 

context of daily life. Decomposing the problem and more accurately selecting experts to meet the needs of the assessment reduces the likelihood that an expert will be aske*d to provide information that is beyond 

the bounds of his or her expertise. Finally, direct assessment of health 

effects has the disadvantage that it may appear to the expert that making 

such an assessment is tantamount to recommending a standard. For some 

experts, this is a responsibility they would wish to avoid. 

To reduce the difficulty of encoding health dose-response knowledge, 

models were built to bridge the gap between exposure data and health impact. Although, in accord with the purpose and resources of this study, 

the models developed are only prototypes—the results verify that quantitative models can be built to serve this function. 

Constructing health models requires a strong grounding of basic 

physiology. It is recommended that future uses of this methodology have 

a medical expert on the team to facilitate the incorporation of this 

physiological understanding into the models. It is not necessary that 

the medical member of the team be an expert in the specific health effect, the pollutant, or in modelling techniques. His or her role would 

be to provide information necessary to create simple models of the physical processes involved. These models must ultimately, of course, be 

criticized by the experts in the health effect, pollutants, and modeling 

techniques. 

14 
P.25 

In the development of the models, the variation of human biological 

systems creates health response uncertainty. The body is not a mechanical system with a given vulnerability to a pollutant; rather it varies 

rapidly and randomly with time in its susceptibility to adverse health * 

effects. Because pollutant levels also vary rapidly and randomly with 

time, many of the deterministic models have an important stochastic element to them. For this reason, it is important that the risk assessment 

team have as a member an analyst who is capable of dealing with probabilistic analysis. Because much of the input at this level may be judgmental, the analyst should be well versed in interviewing, probability encoding, and in Bayesian techniques. 

Existing health models (other than physiological models) were found 

(for CO, at least) to be insufficient for the task at hand. Epidemiological studies could, in principle, serve as the basis for a health risk 

model. A model based on epidemiology would involve the uncertainty and 

controversy of extrapolations and the question of thresholds; there is 

also the problem of adequately separating out the effect of environmental 

cofactors. Epidemiological studies can, of course, serve as important 

calibrating points for models based on understanding physical processes. 

Models based on animal studies were also found to be insufficient for the 

study of CO health effects. Animal data can be precise and well controlled. For the CO effects studied, however, human and animal physiology were sufficiently different that quantitative estimates of human 

health effects had very large (and controversial) uncertainties. 

Each health effect modeled in this project was sufficiently different that it would appear impossible to create a general model applicable 

to all health effects. The creation of specific models for each health 

effect can maintain the quality of simplicity in model structure. This 

is important in communication, criticism and review. It may be desirable 

to incorporate special considerations into each model (such as driving 

time at risk in Chapter VIII) even if this consideration is not important 

in the risk assessment. The ideal here is to balance the complexity of 

the model against the need of the model to readily answer all questions 

and objections addressed during the course of the risk assessment process. 

The use of models that have already been developed for other purposes is not without problems. In this study, the EPA exposure model was 

used. Although the use of this existing model considerably reduced model 

construction time and expense, its structure was not ideally suited to 

the analysis. The level of detail in the model and its output was far 

greater than that required for the analysis. 

One final overall comment on the health effects models is important. 

At the beginning of interviews with many experts, a certain reluctance 

and hostility was evident. When the health effects models were explained, the atmosphere rapidly became cordial and often enthusiastic. 

The experts evidently felt that the models, despite their exceedingly 

simple and preliminary form, represented a logical process that they 

thought reasonable. The models not only remove the need for them to perform mental gymnastics to arrive at a conclusion, but also allow them to 

use their expertise in areas with which they were most familiar, and 

15 
P.26 

indicate areas where other experts could provide better information. 

Uniformly, the reaction at the conclusion of the interview was one of encouragement, and a feeling that this modeling approach, is an advancement 

over previous attempts at health effects quantification. 

Probability Encoding 

The major emphasis of this study was on model building rather than 

direct probability encoding. It was discovered, however, that much of 

the uncertainty in the assessment of health risk was, rather surprisingly, in areas outside the experts' specific realm of expertise. For 

instance, arteriosclerotic heart disease effects depend critically on 

blood pressure and heart rates experienced in daily life; experts on CO 

tend to be familiar only with the impact of these factors in controlled 

environments. 

In one instance, while encoding the distribution on an input parameter, the expert remarked that he had never realized how critical this 

uncertain variable was to the CO question. The existence of the model 

and a simple encoding had suggested an important but neglected area of 

research. 

Care should be taken in encoding distributions on physiological parameters in daily life. Most experts' experience with these parameters 

is under controlled conditions, and it may be difficult to obtain distributions that reflect peak values experienced for short periods of time 

because of some transient emotional or physical effect. 

Model Output 

The models proved flexible and able to answer a large number of 

questions. It was found, however, that different health risks lent themselves to different types of output. For most health risks, for example, 

the number of instances of the health effect per year was relevant. For 

chronic obstructive pulmonary disease this measure was not relevant, but 

life expectancy was (see Chapter VII). If an overall measure of health 

impact (such as EDRA) is not used, meaningful measures must be developed 

for each health risk. 

In general, the concepts of "most sensitive groups" and "margin of 

safety" must be treated with some care. The human organism is not a mechanical system with a fixed susceptibility to harm at some exposure 

level; it is a complex and unpredictable biological system that varies 

over time. Hence, all members of a sensitive group may, at some times 

and under some circumstances, suffer adverse health effects. Similarly, 

there seem to be no thresholds for many health effects, at least in the 

case of the models developed for CO; any ambient level has the potential 

for provoking some adverse health effects. 

16 
P.27 

Overall Characteristics of the Program 

The overall health risk assessment process, as seen in this illustrative example, does not seem to be exorbitant in terms of time or expense. Much of the effort was exploratory in nature; it was difficult to 

determine the type and general structure of models useful for the assessment. In an iteration of the assessment cycle, refinement and elaboration of these models (or the development of new models) would probably 

take significantly less time. The development of models for other pollutants would likely be considerably simpler, given the experience gained 

from this assessment of alternative CO standards. 

In terms of actual computing costs, the health risk models are simple and inexpensive to run. It is hard to see how these models, even 

when refined and elaborated, would entail significant expense. The existing EPA Emissions, Atmospheric Dispersion, and Exposure Models, on the 

other hand, could probably be simplified without compromising the accuracy of the assessment. 

The difficulty of the probability encoding process has been considerably reduced by the realization that, in part, the reluctance of experts to provide quantitative assessments of uncertainty is because of 

their perception that some of the critical elements in the assessment are 

not in their area of expertise. Probabilities can be encoded for each 

element of the process by people best able to judge that particular variable. 

The process lends itself to decision making in the public arena. 

The models are explicit and open to criticism. The input is well defined, and differences of opinion can be tested. Public testimony can be 

well focused and documented. The models are prepared to respond to a 

wide variety of questions and objections. The implications of conflicting opinions on the outcome can be assessed quickly and inexpensively. 

This demonstration has provided several provocative insights and 

lessons for future work. The methodology appears to be well-founded and 

workable despite the complexity of the standards-setting problem. Furthermore, the models developed appear to have value as a basis upon which 

a full quantification of the health risks of CO can be performed. 

17 
P.28 

REFERENCES FOR CHAPTER I 

1. Miley W. Merkhofer, "A Procedure for Assessing Health Risks Associated with Alternative Ambient Air Quality Standards," in Conceptual 

Approaches for Alternative NAAQS, U.S. E.P.A., 1981, pp. 4-1 to 

4-42. 

2. C-A. S. Stael von Holstein and J. E. Matheson, "A Manual for Encoding Probability Distributions," SRI Final Report, Project 7078, SRI 

International, Menlo Park, California, September 1978. 

19 
P.29 

PART II 

EMISSIONS, ATMOSPHERIC DISPERSION, AND EXPOSURE MODELS 

The models used to estimate concentrations of carbon monoxide and 

the exposure of the population to various levels of concentration were 

not developed as part of the work described in this report. A model had 

been previously developed by EPA, and this model has been used to provide 

data for Part III of the report. A final implementation of the risk assessment procedure would, of course, join all parts of the model into a 

single piece of computer code. 

In this part of the report, a general methodology is described, and 

the EPA model is analyzed under this methodology. Unfortunately, because 

of the difficulty and expense of data transmittal, the EPA model was not 

available for sensitivity analysis or probabilistic treatment; for the 

same reason, estimates for only 2 months of the year (January and July) 

were used in the analysis described in subsequent parts of this report. 

EPA has since discovered an error in the data base used in the model; 

this error has the effect of making the estimates derived in this report 

too large. 

21 
P.30 

II EMISSIONS AND ATMOSPHERIC DISPERSION 

The first step in estimating the health effects of an air quality 

standard is to estimate the effect of the standard on the concentrations 

of the pollutant that are encountered. To accomplish this, four different questions must be answered: 

o How much of the pollutant is produced? 

o How much of this pollutant can be controlled or eliminated before 

it enters the ambient atmosphere? 

o Once the pollutant enters the atmosphere, how does it spread geographically? 

o How will regulation and enforcement policies change the situation? 

The Emissions and Atmospheric Dispersion Model addresses these questions 

in a quantitative fashion. 

The discussion below describes the methodology that is appropriate 

to air quality standards for carbon monoxide. The discussion also describes the computer implementation that was developed by the Environmental Protection Agency (EPA) for the Strategies and Air Standards Division 

(SASD). Results obtained from this model were provided by EPA to furnish the basis for later sections of this report. 

Emission and Atmospheric Dispersion Submodel 

The Emissions and Atmospheric Dispersion Model consists of the four 

submodels shown in Figure 2.1: Production Process Submodel; Emission 

Containment and Control Submodel; Atmospheric Dispersion Submodel; State 

and Local Government Regulation and Enforcement Submodel. Each of these 

submodels is discussed below. 

Production Process Submodel 

The production process for CO can be modeled rather simply for the 

following reason: it is estimated that almost all of ambient carbon monoxide is caused by mobile sources. Nationwide data for the United 

States indicate that 75% of man-made CO emissions is because of highway 

vehicles, 9% because of nonhighway vehicles, 9% because of stationary 

fuel combustion, industrial processes and solid waste, and 5% because of 

other sources. Naturally occurring CO emissions are negligible in comparison to man-made sources, especially in urban areas. Because the preponderance of CO emissions is caused by highway vehicles, most attention 

has been paid to reducing CO emissions from this source. 

23 
INPUT ASSUMPTIONS 

ro 

Spatial/Temporal Patterns of Production 

Activities 

• Wind Speeds and Directions 

• Background Concentrations 

• State Implementation Plans # Ai r QualU y Qat a Bas e 

• Emission Control Technology Effectiveness # Microenvironmen t characteristics 

DECISION 

Ambient 

Air Quality 

Standards 

UPSTATE AND LOCAL GOVERNMENT REGULATION 

AND ENFORCEMENT SUBMODEL 

Monitoring 

and Control 

PRODUCTION 

PROCESS 

SUBMODEL 

Emissions 

Monitoring 

EMISSION 

CONTAINMENT 

AND CONTROL 

SUBMODEL 

Emissions 

ATMOSPHERIC 

DISPERSION 

SUBMODEL 

Projections of Ambient 

Concentrations over 

Space and Time 

= > 

FIGURE 2.1 EMISSIONS AND ATMOSPHERIC DISPERSION MODEL 

to 
P.32 

In the EPA implementation, the model separates CO concentrations 

into two parts: emissions caused by local man-made sources and "background" concentrations caused by all other nonlocal sources. The background concentrations are estimated for each urban area from rural monitors located upwind of the urban area. In the present version of this 

model, background concentrations do not vary within the various urban 

areas and do not vary with time. Nonbackground concentrations are obtained by subtracting the estimated background concentrations from actual 

monitored data on CO concentrations. Both these components of CO concentrations, therefore, are given for present levels of CO emission controls. 

The CO levels are disaggregated spatially in the following described manner. Concentrations are obtained from monitored concentrations for six types of neighborhoods: residential, commercial, and 

industrial for both the urban and the suburban portions of each city 

analyzed. The CO data are disaggregated temporally as well: concentrations are given for each hour of the year to account for diurnal and seasonal variations. For each neighborhood in each city, data recorded by 

one monitor during the course of one year have been used as the input 

data. An even more accurate representation of CO concentrations could be 

obtained by using several years worth of observations, but this would 

raise computational costs considerably. 

The model does not incorporate the effects of long-term trends in 

emissions because of such changes as the number of vehicle miles driven 

in each locale. Such changes might occur because of changing population 

densities or changes in vehicle utilization caused by changing fuel 

prices or traffic congestion. 

Emission Containment and Control Submodel 

The purpose of this model is to estimate the effect of efforts to 

contain or control emissions. The Production Process Model gives CO concentrations with the present level of containment and control; the Emission Containment and Control Submodel, therefore, attempts to estimate 

the effect of changes. Only changes in nonbackground CO concentrations 

are considered. The effects of changes in control and containment on 

background concentrations are neglected; these changes should, of course, 

depend on the source of the background concentration and the extent of 

control used. 

There are two general causes of change in nonbackground CO concentrations. First, emissions from highway vehicles and other similar 

sources can be controlled by the use of technology such as improving the 

efficiency of the combustion process or by applying secondary catalytic 

combustion reactors in waste gas streams. Other controls of this type 

are also possible, such as the increased use of alternative fuels for 

vehicles. Second, emissions from highway vehicles can be controlled by 

such means as changing traffic patterns or by curtailing traffic on days 

on which conditions are particularly bad. 

25 
P.33 

It should be noted that, even without changes in present controls, 

there are long term trends that should be reflected in this model. For 

instance, as older cars are retired from service, the present average 

levels of control will probably gradually improve. This improvement, of 

course, may be offset by the long-term trends in vehicle miles driven in 

each locale (contained in Production Process Model). 

The implementation of this model by EPA is discussed below. 

Atmospheric Dispersion Submodel 

The general purpose of this submodel is to estimate CO concentrations throughout a region, given emissions from a particular source such 

as a highway. In general, this type of model can become very complicated, involving an understanding of the physical properties of the pollutant and of the meteorological conditions of the locale. In the case 

of CO, this would be especially difficult. 

The EPA model for CO dispersion follows a simpler, alternative approach. Input CO concentrations are taken from monitored data near highways in each of the six neighborhoods for each city. CO concentrations 

within various microenvironments in each neighborhood are then estimated. 

These microenvironments are: indoors, work or school; indoors, home or 

other; inside a transportation vehicle; outdoors along the roadside; 

other outdoor locations. The CO levels in these microenvironments are 

obtained by multiplying the monitored data by a constant to reflect concentration or dilution of the level in the microenvironment. In addition, a quantity is added to this to reflect CO generated within the microenvironment; some sources of CO within microenvironments are smoking, 

gas stoves, attached garages, motor vehicle exhaust systems, and occupational sources. The factors used for these adjustments have been estimated by measuring CO concentrations in a number of representative microenvironments. 

Thus the CO , the CO concentration in microenvironment m, is estimated in terms or CO , the CO concentration estimated for the centroid of 

c 

the district, as 

CO = A + B x CO [2.1] 

m m m c 

Estimates for the additive coefficients A and the multiplicative coefficients B are presented in Table 2.1. The range on A for home indoor environments represents the possibility of gas stoves and smoking in the 

environment; the model is capable of handling various types of homes and 

different values of A for each hour of the day. The coefficient B for 

transportation vehicles is assumed to incorporate CO generated within the 

vehicle. 

It should be noted that, for the model runs used for this report, 

the values of A were taken to be zero. This means that exposure estimates do not include the effects of CO caused by nonambient sources. 

26 
P.34 

Table 2.1 

ESTIMATED COEFFICIENTS FOR MICROENVIRONMENT CONCENTRATION 

A 

(ppm) 

0.3-4. 0 

3.1 

0 

0 

0 

B 

1.0 

1.0 

2 . 1 

1.2 

1.0 

Indoors (home) 

Indoors (other) 

Transportation vehicle 

Outdoors (along roadway) 

Outdoor (other) 

Finally, because indoor CO concentrations lag outdoor concentrations 

and because CO concentrations decay slowly in enclosed spaces, it may be 

appropriate to use a lagged-time model for indoor microenvironments; this 

type of model is not at present incorporated into the EPA model. 

State and Local Government Regulation and Enforcement Submodel 

The national ambient air quality standard (NAAQS) does not imply any 

particular mechanism for implementation. Each state proposes a State Implementation Plan (SIP) which, if approved by EPA, will provide the regulatory and enforcement mechanism used to attempt to meet the goal. 

The SIPs could influence CO concentrations through the Production 

Process Model. Such measures as encouraging public transportation and 

car-pooling can reduce total emissions. It is thought, however, that 

compliance with the NAAQS would be attempted though measures that affect 

the Emission Containment and Control Model. These measures fall into two 

broad categories. 

First, there are measures that redirect the flow and concentration 

of traffic, including the possibility of restricting traffic on days when 

CO concentrations become particularly high. Thus conditions could be 

ameliorated at those times and in those locations at which the NAAQS is 

likely to be violated. It is quite possible, however, that these measures would not substantially reduce average CO concentrations and might 

even permit higher average CO concentrations. 

A second type of measure would attempt to reduce CO emissions per 

vehicle mile driven. These measures might include a specification of the 

exhaust characteristics required of new and/or used motor vehicles registered in the state. They might also include a mandatory periodical inspection and maintenance program for motor vehicles. This type of measure would reduce CO emissions on all days, reducing both the peak CO 

emissions and the average CO emissions. 

27 
P.35 

SIPs could vary from state to state, or location to location. The 

measures taken will be influenced by many factors besides the need for 

compliance with the CO NAAQS. Most of these measures will affect the 

emissions of other pollutants and compliance with other standards. Measures could be local or statewide, depending on the nature of the problems facing the state. The form of the implementation plan in some 

states may also be affected by projected long-term trends in population 

density, vehicle utilization, and improvement in emissions control. 

Several other questions must be addressed in the model: Are the 

measures effective? Do they ameliorate conditions only on the worst days 

or in the worst location, or do they reduce emissions uniformly? Are the 

measures applied only to certain cities/localities or are they statewide? 

The EPA implementation of this model is discussed below. 

Proposed Ambient Air Quality Standards Analyzed in this Report 

The proposed ambient air quality standards for CO which are analyzed 

in this report are statistical in nature: they specify an averaging 

time, a concentration level, and the expected number of instances in 

which concentrations are permitted to exceed the level over a given 

period of time. The seven proposed standards are presented in Table 2.2. 

Table 2.2 

STANDARDS ANALYZED IN THIS REPORT 

(Daily Maximum) 

Standard i-i 2 

3 

4 

5 

6 

7 

CO 

Concentration 

(ppm) 

25 

35 

9 

12 

9 

12 

25 

Averaging 

Time 

(hour) 

1 

1 

8 

8 

8 

8 

1 

Allowed 

Number of 

Expected 

Exceedances 

per Year 

1 

1 

1 

1 

5 

5 

5 

It should be noted that the present standard roughly corresponds to both 

2 and 3 above; the strictest part of the standard is 3, in that it is 

28 
P.36 

most often exceeded in actuality. It should also be noted that these 

standards, though very similar to the ones presently in force, are exceeded in a large number of cities. 

EPA Model of Implementation of Standards 

For the purposes of this study, a simple situation is addressed. 

This situation assumes that measures are taken that uniformly change 

emissions (measures such as mandatory inspection and maintenance of exhaust systems) and that each city exactly complies with the standard 

(which may mean an improvement from present conditions for some cities 

and a deterioration for other cities) . 

The underlying structure of the Production Process Submodel is actual CO concentrations recorded during 1 year for each of six neighborhoods. In order to assess accurately the effect on CO concentrations of 

standards which involve only a few exceedances a year, analytical distributions (the lognormal or the Weibull distribution) were fitted to the 

data to make the prediction of these rare events computationally tractable. A multiplicative factor was then determined that reduced (or increased) CO levels above background until the data just met the standard 

in terms of exceedances. This procedure was applied to the data for the 

neighborhood with the highest CO concentrations in each city; the multiplicative factor (which is called the "rollback factor") was then applied 

to the data for all the neighborhoods of that city, reflecting the uniform reduction in CO emissions in that city. The rollback factors for 

the seven proposed standards (Table 2.2) are presented in Table 2.3 for 

the four cities analyzed. 

Table 2.3 

ROLLBACK FACTORS FOR FOUR CITIES 

Standard Chicago, IL Los Angeles, CA Philadelphia, PA St. Louis, MO 

1.11 

1.59 

0.54 

0.78 

0.78 

1.13 

1.74 

1 

2 

3 

4 

5 

6 

7 

1.00 

1.43 

0.54 

0.75 

0.66 

0.92 

1.22 

0.78 

1.12 

0.39 

0.55 

0.51 

0.71 

1.07 

1.32 

1.87 

0.60 

0.83 

0.90 

1.23 

1.87 

The background concentrations (to which these rollback factors do 

not apply) are presented in Table 2.4. 

29 
P.37 

Table 2.4 

ESTIMATED BACKGROUND CONCENTRATIONS 

CO 

Concentration 

(ppm) 

Chicago, IL 1.31 

Los Angeles, CA 1.75 

Philadelphia, PA 0.96 

St. Louis, MO 2.27 

Summary 

The Emissions and Atmospheric Dispersion Model provides temporally 

and spatially disaggregated estimates of ambient CO concentrations under 

various proposed standards. In the EPA implementation of this model, 

these data are divided in the following ways: 

o Temporal: hourly concentrations throughout the year. 

o Spatial: four cities (Chicago, IL; Los Angeles, CA; 

Philadelphia, PA; St. Louis, MO). 

-

- Urban and suburban. 

- Residential, commercial, and industrial neighborhoods. 

- Indoors (home), indoors (other), transportation vehicle, outdoors (along roadway), outdoors (other). 

The actual data supplied by EPA for use in this report were slightly simplified because the primary purpose of the report was to develop methodology. Thus the effect of CO generated within a microenvironment (such 

as smokers and gas stoves) was omitted. 

30 
P.38 

REFERENCES FOR CHAPTER II 

1. T. Johnson and R. A. Paul, "The Neighborhood Version of NAAQS Exposure Model (NEM) Applied to Carbon Dioxide," PEDCo Environmental, 

Inc., Durham, NC 27701. Prepared for U.S. Environmental Protection 

Agency, Office of Air Quality Standards Division, Strategies and Air 

Standards Division, Research Triangle Park, NC 27711, September 

1981. 

2. Federal Highway Administration, "Highway Travel Forecasts." U.S. 

Department of Transportation, Washington, D.C, November 1974. 

31 
P.39 

III EXPOSURE MODEL 

The Emissions and Atmospheric Dispersion Model predicts the CO concentrations that will be encountered at various places and times. The 

Exposure Model is intended to answer the questions: 

o Who will be present at these various places and times to experience the CO concentrations? 

o What type of activities will they be engaging in? 

In the subsequent sections, the methodology required for an Exposure 

Model for CO is described, and the EPA model is analyzed in terms of this 

methdology. 

Exposure Model for CO 

The model developed for CO has several properties that are different 

from exposure models for other types of standards. First, in accordance 

with legal requirements, the concern of ambient air quality primary standards is human health effects. Hence only the exposure of human beings 

is modeled; the exposure of animals, vegetation and nonliving objects is 

neglected as being irrelevant to the primary standards. Second, because 

CO is thought to affect humans by combining with hemoglobin in the blood 

to form carboxyhemoglobin (COHb), the output of the model of interest to 

this research is COHb levels present in the population; this is in contrast to the output relevant for other standards, where measures such as 

the amount of pollutant inhaled or ingested might be appropriate. Finally, because COHb levels build up and decrease over a period of several 

hours, a detailed time history of CO exposure must be used for each individual; this is in contrast to other pollutants for which a cumulative 

exposure or peak exposure may be sufficient. 

The Exposure Model has three principal components: the Population 

Dynamics Submodel; the Human Activities Submodel; and the CO-COHb Conversion Submodel (Figure 3.1). 

Population Dynamics Submodel 

The nature of CO demands a particularly detailed form of modeling. 

The effects of CO on the human organism are not instantaneous; they depend on the history of exposure of the individual over a period of over 8 

hours. For this reason, elements of the population must be tracked as 

they move from place to place, exposed to CO levels that vary both by 

time and by location. In addition, different elements of the population 

have different senstivities to CO. For this reason, various groups of 

the population must be tracked separately. 

33 
OJ 

Projections of Ambient 

Concentrations Over 

Space and Time 

[ A 

y 

Population 

Groups i 

POPULATION 

DYNAMICS 

SUBMODEL 

Spatial/Temporal 

Distribution of 

Population Groups 

HUMAN 

ACTIVITIES 

SUBMODEL 

Individual 

Activities 

Profile 

CO-COHb CONVERSION 

SUBMODEL 

A) 

COHb EXPOSURE PROFILES BY: 

STANDARD 

COHORT 

HOUR OF YEAR 

MICROENVIRONMENT 

FIGURE 3.1 EXPOSURE MODEL 

T3 
P.41 

In the EPA implementation of this model, 11 age/occupation groups 

are identified; an age/occupation group for farm workers was eliminated 

as being irrelevant in urban/suburban areas. At the request of SRI, a 

twelfth group was defined to describe people suffering from severe 

chronic obstructive pulmonary disease. The age/occupation groups are 

listed in Table 3.1. 

Table 3.1 

AGE/OCCUPATION CATEGORIES 

Students (over 17 years of age) 

Professionals 

Sales personnel 

Clerical workers 

Craftsmen 

Laborers 

Service workers 

Married women 

Not working 

Children (under 5 years of age) 

Children (between 5 and 15 years of age) 

Special (chronic obstructive pulmonary disease) 

Individuals from these groups move from neighborhood to neighborhood. The six neighborhoods used in the Emissions and Atmospheric Dispersion Model are: residential, commercial, and industrial neighborhoods 

for both urban and suburban areas. For instance, a professional might 

live in a suburban residential area and work in an urban commercial area. 

Census data (and origin-destination data from transportation planning 

agencies in the four cities) were used to define a number of meaningful 

combinations of home/work neighborhoods for each of the age/occupation 

groups and to determine the number of people in each category for each 

city modeled. 

Human Activities Submodel 

Given the general location of any individual at a particular hour, 

it is necessary to specify his microenvironment and exercise level. The 

microenvironment he finds himself in is important in determining the 

level of CO to which he is exposed; his exercise level is important in 

determining the extent to which CO affects him. 

In the EPA implementation of this model, use is made of the results 

of a study undertaken for EPA by SRI International. In this study, estimates are made at each hour of the day of the distribution the population among microenvironments and of the level of exercise undertaken in 

these settings. 

35 
P.42 

Using these results, three representative activity patterns were set 

up for each of the age/occupation groups, and the relevant population was 

divided between these activity patterns. These activity patterns indicate the microenvironment in which an individual finds himself at each 

hour of the day; these microenvironment patterns are different for weekdays and Saturdays and Sundays. For instance, the microenvironment component of one activity pattern for professionals on weekdays is: at home 

until 7 a.m.; transportation vehicle 7-8 a.m.; indoors at work 8 a.m.-

5 p.m.; transportation vehicle 5-7 p.m.; at home after 7 p.m. 

The other component in the three activity patterns is the level of 

exercise the individual engages in. Three levels of exercise are defined: low (lying down, sitting, or standing), medium (walking or other 

mild exercising), and high (running and other heavy exercising). Thus 

the professional discussed above is at a low level of exercise at all 

hours of weekdays; on weekends, however, there are hours during which he 

is at a medium or even high level of exercise. 

In all, the EPA model tracks 98 cohorts (or groups of people) 

through each hour of the year. For each hour of the day, there is a 

specified neighborhood, microenvironment, and exercise level; this pattern is different on weekdays and Saturdays and Sundays. Thus there are 

11 age/occupation groups each with three activities patterns; the twelfth 

age/occupation group (chronic obstructive pulmonary disease) has only one 

activity pattern. This yields 34 groups. Each of these groups can have 

a home microenvironment that is in either an urban residential or a suburban residential neighborhood; this brings the total to 68 groups. Finally, the important combinations of home neighborhoods and work neighborhoods brings the total to 98 cohorts. 

CO-COHb Conversion Submodel 

The previous models enable one to match CO concentrations to the 

population for each hour of the year. For the case of CO, it is desirable to carry the Exposure Model one step further. CO is absorbed by the 

hemoglobin in the blood to produce carboxyhemoglobin (COHb). One of the 

effects of elevated COHb levels is to reduce the oxygen-carrying capacity 

of the blood, as will be discussed in subsequent chapters. Whether this 

is the principal effect of CO on the human organism or not, it is generally accepted that COHb levels are a good index of how severely the human 

organism is affected by CO exposure. 

The COHb levels at any moment are not simply determined by the CO 

concentrations experienced at that moment. COHb builds up in the blood 

over time and is eliminated over time in response to changing CO concentrations and to changing exercise levels. To estimate the COHb level at 

any moment, it is necessary to integrate over about 8 hours of past history of exposure to CO and of exercise levels- This is the reason that 

the Population Dynamics Model and the Human Activities Model are so detailed. 

A model has been developed and at least partially verified that describes the physiology relating COHb levels to ambient CO levels. This 

36 
P.43 

2 

model is embodied in the Coburn equation. This equation predicts the 

rate of change of COHb as a response to changes in ambient CO levels. 

The equation takes into account past history (the level of COHb present 

in the blood .before the change in ambient CO levels), endogenous production of CO, and the physiology of the individual (including the level of 

exercise)., .This model has been clinically tested with a small number of 

subjects. ' The small number of subjects of each sex and the relatively 

low exercise ventilatory rates in these tests indicate that further verification of this model should be sought. 

The EPA implementation of the Exposure Model uses the Coburn equation to calculate the COHb levels present in individuals of each of the 

98 cohorts for each hour of the year. 

This is the final step of the model. There now exists a mechanism 

for estimating how the population is affected by CO exposure under any 

National Ambient Air Quality Standard (NAAQS) for CO. It is the purpose 

of this report to carry this process to its conclusion by answering the 

question: What are the health consequences implied by each proposed 

NAAQS? 

Data Supplied For This Study by EPA Model 

As was mentioned in the Introduction, the purpose of this report is 

primarily methodological in nature. The output data of the combined 

Emissions and Atmospheric Dispersion Model and the Exposure Model were 

not in the precise form needed for the work described below. In addition, the raw output of these models was difficult and expensive to 

transmit to SRI. For these reasons, it was decided to use a simplified 

set of data. The input data used for the Emissions and Atmospheric Model 

was described in Chapter II. In this section, the input data used for 

the Exposure Model is described. 

The Exposure Model follows 96 cohorts hourly through the year, specifying age/occupation group, neighborhood of home and neighborhood of 

work, microenvironments, and activity levels. -For each city analyzed, 

the population is divided among these cohorts. Two additional cohorts 

were defined for the purposes of this study: urban and suburban individuals who suffer from severe chronic obstructive pulmonary disease; these 

cohorts follow the most sedentary activity pattern of the "not working" 

age occupation group. 

In the CO-COHb Conversion Model, various parameters of the Coburn 

equation must be specified. The parameters used by EPA are: Haldane 

constant, 218; hemoglobin concentration, 15 gr per 100 ml (18 gr per 100 

ml for the chronic obstructive pulmonary disease cohorts); endogenous CO 

production rate, 0.0069 ml/min; altitude above sea level, 0 m. Parameters that differ from cohort to cohort are specified in Table 3.2. 

It should be emphasized that the effect of smoking is not included 

in the data supplied for use in this study; other local sources of CO in 

a microenvironment such as gas stoves, attached garages, and occupational 

sources are also excluded. 

37 
P.44 

Table 3.2 

PARAMETERS USED IN COBURN EQUATION 

Age/Occupatd 

Students (over 17) 

Professionals 

Sales personnel 

Clerical workers 

Craftsmen 

Laborers 

Service workers 

Married women 

Not working 

Children (under 5) 

Children (5 to 17) 

Special (pulmonary 

on 

disease) 

CO 

Diffusion 

Rate 

(ml/min/ 

torr) 

27 

27 

27 

27 

30 

30 

27 

24 

27 

6 

21 

20 

Blood 

Volume 

(ml) 

4,800 

4,800 

4,800 

4,800 

5,500 

5,500 

4,800 

4,000 

4,800 

960 

3,200 

4,800 

Ventilation 

(ml/min) 

Rate 

Exercise Level 

Low 

5,500 

5,500 

5,500 

5,500 

6,000 

6,000 

5,500 

5,000 

5,500 

1,100 

3,600 

3,100 

Medium 

8,700 

8,700 

8,700 

8,700 

10,000 

10,000 

8,700 

7,500 

8,700 

1,700 

6,000 

4,000 

High 

17,400 

17,400 

17,400 

17,400 

20,000 

20,000 

17,400 

15,000 

17,400 

3,400 

12,000 

4,000 

Summary 

The Exposure Model, coupled with the Emissions and Atmospheric Dispersion Model, provides estimates of COHb levels in the population under 

various proposed standards. The data transmitted to SRI International 

was disaggregated in the following fashion: 

o Standards: 7 proposed standards and the present as-is situation. 

o Cities: Chicago, IL; Los Angeles, CA; Philadelphia, PA; St. 

Louis, MO. 

o Cohorts: 98 cohorts defined by age/occupation group, neighborhood of home, neighborhood of work, and activity pattern. 

o Time: hourly exposures. 

The three activity patterns describe: 

o Microenvironments: indoors (at home), indoors (other), transportation vehicle, outdoors (along roadway), outdoors (other). 

o Exercise levels: low, medium, high. 

38 
P.45 

Sources of local concentration of CO were not included in the model 

input used to generate the data supplied to SRI. In particular, smoking 

is a source for local concentration of CO (passive smoking) and a source 

of elevated COHb levels for the smoker himself (active smoking). 

Because of the very large quantity of data involved in this disaggregation and because the purpose of this report is methodological, it 

was decided to further limit the amount of data that was to be transmitted to SRI. Thus, hourly exposures were supplied only for the months of 

January and July. These months were chosen to represent seasonal variations; winter typically has higher CO concentrations than summer. 

SRI subsequently reduced the level of disaggregation in accord with 

the needs of the models developed in the following chapters. The data as 

employed by these models is disaggregated in the following fashion. 

o Standards: seven proposed standards and the present as-is 

situation. 

o Cities: Chicago, IL; Los Angeles, CA; Philadelphia, PA; St. 

Louis, MO. 

o Cohorts: five composite cohorts. 

o Microenvironments: all microenvironments; transportation vehicle 

only. 

The data were reduced to a frequency distribution on the number of times 

individuals from the composite cohorts would experience a level of COHb 

in the course of a year. 

Some interesting characterizations of the data can be obtained. 

Table 3.3 shows data on hourly COHb levels for a cohort that contains 

professionals, craftsmen, and laborers; this cohort contains the bulk of 

the population that experiences high COHb levels. The data are for Los 

Angeles, and three situations are presented: the strictest proposed 

standard (9 ppm, 8 hour averaging time, 1 expected exceedance per year), 

the most lenient proposed standard (35 ppm, 1 hour averaging time, 1 expected exceedance per year), and the as-is present situation. It should 

be noted that both the strictest and the most lenient standards are very 

similar to those presently in force. 

Table 3.3 reflects the fact that Los Angeles is not in compliance 

with the strictest standard. COHb in transportation vehicles is, on the 

average, only somewhat higher than in general life. 

Table 3.4 displays the COHb levels (for the same cohort as above) to 

be expected in four cities if the strictest standard is just met. 

Remarkable in Table 3.4 is Philadelphia, where there are occasional 

large CO concentrations. In order to comply with the standard, controls 

have to be such that the average COHb level is a little over half that 

permissible in the other three cities; at the same time, the highest COHb 

levels encountered in Philadelphia would be almost twice that of the 

other cities. This pattern remains true for the other standards as well. 

39 
P.46 

"Table 3.3 

COHb LEVELS: LOS ANGELES, CA 

(Professionals, Craftsmen, Laborers) 

Strictest proposal standard 

Present situation 

Most lenient proposed standard 

Average COHb in 

Average Highest Transportation 

COHb COHb Vehicles 

0.69% 

0.85% 

0.88% 

1.75% 

3.75% 

4.25% 

0.75% 

0.90% 

0.94% 

Table 3.4 

COHb LEVELS UNDER THE STRICTEST PROPOSED STANDARD 

(Professionals, Craftsmen, Laborers) 

Average COHb in 

Average Highest Transportation 

COHb COHb Vehicles 

Chicago, IL 

Los Angeles, CA 

Philadelphia, PA 

St. Louis, MO 

0.58% 

0.69% 

0.35% 

0.61% 

2.25% 

1.75% 

3.25% 

1.75% 

0.72% 

0.75% 

0.42% 

0.68% 

The general conclusion to be drawn from Table 3.3 is that there is a 

fairly high difference in COHb levels for the proposed standards. It 

will be the task of the rest of this report to determine if this implies 

a significant difference in health effects. Table 3.4 indicates that, 

even if standards are exactly met, the pattern of variation of CO concentrations in different cities can lead to far different average levels of 

COHb. This effect increases the difficulty of arriving at national estimates for health risks. 

It should be noted, however, that an error in the EPA data base has 

recently been discovered. Correction of this error will reduce all the 

estimates in this report. 

40 
P.47 

REFERENCES FOR CHAPTER III 

1. Marc F. Roddin, Hazel T. Ellis, and Waheed M. Siddiqee, "Background 

Data for Human Activity Patterns," Vols. 1 and 2. Draft final report prepared for Strategies and Air Standards Division, Office of 

Air Quality Planning and Standards, U.S. EPA, Research Triangle 

Park, NC 27711, August, 1979. 

2. R. F. Coburn, R. E. Forster, and P. B. Kane, "Considerations of the 

Physiological Variables that Determine the Blood Carboxyhemoglobin 

Concentration in Man," Journal of Clinical Investigation, kk_, 1899-

1910 (1965). 

3. J. E. Peterson and R. D. Stewart, "Absorption and Elimination of 

Carbon Monoxide by Inactive Young Men," Arch. Environ. Health, 21, 

165-171 (1970). 

4. J. E. Peterson and R. D. Stewart, "Predicting the Carboxyhemoglobin 

Levels Resulting from Carbon Monoxide Exposures," J. Appl. Physiol., 

39_, 633-638 (1975). 

5. T. Johnson and R. A. Paul, "The Neighborhood Version of NAAQS Exposure Model (NEM) Applied to Carbon Monoxide," PEDCo Environmental 

Inc., Durham, NC 27701. Prepared for U.S. Environmental Protection 

Agency, Office of Air Quality Planning and Standards, • Research Triangle Park, NC 27711, September 1981. 

41 
P.48 

PART III 

HEALTH DOSE-RESPONSE MODELS 

One of the principal tasks described in this report is the development of health dose response models that are capable of linking exposure 

data at ambient CO concentrations to health risks. The essential elements of these models were scattered in the medical literature and existed in the understanding of medical and other experts. The elements 

had not, however, been gathered and used to assist in the estimation of 

risk. 

Because these models represent a first step in completing the unified risk assessment calculator for CO, they must be exposed to public 

scrutiny before the risk assessment can be used. It is anticipated that 

these models may be improved or even substituted by better models; they 

illustrate a structure and provide a challenge. In addition, they identify several important variables that are at present not well known, but 

which appear important for the risk assessment. 

Four areas of potential health impact are studied in the following 

chapters. For each of these areas, a deterministic model is built. Preliminary probability distributions are developed for the most sensitive 

variables in these models, and several measures of health risk are developed. 

It should be reemphasized that these models are preliminary. For 

this reason, and because the data supplied for the study by EPA was simplified and partially incorrect (see Chapters II and III), the health 

risks estimated here should not be used for any decision-making purpose. 

43 
P.49 

IV HEALTH EFFECTS DOSE-RESPONSE MODELS 

There are two major questions that must be addressed before any 

analysis and modeling of the health effects is undertaken: What is the 

approach to be used? What specifically is to be modelled? These two 

questions are addressed in this chapter. 

The Modeling Approach 

There are two distinct approaches with which one can attempt to 

quantify health effects. Both are consistent with the decision analysis 

methodology for handling uncertainties. The choice between these alternatives is, to a certain extent, determined by the amount of information 

available, the complexity of the problem, and the importance of the quantity to be modeled. In addition, different analysts have different philosophies that make them prefer one or the other. 

One method is to encode the probabilities of health effects as a direct function of the exposure to the pollutant. A typical encoding session would involve questions such as the following: "If a group of 1,000 

pregnant women were continuously exposed to COHb levels of 5%, how many 

of these women would suffer loss of the fetus?" The expert must make 

several logical connections mentally to step from the cause to the effect 

in this process. The philosophy behind this approach is that experts 

have information on the magnitude of these effects; this type of question, however, may not be in a form with which the expert is familiar, 

nor one with which he is comfortable. 

The advantage of this approach is that it is relatively quick and 

inexpensive. A wide variety of experts can be interviewed and a good 

picture of current understanding of the effect can be obtained. Furthermore , the output is easily comprehended by nontechnical as well as technical reviewers. 

One problem with the above approach is that the results cannot 

easily be critiqued if there is disagreement among the experts. This is 

a drawback for decision making in the public domain. The assumptions 

that are employed to support an air quality standard must be clear, explicit, and put in terms that many can understand. If not, then the 

analysis is vulnerable to the attacks of anyone who wishes to disagree 

with the experts' opinions. There is often no test possible to distinguish between the conflicting opinions. 

A related drawback is the lack of refutability of the encoded distributions. Only epidemiological evidence can support the experts' opinions. The magnitude of the effects, however, is often too small to be 

detected by such studies. By way of contrast, a good health effects 

45 
P.50 

estimation procedure uses inputs that are, in principle, observable or 

refutable. Where the uncertainty on an input is important, encoding of 

expert opinion is indicated; if, on the basis of the encoded distribution, the value of information is large, then a good area for future research has been identified. If the analysis has been well structured, 

the type of research which is indicated is not only well-defined, but 

also feasible. 

A third criticism is that this approach often fails to make explicit 

the logical processes by which the expert must draw his conclusions. 

This results in two sources of error. First, the expert is asked to assess variables with which he is least familiar, while his extensive 

knowledge of related factors becomes secondary. Second, the expert's 

ability to be logically consistent on an intuitional level is not infallible, especially if the issue is complex. No matter how careful the encoding process, there is a strong possibility of error because of logic 

rather than simple lack of scientific knowledge. 

A second approach to the quantification of health effects tries to 

circumvent the weaknesses of the first by relying on "extrapersonal" models when possible. These make explicit the logical processes that the 

expert must perform implicitly in the first approach. The model describes the physical relationships that are believed to connect the pollutant exposure with the health outcome. Experts aid in formulating and 

refining the logical structure of the model. Once the parameters in the 

model are specified, the model provides a consistent process by which one 

can estimate health effects for different pollutant exposures. 

The information that experts have is separated into two categories: 

effects of the pollutant on physiological parameters and the effects of 

the physiological parameters on health. The connection between the two 

is made "extrapersonally" so that logical errors are reduced. By dividing the problem into its components, additional benefits accrue. 

First, the experts furnish information concerning those variables 

and relationships with which they are most familiar. Second, the model 

and the inputs can be critiqued by anyone familiar with basic physiology. 

One does not need to be an expert on the pollutant to be able to contribute useful information to the risk assessment process. Third, anyone 

dissatisfied with the estimates that result from the model must address 

either the assumptions on the physiological processes or those on the 

physiological variables. This forces discussion away from highly subjective and largely irrefutable statements. Instead, the public debate can 

focus on relationships that can be tested and on variables that can be 

measured. 

The outcome of a risk assessment based on extrapersonal models is a 

clarification of the grounds from which public disagreement arises. Furthermore, directions for valuable future research will be well defined. 

Rather than indicate that more research should be done in the area of 

cardiovascular effects, the models can indicate specific avenues of this 

research, such as the effects of the pollutant on blood pressure. 

46 
P.51 

This risk assessment has followed the second approach, in the belief 

that it is the most fruitful direction for a demonstration of the public 

risks of carbon monoxide. The complete development and critique of such 

models for each possible health effect is a task that is well beyond the 

purpose and resources of this study. For this reason, one should not attempt to use the models presented for actual policymaking at this stage 

in their development. Rather, they could be used as a vehicle to provoke 

public comment on structure and assumptions. Such dialogue will indicate 

weaknesses and errors that should be addressed in each model. This form 

of public critique should be a part of any risk assessment. Within the 

limited budget of the demonstration, the models have received some review 

by appropriate "experts." The extent of this review is not sufficient 

for a finalization of the assessment. While presenting each model, the 

aspects that should clearly be refined or expanded will be indicated. 

Others could probably be unearthed in the public review process. 

Despite the preliminary nature of the models, they represent an advance in the quantification of carbon monoxide health effects. These pilot models are workable, and can readily be elaborated as desired. A 

solid basis for a complete risk assessment now exists. As far as has 

been possible, each model describes the major mechanism by which carbon 

monoxide is believed to affect health. To the extent that the mechanism 

is correctly identified, a full assessment would only require refinement 

and elaboration of the structure. If the mechanism is incorrectly identified, then input from the public may help identify the correct one. A 

new model can be developed to replace the existing one, and an advancement will have been made without a loss in value of the remaining elements of the assessment. 

Choice of Sensitive Groups 

The range of groups who are potentially sensitive to CO is broad. 

Any risk assessment of CO must identify all of these groups. Once identified, the more important groups and health effects can be filtered out 

as candidates for detailed health effects modeling. The less important 

effects are not quantified at this stage because they are likely to be 

insignificant in relation to those health effects that are chosen for 

modeling. One should be careful, however, not to lose sight of these remaining, unquantified effects in the final analysis. 

In the case of CO, the groups identified are listed in Table 4.1, 

followed by the specific health effects associated with the groups. It 

should be noted that effects aggravated by high-altitude hypoxia were 

specifically excluded by EPA from this study. 

Evaluation of relative importance for the first screening was based 

on three considerations. One factor is the size of the sensitive population. Even a small effect, or one of relatively minor importance per 

event, can become a significant public health concern if it occurs very 

commonly within the general population. The second consideration is the 

potential for very grave outcomes such as mortality. These naturally receive the most public attention, and are particularly important when such 

risks are coupled with a large sensitive population. The third 

47 
P.52 

Table 4.1 

SENSITIVE GROUPS AND ASSOCIATED HEALTH EFFECTS 

(1) Cardiovascular disease 

Myocardial infarction 

Sudden death (ventricular fibrillation) 

Aggravation of acute coronary insufficiency 

Aggravation of unstable angina pectoris 

Aggravation of stable angina pectoris. 

(2) Chronic obstructive pulmonary diseases 

Acute exacerbations of pulmonary failure 

Heart failure 

Asphyxiation. 

(3) Fetuses 

Fetal death due to hypoxia 

Nonfatal damage to nervous system 

Malformation. 

(4) Anemics 

Aggravation of hypoxia. 

(5) Hypertensive disease 

Increased risk of cerebrovascular accident. 

(6) People engaged in complex and hazardous activities 

Increased risk of accident. 

(7) Peripheral vascular disease 

Aggravation of intermittent claudication. 

consideration is the relative sensitivity of each group to the pollutant. 

In some cases, small changes in exposure levels can result in large 

changes in the health status of one group of people. Table 4.2 presents 

a check list that indicates which groups rank very high in each of the 

three categories. 

Three of the seven original groups were eliminated in this screening. Anemics with no other complicating diseases were not relatively important on any of the three bases. Although more sensitive to CO than a 

person with normal hemoglobin levels, the effects of CO on this group are 

unlikely to be as large as the effects on the rest of the sensitive 

groups. A similar judgment can be made with respect to peripheral vascular disease. Here the sensitive group is large, but the likelihood of 

major effects is small. The converse is true for hypertensive disease 

patients; if an effect occurs, it is of major significance; the group of 

hypertensives that is likely to be affected is small, however, because 

ambient levels of CO are not known to have large effects on blood pressures or blood flow-rates. 

Cardiovascular disease is extremely prevalent. The potential for 

increased mortality represents a major concern. Although this group is 

48 
P.53 

Table 4.2 

. RANKING OF SENSITIVE GROUPS 

Large Grave Relative 

Group Population Outcomes Sensitivity 

Cardiovascular disease X X 

Chronic obstructive pulmonary disease X X 

Fetuses XX X 

Anemics 

Hypertensive disease X 

Motor vehicle drivers X X 

Peripheral vascular disease X 

probably not extremely sensitive to CO on average, small proportional effects can result in significant outcomes because of the first two factors. 

Chronic obstructive pulmonary disease is quite prevalent, but those 

who would be affected by ambient levels of CO are a small portion of the 

total. The morbidity and mortality of this group is greatly affected by 

oxygen availability. Because even low concentrations of CO interfer with 

oxygenation, its potential effects on this group has been considered more 

fully. 

Fetuses rank high in all categories. Although perhaps not as sensitive as those with compromised pulmonary function, they are unable to 

either indicate stress of hypoxia when it occurs, or to escape such 

events by modification of activity. 

The potential for accidents caused by CO may be small for each individual. Virtually the entire population, however, is subjected to this 

risk in the form of motor vehicle transportation. A small risk may result in major effects when the prevalence of this risk and the relative 

health of this "sensitive" group is accounted for. 

In the following four chapters, models are developed for four areas 

of health effects: 

o Arteriosclerotic Heart Disease 

o Fetal Health Effects 

o Chronic Obstructive Pulmonary Disease 

o Motor Vehicle Accidents. 

49 
P.54 

Only the most important health risks in each of these categories will be 

considered. If it should prove important or desirable, it would not be 

difficult to expand the models to include less important health risks for 

these health effect categories. In addition, models of health effects 

for other sensitive groups and health risks should not be difficult to 

develop, because they will probably have a structure similar to one of 

those developed in the following chapters. 

Measures of Health Risk 

Models such as have been developed in this report are exceedingly 

flexible and can answer a large number of questions. EPA has requested 

that three types of measures of health risk be obtained. Because of the 

specific health effects chosen for evaluation by SRI, not all of these 

measures yield useful results for each of the health effects. It should 

be emphasized that this is because of the choice of health effects and 

the identification of the sensitive groups. Other health effects can be 

identified for which these measures yield useful information. For instance, the probability that 5% of angina patients will suffer an angina 

attack under a definite set of CO exposure and exercise conditions can 

easily be calculated. The applicability of each measure to the effects 

analyzed in this research are discusseed below. 

Measure 1—The probability that the number of persons experiencing, 

and the expected number of incidences of, each adverse health effect 

equals or exceeds a given value in a period of 1 year: 

o For deaths (and nonfatal effects) of fetuses, deaths caused by 

arteriosclerotic heart diseases and deaths caused by motor vehicle accidents, the number of persons experiencing the effect is 

equal to the number of incidences of the effect. 

o For hospitalizations caused by chronic obstructive pulmonary disease, the hospitalization rate of the identified sensitive group 

is such that virtually all individuals are hospitalized at least 

once during the year. Similarly, all angina patients considered 

in this study experience many attacks during a year. Hence all 

individuals in these groups experience adverse health effects 

during a year. 

o For deaths caused by chronic obstructive pulmonary disease, the 

number of deaths per year is not a relevant number (see Chapter 

VII). The measure used is reduction of life expectancy. 

For all these effects, only uncertainties because of the Health Risk 

Models are incorporated. Given the limited amount of data obtained from 

EPA, it was not feasible to include uncertainty on the Emission and Atmospheric Dispersion Model and the Exposure Model. Uncertainty on the 

Coburn equation and its parameters, for instance, is probably important. 

Finally, where fairly accurate base-case data are, in principle, available (e.g., fetal deaths and motor vehicle accident deaths, given today's 

emission levels), these data are taken as certain; this assumption focuses attention on uncertainty associated with changing to a new standard . 

50 
P.55 

Measure 2—The expected number of persons experiencing, and the expected number of incidences of, each adverse health effect in 1 year. 

This measure is just the expected value of the distributions in 

Measure 1, and hence the same observations apply. In presenting the results, it was found useful to express the differences in standards as 

shifts from the present as-is situation and also from the situation under 

the strictest proposed standard. 

• Measure 3—The probability that n percent of the most sensitive 

group, when exposed, will experience an adverse health effect in 1 year 

(n = 1%, 5% and 10%). 

This measure proved to be inapplicable for all the health effects 

analyzed in this report. Because the models show no threshold for risks 

because of CO exposure, all persons in the sensitive groups are continuously exposed. There was no natural way to isolate "most sensitive" 

groups because of the potential for circular reasoning. Finally, either 

the entire identified sensitive group experiences health effects during 

the year (angina patients, chronic obstruction pulmonary disease hospitalizations, and life expectancy reduction) or less than 1% of the group 

experiences the health effect during the year (fetal deaths and nonfatal 

effects, motor vehicle accident deaths). For cardiovascular patients, 

current mortality is about 10% per year, and variations in the CO standard do not affect this dramatically. 

For these reasons, this measure has not been found to be directly 

applicable to the health effects analyzed. As a substitute measure, the 

number of health effects attributable to ambient CO is estimated. It is, 

of course, recognized that it is not feasible to completely eliminate 

ambient CO. One should note, moreover, that although the models should 

predict differences between standards fairly well, the extrapolation to 

the level of zero ambient CO is prone to greater error. 

51 
P.56 

V ARTERIOSCLEROTIC HEART DISEASE 

The modeling (or direct encoding) of arteriosclerotic heart disease 

health effects is difficult. Physiological parameters vary widely among 

individuals, and voluntary actions play a major role in the effects. The 

following model avoids the issues of interpersonal differences by looking 

at a single "composite" patient for a demonstration of model structure. 

Modeling of variations between persons is required of a full risk assessment. However, this demonstration shows that a relatively simple 

quantitative model can be developed for these effects. 

Background and Modeling Issues 

Arteriosclerotic heart disease (ASHD) contributes to more deaths in 

the United States than any other single cause. Approximately 3% of the 

adult population in the United States suffers from some manifestation of 

this disease, such as angina or myocardial infarction. Another 2% of 

adults are2believed to suffer from ASHD without recognized symptoms or 

diagnosis. In fact, for about 25% of those suffering-from ASHD, death 

will be the first apparent symptom of their condition. This group is 

not only large, but many are unaware of the risks to which they are subject. 

ASHD is characterized -by a reduction in the potential oxygenation of 

the myocardial tissue. This is caused by narrowing of the coronary arteries that supply oxygenated blood to the heart. The severity of the 

disease depends on which coronaries are involved as well as the degree of 

narrowing. For a given level of severity, the patient's risk depends on 

his emotional and physical activity patterns. 

A highly active or emotional person places great oxygen demands on 

his heart. A healthy heart can respond to the increased oxygen demand by 

increased blood flow. Those with ASHD cannot respond as well, and the 

oxygen demands of the ASHD heart will sometimes exceed the oxygen supply. 

Myocardial ischemia can have one of three outcomes: the patient may feel 

no pain; he may feel pain that subsides with rest; or he may sustain an 

infarction, with irreversible damage to the heart muscle. In all three 

cases, however, the patient is subjected to risk of death because of fibrillation. The increased propensity to go into fibrillation during ischemia appears to be because of (1) a prolonging of the portion of the 

cardiac cycle when the heart is most susceptible to sustained tachycardia 

and (2) changes in the electrolytic balances-between cells, which make an 

initiating dysrhythmia more likely to occur. 

The very nature of coronary heart disease allows one to suspect that 

ure to carbon moi 

rence of ischemic epis 

exposure to carbon monoxide may be a contributing factor in the occur 

:e of ischemic episodes. Aronow » » » » and Anderson have 

53 
P.57 

"^^^ ^ ** • * 

demonstrated this in clinical studies on stable angina patients. In all 

but one of these studies, anginal patients followed a standard exercise 

protocol described by Redwood ; this protocol is the most reliable in 

measuring the effects of drugs or environmental factors on the supply and 

demand of oxygen to the myocardium at the onset of angina. 

To assess the health effects of CO on ASHD patients one should model 

the relationship of myocardial oxygen consumption (MOC) and maximal oxygen supply capacity (MOS). At any point where MOC rises above MOS, ischemia can be said to occur. This was the consensus of the experts consulted. All of the manifestations of ASHD occur during episodes of ischemia. Therefore, one can estimate changes in the frequency of these 

health effects by changes in the relative proportion of time at which the 

model predicts ischemia to occur. 

The bulk of clinical data looks at patients suffering from painful 

ischemia in the form of stable angina pectoris of effort. The model presented here will estimate frequency in anginal pain as an indication of 

frequency in ischemia in general. This approach makes optimal use of the 

data available. It also provides predictions that can be used to test 

for model accuracy. Pain occurs frequently enough among those suffering 

from angina to allow for a statistical analysis of its relative frequency 

under different environmental conditions. Also, the occurrence of pain 

is a more readily detected event than the occurrence of ischemia. 

One should be aware that the use of pain as the defined event is 

marred by its subjectivity. People have different resistances to pain. 

Also, because many people experience painless myocardial ischemia, one 

can reasonably assume that the degree of ischemia required to induce pain 

may be quite different from case to case. Probably the risks of acute 

infarction and/or fibrillation are related to the degree of ischemia. In 

part, this problem is reduced because the data allow analysis of how the 

susceptibility to pain changes with COHb for given individuals. The relative changes are more important here than are the absolute levels at 

which the pain occurs. These considerations only partially answer the 

problem of subjectivity. Ideally one would define the event of ischemia 

in more measurable, physiological terms such as coronary sinus oxygen 

tension, or electrocardiographic changes. If data on how these variables 

are affected by CO become available, then the model presented here should 

be modified to incorporate such information. Researchers at General 

Motors Research Laboratories are currently developing an animal model 

that relates physiological measurements of ischemia and CO. The outcome 

of their efforts may be directly relevant to such a modification. 

The consumption of oxygen by the myocardium is determined by numerous factors such as heart rate, blood pressure, stroke volume, heart 

size, and inotropic state. Models that estimate MOC quickly become very 

complicated if one tries to account for all of the factors in the most 

realistic way. One commonly used index of MOC is the systolic-time index. This is approximated by the "triple product" (heart rate x mean 

systolic blood pressure x systolic ejection time). Only the third factor 

is difficult to observe. Fortunately, Redwood found that the "double 

product" (heart rate x systolic blood pressure) provides a reasonable 

proxy for MOC under the circumstances of repeated rounds of the standard 

54 
P.58 

protocol. Each stable angina patient has a characteristic value of the 

double product (DP) at the onset of angina that only fluctuates by about 

10%. This implies that each stable angina patient has some MOS beyond 

which increases in MOC result in ischemic pain. When pressed beyond this 

threshold, anginal pain commences as the metabolites of anaerobic respiration build up in the myocardial tissue. 

If, when following the standard protocol, stable angina patients experience a drop in the time until onset of pain but not a drop in the DP 

value at onset, this is evidence that something has occurred to raise the 

MOC for a given level of exercise. If, on the other hand, there is a 

drop in both the time until onset of pain and in the DP at onset, this 

indicates a decrease in MOS. Aronow has carefully documented the existence of a reduction in the MOS of anginal patients who have been exposed 

to various levels of carboxyhemoglobin. His studies indicate that levels 

of COHb that result from ambient exposures do not affect the demand for 

oxygen, but only the supply. 

Deterministic Model 

A model of the magnitude of the possible aggravation of angina in 

the general population must account for the hour to hour changes in both 

myocardial oxygen demand and maximal supply. The basic assumption of 

this model is due to Aronow: fluctuations in COHb cause shifts in the 

supply capacity, but not in the oxygen demand that the patient will experience. The oxygen demand of the heart, however, is assumed to fluctuate 

quite widely, and in a probabilistic manner, over each hour of the day. 

The double product (DP) will be used as a proxy variable to measure MOC 

and MOS. 

The model permits MOS to shift because of changes in COHb. MOS 

clearly does shift for other reasons. This is partly evidenced by the 

fluctuation in DP at onset of pain. In general these shifts occur on a 

regular diurnal basis. For instance, many patients are most susceptible 

to attacks for given stress levels in the mornings. Because these nonCOHb induced shifts in supply capacity are largely predictable by the 

patient, he is assumed to try to compensate for them in his activity 

patterns. Effectively, this results in a fairly constant relationship 

of supply capacity to MOC distribution over all hours of the day for all 

supply effects that are regular in nature. The elements of supply 

changes that are unpredictable, or for which the patient is unable to 

fully compensate, are modeled by a probability distribution on MOS. As 

mentioned above, MOS can fluctuate on a regular basis by as much as 10%. 

The distribution used in this model assumes that MOS deviates more than 

10% from the mean only 20% of the time; this is in accord with statements 

by Aronow and others. COHb shifts the mean, as described below. The 

distribution used is depicted in Figure 5.1, for the case where the mean 

value is 20,000: Pr(MOS|COHb) is the probability density function for 

MOS at a given level of COHb. 

The results from the Aronow studies cited above were used to formulate an estimate of how much the mean value of MOS shifts as a function 

of COHb. The formula was assumed to be linear on the range of ambient 

55 
1.0 

Probability .50 — 

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IS LESS THAN THE VALUE INDICATED: Pr(MOS|COHb=0) IN 

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PAIN, Pr(M0C > MOS). CURVE A IS Pr(MOS|COHb); CURVE B IS Pr(MOS > 

DOUBLE PRODUCT); CURVE C IS Pr(MOC > MOSjCOHb) 
P.65 

To actually calculate the frequency of anginal pain, the inhabitants 

of each city were divided into five groups. Each group would have a different pattern of CO exposure and of incidence of ASHD. Table 5.1 describes these groups. The ranges presented represent differences between 

the four cities; "as-is" refers to the exposures estimated on the actual 

monitoring data. The incidence of angina among the cohorts is not well 

known, and illustrative numbers were supplied for use in the analysis; 

the results of the model are fortunately not sensitive to this assumption. The model assumes the same distributions on MOC and MOS for each 

group. Further development of the model would relax this assumption, allowing for the cohorts to have different distributions on the severity of 

the disease, and on activity patterns. 

Table 5.1 

DEFINITION OF COHORTS USED IN ASHD MODEL 

Cohort 

1 

2 

3 

4 

5 

Description 

Students and clerical workers 

Sales personnel and service workers 

Professionals, craftsmen and laborers 

Married women 

Retired and unemployed 

Range 

of Avg. 

% COHb 

(As-Is) 

.40-.81 

.42-.82 

.42-.85 

.42-.65 

.42-.60 

Percent 

of Cohort 

that is 

Male 

34-38 

49-57 

77 

0 

39-42 

Estimated 

Percent 

With 

Angina 

Men Women 

0 0 

2 .05 

5 .1 

.05 

10 2 

Total attacks of angina per day are calculated as follows: 

E (# angina patients) x V ^ Pr(COHb) x Pr(M0C > MOS | COHb) [5.4] 

in cohort *-^ 

cohorts COHb 

The angina model represents how ischemia changes as a function of 

the ambient exposure to CO. This model was easily developed because of 

the clinical data available. The more severe effects of ASHD, such as 

acute coronary insufficiency, acute myocardial infarction, and sudden 

heart death (ventricular fibrillation) are not easy to relate clinically 

to CO exposures. Controlled tests are out of the question. Only epidemiological and animal data can be collected. 

62 
P.66 

Despite the problems in testing for these health effects in relation 

to CO, an estimate of the relative changes in risk is possible. The angina model predicts relative frequencies of painful ischemia. Whether 

ischemia is painful or painless, the patient is highly susceptible to 

fibrillation at that moment. Thus one would expect that the incidences 

of fibrillation would change roughly in proportion to the changes in frequency of anginal pain. Drs. Kuller, Horvath, and Aronow concur in this 

(see Appendix B). 

Incidence of myocardial infarction can be estimated in a similar 

manner. If elevated COHb is causing additional morbidity along the entire spectrum of ASHD manifestations, then one must conclude that the 

most severe ischemic episodes must also increase. Thus the output of the 

angina model is also used to estimate changes in relative frequency of 

acute myocardial infarctions (AMI). 

Rough estimates of changes in ASHD mortality, due both to AMI and 

fibrillation are calculated as follows: 

(Mortality for given standard) = 

(Mortality at current levels) 

x ( frequency of angina for standard ) [5.5] 

(frequency of angina at current levels) 

Drs. Kuller, Horvath, and Aronow agree that this model is a good preliminary representation of the problem. 

Probabilistic Phase 

Three sources of uncertainty identified in a sensitivity analysis 

were accounted for explicitly. Two parameters describe the variability 

of MOC. The third refers to how much MOS actually shifts for different 

concentrations of COHb. 

The "composite" patient experiences anginal pain at a DP between 

17,000 and 21,000, when COHb is not elevated. A lognormal distribution 

for his MOC was assumed (Figure 5.3), with a minimum value of 4,000. 

Sensitivity tests indicated that this minimum value is not an important 

source of uncertainty. Uncertainty in the two other parameters of the 

lognormal distribution, u and cr , did result in quite a bit of uncertainty. Within the range allowed by physiology discretized distributions 

of y and cr are provided in Figure 5.5. The frequencies of different levels of MOC implied by the three values of y at the mean value of cr are 

listed in Table 5.2; representative values of heart rate (HR) and systolic blood pressure (SBP) are listed for each level of MOC. These probabilities should be closely scrutinized by all those familiar with activity patterns. They should be representative of what the typical stable 

angina patient might be experiencing. One should note that the assumption of lognormality is a strong one. The relative results of the model 

63 
Parameter y 

for MOC 

Parameter a 

for MOC 

Slope of MOS 

as function of 

of % COHb 

4.54 .25 -500 

CT> 

-770 

-1000 

FIGURE 5.5 PROBABILITY TREE FOR ANGINA MODEL. THE PARAMETERS y AND o 

ARE THE PARAMETERS FOR THE LOGNORMAL DISTRIBUTION 
P.68 

Table 5.2 

FREQUENCIES OF DOUBLE PRODUCT 

FOR A RANGE OF VALUES OF y 

DP 

(in 100s) 

40 

60 

80 

100 

110 

120 

130 

140 

150 

160 

170 

180 

190 

200 

210 

Representative 

HR/SBP 

(45/90) 

(60/100) 

(70/115) 

(85/120) 

(90/125) 

(95/125) 

(100/130) 

(100/140) 

(110/140) 

(110/145) 

(115/150) 

(120/150) 

(120/160) 

(125/160) 

(130/160) 

Probability 

y = 4.54 

1 

1 

1 

.93 

.83 

.70 

(17 hr/day) 

.55 

.41 

.30 

.20 

(4-5 hr/day) 

.14 

.09 

.06 

.04 

.02 

(3 hr/week) 

of Exceeding DP In 

y = 4.69 

1 

1 

1 

.98 

.93 

.85 

(20 hr/day) 

.74 

.61 

.49 

.37 

(9 hr/day) 

.28 

.20 

.14 

.10 

.07 

(1-2 hr/day) 

Any Hour of a Day 

y = 4.83 

1 

1 

1 

.99 

.97 

.93 

(all but 1 hr/day) 

.86 

.77 

.67 

.56 

(13 hr/day) 

.45 

.35 

.27 

.20 

.15 

(3-4 hr/day) 

a = .3 in all cases. 

at different standards depend on this assumption. Until better evidence 

is available, this distribution was felt to be the most reasonable way to 

approximate the activity pattern of a sedentary person. Preliminary information and encoding (Appendix B) indicated that this distribution 

was not unreasonable. Within time and budget, however, SRI was not able 

to find a suitable expert in this area. 

The third uncertainty lies in the slope of the function for the mean 

MOS. Aronow's evidence indicates a slope of -770 (Equation 5.1). This 

slope is uncertain because of the paucity of data points for each individual, especially at the higher values of COHb. In addition, it is by 

no means certain that the subjects used by Aronow are representative of 

65 
P.69 

the general population of angina patients. Finally, even by abstracting 

to a single "composite" type, uncertainty remains for that individual. 

Figure 5.6 demonstrates how the measured slope may vary for a single individual. Let the true slope be -700, as indicated by the solid lines. 

MOS can fluctuate by as much as 10% from test to test. During the control test, the patient may be at a relatively low MOS, resulting in point 

A. The CO exposure test may occur at a time when MOS is higher, resulting in point B. The slope measured for this individual (dotted line) 

would be flatter than the true value for that individual. 

Unfortunately, it was not possible to encode experts on this uncertainty. Dr. Aronow was unwilling to comment further on his data, and 

other experts were unwilling to express their judgment without seeing his 

data. Thus, SRI was forced to furnish a preliminary judgment of uncertainty. 

To capture these uncertainties, a probability distribution was 

placed on the slope of -770. The ranges were approximated by an analysis 

of the variation among individuals in Aronow's studies. The discrete approximation used is illustrated in Figure 5.5. 

The probability distribution on average attacks per angina patient 

per day in Los Angeles are provided in Figure 5.7 for the strictest and 

most lenient standards. The shape of the distributions differs very little from standard to standard and city to city. It should be noted that 

the parameters of the distributions could be adjusted so that the "composite" person experiences some given average number of attacks of angina 

per day for the as-is situation. Because there is little reliable information on the average number of angina attacks per day, we have preferred 

to maintain the uncertainty on the parameters of the distribution. 

* These distributions are based on a weighted average across the five 

cohorts. Therefore, to calculate the total number of attacks per year, 

one should multiply by (365 x 120,000), where 120,000 represents the approximate number of people in Los Angeles who suffer from angina. Table 

5.3 presents mean values for total angina attacks in Figure 5.7. 

Table 5.4 summarizes the expected changes in attacks if each of the 

seven standards is implemented in Los Angeles. 

To obtain a distribution on ASHD mortality, Equation 5.5 and distributions on relative changes in anginal pain from current conditions were 

used. Figure 5.8 presents these relative changes in predicted angina 

pain for Los Angeles. Results for the other cities are in Appendix A. 

As discussed, the model assumes that the relative frequency of anginal 

pain is an indicator of relative frequency of ischemia in general, and 

hence in mortality. A value of 1.01 implies a 1% increase in ischemic 

episodes over the level currently being experienced. To calculate actual 

mortality, one multiplies the relative factors by the current number of 

ASHD deaths in the exposed population. For Los Angeles County, approximately 30,000 deaths occur each year because of AMI or ventricular fibrillation. The second set of values in Figure 5.8 indicate how many 

deaths would occur at the different standards, in comparison to an assumed current level of 30,000. Table 5.5 compares these results to the 

66 
22,500 

20,000 

cr*. 

-vi 

Double Product 17 500 

(mm Hg—beats/min) 

15,000 

12,500 

FIGURE 5.6 UNCERTAINTY IN MEASUREMENT OF SLOPE OF 

DOUBLE PRODUCT AT ONSET OF ANGINA 
P.71 

1.0 tx> 

rt 

XI 

o 

l-l 

CU 

0.5 

9 ppm, 8 hr, 1 exex 

35 ppm, 1 hr, 1 exex 

2 3 

Attacks/day/patient 

44 88 132 176 220 

10 Attacks/year 

FIGURE 5.7 DISTRIBUTIONS OF INCIDENCE OF 

ANGINAL PAIN FOR LOS ANGELES 

68 
P.72 

Table 5.3 

MEANS OF TOTAL ANNUAL INCIDENCE 

OF ANGINAL PAIN 

(Los Angeles) 

Standard 

35 ppm 1 hr 1 exex 

25 ppm 1 hr 1 exex 

25 ppm 1 hr 5 exex 

12 ppm 8 hr 1 exex 

12 ppm 8 hr 5 exex 

9 ppm 8 hr 1 exex 

9 ppm 8 hr 5 exex 

Mean , 

(in 10b 

attacks) 

82.3 

80.8 

82.1 

79.9 

80.5 

78.9 

79.7 

Table 5.4 

COMPARISON OF ANNUAL INCIDENCE OF ANGINAL PAIN 

(Los Angeles) 

Standard 

35 ppm 1 hr 1 exex 

25 ppm 1 hr 1 exex 

25 ppm 1 hr 5 exex 

12 ppm 8 hr 1 exex 

12 ppm 8 hr 5 exex 

9 ppm 8 hr 1 exex 

9 ppm 8 hr 5 exex 

Change F 

Million 

Attacks 

0.6 

-0.9 

0.3 

-1.9 

-1.2 

-2.9 

-2.0 

rom As-Is 

Percent 

of As-Is 

0.7 

-1.2 

0.4 

-2.3 

-1.5 

-3.5 

-2.5 

Chang 

Strictest 

Million 

Attacks 

3.5 

1.9 

3.2 

1.0 

1.7 

— 

0.9 

e From 

Standard 

Percent 

of Standard 

4.4 

2.5 

4.1 

1.3 

2.1 

— 

1.1 

69 
P.73 

l.OOrProbability 

(25,1) 

<35,1) 

1.00 1.05 change 

Probability 

1.00 

.75. 

.50. 

• 25_ 

5 exceedances 

per year 

(9,8> 

(12,8) 

25,1) 

.85 

25,000 

.90 

27,000 

.95 

28,500 

1.00 

30,000 

1.05 

31,500 

change 

deaths 

FIGURE 5.8 PROBABILITY THAT FRACTIONAL CHANGE FROM AS-IS 

SITUATION FOR ISCHEMIC EPISODES IS LESS THAN THAT 

INDICATED (LOS ANGELES). ESTIMATE OF DEATHS ASSUMES 

CURRENT ASHD DEATHS IN LOS ANGELES ARE 30,000 PER 

YEAR. STANDARDS ARE INDICATED BY (CONCENTRATION 

[ppm], AVERAGING TIME [hr]) 

70 
P.74 

Table 5.5 

COMPARISON OF PREDICTED CARDIOVASCULAR MORTALITY 

(Los Angeles) 

Standard 

35 ppm 1 hr 1 exex 

25 ppm 1 hr 1 exex 

25 ppm 1 hr 5 exex 

12 ppm 8 hr 1 exex 

12 ppm 8 hr 5 exex 

9 ppm 8 hr 1 exex 

9 ppm 8 hr 5 exex 

Change 

Deaths 

231 

-388 

131 

-761 

-504 

-1,180 

-822 

* 

From As-Is 

Percent 

of As-Is 

0.8 

-1.3 

0.4 

-2.5 

-1.7 

-3.9 

-2.7 

Change From 

Strictest Standard 

Percent 

Deaths of Standard 

1,411 

792 

1,310 

419 

675 

— 

358 

4.9 

2.7 

4.5 

1.5 

2.3 

— 

1.2 

Total ASHD deaths in as-is set at 30,000. 

Total ASHD deaths for strictest standard = 28,820. 

as-is situation and to the expected value for the strictest standard of 9 

ppm, 8 hour average (1 expected exceedance per year). 

It should be noted that there is a large additional uncertainty associated with the model for ASHD mortality. The death rate may increase 

more slowly or more rapidly than the rate of ischemic attacks. We prefer 

not to assign a probability distribution to this uncertainty until the 

model has been exposed to public comment. 

For both anginal attacks and cardiovascular mortality, extrapolation 

of the model to zero ambient CO concentrations provides an estimate that 

perhaps 10% of these effects in Los Angeles can be attributed to ambient 

CO concentrations. 

Discussion 

These estimates of effects on cardiovascular disease patients are 

significant, despite large uncertainties. An expected 3.9% of the annual 

ASHD deaths in Los Angeles might be prevented if the strictest standard 

were met. Uncertainty places a very large range on this value: from 

about 2% to about 8%, as can be seen in Figure 5.8. 

71 
One should keep in mind the crudeness of the inputs that predicted 

these distributions. This model has been based on a single "composite" 

patient, with a fairly arbitrary distribution on both MOC and MOS. Wide 

ranges were placed on the input parameters to maintain explicit recognition of the major uncertainties involved. Despite these large uncertainties, even the very low end of the range results in significant changes 

in mortality (2%). Two percent of 30,000 deaths implies a decrease of 

600 deaths per year by reaching the strictest proposed standard in Los 

Angeles. 

Despite the apparently large absolute effects on ASHD, the percent 

changes from current conditions are quite small. Epidemiological studies 

would be unlikely to detect changes as small as 2%. Changes in ASHD mortality due to ambient CO fluctuations that occur over a short time span 

would probably only appear with borderline significance in population 

studies. If the CO fluctuations in the sample are not as large as those 

of going from one standard to another, then changes in mortality might 

not appear at all. 

A few epidemiological studies of CO and ASHD have been performed. 

Their implications can be compared to the results of this model. A study 

by Kuller et al. of myocardial infarctions and sudden heart deaths in 

an area with moderate ambient CO levels found no significant changes in 

the severe ASHD health effects, in terms of either incidence, or of casefatality rates (the proportion of heart attack victims who do not survive 

the attack). A study by Cohen et al. and one by Hexter and Goldsmith 

in a higher exposure area, however, do find an apparent effect. 

The Cohen study looks only at hospital admissions and case-fatality 

rates for acute myocardial infarction (AMI). It neglects the possibility 

of an increase in sudden deaths on high-exposure days, which never become 

hospital admissions because of the high prehospital mortality of sudden 

death. The data provide no evidence of increased AMI admissions at all. 

There is some suggestion in Cohen's results of a "threshold," or very 

flat dose-response curve in low-CO periods for admission-fatality rates 

(the proportion of AMI hospital admissions who die). In periods of extended high exposures (weekly average CO > 8 ppm), an elevation in AMI 

admission-fatality rates did appear. 

The Hexter study provides much clearer evidence of a correlation between daily average CO and deaths. The exposures in this study are extremely high, in the range of 7.3 ppm to 20.2 ppm daily basin averages. 

All these values are probably located above the apparent "threshold" of 8 

ppm weekly average in the Cohen study. Hexter looks at total deaths 

rather than admission-fatality rates. His paper, published in Science, 

provides the coefficients from a regression of all daily deaths in Los 

Angeles County against the logarithm of CO daily average. Temperature 

and time have also been controlled in the regression. The t-statistic on 

the CO coefficient is significant at the 1% level (t = 3.09, degrees of 

freedom >1000). Similar significance is not found when the same regression is performed for total oxidants. This fact provides evidence that 

the relationship to CO is not spurious, or caused by omitted variables. 

The reason that oxidants do not correlate with deaths is probably because 

of a lag in its health effects, whereas physiological effects of CO are 

72 
P.76 

conducive to immediate response of the organism, with little in the way 

of longer-term effects. 

There are two problems with the use of the Hexter results: (1) the 

regressions are for total mortality, not ASHD alone; and (2) the use of a 

log transform of CO in the regression. Following personal communication, 

Dr. Hexter provided the unpublished results from additional regressions. 

These regressions divided the total deaths into fifteen categories, using 

the same model. Only four of these categories of deaths displayed a significant correlation (at the 1% level) with CO: cardiovascular diseases, 

ASHD in particular, some respiratory diseases, and motor vehicle accidents. The coefficient of log,n CO for daily ASHD deaths is 15.6 

(t = 3.85). Hexter's equation can be translated into a daily mortality 

rate by dividing by the population in Los Angeles County during the 

study, which was approximately 6.6 million. The following curve (where 

all cyclic and temperature effects are assumed at their means) results: 

ASHD daily death rate = 3.965 + 2.37 (log1QCO daily average) [5.6] 

(per 10 pop) 

There remains a problem with this curve. Its specification cannot 

possibly be correct on a global basis. As the daily average of CO approaches zero ppm, the predicted daily deaths become negative. Extrapolation of regression results beyond the range of the original data is a 

very specious practice in any case. Probably a more reasonable specification would have been a sigmoidal shape, as in Figure 5.9. 

With a sigmoidal relationship, lower levels of CO become insignificant. A "threshold" effect appears, as in Cohen's study. At midrange 

levels, the bulk of the sensitive people become affected. The apparent 

effect slows as higher CO levels are reached because the sensitive people 

have all been affected by that point. Hexter apparently made his estimations in the middle range (average daily exposures during the years of 

the study had a minimum value of 7 ppm). Estimates from EPA data for 

this risk assessment, however, indicate exposures mostly in the lower 

range. A simple extrapolation of the Hexter logarithmic curve would be 

unreasonable. (A test run does indeed result in gross underestimates of 

ASHD deaths for the as-is case.) 

One possible approach to resolving this problem is to superimpose a 

threshold. Crudely, this approximates the sigmoidal curve, as shown in 

Figure 5.10. The function would be 

ASHD mortality rate == max[f(C0 = threshold), f(C0)] [5.7] 

Several threshold values can be used to obtain a range on the implications of Hexter's results. 

To estimate the number of deaths from ASHD predicted by this doseresponse relationship, a mathematical expectation is taken over the 

73 
Response 

Dose 

FIGURE 5.9 EXAMPLE OF A SIGMOIDAL DOSE-RESPONSE CURVE 
Daily ASHD 

Mortality 

6

 4 

(deaths /IO pop.) 

Ul 

A 6 

CO Daily Average (ppm) 

FIGURE 5.10 HEXTER DOSE-RESPONSE CURVE WITH 

SUPERIMPOSED THRESHOLD AT CO = 5 ppm 
P.79 

curve. The probability density function used for the expectation is derived from the EPA monitoring data used in the exposure model. For each 

city, a single monitor was chosen as being "representative" of the city 

as a whole. The monitors chosen are described in Table 5.6. 

Table 5.6 

CO MONITORS USED TO TEST IMPLICATIONS OF HEXTER MODEL 

City 

Chicago 

Los Angeles 

Philadelphia 

St. Louis 

SAROAD Code 

141220039F01 

•058720001101 

397140026H01 

260200002G01 

Station Type 

City Commercial 

Suburban Residential 

City Commercial 

Suburban Residential 

Table 5.7 compares the results of the modified Hexter model with the 

predictions of the ASHD model for ASHD mortality. The ranges on the outcomes for the Hexter model are based on a range of CO "thresholds" from 0 

to 5 ppm daily average. The uncertainties are very large, and no real 

conclusions can be drawn from the results. However, the two models are 

in amazingly close agreement. 

Table 5.7 

COMPARISON OF EPIDEMIOLOGICAL RESULTS 

AND OF ASHD MODEL FOR DEATHS IN LOS ANGELES 

Standard 

35 ppm 1 hr 1 exex 

25 ppm 1 hr 1 exex 

25 ppm 1 hr 5 exex 

12 ppm 8 hr 1 exex 

12 ppm 8 hr 5 exex 

9 ppm 8 hr 1 exex 

9 ppm 8 hr 5 exex 

Epidemiological 

Prediction 

(Percent 

of As-Is) 

0.3 to 1.1 

-0.4 to -2.0 

0.2 to 0.7 

-0.7 to -4.7 

-0.5 to -2.8 

-0.7 to -6.7 

-0.7 to 5.2 

ASHD Model 

Prediction 

(Percent 

of As-Is) 

0.8 

-1.3 

0.4 

-2.5 

-1.7 

-3.9 

-2.7 

76 
P.80 

Other Manifestations of ASHD 

Acute coronary insufficiency and other unstable forms of angina are 

syndromes that are intermediate between stable angina (which does not result in irreversible damage) and infarction (which is accompanied by irreversible damage). The main difference between unstable and stable angina pectoris is that attacks of unstable angina* are not preceeded by 

obvious stress, or do not subside when at rest. Serum enzyme concentrations are not elevated, however, and irreversible damage is not apparent. This study assumes that these attacks are similar in nature to 

those of stable angina. The apparent difference is that the myocardial 

oxygen supply capacity, MOS, has become so low in these patients that 

even small increases in MOC cannot be met by the cardiovascular system. 

Attacks occur when no stress has been apparent to the patient. They are 

assumed, however, to be due to some small elevation in MOC or fall in MOS 

that occurs at rest. 

Although unstable angina patients are more susceptible to changes in 

COHb because of the steepness in the MOC distribution at lower values, 

there is assumed to be no intrinsic difference between the mechanisms for 

stable and unstable anginal attacks. To account for unstable angina patients explicitly, one would model their distribution of MOS and their 

MOS responses to COHb separately from that for stable angina. The MOS 

function would be more judgmental than that used for stable angina because there are no clinical data on the responses of these people to 

COHb. The current model assumes that the relative effects of COHb are 

the same for both stable and unstable angina, and treats these patients 

as if they matched the characteristics of the composite patient described 

above. 

Angina decubitus also seems to match the structure of this model. 

The difference appears to be due to myocardial wall stress while reclining. This effectively increases the MOC during moments when the patient 

is lying down, thereby increasing the probability of an attack for a 

given MOS when reclining. The model here does not explicitly treat these 

people because they are a small portion of the population. 

Prinzmetals angina is not handled by this model at all. First of 

all, this is a relatively rare form, with few people in the sensitive 

population. This effect Is believed to be caused by coronary spasms, 

which are a supply effect. 

Other forms of anginal pain caused by very large and irregular reductions in MOS are neglected by this model. Included in this category 

are attacks caused by exposure to cold. There is no obstacle to adding 

them via a greater downside variation in the MOS distribution. However, 

we do not feel that this elaboration would change the relative risk of 

ischemia enough to validate the effort. 

Another painful manifestation of arteriosclerosis is that of intermittent claudication of the extremities in those with peripheral vascular 

disease. The pain is caused again by a deficit of oxygen supply, this 

time to the skeletal muscle. Similarly to angina pectoris, the pain 

seems to occur after some given level or duration of exercise, again 

77 
P.81 

indicating the presence of a relatively stable threshold of oxygen demand. The model for angina thus contains all of the structure that would 

be required to identify the extent of aggravation of intermittent claudication caused by COHb. We have not explicitly modeled this effect in 

this risk assessment for two reasons. The first is that this particular 

manifestation of arteriosclerosis is not as common within the general 

population as the other effects that are modeled. The second reason is 

that it is not considered to be as serious an effect as the others. Because of the large similarities in structure, though, one can obtain a 

rough estimate of the proportional changes in the health effect because 

of COHb from the proportional changes estimated for angina. 

Summary 

ASHD is an extremely common disease in the United States. Even very 

small changes in the risks of people suffering from ASHD can result in 

significant public health effects. The model looks specifically at two 

aspects of ASHD: mortality from AMI and sudden death and morbidity in 

the form of painful ischemia. Despite the large abstractions and the 

great uncertainties, one can clearly see that CO might present a significant health risk to these people. A better quantification of these effects is necessary for a proper input to the standard setting process. 

As can be seen by comparison with the sparse epidemiological studies, 

however, the results at even this preliminary stage may not be far from 

the real situation. 

78 
P.82 

REFERENCES FOR CHAPTER V 

1. "Heart Facts Reference Sheet," American Heart Association, 1975 

(PHE7-74-30M). 

2. U.S. Department of Health, Education and Welfare, Coronary Heart 

Diseases in Adults, United States: 1960-1962. DHEW Publication No. 

(PHS) 79-1019, Hyattsville, Md., 1975. 

3. Richard S. Ross, "Ischemic Heart Disease," Chapter 240, Harrison's 

Principles of Internal Medicine, Maxwell W. Wintrobe, George W. 

Thorn, et al. (eds.), 7th edition, McGraw-Hill Book Company, New 

York, 1974. 

4. Wilbert S. Aronow, Clifford N. Harris, Micahel W. Isbell, et al., 

"Effect of Freeway Travel on Angina Pectoris," Annals of Internal 

Medicine, Vol. 77, 1972, pp. 669-676. 

5. Wilbert S. Aronow and Michael W. Isbell, "Carbon Monoxide Effect on 

Exercise-Induced Angina Pectoris," Annals of Internal Medicine, Vol. 

79, 1972, pp. 669-676. 

6. Wilbert S. Aronow, John Cassidy, Jack Vangrow, et al., "Effect of 

Cigarette Smoking and Breathing Carbon Monoxide on Cardiovascular 

Hemodynamics in Anginal Patients," Circulation, Vol. 50, August 

1978, pp. 340-347. 

7. Wilbert S. Aronow, "Effect of Non-nicotine Cigarettes and Carbon 

Monoxide on Angina," Circulation, Vol. 61, No. 2, 1980, pp. 262-265. 

8. Wilbert S. Aronow, "Aggravation of Angina Pectoris by Two Percent 

Carboxyhemoglobin," American Heart Journal, Vol. 101, No. 2, Febrary 

1981, pp. 154-157. 

9. Einar W. Anderson, Robert J. Andelman, Joseph Strauch, et al., 

"Effect of Low-Level Carbon Monoxide Exposure on Onset and Duration 

of Angina Pectoris," Annals of Internal Medicine, Vol. 79, 1973, pp. 

46-50. 

10. David R. Redwood, Douglas R. Rosing, Robert E. Goldstein, et al., 

"Importance of the Design of an Exercise Protocol in the Evaluation 

of Patients with Angina Pectoris," Circulation, Vol. 43, May 1971, 

pp. 618-628. 

11. Naphtali A. Britman and Herbert J. Levine, "Contractile Element 

Work: A Major Determinant of Myocardial Oxygen Demand," Journal of 

Clinical Investigation, Vol. 43, No. 7, 1964, pp. 1397-1408. 

79 
P.83 

12. Dr. Steven Horvath, personal communication. 

13. Lewis H. Kuller, Edward P. Radford, David Swift, et al., "Carbon 

Monoxide and Heart Attacks," Archives of Environmental Health, Vol. 

30, October 1975, pp. 477-482. 

14. Seymour J. Cohen, Margaret Deane, and John R. Goldsmith, "Carbon 

Monoxide and Survival from Myocardial Infarction," Archives of Environmental Health, Vol. 19, October 1969, pp. 510-517. 

15. Alfred C. Hexter and John R. Goldsmith, "Carbon Monoxide: Association of Community Air Pollution with Mortality," Science, Vol. 172, 

April 16, 1971, pp. 265-267. 

80 
P.84 

VI FETAL HEALTH EFFECTS 

A model has been constructed to estimate fetal health effects caused 

by ambient concentrations of CO experienced by the mother. It was judged 

that the best route to understanding these effects was to model the physiological processes involved. At present, there is great uncertainty in 

the input data for such a model, but data can be collected with time. 

Even with the present uncertainties, the model matches well with the 

sparse epidemiological data. Because of the preliminary nature of the 

model, only fetal death rates have been modeled in detail; rough estimates are obtained for other fetal health effects. 

Physiological Background 

Fetuses develop in an environment that provides sufficient oxygen 

for their needs. Fetal oxygen must depend on the oxygen content in the 

uterine artery of the mother rather than directly on that of the atmosphere. To some extent this vulnerability is reduced because fetal hemoglobin binds more easily with oxygen than adult hemoglobin. However, the 

oxygen tension in fetal tissue is inevitably lower than that in adult 

tissue. This lower state of oxygenation is complicated by a lack of compensatory mechanisms of the fetal cardiovascular system. Children and 

adults are able to respond to increased oxygen needs by increased cardiac 

output, vasodilation and larger oxygen tension gradients. Such responses 

are limited in the fetus. Thus, a given reduction in oxygen availability 

will be more likely to affect a fetus than most adults or children. Although a pregnant woman may display no signs of hypoxia at all, the child 

that she is bearing can be under severe stress due to oxygen deprivation. 

These facts indicate that fetuses may be one of the important groups sensitive to ambient carbon monoxide. 

1 2 

Studies by Hill et al. and by Ginsberg and Myers indicate that the 

presence of COHb in the mother has two distinct effects on fetal oxygenation. The most immediate effect is a decrease in the oxygen available 

for transfer across the placenta to the fetal circulatory system. To 

compensate, either fetal blood flow must rise or oxygen tensions must 

fall. Oxygen tension (P0--,) in the umbilical artery of the fetus has been 

observed to fall as maternal COHb (COHb ) rises in animal studies. This 

is consistent with the fact that the fetal circulatory system has limited 

capacity to change its blood flow. A second effect of COHb exposure is 

the actual transfer of CO across the placenta. Fetal COHb (COHbf) rises 

and falls more slowly than that of the mother, but reaches a higher 

steady-state value. This effect exacerbates the fetal oxygen tension 

problem by shifting the fetal oxygen dissociation curve leftwards. 

The drop in fetal arterial Pn2 because of extremely large maternal 

COHb levels is significant enough to cause major damage to fetal tissue 

81 
P.85 

even when the-fetus is not exposed long enough to have an elevated COHb 

level. Myers has demonstrated that the extent of brain damage, changes 

in cardiovascular function, and neonatal mortality in term fetal monkeys 

is closely related to the levels of P~2 experienced in the fetal abdominal aorta. The maternal exposures must be extremely high to induce such 

striking damages to term fetuses after brief exposures. However, two implications of a decreased P-_ at even low levels of maternal COHb should 

be considered. 

The first implication concerns the effects of lower exposures to 

COHb. A sample of fetuses of any species will have a range of susceptibility to changes in fetal Pn2 and "to changes in COHbf. For some portion 

of the sample, P _ may be at near critical levels because of smoking or 

disease at the time of maternal exposure to elevated ambient CO levels. 

As the CO exposure becomes more extreme, a larger and larger proportion 

of the sample will enter the critical range, in order of their initial 

susceptibility. Therefore, exposures that would not endanger the average 

healthy fetus might still affect a significant number of pregnancies. 

The second implication concerns the potential effects at earlier 

gestational ages. Myers demonstrated these effects on term fetuses. The 

biological systems are almost completely developed at this stage of gestation. There is no evidence of the importance of changes in fetal P_2 

earlier in gestation. When the fetuses' biological systems are very rudimentary and rapidly developing, a given change in oxygenation of the 

tissue may be far more damaging. The potential for malformation, brain 

damage and fetal death at these stages may be greater at each level of 

maternal exposure. This possibility should be considered in future research. 

Deterministic Model 

Two physiological effects have been identified as a source of potential harm of CO to fetuses: decreased oxygen content of the blood results in lowered P „ in the fetus (PfQ 2); increased fetal COHb further 

reduces the Pf02* ***e fir s t effect is immediate. The second partly determines the magnitude of the first effect, and depends on the length of 

the exposure. Hill et al. have developed a model that predicts COHbgiven data on the sequence of COHb . This model works on the same basis 

as the Coburn model, but includes ?he blood-blood transfer of COHb between the mother and fetus. The model predicts both COHbf and Pf .. A 

complete development of the fetal health effects model would involve use 

of this model in conjunction with the EPA exposure model. 

For this demonstration, a simpler model looks only at approximate 

changes in PfQ2

 a s COHb changes. Ff0_ will be predicted on the assumption that COHb,. is at approximately tne same level as that of the mother. 

Within a sample of fetuses, one will observe a range of Pf02 values. 

Over the course of the pregnancy, every fetus will experience variation 

within some portion of this range. Although these values cannot presently be directly observed in humans for ethical reasons, knowledge of 

the distributions on the determinants of Pfn2 allows a judgmental approximation. Dr. Lawrence Longo has provided preliminary estimates of this 

82 
P.86 

distribution: When COHb is at the normal endogeneous level of 'about 

0.75% for pregnant women, the umbilical artery Pf0 2 distribution is described by a normal distribution with mean of 19 mm Hg and standard deviation of 2 mm Hg. This implies that 95% of fetuses will be experiencing 

Pfn 2 values between 15 mm and 23 mm Hg. 

COHb affects this distribution in two ways. First, the mean Pf0 2 

will shift downwards due to the decreased oxygen content of maternal 

blood. A second effect of the decreasing oxygen content appears in the 

decrease of the variance of the distribution. As Pfn 2 values fall, one 

moves into steeper portions of the oxygen dissociation curves: a given 

reduction in oxygen content occurs at smaller reductions in Pf 2. The 

body has a built-in mechanism to avoid dangerously low levels or oxygen 

tension. The effects of COHb thus are assumed to be described by 

changes in the distribution or PfQ 2. The parameters of the assumed normal distribution, Pr(Pf J COHb )—the probability that a value Pf 2 occurs, given the level or COHb —follow the rules below: 

mean = 19 - A x (% COHb ) [6.1] 

m

 l ' 

standard deviation = 2 - B x (% COHb ) [6.2] 

m • 

Although A and B cannot be measured with current tools, discussion 

with Dr. Longo indicated that theoretical knowledge of the adult and 

fetal oxygen dissociation curves and of fetal oxygen consumption allows 

them to be estimated at approximately .5 and .1 (mm Hg)/(% COHb) respectively. Figure 6.1 indicates how the distribution changes from COHb of 

0 to 10%. Note that as the mean of Pr(PfQ2| COHb) falls, the variance decreases. 

Damage to a fetus is assumed to be tied to the levels of Pf0 2 experienced. The blood from the umbilical vein directly supplies the liver, 

heart, and brain tissue. Blood that reaches other tissues, via the descending aorta, is less saturated with oxygen. Part of the descending 

aorta blood is shunted to the umbilical artery without feeding these 

other tissues. Thus the oxygen tension of blood in the umbilical artery 

will be higher than that experienced in much of the body tissue. A Pfn 2 

of 9 mm Hg in the umbilical artery indicates extreme stress to the fetus, 

with tensions approaching zero in some parts of the body. 

The resistance of the fetus to lowered oxygen tensions depends on 

several effects. The extent of acidosis and hypercarbia are important 

variables. High glycogen levels in the tissue can serve as buffers by 

permitting more anaerobic respiration. All of these factors are in part 

determined by the length of the hypoxia. If one were to use the more 

elaborate Hill model , then these effects could be approximated from the 

sequence of COHb . In this model, these effects are assumed to be averaged into the dose-response curve used. 

For obvious ethical reasons, very little data exists to help estimate the risks of human fetuses at various umbilical artery Pfr)2 levels. 

Discussion with Dr. Longo resulted in an approximation of the risk of 

83 
1.00.-

Probability 

00 

-t> 

PfQ 2 (mm Hg) 

FIGURE 6.1 PROBABILITY THAT FETAL OXYGEN TENSIONS IN THE UMBILICAL ARTERY 

ARE LESS THAN THE VALUE INDICATED: Pr(PfQ2|C0Hb) IN CUMULATIVE FORM 
P.88 

fetal death, to be used until better data are available. This doseresponse curve Pr(fetal death|Pf02)—the probability of fetal death given 

a value of Pf02 for 1-hour exposure—is pictured in Figure 6.2. Curve II 

represents the dose-response curve encoded from Dr. Longo; Curves I and 

III represent curves between which the true curve is expected to fall. 

Dr. Longo helped synthesize these data from,knowledge of fetal oxygenation processes and from animal test results . The exposures to each Pfn2 

level are assumed to be for 1 hour, because the data on COHb exposures 

are provided on an hourly basis. Differences in acidosis and hypercarbia 

are averaged into the probability. Future models would want to retain 

information on the correlations of these Pfn2 values over time. In that 

way, biases in assuming that high exposures only occur for a single hour 

can be removed. Chronic and acute exposures can be treated separately. 

It should be noted that the distribution in Figure 6.2 should also reflect the probability of fetal death that is not associated with variation in PfQ2• 

The model calculates the average probability of fetal death as a 

:ion of COHb . This probability is derived . 

r.hted average) taken over the dose-response i 

bution of Pfn2 among fetuses at each COHb level: 

function of COHb . This probability is derived as an expectation 

(weighted average) taken over the dose-response curve, using the distriAverage Prob Fetal Death (COHb ) = 

m 

Pr(fetal death|pfn2) x Pr(PfQ2| COHb) [6.3] 

Pf02 

ranges 

The values for Pr(fetal death| PfQ2) are listed in Figure 6.2. The values 

for Pr(PfQ2| COHb) are taken from curves such as the two presented in Figure 6.1. They are derived using Equations 6.1 and 6.2. The predicted 

numbers of fetal deaths are then found by taking a weighted average over 

these average probabilities of fetal death. The weights for this second 

average come from the distribution of COHb for each cohort derived by the 

exposure model. Five cohorts were aggregated from the 98 of the exposure 

model, as described below. Each cohort has a different exposure experience as well as different pregnancy rates. 

Within each cohort, the same distribution of smoking status was assumed among the pregnant women. Smoking was simulated by adding a constant value to predicted COHb . Values,for these constants were derived 

from various sources in the literature. ' If one believes that there is 

some form of compensatory mechanism that diminishes the effects of CO 

from cigarettes, then that can be approximated by using an additive constant which is less than that actually observed. Health surveys indicated that 30% of young women smoke today. The distribution in Table 

6.1 is used. 

85 
P.89 

"Umbilical Probability 

Artery 

Pf02 I 

1.0 

.75 

'robability ,n 

.25 

0 

<9 .50 

9-10 .68 

10-11 .45 

11-12 .25 

12-13 .12 

,. „_ IT 1/, ns 

\ V 14-15 .01 

V \ i 15-18 .009 

\ \ >18 .009 

N \ X 

\ \ 

sW11 

\ x„ 'III 

\ \ 

\ \ 

\ \ 

\ \ 

\ ^ 

\ \ 

\ \ 

3 10 12 14 16 18 

PfQ2 (mm Hg) 

FIGURE 6.2 PROBABILITY OF FETAL DEATH AS A FUNCTION 

OF OXYGEN TENSION IN FETAL UMBILICAL ARTERY 

(1-HOUR EXPOSURE): Pr(FETAL DEATH|PfQ2) 

86 

of Fetal 

II, 

L.ao 

.80 

.53 

.38 

.20 

.05 

.01 

.009 

.0085 

Death 

III 

1.0.0 

.95 

.72 

.48 

.30 

.14 

.03 

.009 

.006 

. 

-

-
P.90 

Table 6.1 

DISTRIBUTION OF SMOKERS AMONG PREGNANT WOMEN 

Smoking Status 

Nonsmoker, no passive exposure 

Nonsmoker, with daily passive exposure 

Light smoker (<1 pack per day) 

Heavy smoker (>1 pack per day) 

Fraction 

of 

Pregnant 

Women 

.65 

.05 

.25 

.05 

Additive 

Constant 

Level 

(% COHb) 

0 

1 to 3 

1 to 3 

2 to 6 

The total expected number of fetal deaths in a given city is approximately as follows: 

Fetal Deaths - £ (# pregnancies) x ]T (fraction pregnant) 

cohorts (in cohort ) smoking(women in type ) 

types 

x 2J Pr(C0Hb ) x Av Prob Fetal Death(C0Hb +additive constant) [6.4] 

COHb

 m m 

m 

The five cohorts are described in Table 6.2. Total pregnancies that last 

beyond 20 weeks of gestation were estimated from fertility rates characteristic of each city. They were distributed among the cohorts according 

to age and sex distributions for each city provided by PEDCo Environmental, Inc. They were adjusted so that fertile women in different cohorts 

have different relative probabilities of becoming pregnant. All fertile 

working women were assumed to have equal pregnancy rates regardless of 

profession. Students, married nonworking women, and unemployed women 

(all in age range 15-44 years) were estimated to be 20% more likely than 

their employed counterparts to become pregnant; these are illustrative 

numbers, but the results are insensitive to this distribution. Married 

women fall into three categories: nonworking, working, and unemployed. 

Only the nonworking ones are counted in the "married women" cohort. 

Also, the "married women" cohort includes those over 65 years. This represents a double counting with the "retired" category. Those women were 

roughly approximated and removed before pregnancy rates were applied to 

the former cohort. 

The average COHb values presented in Table 6.2 are indicative of how 

exposures differ among the cohorts. The ranges represent differences in 

these cohorts among the four cities. "As-is" represents the currently 

experienced ambient levels rather than those associated with a particular 

87 
P.91 

Cohort 

1 

2 

Table 6.2 

ANNUAL PREGNANCIES BY COHORT 

, 

Description 

Students and clerical workers 

Sales personnel and service 

Range of 

Avg. 

% COHb 

(As-Is) 

.40-.81 

.42-.82 

Number of 

Pregnancies 

Per 1,000 

In Cohort 

25-33 

16-21 

Number of 

Pregnancies 

Per 1,000 

Fertile 

Women 

In Cohort 

40-53 

38-49 

4 

5 

workers 

Professionals, craftsmen and .42-.85 9-12 

laborers 

Married women .42-.65 36-44 

Retired and unemployed .42-.60 15-16 

39-50 

48-56 

48-61 

standard. Note that the highest exposures tend to occur in the cohort 

with the lowest birth rate. By keeping these cohorts separate, some bias 

has been avoided. 

Probabilistic Phase 

The fetal health effects model predicts expected fetal deaths at 

greater than 20 weeks gestation. The national average for metropolitan 

counties was 9.7/1000 in 1977. The model is calibrated so that this average is obtained for the as-is cases in the following manner. There are 

four uncertain parameters that significantly affect the model's estimates 

of the fetal death rate at each COHb level. These are (1) the additive 

factors that simulate the effects of cigarette smoking; (2) the curve for 

the probability of death at each PfQ2 level, Pr(fetal death|PfQ2), and 

(3) the coefficients A and B in Equations 6.1 and 6.2. A and B were adjusted in each case so that when combined with different specifications 

for the first two, the as-is prediction of fetal deaths are in line with 

national statistics. 

The probability tree used for the fetal death prediction is provided 

in Figure 6.3. The probabilities for the additive constant for smokers 

is estimated from ranges found in the literature. ' The resulting distributions for Los Angeles for each of the standards are given in Figure 

6.4. These results are summarized in Table 6.3. Uncertainties because 

of the Emissions and Atmospheric Dispersion Model and the Exposure Model 

are not included because of lack of data. For clarity, it is assumed 

that fetal death rates are exactly known under present conditions; including this uncertainty would only obscure the differences between 

standards. 

88 
P.92 

Additive constants to 

% COHb (light/passive 

smoker; heavy smoker) 

Curve used for 

Pr (fetal death I PfQ2) 

II 

III 

II 

III 

II 

Coefficients 

in Eqs. 6.1, 

6.2 

A B 

.60 .12 

,55 .11 

,55 .11 

.40 .08 

.45 .09 

.40 .08 

,32 .06 

,35 .07 

III ,30 .06 

FIGURE 6.3 PROBABILITY TREE USED WITH FETAL DEATH MODEL 

89 
P.93 

1.00 _ 

(35,1) 

Probability 

1190 1200 1210 1220 

1.00 

,75 

5 Exceedances 

per year 

Probability .50 

.25 

1190 1200 1210 

Fetal deaths 

1220 

FIGURE 6.4 PROBABILITY THAT FETAL DEATHS (> 20 WEEKS GESTATION) 

PER YEAR IN LOS ANGELES WILL BE LESS THAN AMOUNT 

INDICATED; IT IS ASSUMED THAT THERE ARE AT PRESENT 

1,217 FETAL DEATHS PER YEAR FROM ALL CAUSES IN LOS 

ANGELES. STANDARDS ARE INDICATED BY (CONCENTRATION 

[pp mm], AVERAGING TIME [hr]) 

90 
P.94 

Table 6.3 

EXPECTED CHANGES IN FETAL DEATHS (>20 WEEKS GESTATION) 

PER YEAR 

(Los Angeles) 

St 

35 ppm 

25 ppm 

25 ppm 

12 ppm 

12 ppm 

9 ppm 

9 ppm 

andard 

1 

1 

1 

8 

8 

8 

8 

hr 

hr 

hr 

hr 

hr 

hr 

hr 

1 

1 

5 

1 

5 

1 

5 

Ex 

Ex 

Ex 

Ex 

Ex 

Ex 

Ex 

Change Over As-Is 

Number Percent^ 

of Deaths of As-Is 

1.54 .13 

-2.54 

0.87 

-4.83 

-3.26 

-8.11 

-5.22 

-.21 

.07 

-.40 

-.27 

-.67 

-.43 

Change 

Strictest 

Number 

of Deaths o 

9.64 

5.56 

8.97 

3.27 

4.84 

— 

2.88 

s Over 

Standard 

Percent , 

f Standard 

.80 

.46 

.74 

.27 

.40 

— 

.24 

Total fetal deaths in as-is case = 1,217. 

Expected total fetal deaths for strictest standard = 1,209. 

The extent to which the sensitive group is affected is very small 

for this health risk. This is true simply because the sensitive group is 

large (all fetuses) and the fetal death rates small to begin with. For 

Los Angeles, there are 125,000 pregnancies per year; 1% of fetuses die, 

and the difference in deaths between the proposed standards is 0.01% of 

the number of pregnancies. Extrapolation of the model to zero ambient CO 

provided an estimate that perhaps 2% of fetal deaths are caused by ambient CO concentrations. 

Discussion of Results 

One area in which a fair amount of human data is available to check 

the implications of this model is the relative spontaneous abortion rates 

of smoking and nonsmoking mothers. Although the results of such tests 

vary, the ratio of spontaneous abortion rates of smokers to nonsmokers 

appears to be between 1.1 to 2.0, and appears to be positively related 

to the number of cigarettes smoked. Socioeconomic and age factors also 

appear significant. However, to the extent that ambient exposures are 

not correlated with these factors, these factors can be assumed to average out in an estimate of the effects of ambient CO. Cigarette smoke 

contains many other elements such as nicotine, which could account for 

some portion of this excess mortality. These ratios can, however, be 

91 
P.95 

considered as upper bounds on the possible effects of COHb at levels attained by smokers and nonsmokers. 

Each set of parameters used as inputs to the model results in a different prediction of differences in fetal mortality rates between smokers 

and nonsmokers. Using the probabilistic inputs of Figure 6.3, one can 

compare the implied distribution on excess fetal deaths due to the CO 

content of cigarettes with the data on smoking mothers. This distribution has a range of 1.04 to 1.31, with an expected value of 1.16. This 

is not out of line with the epidemiological data. 

Discussion with Dr. Longo has permitted us to look at* these ratios 

more closely. Dr. Longo provided estimates of how this ratio might vary 

according to the smoking experiences of mothers. One should note that 

not all of the excess fetal mortality found in such studies can be attributed to CO. Thus one would expect the epidemiologically derived ratios to be higher than those predicted by a model looking only at the effects of CO. These judgmental ranges are compared with the equivalent 

ones from the model in Table 6.4. It is interesting how well the results 

match. 

Table 6.4 

RATIO OF FETAL DEATH RATES OF SMOKERS TO NONSMOKERS 

Smoking Mother Studies Model's Estimates 

Light smokers (<1 pack/day) 1.1 to 1.4 (mean 1.2) 1.03 to 1.23 (mean 1.12) 

Heavy smokers (>1 pack/day) 1.2 to 1.6 (mean 1.35) 1.09 to 1.72 (mean 1.35) 

This model attempts to quantify only one aspect of fetal health effects, at only one stage of fetal development. Fetuses older than 20 

weeks of gestation are quite well developed, with all of the biological 

systems nearly completely formed. There is little knowledge about the 

relative susceptibility of younger fetuses to hypoxia. One can argue 

that they are likely to be more sensitive because their systems are going 

through the most important and intricate evolutions. As a counterargument one can present evidence that at' earlier stages, ablated tissue 

can often be fully regenerated. The ability to regenerate tissue decreases with gestational age. Thus the damage from hypoxia may be less 

permanent at earlier stages of gestation. Whatever the truth, one should 

realize that the estimates of fetal deaths presented here refer only to 

those sustained by a subgroup of the sensitive population. Further work 

should focus on how this model would apply to younger fetuses. 

A more important concern that has not been addressed by this model 

is the potential for nonfatal effects. Approximately 5% of children are 

born with some form of brain damage, and 2% are born with some physical 

92 
P.96 

abnormality. Hypoxia can induce both effects. If one believes that CO 

can induce fatal hypoxia in fetuses, then one cannot neglect the"spectrum 

of damages that may be suffered by those fetuses that are severely 

stressed but survive the episode of hypoxia. Not only may these effects 

be large, but society may place greater weight on avoiding these forms of 

damage. 

To properly estimate this potentially large set of risks, one would 

need separate dose-response curves such as that in Figure 6.2, each specific to a different effect. To indicate the magnitude of this risk, one 

can make the assumption that changes in risk of fetal death are proportional to changes in risk of brain damage and malformation. This would 

translate into the expected levels of effects for Los Angeles estimated 

in Table 6.5. 

Summary 

Despite the data-availability problems, the output of the model is 

very encouraging. Comparison with smoking-mother studies indicate a very 

Table 6.5 

EXTRAPOLATION OF FETAL DEATH MODEL 

TO OTHER FETAL EFFECTS 

(Los Angeles) 

Effect 

Mental retardation (IQ <70) 

(a) Mild 

(b) Moderate 

(c) Severe (IQ <30) 

Cerebral palsy with no mental retardation 

Seizure disorders 

Speech or language disorder 

Learning disabilities 

Severe physical damage 

Other congenital anomalies 

Current 

Percent 

of Births 

Affected 

2.6 

0.3 

0.1 

0.2 

0.4 

5.0 

5.0 

0.5 

1.5 

Approximate 

Number 

of Births 

Affected 

Annually 

3250 

375 

125 

250 

500 

6250 

6250 

625 

1875 

Maximum 

Change 

in Number 

of Births 

Affected 

Annually 

26 

3 

1 

2 

4 

50 

50 

5 

15 

Maximum change is that from most stringent to least stringent proposed 

standard. 

93 
P.97 

good correspondence. This leads one to believe that the structure and 

inputs are not unreasonable. 

Clearly the most important focus for future research will be on the 

potential effects of COHb on rates of birth defects and brain damage. A 

closer look at how hypoxia can affect fetuses at different stages of gestation will aid in an extension of this model. 

94 
P.98 

REFERENCES FOR CHAPTER VI 

1. Esther P. Hill, Jay R. Hill, et al., "Carbon Monoxide Exchanges between the Human Fetus and Mother: a Mathematical Model," American 

Journal of Physiology, Vol. 232, 1977, pp. H311-H323. 

2. Myron D. Ginsberg and Ronald E. Myers, "Fetal Brain Damage following 

Maternal Carbon Monoxide Intoxication: an Experimental Study," Acta 

Obst. Gynec. Scand•, Vol 53, 1974, pp. 309-317. 

3. Ronald E. Myers, "Threshold Values of Oxygen Deficiency Leading to 

Cardiovascular and Brain Pathological Changes in Term Monkey 

Fetuses," in Advances in Experimental Medicine and Biology, Vol. 

37B, Duane F. Biruley and Haim I. Bicher (eds.), Plenum Press, New 

York: 1973, pp. 1047-1053. 

4. Gordon G. Power, Tom R. Bennett and Lawrence D. Longo, "Survival in 

the Fetal Rabbit Exposed to Intermittent Hypoxia." American Journal 

of Obstetrics and Gynecology, Vol. 127, No. 4, Feb. 15, 1977, pp. 

428-433. 

5. Wayne Ott, A Preliminary Assessment of the Nation's Carboxyhemoglobin Data Base, report to Office of Research and Development, 

U.S.E.P.A., September 1981. 

6. Dr. Steven Horvath, personal communication. 

7. Health United States 1980, Table 27, "Cigarette Smoking Status of 

persons 20 years of age and over, according to sex, race, and age: 

United States, 1965, 1976, 1979," U.S. Department of Health and 

Human Services, Hyattsville, Md., Dec. 1980, p. 162. 

8. Roy Paul, PEDCo Environmental Inc., personal communication. 

9. Vital Statistics of the United States 1977, Vol. II, Mortality. 

U.S. Department of Health and Human Services, (PHS) 80-1102, 

Hyattsville, Md., 1980. 

10. "Pregnancy and Infant Health," Chapter 8 in Smoking and Health: A_ 

Report of the Surgeon General, U.S. Department of Health, Education 

and Welfare, 1979. 

11. Dr. Lawrence D. Longo, personal communication. 

95 
P.99 

VII CHRONIC OBSTRUCTIVE PULMONARY DISEASE 

The model presented here is a simulation model. It follows the sensitive group over time, estimating the rates of hospitalization and mortality that would be experienced as the COHb exposures fluctuate. The 

value of this approach is that it maintains the correlations of COHb over 

time. Because the group considered here is so sensitive to slight 

changes in oxygen availability, significant bias in model predictions 

would result if this correlation were ignored. 

Background and Modeling Issues 

People who suffer from chronic obstructive pulmonary disease, or 

COPD (defined here as pulmonary emphysema and chronic bronchitis), are 

more sensitive to carbon monoxide than those with normal lung functionAyers and Aronow have each found clear evidence of this sensitivity. ' 

This disease affects as much as 4% of the population, but the severity 

of the individual cases lies on a broad continuum. Because normal lungs 

provide a surplus of oxygen-delivery capacity, quite large deteriorations 

in lung function can take place before a person suffering from COPD is 

likely to be more affected than the general population by the relatively 

small changes in COHb induced by the current ambient levels of CO. In 

the end stages of this progressive, irreversible disease, the patient 

becomes chronically hypoxemic. At this point, the lungs cannot provide 

sufficient oxygen for even small exertions. His activities are severely 

restricted, and he is susceptible to exacerbations of his chronic respiratory failure. Except in the case of respiratory infection, the cause 

of these acute attacks is often unknown. 

The symptoms of acute respiratory failure are lowered arterial oxygen tension (PaQ2) with a consequent fall in oxygen saturation of the arterial blood (SaQ 2). Such bouts of acute respiratory failure result in 

hospitalization tor anywhere from a week to over a month. Furthermore, 

stabilization of the problem can be very difficult because efforts to 

raise the oxygen content of the blood through enhanced oxygen concentration of inspired air can cause a suppression of the impetus to breathe. 

As a result of the patient's already weakened state and the delicacy 

needed in controlling blood gases, there is a high fatality rate associated with acute respiratory failure in those with advanced cases of COPD. 

A model of the effects of COHb on COPD patients must define the sensitive group very precisely. The majority of CO health effects will occur within that portion of the population that has become chronically 

hypoxemic. At less advanced stages of the disease the small incremental 

effects of COHb on oxygenation will rarely have an impact. At the very 

final stages of the disease the patient is likely to be on continuous 

oxygen therapy, effectively shielded from small exposures to CO. 

97 
P.100 

Another concern in modeling the effects of CO on COPD patients is to 

account properly for the fact that the sensitive group will be hospitalized quite frequently. While hospitalized, the patient will be removed 

from the risks of ambient CO: blood gases will be closely monitored, and 

oxygen therapy will be immediately available if any deterioration in the 

patient's condition occurs. If high levels of COHb cause increased rates 

of hospitalizations on one day, then the sensitive group at risk will be 

reduced for several days following. Thus time-clustering of high CO concentrations will not have as great of an effect as would be predicted by 

a model that ignores the sequential nature of the exposures. This issue 

leads one to the use of a simulation model. 

The model assumes that a reduction in blood-oxygen content due to 

elevated COHb is one of the factors in exacerbations of respiratory failure. The borderline nature of the patient's oxygenation renders him extremely susceptible to even small changes in blood-oxygen saturation. It 

should be pointed out that the exacerbations that may be "attributable" 

to CO are probabilistic in nature: they result from a combination of 

factors, of which COHb is only one. Because most COPD patients fluctuate 

in their hypoxemic condition from day to day, a relatively large dose of 

COHb on one day may be tolerated easily. Only days later, a smaller dose 

of COHb may be experienced concomitantly with a lowered Pa0o' t*1-^*3 doss 

of COHb may be enough to make the difference between the patient's return 

to a less hypoxemic state and a spiraling out of control of his respiratory function. 

Mortality estimates in this model are best interpreted in terms of 

life expectancy rather than deaths per year. COPD patients in the sensitive group have a life expectancy on the order of 5 years, and usually 

die of causes related to or precipitated by the disease. To the extent 

that the size of the group is stable, the number of deaths each year is 

equal to the number of new patients entering the group. Changes in CO 

conditions are assumed not to alter the number of new patients, but will 

change the life expectancy of members of the group. After several years, 

the size of the group will again stabilize with the annual number of 

deaths equal to the annual number of new patients. Hence, for the health 

effect, changes in CO conditions will have only a transitory effect on 

the annual number of deaths. The effects are better measured in terms of 

changes in life expectancy for the group. 

Deterministic Model 

The sensitive group is defined as that segment of the COPD population that is chronically hypoxemic, but not hospitalized or on domiciliary oxygen therapy. Hypoxemia occurs when PaQ2 falls below 70 mm Hg. 

When the patient's stable level of Pa~2 falls to 55 mm Hg, he will almost 

certainly be on oxygen therapy. Even at 60 mm Hg, doctors may introduce 

supplementary oxygen. The sensitive group is thus defined as those people with stable values of Pan2 between 60 and 70 mm Hg who are currently 

at home, and whose forced expiratory volume in 1 second (FEV ) is less 

than 1 liter. The patients are assumed to be nearly normocapnic, or pHcompensated chronically hypercapnic when in a stable condition. 

98 
p.1 

Exposure estimates were calculated within the EPA exposure model, 

but using different parameters in the Coburn equation. The Coburn equation is derived from assumptions of normal lung characteristics such as 

uniform ratios of diffusion to alveolar ventilation. These assumptions 

do not hold for those with lung diseases. This study was not meant to 

test the reliability of the Coburn equation, and hence its estimates are 

used here without further question. However, the physiological parameters of the Coburn equation were adjusted to attempt to reflect the abnormal characteristics of this sensitive cohort. The values chosen were 

as follows: 

o Capillary Pn« = .7 normal (which is based on atmospheric pressures) • 

o Hemoglobin = 18 gm/100 ml (secondary polycythemia) (normal is 

15). 

o Alveolar ventilation (resting) = 3100 ml/min (normal is 4000-

5500). 

o Alveolar ventilation (elevated exercise level) = 4000 ml/min 

(normal is 5000-20,000). 

o Lung diffusion rate => 20 ml/min/torr (normal is 24 to 30). 

The sensitive types were assumed to be home-bound. They were therefore 

divided into two cohorts per city: suburban and urban residents. The 

daily activity pattern followed was that for the most sedentary group of 

retired and unemployed people. This permitted occasional automobile 

travel within the residential area and exercise that raised alveolar 

ventilation to 4000 ml/min about 5 hours per week. 

The effects for each city are calculated on a per-person basis because the acfual size of this cohort is so uncertain. Table 7.1 describes the cohorts. The fraction in the urban and suburban cohorts was 

determined by the fraction of the total population in each district that 

is implied by the EPA exposure model. 

COPD patients have a heavy incidence of smoking, which is a causal 

and exacerbatory factor in the disease. Even at this advanced stage, a 

large proportion continue to smoke. To simulate the effects of smoking, 

a constant of 5% COHb was.added to the maximum daily COHb estimates for 

that portion of each cohort that is assumed to smoke. 

To estimate the effects of COHb, the model approximates the pattern 

of day-to-day fluctuations in the daily minimum PaQ 2• For the demonstration here, the stable value was placed at 65 mm Hg. An inverse exponential distribution with a maximum of 65 mm Hg was employed. Its cumulative probability distribution (the probability that Pa _ is less than x) 

has the following formula: 

Pr(PaQ2 < x) = i exp[-X(65 - x)] for x < 65 [7.1] 

1 for x > 65 

99 
P.2 

Table 7.1 

DESCRIPTION OF COPD COHORTS 

City 

Chicago (urb) 

Chicago (sub) 

Los Angeles (urb) 

Los Angeles (sub) 

Philadelphia (urb) 

Philadelphia (sub) 

St. Louis (urb) 

St. Louis (sub) 

.Approximate 

Size of 

Cohort 

400 

150 

570 

2430 

280 

720 

225 

225 

Range of 

Maximum 

Daily COHb 

Levels 

Estimated 

(nonsmokers) 

.52-1.09 

.36-1.24 

.36-1.1 

.56-1.91 

.39-3.12 

.27-1.34 

.42-.72 

.45-2.55 

The reciprocal of the parameter X determines the mean value of Pa „. The 

model was run here with X = .19. This implies a mean Pa02 °f 60 mm Hg. 

The distribution is illustrated in Figure 7.1. An extension of this 

model would investigate the most likely parameterization for

 Pa

0 2 * 

The oxygen content of blood is a function of Pa 02' COHb, and total 

hemoglobin. As COHb rises, the fraction of hemoglobin available for 0-

transfer falls. The leftward shift of the oxyhemoglobin-dissociation 

curve also causes the oxygen remaining to be less readily transferred to 

tissue cells. The danger of chronic hypoxemia flaring into an acute exacerbation of respiratory failure increases as hypoxemia increases.-

Thus, the patients with unusually low Pan« will be the most susceptible 

to an elevation of COHb. 

02 

When COHb levels are zero, one can approximate the oxygen supply capacity of the patient by the use of Sa The distribution on this variable is calculated by a change of variable technique on the distribution 

of FaQ_. The resulting distribution is plotted in Figure 7.2. The use 

of Sa_2 in these circumstances is a good approximation since blood volume 

and hemoglobin do not fluctuate rapidly. 

The effect of increased COHb is to reduce the fraction of hemoglobin 

available for carrying oxygen, and hence to reduce the oxygen supply capacity. Although the increase of COHb actually increases Sa02 slightly, 

100 
P.3 

,75 

J3 

CO 

mQ 

o 

U 

PLI 

.50 

.25 

45 50 55 60 65 

PaQ 2 (mm Hg) 

FIGURE 7.1 PROBABILITY THAT Pa0 2 FALLS BELOW THE VALUE 

INDICATED DURING THE DAY FOR A SENSITIVE 

COPD PATIENT 

101 
P.4 

cfl 

md 

o 

u 

PU 

Sa.- (percent) 

FIGURE 7.2 PROBABILITY THAT Sa02 FALLS BELOW THE STATED 

VALUE ON A GIVEN DAY FOR SENSITIVE COPD 

PATIENTS: Pr(SaQ2|COHb=0) 

102 
P.5 

the assumption of this model is that the reduction in oxygen supply capacity for a given level of COHb can be modeled by an effective San2: 

effective SaQ2 - (1 - COHb) x [SaQ2 for zero COHb] [7.2] 

where COHb is the fraction of hemoglobin that is carboxyhemoglobin. 

The morbidity of the patient is assumed to be a function of his oxygenation. The risk of acute respiratory failure (ARF) is then a function 

of the effective level of SaQ2, which is stated in a probabilistic form 

that combines the estimated fluctuations in both Pa02 and COHb. The risk 

curve, Pr(ARF|Sa02), is presented in Figure 7.3. Although based on 

sketchy evidence in the literature, the uncertainty on the curve is accounted for explicitly. 

To estimate expected incidence of acute respiratory failure in a 

given city, the two COPD cohorts are followed over a sequence of 62 days. 

The exposure data for these days are the daily maximum levels of COHb for 

the months of January and July. A sensitivity test indicated that use of 

all 365 days did not significantly affect model estimates. 

On each day, the predicted maximum COHb results in a particular 

probability distribution on effective SaQ2 for that day, Pr,(Sa02). The 

daily average risk of exacerbation, Pr.(ARF), for the cohort is estimated 

by taking a weighted average of risk over this distribution. 

Prd(ARF) = /pr(ARF|Sa02) x Prd(SaQ2) [7.3] 

Sa02 

The number of ARFs for that day and that cohort is this average risk multiplied by the number at risk in the cohort: 

ARF = Pr. (ARF) x (// at risk in cohort on day d) [7.4] 

The unique feature of this model is that the number at risk for each 

cohort fluctuates over time. Each predicted ARF is assumed to result in 

a hospitalization. Thus, for the duration of the hospitalization, the 

cohort size will be reduced by the predicted ARF. The length of hospitalization varies widely, but is estimated to be 14 days on average. 

Thus, the size of the cohort at risk each day is a function of the ARFs 

on the previous 14 days: 

14 

(# in cohort at risk on day d) = (total cohort size) - /^ (ARF. ) [7.5] 

i=l 

103 
Probability 5 Q 

o • PSa 2 (percent) 

FIGURE 7.3 PROBABILITY OF ACUTE RESPIRATORY FAILURE AS FUNCTION 

OF SaQ2—Pr(ARF|SaQ2) 

0> 
P.7 

The total ARF per day for a given city is then obtained by summing over 

cohorts. If two equally high daily maximum COHb exposures occur consecutively, the second day will result in fewer ARFs because the number of 

patients at risk will already be reduced by hospitalizations on the previous day. This calculation avoids the significant double counting that 

would otherwise result for such a highly sensitive group of people. 

Because of the seriousness of ARF, increased morbidity must result 

in decreased life expectancy. With each ARF there is some risk of death, 

and this risk can be approximated from hospital case-fatality rates. It 

increases as the effective San2 at the time of exacerbation falls. This 

neglects the effect of uncontrolled oxygen therapy that is delivered 

prior to admission. When this occurs, risk of death is actually higher. 

The model uses the Sa _ value that would occur without oxygen therapy. 

The risk curve for death, Pr(death| ARF, SaQ 2 ), is provided in Figure 7.4. 

The annual number of deaths for a given cohort is calculated as follows: 

Annual deaths in one cohort = 

365 

^ (ARFd) x / Pr(death|Sa02, ARF) x Prd(SaQ2|ARFd) [7.6] 

d-1 4 0 2 

The final quantity in the equation above can be obtained from Equation 

7.3. 

As discussed above, changes in this mortality figure do not adequately represent the effect of different CO standards. It only reflects 

a change in the death rate for that size cohort. If the number of new 

patients per year remains constant, an increase in life expectancy will 

gradually increase the size of the cohort until the number of deaths each 

year again equals the number of new patients. Rather than model the population dynamics, it was preferred to look at changes in life expectancy 

as the underlying effect of changes in death rates. The average life expectancy for a cohort is approximately: 

(life expectancy) = (# in cohort)/(annual deaths) [7.7] 

Probabilistic Model 

The uncertainties are large in all of the parameters of this model. 

Wide ranges were placed on three of the most sensitive parameters: the 

risk of ARF curve; the risk of death when ARF occurs; and the proportion 

of the sensitive group which smokes. Figure 7.5 provides the probability 

tree employed. Note that Pr(ARF|Sa02) and Pr(death|ARF, SaQ2) have been 

approximated by a discrete function. The distribution on the faction 

that smokes is illustrative, but the results are insensitive to the exact 

probabilities. 

105 
50 

Probability 

.25 

o 

ON 

0 L_ 

40 50 60 70 80 90 

Sa (percent) 

FIGURE 7.4 PROBABILITY OF DEATH AS FUNCTION OF Sa0 2 , GIVEN THAT 

ACUTE RESPIRATORY FAILURE HAS OCCURRED—Pr(DEATH|ARF, SaQ2) 

00 
Pr(ARF|SaQ2) 

(Sa02<.6;.6^Sa02^.8;Sa02>8) 

Pr(death|ARF, Sa „) 02' 

(Sa02<.6;Sa02>.6) 

Fraction of COPD 

sensitive group 

(.5; .1; 0) (.25; .15) 

o 

-•j 

(.25; .05; 0) (.1; .05) 

FIGURE 7.5 PROBABILITY TREE USED FOR COPD MODEL 
P.10 

The probabilistic phase provides distributions on hospitalizations 

and on life expectancy. Figure 7.6 indicates the distribution on average 

hospitalizations for Los Angeles. The lower axis indicates the rate per 

person, averaged over cohorts, and total estimated ARFs per year for Los 

Angeles. Table 7.2 summarizes this data. Figure 7.7 indicates the as-is 

distribution on life expectancy. This is very similar from standard to 

standard. Figure 7.8 provides distributions on changes in life expectancy from the as-is estimate for each standard for Los Angeles. Table 

7.3 summarizes this output. The distributions on hospitalizations and 

life expectancy are virtually identical for all cities since the uncertainty on the as-is levels is so large. Tables and figures that summarize the differences between standards for the remaining three cities are 

provided in Appendix A. 

Although the ranges of the outcomes are very wide, the expected values of effects are quite similar. Expected ARFs per year change from 

2.79 to 3.04 per sensitive COPD patient per year in Los Angeles as one 

goes from the strictest to the most lenient standard. The distribution 

on changes in life expectancy is very large, with a spread as great as 

180 days (expected value = 77 days) when one compares the strictest standard to the as-is situation. However, the size of the population that 

gains this change is small in comparison to other health effects such as 

arteriosclerotic heart disease. This makes the overall public health 

significance of COPD and CO small. 

The effects of all ambient CO are calculated to contribute 8% to 9% 

for Chicago, Philadelphia, and St. Louis and 13% for Los Angeles to the 

total morbidity of this group. As-is life expectancy is decreased by 

equivalent amounts from what it would be if all ambient CO were removed. 

These high values illustrate the sensitivity of this group. 

Summary 

This model is extremely sensitive to some of the parameters used. 

In particular, the shape of the distribution on variation of Pan- over 

time (Figure 7.1) is important but not well known. Given the information 

used in the. analysis, however, two conclusions can be drawn: (1) this 

group is quite sensitive to changes in ambient CO standards, and (2) because of the small size of the group, however, the overall magnitude of 

effects in probably less significant than the effects for arteriosclerotic heart disease patients. 

108 
1.0 

.75 _ 

Probability 0.5 

o 

.25 

exex 

Average Annual Hospitalization/Patient 

18,000 Approximate Total 

Hospitalizations/year 

FIGURE 7.6 PROBABILITY THAT HOSPITALIZATIONS/YEAR IS LESS THAN THE VALUE 

INDICATED FOR THE SENSITIVE COPD POPULATION IN LOS ANGELES 
P.12 

Table 7.2 

COMPARISON OF FREQUENCY OF ACUTE RESPIRATORY FAILURE INCIDENCE 

IN SENSITIVE COPD POPULATION ACROSS STANDARDS 

(Los Angeles) 

Change from 

Standard 

35 ppm 1 hr 1 exex 

25 ppm 1 hr 1 exex 

25 ppm 1 hr 5 exex 

12 ppm 8 hr 1 exex 

12 ppm 8 hr 5 exex 

9 ppm 8 hr 1 exex 

9 ppm 8 hr 5 exex 

Change 

ARF/Yr/ 

Patient 

.11 

-.05 

.02 

-.11 

-.07 

-.14 

-.12 

From As-ls 

Percent 

of As-ls 

3.8 

-1.7 

0.7 

-3.8 

-2.4 

-4.8 

-4.1 

Strictest 

ARF/Yr/ 

Patient 

.25 

.09 

.16 

.03 

.07 

— 

.02 

0. 

Standard 

Percent 

f Standard 

9.0 

.3.2 

5.7 

1.1 

2.5 

— 

0.7 

110 
Probability .50 — 

Life Expectancy (years) 

FIGURE 7.7 PROBABILITY THAT LIFE EXPECTANCY FOR A SENSITIVE COPD 

PATIENT IS LESS THAN THE VALUE INDICATED 
P.M 

1.00*-

Probability 

Probability 

1.00 

75 -

.50 

,25 

5 exceedances 

per year 

25 ppm (1 hr) 

-180 -90 

12 ppm (8 hr) 

9 ppm (8 hr) 

90 180 

Days 

FIGURE 7.8 PROBABILITY THAT THE CHANGE IN LIFE EXPECTANCY FROM 

AS-IS WILL BE LESS THAN THE VALUE INDICATED FOR A 

COPD PATIENT IN LOS ANGELES. AS-IS LIFE EXPECTANCY 

IS PREDICTED TO BE ABOUT 4.48 YEARS IN LOS ANGELES 

FOR THE SENSITIVE GROUP 

112 
P.15 

Table 7.3 

COMPARISON OF LIFE EXPECTANCIES FOR COPD SENSITIVE POPULATION 

ACROSS STANDARDS 

(Los Angeles) 

Standard 

35 ppm 1 hr 1 exex 

25 ppm 1 hr 1 exex 

25 ppm 1 hr 5 exex 

12 ppm 8 hr 1 exex 

12 ppm 8 hr 5 exex 

9 ppm 8 hr 1 exex 

9 ppm 8 hr 5 exex 

Change 

Days per 

Patient 

-50.3 

28.0 

-8.5 

57.4 

36.8 

77.5 

63.3 

from As-ls 

Percent 

of As-ls 

-3.1 

1.7 

-0.5 

3.5 

2.2 

4.7 

3.9 

Change from 

Strictest 

Days per 

Patient c 

-127.8 

-51.1 

-84.0 

-18.3 

-40.2 

— 

-14.6 

Standard 

Percent 

if Standard 

-7.5 

-3.0 

-4.9 

-1.1 

-2.3 

— 

-0.9 

113 
P.16 

REFERENCES FOR CHAPTER VII 

1. Stephen M. Ayres, Hiltrud S. Mueller, et al., "Systemic and Myocardial Hemodynamic Responses to Relatively Small Concentrations of 

Carboxyhemoglobin (COHb)," Arch Env Health, Vol. 18, April 1969, pp. 

• 699-709. 

2. Wilbert S. Aronow, Jack Ferlinz, and Frederick Glauser, "Effect of 

Carbon Monoxide on Exercise Performance in Chronic Obstructive Pulmonary Disease," Am J of Medicine, Vol. 63, December 1977, pp. 904-

908. 

3. Prevalence of Selected Chronic Respiratory Conditions, United States 

- 1970, Vital Health and Statistics, Series 10, No. 84, National 

Center for Health Statistics, Rockville, Maryland, 1973. 

4. R. F. Coburn, R. E. Forster, et al., "Considerations of the Physiological Variables that Determine the Blood Carboxyhemoglobin Concentration in Man," J Clin Invest, Vol. 44, No. 11, 1965, p. 1903. 

115 
P.17 

VIII CENTRAL NERVOUS SYSTEM 

The effects of CO on central nervous system functioning are difficult to model. Clinical studies do not focus on the true public health 

risks that may exist. The model developed here takes an approach originating in the toxicology literature. This approach centers on the use of 

"reference substances" to interpret the risks implied by functional 

tests. The outcome of the model suggests that the health impact in this 

area may be less important than in the other areas considered, and little 

further development of the model is indicated at this time. 

Background and Modeling Issues 

The central nervous system (CNS) is among the first of the physiological system to be impaired when an organism is deprived of oxygen. 

The possibility exists that even small decrements in oxygen availability 

may have noticeable effects on nervous system functions. The presence of 

ambient carbon monoxide may be a contributing factor to these effects if 

they do occur. 

Numerous studies have tested the effects of various levels of COHb 

on different CNS functions. Some find very significant changes, others 

find a significant change, which disappears at higher levels of COHb. 

Many find no effect at all. The inconsistency of results may be caused 

by the difficulty of formulating sensitive tests for most behavioral 

functions. Another problem may be that some aspects of CNS performance 

are more affected than others. 

Taken together, the evidence suggests that some sort of effect of 

COHb on CNS functions does indeed occur, perhaps with a threshold. The 

question that remains is to identify what serious health consequences can 

arise from these effects. Few would argue that the levels of COHb associated with current ambient exposures will induce more than a mild sensory and/or intellectual impairment. Such impairments, however, can contribute to the risk of injury or death in complex sensorimotor tasks that 

require safety precautions. A major sensorimotor task that involves a 

large portion of the population is motor vehicle driving. The levels of 

COHb experienced while driving are generally higher than those found at 

other times. Thus, if there is any significant health effect of CO due 

to CNS impairment, it might be found in a correlation between automobile 

fatalities and COHb levels in drivers. If this is estimated to be a particularly large effect, then investigation of other similar accident 

risks will be indicated. 

Several tests have been made of driving skills and other complex 

tasks under the influence of COHb. As with the general vigilance tests, 

the results are inconclusive. In addition, attempts to find direct 

117 
P.18 

correlations of COHb and drivers in fatal accidents have demonstrated 

only small effects. As will be discussed below, it is easy to overestimate the relevance of these studies when discussing the effects of changing standards. 

Issues that must be addressed in the model include the problem of 

quantitatively relating the results of functional tests of the CNS to the 

implied risks of accident. An accident is a rare event. Tests of accident proneness after exposure to low levels of COHb are both difficult 

and statistically infeasible. Tests of related CNS functions, although 

difficult to design and implement, are an alternative. A decrement in 

functional test scores, however, does not directly indicate the magnitude 

of the associated accident risk. 

Horvath and Frantik have proposed the use of "reference substances" 

in estimating the risks associated with decrements in functional test 

scores. ' A reference substance is one that has a well-documented doseresponse relationship and that has been used in numerous functional 

tests. In this method, one determines the doses of the substance of concern that produce the same effect as that of given doses of the reference 

substance. These are called the "effective levels," and they may be determined for a variety of functional tests. From these comparisons, one 

can use the dose-response curve of the reference substance for the effective level of the substance of concern. 

The main problem in this comparative toxicology is to select the appropriate reference substance. Although preferable, it is not necessary 

that an identical mechanism of toxicity be shared by the two substances. 

A more important question is how well the functional tests model the capabilities whose impairment could lead to the accident. Horvath and 

Frantik point out that the external similarity of the test to the driving 

situation is not ttte only concern. Tests of .vigilance, sustained attention and risk taking are as important to consider as those of driving 

skills. 

The reference substance selected for the purposes of this study is 

alcohol. Alcohol has an exceptionally well-documented relationship to 

driving risks. Furthermore, hypoxia is often described as having an effect similar to the consumption of alcohol. This similarity of effect 

may also occur at low COHb and low blood-alcohol levels. To investigate 

the potential of basing a model on alcohol as a reference substance, the 

literature on the effects of alcohol on tests related to driving skills 

was reviewed briefly. By comparing Figures 8.1 and 8.2, one can see that 

such tests result in decrements of similar magnitudes for blood-alcohol 

and COHb exposures. This is an interesting comparison if the results on 

these functional tests have any implication for changes in the probability of being involved in an automobile accident. 

One can use the evidence from Figures 8.1 and 8.2 to approximate the 

levels of COHb and blood alcohol content (BAC) which result in comparable 

changes in test scores. This comparison can be stated most concisely as 

a mathematical equation relating COHb to an "effective blood alcohol content" (EBAC). On the basis of the EBAC, one can assess risks of accidents from the much more available data on alcohol and driving. 

118 
P.19 

To fully implement the Horvath and Frantik method, one would want to 

compare COHb and BAC for numerous other functional tests. Data are 

available for a more detailed comparison. However, for this demonstration, effective levels for the comparison of COHb and BAC will be estimated from this smaller sample of data on driving-related skills. 

Another important issue in a model of the relationship of COHb and 

traffic accidents is that of "endogeneity." The dose-response curve and 

the relationship of COHb to EBAC levels are not sufficient for an estimation of the effects of CO on expected automobile deaths and injuries. 

As traffic density grows and average vehicle speed slows, CO rises. At 

the same time that COHb is becoming high enough to move people up the 

dose-response curve, the kind of traffic in which they are driving subjects them to less chance of severe accidents. Although small damage accidents may increase, the chances for accidents that result in serious 

injuries and deaths will decrease. Fatal automobile accidents occur 

mainly on open roads where high speeds are obtained. In addition, bad 

weather is negatively correlated with CO levels, so that many accidents 

are most likely to occur on days when CO is low, despite the lower chance 

of accident on those days because of lower COHb. 

To assess properly the impact on driving accidents attributable to 

COHb, one must account for this effect of changing driving conditions. 

This can be done by constructing a relationship between the COHb level 

experienced while in the car and the average physical driving conditions 

that could be associated with that exposure. The physical driving conditions can then be combined with the element of the risk contributed by 

impairment of CNS functioning to assess the average net risk experienced 

at each COHb level. 

Deterministic Model 

Human Element 

Figures 8.1 and 8.2 indicate that both COHb and BAC can have similar 

effects on driving skills. The approximation of COHb in terms of effective levels of BAC (EBAC) requires judgment. Three very different functional forms are chosen to examine possible rescaling: logarithmic, linear, and exponential. Furthermore, there may be some threshold value of 

COHb that must be crossed before this scaling is applied. A zero value 

of BAC is accompanied by some average endogenous level of COHb. If COHb 

can rise before it has any average effect on driving skills, then the 

threshold of the EBAC function may be higher than the average endogenous 

119 
P.20 

0) 

l-i 3 

CO 

CO 

cu 

S 

CS 

c 

o 

o 

c 

3 

En 

CD 

cn 

rt 

<u 

u 

o 

(U 

o 

c 

cu 

cj 

CU 

zuu 

100 • 

0 

(.11) 

(9) 

(9) 

i 

(11) 

(ID 

(7) 

(8) 

(8) 

(11) 

(10) 

(11) 

( I D 

( I D 

(7* 1 0 ) 

' 

.05 .10 

Alcohol Concentration (mg/lOOml) 

FIGURE 8.1 EFFECTS OF ALCOHOL ON DRIVING-SKILL TESTS. 

NUMBERS INDICATE REFERENCE FOR THE STUDY 

FROM WHICH DATA POINT WAS DERIVED 

120 
P.21 

<U 

U 

3 

in 

CO 

CU 

S 

cij 

c 

o 

cj 

c 

3 

fa 

cu 

co 

cd 

cu 

u 

u 

cu 

o 

•u 

c 

CU 

u 

u 

CU 

04 

200 __ 

(3) 

(3) 

100 

oL 

0 

t t 

(4)(6)(5 ) J_L 

t 

(5) 

10 15 20 

COHb (percent) 

FIGURE 8.2 EFFECTS OF COHb ON DRIVING RELATED SKILLSNUMBERS INDICATE REFERENCE FOR THE STUDY FROM 

WHICH DATAPOINT WAS DERIVED. ARROWS INDICATE 

RANGE IN WHICH RESULT IS BELIEVED TO FALL WHEN 

EXACT DATA IS NOT AVAILABLE 

121 
P.22 

level. Letting T be the threshold value, the three alternative relationships might be: 

(a) EBAC = max[(%COHb) - T, 0] [8.1] 

150 

(b) EBAC =0.5 exp(%C0Hb - T) - 1 [8.2] 

100 

(c) EBAC =» inverse of: 

(%C0Hb) = T + 100 x EBAC exp(EBAC) [8.3] 

The coefficients of these functions have been chosen judgmentally. They 

are the coefficients that rescale the COHb data of Figure 8.2 to make 

them conform best with the BAC data of Figure 8.1 within the confines of 

each functional form. Figure 8.3 compares the BAC data with the rescaled 

COHb data to indicate the goodness of fit for each equation. These 

graphs all assume a threshold value of 2.5% COHb, which is consistent 

with data. Differences in threshold would simply shift the rescaled COHb 

data uniformly to the left or the right. Note that the data support a 

higher threshold than 2.5% in Figures 8.3(a) and 8.3(b). This is accounted for in the probabilistic phase where the mean threshold used is 

4%. Figure 8.4 compares the three curves. 

The Department of Transportation has published a curve that represents the probability of involvement in a fatal automobile accident as a 

function of blood alcohol content. ' This probability is relative to 

the probability when the driver has zero^BAC. It is thus applicable to 

drivers of all skill levels. The curve is approximated well by the 

following function in the regions of interest for CO exposure (i.e., for 

BAC between 0 and .05%): 

Relative Probability = exp[300 x (BAC)2] [8.4] 

This curve is illustrated in Figure 8.5. 

When blood alcohol is zero, the relative probability is unity. A 

COHb value of 10% is equivalent to a blood alcohol content of .05%, using 

equation [8.2]. This results in a relative accident probability twice as 

large as when COHb is 2.5% or less. To approximate the relative probability of a fatal accident at each level of COHb, RPFA(COHb), relative to 

a COHb of zero percent, the following formula is used: 

RPFA(COHb) = Relative Probability(EBAC) 

= exp[300 x (EBAC)2] [8.5] 

where EBAC is defined by one of the functions 8.1 through 8.3. 

122 
CO 

CU 

U 

3 

CO 

rt 

CU 

o 

•rl 

U 

Z 3 

UA 

cu 

CO 

rt 

a) 

n 

o 

CU 

200 

100 

0 

X 

• 

• 

1 

(a) 

• 

• 

• 

• • 

X 

• 

• 

• 

• * 

•• 

1 

200 

100 

1 

.05 10 

»* x 

± 

(b) 

200 

100 

J 

0 

.15 .05 .10 .15 

(Effective) Alcohol Concentration (mg/100 ml) 

(c) 

?* x x 

J l_i L 

.05 ,10 

J 

,15 

FIGURE 8.3 COMPARISON OF TEST RESULTS WITH COHb RESCALED TO EFFECTIVE ALCOHOL 

CONCENTRATION AS FOLLOWS: (a) USING EQUATION 8.2, (b) USING EQUATION 

8.1, (c) USING EQUATION 8.3. THRESHOLD =2.5% COHb IN EACH CASE 

X = COHb data 

• = Alcohol data 
Eq. 8.3 

EBAC 

(mg/100 ml) 

N) 

4> 

COHb (percent) 

FIGURE 8.4 COMPARISON OF THREE EBAC FUNCTIONS 

FOR THRESHOLD =2.5% COHb 
Relative 

Probability 

Ln 

BAC (mg/100 ml) 

FIGURE 8.5 PROBABILITY OF FATAL ACCIDENT AS FUNCTION OF BLOOD-ALCOHOL 

CONTENT RELATIVE TO PROBABILITY AT BLOOD-ALCOHOL CONTENT OF ZERO 
Physical Element 

To incorporate the physical element associated with driving conditions, a relationship between COHb levels and the amount of driving time 

during which traffic conditions permit a chance of a fatal accident is 

proposed. While sitting at stop lights, or driving in slow, heavy traffic, the chance of a serious accident is small. These driving conditions, however, result in the greatest increases in COHb. Figure 8.6 

presents a curve illustrating the assumption that the fraction of driving 

time during which there is a chance of a severe accident falls as average 

COHb levels rise. Although this curve for driving time during which a 

fatal accident is possible, DTFA(COHb), is hypothetical, it is potentially measurable. As will be described below, current uncertainty on 

this curve is handled explicitly in the probabilistic phase. 

A change in the CO standard results in a change in the DTFA curve. 

Under a more stingent standard, a given proportion of DFTA would be associated with a lower level of COHb. Thus, the rollback factors enter the 

model in two ways. The first is via the COHb distribution estimated by 

the exposure model. Second, the DTFA curve is shifted by the rollback 

factor for each standard. This is demonstrated in Figure 8.7 for a rollback factor of 0.5. The purpose of this adjustment is to make the average DTFA constant over different standards: one would not expect an inspection and maintenance plan for CO emissions to change the physical 

conditions of driving. Only the human element would be affected. 

Net Accident Risks 

The effective accident risk accounts for the opposing effects on the 

human and physical elements. The effective curve is obtained by multiplying the relative probability of fatal accidents by the fraction of 

driving time during which the person will be in situations where a severe 

accident can occur. This curve is depicted in Figure 8.8. The formula 

for the expected net accident risk, NAR, is: 

NAR =J ^ DTFA(C0Hb, Rollback factor), x RPFA(COHb) x Pr(COHb) [8.6] 

COHb 

The differences in NAR for different standards indicate the relative 

changes in the number of fatal accidents that would occur by moving from 

one standard to another. Current levels of fatal accidents are known 

from public statistics. By applying the proportional changes in NAR from 

the as-is case to the current levels of accident fatalities, one can 

estimate the expected traffic fatalities at different standards. 

The exposure patterns for COHb were derived from the EPA exposure 

model. A probability distribution of COHb was calculated for those hours 

during which the cohorts were said to be in an automobile. All adult 

(driving age) cohorts were aggregated into a single exposure group because there is nothing in this simplified model to discriminate among 

cohorts in terms of sensitivities to COHb. 

126 
Fraction 

of 

Driving 

Time 

COHb (%) 

FIGURE 8.6 HYPOTHETICAL RELATIONSHIP BETWEEN AVERAGE COKb LEVELS WHILE 

DRIVING AND FRACTION OF THE DRIVING TIME WHEN FATAL ACCIDENT 

IS POSSIBLE (DTFA): A AND B ARE VALUES OF COHb AT WHICH SLOPE 

OF DTFA CHANGES 
DTFA (ROLLBACK FACTOR =.5) 

Fraction 

of 

Driving 

Time 

00 

DTFA (ROLLBACK FACTOR = 1) 

% COHb 

FIGURE 8.7 EXAMPLE OF HOW DTFA IS A FUNCTION OF THE 

STANDARD FOR CARBON MONOXIDE 
Relative Scale 

VO 

COHb (percent) 

FIGURE 8.8 DERIVATION OF NET ACCIDENT RISK FUNCTION FROM 

PHYSICAL AND HUMAN ELEMENTS 
P.30 

A summary of average COHb exposures attained by individuals in cars 

is provided in Table 8.1 for each of the four cities. 

Table 8.1 

FATAL ACCIDENT LEVELS AND ESTIMATED COHb EXPOSURE PATTERNS 

(As-ls) 

City 

Chicago 

Los Angeles 

Philadelphia 

St. Louis 

Approximate 

Annual 

Traffic 

Fatalities 

240 

880 

230 

170 

Average 

% COHb 

in Cars 

.90 

.90 

.48 

.72 

Average % COHb 

in All 

Microenvironments 

.59-.72 

.60-.85 

.40-.42 

.55-.67 

Probabilistic Model 

To account for uncertainties, illustrative probability distributions 

were assigned to alternative forms for relating COHb and EBAC, to possible threshold values in the EBAC relationship, and to different values 

for the parameter A in DTFA. Despite the large uncertainties assumed, 

the probability distribution on traffic fatalities results in a very 

small range on the distributions. The probability tree that was used is 

presented in Figure 8.9. 

Other uncertainties that are not included in this probability tree 

were considered in the sensitivity analysis. For instance, if the DTFA 

relationship is removed entirely, the predicted effects do not change 

significantly. Similarly, the relative probability curve for BAC (equation 8.4) is only an approximation. The approximation used here represents an upper bound on the actual slope. Any probability distribution 

on this relative risk curve would result in smaller estimates of changes 

in the number of traffic accidents. 

The distribution of traffic accidents for the most stringent and 

least stringent standards are compared for Los Angeles in Figure 8.10. 

The predicted effects are very small, and the distributions have virtually no range. Even by setting the threshold value at .25% COHb and removing the DTFA curve, the estimated accidents in Los Angeles would rise 

by a maximum of 1.3 and fall by a maximum of 5 automobile deaths. 

130 
Functional form relating 

COHb and EBAC 

Threshold in EBAC 

Relationship 

Parameter A 

in DTFA 

Function 

Exponential COHb 2% COHb 1.5% 

•

50 COHb 2.5% 

COHb 4% 

FIGURE 8.9 PROBABILITY TREE USED FOR TRAFFIC ACCIDENTS MODEL 
1.0 

4J 

•ri 

3 0.5 

ti 

x. 

o 

u 

Co 

to 

0 

-35 ppm, 1 hr, 1 exex 

9 ppm, 8 hr, 1 exex 

.9998 

899.8 

.9999 

899.9 

1.00 

900 

1.0001 

900.1 

Change from as-is 

deaths 

FIGURE 8.10 PROBABILITY THAT TRAFFIC DEATHS IN LOS ANGELES ARE LESS THAN THE VALUE 

INDICATED. TRAFFIC DEATHS IN PRESENT AS-IS SITUATION ARE ASSUMED TO BE 

900 PER YEAR 
P.33 

Discussion 

Figure 8.8 shows that our model estimates that the correlation of 

accident fatalities and driver COHb may be., negative. Hexter in fact 

found a significant negative relationship. The effect of changing 

standards can be seriously overestimated if the changes described in the 

DTFA curve are not addressed. A change in standards affects not only 

COHb levels but also the DTFA curve, and these effects tend to cancel 

each other. 

Summary 

The model for CNS effects of CO suggests that this effect produces 

little contribution to public health risks. Although the model is interesting in its use of data, there is little reason to develop it further 

in its present form. One question that may be worth addressing, however, 

concerns the effect of ambient CO on workers in occupations where safety 

precautions are necessary. This would be especially important if the occupation involved large occupational exposure to CO. Although ambient CO 

might be only a small addition to occupational CO, these workers could be 

on a portion of the dose-response curve where even small increments have 

important consequences. 

133 
P.34 

REFERENCES FOR CHAPTER VIII 

1. M. Horvath and E. Frantik, "Quantitative Interpretation of Experimental Toxicological Data: The Use of Reference Substances," in 

Adverse Effects of Environmental Chemicals and Psychotropic Drugs, 

Vol. 1, Milan Horvath (ed.). Elsevier Scientific Publishing Company, Amsterdam, 1973. 

2. M. Horvath, E., Frantik and K. Kopriva, "Alertness Impairing Effects 

of Chemical Agents in Monotonous Conditions: Laboratory Assessment," in Adverse Effects of Environmental Chemicals and Psychotropic Drugs, Vol. 2, Milan Horvath (ed.). Elsevier Scientific 

Publishing Company, Amsterdam, 1976. 

3. A. M. Ray and T. H. Rockwell, "An Exploratory Study of Automobile 

Driving Performance Under the Influence of Low Levels of Carboxyhemoglobin," Annals New York Academy of Sciences, Vol. 174, pp. 376-

408, 1970. 

4. G. Wright, P. Randell and R. J. Shephard, "Carbon Monoxide and Driving Skills," Arch. Environ. Health, Vol. 27, pp. 349-354, December 

1973. 

5. J. M. Ramsey, "Effects of Single Exposures of Carbon Monoxide on 

Sensory and Psychomotor Response," American Industrial Hygiene Association Journal, Vol. 34, pp. 212-216, 1973. 

6. N. Rummo and K. Sarlanis, "The Effect of Carbon Monoxide on Several 

Measures of Vigilance in a Simulated Driving Task," J. Safety Res., 

Vol. 6, pp. 126-130, 1974. 

7. M. Linnoila, C W. Erwin, D. Ramm and W. P. Cleveland, "Effects of 

Age and Alcohol on Psychomotor Performance of Men," Journal of 

Studies on Alcohol, Vol. 41, pp. 488-495, 1980. 

8. F. MacCarthy and J. E. Tong, "Alcohol and Velocity Perception: II. 

Stimulus Discrimination," Perceptual and Motor Skills, Vol. 51, pp. 

968-970, 1980. 

9. G. J. Connors and S. A. Maisto, "Effects of Alcohol, Instructions 

and Consumption Rate on Motor Performance," Journal of Studies on 

Alcohol, Vol. 41, No. 5, pp. 509-517, 1980. 

10. C. W. Erwin, E. L. Wiener, M. I. Linnoila, T. R. Truscott, "Alcohol 

Induced Drowsiness and Vigilance Performance," J. Stud. Alcohol, 

Vol. 39, p. 505, 1978. 

135 
P.35 

11. M. Linnoila, "Effects of Alcohol on Psychomotor Performance of Men 

and Women," Journal of Alcohol Studies, Vol. 39, p. 745, 1978. 

12. M. W. Perrine, J. A. Waller and L. S. Harris, "Alcohol and Highway 

Safety: Behavioral and Medical Aspects," DOT HS-800-599, U.S. Department of Transportation, NHTSA Technical Report, 1971. 

13. R. E. Borkenstein, R. F. Growther et al., "The Role of the Drinking 

Driver in Traffic Accidents," Indiana Univeresity, Bloomington, 

Indiana, 1964. 

14. Dr. Alfred C. Hexter, personal communication. 

136 
P.36 

PART IV 

SIGNIFICANCE OF RESULTS 

In previous sections of this report, six different health risks were 

analyzed for four different cities under seven proposed standards. Although a complete risk analysis would supply a wealth of information at 

this level, many of the insights useful to a decision maker are best obtained by obtaining a more synthetic view of the problem. 

In this part of the report, an extrapolation is made to an estimate 

of health risks for the national urban population. It is this population 

that will be most affected by the proposed standards. 

The health risks are then combined into a single measure of national 

health impact. The purpose of this health impact measure is not intended 

to establish definitive trade-offs between different health risks, but 

rather to identify which health risks are most important for future development and evaluation. 

Finally, given the current preliminary analysis, the uncertainties 

with the highest value of resolution are identified. This identification 

can help set priorities on future areas of research. 

It should be reemphasized that the estimates derived in this study 

should not be used in their present form for decision-making purposes. 

Not only are the models preliminary, but also the exposure data furnished 

by EPA were simplified and partially in error (see Chapters II and III). 

137 
P.37 

IX HEALTH RISKS FOR THE NATION 

In the previous chapters, health risks have been estimated for four 

cities. Extending these results to obtain an estimate of the health 

risks for the entire nation is not a trivial task. In this chapter, two 

questions will be addressed: Can the results of the Health Risk Models 

be generalized in their relation to the Exposure Model? Given available 

data, what estimates can be made of the health risks for the nation? 

Relation of the Health Risk and Exposure Models 

If the results of the Health Risk Models can be related in a simple 

way to exposure data, then there exists the possibility of using relatively accessible data for various cities to perform the health-risk estimate for each city in the nation. 

Arteriosclerotic heart disease effects depend in a relatively simple 

fashion on COHb. For this reason, it may be possible to obtain a relation between these effects and the average COHb levels experienced in 

each city. 

If an average COHb level is defined as 

av % COHb = \ (fraction of person-occurrences) x % COHb [9.1] 

at COHb level 

COHb 

then a very good fit to the expected value of effects found in the four 

cities for the seven proposed standards and the present "as-is" situation 

can be obtained (Figure 9.1): 

angina attacks 

per day per patient = 1.54 + 0.43 x av % COHb [9.2] 

This relation is not especially convenient, however, in that the age/occupuation distribution differs from city to city. A perhaps more useful 

(but less accurate) fit is (Figure 9.2): 

angina attacks , 

per year per 10 adult pop. = 13.27 + 3.04 x av % COHb [9.3] 

139 
Angina Attacks 

per Day 

per Patient 

o 

.8 

.7 

1 

1 

• ^^ 

• 

1 

• ^^^ 

1 

1 

1 

1 

l 

0.4 0.5 0.6 

Average % COHb 

0.7 0.8 

FIGURE 9.1 ANGINA ATTACKS PER DAY PER PATIENT AS A FUNCTION OF AVERAGE COHb 
Angina Attacks Per 

Year Per 

IO6 Population 

I D 

15 

14 

• 

1 

•^' . 

1 

• ix"^-1^ 

1 

• 

1 

• 

1 

• • 

• • 

• 

• 

1 

1 

• • 

• 

• 

1 

• • 

• 

0.4 0.5 0.6 0.7 0.8 

Average % COHb 

FIGURE 9.2 ANGINA ATTACKS PER YEAR PER MILLION ADULT POPULATION 

AS A FUNCTION OF AVERAGE COHb 

CO 
P.40 

These rates can also be used to estimate cardiovascular mortality through 

Equation 5.5. 

These fits and the fits obtained below are attempts to generalize 

the detailed analysis in such a manner that the results can be extrapolated to cities for which a detailed analysis is impractical within the 

resources of the risk assessment. All the fits are a least-squares fit 

of a straight line to 28 points (4 cities, 7 standards) for expected 

health effects as a function of average COHb levels. A more sophisticated statistical analysis would be appropriate when more extensive data 

are available. 

To use Equation 9.3 for an extrapolation to a national scale, one 

needs the adult population of each city and the average COHb for each 

city under each standard. The problem with this approach is in determining average COHb data in terms of CO data. Because the EPA Exposure 

Model was not readily accessible, it was not possible to discover a reliable relationship between average COHb values and average CO values. The 

structure of the exposure model and fits to available data suggest, however, that a simple relation probably exists between average COHb and the 

population weighted average CO levels (and, if necessary, the variance in 

population weighted CO levels). The CO levels most appropriate for such 

a relationship should probably be those resulting from a 4 to 8 hour 

averaging, because COHb builds up slowly in the body. If such a relation 

exists, then four steps are indicated: 

(1) Determine the relationship between average COHb and average CO 

levels for four cities under seven standards (and the present 

as-is situation) using the Exposure Model. 

(2) Determine average CO levels (for some specified averaging time) 

for various cities for the as-is situation from monitored data. 

(3) Determine the rollback of CO levels for these cities from the 

monitored data. For instance, if a city exceeds 30 ppm CO once 

in a year on an 8-hour averaging basis, the rollback to a 9 

ppm, 8 hr, 1 exex standard is 9/30. This rollback is then applied to the average CO levels; some adjustment for background 

levels of CO may be made. 

(4) Determine the adult population of the city. 

Using these data and Equations 9.3 and 5.5, rough approximations for ASHD 

health effects may be obtained for all cities for which monitored data 

are available. 

The basic problem addressed above is data management and availability: how can a risk analysis be performed economically for the many 

cities involved in a national extrapolation? Given that detailed analysis can be performed only for a small number of cities, this is one possible method to link health effects to available data for the remaining 

cities. Three crucial questions must be answered before this method will 

be useful: (1) Is there a relationship between expected health effects 

and some aggregate measure of dose (such as average COHb)? (2) Is there 

a relationship between the aggregate measure of dose and some aggregate 

142 
P.41 

measure of CO concentration that can be obtained readily for the cities 

in question (such as average 4-hour CO concentrations and perhaps the 

variance of the distribution)? (3) Can the effect of a standard (rollback factor) be estimated for the cities from available data? 

The same procedure can be used for the expected value of fetal effects. Figure 9.3 shows that fetal deaths per pregnancy are closely related to average COHb: 

fetal deaths per 1,000 pregnancies = 9.64 + 0.29 x av % COHb [9.4] 

Given the data developed for ASHD, it is only necessary to determine the 

number of pregnancies per year (or the birth rate) for each city. 

For chronic obstructive pulmonary disease, the situation is somewhat 

more complicated. The input to this model is the daily highest value of 

COHb, and the model functions in a rather complex fashion. In addition, 

the urban/suburban split of these patients differs significantly from 

city to city. In terms of the average daily-high COHb values experienced 

in home microenvironments by these patients (weighted by the urban/suburban split), a fairly good approximation can be obtained (Figures 9.4, 

9.5): 

hospitalizations 

per patient per year = 2.37 + 0.62 x av daily-high % COHb [9.5] 

life expectancy (yr) = 5.40 - 0.78 x av daily-high % COHb [9.6] 

To perform extrapolations using this model, it would be necessary to estimate the size of the sensitive population (incidence rate x population) 

and the average daily-high COHb levels for this group. Some simple relationship between this and some type of CO average probably exists, but 

data were not available to determine it. 

Motor vehicle accidents present a slightly different problem. The 

model used in Chapter VIII has a COHb threshold below which no effects 

occur. For the model, the relevant parameter is average COHb in motor 

vehicles, given that the COHb is above threshold. 

To obtain probabilistic distributions for the nation, equations such 

as the above can be obtained for each setting of the uncertain parameters 

in the models. 

Estimates of Health Risks for Entire Nation 

Unfortunately, not enough data were available to perform the program 

described in the previous section. In particular, a reliable relationship between average COHb and CO levels could not be determined. In addition, it would be a major task to reconcile CO monitor averages 

143 
9.9 

Fetal Deaths per 

1000 Pregancies 9.8 

•P4^ 

9.7 

— 

• 

1 

1 

• 

1 

• 

1 

• 

1 

• 

• 

1 

1 

• • 

^^^^^ • • 

• • 

1 

• 

• 

• 

0.4 0.5 0.6 0.7 0.8 

Average % COHb 

FIGURE 9.3 FETAL DEATHS PER THOUSAND PREGNANCIES AS A FUNCTION 

OF AVERAGE COHb 
Hospitalizations 

per Year 

4>-

Ul 

Average Daily-High % COHb 

FIGURE 9.4 NUMBER OF HOSPITALIZATIONS PER YEAR OF A PATIENT SUFFERING 

CHRONIC OBSTRUCTIVE PULMONARY DISEASE AS A FUNCTION OF THE 

AVERAGE HIGHEST DAILY VALUE OF COHb EXPERIENCED 
5.5 

5.0 _ 

Life Expectancy 

(years) 

•P4.5 ~ 

4.0 

— 

-

1 

• 

1 

• 

• 

• • 

1 

• • 

• 

• 

I 

1 

• • • 

• 

* 

• • 

- • • 

• 

• 

1 

1 

* • 

• 

• 

• 

1 

1 

• • 

1 

-

— 

0.4 0.5 0.6 0.7 0.8 0.9 1.0 

Average Daily-High % COHb 

FIGURE 9.5 LIFE EXPECTANCY OF A PATIENT SUFFERING SEVERE CHRONIC OBSTRUCTIVE 

PULMONARY DISEASES AS A FUNCTION OF THE AVERAGE HIGHEST DAILY 

VALUE OF COHb EXPERIENCED 
P.45 

published'for large cities with the particular monitors used to obtain 

estimates of the rollback for these cities. Finally, given the simplicity and preliminary nature of the health risk models used, it was felt 

that such an ambitious program might lend a specious appearance of validity to the results. 

A simpler means of aggregating the data to a national scale was 

therefore used. After examination of estimated concentrations for various large cities., at the 1 and 5 expected exceedance levels (1 hour and 8 

hour averaging), the adult urban population of the United States was 

judgmentally divided into groups that lived in four types of cities. 

These city types correspond to the four cities analyzed in this report. 

Three possible distributions for the population among these representative city types is defined in Table 9.1. Distribution I roughly corresponds to a classification by CO concentrations at the 1 expected exceedance level (1 hour averaging) experienced by cities of over 1 million 

inhabitants; distribution II was obtained by using the 1 expected exceedance level (8 hour averaging) concentrations; distribution III was obtained by considering these and several other considerations. 

Table 9.1 

POSSIBLE DISTRIBUTIONS OF URBAN POPULATION 

BY REPRESENTATIVE CITY TYPES 

Distribution Chicago, IL Los Angeles, CA Philadelphia, PA St. Louis, MO 

I 

II 

III 

10% 

17% 

38% 

36% 

34% 

9% 

7% 

40% 

19% 

47% 

9% 

34% 

It should be noted that in this type of aggregation, all information 

on the present as-is situation In each city is lost. The national health 

risk estimates will be given in the following form: 

o Assume that all cities exactly comply with the strictest standard 

(9 ppm, 8 hr, 1 exex). For some cities this will be an improvement, for others a deterioration. 

o Estimate the health risk that will be incurred by going to some 

other standard, under the assumption that all cities will exactly 

comply with the new standard. 

This is clearly an oversimplification of the standard setting problem, as 

has been discussed previously. 

For the remainder of this study, it is assumed that standards apply 

only to cities of over 200,000 population; smaller cities either do not 

147 
P.46 

have a problem (and hence the standards will not apply) or are small 

enough in population that they would not substantially affect the national estimates. The national population in urbanized areas of over 

200,000 population was 115 million in 1980. 

Probabilistic Phase 

To account for uncertainty in the categorization of the population 

by city type, each of the three population assignment distributions in 

Table 9.1 were assumed to be equally likely; thus, each distribution has 

1 chance in 3 of occurring. The distributions on health risks developed 

in previous chapters were converted into a per person basis for each city 

and then multiplied by the national population estimated to be in a city 

of that type. The composite distributions for health effects are presented in Figures 9.6-11. 

The expected value of the health risks incurred by changing from 

the strictest proposed standard are presented in Table 9.2. To put these 

figures in perspective for the urbanized areas considered, the models use 

(or predict) estimates of about 330,000 deaths per year from ASHD, 1 billion angina attacks per year, 100,000 attacks of acute respiratory failure per year, 17,000 fetal deaths per year, and 25,000 traffic deaths per 

year. Life expectancy for COPD patients is roughly 5 years; for a sensitive group of roughly 50,000, there are about 80 million days of life expectancy for this group. 

Finally, the percent change in each health effect experienced in 

going from compliance with the strictest standard to compliance with a 

new standard is presented in Table 9.3. 

Summary 

The main problem with estimating health effects for the entire nation is one of efficient use of resources and data. It is impractical to 

perform a complete, detailed analysis for each city in the nation. The 

problem, therefore, is to find a way to generalize the estimates obtained 

from detailed analyses and, using available data, to use these generalizations to estimate the health effects for other cities. Two possible 

programs were outlined in this chapter, but data was available to implement only one of these programs. 

148 
P.47 

1.0 

Cumulative 

Probability 

Cumulative 

Probability 

.5 

1 exceedance 

per 

320 330 340 

320 

25 ppm (1 hr) 

35 ppm (1 hr) 

350 360 

25 ppm (1 hr) 

330 340 350 

Deaths per year (thousands) 

360 

FIGURE 9.6 DISTRIBUTIONS FOR NATIONAL UEBAN DEATHS 

DUE TO ASHD UNDER VARIOUS PROPOSED STANDARDS 

149 
P.48 

1.0 

Cumulative .5 

Probability 

1 exceedance 

per year 

17,200 

l.Or5 exceedances 

per year 

17,400 

Cumulative 

Probability 

.5 

9 ppm (8 hr) 

12 ppm (8 hr) 

17,200 17,400 

Deaths per year 

17,600 

hr) 

17,600 

FIGURE 9.7 DISTRIBUTIONS FOR NATIONAL URBAN FETAL 

DEATHS UNDER VARIOUS PROPOSED STANDARDS 

150 
P.49 

1.0 i— 

Probability 

0.5 

1 Exceedance 

per year 

12 ppm 

25 ppm (1 

Probability 

-2 -1 

Days per year (millions) 

FIGURE 9.8 DISTRIBUTIONS FOR TOTAL DAYS OF LIFE LOST FOR 

NATIONAL URBAN COPD SENSITIVE PATIENTS WHEN THE 

STANDARD IS CHANGED FROM THE STRICTEST STANDARD 

(9 ppm, 8 hr,l EXCEEDANCE) 

151 
P.50 

1.0 i— 

Probability 

0.5 -9 ppm 

24,995 

1.0 

Probability 

0.5 -

24,995 

.2 ppm (8 hr) 

1 Exceedance 

per year 

25,005 25,015 

-25 ppm (1 hr) 

9 ppm (8 hr) 

2 ppm (8 hr) 5 Exceedances 

per year 

J 

25,005 

Deaths per year 

25,015 

FIGURE 9.9 DISTRIBUTIONS FOR NATIONAL URBAN DEATHS 

IN CAR ACCIDENTS UNDER VARIOUS STANDARDS 

152 
Cumulative 

Probability 

Oi 

CO 

200 600 1,000 1,400 

Angina attacks per year (millions) 

1,800 2,200 

FIGURE 9.10 DISTRIBUTION ON ANGINA ATTACKS FOR NATIONAL URBAN ASHD PATIENTS. 

ALL STANDARDS HAVE DISTRIBUTIONS THAT ARE NOT DISTINGUISHABLE 

ON THIS SCALE 

Tl 

CJl 
Cumulative 

Probability 

Cn 

5,000 10,000 15,000 

Hospitalizations per Year 

20,000 

FIGURE 9.11 DISTRIBUTIONS FOR HOSPITALIZATIONS OF NATIONAL URBAN COPD PATIENTS 

FOR THE STRICTEST AND MOST LENIENT PROPOSED STANDARDS. ALL OTHER 

DISTRIBUTIONS LIE BETWEEN THE TWO 
P.53 

Table 9.2 

NATIONAL EXTRAPOLATION OF PREDICTED CHANGES 

FROM STRICTEST STANDARD 

Millions Attacks 

35 

25 

25 

12 

12 

9 

Standard 

ppm 

ppm 

ppm 

ppm 

ppm 

ppm 

1 hr 

1 hr 

1 hr 

8 hr 

8 hr 

8 hr 

1 

1 

5 

1 

5 

5 

exex 

exex 

exex 

exex 

exex 

exex 

Deaths 

from 

ASHD 

15,230 

8,350 

14,230 

3,710 

7,335 

3,230 

Millions 

Attacks 

of Angina 

42.49 

23.40 

39.61 

10.49 

20.54 

9.07 

Days 

Life 

Expectancy 

for COPD 

-1.04 

-0.49 

-0.94 

-0.18 

-0.43 

-0.17 

of 

Acute 

Respiratory 

Failure 

8,854 

3,901 

7,800 

1,418 

3,430 

1,320 

Fetal 

Deaths 

(>20 

weeks) 

130 

71 

122 

32 

63 

28 

Traffic 

Deaths 

2 

0 

2 

0 

0 

0 

Table 9.3 

NATIONAL EXTRAPOLATION OF PREDICTED PERCENT CHANGES 

FROM STRICTEST STANDARD 

Standard 

35 ppm 1 hr 1 exex 

25 ppm 1 hr 1 exex 

25 ppm 1 hr 5 exex 

12 ppm 8 hr 1 exex 

12 ppm 8 hr 5 exex 

9 ppm 8 hr 5 exex 

Deaths 

from 

ASHD 

4.6 

2.5 

4.3 

1.1 

2.2 

1.0 

Angina 

Attacks 

4.1 

2.2 

3.8 

1.0 

2.0 

0.9 

COPD 

Life 

Expectancy 

-5.7 

-2.7 

-5.2 

-1.0 

-2.4 

-0.9 

Acute 

Respiratory 

Failure 

7.1 

3.1 

6.2 

1.1 

2.7 

1.1 

Fetal 

Deaths 

(>20 

weeks) 

0.7 

0.4 

0.7 

0.2 

0.4 

0.2 

Traffic 

Deaths 

0 

0 

0 

0 

0 

0 

155 
P.54 

REFERENCES FOR CHAPTER IX 

1. T. McCurdy, personal communication. 

157 
P.55 

X SIGNIFICANCE WEIGHTING 

Decision analysis suggests that the output from the decision model 

be stated in a form that permits an immediate ranking of the decision 

alternatives. This is especially important when the decision is to be 

based on more than one outcome attribute. 

The evaluation of alternative standards for carbon monoxide presents 

an example of multiattribute outcomes. Some health effects are more 

easily reduced through controls than are others. Thus, the relative contribution to the public health problem of each effect will differ from 

standard to standard. To provide insight and a clear statement of the 

benefits of each level of control, these variations can be compared by a 

significance weighting model which states each health effect in a common 

unit. 

Choice of Units 

A common approach is to translate each outcome into a monetary 

value so that the total monetary value over all of the outcomes can be 

directly compared to the costs of each alternative. There are numerous 

reasons why this approach is inappropriate for the sepcific needs of EPA 

in setting National Ambient Air Quality Standards (NAAQS). 

The first objection is that a monetary value of health effects is 

almost impossible to determine at the societal level. Although numerous 

methods exist for choosing such values, none is acceptable to all members 

of society. The use of such values may obscure the information contained 

in the analysis by introducing the elements of a very difficult and 

emotion-laden question. 

A second argument against the use of monetary values is that it has 

no use in the context of NAAQS. The EPA cannot consider the costs of 

control in its choice of regulatory level. Rather, the health risks 

themselves must be the sole basis for the decision. An outcome variable 

that retains a clear health risk interpretation therefore is preferable. 

In the light of these points, one would prefer a unit that has no 

explicit economic value attached to it and which has a direct health-risk 

connotation. In this report, "equivalent days of restricted activity," 

or EDRAs, are used. This metric allows one to obtain a sense of the severity of an effect without any statement of its absolute societal value. 

Its close relationship to a measurable quantity helps limit the controversy over the number of units that should be assigned to each health effect. Furthermore, it retains some relationship to the actual risk that 

the public faces. 

159 
P.56 

Assignment of EDRAs 

Like any other form of significance weighting, the assignment of 

EDRAs to each health effect does involve value judgments. However, physical fact permits reasonable ranges to be proposed. For instance, a 

health effect that results in a week of hospitalization with no irreversible effects would be represented by seven EDRAs. However, a hospitalization for one week with some irreversible damage remaining afterwards 

would probably be given a higher valuation. If the irreversible damage 

implies a decrease in life expectancy, then the magnitude of this decrease can be used to determine the additional EDRAs. If the damage does 

not affect life expectancy, but impairs the quality of life for the patient, then the frequency or quality of this impairment and the remaining 

length of life can be incorporated into the choice of weighting. 

A final comment relates to the interpretation of the EDRA assignments. The absolute level of EDRAs is not as important as the relative 

weights of the health effects in this total. The usefulness of a significance weighting scheme is that it allows one to determine which health 

effect poses the most severe problem to the public. It does so by combining the two independent factors of incidence and severity. Until this 

is done, coherent summary statements of the problem are extremely difficult to obtain, especially when there are numerous effects to consider. 

Possible EDRA assignments for the effects associated with CO are 

provided in Table 10.1. The third column indicates the range over which 

sensitivity analysis is performed. The values have been drawn from estimates of expected days of restricted activity (ischemia, hospitalization) 

or expected days of life lost (death), modified by an estimate of the 

relative weight society might apply to each effect. The ranges have been 

made broad enough to encompass many points of view. 

Table 10.1 

POSSIBLE EDRA ASSIGNMENTS 

Range for 

Effect EDRAs EDRAs 

Death from myocardial infarction or fibrillation 7.5 years 3-10 years 

Painful episode of myocardial ischemia 0.1 day 0.05-1 day 

Hospitalization for acute respiratory failure 14 days 3-21 days 

One day decreased life expectancy for COPD 

patient 1 day 1-3 days 

Death of fetus (> 20 weeks gestation) 7.5 years 1-75 years 

Death because of automobile accident 30 years 5-50 years 

160 
P.57 

Results 

These weights have been applied to the health effects that have been 

estimated at the national level in Chapter IX. Table 10.2 summarizes the 

predicted changes in EDRAs that would be implied by a decision to reduce 

the standard closest to the current one in force -to each of the remaining, less stringent, standards considered." 

Table 10.2 

CHANGES IN EDRA PER YEAR AT THE NATIONAL LEVEL 

Standard 

35 ppm 1 hr 1 exex 

25 ppm 1 hr 1 exex 

25 ppm 1 hr 5 exex 

12 ppm 8 hr 1 exex 

12 ppm 8 hr 5 exex 

9 ppm 8 hr 5 exex 

Change From 

Strictest Standard 

EDRA/Yr 

(millions) 

47.5 

22.9 

44.3 

11.5 

22.8 

10.0 . 

Approximate 

Percent 

of 

Strictest 

Standard 

3.0 

1.6 

2.8 

0.7 

1.4 

0.6 

Strictest standard estimated to have approximately 1600 x 10 EDRAs associated with the six 

health effects included here. CO is responsible 

for only a small fraction of these EDRAs. 

As noted above, one should not place emphasis on the absolute levels 

indicated in Table 10.2. A more important feature is the relative differences among the standards. It should also be emphasized that the 

changes in EDRAs are for a hypothetical situation. Very few cities included in the sample comply with the strictest standard. The figures 

here only represent the change in EDRAs that would occur if all of the 

cities started in compliance with the strictest standard, and then CO 

levels were to rise so that the cities were exactly in compliance with 

the less stringent standard indicated. 

Figure 10.1 indicates the contribution of each of the six health effects to the total changes in EDRAs for 35 ppm (1 hr, 1 exex) as compared 

161 
ASHD deaths 

Anginal pain 

Change in life 

expectancy for 

COPD 

Fetal deaths 

H 

• GCOPD hospitaliza- • 

tion 

CTV 

to Traffic 

Accidents 

15 30 

EDRAs Per Year (millions) 

45 

FIGURE 10.1 COMPARISONS OF RANGES OF EDRAS ATTRIBUTABLE TO EACH HEALTH EFFECT. 

VALUES ARE CALCULATED AS CHANGES FROM STRICTEST STANDARD TO LEAST 

STRINGENT STANDARD AT NATIONAL LEVEL. CIRCLES REPRESENT THE MOST 

LIKELY VALUES. DOTTED LINE INDICATES POSSIBLE MAGNITUDE ON NONFATAL 

FETAL EFFECTS 
P.59 

to 9 ppm (1 hr, 1 exex). The large circles represent the most likely 

values for EDRAs, as presented in Table 10.1. The ranges indicate how 

the contribution for each health effect will shift as the relative valuations are adjusted across the ranges in Table 10.1. This sensitivity 

analysis is similar for each standard. 

The dotted line on the fetal effects in Figure 10.1 indicates a nominal estimate of how important fetal effects of all forms might be. To 

calculate the change in EDRAs associated with nonfatal effects, the assumption was made that a change in fetal deaths due to changes in CO exposures would also result in a proportional change in nonfatal effects. 

The nominal EDRAs assigned were: 65 years for severe mental retardation 

(IQ <30), cerebral palsy, and severe physical damage at birth; 40 years 

for moderate mental retardation; 20 years for brain seizure disorders; 15 

years for mild mental retardation (IQ <70), speech and learning disorders; and 5 years for all other congenital anomalies. The proportion of 

births affected by each of these is provided in Table 6.5. Ranges of uncertainty were not placed on this estimate because it is very approximate 

to begin with. 

The important feature to note is that manifestations of ASHD dominate in importance regardless of relative valuation. Changes in life expectancy for COPD patients are of secondary relative importance throughout the entire range of possible valuations. Fetal effects are small 

when only spontaneous abortions are considered. However, if nonfatal 

effects were included, this health effect could rise to significant levels. COPD morbidity and traffic accidents are not relatively important 

for any likely valuation. Table 10.3 indicates the percent contributions 

of each health effect to the total change in EDRAs for each standard. 

One can see that there is very little change from standard to standard. 

The most likely values for each effect were used to generate these percentages. 

Table 10.3 

PERCENT CONTRIBUTION OF EACH HEALTH EFFECT TO TOTAL, 

BY STANDARD 

Standard 

35 ppm 1 hr 1 exex 

25 ppm 1 hr 1 exex 

25 ppm 1 hr 5 exex 

12 ppm 8 hr 1 exex 

12 ppm 8 hr 5 exex 

9 ppm 8 hr 5 exex 

COPD 

Life 

ASHD Angina Fetal Expec- COPD Traffic 

Deaths Attacks Death tancy Morbidity Accidents 

87.8 

88.1 

88.9 

88.3 

88.1 

88.3 

8.9 

9.0 

8.9 

9.1 

9.0 

9.1 

0.7 

0.7 

0.8 

0.8 

0.8 

0.8 

2.2 

1.9 

2.1 

1.6 

1.9 

1.7 

0.3 

0.2 

0.2 

0.2 

0.2 

0.2 

0 . 1 

0 

0.1 

0 

0 

0 

163 
P.60 

The data given so far have been based on the expected values of 

EfiRAs for each health effect and standard. Of equal importance is the 

uncertainty on.these values. Figure 10.2 presents probability distributions on the changes in EDRAs from the strictest proposed standard. As 

one can see, the variance increases as the standard becomes less stringent. 

Conclusion 

The relative valuation of different health effects is, in SRI's 

view, a controversial but integral element of any risk assessment, because it can help determine the level of effort applied to each part of 

the assessment. The possible values presented here are not to be taken 

as normative. However, sensitivity analysis on a broad range for each 

valuation indicates that the ranking of relative importance is invariant 

Arteriosclerotic heart disease patients clearly represent the greatest 

component of the public health risks of CO, and analysis of this health 

effect should be done with great care. 

164 
1.0 i— 

Probability 

cr. 

Cn 

0.5 

100 125 

Change from Strictest Standard 

(10 EDRA per year) 

FIGURE 10.2 PROBABILITY THAT THE CHANGE IN EDRAS FROM MOST STRINGENT STANDARD IS 

LESS THAN THE VALUE INDICATED FOR THE SIX LESS STRINGENT STANDARDS, 

ON A NATIONAL BASIS 

KEY: (standard in ppm; averaging time in hours; expected exceedances per year) 

TJ 

cn 
P.62 

XI UNCERTAINTIES WITH HIGH VALUES OF RESOLUTION 

One of the advantages of the approach used in this report is that it 

can identify those areas in which resolution of uncertainty would be most 

useful for the decision-making process. 

As has been emphasized, the primary goal of the next cycle of analysis should be the elaboration of the health risk models. Given that the 

models retain the same type of uncertain input that they presently have, 

however, some estimate can be obtained of the usefulness of resolving uncertainty on these parameters. This can serve as an indication of fields 

of fruitful and useful research. 

For each of the variables assumed to be uncertain, a sensitivity 

analysis has been performed. In this analysis, a distribution is generated under the assumption that each variable in turn takes its highest 

and lowest value (all other variables retain the assigned uncertainty). 

The results of this analysis indicate that resolution of uncertainty 

on the parameters in the arteriosclerotic heart disease model is most important. Knowing the true value of any one of the three parameters in 

Figure 5.5 could change the expected health risk in going from a standard 

similar to the current one to a new one by as much as 25%. Resolution of 

the uncertainty on how the population is assigned to representative city 

types could change the expected health risk because of a change of standard by as much as 15%. Resolution of uncertainty in the variables in 

Figures 6.3, 7.5, and 8.11 would result in less than 5% changes. 

This is illustrated in Figure 11.1, where the health risk in going 

from the strictest standard (9 ppm, 8 hr, 1 exex) to the most lenient (35 

ppm, 1 hr, 1 exex) is presented for three cases. It is assumed that the 

rate of change of the double product at onset of angina as a function of 

COHb can be determined exactly; the three curves in the figure represent 

the case for each value this slope might take. Almost identical curves 

would be obtained if uncertainty were resolved on either of the remaining 

uncertain variables in Figure 5.5. 

The utility of this concept is apparent from the following purely 

hypothetical example. Assume that relaxation of the strictest standard 

is permissible if this relaxation has a 90% probability of increasing the 

health risk by less than 50 million EDRAs. Reference to Figure 10.2 

shows that two standards (25 ppm, 8 hr, 5 exex and 35 ppm, 1 hr, 1 exex) 

are inadmissable. Figure 11.1, however, shows that if the actual slope 

of the Aronow data is around -500, the lenient 35 ppm, 1 hr, 1 exex standard is indeed admissable; the probabilities assumed in Figure 5.5 indicate that there Is 1 chance in 4 that this could happen. 

167 
Cumulative 

Probability 

00 

25 50 75 

EDRA per year (millions) 

100 

FIGURE 11.1 DISTRIBUTIONS FOR THE NATIONAL HEALTH IMPACT PER YEAR UNDER A CHANGE 

FROM THE STRICTEST TO THE MOST LENIENT PROPOSED STANDARD. THE CURVES 

CORRESPOND TO SPECIFIC VALUES FOR THE SLOPE OF MOS AS A FUNCTION OF 

COHb: A,-500; B.-770; C.-1000 
P.64 

Thus, in this hypothetical example, the decision maker knows that a 

program that could completely resolve this uncertainty has a 25% chance 

of making this more lenient standard admissable. This procedure can be 

repeated for other uncertain variables, and priorities can be set on research programs, which can substantially reduce uncertainty. 

169 
P.65 

APPENDIX A 

In this appendix, the data for health risks are presented for three 

cities: Chicago, IL; Philadelphia, PA; St. Louis, MO. Corresponding 

results for Los Angeles, CA, are presented in the body of the report. 

171 
P.66 

Table A.l 

COMPARISON OF INCIDENCE OF ANGINAL PAIN 

(Chicago) 

Standard 

35 ppm 1 hr 1 exex 

25 ppm 1 hr 1 exex 

25 ppm 1 hr 5 exex 

12 ppm 8 hr 1 exex 

12 ppm 8 hr 5 exex 

9 ppm 8 hr 1 exex 

9 ppm 8 hr 5 exex 

* 

Change Over As-ls 

Million Percent 

Attacks of As-ls 

.88 

0 

.47 

-.55 

-.17 

-1.03 

-.73 

2.8 

0 

1.5 

-1.8 

-0.5 

-3.3 

-2.4 

Change Over 

Strictest Standard 

Million Percent 

Attacks of Standard 

1.91 

1.04 

1.49 

.48 

.86 

— 

.29 

6.4 

3.4 

5.0 

1.6 

2.7 

— 

1.0 

As-is predicted incidence = 31 million/year. 

Strictest standard predicted incidence = 20 million/year. 

172 
P.67 

Table A.2 

COMPARISON OF ANNUAL INCIDENCE OF ANGINAL PAIN 

(Philadelphia) 

Standard 

35 ppm 1 hr 1 exex 

25 ppm 1 hr 1 exex 

25 ppm 1 hr 5 exex 

12 ppm 8 hr 1 exex 

12 ppm 8 hr 5 exex 

9 ppm 8 hr 1 exex 

9 ppm 8 hr 5 exex 

* 

Change Over As-ls 

Million Percent 

Attacks of As-ls 

.79 

.31 

.80 

-.17 

.22 

-.46 

-.10 

2.8 

1.1 

2.8 

-0.6 

0.8 

-1.6 

-0.4 

Change 

Strictest 

Million 

Attacks o 

1.25 

.76 

1.25 

.29 

.68 

— 

.36 

Over 

Standard 

Percent 

f Standard 

4.4 

2.7 

4.5 

1.0 

2.4 

— 

1.3 

As-is predicted incidence = 28.6 million/year. 

Strictest standard predicted incidence = 28.2 million/year. 

173 
P.68 

Table A.3 

COMPARISON OF ANNUAL INCIDENCE OF ANGINAL PAIN 

(St. Louis) 

Standard 

35 ppm 1 hr 1 exex 

25 ppm 1 hr 1 exex 

25 ppm 1 hr 5 exex 

12 ppm 8 hr 1 exex 

12 ppm 8 hr 5 exex 

9 ppm 8 hr 1 exex 

9 ppm 8 hr 5 exex 

* 

Change Over As-ls 

Million Percent 

Attacks of As-ls 

.15 

.02 

.20 

-.05 

.03 

-.08 

-.05 

1.3 

0.2 

1.6 

-0.4 

0.2 

-0.7 

-0.4 

Change Over 

Strictest Standard 

Million Percent 

Attacks of Standard 

.24 

.11 

.28 

.04 

.11 

— 

.37 

1.9 

0.9 

2.3 

0.3 

0.9 

— 

0.3 

As-is predicted incidence = 12.3 million/year. 

Strictest standard predicted incidence = 12.2 million/year, 

174 
P.69 

1.0 r 

Cumulative 

Probability 

25 ppm (1 hr) 

35 ppm (1 hr) 

.5 -

0 

1.0 

Cumulative 

Probability 

.5 

5 Exceedances 

per year 

9 ppm (8 hr) 

1 

12 ppm (8 hr) 

•25 ppm (1 hr) 

1 

.85 .90 .95 1.0 1.05 1.10 1.15 change 

5,950 6,300 6,650 7,000 7,350 7,700 8,050 deaths 

FIGURE A.l DISTRIBUTIONS ON CHANGES IN ISCHEMIC EPISODES FROM 

AS-IS IN CHICAGO. ESTIMATES OF APPROXIMATE.DEATHS 

ARE ALSO PROVIDED. CURRENT ASHD DEATHS IN CHICAGO 

ARE ASSUMED TO BE 7000/YEAR 

175 
P.70 

Table A.4 

COMPARISON OF PREDICTED CARDIOVASCULAR MORTALITY 

(Chicago) 

Standard 

35 ppm 1 hr 1 exex 

25 ppm 1 hr 1 exex 

25 ppm 1 hr 5 exex 

12 ppm 8 hr 1 exex 

12 ppm 8 hr 5 exex 

9 ppm 8 hr 1 exex 

9 ppm 8 hr 5 exex 

Change Over 

Number 

of Deaths 

224 

2 

118 

-138 

-43 

-258 

-185 

* 

As-ls 

Percent 

of As-ls 

3.2 

0 

1.7 

-2.0 

-0.6 

-3.7 

-2.6 

Change 

Strictest 

Number 

of Deaths 

482 

260 

377 

120 

216 

— 

73 

Over 

Standard 

Percent 

of Standard 

7.2 

3.9 

5.6 

1.8 

3.2 

— 

1.1 

As-is ASHD deaths assumed to be 7000. 

ASHD deaths predicted for strictest standard = 6742. 

176 
P.71 

1.0 tCumulative 

Probability 

0.5 

Cumulative 

Probability 

0 

1.0 

9 ppm (8 hr) 

0.5 

9 ppm (8 hr) 

1 Exceedance 

per year 

5 Exceedances 

per year 

12 ppm (8 hr) 

•25 ppm (1 hr) 

J 

.94 .97 1.0 1.03 1.06 1.09 1.12 change 

7,990 8,245 8,500 8,755 9,010 9,265 9,520 deaths 

FIGURE A.2 DISTRIBUTIONS ON CHANGES IN ISCHEMIC EPISODES 

FROM AS-IS IN PHILADELPHIA. ESTIMATES OF 

APPROXIMATE DEATHS ARE ALSO PROVIDED. CURRENT 

ASHD DEATHS IN PHILADELPHIA ARE ASSUMED TO BE 

8,500/YEAR 

177 
P.72 

Table A.5 

COMPARISON OF PREDICTED CARDIOVASCULAR MORTALITY 

(Philadelphia) 

Standard 

35 ppm 

25 ppm 

25 ppm 

12 ppm 

12 ppm 

9 ppm 

9 ppm 

1 

1 

1 

8 

8 

8 

8 

hr 

hr 

hr 

hr 

hr 

hr 

hr 

1 

1 

5 

1 

5 

1 

5 

exex 

exex 

exex 

exex 

exex 

exex 

exex 

Change Over 

Number 

of Deaths 

269 

103 

271 

-57 

75 

-152 

-34 

As-ls 

Percent 

of As-ls 

3.2 

1.2 

3.2 

-0.7 

0.9 

-1.8 

-0.4 

Change 

Strictest 

Number 

of Deaths 

421 

256 

423 

96 

228 

— 

119 

. Over 

Standard 

Percent 

of Standard 

5.0 

3.1 

5.1 

1.1 

2.7 

— 

1.4 

As-is ASHD deaths assumed to be 8500. 

ASHD deaths predicted for strictest standard = 8348. 

178 
P.73 

1.0 rCumulative 

Probability 

Cumulative 

Probability 

1 Exceedance 

per year 

0.975 

3,412 

1.00 

3,500 

1.025 

3,587 

1.05 

3,675 

1.075 

3,673 

change 

deaths 

FIGURE A.3 DISTRIBUTIONS ON CHANGES IN ISCHEMIC EPISODES FROM 

AS-IS IN ST. LOUIS. THE APPROXIMATE DEATHS ARE 

ALSO PROVIDED. CURRENT ASHD DEATHS IN ST. LOUIS 

ARE ASSUMED TO BE 3500/YEAR 

179 
P.74 

Table A.6 

COMPARISON OF PREDICTED CARDIOVASCULAR MORTALITY 

(St. Louis) 

Standard 

35 ppm 

25 ppm 

25 ppm 

12 ppm 

12 ppm 

9 ppm 

9 ppm 

1 

1 

1 

8 

8 

8 

8 

hr 

hr 

hr 

hr 

hr 

hr 

hr 

1 

1 

5 

1 

5 

1 

5 

exex 

exex 

exex 

exex 

exex 

exex 

exex 

Change Over 

Number 

of Deaths 

50 

7 

63 

-15 

9 

-26 

-15 

* 

As-ls 

Percent 

of As-ls 

1.4 

0.2 

1.8 

-0.4 

0.3 

-0.8 

-0.4 

Change 

Strictest 

Number 

of Deaths 

76 

34 

90 

12 

36 

— 

12 

. Over 

Standard 

Percent 

of Standard 

2.2 

1.0 

2.3 

0.3 

1.0 

— 

0.3 

As-is ASHD deaths assumed to be 3500. 

ASHD deaths predicted for strictest standard = 3474, 

180 
P.75 

Cumulative 

Probability 25 ppm (1 hr) 

Cumulative 

Probability 

368 

356 360 

Fetal Deaths per Year 

364 368 

FIGURE A.4 PROBABILITY THAT FETAL DEATHS (> 20 WEEKS 

GESTATION) PER YEAR IN CHICAGO, IL, WILL EXCEED 

THE GIVEN AMOUNT. THERE ARE 361 FETAL DEATHS 

PER YEAR FROM ALL CAUSES IN CHICAGO UNDER 

PRESENT CONDITIONS 

181 
P.76 

Table A.7 

EXPECTED CHANGES IN FETAL DEATHS (>20 WEEKS GESTATION) 

PER YEAR 

(Chicago) 

35 

25 

25 

12 

12 

9 

9 

Standard 

ppm 

ppm 

ppm 

ppm 

ppm 

ppm 

ppm 

1 

1 

1 

8 

8 

8 

8 

hr 

hr 

hr 

hr 

hr 

hr 

hr 

1 

1 

5 

1 

5 

1 

5 

exex 

exex 

exex 

exex 

exex 

exex 

exex 

Change Over 

Number 

of Deaths 

1.80 

0.02 

0.94 

-1.11 

-0.34 

-2.05 

-1.47 

* 

As-ls 

Percent 

of As-ls 

0.50 

0.00 

0.26 

-0.31 

-0.09 

-0.57 

-0.41 

Change 

Strictest 

Number 

of Deaths 

3.86 

2.07 

2.99 

0.95 

1.71 

— 

0.58 

i Over 

Standard 

Percent 

of Standard 

1.08 

0.58 

0.84 

0.26 

0.48 

— 

0.16 

Total fetal deaths in as-is case = 361. 

Expected total fetal deaths for strictest standard = 359. 

182 
P.77 

Cumulative 

Probability 

Cumulative 

Probability 

364 366 368 370 

Fetal Deaths per Year 

372 374 

FIGURE A.5 PROBABILITY THAT FETAL DEATHS (> 20 WEEKS 

GESTATION) PER YEAR IN PHILADELPHIA, PA, WILL 

EXCEED GIVEN AMOUNT. THERE ARE 368 FETAL DEATHS 

PER YEAR FROM ALL CAUSES IN PHILADELPHIA UNDER 

PRESENT CONDITIONS 

183 
P.78 

Table A.8 

EXPECTED CHANGES IN FETAL DEATHS (>20 WEEKS GESTATION) 

PER YEAR 

(Philadelphia) 

Standard 

35 ppm 1 hr 1 exex 

25 ppm 1 hr 1 exex 

25 ppm 1 hr 5 exex 

12 ppm 8 hr 1 exex 

12 ppm 8 hr 5 exex 

9 ppm 8 hr 1 exex 

9 ppm 8 hr 5 exex 

Change Over 

Number 

of Deaths 

2.00 

0.73 

2.01 

-0.39 

0.53 

-1.04 

-0.22 

* 

As-ls 

Percent 

of As-ls 

0.54 

0.20 

0.55 

-0.11 

0.14 

-0.28 

-0.06 

Change Over 

Strictest Standard 

Number Percent 

of Deaths of Standard 

3.03 

1.76 

3.05 

0.64 

1.56 

— 

0.81 

0.83 

0.48 

0.83 

0.18 

0.43 

— 

0.22 

Total fetal deaths in as-is case = 368. 

Expected total fetal deaths for strictest standard = 367 

184 
P.79 

1.0 |— 

Cumulative 

Probability 

.5 _ 

1 Exceedance 

per year 

9 ppm (8 hr) 

12 pm (8 

25 ppm (1 hr) 

•35 ppm (1 hr) 

175 

1.0 

Cumulative 

Probability 

176 

5 Exceedances 

per year 

177 178 179 180 

9 ppm (8 hr) 

12 ppm (8 hr) 

175 176 177 178 

Fetal Deaths per Year 

179 180 

FIGURE A.6 PROBABILITY THAT FETAL DEATHS (> 20 WEEKS 

GESTATION) PER YEAR IN ST. LOUIS, MO, WILL 

EXCEED GIVEN AMOUNT. THERE ARE 178 FETAL 

DEATHS PER YEAR FROM ALL CAUSES IN ST. LOUIS 

UNDER PRESENT CONDITIONS 

185 
p^ao 

Table A.9 

EXPECTED CHANGES IN FETAL DEATHS (>20 WEEKS GESTATION) 

PER YEAR 

(St. Louis) 

35 

25 

25 

12 

12 

9 

9 

S tandard 

ppm 

ppm 

ppm 

ppm 

ppm 

ppm 

ppm 

1 

1 

1 

8 

8 

8 

8 

hr 

hr 

hr 

hr 

hr 

hr 

hr 

1 

1 

5 

1 

5 

1 

5 

exex 

exex 

exex 

exex 

exex 

exex 

exex 

Change Over 

Number 

of Deaths 

0.47 

0.07 

0.61 

-0.14 

0.09 

-0.24 

-0.14 

* 

As-ls 

Percent 

of As-ls 

0.26 

0.04 

0.34 

-0.08 

0.05 

-0.14 

-0.08 

Change 

Strictest 

Number 

of Deaths 

0.71 

0.32 

0.85 

0.10 

0.33 

— 

0.10 

i Over 

Standard 

Percent 

of Standard 

0.40 

0.18 

0.48 

0.06 

0.19 

— 

0.06 

Total fetal deaths in as-is case = 178. 

Expected total fetal deaths for strictest standard = 177. 

186 
P.81 

Table A.10 

COMPARISON OF CHANGES IN ACUTE RESPIRATORY FAILURE 

FOR COPD SENSITIVES 

(Chicago) 

Standard 

35 ppm 1 hr 1 exex 

25 ppm 1 hr 1 exex 

25 ppm 1 hr 5 exex 

12 ppm 8 hr 1 exex 

12 ppm 8 hr 5 exex 

9 ppm 8 hr 1 exex 

9 ppm 8 hr 5 exex 

Change Over 

ARF/ 

Patient/Yr 

.05 

.001 

.03 

-.03 

-.01 

-.06 

-.04 

* 

As-ls 

Percent 

of As-ls 

2.0 

0.04 

1.0 

-1.1 

-0.3 

-2.0 

-1.5 

Change Over 

Strictest Standard 

ARF/ Percent 

Patient/Yr of Standard 

' -11. 

.06 

.08 

.03 

.05 

— 

.01 

4.1 

2.1 

3.1 

0.9 

1.7 

— 

0.6 

ARF/yr/patient predicted as-is = 2.73. 

ARF/yr/patient predicted for strictest standard = 2.68. 

187 
P.82 

Table A.11 

COMPARISON OF CHANGES IN ACUTE RESPIRATORY FAILURE 

FOR COPD SENSITIVES 

(Philadelphia) 

Standard 

35 ppm 1 hr 1 exex 

25 ppm 1 hr 1 exex 

25 ppm 1 hr 5 exex 

12 ppm 8 hr 1 exex 

12 ppm 8 hr 5 exex 

9 ppm 8 hr 1 exex 

9 ppm 8 hr 5 exex 

Change Over 

ARF/ 

Patient/Yr 

.24 

.07 

.24 

-.03 

.05 

-.08 

-.02 

* 

As-ls 

Percent 

of As-ls 

7.9 

2.3 

8.1 

-1.1 

1.7 

-2.5 

-.01 

Change Over 

Strictest Standard 

ARF/ Percent 

Patient/Yr of Standard 

.31 

.15 

.32 

.04 

.13 

— 

.06 

11.7 

5.4 

11.8 

1.5 

4.6 

— 

2.1 

ARF/yr/patient predicted as-is = 2.76. 

ARF/yr/patient predicted for strictest standard = 2.69 

188 
P.83 

Table A.12 

COMPARISON OF CHANGES IN ACUTE RESPIRATORY FAILURE 

FOR COPD SENSITIVES 

(St. Louis) 

Standard 

35 ppm 1 hr 1 exex 

25 ppm 1 hr 1 exex 

25 ppm 1 hr 5 exex 

12 ppm 8 hr 1 exex 

12 ppm 8 hr 5 exex 

9 ppm 8 hr 1 exex 

9 ppm 8 hr 5 exex 

Change Over 

ARF/ 

Patient/Yr 

.07 

.01 

.09 

-.02 

.01 

-.04 

-.02 

* 

As-ls 

Percent 

of As-ls 

2.5 

0.4 

3.3 

. -0.8 

0.5 

-1.6 

-0.8 

Change 

Strictest 

ARF/ 

Patient/Yr 

.11 

.06 

.14 

.02 

.06 

— 

.02 

Over 

Standard 

Percent 

of Standard 

4.2 

2.1 

5.0 

0.9 

2.1 

— 

0.8 

ARF/yr/patient for as-is = 2.78. 

ARF/yr/patient for strictest standard = 2.73. 

189 
P.84 

1.0 

Cumulative 

Probability 

0.5 

1 Exceedance/year 

35 ppm (1 hr) 

25 ppm (1 hr) 

Cumulative 

Probability 

•12 ppm (8 hr) 

days 

-80 days 

FIGURE A.7 PROBABILITY THAT CHANGES IN LIFE EXPECTANCY WILL 

BE LESS THAN GIVEN AMOUNT FOR SENSITIVE COPD 

PATIENTS IN CHICAGO. CHANGES ARE CALCULATED 

FROM AS-IS PREDICTION. AS-IS LIFE EXPECTANCY IS 

PREDICTED TO BE .478 YEARS IN CHICAGO 

190 
P.85 

Table A.13 

COMPARISON OF CHANGES IN LIFE EXPECTANCY 

FOR EACH COPD SENSITIVE PATIENT 

(Chicago) 

Standard 

35 ppm 1 hr 1 exex 

25 ppm 1 hr 1 exex 

25 ppm 1 hr 5 exex 

12 ppm 8 hr 1 exex 

12 ppm 8 hr 5 exex 

9 ppm 8 hr 1 exex 

9 ppm 8 hr 5 exex 

Change 

Days/ 

Patient 

-31 

-1 

-16 

18 

6 

33 

24 

* 

Over As-ls 

Percent 

of As-ls 

-1.8 

0 

-0.9 

1.0 

0.3 

1.9 

1.3 

Change Over 

Strictest Standard 

Days/ Percent 

Patient of Standard 

-64 

-34 

-49 

-15 

-28 

— 

-9 

-3.6 

-1.9 

-2.8 

-0.8 

-1.6 

— 

-0.5 

Life expectancy for as-is is predicted to be 4.78 years. 

Life expectancy for strictest standard predicted to be 4.87 years. 

191 
P.86 

1.0 

Cumulative 

Probability 

Cumulative 

Probability 

0.5 

1 Exceedance 

per year 

35 ppm (1 hr) 

25 ppm (1 hr) 

12 ppm (8 hr) 

9 ppm (8 hr) 

days 

9 ppm (8 hr) 

-300 -200 -100 days 

FIGURE A.8 PROBABILITY THAT CHANGES IN LIFE EXPECTANCY WILL 

BE LESS THAN GIVEN AMOUNT FOR SENSITIVE COPD 

PATIENTS IN PHILADELPHIA. CHANGES ARE CALCULATED 

FROM AS-IS PREDICTION. AS-IS LIFE EXPECTANCY IS 

PREDICTED TO BE 4.74 YEARS IN PHILADELPHIA 

192 
P.87 

Table A.14 

COMPARISON OF CHANGES IN LIFE EXPECTANCY 

FOR EACH COPD SENSITIVE PATIENT 

(Philadelphia) 

Standard 

35 ppm 1 hr 1 exex 

25 ppm 1 hr 1 exex 

25 ppm 1 hr 5 exex 

12 ppm 8 hr 1 exex 

12 ppm 8 hr 5 exex 

9 ppm 8 hr 1 exex 

9 ppm 8 hr 5 exex 

Change 

Days/ 

Patient 

-121 

-38 

-123 

20 

-28 

44 

12 

* 

Over As-ls 

Percent 

of As-ls 

-7.0 

-2.2 

-7.1 

1.1 

-1.6 

2.5 

0.7 

Change 

Strictest 

Days/ 

Patient o 

-165 

-82 

-167 

-24 

-72 

— 

-32 

Over 

Standard 

Percent 

f Standard 

-9.3 

-4.6 

-9.4 

-1.4 

-4.1 

— 

-1.8 

* 

Life expectancy for as-is predicted to be 4.74 years'. 

Life expectancy for strictest standard predicted to be 4.86 years 

193 
P.88 

1.0 _ 

Cumulative 

Probability 

0.5 

1 Exce 

per year 

12 ppm (8 hr) 

Cumulative 

Probability 

days 

1.0 

9 ppm (8 hr) 

-150 -100 -50 days 

FIGURE A.9 PROBABILITY THAT CHANGES IN LIFE EXPECTANCY WILL 

BE LESS THAN GIVEN AMOUNT FOR SENSITIVE COPD 

PATIENTS IN ST. LOUIS. CHANGES ARE CALCULATED FROM 

AS-IS LIFE PREDICTION. AS-IS LIFE EXPECTANCY IS 

PREDICTED TO BE 4.70 YEARS IN ST. LOUIS 

194 
P.89 

Table A.15 

COMPARISON OF CHANGES IN LIFE EXPECTANCY 

FOR EACH COPD SENSITIVE PATIENT 

(St. Louis) 

Standard 

35 ppm 1 hr 1 exex 

25 ppm 1 hr 1 exex 

25 ppm 1 hr 5 exex 

12 ppm 8 hr 1 exex 

12 ppm 8 hr 5 exex 

9 ppm 8 hr 1 exex 

9 ppm 8 hr 5 exex 

Change 

Days/ 

Patient 

-34 

-6 

-42 

11 

-7 

24 

11 

* 

Over As-ls 

Percent 

of As-ls 

-1.2 

-0.3 

-2.4 

0.6 

-0.4 

1.4 

0.6 

Change Over 

Strictest Standard 

Days/ Percent 

Patient of Standard 

-57 

-30 

-66 

-13 

-31 

— 

-13 

-3.3 

-1.7 

-3.8 

-0.7 

-1.8 

— 

-0.7 

Life expectancy for as-is predicted to be 4.7 years. 

Life expectancy for strictest standard predicted to be 4.76 years. 

195 
P.90 

APPENDIX B 

A full probability encoding of an uncertain variable can be costly 

as well as time-consuming. For this reason, only the most sensitive variables in an analysis are treated probabilistically. The deterministic 

models of the carbon monoxide risk assessment indicate two important 

areas of uncertainty. One is the slope of the function for maximal myocardial oxygen supply, which depends on COHb. The other important uncertainty is the distribution on peak hourly values of the double product of 

heart rate and mean systolic blood pressure. The uncertainty on the 

first was explicitly stated after review of the individual data in the 

clinical exercise testing studies and discussion with Dr. Aronow; unfortunately, the experts were unwilling to participate in a formal encoding 

session on this variable. 

Encoding sessions were conducted on the other two variables, the 

heart rate and blood pressure. Careful conditioning permitted the results to be combined into a single distribution on double product. This 

very important uncertainty is not one that is particularly familiar to 

the experts on carbon monoxide health effects. To assess it properly, 

exercise physiologists who have monitored daily patterns in heart rates 

and blood pressures would have to be identified. Because such experts 

are currently unidentified, the uncertainty on the peak hourly double 

product was accounted for by use of very broad ranges on the parameters 

of a lognormal distribution. The ranges were chosen to match with physiological facts. 

To check the assumption of lognormal!ty and of the choice of parameters in general, a distribution of peak hourly heart rate and of peak 

hourly mean systolic blood pressure were encoded from a physician familiar with this demonstration project. 

Pitfalls of Encoding 

In concept, the process of probability encoding is deceptively simple. In practice, numerous systematic biases are liable to exist in the 

encodee's mind. If not carefully done, the encoding process may retain 

these biases. Spetzler and von Holstein have outlined these biases and 

indicated several techniques to circumvent them in the encoding process. 

The following section enumerates some of these biases, and indicates ways 

in which they appeared to enter the encoding of heart rate and blood 

pressure. 

Displacement bias results in a distribution that is uniformly too 

low or high. The shape of the distribution remains correct, however, unless variability bias also occurs. Although it is difficult to determine 

whether variability bias occurred during either encoding session, there 

197 
P.91 

is some evidence that a displacement bias exists in the two distributions 

that resulted. The source of this bias is thought to be a difficulty in 

thinking about the peak hourly values of the two variables as they occur 

in daily life. This is a difficult bias to remove, and in fact was not 

even suspected until after the encoding session. Further exploration of 

the question directly with the encodee would perhaps help determine if a 

displacement occurred and, if so, to what extent. 

Motivational bias occurs when the encodee feels that the outcome of 

the session will be used to evaluate him or his job performance. For instance, a clinician who has performed studies on the variable that is being encoded may feel pressure to insist on repeating the results of his 

studies rather than objectively think about the inevitable uncertainties 

that exist in these results. He may be reluctant to place a proper margin of error on his assessment. Much of this kind of bias can be avoided 

through proper conditioning of the subject before actual encoding begins. 

Motivational bias was not a significant problem during the two encoding sessions to be described here. First, the encodee had no formal 

connection to the project or to EPA. Second, he was further assured that 

the encoding was entirely for exploratory purposes, so that the assessments he provided would not be considered the final word on the topic. 

Third, any fear he had of being considered professionally unknowledgable 

was relieved by an agreement that he remain anonymous. Although anonymity is undesirable for assessments that would be integral to a final 

model, it was not felt to be a serious concern for this exploratory encoding . 

Cognitive biases, which result in displacement and variability 

biases, arise from a number of different modes of judgment that cause the 

subject to adjust his assessments systematically in one direction. One 

such mode is availability. In the case of physicians, the most easily 

recalled information is clinical. Efforts to envision scenarios where an 

extreme physiological value could occur usually result in envisioning 

some pathological cause. The day-to-day causes, which are perhaps more 

common, may not receive the appropriate level of emphasis. To avoid 

this, the subject should be encouraged to consider nonclinical scenarios 

before the encoding session begins. 

Anchoring on an initial value is always a problem in encoding. To 

avoid this problem as much as possible, the encoding session should always begin with emphasis on the extreme values that may occur. This is 

part of the conditioning process before actual probabilities are assigned. 

Another common problem is that new information is often improperly 

used to update prior assessments. Some of this problem can be removed by 

encoding conditional variables. This in part removes the need for the 

subject to perform the Bayesian updating mentally. In the case of heart 

rate, nighttime and daytime distributions were encoded separately. In 

the case of blood pressure, the distributions were made dependent on 

heart rate. 

198 
P.92 

Unstated assumptions cause similar problems. The encodee may make 

his assessments entirely on an assumption of "normal conditions." For 

instance, the effects on heart rate of a sudden fright may not be included in the evaluation simply because the subject has implicitly assumed that he is only considering the typical daily events. This is particularly likely to occur if the subject is drawing largely on clinically 

observed data. On the other hand, the efforts of the encoder to be precise in definition may result in too much conditioning. In the case of 

heart rate and blood pressure, it is quite possible that the "composite 

patient" was too carefully defined. Efforts to clarify the values of interest may have narrowed the "patient" down to the point where the encodee was induced to eliminate many possible scenarios. 

The Encoding Sessions 

There were three basic stages to each encoding session. First, several minutes were devoted to a defining and conditioning of the variable 

to be encoded, as well as motivating the necessity to encode the variable. Following that, the actual encoding took place. Finally, the subject was permitted to review and verify the distributions that he had assessed. 

The following excerpt from the first minutes of the first encoding 

session indicates the approach that can be taken to structure and define 

the variable of interest. Because both encodings referred to the same 

"composite patient," this extensive conditioning step was only taken 

once, prior to the encoding of heart rate. One should be aware also 

that, after this step, the encodee was permitted to choose which of the 

two variables he would prefer to encode first, leaving the second variable to be encoded conditionally on the first. 

One will note that little of the excerpt deals with removing of 

motivational biases. As explained above, motivational bias was not considered a serious concern with this subject. Also, the subject had some 

familiarity with the heart disease model and with the goals of the risk 

assessment in general. A discussion of the probability encoding technique had also preceded the interview, and the subject had briefly reviewed the Spetzler and von Holstein paper. Often this initial stage 

will require quite a bit more time. It can become the major part of the 

session, both in terms of time and importance. One should be careful not 

to underemphasize its value for providing a good distribution on the variable at hand. 

Anne Smith What we are interested in is estimating an activity pattern that describes what is happening to a typical angina 

patient. We are only looking at one specific angina patient at this moment. We are not trying to combine all of 

the possibilities for all of the different types of patients, because there is a tremendous amount of (interpersonal) variability. We are using what is called the "double product" as a proxy for an activity pattern because 

that is proportional to oxygen demand of the heart.... We 

199 
are interested in a value of the maximum that will occur 

during an hour for each of the two [heart rate and blood 

pressure], and also we will have to get the correlation 

between the two. 

Encodee 

Anne Smith 

Encodee 

The patient that we decided to look at is currently suffering between one and two attacks per day. He is not on 

medication, so don't try to worry about how the heart rate 

and blood pressure are affected by medications. He is in 

his mid-fifties or early sixties. We haven't decided 

whether he has had a myocardial infarction or not. 

Usually you can tell by looking at the electrocardiogram. 

There is usually an evidence of an old infarct if he has 

already had one. 

Does that affect the way the heart rate and blood pressure 

might change over time? 

Well, that is possible, 

was. 

It depends upon how severe it 

Peter McNamee 

Encodee 

Peter McNamee 

Enodee 

What effect would the severity of the infarct have on the 

blood pressure and the heart rate? 

If it was a really severe infarct, you might not have a 

very strong left ventricular response, and you might not 

be able to achieve blood pressures as high as someone who 

had not had a severe heart attack. 

Then there is a certain extent to which he physiologically 

can't achieve a high blood pressure. 

Yes. 

Peter McNamee How about behavioral changes? 

Encodee Is this all part of the protocol? 

Peter McNamee Yes. 

Encodee 

Anne Smith 

I would guess that someone who has already suffered one 

infarct would be less likely to engage in more strenuous 

kinds of [activity]. But someone who has angina is going 

to be relatively [sedentary], anyway. I am not sure if it 

will make a real difference, though. 

That brings up one other cause of variability that we want 

to look at. We want to take into account the fact that 

there are stresses other than purely physical stresses. 

We don't want to look at what the heart rate would be doing if a person is just sitting at a table.... We want to 

capture the variability that might be induced by quarrels 

and tensions at work as well. 

200 
P.94 

Encodee That is certainly one of the big precipitators of angina 

attacks—anger and repressed anger. 

Anne Smith We are going on that basis, because we realize that people 

are fairly sedentary, and it is unlikely that most of the 

variability in [the double product] will be caused by 

physical activities. 

We have a pretty clear picture of the person we are thinking about.... 

I want to isolate some other details about the pathophysiology of the person's physique. He doesn't have, say, 

left ventricular hypertrophy or.... He doesn't have 

hypertension? 

Anne Smith No. How many angina patients would be likely to have 

hypertension? About 50/50 or...? 

Encodee I couldn't tell you that. 

Anne Smith Are you getting a pretty good picture of who the patient 

is? 

Peter McNamee 

Encodee 

Encodee He doesn't have a sick sinus, either? 

Anne Smith I don't know what that is. 

Encodee That is something that will affect the heart rate, where 

someone who has a lot of fibrosis and scars in the 

heart...they grow minor infarcts that may not be picked 

up, and they also have problems in the sino-atrial node in 

which case they would have a much slower heart rate than 

normal. Even down to the lower 40s. 

Anne Smith But that's not very common? 

Encodee It's common in older people. I would guess, maybe, someone who is in his fifties would have that. 

Anne Smith Well, let's just ignore that, for now. 

Encodee All right. I have an idea of who you are looking at then. 

Following the first phase, the encodee was introduced to the concept 

of the probability encoding wheel, an aid frequently used in encoding 

probability distributions . When he felt that he understood the purpose 

of this tool, the encoding itself began. First, he was asked to indicate 

whether he was more comfortable conditioning blood pressure on heart rate 

or vice versa. Then the units in which he thought of these variables 

were clarified as beats per minute and mm Hg. 

201 
Before actually using the wheel, he was asked to think about very 

high values and low values of the heart rate. The encoder then asked him 

to provide scenarios that could result in even more extreme values. The 

purpose of this step is to avoid anchoring on mid-range values and to 

help him recall small probability events. This helps counteract the bias 

caused by availability. 

The results of the encoding session on heart rate are provided in 

Figures B.l and B.2. One will note that to avoid forcing the subject to 

perform too many mental steps, the variable was conditioned on the time 

of day. The encoder can later combine these through the calculus of 

probability to derive a single distribution. The numbers next to the 

encoded points indicate the order in which the points were encoded; the 

first point encoded in Figure B.l reflects the initial unfamiliarity of 

the encodee with the process. 

After these distributions had been encoded, the subject was asked to 

look at the results and to comment on them. This verification step is 

very useful because it will indicate how comfortable the encodee is with 

the outcome. In this case, the subject actually made some changes to the 

curve, which appear as the dotted line in the figures. After the modifications, he indicated that he was satisfied with the result. In a more 

detailed encoding, more attention would be paid to the important region 

of high heart rates. 

The encoding of the second variable, peak hourly mean systolic blood 

pressure, followed very similar lines. The only difference was that this 

variable was conditioned on given values of peak heart rate. Figures B.3 

and B.4 provide the results of the encoding. During the encoding process 

for blood pressure, the encodee decided that the time of day was not relevant to this variable. 

Several points were noted after this encoding. First, the encodee 

was at ease with concepts of probability. Possibly he could have been 

permitted to draw the distributions directly, rather than rely on the 

wheel. Also, the encodee appeared to have already given some thought to 

the values prior to the session. There is the possibility that he came 

into the session with the distributions almost preconceived. This has 

drawbacks because the conditioning phase would be less effective on such 

a subject. Anchoring and availability may thus have been significant 

problems in these sessions. 

Use of Encoded Distributions 

As mentioned, these two variables were necessary as inputs to derive 

a distribution on the "double product." For some subjects, one might be 

able to encode the double product directly. The subject here was better 

able to consider its components separately. In view of this, the encoders structured the model so that the individual variables could be encoded separately, while probability calculus could be used to replace the 

mental computations of combining them. This approach avoids logical errors and allows the encodee to concentrate on the elements with which he 

is most familiar. 

202 
P.96 

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Probability calculus was used to derive the resultant distribution 

on the double product (Figure B.5) from the distributions in Figures 

B.l-4. Several comments should be made. First, this distribution is 

well approximated by a lognormal distribution. Second, this distribution 

appears to be quite low compared to those implied by the parameterizations in the demonstration model. This may be indicative of a negative 

displacement bias. This tentative conclusion was drawn for two reasons. 

The extreme values implied by this range are lower than those predicted 

for the not uncommon events such as domestic quarrels. This distribution is also not consistent with the statement that the patient has one 

to two attacks of angina each day. Rather, it indicates approximately 

one to two per week. Of course, the assumed value for the double product 

at onset of angina may instead be too high. 

These questions should be addressed fully in the review process. 

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whether the distribution on the peak hourly double product encoded here 

is too low, or whether the double product at angina used in the heart 

disease model is too high. Furthermore, changes in the distribution from 

individual to individual should be discussed at length, to determine 

ranges on the parameters of the lognormal approximation of the double 

product. 

Sunn-nary of Other Interviews 

12/11/81 Dr. Rodney Beard 

Stanford Medical Center 

Dr. Beard has performed several of the vigilance tests of low levels 

of CO. 

(a) He believes that there is some kind of effect at low levels 

(<5% COHb) although he is not sure of the physiological explanation . 

(b) He feels that heart disease effects are the significant public 

health problem. 

(c) He had reservations about using an accident curve from alcohol 

studies to extrapolate to CO-related accidents. However, he 

felt that the similarities in effect between COHb and blood alcohol were very provocative and deserving of further investigation. 

12/28/81 Dr. Victor Laties 

University of Rochester School of Public Health 

The potential central nervous system effects were discussed. Dr. 

Laties is very familiar with all of the vigilance and other CNS tests 

performed on CO. 

(a) He feels that the significant health effects from ambient CO 

are the myocardial and respiratory complications. 

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FIGURE B.5 DISTRIBUTION ON DOUBLE PRODUCT IMPLIED BY ENCODED 

DISTRIBUTIONS ON HEART RATE AND BLOOD PRESSURE 
P.3 

(b) He indicated a personal belief that CNS effects do not occur 

(have not been demonstrated sufficiently to be believed) at 

levels below about 5% COHb. 

(c) The CNS effects per se are not a public health problem. Accident potential is the correct area for concern. 

(d) Provided strong support for the approach of using the relationship of automobile accidents and alcohol. He suggested some 

additional work that should be done to make the analysis more 

thorough. He provided references from epidemiology literature 

that refer to the technique explicitly. 

1/4/82 Dr. Hannah Peavy 

National Institute of Health, Bethesda, MD 

Dr. Peavy is a pulmonary physician. Although not an expert on effects of CO, she was willing to discuss the physiological assumptions and 

relationships in the COPD model. 

(a) She helped pin down the parameters necessary to estimate the 

size of the sensitive group. 

(b) She discussed the assumptions that were used for the Coburn 

equation. Some actual data from patients who were subjected to 

exercise were examined. 

(c) She noted that in many cases, a heart attack will be the actual 

mechanism of death even though it occurs during a respiratory 

exacerbation. Thus there may be some double counting via the 

heart death model. 

(d) She provided references for people to contact concerning the 

assumptions specific to CO. 

1/6/82 Dr. Lewis Kuller 

University of Pittsburgh School of Public Health 

Dr. Kuller has investigated the heart disease health effects of CO. 

He is himself interested in developing an approach to quantifying these 

effects. 

(a) The use of epidemiological studies in developing a doseresponse curve was not discussed. He feels that such studies 

can completely overlook very real effects because the effects 

on the sensitive group become lost in the large proportion of 

the population that is not sensitive. A sigmoidal curve is appropriate, but the curve cannot be estimated when the CO exposures are in the low ranges common today. 

(b) He suggested a more structural approach, which would allow the 

angina model to be tied more closely to that for heart disease 

deaths. The angina model can be used to predict relative 

changes in the amount of time in which angina patients are subjected to significant ischemia (defined in that model as 

209 
P.4 

frequency of anginal pain). There is no known reason why myocardial ischemia sometimes results in irreparable tissue damage 

or fibrillation. He feels that the relative frequency of anginal pain is proportional to the relative risk of irreparable 

damage. One can estimate the relative frequencies from the angina model and apply this change to the group of people susceptible to heart attacks. (This group is larger than the population of angina patients.) 

(c) For this approach to be realistic, the relative frequencies 

would have to be estimated for populations at several levels of 

heart attack risk. The majority of the incremental effects 

would probably occur among the most sensitive group, which includes those with COPD and cor pulmonale. Double counting with 

the COPD model would have to be adjusted for. 

1/8/82 Dr. Jaroslaw Vostal 

Biomedical Sciences Department, General Motors Research Labs 

Warren, Michigan 

The angina model was presented to Dr. Vostal and other interested 

members of his department. Dr. Vostal has been overseeing physiological 

investigations of the effects of CO on myocardial tissue. He and his 

staff thus are very familiar with the issues surrounding the possibility 

of an effect of CO on the health of myocardial patients. 

(a) The structure of the angina model was acceptable in theory. 

The main criticism of the structure was that it appeared to 

attribute all anginal pain to shifts in heart rate and blood 

pressure above their mean values. Apparently supply shifts are 

also of great importance (e.g., vasoconstriction). The model 

assumes that COHb shifts the supply density function uniformly. 

Therefore, the bias introduced by fixing supply at the mean 

value of this density increases as the local curvature of the 

demand density function becomes more pronounced. (Variations 

in supply have since been incorporated into the model used in 

Chapter V.) 

(b) Aronow's studies are weakened because "pain" is subjective. It 

would be preferable to base the supply shifts on some physiological determination of ischemia. This is apparently one of 

GM's research goals. There are no results from the research 

yet, so use of Aronow's work remains the only option at this 

point. 

(c) They were very concerned that the large uncertainties be made 

explicit. 

1/26/82 Dr. Wilbert Aronow 

Long Beach Veterans Administration Hospital 

Long Beach, CA 

Dr. Aronow has performed the majority of clinical studies of the effects of CO on humans. The discussion with Dr. Aronow centered around 

210 
P.5 

arteriosclerotic heart disease and focused closely on the assumptions of 

the angina model and on person-to-person differences. The application of 

the angina model to all cardiovascular effects was also discussed. 

(a) Person-to-person differences are very large, and he does not 

feel that a "composite patient" is possible to describe. He 

feels that the results of any model based on a single, composite patient cannot be used to extrapolate to the entire population. 

(b) The model itself probably describes the effects of CO well. 

• The problem with it is specifying the inputs well enough to 

capture the real situation. 

(c) Causes for variability in the outcome of his studies were enumerated. Bounds were placed on this variability. 

(d) Aronow agreed with Kuller's suggestion for applying the relative changes in angina to an estimation of the relative changes 

in risk of infarction and fibrillation. Epidemiological studies cannot capture "small" effects at low CO levels. He feels 

that the possibility of a true threshold is nil. 

1/29/82 Dr. Lawrence Longo 

Loma Linda Medical School, Loma Linda, CA 

Dr. Longo is an expert in the field of fetal oxygenation. He has 

become involved in the effects of CO and of cigarettes on fetuses. Many 

aspects of the fetal problem and of modeling approaches were discussed. 

(a) A distribution on the effects of cigarette smoking on fetuses 

was encoded. His opinion is that CO may contribute about twothirds of this effect. 

(b) A new, more structural model of the effects of CO on fetuses 

was developed with his assistance. Estimates of the inputs 

were also provided. 

(c) Dr. Longo believes that fetal death is "only the tip of the 

iceberg." He feels that nonfatal effects are the major cause 

for concern for two reasons: (1) the number of effects is 

larger in absolute terms, and (2) each effect represents a 

greater problem to society and to those affected than the loss 

of a fetus. This is a value judgment, but one that should be 

addressed openly. 

(d) He feels that this quantification is needed and expressed 

interest in a more complete development of it in the future. 

2/8/82 Dr. Steven Horvath 

Institute of Environmental Stress 

Santa Barbara, CA 

Dr. Horvath is interested in all health manifestations of CO. Some 

discussion was given to effects on those with pulmonary disease and on 

211 
P.6 

fetuses." The majority of the discussion centered on cardiovascular 

effects. 

(a) Pulmonary disease patients and fetuses are judged to be clearly 

more sensitive to CO, but these problems are not as signficant 

as the cardiovascular problem because of the difference in population sizes. 

(b) Dr. Horvath agreed with using the approach that Kuller suggested for estimating heart disease deaths. He felt that, if 

anything, SRI's preliminary estimates are low. 

(c) Although a full analysis would require greater breakdown on the 

types of patients, he did not feel that SRI was wrong in starting with a single composite patient. 

(d) Estimates of the variability of the double product under different circumstances were provided. A full encoding did not 

occur, but ranges were set. 

(e) He feels that Aronow's results closely approximate the effect 

of CO. This bias in using the slope based on group means did 

not appear significant to him. He did feel, however, that the 

average patient would be more susceptible, because medication 

is lighter within the general population than in Aronow's test 

group. Although Aronow's subjects were not medicated during 

the tests, Horvath feels that the effects of the medication 

were still there residually. 

212 
P.7 

REFERENCES FOR APPENDIX B 

1. C. S. Spetzler and C-A. S. Stael von Holstein, "Probability Encoding 

in Decision Analysis," Management Science, Vol. 22, pp. 340-358, 

1975. 

2. Dr. Steven Horvath, personal communication. 

213