A UNITED STATES DEPARTMENT OF COMMERCE

PUBLICATION NBS TECHNICAL NOTE 746

**rts o»

Estimates

of the Nature and Extent

of Lead Paint Poisoning

in the United States

U.S.

fcPARTMENT

OF

COMMERCE

National

QC

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'US'753
NATIONAL BUREAU OF STANDARDS

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» Part of the Center for Radiation Research.

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* Part of the Center for Building Technology.
IfATlOfiAL BimM OF STAH3ARDS

\qc too Estimates of the Nature and Extent of

U0753IBZ6 Paint Poisoning in the United States

ho- 7V6 —

e ,£.

Judith F. Gilsinn

Applied Mathematics Division

Institute for Basic Standards

National Bureau of Standards

Washington, D.C. 20234

—y^iT^- ~n/ue>. 7U

Report to Lead Paint Poisoning Project

Center for Building Technology

Institute for Applied Technology, NBS

Sponsored by

Department of Housing and Urban Development

Washington, D.C. 20410

NBS Technical Notes are designed to supplement the Bureau's regular publications program. They provide a means for making available scientific data that are of

transient or limited interest. Technical Notes may be

listed or referred to in the open literature. ^PF °4

***fAU Of

*"

U.S. DEPARTMENT OF COMMERCE, Peter G. Peterson, Secretary

NATIONAL BUREAU OF STANDARDS, Lawrence M. Kushner, Acting Director,

Issued December 1972
National Bureau of Standards Technical Note 746

Nat. Bur. Stand. (U.S.), Tech. Note 746, 163 pages (Dec. 1972) CODEN: NBTNAE

For sale by the Superintendent of Documents, U.S. Government Printing Office, Washington, D.C. 20402 (Order by SD Catalog No. C13.46:746). Price $2.00
Preface

The research described in this report was sponsored by the Department

of Housing and Urban Development and was conducted under the overall

direction of the Lead Paint Poisoning Project Manager, Harvey Berger of

the Center for Building Technology (CBT) at the National Bureau of

Standards (NBS) . The staff members who participated in the effort

described herein include members of both NBS' Technical Analysis Division

(TAD) and its Applied Mathematics Division (AMD) . Principal technical

contributions were made by Judith Gilsinn and Lambert Joel of the AMD

and Dr. Michael Thomas of the TAD and the University of Florida.

Supporting staff from TAD included Barbara Reisman, Barry Nocks, Stephen

Karp, Peter Corcoran, Louis Clark, Elizabeth Leyendecker, William O'Neal,

and Dr. Ernst Nilsson.

Many others furnished information and guidance contributing to the

success of this effort. Special thanks are due Gary Tollaksen and

Barbara Ruby of the Aurora Service Center of the Kane County (Illinois)

Council on Economic Opportunity for their cooperation and help in providing,

coding, and interpreting data from Aurora's lead poisoning screening program.

We are also indebted to Elaine Whitmire, Coordinator of the Lead Paint

Poisoning Program, New Haven, Connecticut; Dr. J. Julian Chisolm, Johns

Hopkins University School of Medicine and the Baltimore City Hospitals;

Raymond Tyler, Chief of the Accident Control Division, Philadelphia

Department of Public Health; Dr. Herbert Slutsky, Lead Program Coordinator,

Chicago Board of Health; Daniel Still, Lead Poisoning Control Bureau,

New York City; and members of HEW's Bureau of Community and Environmental

no.
Management. Their discussions with us greatly expanded the staff's

knowledge concerning current expert assessments of the magnitude and

extent of lead poisoning and of the efforts being made to combat it.

We wish to thank Dr. Irwin Billick of the Department of Housing

and Urban Development for his help in identifying sources of lead poisoning information and his help in acquiring that information.

Special thanks are extended to Dr. Alan Goldman, Chief of the

Operations Research Section of the Bureau's AMD, for his many technical

suggestions and for his critical reading of this and other project reports.

We wish to thank Dr. David Waksman and Elizabeth Clark of the Bureau's

CBT for their patient and skillful explanations of a number of technical

points about paint production and building technology.

We also wish to thank Ruth Joel and Karen Rhoades for their patience

in typing the many drafts of this report.

IV
TABLE OF CONTENTS

PAGE

1. Introduction 1

1.1. Description of Lead Poisoning 2

, 1.1.1. Symptoms of Lead Poisoning 3

1.1.2. Lead in the Body 4

1.1.3. Pica 5

1.1.4. Method of Testing for Lead Poisoning 6

1.1.5. Screening 8

1.1.6. Treatment of Lead Poisoning 9

1.1.7. Removing the Lead Hazard 9

1.2. Need for a Model 13

1.3. Procedure 15

1.3.1. Data Acquisition 16

1.3.2. Model Construction 18

1.3.3. Model Validation 19

1.3.4. Model Application 20

2. Data 21

2.1. Incidence Data 22

2.1.1. Survey of Existing Incidence Data 24

2.1.2. Criteria for Choice of Incidence Data Sources ... 32

2.1.3. Selected Sources of Incidence Data 33

2.2. Demographic Data 36

2.2.1. Demographic Characteristics Associated with Lead

Poisoning 36

2.2.2. Sources of Demographic Data 41

2.2.3. Zonal System 42

2.3. Current Data Deficiencies 43

3. The Models 45

3.1. Aggregation Problems 45

3.2. Analysis of the New Haven Data 47
Table of Contents Continued

Page

3.3. Model Form 57

3.4. The Models 61

3.4.1. Model 1 62

3.4.2. Model 1A 63

4. Validation 65

4.1. Introduction 65

4.2. Validation of Model Assumptions - Analysis of the Aurora

Data 67

4.2.1. General Description of the Aurora Data 67

4.2.2. Factors Associated with EBL 70

4.3. Validation of Quantitative Model Performance 97

4.4. Conclusion 102

5. National Estimates by SMSA 104

Appendix A. Other Models 123

A.l. Model 2 123

A. 1.1. Model 2.1 123

A. 1.2. Model 2.2 125

A. 1.3. Model 2.3 125

A. 1.4. Model 2.4 126

A. 1.5. Model 2.5 126

A. 1.6. Model 2.6 127

A. 1.7. Model 2.7 127

A. 1.8. Model 2.8 128

A. 2. Model 3 128

A. 2.1. Model 3.1 129

A. 2. 2. Model 3.2. 129

A. 2.3. Model 3.3 129

A. 2. 4. Model 3.4 130

Appendix B. Models and Estimates of Hazardous Housing 131

B.l. Model Used to Update the New Haven 1960 Housing Condition

Data 131

vi
Table of Contents Continued

Page

B.2. A Model for Estimating the Volume of Housing Containing

Lead Paint 132

B.3. Estimate of the Nationwide Number of Housing Units Containing

Lead Paint 135

B.4. A Nationwide Estimate of Immediately Hazardous Housing . . . 137

Footnotes 144

Bibliography 147

vn
List of Tables

Page

1. Lead Paint Regulations 11

2. Areas Reporting Lead Poisoning Cases 25

3. Current Programs 26

4. Distribution of Blood Lead Levels for Several Programs 29

5. Percent with Elevated Blood Lead Levels of Those Screened in

Several Programs 31

6. Incidence Data Sources 34

7. Housing Characteristics for Use in a Lead Paint Poisoning

Model 37

8. Population Characteristics for Use in a Lead Paint Poisoning

Model . 40

9. New Haven Data 48

10. Percentage of Housing Built Before 1940 for Census Tracts in

Which More than Ten Children were Screened 51

11. Simple Correlation Coefficients for Selected Variables 53

12. Simple Correlation Coefficients for Logarithms of Selected

Variables 54

13. Spearman Rank Correlation Coefficients for Selected Variables . 55

14. Spearman Rank Correlation Coefficients for Logarithms of

Selected Variables .... 56

15. Preliminary Data on EBL Rates 64

16. Aurora Screening Statistics by Census Tract 69

17. Data Items Coded From the Aurora Forms 72

18. Aurora, Illinois Data Statistics 73

19. Blood Lead Levels (yg/100 ml) for Aurora, 111. Children .... 74

20. Breakdowns of EBL's by Tests and Retests 76

21. Breakdowns of EBL's by Census Tract 78

22. Breakdowns of EBL's by Sex of Child 80

23. Breakdowns of EBL's by Age of Child 81

24. Breakdowns of EBL's by Race or Ethnic Origin 83

25. Breakdowns of EBL's by Whether or Not the Child Has a Family

Doctor ..... 84

vm
LIST OF TABLES CONTINUED

PAGE

26. Breakdowns of EBL's by Time of Residence at the Present

Address 86

27. Breakdowns of EBL's by Whether the Family Owns or Rents

Its Home 88

28. Breakdowns of EBL's by Whether or Not the Family Receives

Public Aid 89

29. Breakdowns of EBL's by the Condition of the Housing Unit

Occupied by the Family 91

30. Number and Percent of Families of Each Family Size Having a Given Number of Children with Elevated Blood Lead Levels .... 93

31. Results of Chi Square Tests 96

32. EBL's by Census Tract from the Aurora Data and as Predicted by

Model 1 and Model 1A 98

33. Comparison of Percent of High Risk Children Found with EBL with

Those Predicted by Model 1A 100

34. Children Tested in the Aurora Screening Program Versu's Children

at Risk as Calculated by the Models 101

35. EBL's Predicted by the Models for Each SMSA 106

36. 25 SMSA's With the Greatest Number of EBL's (Model 1 Estimates). 114

37. The Number of Children in Each SMSA with Blood Lead Levels of

40 and Above, 50 and Above, 60 and Above, and 70 and Above

(Measured in yg/100 ml) 115

38. Variables and Symbols Used in the Models 124

39. Estimate of the Immediately Hazardous Housing in Each SMSA . . . 138

IX
LIST OF FIGURES

PAGE

1. New Haven Census Tracts 50

2. Two Curves Passing Through the Same Three Points 60

3. Map of Aurora Census Tracts Having High EBL Rates 79

4. Predicted Nat ion-Wide Incidence of Lead Based Paint Poisoning . 112

x
Estimates of the Nature and Extent

of Lead Paint Poisoning in the United States

Judith Gilsinn

This report evaluates the nationwide magnitude and extent

of pediatric lead poisoning resulting from the ingestion of

lead-based paint. Estimates are given of the number of children

who have elevated blood lead levels (40 yg or more of lead per

100 ml of whole blood) in each of 241 Standard Metropolitan

Statistical Areas throughout the country. The mathematical

models used to obtain these estimates are documented together

with the assumptions and data upon which those models are

based. Partial validation of both models and assumptions is

also reported.

Key words: Childhood diseases; estimation; health problems;

lead; lead paint; lead paint poisoning; lead poisoning;

mathematical modeling; models; urban health problems.

1. INTRODUCTION

Lead poisoning resulting from the ingestion of lead based paint is

a serious illness which has recently been recognized as a major pediatric

problem. Several cities have initiated programs to locate and treat

lead poisoned children, and in January 1971 the United States Congress

enacted PL 91-695, the "Lead Based Paint Poisoning Prevention Act", to

provide Federal assistance to help eliminate the disease. Title III of

this act calls for research to determine the nature and extent of the
lead paint poisoning problem. This research is to be coupled with

analyses of lead detection procedures and removal methods, to provide a

set of recommendations that can form the basis for future action against

lead poisoning.

1.1. Description of Lead Poisoning

Lead poisoning today is a disease primarily affecting children 1 to

6 years old 1

, although some cases of adult poisoning are still reported.

These latter fall mainly into two groups: industrial or work related,

such as poisoning of painters who have worked for many years with lead

paints, and domestic, e.g., individuals poisoned by eating or drinking an

acidic substance such as orange juice or applesauce which had been stored

in a ceramic container with an improperly fired glaze from which lead had

been leached by the acid.

It has been estimated that about 90 percent of the pediatric lead

poisoning cases result from a child eating peeling, cracking, or flaking

paint or painted plaster inside his home. (Plaster itself does not contain lead, but its upper layers absorb lead-containing oil from the

paint.) Other sources of lead which have led to poisoning include putty

and caulking, lead-painted gutter pipes, fences, imported toys, and pencils,

toothpaste tubes made of a lead alloy, lead shot in beanbags, lead fishing

sinkers, and lead pottery glazes. Air pollution has also been cited as a

cause of lead poisoning.-' Recently (early 1972), several children living

near a smelter in El Paso, Texas, were found to show signs of undue lead

absorption. Soil samples taken near the smelter showed very high lead

levels, indicating that lead in the air had settled and accumulated in

2
the soil. The major source of lead pollutants in the air, however, is

non-industrial: leaded gasoline fumes. The relative importance of lead

in gasoline and lead in paint as causative factors of lead poisoning is

currently being debated, but most experts conducting child screening programs believe that paint eating, if not the only factor, at least is the

major factor precipitating the onset of the disease.

The following sections concern the characteristics of, testing for,

and treatment of lead poisoning. They contain some medical and chemical

terms which are not explained in the text. The interested reader is referred to [7], [11], [12], [18], and [40] for further information.

1.1.1. Symptoms of Lead Poisoning

The initial symptoms of lead poisoning are very non-specific and

might be due to many other agents, making it easy for physicians and

parents to misdiagnose the disease or to overlook it entirely. These

symptoms include nausea, vomiting, abdominal pain, constipation, anemia,

irritability, anorexia and listlessness. If untreated, the disease may

lead to central nervous system involvement (lead encephalopathy) which can

result in blindness, paralysis, mental retardation, and finally death.

Less severe cases of lead absorption may leave the victim with learning

problems, partial loss of sensory perception, and behavioral and other

emotional difficulties. Some experts have also noted cardiovascular and

renal damage.
1.1.2. Lead in the Body

The effects of lead in the human body are incompletely known, but it

fulfills no apparent necessary role in the body chemistry. It has been

estimated that approximately 5 to 10 percent of ingested lead is retained

in the bones and soft tissues, the rest being excreted, while about 40

percent of respired lead is retained. Using these figures, the daily

permissible intake (DPI) of lead has been set at 300 yg/day.° About half

of this would be absorbed from normal contamination levels in food, water,

and the air. The other 150 yg allow for abnormal levels in food, water,

and air and for other possible sources such as paint. Since a single paint

chip the size of a thumbnail may contain several times the DPI, a child

eating any lead paint at all runs a great risk of being poisoned.

A phenomenon generally noted by those working with lead poisoning is

that the number of cases rises in the summer. Several explanations have

been offered, but none has been completely satisfactory. A possible

physiological explanation lies in the (incompletely understood) relationship between lead absorption and the amount of ultraviolet light to which

the child is exposed. Lead follows the same metabolic path as calcium,

which has a higher absorption rate in the presence of vitamin D, whose

production in turn is affected by ultraviolet light. In addition, some

experts have hypothesized that ultraviolet light is a stimulus for release

into the blood of lead stored in the bones.

On the other hand, in general the number of reported cases rises as

more effort is spent in looking for them. It is in the summer that many

cities are able to mount community outreach programs using volunteer workers

to go into neighborhoods to encourage parents to bring their children to

clinics for testing. 4
Yet another possible explanation for the higher summer rates is that

during the warmer months children spend more time out-of-doors, with

greater exposure to additional sources of lead in dirt, automobile exhaust

fumes, and peeling leaded exterior paint.

1.1.3. Pica

Another condition closely associated with lead poisoning is pica,

an unreasonable craving for non-food substances. All children go through

an oral exploratory stage, lasting up to about 3 years of age, during which

they mouth objects within their reach. However some children continue the

stage beyond this age, or are unusually persistent in "attacking" certain

objects. Typically, a child might strike at a wall with a toy in order to

break off pieces of plaster to eat, or might rearrange furniture to enable

him to climb up to get medicine placed in a cabinet high above his reach,

in spite of repeated admonitions by parents. It is virtually impossible

to keep such a child from eating chips of lead paint if they are accessible

to him; no mother can constantly supervise an active two year old child

determined to chew on paint.

Pica was originally regarded as linked to nutritional deficiency,

but studies conducted at Children's Hospital in Washington, D.C. have

found no relationship between the two. ^ Pica has been found, however,

to be associated with situations of emotional stress in the child's family.

Another factor is the mother's tolerance of pica. If she had pica as a

child or comes from an area where such practices as clay or starch eating

are common, she may consider the child's pica as normal, and thus do

nothing to correct it. This attitude may also degrade the usefulness

5
of certain questions frequently appearing on questionnaires designed to

elicit information on pica: some mothers may in all honesty answer that

their child does not eat "unusual substances"—don't all children eat

matches, dirt, or paint?

1.1.4. Method of Testing for Lead Poisoning

Because the initial symptoms of lead poisoning are so inconclusive,

while permanent damage has already been done by the time more identifiable

symptoms appear, it is necessary to have a method of measuring the total

body burden of lead and of identifying children in danger of being poisoned

Although several tests are available, the Public Health Service recommends

the blood lead determination as "the most consistent indicator of acute

exposure." The "normal" median blood lead level of children is believed

to be 16 to 27 yg lead per 100 ml of whole blood (abbreviated yg/100 ml)

,

and levels in the range of 15 to 40 yg/100 ml are considered normal. The

Public Health Service recommends that a blood lead concentration of 80

yg/100 ml or more, regardless of other laboratory test results or of the

presence or absence of other symptoms, be considered unequivocally

definitive evidence of lead poisoning and be treated as a medical

emergency requiring hospitalization for chelation therapy.

The Public Health Service also recommends that children whose blood

lead level is in the 50-70 yg/100 ml range should be referred for further

diagnosis. The presence of symptoms of lead poisoning unexplained by other

circumstances, or of positive results on any of the following confirmatory

tests, is regarded by the Public Health Service as suggestive of lead

poisoning:
1. Urinary excretion in 24 hours of more than 1 yg of lead per mg of

Ca-EDTA administered intra-muscularly at a dose of 50 mg per kg

of body weight;

2. Serum delta-aminolevulinic (ALA) level of greater than 20. yg per

100 ml of whole blood established using the Haeger-Aronson method;

3. Urinary output of coproporphyrin greater than 150 yg per 24 hours;

4. Urinary output of delta-aminolevulinic acid greater than 5 mg per

24 hours, (this will be called the ALA urine test in later sections

of this paper)

5. The presence of basophilic stippling of red blood cells, "lead

lines" in long bone x-rays, or a strongly positive urine spot

1 o test for coproporphyrin

.

The Public Health Service recommends that children whose blood lead

level falls in the 40 to 49 yg/100 ml range should be recalled every 6 to

8 weeks for retest and evaluation with determination of blood lead level,

as long as they remain in a hazardous environment.

Several analytical methods are available for measuring blood lead.

They include:

1. the spectrophotometry dithizorie technique

2. atomic absorption spectrophotometry (AA) , and

3. anodic stripping voltammetry (ASV)

.

The first of these is a wet chemistry technique requiring a macro-blood

sample (about 5 ml) obtained by venipuncture. The other two methods can

be used to analyze macro samples as well as small samples (20-100 yl)

obtained by a finger prick. A method requiring only a finger prick is

desirable on grounds of both public acceptance, since venipuncture is

7
painful, and cost, since venipuncture requires more highly trained

personnel. However, recent experience has shown that the risk of contamination of the small finger prick samples is very great, since the

amount of lead in such a small sample is minute even at critically high

levels, and lead may easily be introduced from lead -polluted dirt and air.

1.1.5. Screening

Several cities have set up programs for screening children for lead

poisoning, testing all children in an area or all who come to a particular

clinic or hospital, to identify those with dangerously elevated blood lead

levels. Most programs carefully choose the population to be tested so as

to maximize the fraction of those tested expected to be identified as lead

poisoning cases. Some programs restrict their testing to areas with substandard old housing in a deteriorating condition which offers a peeling

lead paint hazard. Others test all children coming to health clinics or to

hospitals, hoping to catch those with symptoms of the disease. Thus most

programs, having limited funds, have made a (sometimes tacit) decision as

to which children are at greatest risk, and have concentrated efforts on

such children. From a health viewpoint the most desirable child screening

program is one that would test all children, but a program of such magnitude

is not possible under current or projected funding levels. Therefore, better

estimates of the characteristics of these children at greatest risk are

needed to permit a more accurate and reliable focusing of screening efforts.

An alternative screening procedure is possible—test all homes in an

area for lead content, and once a lead hazard environment is identified,

test the children living there. This procedure could be very useful if

8
the program includes removal of the lead hazard from every home where one

was found, as well as treatment of children with dangerously high blood

lead levels. However, since most programs have funds only to test and treat

children, the "screen house first" approach is not being used at present.

1.1.6. Treatment of Lead Poisoning

The primary goal of lead poisoning treatment is the removal of the

excess lead from the child's body. This is accomplished through chelation

13 therapy, which must be performed under carefully controlled supervision,

since the initial response may be an increase in blood lead level as lead

is released from the bones. Chelation may also draw calcium from the bones

if too high a dosage is prescribed.

1.1.7. Removing the Lead Hazard

In addition to reducing the body burden of lead, which lessens the

immediate danger to the child, it is necessary to remove the source of

lead poisoning from his environment. If the child is returned to an unchanged environment he runs the same risk as before; the lead is still

there to be ingested. More intense supervision can alleviate the danger

somewhat, but the only way to insure protection of this child —and others

living in the same housing unit now and in the future—is to remove the

lead hazard. The Public Health Service recommends that no child be returned to his home until the source of lead exposure has been identified

and either eliminated or made unavailable. It suggests that convalescent

homes, halfway houses, public housing, or hazard-free relatives' homes be

14 employed during the interim.
Although there are many areas with small programs to identify and

treat children with lead poisoning, few areas have programs to remove the

lead paint hazard from dwelling units. There are several reasons for this,

the principal one being lack of money. Deleading a dwelling unit can be

very expensive. Costs can range from a low of a few hundred dollars to

scrape off loose and peeling paint, to a high of five or six thousand

dollars for a major rehabilitation effort. Since units offering an

immediate lead hazard in the form of peeling and flaking lead paint are

often rental units owned by landlords making marginal profits or units

owned by lower income residents, the owners are unable and/or unwilling

to make the repairs necessary to eliminate the hazard. Limited city funds

are inadequate to delead more than a token number of units. Some federal

funds are available, but eligibility for such grants is limited. In

addition, there is a several-month time lag between application for

federal funds and the actual granting of such funds. During this time

period the child remains exposed to the danger of lead poisoning.

Many cities have laws against the sale of lead paint and its use on

interior surfaces accessible to children. Such laws often assign enforcement powers to Health or Housing Departments in the city or state. Table

1 summarizes the characteristics of such regulations for several areas.

The table lists which authority (health or housing) has jurisdiction,

whether there is a statute requiring cautionary labeling of lead paint

for sale, whether there is a law banning the use of lead paint on interior

surfaces, whether the authority includes enforcement powers, and finally

if the existence of peeling paint is a violation of the code.

10
Table 1 Lead Paint Regulations

Area

Health

or Housing

Label

Paint

for Sale

Not On Interior

Surfaces

Can

Force Removal

No Peeling

Paint Source

Baltimore Housing yes yes yes yes Baltimore Lead Paint Labeling Ordinance

#1504, 6/9/S8, Housing Code, Ordinance

#902, 1951 Boston Health yes yes Chicago Housing yes yes yes yes Municipal Code of Chicago, Section 78-17.2 Cincinnati Health yes yes yes Cincinnati Lead Ordinance, 1960 Connecticut Health,

Housing

yes yes ("accessible

surfaces")

yes yes Public Act 194 - An Act to Enforce the Elimination of Lead Based Paint in Housing

Accomodations, 1971 Jersey City Health yes yes yes City Ordinance #G-36 - An Ordinance Regulating the Sale of Lead Paint in the City

of Jersey City, 1962 Massachusetts Health yes yes + exterior

yes Chapter 1081, An Act Providing for a Com- prehensive Program of Lead Poisoning Pre- vention and Control-1971

Newark Health yes yes yes Ordinance Numbers 8B102170, 8C102170,

8D102170, 8E102170, and 8F102170 of the City of Newark, N.J., Nov. 1970. New Haven Health,

Housing

yes yes + (accessible

exterior)

yes Amendment of Housing Code Relative to Lead Paint on Dwelling Units 1968 - Lead Paint Ordinance - 1968 New Orleans Health yes yes + (accessible

exterior)

yes Ordinance 828 amended 1971

New York State Health yes yes + (accessible

exterior)

yes Official Compilations of Codes, Rules and Regulations of the State of New York

(Health) 1970. An Act to Amend the Public Health Law in Relation to the Prevention

and Control of Lead Poisoning New York City Health yes yes yes New York Administrative Code - Lead Paint Regulations 1970 Norfolk Housing yes Rhode Island Health yes Philadelphia Health yes yes yes Regulations Relating to Labeling, Application, and Removal of Lead Paint 1966

St. Louis Housing yes yes + (disintegrating)

yes yes Ordinance 55638-Lead Poisoning Ordinance

1970

Washington, D.C. Health,

Housing

yes yes yes Health Regulations, Part 9 Use of Lead

Paint, 1970, Housing Regulations Section

2605, 1970 Wilmington Health yes yes yes Ordinance Regulating and Governing the Hygiene of Housing and Enacting a Housing

Code in Wilmington, Del., section 7A, Lead Paint, 1963

11
In some areas the primary responsibility for lead paint poisoning

prevention lies with the health department. This has some advantages, in

that health problems in general receive more immediate attention than

housing problems. Also, health officials are often the ones making first

contact with a poisoned child, so that leaving deleading enforcement in

their hands requires fewer interdepartmental delays. The disadvantage,

when primary enforcement of deleading is based on establishing the existence of a health code violation, is that court proceedings often require

demonstration that the presence of lead paint in the unit is the "cause"

of lead poisoning in the child. For instance, in a recent Maryland

decision the court found in favor of a landlord, saying the mother may

have been contributorially negligent in not having supervised the child

carefully enough.

In other cities, primary deleading enforcement responsibility lies in

the hands of housing officials. This simplifies the problem of proving

landlord negligence, since most laws require only proof that an interior

surface contains paint with a lead content in excess of a given level

(usually 1 percent) . Cities lacking specific ordinances against lead

paint often use regulations against peeling paint (regardless of lead content) to enforce removal. This is less desirable, since lead paint which

is tightly adhered today may become loosened in the future.

Although enforcement powers have been given to officials in many cities,

they are reluctant to use them for several reasons. Many buildings in a

condition requiring deleading are marginal income investments (i.e., provide

little if any profits) for the landlords. The expense of deleading would

force them into abandoning their property. Cities already faced

12
with a shortage of low income housing do not wish to trigger further

abandonments. In addition, deleading enforcement requires court action,

an expensive and time consuming procedure for already overtaxed city

departments. Thus most cities which issue deleading orders report high

levels of non-compliance with those orders.

The lack of active deleading programs contributes greatly to the

high number of repeaters, children who were treated once yet have dangerously elevated blood lead levels again a few months later. Of course, the

danger that such children will suffer permanent damage is very great.

1.2. Need for a Model

As described above, pediatric lead poisoning is a solvable problem.

It has a known cause (the ingestion of lead based paint), there is a known

test for the early identification of children with the disease, the method

of treating children with the disease is known, and the lead hazard in

homes can be identified and removed. However the cost to do all this is

high and may be prohibitive. The magnitude and extent of lead poisoning in

the United States must therefore be estimated, to ascertain what level of

commitment of resources is most appropriate to alleviate the problem and to

determine where those resources can most effectively be applied. If humanitarian considerations were set aside, one must know how many children are

afflicted to be able to estimate the economic cost to society of doing

nothing about the disease, both the direct costs of custodial care required

for brain damaged children and the indirect costs of the loss of potentially

productive members of society. One must know where the disease occurs to

13
verify that the problem is of nationwide, rather than regional, extent and

to ascertain in which geographical areas the urgency for intensive screening

and deleading efforts is greatest.

Current lead poisoning screening programs are for the most part

funded by the cities or states. Only two cities, New York and Chicago,

have programs funded at a level permitting mass child screening. These

two programs also have (limited) funds to be used for deleading homes.

Many areas have no screening programs, and authorities in some areas are

still unaware of the problem or believe it does not exist in their locale,

although the environment is similar to that in other areas where the

disease has been found. Some cities and states do not require reporting

of lead poisoning cases to health authorities, while others report several

hundred cases a year. At present, the number of cases found appears to

depend more on the effort spent looking for lead poisoning than on the

size of the city. Because the symptoms are similar to those of many other

ailments, many cases may be misdiagnosed unless local physicians are aware

of lead poisoning and test for it. This diversity of local programs,

attitudes, and sensitivity to the problem increases the need for a nationwide estimate based on consistent evaluations throughout the country.

The mathematical model described later in this report will relate the

magnitude of lead poisoning in an area to characteristics of the people and

housing in that area. The underlying assumption is, of course, that one can

characterize those areas now reporting lead poisoning well enough to predict

which other areas are similar and thus (on the average) should have the same

14
levels of poisoning. The primary purpose of the current model is to

estimate the nationwide magnitude and extent of lead poisoning. This will

be done by estimating the number of children with elevated blood lead

levels (EBL: 40 yg/100 ml or more) separately for 241 metropolitan areas

of the country. The present model has not been designed to predict EBL's

for separate neighborhoods within a city. However, future work may determine that the same model is applicable at this finer level of geographical

detail.

Because of project time and budget constraints, it was necessary to

develop the model without mounting a supporting data collection effort.

The data base for the model was therefore limited to currently available

data, more specifically to those available in a form requiring little

further processing. It follows that the results of the present model can

only be regarded as "current best estimates", subject to further confir1 ft mation and possible modification. Current data do not include information

on several aspects of the problem, such as typical EBL rates for rural

children, for middle class children living in older well-maintained housing,

or for children in the South and West where building practices as well as

decorating and painting methods may differ from those in the urban East and

Midwest. Thus care must be taken in interpreting model results, particularly

in those areas influenced most by these unanswered questions.

1.3. Procedure

The procedure followed in obtaining estimates of the nationwide

magnitude and extent of lead poisoning may be divided into four steps:

15
1. data acquisition,

2. model construction,

3. model validation, and

4. application of the model.

The remainder of this report will present each of these topics in order,

describing the background of and work done on each step for the lead

poisoning estimation model. However, because these four steps are common

to most modeling efforts, we will describe in general terms in this

section the objects of and the tasks involved in each.

1.3.1. Data Acquisition

Data acquisition forms a critical facet of all mathematical modeling

efforts, particularly when one is modeling an incompletely understood

phenomenon, and when no further structured data acquisition can be undertaken.

There are several tasks involved in obtaining the necessary data for such

an effort. Some of these are conceptual, some "physical", but all involve

costs in time and money and therefore all must be included in planning such

an effort.

The first task is the determination of relevant data items. This

process should, sequentially, choose the dependent variable (that which is

to be estimated) , identify associated independent variables (the factors

on which the estimation will be based) , and analyze these independent

variables. The choice of dependent variable and associated independent

variables may be aided by the use of published material describing the

phenomenon being modeled, by an analysis of previous modeling attempts,

16
and by discussions with those having expert knowledge of and experience with

the phenomenon. At this early stage one should include any data item which

seems at all relevant; excess ones can easily be eliminated later, and

repetition of search effort avoided.

The second task is the determination and evaluation of sources of

information on these items. This includes the establishment of criteria

for comparison of sources, as well as localization of the sources. The

evaluation involves an assessment of the real applicability of the desired

data, the form in which it is available^ and the amount of further processing required for its use. Some evaluation of the quality of data

available from a particular source must be made, and if several sources

are to be used then the comparability of their data must be examined.

The third and fourth tasks are the acquisition and assimilation of

the data. Obtaining copies of existing data may be relatively easy.

However, if they must be obtained from several different sources or are

in many pieces, the difficulties multiply. "Assimilating" the data refers

to several kinds of further processing required to transform raw information

into the form required by the modeling process. This may consist of handcomputing of totals, aggregating information into desired categories, coding

items into numeric form, punching computer cards, or processing magnetic

computer tapes. Combinations of these activities may be necessary, and

data from different sources may require different processing entirely.

The final task in the acquisition of data is the preliminary analysis

of these data. This is performed to check initial evaluations of the data,

to assess the need for more data, to evaluate the relevancy of already

17
available data items and decide if or which others are needed, and to

determine if gaps in empirical information exist which must be filled by

additional modeling.

1.3.2. Model Construction

Constructing the model involves three stages. The first two of these

require that decisions be made about how the model will be used, what kind

of model can be obtained, and what form it will take. These decisions are

not just technical, but policy decisions set by the "sponsor" as well as

analysts. Additional input to these decisions comes from conversations

with experts (on the phenomenon being modeled) , information gleaned from

relevant literature and previous modeling efforts, and the analysis of

available data. The first decision concerns the purpose and scope of the

model: Is it to be applied primarily by the modeler in order to produce

certain desired output, or is it to be applied in situations not subject

to the guidance of the modeler? Are the area and level of application

known in advance? Are the ranges of the variables known and limited?

The second decision requires the determination of the methodological

basis of the model and the form it will take. This will determine whether

the bulk of the analysis will be qualitative, involving development of

theories, or quantitative, relying mainly on manipulation of numerical

data. Some phenomena are amenable to description by a mathematical formula

which directly relates some combination of the magnitudes of the causative

factors to the magnitude of the effect on the phenomenon involved. Such

phenomena are usually physical or chemical in nature, whereas phenomena

involving social factors rarely fall into this category and modeling their

18
behavior must rely on statistical techniques. The actual mathematical

form of the model is then determined in part by the techniques used and in

part by the data analysis, rather than through translation into numerical

terms of a known understood process.

The final stage in the construction of models for which no a priori

mathematical formula is known involves the process of curve-fitting to

available data. Known as "calibration", this process requires the determination of those values for the parameters of a particular model form

which yield the best matching for the available data set. The resulting

models are then compared on the basis of reasonableness of behavior and

goodness of fit to the data, and a best overall model is Chosen.

1.3.3. Model Validation

Once the model has been constructed it must be validated, i.e.,

checked out to insure that (1) data anomalies peculiar to one situation

are recognized as such and do not become major determinants of the model,

and (2) model hypotheses based on the analysis of the phenomenon in one

context carry over into others. There are two levels of validation required . The first involves checking the assumptions upon which the model

rests, both those determining which factors are important and which can

be omitted, as well as those determining model form. The second level of

validation is the comparison of the magnitudes of the phenomenon as predicted by the model and as actually occurring, to insure the model is a

good estimator. Validity of models lacking an a priori mathematical

19
formulation can seldom be completely assured, but confidence in the model

estimates can be increased by this process.

1.3.4. Model Application

Once the model has been constructed and validated, it can be applied

to yield desired outputs. The outputs can be analyzed in the light of both

model formulation processes and the validation exercises, and implications

can be drawn from them. It should be noted here that care should always

be exercised in examining the outputs of a model formulated primarily

through statistical manipulation of data. Conclusions drawn for situations

quite different from those represented by the calibration and validation

data sets may be misleading or erroneous. Potential pitfalls resulting

from levels of aggregation and from the limited numeric ranges of input

data must be comprehended before the output can be interpreted intelligently.

Thus one should not blindly accept the model output without an understanding

of the whole process from which it has emerged.

20
2 . DATA

Data are used in the modeling process in four ways:

1. for model development

2. for model calibration,

3. for model validation, and

4. for model application.

Preliminary analysis of the data can aid in the model formulation by

limiting the number of variables required, and can help determine the

model's form. Data are used in the model calibration process for determining the best estimates for model parameters. Model validation requires

comparing the predicted values of the model's dependent ("output") variable

with actual values. The data required for validation must be a different

set from that used for model development, but should be from a context as

similar as possible to those in which the model will be applied. Finally,

it is necessary to have values for the independent model variables in

order to apply the model. The first three of the four headings listed

above thus require values for both the independent and the dependent model

variables, while the last requires values only for the independent variables.

Values for a large variety of data items are required during model development, since the model form and content have not been established at that

time. Once the model has been formulated, fewer data are required. The

data acquisition, assimilation, and analysis process described in Chapter

1 is much the same for all four stages. Since many of the data items required are the same for all stages, one data search should uncover most

21
sources of data for all stages, although hard-to-obtain items may require

an ongoing effort to locate further sources.

The data requirements for the lead paint poisoning model may be divided

into two categories: medical data on poisoning incidence, to be used

for the dependent variable, and demographic data concerning housing and

population, to be used for the independent variables. A distinction between these two categories of data is made, because the sources of the

two types of data are quite different and the incidence data are not

needed for the model application stage.

2.1. Incidence Data

There were several possible dependent variables for the lead poisoning

model, i.e., several plausible measures of "the lead paint poisoning problem":

1. the number of lead poisoning cases t

2. the number of children with blood lead levels at or above some

cutoff level, and

3. the number of housing units offering a hazard.

This last is useful for estimating the costs of a program for deleading

housing, but the primary measure of the lead poisoning problem is the

number of children with the disease. The definition of a lead poisoning

"case" is, however, quite vague, differing from place to place and doctor

to doctor, and sometimes not even being described. In addition, the determination thatf a child indeed has a case of lead poisoning requires additional

diagnostic procedures beyond the preliminary screening. Therefore,

22
use of the number of cases of lead poisoning as the measure of the

magnitude of the problem would not allow calculation of the diagnostic

costs needed to identify a case (and non-cases).

Thus the measure of the lead poisoning problem estimated by the model

described in this report is the (relatively unambiguous) number of children

with elevated blood lead levels (EBL) . The cutoff level which will be

used is 40 yg/100 ml, the level recommended by the SurgeQn General as

showing undue absorption of lead. Not all children with blood lead levels

above this cutoff require treatment, but all require further diagnostic

work. This cutoff level thus distinguishes those children who require

further scrutiny in addition to the initial measurement of blood lead level,

even in the absence of accompanying symptoms. The distribution of

blood lead levels above 40 yg/100 ml can be estimated from existing blood

lead data, so that fractions of the child population with higher levels

can also be obtained.

It is important to remark at this point that all current screening

programs give a sort of "snapshot" view of the magnitude of the lead

poisoning problem. The rates observed in such programs can be used with

greatest confidence only to estimate how many children could be expected

*• to be found by a similar program. One cannot interpret these results

to yield a probability over a given length of time that a typical child

will have an EBL, because this one-time snapshot view does not yield any

information on how blood lead level fluctuates in one child over time.

On the other hand, the probability that a child will experience (or has

23
experienced) EBL at some time is undoubtedly greater than that described

by the average rate of EBL's observed at one point in time.

2.1.1. Survey of Existing Incidence Data

Table 2 lists several areas reporting lead poisoning cases in the

last few years. Many cities and states and the Federal government do not

require the reporting of lead poisoning to responsible authorities, thus

making an accurate nationwide enumeration of the number of cases impossible.

Most of the areas listed in Table 2 do not have active programs screening

children, but rather rely on information received from existing medical

facilities.

Table 3. lists most of the areas in which any screening program exists

and includes comments on the programs. They fall roughly into three categories.

The first includes areas where authorities are aware of the problem, but

are unable to fund any mass screening effort, and therefore only test

children brought into medical facilities such as hospitals or clinics.

In some areas testing is done only if the child exhibits symptoms unexplained by other conditions. The second category includes those areas

which have limited mass screening of children living in high risk environments. One such effort was carried out by the Bureau of Community

and Environmental Management (BCEM) of HEW, and several of the cities

listed in Table 3 participated in this BCEM survey. The final category

includes cities with true mass screening programs, screening a significant

portion of the children who are at greatest risk. Only a few programs,

24
Table 2

Areas Reporting Lead Poisoning Cases18

Baltimore

Boston

Chicago

Cincinnati

Cleveland

Connecticut

Delaware

Denver

Detroit

Dover, Del.

Hartford

Honolulu

Illinois

Kansas City

Louisville

Milwaukee

Nassau County, N.Y,

Newark, N.J.

New Haven

New Orleans

New York City

New York State

Norfolk

Norristown, Pa.

Oklahoma City

Philadelphia

Portland, Maine

Providence

Rochester, N.Y.

St. Louis

Washington, D.C.

Wilmington

25
Table 3 Current Programs

Area Number

Screened Year Description of Program

Baltimore 746

939

1969

1970 Mostly by physicians or in hospitals

Buffalo Small screening effort Chicago 28008 40785

1967

1968 47S27 1969 Mass screening

44347 1970

28973 1971 Cincinnati Small screening effort, test children with symptoms Columbus Test children with symptoms

Connecticut

:

Using primarily the ALA urine test Bridgeport 2 cases reported, small screening, also test in clinics Hartford 147 Test in hospitals and test children with symptoms

New Haven 1897 1970 Mass screening in 1970, not funded in 1971, now screening

in hospitals and clinics primarily

Stamford 130 Small screening effort Waterbury 500 Small screening effort Delaware

:

Wilmington BCEM survey, small screening, in hospitals

Denver Small screening effort. Illinois

:

Aurora 1708 1971 Mass screening, 12% of all children screened Springfield 670 1971 -

Peoria 387 1971

E. St. Louis 376 1971 Decatur 763 1971 Joliet

Rock Island

383

285

1971

1971 Screening in selected areas

E. Moline 293 1971 Robbins 103 1971 Harvey 226 1971 Carbondale 264 1971 -

Indianapolis BCEM survey, small screening effort

Kansas City Testing in hospitals Massachusetts

:

Boston Pilot project, initiated by community groups Cambridge In hospitals, planning future efforts Lowell In clinics Worcester Screening in Model Cities areas

26
Table 3 Continued

Area Number

Screened Year Description of Program

Milwaukee Test children with symptoms

Minneapolis Test children with symptoms

Nashua, N.H. BCEM survey Nashville 97 Pilot project

New Jersey:

Jersey City Testing in hospitals, testing children with symptoms Newark 3048 1971 Some mass screening and also in hospitals Paterson Testing children with symptoms Trenton Testing in hospitals

New Orleans 727 Using a finger prick test New York City 2648 1969

-

84368 1970 Mass screening

87559 1971 -

Norfolk About

1200

1971 Grant from BCEM to conduct small screening

Omaha Small screening effort Philadelphia About

5000 Screening in Model Cities Areas, testing in hospitals and not using blood lead test clinics

Portland, Maine About

1000

1970 Small screening effort, testing in hospitals, using ALA urine test

Portland, Ore BCEM survey

Rhode Island 2600 Screening using hair samples

Rochester, N.Y. Screening, also test children with symptoms Sacramento BCEM survey, small screening effort

St. Louis 4027 1971 Mass screening in selected areas

San Antonio Test children with symptoms

San Francisco Small screening effort

Salt Lake City BCEM survey

South Bend Small screening effort Syracuse Small screening effort, test children with symptoms

Washington, D.C. 808 1821

1970 Small screening effort and testing in clinics

Yonkers Small screening effort, test children with symptoms

27
such as those in Chicago, New Haven, Aurora, New York, and St. Louis

fall into this category. Several screening programs use test methods

other than blood lead determinations, such as the ALA urine test or the

determination of the lead content of hair clippings.

Table 4 gives the distribution of blood lead levels (measured in

yg/100 ml) of children screened by the major programs, and the total

number of children screened by each program in the time period indicated.

None of these programs occurs west of the Mississippi River, and only

New Orleans and Norfolk are in the South. Most programs have tested

children once, in a small pilot effort. Only New York and Chicago have

allotted funds for continuing screening programs, testing and retesting

children in the same areas year after year.

Table 5 lists the percentages of screened children found to have EBL

in each of the programs. The first column records the percentage for each

recording year and the second column gives the average percent for each

program. The percentages range from a low of 6.1 in Washington in 1970 to a

high of 46.3 in New York in 1969. The screening of children in New York

in 1969 differed from the screening there in 1970, in that in 1969 sick

children were screened in hospitals and clinics, while in 1970 the

screening program used a community outreach approach to screen in areas

having large concentrations of poor quality housing. The Illinois State

survey found EBL percentages varying from a low of 9.5 in Rockford to a

high of 31.3 in Peoria, indicating considerable difference in rates in

different cities.

28
01ugo

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29
Rock- ford, 111. 1971 m

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in cm

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Blood Lead Level 0-910-19 20-29 30-39 40-49 50-59

ON

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30
Table 5

Percent with Elevated Blood Lead Levels of Those Screened in Several Programs

% EBL

% EBL Program

Average

New York 1969 46.3

•"!

New York 1970 28.7 32.9

New York 1/71-8/71 23.7 - Newark 25.4 25.4

Norfolk 24.1 24.1

St. Louis 1970 37.3

32.2

St. Louis 1/71-11/71 27.1

Washington 1970 6.1

22.6

Washington 4/26-10/29, 1971 39.2 . New Orleans 28.4 28.4

New Haven 22.9 22.9

Baltimore 1968 25.3

Baltimore 1969 27.9 28.2

Baltimore 1970 31.5

Chicago 1968 11.5

Chicago 1969 6.7 8.8

Chicago 1970 8.4

Chicago 1/71-5/71 8.5

Aurora 23.6

Springfield 30.1

Peoria 31.3

E. St. Louis 24.7

Decatur 12.2

Joliet 24.3

19.4

Rock Island 21.1

E. Moline 11.4

Robbins 12.6

Harvey 16.4

Carbondale 17.0

Rockford 9.5

Average 22.7 26.1

31
2.1.2. Criteria for Choice of Incidence Data Sources

Several criteria were used in evaluating various sources of incidence

data.

1. The data should be acquired from an area with a mass screening

program.

2. The data must be in a form which can be readily aggregated by

the same geographical zonal system as the demographic data and

as that required for model output.

3. Incidence data should be determined by blood lead level test.

Data obtained from general mass screening are obviously more likely

to yield incidence rates characteristic of the population of an area than

are rates derived from samples with any kind of systematic bias. Assured

representativeness of data from mass screening is required to permit

accurate extrapolations from the small samples to incidence rates for

area wide populations.

It is necessary that the geographic level of aggregation of the

incidence data be compatible with that for the demographic data, described

below in section 2.2. Most incidence data sources do not themselves have

their data already aggregated by any zonal system, but have only the home

address of each child tested, together with blood lead level and other

information needed by the program. In many instances these data are not

yet in machine readable form but appear on handwritten file cards. Even

32
computer coded data which have no zonal classification other than the

street address must be further processed by hand to obtain zone numbers,

since the present state of the art of computerized address coding is quite

rudimentary and still experimental.

The third criterion required of the incidence data is that they result

from a macro-blood lead determination. In section 1.1.4. we discussed

testing methods and cited the Surgeon General's recommendation that the

primary testing method be the blood lead test. Although micro-analysis

procedures are available to analyze the amount of lead in small samples

of blood obtained by finger pricks, the experience of several experts has

been that the risk of the contamination of such samples is very great.

Therefore we have restricted our data sources to those using a macro-blood

lead test to screen.

2.1.3. Selected Sources of Incidence Data

Criteria 1 and 3 of section 2.1.2 and project time restrictions

limited the potential data sources to the four listed in Table 6. The

volume of data requiring hand coding of addresses precluded the use of

the Chicago or New York data in the modeling effort described in this

report. Thus the two data sources from which data were used for developing,

calibrating, and validating the lead paint poisoning model are New Haven,

Connecticut, and Aurora, Illinois.

When this study began the New Haven data were the only ones immediately

available already aggregated by an appropriate zonal system. The city of

33
Table 6

Incidence Data Sources

Source Form Comments

New Haven published already aggregated by

report census tract

New York magnetic only has addresses,

tape large amount to process

Chicago file only has addresses,

cards must be coded, large

amount to process

Aurora, forms only has addresses,

Illinois need to process fewer

than 2000 forms

34
New Haven conducted a mass screening program in 1970, testing 1897 children;

funding was not available to continue this in 1971, so that for this

year only children brought into hospitals or clinics were tested. Some

of the children tested in 1970 received only an ALA urine test and no

follow-up blood test. These children were omitted from the data set,

leaving about 1300 tests which were used in model development and calibration. This represents about 3 percent of New Haven children six years

old or younger.

In July of 1971 the Illinois Department of Public Health tested 449

children in Aurora, Illinois. Subsequently the Kane County Council of

Economic Opportunity screened an additional 1258 high risk children making

a total of 1707 tests, representing 12.8% of all children in Aurora.

These data were originally recorded on forms which contained additional

information about the child and his family. Since the only geographical

information available on each child was a street address, it was necessary

to code a zone for each address as well as to code the other information

prior to committing it to punched cards. Whereas the New York and Chicago

data sets were too large to permit this type of hand coding, project time

and budget constraints did allow the hand processing of the smaller Aurora

data set.

Both the New Haven and Aurora data contained some bias, since both

focused their programs on testing "high risk children", those living in

- neighborhoods containing poor quality old housing which is likely to

contain peeling lead. No current program has tested a random sampling of

children; all have concentrated on testing only those children running the

35
greatest risk of poisoning. The definition of "high risk" is, however,

vague (or often not stated at all) , making difficult an exact characterization of those children screened in relation to the total population of

children.

2.2. Demographic Data

The lead poisoning model will relate the incidence of EBL in an area

to demographic characteristics of that area. The demographic factors

which have been associated with lead poisoning, in the literature and in

conversations with experts, can be divided into two types: 1) those

concerned with the environment in which the lead poisoned child lives,

and 2) those concerned with the socio-economic characteristics of the

child's family.

2.2.1. Demographic Characteristics Associated with Lead Poisoning

Table 7 lists some of the housing data items which have been

associated with lead poisoning. The most obvious characteristic of the

housing in which a lead poisoned child lives is the presence of lead

paint in a form the child can easily ingest. Until the 1940 's lead paint

was the predominantly used wall covering house paint in this country.

Subsequently lead paint usage declined as latex paints were introduced.

In 1955 the American Standards Association (now ANSI) adopted a voluntary

standard prohibiting interior house paint from containing more than

1% lead by weight, and requiring a message cautioning against use

36
Table 7

Housing Characteristics for Use in a Lead Paint Poisoning Model

19

1. the number of units currently standing which were built in various periods

a. built before 1940

b. built between 1940 and 1950

c. built between 1950 and 1960

20

2. the number of sound housing units

3. the number of dilapidated housing units

4. the number of deteriorating housing units

5. the median dollar value of owner occupied housing units

6. the average monthly contract rent

7. the housing vacancy rate

8. the number of multiple unit structures

9. the number of renter occupied units

10. the number of renter occupied units

37
on surfaces accessible to children to be printed on the label of all leaded

21 paints. However, recent surveys have found some leaded paints on the market

to lack adequate cautionary labeling. In addition, lead exterior paints

are still sold and children may eat paint chips from exterior railings,

porches, garages, exterior windowsills, and accessible exterior walls.

Therefore, some walls being painted today may well offer a potential

hazard to children in the future. It is impossible to set a cutoff date

for which one can say housing built before that time is probably hazardous

and housing built later is probably not. Without a major survey of

housing, one can only speculate on the fraction of housing built in any

year which has lead paint in it

In the absence of more definitive information on the correlation

between the age of a dwelling and the presence of lead paint, it may be

necessary to use the condition of the dwelling unit. This may actually be

a more reliable indicator, since lead paint on a surface does not necessarily

offer an immediate hazard to a child if the surface has been well maintained,

paint is not chipping or peeling, and the surface does not present a chewable

edge to the child. Such surfaces are, of course, a potential hazard, since

if the dwelling unit is allowed to deteriorate, at some future time peeling

and chipping paint may provide a source of lead for a child to ingest.

Other items listed in Table 7 may be used to identify poor people

living in unsound housing (those running the greatest risk of lead poisoning). Presumably such housing is lower in value, and monthly rent for

such housing is lower than rental costs for better quality housing in the

same area. (It should be noted, however, that inner city rents for poor

38
quality housing are sometimes higher than rents for better quality suburban

units.) Vacancy rates for such units are usually higher than rates for

better quality housing. Lead poisoning is found primarily among the poor,

who are usually unable to own their own housing. Thus the number of

owner occupied versus rental units is of interest, as is the number of

multiple unit structures (which are most often rental units) versus

single unit structures.

Table 8 lists population characteristics associated with lead poisoning. All but a small number of reported cases have occurred in children

six years old or younger, and most programs concentrate their testing

on children in this age range. The children who develop lead poisoning

come primarily from poor families, those most likely to be living in deteriorating or dilapidated housing. Many of the characteristics listed in

Table 8 are associated with such families. They live under crowded conditions, a disproportionate number of the families are headed by females,

many of the families are newly arrived to the city from the rural South,

the head of the household often has not completed high school, the household head if employed at all works in a menial job, and finally a disproportionate number of such families belong to minority groups. Of

course, not all children with lead poisoning belong to families with all

or most of these characteristics, but one can often identify neighborhoods

which contain high risk children as those in which many families do fall

into these categories.

39
Table 8

Population Characteristics for Use in a Lead Paint Poisoning Model

22

1. the number of children in various age categories

a. under 5

b. 5 years old

c. 6 years old

2. the median family income

3. the number of families with incomes below the poverty level

4. the number of people living under crowded conditions

a. crowded - 1.01 persons per room

b. crowded - 1.51 persons per room

5. the number of female household heads

23

6. the distribution ~ of region of birth of the household head

7. the distribution, by years of school completed, of the educational

level of the household head

8. the distribution of the work status (employed or unemployed, job

category) of the head of household

9. the distribution of persons by race

40
2.2.2. Sources of Demographic Data

There are basically two sources of the types of data listed in

Tables 7 and 8, namely the U. S. Census Bureau and various local data

sources such as property tax records and welfare agency files. It was decided

to use data available from the U. S. Census Bureau rather than those from

local sources, for three reasons:

1) Census data are of uniform type and quality across the country.

2) The reputation and resources of the Census Bureau make it likely

that Census reports have the greatest possible accuracy subject

to criterion 1.

3) The Census data were available on computer magnetic tape from

virtually a single source. Their use did not necessitate a

search of possible sources and separate evaluation and reconcilation

of the form and quality of each.

Although the 1970 Census was conducted 2 years ago, not all the

data then collected were released in time to be used in this effort; one

main data item, the distribution of housing by condition of house, was

not collected in 1970. Therefore it has been necessary to investigate

the availability of data from 3 different Census sources: the 1970 Fourth

Count, the 1970 First Count, and the 1960 Census.

The 1970 Census had 3 levels of sampling; one included questions

asked of all persons, a second was asked of 15 percent of the population,

and a third was asked of only 5 percent. The Census First Count data

includes responses only from the 100 percent sampled questions, which do

not include questions concerning the age of the dwelling unit, the family

income, or the region of birth, educational level, or work status of

41
household head, all of which appear in the Fourth Count data. In addition,

the Census Bureau discontinued questions concerning the condition of the

dwelling unit after the 1960 Census, because of the subjectiveness and

lack of uniformity encountered in interpreting the definitions of sound,

deteriorating and dilapidated. Therefore, the data used by this project

which concerned condition of housing were taken from 1960 Census published

reports. 1970 Fourth Count data were not released in time to be utilized.

Thus the 1970 Census First Count was the primary data source for current

population and housing statistics, and 1960 Census data were used whenever

1970 data had not yet been released.

2.2.3 Zonal System

The decision to use data from the U. S. Census Bureau means that the

zonal system used in the modeling process must be compatible with that of

the Census. Several levels of aggregation are available:

1. the block group (in urban areas) and its rural equivalent, the

enumeration district,

2. the census tract,

3. the county, and

4. the Standard Metropolitan Statistical Area (SMSA)

There are approximately 250,000 block groups, 50,000 census tracts, 3000

counties, and 243 SMSA's in the U.S. A census tract contains approximately

4000 to 5000 people and consists of about 4 to 6 block groups. An SMSA is

a metropolitan area consisting essentially^ of a central city of at least

50,000 people together with its surrounding suburbs.

42
It was decided to use census tract level data for model development

and calibration, because 1) incidence data were available for only a few

cities so that to obtain enough data it was necessary to use a level of

aggregation lower than city wide and 2) census tract data are more easily

available than block group data, and there are fewer census tracts so the

data set is more manageable.

The SMSA level of aggregation was chosen for national predictions.

County level was rejected because 1) there are too many counties to code

the data and display them effectively, 2) different states have different

size counties, and 3) all available incidence data are for urban populations, and it is not known whether the model can predict as well in

rural areas. SMSA predictions could be done in two ways: 1) predict for

each census tract on the basis of the characteristics of the tract and

aggregate to the whole SMSA or 2) predict for the whole SMSA on the basis

of aggregate and averaged variables. This second method was used because

of the volume and cost of obtaining data for the first procedure.

2.3. Current Data Deficiencies

Current data lack information of several types. Most experts involved in lead poisoning programs believe that the greatest source of

poisoning is peeling and chipping lead paint. However, there is little

actual data to indicate the fraction of cases caused by paint as opposed

to other sources. At present there are no data to establish a relationship

between the age of housing in a particular area and the presence of lead

43
paint in the housing. The arbitrary cutoff year 1940 has been used by

some as the year after which lead paint was not used, but paint production

figures indicate a substantial amount of lead paint was used well into the

1950' s. Therefore some housing built after 1940 may and very likely will

contain lead paint.

Current incidence data apply primarily to inner city poor children

from the East and Midwest. EBL rates for middle class children, for rural

and suburban children, and for children living in the South and West are

unknown. It is generally believed that rates for these children are

25

lower than for the Eastern urban poor, but screening of these groups is

needed to establish the magnitude of that rate difference.

The demographic data available for the modeling effort described in

this report were lacking in two respects, namely the late release of the Census

Fourth Count data and the lack of 1970 housing condition data. The first

of these deficiencies has been remedied (but too late for this modeling

effort), since the Fourth Count data have now been released. The lack of

housing condition data can also be remedied through the use of a model

developed by the Census Bureau to predict the distribution of housing, by

condition of unit, from Census Fourth Count data.

44
3. THE MODELS

3.1. Aggregation Problems

The word "aggregation", in the context of a statistical study or the

construction of a mathematical model, refers to the way the subjects of

the study or model are grouped in the analysis or subsequent application.

The degree to which subjects, populations or observations are combined is

called the level of aggregation. Problems may arise when one level

of aggregation is used for model calibration and another for model application.

Difficulties and costs in obtaining and processing data for 50,000 census

tracts, rather than 250 SMSA's, discouraged using the census tract level

of aggregation for the estimation of the national incidence of lead paint

poisoning. On the other hand, incidence data were not available from

enough different areas all at one level of aggregation, such as the city,

to calibrate a model at a higher level of aggregation than that of the

census tract. The data described in section 2.1 are most often available

for selected sections of a city rather than the whole city. Thus it was

decided to calibrate the model at the census tract level, but to apply it

at the SMSA level. On the basis of this decision, model evaluation included steps to check for possible aggregation problems.

Problems of different levels of aggregation may arise whenever the

form of the model is non-linear (that is, not strictly proportional to a

sum of variables). For example, suppose the incidence I of lead poisoning

could be calculated by the formula

I = /T" ,

45
where K is the number of children in the area. Consider a region containing 25 children. The formula predicts 5 victims of lead poisoning. If

the same region is broken into 2 sub areas, one containing 16 children

and the second containing 9, the predicted incidence for the first area

would be 4 and for the second area 3, making a total of 7 for the whole

region, a difference of 40% from the original estimate.

Problems may also arise if the variables are non-linearly related

to the populations they represent. Data expressed as medians or rankings

are examples of such types of variables. Suppose, for instance, that the

fraction L of housing containing lead is related to the median age A of

housing in an area, by the formula

J

.02A for Ai 50

L =

1

(^ 1.0 for A > 50

Suppose one area has 20 houses all 50 years old, and an adjacent area has

10 houses all 10 years old. The median age of housing in the first area

is 50 years, so the fraction of housing there having lead is 1, i.e., all

20 houses contain lead. In the second area the median age is 10 years,

so that the fraction of housing containing lead is .2, or 2 houses. The

total number of houses in the two areas which would contain lead, if one

calculates for the two areas separately, is 20+2 or 22. If one considers

the whole region as one area containing 30 houses, 10 of which are 10

years old and 20 of which are 50 years old, the median age is 50 years.

On this basis 100 percent of the housing, or 30 houses should contain

lead, an increase of 36 percent over the first method of calculation.

46
Higher levels of aggregation may exhibit a homogeneity among areas,

which does not exist at lower levels. For instance, median as well as

mean incomes may vary markedly between poor and rich neighborhoods, whereas

the median (or mean) incomes for different metropolitan areas do not.

Therefore data at the higher level of aggregation may not vary enough to

discriminate among levels of occurrence of phenomena. On the other hand,

the tendency towards homogeneity at higher levels of aggregation may be

an advantage if there are compensating errors. An example of this occurs

in the current work where because of the late release of some of the

1970 housing data, it was necessary to employ 1960 data for the

distribution of housing by age. whereas the fraction of housing built

before 1940 and still standing in 1960 may be very different because of a

concentration of urban renewal projects from that still standing in 1970

in a single census tract, the total amount of housing torn down

in renewal projects over the whole SMSA during the last 10 years is

a small fraction of either the total housing or the pre-1940 housing and

thus will have much less affect on these figures.

3.2. Analysis of the New Haven Data

Table 9 contains demographic and screening data by census tract for

the city of New Haven. The demographic data items tabulated include 1970

population, the number of children six years old or less, the number of

female household heads, the number of people living in crowded households

with more than 1 person per room, or again with more than 1 1/2 persons

per room, the total number of housing units, and the number of rented

47
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48
units. In addition to these 1970 data, 1960 data are included for median

family income, total housing, the number of deteriorating housing units,

the number of dilapidated housing units, and the number of units standing

in 1960 which were built before 1940.

Figure 1 is a map of the census tracts in New Haven, with high and

low incidence tracts indicated by heavy dotted areas and diagonal lines

respectively. The 14 tracts in which fewer than 10 children were screened

are left blank. The New Haven program concentrated screening in neighborhoods which had older housing in poor condition, thus offering an Immediate

lead hazard, so that presumably those tracts in which few children were

screened are lower risk areas.

There are two observations which should be made about the demographic

data in Table 9. Between 1960 and 1970 the total number of housing units

declined in 17 of the 28 census tracts, and the net decline for all tracts

was about 2500. It is not expected that this is typical of most SMSA's,

since in general the number of housing units built in the decade from

1960 to 1970 is greater than the number demolished, leading to a net rise

in the total number of housing units.

A second observation is that 86.5 percent of the 1960 New Haven

housing stock was built before 1940. This is probably representative of

the older cities in the Northeast, but much less so of those in the West

which sustained their major growth in the post-World War II period.

Table 10 lists the percent of 1960 housing stock built before 1940 for

each of the 14 census tracts in which more than 10 children were screened.

In only 4 out of the 14 tracts is less than 80% of the housing pre-1940,

49
Figure 1

New Haven Census Tracts

1 1

1

1 1 1 1 1 1 1 1

1

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INCIDENCE RATE<

THE AVERAGE

FEWER THAN 10

CHILDREN SCREENED

50
Table 10

Percentage of Housing Built Before 1940 for Census Tracts in

Which More Than Ten Children Were Screened

Tract Percent Built Before 1940

1403 97.9

1404 99.7

1405 99.4

1406 98.9

1407 97.9

1412 41.9

1413 43.5

1415 97.5

1416 73.1

1418 66.6

1421 84.2

1423 98.8

1424 99.3

1425 85.3

51
and in 8 of the tracts the percent is greater than 97%. Thus, although it

was hoped that the fraction of housing stock built before 1940 could be

used in the model to differentiate the lead hazard in different regions

of the United States, the values of this variable in New Haven do not

differ enough to discriminate high and low risk areas.

Tables 11 and 12 give the simple correlation coefficients and

27 Tables 13 and 14 the Spearman rank correlation coefficients calculated

on the tract data for New Haven. Tables 11 and 13 are calculated using

the values of the variables themselves and Tables 12 and 14 are calculated

using the logarithms of the variables. A correlation coefficient of ±1 in

all tables indicates the two variables in question differ at most by a

constant scale factor, while a coefficient of indicates there is no

discernable relationship between the two variables.

The analysis of the correlations is used in the modeling process

in three ways. The first of these is the isolation of the most promising

predictor variables. The column labeled "Fraction of children screened

having EBL" gives the correlation coefficient between the dependent

variable and each of the independent variables. Those with the highest

numbers (in absolute value) are most closely related to the incidence of

EBL and are most likely candidates for good predictor variables. As can

be seen from Tables 11 through 14, the characteristics most correlated

with the incidence of EBL are the age and condition of housing.

A second way correlations can be used is to avoid fitting error. If

two (or more) of the model parameters are assumed to vary independently,

whereas in fact they are highly correlated, spurious parameter values may

52
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56
occur in the process of curve fitting. This may lead to serious misinterpretations of the interactions among variables and of the sensitivity

of the dependent variable to changes in the independent variables.

Correlations among observations need not always give cause for alarm.

They can be utilized fruitfully to indicate possible substitute variables

for unavailable or less desirable (e.g., not well defined or less precise)

ones. As an example, we believe lead poisoning to be highest among the

poor, and desire to use family income as a determining variable, but

current income data were not available in time to be used in the modeling

effort. Because of the high correlations (greater than .7 in absolute

value in all tables) between 1960 income and the fraction of 1970 population which are female household heads, we have ventured to use the

female household head fraction variable as a substitute for income in the

modeling effort.

3.3. Model Form

The choice of an initial model structure in the absence of a highly

detailed explanatory hypothesis rests on two principal criteria: simplicity

and conceptual plausibility. The first affects ease of calibration, and also

amenability to modification when (inevitable) improvements in data availability occur. The second is a measure of how comfortable one is with

whatever ideas support a given simple formulation.

The two simplest kinds of representation of the dependency of a

numerical measure of some phenomenon on the values of a set of others are

57
grouping them additively (called a linear model) , or grouping them

multiplicatively (called an exponential model) . An example of a linear

model with 2 independent variables (X-^ and X2) would be

Y = a

Q + a.^ + a^. (3.1)

An exponential model with the same 2 variables would be

b b

Y = b

Q

• X

1

• X

2

. (3.2)

No attempt at the formulation of a linear model of the incidence of EBL

was remotely successful. Thus the models described in this chapter and

in Appendix A are all exponential models, in which the EBL incidence rate

is estimated by multiplying the relative frequencies of occurrence of

suspected causative factors.

In the models described below, several parameters (the b's of equation

3.2) must be estimated; this procedure is called curve fitting. The

standard procedure involves the least squares approximation to the linear

form. The model form chosen for the lead paint poisoning is multiplicative

rather than linear, but can be made linear by taking the logarithms of both

sides of the equation. In equation 3.2

b b

Y=b X

l

X

2

'

Taking logarithms of both sides yields

log Y = log b

Q + b, • log X-, + b

2

• log X

2

«

Changing variables for clarity: letting b' = log b

Q

, Y' = log Y, X' = log X, ,

and X ' = log X~, we have

58
Y' = b' + b^' + b

2X

2

' (3.4)

which is a linear model form equivalent to equation 3.2.

Several criteria can be used for comparing models obtained by this

procedure, but care must be taken in the analysis since the models result

from a fit to a single data set and may reflect anomalies peculiar to that

set. Figure 2 illustrates this situation. Curves a_ and b_ both pass

through the three points P.. , P~, and P~ but their values at the vertical

dotted line are quite different. Both curves fit the data exactly, but

both will not predict equally well. Therefore judging the quality of a

model requires, in addition to the mechanical criteria used in curve fitting,

some determination of whether the model actually mirrors the process it

is supposed to describe.

Two major criteria have been used to compare models for lead paint

poisoning. The first of these involves the calculation of a statistical

measure of the accuracy of the fit of the model to the data, the square

2 of the multiple correlation coefficient, called R-square and written R .

R - 1

,

where y. are the observed values of the phenomenon and y. are those

2 estimated by the model. A model is deemed acceptable if 1) R (for the log

linear form) exceeds .9 and 2) for refinements produced by the introduction

of additional variables, no attempted refinement has an R which is greater

than that of the original model by .05. This latter criterion sets a

tradeoff between model complexity and fit-improvement.

59
Figure 2

Two Curves Passing Through the Same Three Points

CURVE a

60
As was stated above, no model should be judged solely on a goodness

2 of fit criterion such as R , but should be evaluated also on its behavior.

One relatively simple test that can be applied to the calculated parameters

(coefficients and exponents) is whether their signs are consistent with our

judgments (or prejudices). For example, we would expect the variable

"median family income" to appear in the lead poisoning model with a

negative exponent, because we believe the incidence of EBL in an area to

decrease as the income level rises. If the characteristics of a parameter

are "counterintuitive" then the data sample may be distorted, the variable may

have been selected by erroneous reasoning, or indeed the rationale for

the sign criterion may be mistaken.

3.4. The Models

The simplest multiplicative model would apply if the concurrent

presence of the listed factors insured the presence of lead poisoning.

One possible form for such a model for lead poisoning is that of a conditional probability model in which the expected number of children with

EBL is the child population size, times the probability that a child will

live in a hazardous environment, times the probability a child so situated

will ingest lead. The models described below equate a hazardous environment

with housing which is either dilapidated or deteriorating. Thus the models

are of the form

E = K • |

• I (3.3)

where E : the number of children with EBL

K : the number of children

61
D : the number of dilapidated or deteriorating housing units

H : the number of housing units

I : an EBL incidence rate for children living in a hazardous

environment

(Interpreting — as the probability that a child will live in a hazardous

environment involves the assumption that the number of children per

household is the same for a high risk environment as for a lower risk one.)

The data base and our understanding of the lead ingestion process

do not lend themselves to such an exact interpretation of I, the probability

that a child living in a hazardous environment will ingest lead. Two

different "models" for I have been developed separately: one has I

constant and the other uses not a simple multiplicative form, but rather

one in which non-unitary exponents are introduced to reflect the different

levels of contributions of the factors.

3.4.1. Model 1

Model 1 is a simple model developed early in the project. It has

the form

E = K

6 *H 'h

where E is the predicted number of children with EBL for an appropriate

population

K>. is the number of children 6 years of age or less

D is the number of unsound housing units (dilapidated or deteriorating)

H is the total number of housing units

Iq is a (constant) EBL incidence rate for high risk children.

62
In was estimated as the average (.24) of the EBL rates listed in Table 15.

The rate for high risk children is thus taken as constant, and the rate

for the whole child population in an area depends only on the fraction of

unsound housing units in that area.

3.4.2. Model 1A

Model 1A is similar to Model 1, but the constant rate I~ is replaced

by a variable rate I calculated as a function of the characteristics

of the area in question:

(tYls)

.2967 k .2484

I = .747

where K, , D, and H are as in Model 1 and

6

P is the total population.

The parameters (the numbers .747, .2967, and .2484) were estimated using

the New Haven data listed in Table 9, and I was calculated as the number

of children with EBL divided by the number of children screened. Only

those tracts in which more than 10 children were screened were used in

2 the calibration. The R associated with this calibration is high (.945),

and the signs of both exponents are as expected since incidence rises as the

fraction of housing in poor condition rises or as the fraction of total

population which is children 6 or under rises. Other models of this

type are described in Appendix A. This model has been judged best

according to the criteria given in section 3.3.

63
Table 15

Preliminary Data on EBL Rates

City EBL Rate

%

Source

New York 1969 45.5 Cong. Record 10/18/71; article by

Guinee, submitted by Cong. Ryan

New York 1970 28.8 Lead Poisoning Control Bureau,

City of New York v Aurora, 111. 20.8 Kane Co. Council for Economic

Opportunity, G. Tollaksen

Aurora, 111. 24.3

Springfield, 111. 30.1

Peoria, 111. 31.3

E. St. Louis, 111. 24.7

Decatur, 111. 12.2 "Pediatric Lead Poisoning in

Joliet, 111. 24.3 Illinois", Phillip R. Fine and

Rock Island, 111. 21.1

Richard H. Suhs

E. Moline, 111. 11.4

Robbins, 111. 12.6

Harvey, 111. 16.4

Carbondale, 111. 17.0

Norfolk, Va. 22.7 Lead Poisoning Program, Norfolk

Health Dept . , Ebbut

New Haven, Conn. 23.7 New Haven Lead Paint Poisoning

Program, Elaine Whitmire

Baltimore 1968 25.3 1 "Undue Absorption of Lead Among

Baltimore 1969 27.3

Children - A New Look at an Old

Problem", Jane Lin-Fu, PHS

Baltimore 1970 31.5

Washington, D.C. 1971 19.5 D.C. Model Cities, Dudley Anderson

64
4 . VALIDATION

4.1. Introduction

Model validation ideally refers to a process wherein the results

predicted by a mathematical model in specific instances are compared with

the corresponding events in the real world, in order to increase confidence in all model predictions, or alternatively, to learn that the

model needs to be revised or discarded.

There are two levels of validation required. The first involves

checking the assumptions upon which the model rests, those determining

which factors are important and which can be omitted, as well as those

determining the general mathematical form of the model. The second level

of validation is the comparison of the magnitude of the phenomenon as predicted by the model and as actually occurring, to ensure that the model

is a good estimator.

There are two major reasons it is necessary to validate a model.

1. Data anomalies peculiar to one region or situation may not be

recognized as such and may become major determinants in the model

form.

2. Model hypotheses based on the analysis of a phenomenon in one

context may not carry over into others.

The only variables which emerged from the analysis of the New Haven

data described in Chapter 3 as major determinants of EBL's were the

relative size of the child population six years and under, and the extent

of dilapidated and deteriorating housing. Thus the model was based on the

65
hypothesis that young children living in poor housing run the greatest

risk of lead poisoning. This hypothesis does not rest solely on the New

Haven experience but comes also from the general findings in New York and

Chicago. However the actual degree and form of the dependence, as well

as the exclusion of other factors, result from the analysis of New Haven's

data and must be verified elsewhere by the model validation process.

When the models were being developed, New Haven was the only source

of data which had EBL's aggregated by Census tract. An additional data

source became available late in 1971. In the city of Aurora, Illinois, a

task force (spurred by the finding that 91 out of about 450 children

tested there by the Illinois Department of Public Health exhibited EBL,

and by the subsequent death of one of these children) screened over 1700

children between July and October of 1971.

The two cities, Aurora and New Haven, differ in many respects, and

validation of the models with data from Aurora will thus widen the known

range of the models' applicability. Aurora is in a different part of the

country (the Midwest as compared with the East) from New Haven, the calibration city, and has a different character as a city. New Haven itself

forms an SMSA, but Aurora is part of the larger Chicago SMSA and is partly

a suburban bedroom community. The center of Aurora is an old city

originally built along the Fox River and the Burlington Railroad yards,

at a distance of about 35 miles from the center of Chicago. At present,

Aurora is included among the expanding suburbs of Chicago, and its

small town character has changed much in recent years.

Verification of the models' estimates of EBL frequency for Aurora

will not, of course, assure the accuracy of the models' other estimates.

66
In fact, although good agreement with actual Aurora findings will encourage

acceptance of the models' estimates for small and medium size cities elsewhere

in the East and Midwest, we will still lack verification of the estimates for

other areas of the country, such as the South, the Mountain States, and

the West Coast. These areas have quite different climatic characteristics,

possibly different techniques of home construction and decoration, and

different proportions of older housing. Data on EBL from some cities in

these areas are needed to really establish the validity of the estimates

there. In the current absence of such data, validation using Aurora data

can at least improve our confidence in the general assumptions upon which

the models were built, and in particular can alleviate the risk that the

models reflect unduly the specific character of New Haven.

4.2. Validation of Model Assumptions - Analysis of the Aurora Data

4.2.1. General Description of the Aurora Data

In July of 1971 the Illinois Department of Public Health tested 449

children in Aurora, Illinois. Of these, 91 were found to have EBL's,

five with high enough levels to warrant hospitalization. This testing in

Aurora was part of a 10 city survey conducted by the Department to ascertain why, of the total of over 2000 lead poisoning cases reported to

them each year, only a handful occurred outside the City of Chicago,

although less than half of the children in the applicable age range live

in Chicago, and housing conditions similar to those in Chicago's poorer

67
areas are also found in other cities in the state. The Aurora tests and

those in other Illinois cities revealed that the disease is widespread

outside Chicago but had remained largely unrecognized. This is an

excellent example of the circumstance mentioned previously, that the

number of children found with EBL's depends directly on the effort spent

in searching.

Once the presence of lead poisoning in Aurora became known, a program

directed by the Aurora Service Center of the Kane County Council of

Economic Opportunity (staffed almost wholly by volunteers) was launched

to test the remainder of those children in Aurora living in neighborhoods

containing poor housing. In 1971, 1707 children, 12.8% of all children

6 years of age or less in Aurora, were tested. (This figure includes

the 449 originally tested by the Illinois Department of Public Health,

but does not include 86 ostensibly non-high risk "middle class" children

tested for statistical control purposes.) 321 of these, or 18.8 percent,

had EBL's. Table 16 gives, for each census tract in Aurora, the total

number of children 6 years and under (from the 1970 Census) , the number

of children screened, the percent of children screened, the number of those

children screened found with EBL's, and the percentage of those screened

with EBL. The extent of the screening coverage can be measured by the

fact that in only one of Aurora's 16 census tracts were less than 5 percent of

the children screened, while in four tracts more than 20 percent were screened,

Aurora officials have estimated that all high risk children have

been screened in this program. Their estimate of 1700 children in high

risk neighborhoods is quite naturally greater than the NBS estimate of

68
Table 16

Aurora Screening Statistics by Census Tract

CENSUS TOTAL CHILDREN PERCENT TOTAL PERCENT

TRACT CHILDREN SCREENED SCREENED EBL EBL

8529 2116. 173. 8.2 25.. 14.5

8530 2109. 73. 3.5 9. 12.3

8531 391. 20. 5.1 5. 25.0

8532 944. 100. 10.6 28. 28.0

8533 380. 49. 12.9 6. 12.2

8534 1122. 283. 25.2 64. 22.6

8535 556. 96. 17.3 11. 11.5

8536 772. 225. 29.1 48. 21.3

8537 30. 5. 16.7 1. 20.0

8538 386. 49. 12.7 11. 22.4

8539 826. 41. 5.0 7. 17.1

8540 1099. 240. 21.8 50. 20.8

8541 517. 82. 15.9 19. 23.2

8542 527. 58. 11.0 7. 12.1

8543 827. 42. 5.1 3. 7.1

8544 788. 172. 21.8 27. 15.7

Total 13390. 1708. 12.8 321. 18.8

Values in the tables in this section are computer output and subject to

round-off error. For this reason totals from different tables may not

agree and percentages may not total 100.

69
over 1300 in high risk housing units , since some of the housing in a high

risk neighborhood may be in good condition. It is clear that any program

intending to test all high risk children will have to screen more than the

bare minimum of children, since it is impossible to draw neighborhood

boundaries strictly enough to include only children living in poor housing.

Also, programs run by government affiliated groups must accept all children

whose parents request a test. In addition, health officials see great

benefit in testing any child, regardless of socio-economic status, who

might have been exposed to the lead hazard. The problem for most programs

is not that of having to turn away children desiring the test, but that of

persuading parents of exposed children to allow the test to be done. The

Aurora program which tested over 12 percent of all children has succeeded

well in this respect and it is very probable that most, if not quite all,

of the high risk children have been tested.

4.2.2. Factors Associated with EBL

The Aurora officials allowed NBS staff access to the forms which

were filled out for each child. These forms had information concerning

the child and his family in addition to the blood lead level. This information was coded and punched at NBS on computer cards for an analysis

of characteristics actually associated with children with EBL. The forms

contained only the street address for each child's home, but the correct

census tract was coded for each address by hand using a street directory

and street maps of the Aurora area. A small number (fewer than 10 forms)

70
could not be coded because the street could not be found in the directory

or on the maps, the street was found in a tract not included in our study,

or only a Post Office box number was given as an address.

Table 17 lists the data items coded from the Aurora forms. Not all

forms contained all twelve items, of course. Some items were not filled

in on some forms, and these items were coded as blanks, unless other

information, such as a form for another child in the same family (which

could be used for all but items 1, 3, 4 and 12) or the child's name

(which could be used to determine the child's sex), was available to fill

in the blank. Three different forms had been used for the three different

test dates on which the survey was run: the first contained only items 1

through 6, the second contained all items except 11; and the third contained

all 12 items.- The use of these different forms thus accounts for many of the

blanks observed in the data. Table 18 records the number of blanks found for

each of items 2 through 12. There are no blanks for blood lead level, since

any form lacking this information was discarded. Only 19 retests were included in this study, and therefore any conclusions about them are based on

a very small sample.

Table 19 displays the distribution of blood lead levels for the

Aurora children, separately for first time tests and for retests. As just

noted, the number of retests is too small to make possible sound generalizations concerning EBL rates for children in programs with periodic retesting. The fact that only those children whose initial tests were high

were retested may explain why the distribution of retest blood lead levels

71
Table 17

Data Items Coded From the Aurora Forms

1. blood lead level

2. census tract

3. sex of ciild

4. age of child

5. race or ethnic origin

6. whether or not the child has a family doctor

7time of residence at t

1^-3. present address

8. whether the family owns or rents its home

9. whether or not the family receives public aid

10. a family number to identify children in the same family

11. condition of the house the child lives in

12. retest blood lead level, if child was retested.

72
Table 18

Aurora, Illinois Data Statistics

There are 3 blanks in the column for (2) tract

(3) sex

17 (4) age

22 (5) race

20 (6) doctor

331 (7) transiency

363 (8) tenure

352 (9) aid

1361 (11) condition

There is a total of 1726 tests of which 19 are retests,

The total number of families is 1044. The numbers in

parentheses are the indices used in Table 17.

73
Table 19

Blood Lead Levels (yg/100 ml) for Aurora, 111. Children

Blood Number of

Lead Level Initial Tests

0-9 246.

10 - 19 389.

20 - 29 461,

30 - 39 293.

40 - 49 178.

50 - 59 71.

60 - 69 29.

70 - 79 17.

80 - 89 12.

90 - 99 6.

100 and Up 8.

Blood

Lead Level

0-9

10 - 19

20 - 29

30 - 39

40 - 49

50 - 59

60 - 69

70 - 79

80 - 89

90 - 99

100 and Up

%

Initial Tests

14.3

22.5

26.7

17.0

10.3

4.1

1.7

1.0

.7

.3

.5

Blood Number of

Lead Level Retests

0-9 0.

10 - 19 3.

20 - 29 1.

30 - 39 5.

40 - 49 6.

50 - 59 4.

60 - 69 0.

70 - 79 0.

80 - 89 0.

90 - 99 0.

100 and Up 0.

Blood %

Lead Level Retests

0-9 .0

10 - 19 15.8

20 - 29 5.3

30 - 39 26.3

40 - 49 31.6

50 - 59 21.1

60 - 69 .0

70 - 79 .0

80 - 89 .0

90 - 99 .0

100 and Up .0

74
has a higher mean than that for first time tests. Table 19 contains not

only (on the right) the percentage distribution of blood lead levels, but

also (on the left) the actual numbers found in each category. While

percentages do not seem to have a great impact on our emotions, the observation that 26 children tested in Aurora had blood lead levels greater

than 80 yg/100 ml, which the Surgeon General recommends as the level

at which the child be considered lead poisoned and possibly suffering

irreparable brain damage, conveys the enormity of the problem more

graphically. One of these children has died. We should remember that

Aurora is only a small city, and that there are many others like it with

no programs to screen for or treat lead poisoning. In addition to

the 26 children with defined lead poisoning cases, some of the 46 children

with levels between 60 yg/100 ml and 80 yg/100 ml may be exhibiting other

28

symptoms and may have suffered brain damage.

Table 20 gives a breakdown of the children with EBL by first time

tested versus retests. Again, both the absolute numbers as well as the

percentages are recorded, as they will be in succeeding tables. Only

1.1 percent of all tests were retests, and only children whose first test

indicated an EBL were retested. Almost half of the children's blood levels

were below 40 yg/100 ml on retest. whether this is because of treatment,

inexactitude in one of the tests, or increased surveillance by a parent is not

known. It means however, that slightly more than half have levels still above

40 yg/100 ml and thus remain in danger. This agrees with findings in other

cities that indicate high incidence of recurrence of EBL if the child is

returned to a contaminated environment. Aurora authorities ran their

75
Table 20

Breakdowns of EBL's by Tests and Retests

Testing

Number

Under

40

Number

40 and

Above

Total

First Time

Retests

1390.

9.

318.

10.

1708.

19.

Testing

%

Under

40

%

40 and

Above

Total

First Time

Retests

81.4

47.4

18.6

52.6

98.9

1.1

The percentages in the right hand column refer to the percent of all

children tested falling into each of the categories "First Time" and

"Retests".

76
Screening program virtually on a shoestring, and were thus able to do no

more about the environment in which the children lived than to warn parents

of the danger. Only one house, that of the child who died, was deleaded.

It is not known if there are other cases in which a landlord or homeowner,

informed of the danger by the screening program, deleaded a property on

his own initiative.

Table 21 lists the number of EBL's found in each census tract.

Figure 3 is a map of Aurora showing the geographic locations of all

sixteen of these Census tracts, with those having high EBL rates filled

in with diagonal lines. As can be seen from the table, the percentage of

children with EBL varies from a low of 8.1 in tract 8543 to a high of 28

in tract 8532. However, the tract with the lowest absolute number of

children with EBL is 8537, a downtown area with few residences. The

highest number of EBL's (64) is found in 8534. Other tracts with many

EBL's are 8540 with 50 and 8536 with 48.

Table 22 gives the breakdown of EBL's by sex of child. The Aurora

screening program tested girls and boys in almost equal numbers and the

fractions of those tested having EBL are almost the same. From this

table one can conclude that lead poisoning is a disease afflicting both

males and females, with very little difference in the incidence rates

for the two, a fact which agrees with findings elsewhere.

Table 23 gives the breakdown of EBL's by age of the child. Aurora

screened approximately equal numbers of children in each of the yearly age

ranges 1 to 6. (Only about 4 percent of the children screened were 7 or 8.)

77
Table 21

Breakdowns of EBL's by Census Tract

Number Number

Tract Under 40 and Total

40 Above

8529 148. 25. 173.

8530 64. 9. 73.

8531 15. 5. 20.

8532 72. 28. 100.

8533 43. 6. 49.

8534 219. 64. 283.

8535 85. 11. 96.

8536 177. 48. 225.

8537 4. 1. 5.

8538 38. 11. 49.

8539 34. 7. 41.

8540 190. 50. 240.

8541 63. 19. 82.

8542 51. 7. 58.

8543 39. 3. 42.

8544 145. 27. 172.

Total 1387. 321. 1708.

% %

1

Tract Under 40 and Total

40 Above

8529 85.5 14.5 10.1

8530 87.7 12.3 4.3

8531 75.0 25.0 1.2

8532 72.0 28.0 5.9

8533 87.8 12.2 2.9

8534 77.4 22.6 16.6

8535 88.5 11.5 5.6

8536 78.7 21.3 13.2

8537 80.0 20.0 .3

8538 77.6 22.4 2.9

8539 82.9 17.1 2.4

8540 79.2 20.8 14.1

8541 76.8 23.2 4.8

8542 87.9 12.1 3.4

8543 92.9 7.1 2.5

8544 84.3 15.7 10.1

Total 81.2 18.8

7.8
Figure 3

Map of Aurora Census Tracts Having High EBL Rates

8544

EBL INCIDENCE RATE >

THE AVERAGE RATE (18.8%)

EBL INCIDENCE RATE <

THE AVERAGE RATE (18.8%)

79
Table 22

Breakdowns of EBL's by Sex of Child

Sex

Number

Under

40

Number

40 and

Above

Total '<

Male

Female

706.

683.

176.

145.

882.

828.

Sex

%

Under

40

%

40 and

Above

Total

Male

Female

80.0

82.5

20.0

17.5

51.6

48.4

80
Table 23

Breakdowns of EBL's by Age of Child

Number Number

Age Under 40 and Total

40 Above

1 192. 40. 232.

2 231. 69. 300.

3 243. 60. 303.

4 212. 51. 263.

5 239. 50. 289.

6 202. 39. 241.

7 50. 9. 59.

8 5. 0. 5.

% %

Age Under 40 and Total

40 Above

1 82.8 17.2 13.7

2 77.0 23.0 17.7

3 80.2 19.8 17.9

4 80.6 19.4 15,5

5 82.7 17.3 17.1

6 83.8 16.2 14.2

7 84.7 15.3 3.5

8 100.0 .0

i J-

.3

81
The EBL rates for each of the ages 1 to 6 are approximately the same, with

only a slightly higher rate for 2 year olds. This differs from results

reported by other programs, which found relatively many more children in

the 2 to 3 year old range to have EBL's. The reason for the discrepancy is

not known.

Table 24 gives the breakdowns of EBL's by race or ethnic origin of

the child. Black children have the highest rate, 24.2 percent. Spanish

surnamed children have the intermediate rate of 20.6 percent. White

children are lowest with 14.2 percent. The Aurora sample contained a

larger proportion of black and Spanish surnamed children than would be

found in the population as a whole. This is undoubtedly because the

program sought to test all high risk children (i.e., those living in poor

quality housing) , and it is known that these minority groups make up a

disproportionate fraction of the lower income groups living in such housing.

Table 25 gives the breakdowns of EBL's by whether or not the child has a

family doctor. This table is included to test the hypothesis that a child

who has regular contact with the medical establishment has less chance to

have an EBL. As can be seen clearly, however, this is not true in

Aurora. The EBL rates are almost identical whether or not the child has

a family doctor. The most surprising statistic to emerge from this table

is that over 90 percent of the children were reported as having a family

doctor. The actual figure may not in fact be quite so high. In at least

two cases the mother noted on the form that the doctor listed was "mother's

baby doctor". Also, since treatment of EBL's was done by local physicians,

we believe that mothers were urged to write down the name of the doctor

they would take their child to if he had an EBL.

82
Table 24

Breakdowns of EBL's by Race or Ethnic Origin

-"—

Number Number

Race Under 40 and Total

40 Under

White 647. 107. 754.

Black 338. 108. 446.

Spanish 389. 101. 490.

Race

%

Under

40

%

40 and

Above

Total

White

Black

Spanish

85.8

75.8

79.4

14.2

24.2

20.6

44.6

26.4

29.0

83
Table 25

Breakdowns of EBL's by Whether or Not the Child Has a Family Doctor

Doctor

Number

Under

40

Number

40 and

Above

Total

Yes 1242. 289. 1531.

. k

No 130. 29. 159.

Doctor

%

Under

40

%

40 and

Above

Total

Yes

No

81.1

81.8

18.9

18.2

90.6

9.4

84
The 90 percent figure is suspiciously high, but even a figure of 70

or 80 percent would indicate a greater contact of these small city

families with the regular medical profession than that of large city poor

families. Increasing the doctors' awareness of the danger posed by lead

paint and of the symptoms of lead poisoning may by itself provide a great

payoff in lead poisoning control.

Table 26 gives the breakdown of EBL's by duration of the family's

residence at its present address. It has been speculated that poorer

families are more transient. Therefore it was hoped that transiency rates

could be used to distinguish those with high EBL rates. However Table 26

shows that there is not a great deal of difference in EBL rate among the

various lengths of residence, and the differences which exist do not form

the pattern expected under the above hypothesis. Why the highest rate,

27.1 percent, should be for those who have lived at their present address

for four years is not at all clear, when the corresponding rate for three

years is 20.8 percent and for 5 years is 19.0. The low rate of 16.5 for

those living at the same address 5 or more years is in the direction expected, but is not different enough* to be statistically significant. The

figures in Table 26 may just reflect the fact that frequent changes of

residence are characteristic of life today. One demurrer must be placed

on these statistics, however. Many of the forms only recorded the address

of the family back to the child's birth (only up to 3 years ago, for

instance, for a 3 year old child) and left the other spaces blank. If

the address was the same as the present address, the family was usually
Table 26

Breakdowns of EBL's by Time of Residence at the Present Address

-———

Time of Residence

at this Address

Number

Under

40

Number

40 and

Above

Total

Less than 1 month

Less than 6 months

Less than 1 year

Less than 2 years

Less than 3 years

Less than 4 years

Less than 5 years

At least 5 years

47.

161.

137.

143.

99.

70.

34.

405.

12.

50.

36.

45.

26.

26.

8.

80.

59.

211.

173.

188.

125.

96.

42.

485.

Time of Residence

at this Address

%

Under

40

! %

40 and

Above

Total

Less than 1 month 79.7 20.3 4.3

Less than 6 months 76.3 23.7 15.3

Less than 1 year 79.2 20.8 12.5

Less than 2 years 76.1 23.9 13.6

Less than 3 years 79.2 20.8 9.1

Less than 4 years 72.9 27.1 7.0

Less than 5 years 81.0 19.0 3.0

At least 5 years 83.5 16.5 35.2

86
coded as having lived there at least 5 years since there was no other

address listed. This practice may have led to the high percentage of

those tested recorded as living at their present address "at least 5 years",

and low percentages for less than 4 or 5 years.

Table 27 gives the breakdown of EBL's by whether the family owns or

rents its home. In the larger cities a high percentage of the population

at all income levels rents housing. In a smaller city one would expect

that a smaller percentage rents, and that th£ poor are more likely to rent

than those more affluent. Thus lead poisoning may he higher among renters.

The Aurora sample of high risk children is divided almost evenly among

renters and owners. The EBL rates are almost the same for the two categories, and thus it is not the case that children of renters show a higher

probability of contracting EBL.

Table 28 gives the breakdown of EBL's by whether or not the family receives some form of public aid. The most common kinds of aid listed were food

stamps and ADC (Aid to Dependent Children), commonly called welfare. This

was the only item on the forms which was specifically related to income,

since a family must be in the lowest income bracket to receive public aid.

As expected, those on an aid program had a much higher probability of

having an EBL than those not on aid. A more detailed breakdown of income

and EBL would have been desirable, but it is clear from Table 28 that

those below the poverty level have higher EBL rates than even other high

risk children. This may be modified somewhat in Aurora in the future

since a new public housing project, presumably not containing lead paint,

87
Table 27

Breakdowns of EBL's by Whether the Family Owns or Rents Its Home

Tenancy

Number

Under

40

Number

40 and

Above

Total

Own

Rent

530.

542.

124.

151.

654.

693.

Tenancy

%

Under

40

%

40 and

Above

Total

Own

Rent

81.0

78.2

19.0

21.8

48.6

51.4

88
Table 28

Breakdowns of EBL's by Whether or Not the Family Receives Public Aid

Public Aid

Number

Under

40

Number

40 and

Above

Total

On Aid

Not

142.

939.

53.

224.

195.

1163.

Public Aid

%

Under

40

%

40 and

Above

Total

On Aid 72.8 27.2 14.4

Not 80.7 19.3 85.6
has recently been opened. However, until the units formerly occupied by

these new public housing residents are deleaded, the hazard will still exist

for whoever will live in those units.

Table 29 gives a breakdown of EBL's by the condition of the house

occupied by the child. The conditions listed are "(1) very poor, paint is

chipped and peeling on doors, windowsills, and/or on the outside of the

house, (2) not too bad, some peeling, could easily be improved, and (3)

good, walls are firmi no chipping or peeling on outside or inside of

house". The responses are of course subjective assessments by the

residents of their own housing. What one family might term as good,

because it was much better than their previous housing, another might

think is only fair. A homeowner might believe some item could be easily

repaired and therefore term his house in fair condition, while a renter,

who has to rely on someone else to fix the item, might feel it was more

difficult to repair and thus term the same home in poor condition. However,

even taking into account the subjectivity of this categorization, there

is a great difference (a factor of 2) in the EBL rates for housing termed

good versus that termed fair or poor: This is certainly a corroboration

of the basic premise of the model, that children living in housing in

poor condition suffer a higher risk of EBL.

The dependence of EBL incidence on housing condition also increases confidence in the assumption on which all our work has been based, that the

primary cause of pediatric lead poisoning is the ingestion of leaded

paints, since the total body burden of lead (as measured by that in the

90
Table 29

Breakdowns of EBL's by the Condition of the Housing Unit Occupied by the Family

Number Number

Housing Condition Under 40 and Total

40 Above

Poor 9. 2. 11.

Fair 90. 24. 114.

Good 204. 24. 228.

Housing Condition

%

Under

40

%

40 and

Above

Total

Poor

Fair

Good

81.8

78.9

89.5

18.2

21.1

10.5

3.1

32.3

64.6

91
blood) is associated with the condition of the unit in which the child

resides.

Table 30 gives a breakdown by family size of the number of children

in the same family with EBL's. This table shows that as the number of

children in a family goes up, the probability that at least one child in

the family will have an EBL also rises (except for a slight decrease in

5 children families), which would be expected even if EBL's do not tend

to run in families. If the tendency of EBL to run in families were perfect, i.e., if all children in a family had EBL whenever any one of them

did, the table would be as follows.

1 —

1 2 3 4

1 471 109 - - -

2 245 57 - -

3 99 23 -

4 25 6

(We have omitted 5 children families because there are too few of them to

generalize from.) This is not the case in Table 30, since non-zero

entries occur. At the opposite extreme, if there were no such tendency

at all and the probability that a child had an EBL were the average .188

observed for all children, the numbers occurring in Table 30 (omitting

5 children families as above) would be as follows (on page 94)

92
Table 30

Number of Families of Each Family Size Having a Given Number of

Children with Elevated Blood Lead Levels

Family Size

(Children 7 and Under)

Number of Children in the Same Family

With Elevated Blood Lead Levels

1 2 3 4 5 All

1

2

3

4

5

471.

196.

72.

17.

4.

109.

91.

36.

10.

1.

15.

11.

4.

2.

3.

0.

0.

0.

0. 0.

580.

302.

122.

31.

7.

Percent of Families of Each Family Size Having a Given Number of

Children with Elevated Blood Lead Levels

Family Size

(Children 7 and Under)

Number of Children in the Same Family

With Elevated Blood Lead Levels

1 2 3 4 5 All

1

2

3

4

5

81.2

64.9

59.0

54.8

57.1

18.8

30.1

29.5

32.3

14.3

5.0

9.0

12.9

28.6

2.5

.0

.0

.0

.0 .0

55.7

29.0

11.7

3.0

.7

93
1 2 3 4 All

1 471 109 - - - 580

2 199 92 11 - - 302

3 65 45 11 1 - 122

4 13 13 4 1 31

The differences between those observed in Table 30 and those expected

under the assumption that EBL's do not run in families are displayed

below. These differences are all quite small.

1 2 3 4

1 - - -

2 -3 -1 4 - -

3 7 -9 2 -

4 4 -3 -1

29

A Chi-square test ' was performed separately on each of rows 1, 2, and 3

(the last two entries in row 4 of Table 30 are too small for the test to

be applicable there) to test if, for families of a fixed size, the tendency

toward EBL runs in the family. This was strongly not the case for each of

the family sizes 1 child, 2 children, and 3 children 7 years or younger.

The fact that EBL's do not run in families means that testing and

deleading programs cannot take advantage of the spatial concentration of

children in families. If EBL's ran in families, one could screen

94
families by testing only one child in the family. Furthermore, for a

given EBL rate, fewer units would require deleading. But since the

probability that a child has an EBL does not depend significantly on

whether others in his family have EBL's, screening programs must screen

as many children as possible, regardless of the number of families they

represent. The number of units to be deleaded is approximately the

number of EBL's found divided by the average number of children in the

same family in the applicable age range.

Chi square tests, designed to reveal which factors are statistically

associated with EBL, were run on the 10 factors shown in Tables 20 through

29 and discussed above. Table 31 summarizes the results of these tests.

The significant difference for retests stems from the fact, noted in the

discussion of Table 20, that children were retested only if they had an

EBL the first time. Thus without a deleading of the child's environment

one would expect retests to have higher blood lead levels than the

ordinary population. The significant difference for the "census tract"

factor reassures us that the modeling effort is indeed feasible, for if

blood lead levels did not differ from one tract to the next, one could

never hope to calibrate a model, i.e., one cannot model differences in

EBL rates among census tracts if such differences do not exist.

The other three items which turn out to be statistically significant

also reinforce our confidence in the model which has been developed.

Housing condition has proved to be significant, and being a member of a

minority group or on public aid are closely associated with income level,

95
Table 31

Results of Chi Square Tests

Item Chi

Square

Degrees of

Freedom

Significance

Level*

Retest 14.12 1 .01

Census Tract 27.35 15 .05

Sex 1.67 1 -

Age 7.25 7 -

Race 20.19 2 .01

Doctor .04 1 -

Transiency 10.08 7 -

Tenure 1.66 1 -

Public Aid 6.45 1 .05

Housing 7.08 2 .05

The observed value of Chi-square will be exceeded with probability

less than the significance level indicated if in fact there is no difference for the factor considered.

96
which determines who lives in housing in poor condition. Such factors

as the age of the child (note that all children tested were under 8 and

most were 6 and under, so we are not referring to older children), the

child's sex, whether the child has a family doctor, how long the family

has resided at its present address, or whether the family owns or rents

its home are not statistically significant, although at first thought

most of these would be suspected to have some influence on EBL. The

emphasis on poor housing to the exclusion of other factors, discovered in

the course of the calibration on New Haven data, is confirmed by the data

from Aurora. The Aurora data are particularly useful for this kind of

test, since one could associate the particular characteristics of one

child with his blood lead level.

The analysis of the Aurora data generally supports the assumptions

built into the lead poisoning model developed by NBS. This analysis

has also increased our knowledge of factors associated with EBL, even if

only by ruling out some plausible associates. Such negative results

help us focus on the major factors: poor housing condition and low income,

4.3. Validation of Quantitative Model Performance

A second part of the validation process will be reported in this

section: the checking of model-predicted EBL frequencies against those

discovered in the Aurora screening program. Table 32 gives the number of

EBL's found in the Aurora screening effort and those predicted by each of

the two models, Model 1 and Model 1A, described in Chapter 3. As can be

seen from a glance at this table, there is substantial agreement between

97
Table 32

EBL's by Census Tract from the Aurora Data

and as Predicted by Model 1 and Model 1A

| Census Aurora Model 1 Model 1A

Tract Screening Predicted EBL Predicted EBL

8529 25 30 26

8530 9 30 27

8531 5 6 5

8532 28 15 14

8533 6 5 4

8534 64 48 57

8535 11 24 27

8536 48 42 51

8537 1 1 1

8538 11 16 19

8539 7 10 8

8540 50 31 31

8541 19 23 26

8542 7 7 6

8543 3 9 7

8544 27 22 22

Total 321 319 331

98
the predictions of the two models for each census tract. In addition, the

"highs" and "lows" of the model predictions agree quite well with those

of the EBL 1 s actually found in the program. To confirm this, a statistical

30

test associated with Kendall's rank correlation coefficient tau was performed, leading to a 99 percent confidence level for the agreement between

the predictions of Model 1 and also of Model 1A with the Aurora data.

Since this statistical test only compares the rank orders within the two

columns, one might still question the agreement, were it not for the fact

that there is only about 3 percent difference in the total predicted by

Model 1A and that actually found, and Model 1 differs by less than 1 percent.

The degree of agreement between predicted EBL's and those actually found is

in our opinion astonishing, in view of the many problems known to exist

with the data.

Table 33 gives the percentage of high risk children predicted by

Model 1A to have EBL, and the percentage of the screened children with

EBL. These two columns of numbers are not at all in the same kind of

agreement as those in Table 32. Even the overall percentages differ by 5

percentage points. Thus in spite of the fact that the number of children

found with EBL agrees with the number predicted by either Model 1 or Model 1A,

the fractions of high risk children with EBL as calculated by those models

do not agree with the fractions observed in children screened. An explanation for the fact that the number of EBL's are in agreement but the

percentages of high risk children predicted and observed do not agree,

lies in the equating of high risk children with those screened. Table 34

gives the number of children screened in each census tract and the number

99
Table 33

Comparison of Percent of High Risk Children

Found with EBL with Those Predicted by Model 1A

Census

Tract

% EBL as Found

% EBL as Predicted By

Model 1A

"

8529 14.4 20.7

8530 12.3 22.0

8531 25.0 19.9

8532 28.0 21.9

8533 12.2 18.9

8534 22.6 28.8

8535 11.4 27.4

8536 21.1 29.5

8537 20.0 23.4

8538 22.2 28.0

8539 17.0 18.3

8540 20.8 24.1

8541 23.1 26.6

8542 12.0 19.4

8543 7.1 18.3

8544 15.7 24.6

Total 18.8 24.9

100
Table 34

Children Tested in the Aurora Screening Program

Versus Children at Risk as Calculated by the Models

Census Children Children Difference

Tract Tested At Risk (Tested -Predicted)

8529 173 124 49

8530 73 124 - 51

8531 20 27 - 7

8532 100 64 36

8533 49 20 19

8534 283 199 84

8535 96 98 - 2

8536 227 174 53

8537 5 6 - 1

8538 49 69 - 20

8539 41 42 - 1

8540 240 127 113

8541 82 96 - 14

8542 58 26 32

8543 42 39 3

8544 172 91 81

total 1710 1326 470 - 96 = 374

101
of high risk children as predicted by the models. The model regards

high risk children as those living in unsound (i.e., dilapidated or

deteriorating) housing and calculates the number of them as

where K, is the number of children six years old or less,

D is the number of unsound housing units, and

H is the total number of housing units.

The Aurora screening program defined high risk children as those

living in poor neighborhoods which contain poor housing. As discussed in

section 4.2.1, it is impossible for a program such as that in Aurora

to draw boundaries to specify exactly which children will be tested.

Survey workers cannot agree to test one child because he lives in poor

housing and refuse to test a neighbor because his house is in better

condition. They can choose a blood sampling site to maximize their chances

of at least testing all children living in housing presenting a lead hazard,

but then must test all children who ask to be tested. They can also

concentrate announcements of the test in areas of greatest risk. Table 34

shows that 374 more children were tested than were predicted to be high

risk by the models. If these are actually children living in good housing

and thus less likely to have EBL's, this would explain the lower rates

observed

4.4. Conclusion

The full validity of the models is not proved by the analysis of the

data from Aurora. As noted in section 4.1, this would not have been

102
possible to achieve anyway, since we still do not have information concerning EBL rates in such areas of the United States as the South and West.

The analysis of the Aurora data has, however, enhanced the believability of the models in two ways. Analysis of various factors which

could be associated with lead poisoning shows that the major factor is

poor housing. This was the main assumption upon which the models were

built, and the analysis has corroborated that assumption. The second

finding which supports the acceptability of the models is the remarkable

agreement between the number of EBL's predicted and those actually found.

This indicates that at least for Aurora the models could only be underpredicting the number of children with EBL, since the number predicted has

already been found and only 12 percent of all children have been tested.

The models included rates only for high risk children since little information is yet available on other groups. Such data are now obviously

needed to obtain better estimates of the magnitude of the problem in all

segments of the population. In addition to the agreement between the total

EBL's predicted and found, there was surprising agreement between the two

for each census tract.

Thus, although the results of this validation exercise do not conclusively prove the validity of Model 1 and Model 1A, they have increased

our confidence both in the order of magnitude of the estimates and in their

relative sizes for different SMSA's. Further information, including data

from screening in the South and West, data to determine an accurate description of the children at risk, and data on non-high risk EBL's, is

needed to be able more fully to assure the validity of the models.

103
5. NATIONAL ESTIMATES BY SMSA

This chapter is devoted to the presentation of the model outputs. Care

should be taken in interpreting these outputs, in light of the modeling

problems described in Chapter 3 and the incompleteness of validation to

date as described in Chapter 4. In particular, estimates for SMSA's in the

South and West are based on a model which has not been validated for those

31 areas. It is believed by many experts that lead poisoning incidence is

lower in these regions than in the East and Midwest, so that the estimates

given in this report (and based on data from only the East and Midwest)

may be too high for SMSA's in the South and West.

The estimates given below only apply to SMSA's (which are large and

medium metropolitan areas) representing approximately 68% of the total

population and housing in the United States. Our cautious disclaimers

notwithstanding, there is some evidence to suggest that the incidence of

EBL in small cities, which are not large enough to quality as SMSA's, is

similar to or only slightly lower than that of the larger metropolitan

areas with similar housing characteristics. At present there is no information available concerning EBL rates for children living in rural areas.

32

At least one study has reported lower average blood lead levels for rural

children, which (if the standard deviation for the distribution of rural

blood lead levels is similar to that for urban ones) would lead one to

expect a lower EBL rate for rural children. Thus the total EBL's as

summed over all SMSA's will be less than the nationwide incidence by an

unknown amount. However, if one takes into account the probable overestimation of model-calculated EBL's in the Southern and Western SMSA's

noted above, the SMSA totaled EBL's (about 600,000) may be a fairly good

104
national estimate of the number of children aged 1 to 6 who currently have

EBL's. This estimate does not include either those who have had an EBL

and are suffering the effects (but do not now have EBL) or those who will

(without remedial action) first develop EBL's in the future.

Table 35 lists for each SMSA the number of children 6 years of age

and under (according to the 1970 Census), the estimated number of high

risk children (those living in dilapidated or deteriorating housing) and

the number of EBL's estimated by the models 1 and 1A described in Chapter

3. These numbers have all been rounded to the nearest hundred to indicate

that the model estimations are only approximate. At the end of the table,

33 totals over 241 SMSA's are listed, showing about 17 million children

6 years of age or less, an estimated 2 and a half million high risk

children among them, and about 600,000 EBL's to be expected in these SMSA's.

Figure 4 shows the 25 SMSA's (also listed in Table 36) which have the

greatest predicted incidence of EBL according to model 1. Table 36 also

lists the population rank (with number 1 being the largest) or each of

the 25 SMSA's, to emphasize the fact that the rank of a city as determined

by the estimated number of EBL's is due in large part to its population

rank. Only 4 of the top 25 SMSA's in EBL are not in the top 25 SMSA's in

population.

Table 37 gives estimates of the number of children, in each SMSA,

with blood lead levels of 40 yg/100 ml or more, 50 or more, 60 or more,

and 70 or more. These estimates were calculated using Model 1, from

Chapter 3, with the values of 1^. obtained as the observed fractions of

children screened falling into these categories in the 1970 New York

screening program. The relevant percentages aggregated from Table 4 in

Chapter 2 are 28.9% for 40 and above, 12.7% for 50 and above, 6% for 60

105
Table 35

EBL's Predicted by the Models for Each SMSA

SMSA NAME Children

6 and Under

Children

At Risk

Model 1 EBL Model 1A EBL Abilene, Texas 13200 2800 700 700

Akron, Ohio 83700 11500 2800 2800

Albany, Georgia 13500 4400 1100 1400

Albany-Schenectady-Troy , N.Y. 86600 14100 3400 3600

Albuquerque, N.Mex. 42300 5200 1300 1300

Allentown-Bethlehem-Easton, Pa. -N.J. 59900 7200 1700 1700

Altoona, Pa. 15400 4200 1000 1200

Amarillo, Tex. 17800 2800 700 700

Anaheim -Santa Ana-Garden Grove, Cal. 184600 12600 3000 2700

Anderson, Ind. 18100 4200 1000 200

Ann Arbor, Mich. 27700 3600 900 900

Appleton-Oshkosh, Wise. 37200 5800 1400 1500

Asheville, N.C. 16800 4600 1100 1300

Atlanta, Ga. 185300 33700 8100 9100

Atlantic City, N.J. 19500 2500 600 600

Augusta, Ga. 31800 8600 2100 2500

Austin, Tex. 35400 6700 1600 1800

Bakersfield, Cal. 42300 9100 2200 2500

Baltimore, Md. 255600 32600 7800 7900

Baton Rouge, La. 38400 7800 1900 2100

Bay City, Mich. 16300 3000 700 800

Beaumont-Port Arthur-Orange, Tex. 38300 8500 2000 2300

Billings, Mont. 10400 2500 600 700

Biloxi-Gulfport , Miss. 17600 5600 1400 1700

Binghampton, N.Y. 38500 5600 1300 1400

Birmingham, Ala. 86400 22700 5500 6400

Bloomington-Normal , 111. 11500 2900 700 800

Boise City, Ida. 13800 2600 600 700

Boston, Mass. 316900 39100 9400 9100

Bridgeport, Conn. 46100 4800 1100 1100

Bristol, Conn. 8700 1100 300 300

Brockton, Mass. 27500 3300 800 800

Brownsville-Harlingen-San Benito, Tex. 21800 7700 1800 2600

Bryan-College Station, Tex. 7600 2400 600 800

Buffalo, N.Y. 163000 21900 5200 5300

Canton, Ohio 45700 8000 1900 2100

Cedar Rapids, Iowa 22800 3600 700 900

Champaign-Urbana, 111. 18400 2600 600 600

Charleston, S.C. 43100 12000 2900 3600

Charleston, W. Va. 25000 6200 1500 1700

Charlotte, N.C. 53900 10500 2500 2800

Chattanooga, Tenn. 36500 9300 2200 2600

106
Table 35 Continued

SMSA NAME Children

6 and Under

Children

At Risk

Model 1 EBL Model 1A EBL Chicago, 111. 872500 100300 24100 23600

Cincinnati, Ohio-Ky. -Ind. 179700 26700 6400 6800

Cleveland, Ohio 250700 26500 6400 6100

Colorado Springs, Col. 30300 4000 900 1000

Columbia, Missouri 9100 2100 500 600

Columbia, S.C. 38700 8800 2100 2400

Columbus, Ga. 31300 8400 2000 2500

Columbus, Ohio 116000 19600 4700 5100

Corpus Christi, Tex. 40400 9300 2200 2700

Dallas, Tex. 211800 35600 8600 9500

Davenport-Rock Island -Moline, Iowa-Ill. 47600 7900 1900 2100

Dayton, Ohio 108100 13300 3200 3200

Decatur, 1-1. 15700 2600 600 700

Denver, Colo. 154700 16900 4100 3900

Des Moines, Iowa 35600 6900 1600 1900

Detroit, Mich. 546200 59100 14100 13900

Dubuque, Iowa 13600 3300 800 1000

Duluth-Superior , Minn. -Wise. 29600 5600 1400 1500

Durham, N.C. 21900 4800 1200 1300

El Paso, Tex. 52800 10100 2400 2800

Erie, Pa. 33800 5200 1200 1300

Eugene, Ore. 24700 4300 1000 1100

Evansville, Ind.-Ky. 26800 5200 1300 1400

Fall River, Mass. -R.I. 18000 2500 600 600

Fargo-Morehead, N. Dak. -Minn. 14000 2300 500 600

Fayetteville, N.C. 29300 9800 2300 3100

Fitchburg-Leominster , Mass. 12000 3000 700 900

Flint, Mich. 73000 10900 2600 2900

Fort Lauderdale-Hollywood, Fla. 60700 4500 1100 900

Fort Smith, Ark.-Okla. 19800 8100 1900 2600

Fort Wayne, Ind. 38000 5500 1300 1400

Fort Worth, Tex. 99600 16700 4000 4400

Fresno, Cal. 51800 11300 2700 3100

Gadsden, Ala. 11200 4300 1000 1300

Gainesville, Fla. 12400 3800 900 1100

Galveston-Texas City, Texas 21300 4900 1200 1400

Gary-Hammond-East Chicago, Ind. 84700 13000 3100 3400

Grand Rapids, Mich. 72700 9800 2400 2500

Great Falls, Mont. 10700 3100 700 900

Green Bay, Wise. 23300 3100 800 800

Greensboro-Winston Salem-High Point, N.C. 73500 14500 3500 3900

Greenville, S.C. 37000 9200 2200 2600

Hamilton-Middletown, Ohio 28100 4000 1000 1000

Harrisburg, Pa. 46100 5700 1400 1300

107
Table 35 Continued

SMSA NAME Children

6 and Under

Children

At Risk

Model 1 EBL Model 1A EBL Hartford, Conn. 81900 7200 1700 1600

Honolulu, Hawaii 84900 18100 4300 5100

Houston, Tex. 273300 40300 9700 10400

Huntington-Ashland, W. Va. -Ky . -Ohio 29200 7700 1900 2100

Huntsville, Ala. 32800 7200 1700 2100

Indianapolis, Ind. 145900 21900 5300 5600

Jackson, Mich. 18300 3200 800 800

Jackson, Miss. 34600 8000 1900 2300

Jacksonville, Fla. 66200 13700 3300 3700

Jersey City, N.J. 66600 10300 2500 2500

Johnstown, Pa. 29000 7300 1700 2000

Kalamazoo, Mich. 24500 3600 900 900

Kansas City, Mo. -Kansas 155500 24100 5800 6100

Kenosha, Wise. 15700 2100 500 500

Knoxville, Tenn. 45200 10800 2600 3000

La Crosse, Wise. 9400 2300 500 600

Lafayette, La. 16100 4800 1200 1500

Lafayette, West Lafayette, Ind. 13100 3000 700 800

Lake Charles, La. 20100 4100 1000 1200

Lancaster, Pa. 39700 4700 1100 1100

Lansing, Mich. 50700 7800 1900 2000

Laredo, Tex. 12000 6800 1600 2600

Las Vegas, Nev. 37800 3100 800 700

Lawrence-Haverhill, Mass.-N.H. 29300 5000 1200 1300

Lawton, Okla. 13600 2900 700 800

Lewiston-Auburn, Maine 9100 2300 500 700

Lexington, Ky. 21600 4200 1000 1100

Lima, Ohio 22800 4500 1100 1200

Lincoln, Neb. 18900 3800 900 1000

Little Rock-North Little Rock, Ark. 40700 8800 2100 2400

Lorain-Elyria, Ohio 36400 4600 1100 1200

Los Angeles-Long Beach, Cal. 839100 60900 14600 12600

Louisville, Ky.-Ind. 103300 16900 4100 4300

Lowell, Mass. 31600 4600 1100 1200

Lubbock, Tex. 24000 4500 1100 1200

Lynchburg, Va. 14500 3700 900 1100

Macon, Ga. 27100 8000 1900 2400

Madison, Wise. 36300 3800 900 900

Manchester, N.H. 13100 2600 600 700

Mansfield, Ohio 16600 3800 900 1100

McAllen-Pharr-Edenburg, Tex. 28900 9900 2400 3300

Memphis, Tenn. -Ark. 101000 18800 4500 5100

Meriden, Conn. 6900 1000 200 300

Miami, Fla. 127300 10700 2600 2200

108
Table 35 Continued

SMSA NAME Children

6 and Under

Children

At Risk

Model 1 EBL Model 1A EBL Midland, Tex. 8100 1500 400 400

Milwaukee, Wise. 177600 17600 4200 4000

Minneapolis-St . Paul, Minn. 244400 27900 6700 6700

Mobile, Ala. 50300 14000 3400 4200

Modesto, Cal. 24200 3100 700 800

Monroe, La. 15900 5100 1200 1600

Montgomery, Ala. 26000 7200 1700 2100

Muncie, Ind. 16400 2900 700 700

Muskegon-Muskegon Heights, Mich. 20900 3500 800 900

Nashua, N.H. 10200 1800 400 500

Nashville, Tenn. 63700 13000 3100 3500

New Bedford, Mass. 16800 2000 500 500

New Britain, Conn. 16900 1700 400 400

New Haven, Conn. 40800 5400 1300 1300

New London-Groton-Norwich, Conn. 27800 4500 1100 1200

New Orleans, La. 140300 30100 7200 8500

New York, N.Y. 1316700 169100 40600 39800

Newark, N.J. 222000 28500 6800 6800

Newport News -Hampton, Va. 38700 6900 1700 1800

Norfolk-Portsmouth, Va. 86300 15200 3600 4000

Norwalk, Conn. 14300 1500 400 300

Odessa, Tex. 12200 2600 600 700

Ogden, Utah 17600 2500 600 600

Oklahoma City, Okla. 78300 13900 3300 3600

Omaha, Nebr.-Iowa 73400 11400 2700 3000

Orlando, Fla. 51600 8000 1900 2000

Owensboro, Ky. 10600 2300 500 600

Oxnard-Ventura, Cal. 53500 8300 2000 2200

Paterson-Clifton-Passaic, N.J. 150500 11400 2700 2300

Pensacola, Fla. 31300 7500 1800 2100

Peoria, 111. 42700 6800 1600 1700

Petersburg-Colonial Heights, Va. 15900 6500 1600 2100

Philadelphia, Pa. -N.J. 578900 60100 14400 13600

Phoenix, Ariiz. 122300 18500 4400 4700

Pine Bluff, Ark. 11700 6000 1400 2100

Pittsburgh, Pa. 259900 43900 10500 10900

Pittsfield, Mass. 9700 1700 400 400

Portland, Maine 16700 2100 500 500

Portland, Ore. -Wash. 116000 17300 4100 4200

Providence-Pawtucket -Warwick, R.I. -Mass108400 13400 3200 3200

Provo-Orem, Utah 20200 2600 600 700

Pueblo, Colo. 14200 3500 800 1000

Racine, Wise. 23500 2900 700 700

109
Table 35 Continued

SMSA NAME Children

6 and Under

Children

At Risk

Model 1 EBL Model 1A EBL Raleigh, N.C. 27900 6600 1600 1900

Reading, Pa. 32000 4100 1000 900

Reno, Nev. 14000 2400 600 600

Richmond, Va. 61300 8400 2000 2000

Roanoke, Va. 20600 2900 700 700

Rochester, Minn. 12300 2200 500 600

Rochester, N.Y. 115800 12700 3000 3000

Rockford, 111. 37500 5100 1200 1300

Sacramento, Cal. 93700 10600 2500 2400

Saginaw, Mich. 32300 5700 1400 1600

St. Joseph, Mo. 10000 3100 700 900

St . Louis , Mo . -Ill

294700 47800 11500 12300

Salem, Ore. 20700 5300 1300 1500

Salinas-Monterey, Cal. 30000 2000 500 400

Salt Lake City, Utah 84700 10500 2500 2700

San Angelo, Tex. 8200 1900 500 500

San Antonio, Tex. 117900 25000 6000 7000

San Bernardino-Riverside-Ontario, Cal. 142200 17600 4200 4200

San Diego, Cal. 156400 13500 3300 2900

San Francisco-Oakland, Cal. 336800 29200 7000 6200

San Jose, Cal. 143300 11400 2700 2500

Santa Barbara, Cal. 30500 3400 800 800

Santa Rosa, Cal. 23600 3400 800 800

Savannah, Ga. 24000 6200 1500 1800

Scranton, Pa. 23600 4300 1000 1100

Seattle-Everett, Wash. 172800 22100 5300 5300

Shreveport, La. 39200 10000 2400 2900

Sioux City, Iowa-Neb. 14100 3700 900 1100

Sioux Falls, South Dakota 12000 2400 600 700

South Bend, Ind. 33500 4100 1000 1000

Spokane, Wash. 22900 4900 1200 1200

Springfield, 111. 19400 3600 900 900

Springfield, Mo. 17300 3200 800 800

Springfield, Ohio 20500 3800 900 1000

Springfield-Chicopee-Holyoke , Mass . -Conn62000 8200 2000 1900

Stamford, Conn. 22200 1800 400 300

Steubenville-Wierton, Ohio-W. Va. 18800 4200 1000 1100

Stockton, Cal. 34500 6400 1500 1700

110
Table 35 Continued

SMSA NAME Children

6 and Under

Children

At Risk

Model 1 EBL Model 1A EBL Syracuse, N.Y. 83600 14000 3400 3700

Tacoma, Wash. 48300 7200 1700 1800

Tallahassee, Fla. 12200 3700 900 1100

Tampa-St. Petersburg, Fla. 99000 12800 3100 2900

Terre Haute, Ind. 18600 4900 1200 1300

Texarkana , Tex . -Ark

12600 5400 1300 1800

Toledo, Ohio-Mich. 88300 11600 2800 2800

Topeka, Kansas 19000 3400 800 900

Trenton, N.J. 34800 3800 900 900

Tucson, Ariz. 41600 5600 1300 1300

Tulsa, Okla. 57900 11900 2900 3200

Tuscaloosa, Ala. 12900 4300 1000 1300

Tyler, Tex. 11900 4100 1000 1300

Utica-Rome, N.Y. 42500 8900 2100 2400

Vallej o-Napa, Cal. 30400 5200 1200 1300

Vineland -Mil lvi lie -Bridge ton, N.J. 16100 3400 800 1000

Waco, Tex. 15600 3700 900 1000

Washington, D.C.-Md.-Va. 370000 26300 6300 5600

Waterbury, Conn. 26200 3000 700 700

Waterloo, Iowa 16900 2700 600 700

West Palm Beach, Fla. 36900 5300 1300 1300

Wheeling, W. Va.-Ohio 19500 4800 1200 1300

Wichita, Kansas 48700 7300 1700 1800

Wichita Falls, Tex. 14200 3100 700 800

Wilkes Barre-Hazleton, Pa. 34200 5100 1200 1200

Wilmington, Del . -N. J. -Md. 64700 8400 2000 2100

Wilmington, N.C. 13800 4600 1100 1400

Worcester, Mass. 39600 5300 1300 1300

York, Pa. 40100 5000 1200 1200

Youngstown-Warren, Ohio 63200 9800 2400 2400

Total for all SMSA's 17112800 2458100 590000 624100

All numbers have been rounded to the nearest hundred. The totals are rounded after

summing, rather than being sums of the rounded numbers.

Ill
Figure 4

112
Figure 4 Continued

LEGEND

I PREDICTED NUMBER OF YOUTHS

WITH LEAD POISONING

X UNSOUND HOUSING UNITS (DETERIORATED & DILAPIDATED) RANGE: OX-37.SX REPRESENTATIVE SIZES o

800 CHILDREN o

1,000 CHILDREN

25,000 HOUSES

80,000 HOUSES

MEMIIfDBY:

APPLIED URBANETICS,

INC.

39,000 CHILDREN 3,000,000 HOUSES

113
Table 36

25 SMSA's With the Greatest Number of EBL's (Model 1 Estimates)

SMSA EBL Population

Rank

1. New York, N.Y. 40600 1

2. Chicago, 111. 24100 3

3. Los Angeles - Long Beach, Cal. 14600 2

4. Philadelphia, Pa. 14400 4

5. Detroit, Mich. 14200 5

6. St. Louis, Mo. -111. 11500 10

7. Pittsburgh, Pa. 10500 9

8. Houston, Tex. 9700 13

9. Boston, Mass. 9400 8

10. Dallas, Tex. 8600 16

11. Atlanta, Ga. 8100 19

12. Baltimore, Md. 7800 11

13. New Orleans, La. 7200 30

14. San Francisco-Oakland, Cal. 7000 6

15. Newark, N.J. 6800 14

16. Minneapolis-St . Paul, Minn. 6700 15

17. Cincinnati, Ohio-Ky.-Ind. 6400 20

18. Cleveland, Ohio 6400 12

19. Washington, D.C.-Md.-Va. 6300 7

20. San Antonio, Tex. 6000 37

21. Kansas City, Kan. -Mo. 5800 25

22. Birmingham, Ala. 5500 43

23. Seattle-Everett, Wash. 5300 18

24. Indianapolis, Ind. 5300 28

25. Buffalo, N.Y. 5200 23

Total 253400

114
Table 37 The Number of Children in Each SMSA with Blood Lead Levels of 40 and Above, 50 and Above, 60 and Above, and 70 and Above (Measured in yg/100 ml; based on

1970 New York City EBL rates)

SMSA NAME 40 And Above

50 And Above60

And Above

70 And Above

Abilene, Tex. 800 400 160 80 Akron, Ohio 3300 1500 690 310

Albany, Ga. 1300 600 260 120

Albany-Schenectady-Troy , N.Y. 4000 1800 840 380

Albuquerque, N. Mex1500 700 310 140

Al lent own -Bethlehem -East on, Pa. -N.J. 2000 900 430 200

Altoona, Pa. 1200 500 250 110

Amarillo, Tex. 800 400 170 80

Anaheim-Santa Ana-Garden Grove, Cal. 3600 1600 750 340

Anderson, Ind. 1200 500 250 110

Ann Arbor, Mich. 1000 500 220 100

Appleton-Oshkosh, Wise. 1700 700 350 160

Asheville, N.C. 1300 600 270 120

Atlanta, Ga. 9700 4300 2020 910

Atlantic City, N.J. 700 300 ISO 70 Augusta, Ga

2500 1100 510 230

Austin, Tex. 1900 900 400 180

Bakersfield, Cal. 2600 1200 540 250

Baltimore, Md. 9400 4100 1950 880

Baton Rouge, La. 2200 1000 470 210

Bay City, Mich. 900 300 180 80 Beaumont-Port Arthur-Orange, Tex. 2400 1100 510 230

Billings, Mont. 700 300 150 70 Biloxi-Gulfport , Miss. 1600 700 340 150

Binghampton, N.Y. 1600 700 340 150

Birmingham, Ala. 6500 2900 1360 610

Bloomington-Normal , 111. 800 400 170 80

Boise City, Idaho 800 300 160 70 Boston, Mass. 11300 5000 2350 1060

Bridgeport, Conn. 1400 600 290 130

Bristol, Conn. 300 100 60 300

Brocton, Mass. 900 400 200 90 Brownsville-Harlingen-San Benito, Tex. 2200 1000 460 200

Bryan-College Station, Tex. 700 300 150 60 Buffalo, N.Y. 6300 2800 1310 590

Canton, Ohio 2300 1000 480 220

Cedar Rapids, Iowa 1000 500 220 100

Champaign-Urbana, 111. 700 300 150 70 Charleston, S.C. 3500 1500 720 320

Charleston, W. Va. 1800 800 370 170

115
Table 37 Continued

SMSA NAME 40

And Above

50 And Above60

And Above

70 And Above

Charlotte, N.C. 3000 1300 630 280

Chatenooga, Tenn. 2700 1100 560 250

Chicago, 11128900 12700 6020 2710

Cincinnati, Ohio-Ky. -Ind. 7700 3400 1600 720

Cleveland, Ohio 7600 3400 1600 720

Colorado Springs, Colo. 1100 500 240 110

Columbia, Mo. 600 300 130 60 Columbia, S.C. 2500 1100 530 240

Columbus, Ga. 2400 1100 500 230

Columbus, Ohio 5700 2500 1180 530

Corpus Christi, Tex. 2700 1200 560 250

Dallas, Tex. 10300 4500 2140 960

Davenport-Rock Island-Moline, Iowa-Ill. 2300 1000 470 210

Dayton, Ohio 3800 1700 800 360

Decatur, 111. 800 300 160 70

Denver, Colo. 4900 2200 1020 460

Des Moines, Iowa 2000 900 420 190

Detroit, Mich. 7000 7500 3550 1600

Dubuque, Iowa 1000 400 200 90 Duluth- Superior , Minn-Wise. 1600 700 340 150

Durham, N.C. 1400 600 290 130

El Paso, Tex. 2900 1300 610 270

Erie, Pa. 1500 700 310 140

Eugene, Ore. 1300 600 260 120

Evansville, Ind.-Ky. 1500 700 310 140

Fall River, Mass. -R.I. 700 300 150 70 Fargo-Morehead, N. Dak. -Minn. 700 300 140 60

Fayetteville, N.C. 2800 1200 590 260

Fitchburg-Leominster , Mass. 900 400 180 80

Flint, Mich. 3100 1400 650 290

Fort Lauderdale-Hollywood, Fla. 1300 600 270 120

Fort Smith, Ark.-Okla. 2300 1000 480 220

Fort Wayne, Ind. 1600 700 330 150

Fort Worth, Tex. 4800 2100 1000 450

Fresno, Cal. 3200 1400 680 300

Gadsden, Ala. 1200 500 260 120

Gainesville, Fla. 1100 500 230 100

Galveston-Texas City, Tex. 1400 600 290 130

Gary-Hammond -East Chicago, Ind. 3800 1700 780 350

Grand Rapids, Mich. 2800 1200 590 270

Great Falls, Mont. 900 400 190 80 Green Bay, Wise. 900 400 190 90

116
Table 37 Continued

SMSA NAME 40

AND ABOVE

50 AND ABOVE

60

AND ABOVE

70

AND ABOVE

Greensboro-Winston Salem-High Point, N.C. 4200 1800 870 390

Greenville, S.C. 2700 1200 550 250

Hamilton-Middletown, Ohio 1100 500 240 110

Harrisburg, Pa. 1600 700 340 150

Hartford, Conn. 2100 900 430 190

Honolulu, Hawaii 5200 2300 1090 490

Houston, Tex. 11600 5100 2420 1090

Huntington-Ashland, W.Va. -Ky. -Ohio 2200 1000 460 210

Huntsville, Ala. 2100 900 440 200

Indianapolis, Ind. 6300 2800 1320 590

Jackson, Mich. 900 400 190 90 Jackson, Miss. 2300 1000 480 220

Jacksonville, Fla. 3900 1700 820 370

Jersey City, N.J. 3000 1300 620 280

Johnstown, Pa. 2100 900 440 200

Kalamazoo, Mich. 1000 500 210 100

Kansas City, Mo. -Kansas 6900 3100 1450 650

Kenosha, Wise. 600 300 130 60 Knoxville, Tenn. 3100 1400 650 290 La Crosse, Wise. 700 300 140 60 Lafayette, La. 1400 600 290 130

Lafayette, West Lafayette, Ind. 900 400 180 80

Lake Charles, La. 1200 500 250 110

Lancaster, Pa. 1300 600 280 130

Lansing, Mich. 2300 1000 470 210

Laredo, Tex. 2000 900 410 180

Las Vegas, Nev. 900 400 190 90 Lawrence-Haverhill, Mass. 1400 600 300 130

Lawton, Okla. k 800 400 180 80 Lewiston-Auburn, Maine 700 300 140 60 Lexington, Ky. 1200 500 260 120

Lima, Ohio 1300 600 270 120

Lincoln, Neb. 1100 500 230 100

Little Rock-North Little Rock, Ark. 2500 1100 530 240

Lorain-Elyria, Ohio 1300 600 280 130

Los Angeles-Long Beach, Cal. 17500 7700 3650 1640

Louisville, Ky.-Ind. 4900 2100 1010 460

Lowell, Mass. 1300 600 270 120

Lubbock, Tex. 1300 600 270 120

Lynchburg, Va. 1100 500 220 100

Macon, Ga. 2300 1000 480 220

Madison, Wise. 1100 500 230 100

Manchester, N.H. 800 300 160 70

117
TABLE 37 CONTINUED

SMSA NAME 40

AND ABOVE

50 AND ABOVE60

AND ABOVE

70

AND ABOVE

Mansfield, Ohio 1100 500 230 100

McAllen-Pharr-Edenburg , Tex. 2900 1300 600 270

Memphis, Term. -Ark. 5400 2400 1130 510

Meriden, Conn. 300 100 60 30

Miami, Fla. 3100 1400 640 290

Midland, Tex. 400 200 90 40 Milwaukee, Wise. 5100 2200 1060 480

Minneapolis-St . Paul, Minn. 8000 3500 1670 750

Mobile, Ala. 4000 1800 830 380

Modesto, Cal. 900 400 190 80

Monroe, La. 1500 600 300 140

Montgomery, Ala. 2100 900 430 190

Muncie, Ind. 800 400 170 80 Muskegon-Muskegon Heights, Mich. 1000 400 210 90 Nashua, N.H. 500 200 110 50 Nashville, Tenn. 3700 1600 780 350

New Bedford, Mass. 600 300 120 50 New Britain, Conn. 500 200 100 50 New Haven, Conn. 1600 700 320 150

New London-Groton-Norwich, Conn. 1300 600 270 120

New Orleans, La. 8700 3800 1800 810

New York, N.Y. 48700 21500 10150 4570

Newark, N.J. 8200 3600 1710 770

Newport News-Hampton, Va. 2000 900 410 190

Norfolk-Portsmouth, Va. 4400 1900 910 410

Norwalk, Conn. 400 200 90 40 Odessa, Tex. 700 300 150 70 Ogden, Utah 700 300 150 70 Oklahoma City, Okla. 4000 1800 830 370

Omaha, Nebr.-Iowa. 3300 1500 690 310

Orlando, Fla. 2300 1000 480 220

Owensboro, Ky. 700 300 140 60 Oxnard-Ventura, Cal. 2400 1100 500 230

Paterson-Clifton-Passaic , N.J. 3300 1400 680 310

Pensacola, Fla. 2200 1000 450 200

Peoria, 111. 1900 900 410 180

Petersburg-Colonial Heights, Va. 1900 800 400 180

Philadelphia, Pa. -N.J. 17300 7600 3610 1620

Phoenix, Ariz. 5300 2400 1110 500

Pine Bluff, Ark. 1700 800 360 160

Pittsburgh, Pa. 12600 5600 2640 1190

Pittsfield, Mass. 500 200 100 50 Portland, Maine. 600 300 130 60

118
Table 37, Continued

SMSA NAME 40 AND ABOVE

50 AND ABOVE60 AND ABOVE

70

AND ABOVE

Portland, Ore. -Wash.

Providence-Pawtucket-Warrwick, R. I

. -Mass

Provo-Orem, Utah

Pueblo, Colo.

Racine, Wise.

Raleigh, N.C.

Reading, Pa. Reno, Nev. Richmond, Va. Roanoke, Va. Rochester, Minn.

Rochester, N.Y. Rockford, 111.

Sacramento, Cal.

Saginaw, Mich.

St. Joseph, Mo.

St. Louis, Mo. -111.

Salem, Ore.

Salinas-Monterey, Cal.

Salt Lake City, Utah

San Angelo, Tex. San Antonio, Tex. San Bernardino-Riverside-Ontario, Cal.

San Diego, Cal.

San Francisco-Oakland, Cal.

San Jose, Cal.

Santa Barbara, Cal.

Santa Rosa, Cal.

Savannah, Ga. Scranton, Pa. Seattle-Everett, Wash.

Shreveport, La. Sioux City, Iowa-Neb.

Sioux Falls, S. Dak.

South Bend, Ind.

Spokane, Wash.

Springfield, 111.

Springfield, Mo. Springfield, Ohio

Springfield-Chicopee-Holyoke , Mass . -Conn.

Stamford, Conn.

Stubbenville-Wierton, Ohio-W. Va. Stockton, Cal.

5000

3800

800

1000

800

1900

1200

700

2400

800

600

3600

1500

3100

1600

900

13800

1500

600

3000

500

7200

5100

3900

8400

3300

1000

1000

1800

1200

6400

2900

1100

700

1200

1400

1000

900

1100

2300

500

1200

1800

2200

1700

300

400

400

800

500

300

1100

400

300

1600

700

1300

700

400

6100

700

300

1300

200

3200

2200

1700

3700

1400

400

400

800

600

2800

1300

500

300

500

600

500

400

500

1000

200

500

800

1040

800

160

210

180

400

250

140

500

180

130

760

310

640

340

180

2870

320

120

630

110

1500

1060

810

1750

680

200

210

370

260

1330

60 220

150

250

290

210

190

230

490

110

250

380

470

360

70 100

80

180

110

60

230

80

60 340

140

290

150

80

1290

140

50 280

50 680

480

370

790

310

9090 170

120

600

270

100

70 110

130

100

90 100

220

50 110

170

119
Table 37 Continued

SMSA NAME 40

AND ABOVE

50 AND ABOVE60 AND ABOVE

70 AND ABOVE

Syracuse, N.Y. 4000 1800 840 380

Tacoma, Wash. 2100 900 430 190

Tallahassee, Fla. 1100 500 230 100

Tampa-St. Petersburg, Fla. 3700 1600 770 350

Terre Haute, Ind. 1400 600 290 130

Texarkana, Tex. -Ark. 1600 700 320 150

Toledo, Ohio-Mich. 3300 1500 700 310

Topeka, Kansas 1000 400 210 90

Trenton, N.J. 1100 500 230 100

Tucson, Ariz. 1600 700 330 150

Tulsa, Okla. 3400 1500 720 320

Tuscaloosa, Ala. 1200 500 260 120

Tyler, Tex. 1200 500 250 110

Utica-Rome, N.Y. 2600 1100 530 240

Vallejo-Napa, Cal. 1500 700 310 140

Vine land -Mi llvi lie -Bridge ton, N.J. 1000 400 210 90 Waco, Tex. 1100 500 220 100

Washington, D.C.-Md.-Va. 7600 3300 1580 710

Waterbury, Conn. 900 400 180 80

Waterloo, Iowa 800 300 160 70 West Palm Beach, Fla. 1500 700 320 140

Wheeling, W. Va.-Ohio 1400 600 290 130

Wichita, Kansas 2100 900 440 200

Wichita, Falls, Tex. 900 400 190 80

Wilkes Barre-Hazleton, Pa. 1500 700 310 140

Wilmington, Del . -N. J. -Md. 2400 1100 500 230

Wilmington, N.C. 1300 600 280 130

Worcester, Mass. 1500 700 320 140

York, Pa. 1400 600 300 130

Youngstown-Warren, Ohio 2800 1200 590 270

Total for all SMSA's 708000 312200 147490 66370

The columns for 40 and above and 50 and above have been rounded to the nearest hundred;

those for 60 and above and 70 and above to the nearest ten. The totals have been rounded

after summing. The fact that the last two columns have been rounded to the nearest 10 should not be taken as an indication of a greater precision in these estimates. The

rounding for these columns was chosen to allow comparisons between estimates for different SMSA's, which would have been blurred if they were rounded to the nearest hundred.

120
and above, and 2.7% for 70 and above. We note that the percentage of

EBL's observed in the New York program is greater by A. 8 percentage points

than the average figure which was used in the calculations of the model 1

estimates given in table 35. This higher New York EBL rate leads to an

alternative national estimate of 700,000 EBL's, 300,000 of which are at a

level of 50 yg/100 ml or more, 150,000 of which are 60 or more, and

66,000 of which are at the level of 70 yg/100 ml or more. In this table

the estimates for 40 and above and 50 and above have been rounded to the

nearest hundred, indicating as before the approximate nature of the numbers,

The estimates for 60 and above and 70 and above have been rounded to the

nearest 10, which should not be taken to mean that these estimates are

more precise than the others. It was done because the magnitudes of the

numbers are close to one hundred and rounding would blur comparisons.

(For example, 149 is almost 3 times 51 but both would round to 100.)

As noted earlier, the estimates given in this report are at best as

good as the data and information currently available, and information

is lacking in several areas. Therefore the estimates produced must be

assessed with these data lacks in mind. We have assumed in formulating

the models that the primary cause of pediatric lead poisoning is the

< ingestion of old lead based paint. There is some evidence supporting

this view, such as the fact that lead often shows up in X-rays of the

stomach and intestines of poisoned children, and the high correlation

between EBL and the condition of the child's house discovered in the

analysis of the Aurora data. However, some experts have emphasized air

pollution (particularly leaded gasoline fumes) as a causative factor.

121
citing the lower average blood lead level for rural children as evidence

of its importance. Thus to really insure a solution to the problem of

lead poisoning as well as to assess its magnitude, it is necessary to

know, with more certainty than is now known, what are the sources of the

lead and how much each contributes to the poisoning problem.

To date, lead poisoning has been found primarily among the poor

inner city dwellers of the Northeast and Midwest. On the other hand, it

has often been noted that the number of cases found is in direct proportion

to the effort spent looking for them, and so far they have generally been

sought within this group. To fully assess the extent of the problem,

it is thus necessary to screen middle income children, rural children,

and children in the South and West to estimate EBL rates among these

groups. The current estimates must be viewed with reservation in the

absence of such additional screening information.

The estimates given in this report confirm that pediatric lead poisoning is a major urban health problem in this country. The models suggest

that if tested, approximately 600,000 children would show undue absorption

of lead, as measured by the level of lead in their blood. The effects of

this, in terms of the number of children suffering permanent brain damage

or damage to other organs, is not known. How many of the children who

have only slightly elevated blood levels will later on exhibit learning

difficulties is not known, but it is believed that the problem will persist as long as peeling lead painted surfaces are ascessible to young

children. Discovering and treating children with EBL's will partially

alleviate the problem, but its full solution requires the removal of

lead paint on all surfaces accessible to children.

122
APPENDIX A

OTHER MODELS

This appendix documents several trial models formulated in the course

of model development, but rejected in favor of the two given in

Chapter 3. They are similar in form to the favored models, differing

chiefly in the combination of parameters employed. In the primitive

screening process by which the models were selected, we relied principally

on the ability of the models to reproduce the data observations,

and on the intuitive plausibility of the signs of coefficients or exponents,

o We termed a fit "good" if R - .9, and signs "wrong" if they were opposite

to those expected. These criteria are discussed at greater length in

Section 3.3. of the main report.

Table 38 lists the symbols used in the models.

A.l. Model 2

The versions of Model 2 are exponential in form and all use unsound

housing as a variable.

A. 1.1. Model 2.1.

E = a

Q

a, a. a~ a,

ft) (i) (W (¥)

123
Table 38

Variables and Symbols Used in the Models

a~, a,,... parameters estimated by the curve fitting process

C,~, crowded population (- 1.01 per room)

C, c-i crowded population (^ 1.51 per room)

C„ crowded (1.01) population with female household head

D dilapidated or deteriorating housing units (number)

E number of children with elevated blood lead levels

(> 40 yg/100 ml.)

F number of female household heads

H number of housing units

I fraction of high risk child population with elevated

blood lead levels

K, number of children 4 years old or younger

K^ number of children 6 years old or younger

L number of housing units lacking one or more plumbing

facilities

P total population

R median rent $/mo.

R, number of units with rent 1 $40

R^n number of units with rent 1 $60

Y median family income $/ann.

124
-2.222

where a^ = e a

± = .3197

a

2 = .1305

a

3 = -.7846

a. = 1.757

4 R

2 = .719

The fit of this model is not too good and a- has the wrong sign,

since one would expect areas with more young children to have a higher

incidence of lead poisoning.

A. 1.2. Model 2.2

a

l a2 (f) (r> {»

a

3 a

4

E = a 0lpj U) \-l < C 151>

, -3.912

where a_ = e

a

± = -.1911

a

2 = .3232

a

3 = .1969

a. = 1.052

4

R

2 = .955

Although the fit for this model is very good, the sign of a., is wrong,

A. 1.3. Model 2.3 a

l

a

2 a

3, „ v a

4 a5

--•o(f) (l) « (50 <C

151>

125
12.798 where a

n = e

a

± = -.6470

a

2 = .3483

a

3 = -2.1726

a. = .4338

4 a

5 = .9413

R

2 = .960

Although the fit is quite good, the sign of a., is wrong.

A. 1.4. Model 2.4

a

2 a

3 / K, \

a

4 a

5

^•od)

1

^)

2 ^

3

^) «W

6.895 where a

n = e

a

x = -.4757

a

2 = .3560

a

3 = -.9926

a

4 = .2663

a- = -.3433

R

2 = .960

Although the fit is quite good, the signs of both a, and a- are wrong.

A.1.5. Model 2.5

a

l a

2 a

3 a4 . „ % a

5 -4» (f) fl) - (¥)

126
7.654

where a

n = e

a

± = .9697

a

2 = -.5163

a

3 = .3113

a. = -.9008

4 a

5 = -.374

R

2 = .967

Although the fit is quite good, the signs of a, and a^ are wrong,

A. 1.6. Model 2.6

a

where a» = e2.4785

(W '(f) (!) ' »

'(»

a

1 = .8806

a

2 = -.3186

a = .3062

a, = .3062

a

5 = -.6908

R

2 = .967

Although the fit is quite good, the signs of a

? and a,, are wrong,

A. 1.7. Model 2.7

= >o(W

aW

2

(f)

a

3 a

4

127
.5306 where a- = e

a

± = .6416

a

2 = -.4574

a

3 = .3001

a, = .1729

4

R

2 = .963

Although the fit is quite good, the signs of a~ and a, are wrong,

A. 1.8. Model 2.

a

l a

2 a

3

*-4& (i) <*>

-.3362

where a~ = e

a, = .3003

a

2 = .2485

a. = .0129

R

2 = .945

Although the fit is quite good, the sign of a- is wrong.

A. 2. Model 3

All of the versions of Models 1 and 2 involved a housing quality

variable. However, because data on this factor were not collected in the

1970 Census, an attempt was made to construct a model using only currently

available 1970 Census First Count Data items.

128
A. 2.1. Model 3.1

-.&)

a, a

2

(L)

.8428

where a_ = e

a

± = .6747

a

2 = .2479

R

2 = .908

The fit is good and the signs of all parameters are correct,

A. 2. 2. Model 3.2

a

l a2 . „ k a

3

I m » (w = a

Q l^/

.'8853

where a

n = e

a

x = .5825

a

2 = .0712

a

3 = .2354

R

2 = .908

The fit is good and all signs of parameters are correct. However the

third factor only reduces the residual sum of squares .03 from model

3.1, so that is the preferred model.

A. 2. 3. Model 3.3

I = a

o

a, a„ a_ a.

129
1.9422 where a~ = e

a

± = .6104

a

2 = .4743

a

3 = .3481

a. = -.3603

4

R

2 = .921

Although the residual sum of squares has been reduced from Model 3.2, the

sign of a, is wrong.

A. 2. 4. Model 3.4

a

l a

2 . „ ^ a3 a

4 = ao(v) (L

> (r) <*60>

1.6604 where a~ = e

a

±

= .6869

a

2 = .3898

a

3 = .1670

a, = -.2349

4

R

2 = .933

This fit was done as a weighted regression where the weight for each

census tract was the fraction of its total child population that was

screened. The sign of a, is wrong.

130
APPENDIX B

MODELS AND ESTIMATES OF HAZARDOUS HOUSING

B.l. Model Used to Update the New Haven 1960 Housing Condition Data

To remedy the discontinuation in 1970 of tabulation of "condition of

housing" data, the Census Bureau has developed a method for predicting

that information from the 1970 data and 1960 Fourth Count Census items including (1) units with central heating facilities, (2) rent, (3) units with

crowded population; 1.01 or more per room, (4) multiple unit structures,

(5) educational level of household head, (6) race of household head, (7)

owned or rented, and (8) vacant units. The dwelling units are divided

into categories determined by cutoff levels of the above attributes, and

separate growth patterns are applied to each of the categories. However,

since the Census Fourth Count data were not available in time, this model

could not be used to furnish inputs to the lead poisoning models at the

present stage.

The following make-shift housing adjustment, therefore, was applied to

the census tract level data, on the assumption that the results, even though

crude, were more likely than raw 1960 condition of housing figures to be

consistent with available 1970 EBL incidence data from New Haven tracts.

Let: H,_ - total 1960 housing units

H

70 - total 1970 housing units

D,

n - total 1960 unsound units

D- - total 1970 unsound units

131
If H

7Q

* H

60 , then

If H

70

< H

60' then

D

70

= D 60-

D

70

= max [.05D

60 , (D 60

- .9(H

6Q -H^))].

This means that if the number of housing units in a tract has increased,

we don't attempt to adjust the number of unsound units. If housing has

decreased, we assumed that 90 percent of the decrease represents

demolition of unsound housing. If "net demolitions", however, appear to

exceed the total 1960 unsound housing stock, then 5 percent of this unsound stock was assumed still standing. The choices of the .05 and .9

are arbitrary but have been chosen to approximate patterns stated to

exist in New Haven. (This "model" could not be applied usefully at the

SMSA level; it would predict "no change" in unsound stock since almost

all SMSA's, as opposed to tracts, have experienced net housing increases

in the last 10 years. Units demolished in the central cities are more

than counter -balanced by those built in the suburbs.) Thus the housing

figures used in the modeling process are not as current as one would

desire and may be altered in future modeling procedures.

B.2. A Model for Estimating the Volume of Housing Containing Lead Paint

In response to a request from the Department of Housing and Urban

Development, the present study includes a method for rough estimation of

housing units in each SMSA believed to contain sufficient concentrations

132
of lead on painted surfaces to constitute lead poisoning hazards. (Although this estimated number of "hazardous environments" has not yet been

incorporated as a variable into the EBL models tested, the formulation of

the method is presented here to make the current record complete.)

The housing estimation embodies the following assumptions:

(1) Almost all housing units constructed before 1940 in urbanized

regions contain lead paint from original surfacing and /or

subsequent refurbishment.

(2) About half of the housing units built between 1940 and 1949 in

urbanized areas are similarly contaminated. (This is an informal

estimate based on the reduction in the quantity of lead paint

manufactured in this decade, relative to housing construction in

the same period.)

(3) Only 5% of housing constructed between 1950 and 1959 has interior

lead paint, and post 1960 housing can be ignored in the estimates,

(4) Areas in which non-negligible fractions of the housing stock

were largely unpainted or merely whitewashed (substantially in

the rural South) or other areas in which the climate allowed

painting materials to be selected without great concern for

weathering properties (such as the south west and far west) can

be distinguished crudely from high hazard concentration areas by

differences in the fraction of dwelling units which are single

unit structures.

Reasoning from (4) above, the estimates by age in (1), (2), and (3)

are attenuated in the model by the fourth root of the fraction of dwelling

133
units in multi-unit structures. (Weighting by the actual fractions of

multi-unit structures leads to counts of contaminated dwelling units

which are suspiciously low, so they have been adjusted upward by using

the fourth root, the square root of the square root, of these fractions.

The fourth root has desirable properties of being easy to calculate, and

remaining smaller than 1.0 for fractions. It also compresses the range of

the weights.) Typical values of the fraction of total dwelling units which

are in multiunit buildings in southern cities and SMSA's are .3 and .2, respectively, in northeastern cities and SMSA's, .7 and .5 respectively. The

fourth roots of these numbers are, in the order listed, .74, .67, .91, and .84,

The count of housing units by age in each SMSA has to be estimated,

because while 1970 national figures for % of housing by decade of construction have been published by the Bureau of the Census, counts by

SMSA were not available in time to be utilized in this effort. The

estimates are made by multiplying the 1960 census counts by the ratios of

national percentages in 1960 and 1970. Thus the estimate has the form

(

hrL h

40/70 5

h

50/70 , h

60/7o \

40/60 hA0/60 50/60 h

50/60

60/6° h60/60/

where : HL is the estimated number of contaminated housing units in an SMSA

H

40/60' H50/60' H

60/60 are the COuntS ° f 196 ° housin8 stock in the

SMSA built before 1940, during 1940-49, and during 1950-59,

according to 1960 census data.

134
^An/fin* k^n/fir' ^fin/fin are t*ie fract i°ns °f tne I960 national

housing stock built before 1940, during 1940-49, and during

1950-59, according to 1960 census data

h, _.,.,_., h c _,-,_, h,,.,-- are the fractions of the 1970 national

40/70 50/70 60/70

housing stock built before 1940, during 1940-49 and during

1950-59, according to 1970 census data.

B.3. Estimate of the Nationwide Number of Housing Units Containing

Lead Paint

Because of the late release of the 1970 Fourth Count Census data,

the model described in B.2 was not applied for each SMSA. The nationwide

total of potentially hazardous housing was desired, however, so an attempt

was made to estimate it using a different procedure.

Although we know that some housing built before 1940 may be uncontaminated by interior lead paint, and that a nonnegligible portion of

housing built in 1950-1959 does include lead paint-bearing surfaces (as

well as some built after 1960), we have relied, as a rule of thumb, on

the supposition that the potentially "lead hazardous" housing stock is

substantially equivalent to the standing stock built before 1940.

Bureau of Census publication Current Housing Reports—Housing

Vacancies (Series H-lll, No. 63, Part II, March 1971), contains on page 2

the following statements:

"Two-fifths of the 1970 stock of homeowner housing were at least

30 years old"

135
"About 55 percent of the rental units were at least 30 years old"

The following data were provided by the Housing Division of the

Bureau of Census in a telephone conversation on January 5, 1972.

A - Total H.U. 67 656 566

B - Total occupied 63 449 747

C - Renter occupied 23 564 567

D - Vacant for rent 1 655 390

E - Owner occupied 39 885 180

F - Vacant owner units 477 371

G - Rented or sold-awaiting occupancy 334 295

H - Occasional use units 760 237

J - Otherwise year round vacant 979 526

Using these data and the estimated rates quoted above, we have the

following formulations:

Pre 1940 occupied or awaiting occupancy:

.55(0 + .37G) + .4(E + .63G) = 29,000,000

or about 46% of occupied housing units.

Pre 1940 total housing:

.55(C + D + .37(G + H + J)) + .4(E + F + . 63 (G + H + J)) = 31,000,000

or about 46% of all housing units.

The three categories G, H and J (which were not broken down in the

census figures as to type of tenure) have been distributed between rental

and homeowner units in the same ratio as the renter /owner occupancy rates,

136
B . 4 . A Nationwide Estimate of Immediately Hazardous Housing

In constructing the EBL incidence models, NBS equated the immediatelyhazardous housing with 1960 "unsound" (deteriorating housing units plus

dilapidated housing units), 1960 "substandard", or very crude estimates

of both of these updated to 1970, primarily on the basis of total net housing

change and demolition ratss. As far as a national estimate goes, one may

use at this point the 6,9 million estimate of total "substandard" housing

37 units for 1970 made by the National Commission on Urban Problems. Aside

from doubts as to the accuracy of the estimate, using Census "substandard"

as a surrogate for "acute lead hazard" housing should not raise serious

objections. Virtually all "unsound" housing is old enough to have peeling

lead paint, and accessible lead paint is most likely to be found in otherwise sound houses where there are plumbing deficiences—a condition often

associated with age and indifferent maintenance. To the extent that this

latter is not true, for example in rural areas, it is very likely counterbalanced b( y older urban housing units with peeling lead paint which are

nevertheless ruled sound according to the census definitions.

An estimate was also made using the 1960 unsound housing rates, under

the assumption (which undoubtedly overestimates the current unsound housing

stock) that these rates also apply at the SMSA level in 1970. Table 39

lists the hazardous housing units estimated in this manner for each SMSA.

The total number of units for all SMSA's is 5,270,000. Since housing in

SMSA's represents 68% of all housing units, this procedure leads to an

estimate of 7,750,000 unsound units nationwide.

137
Table 39 Estimate of the Immediately Hazardous Housing in Each SMSA

SMSA

Hazardous

Housing

Abilene, Tex. 8400

Akron, Ohio 22100

Albany, Ga. 6800

Albany-Schenectady-Troy, N.Y. 36100

Albuquerque, N. Mex. 9500

Allentown-Bethlehem-Easton, Pa. -N.J. 19100

Altoona, Pa. 11900

Amarillo, Tex. 7300

Anaheim-Santa Ana-Garden Grove, Cal

15400

Anderson, Ind. 9400

Ann Arbor, Mich. 6700

Appleton-Oshkosh, Wise. 4500

Asheville, N.C. 11500

Atlanta, Ga. 56100

Atlantic City, N.J. 8400

Augusta, Ga. 17100

Austin, Tex. 12500

Bakersfield, Cal. 20900

Baltimore, Md. 66200

Baton Rouge, La. 13400

Bay City, Mich. 6000

Beaumont-Port Arthur-Orange, Tex. 21700

Billings, Mont. 6200

Biloxi-Gulfport, Miss. 6600

Binghampton, N.Y. 9700

Birmingham, Ala. 51300

Bloomington-Normal, 111. 7000

Boise City, Ida. 5900

Boston, Mass. 100400

Bridgeport, Conn. 11000

Bristol, Conn. 1700

Brockton, Mass. 5500

Brownsville-Harlingen-San Benito, Tex. 14800

Bryan-College Station, Tex. 3000

Buffalo, N.Y. 54900

Canton, Ohio 18200

Cedar Rapids, Iowa 6900

Champaign-Urbana, 111. 5400

Charleston, S.C. 17200

Charleston, W. Va. 19100

138
Table 39 Continued

SMSA Hazardous

Housing

Charlotte, N.C. 16000

Chattanooga, Tenn. 22400

Chicago, 111. 229600

Cincinnati, Ohio-Ky. - Ind. 51200

Cleveland, Ohio 59600

Colorado Springs, Cold. 6300

Columbia, Miss. 2500

Columbia, S.C. 15600

Columbus, Ga. 15800

Columbus, Ohio 36200

Corpus Christi, Tex. 15800

Dallas, Tex. 61000

Davenport-Rock Island-Moline, Iowa 14100

Dayton, Ohio 26200

Decatur, 111. 6600

Denver, Colo. 33600

Des Moines , Iowa 17300

Detroit, Mich. 124900

Dubuque, Iowa 5400

Duluth-Superior , Minn. -Wis. 18800

Durham, N.C. 7200

El Paso, Tex. 16500

Erie, Pa. 12100

Eugene, Ore. 9100

Evansville, Ind.-Ky. 12900

Fall River, Mass. -R.I. 6600

Fargo-Morehead, N. Dak. -Minn. 5200

Fayetteville , N.C. 12200

Fitchburg-Leominster , Mass. 7300

Flint, Mich. 16800

Fort Lauderdale-Hollywood, Fla. 9600

Fort Smith, Ark.-Okla. 19500

Fort Wayne, Ind. 10500

Fort Worth, Tex. 32700

Fresno, Cal. 25900

Gadsden, Ala. 11500

Gainesville, Fla. 2600

Galveston-Texas City, Tex. 11700

Gary-Hammond-East Chicago, Ind. 26000

Grand Rapids, Mich. 15300

Great Falls, Mont. 6900

Green Bay, Wise. 4800

Greensboro-Winston Salem-High Point, N.C. 25700

Greenville, S.C. 16000

139
Table 39 Continued

SMSA Hazardous

Housing

Hamilton-Middleton, Ohio 8300

Harrisburg, Pa. 13800

Hartford, Conn. 14400

Honolulu, Haw. 26800

Houston, Tex. 60200

Huntington-Ashland, W. Va. -Ky. -Ohio 21200

Huntsville, Ala. 7400

Indianapolis, Ind. 33600

Jackson, Mich. 7200

Jackson, Miss. 12400

Jacksonville, Fla. 29200

Jersey City, N.J. 31800

Johnstown, Pa. 21500

Kalamazoo, Mich. 7600

Kansas City, Mo. -Kan. 55000

Kenosha, Wise. 4500

Knoxville, Tenn. 26900

La Crosse, Wise. 3700

Lafayette, La. 7100

Lafayette-W. Lafayette, Ind. 3200

Lake Charles, La. 8900

Lancaster, Pa. 9900

Lansing, Mich. 14000

Laredo, Tex. 9700

Las Vegas, Nev. 3600

Lawrence-Haverhill, Mass.-N.H. 10700

Lawton, Okla. 5400

Lewiston-Auburn , Maine 5900

Lexington, Ky. 7900

Lima, Ohio 6300

Lincoln, Neb. 10100

Little Rock-North Little Rock, Ark. 16600

Loraine-Elyria, Ohio 7900

Los Angeles-Long Beach, Cal. 172000

Louisville, Ky.-Ind. 36600

Lowell, Mass. 7000

Lubbock, Tex. 9300

Lynchburg, Va. 8200

Macon, Ga. 16000

Madison, Wise. 7100

Manchester, N.H. 6600

Mansfield, Ohio 3800

140
Table 39 Continued

SMSA

Hazardous

Housing

McAllen-Pharr-Edenburg , Tex. 3200

Memphis, Tenn.-Ark. 34500

Meriden, Conn. 2500

Miami, Fla. 29300

Midland, Tex. 4100

Milwaukee, Wise. 37200

Minneapolis-St . Paul, Minn. 52900

Mobile, Ala. 25500

Modesto, Cal

1700

Monroe, La. 9900

Montgomery, Ala. 13700

Muncie, Ind. 6100

Muskegon-Muskegon Heights, Mich. 7800

Nashua, N.H. 2200

Nashville, Tenn. 24600

New Bedford, Mass. 6200

New Britain, Conn. 4000

New Haven, Conn. 13400

New London-Groton-Norwich, Conn. 8100

New Orleans , La. 58400

New York, N.Y. 467900

Newark, N.J. 68900

Newport News -Hampton, Va. 11500

Norfolk-Portsmouth, Va

29400

Norwalk,' Conn. 3300

Odessa, Tex. 6200

Ogden, Utah 4600

Oklahoma City, Okla 30600

Omaha , Nebr . - Iowa 22300

Orlando, Fla. 16600

Owensboro, Ky. 2900

Oxnard-Ventura, Cal. 9500

Paterson-Clifton-Passaic, N.J. 28000

Pensacola, Fla. 14600

Peoria, 111. 14700

Petersburg-Colonial Heights, Va. 4800

Philadelphia, Pa. -N.J. 138500

Phoenix, Ariz. - 32100

Pine Bluff, Ark. 12800

Pittsburgh, Pa. 125200

Pittsfield, Mass. 4200

Portland, Maine 5300

141
Table 39 Continued

SMSA Hazardous

Housing

Portland, Ore. -Wash.

Proyidence-Pawtucket-Warwick, R.I. -Mass

Provo-Orem, Utah

Pueblo, Colo.

Racine, Wise.

Raleigh, N.C.

Reading, Pa. Reno, Nev. Richmond, Va. Roanoke, Va. Rochester, Minn.

Rochester, N.Y. Rockford, 111.

Sacramento, Cal.

Saginaw, Mich.

St. Joseph, Mo.

St. Louis, Mo. -111.

Salem, Ore.

Salinas-Monterey, Cal.

Salt Lake City, Utah

San Angelo, Tex. San Antonio, Tex. San Bernardino-Riverside-Ontario , Cal.

San Diego, Cal.

San Francisco-Oakland, Cal.

San Jose, Cal

Santa Barbara, Cal.

Santa Rosa, Cal.

Savannah, Qa.

Scranton, Pa. Seattle-Everett, Wash.

Shreveport, La. Sioux City, Iowa-Neb.

Sioux Falls, S.D. South Bend, Ind.

Spokane, Wash.

Springfield, 111.

Springfield, Mo. Springfield, Ohio

Springf ield-Chicopee-Holyoke, Mass . -Conn,

Stamford, Conn.

Steubenville-Weirton, Ohio-W. Va.

43300

33200

3700

8600

5500

11600

11800

5000

16900

7100

2300

20300

9000

18600

9800

9700

107300

12800

1400

14200

5000

41700

38200

29400

84900

15800

6300

1700

15400

13700

50200

22800

9300

5200

9000

14500

9200

8300

7500

20000

4400

11400

142
Table 39

Continued

SMSA Hazardous

Housing

Stockton, Cal. 14900

Syracuse, N.Y. 29800

Tacoma, Wash!. 15700

Tallahassee, Fla. 6500

Tampa-St. Petersburg, Fla. 38800

Terre Haute, Ind. 9700

Texarkana, Tex. -Ark. 13900

Toledo, Ohio-Mich. 19300

Topeka, Kan. 8300

Trenton, N.J. 8700

Tucson, Ariz. 11400

Tulsa, Okla. 30000

Tuscaloosa, Ala. 9800

Tyler, Tex. 9900

Utica-Rome, N.Y. 22400

Vallejo-Napa, Cal. 10700

Vineland-Millville-Bridgeton, N.J. 2400

Waco, Tex. 11900

Washington, D.C.-Md.-Va. 44000

Waterbury, Conn. 6600

Waterloo, Iowa 6000

West Palm Beach, Fla. 12800

Wheeling, W. Va.-Ohio 15600

Wichita, Kan. 17000

Wichita Falls, Tex. 9000

Wilkes Barre-Hazleton, Pa. 17100

Wilmington, Del . -N. J. -Md. 14800

Wilmington, N.C. 10400

Worcester, Mass. 13300

York, Pa. 9600

Youngstown-Warren, Ohio 23800

Total for all SMSA's 5,267,600

143
Footnotes

1. [18], [28]

2. [27]

3. [18]

4. [27] and conversations with Ray Tyler, Chief of the Accident Control

Division, Philadelphia Department of Public Health; Mrs. Elaine

Whitmire, Coordinator of the Lead Paint Poisoning Program, New Haven,

Connecticut; Dan Still, Lead Poisoning Control Bureau, New York City;

and Dr. Herbert L. Slut sky, Lead Program Coordinator, Chicago Board

of Health.

5. [18], [6]

6. [11], [35], [36]

7. [11]

8. [11], [43]

9. [43]

10. [16], [42]

11. [18]

12. [18]

13. [18]

14. [18]

15. "Paint Suit Lost by Blind Child", Washington Star, Friday, November

19, 1971, Section C, page 1.

16. Chapter 4 of this report documents the substantiation effort which has

been possible to date; its findings, though limited in scope, can be

characterized as distinctly confirmatory, rather than the opposite.

17. Throughout this report the term "data items" will be used to refer to

categories of information, while "data" will refer to numbers in those

categories.

18. These refer to areas reporting lead poisoning up through 1971.

19. The data available for many of these characteristics may be obtained

as the number of or the fraction of , only one of which

will be listed.

144
20. The following U.S. Census definitions may clarify the data items listed

Housing Categories :

Sound - Having no defects that could not be alleviated by routine

maintenance procedures.

Deteriorating - Containing some serious defects: holes in wallboards several inches in diameter, some rotting

in structural members, heavy splintering, etc.

Dilapidated - Severe surface damage, fractures in structural

members, etc.

Unsound - Deteriorating or Dilapidated.

Dwelling Unit; Housing Unit - Living quarters for a household;

primarily a house or apartment. For our purposes the terms are interchangeable. Census used "Dwelling Unit" through 1960 and

"Housing Unit" with minor technical changes in the definition,

thereafter.

21. In a New York sample of paints, 8 out of 76 sampled were found to

have greater than 1% lead as reported in the New York Times of

July 24, 1971. An intentionally biased survey directed by the

National Bureau of Standards for HUD and reported to the Senate

Subcommittee on Health in March of 1972 also found some interior

house paints with a lead content of greater than 1%.

22. As in Table 7 this may be obtained either as the number of or

a fraction of .

23. The data available for this characteristic and the others described

as "distributions" is the fraction of the persons falling in each

subcategory of the characteristic.

24. Exceptions are primarily "twin-cities" located less than 20 miles

apart and having a combined population of more than 50,000.

25. Rural poor in substandard housing, particularly in the South and

Southwest may live in dwelling units that are unpainted or whitewashed, rather than in housing with old leaded paint. Most housing

in the West and in suburban areas throughout the country is on the

average of more recent vintage than that in the urban East and

Midwest, and thus less likely to contain lead paint.

26. [63]

27. [65]

145
28. Follow-up medical records were not available to project staff.

29. [64]

30. [65]

31. Conversations with experts in Eastern cities (see footnote 4),

Dr. J. Julian Chisolm of Baltimore and members of HEW's Bureau of

Community and Environmental Management

32. [27]

33. In 1970 there were 243 SMSA's, but only 189 in 1960. [61] was used

to obtain 1960 data for all but 2 of the 243 1970-SMSA's. Data for

these 2 could have been obtained, but with much greater difficulty.

34. Reported orally during a field trip to the New Haven Lead Poisoning

Prevention Program.

35. Data in the Census of Manufacturers and the Minerals Yearbook were

analyzed to calculate these figures.

36. [61]

37. [62]

146
Bibliography

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147
14. Clark, Alan J. and George W. Hallett, "Lead Poisoning Survey—Portland,

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18. "Control of Lead Poisoning in Children", HEW, PHS, BCEM, Dec. 1970.

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20. "Facts About Lead and Pediatrics", Lead Industries Association Inc.

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24. Greengard, Joseph, "Lead Poisoning in Childhood: Signs, Symptoms,

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148
28. "Hearing Before the Subcommittee on Health of the Committee on Labor

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32. Jacobziner, Harold and Harry W. Raybin, "The Epidemiology of Lead

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35. Kehoe, Robert A., "The Metabolism of Lead in Man in Health and

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38. "Lead Poisoning in Children: Diagnostic Criteria", HEW, May 1959.

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42. Lourie, Reginald S., Emma M. Layman, and Frances K. Millican, "Why

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149
43. Lutz, Garson A., Arthur A. Levin, Sanford G. Bloom, Kaj L. Nielsen,

Jack L. Cross, and David L. Morrison, "Technical, Intelligence, and

Project Information System for the Environmental Health Service,

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44. McLaughlin, Mary, "Two Health Problems—One Solution", Bull. N. Y.

Acad. Med., Vol. 46, No. 6, June 1970.

45. "Medical Aspects of Childhood Lead Poisoning", HSMHA Health Reports,

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46. Meigs, J. Wister and Elaine Whitmire, "Epidemiology of Lead Poisoning in New Haven Children—Operational Factors", Connecticut Medicine, Vol. 35, No. 6, June 1971.

47. Mellins, Robert B. and C. David Jenkins, "Epidemiological and

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158, No. 1, May 1955.

48. Mushkin, Selma J. and Ralph Freidin, "Lead Poisoning in Children: The

Problem in D.C. and Preventive Steps", Public Services Laboratory,

Georgetown University, September 1971.

49. Oberle, Mark W. , "Lead Poisoning: A Preventable Childhood Disease

of the Slums", Science, Vol. 165, Sep. 1969.

50. Public Law 91-695, Ninety-first Congress, H. R. 19172, Jan. 13, 1972.

51. Rait, J. R. and C. Teng, "Lead Poisoning Issue Study", New York City

Rand Institute, Feb. 1970.

52. "Report of Governor's Task Force on Lead Poisoning", State of

Connecticut, Dec. 1970.

53. Sachs, Henrietta K., Larry A. Blanksma, Edward F. Murray, and Morgan

J. O'Connell, "Ambulatory Treatment of Lead Poisoning, Report of

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56. "The Surgeon General's Policy Statement on Medical Aspects of Childhood Lead Poisoning", Nov. 1970.

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150
58. Tiboni, Emil A., "Control of Childhood Lead Poisoning", Public

Health Reports, Vol. 79, No. 2, Feb. 1964.

59. Tyler, Raymond L., "Philadelphia Combats 'Silent Epidemic' in the

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II. Demographic Data

61. City and County Data Book 1967 , U. S. Department of Commerce, Bureau

of the Census.

62. Kristoff, F. S., Urban Housing Needs Through the 1980 's: An Analysis

and Projection , Report Number 10, The National Commission on Urban

Problems, Washington, D.C., 1968.

III. Statistical Analysis

63. Mood, Alexander M., and Franklin A, Graybill, Introduction to the

Theory of Statistics , McGraw-Hill, New York, 1963.

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Standards Handbook 91, 1966.

65. Siegel, Sidney, Nonparametric Statistics for the Behavioral Sciences , McGraw-Hill, New York, 1956.

151

FORM NBS-H4A (1-71) 2. Gov't Accession No.

U.S. OEPT. OF COMM. BIBLIOGRAPHIC DATA

SHEET

TT PUBLICATION OR REPORT NO. NBS TN-746

3. Recipient's Accession No.

4. TITLE AND SUBTITLE

Estimates of the Nature and Extent of Lead Paint Poisoning

in the United States

5. Publication Date December 1972

6. Performing Organization Code

7. AUTHOR(S) Judith F. Gilsinn

8. Performing Organization

10. Project/Task/Work Unit No. 4314518

9. PERFORMING ORGANIZATION NAME AND ADDRESS

NATIONAL BUREAU OF STANDARDS

DEPARTMENT OF COMMERCE

WASHINGTON, D.C. 20234

11. Contract/Grant No.

12. Sponsoring Organization Name and Address

Department of Housing and Urban Development

13. Type of Report & Period Covered Final

1 A, Sponsoring Agency Code

15. SUPPLEMENTARY NOTES

16. ABSTRACT (A 200-word or less factual summaty of most significant information. If document includes a significant bibliography or literature survey, mention it here.) This report evaluates the nationwide magnitude and extent of pediatric lead

poisoning resulting from the ingestion of lead-based paint. Estimates are given

of the number of children who have elevated blood lead levels (40 yg or more of

lead per 100 ml of whole blood) in each of 241 Standard Metropolitan Statistical

Areas' throughout the country. The mathematical models used to obtain these

estimates are documented together with the assumptions and data upon which those

models are based. Partial validation of both models and assumptions is also

reported.

17. KEY WORDS (Alphabetical order, separated by semicolons) r>v.jui a jj _ ...»„ *. • u t *.t. ' Childhood diseases; estimation; health

problems; lead; lead paint; lead paint poisoning; lead poisoning; mathematical

modeling; models; urban health problems.

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21. NO. OF PAGES

163

22. Price $2.00

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