NATIONAL BUREAU OF STANDARDS REPORT

10 657

DATA COLLECTION AND ASSIMILATION

FOR THE LEAD PAINT POISONING MODEL

LI erne

U.S. DEPARTMENT OF COMMERCE

NATIONAL BUREAU OF STANDARDS
NATIONAL BUREAU OF STANDARDS

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NATIONAL BUREAU OF STANDARDS REPORT

NBS PROJECT

4314518 December 7, 1971

NBS REPORT

10 657

DATA COLLECTION AND ASSIMILATION

FOR THE LEAD PAINT POISONING MODEL

Milestone 2

by

Judith Gilsinn

Applied Mathematics Division

Lead Paint Poisoning Project

Building Research Division

Institute for Applied Technology

National Bureau of Standards

01 /of refe****^

Sponsored by

Department of Housing and Urban Development

IMPORTANT NOTICE NATIONAL BUREAU OF STA

for use within the Government, t and review. For this reason, the whole or in part, is not authoriz Bureau of Standards, Washington

the Report has been specifically p

Approved for public release by the

Director of the National Institute of

Standards and Technology (NIST)

on October 9, 2015.

s accounting documents intended

;u bjected to additional evaluation

listing of this Report, either in Office of the Director, National

the Government agency for which

pies for its own use. <NBS>

U.S. DEPARTMENT OF COMMERCE

NATIONAL BUREAU OF STANDARDS

PREFACE

This report can be grouped, conceptually, with 4 other NBS Reports

under the blanket title "Pediatric Lead Paint Poisoning in the United

States—A Survey with Preliminary Estimates." Under this arrangement,

the "parts" of the study would be listed:

Part I NBS Report # 10499 "The Nature of the Lead Paint Poisoning

Hazard"

Part II NBS Report # 10657 "Data Collection and Assimilation for

the Lead Paint Poisoning Model"

Part III NBS Report # 10653 "Effect of Data Aggregation in Modelling"

Part IV NBS Report # 10654 "A Model to Estimate the Incidence of

Lead Paint Poisoning"

Part V NBS Report # 10651 "National Estimates of Lead Based

Paint Poisoning of Children (Estimated by Standard

Metropolitan Statistical Area)"

These papers were intended as interim progress reports covering work

done up to the time of publication. Reports describing validation and

refinement of the models and outputs as well as analysis of the outputs

will be issued subsequently. A summary report encompassing revision of

the current ones and those projected above, under one cover, is anticipated.
1
ABSTRACT

Criteria are given for the choice of data items and data sources

for use in a model to predict the incidence of pediatric lead paint

poisoning in the United States. The required environmental and population variables are described, and the sources and availability of these

data are compared. Sources of lead poisoning incidence data are also

identified and appraised

TABLE OF CONTENTS

Page

1. INTRODUCTION 1

1.1 Relevant Data 1

1 . 2 Data Sources 2

1.2.1 Data Quality 2

1.2.2 Data Accessibility 4

1.3 Obtaining Data 5

1.4 Data Assimilation 5

2. GENERAL DESCRIPTION OF DEMOGRAPHIC REQUIREMENTS 9

2.1 Environmental Factors 11

2.2 Population Factors 12

3. CHOICE OF DEMOGRAPHIC DATA SOURCE 15

3.1 Uniformity 15

3.2 Accuracy 16

3.3 Accessibility 16

3.3.1 1960 Census Data 17

3.3.2 1970 First Count Census Data 18

3.3.3 1970 Fourth Count Census Data 19

3.4 Choice of Zonal System 19

4. INCIDENCE DATA 23

4.1 Mass Screening 24

4.2 Bias 25

4.3 Zonal System Coding 26

4.4 Test Used to Screen 27

4.5 Sources 27

5. FUTURE DATA COLLECTION 34

5.1 Census Data 34

5.2 Existing Incidence Data 35

5.3 Collection of Extra Incidence Data 36

i
LIST OF TABLES

Page

TABLE 1: Housing Characteristics Associated

with Lead Paint Poisoning 9

TABLE 2: Population Characteristics Associated

with Lead Paint Poisoning 9

TABLE 3: Lead Poisoning Cases by Age

in Months 12

TABLE 4: Census Data Status 19

TABLE 5: Lead Poisoning Screening Programs 27

TABLE 6 : Incidence Data Sources and Problems i 31

li
DATA COLLECTION AND ASSIMILATION

FOR THE LEAD PAINT POISONING MODEL

1. INTRODUCTION

In any project involving the construction of a mathematical

model, a major effort must be made to collect and assimilate data

to facilitate model development, calibration, validation, and

application. Such an effort includes:

a. determination of relevant data items,*

b. determination of the sources of those items,

c. obtaining the data items, and

d. assimilating them into a form to enable further processing.

Once the data have been assimilated, preliminary analysis can be

conducted in order to decide which items are most relevant to the

process being modelled and which predict best. Further analysis

and perhaps modelling may be necessary to fill in any gaps in

the existing data.

1.1 Relevant Data

The first step in a data collection effort is the determination

of what data items to collect. It may seem at first to be

overly pedantic to consider this as a separate step, but if care is

not taken at this stage, decisions may be made which are later

regretted. It is often better to collect too much data than too

little, but there is a practical limit on how much can realistically

In this report, ’’data items” will refer to categories of information,

while ”data” will refer to the numbers in a category.

1
be assimilated and checked. Deciding which data items to collect

involves first learning about the process being modelled in order

to see what has an effect on that process. It is at this point

that the decision should be made on what the dependent variable

of the model should be, i.e., what exactly is being modelled.

Relevant literature and conversations with experts in the field

can help determine those data items which are being modelled, and

may be used to eliminate other items. A preliminary search of

data sources may also uncover items which one’s intuition may

suggest as relating to the process being described. Any previous

modelling of the same phenomenon may also provide insight as to

the choice of data items to collect.

1.2 Data Sources

Once the kind of data to be collected has been decided upon, one

must search for sources of such data items. Of course, the decision

on which data items to collect is not entirely independent of which

items are available. However, if one is too influenced by sources he

knows about, he is unlikely to investigate less obvious sources for a

desirable item which is more difficult to acquire. The two major

criteria which must be used in assessing the different data sources

are the quality and accessibility of the data.

1.2.1 Data Quality

Quality includes accuracy, uniformity, and compatibility with

other data sources. In assessing data accuracy it is necessary to know

the reputation of the organization collecting the data and to understand

their sampling procedures. It has often been said that one can "lie

2
with statistics,” that one can obtain data supporting almost any

thesis he wishes. It is therefore necessary to investigate biases

in data to eliminate, as much as possible, prejudging the outcome of

the modelling process. Another element in assessing the quality of

data is the uniformity with which it is collected. In the case of

lead poisoning, for instance, it is desirable to have uniformity both

with respect to geographical location and socio-economic status, since

we must be able to predict for all people in all parts of the

country. This ideal is unlikely to be met in full, but the degree to

which it can be met by any source will be a major factor in deciding

for or against it as a data source. A final aspect of the quality

of a data source is its compatibility with data from other sources.

This involves, in part, the level of geographical aggregation of the

two sources. This problem will be discussed in greater detail in

Effect of Data Aggregation in Modelling , ~ but it will be noted

here that the data sources must either be at the same level of

aggregation or else be capable of being aggregated or disaggregated,

so that all model variables are at the same level. This may require

some modelling, particularly if data must be disaggregated.

*NBS Report #10653, Effect of Data Aggregation in Modelling.

3
1.2.2 Data Accessibility

A second criterion in addition to data quality is the

accessibility of the data from any source. It should be noted here

that this project has neither the funds nor the time to engage in any

major data collection effort. Such efforts require a large staff,

many trips into the field, and a great amount of time in both planning

and data reduction phases. Since this project is a short term,

relatively low budget effort, it was decided to utilize only

already existing data sources. This, however, did necessitate a search for those sources and an examination into the real

availability of obtaining the information contained in these

sources, within the time and budget constraints of the project.

A major consideration of data accessibility is the form that

the data are in. Many people assume that because they have a filing

cabinet full of papers with numbers on them, they have lots of

data. However, if it would take several man-months of work to

reduce this file to a manageable form, then these data are not

really accessible. To be accessible, data ideally should be in

machine readable form (on punched cards or magnetic tape) or else be

few enough in number to be reducible to machine readable form with a

small effort. One might reasonably expect to code 2000 forms

in 2 man weeks, and that is a project one would be willing to

undertake, whereas the coding of 200,000 items would be out of the

question. Even machine readable data might not be truly available,

if it requires either additional hand- coding or very sophisticated

data processing techniques to extract the necessary infomation.

4
As an example of this, it is necessary for this project to have data

broken down by geographical areas. Some sources have such information,

but their only geographical reference point is a street address. The

state of the art of computer address coding is not very advanced; it

has been successfully applied only for a small geographical area.

The alternative is to hand code the addresses to our geographical

zone system, which is a reasonable procedure if the number to be

coded is not too great. Thus, it is necessary to investigate the

data sources in detail to ascertain how much work will be necessary

to reduce the data to a form which can be used as input to the

computer modelling process.

1. 3 Obtaining Data

Once the source or sources of data have been chosen one must

actually obtain the data. This process is likely to be time consuming

and frustrating, but in principle at least it is relatively straightforward, since presumably in evaluating the sources the availability

of the data was investigated.

1.4 Data Assimilation

The final stage in a model data collection effort is data

assimilation, by which is meant collecting the data in one place

and putting it into the final form for which it becomes input into

the modelling process. This includes:

1. putting all the data into machine readable form on tape

or on punched cards,

2. data processing to select and reformat to facilitate

entry into the modelling process,

5
3 . aggregation and disaggregation to ensure all data items

appear at the same geographical zonal level,

4. modelling to fill in any gaps in the necessary

data, and

5. preliminary analysis to narrow down the number of data

items to be used in the model and to better understand

the relationships between these variables.

These steps are thus designed to bring all the data together in one

place in one form SO that they can be examined critically and massaged, if

necessary, before it is used directly in a modelling effort.

There is a gray area between what is properly data collection and

analysis and what is properly modelling. The two processes are not

separate and must be meshed to ensure that the model is developed from

an adequate data base. Much can also be learned aobut the process being

modelled from a careful analysis of this data base. Some kinds of

processes are such that one can discover q functional cause and effect

relationship among the variables, and thus build a model before

examining the data. However, social processes do not generally fall

into this category, and lead paint poisoning is no exception. Although

most authors writing about lead paint poisoning have noted characteristics

of the environment and population associated with the disease, few

of these characteristics, with the exception of the presence of

peeling paint, can be shown definitely to have a cause and effect

relationship. Therefore, it is necessary to study the data to ascertain

which variables and which combinations of variables are most associated

with the incidence of lead poisoning. A second reason for analyzing

6
the data is to discover correlations among the variables. Since most

statistical estimation procedures require the variables to be

independent, it is desirable to use in the model only variables which

are relatively uncorrelated.

Data is used in the modelling process in four ways:

1. for model development,

2. for model calibration,

3. for model validation, and

4. for model application.

The data analyses described above can be used to aid in deciding which

variables to include in the model and to help deteimine the model

form. Data is used in the model calibration process for determining

the best estimates for model parameters. The model validation process

requires comparing the predicted values of the model’s dependent

variables with actual values. Finally, it is necessary to have

values for the independent model variables in order to apply the model.

The first three thus require values for both the independent and

the dependent variables, while the last requires only the

independent variables. Values for more variables are also required

during the model development process since the model form has not

been established at that time. However, the data collection,

assimilation, and analysis process is very much the same for all

four of these stages. Since many of the data items required are the

same for all four stages, one data search should uncover most sources

of data for all stages, although items which are difficult to obtain

may require an ongoing effort to improve or locate further sources.

7
This concludes the general description of the data collection

process. The following sections will describe the specific application

of this process to the collection and assimilation of data for the

lead paint poisoning model.

8
2. GENERAL DESCRIPTION OF DEMOGRAPHIC DATA REQUIREMENTS

The data requirements for the lead paint poisoning model can be

divided into two types 1) demographic and 2) incidence. This division

is made primarily because the sources of these two types of data

differ, and because incidence data is not needed for prediction,

since if it were available for the whole country it would not

be necessary to construct a model. Incidence data is_ required for

model development, calibration and validation.

The demographic variables which have been identified with

lead paint poisoning can be divided into two types: 1) those

concerned with the environment in which the child with lead poisoning

lives and which will primarily be associated with housing conditions

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

the family of the child. Tables 1 and 2 contain the lists of

possible housing and population characteristics which have been

associated with pediatric lead poisoning in the literature and in

conversations with experts in the field. It can be seen that many

of these variables are related. For instance, low income people

are more likely to live in housing of poor condition. Therefore,

it is probable that only some of these variables will actually

appear in any model formulation. However, those that will be the

most relevant and the best predictors cannot be known until values

for all are in hand and can be analyzed.

9
TABLE 1: Housing Characteristics

Associated with Lead Paint Poisoning

1. Age of housing

2. Condition of housing - dilapidated or deteriorating

3. Value of house

4. Number of renter occupied dwelling units

5. Vacancy rate

6. Number of multiple unit structures

1.

TABLE 2: Population Characteristics

Associated with Lead Paint Poisoning

Age of children

2. Number of female household heads

3. Population crowding

4. Mobility

5. Income

6. Educational level of head of household

7. Work status of head of household

8. Race

9. Region of birth

As described, some of these variables pertain to individual households, others to area aggregates. The individual household variables can

be redefined at area levels when it is desired to incorporate them into

area models. Thus, e.g., "Age of housing" may be represented:

mean age of dwelling units in area

mean age of structures in area

median age of dwelling units in area

percentage of dwelling units in area at various age levels

and so forth

10
2 . 1 Environmental Factors

The most obvious pertinent environmental factor is the presence

of lead paint in a form that the child can easily ingest. Until the

1940's lead paint was the major good quality interior house paint

used in this country. Subsequently, lead paint usage declined as

the new 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 its use on surfaces accessible

to children to be printed on the label of any leaded paints.

However, a recent New York survey discovered lead in paints being

sold for interior use in that city, in August of this year.

Lead exterior paint is still sold, and there is little to prevent

one from ignoring the warning message and using it on interior

surfaces. In addition, children have access to exterior surfaces

as well as interior ones and may eat paint chips from railings,

porches, garages, exterior window sills and other exterior surfaces.

Therefore, some surfaces being painted today may well offer a potential hazard to children in the future. It is Impossible to set

a cut-off date for which one could say that housing built prior to

that time is probably hazardous, and without a major survey of

actual housing one can only speculate on the fraction of housing

built in any year which has any lead paint in it. Also, although a dwelling unit may be free of lead paint except for one windoAV sill,

that surface may have a high enough lead content to kill a child or

cause permanent brain damage.

11
In the absence of more definitive information on the correlation

between the age of a house and the presence of lead paint , the condition

of the dwelling unit becomes more important. The mere presence of

lead paint on a surface does not necessarily offer an immediate

hazard to a child, if the surface has been well maintained, the 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. Indeed, several cities with lead

poisoning programs cited once good homes, which have been divided

into apartment units and which have since deteriorated, as fairly

typical lead poisoning environments.

Another characteristic of the housing environment associated

with children who get lead paint poisoning, is that most of these

children live in rented quarters. A factor here is, of course,

the fact that lead poisoning occurs mainly among the poor, who are

less able to own their own property and are more likely to live

in substandard housing.

2.2 Population Factors

The major population characteristic associated with lead poisoning

is the age of the child. All but a small number of reported cases have

occurred in children under 6 years old. Table 3 shows that over

85% of lead paint poisoning cases in Phildelphia occurred in

children 18 to 35 months old.

12
TABLE 3:

Lead

Poisoning

Cases

by

Age

in

Months

COo

oo

tO

vO

to

to

co

toc

to

oc

co

vO

CO

CO

co

COoCO

oo

\Do

\o

13

Age

in

Months
The children who develop lead poisoning come from poor families

that have all of the problems of such families. The families have

a disproportionate number of female household heads, they live

in crowded conditions, they move often, the household head is employed,

if at all, in an unskilled or semi-skilled job, and a disproportionate

number come from minority groups such as blacks and Spanish speaking

Americans

These characteristics of housing and children have been noted

before and have been used by groups that are screening children

to locate those with the greatest risk of developing lead poisoning.

Some communities which had children with high risk characteristics

living in hazardous environments, had no previously reported cases

but found lead poisoning when they screened the children for it.

Thus, the variables listed in Tables 1 and 2 constitute those

demographic data most commonly associated with identified cases of

lead poisoning.

14
3. CHOICE OF DEMOGRAPHIC DATA SOURCE

The major sources of the demographic data listed in Tables 1 and

2 are the U.S. Census and various local sources. These have been judged

by the criteria described in Part 1 of this report, and the U.S. Census

data has emerged as by far the best source of demographic data for use

in a nationwide lead paint poisoning model. The reasoning behind this

decision is described below.

3.1 Uniformity

A main deficiency of local data is its lack of uniformity across

the nation. The quality and availability of data generally depends

on the city size and/or the abilities of a department head. Large

cities have the funds and staff to collect, maintain and analyze

data. Pilot programs in some cities have led to the collection and

processing of some information. However, in general, the kinds

and quality of the data retained by different localities vary

markedly from place to place and data from one city is not necessarily

likely to be compatible with data from another. The size of local

zonal systems will very likely vary. (In fact, the zonal systems

used by different departments such as health and police within the

same city are likely to be different.) The type and definition of the

data collected may vary. For example, one jurisdiction may date the

age of a structure from the time it was first assessed, while another

may use the date of issuance of a building permit.

It is one of the goals of the U.S. Census Bureau of collect

and make data available on a nationwide basis so that different areas

of the country may be compared. There may be some uncertainty that the

15
Census Bureau has been able to achieve this goal, but for the most

part the Census data is much more uniform than any other alternative.

3.2 Accuracy

In comparing the U.S. Census data with local sources on the

basis of accuracy, local sources in some areas may be better. However,

again because of the lack of nationwide uniformity, some local

sources may be less accurate. In fact, many municipalities themselves

depend on Census statistics. The main area in which local data

would be most useful and most accurate is in the area of housing

statistics, since most local governments must assess property to

obtain property taxes. Also many local building departments

have a great deal of information in the form of experienced and

knowledgeable personnel, but this is not codified in any form

and cannot truly be called data. City personnel may, for instance,

be able to point out dilapidated units, but until the address and

number of dwelling units in each such structure is actually listed,

that information is not available data. Thus, although local

municipalities may provide a potential data source which might in

some cases be more accurate, more current and better suited to our

needs than the U.S. Census, the lack of uniformity and the problems

of obtaining such data again lead us to the U.S. Census as the better

data source.

3.3 Accessibility

Another consideration upon which the decision to use U.S. Census

data was based is the accessibility of the data. As described above,

much of the data potentially available from local sources is not in

16
a form in which they can be processed. Some are not even written down

and what is written down must be extracted, aggregated by some zonal

system, and put into machine readable form. Few cities, except large

ones such as New York, have such data on tape or punched cards, and

even those would require some hand coding.

The main drawback to using local sources for data is the sheer

magnitude of the effort required to obtain information from all of

the major cities in the country. This project lacks both the funds

and, more importantly, the time to engage in such an effort. Local

sources are, however, being consulted in the cities of New Haven,

Connecticut, and Aurora, Illinois, which are principal sources of

incidence data that will be used for model development and validation.

The city of New Haven has provided us with a map showing current areas

of urban blight. This has been used to aid in modelling current

unsound housing figures.

Although the 1970 Census was conducted a year and a half ago,

not all of the data is currently available. Therefore, it has been

necessary to investigate 3 different sets of census data: the 1960

Census, the 1970 Census First Count data, and 1970 Census Fourth

Count data.

3.3.1 1960 Census Data

The 1960 data are already available, tested, and known to be

reliable at different levels of aggregation. However, these data

are more than 10 years old and urban renewal, changing patterns

of residence, and land use differences have all contributed to the

present inaccuracies of the data. It is desirable, therefore,

17
to use more current data wherever possible. Be that as it may, one

variable listed in Table 1, namely condition of housing, will not

be available to this project from the 1970 Census. In the interest

of uniformity, the Census Bureau eliminated this variable ftom the

1970 Census, because it required a subjective decision by the enumerator.

Thus, without further modelling, the 1960 Census is the sole source

of this data item. In addition, because of delayed publication of

the 1970 Fourth Count data, the 1960 Census will be the only source

of several other variables listed in Table 2. The 1960 Census data

appear in books available from the Commerce Library and on tape. Because of

the difficulties associated with extracting the desired data from

the tapes and the number of tapes involved, it has been decided

to hand code the data items from the 1960 Census as required.

3.3.2 1970 First Count Census Data

1970 First Count Census data is currently available both from

the Census Bureau and from private vendors who have processed it

for easier use. We have obtained 1970 First Count, data and

have reformatted it for our own use for the selected areas required

by the modelling process. The major limitation of the First Count

data is that certain key items such as age and condition of housing,

income, educational level, work status, mobility, and region of

birth are not included. As indicated before, the condition of

housing will not be available for 1970 at all since that data

item was not collected.

18
3.3.3 1970 Fourth Count Census Data

1970 Fourth Count Census data will contain all the data items in

Tables 1 and 2 except the condition of housing. However, Fourth Count

data is not yet available, although it was orginally slated for

release last August. Current scheduling calls for the release of this

data in the Spring of 1972. Thus, it will not be available in time

to be used for the January report to Congress, but may possibly, if

released early in the Spring, be useful for the final report and

modelling effort. The Census Bureau has developed a model for

predicting housing condition as a function of Fourth Count

variables. This model may be particularly useful when predicting

lead poisoning incidence for areas in a city which have changed

greatly in the last 10 years because of urban renewal projects

and changing land use patterns.

Because of data unavailability, it has been necessary to make

use of a combination of 1960 data and 1970 First Count, using the

more current data whenever possible. Table 4 lists the Census

data categories, the required data in each category, the current

status of that data, and problems with that category. Data which

is "on cards" is on punched cards which have been used; data on

tape has been reformatted to permit easier handling on the NBS

computer

3.4 Choice of Zonal System

The decision to use Census data includes also the decision to

use the Census zonal system. This system has many levels of aggregation,

but the primary ones for our purposes are the Census block group, the

19
TABLE 4: Census Data Status

Data Item Availability Problems

1. 1960 Census

A. Census tract level 10 years old

1. New Haven on cards

2. Aurora, 111. on cards

3. Chicago on cards

B. SMSA level (whole U.S.) on cards

2. 1970 First Count

A. Census tract level

Does not contain

1. New Haven on cards

age or condition of

housing, income.

2. Aurora, 111. on cards education, or work

status

3. Chicago on tape

4. New York City on tape

B. SMSA level (whole U.S.) on cards

j C. County level (whole U.S.)

!

! on tape i

j

t

3. 1970 Fourth Count

,

. f not yet May not be available

in time, will not

contain condition

; of hous ing

{

20
Census tract, and the Standard Metropolitan Statistical Area (SMSA)

There are about 250,000 Census block groups in the country, 50,000

Census tracts and 243 SMSA’s. There are about 3,000 to 10,000

people in a Census tract, and 4 to 6 block groups per census tract.

The New York SMSA has over 2,000 Census tracts in it. It was

decided to use Census tract level data for model development and

calibration because 1) incidence data was available only for a few

cities so that it was necessary to use a level of aggregation less

than a whole city, and 2) census tracts are more easily available,

there is a relatively small number of them, and the data set is more

manageable than if block groupings were to be used. In the future

it may be desirable to make use of block groupings, since because

they are smaller, they should be more homogeneous. On the other

hand it was decided to predict for the entire United States on the

SMSA level. This can be done in two ways: 1) predict for

each Census tract on the basis of the characteristics of that tract

and aggregate to the whole SMSA or 2) predict for the whole SMSA

on the basis of aggregate and averaged variables. It was decided

to take the second choice, because of the volume and cost of obtaining

data for the first procedure. A third alternative is to predict for

each county in the country. This was rejected for three reasons:

1) There are over 3,000 counties which is a large volume of 1960

data to code and display effectively. 2) Different states have

quite different size counties. 3) All available incidence data is

for urban populations, and it is believed that the model will not

predict as well for rural as for urban areas. Many counties are

21
rural, but SMSA's, by definition are urban and suburban.

There are problems of consistency between 1960 and 1970

Census at all levels of aggregation. Counties in Virginia do not

remain the same, since cities can become independent of counties.

Census tracts are split, sometimes into several pieces., as areas

grow. Even the block groupings change with population shifts. In

1960 there were fewer than 200 SMSA’s, but in 1970 there are 243.

Some of these SMSA’s are new, but in at least one case 2 formerly

separate ones (Greensboro -High Point and Winston Salem) have been

combined. Thus, special care must be taken whenever 1960 and 1970

data are combined.

The major criteria involved in the decision as to the choice

of a demographic data source have been the time for locating, obtaining,

and assimilating data from any source, the uniformity and applicability

of the data, and its availability. The final source is a combination

of 1960 Census data and 1970 First Count Census data at the

census tract level for selected cities and the SMSA level for the

country.

22
4. INCIDENCE DATA

In addition to the demographic data needed for the modelling process,

data on the incidence of lead poisoning are also needed. It became

obvious at the outset of this project that the definition of a lead

poisoning "case” was unclear and differed from one community to

another and from one doctor to another. Different cities use

different tests to determine which children have lead poisoning, and

these tests have differing degrees of reliability. Therefore, the

Surgeon General's recommendation of a lead content of 40 pg per 100 ml

of whole blood is being used as a precise definition of lead poisoning

incidence as it relates to the model and to the data to be obtained.

Thus, the incidence data required are the number of children screened

in each zone and the number of those screened with blood lead levels

greater than 40 yg/100 ml. It would be more desirable, in fact if

the whole blood lead level distribution were available so that one

could estimate the number over any desired limit value.

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 screening program should be as unbiased as possible,

3. The data must be able to be readily aggregated by the

geographical zonal system chosen for the modelling process

4. Data should be determined by a blood lead level test.

23
4.1 Mass Screening

Data should be obtained from a mass screening program in order

to eliminate as much bias as possible and to minimize the

difficulties in factoring screening to the whole population. Many

testing programs around the country have only screened those children

whose parents brought them into a clinic or hospital. They have no

community outreach programs to go out and contact children to be

tested. Children who are brought in to well baby clinics are apt

to be those whose parents are most concerned about them, and who

are most able to spend the time, effort and money to bring the

child into a central facility. Thus, one might expect such

children to suffer from a lower incidence of lead poisoning.

On the other hand, a child already exhibiting the symptoms of lead

poisoning would be very likely to be taken into a clinic of

hospital because the child is sick. Thus, a program which lacks

community outreach introduces a bias, but it is difficult to decide

whether the bias is in favor of higher or lower incidence rates

and it is quite impossible to know the size of the bias. Only testing

a large proportion of all children in an area and in particular

testing those children who have little or no contact with the organized

medical establishment can lessen possible bias.

In addition to reducing the bias , large screening programs reduce

some of the problems associated with factoring a screening sample to

the whole population of children. If one has the number of children

screened and the fraction of those with lead poisoning, one must

still estimate the total number of children with the disease.

24
This estimation process is called factoring. In programs which

screen children only in a hospital, for instance, some area may be

represented by only a small number of children, a high fraction of

which have elevated blood lead levels. This fraction need not

reflect the true incidence of the area because it is based on a small

biased sample. In addition, lead poisoning is a phenomenon which,

prevalent at a high enough level to be of epidemic proportions,

is found in significantly less than half the children tested. A

small sample might miss it entirely, and certainly if one wanted to

estimate the fraction to two digits, one ought to screen at least

100 children in each zone. Thus, a mass screening effort is

necessary for an accurate predictive model and zones for which the

number screened is too small, should be dropped from the data list.

4.2 Bias

Bias is, however, present to some degree in all existing programs.

Since time and budgetary constraints do not permit this project to

conduct screening, it is necessary to obtain data from already

existing sources. The primary purpose of existing screening programs

is to find as many lead-poisoned children as possible, rather than

to provide data for a lead poisoning model. Therefore, they focus

their screening efforts on those areas of the city or state whose

characteristics, as described in Tables 1 and 2 above, identify them

as high risk areas. Thus, we cannot hope to obtain data free from

such a bias toward high risk areas. However, it is desirable that

data be as free from other known biases as possible.

25
4.3 Zonal System Coding

Another criterion, which has in fact proved to be a major limiting

factor in the choice of data sources, is that it is necessary that the

form of the data be such as to facilitate aggregation by a zonal

system. Few of the data sources have any zonal system classification

built into their data; most just record the child's street address.

Since, as previously mentioned, the state-of-the-art of computer

address coding is quite rudimentary, any coding of zones from

addresses must be done by hand. A partial solution to this

problem is to sample from the complete data base, taking a small

enough sample so that it can be hand coded within project time and

budget constraints. There is, however, the difficulty of deciding

how representative a small sample is, and this can lead to problems

in factoring the sample to the whole population. The ideal data

source would be a small city in which only a relatively small (in

absolute) number of children need be screened in order to account

for a large percentage of the total number of children.

Two points must be made here. The first is that it is necessary

to code both the addresses of children who have elevated blood lead

levels and those who do not. One must have the number of children

screened in order to be able to factor up the number of children

with elevated blood lead levels to those which might be expected

if all children had been screened. Thus, if 25 children out of

the 100 screened had elevated blood lead levels and there were 500

children in the zone, one would expect 125 children in the zone to

have elevated blood lead levels. (This of course assumes the 100

26
children screened were representative of all children in the zone.)

It would not be enough to just know that 25 children in the zone

had elevated blood lead levels, since the incidence rate could be

anything from 5 to 100 percent. A second suggestion has been to code

only data from a few zones. The difficulty with this alternative

is that one cannot know the zone before the address is coded, so

that all must be coded to know which to pick and which to discard.

However, if there is other information, such as the drawing site, which

can be used to eliminate a large proportion of the data such a plan

might be feasible.

4.4 Test Used to Screen

The final criterion on which to base the choice of sources

for incidence data is a medical one. The data must be based on

blood lead level determinations. Other methods are less

reliable; in particular they result in too many false negatives

and thus could lead to under predication by as much as 30 percent.

The blood lead determination is the test recommended by the Surgeon

General. Recent efforts have been made to test out a micro-blood

test, but until this has been proven, the standard blood lead test

requiring 5 ml of blood is generally considered the most reliable.

4.5 Sources

Table 5 lists known programs, their sizes, known biases, and

test methods. Question marks indicate that the particular item

is not known. The major mass screening programs are in Chicago an

New York. Philadelphia is currently screening in model cities areas

but is not using the standard blood lead determination test. In

27
TABLE 5 : Lead Poisoning Screening Programs

SIZE BIAS TEST

Aurora, 111. 1478

(7/ 71 -now)

mass screening blood

Baltimore small hospitals blood

BCEM 50-100 in each

of 30 cities

selected areas micro

Boston small community groups mi cro

Connecticut New Haven 1897

Hartford 147

Waterbury 500

Bridgeport 2 cases

New London 1 case

Stanford 130

some screening

but through clinics

etc.

ALA urine

Chicago 28008 (1967)

40785 (1968)

47527 (1969)

44347 (1970)

28973 (1971)

mass screening blood

Cincinnati small hospitals ?

Cleveland planning ? ?

Delaware Dover 3 cases

Wilmington 6 cases

no mass screening ?

Denver none

Detroit starting ?

28
SIZE BIAS TEST

Illinois Aurora 449

Springfield 670

Peoria 387

E. St. Louis 376

Decatur 793

Joliet 383

Rock Island 285

E. Moline 293

Robbins 103

Harvey 226

Carbondale 264

selected areas blood

Kansas City, Mo. planning hospital based ?

Louisville 2 cases no screening ?

Merimac Valley, Mass. small ? ?

Milwaukee none

Minneapolis small student committee

organized

ALA urine

Nassau Co. N.Y. 3 cases ? ?

Newark 1973

(1/69-3/70)

selected areas ALA urine

New Haven 1897 (1969)

1990 (1970)

364 (1/71-6/71)

mass screening in

1970, but only

clinics and

hospitals in 1971

blood

(some only

ALA urine)

New Orleans 300 health centers $

small screening

micro

New York 80,000 per

year (1970-71)

mass screening blood

New York State reportable —

(see Nassau Co.)

29
SIZE ' BIAS TEST

Norfolk 1,200 well baby clinics micro

Oklahoma City 4 cases ? ?

Philadelphia 5,000 to be

screened (2/71-

6/71)

model cities areas blood enzyme

Portland, Me. 1,000 (1970) small mass

screening, will

have in well -baby

clinics

ALA urine

Rhode Island Providence 2,600 head start, clinics hair sample, i

positive , take

blood

Rochester, N.Y. small hospital and

community groups ?

San Francisco small (1,000) ? ?

St . Louis 1847

(since 6/28/70)

well baby clinics

and community groups

micro

Washington, D.C. small

(6/70-808)

hospitals

,

UPO centers and

well baby clinics

808 micro,

blood in

hospitals

30
1970 New Haven conducted a mass screening program, but because of lack

of funds this program was not continued in 1971. Since July 22, 1971,

Aurora, Illinois, has screened over 1400 children out of about

10,000 children in the age group 1-6, of Which 2,000 are estimated

as high risk children. These five programs are the only ones which

can be described as mass screening programs. Two other programs

should be noted here, because although neither is a mass screening

program in the sense of screening a large proportion of the children

in an area, both are attempting to screen children from different

cities to ascertain the geographical range of lead poisoning.

These two programs are the Illinois screening of 10 Illinois cities

and HEW' s Bureau of Community and Environmental Management's (BCEM)

program of screening in 30 U.S. cities spread throughout the country.

Table 6 lists the major incidence data sources and problems

associated with each. The screening programs in New York and Chicago

have a great deal of data potentially available. However, the

problem of address coding these data precludes their immediate use.

The Chicago data is not in machine readable form and would have to

be coded, but New York has put its case and screening data on tapes.

Philadelphia's program is new and is not using the standard blood

lead determination. The two programs, therefore, which offer

greatest possibility of use are those in New Haven and Aurora. The

New Haven data are immediately available, and there is a published report

containing the number of children screened and the number of elevated

blood lead levels (greater than 40 pg /100 ml) for each of the

census tracts in that city. Aurora is also a good potential data source,

31
TABLE 6: Incidence Data Sources and Problems

SOURCE AVAILABILITY PROBLEMS

1 . New Haven in use

2. New York on tape in N.Y. Only has addresses, large

amount to process

3. Chicago on file cards Large number screened,

in Chicago needs to be put on tape,

will cost ll<|:/child

4. Aurora, 111. on forms, can Will probably need, some

photo reproduce geographical coding

but only 2000 records

5. BCEM 5 cities on Only has distribution of

hand prelim. blood leads, no geographic

information, small sample

6. State of Illinois 6 cities on hand Only has distribution of

blood leads

7. Philadelphia, Baltimore, Forms, etc. Too small samples,

Washington not mass screening,

not representative of

entire city.

32
since although a large percentage of the children at risk have been

screened, this represents a relatively small absolute number of children

and one which can be hand coded within the project time frame and

budget limitations. The 0E0 office in Aurora, which has been

directing the screening program there, has been very cooperative,

is currently coding the screening results. This source will, in

the near future make these available aggregated by census tract.

33
5. FUTURE DATA COLLECTION

There are still some deficiencies in the data sets described

above. Future data collection efforts will be aimed at alleviating

these deficiencies as well as obtaining additional data for a more

detailed model.

Although the main lack of data lies in the incidence data area,

there is still some deficiency in the Census demographic data. The

major problem here is the late release of the 1970 Census Fourth

Count data. If the Fourth Count data becomes available for any

part of the country, it will be obtained and assimilated for use in

the modelling process as soon after its release as possible.

5.1 Census Data

Further Census First Count data may be desired for future model

versions, in particular data at the block group level for a more

detailed model. The major thrust for the first phase modelling

effort has been toward developing a model to predict the national

lead poisoning incidence. It is expected that future modelling

efforts may be directed toward developing a model for use by a city

to determine the geographical extent and level of lead poisoning

throughout the city. It is undesirable to use census tracts because

they are too large and inhomogenous to predict well for this effort.

Therefore, it will be necessary to obtain Census data at the block

level of aggregation for a selected set of cities for the future

model process. Incidence data will also have to be obtained at this

level for model development, calibration, and validation.

34
5.2 Existing Incidence Data

Deficiencies in incidence data can be remedied in two ways:

1) by utilizing existing data sources which have too much information

to be assimilated in the short term, and 2) by designing a data

collection effort to go out and screen selected small groups of children.

Neither of these methods have been used for the first phase because

of the time and cost involved. However, since the major difficulty

in model development during the first phase has been the lack of

incidence data, a heavy push toward obtaining extra data has a high

expectation of producing a much better model and is particularly

necessary for applying the model to geographical areas at lower

levels of aggregation.

There are three possible existing sources which may be utilized

in a further data collection effort; New York, Chicago, and Aurora.

New York's screening data is currently on magnetic tape, but

the volume which would have to be address coded precluded its use

for the first phase of modelling. One way of possibly avoiding

this large volume is to sample from the data base. Since the

Lead Poisoning Control Bureau of the City of New York has been very

cooperative, this procedure seems to offer much promise, although

the difficulties of factoring a 1 or 2 percent sample to the total

population must be investigated. Chicago's data is not in machine

readable form. A computer firm in Chicago has submitted a proposal

to the City of Chicago to put the screening data on tape, including

address coding, for about 11 cents a child plus some fixed costs.

This amounts to about $25,000 for all four years' data, but the

35
firni has estimated the cost for one years’ data at about $8,000.

This cost does not seem exhorbitant and would free project staff

somewhat from having to supe:~vise the coding effort. A third

source of extra data is the Aurora screening effort which is being

used for validation. Aurora has records containing information

about each child screened. If proprietary and invasion of

privacy considerations can be worked out between the Aurora OEO

office and ourselves, we may be able to obtain this extra information

which could be used to discriminate between the characteristics of

the lead poisoned child and one who is healthy.

5.3 Collection of Extra Incidence Data

Even if all three sources of further data were to be exploited,

there are still some data deficiencies remaining. The first of

these is the lack of information about blood lead levels of

children who are not living in the environment described in section 2

of this report. In particular, it would be desirable to screen a

sample of white middle-class children living in older, but well

kept homes. In addition, it would be helpful to be able to establish

a correlation between the age of the house and the presence of lead

paint. This latter would entail a housing screening program.

A second effort at obtaining more screening data would be to

screen a large percentage of children in selected block groups in

several cities. Some of these block groups might be chosen at random

in order to obtain some idea of the blood lead levels of a normal

urban child. Others could be chosen more specifically to study

such things as regional effects. However, the main reason for

36
for using block groups is to be able to obtain screening data associated

with areas which are more homogeneous with respect to socio-economic

characteristics. If the block groups are in different cities they

may be more representative of the nation as a whole, than if they

were all in the same city.

Thus, future data collection efforts may be broken down into

three areas: 1) obtain more Census data as needed, 2) try to utilize

existing sources which could not be used in the first phase, and

3) request small, well designed screening efforts to fill in gaps

in existing data.

37


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