Developing a Concentration-Response Function for Pb

Exposure and Cardiovascular Disease-Related Mortality 

June 2014 

Prepared for: 

National Center for Environmental Economics 

Office of Policy 

U.S. Environmental Protection Agency 

1200 Pennsylvania Ave., NW 

Washington, D.C. 20460 

Prepared by: 

Abt Associates Inc. 

4550 Montgomery Avenue 

Suite 800 North 

Bethesda, MD 20814 

 
Contents ▌pg. ii

Developing a Concentration-Response Function for Pb Exposure and 

Cardiovascular Disease-Related Mortality 

Table of Contents 

1. Introduction ............................................................................................................................. 1-1

1.1 Available Literature ........................................................................................................ 1-1

1.1.1 EPA ISA ........................................................................................................... 1-1

1.1.2 The NTP Monograph ........................................................................................ 1-2

1.2 Report Outline ................................................................................................................ 1-3

2. Selection of Cardiovascular Disease-Related Mortality as the Health Endpoint .............. 2-1

2.1 Mode of Action for Pb and Cardiovascular Impacts ...................................................... 2-1

2.2 EPA ISA and NTP Monograph Cardiovascular Morbidity Findings ............................. 2-1

2.3 EPA ISA and NTP Monograph Cardiovascular Disease-Related Mortality Findings ... 2-5

3. Assessment of Literature ........................................................................................................ 3-1

3.1 Detailed Summary of Schober et al. (2006) ................................................................... 3-3

3.2 Detailed Summary of Menke et al. (2006) ..................................................................... 3-5

3.3 Detailed Summary of Khalil et al. (2009) ...................................................................... 3-9

3.4 Detailed Summary of Weisskopf et al. (2009) ............................................................. 3-12

3.5 Summary of Study Selection ........................................................................................ 3-14

4. Derivation of Blood Pb Concentration-Response Function ................................................ 4-1

5. Generalizability of the Concentration-Response Function from Menke et al. .................. 5-1

5.1 Generalizability to the Adult Population ........................................................................ 5-1

5.2 Range of Blood Pb Levels over Which the Concentration-Response Function Should Be 

Applied ........................................................................................................................... 5-1

5.2.1 Apply Study to All Blood Pb Levels ................................................................ 5-1

5.2.2 Apply Study to Blood Pb Levels Exceeding a Certain Value .......................... 5-2

5.3 Issues Regarding the Profile and Measurement of Lead Exposure and Risk ................. 5-4

5.4 Sample Benefits Calculation .......................................................................................... 5-6

5.4.1 Cardiovascular Disease Mortality Rate ............................................................ 5-6

5.4.2 Blood Pb Levels ................................................................................................ 5-7

5.4.3 Population ......................................................................................................... 5-9

5.4.4 Beta ................................................................................................................. 5-10

5.4.5 Summary of Inputs ......................................................................................... 5-10
Contents ▌pg. iii

5.4.6 Results ............................................................................................................ 5-11

6. Discussion on Uncertainty and Variability in the Concentration-Response and Health 

Impact Functions .................................................................................................................... 6-1

6.1 Uncertainty in the Concentration-Response Function .................................................... 6-1

6.1.1 Effect (β) Estimate ............................................................................................ 6-1

6.1.2 Blood Pb Estimates ........................................................................................... 6-3

6.1.3 Functional Form ............................................................................................... 6-3

6.2 Uncertainty in the Health Impact Function .................................................................... 6-4

6.2.1 Baseline Mortality Rates................................................................................... 6-4

6.2.2 Population Impacted by the Rule ...................................................................... 6-4

7. Next Steps ................................................................................................................................ 7-1

8. References ................................................................................................................................... 1

Appendix A Overview of Studies Not Selected for Additional Review ........................................ A-1

Cocco et al. (2007) ................................................................................................................... A-1

Lin et al. (2011) ........................................................................................................................ A-1

Lustberg & Silbergeld (2002) .................................................................................................. A-1

Møller & Kristensen (1992) ..................................................................................................... A-2

Neuberger, Hu, Drake, & Jim (2009) ....................................................................................... A-2

Appendix B Discussion of Blood Pb, Bone Pb, and Their Interrelationship ............................... B-1

Appendix C Absolute Change ......................................................................................................... C-1

Appendix D Percentage Change ...................................................................................................... D-1

 
Contents ▌pg. iv

List of Exhibits 

Exhibit 1. Conclusions from the EPA ISA and NTP Monograph on Readily Quantifiable 

Cardiovascular Morbidity Effects Experienced in Adults Associated with Pb Exposure 2-

3

Exhibit 2. Conclusions from the EPA ISA and NTP Monograph on Cardiovascular Disease-Related 

Mortality in Adults Associated with Pb Exposure ......................................................... 2-5

Exhibit 3. Summary of Population Characteristics for Studies Examining the Association between 

Blood Pb Levels and Cardiovascular Disease-Related Mortality ................................... 3-2

Exhibit 4. Multivariable Adjusted Relative Risks for All-Cause, Cancer, and Cardiovascular 

Disease-Related Mortality by Blood Level and Age Category (Schober et al., 2006) ... 3-5

Exhibit 5. Hazard Ratios and 95% Confidence Intervals of All-Cause, Cardiovascular Disease, 

Myocardial Infarction, and Stroke Mortality Associated with Tertile of Pb (Menke et al., 

2006) ............................................................................................................................... 3-6

Exhibit 6 Multivariate Adjusted Relative Hazard of Mortality Associated with Blood Lead Levels 

between 0.05 μmol/L (1 μg/dL) and 0.48 μmol/L (10 μg/dL) ....................................... 3-7

Exhibit 7. Multivariate Adjusted Relative Hazards1

 of All-Cause and Cardiovascular DiseaseRelated Mortality ............................................................................................................ 3-8

Exhibit 8. Illustrative Example of the Function from Menke et al. (2006) Representing the Risk of 

CVD Mortality as a Result of a Change in Blood Pb Relative to a Reference Level of 1 

μg/dL .............................................................................................................................. 3-9

Exhibit 9. Hazard Ratios and 95% Confidence Intervals of All-Cause Mortality by Blood Pb 

Concentrations .............................................................................................................. 3-11

Exhibit 10. Hazard Ratios and 95% Confidence Intervals for All-Cause, Cardiovascular Disease, 

Ischemic Heart Disease, and Other Cardiovascular by Tertile of Patella Pb at Baseline . 3-

13

Exhibit 11. Summary of Studies under Consideration as the Basis of the Concentration-Response 

Function ........................................................................................................................ 3-14

Exhibit 12. Summary Statistics for Studies Considered as the Basis for the Concentration-Response 

Function Relating Blood Pb Levels to Cardiovascular Disease-Related Mortality ...... 3-16

Exhibit 13. Proportion of the Population included in Benefits Analysis Based on Various Cutoff 

Points .............................................................................................................................. 5-2

Exhibit 14. Cardiovascular Disease-Related Mortality in the United States in 2010 ........................ 5-7

Exhibit 15. Adult Blood Lead Levels in 2011-2012 ......................................................................... 5-9

Exhibit 16. Input Parameters for a Hypothetical Benefits Analysis ................................................ 5-10

Exhibit 17. Quantified Benefits from Absolute Change for Varying Blood Pb Cutoff Levels for LRRP 

Type Rule ..................................................................................................................... 5-12

Exhibit 18. Benefits from Percentage Change for Varying Blood Pb Cutoff Levels for LRRP Type 

Rule............................................................................................................................... 5-13
Developing a Concentration-Response Function for Pb Exposure and Cardiovascular Disease-Related 

Mortality 

 Introduction ▌pg. 1-1

1. Introduction 

Lead (Pb) is a highly toxic pollutant that can damage neurological, cardiovascular, immunological, 

developmental, and other major organ systems (U.S. EPA, 2013). The neurological effects are 

particularly pronounced in children. Additionally, Pb exposure has been identified as one of the top 

15 mortality risk factors (and top 10 cardiovascular risk factors) in the United States (U.S. Burden of 

Disease Collaborators, 2013). Typically, EPA Regulatory Impact Assessments for rules reducing Pb 

releases have focused on quantifying children’s IQ as the primary category of monetized benefits, 

relying on an established methodology (U.S. EPA, 2008a). Benefits realized in the adult population 

are only discussed qualitatively. However, recent studies in the public health literature have found 

that a wide spectrum of adverse health outcomes can occur in people of all ages (U.S. EPA, 2013). In 

addition, a level of Pb exposure below which adverse effects do not occur has not been identified. 

This suggests that further declines in Pb exposure below today’s levels could still yield important 

benefits in the adult population that are not currently being quantified in benefits estimates. Recent

evidence has suggested that exposure to Pb in adults can result in cardiovascular disease (CVD) 

impacts; specifically, increases in hypertension, coronary heart disease, CVD, and cardiovascular 

disease-related mortality (CVD mortality) (National Toxicology Program, 2012; U.S. EPA, 2013). 

A well-established quantitative approach to evaluating benefits due to reductions in Pb releases for 

adults does not exist. The objective of this project is to create a rigorous approach to quantify adult 

health benefits from a reduction in Pb exposure for CVD mortality, which would strengthen EPA’s 

regulatory analyses and more completely characterize the effects of its programs. While we recognize 

the uncertainties and complexities in quantifying adult exposures to Pb, the process for estimating 

exposure is outside the scope of this report. The purpose of this report is to present a concentrationresponse function to estimate the number of cardiovascular disease deaths which would be avoided as 

a result of a decrease in Pb exposure. 

In order to accomplish this objective, we reviewed the literature, identified key studies, and assessed 

them for their applicability to adult benefits estimation. We used the results from the most applicable 

study as the basis of a proposed concentration-response function for use in benefits estimation. 

1.1 Available Literature 

Two recent comprehensive government documents summarize the literature on the health impacts of 

Pb exposure: EPA’s Integrated Science Assessment for Lead (U.S. EPA, 2013) (hereafter referred to 

as the EPA ISA or the EPA ISA report) and the National Toxicology Program Monograph on Health 

Effects of Low-Level Lead (National Toxicology Program, 2012) (hereafter referred to as the NTP 

Monograph). Given that these two documents already reviewed the literature through 2012 on 

adverse health effects associated with Pb exposure, we used them to identify studies that could serve 

as the basis of a function relating CVD to an adverse health outcome as a result of Pb exposure in the

adult population. 

1.1.1 EPA ISA 

The EPA ISA report surveyed and evaluated policy-relevant science examining the relationship 

between Pb and human health. The report determined causality by an evaluation and synthesis of 

evidence from controlled human exposure, epidemiologic, and toxicological studies published since 
Developing a Concentration-Response Function for Pb Exposure and Cardiovascular Disease-Related 

Mortality 

 Introduction ▌pg. 1-2

the last review (this occurred in 2006). From the review of this literature and the conclusions reached 

in the previous review, the EPA ISA classified the relationship between Pb exposure and adverse 

health effects. 

The EPA ISA causal determination categories are as follows: 

• Causal relationship: Pollutant has been shown to result in health effects in studies in which 

chance, bias, and confounding could be ruled out with reasonable confidence. 

• Likely to be a causal relationship: Pollutant has been shown to result in health effects in studies 

in which chance and bias can be ruled out with reasonable confidence, but potential issues 

remain. 

• Suggestive of a causal relationship: Evidence is suggestive of a causal relationship with 

relevant pollutant exposures, but is limited. 

• Inadequate to infer a causal relationship: Available studies are of insufficient quantity, quality, 

consistency, or statistical power to permit a conclusion regarding the presence or absence of an 

effect. 

• Not likely to be a causal relationship: Evidence is suggestive of no causal relationship with 

relevant pollutant exposures. 

The EPA ISA report determined there was a causal relationship between Pb exposure and health 

outcomes in the nervous, cardiovascular, renal, immune, and reproductive and developmental systems 

(U.S. EPA, 2013). The EPA ISA also determined a causal relationship between Pb exposure and 

effects on heme synthesis and red blood cell function. Lastly, they concluded there is a likely causal

relationship between Pb exposure and cancer (U.S. EPA, 2013). 

1.1.2 The NTP Monograph 

The NTP Monograph summarizes the entire epidemiologic body of evidence for human health effects 

associated with low-level Pb exposure (less than 10 micrograms of Pb per deciliter of blood 

(<10 μg/dL)). This monograph does not focus on health effects at blood Pb levels >10 μg/dL because 

these effects are well established (National Toxicology Program, 2012). The NTP conducted a review 

of the epidemiological literature for low-level Pb association with the following health endpoints: 

cardiovascular, immunological, neurological, renal, and reproductive and developmental effects. 

From this evaluation, the NTP categorized its conclusions for these endpoints as follows: 

• Sufficient evidence of association: Chance, bias, and confounding could be ruled out with 

reasonable confidence. 

• Limited evidence of association: Chance, bias, and confounding could not be ruled out with 

reasonable confidence. 

• Inadequate evidence of association: Available studies are insufficient in quality, consistency, or 

statistical power; or an association between exposure and health outcome is absent; or no data in 

humans are available. 

• Evidence of no association: Several adequate studies covering the full range of levels of 

exposure that humans are known to encounter (in this case limited to blood Pb levels <10 μg/dL) 
Developing a Concentration-Response Function for Pb Exposure and Cardiovascular Disease-Related 

Mortality 

 Introduction ▌pg. 1-3

are mutually consistent in not showing an association between exposure to the agent and any 

studied endpoint. 

NTP found sufficient evidence of an association for select neurologic, cardiovascular, and 

reproductive endpoints in adults exposed with blood Pb levels < 10 μg/dL (National Toxicology 

Program, 2012). 

1.2 Report Outline 

We looked at the evidence presented in the EPA ISA (U.S. EPA, 2013), in conjunction with that in 

the NTP Monograph (National Toxicology Program, 2012), with the goal of choosing the most 

relevant endpoints for benefits estimation for regulations reducing Pb exposure in adults. Through 

this exercise, we selected CVD mortality as the best endpoint for developing a concentration-response 

function to inform a quantitative risk assessment–based approach to evaluating benefits due to 

reductions in Pb exposure. The remainder of this report explains our process for choosing CVD 

mortality as the selected endpoint (Section 2), the studies we considered as the potential basis for a

concentration-response function (Section 3), and the derivation of the concentration-response function

(Section 4). Additionally, in this report we explore the generalizability of the derived concentrationresponse function and present the benefits associated with a hypothetical example rule (Section 5) and

propose a method by which to analyze the inherent uncertainty in the function (Section 6). Lastly, we 

discuss what additional steps could be taken to refine the Pb-CVD mortality function (Section 7). 
Developing a Concentration-Response Function for Pb Exposure and Cardiovascular Disease-Related 

Mortality 

 Selection of Health Endpoint ▌pg. 2-1

2. Selection of Cardiovascular Disease-Related Mortality as the 

Health Endpoint 

The EPA ISA and NTP Monograph reviewed the association between Pb exposure and adverse health 

impacts for more than 15 endpoints within six physiologic systems. This report focuses on the 

cardiovascular endpoint, and this section explains the process for selecting CVD mortality as the 

health endpoint of interest. In order to draw the conclusion that an exposure may result in an adverse

health outcome, there must be a plausible mode of action by which the exposure can cause the health 

outcome. This is presented in Section 2.1. Sections 2.2 and 2.3 discuss the weight of evidence for 

cardiovascular morbidity and mortality, respectively. 

2.1 Mode of Action for Pb and Cardiovascular Impacts 

Pb is thought to impact the cardiovascular system in several ways. According to the EPA ISA, the 

mechanistic evidence from toxicological studies is strongest for the role of Pb-induced oxidative 

stress in hypertension (U.S. EPA, 2013). Multiple studies cited in the EPA ISA show that Pb changes 

enzymatic activity, leading to the increased formation and decreased breakdown of reactive oxygen 

species, which inactivate and sequester nitrogen dioxide, a vasodilator. The decrease in nitrogen 

dioxide, as a result of increased reactive oxygen species, results in constriction of blood vessels and 

therefore increased blood pressure. High blood pressure is a well-recognized risk factor for CVD and 

CVD mortality (Ezzati et al., 2006). 

Another potential mechanism through which Pb exerts its cardiovascular toxicity is by altering the 

normal function of vascular cells, including endothelial and vascular smooth muscle cells (U.S. EPA, 

2013). For example, Pb induces inflammatory damage to endothelial cells, which line the interior of 

blood vessels and help to regulate blood pressure (Cines et al., 1998; U.S. EPA, 2013). There is also 

evidence that Pb exposure stimulates vascular smooth muscle cell migration and proliferation. Each 

of these is a key event in the pathogenesis of atherosclerosis. Atherosclerosis is a key component of 

the pathological process of peripheral arterial disease, stroke, and coronary heart disease, all of which 

can cause CVD mortality (CDC, 2004). 

Pb may also exert cardiovascular toxicity through disruption of calcium homeostasis. Evidence 

suggests that changes to calcium levels cause changes in heart rate variability, which have been 

associated with cardiovascular morbidity and mortality in older adults (U.S. EPA, 2013). Calciuminduced pro-coagulant activity may also lead to thrombosis, a risk factor for stroke and heart attack. 

Hypertension and atherosclerosis have been linked to additional Pb-induced modes of action, 

including hormonal system dysfunction, sympathetic nervous system activation, renal system 

dysfunction, and vasomodulator imbalance. For more information on Pb’s mode of action, the reader 

is referred to pages 4-324 through 4-404 of EPA’s ISA report. 

2.2 EPA ISA and NTP Monograph Cardiovascular Morbidity Findings 

Many endpoints fall under the category of cardiovascular morbidity. The EPA ISA report specifically 

examined and made explicit conclusions on the association between Pb exposure and increased blood 

pressure or the resulting clinical outcome of hypertension, subclinical atherosclerosis, coronary heart 
Developing a Concentration-Response Function for Pb Exposure and Cardiovascular Disease-Related 

Mortality 

 Selection of Health Endpoint ▌pg. 2-2

disease, and cerebrovascular disease. The NTP monograph examined and made explicit conclusions 

on blood pressure or the resulting clinical outcome of hypertension, heart rate variability, 

electrocardiogram abnormalities, clinical CVD (in general and by specific endpoint), and CVD 

mortality. 

Given that the purpose of this report is to identify an association between Pb exposure and monetary 

benefits from reduced exposure, we concentrate on summarizing the evidence for only those 

endpoints that are readily quantifiable and monetizable. Therefore, in this section we summarize the 

evidence for increased blood pressure or the resulting clinical outcome of hypertension, clinical CVD,

and cerebrovascular disease. The weight of evidence for these endpoints was examined by both the 

EPA ISA and the NTP monograph. The NTP and EPA ISA conclusions are summarized in Exhibit 1. 
Developing a Concentration-Response Function for Pb Exposure and Cardiovascular Disease-Related Mortality 

Selection of H

Exhibit 1. Conclusions from the EPA ISA and NTP Monograph on Readily Quantifiable Cardiovascul

Experienced in Adults Associated with Pb Exposure 

Effect Definition EPA ISA Conclusion NTP 

Blood pressure The force exerted by the heart 

against the walls of the arteries (measured in millimeters of mercury (mmHG)), with a maximum during the pumping phase of the heartbeat (systolic blood pressure, SBP) and a minimum when the heart muscle relaxes between beats (diastolic blood pressure, DBP). 

Causal: Prospective epidemiologic studies adjusting for potential confounders consistently find associations. Supported by crosssectional studies, meta-analyses, animal studies and plausible modes of action. Uncertainties remain regarding the timing, frequency, duration, and level of Pb exposures contributing to the effects observed in epidemiologic studies (p. lxxiv). 

Sufficient evidlevels <10 μgwith increase(BP) and hypassociation blevels and higconsistent in 80).

Hypertension Medical term for high blood 

pressure (currently, SBP ≥140 or 

DBP ≥90) compared to an optimal 

BP of <120/80 mmHg. BPs of 120-139/80-89 mmHg are considered prehypertension. 
Developing a Concentration-Response Function for Pb Exposure and Cardiovascular Disease-Related Mortality 

Selection of H

Effect Definition EPA ISA Conclusion NTP 

Cardiovascular Disease 

Coronary heart disease 

Narrowing of the arteries that supply blood and oxygen to the heart muscle, caused by atherosclerosis, which can result in a myocardial infarction (also called ischemic heart disease (IHD)). 

Causal: Prospective studies consistently find associations of Pb biomarkers with cardiovascular disease -related mortality and morbidity, specifically myocardial infarction (MI), IHD, or heart rate variability. This is supported by animal studies. Uncertainties remain regarding the timing, frequency, duration and level of Pb exposures contributing to the effects observed in epidemiologic studies (p. lxxxv). 

Clinical CVD 

evidence that μg/dL are assCVD (p. 97). Clinical CVD 

evidence to eassociation band specific C

Peripheral artery disease (for EPA included in the category of subclinical atherosclerosis (U.S. EPA, 2013, p. 1-27) 

Narrowing of arteries carrying blood to the arms and legs, caused by atherosclerosis. 

Suggestive of a causal relationship: (categorized as subclinical atherosclerosis by EPA). Crosssectional analyses of National Health and Nutrition Examination Survey (NHANES) data find associations. Animal studies are limited to modeof-action studies, which demonstrate how Pb may initiate atherosclerotic vessel disease.

Cerebrovascular Disease 

Disease of the blood vessels which supply the brain. Often caused by atherosclerosis and can lead to stroke. 

Inadequate to infer a causal 

relationship: Available data are of insufficient quantity, quality, and/or consistency, but modes of action are demonstrated (p. lxxxv). 

Limited evidereplicated stu

Sources: American Heart Association (2011); MedicineNet.com (2012); National Toxicology Program (2012); U.S. EPA (2013) 
Developing a Concentration-Response Function for Pb Exposure and Cardiovascular Disease-Related 

Mortality 

 Selection of Health Endpoint ▌pg. 2-5

2.3 EPA ISA and NTP Monograph Cardiovascular Disease-Related Mortality 

Findings 

In addition to examining the impacts from cardiovascular morbidity, CVD mortality was also 

examined by the EPA ISA (under the category of coronary heart disease) and the NTP Monograph. A 

summary of the findings on the conclusions from the EPA ISA and NTP Monograph for CVD 

mortality are summarized in Exhibit 2. Detailed summaries of all the studies considered can be found 

in Section 3 and Appendix A of this document. 

Exhibit 2. Conclusions from the EPA ISA and NTP Monograph on Cardiovascular 

Disease-Related Mortality in Adults Associated with Pb Exposure

Effect Definition EPA ISA Conclusion NTP Conclusion

Cardiovascular 

disease-related 

mortality 

(for EPA 

included under 

the category for 

coronary heart 

disease) 

Death attributed to heart 

or circulatory causes 

Included in causal 

determination for 

coronary heart disease 

(see Exhibit 1). Despite 

the differences in design 

and methods across 

studies, with few 

exceptions associations 

between higher levels of 

Pb biomarkers and 

higher risk of [coronary 

heart disease]-related 

mortality were 

consistently observed (p. 

4-412). 

Limited evidence that 

blood Pb levels <10 

μg/dL are associated 

with increased mortality 

from cardiovascular 

causes (p. 90) 

Sources: (American Heart Association, 2011; National Toxicology Program, 2012; U.S. EPA, 2013) 

The EPA ISA deemed the association between Pb exposure and coronary heart disease (including 

cardiovascular-related mortality) to be causal. Specifically, EPA stated, “despite the differences in 

design and methods across studies, with few exceptions associations between higher levels of Pb 

biomarkers and higher risk of [coronary heart disease]-related mortality were consistently observed” 

(p. 4-412, U.S. EPA, 2013). 

On the other hand, the NTP Monograph concluded that there is “limited evidence that blood Pb levels 

<10 μg/dL are associated with increased mortality from cardiovascular causes” and that the 

“association between increased CVD mortality and increased blood Pb was supported by three 

prospective studies but not supported by two prospective studies, one of which reported a significant 

association with bone Pb” (National Toxicology Program, 2012, p. 90). 

The two prospective studies NTP is referring to that do not support the association between blood Pb 

increases and CVD mortality are Møller and Kristensen (1992) and Weisskopf et al. (2009). 

However, Møller and Kristensen (1992) examined the risk of developing coronary heart disease and 

cardiovascular disease using both fatal and nonfatal cases. They did not examine the risk of CVD 

mortality without the non-fatal cases included in the analysis. Weisskopf et al. (2009) found no 
Developing a Concentration-Response Function for Pb Exposure and Cardiovascular Disease-Related 

Mortality 

 Selection of Health Endpoint ▌pg. 2-6

association between blood Pb and cardiovascular-related mortality but did find an association when 

bone Pb was used as the marker of exposure (more detail on this study can be found in Section 3.4). 

The findings should be considered alongside the fact that the method of selection for the Normative 

Aging Study (NAS) cohort used in the Weisskopf study suffers from selection bias. The NAS cohort 

used in the Weisskopf et al. (2009) study is weighted toward individuals without CVD, given that in 

order to be entered into the cohort you could not have prior CVD. For older individuals this creates a

strong selection bias toward heart-healthy people (Weisskopf, 2013). Therefore, the null results may 

be a product of this selection bias. The details about the Weisskopf study are outlined in more detail

in Section 3.4. Regardless, personal communication with Dr. Weisskopf revealed that there were 

errors in the analysis, a correction is to be published in Circulation, and the currently published 

results should not be used. Once the corrected results are published, we will reexamine the study. 

As described in Section 2.1, both the EPA ISA and NTP Monograph gave the strongest weight-ofevidence designation to increases in blood pressure. However, EPA also determined a “causal” 

weight of evidence for coronary heart disease, and included CVD mortality in this category. Although 

NTP determined the association between Pb and CVD mortality to be “limited” (its second highest 

category under “sufficient”), this determination was based on the fact that two studies did not support 

the association. However, as stated in the previous paragraph, at least one of the two studies that do

not support the association has issues which are being corrected by the authors. Therefore, given that

CVD mortality is more useful from a benefits analysis perspective and there is ample weight of 

evidence to support the association, we chose this endpoint for the development of a concentrationresponse function. The next section evaluates the literature selected by the EPA ISA (U.S. EPA, 

2013) and the NTP Monograph (National Toxicology Program, 2012) to determine the best evidence 

from which to develop a concentration-response function explaining the relationship between Pb 

exposure and CVD mortality. 
Developing a Concentration-Response Function for Pb Exposure and Cardiovascular Disease-Related 

Mortality 

 Assessment of Literature ▌pg. 3-1

3. Assessment of Literature 

To estimate a concentration-response function between blood Pb and CVD mortality, it is necessary 

to identify one or more suitable studies as the basis of the function. To be useful for benefits analyses, 

a study should be applicable to the general population and use sound scientific methods in the 

approach. We reviewed each of the eight studies considered by the EPA ISA and the NTP Monograph 

to see if their results could be extrapolated to the general population. For this initial screening, we 

considered the following criteria: 

• The study sample is representative of the adult general population or a large sector of the general 

adult population (e.g., men/women over the age of 40). Studies of patients with a particular 

disease, for example, do not represent a major portion of the population and would not be very 

useful for estimating the primary concentration-response function for a benefits analysis 

applicable to the general population. 

• The study reports average blood Pb levels <10 μg/dL. These levels are more representative of the 

current blood Pb levels in the United States, and consistent with the criteria for inclusion used by 

NTP. 

• The study used established methods and was included in the NTP or EPA weight-of-evidence 

determinations. 

Exhibit 3 displays the eight studies identified by the NTP Monograph and the EPA ISA report that 

examined the association between blood Pb and CVD mortality. Only one study, Weisskopf et al. 

(2009), also considered bone Pb as a biomarker. There were no studies identified that looked only at 

bone Pb and CVD mortality. 

As summarized in Exhibit 3, Menke et al. (2006), Schober et al. (2006), Khalil et al. (2009), and 

Weisskopf et al. (2009) all have populations in their study that would allow their results to be 

generalizable to all or significant portions of the U.S. adult population for a national benefits 

analysis.1 We closely examined these four studies to determine if they would be useful in creating a 

concentration-response function for blood Pb level and risk of CVD mortality. This included 

evaluating the statistical significance of their findings. Summaries of the other five2 studies can be 

found in Appendix B of this document. 

 

1

 The Weisskopf et al. (2009) cohort excluded people with prior CVD. If these findings are considered in the 

future, it should be noted that 3.6% of the population between the ages of 18 and 44 and 12.8% of the 

population between the ages of 45 and 64 have cardiovascular disease (CDC, 2012b). Additionally, 

consideration will need to be given to applying the results to older individuals, over 65 years of age, given 

that at least 25% of the older population has cardiovascular disease (CDC, 2012b). 

2

 Given that Møller and Kristensen’s 1992 study did not examine blood Pb as it relates specifically to

cardiovascular disease-related mortality, we will not further consider it in the development of the 

concentration-response function. It is included in Appendix B since it was considered in the NTP 

Monograph. 
Developing a Concentration-Response Function for Pb Exposure and Cardiovascular Disease-Related 

Mortality 

 Assessment of Literature ▌pg. 3-2

Exhibit 3. Summary of Population Characteristics for Studies Examining the 

Association between Blood Pb Levels and Cardiovascular Disease-Related 

Mortality 

Study 

Population 

Representative 

of a Significant 

Portion of the 

Adult 

Population 

Reported Average 

Blood Pb Levels 

<10 μg/dL 

(Mean Blood Pb Level 

unless Otherwise 

Noted) 

Cocco et al. (2007) 

Causes of death among lead smelters in relation to 

the glucose-6-phosphate dehydrogenase 

polymorphism

a 

Khalil et al. (2009) 

Association of blood lead concentrations with 

mortality in older women: a prospective cohort 

study

X 

X 

(5.3 μg/dL) 

Lin et al. (2011) 

Association of blood lead levels with mortality in 

patients on maintenance hemodialysis

(median: 10.4 μg/dL)

Lustberg & Silbergeld (2002) 

Blood lead levels and mortality X (14.0 μg/dL) 

Menke et al. (2006) 

Blood lead below 0.48 μmol/l (10 μg/dL) and 

mortality among US adults

X 

X 

(2.58 μg/dL) 

Neuberger et al. (2009) 

Potential health impacts of heavy-metal exposure at

the Tar Creek Superfund site, Ottawa County, 

Oklahoma

X a

Schober et al. (2006) 

Blood lead levels and death from all causes, CVD, 

and cancer: results from the NHANES III mortality 

study

X 

X 

(median: 4.14 μg/dL)b 

Weisskopf et al. (2009) 

A prospective study of bone lead concentration and 

death from all causes, CVD, and cancer in the 

Department of Veterans Affairs Normative Aging 

Study

X 

X 

(5.6 μg/dL) 

a

 No blood Pb data presented for this study. 

b

 No overall mean or median blood Pb level was presented in Schober et al. (2006). This value is a calculated weighted 

median blood Pb level based on the cohort population characteristics presented in Table 1 of Schober et al. (2006).
Developing a Concentration-Response Function for Pb Exposure and Cardiovascular Disease-Related 

Mortality 

 Assessment of Literature ▌pg. 3-3

In our evaluation of Menke et al. (2006), Schober et al. (2006), Khalil et al. (2009), and Weisskopf et 

al. (2009), we determined there are two categories into which the studies may fall: 

• The study presents a continuous concentration-response function or presents a result (e.g., a 

relative risk associated with a given change in blood Pb levels) based on an underlying 

continuous concentration-response function for blood Pb and CVD mortality risk. This type of 

study is the most useful. 

• The study presents categorical comparisons of risk of CVD mortality associated with different 

categories of blood Pb level based on a categorical analysis. If applicable, we contacted the 

authors to see if the underlying data are available to estimate a continuous concentration-response 

function. Since using categorical data to estimate the continuous function requires additional 

assumptions and modeling, we gave preference to studies that provide continuous data. 

As discussed above, Schober et al. (2006), Menke et al. (2006), Khalil et al. (2009), and Weisskopf et

al. (2009) have results that can be extrapolated to a large portion of the population for a national 

benefits analysis. In some cases, we contacted study authors for details and/or data not available in 

the publication. A more detailed overview of the methods and results of each of these four studies is 

presented in the subsequent subsections. 

3.1 Detailed Summary of Schober et al. (2006) 

Schober et al.’s (2006) Blood Lead Levels and Death from All Causes, Cardiovascular Disease, and 

Cancer: Results from the NHANES III Mortality Study examined the association between blood Pb 

levels and all-cause and cause-specific mortality of 9,686 participants in NHANES III who were 40 

years of age or older. The mean blood Pb level for the population was not presented, but 

approximately 94% of the study participants had blood Pb levels less than 10 μg/dL. We calculated 

the median blood Pb level to be approximately 4.14 μg/dL based on the median blood Pb levels and 

sample sizes presented in Table 1 of Schober. The median length of follow-up was 8.55 years, during 

which there were 2,515 deaths. Using the International Classification of Disease (Tenth Revision), 

Schober et al. identified death due to malignant neoplasms (ICD-10 codes C00-C97) and major CVD 

(ICD-10 codes I00-I78). 

Schober et al. categorized blood Pb into three categories: <5 μg/dL, 5 to <10 μg/dL, and ≥10 μg/dL. 

The authors used Cox proportional hazard regression analysis, with age3

 as the time scale, to examine 

the hazard of mortality from all causes, cancer, and CVD using the categories outlined in the previous

paragraph. The baseline hazard was stratified by age, using 6-year intervals, controlling for potential 

cohort difference in cumulative exposure to Pb before the late 1970s. Additionally, because current 

Pb exposure (and subsequently blood Pb levels) continued to decline over the 6-year period of blood 

collection, the authors also stratified based on survey phase. Multivariate proportional hazard models

were used to examine the association between blood Pb and mortality, while adjusting for potential 

confounders. Additionally, all two-way interactions with blood Pb category were assessed. Further, 

Schober et al. stated that because the cancer mortality and blood Pb relationship was different for men 

 

3

 Age was defined as a participant’s age at the baseline examination. 
Developing a Concentration-Response Function for Pb Exposure and Cardiovascular Disease-Related 

Mortality 

 Assessment of Literature ▌pg. 3-4

and women in a previous study of the NHANES II cohort, they also stratified multivariate models 

separately for males and females, and sex was included in their final model as a confounder. 

Schober et al. assessed the concentration-response relationship of blood Pb and mortality in two 

ways. First, the multivariate adjusted relative risks for the three blood Pb categories were tested for 

trend. The median values for each Pb group were placed in a linear term and analyzed using a Wald 

test. Second, using a five-knot cubic regression spline, they evaluated the log-transformed4 blood Pb 

concentrations as a continuous variable, and used a Wald test to evaluate the concentration-response 

relationship. 

The number of deaths and multivariate-adjusted relative risks of mortality due to all causes, CVD, 

and cancer are presented in Exhibit 4. There were a total of 2,485 deaths from all causes, 1,189 from 

CVD, and 543 from cancer. All multivariate models were adjusted for sex, race/ethnicity, education 

level, and smoking status. None of the interaction terms had large effects on the model, nor did they 

alter the direction of the Pb-mortality relationship, and therefore they were not included in the final 

models. For mortality due to all causes and CVD, there was a pattern of increasing risk with 

increasing blood Pb. For all-cause, cardiovascular, and cancer deaths for all age groups, the trend tests 

exploring a concentration-response relationship were significant. The results of this analysis are 

presented in Exhibit 4. 

 

4

 Because of the skewed distribution of blood Pb levels, the authors log-transformed the blood Pb 

measurements. 
Developing a Concentration-Response Function for Pb Exposure and Cardiovascular Disease-Related 

Mortality 

 Assessment of Literature ▌pg. 3-5

Exhibit 4. Multivariable Adjusted Relative Risks for All-Cause, Cancer, and 

Cardiovascular Disease-Related Mortality by Blood Level and Age 

Category (Schober et al., 2006) 

Cause of Death/ 

Blood Pb Level 

Number of 

Deaths 

Relative Risk (95% CI) by Age Category (Years) 

40-74 75-84 >85 All 

All causes

<5 μg/dL 1,402 1 1 1 1 

<5-9 μg/dL 828 1.30 

(1.03-1.65) 

1.38 

(1.04-1.83) 

0.98 

(0.85-1.14) 

1.24 

(1.05-1.48) 

≥10 μg/dL 255 1.73*** 

(1.28-2.35) 

1.39** 

(0.93-2.08) 

1.67 

(1.11-2.53) 

1.59*** 

(1.28-1.98) 

Cardiovascular disease

<5 μg/dL 684 1 1 1 1 

<5-9 μg/dL 394 1.11 

(0.79-1.56) 

1.41 

(0.87-2.28) 

1.07 

(0.87-1.31) 

1.20 

(0.93-1.55) 

≥10 μg/dL 111 1.47 

(0.93-2.33) 

1.71** 

(0.94-3.09) 

1.45 

(0.85-2.48) 

1.55* 

(1.16-2.07) 

Cancer

<5 μg/dL 282 1 1 1 1 

<5-9 μg/dL 194 1.44 

(0.91-2.28) 

1.46 

(1.03-2.07) 

1.44 

(0.92-2.26) 

1.44 

(1.12-1.86) 

≥10 μg/dL 67 2.27* 

(1.38-3.74) 

0.80 

(0.38-1.69) 

2.2* 

(1.13-4.29) 

1.69* 

(1.14-2.52) 

Note: Variables adjusted for sex, race/ethnicity, education, and smoking status. 

*p-value for trend test <0.01 

**p-value for trend test <0.05 

***p-value for trend test <0.001 

Source: Schober et al. (2006, Table 2). 

The work by Schober et al. (2006) provides supporting evidence that blood Pb is associated with allcause and CVD mortality. We contacted the study authors to determine if a concentration-response 

function was available for the relationship between CVD mortality and blood Pb. Although Dr. 

Schober and her co-author, Dr. Mirel, were willing to help, it turned out not to be feasible to retrieve 

actual functions. Therefore, if this study were used, a concentration-response function would need to 

be estimated from Schober’s categorical analysis. 

3.2 Detailed Summary of Menke et al. (2006) 

Similar to Schober et al. (2006), Menke et al. (2006) also used NHANES III data to examine the 

association between blood Pb levels and all-cause and cause-specific mortality among U.S. adults 

who have blood Pb levels below 10 μg/dL in their study, Blood Lead Below 0.48 μmol/L (10 μg/dL) 

and Mortality Among US Adults. However, Menke et al. (2006) included 13,946 participants 20 years 

of age and older. Additionally, mortality follow-up was approximately 12 years, and Menke et al. 

(2006) included additional ICD-9 codes when examining the cause of death. The mean blood Pb level 

for the participants in the Menke et al. (2006) study was 2.58 μg/dL. 
Developing a Concentration-Response Function for Pb Exposure and Cardiovascular Disease-Related 

Mortality 

 Assessment of Literature ▌pg. 3-6

Menke et al. (2006) performed several statistical analyses. In one statistical analysis, Menke et al. 

categorized the participants into blood Pb tertiles based on the weighted population distribution. The

tertiles were <1.93 μg/dL, 1.94 μg/dL – 3.62 μg/dL, ≥3.63 μg/dL. To analyze the association between 

blood Pb and mortality, follow-up for each participant was calculated as the time between their 

NHANES III examination and the date of death, the date on which they turned 90 years of age, or 

December 31, 2000. The HRs and 95% CIs were calculated by multivariable Cox regression models 

for all-cause cardiovascular, myocardial infarction, stroke, and cancer mortality by comparing each 

tertile with the first (low Pb) tertile. When comparing the second (middle) tertile to the first tertile an 

increase was seen, but this was not statistically significant. When comparing the third tertile to the

first tertile, a statistically significant increase in risk was found (see Exhibit 5). As shown in Exhibit 

5, the study authors performed three analyses to adjust for various potential confounders, and CVD 

mortality remained statistically significantly associated with blood Pb levels in all three models. 

Cancer mortality was not found to be associated with Pb exposure. 

Exhibit 5. Hazard Ratios and 95% Confidence Intervals of All-Cause, 

Cardiovascular Disease, Myocardial Infarction, and Stroke Mortality 

Associated with Tertile of Pb (Menke et al., 2006) 

Tertile 1

(<0.09 

μmol/L or 

<1.93 μg/dL) 

Tertile 2

(0.09-0.17 μmol/L 

or 

1.94-3.62 μg/dL) 

Tertile 3

(≥0.18 μmol/L 

 or 

≥3.63 μg/dL) Ptrend 

All-cause mortality, n 252 470 939 

Age, race-ethnicity, and sex adjusted 1.00 0.97 (0.76–1.23) 1.37 (1.15–1.64) <0.001 

Multivariable 1 adjusted* 1.00 0.93 (0.73–1.19) 1.30 (1.08–1.56) <0.001 

Multivariable 2 adjusted† 1.00 0.91 (0.72–1.15) 1.25 (1.04–1.51) 0.002 

Cardiovascular disease mortality, n 104 219 443 

Age, race-ethnicity, and sex adjusted 1.00 1.01 (0.68–1.51) 1.51 (1.07–2.14) 0.004 

Multivariable 1 adjusted* 1.00 1.06 (0.70–1.60) 1.64 (1.14–2.35) 0.001 

Multivariable 2 adjusted† 1.00 1.03 (0.69–1.55) 1.55 (1.08–2.24) 0.003 

Myocardial infarction mortality, n 50 83 234 

Age, race-ethnicity, and sex adjusted 1.00 0.99 (0.55–1.79) 1.70 (0.99–2.90) 0.011 

Multivariable 1 adjusted* 1.00 1.05 (0.56–1.97) 2.01 (1.12–3.61) 0.003 

Multivariable 2 adjusted† 1.00 1.02 (0.55–1.89) 1.89 (1.04–3.43) 0.007 

* Adjustment included age, race-ethnicity, sex, diabetes mellitus, body mass index (BMI), current or former 

smoking, alcohol consumption, physical activity, low income, c-reactive protein (CRP), total cholesterol, 

high school education, urban residence, and post-menopausal status. 

† Adjustment includes variables in model 1, hypertension, and level of kidney function. 

Sample sizes (n) refer to the number of events. 

Source: Menke et al. (2006, Table 2).

Tests for linear trend across tertiles of blood Pb were computed by including tertile of Pb as 

continuous variable in the Cox regression models. The trend analysis found statistically significant 

increases in mortality risk for all causes of mortality analyzed except cancer. The results of this 

analysis are also presented in Exhibit 5 and support the finding of a concentration-response 

relationship between blood Pb and CVD mortality. 

Menke et al. (2006) also explored the concentration-response relationship of blood Pb level with allcause, myocardial infarction, and stroke mortality using a restricted quadratic spline with knots at the 
Developing a Concentration-Response Function for Pb Exposure and Cardiovascular Disease-Related 

Mortality 

 Assessment of Literature ▌pg. 3-7

10th, 50th, and 90th percentiles of blood Pb distribution (Exhibit 6). This analysis revealed that the 

increase in all-cause and myocardial infarction occurred at blood Pb levels > 2.0 μg/dL (0.10 

μmol/L). Stroke increased monotonically at all blood Pb levels included in the spline analysis (1 to 10 

μg/dL). Quadratic spline results were not presented for CVD. 

In a third analysis, due to the skewness of the distribution of blood Pb, Pb was log-transformed and 

treated as a continuous variable. The study authors calculated HRs for a 3.4 increase in blood Pb 

levels or the difference between the logged 80th (4.92 μg/dL) and 20th (1.46 μg/dL) percentiles of 

blood Pb distribution. After the multivariate adjustment,5 the HR for a 3.4-fold increase in blood Pb 

level was 1.34 (95% CI = 1.16 to1.54) for all-cause mortality, 1.53 (1.21-1.94) for CVD mortality, 

1.78 (1.18 to 2.67) for myocardial infarction, and 1.59 (1.08-2.34) for stroke mortality. The results 

from the all-cause and CVD mortality analyses are presented in Exhibit 7. 

Menke et al. (2006) also determined the association between blood Pb as a continuous variable and 

mortality for subgroups defined by age, race-ethnicity, sex, menopausal status, urban or rural 

residence, cigarette smoking, overweight, diabetes mellitus, hypertension, and level of estimated 

glomerular filtration rate (GFR). However, no subgroup interaction terms were statistically significant 

at the 5% level. Their findings are summarized in Exhibit 7. 

Exhibit 6 Multivariate Adjusted Relative Hazard of Mortality Associated with 

Blood Lead Levels between 0.05 μmol/L (1 μg/dL) and 0.48 μmol/L (10 

μg/dL) 

Source: Menke et al. (2006, Figure 1). Note: Histogram of blood lead levels is superimposed in the 

background and displayed on the right axis. 

 

5

 Adjustment included age, race-ethnicity, sex, diabetes mellitus, BMI, current or former smoking, alcohol 

consumption, physical activity, low income, c-reactive protein (CRP), total cholesterol, high school 

education, urban residence, and post-menopausal status, hypertension, and level of kidney function 

(Personal Communication with Andy Menke, 2013). 
Developing a Concentration-Response Function for Pb Exposure and Cardiovascular Disease-Related 

Mortality 

 Assessment of Literature ▌pg. 3-8

Exhibit 7. Multivariate Adjusted Relative Hazards1 of All-Cause and 

Cardiovascular Disease-Related Mortality 

Subgroup 

HR of All-Cause 

Mortality (95% CI) 

HR of Cardiovascular 

Disease-Related 

Mortality (95% CI) 

Age (years)

<60 1.75 (1.25-2.44) 2.00 (1.24-3.22) 

≥60 1.31 (1.08-1.58) 1.49 (1.12-1.99) 

Race-ethnicity

Non-Hispanic white 1.32 (1.09-1.60) 1.49 (1.12-1.99) 

Non-Hispanic black 1.23 (0.99-1.52) 1.13 (0.79-1.61) 

Mexican-American 1.17 (0.86-1.60) 1.55 (0.90-2.68) 

Sex and menopausal status

Male 1.41 (1.11-1.78) 1.35 (0.84-2.18) 

Female 1.24 (1.00-1.54) 1.63 (1.25-2.11) 

Pre-menopausal 1.02 (0.54-1.95) 2.71 (0.93-7.91) 

Post-menopausal 1.24 (1.00-1.54) 1.46 (1.04-2.03) 

Residence

Rural 1.28 (1.05-1.54) 1.41 (1.01-1.96) 

Urban 1.42 (1.18-1.72) 1.75 (1.19-2.56) 

Smoking

Never 1.21 (0.93-1.58) 1.57 (1.10-2.24) 

Former 1.61 (1.33-1.94) 2.07 (1.49-2.89) 

Current 1.34 (0.96-1.87) 1.05 (0.54-2.04) 

Body mass index (kg/m2)

<25 1.51 (1.16-1.96) 2.02 (1.32-3.11) 

≥25 1.28 (1.03-1.58) 1.34 (0.94-1.91) 

Hypertension

No 1.31 (1.08-1.58) 1.48 (0.96-2.26) 

Yes 1.32 (1.09-1.60) 1.49 (1.15-1.94) 

Diabetes

No 1.37 (1.19-1.58) 1.59 (1.31-1.92) 

Yes 1.12 (0.73-1.71) 1.16 (0.67-2.00) 

Estimated glomerular filtration rate (ml/min/1.73m2)

<60 1.44 (1.01-2.06) 1.75 (1.06-2.88) 

≥60 1.32 (1.12-1.56) 1.49 (1.18-1.89) 

Overall 1.34 (1.16-1.54) 1.53 (1.21-1.94)

1Hazard ratios were calculated for a 3.4-fold increase in blood Pb with log–blood Pb 

as a continuous variable. This increase corresponds to the difference between the 

80th and 20th percentiles of the blood Pb distribution (4.92 μg/dL versus 1.46 μg/dL, 

respectively). 

Source: Menke et al. (2006, Figure 2). 

The results presented in Exhibit 7 are based on a continuous function that can be used as the basis of

the concentration-response function for a benefits analysis. For each 3.4-fold increase in blood Pb 

level, there is a subsequent 53% increase in risk of CVD mortality for the adult population. This 

function is represented by the plot in Exhibit 8, which we generated based on the overall HR. 
Developing a Concentration-Response Function for Pb Exposure and Cardiovascular Disease-Related 

Mortality 

 Assessment of Literature ▌pg. 3-9

Exhibit 8. Illustrative Example of the Function from Menke et al. (2006) 

Representing the Risk of CVD Mortality as a Result of a Change in 

Blood Pb Relative to a Reference Level of 1 μg/dL 

Menke et al.’s (2006) study results meet the criteria of having a generalizable concentration-response

function that can be used, along with other data, to estimate benefits for adults who would have a 

reduction in Pb exposure as the result of a hypothetical regulation. Additionally, this study uses the

same NHANES cohort as the Schober et al. (2006) paper but includes a wider age range of 

individuals, making their results more applicable to the adult general population. It is important to 

note that in addition to examining the relationship between blood Pb and CVD mortality, Menke et al. 

(2006) also found a statistically significant relationship between blood Pb and all-cause mortality, 

myocardial infarction mortality, and stroke mortality. CVD mortality was selected as the endpoint of 

preference because there was strong weight of evidence in the literature and it proved most useful for

a benefits analysis compared to other endpoints (see Section 2). 

The details of developing a concentration-response function with the data presented in Menke et al. 

(2006) are presented in Section 4. 

3.3 Detailed Summary of Khalil et al. (2009) 

Khalil et al.’s (2009) Association of Blood Lead Concentrations with Mortality in Older Women: A 

Prospective Cohort Study used the ancillary Pb study of the Study of Osteoporotic Fractures, a 

0

0.5

1

1.5

2

2.5

0.5 1 1.5 2 2.5 3 3.5 4 4.5 5 5.5 6 6.5 7 7.5 8 8.5 9 9.5 10

Relativfe Risk of Cardiovascular Mortality

Blood Pb Levels (μg/dL)
Developing a Concentration-Response Function for Pb Exposure and Cardiovascular Disease-Related 

Mortality 

 Assessment of Literature ▌pg. 3-10

longitudinal cohort study that enrolled white women aged 65–87 at either the University of Pittsburgh 

or University of Maryland clinics, to evaluate the blood Pb levels of 533 women aged 65–87 in 

association with all-cause and cause-specific mortality. The population’s mean blood Pb 

concentration was 5.3 ± 2.3 μg/dL. The study authors categorized the participants into three groups 

depending on their blood Pb levels. The groups were ≤3 μg/dL (lower 15th percentile), 4-7 μg/dL, and 

≥8 μg/dL (upper 15th percentile). This categorization was determined a priori based on a previous 

study of blood Pb and cognitive function; however, initial analysis suggested that mortality was only 

significant at the highest 15th percentile and that only the top quintile (80th percentile) showed 

elevated risk of death; therefore, the study authors combined categories to create only two categories, 

<8 μg/dL and ≥8 μg/dL. This varies from the Menke et al. (2006) findings in post-menopausal 

women. It is worth noting that this study has a substantially smaller sample size than Menke et al. 

(2006). 

Deaths were confirmed by death certificate. The authors recorded the underlying cause of death using 

ICD-9-CM codes for CVD including all diseases of the circulatory system except those involving 

veins and lymphatics [ICD-9-CM codes 425, 429.2, 440–444, 428, 401–404, 410–414, 430–438, and 

798]; coronary heart disease (CHD) [ICD-9-CM codes 410–414]; stroke [ICD-9-CM codes 430–438]; 

cancer [ICD-9-CM codes 140–239]; and all other deaths. 

Using Cox proportional hazards regression analysis, Khalil et al. (2009) estimated the HRs and 95% 

CIs of death in the high Pb group (≥8 μg/dL) compared to the low Pb group (<8 μg/dL) using a 

multivariable model. Their findings are summarized in Exhibit 9. 
Developing a Concentration-Response Function for Pb Exposure and Cardiovascular Disease-Related 

Mortality 

 Assessment of Literature ▌pg. 3-11

Exhibit 9. Hazard Ratios and 95% Confidence Intervals of All-Cause Mortality by 

Blood Pb Concentrations 

Cause of Death Deaths 

Blood Pb Concentration (μg/dL) [μmol/L]

Pvalue (< 8) [< 0.384] (≥ 8) [≥ 0.384]

All cause death, n (Col %) 123 96 (21%) 27 (34%) 0.018* 

Age, clinic adjusted 1.0 1.73 (1.12, 2.68) 0.014 

Multivariate adjusted a 1.0 1.59 (1.02, 2.49) 0.041 

Cardiovascular disease b, n(%) 54 41 (9) 13 (16) 0.044* 

Age, clinic adjusted 1.0 1.90 (1.00, 3.63) 0.054 

Multivariate adjusted a 1.0 1.78 (0.92, 3.45) 0.089 

Coronary heart disease c, n(%) 23 15 (4) 8 (11) 0.006* 

Age, clinic adjusted 1.0 3.54 (1.48, 8.45) 0.004 

Multivariate adjusted a 1.0 3.08 (1.23, 7.70) 0.016 

Stroke d, n(%) 21 17 (4) 4 (5) 0.578* 

Age, clinic adjusted 1.0 1.16 (0.34, 4.00) 0.816 

Multivariate adjusted a 1.0 1.13 (0.34, 3.81) 0.840 

Cancer e, n(%) 38 30 (7) 8 (10) 0.262* 

Age, clinic adjusted 1.0 1.70 (0.77, 3.75) 0.185 

Multivariate adjusted a 1.0 1.64 (0.73, 3.71) 0.231 

All other deaths f, n(%) 31 25 (7) 6 (10) 0.289* 

Age, clinic adjusted 1.0 1.51 (0.61, 3.72) 0.370 

Multivariate adjusted a 1.0 1.22 (0.48, 3.10) 0.673 

*Chi-square p-value only for percentage of deaths in two blood Pb strata; the rest are hazard ratio 

p-values. 

a

 The multivariate model included the following: age, clinic, BMI, education, smoking, alcohol 

intake, estrogen use, hypertension, walking for exercise, diabetes, and total hip bone mass 

density. 

b

 ICD9 Code: All deaths due to CVD, including all diseases of circulatory system except those 

involving veins and lymphatics: 425, 429.2, 440–444, 428, 401–404, 410–414, 430–438, and 798. 

c

 ICD9 Code: Deaths due to coronary heart disease: 410–414. 

d

 ICD9 Code: Deaths due to stroke: 430–438. 

e

 ICD9 Code: Deaths due to cancer: 140–239. 

f

 ICD9 Code: All other deaths: Non CVD and noncancer deaths. 

Source: Khalil et al. (2009, Table 5). 

Although the Khalil et al. (2009) results support the association between Pb exposure and 

cardiovascular-related mortality to a portion of the adult U.S. population, we will not use the study to 

develop a concentration-response function because the results are less applicable to the entire adult 

U.S. population due to the focus on older women and the high average blood Pb levels among 

participants. Additionally, there is no underlying continuous concentration-response function 

reported. 
Developing a Concentration-Response Function for Pb Exposure and Cardiovascular Disease-Related 

Mortality 

 Assessment of Literature ▌pg. 3-12

3.4 Detailed Summary of Weisskopf et al. (2009) 

Weisskopf et al.’s (2009) A Prospective Study of Bone Lead Concentration and Death from All 

Causes, Cardiovascular Diseases, and Cancer in the Department of Veterans Affairs Normative 

Aging Study examined the association of both blood and bone Pb levels and mortality. The authors 

measured bone Pb levels in the tibia (n = 863), patella (n = 860), and blood Pb (n = 1,235) in 

participants in the Department of Veterans Affairs Normative Aging Study, a cohort of communitydwelling elderly men from the greater Boston area. The average patella and tibia bone Pb 

concentrations were 31.2 (SD = 19.4) and 21.8 (SD = 13.6) μg/g bone mineral, respectively. The 

average blood Pb concentration measured at baseline was 5.6 (SD = 3.4) μg/dL, and the geometric 

mean was 4.8 μg/dL. To ascertain death, the study authors sent birthday cards and supplemental 

questionnaires to study participants. Next of kin or postal authorities notified the study authors if an 

individual had passed. Additionally, vital records of the Veterans Affairs and the Social Security 

Administration Death Master File were used to pick up possible unreported death, allowing for nearly 

100% mortality follow-up. Cause-specific mortalities were classified using ICD-9 codes. 

For the statistical analysis, the study authors performed direct standardization by age, given that bone 

Pb is strongly associated with age. The standardization was achieved by calculating a weighted 

average of the age-specific averages (continuous variables) or percentages (in 5-year groups). The 

authors used multivariable Cox proportional hazard regression to estimate hazard ratios and 95% 

confidence intervals (CIs). Tests for linear trend across tertiles were computed by including the tertile 

of Pb biomarkers as a continuous variable in the models. Non-linearity of Pb terms was also tested 

with penalized spline terms for the Pb biomarkers. 

In multivariable adjusted analysis, all-cause, cardiovascular, and ischemic heart disease deaths 

showed significant associations with patella Pb. Among categories of cardiovascular deaths, the 

multivariable-adjust HR for mortality from ischemic heart disease was significantly elevated in the 

highest patella Pb tertile. However, there were too few ischemic heart disease deaths among 

participants without heart disease or stroke at baseline to allow for stable, multivariable-adjusted 

models of this outcome in this subset of participants. The multivariable HR for other cardiovascular 

deaths did not appear to increase with increasing bone Pb. The results are summarized in Exhibit 10. 

In the spline regression model, there was some suggestion that the association with all-cause mortality 

and ischemic heart disease plateaus at higher bone Pb concentrations, although the authors note that 

in these ranges the data were sparse and the confidence intervals were wide. The results for tibia Pb 

and mortality were much weaker than those for patella Pb. Additionally, contrary to the Schober et 

al., Menke et al., and Khalil et al. results, no association was found between blood Pb and mortality 

(p-trend > 0.05 for all CVD deaths and for all-cause mortality) (Menke et al., 2006; Schober et al., 

2006). The authors hypothesized that their results vary from the Schober et al. and Menke et al. 

results due to a smaller sample size (i.e., not enough power to detect a relationship) or potentially due 

to greater variability in lead exposure in the Greater Boston area. The variability could impact the 

result because with more fluctuation in blood Pb levels “any single blood lead measure would be less 

correlated with overall lead exposure in our cohort and show a reduced effect estimate for mortality if 

it is truly cumulative exposure that is important for mortality outcomes” (Weisskopf et al., 2009, 

p.1061) 
Developing a Concentration-Response Function for Pb Exposure and Cardiovascular Disease-Related 

Mortality 

 Assessment of Literature ▌pg. 3-13

As stated previously, personal communication with study authors revealed plans to reanalyze the data 

and publish a correction in Circulation (Personal Communication with Mark Weisskopf, 2013). 

Therefore, any analysis on this paper should be based on the correction when it becomes available, 

not the currently published paper. Further, the results, although potentially applicable to a portion of 

the U.S. population, may be less applicable to the entire U.S. adult population given that the cohort 

consisted only of older men. 

Exhibit 10. Hazard Ratios and 95% Confidence Intervals for All-Cause, 

Cardiovascular Disease, Ischemic Heart Disease, and Other 

Cardiovascular by Tertile of Patella Pb at Baseline

Tertile of Patella Pb 

1 (<22 μg/g) 2 (22 to 35 μg/g) 3 (>35 μg/g) p for trend 

N 298 283 279 

follow-up, y 2763 2532 2387 

All-cause 

Deaths 55 75 111 

Crude Reference 1.47 (1.04-2.08) 2.45 (1.77-3.39) <0.0001 

Multivariable 1a Reference 1.44 (0.79-3.26) 1.76 (0.95-3.25) 0.07 

Multivariable 2b Reference 1.75 (0.82-3.75) 2.52 (1.17-5.41) 0.02 

All cardiovascular 

Deaths 33 41 63 

Crude Reference 1.36 (0.86-2.15) 2.33 (1.53-3.55) <0.0001 

Multivariable 1a Reference 1.39 (0.61-3.19) 2.45 (1.07-5.60) 0.03 

Multivariable 2b Reference 1.63 (0.51-5.18) 5.63 (1.73-18.3) 0.003 

Ischemic Heart Disease (Subset of All cardiovascular) 

Deaths 14 18 30 

Crude Reference 1.41 (0.70-2.85) 2.69 (1.42-5.08) 0.002 

Multivariable 1a Reference 2.99 (0.40-22.6) 8.37 (1.29-54.4) 0.01 

Other Cardiovascular (Subset of All cardiovascular)

Deaths 19 23 33 

Crude Reference 1.31 (0.72-2.41) 2.07 (1.18-3.64) 0.01 

Multivariable 1a Reference 1.01 (0.38-2.70) 1.16 (0.40-3.39) 0.79 

Multivariable 2b Reference 0.64 (0.15-2.80) 1.35 (0.30-6.09) 0.73 

a Adjusted for age, smoking (never/former/current and pack years), and education. 

b Same model but excluding the 154 participants (53 death) who had heart disease (146) or stroke (11) at

baseline. 

Source: Weisskopf et al. (2009) 
Developing a Concentration-Response Function for Pb Exposure and Cardiovascular Disease-Related 

Mortality 

 Assessment of Literature ▌pg. 3-14

3.5 Summary of Study Selection 

A summary of the rationale for our study selection for the estimation of the concentration-response 

function is available in Exhibit 11. Menke et al. (2006) estimated a continuous function and used the 

largest sample size and the most representative adult population based on age, sex, and blood Pb 

levels of all studies considered. Additionally, the EPA ISA specifically pointed to the Menke et al. 

study as the “strongest...presently published for estimating the effects of Pb on cardiovascular 

disease-related mortality” (p. 5-355). The EPA ISA supported this statement by explaining that the 

study uses a nationally representative sample of men and women and addresses some key weaknesses 

of previous NHANES analyses, such as examining confounding by many factors including 

hypertension and kidney function (U.S. EPA, 2013). 

Exhibit 11. Summary of Studies under Consideration as the Basis of the 

Concentration-Response Function 

Study 

Presentation/Use of a 

Continuous ConcentrationResponse Function in the 

Study? 

Will Study Be Used to 

Develop the ConcentrationResponse Function? 

Schober et al. (2006) 

Blood lead levels and death 

from all causes, CVD, and 

cancer: Results from the 

NHANES III Mortality Study

Yes 

No. While the study bases 

some of its findings on a 

continuous concentrationresponse function, these 

functions were not presented in 

the study, and the authors were 

unable to retrieve them. 

Menke et al. (2006) 

Blood lead below 0.48 μmol/L 

(10 μg/dL) and mortality among 

US adults

Yes 

Yes. The study presented 

results of an increase in CVD 

mortality risk as a result of a 

continuous increase in blood 

Pb level (3.4-fold increase) for 

adults age 20 and above. 

Khalil et al. (2009) 

Association of blood lead 

concentrations with mortality in 

older women: a prospective 

cohort study

No 

No. While results support the 

association between Pb 

exposure and mortality, the 

study does not present or use a 

continuous concentrationresponse function. Additionally, 

the results may be less 

applicable to the general adult 

population. 

Weisskopf et al. (2009) 

A prospective study of bone 

lead concentration and death 

from all causes, CVD, and 

cancer in the Department of 

Veterans Affairs Normative 

Aging Study

No 

No. The study does not present 

or use a continuous 

concentration-response 

function. Additionally, the 

results may be less applicable 

to the general adult population. 

Finally, the study authors 

recommended against using 

the results until a correction is 

published. 
Developing a Concentration-Response Function for Pb Exposure and Cardiovascular Disease-Related 

Mortality 

 Assessment of Literature ▌pg. 3-15

Exhibit 12 summarizes the populations used and the key findings for the four studies, all of which 

used Cox proportional hazards models to derive their hazard ratios. Examining this exhibit reveals 

potential explanations as to why Schober et al. (2006) and Menke et al. (2006), although both using 

NHANES III data, came up with different effect estimates—they used different subsets of the 

NHANES dataset, used different ICD codes to code for CVD mortality, and controlled for different 

variables. Menke also used a more inclusive set of participants, including adults age 20 and above, 

while Schober et al. focused on adults age 40 and above. 

Exhibit 12 also shows that Khalil et al. (2009) did not find a statistically significant relationship 

between blood Pb and CVD mortality in their older population of women with a higher mean blood 

Pb level. It is possible that the only reason Khalil et al.’s (2009) estimates were not statistically 

significant was due to a lack of power (i.e., a smaller number of subjects in their study population). 

Weisskopf et al. (2009) did not find a statistically significant relationship between blood Pb and CVD

mortality, though they did find a statistically significant relationship when bone is the measure of Pb 

exposure. Selection bias may be one cause for the lack of an association between blood Pb and CVD 

mortality. Selection criteria for the Normative Aging Study are heavily weighted toward individuals 

without CVD, given that in order to be entered into the cohort an individual could not have prior 

CVD. For older individuals this creates a strong selection bias toward heart-healthy people 

(Weisskopf, 2013). Further, there were fewer than 700 subjects in the Weisskopf et al. (2009) analysis

of CVD mortality, potentially leading to a lack of power to detect an association. In addition, as 

pointed out in the Weisskopf et al. (2009) discussion, the lack of a finding may be due to greater 

variability in lead exposure in the Greater Boston area. The variability could impact the result because 

with more fluctuation in blood Pb levels, “any single blood lead measure would be less correlated 

with overall lead exposure in our cohort and show a reduced effect estimate for mortality if it is truly 

cumulative exposure that is important for mortality outcomes” (Weisskopf et al., 2009, p. 1061). The 

difference in findings based on the biomarker of exposure in the study may be a result of exposure 

misclassification. It is possible that bone may be the more accurate measure of Pb exposure in relation 

to cardiovascular disease mortality risk and the exposure misclassification may not be as prominent of

an issue with bone Pb. Alternatively, it may be that a stronger association is measured between bone 

lead and cardiovascular disease which may explain why a statistically significant association was 

found when using bone Pb as the biomarker of exposure but not when using blood Pb. However, the 

authors have cautioned that a correction to the analysis is forthcoming, and that results from the 2009 

study should not be used. 

Due to heterogeneity across the four studies in terms of population, health effect (ICD codes), and Pb

reference level, as well as the overlap in study populations between Menke et al. and Schober et al., it 

is not appropriate to synthesize the blood Pb hazard increase estimates from these studies 

quantitatively, for example using meta-analysis. 
Developing a Concentration-Response Function for Pb Exposure and Cardiovascular Disease-Related Mortality 

Assessmen

Exhibit 12. Summary Statistics for Studies Considered as the Basis for the Concentration-Response 

Blood Pb Levels to Cardiovascular Disease-Related Mortality 

Study Population Examined Mean Pb Levela ICD Codes 

Reference 

Levela Pb Levela 

Khalil et al. 

(2009) 

533 women aged 65-87 years enrolled in the Study of Osteoporotic Fractures 

5.3 ± 2.3 μg/dL 

ICD9: 425, 429.2, 440-444, 428, 401-404, 410-414, 430-438, and 798 

<8μg/dL > 8 μg/dL 

Menke et al. 

(2006) 

13,946 NHANES III participants ≥ 20 years of 

age 

2.58 μg/dL (geometric mean) 

ICD-9: 390 – 434; 436 - 459; ICD-10: I00-

I99 

<1.94 μg/dL 

1.94-3.62 μg/dL 

>3.62 μg/dL 

1.46 μg/dL (20th

percentile) 

 4.92 μg/dL (80th

percentile) 

Schober et al. 

(2006) 

9,686 NHANES III participants ≥40 years of 

age 

Median: 4.14 

μg/dLa ICD-10: I00–I78 <5 μg/dL 

5-9 μg/dL 

>10 μg/dL 

Weisskopf et al. (2009) 

Men from the VA Normative Aging study n = 860 for patella Pb; n = 863 tibia Pb; and n = 1,235 blood Pb) 

Patella Pb: 31.2 μg/g Tibia Pb: 21.8μg/g Blood Pb: 4.8 μg/dL (geometric mean) 

ICD-9 codes: 390 to 459 

Patella Pb: < 22 μg/g 

Patella Pb: >35 μg/g 

Blood Pb: <4 μg/dL 

Blood Pb: >6 μg/dL 
Developing a Concentration-Response Function for Pb Exposure and Cardiovascular Disease-Related Mortality 

Assessmen

Study Population Examined Mean Pb Levela ICD Codes 

Reference 

Levela Pb Levela 

a Reported values reflect blood Pb levels unless stated otherwise. b This model was adjusted for age, clinic where examination occurred, BMI, education, smoking, alcohol intake, estrogen use, hyperten

for exercise, diabetes, and total hip bone density.

c This model was adjusted for age, race-ethnicity, sex, diabetes, BMI, current or former smoking, alcohol consumption, physical activity

CRP, total cholesterol, high school education, urban residence, postmenopausal status, hypertension and level of kidney function. 

d This model was adjusted for sex, race/ethnicity, education, and smoking status. e This model was adjusted for age, smoking (never/former/current and pack years), and education. It also excluded the 154 participants 

heart disease or stroke at baseline (n=706). 

f This model was adjusted for age, smoking (never/former/current and pack years), and education. It also excluded the 212 participants 

ischemic heart disease, or stroke at baseline (n= 648).
Developing a Concentration-Response Function for Pb Exposure and Cardiovascular Disease-Related 

Mortality 

Derivation ▌pg. 4-1

4. Derivation of Blood Pb Concentration-Response Function 

As discussed in Section 3.5, Menke et al. (2006) is the best study to use for the estimation of the 

concentration-response function due to its underlying continuous concentration-response function and 

applicability to the adult general population. 

Exhibit 7 shows the multivariate adjusted relative hazards of CVD mortality, with 95% CIs, based on 

a Cox proportional hazards model using the log of the blood Pb level. We calculated HRs for the 

difference between the (log-transformed) 80th and 20th percentiles of the blood Pb distribution given 

in the study. If blood Pb level had not been log-transformed first, the concentration-response 

relationship between blood Pb level and mortality resulting from the Cox proportional hazards model 

would be log-linear in form (i.e., the natural log of mortality would be a linear function of blood Pb

level). Since Menke et al. (2006) log-transformed blood Pb level before estimating the Cox 

proportional hazards model, however, the resulting concentration-response relationship between 

blood Pb level and mortality is log-log in form (i.e., the natural log of mortality is a linear function of 

the natural log of blood Pb level). 

The basic form of the concentration-response function underlying the HRs presented in Exhibit 7 is 

 ln( y ) = α + β * ln( x )

or 

 

β

y=B*x (1) 

Where: 

α B =e , 

where y is the probability of the adverse health effect (e.g., CVD mortality), α includes the other 

independent variables in the model and x is blood Pb level. 

This concentration-response relationship could be used in a health impact function as follows. If 0

y

denotes the baseline probability of the health effect when blood Pb level is at baseline level 0

x and 

1

y denotes the (lower) probability of the health effect associated with a (lower) blood Pb level of 1x

(the blood Pb level associated with a rule), then it can be shown that the reduced risk of the health 

effect, ( ) 0 1 ∆y= y −y , associated with a change in blood Pb level from 0

x to 1

x is: 

 ]

0

1

* 1[

0

β

















∆ = −

x

x

y y . (2) 
Developing a Concentration-Response Function for Pb Exposure and Cardiovascular Disease-Related 

Mortality 

Derivation ▌pg. 4-2

If pop denotes the population for whom this change in blood Pb level occurs, then the number of 

avoided cases of the health effect is 

 pop

x

x

cases avoided y ] *

0

1

* 1[

0

β

















= − . (3) 

The coefficient (estimate of β ) and the standard error for CVD mortality for the overall population 

are 0.35 (0.10).6 Menke et al. noted that subgroup interaction terms were not statistically significant, 

supporting the use of the coefficient for the overall population in the remainder of this analysis. 

Adjustment in the analysis included age, race-ethnicity, sex, hypertension, diabetes, BMI, current or 

former smoking, alcohol consumption, physical activity, low income, C-reactive protein, total 

cholesterol, level of kidney function, high school education, urban residence, and post-menopausal 

status. 

The CVD mortality probability associated with every value of 0

x is not available, and therefore to 

apply this function we will use the baseline incidence rate (or, equivalently, the baseline per-person

probability of CVD mortality) for the adult general population, regardless of starting blood Pb level,

for 0

y , which is standard practice for this type of analysis (U.S. EPA, 2008c, 2012a). This assumption 

will overstate the probability of CVD mortality for some people and will understate it for others. It is 

generally assumed that these over- and underestimates of risk will largely cancel each other out when 

the number of cases of CVD mortality avoided in the exposed population are calculated (U.S. EPA, 

2008c, 2012a). The CDC has data on mortality rates at various age ranges down to the county level in 

the National Vital Statistics System (CDC, 2012a). For the purpose of the benefits analysis, we could 

apply different baseline CVD mortality rates throughout the United States at the county, city, or state 

level, resulting in more precise estimates than applying one baseline CVD mortality rate to the entire

country.

 

6

 Drs. Menke and Guallar, two of the study authors, sent us the coefficients (betas), and their standard errors, 

in the log-log concentration-response functions for both all-cause mortality and CVD mortality underlying 

the HRs and 95% CIs shown in Figure 2 of their study and Exhibit 77 in the previous section. We were also 

able to back-calculate these values from the published results, and our back-calculated coefficients matched 

the coefficients that Drs. Menke and Guallar sent us, confirming our understanding of the form of the 

underlying concentration-response functions. 
Developing a Concentration-Response Function for Pb Exposure and Cardiovascular Disease-Related 

Mortality 

Generalizability ▌pg. 5-1

5. Generalizability of the Concentration-Response Function from 

Menke et al. 

In order for the concentration-response function to be most useful in a benefits analysis, it should be 

generalizable to a large portion of the population. To properly use the concentration-response 

function derived in the previous section, the function must be applied to the appropriate population 

(Section 5.1) and the appropriate blood Pb concentrations (Section 5.2), and over the appropriate time

span (Section 5.3). With these considerations, sample calculations presenting the benefits based on a 

hypothetical example are shown in Section 5.4. 

5.1 Generalizability to the Adult Population 

As noted in Exhibit 9, effect estimates were derived for many subsets of the populations (e.g., 

smokers, diabetics, individuals over 60). Given that the Menke et al. (2006) findings of statistical 

significance for the CVD mortality endpoint persisted after the adjustment for many confounders (see 

Exhibit 5) and tests for the interaction for between blood Pb levels and different subgroups resulted in 

no statistically significant relationships, it is appropriate to use the beta estimate for the entire 

population in Equation 3. Therefore, the function can be applied to adults generally. 

5.2 Range of Blood Pb Levels over Which the Concentration-Response 

Function Should Be Applied 

The blood Pb levels analyzed in Menke et al. (2006) also merit special consideration. While the 

geometric mean blood Pb level of 2.58 μg/dL reported in Menke et al. is low relative to other 

published studies, blood Pb levels have continued to decline in the decades since the data were 

collected (1988-1994), falling to a geometric mean of 1.09 μg/dL in 2011-2012 (see Exhibit 15) 

(CDC - National Center for Health Statistics, 2013). This raises the question of whether a 

concentration-response function based on the Menke study should be applied to populations with 

lower mean blood Pb levels than those reported in the study – i.e., should the function be applied to 

all blood Pb levels in the population or only to those blood Pb levels exceeding a specified value. 

5.2.1 Apply Study to All Blood Pb Levels 

One option is to apply the function from Menke et al. (2006) to the entire population, regardless of 

blood Pb level. The arguments for this conceptual model are based on a couple of factors. The first 

factor is that no threshold has been identified for adverse effects of Pb exposure. This has been 

demonstrated in both the toxicologic and epidemiologic literature (U.S. EPA, 2013) and is further 

evidenced by the fact that the hazard ratio presented in Exhibit 7 is based on the log-blood Pb 

increase, with blood Pb being a continuous variable without a threshold in a linear model. Therefore, 

for every 3.4-fold increase, regardless of where on the concentration-response curve the blood Pb 

level falls, the increase in hazard for CVD mortality is 53% (95% CI: 21%–94%). The second factor 

is that when quantifying benefits, this assumption ensures that no one is missed in the benefits 

evaluation. There is precedent in other EPA assessments for taking this approach, such as when 

quantifying IQ loss from blood Pb exposure in children in the National Ambient Air Quality 

standards (NAAQS). Benefits were estimated below the lowest measured blood Pb level of 1.47 

μg/dL in the Lanphear model (U.S. EPA, 2008b). 
Developing a Concentration-Response Function for Pb Exposure and Cardiovascular Disease-Related 

Mortality 

Generalizability ▌pg. 5-2

5.2.2 Apply Study to Blood Pb Levels Exceeding a Certain Value 

It can be argued that applying the Menke et al. (2006) function to all individuals regardless of blood

Pb level may not be appropriate, that the concentration-response function should be used only over 

the range of levels assessed in the study, or applied only to those with blood Pb levels above some 

other cutoff level. With environmental data, there is often censoring to the left at the limit of detection 

(LOD). Thus, it could be argued that the concentration-response function may be applied at levels 

above the LOD (i.e., 1 μg/dL). There may also be other cutoff points such as applying the function 

only above the 20th percentile (i.e., 1.46 μg/dL), above the inflection point from the Menke et al. 

spline functions (i.e., 2 μg/dL) or above the third tertile of Menke et al.’s tertile analysis (i.e., 3.63 

μg/dL). The reasoning for and against using each of several different cutoff points based on results 

from the Menke et al. study are provided below. 

One issue with using a cutoff level is that potential benefits will be missed in populations with blood 

Pb levels below this level. For example, only 20% of U.S. adults currently have blood Pb levels 

above 2 μg/dL. This is demonstrated in Exhibit 13, which shows several potential thresholds one 

could use for this analysis and the corresponding proportion of the population to which the benefits 

would be limited if each threshold were used. 

Exhibit 13. Proportion of the Population included in Benefits Analysis Based on 

Various Cutoff Points 

Source: National Health and Nutrition Examination Survey (NHANES) 2011-2012. 

Greater Than 1 μg/dL 

For NHANES III, the cohort examined in the Menke et al. (2006) analysis, the blood Pb LOD was 

1 μg/dL. In their analysis, the concentration of any sample below this LOD (for 8.1% of study 

No Threshold LOD 20th Percentile Spline Function 

Inflection Point

Tertile Analysis

0

10

20

30

40

50

60

70

80

90

100

0 1 1.46 2 3.63

Percent of Adult U.S. Population 

over 20 years old

Blood Lead Level
Developing a Concentration-Response Function for Pb Exposure and Cardiovascular Disease-Related 

Mortality 

Generalizability ▌pg. 5-3

participants) was replaced with a concentration of 0.7 μg/dL (Menke et al., 2006), introducing 

measurement error into the data at low blood Pb levels. It is unlikely that all samples below the LOD 

have the same concentration; instead, there is a distribution of levels between 0 and the LOD (Belova,

Greco, Riederer, Olsho, & Corrales, 2013). Because reliable estimates of blood Pb in Menke et al. 

(2006) are available only at levels above the LOD, it can be argued that the regression coefficient 

should instead be applied only to individuals with blood Pb levels above the LOD of 1 μg/dL. This is 

consistent with what was done for children’s IQ in EPA’s Renovation and Repair Rule (U.S. EPA, 

2008a). 

Greater Than 1.46 μg/dL 

Given that the hazard ratio (see Exhibit 7) was based on a comparison of those with a blood Pb level 

of 4.92 μg/dL (80th percentile) to those with a blood Pb level of 1.46 μg/dL (20th percentile), another 

potential cutoff point is 1.46 μg/dL. It should be recognized that this cutoff point is based on an 

arbitrary comparison, the 20th to 80th percentile from which the hazard ratio was developed. The 

hazard ratio could have been developed based on a comparison of any two blood Pb levels within the 

range of blood Pb levels from the study. 

Greater Than 2 μg/dL 

Menke et al. (2006) present the results from spline regression models that demonstrate an increase in 

all-cause and myocardial infarction (MI) deaths; both of these models have an inflection point at 

approximately 2 μg/dL(0.1 μmol/L; Exhibit 6). That is, the spline figure shows negative slopes 

between 0 and 2 μg/dL for risks of all-cause and MI deaths. It should be noted that the 20th percentile 

blood Pb level in Menke et al. was 1.46 μg/dL, suggesting that there may have been low power below 

2 μg/dL. Additionally, as mentioned previously, the LOD in the sample was 1 μg/dL and any 

concentration below 1 μg/dL was assigned 0.7 μg/dL. The authors state that 8.1% of participants 

were below this level. The smaller sample size in this area of the graph, along with the measurement 

uncertainty due to the measurements below the LOD in this region, could contribute to the 

counterintuitive sign seen in Menke et al.’s (2006) spline model analysis for myocardial infarction 

and all-cause mortality below 2 μg/dL (see Figure 1 of Menke et al., 2006). 

It should be noted when considering this cutoff point that Menke et al. also estimated a spline 

function for stroke mortality and this function does increase monotonically with blood Pb above 

1 μg/dL. Total CVD-related mortality, the endpoint we are examining in this report, is not included in 

the figure, and therefore it is not known whether the same relationship exists for CVD mortality as in

the MI, all-cause, or stroke spline functions. 

Last, the 0.35 coefficient for CVD mortality was derived using data from the entire distribution of 

blood Pb levels in the study. If the inflection point of 2 μg/dL in the MI and all-cause mortality spline 

functions also holds for CVD mortality, 0.35 may underestimate the relationship between blood Pb 

increase and CVD mortality risk for individuals with blood Pb above 2 μg/dL. 

Greater Than 3.63 μg/dL 

Menke et al.’s tertile analysis found a positive, but not statistically significant, difference between the 

risk of CVD mortality in the second tertile (1.93–3.62 μg/dL) when compared to the first tertile 

(<1.93 μg/dL). Statistical significance in the tertiles was detected only when comparing the third 

tertile (≥ 3.63 μg/dL) to the first tertile (see Exhibit 5). Therefore, it could be argued that the 
Developing a Concentration-Response Function for Pb Exposure and Cardiovascular Disease-Related 

Mortality 

Generalizability ▌pg. 5-4

concentration-response function derived from the continuous analysis in Menke et al. (2006) should 

only be applied to individuals with blood Pb levels greater than or equal to 3.63 μg/dL. However, in 

the trend analysis of the tertiles, Menke et al. did find a statistically significant increase across these 

three categories, and the hazard ratio from which our function is derived is developed without a 

threshold. In addition, the lack of a significant difference between the first two tertiles could be driven 

by the inflection point around 2 μg/dL seen in the MI and all-cause mortality spline functions. If CVD

mortality exhibits a similar functional form, then it is unsurprising that CVD mortality risk from the

first two tertiles would not be statistically significantly different on average because the upward trend 

in the blood Pb-CVD mortality relationship only begins around 2 μg/dL. 

5.3 Issues Regarding the Profile and Measurement of Lead Exposure and 

Risk 

Questions still remain about the time frame over which the biomarker most accurately represents risk. 

As stated by the EPA ISA, “uncertainties remain regarding the timing, frequency, duration and level 

of Pb exposures contributing to the effects observed in epidemiologic studies” (U.S. EPA, 2013, p. 

1xxxv). These uncertainties are not addressed by Menke et al. (2006) or any of the other 

epidemiologic studies we examined. This is because all of the findings are based on a single 

biomarker (blood or bone) measurement per study subject. Therefore, there is no clear answers to 

issues regarding cessation lag and latency,7 and there is uncertainty in which conceptual model best 

represents the relationship between the biomarkers or Pb exposure and current risk of CVD mortality. 

The main conceptual models are: 

1. Current CVD mortality risk = f(current blood Pb)

2. Current CVD mortality risk = f(average blood Pb over X years) 

3. Current CVD mortality risk = f(average blood Pb over X years) + latency 

4. Current CVD mortality risk = f(peak blood Pb) 

5. Current CVD mortality risk = f(current bone Pb) 

6. Current CVD mortality risk = f(average bone Pb over X years) 

7. Current CVD mortality risk = f(average bone Pb over X years) + latency 

8. Current CVD mortality risk = f(peak bone Pb) 

where current CVD mortality risk represents the CVD mortality risk within the next y years and 

where X years can include years in the follow up period, in addition to past years. Additionally, X

could be many values, even less than a year. Other conceptual models that incorporate multiple peaks 

or different biomarkers are also possible.8

 

 

7

 See U.S. EPA (2010) for definitions of latency and cessation lag. 

8

 Note that Models 1 and 2 can be considered special cases of Model 3 in which latency is set equal to zero. 

Similarly, Models 5 and 6 can be considered special cases of Model 7. 
Developing a Concentration-Response Function for Pb Exposure and Cardiovascular Disease-Related 

Mortality 

Generalizability ▌pg. 5-5

Using Menke et al. (2006) assumes that conceptual model 1 in the above list is true. This is because 

the Menke et al. study used an individual’s current blood Pb level to model the potential relationship

between blood Pb and CVD mortality and because they used the Cox regression model to model this 

relationship. The Cox regression model assumes that the hazard ratio will be the same regardless of 

the follow-up time frame. An inherent assumption of the Cox model, known as the proportional 

hazards assumption, is that an effect of a variable (main exposure or covariate) does not change over 

time (i.e., the hazard ratio is a fixed constant over time). Though hazard rates may vary with time, the 

hazard ratio will always be the same because the proportionality assumption is met and time is 

cancelled out when the ratio is calculated (Allison, 2000; Tibshirani, 1982). 

In conceptual models 2 and 3 it is unclear if the health impact function would over- or underestimate 

the number of cases. The Menke et al. (2006) analysis is based on a single blood Pb measurement per 

individual, which is reflective of both recent exposures (<30 days) from exogenous sources and past 

exposures (years to decades) that had been stored in tissues (e.g., bone) and released endogenously 

(National Toxicology Program, 2012; U.S. EPA, 2013). Therefore, this point-in-time estimate may be 

higher or lower than an individual’s average exposure over a specified period of time, depending on 

the individual’s exposure profile and other physiological characteristics that may contribute to the 

release of Pb from bone. 

In conceptual model 4, if peak exposures are most important in predicting risk, it would be hard to 

predict in which direction the results will be biased because we do not know when the NHANES 

measurements were taken in relation to the timing of the peak exposure. External exposures are 

represented in blood Pb for about 30 days, during which Pb is eliminated or stored in bone, and 

therefore the spot blood Pb sample would not be likely to capture the higher blood Pb concentration 

as a result of the external exposure spike, unless it occurred within the last 30 days. 

If the conceptual models that incorporate latency (3 and 7 from the above list) are correct, using 

Menke et al.’s results as the basis of our health impact function is likely to overestimate the 

monetized (i.e., dollar-valued) benefits that would result from any rule that decreases Pb exposure. 

This is because benefits would be realized later than predicted when not considering latency. Since 

benefits in the future are discounted, ignoring latency (and not discounting benefits) would result in

an overestimation of monetized benefit. In benefits estimation, an assumption about latency could be 

built in to a sensitivity analysis. 

If risk is more accurately represented by bone Pb (conceptual models 5 through 8), which is thought 

to be a better marker for cumulative Pb exposure, the health impact function may underestimate the 

benefits. If bone is the more accurate measure and if using blood Pb as a proxy measurement for bone 

Pb adds random, non-biased error, using bone Pb should result in less exposure misclassification than 

using blood Pb. Therefore, using blood Pb may bias the result toward the null. That is, a single blood

Pb measurement is highly variable, and therefore using the highly variable measurement to predict an 

outcome that may be a consequence of a long-term exposure results in an underestimation of the true 

effect. 

Also, for all of the conceptual models, the data are not sufficiently detailed to indicate whether 

several short periods of high exposure would impact an individual differently from a continuous 

lower level of exposure. It would be difficult to separate these two scenarios given that they may 
Developing a Concentration-Response Function for Pb Exposure and Cardiovascular Disease-Related 

Mortality 

Generalizability ▌pg. 5-6

result in the same cumulative average exposure (which can be represented by the bone Pb 

measurement). Additional information on bone and blood Pb biomarkers is provided in Appendix B. 

The concentration-response function derived from Menke et al. (2006) was estimated with a static 

version of the Cox proportional hazards model. That is, Menke et al. (2006) explored the relationship 

between the level of blood Pb measured at a certain date and whether the study participant died 

between the date of blood sample collection and December 31, 2000. Because exposure is represented 

by a static/point-in-time measurement, this type of modeling cannot answer questions about the 

existence of risk cessation lag. Dynamic survival models could be used to explore the idea of 

cessation lag in this context, but they will require information on the exposure time profile (e.g., 

blood Pb measurements at several points in time). The current literature does not contain this type of

analysis. 

5.4 Sample Benefits Calculation 

To gain an understanding of the magnitude of the benefits that would result from using the function 

derived in Section 4, we present hypothetical examples. Recall from the previous section that 

Equation 3, the health impact function based on the Menke et al. concentration-response function, is: 

 pop

x

x

cases avoided y *]

0

1

1[*

0

β

















= − . (3) 

Where: 

y0 = CVD disease mortality rate 

x1 = blood Pb level with rule 

x0 = blood Pb level without rule 

β = 0.35 (SE = 0.10) (from Menke et al., 2006) 

pop = U.S. adult population over 20 years old 

The hypothetical examples presented in this section represent a decrease in short-term exposure 

spikes from an event such as a renovation. 

5.4.1 Cardiovascular Disease Mortality Rate 

We use various inputs for each of the parameters outlined in Equation 3 except for y0, the baseline 

cardiovascular disease mortality rate. For the hypothetical example calculations, we use the age 

(within 10 years) and gender specific CVD mortality rates represented in Exhibit 14, for all scenarios. 
Developing a Concentration-Response Function for Pb Exposure and Cardiovascular Disease-Related 

Mortality 

Generalizability ▌pg. 5-7

Exhibit 14. Cardiovascular Disease-Related Mortality in the United States in 2010 

Age (years) Gender Number of 

Deaths 

CVD Mortality 

Incidence, Y0

20-29 

M 1,562 7.2E-05 

F 780 3.7E-05 

30-39 

M 4,867 2.4E-04 

F 2,491 1.2E-04 

40-49 

M 18,005 8.3E-04 

F 8,644 3.9E-04 

50-59 

M 45,207 2.2E-03 

F 20,220 9.4E-04 

60-69 

M 64,548 4.6E-03 

F 35,298 2.3E-03 

70-80 

M 93,258 1.2E-02 

F 75,440 7.6E-03 

Total (20-80 

years) 

M 227,447 2.2E-03 

F 142,873 1.3E-03 

Both 370,320 1.7E-03 

Source: CDC – National Center for Health Statistics (2010) 

5.4.2 Blood Pb Levels 

For the ratio of with and without rule blood Pb levels (



) we plan to take two approaches, one that 

assumes all individuals experience the same percentage change in their blood Pb level as a result of 

the rule and one that assumes all individuals experience the same absolute change as a result of the 

rule. Both of these approaches are oversimplifications, as each individual in reality may have 

different exposure patterns, but they are applied here for illustrative purposes. 

As discussed in the previous section, it is unclear from the available data if peak blood Pb levels or

average blood Pb levels are more important in regard to the risk of cardiovascular disease mortality. 

Therefore, to examine how benefits from a rule may vary based on the average or peak blood Pb 

level, we will use two values, 0.05 μg/dL or 1 μg/dL above the current geometric mean levels from 

the most recent NHANES analysis (see Exhibit 15) for the appropriate age and gender category.9

 That 

is: 

 

9

 For this hypothetical example we are making simplifications to focus on the concentration-response 

function, and understand that modeling blood Pb changes is not trivial; however, that discussion is beyond 

the scope of this report. 
Developing a Concentration-Response Function for Pb Exposure and Cardiovascular Disease-Related 

Mortality 

Generalizability ▌pg. 5-8

x0 = 0.05 +BLLb or 

x0 = 1.00 + BLLb 

Where: 

BLLb = the geometric mean blood Pb level from NHANES, see Exhibit 15 

The 0.05 μg/dL increase over the current geometric mean blood Pb levels represents the average 

blood Pb levels over the course of an event such as a renovation, whereas the 1 μg/dL increase 

represents a potential peak increase in blood Pb level. Then, assuming the rule negates all potential 

exposure from an event such as a renovation, we calculate x1 to be x0 minus the increase in exposure 

caused by the renovation, either 0.05 μg/dL or 1 μg/dL. That is: 

x1 = x0 - 0.05 = BLLb or 

x1 = x0 - 1.00 = BLLb

For the percentage increase we will use the equations below to examine the sensitivity of the results 

to the ratio of baseline to post-rule blood Pb levels. 

That is: 

x0 = BLLb *1.05 or 

x0 = BLLb *1.50, and 

x1 

= BLLb 

In the case of a rule that would decrease ambient Pb exposure levels, x0 and x1 would be calculated 

differently. We would assume that x0 is equal to the current baseline blood Pb levels and x1 is equal x0

minus a specified decrease in blood Pb levels. That is, instead of assuming we are avoiding an 

increase in blood Pb level, as we are for an event in this LRRP-type rule, we would assume we are 

decreasing blood Pb levels. 
Developing a Concentration-Response Function for Pb Exposure and Cardiovascular Disease-Related 

Mortality 

Generalizability ▌pg. 5-9

Exhibit 15. Adult Blood Lead Levels in 2011-2012 

Age 

(years) Gender

Geometric 

Mean 

Std. 

Error

75th

Percentile

95th

Percentile

Percentage above: 

1 

μg/dL

1.46 

μg/dL

2 

μg/dL

3.62 

μg/dL 

20-29 

M 0.93 0.05 1.29 2.85 37% 19% 10% 4% 

F 0.56 0.02 0.74 1.43 11% 5% 2% 0% 

30-39 

M 1.00 0.04 1.44 2.52 46% 24% 11% 1% 

F 0.62 0.03 0.86 1.61 16% 6% 3% 1% 

40-49 

M 1.25 0.05 1.74 3.64 60% 36% 18% 5% 

F 0.93 0.03 1.28 2.48 39% 17% 9% 2% 

50-59 

M 1.61 0.10 2.24 5.20 76% 52% 30% 8% 

F 1.23 0.04 1.78 2.73 63% 35% 16% 3% 

60-69 

M 1.78 0.09 2.61 4.60 85% 59% 39% 13% 

F 1.28 0.08 1.76 3.10 66% 39% 15% 4% 

70-80 

M 1.86 0.07 2.65 4.02 89% 68% 42% 10% 

F 1.45 0.06 2.13 4.00 72% 49% 28% 7% 

All 20+ 

M 1.31 0.04 1.94 4.00 63% 40% 23% 6% 

F 0.93 0.03 1.42 2.76 43% 24% 12% 3% 

Both 1.09 0.03 1.67 3.36 52% 32% 17% 4% 

Source: National Health and Nutrition Examination Survey (NHANES) 2011-2012. 

5.4.3 Population 

For this example we assumed that 0.5% of the population (approximately 1 million people) is 

impacted by the rule. We also stratified our analysis because, as discussed in Section 5.2.2, in certain 

cases it may be appropriate to apply the function only above different cutoff levels of blood Pb. For 

illustrative purposes, we apply the function to the entire population affected by the rule and then 

restrict the benefits to only the proportion of the population that has blood Pb concentrations greater 

than 1, 1.46, 2, and 3.63 (see Exhibit 15). We calculated the following parameters for each 

stratification level: 

• We calculated mean blood Pb levels (BLLb ) as geometric means of blood Pb levels from 

NHANES 2011-2012, for only those with blood Pb levels above the starting level of interest. 

• We multiplied the affected population (pop) by the share of the population with blood Pb levels 

above the cutoff level of interest, based on NHANES 2011-2012. 

• We calculated the without-rule blood Pb level (x0) in two ways. For percentage changes, we 

calculated 5% and 50% increases in our estimates from mean blood Pb levels (x1). For absolute 

changes, we added 1.0 μg/dL and 0.05 μg/dL to each NHANES participant’s blood Pb level, and 

then calculated the geometric means for each stratification. We used these geometric means as x0

in the function. 
Developing a Concentration-Response Function for Pb Exposure and Cardiovascular Disease-Related 

Mortality 

Generalizability ▌pg. 5-10

5.4.4 Beta 

Lastly, the central estimate of beta is 0.35 μg/dL (SE=0.10). In order to understand the sensitivity of 

the results to the value of this parameter, we will run all of our model estimates with the central 

estimate of beta and the upper bound (β = 0.55) and lower bound (β = 0.15) of the 95% confidence 

interval. 

5.4.5 Summary of Inputs 

A summary of the various inputs we use to calculate benefits for various hypothetical examples are 

presented in Exhibit 16. 

Exhibit 16. Input Parameters for a Hypothetical Benefits Analysis 

Parameter Assumption 

CVD mortality 

rate, y0

Age (within a 10-year range) and gender-specific CVD disease mortality rates 

(ICD-9 codes 390-434 and 436-459; ICD-10 codes I00-I99) (see Exhibit 14). 

Blood Pb levels, 

x1,x0

Two approaches will be taken: 

• A percent increase (approximately 5% or 50%): 

o x0 = NHANES geometric mean blood Pb multiplied by 1.05 or 1.50 

o x1 = NHANES geometric mean blood Pb 

• An absolute change: 

o x0 = NHANES geometric mean blood Pb plus 0.05 μg/dL or 1 μg/dL 

o x1 = NHANES geometric mean blood Pb 

Β 0.35 (SE = 0.10) (from Menke et al., 2006) 

Population • For LRRP-type rule, 0.5% of the population or approximately 1 million 

people 

• Blood Pb cutoffs: 

o >1 

o >1.46 

o >2 

o >3.63 

For this hypothetical example we assume that all benefits will be experienced within 1 year. This is 

based on the observation that blood Pb declines rapidly following short-term elevated exposures in 

individuals with relatively low blood Pb levels, even though bone Pb and total body burden decline 

much more slowly (Figure 3-11, U.S. EPA, 2013). Further refinements of this analysis could instead 

use predicted blood Pb levels from a biokinetic model such as Leggett (Cal OEHHA, 2013; Leggett, 

1993) to estimate changes in blood Pb over the length of time blood Pb is estimated to remain 

elevated relative to baseline levels. 

In situations where blood Pb levels in a population are higher for longer periods of time, and a 

proposed rule is anticipated to reduce these levels significantly, it may take more time for the blood

Pb levels to decrease due to the contribution of body burden (bone stores) on blood Pb levels. In this

case, it may be more appropriate to consider a more nuanced approach to estimating the benefits than 

these hypothetical examples. 
Developing a Concentration-Response Function for Pb Exposure and Cardiovascular Disease-Related 

Mortality 

Generalizability ▌pg. 5-11

5.4.6 Results 

Using Equation 3 along with the assumptions and data points outlined in the preceding sections and 

Exhibit 16, we calculated the total number of CVD mortality cases avoided under the different 

scenarios. By multiplying the number of cases by a value of a statistical life (VSL) in 2012 dollars of 

$8.43 million ($4.799 million in 1990 USD with an inflation factor of 1.76), (Bureau of Labor 

Statistics, 2013; U.S. EPA, 2010), we calculated the total annual benefits. These results are 

summarized in Exhibit 17 and Exhibit 18. The benefits of an LRRP-type rule affecting approximately 

1 million people range from $1.9 million, with a 0.05 μg/dL decrease in blood Pb in individuals with 

blood Pb levels greater than 3.62 μg/dL and the lower estimate of beta, to $4.02 billion, with a 

1 μg/dL decrease in blood Pb when considering all individuals regardless of blood Pb with the upper 

beta estimate. 
Developing a Concentration-Response Function for Pb Exposure and Cardiovascular Disease-Related Mortality 

 Generalizability ▌pg. 5-12

Exhibit 17. Quantified Benefits from Absolute Change for Varying Blood Pb Cutoff Levels for LRRP Type 

Blood 

Pb 

Cutoff 

(μg/dL)

Gender

0.05 μg/dL decrease 1.0 μg/dL decrease 

Annual Cases Avoided Annual Benefits (2012$ million) Annual Cases Avoided Annual B

L1 C2 U3 L C U L C U L 

None4

M 5.80 13.49 21.12 $48.90 $113.70 $178.00 82.68 183.53 274.59 $697.00 

F 4.69 10.90 17.06 $39.60 $91.90 $143.80 62.27 136.82 202.73 $525.00 

Both 10.50 24.39 38.18 $88.50 $205.60 $321.80 144.96 320.35 477.32 $1222.00 

1 

M 3.71 8.64 13.54 $31.30 $72.80 $114.10 57.05 127.69 192.59 $481.00 

F 2.09 4.87 7.63 $17.60 $41.00 $64.30 31.65 70.59 106.09 $266.80 

Both 5.81 13.51 21.17 $48.90 $113.90 $178.40 88.71 198.28 298.67 $747.80 

1.46 

M 2.13 4.96 7.79 $18.00 $41.90 $65.60 34.42 77.54 117.72 $290.10 

F 1.03 2.40 3.76 $8.70 $20.20 $31.70 16.47 37.02 56.07 $138.80 

Both 3.16 7.37 11.55 $26.70 $62.10 $97.40 50.89 114.57 173.79 $429.00 

2 

M 1.04 2.42 3.80 $8.80 $20.40 $32.00 17.51 39.71 60.66 $147.60 

F 0.40 0.93 1.46 $3.40 $7.80 $12.30 6.71 15.19 23.18 $56.50 

Both 1.44 3.35 5.26 $12.10 $28.30 $44.30 24.22 54.90 83.84 $204.10 

3.62 

M 0.16 0.38 0.60 $1.40 $3.20 $5.00 2.92 6.70 10.34 $24.60 

F 0.06 0.13 0.21 $0.50 $1.10 $1.80 1.04 2.37 3.66 $8.70 

Both 0.22 0.51 0.81 $1.90 $4.30 $6.80 3.96 9.07 14.00 $33.40 

Note: 1 L = lower beta estimate 2 C = central beta estimate 3 U = upper beta estimate 4 “None” represents an unstratified analysis and includes the entire population regardless of blood Pb level 
Developing a Concentration-Response Function for Pb Exposure and Cardiovascular Disease-Related Mortality 

 Generalizability ▌pg. 5-13

Exhibit 18. Benefits from Percentage Change for Varying Blood Pb Cutoff Levels for LRRP Type Rule 

Blood Pb 

Cutoff (μg/dL) Gender

5% Decrease 50% Decrease 

Annual Cases 

Avoided 

Annual Benefits (2012$ 

million) 

Annual Cases 

Avoided Annual Be

L1 C2 U3 L C U L C U L 

None4

M 8.3 19.3 30.1 $69.90 $162.30 $253.80 67.1 150.5 227.3 $565.70 

F 5.2 12.1 18.9 $43.90 $102.00 $159.50 42.2 94.5 142.8 $355.30 

Both 13.5 31.4 49.0 $113.80 $264.30 $413.30 109.3 245.0 370.1 $921.00 

1 

M 6.8 15.7 24.5 $57.00 $132.30 $206.90 54.7 122.7 185.3 $461.20 

F 3.5 8.0 12.5 $29.10 $67.60 $105.80 28.0 62.7 94.7 $235.70 

Both 10.2 23.7 37.1 $86.10 $200.00 $312.70 82.7 185.3 280.0 $696.80 

1.46 

M 4.8 11.3 17.6 $40.90 $94.90 $148.40 39.2 87.9 132.9 $330.60 

F 2.2 5.0 7.9 $18.30 $42.60 $66.60 17.6 39.5 59.6 $148.30 

Both 7.0 16.3 25.5 $59.20 $137.40 $214.90 56.8 127.4 192.5 $479.00 

2 

M 3.0 7.0 10.9 $25.40 $59.00 $92.30 24.4 54.7 82.6 $205.60 

F 1.1 2.6 4.1 $9.40 $21.80 $34.10 9.0 20.2 30.6 $76.10 

Both 4.1 9.6 15.0 $34.80 $80.80 $126.40 33.4 74.9 113.2 $281.70 

3.62 

M 0.8 1.9 2.9 $6.80 $15.80 $24.60 6.5 14.6 22.1 $54.90 

F 0.3 0.6 1.0 $2.30 $5.30 $8.30 2.2 4.9 7.5 $18.60 

Both 1.1 2.5 3.9 $9.10 $21.10 $33.00 8.7 19.6 29.5 $73.50 

Note: 1 L = lower beta estimate 2 C = central beta estimate 3 U = upper beta estimate 4 “None” represents an unstratified analysis and includes the entire population regardless of blood Pb level
Developing a Concentration-Response Function for Pb Exposure and Cardiovascular Disease-Related 

Mortality 

 Generalizability ▌pg. 5-14

To put these values in perspective, according to CDC, in 2010, 1,852 individuals out of a million 

(0.185%) would be expected to die from CVD in the adult general population based on the CVD 

mortality incidence rates (CDC - National Center for Health Statistics, 2010). The absolute change 

examples presented avoid from 1.2% (0.05 μg/dL decrease in blood Pb) to 16% (1 μg/dL decrease in 

blood Pb) of CVD-related deaths, and the percentage increase examples avoid between 1.6% and 

12.3% of CVD-related deaths. All of these examples are assuming the function will apply to the 

entire affected population regardless of blood Pb level and are based on the central beta estimate. 
Developing a Concentration-Response Function for Pb Exposure and Cardiovascular Disease-Related 

Mortality 

 Uncertainty and Variability ▌pg. 6-1

6. Discussion on Uncertainty and Variability in the ConcentrationResponse and Health Impact Functions 

The hypothetical examples presented in Section 5.4 used point estimates for each of the parameters 

(y0, β, and pop). However, in reality each of the parameters used in the function are uncertain and 

variable. Consequently, as with any benefits analysis, there is uncertainty and variability of the 

concentration-response function and the health impact function. As a result of the uncertainty in the 

inputs to the benefits estimation, the resulting benefits numbers are also uncertain. In this section we 

describe the additional sources of uncertainty and variability for both the concentration-response 

function (Section 6.1) and the health-impact function (Section 6.2). Where possible, we also describe 

approaches for characterizing the uncertainty and variability. 

6.1 Uncertainty in the Concentration-Response Function 

Recall from Section 4 the concentration-response function is of the form: 

 ln( y ) = α + β * ln( x )

or 

β

y=B*x , ( α B =e ), 

where y is the probability of the adverse health effect (e.g., CVD mortality), x is blood Pb level, and β

is 0.35 (SE = 0.10), as provided by Menke et al. (2006). The uncertainty and variability associated 

with each of these parameters is discussed in the following sections. 

6.1.1 Effect (β) Estimate 

The beta estimate of 0.35 (SE=0.10) has two main uncertainties associated with it: (1) sampling 

uncertainty, and (2) missing data/imputation uncertainty. Sampling uncertainty is the uncertainty 

associated with sampling a random sample of a group as opposed to every individual in the group. 

This uncertainty can be characterized by using a distribution of estimates for the beta as opposed to 

the single value of 0.35. Based on the sampling design for NHANES III, the distribution for the beta 

is a t-distribution with 49 degrees of freedom (Personal Communication with Andy Menke, 2013). An 

example of the lower and upper bound estimates of the number of CVD deaths avoided as a result of 

a hypothetical situation was calculated in the previous section using the upper and lower bounds of 

the 95% confidence interval on the central beta estimate. 

There is additional uncertainty when the estimated concentration-response function is used to make 

predictions for blood Pb levels below the LOD because blood Pb levels below the LOD were 

imputed. Unfortunately, these missing data/imputation uncertainties cannot be categorized without 

doing a complete reanalysis of the Menke et al. (2006) data. 

As for variability associated with the β estimate, Menke et al. (2006) examined effect modification by 

all of the groups presented in Exhibit 7 and found no statistically significant interactions. 
Developing a Concentration-Response Function for Pb Exposure and Cardiovascular Disease-Related 

Mortality 

 Uncertainty and Variability ▌pg. 6-2

There is also an uncertainty in regard to the NHANES III one-time blood Pb levels, since it is 

unknown if they accurately represent the Pb exposure that will result in future CVD mortality cases. 

As stated previously, a major limitation of the Menke et al. (2006) findings is that they are based on a 

single blood Pb measure, which is reflective of both recent exposures (<30 days) from exogenous 

sources and past exposures (years to decades) that had been stored in tissues (e.g., bone) and released 

endogenously (National Toxicology Program, 2012; U.S. EPA, 2013). Therefore, it is unclear 

whether the impact of Pb on CVD mortality risk observed in Menke et al. (2006) is associated with 

current, past, or cumulative exposures. 

It is possible that the relationship predicted by using blood Pb may underestimate the true risk of 

CVD mortality associated with Pb exposure. This is because using blood Pb will likely result in 

exposure misclassification, biasing the result toward the null. This occurs because a single blood Pb 

measurement is highly variable, and, therefore, using the highly variable measurement to predict an 

outcome adds noise to the model, resulting in an underestimation of the true effect (Personal 

Communication with Mark Weisskopf, 2013; Rotheman, 1998). 

An additional consideration is that of applying the beta estimate derived from a population whose 

blood Pb levels were measured between 1988 and 1994 to estimate the benefits in the current 

population. General population blood lead levels fell relatively quickly in the 1980s and 1990s, and 

continued to fall, but at a slower rate, after the year 2000. The lifetime Pb exposure profile for an

adult with a particular blood Pb measurement in 2014 is likely different from that of an individual of

the same age with the same blood Pb level in 1992. The individual in 1992 would likely have had 

higher past Pb exposure. It is unclear how the different past exposure profiles would impact the 

resulting effect estimate. This is because based on the current data we are unaware what the best 

model is to relate blood Pb measurements to cardiovascular disease mortality risk (see discussion in 

Section 5.3). 

Additionally, blood Pb levels decreased (from a geometric mean of 2.72 μg/dL in1988-1994 to 1.64 

μg/dL in 1999-2002) during the sampling and follow-up period of the NHANES cohort used by of 

Menke et al. (2006). The implications of this decrease in blood Pb levels throughout the NHANES III 

time period are unclear. It could be argued that applying the beta estimate based on these higher blood 

Pb levels to lower blood Pb levels may overestimate the risk. On the contrary, the argument could be 

made that due to exposure misclassification the beta estimate has been attenuated and is actually 

underestimating the relationship, assuming the misclassification is random. As the authors point out, 

“the decrease in blood lead observed at the population level implies that the results of the present 

study are conservative and that the lead–mortality relationship may be stronger than reported” 

(Menke et al., 2006, p. 1392-1393).

As discussed in Section 5.2.2, the function used in Menke et al. (2006) was developed using past 

blood Pb levels (adults in 1988-1994), which are greater than current blood Pb levels. The NHANES 

III levels were analyzed with a technique that had a limit of detection of 1 μg/dL (CDC, 1996, p. VIIH-12). Therefore, assuming that the linear relationship between log blood Pb and log CVD mortality 

would hold below the observable range in Menke et al. (2006) introduces uncertainty, as it could be 

possible that the slope is different at lower blood Pb levels relevant for portions of current and future 

populations (i.e., blood Pb levels < 1 μg/dL). 
Developing a Concentration-Response Function for Pb Exposure and Cardiovascular Disease-Related 

Mortality 

 Uncertainty and Variability ▌pg. 6-3

The multivariate regression estimates reported in Menke et al. (corresponding to Exhibit 7 and 

Exhibit 12) could also underestimate the relationship between blood Pb and CVD mortality due to the 

inclusion of hypertension and kidney function as control variables. The EPA ISA (2013) found 

associations between Pb exposure and hypertension (causal relationship), as well as reduced kidney 

function (suggestive of a causal relationship). Controlling for these conditions in the regression means 

that the coefficient estimate on Pb exposure will not capture any potential indirect effects of Pb 

exposure in increasing the risk of CVD mortality by the mechanisms of hypertension or impaired 

kidney function. As can be seen in Exhibit 5, when not controlling for hypertension or kidney 

function, the hazard ratios were slightly larger compared to controlling for them. 

Quantifying these uncertainties is currently not possible. However, if follow-up work from Weisskopf 

et al. provides a function relating bone Pb to CVD mortality, both the bone and blood Pb functions 

could be used to estimate benefits to determine how the various models impact the resulting benefits 

numbers. Additionally, an analysis similar to Menke et al. (2006) is underway by the National Center 

for Health Statistics to use the more recent NHANES blood Pb data that could help better identify the 

relationship between blood Pb and CVD mortality at more recent blood Pb levels. If a function is 

estimated by this analysis, it too could be used to characterize the uncertainty of the relationship 

between Pb exposure and CVD mortality. These ongoing studies are discussed further in Section 7. 

6.1.2 Blood Pb Estimates 

The blood Pb levels used in the hypothetical example were based on data from the most recent 

NHANES analysis (NHANES 2011-2012). Sampling uncertainty exists with these values and can be 

characterized by using a distribution of blood Pb levels with the proper number of degrees of freedom 

to account for the complex sampling design used in NHANES 2011-2012. However, blood Pb levels 

used in benefits estimation for a proposed rule will likely be estimated using exposure modeling. If 

the exposure model is designed to output a distribution of total blood Pb values at baseline and under

regulation, then the resulting distributions can be used to calculate a range of benefits as opposed to 

just using the mean value. Additional uncertain variables will undoubtedly exist in the exposure 

modeling. However, given that this report is focused on applying the concentration-response function 

and not the exposure modeling, we do not further analyze the uncertainty of potential exposure 

models in this report. 

Additionally, blood Pb levels will vary between people (e.g., based on age, gender, sociodemographic group membership). For the hypothetical analysis we characterize some of this 

variability by deriving benefits estimates for gender/age groups, whose blood Pb levels do vary. 

However, as with the uncertainty, in order to capture this variability in modeled blood Pb levels, 

blood Pb estimates could be modeled for various groups of people to determine how the blood Pb 

concentrations may vary based on an individual’s characteristics. Again, given that this report is 

concentrating on applying the concentration-response function and not the exposure modeling, we do 

not further analyze the uncertainty of potential exposure models in this report. 

6.1.3 Functional Form 

As with many concentration-response functions, there is uncertainty about the functional form of the 

relationship between exposure and response. In considering the relationship between Pb exposure and 

CVD, Menke et al. (2006) examined several functional forms and concluded that the linear function 

between log of blood Pb and log of CVD mortality risk best represented the concentration-response 
Developing a Concentration-Response Function for Pb Exposure and Cardiovascular Disease-Related 

Mortality 

 Uncertainty and Variability ▌pg. 6-4

relationship. Additionally, although it is possible for the functional form to vary between population

groups, Menke et al. (2006) explored this and found no difference between groups. 

6.2 Uncertainty in the Health Impact Function 

A health impact function was derived from the concentration-response function. This function allows 

for a quantification of the number of cases avoided among populations impacted, as illustrated in our 

hypothetical example. As a reminder this equation is: 

pop

x

x

cases avoided y *]

0

1

1[*

0

β

















= − (3) 

where y0 is the baseline CVD mortality per capita rate, x1 is the blood Pb level with a rule in place, x0

is the blood Pb level without the rule, and pop is the population the rule will impact. In addition to the 

uncertainty associated with blood Pb measurement and the concentration-response function 

coefficient estimate (discussed in the previous section), incorporating the baseline mortality rate and 

the affected population adds additional uncertainty and variability to the benefits estimate. The 

subsequent sections discuss these additional sources of uncertainty and variability. 

6.2.1 Baseline Mortality Rates 

The method to characterize uncertainty and variability in the baseline mortality rates for the benefits 

analysis will be specific to the economic analysis approach and the data sources being used. For 

example, a specific analysis can either assume that the mortality rate for a given population is the 

same as the most recent year(s) of data, or it can project what the mortality rate may be in the future 

year when a rule may be implemented. Uncertainties associated with both approaches need to be 

examined by the economists developing the analysis in the context of the rule, and then the 

characterization of these uncertainties can be presented. As for variability, the baseline mortality rate 

varies by age, gender, socio-demographic group, and location. As with uncertainty, variability should 

be characterized in the context of the rule. For example, if it is important to understand how benefits 

vary in different locations across the country, CVD mortality rates may be needed at a smaller 

geographic resolution compared to the hypothetical example, which used a national estimate. Because 

the characterization of uncertainty and variability depends on the data sources, approach, and rulespecific needs, exact methods of characterization will not be discussed further in this report. 

6.2.2 Population Impacted by the Rule 

For any benefits analysis, the population affected by the rule needs to be defined. In the hypothetical 

example presented, it was assumed 1 million people would be impacted, which is only 0.5% of the 

total U.S. adult population. The number of people impacted will vary by the policy scenario being 

considered and will also vary according to many of the same variables that are mentioned when 

considering uncertainty and variability for the other components of the concentration-response 

function. Additionally, spatial variation may exist. That is, benefits will be dependent on the area of 

the United States that is being impacted and the size of the population in that area. The magnitude of

the variability of the population impacted could be explored by examining different areas where the 

rule will be implemented and different population groups that may be impacted. Additionally, 

uncertainty surrounding the population estimates in certain areas can be characterized by data 
Developing a Concentration-Response Function for Pb Exposure and Cardiovascular Disease-Related 

Mortality 

 Uncertainty and Variability ▌pg. 6-5

provided by the U.S. Census, which can be obtained at the Census tract level. The U.S. Census data 

provide information on measurement uncertainty in these estimates and could be used to characterize 

uncertainty surrounding population estimates. 
Developing a Concentration-Response Function for Pb Exposure and Cardiovascular Disease-Related 

Mortality 

 Next Steps ▌pg. 7-1

7. Next Steps 

We have developed a concentration-response function that allows for the estimation of benefits due to 

a reduction in risk from CVD mortality as a result of reduced lead exposure in adults. This function is 

based on Menke et al. (2006), who used one-time blood Pb samples from NHANES III (1988-1994). 

To understand how this function may change with reduced blood Pb levels (past blood Pb levels are 

higher than current blood Pb levels), the National Center for Health Statistics is conducting an 

analysis similar to that conducted by Menke et al. (2006) with more recent NHANES blood Pb 

samples. When these data become available, we will evaluate them and, if appropriate, use them to 

update the current function with a new function based on more recent blood Pb levels. 

In addition, work is currently underway by Weisskopf et al. to understand the extent to which 

selection bias may impact the results of their 2009 paper. When these data become available, we will 

evaluate them to determine if a bone Pb-CVD mortality risk function could be developed. We will 

also examine the results from the revised blood Pb analysis. This would allow for a deeper 

understanding of the relationship between current versus cumulative exposure and increased CVDrelated mortality risk. 

Further, in order to understand additional benefits that may result from reduced Pb exposure, it may 

be feasible to pursue the development of concentration-response functions for additional endpoints. 

To select additional endpoints for inclusion in a benefits assessment, we would need to determine if 

estimates can be provided for morbidity cases (e.g., non-fatal myocardial infarction or increases in 

blood pressure) that do not result in mortality (e.g., fatal myocardial infarction) and if the 

interrelationship of the various toxicity endpoints associated with Pb exposure such as cardiovascular

and renal endpoints could be separated for the purposes of estimating benefits.
Developing a Concentration-Response Function for Pb Exposure and Cardiovascular Disease-Related 

Mortality 

 References ▌pg. 1

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lead level prediction models and their application to examine the relationship of lead 

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Personal Communication with Andy Menke (2013, 9/2/13). 

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injuries, and risk factors. JAMA, 310(6), 591-606. doi: 10.1001/jama.2013.13805 

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Final Rule for Target Housing and Child-Occupied Facilities. Washington, DC: U.S. EPA. 

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Park, NC. 

Weisskopf, M. G. (2013). What you don't see can hurt you: Selection bias in cohort studies - are 

environmental studies at particular risk? Paper presented at the International Society for 

Environmental Epidemiology, Basel, Switzerland. 

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prospective study of bone lead concentration and death from all causes, cardiovascular 
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Mortality 

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diseases, and cancer in the Department of Veterans Affairs Normative Aging Study. 

Circulation, 120(12), 1056-1064. doi: 10.1161/circulationaha.108.827121 

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IV. Distribution of lead in the human skeleton. Arch Environ Health, 43(6), 381-391. doi: 

10.1080/00039896.1988.9935855 
Developing a Concentration-Response Function for Pb Exposure and Cardiovascular Disease-Related 

Mortality 

 Studies Not Selected ▌pg. A-1

Appendix A Overview of Studies Not Selected for Additional Review 

This section presents summaries of five studies identified by the EPA ISA and the NTP Monograph 

that examine the association between Pb exposure and CVD mortality. These are the studies that we 

did not select for further evaluation as the basis for the development of a concentration-response 

function. 

Cocco et al. (2007) 

Cocco et al.’s 2007 study of causes of death among lead smelters in relation to the glucose-6-

phosphate dehydrogenase polymorphism (G6PD) assessed the adverse health effects of 933 male Pb 

smelters in Sardinia, Italy. The study focused on differences in Pb toxicity susceptibility of two 

cohorts with divergent G6PD polymorphisms. Blood Pb levels were not measured, but the 

standardized mortality ratios (SMRs) for the Pb smelters’ all-cause and CVD deaths were 56 (95% 

CI: 46, 68) and 37 (95% CI: 25, 55), respectively, when comparing all of the Pb smelters regardless 

of their G6PD polymorphism with the general population. Given that blood Pb levels were not 

reported in this study and that the study was in Pb smelters, a group not representative of the adult 

general population, we did not evaluate the study further to determine if the data presented would be 

useful in the estimation of a concentration-response function. 

Lin et al. (2011) 

Lin et al.’s 2011 study, Association of Blood Lead Levels with Mortality in Patients on Maintenance 

Hemodialysis, considered the association between blood Pb levels and mortality in patients on 

maintenance hemodialysis. The study included 927 patients in Taiwan who had a mean hemodialysis 

duration of 5.6 ± 2.1 years and were on average 55.7 ± 13.4 years old. The median blood Pb level was 

10.4 μg/dL, and all subjects were stratified into three equal groups: low blood Pb level (<8.51 μg/dL), 

medium blood Pb level (8.51-12.64 μg/dL), and high blood Pb level (>12.64 μg/dL). A Cox 

multivariate analysis was used to associate baseline blood Pb levels >12.64 μg/dL with higher allcause mortality (HR = 4.70; 95% CI = 1.92-11.49), cardiovascular-cause mortality (HR = 9.71; 95% 

CI = 2.11-23.26), and infection-cause mortality (HR = 5.35; 95% CI = 1.38-20.83) in these patients 

over 18 months of follow-up. We will not use this study in the estimation of a concentration-response 

function because the association of blood Pb levels and mortality in patients on maintenance 

hemodialysis is not applicable to the adult general population. 

Lustberg & Silbergeld (2002) 

Lustberg & Silbergeld’s 2002 study of blood Pb levels and mortality evaluated the association of 

blood Pb levels and mortality in the United States using the 1992 follow-up data for participants in 

the Second National Health and Nutrition Examination Survey (NHANES II) conducted from 1976 to 

1980. The NHANES II survey interviewed a nationally representative sample of 20,322 people, of 

whom blood Pb levels were available for 4,292 participants. Individuals with blood Pb levels of at 

least 30 μg/dL were excluded, leaving 4,190 participants. In this group, 19.5% of participants had 

blood Pb levels <10 μg/dL, 65.3% had blood Pb levels of 10-19 μg/dL, and 15.2% had blood Pb 

levels of 20-29 μg/dL. The average blood Pb level of the 4,190 participants was 14.0 ± 5.1 μg/dL. 

Adults with blood Pb levels of 20-29 μg/dL were found to have 46% increased risk for all-cause 

mortality (risk ratio (RR) = 1.46; 95% CI = 1.14-1.86) and 39% increased risk for circulatory 
Developing a Concentration-Response Function for Pb Exposure and Cardiovascular Disease-Related 

Mortality 

 Studies Not Selected ▌pg. A-2

mortality (RR = 1.39; 95% CI = 1.01-1.91) compared to those with blood Pb levels of <10 μg/dL. 

Increased all-cause and circulatory mortality was also found in individuals with blood Pb levels of 

10-19 μg/dL relative to those with <10 μg/dL. Given that the average blood Pb level of participants 

was 14.0 ± 5.1 μg/dL, we will not further consider this study in the development of the concentrationresponse function because the reported average blood Pb level is too high to represent the current 

average blood Pb levels in the U.S. general population. 

Møller & Kristensen (1992) 

Møller and Kristensen’s 1992 study Blood Lead as a Cardiovascular Risk Factor examined the 

association between blood Pb and blood pressure, total mortality, coronary heart disease and 

cardiovascular disease mortality. Their cohort included 1,052 men and women from Copenhagen 

County, Denmark, who had blood Pb measurements taken three times, in 1976, 1981, and 1987 (in 

1987 only men were examined). Mean blood Pb concentrations decreased from 13.6 to 8.3 μg/dL 

from 1976 to 1987 for men, and from 9.6 to 6.8 μg/dL from 1976 to 1981 for women. All participants 

in the study were followed regarding hospital admissions and deaths for a 14-year follow-up period. 

The authors found a significant univariate association with total mortality, coronary heart disease, and 

cardiovascular disease. With regard to coronary heart disease and cardiovascular disease, the 

association disappeared when the model included confounders. However, the relationship between 

blood Pb and total mortality remained statistically significant after controlling for confounders. Given 

that this study did not examine blood Pb as it relates specifically to cardiovascular disease-related 

mortality, we will not further consider it in the development of the concentration-response function. 

Neuberger, Hu, Drake, & Jim (2009) 

Neuberger et al.’s 2009 study, Potential health impacts of heavy-metal exposure at the Tar Creek 

Superfund site, Ottawa County, Oklahoma, compared Oklahoma State Department of Health 

mortality data for residents of five Ottawa County towns located within the boundaries of the Tar 

Creek Superfund site (i.e., the exposed area) with residents of four Ottawa County towns not within 

the boundaries of the Tar Creek Superfund site (i.e., the unexposed area). The Tar Creek Superfund 

site is part of the Tri-State Mining District of Kansas, Missouri, and Oklahoma. In the mid-1980s, 

parts of the mining district were declared a Superfund site because of the presence of high 

concentrations of lead, zinc, and cadmium in the mine wastes and tailings, as well as the presence of 

acid mine water laden with heavy metals emerging from surface and groundwater into the 

surrounding creeks, especially in Oklahoma. Neuberger et al. compared the occurrence of selected 

mortality outcomes in Ottawa County (both exposed and unexposed areas) with data for the entire 

state of Oklahoma. The SMR for death due to hypertension for Ottawa county compared to the state 

in 1999–2001 was 112.5 (95% CI: 64.3–182.7); the SMR for death due to stroke was 121.6 (95% CI: 

119.2–123.9); and the SMR for death due to heart disease was 114.1 (95% CI: 113.1–115.2). 

Neuberger et al. also compared the occurrence of selected mortality outcomes for the five exposed 

Ottawa County cities to the remainder of Ottawa County’s population. The SMR for mortality due to 

hypertension for the five exposed Ottawa County towns compared to data for the rest of the county in 

1999–2001 was 144.9 (95% CI: 39.5–370.9); the SMR for death due to stroke was 69.9 (95% CI: 

37.2–119.6); and the SMR for heart disease-caused mortality was 90.9 (95% CI: 71.0–114.7). The 

Neuberger et al. (2009) study also discusses which Oklahoma cities had the highest percentages of 

elevated blood Pb levels for children under 6 years old in 1996–2000. Given that exposure in this 
Developing a Concentration-Response Function for Pb Exposure and Cardiovascular Disease-Related 

Mortality 

 Studies Not Selected ▌pg. A-3

study is determined by residence and not blood Pb levels, and blood Pb levels are not reported for 

adults, we cannot use this study to estimate the concentration-response function. 
Developing a Concentration-Response Function for Pb Exposure and Cardiovascular Disease-Related 

Mortality 

 Blood and Bone Pb ▌pg. B-1

Appendix B Discussion of Blood Pb, Bone Pb, and Their Interrelationship 

Once Pb enters the body (through either inhalation or ingestion), it enters the blood stream and has a

clearance half-life of approximately 30 days. Clearance of Pb from blood occurs through “the 

distribution into soft tissues and bone as well as excretion, primarily through kidney filtration and 

elimination in urine” (Hu, Shih, Rothenberg, & Schwartz, 2007, p. 456). The loss of bone Pb occurs 

slowly through bone resorption,10 the dominant transfer process for bone Pb, and by diffusion (U.S. 

EPA, 2013). Half-time of Pb in bone is dependent on age, intensity of exposure, and bone type. EPA 

states that for cortical bone, the half-time of lead at birth is 0.23 years, 1.2 years at 5 years of age, 3.7 

years at 15 years of age, and 23 years in adults; for trabecular bone, the half-time of lead at birth is 

0.23 years, 1.0 years at 5 years of age, 2.0 years at 15 years of age, and 3.9 years in adults (U.S. EPA, 

2013). 

Although Pb can circulate throughout the body and can be found in all organs and tissues, blood is the

most readily available biomarker for Pb exposure. It is reflective of both recent exposures (<30 days)

from exogenous sources and past exposures (years to decades) that had been stored in tissues (e.g., 

bone) and released endogenously (National Toxicology Program, 2012; U.S. EPA, 2013). As stated 

previously, the half-life of blood Pb is approximately 30 days, but Hu et al. (2007) point out that this 

half-life is: 

“a reflection of the 120-day lifespan of erythrocytes and only applies in practice if the total exposure is short (e.g., 

<30 days). If lead exposure is long-term (i.e., with a duration of years), upon cessation the kinetics of clearance of 

lead from blood is considerably more complicated, with an initial rapid decline in levels reflecting partial 

clearance from blood and other soft tissues followed by a much slower clearance, reflecting the replenishment of 

soft tissue polls of lead with lead from long-lived deposits in bone” (p. 457). 

Further, when we discuss blood Pb, we are referring to Pb in whole blood. However, plasma Pb, 

which is less than 1% (U.S. EPA, 2013) to less than 5% (National Toxicology Program, 2012) of 

whole blood Pb, is the portion of blood Pb that enters specific tissues and is what is of interest when 

understanding the toxic impacts of Pb. Unfortunately, plasma Pb is difficult to measure and is 

subsequently not readily available or used in epidemiological studies. This can result in exposure 

misclassification given that the ratio of whole blood Pb to plasma blood Pb is not well characterized,

and therefore it is unclear how much Pb reaches a target organ. It is also important to note that 

different techniques for quantifying Pb in whole blood or plasma measurements can limit 

comparability across studies, especially when methods with high levels of detection are used 

(National Toxicology Program, 2012). 

Blood Pb is only representative of approximately 1% of an individual’s body burden, and therefore 

additional exposure misclassification may be introduced especially with studies that only provide one 

blood Pb measurement at one point in time (U.S. EPA, 2013). That is, simply because an individual 

has a high blood Pb level at one time, this does not imply that he/she has had a lifetime of high Pb 

exposures. 

 

10 Bone resorption is the breakdown of bone tissue in order to release calcium to the blood. 
Developing a Concentration-Response Function for Pb Exposure and Cardiovascular Disease-Related 

Mortality 

 Blood and Bone Pb ▌pg. B-2

However, methods have been developed to estimate cumulative Pb exposure or indicators of 

cumulative Pb exposure based on blood Pb measurements. One such method is time-integrated blood 

Pb, often referred to as the cumulative blood lead index (CBLI),11 which uses multiple blood Pb 

measurements and the trapezoidal rule12 to estimate an acceptable cumulative Pb dose surrogate (Hu 

et al., 2007). Another such technique, developed by Park et al. (Park et al., 2009), uses blood Pb 

levels and other standard covariates (e.g., blood Pb, age, education, occupation, cumulative cigarette

smoking, and smoking status) to predict bone Pb levels. This model used a subset of data from the 

Normative Aging Study and found that blood Pb accounted for approximately 9% (tibia) to 13% 

(patella) of the variability in bone Pb levels. Inclusion of age in the regression model accounted for an 

additional 7-10% of the variability in bone Pb (Park et al., 2009). 

Bone Pb, a more recently developed biomarker of Pb exposure, can be interpreted as a person’s 

cumulative exposure. This is because of the long half-life of Pb in bone that results in bone lead 

representing 90% of an adult’s body burden (U.S. EPA, 2013). There are two types of bone, cortical 

and trabecular, both of which can accumulate Pb. Trabecular bone is a spongier, more porous bone, 

such as the interior of the patella (~20% of adult bone), and cortical bone is a denser bone such as the 

shaft of the tibia (~80% of adult bone) (U.S. EPA, 2013; Wittmers Jr., Aufderheide, Wallgren, Rapp, 

& Alich, 1988). The trabecular bone, given its shorter half-life of approximately 4 years, is more 

representative of recent exposure. Additionally, trabecular bone may provide more of the Pb in 

circulation “due to its larger surface area allowing for more Pb to bind via ion exchange mechanisms 

and more rapid turnover, making it more sensitive to changing patterns of exposure” (U.S. EPA, 

2013, p. 3-68). Cortical bone, on the other hand, has an approximate half-life of more than 20 years 

and is more representative of cumulative exposure (U.S. EPA, 2013). 

According to EPA, uptake of Pb to bone depends on calcification rates (the rate at which the bone is 

being formed or remodeled13). In infancy and childhood, calcification is most active in trabecular 

bone. Additionally, given the high bone formation rate in early childhood, there is a rapid uptake of 

Pb into mineralizing bone. However, there is also a high bone resorption rate, and therefore “much of 

the Pb acquired early in life is not permanently fixed in the bone” (U.S. EPA, 2013, p. 1-12). In 

adulthood calcification occurs mainly at sites of remodeling in cortical and trabecular bones (U.S. 

EPA, 2013). Further, during times of physiologic stress such as pregnancy, lactation, menopause, 

extended bed rest, hyperparathyroidism, or severe weight loss, mobilization of Pb from bone into 

blood increases. Interestingly, Nie et al. (2009) found that although bone lead was significantly 

associated with blood Pb in a population of elderly men (from the Normative Aging Study), age and 

bone resorption rates did not significantly modify this association. They hypothesize that lead 

distribution in bone is uneven and that lead is stored in less active bone sites for elderly people. 

Further research on this is needed to confirm this finding. The EPA ISA (U.S. EPA, 2013) states that 

 

11 The process for deriving the CBLI can be found in Appendix A of Hu et al. (2007). 

12 A technique for approximating the area under a graph of a function. 

13 Bone remodeling is the process of breaking down of bone tissue in order to release calcium to the blood 

(referred to as resorption) and forming of new bone. It occurs throughout the life of an individual. 
Developing a Concentration-Response Function for Pb Exposure and Cardiovascular Disease-Related 

Mortality 

 Blood and Bone Pb ▌pg. B-3

based on limited studies, bone Pb stores can contribute as much as 40–70% to blood Pb. However, Hu 

et al. (2007) state that the variation in blood Pb is mainly due to changes in external exposure. 

Bone Pb is most often measured with a k-xray fluorescence (K-XRF) machine. However, the most 

commonly used K-XRF machines have a wide measurement error (National Toxicology Program, 

2012; U.S. EPA, 2013). As with blood Pb, comparing studies that uses different calibration 

techniques may limit one’s ability to compare the results between studies. Further, some methods 

may be impacted by the thickness of skin over the measurement site, which may be a concern for 

studies that include obese people (National Toxicology Program, 2012). Additionally, measurements 

from K-XRF are often given in terms of bone density, which can introduce uncertainty in regard to 

the Pb measurements and may have implications for studies in individuals with low bone density 

(e.g., older women) (U.S. EPA, 2013). 
Developing a Concentration-Response Function for Pb Exposure and Cardiovascular Disease-Related Mortality 

Ab

Appendix C Absolute Change Exhibit C-1. Benefits from Absolute Change in Unstratified Blood Pb Cutoff 

Age 

Group Gender

0.05 μg/dL increase 1.0 μg/dL increase 

Annual Cases Avoided Annual Benefits (2012$ million) Annual Cases Avoided Annual Be

L C U L C U L C U L 

20-29 M 0.07 0.16 0.25 $0.60 $1.40 $2.10 0.86 1.86 2.72 $7.30 

F 0.06 0.13 0.20 $0.50 $1.10 $1.70 0.57 1.20 1.72 $4.80 

30-39 M 0.21 0.48 0.74 $1.70 $4.00 $6.30 2.54 5.51 8.08 $21.40 

F 0.16 0.38 0.59 $1.40 $3.20 $5.00 1.73 3.67 5.26 $14.60 

40-49 M 0.62 1.44 2.25 $5.20 $12.10 $19.00 8.17 17.92 26.52 $68.90 

F 0.39 0.90 1.41 $3.30 $7.60 $11.80 4.72 10.22 14.94 $39.80 

50-59 M 1.21 2.81 4.40 $10.20 $23.70 $37.10 17.16 38.02 56.80 $144.60 

F 0.68 1.59 2.48 $5.80 $13.40 $20.90 9.10 19.97 29.56 $76.70 

60-69 M 1.61 3.73 5.85 $13.50 $31.50 $49.30 22.95 51.02 76.47 $193.40 

F 1.14 2.66 4.16 $9.60 $22.40 $35.10 15.43 33.93 50.30 $130.10 

70-80 M 2.09 4.86 7.62 $17.60 $41.00 $64.20 31.01 69.18 104.00 $261.50 

F 2.26 5.25 8.22 $19.10 $44.30 $69.30 30.72 67.83 100.96 $259.00 

Total 

(20-80 

years) 

M 5.80 13.49 21.12 $48.90 $113.70 $178.00 82.68 183.53 274.59 $697.00 

F 4.69 10.90 17.06 $39.60 $91.90 $143.80 62.27 136.82 202.73 $525.00 

Both 10.50 24.39 38.18 $88.50 $205.60 $321.80 144.96 320.35 477.32 $1,222.00 
Developing a Concentration-Response Function for Pb Exposure and Cardiovascular Disease-Related Mortality 

Ab

Exhibit C-2. Benefits from Absolute Change for a Blood Pb Cutoff of Above 1.0 μg/dL 

Age 

Group Gender

0.05 μg/dL increase 1.0 μg/dL increas

Annual Cases Avoided Annual Benefits (2012$ million) Annual Cases Avoided Annua

L C U L C U L C U L 

20-29 M 0.01 0.03 0.05 $0.10 $0.30 $0.40 0.20 0.44 0.66 $1.70 

F 0.00 0.01 0.01 $0.00 $0.00 $0.10 0.03 0.07 0.11 $0.30 

30-39 M 0.06 0.13 0.20 $0.50 $1.10 $1.70 0.82 1.81 2.71 $6.90 

F 0.01 0.02 0.04 $0.10 $0.20 $0.30 0.15 0.32 0.48 $1.20 

40-49 M 0.24 0.56 0.87 $2.00 $4.70 $7.40 3.61 8.05 12.10 $30.40 

F 0.08 0.19 0.30 $0.70 $1.60 $2.50 1.23 2.73 4.09 $10.40 

50-59 M 0.71 1.65 2.59 $6.00 $13.90 $21.80 10.83 24.20 36.46 $91.30 

F 0.30 0.71 1.11 $2.60 $5.90 $9.30 4.53 10.08 15.11 $38.20 

60-69 M 1.09 2.53 3.97 $9.20 $21.30 $33.40 16.68 37.35 56.36 $140.60 

F 0.54 1.26 1.97 $4.60 $10.60 $16.60 8.13 18.09 27.15 $68.50 

70-80 M 1.61 3.74 5.86 $13.50 $31.50 $49.40 24.92 55.83 84.29 $210.10 

F 1.15 2.68 4.21 $9.70 $22.60 $35.40 17.59 39.29 59.15 $148.30 

Total 

(20-80 

years) 

M 3.71 8.64 13.54 $31.30 $72.80 $114.10 57.05 127.69 192.59 $481.00 

F 2.09 4.87 7.63 $17.60 $41.00 $64.30 31.65 70.59 106.09 $266.80 

Both 5.81 13.51 21.17 $48.90 $113.90 $178.40 88.71 198.28 298.67 $747.80 
Developing a Concentration-Response Function for Pb Exposure and Cardiovascular Disease-Related Mortality 

Ab

Exhibit C-3. Benefits from Absolute Change for a Blood Pb Cutoff of Above 1.46 μg/dL 

Age 

Group Gender

0.05 μg/dL increase 1.0 μg/dL increas

Annual Cases Avoided Annual Benefits (2012$ million) Annual Cases Avoided Annu

L C U L C U L C U L 

20-29 M 0.01 0.01 0.02 $0.00 $0.10 $0.10 0.08 0.17 0.26 $0.70 

F 0.00 0.00 0.00 $0.00 $0.00 $0.00 0.01 0.03 0.04 $0.10 

30-39 M 0.02 0.05 0.08 $0.20 $0.40 $0.70 0.34 0.77 1.16 $2.90 

F 0.00 0.01 0.01 $0.00 $0.10 $0.10 0.04 0.09 0.14 $0.30 

40-49 M 0.11 0.26 0.41 $0.90 $2.20 $3.40 1.78 4.01 6.08 $15.00 

F 0.03 0.06 0.09 $0.20 $0.50 $0.80 0.40 0.91 1.37 $3.40 

50-59 M 0.39 0.91 1.43 $3.30 $7.70 $12.00 6.26 14.09 21.38 $52.80 

F 0.13 0.31 0.48 $1.10 $2.60 $4.00 2.07 4.65 7.04 $17.50 

60-69 M 0.58 1.36 2.13 $4.90 $11.40 $17.90 9.45 21.32 32.41 $79.70 

F 0.25 0.57 0.90 $2.10 $4.80 $7.60 3.90 8.74 13.23 $32.80 

70-80 M 1.02 2.38 3.73 $8.60 $20.00 $31.40 16.50 37.17 56.42 $139.10 

F 0.63 1.46 2.29 $5.30 $12.30 $19.30 10.05 22.60 34.26 $84.70 

Total 

(20-80 

years) 

M 2.13 4.96 7.79 $18.00 $41.90 $65.60 34.42 77.54 117.72 $290.10 

F 1.03 2.40 3.76 $8.70 $20.20 $31.70 16.47 37.02 56.07 $138.80 

Both 3.16 7.37 11.55 $26.70 $62.10 $97.40 50.89 114.57 173.79 $429.00 
Developing a Concentration-Response Function for Pb Exposure and Cardiovascular Disease-Related Mortality 

Ab

Exhibit C-4. Benefits from Absolute Change for a Blood Pb Cutoff of Above 2.0 μg/dL 

Age 

Group Gender

0.05 μg/dL increase 1.0 μg/dL increa

Annual Cases Avoided Annual Benefits (2012$ million) Annual Cases Avoided Ann

L C U L C U L C U L 

20-29 M 0.00 0.00 0.01 $0.00 $0.00 $0.10 0.03 0.07 0.11 $0.30 

F 0.00 0.00 0.00 $0.00 $0.00 $0.00 0.00 0.01 0.01 $0.00 

30-39 M 0.01 0.02 0.03 $0.10 $0.10 $0.20 0.12 0.28 0.42 $1.00 

F 0.00 0.00 0.00 $0.00 $0.00 $0.00 0.01 0.03 0.05 $0.10 

40-49 M 0.04 0.09 0.15 $0.30 $0.80 $1.20 0.68 1.54 2.35 $5.70 

F 0.01 0.02 0.04 $0.10 $0.20 $0.30 0.17 0.39 0.59 $1.40 

50-59 M 0.17 0.40 0.62 $1.40 $3.30 $5.30 2.86 6.49 9.93 $24.1

F 0.05 0.11 0.17 $0.40 $0.90 $1.50 0.78 1.77 2.70 $6.60 

60-69 M 0.31 0.72 1.13 $2.60 $6.10 $9.50 5.23 11.88 18.16 $44.1

F 0.07 0.16 0.25 $0.60 $1.30 $2.10 1.14 2.58 3.95 $9.60 

70-80 M 0.51 1.19 1.86 $4.30 $10.00 $15.70 8.59 19.46 29.70 $72.4

F 0.27 0.64 1.00 $2.30 $5.40 $8.40 4.59 10.41 15.89 $38.7

Total 

(20-80 

years) 

M 1.04 2.42 3.80 $8.80 $20.40 $32.00 17.51 39.71 60.66 $147.6

F 0.40 0.93 1.46 $3.40 $7.80 $12.30 6.71 15.19 23.18 $56.5

Both 1.44 3.35 5.26 $12.10 $28.30 $44.30 24.22 54.90 83.84 $204.1
Developing a Concentration-Response Function for Pb Exposure and Cardiovascular Disease-Related Mortality 

Ab

Exhibit C-5. Benefits from Absolute Change for a Blood Pb Cutoff of Above 3.62 μg/dL 

Age 

Group Gender

0.05 μg/dL increase 1.0 μg/dL increa

Annual Cases Avoided Annual Benefits (2012$ million) Annual Cases Avoided Ann

L C U L C U L C U L 

20-29 M 0.00 0.00 0.00 $0.00 $0.00 $0.00 0.01 0.02 0.03 $0.10 

F 0.00 0.00 0.00 $0.00 $0.00 $0.00 0.00 0.00 0.00 $0.00 

30-39 M 0.00 0.00 0.00 $0.00 $0.00 $0.00 0.01 0.01 0.02 $0.10 

F 0.00 0.00 0.00 $0.00 $0.00 $0.00 0.00 0.00 0.01 $0.00 

40-49 M 0.01 0.01 0.02 $0.00 $0.10 $0.20 0.10 0.22 0.35 $0.80 

F 0.00 0.00 0.00 $0.00 $0.00 $0.00 0.02 0.05 0.08 $0.20 

50-59 M 0.02 0.05 0.07 $0.20 $0.40 $0.60 0.36 0.83 1.28 $3.00 

F 0.00 0.01 0.02 $0.00 $0.10 $0.10 0.07 0.17 0.26 $0.60 

60-69 M 0.06 0.14 0.22 $0.50 $1.20 $1.90 1.08 2.47 3.81 $9.10 

F 0.01 0.03 0.04 $0.10 $0.20 $0.40 0.21 0.47 0.73 $1.70 

70-80 M 0.08 0.18 0.28 $0.60 $1.50 $2.40 1.38 3.15 4.85 $11.6

F 0.04 0.10 0.15 $0.30 $0.80 $1.30 0.74 1.68 2.60 $6.20 

Total 

(20-80 

years) 

M 0.16 0.38 0.60 $1.40 $3.20 $5.00 2.92 6.70 10.34 $24.6

F 0.06 0.14 0.21 $0.50 $1.10 $1.80 1.04 2.37 3.66 $8.70 

Both 0.22 0.51 0.81 $1.90 $4.30 $6.80 3.96 9.07 14.00 $33.4
Developing a Concentration-Response Function for Pb Exposure and Cardiovascular Disease-Related Mortality 

Ab

Appendix D Percentage Change Exhibit D-1. Benefits from Percentage Change for an Unstratified Blood Pb Cutoff 

Age 

Group Gender

5% increase 50% increase 

Annual Cases Avoided Annual Benefits (2012$ million) Annual Cases Avoided Annua

L C U L C U L C U L 

20-29 M 0.06 0.13 0.21 $0.50 $1.10 $1.70 0.46 1.03 1.56 $3.90 

F 0.03 0.07 0.10 $0.20 $0.60 $0.90 0.23 0.52 0.78 $1.90 

30-39 M 0.18 0.41 0.64 $1.50 $3.50 $5.40 1.44 3.22 4.86 $12.10 

F 0.09 0.21 0.33 $0.80 $1.80 $2.80 0.74 1.65 2.49 $6.20 

40-49 M 0.66 1.52 2.38 $5.50 $12.80 $20.10 5.31 11.91 18.00 $44.80 

F 0.32 0.73 1.14 $2.70 $6.20 $9.60 2.55 5.72 8.64 $21.50 

50-59 M 1.65 3.83 5.99 $13.90 $32.30 $50.50 13.34 29.91 45.18 $112.40 

F 0.74 1.71 2.68 $6.20 $14.40 $22.60 5.97 13.38 20.21 $50.30 

60-69 M 2.35 5.46 8.55 $19.80 $46.10 $72.00 19.04 42.70 64.51 $160.50 

F 1.29 2.99 4.67 $10.80 $25.20 $39.40 10.41 23.35 35.28 $87.80 

70-80 M 3.40 7.90 12.35 $28.70 $66.60 $104.10 27.51 61.69 93.21 $231.90 

F 2.75 6.39 9.99 $23.20 $53.80 $84.20 22.26 49.91 75.40 $187.60 

Total 

(20-80 

years) 

M 8.29 19.26 30.11 $69.90 $162.30 $253.80 67.11 150.46 227.32 $565.70 

F 5.21 12.10 18.92 $43.90 $102.00 $159.50 42.15 94.51 142.79 $355.30 

Both 13.50 31.35 49.03 $113.80 $264.30 $413.30 109.26 244.97 370.12 $921.00 
Developing a Concentration-Response Function for Pb Exposure and Cardiovascular Disease-Related Mortality 

Ab

Exhibit D-2. Benefits from Percentage Change for a Blood Pb Cutoff of above 1.0 μg/dL 

Age 

Group Gender

5% increase 50% increase 

Annual Cases Avoided Annual Benefits (2012$ million) Annual Cases Avoided Annua

L C U L C U L C U L 

20-29 M 0.02 0.05 0.08 $0.20 $0.40 $0.60 0.17 0.38 0.57 $1.40 

F 0.00 0.01 0.01 $0.00 $0.10 $0.10 0.03 0.06 0.09 $0.20 

30-39 M 0.08 0.19 0.30 $0.70 $1.60 $2.50 0.67 1.50 2.26 $5.60 

F 0.01 0.03 0.05 $0.10 $0.30 $0.40 0.12 0.26 0.39 $1.00 

40-49 M 0.39 0.91 1.43 $3.30 $7.70 $12.00 3.18 7.12 10.76 $26.80 

F 0.12 0.29 0.45 $1.00 $2.40 $3.80 1.00 2.25 3.40 $8.50 

50-59 M 1.25 2.90 4.53 $10.50 $24.40 $38.20 10.10 22.64 34.20 $85.10 

F 0.47 1.09 1.70 $3.90 $9.10 $14.30 3.78 8.48 12.81 $31.90 

60-69 M 1.99 4.62 7.23 $16.80 $39.00 $60.90 16.11 36.12 54.57 $135.80 

F 0.85 1.98 3.10 $7.20 $16.70 $26.20 6.91 15.50 23.42 $58.30 

70-80 M 3.03 7.03 10.99 $25.50 $59.20 $92.60 24.49 54.90 82.95 $206.40 

F 1.99 4.63 7.23 $16.80 $39.00 $61.00 16.12 36.14 54.61 $135.90 

Total 

(20-80 

years) 

M 6.76 15.70 24.55 $57.00 $132.30 $206.90 54.70 122.65 185.31 $461.20 

F 3.46 8.02 12.55 $29.10 $67.60 $105.80 27.96 62.69 94.71 $235.70 

Both 10.22 23.72 37.09 $86.10 $200.00 $312.70 82.66 185.34 280.02 $696.80 
Developing a Concentration-Response Function for Pb Exposure and Cardiovascular Disease-Related Mortality 

Ab

Exhibit D-3. Benefits from Percentage Change for a Blood Pb Cutoff of above 1.46 μg/dL 

Age 

Group Gender

5% increase 50% increase 

Annual Cases Avoided Annual Benefits (2012$ million) Annual Cases Avoided Annua

L C U L C U L C U L 

20-29 M 0.01 0.03 0.04 $0.10 $0.20 $0.30 0.09 0.20 0.30 $0.70 

F 0.00 0.00 0.01 $0.00 $0.00 $0.00 0.01 0.03 0.04 $0.10 

30-39 M 0.04 0.10 0.16 $0.40 $0.80 $1.30 0.35 0.78 1.18 $2.90 

F 0.01 0.01 0.02 $0.00 $0.10 $0.20 0.05 0.10 0.15 $0.40 

40-49 M 0.24 0.55 0.87 $2.00 $4.70 $7.30 1.93 4.32 6.53 $16.30 

F 0.05 0.13 0.20 $0.50 $1.10 $1.70 0.44 0.98 1.49 $3.70 

50-59 M 0.86 2.01 3.14 $7.30 $16.90 $26.50 6.99 15.68 23.69 $58.90 

F 0.26 0.61 0.95 $2.20 $5.10 $8.00 2.11 4.73 7.14 $17.80 

60-69 M 1.38 3.21 5.01 $11.60 $27.00 $42.30 11.17 25.05 37.85 $94.20 

F 0.50 1.15 1.80 $4.20 $9.70 $15.20 4.02 9.01 13.61 $33.90 

70-80 M 2.31 5.36 8.39 $19.50 $45.20 $70.70 18.69 41.91 63.32 $157.60 

F 1.36 3.15 4.92 $11.40 $26.50 $41.50 10.97 24.60 37.17 $92.50 

Total 

(20-80 

years) 

M 4.85 11.25 17.60 $40.90 $94.90 $148.40 39.22 87.94 132.86 $330.60 

F 2.17 5.05 7.90 $18.30 $42.60 $66.60 17.60 39.45 59.61 $148.30 

Both 7.02 16.30 25.49 $59.20 $137.40 $214.90 56.82 127.39 192.47 $479.00 
Developing a Concentration-Response Function for Pb Exposure and Cardiovascular Disease-Related Mortality 

Ab

Exhibit D-4. Benefits from Percentage Change for a Blood Pb Cutoff of above 2.0 μg/dL 

Age 

Group Gender 

5% increase 50% increas

Annual Cases Avoided Annual Benefits (2012$ million) Annual Cases Avoided Ann

L C U L C U L C U L 

20-29 M 0.01 0.01 0.02 $0.00 $0.10 $0.20 0.05 0.10 0.16 $0.40 

F 0.00 0.00 0.00 $0.00 $0.00 $0.00 0.01 0.01 0.02 $0.00 

30-39 M 0.02 0.05 0.07 $0.20 $0.40 $0.60 0.16 0.35 0.53 $1.30 

F 0.00 0.01 0.01 $0.00 $0.10 $0.10 0.02 0.05 0.08 $0.20 

40-49 M 0.12 0.28 0.43 $1.00 $2.30 $3.60 0.96 2.15 3.25 $8.10 

F 0.03 0.07 0.11 $0.20 $0.60 $0.90 0.24 0.53 0.80 $2.00 

50-59 M 0.50 1.16 1.82 $4.20 $9.80 $15.30 4.05 9.09 13.73 $34.2

F 0.12 0.28 0.44 $1.00 $2.40 $3.70 0.97 2.18 3.30 $8.20 

60-69 M 0.93 2.15 3.36 $7.80 $18.10 $28.30 7.49 16.79 25.36 $63.1

F 0.20 0.45 0.71 $1.60 $3.80 $6.00 1.58 3.54 5.35 $13.3

70-80 M 1.44 3.35 5.24 $12.20 $28.30 $44.20 11.69 26.20 39.59 $98.5

F 0.77 1.78 2.79 $6.50 $15.00 $23.50 6.21 13.93 21.04 $52.4

Total 

(20-80 

years) 

M 3.01 7.00 10.94 $25.40 $59.00 $92.30 24.39 54.69 82.62 $205.6

F 1.12 2.59 4.05 $9.40 $21.80 $34.10 9.03 20.24 30.57 $76.1

Both 4.13 9.59 14.99 $34.80 $80.80 $126.40 33.42 74.92 113.20 $281.7
Developing a Concentration-Response Function for Pb Exposure and Cardiovascular Disease-Related Mortality 

Ab

Exhibit D-5. Benefits from Percent Change for a Blood Pb Cutoff of Above 3.62 μg/dL 

Age 

Group Gender 

5% increase 50% increas

Annual Cases Avoided Annual Benefits (2012$ million) Annual Cases Avoided Ann

L C U L C U L C U L 

20-29 M 0.00 0.01 0.01 $0.00 $0.00 $0.10 0.02 0.04 0.06 $0.2

F 0.00 0.00 0.00 $0.00 $0.00 $0.00 0.00 0.00 0.00 $0.0

30-39 M 0.00 0.01 0.01 $0.00 $0.00 $0.10 0.02 0.04 0.06 $0.1

F 0.00 0.00 0.00 $0.00 $0.00 $0.00 0.01 0.01 0.02 $0.1

40-49 M 0.03 0.08 0.12 $0.30 $0.60 $1.00 0.27 0.60 0.90 $2.2

F 0.01 0.02 0.03 $0.10 $0.10 $0.20 0.06 0.14 0.21 $0.5

50-59 M 0.13 0.29 0.46 $1.10 $2.50 $3.90 1.03 2.30 3.48 $8.7

F 0.02 0.05 0.07 $0.20 $0.40 $0.60 0.16 0.37 0.55 $1.4

60-69 M 0.30 0.69 1.08 $2.50 $5.80 $9.10 2.41 5.41 8.17 $20.3

F 0.06 0.13 0.20 $0.50 $1.10 $1.70 0.44 0.99 1.50 $3.7

70-80 M 0.34 0.80 1.25 $2.90 $6.70 $10.50 2.78 6.23 9.41 $23.4

F 0.19 0.44 0.69 $1.60 $3.70 $5.80 1.53 3.43 5.18 $12.9

Total 

(20-80 

years) 

M 0.81 1.87 2.92 $6.80 $15.80 $24.60 6.52 14.61 22.08 $54.9

F 0.27 0.63 0.99 $2.30 $5.30 $8.30 2.20 4.94 7.47 $18.6

Both 1.08 2.50 3.91 $9.10 $21.10 $33.00 8.72 19.55 29.54 $73.5