## Developing a Concentration-Response Function for Pb

Exposure and Cardiovascular Disease-Related Mortali ty

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 20814Contents ▌ 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 Mortali ty 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-Re lated 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 Funct ion ............................................... . 4-1

5. Generalizability of the Concentration-Response Func tion 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 Concentrati on-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 Lea d 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 .................................

................................................... ..... 5-10Contents ▌ pg. iii 5.4.6 Results ...........................................

................................................... .............. 5-11

6. Discussion on Uncertainty and Variability in the Co ncentration-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 Add itional 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 The ir Interrelationship .............................. . B-1

Appendix C Absolute Change ........................

................................................... .............................. C-1

Appendix D Percentage Change ......................

................................................... ............................. D-1Contents ▌ pg. iv



### List of Exhibits

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

Cardiovascular Morbidity Effects Experienced in Adu lts Associated with Pb Exposure 2-
3
Exhibit 2. Conclusions from the EPA ISA and NTP Monograph on C ardiovascular Disease-Related

Mortality in Adults Associated with Pb Exposure ...

................................................... ... 2-5
Exhibit 3. Summary of Population Characteristics for Studies E xamining 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 Ca tegory (Schober et al., 2006) ... 3-5
Exhibit 5. Hazard Ratios and 95% Confidence Intervals of All-C ause, Cardiovascular Disease,

Myocardial Infarction, and Stroke Mortality Associa ted 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 μ g/dL) ....................................... 3-7
Exhibit 7. Multivariate Adjusted Relative Hazards 1 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 R elative to a Reference Level of 1 μg/dL .............................................

................................................... .............................. 3-9
Exhibit 9. Hazard Ratios and 95% Confidence Intervals of All-C ause 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 Ba sis 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 A nalysis Based on Various Cutoff

Points ............................................

................................................... ............................... 5-2
Exhibit 14. Cardiovascular Disease-Related Mortality in the Uni ted States in 2010 ........................ 5-7
Exhibit 15. Adult Blood Lead Levels in 2011-2012 ..............

................................................... ........ 5-9
Exhibit 16. Input Parameters for a Hypothetical Benefits Analys is ................................................ 5-10 Exhibit 17. Quantified Benefits from Absolute Chang e for Varying Blood Pb Cutoff Levels for LRRP

Type Rule .........................................

................................................... ......................... 5-12 Exhibit 18. Benefits from Percentage Change for Var ying Blood Pb Cutoff Levels for LRRP Type

Rule...............................................

................................................... ............................. 5-13Developing 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 dama ge 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 cardiovascula r risk factors) in the United States (U.S. Burden o f Disease Collaborators, 2013). Typically, EPA Regula tory 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, 20 08a). Benefits realized in the adult population are only discussed qualitatively. However, recent s tudies 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 advers e 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 curre ntly being quantified in benefits estimates. Recent evidence has suggested that exposure to Pb in adult s can result in cardiovascular disease (CVD) impacts; specifically, increases in hypertension, c oronary heart disease, CVD, and cardiovascular disease-related mortality (CVD mortality) (National Toxicology Program, 2012; U.S. EPA, 2013).

A well-established quantitative approach to evaluat ing benefits due to reductions in Pb releases for adults does not exist. The objective of this projec t is to create a rigorous approach to quantify adul t health benefits from a reduction in Pb exposure for CVD mortality, which would strengthen EPA’s regulatory analyses and more completely characteriz e the effects of its programs. While we recognize the uncertainties and complexities in quantifying a dult exposures to Pb, the process for estimating exposure is outside the scope of this report. The p urpose of this report is to present a concentration

response function to estimate the number of cardiov ascular 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 assesse d them for their applicability to adult benefits esti mation. We used the results from the most applicabl e study as the basis of a proposed concentration-resp onse function for use in benefits estimation.



### 1.1 Available Literature

Two recent comprehensive government documents summa rize 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 ref erred 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 adver se 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-re levant science examining the relationship between Pb and human health. The report determined causality by an evaluation and synthesis of evidence from controlled human exposure, epidemiolo gic, and toxicological studies published sinceDeveloping 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 r eview of this literature and the conclusions reache d 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 effec ts in studies in which chance, bias, and confounding could be ruled out wi th reasonable confidence.

• Likely to be a causal relationship: Pollutant has been shown to result in health effec ts in studies
in which chance and bias can be ruled out with reas onable confidence, but potential issues

remain.

• Suggestive of a causal relationship: Evidence is suggestive of a causal relationship wi th relevant pollutant exposures, but is limited.

• Inadequate to infer a causal relationship: Available studies are of insufficient quantity, qu ality, consistency, or statistical power to permit a concl usion regarding the presence or absence of an

effect.

• Not likely to be a causal relationship: Evidence is suggestive of no causal relationship w ith relevant pollutant exposures.

The EPA ISA report determined there was a causal re lationship between Pb exposure and health outcomes in the nervous, cardiovascular, renal, imm une, and reproductive and developmental systems (U.S. EPA, 2013). The EPA ISA also determined a cau sal relationship between Pb exposure and effects on heme synthesis and red blood cell functi on. Lastly, they concluded there is a likely causal relationship between Pb exposure and cancer (U.S. E PA, 2013).

1.1.2 The NTP Monograph

The NTP Monograph summarizes the entire epidemiolog ic body of evidence for human health effects associated with low-level Pb exposure (less than 10 micrograms of Pb per deciliter of blood (<10 μ g/dL)). This monograph does not focus on health eff ects at blood Pb levels >10 μ g/dL because these effects are well established (National Toxico logy 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 concl usions for these endpoints as follows:

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

reasonable confidence.

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

reasonable confidence.

• Inadequate evidence of association: Available studies are insufficient in quality, con sistency, or statistical power; or an association between exposu re and health outcome is absent; or no data in

humans are available.

• Evidence of no association: Several adequate studies covering the full range o f levels of exposure that humans are known to encounter (in thi s case limited to blood Pb levels <10 μ g/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 associati on 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, 201 2), with the goal of choosing the most relevant endpoints for benefits estimation for regu lations reducing Pb exposure in adults. Through this exercise, we selected CVD mortality as the bes t endpoint for developing a concentration-response function to inform a quantitative risk assessment–b ased approach to evaluating benefits due to reductions in Pb exposure. The remainder of this re port 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 th e derivation of the concentration-response function (Section 4). Additionally, in this report we explor e the generalizability of the derived concentration

response function and present the benefits associat ed with a hypothetical example rule (Section 5) and propose a method by which to analyze the inherent u ncertainty in the function (Section 6). Lastly, we discuss what additional steps could be taken to ref ine 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. S e lection of Cardiovascular Disease-Related Mortality as the Health Endpoint

The EPA ISA and NTP Monograph reviewed the associat ion between Pb exposure and adverse health impacts for more than 15 endpoints within six physi ologic 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 c onclusion that an exposure may result in an adverse health outcome, there must be a plausible mode of a ction 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, respectivel y.



### 2.1 Mode of Action for Pb and Cardiovascular Impact s

Pb is thought to impact the cardiovascular system i n 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 s tudies cited in the EPA ISA show that Pb changes enzymatic activity, leading to the increased format ion and decreased breakdown of reactive oxygen species, which inactivate and sequester nitrogen di oxide, a vasodilator. The decrease in nitrogen dioxide, as a result of increased reactive oxygen s pecies, results in constriction of blood vessels an d therefore increased blood pressure. High blood pres sure 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 endoth elial 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 smoot h muscle cell migration and proliferation. Each of these is a key event in the pathogenesis of athe rosclerosis. Atherosclerosis is a key component of the pathological process of peripheral arterial dis ease, stroke, and coronary heart disease, all of wh ich can cause CVD mortality (CDC, 2004).

Pb may also exert cardiovascular toxicity through d isruption of calcium homeostasis. Evidence suggests that changes to calcium levels cause chang es in heart rate variability, which have been associated with cardiovascular morbidity and mortal ity in older adults (U.S. EPA, 2013). Calciuminduced pro-coagulant activity may also lead to thr ombosis, a risk factor for stroke and heart attack.

Hypertension and atherosclerosis have been linked t o 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 I SA report.



### 2.2 EPA ISA and NTP Monograph Cardiovascular Morbid ity Findings

Many endpoints fall under the category of cardiovas cular morbidity. The EPA ISA report specifically examined and made explicit conclusions on the assoc iation between Pb exposure and increased blood pressure or the resulting clinical outcome of hyper tension, subclinical atherosclerosis, coronary hear tDeveloping 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 monog raph examined and made explicit conclusions on blood pressure or the resulting clinical outcome of hypertension, heart rate variability, electrocardiogram abnormalities, clinical CVD (in g eneral and by specific endpoint), and CVD mortality.

Given that the purpose of this report is to identif y an association between Pb exposure and monetary benefits from reduced exposure, we concentrate on s ummarizing the evidence for only those endpoints that are readily quantifiable and monetiz able. Therefore, in this section we summarize the evidence for increased blood pressure or the result ing 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 PbExposure and Cardiovascular Disease-Related MortalitySelection of Health Endpoint▌pg.2-3Exhibit 1. Conclusions from the EPA ISA and NTP Monograph on Readily Quantifiable Cardiovascular Morbidity EffectsExperienced in Adults Associated with Pb ExposureEffectDefinitionEPA ISA ConclusionNTP ConclusionBlood pressureThe force exerted by the heartagainst the walls of the arteries(measured in millimeters of mercury(mmHG)), with a maximum duringthe pumping phase of the heartbeat(systolic blood pressure, SBP) and aminimum when the heart musclerelaxes between beats (diastolicblood pressure, DBP).Causal:Prospective epidemiologicstudies adjusting for potentialconfounders consistently findassociations. Supported by crosssectional studies, meta-analyses,animal studies and plausible modesof action. Uncertainties remainregarding the timing, frequency,duration, and level of Pb exposurescontributing to the effects observed inepidemiologic studies (p. lxxiv).Sufficientevidence that blood Pblevels <10 μg/dL are associatedwith increases in blood pressure(BP) and hypertension. Theassociation between higher Pblevels and higher BP is mostconsistent in studies of bone Pb (p.80).HypertensionMedical term for high bloodpressure (currently, SBP≥140 orDBP≥90) compared to an optimalBP of <120/80 mmHg. BPs of 120-139/80-89 mmHg are consideredprehypertension.Developing a Concentration-Response Function for PbExposure and Cardiovascular Disease-Related MortalitySelection of Health Endpoint▌pg.2-4EffectDefinitionEPA ISA ConclusionNTP Conclusion

Cardiovascular DiseaseCoronary heartdiseaseNarrowing of the arteries that supplyblood and oxygen to the heartmuscle, caused by atherosclerosis,which can result in a myocardialinfarction (also called ischemic heartdisease (IHD)).Causal: Prospective studiesconsistently find associations of Pbbiomarkers with cardiovasculardisease -related mortality andmorbidity, specifically myocardialinfarction (MI), IHD, or heart ratevariability. This is supported byanimal studies. Uncertainties remainregarding the timing, frequency,duration and level of Pb exposurescontributing to the effects observed inepidemiologic studies (p. lxxxv).Clinical CVD (general):Limitedevidence that blood Pb levels <5μg/dL are associated with clinicalCVD (p. 97).Clinical CVD (specific):Inadequateevidence to evaluate a potentialassociation between Pb exposureand specific CVDs (p. 97).Peripheralartery disease(for EPAincluded in thecategory ofsubclinicalatherosclerosis(U.S. EPA,2013, p. 1-27)Narrowing of arteries carrying bloodto the arms and legs, caused byatherosclerosis.Suggestive of a causal relationship:(categorized as subclinicalatherosclerosis by EPA). Crosssectional analyses of National Healthand Nutrition Examination Survey(NHANES) data find associations.Animal studies are limited to modeof-action studies, which demonstratehow Pb may initiate atheroscleroticvessel disease.Cerebrovascular DiseaseDisease of the blood vessels whichsupply the brain. Often caused byatherosclerosis and can lead tostroke.Inadequateto infer a causalrelationship:Available data are ofinsufficient quantity, quality, and/orconsistency, but modes of action aredemonstrated (p. lxxxv).Limitedevidence: There are fewreplicated studies (p. 76).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 Diseas e-Related Mortality Findings

In addition to examining the impacts from cardiovas cular morbidity, CVD mortality was also examined by the EPA ISA (under the category of coro nary 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 sum maries 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 Mo nograph 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; Nationa l Toxicology Program, 2012; U.S. EPA, 2013)

The EPA ISA deemed the association between Pb expos ure and coronary heart disease (including cardiovascular-related mortality) to be causal . Specifically, EPA stated, “despite the difference s in design and methods across studies, with few excepti ons associations between higher levels of Pb biomarkers and higher risk of [coronary heart disea se]-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 f rom cardiovascular causes” and that the “association between increased CVD mortality and in creased blood Pb was supported by three prospective studies but not supported by two prospe ctive studies, one of which reported a significant association with bone Pb” (National Toxicology Prog ram, 2012, p. 90).

The two prospective studies NTP is referring to tha t do not support the association between blood Pb increases and CVD mortality are Møller and Kristens en (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 nonfata l cases. They did not examine the risk of CVD mortality without the non-fatal cases included in t he analysis. Weisskopf et al. (2009) found noDeveloping 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-rel ated mortality but did find an association when bone Pb was used as the marker of exposure (more de tail on this study can be found in Section 3.4). The findings should be considered alongside the fac t that the method of selection for the Normative Aging Study (NAS) cohort used in the Weisskopf stud y suffers from selection bias. The NAS cohort used in the Weisskopf et al. (2009) study is weight ed toward individuals without CVD, given that in order to be entered into the cohort you could not h ave 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 ab out 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 publi shed in Circulation, and the currently published

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

As described in Section 2.1, both the EPA ISA and N TP 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 m ortality to be “limited” (its second highest category under “sufficient”), this determination wa s based on the fact that two studies did not suppor t 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 be ing corrected by the authors. Therefore, given that CVD mortality is more useful from a benefits analys is 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 l iterature selected by the EPA ISA (U.S. EPA, 2013) and the NTP Monograph (National Toxicology Pr ogram, 2012) to determine the best evidence from which to develop a concentration-response func tion 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 betwe en blood Pb and CVD mortality, it is necessary to identify one or more suitable studies as the bas is of the function. To be useful for benefits analy ses, a study should be applicable to the general populat ion and use sound scientific methods in the approach. We reviewed each of the eight studies con sidered by the EPA ISA and the NTP Monograph to see if their results could be extrapolated to th e general population. For this initial screening, w e considered the following criteria:

• The study sample is representative of the adult gen eral population or a large sector of the general adult population (e.g., men/women over the age of 4 0). Studies of patients with a particular disease, for example, do not represent a major port ion of the population and would not be very useful for estimating the primary concentration-res ponse 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 c onsistent 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 m ortality. Only one study, Weisskopf et al. (2009), also considered bone Pb as a biomarker. The re were no studies identified that looked only at bone Pb and CVD mortality.

As summarized in Exhibit 3, Menke et al. (2006), Sc hober et al. (2006), Khalil et al. (2009), and Weisskopf et al. (2009) all have populations in the ir 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 determin e 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 fi ndings. Summaries of the other five 2 studies can be found in Appendix B of this document.

1 The Weisskopf et al. (2009) cohort excluded peopl e with prior CVD. If these findings are considered in the future, it should be noted that 3.6% of the populat ion between the ages of 18 and 44 and 12.8% of the population between the ages of 45 and 64 have cardi ovascular 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 cardi ovascular 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 n ot 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 fo r Studies Examining the

Association between Blood Pb Levels and Cardiovascu lar 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 prese nted in Schober et al. (2006). This value is a calc ulated weighted median blood Pb level based on the cohort populatio n characteristics presented in Table 1 of Schober e t 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 e t 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-respo nse function or presents a result (e.g., a relative risk associated with a given change in blo od Pb levels) based on an underlying continuous concentration-response function for bloo d 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 stu dies 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 stud y authors for details and/or data not available in the publication. A more detailed overview of the me thods 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, Cardio vascular Disease, and Cancer: Results from the NHANES III Mortality Study examined the association between blood Pb levels and all-cause and cause-specific mortality o f 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 blo od 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 m edian length of follow-up was 8.55 years, during which there were 2,515 deaths. Using the Internatio nal Classification of Disease (Tenth Revision), Schober et al. identified death due to malignant ne oplasms (ICD-10 codes C00-C97) and major CVD (ICD-10 codes I00-I78).

Schober et al. categorized blood Pb into three cate gories: <5 μg/dL, 5 to <10 μg/dL, and ≥ 10 μg/dL. The authors used Cox proportional hazard regression analysis, with age 3 as the time scale, to examine the hazard of mortality from all causes, cancer, an d CVD using the categories outlined in the previous paragraph. The baseline hazard was stratified by ag e, using 6-year intervals, controlling for potentia l cohort difference in cumulative exposure to Pb befo re the late 1970s. Additionally, because current Pb exposure (and subsequently blood Pb levels) cont inued to decline over the 6-year period of blood collection, the authors also stratified based on su rvey 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 morta lity and blood Pb relationship was different for me n

3 Age was defined as a participant’s age at the bas eline 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 coho rt, they also stratified multivariate models separately for males and females, and sex was inclu ded 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 ris ks for the three blood Pb categories were tested fo r trend. The median values for each Pb group were pla ced in a linear term and analyzed using a Wald test. Second, using a five-knot cubic regression sp line, they evaluated the log-transformed 4 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 rela tive 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 w ere 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 relationshi p, and therefore they were not included in the fina l models. For mortality due to all causes and CVD, th ere was a pattern of increasing risk with increasing blood Pb. For all-cause, cardiovascular, and cancer deaths for all age groups, the trend te sts exploring a concentration-response relationship wer e significant. The results of this analysis are presented in Exhibit 4.

4 Because of the skewed distribution of blood Pb le vels, 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 fo r All-Cause, Cancer, and

Cardiovascular Disease-Related Mortality by Blood L evel 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, e ducation, 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 supporti ng evidence that blood Pb is associated with allcause and CVD mortality. We contacted the study aut hors 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 retrie ve actual functions. Therefore, if this study were use d, 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. (200 6) also used NHANES III data to examine the

association between blood Pb levels and all-cause a nd cause-specific mortality among U.S. adults who have blood Pb levels below 10 μg/dL in their st udy, Blood Lead Below 0.48 μmol/L (10 μg/dL) and Mortality Among US Adults. However, Menke et al. (2006) included 13,946 partic ipants 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 examini ng the cause of death. The mean blood Pb level for the participants in the Menke et al. (2006) stu dy 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 a nalyses. 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 particip ant was calculated as the time between their NHANES III examination and the date of death, the d ate on which they turned 90 years of age, or December 31, 2000. The HRs and 95% CIs were calcula ted 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 compa ring the second (middle) tertile to the first terti le an increase was seen, but this was not statistically s ignificant. When comparing the third tertile to the first tertile, a statistically significant increase in risk was found (see Exhibit 5). As shown in Exh ibit 5, the study authors performed three analyses to ad just for various potential confounders, and CVD mortality remained statistically significantly asso ciated with blood Pb levels in all three models. Cancer mortality was not found to be associated wit h Pb exposure.

Exhibit 5. Hazard Ratios and 95% Confidence Interva ls 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) P trend 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) 1.3 0 (1.08–1.56) <0.001 Multivariable 2 adjusted† 1.00 0.91 (0.72–1.15) 1.2 5 (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) 1.6 4 (1.14–2.35) 0.001 Multivariable 2 adjusted† 1.00 1.03 (0.69–1.55) 1.5 5 (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) 2.0 1 (1.12–3.61) 0.003 Multivariable 2 adjusted† 1.00 1.02 (0.55–1.89) 1.8 9 (1.04–3.43) 0.007 * Adjustment included age, race-ethnicity, sex, dia betes mellitus, body mass index (BMI), current or f ormer smoking, alcohol consumption, physical activity, lo w income, c-reactive protein (CRP), total cholester ol, high school education, urban residence, and post-me nopausal status. † Adjustment includes variables in model 1, hyperte nsion, 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. T he trend analysis found statistically significant increases in mortality risk for all causes of morta lity analyzed except cancer. The results of this analysis are also presented in Exhibit 5 and suppor t 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 t heDeveloping a Concentration-Response Function for Pb Exposure and Cardiovascular Disease-Related Mortality

Assessment of Literature ▌ pg. 3-7 10 th , 50 th , and 90 th

percentiles of blood Pb distribution (Exhibit 6). This analysis revealed that the increase in all-cause and myocardial infarction occ urred at blood Pb levels > 2.0 μg/dL (0.10 μmol/L). Stroke increased monotonically at all bloo d Pb levels included in the spline analysis (1 to 1 0 μg/dL). Quadratic spline results were not presented for CVD.

In a third analysis, due to the skewness of the dis tribution 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 80 th (4.92 μg/dL) and 20 th

(1.46 μg/dL) percentiles of blood Pb distribution. After the multivariate adjus tment, 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 p resented 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-ethnic ity, sex, menopausal status, urban or rural residence, cigarette smoking, overweight, diabetes mellitus, hypertension, and level of estimated glomerular filtration rate (GFR). However, no subgr oup interaction terms were statistically significan t at the 5% level. Their findings are summarized in E xhibit 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: Histog ram of blood lead levels is superimposed in the background and displayed on the right axis.

5 Adjustment included age, race-ethnicity, sex, dia betes mellitus, BMI, current or former smoking, alc ohol consumption, physical activity, low income, c-react ive protein (CRP), total cholesterol, high school education, urban residence, and post-menopausal sta tus, 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 Hazards 1 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/m 2 ) <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 (m l/min/1.73m 2 ) <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)

1 Hazard ratios were calculated for a 3.4-fold increa se 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 distribut ion (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 c ontinuous 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 o f CVD mortality for the adult population. This function is represented by the plot in Exhibit 8, w hich 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 fr om 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 criter ia of having a generalizable concentration-response function that can be used, along with other data, t o estimate benefits for adults who would have a reduction in Pb exposure as the result of a hypothe tical regulation. Additionally, this study uses the same NHANES cohort as the Schober et al. (2006) pap er but includes a wider age range of individuals, making their results more applicable t o 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 relat ionship between blood Pb and all-cause mortality, myocardial infarction mortality, and stroke mortali ty. CVD mortality was selected as the endpoint of preference because there was strong weight of evide nce in the literature and it proved most useful for a benefits analysis compared to other endpoints (se e 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 Morta lity in Older Women: A Prospective Cohort Study used the ancillary Pb study of the Study of Osteop orotic 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 morta lity. The population’s mean blood Pb concentration was 5.3 ± 2.3 μg/dL. The study author s categorized the participants into three groups depending on their blood Pb levels. The groups were ≤ 3 μg/dL (lower 15 th percentile), 4-7 μg/dL, and

≥ 8 μg/dL (upper 15 th

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 15 th percentile and that only the top quintile (80 th percentile) showed elevated risk of death; therefore, the study author s combined categories to create only two categories

, <8 μg/dL and ≥ 8 μg/dL. This varies from the Menke et al. (2006) f indings in post-menopausal women. It is worth noting that this study has a sub stantially smaller sample size than Menke et al. (2006).

Deaths were confirmed by death certificate. The aut hors recorded the underlying cause of death using ICD-9-CM codes for CVD including all diseases of th e circulatory system except those involving veins and lymphatics [ICD-9-CM codes 425, 429.2, 44 0–444, 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 deat hs.

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) us ing 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 Interv als of All-Cause Mortality by

Blood Pb Concentrations

Cause of Death Deaths Blood Pb C oncentration (μg/dL) [μmol/L] P value (< 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 i n 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 exe rcise, diabetes, and total hip bone mass density.

b ICD9 Code: All deaths due to CVD, including all di seases 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: 4 10–414.

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 t he association between Pb exposure and cardiovascular-related mortality to a portion of th e adult U.S. population, we will not use the study to develop a concentration-response function because t he 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), pat ella (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 a uthors sent birthday cards and supplemental questionnaires to study participants. Next of kin o r 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 mortalitie s were classified using ICD-9 codes.

For the statistical analysis, the study authors per formed direct standardization by age, given that bo ne Pb is strongly associated with age. The standardiza tion was achieved by calculating a weighted average of the age-specific averages (continuous va riables) 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 tert ile

of Pb biomarkers as a continuous variable in the mo dels. Non-linearity of Pb terms was also tested with penalized spline terms for the Pb biomarkers.

In multivariable adjusted analysis, all-cause, card iovascular, and ischemic heart disease deaths showed significant associations with patella Pb. Am ong 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 bas eline to allow for stable, multivariable-adjusted models of this outcome in this subset of participan ts. The multivariable HR for other cardiovascular deaths did not appear to increase with increasing b one Pb. The results are summarized in Exhibit 10.

In the spline regression model, there was some sugg estion that the association with all-cause mortalit y and ischemic heart disease plateaus at higher bone Pb concentrations, although the authors note that in these ranges the data were sparse and the confid ence intervals were wide. The results for tibia Pb and mortality were much weaker than those for patel la Pb. Additionally, contrary to the Schober et al., Menke et al., and Khalil et al. results, no as sociation was found between blood Pb and mortality ( p-trend > 0.05 for all CVD deaths and for all-cause mortal ity) (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 eno ugh power to detect a relationship) or potentially due to greater variability in lead exposure in the Grea ter Boston area. The variability could impact the

result because with more fluctuation in blood Pb le vels “any single blood lead measure would be less correlated with overall lead exposure in our cohort and show a reduced effect estimate for mortality i f it is truly cumulative exposure that is important f or 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 s tudy 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 bas ed on the correction when it becomes available, not the currently published paper. Further, the res ults, 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 Interv als 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 1 a Reference 1.44 (0.79-3.26) 1.76 (0.95-3.25) 0.07

Multivariable 2 b 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 1 a Reference 1.39 (0.61-3.19) 2.45 (1.07-5.60) 0.03

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

Ischemic Heart Disease (Subset of All cardiovascula r)

Deaths 14 18 30
Crude Reference 1.41 (0.70-2.85) 2.69 (1.42-5.08) 0 .002

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

Multivariable 2 b 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 d eath) 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 adu lt 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 est imating 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 me n and women and addresses some key weaknesses of previous NHANES analyses, such as examining conf ounding 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/ U se of aContinuous ConcentrationResponse Function in the Study? Will Study Be Used toDevelop 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 acontinuous 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 acontinuous 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 thei r hazard ratios. Examining this exhibit reveals potential explanations as to why Schober et al. (20 06) and Menke et al. (2006), although both using NHANES III data, came up with different effect esti mates—they used different subsets of the NHANES dataset, used different ICD codes to code fo r 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 a bove.

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 p opulation of women with a higher mean blood Pb level. It is possible that the only reason Khali l et al.’s (2009) estimates were not statistically significant was due to a lack of power (i.e., a sma ller number of subjects in their study population).

Weisskopf et al. (2009) did not find a statisticall y significant relationship between blood Pb and CVD mortality, though they did find a statistically sig nificant relationship when bone is the measure of P b exposure. Selection bias may be one cause for the l ack of an association between blood Pb and CVD mortality. Selection criteria for the Normative Agi ng 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 se lection bias toward heart-healthy people (Weisskopf, 2013). Further, there were fewer than 7 00 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) discussi on, 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 becau se with more fluctuation in blood Pb levels, “any sing le 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 tru ly cumulative exposure that is important for mortality outcomes” (Weisskopf et al., 2009, p. 1061). The difference in findings based on the biomarker of ex posure 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 relatio n to cardiovascular disease mortality risk and the ex posure misclassification may not be as prominent of an issue with bone Pb. Alternatively, it may be tha t a stronger association is measured between bone lead and cardiovascular disease which may explain w hy a statistically significant association was found when using bone Pb as the biomarker of exposu re but not when using blood Pb. However, the

authors have cautioned that a correction to the ana lysis is forthcoming, and that results from the 200 9 study should not be used.

Due to heterogeneity across the four studies in ter ms of population, health effect (ICD codes), and Pb reference level, as well as the overlap in study po pulations between Menke et al. and Schober et al., it is not appropriate to synthesize the blood Pb hazar d increase estimates from these studies quantitatively, for example using meta-analysis.Developing a Concentration-Response Function for PbExposure and Cardiovascular Disease-Related MortalityAssessment of Literature▌pg.3-16Exhibit 12. Summary Statistics for Studies Considered as the Basis for the Concentration-Response Function RelatingBlood Pb Levels to Cardiovascular Disease-Related MortalityStudyPopulation ExaminedMean Pb LevelaICD CodesReferenceLevelaPb LevelaHazardIncrease(95% CI)Khalilet al.(2009)533 women aged 65-87years enrolled in theStudy of OsteoporoticFractures5.3 ± 2.3 μg/dLICD9: 425, 429.2,440-444, 428,401-404, 410-414, 430-438,and 798<8μg/dL > 8 μg/dL1.78b(0.92-3.45)Menkeet al.(2006)13,946 NHANES IIIparticipants≥20 years ofage2.58 μg/dL(geometricmean)ICD-9:390 – 434; 436 -459; ICD-10:I00-I99<1.94 μg/dL1.94-3.62μg/dL1.03 (0.69-1.55)c>3.62 μg/dL1.55c(1.08-2.24)1.46 μg/dL(20thpercentile)4.92 μg/dL(80thpercentile)1.53c(1.21-1.94)Schoberet al.(2006)9,686 NHANES IIIparticipants≥40 years ofageMedian: 4.14μg/dLaICD-10: I00–I78 <5 μg/dL5-9 μg/dL1.20d(0.93-1.55)>10 μg/dL1.55d(1.16-2.07)Weisskopf et al.(2009)Men from the VANormative Aging study n= 860 for patella Pb; n =863 tibia Pb; and n =1,235 blood Pb)Patella Pb: 31.2μg/gTibia Pb:21.8μg/gBlood Pb: 4.8μg/dL (geometricmean)ICD-9 codes: 390to 459Patella Pb:< 22 μg/gPatella Pb:>35 μg/g5.63e(1.73-18.3)Blood Pb: <4μg/dLBlood Pb:>6 μg/dL0.69f(0.33-1.47)Developing a Concentration-Response Function for PbExposure and Cardiovascular Disease-Related MortalityAssessment of Literature▌pg.3-17StudyPopulation ExaminedMean Pb LevelaICD CodesReferenceLevelaPb LevelaHazardIncrease(95% CI)aReported values reflect blood Pb levels unless stated otherwise.bThis model was adjusted for age, clinic where examination occurred, BMI, education, smoking, alcoholintake, estrogen use, hypertension, walkingfor exercise, diabetes, and total hip bone density.cThis model was adjusted for age, race-ethnicity, sex, diabetes, BMI, current or former smoking, alcohol consumption, physical activity, low income,CRP, total cholesterol, high school education, urban residence, postmenopausal status, hypertension and level of kidney function.dThis model was adjusted for sex, race/ethnicity, education, and smoking status.eThis model was adjusted for age, smoking (never/former/current and pack years), and education. It also excluded the 154 participants who hadheart disease or stroke at baseline (n=706).fThis model was adjusted for age, smoking (never/former/current and pack years), and education. It also excluded the 212 participants who hadischemic 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 Fu nction

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 underlyi ng 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 t he blood Pb level. We calculated HRs for the difference between the (log-transformed) 80 th and 20 th percentiles of the blood Pb distribution given in the study. If blood Pb level had not been log-tr ansformed first, the concentration-response relationship between blood Pb level and mortality r esulting 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 b lood 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 functi on of the natural log of blood Pb level).

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

) ln( * ) ln( x y β α + =

or

β 

## x B y * = (1)

Where:

α 

## e B =

,

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 u sed in a health impact function as follows. If 0 

## y

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

## x and

1 y denotes the (lower) probability of the health effec t associated with a (lower) blood Pb level of
1 x (the blood Pb level associated with a rule), then i t can be shown that the reduced risk of the health

effect, 

## ) ( 1 0 

## 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 y avoided cases * ]

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 overa ll population in the remainder of this analysis. Adjustment in the analysis included age, race-ethni city, sex, hypertension, diabetes, BMI, current or former smoking, alcohol consumption, physical activ ity, 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 incide nce 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 analy sis (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 underestimat es 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 throug hout the United States at the county, city, or stat e level, resulting in more precise estimates than app lying 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 standa rd errors, in the log-log concentration-response functions for both all-cause mortality and CVD mortality underly ing the HRs and 95% CIs shown in Figure 2 of their stud y and Exhibit 77 in the previous section. We were a lso able to back-calculate these values from the publis hed results, and our back-calculated coefficients m atched

the coefficients that Drs. Menke and Guallar sent u s, 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 F unction from
Menke et al.

In order for the concentration-response function to be most useful in a benefits analysis, it should b e generalizable to a large portion of the population. To properly use the concentration-response function derived in the previous section, the funct ion must be applied to the appropriate population (Section 5.1) and the appropriate blood Pb concentr ations (Section 5.2), and over the appropriate time span (Section 5.3). With these considerations, samp le 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 derive d for many subsets of the populations (e.g., smokers, diabetics, individuals over 60). Given tha t the Menke et al. (2006) findings of statistical significance for the CVD mortality endpoint persist ed after the adjustment for many confounders (see Exhibit 5) and tests for the interaction for betwee n blood Pb levels and different subgroups resulted in no statistically significant relationships, it is a ppropriate to use the beta estimate for the entire population in Equation 3. Therefore, the function c an be applied to adults generally.



### 5.2 Range of Blood Pb Levels over Which the Concent ration-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 μ g/dL reported in Menke et al. is low relative to ot her published studies, blood Pb levels have continued t o decline in the decades since the data were collected (1988-1994), falling to a geometric mean of 1.09 μ g/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 t he study – i.e., should the function be applied to all blood Pb levels in the population or only to th ose 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 a l. (2006) to the entire population, regardless of blood Pb level. The arguments for this conceptual m odel 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 epidemiolo gic literature (U.S. EPA, 2013) and is further evidenced by the fact that the hazard ratio present ed 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 mortali ty 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 assessm ents for taking this approach, such as when quantifying IQ loss from blood Pb exposure in child ren in the National Ambient Air Quality standards (NAAQS). Benefits were estimated below th e 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 Ce rtain Value

It can be argued that applying the Menke et al. (20 06) function to all individuals regardless of blood Pb level may not be appropriate, that the concentra tion-response function should be used only over the range of levels assessed in the study, or appli ed 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 dete ction (LOD). Thus, it could be argued that the concentrat ion-response function may be applied at levels above the LOD (i.e., 1 μg/dL). There may also be ot her cutoff points such as applying the function only above the 20 th

percentile (i.e., 1.46 μg/dL), above the inflectio n 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 potenti al benefits will be missed in populations with bloo d Pb levels below this level. For example, only 20% o f 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 p roportion of the population to which the benefits

would be limited if each threshold were used.

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

Various Cutoff Points

Source: National Health and Nutrition Examination S urvey (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 an y 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 lev els. It is unlikely that all samples below the LOD have the same concentration; instead, there is a di stribution 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 bas ed on a comparison of those with a blood Pb level of 4.92 μg/dL (80 th

percentile) to those with a blood Pb level of 1.46 μg/dL (20 th 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 20 th to 80 th percentile from which the hazard ratio was develop ed. The hazard ratio could have been developed based on a c omparison 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; bo th 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 20 th percentile blood Pb level in Menke et al. was 1.46 μg/dL, sugg esting 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 t his area of the graph, along with the measurement uncertainty due to the measurements below the LOD i n 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 poi nt that Menke et al. also estimated a spline function for stroke mortality and this function doe s increase monotonically with blood Pb above

1 μ g/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 t he 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 de rived using data from the entire distribution of blood Pb levels in the study. If the inflection poi nt of 2 μg/dL in the MI and all-cause mortality spl ine functions also holds for CVD mortality, 0.35 may un derestimate the relationship between blood Pb increase and CVD mortality risk for individuals wit h blood Pb above 2 μg/dL.

Greater Than 3.63 μg/dL
Menke et al.’s tertile analysis found a positive, b ut not statistically significant, difference betwee n 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 tert iles 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 theDeveloping a Concentration-Response Function for Pb Exposure and Cardiovascular Disease-Related

Mortality

Generalizability ▌ pg. 5-4 concentration-response function derived from the co ntinuous 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. di d find a statistically significant increase across these three categories, and the hazard ratio from which o ur function is derived is developed without a threshold. In addition, the lack of a significant d ifference between the first two tertiles could be d riven 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 signi ficantly different on average because the upward tr end in the blood Pb-CVD mortality relationship only beg ins 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 wh ich the biomarker most accurately represents risk. As stated by the EPA ISA, “uncertainties remain reg arding the timing, frequency, duration and level of Pb exposures contributing to the effects observe d in epidemiologic studies” (U.S. EPA, 2013, p. 1xxxv). These uncertainties are not addressed by Me nke 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 sub ject. 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 o ver X years)

7. Current CVD mortality risk = f(average bone Pb o ver 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, i n 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 m odel 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 ov er time). Though hazard rates may vary with time, t he hazard ratio will always be the same because the pr oportionality 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 h ealth impact function would over- or underestimate the number of cases. The Menke et al. (2006) analys is is based on a single blood Pb measurement per individual, which is reflective of both recent expo sures (<30 days) from exogenous sources and past exposures (years to decades) that had been stored i n 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 exposu re over a specified period of time, depending on the individual’s exposure profile and other physiol ogical characteristics that may contribute to the

release of Pb from bone.

In conceptual model 4, if peak exposures are most i mportant in predicting risk, it would be hard to predict in which direction the results will be bias ed because we do not know when the NHANES measurements were taken in relation to the timing o f the peak exposure. External exposures are represented in blood Pb for about 30 days, during w hich Pb is eliminated or stored in bone, and therefore the spot blood Pb sample would not be lik ely 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 i mpact 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 th an predicted when not considering latency. Since benefits in the future are discounted, ignoring lat ency (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, t he 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 sho uld 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 us ing the highly variable measurement to predict an outcome that may be a consequence of a long-term ex posure results in an underestimation of the true

effect.

Also, for all of the conceptual models, the data ar e 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 s eparate these two scenarios given that they mayDeveloping a Concentration-Response Function for Pb Exposure and Cardiovascular Disease-Related

Mortality

Generalizability ▌ pg. 5-6 result in the same cumulative average exposure (whi ch can be represented by the bone Pb measurement). Additional information on bone and bl ood Pb biomarkers is provided in Appendix B.

The concentration-response function derived from Me nke 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 Dec ember 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 m odels could be used to explore the idea of cessation lag in this context, but they will requir e information on the exposure time profile (e.g., blood Pb measurements at several points in time). T he current literature does not contain this type of analysis.



### 5.4 Sample Benefits Calculation

To gain an understanding of the magnitude of the be nefits that would result from using the function derived in Section 4, we present hypothetical examp les. 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 y avoided cases *]

0 1 1[* 0 β

       

− = . (3)

Where:

y 0 = CVD disease mortality rate

x 1 = blood Pb level with rule

x 0 = 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 ou tlined in Equation 3 except for y 0 , the baseline cardiovascular disease mortality rate. For the hypo thetical 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 Mortalit y in the United States in 2010

Age (years) Gender Number of Deaths CVD Mortality

Incidence, Y 0

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 lev els (    ) 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 exper ience the same absolute change as a result of the rule. Both of these approaches are oversimplificati ons, as each individual in reality may have different exposure patterns, but they are applied h ere 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 regar d 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/d L above the current geometric mean levels from the most recent NHANES analysis (see Exhibit 15) fo r the appropriate age and gender category. 9 That

is:

9 For this hypothetical example we are making simpl ifications to focus on the concentration-response function, and understand that modeling blood Pb cha nges is not trivial; however, that discussion is be yond the scope of this report.Developing a Concentration-Response Function for Pb Exposure and Cardiovascular Disease-Related

Mortality

Generalizability ▌ pg. 5-8 x 0 = 0.05 +BLL b or

x 0 = 1.00 + BLL b

Where:

BLL b = the geometric mean blood Pb level from NHANES, s ee 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 le vel. Then, assuming the rule negates all potential exposure from an event such as a renovation, we cal culate x 1 to be x 0 minus the increase in exposure caused by the renovation, either 0.05 μg/dL or 1 μg /dL. That is:

x 1 = x 0 - 0.05 = BLL b or

x 1 = x 0 - 1.00 = BLL b

For the percentage increase we will use the equations below to examin e the sensitivity of the results to the ratio of baseline to post-rule blood Pb leve ls.

That is:

x 0 = BLL b *1.05 or

x 0 = BLL b *1.50, and

x 1 = BLL b

In the case of a rule that would decrease ambient P b exposure levels, x 0 and x 1 would be calculated differently. We would assume that x 0 is equal to the current baseline blood Pb levels a nd x 1 is equal x 0 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 75 th
Percentile 95 th

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 S urvey (NHANES) 2011-2012.

5.4.3 Population

For this example we assumed that 0.5% of the popula tion (approximately 1 million people) is impacted by the rule. We also stratified our analys is because, as discussed in Section 5.2.2, in certa in cases it may be appropriate to apply the function o nly 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 greate r 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 (BLL b ) as geometric means of blood Pb levels from NHANES 2011-2012, for only those with blood Pb leve ls above the starting level of interest.

• We multiplied the affected population ( pop ) by the share of the population with blood Pb leve ls above the cutoff level of interest, based on NHANES 2011-2012.

• We calculated the without-rule blood Pb level ( x 0 ) in two ways. For percentage changes, we
calculated 5% and 50% increases in our estimates fr om mean blood Pb levels ( x 1 ). 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 strati fication. We used these geometric means as x 0 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 o f 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 Ben efits Analysis

Parameter Assumption CVD mortality rate, y 0 Age (within a 10-year range) and gender-specific CV D disease mortality rates

(ICD-9 codes 390-434 and 436-459; ICD-10 codes I00- I99) (see Exhibit 14).

Blood Pb levels,

x 1 ,x 0 Two approaches will be taken:

• A percent increase (approximately 5% or 50%):

o x 0 = NHANES geometric mean blood Pb multiplied by 1.05 or 1.50

o x 1 = NHANES geometric mean blood Pb

• An absolute change:

o x 0 = NHANES geometric mean blood Pb plus 0.05 μg/dL o r 1 μg/dL

o x 1 = 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 appr oximately 1 million people

• Blood Pb cutoffs:

o >1

o >1.46

o >2

o >3.63

For this hypothetical example we assume that all be nefits will be experienced within 1 year. This is based on the observation that blood Pb declines rap idly following short-term elevated exposures in individuals with relatively low blood Pb levels, ev en though bone Pb and total body burden decline much more slowly (Figure 3-11, U.S. EPA, 2013). Fur ther refinements of this analysis could instead use predicted blood Pb levels from a biokinetic mod el such as Leggett (Cal OEHHA, 2013; Leggett, 1993) to estimate changes in blood Pb over the leng th 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 bo dy 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 dat a points outlined in the preceding sections and Exhibit 16, we calculated the total number of CVD m ortality cases avoided under the different scenarios. By multiplying the number of cases by a value of a statistical life (VSL) in 2012 dollars o f $8.43 million ($4.799 million in 1990 USD with an i nflation factor of 1.76), (Bureau of Labor Statistics, 2013; U.S. EPA, 2010), we calculated th e total annual benefits. These results are

summarized in Exhibit 17 and Exhibit 18. The benefi ts 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 low er estimate of beta, to $4.02 billion, with a 1 μg/dL decrease in blood Pb when considering all i ndividuals regardless of blood Pb with the upper

beta estimate.Developing a Concentration-Response Function for PbExposure and Cardiovascular Disease-Related MortalityGeneralizability▌pg.5-12Exhibit 17. Quantified Benefits from Absolute Change for Varying Blood Pb Cutoff Levels for LRRP TypeRuleBloodPbCutoff(μg/dL)Gender0.05 μg/dL decrease1.0 μg/dL decreaseAnnual Cases AvoidedAnnual Benefits (2012$million)Annual Cases AvoidedAnnual Benefits (2012$ million)L1C2U3LCULCULCUNone4M 5.80 13.49 21.12 $48.90 $113.70 $178.00 82.68 183.53 274.59 $697.00 $1547.10 $2314.80F 4.69 10.90 17.06 $39.60 $91.90 $143.80 62.27 136.82 202.73 $525.00 $1153.40 $1709.00Both 10.50 24.39 38.18 $88.50 $205.60 $321.80 144.96 320.35 477.32 $1222.00$2700.50 $4023.801M 3.71 8.64 13.54 $31.30 $72.80 $114.10 57.05 127.69 192.59 $481.00 $1,076.40 $1,623.50F 2.09 4.87 7.63 $17.60 $41.00 $64.30 31.65 70.59 106.09 $266.80 $595.10 $894.30Both 5.81 13.51 21.17 $48.90 $113.90 $178.40 88.71198.28 298.67 $747.80 $1,671.50 $2,517.801.46M 2.13 4.96 7.79 $18.00 $41.90 $65.60 34.42 77.54 117.72 $290.10 $653.70 $992.40F 1.03 2.40 3.76 $8.70 $20.20 $31.70 16.47 37.02 56.07 $138.80 $312.10 $472.70Both 3.16 7.37 11.55 $26.70 $62.10 $97.40 50.89 114.57 173.79 $429.00 $965.80 $1,465.102M 1.04 2.42 3.80 $8.80 $20.40 $32.00 17.51 39.71 60.66 $147.60 $334.70 $511.40F 0.40 0.93 1.46 $3.40 $7.80 $12.30 6.71 15.19 23.18 $56.50 $128.00 $195.40Both 1.44 3.35 5.26 $12.10 $28.30 $44.30 24.22 54.90 83.84 $204.10 $462.80 $706.803.62M 0.16 0.38 0.60 $1.40 $3.20 $5.00 2.92 6.70 10.34$24.60 $56.40 $87.10F 0.06 0.13 0.21 $0.50 $1.10 $1.80 1.04 2.37 3.66 $8.70 $20.00 $30.90Both 0.22 0.51 0.81 $1.90 $4.30 $6.80 3.96 9.07 14.00 $33.40 $76.50 $118.00Note:1L = lower beta estimate2C = central beta estimate3U = upper beta estimate4“None” represents an unstratified analysis and includes the entire population regardless of blood PblevelDeveloping a Concentration-Response Function for PbExposure and Cardiovascular Disease-Related MortalityGeneralizability▌pg.5-13Exhibit 18. Benefits from Percentage Change for Varying Blood Pb Cutoff Levels for LRRP Type RuleBlood PbCutoff (μg/dL)Gender5% Decrease50% DecreaseAnnual CasesAvoidedAnnual Benefits (2012$million)Annual CasesAvoidedAnnual Benefits (2012$ million)L1C2U3LCULCULCUNone4M 8.3 19.3 30.1 $69.90 $162.30 $253.80 67.1 150.5 227.3 $565.70 $1,268.40 $1,916.30F 5.2 12.1 18.9 $43.90 $102.00 $159.50 42.2 94.5 142.8 $355.30 $796.70 $1,203.80Both 13.5 31.4 49.0 $113.80 $264.30 $413.30 109.3 245.0 370.1 $921.00 $2,065.10 $3,120.101M 6.8 15.7 24.5 $57.00 $132.30 $206.90 54.7 122.7 185.3 $461.20 $1,034.00 $1,562.20F 3.5 8.0 12.5 $29.10 $67.60 $105.80 28.0 62.7 94.7$235.70 $528.40 $798.40Both 10.2 23.7 37.1 $86.10 $200.00 $312.70 82.7 185.3 280.0 $696.80 $1,562.40 $2,360.601.46M 4.8 11.3 17.6 $40.90 $94.90 $148.40 39.2 87.9 132.9 $330.60 $741.30 $1,120.00F 2.2 5.0 7.9 $18.30 $42.60 $66.60 17.6 39.5 59.6 $148.30 $332.60 $502.50Both 7.0 16.3 25.5 $59.20 $137.40 $214.90 56.8 127.4 192.5 $479.00 $1,073.90 $1,622.502M 3.0 7.0 10.9 $25.40 $59.00 $92.30 24.4 54.7 82.6$205.60 $461.00 $696.50F 1.1 2.6 4.1 $9.40 $21.80 $34.10 9.0 20.2 30.6 $76.10 $170.60 $257.70Both 4.1 9.6 15.0 $34.80 $80.80 $126.40 33.4 74.9 113.2 $281.70 $631.60 $954.303.62M 0.8 1.9 2.9 $6.80 $15.80 $24.60 6.5 14.6 22.1 $54.90 $123.20 $186.10F 0.3 0.6 1.0 $2.30 $5.30 $8.30 2.2 4.9 7.5 $18.60$41.70 $62.90Both 1.1 2.5 3.9 $9.10 $21.10 $33.00 8.7 19.6 29.5$73.50 $164.80 $249.00Note:1L = lower beta estimate2C = central beta estimate3U = upper beta estimate4“None” represents an unstratified analysis and includes the entire population regardless of blood PblevelDeveloping a Concentration-Response Function for Pb Exposure and Cardiovascular Disease-Related Mortality

Generalizability ▌ pg. 5-14 To put these values in perspective, according to CD C, in 2010, 1,852 individuals out of a million (0.185%) would be expected to die from CVD in the a dult general population based on the CVD mortality incidence rates (CDC - National Center fo r Health Statistics, 2010). The absolute change examples presented avoid from 1.2% (0.05 μg/dL decr ease 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 l evel 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 ( y 0 , β , and pop ). However, in reality each of the parameters used in the function are uncertain and variable. Consequently, as with any benefits analys is, there is uncertainty and variability of the concentration-response function and the health impa ct function. As a result of the uncertainty in the inputs to the benefits estimation, the resulting be nefits 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 functi on (Section 6.2). Where possible, we also describe approaches for characterizing the uncertainty and v ariability.



### 6.1 Uncertainty in the Concentration-Response Funct ion

Recall from Section 4 the concentration-response fu nction is of the form:

) ln( * ) ln( x y β α + =

or

β 

## x B y * = , ( α



## e B = ),

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. (2 006). The uncertainty and variability associated with each of these parameters is discussed in the f ollowing sections.

6.1.1 Effect ( β ) Estimate

The beta estimate of 0.35 (SE=0.10) has two main un certainties associated with it: (1) sampling uncertainty, and (2) missing data/imputation uncert ainty. 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 di stribution of estimates for the beta as opposed to the single value of 0.35. Based on the sampling des ign for NHANES III, the distribution for the beta is a t-distribution with 49 degrees of freedom (Per sonal Communication with Andy Menke, 2013). An example of the lower and upper bound estimates of t he number of CVD deaths avoided as a result of a hypothetical situation was calculated in the prev ious section using the upper and lower bounds of
the 95% confidence interval on the central beta est imate.

There is additional uncertainty when the estimated concentration-response function is used to make predictions for blood Pb levels below the LOD becau se blood Pb levels below the LOD were imputed. Unfortunately, these missing data/imputati on uncertainties cannot be categorized without doing a complete reanalysis of the Menke et al. (20 06) data.

As for variability associated with the β estimate, Menke et al. (2006) examined effect modi fication 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 NHANE S III one-time blood Pb levels, since it is unknown if they accurately represent the Pb exposur e that will result in future CVD mortality cases. As stated previously, a major limitation of the Men ke et al. (2006) findings is that they are based on a single blood Pb measure, which is reflective of bot h recent exposures (<30 days) from exogenous sources and past exposures (years to decades) that had been stored in tissues (e.g., bone) and release d endogenously (National Toxicology Program, 2012; U. S. EPA, 2013). Therefore, it is unclear whether the impact of Pb on CVD mortality risk obse rved in Menke et al. (2006) is associated with current, past, or cumulative exposures.

It is possible that the relationship predicted by u sing 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 towa rd the null. This occurs because a single blood Pb measurement is highly variable, and, therefore, usi ng the highly variable measurement to predict an outcome adds noise to the model, resulting in an un derestimation 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 fe ll 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 201 4 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 diff erent 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 cardiov ascular disease mortality risk (see discussion in Section 5.3).

Additionally, blood Pb levels decreased (from a geo metric 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 decre ase in blood Pb levels throughout the NHANES III time period are unclear. It could be argued that ap plying the beta estimate based on these higher bloo d 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 bet a estimate has been attenuated and is actually underestimating the relationship, assuming the misc lassification is random. As the authors point out, “the decrease in blood lead observed at the populat ion 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 gr eater 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 relation ship between log blood Pb and log CVD mortality would hold below the observable range in Menke et a l. (2006) introduces uncertainty, as it could be possible that the slope is different at lower blood Pb levels relevant for portions of current and fut ure 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 M enke et al. (corresponding to Exhibit 7 and Exhibit 12) could also underestimate the relationsh ip between blood Pb and CVD mortality due to the inclusion of hypertension and kidney function as co ntrol 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). Con trolling for these conditions in the regression mea ns that the coefficient estimate on Pb exposure will n ot 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 co mpared to controlling for them.

Quantifying these uncertainties is currently not po ssible. 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 ongoin g studies are discussed further in Section 7.

6.1.2 Blood Pb Estimates

The blood Pb levels used in the hypothetical exampl e were based on data from the most recent NHANES analysis (NHANES 2011-2012). Sampling uncert ainty exists with these values and can be characterized by using a distribution of blood Pb l evels 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 wil l likely be estimated using exposure modeling. If the exposure model is designed to output a distribu tion 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 t o just using the mean value. Additional uncertain var iables will undoubtedly exist in the exposure modeling. However, given that this report is focuse d on applying the concentration-response function and not the exposure modeling, we do not further an alyze the uncertainty of potential exposure models in this report.

Additionally, blood Pb levels will vary between peo ple (e.g., based on age, gender, sociodemographic group membership). For the hypothetical analysis we characterize some of this variability by deriving benefits estimates for gend er/age groups, whose blood Pb levels do vary. However, as with the uncertainty, in order to captu re this variability in modeled blood Pb levels, blood Pb estimates could be modeled for various gro ups of people to determine how the blood Pb concentrations may vary based on an individual’s ch aracteristics. Again, given that this report is concentrating on applying the concentration-respons e function and not the exposure modeling, we do not further analyze the uncertainty of potential ex posure models in this report.

6.1.3 Functional Form

As with many concentration-response functions, ther e is uncertainty about the functional form of the relationship between exposure and response. In cons idering the relationship between Pb exposure and CVD, Menke et al. (2006) examined several functiona l forms and concluded that the linear function between log of blood Pb and log of CVD mortality ri sk best represented the concentration-responseDeveloping 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 conce ntration-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 i s:

pop

x x y avoided cases *]

0 1 1[* 0 β

       

− = (3)

where y 0 is the baseline CVD mortality per capita rate, x 1 is the blood Pb level with a rule in place, x 0 is the blood Pb level without the rule, and pop is the population the rule will impact. In additio n to the uncertainty associated with blood Pb measurement an d the concentration-response function coefficient estimate (discussed in the previous sec tion), incorporating the baseline mortality rate an d the affected population adds additional uncertainty and variability to the benefits estimate. The subsequent sections discuss these additional source s of uncertainty and variability.

6.2.1 Baseline Mortality Rates

The method to characterize uncertainty and variabil ity in the baseline mortality rates for the benefit s 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 futur e 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 pres ented. As for variability, the baseline mortality r ate varies by age, gender, socio-demographic group, and location. As with uncertainty, variability should

be characterized in the context of the rule. For ex ample, if it is important to understand how benefit s 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 w ill 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 hypothetica l 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 i mpacted 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 oth er components of the concentration-response function. Additionally, spatial variation may exist . That is, benefits will be dependent on the area o f the United States that is being impacted and the si ze 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 g roups that may be impacted. Additionally, uncertainty surrounding the population estimates in certain areas can be characterized by dataDeveloping 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 t hese 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 i s based on Menke et al. (2006), who used one-time blo od Pb samples from NHANES III (1988-1994). To understand how this function may change with red uced 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 bas ed on more recent blood Pb levels.

In addition, work is currently underway by Weisskop f 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 mortali ty 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 v ersus 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 concentrat ion-response functions for additional endpoints. To select additional endpoints for inclusion in a b enefits 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 purp oses of estimating benefits.Developing a Concentration-Response Function for Pb Exposure and Cardiovascular Disease-Related Mortality

References ▌ pg. 1 

## 8. References

Allison, P. D. (2000). Estimating Cox Regression Mo dels with PROC PHREG Survival Analysis Using the SAS System: A Practical Guide (pp. 113-118). American Heart Association. (2011). Cardiac Glossar y Retrieved September 10, 2012, 2012, from http://www.heart.org/HEARTORG/Conditions/HeartAttac k/HeartAttackToolsResources/Car diac-Glossary_UCM_303945_Article.jsp Belova, A., Greco, S., Riederer, A., Olsho, L., & C orrales, M. (2013). A Method to screen US environmental biomonitoring data for race/ethnicity and income-related disparity. Environmental Health Perspectives, 12 (1), 114. Bureau of Labor Statistics. (2013). Consumer Price Index History Table. All Urban Consu mers - (CPI-U), U.S. city average, All items, 1982-84=100

. . Retrieved from tp://ftp.bls.gov/pub/special.requests/cpi/cpiai.txt

. Cal OEHHA. (2013). Legget Model Code . Retrieved from http://oehha.ca.gov/air/legget.html

. CDC - National Center for Health Statistics. (2010) . Underlying Cause of Death 1999-2010 on CDC WONDER Online Database, released 2012. CDC - National Center for Health Statistics. (2013) . NHANES 2011-2012: Blood Lead, Cadmium, Total Mercury, Selenium, and Manganese (PbCd_G ) da ta file. In CDC (Ed.). Hyattsville,
MD. CDC. (1996). Laboratory Procedures Used for the Third National H ealth and Nutrition Examination Survey (NHANES III), 1988-1994 . Atlanta, GA and Hyattsville, MD: Retrieved from http://www.cdc.gov/nchs/data/nhanes/nhanes3/cdrom/n chs/manuals/labman.pdf

. CDC. (2004). The Health Consequences of Smoking: A Report of the Surgeon General . Retrieved
from http://www.cdc.gov/tobacco/data_statistics/sgr/2004 /pdfs/insidecover.pdf

. CDC. (2012a). Mortality Data File for 2010 with all county identifiers.CD-ROM: Available by request from the National Association for Public He alth Statistics and Information Systems (NAPHSIS). http://www.naphsis.org/Pages/VitalStatisticsDataRes earchRequestProcess.aspx

. CDC. (2012b). Summary Health Statistics for U.S. Adults: National Health Interview Survey

. Washington, DC. Cines, D. B., Pollak, E. S., Buck, C. A., Loscalzo, J., Zimmerman, G. A., McEver, R. P., . . . Stern, D. M. (1998). Endothelial cells in physiology and in t he pathophysiology of vascular disorders. Blood, 91 (10), 3527-3561. Cocco, P., Fadda, D., Atzeri, S., Avataneo, G., Mel oni, M., & Flore, C. (2007). Causes of death among lead smelters in relation to the glucose-6-ph osphate dehydrogenase polymorphism. Occup Environ Med, 64 (6), 414-416. doi: 10.1136/oem.2006.028779 Ezzati, M., Vander Hoorn, S., Lopez, A., Danaei, G. , Rodgers, A., Mathers, C., & Murray, C. (2006). Comparative quantification of mortality and burden of disease attributable to selected risk
factors Global Burden of Disease and Risk Factors . New York, NY: Oxford University Press. Hu, H., Shih, R., Rothenberg, S., & Schwartz, B. S. (2007). The epidemiology of lead toxicity in adults: measuring dose and consideration of other m ethodologic issues. Environmental Health Perspectives, 115 (3), 455-462. Khalil, N., Wilson, J., Talbott, E., Morrow, L., Ho chberg, M., Hillier, T., . . . Cauley, J. (2009).
Association of blood lead concentrations with morta lity in older women: a prospective cohort study. Environmental Health, 8 (1), 1-10. doi: 10.1186/1476-069x-8-15 Leggett, R. (1993). An age-specific kinetic model o f lead metabolism in humans. Environ Health Perspect, 101 (7), 598-616. Lin, J. L., Lin-Tan, D. T., Hsu, C. W., Yen, T. H., Chen, K. H., Hsu, H. H., . . . Hsu, K. H. (2011). Association of blood lead levels with mortality in patients on maintenance hemodialysis. Am J Med, 124 (4), 350-358. doi: 10.1016/j.amjmed.2010.10.022Developing a Concentration-Response Function for Pb Exposure and Cardiovascular Disease-Related Mortality

References ▌ pg. 2 Lustberg, M., & Silbergeld, E. (2002). Blood lead l evels and mortality. Arch Intern Med, 162 (21),
2443-2449. MedicineNet.com. (2012). Defintion of Cerebrovascul ar Disease Retrieved November 6, 2013, from http://www.medterms.com/script/main/art.asp?article key=40116 Menke, A., Muntner, P., Batuman, V., Silbergeld, E. K., & Guallar, E. (2006). Blood lead below 0.48 micromol/L (10 microg/dL) and mortality among US ad ults. Circulation, 114 (13), 1388-
1394. doi: 10.1161/circulationaha.106.628321 Møller, L., & Kristensen, T. S. (1992). Blood Lead as a Cardiovascular Risk Factor. American Journal of Epidemiology, 136 (9), 1091-1100. National Toxicology Program. (2012). Prepublication Copy: NTP Monograph on Health Effect s of
Low-Level Lead

. Neuberger, J., Hu, S., Drake, K., & Jim, R. (2009). Potential health impacts of heavy-metal exposure at the Tar Creek Superfund site, Ottawa County, Okl ahoma. Environmental Geochemistry and Health, 31 (1), 47-59. doi: 10.1007/s10653-008-9154-0 Nie, H., Sanchez, B. N., Wilker, E., Weisskopf, M. G., Schwartz, J., Sparrow, D., & Hu, H. (2009). Bone lead and endogenous exposure in an environment ally exposed elderly population: the normative aging study. J Occup Environ Med, 51 (7), 848-857. Park, S. K., Mukherjee, B., Xia, X., Sparrow, D., W eisskopf, M. G., Nie, H., & Hu, H. (2009). Bone lead level prediction models and their application to examine the relationship of lead exposure and hypertension in the Third National Hea lth and Nutrition Examination Survey. J Occup Environ Med, 51 (12), 1422-1436. doi: 10.1097/JOM.0b013e3181bf6c8d Personal Communication with Andy Menke (2013, 9/2/1 3). Personal Communication with Mark Weisskopf (2013). [Phone Conversation on July 25th]. Rotheman, K. (1998). Modern Epidemiology : Lippincott-Raven. Schober, S. E., Mirel, L. B., Graubard, B. I., Brod y, D. J., & Flegal, K. M. (2006). Blood lead levels and death from all causes, cardiovascular disease, and cancer: results from the NHANES III mortality study. Environ Health Perspect, 114 (10), 1538-1541. Tibshirani, R. (1982). A plain man's guide to the p roportional hazards model. Clinical & Investigative Medicine - Médecine Clinique et Experimentale, 5 (1), 63-68. U.S. Burden of Disease Collaborators. (2013). The s tate of US health, 1990-2010: Burden of diseases, injuries, and risk factors. JAMA, 310 (6), 591-606. doi: 10.1001/jama.2013.13805 U.S. EPA. (2008a). Economic Analysis for the TSCA Lead Renovation, Rep air and Painting Program Final Rule for Target Housing and Child-Occupied Fa cilities . Washington, DC: U.S. EPA. U.S. EPA. (2008b). Regulatory Impact Analysis of the Proposed Revision s to the National Ambient Air Quality Standards . Research Triangle Park. U.S. EPA. (2008c). Risk and Exposure Assessment to Support the Review of the NO 2 Primary National Ambient Air Quality Standard . Washington, DC. U.S. EPA. (2010). Guidelines for Preparing Economic Analyses, EPA 240 -R-10-001

. U.S. EPA. (2012a). Health Risk and Exposure Assessment for Ozone - Fir st External Review Draft

. Washington, DC. U.S. EPA. (2012b). Integrated Science Assessment for Lead - 3rd Draft . Research Triangle Park. U.S. EPA. (2013). Integrated Science Assessment for Lead . (EPA/600/R-10/075F). Research Triangle 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. Weisskopf, M. G., Jain, N., Nie, H., Sparrow, D., V okonas, P., Schwartz, J., & Hu, H. (2009). A prospective study of bone lead concentration and de ath from all causes, cardiovascularDeveloping a Concentration-Response Function for Pb Exposure and Cardiovascular Disease-Related Mortality

References ▌ pg. 3 diseases, and cancer in the Department of Veterans Affairs Normative Aging Study. Circulation, 120 (12), 1056-1064. doi: 10.1161/circulationaha.108.82 7121 Wittmers Jr., L. E., Aufderheide, A. C., Wallgren, J., Rapp, G., Jr., & Alich, A. (1988). Lead in bone

. IV. Distribution of lead in the human skeleton. Arch Environ Health, 43 (6), 381-391. doi:
10.1080/00039896.1988.9935855Developing 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 Add itional Review

This section presents summaries of five studies ide ntified by the EPA ISA and the NTP Monograph that examine the association between Pb exposure an d 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) assesse d the adverse health effects of 933 male Pb smelters in Sardinia, Italy. The study focused on d ifferences 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 sme lters’ 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 populat ion. 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 f urther to determine if the data presented would be useful in the estimation of a concentration-respons e 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 pa tients 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 μ g/dL, and all subjects were stratified into three e qual groups: low blood Pb level (<8.51 μ g/dL), medium blood Pb level (8.51-12.64 μ g/dL), and high blood Pb level (>12.64 μ g/dL). A Cox multivariate analysis was used to associate baselin e blood Pb levels >12.64 μg/dL with higher allcause mortality (HR = 4.70; 95% CI = 1.92-11.49), c ardiovascular-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 s tudy 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 leve ls 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 Examinatio n 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 parti cipants. Individuals with blood Pb levels of at least 30 μg/dL were excluded, leaving 4,190 partici pants. In this group, 19.5% of participants had blood Pb levels <10 μg/dL, 65.3% had blood Pb level s of 10-19 μg/dL, and 15.2% had blood Pb levels of 20-29 μg/dL. The average blood Pb level o f the 4,190 participants was 14.0 ± 5.1 μg/dL. Adults with blood Pb levels of 20-29 μg/dL were fou nd 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 circulatoryDeveloping 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 a lso 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 bloo d Pb level is too high to represent the current average blood Pb levels in the U.S. general populat ion.



### 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, to tal mortality, coronary heart disease and cardiovascular disease mortality. Their cohort incl uded 1,052 men and women from Copenhagen County, Denmark, who had blood Pb measurements take n three times, in 1976, 1981, and 1987 (in 1987 only men were examined). Mean blood Pb concent rations decreased from 13.6 to 8.3 μg/dL from 1976 to 1987 for men, and from 9.6 to 6.8 μg/d L from 1976 to 1981 for women. All participants in the study were followed regarding hospital admis sions and deaths for a 14-year follow-up period. The authors found a significant univariate associat ion with total mortality, coronary heart disease, a nd cardiovascular disease. With regard to coronary hea rt disease and cardiovascular disease, the association disappeared when the model included con founders. However, the relationship between blood Pb and total mortality remained statistically significant after controlling for confounders. Giv en that this study did not examine blood Pb as it rela tes specifically to cardiovascular disease-related mortality, we will not further consider it in the d evelopment 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 Ka nsas, Missouri, and Oklahoma. In the mid-1980s, parts of the mining district were declared a Superf und site because of the presence of high concentrations of lead, zinc, and cadmium in the mi ne wastes and tailings, as well as the presence of acid mine water laden with heavy metals emerging fr om surface and groundwater into the surrounding creeks, especially in Oklahoma. Neuberg er et al. compared the occurrence of selected mortality outcomes in Ottawa County (both exposed a nd unexposed areas) with data for the entire state of Oklahoma. The SMR for death due to hyperte nsion for Ottawa county compared to the state in 1999–2001 was 112.5 (95% CI: 64.3–182.7); the SM R for death due to stroke was 121.6 (95% CI: 119.2–123.9); and the SMR for death due to heart di sease was 114.1 (95% CI: 113.1–115.2).

Neuberger et al. also compared the occurrence of se lected mortality outcomes for the five exposed Ottawa County cities to the remainder of Ottawa Cou nty’s population. The SMR for mortality due to hypertension for the five exposed Ottawa County tow ns compared to data for the rest of the county in 1999–2001 was 144.9 (95% CI: 39.5–370.9); the SMR f or death due to stroke was 69.9 (95% CI: 37.2–119.6); and the SMR for heart disease-caused m ortality 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 thisDeveloping 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 l evels, and blood Pb levels are not reported for adults, we cannot use this study to estimate the co ncentration-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 The ir 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. Clear ance 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, & Schw artz, 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 lea d at birth is 0.23 years, 1.2 years at 5 years of a ge, 3.7 years at 15 years of age, and 23 years in adults; f or 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. E PA, 2013).

Although Pb can circulate throughout the body and c an be found in all organs and tissues, blood is the most readily available biomarker for Pb exposure. I t 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 Toxicolog y Program, 2012; U.S. EPA, 2013). As stated previously, the half-life of blood Pb is approximat ely 30 days, but Hu et al. (2007) point out that th is

half-life is:

“a reflection of the 120-day lifespan of erythrocyt es and only applies in practice if the total exposu re is short (e.g., <30 days). If lead exposure is long-term (i.e., wit h a duration of years), upon cessation the kinetics of clearance of lead from blood is considerably more complicated, w ith an initial rapid decline in levels reflecting p artial

clearance from blood and other soft tissues followe d by a much slower clearance, reflecting the replen ishment 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 ent ers specific tissues and is what is of interest whe n understanding the toxic impacts of Pb. Unfortunatel y, plasma Pb is difficult to measure and is subsequently not readily available or used in epide miological studies. This can result in exposure misclassification given that the ratio of whole blo od Pb to plasma blood Pb is not well characterized,

and therefore it is unclear how much Pb reaches a t arget organ. It is also important to note that different techniques for quantifying Pb in whole bl ood or plasma measurements can limit comparability across studies, especially when metho ds 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 introd uced 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 no t imply that he/she has had a lifetime of high Pb

exposures.

10 Bone resorption is the breakdown of bone tissue i n 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 cu mulative Pb exposure or indicators of cumulative Pb exposure based on blood Pb measuremen ts. 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 rule 12 to estimate an acceptable cumulative Pb dose surro gate (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 P b, age, education, occupation, cumulative cigarette smoking, and smoking status) to predict bone Pb lev els. This model used a subset of data from the Normative Aging Study and found that blood Pb accou nted for approximately 9% (tibia) to 13% (patella) of the variability in bone Pb levels. Inc lusion of age in the regression model accounted for an additional 7-10% of the variability in bone Pb (Par k 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 ha lf-life of Pb in bone that results in bone lead representing 90% of an adult’s body burden (U.S. EP A, 2013). There are two types of bone, cortical and trabecular, both of which can accumulate Pb. Tr abecular 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 t he shaft of the tibia (~80% of adult bone) (U.S. EPA, 2013; Wittmers Jr., Aufderheide, Wallgren, Rapp, & Alich, 1988). The trabecular bone, given its shor ter half-life of approximately 4 years, is more representative of recent exposure. Additionally, tr abecular bone may provide more of the Pb in circulation “due to its larger surface area allowin g for more Pb to bind via ion exchange mechanisms and more rapid turnover, making it more sensitive t o changing patterns of exposure” (U.S. EPA, 2013, p. 3-68). Cortical bone, on the other hand, h as 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 c alcification rates (the rate at which the bone is being formed or remodeled 13 ). In infancy and childhood, calcification is most active in trabecular bone. Additionally, given the high bone formation r ate 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 fi xed in the bone” (U.S. EPA, 2013, p. 1-12). In adulthood calcification occurs mainly at sites of r emodeling in cortical and trabecular bones (U.S. EPA, 2013). Further, during times of physiologic st ress such as pregnancy, lactation, menopause, extended bed rest, hyperparathyroidism, or severe w eight loss, mobilization of Pb from bone into blood increases. Interestingly, Nie et al. (2009) f ound 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 sto red 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 gr aph of a function.

13 Bone remodeling is the process of breaking down o f bone tissue in order to release calcium to the bl ood (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 contri bute 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 fluore scence (K-XRF) machine. However, the most commonly used K-XRF machines have a wide measuremen t 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 r esults between studies. Further, some methods may be impacted by the thickness of skin over the m easurement site, which may be a concern for studies that include obese people (National Toxicol ogy 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 s tudies in individuals with low bone density (e.g., older women) (U.S. EPA, 2013).Developing a Concentration-Response Function for PbExposure and Cardiovascular Disease-Related MortalityAbsolute Change▌pg.C-1Appendix C Absolute ChangeExhibit C-1. Benefits from Absolute Change in Unstratified Blood Pb CutoffAgeGroupGender0.05 μg/dL increase1.0 μg/dL increaseAnnual Cases AvoidedAnnual Benefits (2012$million)Annual Cases AvoidedAnnual Benefits (2012$ million)LCULCULCULCU20-29M 0.07 0.16 0.25 $0.60 $1.40 $2.10 0.86 1.86 2.72 $7.30 $15.70 $22.90F 0.06 0.13 0.20 $0.50 $1.10 $1.70 0.57 1.20 1.72 $4.80 $10.20 $14.5030-39M 0.21 0.48 0.74 $1.70 $4.00 $6.30 2.54 5.51 8.08 $21.40 $46.50 $68.10F 0.16 0.38 0.59 $1.40 $3.20 $5.00 1.73 3.67 5.26 $14.60 $31.00 $44.4040-49M 0.62 1.44 2.25 $5.20 $12.10 $19.00 8.17 17.92 26.52 $68.90 $151.10 $223.50F 0.39 0.90 1.41 $3.30 $7.60 $11.80 4.72 10.22 14.94 $39.80 $86.20 $125.9050-59M 1.21 2.81 4.40 $10.20 $23.70 $37.10 17.16 38.02 56.80 $144.60 $320.50 $478.80F 0.68 1.59 2.48 $5.80 $13.40 $20.90 9.10 19.97 29.56 $76.70 $168.30 $249.2060-69M 1.61 3.73 5.85 $13.50 $31.50 $49.30 22.95 51.02 76.47 $193.40 $430.10 $644.70F 1.14 2.66 4.16 $9.60 $22.40 $35.10 15.43 33.93 50.30 $130.10 $286.00 $424.0070-80M 2.09 4.86 7.62 $17.60 $41.00 $64.20 31.01 69.18 104.00 $261.50 $583.20 $876.70F 2.26 5.25 8.22 $19.10 $44.30 $69.30 30.72 67.83 100.96 $259.00 $571.80 $851.10Total(20-80years)M5.80 13.49 21.12 $48.90 $113.70$178.0082.68 183.53 274.59 $697.00 $1,547.10$2,314.80F4.69 10.90 17.06 $39.60 $91.90 $143.8062.27 136.82 202.73 $525.00 $1,153.40$1,709.00Both10.50 24.39 38.18 $88.50 $205.60$321.80144.96320.35 477.32 $1,222.00$2,700.50$4,023.80Developing a Concentration-Response Function for PbExposure and Cardiovascular Disease-Related MortalityAbsolute Change▌pg.C-2Exhibit C-2. Benefits from Absolute Change for a Blood Pb Cutoff of Above 1.0 μg/dLAgeGroupGender0.05 μg/dL increase1.0 μg/dL increaseAnnual Cases AvoidedAnnual Benefits (2012$million)Annual Cases AvoidedAnnual Benefits (2012$million)LCULCULCULCU20-29M 0.01 0.03 0.05 $0.10 $0.30 $0.40 0.20 0.44 0.66 $1.70 $3.70 $5.60F 0.00 0.01 0.01 $0.00 $0.00 $0.10 0.03 0.07 0.11 $0.30 $0.60 $0.9030-39M 0.06 0.13 0.20 $0.50 $1.10 $1.70 0.82 1.81 2.71 $6.90 $15.30 $22.90F 0.01 0.02 0.04 $0.10 $0.20 $0.30 0.15 0.32 0.48 $1.20 $2.70 $4.0040-49M 0.24 0.56 0.87 $2.00 $4.70 $7.40 3.61 8.05 12.10$30.40 $67.90 $102.00F 0.08 0.19 0.30 $0.70 $1.60 $2.50 1.23 2.73 4.09 $10.40 $23.00 $34.5050-59M 0.71 1.65 2.59 $6.00 $13.90 $21.80 10.83 24.20 36.46 $91.30 $204.00 $307.40F 0.30 0.71 1.11 $2.60 $5.90 $9.30 4.53 10.08 15.11$38.20 $85.00 $127.4060-69M 1.09 2.53 3.97 $9.20 $21.30 $33.40 16.68 37.35 56.36 $140.60$314.90 $475.10F 0.54 1.26 1.97 $4.60 $10.60 $16.60 8.13 18.09 27.15 $68.50 $152.50 $228.9070-80M 1.61 3.74 5.86 $13.50 $31.50 $49.40 24.92 55.83 84.29 $210.10$470.70 $710.60F 1.15 2.68 4.21 $9.70 $22.60 $35.40 17.59 39.29 59.15 $148.30$331.20 $498.60Total(20-80years)M3.71 8.64 13.54 $31.30 $72.80 $114.1057.05 127.69 192.59 $481.00$1,076.40$1,623.50F2.09 4.87 7.63 $17.60 $41.00 $64.30 31.65 70.59 106.09 $266.80$595.10 $894.30Both5.81 13.51 21.17 $48.90 $113.90$178.4088.71 198.28 298.67 $747.80$1,671.50$2,517.80Developing a Concentration-Response Function for PbExposure and Cardiovascular Disease-Related MortalityAbsolute Change▌pg.C-3Exhibit C-3. Benefits from Absolute Change for a Blood Pb Cutoff of Above 1.46 μg/dLAgeGroupGender0.05 μg/dL increase1.0 μg/dL increaseAnnual Cases AvoidedAnnual Benefits (2012$million)Annual Cases AvoidedAnnual Benefits (2012$million)LCULCULCULCU20-29M 0.01 0.01 0.02 $0.00 $0.10 $0.10 0.08 0.17 0.26 $0.70 $1.50 $2.20F 0.00 0.00 0.00 $0.00 $0.00 $0.00 0.01 0.03 0.04 $0.10 $0.20 $0.3030-39M 0.02 0.05 0.08 $0.20 $0.40 $0.70 0.34 0.77 1.16 $2.90 $6.50 $9.80F 0.00 0.01 0.01 $0.00 $0.10 $0.10 0.04 0.09 0.14 $0.30 $0.80 $1.2040-49M 0.11 0.26 0.41 $0.90 $2.20 $3.40 1.78 4.01 6.08 $15.00 $33.80 $51.30F 0.03 0.06 0.09 $0.20 $0.50 $0.80 0.40 0.91 1.37 $3.40 $7.60 $11.6050-59M 0.39 0.91 1.43 $3.30 $7.70 $12.00 6.26 14.09 21.38 $52.80 $118.80$180.20F 0.13 0.31 0.48 $1.10 $2.60 $4.00 2.07 4.65 7.04 $17.50 $39.20 $59.3060-69M 0.58 1.36 2.13 $4.90 $11.40 $17.90 9.45 21.32 32.41 $79.70 $179.80$273.20F 0.25 0.57 0.90 $2.10 $4.80 $7.60 3.90 8.74 13.23$32.80 $73.70 $111.5070-80M 1.02 2.38 3.73 $8.60 $20.00 $31.40 16.50 37.17 56.42 $139.10$313.40$475.60F 0.63 1.46 2.29 $5.30 $12.30 $19.30 10.05 22.60 34.26 $84.70 $190.50$288.80Total(20-80years)M2.13 4.96 7.79 $18.00 $41.90 $65.60 34.42 77.54 117.72 $290.10$653.70$992.40F1.03 2.40 3.76 $8.70 $20.20 $31.70 16.47 37.02 56.07 $138.80$312.10$472.70Both3.16 7.37 11.55 $26.70 $62.10 $97.40 50.89 114.57 173.79 $429.00$965.80$1,465.10Developing a Concentration-Response Function for PbExposure and Cardiovascular Disease-Related MortalityAbsolute Change▌pg.C-4Exhibit C-4. Benefits from Absolute Change for a Blood Pb Cutoff of Above 2.0 μg/dLAgeGroupGender0.05 μg/dL increase1.0 μg/dL increaseAnnual Cases AvoidedAnnual Benefits (2012$million)Annual Cases AvoidedAnnual Benefits (2012$million)LCULCULCULCU20-29M 0.00 0.00 0.01 $0.00 $0.00 $0.10 0.03 0.07 0.11 $0.30 $0.60 $0.90F 0.00 0.00 0.00 $0.00 $0.00 $0.00 0.00 0.01 0.01 $0.00 $0.10 $0.1030-39M 0.01 0.02 0.03 $0.10 $0.10 $0.20 0.12 0.28 0.42 $1.00 $2.30 $3.60F 0.00 0.00 0.00 $0.00 $0.00 $0.00 0.01 0.03 0.05 $0.10 $0.30 $0.4040-49M 0.04 0.09 0.15 $0.30 $0.80 $1.20 0.68 1.54 2.35 $5.70 $12.90 $19.80F 0.01 0.02 0.04 $0.10 $0.20 $0.30 0.17 0.39 0.59 $1.40 $3.20 $5.0050-59M 0.17 0.40 0.62 $1.40 $3.30 $5.30 2.86 6.49 9.93 $24.10 $54.70 $83.70F 0.05 0.11 0.17 $0.40 $0.90 $1.50 0.78 1.77 2.70 $6.60 $14.90 $22.7060-69M 0.31 0.72 1.13 $2.60 $6.10 $9.50 5.23 11.88 18.16$44.10 $100.10$153.10F 0.07 0.16 0.25 $0.60 $1.30 $2.10 1.14 2.58 3.95 $9.60 $21.80 $33.3070-80M 0.51 1.19 1.86 $4.30 $10.00 $15.70 8.59 19.46 29.70 $72.40 $164.00$250.40F 0.27 0.64 1.00 $2.30 $5.40 $8.40 4.59 10.41 15.89$38.70 $87.70 $133.90Total(20-80years)M1.04 2.42 3.80 $8.80 $20.40 $32.00 17.51 39.71 60.66 $147.60$334.70$511.40F0.40 0.93 1.46 $3.40 $7.80 $12.30 6.71 15.19 23.18$56.50 $128.00$195.40Both1.44 3.35 5.26 $12.10 $28.30 $44.30 24.22 54.90 83.84 $204.10$462.80$706.80Developing a Concentration-Response Function for PbExposure and Cardiovascular Disease-Related MortalityAbsolute Change▌pg.C-5Exhibit C-5. Benefits from Absolute Change for a Blood Pb Cutoff of Above 3.62 μg/dLAgeGroupGender0.05 μg/dL increase1.0 μg/dL increaseAnnual Cases AvoidedAnnual Benefits (2012$million)Annual Cases AvoidedAnnual Benefits (2012$million)LCULCULCULCU20-29M 0.00 0.00 0.00 $0.00 $0.00 $0.00 0.01 0.02 0.03 $0.10 $0.20 $0.20F 0.00 0.00 0.00 $0.00 $0.00 $0.00 0.00 0.00 0.00 $0.00 $0.00 $0.0030-39M 0.00 0.00 0.00 $0.00 $0.00 $0.00 0.01 0.01 0.02 $0.10 $0.10 $0.20F 0.00 0.00 0.00 $0.00 $0.00 $0.00 0.00 0.00 0.01 $0.00 $0.00 $0.1040-49M 0.01 0.01 0.02 $0.00 $0.10 $0.20 0.10 0.22 0.35 $0.80 $1.90 $2.90F 0.00 0.00 0.00 $0.00 $0.00 $0.00 0.02 0.05 0.08 $0.20 $0.40 $0.6050-59M 0.02 0.05 0.07 $0.20 $0.40 $0.60 0.36 0.83 1.28 $3.00 $7.00 $10.80F 0.00 0.01 0.02 $0.00 $0.10 $0.10 0.07 0.17 0.26 $0.60 $1.40 $2.2060-69M 0.06 0.14 0.22 $0.50 $1.20 $1.90 1.08 2.47 3.81 $9.10 $20.80 $32.10F 0.01 0.03 0.04 $0.10 $0.20 $0.40 0.21 0.47 0.73 $1.70 $4.00 $6.1070-80M 0.08 0.18 0.28 $0.60 $1.50 $2.40 1.38 3.15 4.85 $11.60 $26.50 $40.90F 0.04 0.10 0.15 $0.30 $0.80 $1.30 0.74 1.68 2.60 $6.20 $14.20 $21.90Total(20-80years)M0.16 0.38 0.60 $1.40 $3.20 $5.00 2.92 6.70 10.34 $24.60 $56.40 $87.10F0.06 0.14 0.21 $0.50 $1.10 $1.80 1.04 2.37 3.66 $8.70 $20.00 $30.90Both0.22 0.51 0.81 $1.90 $4.30 $6.80 3.96 9.07 14.00 $33.40 $76.50 $118.00Developing a Concentration-Response Function for PbExposure and Cardiovascular Disease-Related MortalityAbsolute Change▌pg.D-1Appendix D Percentage ChangeExhibit D-1. Benefits from Percentage Change for anUnstratified Blood Pb CutoffAgeGroupGender5% increase50% increaseAnnual Cases AvoidedAnnual Benefits (2012$million)Annual Cases AvoidedAnnual Benefits (2012$million)LCULCULCULCU20-29M 0.06 0.13 0.21 $0.50 $1.10 $1.70 0.46 1.03 1.56 $3.90 $8.70 $13.20F 0.03 0.07 0.10 $0.20 $0.60 $0.90 0.23 0.52 0.78 $1.90 $4.30 $6.6030-39M 0.18 0.41 0.64 $1.50 $3.50 $5.40 1.44 3.22 4.86 $12.10 $27.10 $41.00F 0.09 0.21 0.33 $0.80 $1.80 $2.80 0.74 1.65 2.49 $6.20 $13.90 $21.0040-49M 0.66 1.52 2.38 $5.50 $12.80 $20.10 5.31 11.91 18.00 $44.80 $100.40 $151.70F 0.32 0.73 1.14 $2.70 $6.20 $9.60 2.55 5.72 8.64 $21.50 $48.20 $72.8050-59M 1.65 3.83 5.99 $13.90 $32.30 $50.50 13.34 29.91 45.18 $112.40$252.10 $380.90F 0.74 1.71 2.68 $6.20 $14.40 $22.60 5.97 13.38 20.21 $50.30 $112.80 $170.4060-69M 2.35 5.46 8.55 $19.80 $46.10 $72.00 19.04 42.70 64.51 $160.50$360.00 $543.80F 1.29 2.99 4.67 $10.80 $25.20 $39.40 10.41 23.35 35.28 $87.80 $196.80 $297.4070-80M 3.40 7.90 12.35 $28.70 $66.60 $104.1027.51 61.69 93.21 $231.90$520.10 $785.70F 2.75 6.39 9.99 $23.20 $53.80 $84.20 22.26 49.91 75.40 $187.60$420.70 $635.60Total(20-80years)M8.29 19.26 30.11 $69.90 $162.30$253.8067.11 150.46 227.32 $565.70$1,268.40$1,916.30F5.21 12.10 18.92 $43.90 $102.00$159.5042.15 94.51 142.79 $355.30$796.70 $1,203.80Both13.50 31.35 49.03 $113.80$264.30$413.30109.26244.97 370.12 $921.00$2,065.10$3,120.10Developing a Concentration-Response Function for PbExposure and Cardiovascular Disease-Related MortalityAbsolute Change▌pg.D-2Exhibit D-2. Benefits from Percentage Change for aBlood Pb Cutoff of above 1.0 μg/dLAgeGroupGender5% increase50% increaseAnnual Cases AvoidedAnnual Benefits (2012$million)Annual Cases AvoidedAnnual Benefits (2012$million)LCULCULCULCU20-29M 0.02 0.05 0.08 $0.20 $0.40 $0.60 0.17 0.38 0.57 $1.40 $3.20 $4.80F 0.00 0.01 0.01 $0.00 $0.10 $0.10 0.03 0.06 0.09 $0.20 $0.50 $0.8030-39M 0.08 0.19 0.30 $0.70 $1.60 $2.50 0.67 1.50 2.26 $5.60 $12.60 $19.10F 0.01 0.03 0.05 $0.10 $0.30 $0.40 0.12 0.26 0.39 $1.00 $2.20 $3.3040-49M 0.39 0.91 1.43 $3.30 $7.70 $12.00 3.18 7.12 10.76$26.80 $60.00 $90.70F 0.12 0.29 0.45 $1.00 $2.40 $3.80 1.00 2.25 3.40 $8.50 $18.90 $28.6050-59M 1.25 2.90 4.53 $10.50 $24.40 $38.20 10.10 22.64 34.20 $85.10 $190.80 $288.30F 0.47 1.09 1.70 $3.90 $9.10 $14.30 3.78 8.48 12.81$31.90 $71.50 $108.0060-69M 1.99 4.62 7.23 $16.80 $39.00 $60.90 16.11 36.12 54.57 $135.80$304.50 $460.10F 0.85 1.98 3.10 $7.20 $16.70 $26.20 6.91 15.50 23.42 $58.30 $130.70 $197.4070-80M 3.03 7.03 10.99 $25.50 $59.20 $92.60 24.49 54.9082.95 $206.40$462.80 $699.30F 1.99 4.63 7.23 $16.80 $39.00 $61.00 16.12 36.14 54.61 $135.90$304.70 $460.30Total(20-80years)M6.76 15.70 24.55 $57.00 $132.30$206.9054.70 122.65185.31$461.20$1,034.00$1,562.20F3.46 8.02 12.55 $29.10 $67.60 $105.8027.96 62.69 94.71 $235.70$528.40 $798.40Both10.22 23.72 37.09 $86.10 $200.00$312.7082.66 185.34280.02$696.80$1,562.40$2,360.60Developing a Concentration-Response Function for PbExposure and Cardiovascular Disease-Related MortalityAbsolute Change▌pg.D-3Exhibit D-3. Benefits from Percentage Change for aBlood Pb Cutoff of above 1.46 μg/dLAgeGroupGender5% increase50% increaseAnnual Cases AvoidedAnnual Benefits (2012$million)Annual Cases AvoidedAnnual Benefits (2012$million)LCULCULCULCU20-29M 0.01 0.03 0.04 $0.10 $0.20 $0.30 0.09 0.20 0.30 $0.70 $1.70 $2.50F 0.00 0.00 0.01 $0.00 $0.00 $0.00 0.01 0.03 0.04 $0.10 $0.20 $0.3030-39M 0.04 0.10 0.16 $0.40 $0.80 $1.30 0.35 0.78 1.18 $2.90 $6.60 $10.00F 0.01 0.01 0.02 $0.00 $0.10 $0.20 0.05 0.10 0.15 $0.40 $0.90 $1.3040-49M 0.24 0.55 0.87 $2.00 $4.70 $7.30 1.93 4.32 6.53 $16.30 $36.50 $55.10F 0.05 0.13 0.20 $0.50 $1.10 $1.70 0.44 0.98 1.49 $3.70 $8.30 $12.5050-59M 0.86 2.01 3.14 $7.30 $16.90 $26.50 6.99 15.68 23.69 $58.90 $132.20 $199.70F 0.26 0.61 0.95 $2.20 $5.10 $8.00 2.11 4.73 7.14 $17.80 $39.90 $60.2060-69M 1.38 3.21 5.01 $11.60 $27.00 $42.30 11.17 25.05 37.85 $94.20 $211.20 $319.00F 0.50 1.15 1.80 $4.20 $9.70 $15.20 4.02 9.01 13.61$33.90 $75.90 $114.7070-80M 2.31 5.36 8.39 $19.50 $45.20 $70.70 18.69 41.91 63.32 $157.60$353.30 $533.80F 1.36 3.15 4.92 $11.40 $26.50 $41.50 10.97 24.60 37.17 $92.50 $207.40 $313.40Total(20-80years)M4.85 11.25 17.60 $40.90 $94.90 $148.4039.22 87.94 132.86$330.60$741.30 $1,120.00F2.17 5.05 7.90 $18.30 $42.60 $66.60 17.60 39.45 59.61 $148.30$332.60 $502.50Both7.02 16.30 25.49 $59.20 $137.40$214.9056.82 127.39192.47$479.00$1,073.90$1,622.50Developing a Concentration-Response Function for PbExposure and Cardiovascular Disease-Related MortalityAbsolute Change▌pg.D-4Exhibit D-4. Benefits from Percentage Change for aBlood Pb Cutoff of above 2.0 μg/dLAgeGroupGender5% increase50% increaseAnnual Cases AvoidedAnnual Benefits (2012$million)Annual Cases AvoidedAnnual Benefits (2012$million)LCULCULCULCU20-29M 0.01 0.01 0.02 $0.00 $0.10 $0.20 0.05 0.10 0.16 $0.40 $0.90 $1.30F 0.00 0.00 0.00 $0.00 $0.00 $0.00 0.01 0.01 0.02 $0.00 $0.10 $0.1030-39M 0.02 0.05 0.07 $0.20 $0.40 $0.60 0.16 0.35 0.53 $1.30 $3.00 $4.50F 0.00 0.01 0.01 $0.00 $0.10 $0.10 0.02 0.05 0.08 $0.20 $0.40 $0.6040-49M 0.12 0.28 0.43 $1.00 $2.30 $3.60 0.96 2.15 3.25 $8.10 $18.20 $27.40F 0.03 0.07 0.11 $0.20 $0.60 $0.90 0.24 0.53 0.80 $2.00 $4.40 $6.7050-59M 0.50 1.16 1.82 $4.20 $9.80 $15.30 4.05 9.09 13.73$34.20 $76.60 $115.80F 0.12 0.28 0.44 $1.00 $2.40 $3.70 0.97 2.18 3.30 $8.20 $18.40 $27.8060-69M 0.93 2.15 3.36 $7.80 $18.10 $28.30 7.49 16.79 25.36 $63.10 $141.50$213.80F 0.20 0.45 0.71 $1.60 $3.80 $6.00 1.58 3.54 5.35 $13.30 $29.90 $45.1070-80M 1.44 3.35 5.24 $12.20 $28.30 $44.20 11.69 26.20 39.59 $98.50 $220.90$333.70F 0.77 1.78 2.79 $6.50 $15.00 $23.50 6.21 13.93 21.04 $52.40 $117.40$177.40Total(20-80years)M3.01 7.00 10.94 $25.40 $59.00 $92.30 24.39 54.69 82.62 $205.60$461.00$696.50F1.12 2.59 4.05 $9.40 $21.80 $34.10 9.03 20.24 30.57$76.10 $170.60$257.70Both4.13 9.59 14.99 $34.80 $80.80 $126.40 33.42 74.92 113.20$281.70$631.60$954.30Developing a Concentration-Response Function for PbExposure and Cardiovascular Disease-Related MortalityAbsolute Change▌pg.D-5Exhibit D-5. Benefits from Percent Change for a Blood Pb Cutoff of Above 3.62 μg/dLAgeGroupGender5% increase50% increaseAnnual Cases AvoidedAnnual Benefits (2012$million)Annual Cases AvoidedAnnual Benefits (2012$million)LCULCULCULCU20-29M 0.00 0.01 0.01 $0.00 $0.00 $0.10 0.02 0.04 0.06 $0.20 $0.30 $0.50F 0.00 0.00 0.00 $0.00 $0.00 $0.00 0.00 0.00 0.00 $0.00 $0.00 $0.0030-39M 0.00 0.01 0.01 $0.00 $0.00 $0.10 0.02 0.04 0.06 $0.10 $0.30 $0.50F 0.00 0.00 0.00 $0.00 $0.00 $0.00 0.01 0.01 0.02 $0.10 $0.10 $0.2040-49M 0.03 0.08 0.12 $0.30 $0.60 $1.00 0.27 0.60 0.90 $2.20 $5.00 $7.60F 0.01 0.02 0.03 $0.10 $0.10 $0.20 0.06 0.14 0.21 $0.50 $1.10 $1.7050-59M 0.13 0.29 0.46 $1.10 $2.50 $3.90 1.03 2.30 3.48 $8.70 $19.40 $29.30F 0.02 0.05 0.07 $0.20 $0.40 $0.60 0.16 0.37 0.55 $1.40 $3.10 $4.7060-69M 0.30 0.69 1.08 $2.50 $5.80 $9.10 2.41 5.41 8.17 $20.30$45.60 $68.80F 0.06 0.13 0.20 $0.50 $1.10 $1.70 0.44 0.99 1.50 $3.70 $8.40 $12.6070-80M 0.34 0.80 1.25 $2.90 $6.70 $10.50 2.78 6.23 9.41$23.40$52.50 $79.40F 0.19 0.44 0.69 $1.60 $3.70 $5.80 1.53 3.43 5.18 $12.90$28.90 $43.70Total(20-80years)M0.81 1.87 2.92 $6.80 $15.80 $24.60 6.52 14.61 22.08$54.90$123.20$186.10F0.27 0.63 0.99 $2.30 $5.30 $8.30 2.20 4.94 7.47 $18.60$41.70 $62.90Both1.08 2.50 3.91 $9.10 $21.10 $33.00 8.72 19.55 29.54$73.50$164.80$249.00