CALIFORNIA STATE UNIVERSIT"i, NORTHRIDGE
THE PREDICTION OF CHILDHOOD
1\
BLOOD LEAD LEVELS
A thesis submitted in partial satisfaction of the
requirements for the degree of Master of Science in
Health Science
by
Suthi
~ipanyakom
July; 1975
The thesis of Suthi Athipanyakom is approved:
California State University, Northridge
July, 1975
ii
ACKNOWLEDGEMENTS
I am indebted to the Department of Health Services, Community
Health Services, Los Angeles County, California, for providing the
raw data.
My special thanks and grateful appreciation are expressed to
Dr. Bernard Hanes, academic advisor, for his kindness, assistance and
cooperation throughout this study.
I thank Dr. Ram Swaroop for his suggestions, and I also thank
Miss Arlene Ketchum for typing this thesis.
iii
TABLE OF CONTENTS
APPROVAL PAGE • •
ii
ACKNOWLEDGEMENTS.
iii
LIST OF TABLES.
v
ABSTRACT • . • • • •
vi
CHAPTER
I
II
III
IV
V
INTRODUCTION
1
Objective of the Study •
1
REVIEW OF LITERATURE •
2
Introduction to Lead Poisoning •
2
Prediction Models • •
6
Regression Equations
8
RESEARCH DESIGN. • • • •
11
Subjects and Sampling • •
11
Measurement.
11
Method of Analysis
11
Regression Equation. •
12
RESULTS. •
14
CONCLUSIONS • •
21
BIBLIOGRAPHY.
22
APPENDICES. •
24
1
Questionnaire.
24
2
Quan·titative Determination of Lead on Painted
Surfaces Using a Radioisotope X-ray Fluorescence
Arlalyzer . .. . . . . . • . . . . . . . . • .
30
3
Multiple Regression with Case Combinations
{B~.J)
03R) •
45
4
Raw Data
56
5
Graphs • • •
59
iv
LIST OF TABLES
Page
Table No.
1.
List of the Variables and Labels •
13
2.
The Outcome of the Multiple Linear Regression with All
Eighteen Independent Variables
• • • • • • •
15
3.
Correlation Coefficient Matrix of All Variables
16
4.
The Outcome of the Multiple Linear Regression with
Three Independent Variables
• • • • •
17
5.
Correlation Coefficient Matrix
19
6.
The Relative Contribution of Predictor Variables
20
"
v
ABSTRACT
THE PREDICTION OF CHILDHOOD
BLOOD LEAD LEVELS
by
Suthi Athipanyakom
Master of Science in Health Science
July, 1975
'l'he purpose of this study was to predict childhood blood lead
levels, and to determine which are the important predictor variables.
Children ages one thru six, living in the Venice District, the County
of Los Angeles, California, were sampled.
Atomic absorption spectro-
scopy was used to measure blood lead in the subjects.
A portable
radioisotope X-ray fluorescent (XRF) instrument was used to measure
lead on painted surfaces in the subject's household.
The computer package program BMD 03R (biomedical computer program7
multiple regression vlith case combinations) was used to analyze the
data.
The multiple linear regression equation based upon three
v;:.riables is:
vrhere
y
expected blood lead level
father's montly pay
measurement of lead on exterior walls
vi
predicto~
1
x
3
measurement of lead on interior walls
2
This model produces a multiple correlation coefficient (R ) of
.50.
The father's monthly pay appeared to be the most important
predictor variable.
vii
CHAPTER I
INTRODUCTION
Childhood lead poisoning is a well known and an important public
health problem in the United States.
It is estimated that more than
200,000 children are currently poisoned with lead (1:198).
Childhood
lead poisoning results from ingestion of lead-based paint chips,
atmospheric inhalation of lead and perhaps the eating of dirt that
contains lead.
In 1971 the United States Congress passed the "Lead-Based Paint
Poisoning Prevention Act", for purposes of researching the nature
and extent of the lead paint problem (2:1).
The Public Health Service recommends that the normal median blood
lead level of children is between 16 to 27 micrograms lead per 100
milliliters of whole blood.
Children with blood lead level exceeding
40 micrograms per 100 ml. are to be referred for medical services.
Children having a high blood lead concentrati.on of 80 microgra..'lts per
100 ml. or more, although absence of other symptoms, are required to
be treated as a medical emergency for chelation therapy (2:6-7).
Objective of Study
The major objective of this study was to develop a multiple
regression equation that will
pr~dict
lead blood levels in children
and to assess the importance of the predictor variables.
1
CHAPTER II
REVIEW OF LITERAWRE
Lead poisoning is a chronic disease which is a major pediatric
problem.
Several cities in the United States have lead screening
programs (2:8-9).
It is estimated that 90 percent of the pediatric
lead poisoning is caused from eating paint or paint plaster which has
peeled, cracked or flaked.
Other sources of lead come from toys,
furniture and household supplies.
It is also believed that air pollu-
tion, lead absorption in the soil and leaded gasoline fumes contribute
to the lead poison problem.
The initial symptoms of lead poisoning are not conclusive.
These
symptoms may include nausea, vomiting, abdominal pain, constipation,
anemia, irritability, anorexia and listlessness.
If the disease leads
to central nervous system involvement, the result is blindness, paralysis, mental retardation and finally death.
The daily permissable intake (DPI)
300 micrograms per day.
(2:4) of lead has been set at
About 150 micrograms per day are absorbed
from normal contamination levels in food, water and air.
The diagnostic criteria indicative of lead poisoning are (2:6-7):
1)
More than 1 microgram of lead per mg. of edathamil calcium
disodium (CaEDTA) administered intramuscularly at a dose of
50 mg. per kg. of body weight excreted in a 24 hour urine
2)
Serum delta-aminolevulinic (ALA) level of greater than 20
'
micrograms per 100 ml. of whole blood using the Haeger-Aronson!
method
3)
Urinary output of coproporphyrin greater than 150 micrograms
per 24 hours
4)
Urinary output of delta-aminolevulinic acid greater than 5 mg.,
per 24 hours
5)
The presence of basophilic stippling of red blood cells,
"lead lines" in long bone x-ra.ys, or a strong positive
2
3
urine spot test for coproporphyrin
The laboratory techniques used to measure lead in the blood are:
1)
The spectrophotometric dithizone technique
2)
Atomic absorption spectrophotometry (AA) technique
3)
Anodic stripping voltammetry (ASV) technique
The first test is a wet chemistry technique using a macro-blood
sample (about 5 ml.) obtained by venipuncture.
The other two methods
use only small samples (about 20-lOOJUl) which can be readily gotten
from a finger prick.
Screening
Many cities have programs of screening children for lead poisoning.
Most programs carefully choose the population at risk.
Some
programs restrict their screening to geographic areas which contain
sub-standard housing in a deteriorating condition.
An alternative
screening procedure is the determination of lead content of sample
dwellings and identification of those with lead hazard environments.
Chelation tl1erapy, removal of body lead, 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 therapy may also result in depletion of calcium from the
bones if too high a dosage is prescribed (2: 8-11).
~1e
Public Health Service recommends that no child be returned
to the home until the source of lead has been identified and eliminated (2: 9).
Many cities have laws against the sale of lead paint and its use
on interior surfaces which may be accessible to children.
Many health'
or housing departments in cities or states have authority and powers
to limit the amount of lead in interior paint (2: 10-12).
There are two major factors associated with lead poisoning,
environmental and socio-economic.
'I'he environmental factors are con-
4
cerned with the physical environment where the children live and play.
Several studies indicate that there is a relationship between blood
lead levels in children and dwelling location (3:1430-1433).
Children
living in high density areas are also exposed to the risk of ingestion
of lead substances.
Cohen, et al. studied blood lead levels of 230
rural and 272 urban children.
The mean blood lead level for the rural
children was 10 micrograms per 100 ml., lower than that of urban
children (22.8 micrograms compared to 32.7 micrograms per 100 rnl.).
Nine percent of the rural children had blood lead level at least 40
micrograms per 100 ml. compared to 23 percent of the urban children.
Elevated lead levels were present in all cases of the urban children.
The presence of leaded paint on accessible surfaces was .also documented:.
In 1971, specific areas in Rochester (4:167-170) were studied.
Differences in exposure to lead in terms of lead residual on the hands
and lead content in the dwellings were highly significant between
inner-city children and suburban children.
It was not indicated
whether lead carne from airborne sources, chalking of paints or from
paint chips shed from walls.
However, it was suggested that dust is
a source of child lead poisoning.
It is thus possible that multiple
sources of environmental contamination in the inner-city were responsible for the observed blood lead level elevation.
Caprio, et al.
studied a population of 5,226 children between the ages of one and
five years in New Jersey during 1971.
The results indicate that
there is an association between blood lead concentration and distance
of residence from major traffic arteries.
There was an association
between blood lead levels and exposure to varying average weekday
vehicle densities (AWVD) in children living within 200 feet from a
major highway (5:195-196).
Thus high traffic densities contribute
substantially to lead absorption in children.
Residential areas
located adjacent to major urban highways exhibit higher rates of
blood lead absorption in children than do other areas.
The authors
felt that the healtl1 hazards concomitant with high traffic densities
adjacent to residential areas are very real, and thi.s problem can be
corrected by reducing heavy traffic away from areas of high population
densi·ty (5:197).
5
Chisolm (6:955-956) reported that environmental, behavioral
social factors are associated with lead poisoning in children.
Chisolm
concludes that poor maintenance of modern dwellings causes accumulation
of many layers of lead paint on wall surfaces and this condition increases the risk of exposure.
A survey of housing in Southeast Los Angeles in 1972 conducted
by the Department of Community Health Services of Los Angeles County
(7:28-29) discovered that 100 percent of the interior surfaces have
one to eight percent lead content.
Rocke's study (8:32) in Burbank, California, found that 58 percent
2
of interior surfaces contained above 1 mg/cm lead content, 87 percent
2
of exterior surfaces had readings above 1 mg/cm lead content, and
70 percent of all surfaces had a reading of 1 mg/cm 2 or more lead content.
Gilsinn (2:114, 140) estimates that there are 14,600 children
in Los Angeles and Long Beach, California, that have elevated blood
lead levels and live in 172,000 hazardous housing units.
Chisolm
(6:956) concludes that the environmental factors were important in
the ingestion of small quantities of leaded-paint flakes.
Chisolm
recommends that removal of lead from the child's environment is the
only adequate protective measure.
Browder, et al. (9:914-915) studied the evaluation of screening
·programs for childhood lead poisoning in Newark, New Jersey, during
the years 1967-1972.
They suggested that lead poisoning is not de-
pendent on ethnic grouping but is related more to the environmental
risk ·to which children are exposed.
Their study indicates that a
program which does not protect children from re-exposure will have a
rising average age of admission.
Cohen (3:1432) discovered that lead content of soil contains
visible paint chips near old dwellings where the children play.
Soil
from a city park and near a ga.s station contained . 022 percent and
.024 percent of lead, respectively.
Cohen feels that. lead in paint as
-v;;·,;;ll as ai:cbo:.-ne lead contributes t.o the increased body burden of
lead.
6
The concentration of lead in the air near major highways in· large
urban communities is dependent on the traffic density and can vary from
4 to 511 micrograms/m 3 • Increased blood lead levels have been found in i
persons exposed to high traffic density.
Goldsmith and Hexter (10:132,'
134) evaluated existing epidemiological data and observed a logarithmic'
dose-response relationship between blood lead level and atmospheric
lead concentrations for atmospheric concentration greater than 2 micro-:
3
grams/m • Airborne lead may contribute to an urban child's intake by
I
direct inhalation or more importantly by ingestion of grass and soil
covered with atmospheric lead fallout (10:132-134}.
Hemphill, et al. (11:190-194) studied lead content of soil and
vegetation along highways used by trucks to transport lead sulfide
from mines and mills to smelters.
Analyses of vegetation samples in-
dicate that concentrations on or in plants varied greatly between
individual sampling sites.
The highest levels present were in samples
collected near smelters, mines, mills and along rough areas in the
highway surface that would cause the bouncing of the truck transports.
Lead content decreased very rapidly as the distance from the roadway
increased.
Charles (7:36-38)reported that 48 percent of the parents know
that t.heir children have a habit of eating paint chips and/or other
non-food substances.
He stated that this situation may be an
indicate~
of a lack of adequate parental supervision.
Printed media, newspapers and magazines, also contain lead.
Printed media use lead-based or lead-contaminated ink for printing.
Lead contents of printed media varies considerably, depending on the
intensity and shading of the color.
Children may also ingest lead
by chewing printed media (12:238-240).
Predic·tion Jvlodels
'Ihe purpose of Gilsinn's research (2:1) was to .evaluate the
natior:vJide magnitude and extent of lead poisoning in the United States.·,
Estimates are given of the number of children who have elevated blood
7
lead levels
(40~g.
or more of lead per 100 ml. of whole blood) in
each of the 241 standard metropolitan statistical areas throughout the
country.
The procedure followed in obtaining estimates of the nationwide
magnitude and extent of lead poisoning may be divided into four
steps (2:15-20):
1.
Data acquisition
a)
The determination of relevant data wanted
b)
The determination and evaluation of sources of information
of the data, this includes the establishment of criteria
for comparison of sources
c)
The acquisition and assimilation of the data.
The data
may be obtained from several different sources and requires manipulation into a required form.
This process
consists of aggregating information into desired categories, coding, etc.
d)
2.
The preliminary analysis of the data
Model construction, involves three stages:
a)
Decisions about the purpose and scope of the model
b)
Decisions about the detexmination of the methodological
basis of the model and the form it will take
c)
Curve-fitting for predic·tion purpose, the resulting
models are then compared on the basis of reasonableness
of behavior and goodness of fit to the data
3.
t-iodel validation, there are two levels of validation:
a)
Checking the
b)
The compariscn of the magnitude of the phenomenon as
asm.m:-.~tions
upon which the model rests
predicted :b;· the model and as actually occurring
4•
Model appl ica ti.on
The model is applied to yield desired outputs.
The outputs
are analyzed. in terms of model formulation and validation
agaJ.ns·t reality.
8
:Regression Equations
The simplest model is linear which usually takes the
forms (2:57-61):
where
y
the dependent variable to be estimated
X.
the predictor variable
b.
the sample regression coefficient of yon X.
log y
=
1
1
1
or
log b
0
+ b
1
log x
+ b
1
2
log x
2
2
The multiple correlation coefficient (R ) is used as a
simple criteria of best fit.
R
2
=
R
2
is given as
SSR
SST
where
SSR
sum of square attributable to the regression
SST
sum of square of total (the dependent}
Other regression models
The simplest multiplication model used to estimate the
number of children with elevated blood lead levels is (2:6162) :
E
=
K. D • I
H
•
where
E
the number of children wiLh elevated blood lead
K
L'<.e number of children
D
the Dumber of dilapidated or deteriorating housingi
units
H
the numl>er of housing units
I
an elevated incide:tlCC! rat:f;; for children living
9
in a hazardous environment
This generic model form has been developed separately into
two different models.
Model A is (2:62):
= K6
E
• D • I
0
H
where
E
the predicted number of children with elevated
blood lead levels for an appropriate population
K
6
D
the number of children 6 years of age or less
the number of unsound housing units (dilapidated
or deteriorating}
H
the total housing units
I0
an elevated blood lead levels incidence rate for
high risk children {constant)
Model B is the same as Model A, but I
0
is different.
It is
replaced by I that is computed from the equation (2:63):
where
K , D and H are the same in Model A
6
P is the total population
Parameters (the numbers, .747, .2967 and .2484) were
estimated using New Haven data
This model has been judged best according to the
2
R = .945 (2:63)~
'l'ola, et al.
i.
criteri~
(3:134-136) studied prediction of blood
lead levels concerned with absorption and biological effects.
The subjects consisted of 33 workers employed in battery
plants.
gression.
The method of analysis was a forward stepwise reThe model of their study was:
10
=
y
46.203 + .00567x
1
+ .1564x
2
.000221x
2
2
. 2
.630Bx
3
where
blood lead concentration
y
x
x
x
1
2
3
coproporphyrin concentrations
urinary lead concentrations
logarithmic value for erythrocyte
~ - aminolevulinic acid dehydratase activities
2
and given, R = .605
CHAPTER III
RESEARCH DESIGN
Subjects and Sampling
The subjects for this study were children aged one thru six,
living in the Venice District, Los Angeles, California.
TWenty-six·
children who live in 26 households were randomly selected by using
a random number table.
Measurement
The questionnaire form (Appendix 1) was constructed by the
Department of Health Services, Community Health Services, Los Angeles
County, California, to obtain information concerning children's background, relationships and environmental variables.
The lead content in painted surfaces was determined by using a
portable radioisotope x-ray fluorescence
(XRF)
instrument.
tion of the technique is provided in Appendix 2.
XRF
The descrip-;
.
'
The radioisotope
analyzer will provide the weight of lead per unit surface area
(mg/cm 2 ) •
The sensitivity of this instrument is adequate to identify
the presence of lead in excess of 0.5 mg/cm 2 through levels up to
20 mg/cm
2
(14:3-4).
For this study atomic absorption spectroscopy (AAS) was the
method chosen to measure the blood lead content.
This method requires
'
only a small blood :;::ample that is obtained by a finger prick (15: 33-42)j.
Met-..hod of Analysis ·
The
util.iz\~d
m-~1 "t.iple
regression wi t.h 9ase combinations . (BMD 03R) was
t.o compute a linear prediction equation (16: 331-340).
11
12
Regression Equation
The goal of this study was to use a regression model for the
prediction of children's blood lead level.
The multiple regression
model may be written as (17:381):
where
y
predicted blood lead level for given x , x , • • •
1
2
values of predictor variables
x.
the values of predictor variables
b
a constant value called intercept
1
0
b.
1
~
parameters, partial regression coefficient of y on x.
1
In this study there are eighteen predictor variables and one
dependent variable (list of variables are in Table 1).
The statis-
tical analysis of the data was performed with the use of the BMD 03R
(Biomedical computer program multiple regression with
~ase
combina-
tions), on the California State University, Northridge computer.
Details of this program are given in Appendix 3.
g
13
TABLE 1
LIST OF THE VARIABLES AND LABELS
ry
xl
living with child
x2
mother's monthly pay
x3
father's monthly pay
x4
mother - months on job
xs
father - months on job
x6
mother's grade
x7
father's grade
xs
pottery
x9
sex
xlO
blood lead level (the dependent variable)
xll
measurement of lead on exterior wall
xl2
wall condition exterior
xl3
measurement of lead on interior wall
xl4
wall condition interior
xlS
measurement of lead on exterior trim
xl6
trim condition exterior
xl7
measurement of lead on interior trim
xl8
trim condition interior
xl9
length lived in dwelling
CHAPTER IV
RESULTS
Table 2 presents the multiple regression equation using all the
variables.
(R
2
The multiple correlation coefficient R is very high
The regression models with a limited number of independent'
= • 91).
variables are easier to analyze and understand, but as stated by Neter
and Wasseman (18:372) the problem is how to select a small number of
2
predictor variables so as to obtain best prediction (high value of R ).
This best set needs to be small enough so that maintenance costs are
manageable and analysis is facilitated.
Table 3 shows the correlation matrix of all variables.
It will
be seen that the independent variables, which have high correlation
with blood lead levels, are months-mother on the job (x ), father's
4
grade (x ), and father's monthly pay (x ), with respective correlations:
7
3
of .60, -.53, and -.50.
Other variables have correlation coefficients
lower than .50.
Table 4 presents results of above thre'e predictor .variables,
chosen for their importance.
2
is .7109 and R
The multiple correlation cdefficient R
is .50.
The estbnated parameter equation of the multiple linear regression
model with three predictor independent variables is:
y- 32.75- .54x
1
+ .76x 2 - 3.50x
3
where
eX'.i?ected blood lead level
y
,,
x
1
father's raonthly pay
measurement of lead on ex·terior walls
x.;
measurement of lead on interior walls
·-'
.1 ~
15
TABLE 2
THE OUTCOME OF THE MULTIPLE LINEAR
REGRESSION WITH ALL EIGHTEEN INDEPENDENT VARIABLES
(USING BMD 03R)
Sample size.
• 26
The number of variables.
• 19
Dependent variable is now NO
10
Coefficient of determination (R 2 ).
0.9138
Multiple correlation coefficient {R)
0.9559
Sum of squares attributable to regression.
• 1182.82394
Sum of squares of deviation from regression.
111.63760
Variance of estimate • • •
15.94823
Standard error of estimate
3.99352
Intercept value • • . • • •
• • 46.36558
ANALYSIS OF VARIANCE FOR
THE MULTIPLE LINEAR REGRESSION
Source of variable
D.F.
Sum of sq.
M.S.
F ·
Due to regression • . • • • 18
1182.82394
65.71244
Deviation about regression •• 7
111.63760
15.94823
Total • .
• 25
4.1204
1294.46154
t-VALUES OF VARIABLES
xl
0.37370
x7
0.03497
xl4
1.00734
x2
x_
0.23186
X8
-L94386
xl5
-1.21652
-1.02463
x9
-1.24328
xl6
0.24015
3.31923
xll
-0.27394
xl7
1.06458
x5
0.74812
xl2
-1.39615
xlB
1.41960
x6
·-1. 68465
xl3
1.17454
xl9
-0.90004
.:5
X
4
TABl:..E 3
'x x1
x5
x4
.016 ~322 .148 7209
X ·2
.072 <624 -:116
1
x2
x3
CORHELATION COBFFICIENT MATRIX OF ALL VARIABLES
xll
x6
x7
X8
x9
xl3
xlo
x14
x16
x12
x15
.024 -:374 .154 .048 .068 .095 .040 -:120 -:108 -:166 .064
.281 -:042 .111 .164 .264 .296 -:196 -;-088 .147 .319 -:071
-:192
-:144
xlB
.188
T056
x19
-:103
.250
• 148 .381 .128 .211 -;-497
xl7
'•.,::',......··"'
~':l
T225 T097
·>
x4
X.~
::,
Jt6
.171
T302
-;-254
.113
T064
.177
7047
.118
-:050 .
.074 -:302 .061 .186 .590
.367
.130
T066
-:403
.279
T233
.086
-:282
.257
T040 .131 .089 7201 -:061
.080
.156
-:086
T064
-:039
.041
.187
.119
.673
-:305
.317
• 165
-;-075
.184
":"136
.364
-:396
.083
• 039 .
.287 .249 ":"534
.297
.124
.049
.260
-:064
-;-313
-:005
• 073
-:183
.288 ";'267
• 022
-:192
.093
-:140
.091
.044
.110
-:154
.. o78 .
.010
.0~2
'0166
.o91
.097
-:074
.077
-:088
'0152
";'308
.360
.043
.. 181
.031
T098
-:175
-;-198
T002
.136
I
T480
.334
-:118
.380
-:625
.026
7532
• 365
.I
':'080
':'113
":"029
.441
.210
.222
.os5
1
":"315
.763
":"377
.392
-:529
-:172
I
':'660
.569
T.723
--~-
T086
-
.488 -:114
~7
~
8
x9
-
xlO
!i:ll
l'tl2
.ooo
.
-
''13
"14
~
··1s
':'489
:1(16
:;~17
>tlS
---
-
Se~
Table 1 for name3 of variable;3
'{19
"l-lOTE:
.. , . . _,
..._....,~ _,_,._~.·----~-~---~_,.,
. .,_ _.,__.__ .. . -.,., ,.... ,... ~,....
----~-w ·-·~-~->~-·-"":"••·
'•
~· ···;~;
··~
- ..
"~l':
•••.,,,_...'1'·•·-~..--
I
.
.612
.223
-:-058
T464
-=-o11 1
':'439
.468
-:-084
':'119
':'138
l
.120
I
I
l
-·-·•t·,......
-~
......
..
;
... ·.. ..:.. ·,.; ··-·
~
. :_, __ ...
-~-
...
~-
-- ·-
1-'
(j)
17
TABLE 4
THE OUTCOME OF THE MULTIPLE LINEAR
SUBSET WITH THREE INDEPENDENT VARIABLES
(USING BMD 03R)
Sample size •
26
The number of variables • •
4
Dependent variable is now No • •
10
Coefficient of determination (R2 )
0.5054
Multiple correlation coefficient (R).
0.7109
Sum of squares attributable to regression •
654.27271
Sum of squares of deviation from regression . • 640.18883
Variance of estimate • • • •
29.09949
Standard error of estimate.
5.39439
Intercept value •
32.75342
ANALYSIS OF VARIANCE FOR
THE MULTIPLE LINEAR REGRESSION
Source of variable
Due to regression •
M.S.
3
654.27271
218.09090
22
640.18883
29.09949
• • • • • • 25
1294.46154
Deviation about regression
Total • •
s.s.
D.F.
F
7.4947
•
Variables
Mean
s
b.
l..
sb.
t
l.
xl
7.65385
7.24951
-0.53836
0.15456
-3.48312
x..,,..
2.45769
3.97021
0.76434
0.28964
2.63894
x3
0.30000
0.94657
-3.49948
1.23757
-2.82771
29.46154
7.19573
y
where
s
standard deviation
bi
regression
s
.
b ..
l.
coefficie~t
standard error of regression coefficient
18
Table 5 derived from Table 3 presents the correlations amon'g the
variables used in the final regression.
The correlations among the
variabLes x 1 , x and x appear to be substantial and may in part ex2
3
plain the low R2 •
Table 6 illustrates the percentage contribution of each of the
2
predictor variables to R • Apparently the economic variable is relatively more important than hazard variables.
19
TABLE 5
CORRELATION COEFFICIENT MATRIX OF FOUR VARIABLES
Variables
xl
(Fath~r's
monthly pay)
x2 (Measurement of lead
on exterior wall)
x3 (Measurement of lead
on interior wall)
y
''
(Blood lead levels)
xl
1.0000
x2
x3
y
-0.17105
-.25415
-.49753
1.00000
.33443
.36055
1. 0000
-.18146
1.00000
20
TABLE 6
THE RELATIVE CONTRIBUTION OF PREDICTOR VARIABLES
'-1
r
x.y
I
Variables
'it
s
X
b
b
X
sx
xsy
2
R xy
Father's monthly
pay
7.24951
-0.53836
-.54238
-.49753
.26985
'{, 'L
Measurement of lead
on exterior wall
3.97021
0.76434
.42172
.36055
.15205
. .J
Measurement of lead
on interior wall
0.94657
-3.49948
-.46034
-.18146
.08355
Blood lead level (y)
7.19573
f'-\'1!.
where
s
b
standard deviation of variables
X
regression coefficient of variables
X
standard deviation of blood lead level
correlation coefficient of predictor variable with
blood lead level
=
contribution of pre~ictor variable to coefficient
of determination (R )
CHAPTER V
CONCLUSIONS
The goal of this study was to construct a model that predicts
childhood blood lead levels and determines the relative importance of
the selected predictor variables.
The results of this study provide
i
a model that contains only three predictor variables, father's monthly i
pay, lead on exterior and interior walls.
Father's monthly pay assume$
the greatest relative importance in the prediction, followed by measurement of lead on exterior walls and interior walls, respectively.
The environmental factors are known to~fect the blood lead
levels.
Families whose monthly rate of pay is low, maintain usually
a hazardous dwelling where 20 percent of interior walls, and 30
of exterior walls are in deteriorated condition.
percen~
Lead content on ex-
!
terior walls are higher than lead content on interior walls (2.5 and
0.3 mg/cm 2 , respectively).
It is hypothesized that living in substandard housing is a function of father's income.
One then wonders what form the real causa-
tive agents of lead poisoning takes, economic, which may force one to
live in a high risk area or the Lraditional environmental contaminants.:
0
21
22
BIBLIOGRAPHY
1.
Balch, R.W., "Laboratory Diagnosis of Increased Lead Absorption",
Archives of Environmental Health, 28: 198-207, 1974.
2.
Gilsinn, J.F., Estimates of the Nature and Extent of Lead Paint
Poisoning in the United States, u.s. Department of Commerce,
National Bureau of Standards Technical Note 746, 1-143,
December, 1972.
3.
Cohen, C.J., Bowers, G.N., and Lepow, M.L., "Epidemiology of Lead
Poisoning a Comparison Between Urban and Rural Children",
Journal of the American Medical Association, 226: 1430-1433,
1973.
4.
Sayre, J .W., et al. "House and Hand Dust as a Potential Source
of Childhood Lead Exposure", American Journal of Disease of
Children, 127: 167-170, 1974.
5.
Caprio, R.J., et al. "Lead Absorption in Children and Its Relationship to Urban Traffic Densities", Archives of Environmental
Health, 28: 195-197, 1974.
6.
Chisolm, J.J., and Harrison, H.E., "The Exposure of Children to
Lead", Pediatrics, 18: 955-956, 1956.
7.
Charles, C.B., "A Survey for Children Exposed to Lead Paint in
the Southeast Health District of Los Angeles County", Master
of Science in Health Science thesis, California State University, Northridge, 28-29, 36-38, June, 1974.
8.
Rocke, A., "A Survey of Lead Paint in Specific Census Tract in
Burbank, California", Master of Science in Health Science
thesis, California State University, Northridge, 32, October,
1973.
9.
Browder, A., et al. "Evaluation of Screening Programs for Childhood Lead Poisoning by Analysis of Hospital Admissions",
Araerican Journal of Public Health, 64: 914-915, 1974.
10.
Goldsmi r.h .• ,J .R., and Hexter, A.C., "Respiratory Exposure to Lead:
Epidemiological and Experimental Dose Response Relationships;;,
Sci~~, 158: 132-134, 1967.
11.
Hemphill, D.D., et al. "Roadside Lead Contamination in the Missouri\
Lead Belt'', Archives of Environmental Health, 28: 190-194, 1974.
('
23
12.
Joselow, M.M., and Bogden, J.D., "Lead Content of Printed Media
(Warning: Spit.balls May Be Hazardous to Your Health)",
American Journal of Public Health, 64: 238-240, 1974.
13.
T:Jla, s., et al. "Parameters Indicative of Absorption and Biological Effect in New Lead Exposure: A Prospective Study",
British Journal of Industrial Medicine, 30: 134-136, 1973.
14.
Nuclear-Chicago-Texas Nuclear Division, Quantitative Determination:
of Lead on Painted Surfaces Using a Radioisotope X-ray Fluores~
cence Analyzer, (Principles of Instrument and Primary Survey
Results), Austin, Texas, September, 1970.
15.
King, B.G., and Bratzel, P.M., Technologies in Detecting Lead
Poisoning, U.S. Department of Health, Education, and Welfare,
Cincinnati, Ohio, November, 1972.
16.
Dixon, W.J., "BMD 03R: Multiple Regression with Case Combinations", BMD Biomedical Computer-Programs, University of California Press, Berkeley and Los Angeles, California, 331-340,
1973.
17.
Snedecor, G.W., and Cochran, W.G., "Multiple Regression",
Statistical Method, Sixth Edition, The Iowa State University
Press, 381, 1973.
18.
Neter, J., and Wasserman, w., Applied Linear Statistical Models,
Richard D. Irwin, Inc., 372, 1974.
APPENDIX 1
•
24
25
LEAD PROJECT STUDY
Case Nmnber
No.
------of brothers and sisters
Name
--------
-----------------No
All same surname Yes
Sibling surnames if different from child's
-----------------'
Does child live with
If parent(s), are they
1)
Parent(s)
2)
Other, please specify - - - - - - - - - - - - - 1)
together
2)
separated
3)
divorced
4)
single
The following information should be gotten for each parent or for
the person with whom the child lives.
Mother
Father
Other (Specify)
-=~
Grew up in what state or country
Occupation
(Supplemental
income)
Monthly or hourly rate of pay
---
No. months on present job
Highest grade in school completed
Who takes care of the child at
least 4 hours during the day?
Where do the children play
most of the time?
---
1)
Mother
2)
Adult relative
3)
4)
Older sister or brother of
child
Baby sitter
5)
Nursery school
6)
Other, please specify ------
1)
In the house
26
2)
In the yard
3)
On sidewalk or street
4)
In the yard, and on sidewalk
or street
5)
In the house, and on sidewalk
or street
6)
In the house, and in the yard
Does the child eat any non-food items?
If yes,
1)
Paint chips
2)
Dirt
3)
Other, please identify
4)
No
5)
Paint chips, and dirt
6)
Dirt, and other, please identify
7)
Paint chips, and other, please identify
8)
Paint chips, dirt, and other, please identify
Does the mother (or person caring for child) eat any non-food substance?
---------------------
If yes, what?
Is pottery used for cooking or storing of food or drink? 1) Yes
Does child have occasional
0)
Blank
1)
Stomach aches
2)
Vomiting
No
3) ..:___ Irritability
4)
Poor appetite
5)
Stomach aches, and vomiting
6)
Vomiting, and irritability
7)
Stomach aches, vomiting: irritability, and poor appetite
Stomach aches, irritability, and
poor appetite
Vomiting, irritability, and
poor appetite
8)
9)
'
27
COUNTY OF LOS ANGELES DEPARTMENT OF HEALTH SERVICES
COMMUNITY HEALTH SERVICES
313 North Figueroa Street, Los Angeles, California
ADDRESS
1.
90012
------------------------------FAMILY NAME------------------
Age of Dwelling
Pre-1940
Post-1940
2.
Condition and Lead Level of Paint Surfaces
EXTERIOR
XRF
XRF
Readings
INTERIOR
Condition*
Readings
Condition*
Locations:
Walls
Trim
(incl. baseboards)
Other Surfaces
Fences
Garages
Others
3.
-------
Distance from freeway or major traffic artery
200 feet or less
200 feet to 300 yards
more than 300 yards
4.
Lead levels in dirt (soil)
Dirt sample collected from foundation site
Dirt
5.
sa~ple
collected from property line
Type of Construction of Dwelling
Wood frame
Stucco
----- Other
Lab Results
Lab Results
28
6.
How long have the occupants lived in this dwelling by number
of months?
------ Months
* Specify if chewed (X) or other types of deterioration such as
peeling, chipping, flaking, etc. (D) or both, (X,D); good
intact surfaces, not deteriorated (I).
*u.s.
GOVERNMENT PRI,HING OFFICE- 1974-740..()74
29
DEPAHTMENT OF
HEALTH, EDUCATION, AND WELFARE 'PUBLIC HEALTH SERVICE
Center for D•scasc Cvntr0l
Bureau of State Serv<ces
Atlanta, Georgia 30333
NATIONAL COMMUNITY LEAD POISONING DATA SYSTEM
REQUEST FOR BLOOD LEAD ANALYSIS
-
Nrw
-
Corrrcllon
CASE NUMcllR
REQUEST NUMBER
28
42
LAST.
•43
MID. !NIT.
1-
z
0
44
I ·I I I
o~
OA TE OF BIRTH
UNIQUE
Mal•
.~em.Ur
50
49
51
CJ
z
I
<
a:
First Blood
Conhrmat&on Blood
Other
I I I II I
78
STREET
64
b3
HOUSE NUMBER
I
2
3
57
56
DATE SAMPLE
COLLECTED
I I I I
I I I II
CHILD'S
HOME
ADDRESS se
;:
D
I I
SEX
~
15
D
1
27
26
FIRST
1,7
9
8
D
I.
I
CHILD'S
NAME
I I II i I II
I I I I I I I
2
17
APT.
19
24
ZtP
20
0
25
I I+I
TELEPHONE NO.
D~
31
D
Yu
No
v..
D
No
SIBLING
OF VICTiM
ETH!IItC
GROUP
51
48
47
HOSPITAL CHART NO.
HOSPITAL
CODE NO.
PARENT/
4
Spanish Speoakinl
Wh1t.r
Sp.1.nish Speakml
Other Black
5
Oriental
6
7
A m~r. Indian
Other
D
Oth~r
Bh~o<'k.
I Oi
I I
I I I I I I I I
~h1t~.
I
2
3
34
33
32
PARENTAL
CONSENT
36
I
2
. 35
No
Unk.
TESTED
FOR LEAD
PREVIOUSLY
~crermn1.
I
2
Hosplt&JizedD
Outpatient
I
Siblin& of Victim
L~ad '" OweJhn11
RrlerTt>d•Pnvate Phys..
3
4
53
52
PATIENT
STATUS
y.,
I
2
3
Ref~ned/Hosp1t.al
5
PURPOSE
6
FOR TESTING
OR AWING
POINT NO
Suspect. Ca$4!:
GUARDIArN~--~-r~~,--r~r-,-~~
I~......_.__..,L_.L...-.;L--.L--1---'---'---'-1__.]
NAME
f,cst
IJ
67
.J
ldSI
17
FOR USE BY CONTROL POINT
..J
o._
a:z
....
_
zo
o~
rn
[I]
35
u
37
36
~URSE
J.
JS
39
CENStJSILOC.
44
SAN IT
FOR USE BY LABORATORY
I I I I
45
>
a:
0
1-
TEST
RESULTS
I I IJ
59
61
LEAD
IN BLOOD
<
a:
I
I
I
DATE SAMPLE REC'D
~
rn
62
..__I..__I..__I..__I..__.I1.....__.__.1I
.__I
~1
LASO.RATORY NUVSER
I I
63
64
HEMATOCRIT •
D
57
66
SICKLE
CELL •
HEMOGLOBIN"
:-.i ol A ppltcablf'
0
I
2
3
4
?<•SILtv~
NPit,dtl\'E'
68
Dasf'.l~
Tra11
6'l
D
0
l"5<.ih:,•
1
fir,.~~n
2
ln-lg:ld:t·<~nt
C0~8'T1CN
3
Ponr
(J",Hfi~lt)'
COHb•
CF SA\•?LE
0
Ill
c(
u
..J
71
ME !HOD
USED
i
!\hero AA
'l
MtCTO AA
J
ASV
4
Protoporphynn
5
o ••
6
OLhrr
I I I I II I
OA TE TESJ
77
A£SUt TS
AEPORTfO
•Optional, but code Zero's if not applicable.
- - -r
REQUEST
NUMBER
CHILD'S
NAME
ITI
l
l I I I
O,____.__.._f..I~ I I ·J I
I
REQUEST
NUMBER
[II
CHILD'S
NAME
ITI
CDC 7.16·2
White- Originator
11·73
G-HH.~o
l
,.·.;,
I
] ITJI
I
I T[J l
and P 1nk --- L Bbor.otorv • .and then
-.·.···
-- --- --l II
. .__[I~__.LJ_LJ
I
·,
.,
REQUEST
NUMBER
CHILD'S
NAME
--------------------·
Form Appro' e<:l
,-.. o\t
n
"'"·'
t."?.;,·,l--t'·"
APPENDIX 2
30
31
•
QUl".NJ:'ITJl.TIVE
DE'I'ERl,~INATION
OF LEAD
ON PAINTED.SURFACES
USING A
P~DIOISOTOPE
FLUORESCENCE
' " ' ~-.! ·- -
.! .... ~ -
-
\l:' .t...i..UI.,.;.I..,P.Lt::;;>
-
L:
V.L.
X-RAY
Jl~ALYZER
~ -
-· ,_ ........... --~ .........
...L.H.::> '-.t. U!IICU
,J.,
~
and
pre l.l.riu..n ary s urve:y . . . -_ ... ,.J-_,
.t.C.::>U...L.~;;>J
Nuclear-Chicago - Texas Nuclear Division
8 September 1970
32
•
INTRODUCTION TO 'l'L.G PROBLE~·~ AND 'i'IIE l~SSAY 'l'ECIINIQUE
The incidence of lead poisoning, especially among young
children living in old tenement dwellings, has for many years
been recognized as a matter of
grea~ concern. 1
The problem is
particularly acute in the urban. ghetto areas of major cities,
but i t is by no means exclusive t6 these designated "lead belts".
Although several possible sources of lead may be faulted, by far
the most prevalent is the
pigment.
i~gestion
of paint \vhich contains lead
Older houses, most of which are subdivided into apartment
dwellings, invariably have many layers of paint on the interior
surfaces.
The sub-surface paint, usually pre-1940 vintage, has
been found to contain incredibly high amounts of lead.
Paint
chips, flakes and chewable areas of doors, window frames and
railings P,resent invi::..:..>lg objects to the pica-afflicted child.
Control measures are nm.,r in force in several cities in an effort
to eliminate the problem, and hundreds of dHellings have been
successfully cleared.
The magnitude of the task however is almost
beyond comprehension.
Hundreds of thousands of apartments are
involved and in some cities total clearance is estimated to cost
as much as a billion dollars.
2
Patch-work remedies could be the
solution if an efficient quantitative on-site survey were possible.
An issue no\v facing many authorities is how to implement such a
survey.
33
Chemical methods of analysis not only place excessive
demands c'n the laboratory, but alsc entail considerable effort in
Destructive
.,
the field to gather a meaningful number of samples.
sampling can also create some difficulties with the ter:.ant.
many cases also the validity of a
d~screte
In
sample taken from a
region of highly variable composition can be suspect.
Still more
succinct is the question of interpretation of the analysis itself.
Present-day regulations are based on the 1964 A.S.A •
. paint standard
3
\vhich stip·1lates that paint exceeding 1% lead by
weight is regarded as hazardous for indoor use. Such a standard
when applied to a painted surface is clearly in appropriate.
It suggests that:
(1)
if the lead containing paint layer is
sampled along \vith a large number of layers of non-leaded paint
(\·lith perhaps some plaster or wood), leaded paint containing 10%
Pb or more could be
~~nsidered
tolerable; and
(2)
{to accentuate
the inaptitude of the standard still further) a very thick paint
surface of 1% lead paint is no less hazardous than a thin layer
of the same paint.
Potential toxicity is clearly a
f~~ction
of the total
lead content of the painted surface, or to be more definitive,
the weight of lead per
uni~
area of painted surface.
The most meaningful measurement therefore is one \·lhich
establishes this quantity on-site since there is alwtys the possibility that a detached sample may not carry with it all the avuil0blc
lead.
·.
34
On-site quantitative determination of lead in painted
surfaces is possible using the X-ra.y fluorescence technique.
The
te.chnique is a well established analytical method, in which the
atoms of the sought element are caused to fluoresce, emitting
characteristic X-radiation, under tQe influence of an incident beam
of stimulating radiation.
By using a radioisotope source to
provide the stimulating radiation and an efficient detection system,
a portable, compact and sensitive instrumental method of analysis
is made possible.
Fortuitously, considering the nc.ture of the
paint deposit, the K X-rays of lead, excitable with a radioisotope source, possess an energy of 75 keV.
Thus the X-radiation
from a deeply sited layer can emerge from the surface with virtually
no attenuation in the overlying material.
Another ad van t.age
in using the penetrating K X-radiation of lead (as opposed to the
lower energy L X-radiation of the element) is· that each successive
layer of lead-containing-paint contributes to the measured intensity.
In effect,
thr.~efore,
a signal corresponding to the total quantity
of lead within the influence of the detection system is obtained .
•
The principles of a radioisotope X-R-F instrument designed
specifically for lead are described in the following section.
instrQment is both portrihle and compact
remote and awkwardly located situations.
a~d
The
thus applicable to
Ceilings, windm-; frames 1
stair rails, furniture, etc. can be rapidly scanned.
Sensitivity
is certainly adequate to identify the presence of lead in excess
35
of 0.5
~g/cm 2 , though, as the results of a series of field surveys
have shm.;n, levels up to 20 mg/cm
2
are typically encountered .
...
There is no reason to believe that the instrumental technique
would be questioned in a court· of iaH, but even if dGbarred from
•
this privilege, the technique could. certainly reduce the number of
samples no\v taken for chemical analyses.
surveys could then become a reality.
Total block by block
Finally, there can be no
question of the tremendous advantage of being able to check rapidly
from spot to spot over a suspected region of lead contamination.
The psychological feed-back factor provided through the instrument
,.--,
operator would be a conside'rable improvement over the "present
sam~ ling'!\ technique ..
36
PRINCIPLES OF l{nDIOISO'i'OPE X-H-F HETHOD FOR LEAD DETER>.iiN.:\TIO:l
The operating principles and construction of the
Nuclear Chicago 9200 series portable analyzer are fully described
in the attached brochure
{Appe~dix
graph of the instrument as it
m~ght
1).
F~gure
1 shov;s a photo-
be used for the determinatio:1 •
of lead in a painted surface.
Briefly, the technique used in this insturment is nondispersive X-ray fluorescence analysis in which a lmv intensity
radioisotope source is used to excite the characteristic X-rays
or the desired element.
Coarse energy discrimination against
backscattered and undesired. X-rays is afforded by electronic
pulse height selection and fine discrimination (where necessary)
is achieved by means of a pair of absorption edge filters.
The
thickness of these filters are adjusted so that the transmitted
ra:diation i!ltensi ty :;.s equal over the selected e·ne:r-gy region
except for the narrow "pass band" bet\·1een their two absorption
edges.
By choice of filter materials this pass band is arranged
to bracket the energy of the desired x-ray.
In operation the
filters are placed sequentjally over the detector window and the
difference in measured intensity is recorded.
This difference
reading is a function of the amount of the fluorescent
present in
~~e
el~ment
sample.
The radioisotope source and the filter materials are
selected for each application.
The source is located in the
center of the detector aperture, a· location which
a~fords
maxi-
mum detection sensitivity for lllinimum ·source activity, and is
.....
~-
..
.I
·.
,..
/'
.........
..
-
··.
•.. ,:_..a ...
·-
.......
-..
·- ....
_
.,
•
.......
·4
"·.
. ;· ·_::~
·l
...
• >
'.
.· ~
'1
'.
..,··-~
~,..,
'
I
..
I
..--....~~.,.~ -~ ~ ~--~·~ ---~...
••
..
......
I
.. 1*
•••••
J
~ -~·--. . . --~ --·,
.. _ - . _ _ ........... --
'
·"
t ...... " .... -.. .. ;,...
'
.J.. .. ~. ..... ~ ........ ::..~
·~•• -.
•.
.
~~-- ..
38
completely shielded unt:il the probe is. placed on the sample to
be r.1easnrcd.
pin is
aside.
\'lhen the probe is placed in position, a retractable
a~tomaticu.lly
depressed and the source shutter is swung
On removing the probe from the surface the shutter auto-
rna tically sHi!lgs back in posi t~on to cover the source.
•rhe ra.'O.ia-
tion dose is virtually nil when the shutter, or the sample,is in
.
place, personnel monitoring is not in fact
~equired
for the use
of the instrument.
The detector used is a Sodium Iodide (Thallium activated)
crystal scintillation counter.
This detector affords a large
sensitive area for maximum detection efficiency r>nd is both rugged and compact for ease of,use in a portable instrument.
The application of the radioisotope X-ray fluorescence
analyzer to the oeterminat1on of lead in paint is illustrated
schematically in Figure 2.
. '•
.._-~-t;::
<-···.. ·····'·' ._,...
..
The optimum location of the sample,
"
relative 'to the probe l·ead, is fixed'by means of the stand-off
spacer.
In this location the measured count rate is a maximum
and is relatively uneffected by sample displacement of ± 1 mm or
so.
Thus,
geo~ctrical
errors arising from variation of the
(d) of the lead paint layer are virtually eliminated.
dep~h
Determina-
tion is still possible at larger separations of the sample from
the head 1 as may be occasioned for example by an av.'kwardly
"'located surface such as· the mouldi!lg beneath a \'Iindow sill; pro=
vided the source shutter pin is depressed.
The overlying paint layers and the presenc<; of other
·metal pigments can effect the intensity of the characteristic
X-rays orginating from deep in the sample simply by their effect
39 .
..
...
"
Detector
Filter
Space
X-Ray Source
~Paint
Sample
~~~~--~~~~~~~~·~~~~~~~~-~-----~--~
(1) Schematic of Measurement Technique
Layers of Nevl
Paint and UnderCoats
t
d
+
Undercoat
-·.-
Plaster
(2) Idealized Sample Distribution
..
Figure 2 - Non-Dispersive X-Ray Determin2tion of Lead in Paint
.
40
on the abso.:::-ption of the i!lgoing and out.going radiation.
m~gnitude
The .
cf the effect depends on the energies of the exciting
and fluorescent radiation.
It is preferable therefore in the
determination of lead to excite the Pb K X-rays
...
(energy 75 keV)
rather than the Pb L X-rays (energy 13 keV) since the latter
~
radiation would be heavily attenuated in the overlaying paint
layers.
A suitable radioisotope source for exciting
Pb K X-rays is Cos 7 which emits. gamma rays of 122 keV.
activity of "'300 l-!Ci is adequate
A source
though an activity of 1 mCi is
preferred for rapid (10 's seconds} measurement and t.o provide a
long useful life (18 months to
2 years) between source exchanges.
The dose rate from an unshielded, 1 mCi Cos 7 source is about
3rn"R/h r
r~
t: 1 0 r.m.
F~gure
3 shows a typical pulse height-energy distribu-
tipn obtained from a sample containing _lead.
The intrinsic
energy resolution of the detector is not sufficient to resolve
the peaks corresponding to the lead K X-rays {75 keV} from the
intense backsca:tered radiation (at '\,83 keV) and hence balanced
filters are necessary to isolate the K X-ray peaks.
Sui table
filter materials are Tungsten and Gold which have absorption
edges at 69.5 keV and 80.7 keV, respectively.
this filter is also illustr~ted in Figure 3.
balanced {by thickness adjustment}
The operation of
The filters are
to give equal transmitted
intensities for radiation outside the energy pass banrl defined
by the Tungsten and Gold absorption edges.
..
41.
50
/
Tungsten
Filter
/
/
/
//1
I
I
I
Gold
Filter
I
I
I
f;
I
1
60
80
70
Energy
90
(keV}
Balanced Filter Operation
Backscattered
,~ Radiation
~.
t.:!i re so 1 ve d
PbK Fluorescent
Radiation
--
-...
I
I
I
I I
I .J
I I
I
60
70
I
80
90
100
Energy
(keV)
DETECTED SPECTRUM
Figure
3- Showing Unfiltered Rc:.diation ~pectrurn and Operation of
Filters for the determination of lead.
42
The r..easurcmcnt technique
t11e}:c~forc
requires taking
two radi2.t.i..on measure:.:cnts, the first vith tlw_ gold filter in
position which includes essentially t11e backscatter intensity
plus the Lead X-ray intenstiy, and the second with the tungsten
filter in position \..;hich comprises essentially only the bu.ck- •.
scatter intensity.
The difference between the measurements is
thus proportional to the intensity of the lead X radiation.
On the 9200 series analyzer, the subtraction operation
is accomplished by means of a differential digital scaler {see
brochure).
Nhen the gold filter is placed in position, the
.
sca __1 er 1.s
caused to count 1l!:J.
At the end of the
p~eset
count
time, the scaler automatica~ly stop~ and the count is stored
in the scaler memory.
The tungsten filter is then switched into
position and the 'count down• mode is
are automatically subtracted from the stored count and the
scc;ler counts dovm for the same preset time.
At the end of the
counting period, the scaler stops and the residual count is
displayed.
One
~eature
of the balanced filter technique which is
of considerable practical value in the application to in-situ
lead paint assay is in- regard to the system stability.
Since
the filters themselves provide the energy discrimination, no
.
electronic pulse height discrimination is required.
problems of electronic
by wide fluctuations in
virtually non-existent.
insta~ility,
tempera~ure
Consequently!
as may be caused for example
on the measuring unit, are
This is an important feature consider-
ing the tcnpera ture variations likely to be encountered in going
from house to house, especially in winter time •
..... ..•
•
,_
43
CALIBRA'l'IO~
From thi foregoing discus~ion, the procedure for
instrument calibration in units of mg/cm 2 .is seen to be comparatively straightfon:ard.
f)
By means of standard knm:n samples
the sensitivity per unit weight is established and the unknmm
is readily inferred by dividing the displayed count by the
sensitivity factor cir by reading off a calibration curve.
An alternative approach, \vhich would have considerable
merit in the use of the instrument in unskilled hands, \·;ould be
to pre-adjust the timing period so that the displayed count would
·correspond directly to mg/cm 2 units.
This is relatively easy
to do and would only require monthly readjustment (to allow for
source decay) to maintain the calibration over the useful life
of the source.
At this time the subject of calibration units 1 i.e.
whether %Pb or mg/cm 2 1 is receiving a considerable amount of
attention •
No doubt the matter \vi 11 eventually be resolved in
the adoption or the mg/cm 2 unit which,
dS
already discussed,
appears to be a more meaningful indicator of toxicity.
Mean-
while, however, the %Pb unit is still in use and it is therefore.
pertinent to the_evaluation of the instrument to consider also
the calibration in terms of this unit.
%Pb calibration is onlv
possible if an assumption, or additional measurement, is
the paint film thickness.
--··
~ade
of
A calibration curve simila: to that
I
in Figure 5 could be generated from standard samples and similar
techniques tci those discussed in relation to the mg/cm 2 determination could be used.
44
survey~
In the analysis of results of field
in the section to follow, no data was
filnt thic'::ncss.
availa~le
An average value of 0. 2 g/cm
2
described
on the paint
was therefore used
in making an attempt to correlate the instrument reading with the
..
results of chemical analysis.
(.)
APPENDIX 3
•
45
46
BMD03R
MULTIPLE REGRESSION WITH CASE COMBINATIONS
f)
1.
GENERAL DESCRIPTION
a.
This program performs multiple regression and correlation
analyses on the data within each selection of subsamples from
the same population. A selection may be any specified set of
subsamples. For example, if subsamples are classified
according to some method of collecting data, we might wish to
analyze as follows:
Selection
Selection
Selection
Selection
1
2
3
4
Subsamples
Subsamples
Subsamples
Subsamples
1, 2, 3, 4, 5, 6
1, 2, 3
4, 5, 6
1, 3, 5
•••
As illustrated above, any set of subsamples arranged in blocks,
namely subsa1nple 1 data together, subsample 2 data together,
etc., may be combined.
b.
Output for this program includes:
(1)
(2)
(3}
(4)
(5)
(6)
(7)
(8)
(9)
( 1 0)
(11)
( 12)
Sums and sums of squares
Cross -products of deviations
Correlation matrix
Inverse of correlation matrix
Means and standard deviations
Regression coefficients, their standard errors and t-values
Sums of squares and mean squares due to regression and
and deviation about regression, with degrees of freedom
and F-value
Sums of squares due to regression for each variable
Standard error of estimate
Intercept
Partial correlation coefficients
Multiple correlation coefficient, R and R 2
For each selection and for each subproblem, the following can
be obtained:
(13)
Table of residuals
47
(14)
c.
Analysis of extreme residuals
Limite tions:
Limitations per problem:
( 1}
(2)
(3)
(4)
(5)
M,
p,
N,
k,
S,
number of subsamples ( 1 ~ M .:5 28)
number of original variables , (2 $. p ~ 50)
total sample size of all subsamples combined (N
number of Variable Format Cards ( 1 $ k .$ 10)
number of Selection Cards (1 .:5 S .:5 99)
.,
:5 99, 999)
Limitations per selection:
(1)
(2)
(3)
(4)
(5)
(6)
m, number of subsamples selected (1 .:5 m $. 28)
p, number of original variables (2 ::; p ~ 50)
q, number of variables added to the original set after
transgeneration (-9 $. q $. 48), (2 .:5 p+q $50)
n, total sample size of subsamples selected (p+q $. n < N)
t, number of Transgeneration Cards (0 .:5 t .:5 50)
r, number of Replacement-Deletion Cards (0 .:5 r .:5 99}
Limitation per replacement-deletion:
( 1)
d,
number of variables deleted (d ..:5 28)
Additional Limitation:
This program may give erroneous results when the correlation
matrix is singular or near singular. There are no special
routines in the program which test or make adjustments for
singularity of the correlation matrix before or after computing
the inverse matrix.
d.
Estirnation of running time and output pages per problem:
Number of seconds
Number of pages
=
=
60 + 60 S + 10 r
10 S + 5 r
{for IBM 7094)
e.
Within eLcn selection of subsamples, the trans generation feature
may be u ;:; ed. Codes 01 through 1 7 of the transgeneration list
are allowed.
f.
Within each selection, Replacement-Deletion Cards. may be
used in multiple regression and correlation analyses to rep:ace
the dep·~ndent variable and to delete independent variables.
\
48
2.
ORDER OF CARDS IN JOB DECK
Cards indicated by letters enclosed in parentheses are optional.
All other cards must be included in the order shown.
[Introduction, IV]
a.
System Cards
b.
Problem Card
c.
Sample Size Card(s)
d.
F-type Variable Format Card(s)
[Introduction, III-C]
e.
DATA INPUT Cards
(Place data input deck here if
data input is from cards.)
[Introduction, II ]
f.
Selection Card
(g.) Standard Transgeneration Card(s)
[Introduction, III-B]
(h.) Replacement-Deletion Card(s)
...
Repeat f. through (h.) as desired.
i.
Finish Card
[Introduction, Ill ]
Deck Set-up:
FINISH - Finish Card
Repeat as
desired
- Replacement Deletion Card(s)
TRNGEN - Transgeneration Card(s)
F- Type Variable Format Card( s)
- Sample Size Card(s)·
b
Problem Card
49
3.
CARD PREPARATION (SPECIFIC FOR THIS PROGRAM)
Preparation of the cards listed below is specific for this program.
All other cards listed in the preceding section are prepared
according to instructions in the Introduction.
b.
c.
Problem Card
Col. 1-6
PR~BLM
Col. 7, 8
Number of subsamples ( 1 :; M !5 28)
Col. 9, 10
Number of origi101.al variables
Col. 11-15
Total sample size of all sub samples combined
(N ~ 99, 999)
Col. 16, 17
Alternate tape number on which only input data are
recorded.
Col. 18-70
Blank
Col. 71-72
Number of Variable Format Cards (1
(Mandatory)
(2 :; p $50)
~
k
~
Sample Size Card(s)
Col. 1-6
SAMSIZ
{Mandatory)
Col. 7-12
Size of 1st subsample
Col. 13-18
Size of 2nd subsample
Col. 19-24
Size of 3rd sub sample
Col. 6 7-72
Size of 11th subsamp1e
If there are more than 11 subsamp1es, the ~econd card is
keypunched as follows:
'
Col. 1-6
5-AMSIZ
Col. 7-12
Size of 12th subsamp1e
.
...
10)
!
50
f.
Selection Card
(Mandatory}
Col. 1-6
SELECT
Col. 7, 8
Selection number (for identification only; may be
arbitrary)
Col. 9·, 10
02
If the table of residuals and analysis of
extreme residuals are desired. \llhen the
sample size is large, 02 punch is not
recommended.
01
If only -the analysis of extreme residuals is
desired.
00
If neither one is desired.
(0 ::: t ~ 50)
Col. 11, 12
Number of Transgeneration Cards
Col. 13, 14
Number of variables added to the original set after
trans generation. ( -9 ~ q ~ 48 )7 (2 ::: p+q 5 50)
Col. 15, 16
Number of subsamples to be selected ( 1 .:S m .:S 28)
Col. 17, 18
1st subsample to be selected
Col. 19, 20
2nd subsample to be selected
...
Col. 71, 72
28th subsample to be selected
(h.) Replacement-Deletion Card(s)
This card has a threefold purpose:
(1)
(2)
(3)
It designates the dependent variable.
It indicates the deletion of independent variables.
It controls the output.
Designation of a dependent variable and deletion of different
sets of va~iables can be done as many times as desired. Thus,
it is possible for the program to perform multiple regres sian and
correlation analyses with selected sets of variables. The program
will retain the original set of variabl ~s or the set of variables
after transgenerations as long as the selection number is the
same.
51
The p-reparation of the Replacement-Deletion Card is as follows:
Col. 1-6
REPDEL
Col. 7,8
Selection number (must be the same as that in
Col. 7-8 of the Selection Card)
Col. 9, 10
Subproblem number (for identification)
Col. 11, 12
02
If the table of residuals and analysis of
extreme residuals are desired. When the
sample size is large, 02 punch is not
recommended.
01
If only the analysis of extreme residuals is
desired.
00
If neither one is desired.
(Mandatory)
Col. 13, 14
Index (after transgeneration, if any) of the variable
to be treated as the dependent variable (must be
specified)*.
Col. 15,16
d, number of variables to be deleted (0 =:: d
Col. 17, 18
Index (after transgeneration, if any) of the 1st
variable to be deleted.
Col. 19, 20
Index (after transgeneration, if any) of the 2nd
variable to be deleted.
Col. 71, 72
Index (after transgeneration, if any) of the 28th
variable to be deleted.
~
28)
The indices of the variables to be deleted must be punched in
ascending order (e. g., if x 2 and X7 are among the set of
variables to be deleted, 2 would appear on the ReplacementDeletion Card before 7).
It is informative to note the difference between subsamples and
variables: each case or obseryation (that is, each card or set
of cards) contains values for all p variables. These are the
variables updn which (transgeneration and) analysis will be
performed. However, there may be some way of clas sHying
the different cases into groups, or subsamples. The program
is informed of which cases belong to which subsamples by the
order for any analysis to be
must be specifically indicated.
>!(In
perforn~ed,
the dependent variable
52
numbers punched on the Sample Size Card: if these numbers
are n 1 n ~ ••• 1 nm, the program will regard the first n cases
2
1
1
as belonging to the first subsample, the next
belonging to the second sample, .•. , and the
as belonging to the mth sample. Every case,
observations for the same p variables, in the
n 2 cases as
last n
cases
m
of course, has
same order.
After reading in the data for the m samples, the program will
read as many Selection Cards as there are present {there
must be at least one, si:o.ce no computation is performed until
a Selection Card is read). The procedure for each Selection
Card is as follows:
The indicated transgenerations (if any) are performed
upon the original variables (the original variables
remaining unchanged in storage). All the cases from
the indicated subsamples are pooled into one homogeneous sample (the original organization of cases into
subsamples also being retained unchanged in storage).
One complete analysis is performed, using all the
variables {after transgeneneration) with the last
variable (after transgeneration) being regarded as
the dependent variable. The output is controlled by
the entries in Columns 9-10 of the Selection Card.
Then, the Replacement-Deletion Cards, if any, are
read in. The procedure for each ReplacementDeletion Card is as follows:
Using the pooled sample of transgenerated
variables from above, the indicated variable
is macie the dependent variable, the variables
chosen for deletion are deleted, and a complete
analysis is performed. The output is controlled
by the entries in Columns 11-12 of the Replacement-Deletion Card.
Thus, for each Selection Card the indicated transgeneration is
upon the original variables, and a pooled sample is
formed from the indicated original subsamples. For each
Replacement-Deletion Card, deletion of variables and selection
of the dependent variable· is made for the pooled sample from
the preceding Selection Card, on th•' basis of the indexing
formed in and peculiar to that Selection Card.
perform~d
53
4.
COMPUTATIONAL PROCEDURE
Step 1.
( 1}
Preliminary computations.
Selection and trans generation: Subsamples are selected according to the specification on the Selection Card and the data are
transgenerated according to the codes specified on the Transgeneration Cards (if any).
n
(2}
Sums:
LX .. '
j
. 1 1J
1=
= 1, 2, ••• , p+q
where n is the number of cases and p+q is the total number of
variables {after transgeneration, if any}.
(3)
n
2
LX
..
. 1 1J
Sums of squares:
j
= 1,
2, ..• ' p+q
j
= 1'
2, •.• , p+q
1=
(4)
Means:
f
=.
X.
J
1=
1
n,
X .. /
1J
n
L
=
s.
{5)
Standard deviations:
(6)
Cross -product sums: Sk.
J
(7)
J
=
(X .. - x.)2
1
J
J
j
n-1
n
L X.k1 X 1J..
. 1
1=
= 1'
k
= 1,
j
= 1, 2,
2,
.•• , p+q
... ,
... '
p+q
p+q
... '
... ,
p+q
p+q
Cross -products of deviations:
n
Dk.
J
(8)
i:: 1
c=
L
i=1
(X.k -
1
X~)
(X ..
1J
X.)
J
j
k
= 1,
= 1,
2,
2,
Simple correlation coefficients:
ffJ::
JJ
j
k
= 1,
=
2,
1, 2,
••• J
••• J
p+q
p+q
•
54
Step 2.
(1)
Regression computations.
f,pecification: Either the entire set of variables or a subset
<.s specified by the Replacement-Deletion Cards are selected.
For simplicity of exposition, assume that x , ••. , x
are
1
0
the independent variables specified and Y
Xz; the dependent
..
=
variable 'specified.
{2)
Inversion:
Let
The regression model has the form
{ rij}
denote the Q x Q matrix of simple
correlation coefficients of the independent variables
ij}
Its inverse { r
is obtained and used to compute
=
c ..
1J
{3)
rij r ..
1J
D ..
1J
i, j
= 1,
•.•
J
x 1,
••• ,
x .
0
Q
Regression coefficients:
Q
{3.
1
{4)
=L
c .. D.v
. 1 1J
J
i=1, ••• ,Q
J=
Intercept:
Q
= Y - .L1 {3.1 X.1
a
1=
(5)
Sum of squares attributable to regression:
Q
RS
= .L: l
{3. D. , ,
1=
(6)
1v
Sum of squares of deviation from regression:
DS
(7)
1
=
DlJ1J
Coefficient of determination and multiple correlation coefficient:
R
2
= RS/Dvv
R
=
JR2
55
(8)
Variance and standard error of estimate:
s
(9)
2
= DS/{n-Q-1)
,
fJ
s=
Standard deviations of regression coefficients:
i=1, ••• ,Q
(10)
t-values:
t.
1
(11)
= f3./s.;
1
1
•
i
= 1,
•••• Q
Partial correlation coefficients:
R.1
-r
=
i, Q+l
i=l, ••• , Q
Jr ii r i, Q+l
where { rij} is the inverse of the (Q+l) x {Q+1) matrix of
correlation coefficients for the variables x , ••• , x , · Y.
1
0
(12}
Sums of squares and proportion of total variance added:
The kth term printed in the output under· the heading SUM OF
SQUARES ADDED corresponds to the difference between the
"due regression" sum of squares obtained by entering the
first k variables xl, •..• xk and that obtained by entering only
the first k-1. The corresponding PROPORTION OF TOTAL
VARIANCE ADDED is also printed.
( 13)
Comparison check on final coefficient:
The regression coefficient for the last independent variable is
computed by a different method (explained in Bennett and
Franklin, pages 312-315, see Section 5, References) to check
the accuracy of the above .computational procedure.
Step 3.
.
Analysis of extreme residuals •
The following are computed:
,..
;..
max
(Y.- Y.)
1
1
1 ~ i ~ n
min
1 $ i :5 n
"'
,..
(rnax (Y. - Y.) - min (Y. - Y.)) / s
1
1
l
1
(Y.- Y.)
l
1
APPENDIX 4
•
56
"
RAW DATA
.-1
C'~
M<:l'
7585 oo 1 1
4558 04 1 1
4588 00 1 1
4560 05 1 1
4565 01 l 1
4574 06 1 1
4554 Ol 2 2
4566 02 1 1
4573 05 1 :i..
4538 01 1 1
4545 00 }_ 1
4544 00 111'1
4577 0511 2
4583 03 1 2
4543 00 111
4542 00 1 l
4562 02 1 l.
4555 03 l 1
4575 03 1 1
4541 06 1 3
4581 01 111
4582100 . 4
4570 02 1 2
4579110 1 1
4571.04 111
45solo21i3
lfl
\fl
!'-
CO
06 07 oo2 024
012
07
012
12
060
07
144
13.
060
041
1003
17
033
G9
012
21
060
13 21
012
00 20 000 012
12
072
07
11
18
000
001
. 015
012
004
00 11 000
07
002
05 1
005
06,. .
I
•
I
I
400
Or-INM<::l'l.{)\!l!'-C00"\0
0"1.-fr-irlr-l.-i.-irlr-lr-lr-IN
2
2
1
0
1
1
1
1
2
2
3
3
2
r-1
N
2 2 6 612 o 2 1 1 112'032
2 1 6 8 2 0 2 1 1 1 2 021
2 1 6 4 12 7 2 2 1 1 2 028
1 1 6 3 12 8 1.2 1 1 1 2 029
2 1 6 6 2 9 ,1 2 1 1 2 025
1!1 41312 01611 l 1 2 028
7 61'812 0 11112 034
1 1 6 2 2 0 2 2 1 1 2 039
0 1 6 6 1
14 1 l ]_ 2 029
3 1 4 4 2 0 . 3 1 1 2 2 021
3 1 6 5 2 ,o 4 2 1 2 2 021
3 1 1 3 21'4 2 2 1 2 2 )019
3 6 2 2 1 4 211 4 21050
116 4 2 1 6 211 4 2 '038
1 2 1 2 3 210 6 2 1 1 2 028
2 2 1 1 4 2 0 2 2 1 1 2 026
2 3 1 6 3 2 0 2 2 1 l 2 028
1·1 1 6 3 2 0 6 2 1 1 ,2 030
211 1 6 4 1 0 3 1 l 1 2 043
2I 1 3
2 0 3 1 1 4 1 020
2 2 1 1 1 5 12 0 2 1 1 4 2 030
2 2 1 2 4 12 1 4 2 1 4 2 025
2 3 1 2 2 2 1 5 1 1 4 2 031
2 2 1 2 4 2 4 3 1 1 4 2 031
3 3 1 6 4 2 0 l. 2 1 4 2 027
41
2
N
N
M
N
37
1127
1 042
1 088
1 017
1 017
1 097
1005
1 014
1 037
1 000
1 000
1 000
1 094
1 095
2 000
2 000
2 000
2 000
2 006
2 000
2 000
2 000
2 000
2 000
2 000
43
40
40
39
35
39
37
37
38
36
38
36
37
37
36
33
37
<::l'
N
11)
N
2
4
2
4
4
2
4
2
4
4
2
4
4
2
4
2
4
4
4
2
4
4
4
4
4
l!l
N
oo5
045
018
000
000
000
000
000
000
000
000
000
000
010
000
000
000
000
000
000
000
000
000
000
000
!'-CO
N
N
4 081
2 342
4 144
21134
2 134
4 022
4 062
4 023
4 061
4 107
4 126
4 000
2 169
4 066
4 000
4 006
4 009
4 045
4 013
2 05 2
4 000
4 003
4 000
4 000
4 000
0"1
N
2
2
2
2
2
2
4
4
4
4
4
t1
2
2
4
2
4
4
2
2
4
4
4
4
4
0
M
r-INM
MMM
ooo 4
206 1
000 2
255 4
255 4
015 2
00914
005 2
000 2
001 2
004 1
010 1
000 2
031 3
000 2
002 2
000 2
009 2
004 2
000 2
000 3
000
000 2
000 2
000 2
3
1 1
3 1
2 3
2 3
2 1
21
2 1
2 1
2 2
1 2
1 2
2 1
3 1
2 2
2 2
2 2
2 2
2 2
2 2
3 2
2
2 2
2 2
2 2
<::l'
M
096
001
008
024
024
060
015
012
072
040
024
024
072
024
036
001
001
012
006
048
006
009
001
144
004
12 4 2 o 5 114;2 033 35 2 ooo 4·ooo 4 oo14 ooo 313 2 o1o
U1
-.1
58
- ·-· .. ·-·--· -· ·-·
,,
1
Case No.
2
Number of siblings
3
Living with child
4
Parent living arrangements
5
Mother's monthly pay
6
Father's monthly pay
7
Mother-months on job
8
Father-months on job
9
Mother's grade
10
Father's grade
11
Responsibility for child
12
Children's play area
13
Non-food eating
14
Pottery
15
Physical symptoms
16
Age of children
17
Sex of children
18
Kind of sample
19
Ethnic group
20
Previous lead test
21
Lead in blood
22
Hematocrit
23
Age of dwelling
24
Measurement of lead on exterior wall
25
Wall condition exterior
26
Measurement of lead on interior wall
27
Wall condition interior
28
Measurement of lead on exterior wall
29
•rrim condition exterior
30
Measurement of lead on interior trim
31
'I'r:i.m condition interior
32
Distance from major traffic arteries
33
Type of dwelling construction,
34
-~-~------···---···-~-
Length live in dwelling
.. .,
;
APPENDIX 5
59
60
~ .·
i',/
100
80
s::
«!
l-1-
'tl
r-1
·r-1
6
4-l
0
.j..)
)
s::<ll
0
j.j
«!
AI
Parent
Figure 1.
i)•
Other
Living with Child
Unknown
61
,.
80'
..
20
~
Together
Figure 2.
Separated
Divorced
Parent Living Arrangements
Single
62
~
!
30
I"'
-
.
Ul
1-l
(1)
.a
20
~
10
..
~
Ill
r:..
11-1
0
~
s::
C1)
0
1-l
.(1)
t:l<
)
-
,I~
0
200
400
600
800
1000
Dollars Per Month
Figure 3.
Father's Monthly Rate of Pay
:1.20 -
unknown
63
N
~
I
IJ)
50 1-
.
........
r-I
40
~
.......
..
~
Q)
til
1-1
Q)
£
~
~
~
30
sQ)
.
~
~
4-i
)
........
0
("')
0!
........
&!
Q)
0
1-1
til
20
~
I
~
~
Q)
t')
Jl.t
~
til
+l
Q)
r--
Q)
~
t1l
Q)
1-1
~
~
0
0
~
t>
0
0
..c:t')
10
Ul
""
z0
0
Figure 4.
Mother's Highest School Grade
-~
'"
'j
Percent
~
')
"lI
i
0
hj
1-'·
1-'
0
'
IV
·W
0
b
I
'
"'0"
i
'§
11
(D
U1
No School or Res] onse
hj
Ill
g:.
(D
11
Ill
::r:
1-'•
'
'
Elementarj' (1-7)
'§.
!
(D
i
Ill
rt
i
(/)
..
n
50
!
:
Hig h School (8-12)
1-'
Gl
11
Ill
:
~
i
I
~--·
College (13-17)
m
.!::>
65
100
I"'
80 -
60 r-
+>
s::Q)
0
1-1
Q)
AI
40
.
20
~
Mother
Figure 6.
Adult
Relative
Older Sister Others
or Brother
Responsibility for Child
66
~
II
. 60
r-
.
50~
40 r-
30-
20 ..
10-
0~--~----~--_.------L---~-----L--~------~--~----_.---
In the
House and
the Yard
Figure 7=
In the
Yard
In the
House
•
Children's Play Area
In the Yard On Sidewalk
or Street
and on
Sidewalk
,.
Percent of Children
l'lj
......
w
1\.)
1--'
0
0
0
0
•
•
•
0
"""
•
~
I"!
([)
0:1
NONE
:
!2:
0
::l
I
HJ
0
0
P•
OTHER
1-1 •
(i•
(!I
sU::
l:tl
llJI
.
DIRT
I
~r
([I
:
::I
I
'
tr
'<'
'
()
::r'
DIRT AND OTHER
:
1-'·
1-.J
C:!.
'"'
(1)
~~
PAINT CRIPE
AND DIRT
:
I
I
I
I
PAINT CHIP!:
DIRT & OTHE R
I
..
. ..
0'1
--.J
.. J
68
100
80
.
.
•
60
-
40
-
20
~
't:l
<I)
til
::>
+I
s::<I)
0
1-1
<I)
~
)
0
No
Figure 9.
Yes
Pottery Used for Cooking, Storing or
Drinking
-·
~~~>
--~--
Percent
ttj
1-'
0
0
IV
0
'
1-'·
I.Q
w
0
'
"'0"
L-..
0
0\
0
"T
I
lJ1
~
11
(!)
NONE
.
1-'
0
'0
::r
.'<!
Ul
1-'·
0
STOMACH ACHES
Ill
1-'
til
~rt
0
sUl
•
•
POOR APPETITE
1-'·
~
0
::r
1-'·
1-'
p.
11
(!)
~
ACHES, VOMITING, IRRITABILITY, POOR APPETITE
STOMACH ACHES , IRRITABILITY 1 POOR APPETITE
VOMITING, IRRITABILITY, POOR APPETITE
0\
1.0
70
40
----·--·---------··
--
---- -------
---
30
-----
..
•
~
(I)
~
't1
.-!
·o-f
..c:
0
ti-l
0
- ... ------- --
- 20
.j.J
~
(I)
0
~
(I)
Pi
)
10
..
0~----~~----~-----L----~------~----~----~---Years
1
2
Figure 11. Age of Child
3
4
5
6
71
---~-___;.
~--------
______ ;_____
-50~--
----- --------- ----- ------- . .-------4 0 -. ---· --- ---:
.j.J
s::
---------------------'--------4l
.
0 --------- ---------
1-1
4l
Ill
20 ""--
10-
0~------L-------~------~-------L------
Male
Figure 12.
Sex of Child
Female
72
60
..
...
so
~
40
~
.j..J
2u
30
~
(!)
fl.!
20
~
10 -
0
White,
Spanish
Speaking
Figure 13.
E~h-~ic
Other
White
Other
Black
Group
·•·
I
...
$;'!-,.:."1('1
73
------
..
--
.
---------
·-···
I. - - . --
--10 1--
ob---~~------L-------L-------~------~------~-19
26
33
40
Micrograms per 100 ml.
Figure 14.
Lead in Blood of Children
47
54
74
so,..
401-
30 r-
20-
10-
0~---L----~~--~------~--~----~~-Unknown
Post-1940
Pre-1940
Figure 15.
Age.of Dwelling
80-· -·····- -··- ·-··------· ------------------------- ____,__ ----------·- -·-·-- ---·' ------------: -----· -- ..
:
:
~
. ---~---------- -~-.
----- ..:....
_____
~----·:
----
__________ __
____;__
----------60
40
·-------:--·
20
0
~--~------~--------~------L-------~------~------1.50
0.90
1.20
o.oo
0.30
0.60
2
mg/cm
Figure 16.
Measurement of Lead on Exterior Walls of Dwellings
•
~--··___;
_____~---·
76
·-
,·
--------- -· -----
-------· --·
-~--
----~·
---------
.......
--
-·-
"
-
-~<-------------;------------------------1-------------
60
40-
mg/crn
Figure 17.
2
Measurement of Lead on Interior Walls of Dwellings
..... .L ................. ----- ........... ---------·····-·
77
:-:-----------:_·-----~----------·-------------~------~-----------~------~----------------<.----------
-·
"
---------------- so-...----
-6o-
~-
-40
~
0'1
~
"""r-l
...-{_
~
4-l
--
0
+l
~
Q)
t)
1-1
Q)
Ill
20 11-1
0
1-1
•rl
1-1
0
1-1
·rl
1-1
0
1-1
0
1-1
·rl
•rl
1-1
Q)
Q)
Q)
Q)
.j..J
~
+l
.j..J
.j..J
r:z:.1
H
H
>:
~
>:
r:z:.1
0
Good Intact
Surface
Figure 18.
Peeling, Chipping
or Flaking
Interior and Exterior Wall Condition
78
50~
--- -....----w
"
40
~-
··-··-·· -----
_______ .:__ _______..,...
~-:-----
----·----- ___ ,
--··
·----r---~-----··------··.
·,
-- ·-··
-----·-
--------·---·------··---- ..
---
- - 30-
20
io>
10-
0 ~--o~.-o----.~3~o----.-6~o-----.9~o----~1~.2-o----1~.-s-o---1~.~a~Jf~1~3-.-4-2~-­
mg/cm2
Figure
19.
Measurement of Lead on Exterior Trims of Dwellings
79
80 ..
60 '"
40
20 ..
Figure 20.
Measurement of Lead on Interior Trims of Dwellings
'
(['""'~~li1'
_,,_.
80
-.-..------- ---------
-~~-----·-
··--. ------ - . .,....... ________ _. __ _
'
40 -
20
l-1
0
•r-1
l-1
IV
+!
J:::
0
H
l-1
0
·r-1
l-1
IV
+!
~
Good Intact
Surface
Figure 21.
t:
-r-1
l-1
IV
+!
J:::
H
l-1
0
•r-1
l-1
IV
+!
X
IJ::j
Peeling, Chipping
or Flaking
l-1
0
-r-1
l-1
+!
J:::
H
Chewed
Interior and Exterior Trim Condition of Dwellings
81
60
~
50 1-
40 1-
20 1-
10
~
r
0
200 Feet
300 Yards
Figure 22.
More than
300 Yards
200 Feet
or less
Unknown
Distance from Major Traffic Artery
Yards
82
60 ...
.,
50 -
40 1-
20
~
10 •
0~---L------~----~------J-------L------L-Other
Wood Frame
Stucco
Figure 23.
\_.;).
Type of Dwelling Construction
83
50 ,...
40 1--
30 ...
20 ~
10 1-
0
.___o.L---l.L.s-·--3Lo---4L-S--6-0L---7...JS
~~Q~Rs
Number of Months
Figure 24.
A Length of Time Lived in Dwelling