Self-Report Methods of

Estimating Drug Use:

Meeting Current Challenges

to Validity

Editors:

Beatrice A. Rouse, Ph.D.

Nicholas J. Kozel, M.S.

Louise G. Richards, Ph.D.

Division of Epidemiology and Statistical Analysis

National Institute on Drug Abuse

NIDA Research Monograph 57

1985

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Self-Report Methods of Estimating

Drug Use: Meeting Current

Challenges to Validity
ACKNOWLEDGMENT

This monograph is based upon papers presented at a technical review

on validity issues in self-reported drug use which took place on

May 8 and 9, 1984, at Bethesda, Maryland. The meeting was sponsored

by the Division of Epidemiology and Statistical Analysis, National

Institute on Drug Abuse.

COPYRIGHT STATUS

Material in this volume is in the public domain and may be used or

reproduced without permission from the Institute or the authors.

Citation of the source is appreciated.

Opinions expressed in this volume are those of the authors and do

not necessarily reflect the opinions or official policy of the

National Institute on Drug Abuse or any other part of the U.S.

Department of Health and Human Services.

DHHS publication number (ADM)85-1402

Printed 1985

NIDA Research Monographs are indexed in the Index Medicus. They are

selectively included in the coverage of American Statistics Index

Biosciences Information Service, Chemical Abstracts, Current

Contents, Psychological Abstracts, and Psychopharmacology Abstracts.

iv
PREFACE

In many areas of research, including those sponsored by the

National Institute on Drug Abuse, the self-report has become an

integral component of the research methodology. While there is a

growing body of literature that supports the general veridicality

of the self-report, there are also studies that suggest underreporting in certain populations. This is true of a number of

behaviors, including the self-administration of drugs such as

tobacco, alcohol, marijuana, cocaine, and heroin. For some behaviors which are highly stigmatized and/or relatively rare events,

such as heroin use, the self-report in sample surveys may not be

the most appropriate technique. Thus, methods other than selfreport have been used to estimate the prevalence of heroin

addiction. Self-reporting may vary based on the social acceptance, or perceived acceptance, of the behavior in question. While

the level of stigma associated with heroin use has been constant,

there have been sharp reversals in the perception of the acceptability of such behaviors as smoking tobacco, marijuana use, and

cocaine use. Shifts in society's attitudes toward these behaviors

raise legitimate and necessary questions regarding the continued

veridicality of the self-report for these particular behaviors,

and it then becomes incumbent upon the researcher to address these

issues. This monograph is based, in large part, on a technical

review that was held to discuss issues of validity of selfreported data as well as various estimation techniques. This

meeting was another step in a continuing effort designed to maintain excellence in the Institute's research in this area and to

contribute to the field in general.

Edgar H. Adams

Acting Director

Division of Epidemiology

and Statistical Analysis

National Institute on Drug Abuse

v

CONTENTS

Preface . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

Introduction and Overview . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

Beatrice A. Rouse, Nicholas J. Kozel,

and Louise G. Richards

A Discussion of Validity . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

David N. Nurco

Validation of Self-Report: The Research Record..............

Adele V. Harrell

Influence of Privacy on Self-Reported Drug Use by Youths . . . .

Joseph Gfroerer

Issues of Validity and Population Coverage in Student

Surveys of Drug Use..........................................

Lloyd D. Johnston and Patrick M. O’Malley

Sampling and Coverage Difficulties in Canadian Drug Use

Surveys and Efforts to Avoid Them . . . . . . . . . . . . . . . . . . . . . . . . . . . .

Reginald G. Smart

Dynamic Simulation Models: How Valid Are They?...............

Raymond C. Shreckengost

Telephone Surveying for Drug Abuse: Methodological Issues

and an Application . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

Blanche Frank

A Pilot Study Assessing Maternal Marijuana Use by Urine

Assay During Pregnancy

Barry S. Zuckerman, Ralph W. Hingson, Suzette Morelock,

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

Hortensia Amaro, Deborah Frank, James R. Sorenson,

Herbert L. Kayne, and Ralph Timperi

History of Heroin Prevalence Estimation Techniques...........

Marc D. Brodsky

v

1

4

12

22

31

55

63

71

84

94

vii
The Nominative Technique: A New Method of Estimating Heroin

Prevalence . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 104

Judith Droitcour Miller

Heroin Incidence: A Trend Comparison Between National

Household Survey Data and Indicator Data..................... 125

Raquel A. Crider

Estimating Heroin Imports into the United States............. 141

Keith L. Gardiner and Raymond C. Shreckengost

Estimating the Size of a Heroin-Abusing Population Using

Multiple-Recapture Census . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 158

J. Arthur Woodward, Douglas G. Bonett, and M.L. Brecht

Participants . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 172

List of Monographs........................................... 175

viii
INTRODUCTION AND OVERVIEW

Beatrice A. Rouse, Nicholas J. Kozel

Louise G. Richards

Various methods of identifying drug users have been developed to

improve or validate estimates based on direct questioning of

individuals regarding their use of drugs. These methods include

biochemical analysis of different body fluids, indirect questioning techniques, and statistical modeling procedures. Examples of

each of these methods are included in this volume.

Chapters in this volume are the product of a National Institute on

Drug Abuse (NIDA) Technical Review convened to examine various

methodological issues regarding the validity of self-report data.

The meeting was held May 8-9, 1984, at the National Institutes of

Health.

While validity covers a wide range of methodological concerns, the

Technical Review participants focused on three areas: 1) underreporting of drug use on direct questioning, 2) noncoverage of

groups in the population who are at risk, and 3) procedures for

estimating low prevalence drugs, such as heroin. The papers

presented in this volume attest to the rich array of concepts,

methods, empirical results, and evaluations engendered by the

meeting.

In the first chapter, Nurco identifies and describes the different

types of validity, raises various validity issues in drug use and

crime research, and suggests strategies for improving the validity

of self-report data.

Harrell reiterates the fact that validity is a multidimensional

concept and discusses those research conditions and respondent

characteristics found in methodological research that affect the

levels of underreporting. The results of Gfroerer's analysis of

privacy support the importance of the conditions under which an

interview takes place.

Johnston and O'Malley discuss the advantages and limitations of

school surveys in general and describe the Monitoring the Future

1
study in particular. They also address the issue of noncoverage

and examine the effect on drug estimates of omitting absentees and

dropouts.

Smart presents the Canadian experience with school surveys and

suggests some strategies for increasing the cooperation of school

boards--a major source of noncoverage in school surveys. In addition, he describes an informant method of deriving drug use estimates.

Shreckengost presents various tests to evaluate the validity of

dynamic simulation models and points out that statistical models

may often illuminate data that are erroneous.

Frank delineates the methodological issues that affect the validity of telephone surveys and describes the creative ways that the

research group at the New York State Division of Substance Abuse

Services has dealt with these issues. One of these validation

efforts involved the use of a randomized response technique over

the telephone.

Zuckerman and colleagues report their results using urine tests

with pregnant women to validate self-reported use of marijuana.

Brodsky presents a historical outline of the different techniques,

with varying levels of statistical sophistication, used to derive

national estimates of heroin users and points out that these

methods have changed as both the legal status of opiates and the

population subgroups using opiates have changed.

Miller describes the nominative technique, which has been used in

the National Institute on Drug Abuse's National Household Survey

of Drug Abuse. She presents the logic of the technique and the

formula for obtaining a prevalence estimate based on this

approach, and compares the rates obtained with those from direct

self-report.

Crider compares the trends in incidence of heroin use based on

self-report from the National Household Survey of Drug Abuse with

such indicator data as hepatitis B cases and heroin-related

emergency room visits, deaths, and treatment admissions.

Gardiner and Shreckengost present a dynamic simulation model of

the heroin system. They focus on the relationship between supply

(inventory) and demand (desired inventory) and how this relationship affects the price of heroin, its purity, the number of

heroin-related deaths, and the number of heroin users.

Finally, Woodward, Bonett, and Brecht suggest the multiplerecapture census as a supplemental approach to estimating the

prevalence of heroin. They present several mathematical models of

this sampling process and the conditions under which each model is

most appropriate.

2
The rich array of methodological issues and techniques presented

both during the technical review and in this volume attest to the

accomplishments of methodology during the short history of drug

abuse epidemiology. Ten years ago, at a similar meeting, the

problems were rampant and the solutions sparse. The challenges

today also seem numerous, but they are more sophisticated. They

require attention to established knowledge as well as creative

solutions to meet them. This strategy that evaluates previous

experience undergirds all solid development and should lead to

even higher quality estimates of drug abuse 10 years from now.

COEDITORS

Beatrice A. Rouse, Ph.D.

Nicholas J. Kozel, M.S.

Louise G. Richards, Ph.D.

Division of Epidemiology

and Statistical Analysis

National Institute on Drug Abuse

3
A DISCUSSION OF VALIDITY

David N. Nurco

Scientific research rests on a foundation of measurement and

classification. The translation or "operationalization" of

concepts into indicators or measuring devices and the application

of those devices define the process of measurement, without which

formal science cannot exist. The term validity enters the researcher's vocabulary when we self-consciously ask ourselves how

well we have succeeded in performing the measurements we have

undertaken, or when we ask how much trust we can place in the

resultant data. Discussion of the validity of estimates of substance abuse in this volume has indicated a role for laboratory

procedures in improving the accuracy of self-reports; other discussions have concentrated on our inevitable dependence on selfreports. It seems clear that we are talking about a variety of

approaches to validity.

TYPES OF VALIDITY

The concept "validity" is usually defined by the question "Are we

measuring what we intend to measure?" Philosophically, this question is either tautological or it is unanswerable. It implies

that there is some ultimate reality (perhaps in the mind of God),

but that the reality is not available to mere mortals; if it were,

we would approach it directly and the validity question would be

meaningless. If ultimate truth is out of our reach, then we must

be talking about approximate truth (if the logicians will forgive

us) and, further, about degrees of approximation. We then must

face the fact that we have no objective way to determine which of

our approximations is closest to the real thing. Therefore, we

must rely upon consensus or common sense to decide, for example,

which of two measures is the criterion and which is the candidate

for validation.

In dealing with self-reports of substance abuse, we have a further

complication. It is fair to assume that objective truth exists.

For example, the teenager did or did not use amphetamines. But

the validity question is twofold. We use the term "veridicality"

4
to describe the extra link between reality and report. In this

age of look-alikes, do respondents really know what they used? In

addition, are they telling us the truth as they know it? I might

point out in passing that all we can ever hope for in self-reports

is that our subjects try to tell us the truth as they believe it

exists. We cannot pretend to measure the swindles of look-alikes

in surveys.

Validity, which has to do with the accuracy or correctness of what

is being measured, must, of course, be differentiated from reliability, which refers to the consistency or reproducibility of

measurement, as in repeated measures. If a measurement procedure

is not reliable, it will not even agree with itself, so reliability is a necessary but not sufficient precondition for validity.

However, the converse is not true; a measure can give consistent

(reliable) results and still be invalid, as with a thermometer

that always reads 5 degrees too high.

Although reliability and validity are the most often discussed

psychometric desiderata, a fuller treatment of the quality of

measurement would also refer to: 1) objectivity, or the degree to

which the measurement is independent of the person performing it;

2) precision, or the extent to which the measurement is capable of

detecting small differences, which may or may not be important;

3) utility, or the adequacy of the measurement for its purpose.

For example, the invalid thermometer that always reads 5 degrees

too high may be just as useful as a correct one if the purpose is

to measure the relationship between temperature variation and

discomfort. Much of the art or science of psychometrics deals

with ingenious ways to operationalize reliability and validity in

order to evaluate the success of the measurement process.

An index of reliability is often calculated by the test-retest

(e.g., reinterview) technique, a procedure that yields what is

more properly referred to as a stability coefficient of reliability. Any test-retest procedure must deal with such embarrassing

considerations as whether the phenomenon itself has changed

between measurements or, in the case of cyclic behavior, e.g.,

hunger, drowsiness, sex drive, whether one is dealing with a

different sample of the trait. Reliability has sometimes been

taken to mean an agreement among observers, as illustrated by

Stephens (1972), who defined reliability as agreement between

client and counselor on a number of items. Reliability defined as

inter-rater agreement usually refers to situations in which

"judges" or equivalent observers are asked to rate or otherwise

evaluate certain characteristics. Reliability of scales or tests

is frequently measured by internal consistency, indicating the

degree to which the separate items making up the test or scale are

correlated with one another. Many psychometricians consider this

the most important kind of reliability, and it is frequently

indexed by Cronbach's coefficient alpha (1970). Split-half reliability is a special case of coefficient alpha, since it measures

the agreement between halves of an instrument that has been

5
partitioned in only one of the many ways possible. Coefficient

alpha, on the other hand, is the average of all possible splithalves (Cronbach 1970, Nunnally 1978).

When we get into the area of validity, the picture becomes even

more complex. Although distinctions are sometimes blurred,

psychometricians approach validity in a variety of ways. Face

validity means that, in some expert's judgment, an instrument,

questionnaire, or other device appears to measure what it purports

to measure. Content validity goes further in that the instrument

consists of items that explicitly deal with the issue at hand, as

in a vocabulary test that consists of words to define. Concurrent

validity refers to the degree of agreement between the test

results and some other measure of the same thing that is obtained

(approximately) concurrently and that is generally regarded as

valid on a riori grounds. Predictive validity is much the same

as concurrent validity except that the behavior or event to be

predicted, termed the criterion and believed to be a better

indicator of the phenomenon in question, will occur sometime in

the future. Finally, and perhaps most important for theoretical

purposes, there is construct validity. Although this is somewhat

difficult to define, it is closely linked with factor analytic

strategies and the notion of a latent variable. A latent variable

(or its equivalent, a hypothetical construct) has no single, pure

criterion or indicator of itself. Rather, it is an abstraction

measured by the relationships among a number of observable variables believed to be partially determined by it. Some examples of

latent variables that are widely used in the social and behavioral

sciences are intelligence, anxiety, depression, socioeconomic

status, and gross national product. In effect, these latent

variables are measured by obtaining a weighted combination of the

several indicators believed to be determined by them. Note that

it is impossible to directly validate such a measure because no

direct criterion exists. However, if the results of our measure

are in accord with theoretical expectation, e.g., if persons under

a psychiatrist's care score higher on a measure of anxiety than do

people not under such care, we conclude that the measure is

behaving in a manner consistent with theory.

Turning more directly to the issues that are of present concern,

we might begin by saying that reliability is only a means to an

end--validity--and that validity is a means to an end--utility.

The real question is "How are you going to use the valid measure?"

For example, the precision of each individual's response is

important if the objective is individual diagnosis, but a considerable amount of imprecision may be tolerable over a sizable

sample of individuals if the objective is the study of correlates

of a response. Even if the objective involves determination of a

prevalence estimate for a population, and policy is being made on

the basis of the estimate, only policymakers can tell you whether

they would behave differently if there were 1 million heroin

addicts than if there were 900,000.

6
To illustrate the difference between individual diagnosis and

group findings, consider evaluation of drug use among Vietnam

veterans (Nurco, unpublished data). Based on self-reported data,

10 percent of the subjects admitted drug abuse; laboratory reports

of these same subjects showed that 10 percent had positive urine

tests. However the admitted drug users were not all the same 10

percent of the subjects with the positive urine tests.

Sometimes, we are tempted to speak glibly of comparing selfreported drug use against a criterion of medical records or other

ostensibly superior sources. We tend to forget that what appears

in the medical records is what the subject told the recordkeeper.

In effect, we are merely comparing the results of one interview

with the results of another interview. We must remember that, at

best, we have highly fallible criteria, such as clinic records of

uneven quality, ancillary reports of family and friends, police

records of varying degrees of completeness and accuracy, counselor

appraisals of questionable reliability and objectivity, and

inconsistent laboratory results.

VALIDITY ISSUES IN DRUG USE AND CRIME RESEARCH

With regard to more objective criteria of legal involvement, the

deficiencies inherent in official criminal records have been

thoroughly reviewed and documented by Collins and his associates

(1982). In their summary regarding various aspects of data

quality, they state:

There is considerable evidence in the literature

that individuals, when asked about arrests, attempt

to report that information accurately. It is also

clear from the literature that individuals sometimes

do not report their involvement in crime and that

the accuracy of arrest self-reports varies on the

basis of length of recall period, type of criminal

behavior, and data collection methodology. It is

not possible to summarize the nature of the systematic bias that exists in individual reporting

patterns in any simple way. The evidence is not

consistent.

It is also clear from the literature review that

official records are often deficient. Arrests are

not always entered on an individual's record, and

the accuracy of a given record depends on the types

of offenses, where the arrest took place, and when

the arrest occurred. In the past there has been a

tendency for researchers to be concerned about the

validity of self-reports and to ignore the deficiencies in official crime records. The preceding

review clearly shows that official records should

not be viewed as complete and accurate (Collins et

al. 1982, pp. 16-17).

7
An issue not raised by Collins et al. but sometimes alleged is

that official reports of crimes may be subject to political

pressures to show a decrease in certain types of criminal

activity. If this is true, there is even further reason to

question the value of official records as a criterion against

which to validate information from other sources.

On a more optimistic note, I want to emphasize and elaborate on

some points made in Harrell's paper in this volume. For those of

us in narcotic addiction research, it is encouraging to encounter

the view that addicts might indeed be more honest respondents

regarding drug abuse than others in the general population by the

very fact that they have little to conceal. The addicts that we

deal with in our studies are usually well known to authorities and

have already been involved in rehabilitative efforts.

Also, admitting to addiction may have its compensations. Though

generally we must be concerned about underreporting, overreporting

may be a problem in some instances. For example, individuals

arrested for violent crimes, with the prospect of prolonged

incarceration, may present themselves as victims of narcotic

dependence--not entirely responsible for their actions and more in

need of treatment than punishment. Lack of veridicality in the

form of overreporting, therefore, may present more of a problem to

judicial and correctional personnel than to survey researchers in

the community. Overreporting is also an issue that has to be

dealt with in any desirable or popular treatment program. For

example, overreporting tends to be a problem in determining eligibility for methadone maintenance programs and has given rise to

the use of narcotic antagonist challenges to confirm addiction.

(This raises the problem of frequency of use versus actual physical dependence in determining addiction. Addicts may be truthfully reporting extent of use but may not actually be addicted.)

IMPROVING VALIDITY OF SELF-REPORTED DATA

We should not, however, give up on self-report data. Rather, we

should concentrate on making it better. From information

presented by Harrell (this volume), we can devise measures to

reduce concealment or underreporting.

Among strategies to be considered are:

1) Assuring confidentiality of information

2) Establishing rapport

a. Selecting empathic and skillful interviewers

b. Enlisting respondent support by presenting general

objectives of the study, e.g., appeal to altruism

3) Checking records and informing subject of intent, which

should be beneficial not only as a concurrent check but

may actually improve accuracy of self-report

8
4) Urine monitoring and informing subject of this intent,

which should be beneficial not only as a concurrent check

but may actually improve accuracy of self-report

5) Concentrating on recent events

6) Making questions less specific

Dr. Harrell's suggestion that researchers check questionnaires for

bias during test construction is worth repeating: the preparation

of any study using self-reported data should include pretesting of

the questionnaire and field work procedures to evaluate the potential response bias associated with the mode of inquiry. This fits

into the widely followed practice of tailoring questionnaires to a

specific purpose and of careful test construction as part of most

research conducted in the area of drug abuse. The notions seem to

be strongly supported by Mr. Gfroerer's analysis of responses

under varying conditions of privacy, since privacy in this

instance is part of the assurance of confidentiality. As Mr.

Gfroerer points out, his findings are clouded by the fact that the

level of privacy in each interview was not randomly assigned, so

that unsuspected exogenous variables may have exerted an effect.

Dr. Zuckerman (this volume) discusses the validity of reported

marijuana use as determined by concurrent laboratory findings.

Researchers also should be concerned about the validity of reports

of alcohol use and smoking. Both of these activities are extremely important with respect to pregnancy, and it is imperative that

we learn more about them. Laboratory tests for these substances

have improved and are available; therefore, the validity of

reports of alcohol and tobacco use as well as marijuana should be

checked. In fact, an intriguing design would be to test the

hypothesis that validity of self-report diminishes with increased

social undesirability of reported activity, e.g., use of cigarettes, alcohol, and marijuana.

Researchers' main concern has been obtaining valid results, but

consideration of this objective prompts a related concern:

granted the validity of the findings, how valid is their generalization to the population in question? Or, put another way, are

our results valid for only a subsection of our population? This

raises the issue, discussed earlier, of utility as the ultimate

objective of validity.

To illustrate this point, consider our experiences in our natural

history study (Nurco 1975, Nurco et al. 1981a, 1981b, 1981c),

which involved the examination of addict careers. We used a

roster of individuals already identified in the arrest and investigation files of the Baltimore City Police Department's Narcotic

Squad. Since we were planning to study narcotic addicts, we were

concerned with how representative our sample was. With this in

mind, we identified a number of addicts in the State mental hospitals in Maryland who were not then known to the police. When we

9
checked, 3 years later, we found that virtually all these appeared

on an updated police roster. The fact that all addicts eventually

became known to the police helped to calm our fears about the

generalizability (and thus the utility) of the original sample.

A full consideration of representativeness not only involves

sample identification but is also concerned with accessibility and

subsequent attrition. In the first wave of our study, we selected

10 white males and 10 black males from those newly identified

during each year of the period 1952 through 1966, and 5 white

males and 5 black males for the period 1967 through 1971. (We

oversampled in the earlier years because of our interest in the

careers of addicts). We located 98 percent of our sample. Our

interview response rate was in excess of 92 percent, with an

interview that took approximately 3 hours to administer. Unfortunately, the success of this endeavor in producing this level of

response represents the exception rather than the rule in drug

abuse research. I present it here to emphasize the importance of

obtaining a high response rate and as an example of what can be

achieved through perseverance.

With regard to the veridicality of the information obtained, we

wanted detailed data about drug use, employment, criminal behavior, and social relationships for each period of addiction over a

lifetime involvement with drugs. In a pilot phase, we found that

the typical subject would collapse his periods of addiction with

periods of nonaddiction as a way of bringing a long interview to

conclusion. As a result of this experience, our subsequent strategy was to determine the dates of successive addiction periods

before asking detailed questions about each one. After eliciting

these dates, we asked questions about preaddictive behavior and

then moved to each of the on-and-off periods we were interested

in, reminding the subject of the dates we had originally elicited

from him. In this way we obtained the information we needed.

I am not suggesting that a tactic similar to the above be used in

every study. I am recommending, however, that researchers in the

field steep themselves in the nuances of veridicality until they

appreciate the magnitude of the problem and are prepared to devise

anticipatory strategies to avoid its many pitfalls.

REFERENCES

Collins, J.J.; Hubbard, R.L.; Rachal, J.V.; Cavanaugh, E.R;

Craddock, S.G.; and Kristiansen, P.L. Criminality in a Drug

Treatment Sample: Measurement Issues and Initial Findings.

Report to the National Institute on Drug Abuse under Contract 271-79-3611. Research Triangle Park, N.C.: Research

Triangle Institute, 1982.

Cronbach, L.J. Essentials of Psychological Testing. 3rd ed. New

York: Harper and Row. 1970.

Nunnally, J.C. Psychometric Theory. 2nd ed. New York: McGrawHill, 1978.

10
Nurco, D.N.; Bonito, A.J.; Lerner, M.; and Balter, M.B. Studying

addicts over time: Methodology and preliminary findings. Am J

Drug and Alcohol Abuse, 2:183-196, 1975.

Nurco, D.N.; Cisin, I.H.; and Balter, M.B. Addict careers. I. A

new typology. Inter J Addict, 16:1305-1325, 1981a.

Nurco, D.N.; Cisin, I.H.; and Balter, M.B. Addict careers. II.

The first ten years. Inter J Addict, 16:1327-1356, 1981b.

Nurco, D.N.; Cisin, I.H.; and Balter, M.B. Addict careers. III.

Trends across time. Inter J Addict, 16:1357-1372, 1981c.

Stephens, R. The truthfulness of addict respondents in research

projects. Inter J Addict, 7:549-558, 1972.

AUTHOR

David N. Nurco, D.S.W.

Department of Psychiatry

University of Maryland

1229 West Mt. Royal Avenue

Baltimore, MD 21217

11
VALIDATION OF SELF-REPORT:

THE RESEARCH RECORD

Adele V. Harrell

Self-reported data are the mainstay of much social research.

Indeed, questionnaires and checklists have become a way of life as

we record our food preferences or television viewing for the

latest survey. The popularity of self-reported data can be easily

understood. It is relatively simple to collect--by mail, by telephone, by face-to-face interview, or with self-administered

questionnaires. The ability to manipulate the mode of questioning

and the questionnaire content provides a great deal of flexibility

in designing studies. In addition, certain types of information

can be collected from individuals with less effort and often more

accurately than from alternative data sources. Take, for example,

questions such as "How old were you when you got a driver's

license?" or "How often have you been hospitalized?" Searches of

the records either at the Division of Motor Vehicles or at hospitals would be far more time-consuming than a questionnaire and

would depend heavily on the accuracy of the officially maintained

records. More important, there is certain information that can

come only from the individual. This includes, for example, information on private personal behavior, such as voting behavior, and

information about individual attitudes. Small wonder that we rely

so heavily on easy-to-get self-reported data. However, in the

face of the good news about self-reported data, it is necessary to

take some time to consider the bad news--or at least the potential

for bad news.

The potential for bad news comes in the form of multiple threats

to the validity of self-reported data. Validity in this context

refers to whether the data recorded by the researcher accurately

reflect the phenomenon under investigation. This simple statement

conceals what is in actuality a complex, multidimensional concept.

Validity can take on a variety of meanings, depending in part on

the method used to evaluate the extent to which the data reflect

the phenomenon under investigation. Face validity, for example,

refers to the extent to which the data appear to "make sense" as a

reasonable indicator of the purported phenomenon. Predictive

validity refers to the extent to which the data correlate with

12
subsequent outcomes to which they should be related on logical

grounds. Criterion validity, the primary focus of this discussion

of self-reported data, refers to the extent to which the subjective self-reported data are "verified" by agreement with another

indicator of the same phenomenon believed to be of higher validity

(the criterion). Criteria used for this purpose have included

official or medical records; reports of others such as family,

friends, or counselors; and biochemical tests (Stanton 1972).

Any number of factors can undermine the validity of self-reported

data. These include careless field procedures (Deming 1950),

question design and content (Bradburn et al. 1979), memory lapse

(Deming 1950), and status bias (Cahalan 1968). In general, the

validity threats cited by researchers fall into three categories:

1)

2)

3)

Aspects of the mode of inquiry; factors in the

questioning situation that influence the response.

Examples include question wording, interviewer

expectations, and degree of anonymity.

Inability to provide correct information; respondent

never knew or has forgotten the answer and thus cannot provide valid data.

Unwillingness to provide the information; respondents' answers are designed to present them in a

socially favorable way and/or to promote their

personal interests. In this case, the respondent is

unwilling to provide information requested.

Of these threats to validity, it is the third category that is a

crucial issue in studies of illicit drug use. Illicit drug use is

behavior that carries with it the threat of social sanctions and

the stigma of illegality. The negative social status of illicit

drug use may deter some survey respondents from accurately reporting their drug use experiences--either in an attempt to avoid

adverse reactions from parents, employers, or teachers (if not

peers) or in an attempt to present themselves in a favorable way

during the interview. This concern is not without theoretical

foundation. Social desirability theory (Edwards 1957) rests on

the premise that the more highly stigmatized and negatively sanctioned a behavior, the stronger the tendency to deny having

engaged in it. This theoretical perspective indicates that distorted responses, either underreporting or overreporting, will

occur as a function of the perceived acceptability of the correct

response. The following review, which begins with a general look

at the validity of self-reported data and goes on to examine drug

use validity studies, provides empirical evidence consistent with

this thesis.

The focus on veridicality as a central issue is not new. Hyman

aptly entitled his 1944 article "Do they tell the truth?" His

subject was the socially sensitive issue of the redemption of war

13
bonds. In the midst of World War II, cashing in war bonds was

widely thought to be unpatriotic and the basis for strong social

censure. Hyman found that a substantial percentage of persons

known to have cashed in war bonds denied having done so within a

week of the redemption. Apparently, respondents were unwilling to

admit such socially unacceptable behavior.

The importance of the apparent "degree of deviance" is illustrated

more clearly in the Denver validity study (Parry and Crossley

1950), which compares the accuracy of response for socially neutral and socially positive behaviors. Respondents were asked

about their voting record, charitable contributions, possession of

a library card, possession of a driver's license, and ownership of

specific items such as a telephone. Records from the polls, the

Community Chest, the library, etc., were used as the criteria.

Rates of distortion appeared clearly linked to the desirability

of the correct response and/or the ease with which the correct

response could be verified. Easily verified questions of fact--

e.g., telephone ownership, age--attained accuracy rates of 90

percent or higher against external criteria. In contrast, questions about socially desirable behavior produced response

distortion. For example, 34 percent of the respondents reported

that they had made contributions to the Community Chest that were

not recorded in Community Chest records. Further evidence of

exaggerated responses about socially positive behavior is found in

the overreporting of voting: 16 percent inaccurately reported

voting in a recent election, while 42 percent overreported voting

in the past six elections. Ownership of a library card, a more

neutral behavior, was apparently exaggerated by only 9 percent of

the sample. Interestingly, the rate of error in the other

direction--failure to report a real contribution to the Community

Chest or a vote--was consistently small (under 5 percent) and

probably should be attributed to memory lapse, field procedure

problems, and other sources of unreliability.

Research on the validity of data on socially undesirable behavior

produced similar results. Cannell and Fowler (1963) compared

self-reported data on hospitalization to data from records. They

found that the denial of hospitalization for threatening or embarrassing disorders was considerably higher than the denial of

hospitalization for other kinds of disorders. Phillips and Clancy

(1970) found that the willingness to report symptoms of mental

illness was directly related to the respondent's view of the

social undesirability of various psychological disorders.

The tendency to exaggerate socially positive behavior and to

underreport socially negative behavior does not appear to have

diminished with time. In 1979, 29 years after the Denver validity

study, Bradburn et al. again report substantial distortion of

voting behavior--rates of overreporting in response to whether the

person voted in a recent primary ranged from 36 percent to 48

percent depending on the mode of inquiry. Drunken driving, with

14
its socially negative status, elicited respondent denial at rates

that ranged from 35 percent to 54 percent, depending on the questioning technique. As before, a much lower rate of overreporting

characterized responses to the more neutral question of whether

the respondent had a library card. These highlights of the literature on self-reported data demonstrate that in general there is a

tendency for respondents to give answers that make them "look

good."

Not everyone, it appears, is equally likely to give a distorted

response. Hyman (1944), for example, found that the rate of

denial for war bond redemption increased with income; that is,

persons with higher incomes were more reluctant to admit to this

behavior than persons with lower incomes. Cahalan (1968) reported

that the distorted responses to the Denver validity study varied

by sex, age, and socioeconomic status. Compared to men, women

were slightly less likely to exaggerate their voting record, and

noticeably less likely to exaggerate their Community Chest contributions and possession of a valid driver's license. Younger

persons were more likely than older persons to overreport socially

desirable behaviors such as voting and contributing. In addition,

contributions were more likely to be exaggerated by lower socioeconomic status respondents than by higher status respondents. A

similar conclusion was reached by Weiss (1968). She examined

self-reported registration and voting among black welfare mothers

and found that rates of voting exaggeration were related to age,

education, and social status (as well as rapport with and social

distance from the interviewer). These exaggerated reports

belonged to women she refers to as the "almost voters"--older,

more educated women who were more experienced in the labor market

and who held middle-class views. They were, in short, women who

valued voting and thus tended to present a view of themselves that

included voting.

These sociodemographic differences in validity of self-reported

data appear in many instances to be a function of personal norms

and self-expectations. For example, we can speculate that having

a driver's license was, at the time of the Denver Validity Study,

less common for women than men. Thus, women may have responded on

the basis that having a driver's license was not socially expected

behavior--not the norm. Similarly, the wealthy may have held more

stringent expectations of their financial responsibilities for the

war effort and, therefore, responded as they felt they should have

behaved. Weiss clearly believes the personal value system of her

sample of mothers influenced the validity of their responses.

This interpretation is supported by one study of the validity of

self-reported data on deviant behavior (Clark and Tifft 1966).

Using anonymous questionnaires, interviews, and polygraph tests to

gather information on behavior and attitudes, Clark and Tifft

concluded that response inaccuracy was highly related to declared

personal norms and reference group norms and was related as well

to the generally understood "deviance" of the behavior. Such

interpretations are consistent with social desirability theory.

15
These findings are significant for the study of illicit drug use--

a phenomenon that has sharply divided our society into segments

characterized by divergent drug use norms both in terms of what is

typical or expected behavior and in terms of what is desirable

behavior. Thus, the validity of self-reported data on illicit

drug use may well vary by age, location, or other correlates of

these normative differences.

Much less information is available on the second threat to

validity--the respondent's ability to provide the information. In

one test of response validitv. Jeager and Pennock (1961) interviewed appliance owners twice; at a 1-year interval, on the kind

and condition of their washing machines and the year of purchase.

The results indicate widespread inability to recall detailed

information such as the year of purchase. Almost 60 percent of

the respondents contradicted themselves on the year of purchase,

although 75 percent of the time the difference was 2 years or

less. Agreement on kind and condition of the machine was much

higher, illustrating the higher validity for easier questions.

This provides clear warning about attempting to collect detailed

data without adequate memory aids and carefully designed questioning procedures. However, it is not clear that these results

provide any more than general guidelines for the study of illicit

drug use. Memory is a selective process, known to be affected by

the salience of an event. Events as different as purchasing a

washing machine and using cocaine for the first time may well

differ in salience, with unknown consequences for the validity of

self-reported data.

A third threat to validity in the list was the mode of inquiry.

The literature abounds with evidence on how relatively subtle

differences in the context of an interview can influence the

responses. A classic example was reported in early surveys of

drinking, in which interviewers who drank elicited reports of more

drinking among respondents than did interviewers who abstained

(Mulford and Miller 1959, 1963; Mulford 1964). Similarly, Weiss

(1968) reported that interviewer rapport with her sample of

welfare mothers influenced the validity of their self-reported

voting behavior.

Variation in the mode of questioning can affect the degree to

which responses are anonymous and thus the degree of selfdisclosure required (Jourard 1971). The work of Bradburn et al.

(1979) is particularly interesting because it relates variations

in degree of anonymity to the validity of responses to questions

about behaviors that vary in social desirability. They evaluated

the responses to questions ranging from drunk driving to voting

collected by telephone, by mail, by face-to-face interview, or

with self-administered questionnaire. Although theoretically one

might expect more authentic responses under conditions of greater

anonymity, the reported validity differences due to variation in

questioning techniques are so small as to have little practical

import, regardless of the behavior's social desirability.

16
This general overview provides the background for evaluating the

relatively sparse literature on the validity of self-reported data

on illicit drug use.

Much of the research on the validity of self-reported data on drug

use has focused on the veridicality of former narcotic addicts.

These studies compare addicts' reported drug use, arrest record,

and demographic information to data from hospital records, law

enforcement records, biochemical tests, and/or reports of significant others (Ball 1967, Cottrell and O'Donnell 1967; Robins and

Murphy 1967, Stephens 1972, Amsel et al. 1976, Maddux and Desmond

1975; Bonito et al. 1976). In one such study, Ball (1967) compared the responses of 59 narcotic drug addicts in a structured

interview to data from hospital records, FBI records, and urine

tests conducted immediately after the interview. His expressed

goal was to determine whether "deviant groups, especially those

engaged in illegal behavior, are motivated to--and do--conceal or

deny their proscribed behavior" (Ball 1967, p. 650). Five items

were used for comparison: 1) the age of the subject, 2) age at

onset of drug use, 3) type and place of first arrest, 4) total

number of arrests, and 5) drug use at the time of interview.

Responses to the items related to deviant behavior "indicate a

rather surprising veracity on the part of former addicts" (Ball

1967, p. 653). In fact, the only response bias noted was that the

females in the sample were significantly less reliable than the

males in reporting their age. In general, most research on former

addicts concludes that addicts are willing to reveal the facts of

their drug use and arrest record, although Amsel et al. (1976)

report relatively high denial rates for drug use.

Recall of detailed information does, however, appear to pose a

threat to validity for some drug use items. Higher rates of

distortion are reported for exact information, e.g., age at first

arrest and age of first drug use (Cottrell and O'Donnell 1967,

Ball 1967) than for "easier" questions, such as "Have you used

marijuana?" Because the addicts appeared willing to provide

authentic drug information, the implication is that faulty memory

produces these inaccurate answers.

Although these results are quite encouraging for those who wish to

gather drug use data in interviews, the generalizability of the

addict studies to surveys of the general household population is

questionable. Previously hospitalized addicts have already been

publicly labeled deviants and identified as drug users. They are

well aware that records of their drug-use history exist. This

reduces the amount of new self-disclosure required. The use of

urinalysis in some studies further discourages attempts at concealment by reducing the chances of successful concealment (Amsel

et al. 1976). In contrast, nonaddict drug users in the household

population may be actively engaged in concealing their use from

others and may believe the drug-use behavior to be unknown except

to selected persons. In these cases, there may be greater incentives for denial and greater chances of successful concealment.

17
One study of the validity of self-reported data conducted with

members of the household population was reported by Parry et al.

(1971). The research evaluated the accuracy of responses to

questions on the use of psychotherapeutic prescription drugs,

using as a criterion a complete file of all prescriptions filled

in a small midwestern town. Antibiotic use, believed to be less

stigmatized than use of tranquilizers, stimulants, or sedatives,

was used for comparison. The overall level of response accuracy

can only be described as fairly good: the percentage of respondents who correctly reported their drug use ranged from 64 percent

to 80 percent. However, the accuracy appears to be unrelated to

the social desirability of the drug. Indeed, the accuracy rate

for psychotherapeutic drug use averaged 74 percent, exceeding the

accuracy rate of 64 percent for antibiotics. The tendency of

adults in the household population to underreport psychotherapeutic drug use is also reported by Fejer and Smart (1973), who

found that the rate of psychotherapeutic drug use calculated from

pharmacy dispensing records was substantially higher than the rate

estimated from self-reported survey data.

Once again, respondent inability to give accurate data may have

posed a problem. The use of a long-form questionnaire plus drug

charts in color by Parry et al. (1971) reduced the percentage of

incorrect responses on tranquilizer use from 27 percent to 15

percent. The effectiveness of these techniques in reducing

incorrect answers suggests that respondents will provide valid

answers if questions are presented in a way that facilitates

recall. The study further indicates the effect of time on the

accuracy of the responses; inaccurate responses were twice as

prevalent among former tranquilizer users as among current users.

This is a sharp decline in view of the relatively narrow time span

used to define former users (used in 1966 or 1967, but not in 1968

or 1969) and current users (used in 1968 or 1969). The results

thus indicate a marked drop in accurate response within a year or

two of the drug use. It should be noted, however, that this study

differs in one important aspect from surveys of illicit drug use.

While the medical use of the psychotherapeutic drugs may carry a

negative social image, it would generally be viewed less negatively than illicit use of drugs such as cocaine or heroin.

Of all the studies reviewed, the one that comes closest to

addressing the validity of self-reported drug-use data collected

in a household interview was conducted in San Francisco just prior

to the first National Survey on Drug Abuse (Cisin and Parry 1980).

The design compared responses of patients at selected drug abuse

clinics with responses of a matched control group and with the

clinic records. Questions covered nonmedical use of psychotherapeutic drugs; use of marijuana or hashish; and use of heroin,

other opiates, hallucinogens, or cocaine. For most drugs the

questionnaire data revealed at least as much use as the clinic

data, though both types of data were subject to response errors.

However, for heroin, the clinic records revealed considerably more

use. For example, the questionnaires detected 73 percent of the

18
cocaine use reported to the clinic, while the clinic records

listed 60 percent of the cocaine use reported on the questionnaires. In contrast, the questionnaires detected only 41 percent

of the heroin use reported to the clinic, while the clinic records

listed 85 percent of the heroin use reported on the

questionnaires.

Thus, it seems that the answer to Hyman's question, "Do they tell

the truth?" is: not always. There is a clear tendency for

respondents to exaggerate the extent of their socially desirable

behavior and minimize or deny their socially undesirable

behavior. However, beliefs as to what constitutes socially

desirable or undesirable behavior seem to vary widely. Differences in personal values, expectations, and reference group norms

appear to be key factors in how willing respondents are to provide

authentic responses. In the area of drug use, ex-addicts appear

relatively truthful--willing to admit candidly their arrest record

and drug experience. However, household interviews with drug

clinic patients, a group less "deviant" than previously hospitalized addicts, revealed a reluctance to admit to the use of heroin.

As Bonito et al. (1976) put it, "Despite the consistently positive

evidence for addict veridicality, we believe it important that

every study relying on self-disclosure provide evidence for the

essential accuracy of the information obtained" (p. 720).

A second finding is that there are definitely limits to the kind

of detailed information respondents are able to recall.

Certainly, recent events are more accurately recalled than past

events. Likewise, precise information is more difficult to recall

than general information. That is, use of marijuana is easier to

recall than the number of times marijuana was used. Some types of

information, e.g., the name of the particular tranquilizer used,

require the use of memory aids to achieve accuracy. In this

regard, it is wise to consider carefully the degree of precision

that is actually required for research purposes. In most, if not

all, cases, it is far better to get valid answers to general questions than biased answers or nonresponse.

The ability of a survey to elicit truthful answers based on accurate recall may be a function in part of the mode of inquiry, of

the situation in which the respondent is asked to reveal personal

information. The cumulative evidence suggests that bias related

to the mode of inquiry may vary from one research setting to

another. That is, anonymity may be a necessary component in some

situations, while interviewer characteristics may be important in

others. It is safe to say that the preparation for any study

using self-reported data should include pretesting of the questionnaire and field work procedures to evaluate the potential

response bias associated with the mode of inquiry.

Although these potential threats to the validity of self-reported

data do indeed require thought and preparation on the part of any

researcher, they by no means should be construed as indications

19
that self-reports should be abandoned. Self-reported data remains

a flexible and relatively efficient method of gathering information that might otherwise be inaccessible. The message is that we

must be constantly vigilant and ready to examine such data with a

critical eye.

REFERENCES

Amsel, Z.; Mandell, D.; Matthias, C.; et al. Reliability and

validity of self-reported illegal activities and drug use

collected from narcotic addicts. Int J Addict, 11(2):325-336,

1976.

Ball, J. The reliability and validity of interview data from 59

narcotic drug addicts. AJS, 72:650-654, 1967.

Bonito, A.J.; Nurco, D.N.; and Shaffer, J.W. The veridicality of

addicts' self reports in social research. Int J Addict,

11(5):719-724, 1976.

Bradburn, N.M.; Sudman, S.; et al. Improving Interview Methods

and Questionnaire Design. Washington, D.C.: Jossey-Bass,

1979. 214 pp.

Cahalan, D. Correlates of respondent accuracy in the Denver

validity survey. Public Opin Q, 32:607-621, 1968.

Cannell, C.F., and Fowler, F.J. Comparison of a self-enumerative

procedure and a personal interview: A validity study. Public

Opin Q, 27:250-264, 1963.

Cisin, I.H., and Parry, H.L. Sensitivity of survey techniques in

measuring illicit drug use. In: Rittenhouse, J.D., ed.

Developmental Papers: Attempts to Improve the Measurement of

Heroin in the National Survey. Washington, D.C.: Supt. of

Docs., U.S. Govt. Print. Off., 1980. pp. 3-46.

Clark, J.P., and Tifft, L.L. Polygraph and interview validation

of self-reported deviant behavior. Am Social Rev, 31:516-523,

1966.

Cottrell, E.S., and O'Donnell, J.A. Reliability of admission

data. Unpublished report. National Institute of Mental Health

Clinical Research Center, Lexington, KY, 1967.

Deming, W.E. Some Theory of Sampling. New York: Wiley, 1950.

Edwards, A.L. The Social Desirability Variable in Personality

Assessment and Research. New York: Dryden, 1957. 602 pp.

Fejer, D., and Smart, R.G. The use of psychoactive drugs by

adults. Can J Psychiatry, 18:313-320, 1973.

Hyman, H. Do they tell the truth? Public Opin Q, 8:557-559,

1944.

Jeager, C., and Pennock, J.L. An analysis of consistency of

response in household surveys. J Am Stat ASSOC, 56:320-327,

1961.

Jourard, S.M. Self-disclosure: An Experimental Analysis of the

Transparent Self. New York:

Maddux, J.F., and Desmond, D.P. Reliability and validity of

information from chronic heroin users. J Psychiatr Res,

12(2):87-95, 1975.

Mulford, H.A. Drinking and deviant drinking, U.S.A., 1963. Q J

Stud Alc, 25:634-650, 1964.

20
Mulford, H.A., and Miller, D.E. Drinking in Iowa: I. Sociocultural distribution of drinkers. Q J Stud Alc 20:704-726,

1959.

Mulford, H.A., and Miller, D.E. The prevalence and extent of

drinking in Iowa, 1961: A replication and an evaluation of

methods. Q, J Stud Alc, 24:39-53, 1963.

Parry, H.J., and Crossley, H.M. Validity of response to survey

questions. Public Opin Q, 14:61-80, 1950.

Parry, H.J.; Balter, M.B.; and Cisin, I.H. Primary levels of

under-reporting psychotherapeutic drug use. Public Opin Q,

34:582-592, 1971.

Phillips, D.L. Knowledge From What? Theory and Methods in Social

Research. Chicago: Rand-McNally, 1972. 204 pp.

Phillips, D.L., and Clancy, K.J. Response bias in field studies

of mental illness. Am Social Rev 35:503-515, 1970.

Robins, L.N., and Murphy, D.E. Drug use in normal population of

young Negro men. Am J Public Health, 57:1580, 1967.

Smart, R. Percent studies of the validity and reliability of

self-reported drug use, 1970-1974. Can J Criminol Corr,

16(4):326-333, 1974.

Stephens, R. The truthfulness of addict respondents in research

projects. Int J Addict, 7(3):549-558, 1972.

Weiss, C. Validity of welfare mothers' interview responses.

Public Opin Q, 32:622-633, 1968.

AUTHOR

Adele V. Harrell, Ph.D.

Institute for Social Analysis

1625 "K" Street

Washington, DC 20006

21
INFLUENCE OF PRIVACY

ON SELF-REPORTED DRUG USE

BY YOUTHS

Joseph Gfroerer

INTRODUCTION

Underreporting of drug use by survey respondents has always been a

major concern of drug abuse survey researchers. This concern particularly applies when the respondents are youths since they might

fear being punished if their use of drugs was discovered by

parents. A number of studies have been conducted to determine

whether underreporting is a serious problem and to identify procedures that can be used to obtain the most valid data. While some

of the results have been contradictory, most studies conclude that

reliable, valid self-reported drug use data can be obtained (Smart

and Jarvis 1981: O'Malley et al. 1983; Hubbard et al. 1976; Single

et al. 1975; Smart 1975).

Factors that have been identified as possibly affecting the

reporting of drug use by youths include the type of questionnaire

(interview vs. self-administered), characteristics of the interviewer, the degree of anonymity of the respondent, the setting

(home vs. school), and the degree of privacy during the interview

(Johnston, in press; Sudman and Bradburn 1974). Degree of privacy

refers to the presence of a parent or other person in the same

room during the interview.

In several studies, self-administered questionnaires have been

shown to produce higher reported prevalence of drug use than

interviews. In one study (Hochstim 1967), women were found more

likely to report having used alcohol on a self-administered questionnaire than in a face-to-face interview. A study of college

students (Krohn et al. 1975) showed higher prevalence of various

illegal behaviors such as marijuana use and drinking under age

reported on a self-administered questionnaire, although the sample

size for the study was very small and differences were not statistically significant. This study also showed that respondents are

more likely to report illegal behavior to "hip" interviewers than

to "straight" interviewers.

22
Anonymity and setting have not been clearly shown to be important

factors affecting underreporting. King (1970) compared drug use

data from identifiable and anonymous questionnaires filled out by

recent college graduates and found no significant differences in

drug use prevalence between the two approaches. A study of undergraduate students (Luetgert and Armstrong 1973) found similar

results. This study also found that with less anonymity, current

marijuana users may report their use as being in the past rather

than current, unless they are frequent users. A study of youths

involved completing a drug and health questionnaire both at school

and at home, with parents also completing the questionnaire in a

different room (Needle et al. 1983). Results indicated that the

setting in which respondents complete questionnaires is not

related to reporting bias. Similar results were found in another

study (Zanes and Matsoukas 1979) using matched samples of youths.

This study also concluded that the presence of parents in the room

during the completion of a self-administered questionnaire had no

effect on the reporting of youth drug use.

While many of the factors that may affect underreporting can be

controlled by researchers, it is not possible to achieve complete

privacy (i.e., no other person in the room) in every interview

when conducting a household survey. Given this limitation, it is

important to assess the impact of the lack of privacy on the

results of a survey, both to assess the potential impact on the

validity of data from that survey and also to provide general

information on the importance of privacy for future surveys. Only

one previous study is known to address this specific issue for

self-administered questionnaires, and it was based only on a

sample of eleventh graders in one school located in a stable,

mostly white, middle- and upper-middle-class neighborhood (Zanes

and Matsoukas 1979). The present study attempts to further

examine this issue using data from a national probability sample

of youths 12 to 17 years of age in households.

METHOD

Data from the 1979 (Fishburne et al. 1980) and 1982 (Miller et al.

1983) National Survey on Drug Abuse were analyzed. In these

household interview surveys, data were collected from nationally

representative samples of youths ages 12 to 17 years and adults

aged 18 years and older. Depending on household composition,

interviews were conducted with one adult only, one youth only, or

both an adult and a youth. Sample sizes for youths were 2,165 in

1979 and 1,581 in 1982. The surveys collected data on whether

respondents had used various licit and illicit drugs in the past

month (monthly use), in the past year (annual use), or ever

(lifetime use).

Interviewers tried to ensure privacy by requesting that the interview be conducted in a private room. Also, most drug-use questions were filled out on an answer sheet by the respondent, so no

verbal responses could be overheard by other persons in the house.

23
Furthermore, confidentiality was promised by having the respondent

place completed answer sheets in an envelope and sealing the

envelope, assuring the respondent that the interviewer would never

see the responses. After each youth interview, the interviewer

filled in a question regarding the degree of privacy during

questioning. Interviewers rated privacy on a scale of 1 to 9 as

follows:

1 - completely private

3 - minor distractions

5 - parent in room around one-third of the time

7 - serious interruptions of privacy more than half of

the time

9 - constant presence of parent

Values of 2, 4, 6 or 8 could also be used by interviewers.

Respondents with unknown privacy were excluded from this analysis,

resulting in sample sizes of 2,148 12- to 17-year-olds in 1979 and

1,538 12- to 17-year-olds in 1982.

The relationship between privacy and reported lifetime use of

several drugs was analyzed using weighted linear regression analysis (Draper and Smith 1966). Lifetime use was chosen because of

the very small proportion of youths with annual or monthly use of

some drugs and because underreporting is believed to be less serious for lifetime use than for annual or monthly use (Luetgert and

Armstrong 1973). Thus, if lack of privacy is found to affect

reporting of lifetime use, reporting of current use is probably

affected at least as much.

Because of the strong positive correlation between age and

lifetime drug use for youths, age was included as an independent

variable in the regression models, along with privacy. This made

it possible to test the significance of the effect of privacy with

age controlled. Lifetime prevalence of drug use was computed for

each drug and for every possible combination of age and level of

privacy. Each regression model was based on a maximum of 54

observations, one observation for each possible combination of age

(12 to 17) and privacy (1 to 9). Each observation was weighted by

the sample size for the age-privacy combination, to provide

unbiased estimation. Some combinations did not occur in the

samples. The dependent variable was the survey estimate of

24
prevalence of lifetime use of the drug for the age and privacy

combination. The model used can be described as follows:

P = B0 + B1X1 + B2X2 + e, where

P = survey estimate of prevalence of a drug for

the age-privacy combination

X1= age (values range from 12 to 17)

X2= privacy level (values range from 1 to 9).

The regression coefficients were computed for each of six drugs in

both 1979 and 1982. Models were run using the procedure REG of

the Statistical Analysis System (SAS Institute 1982). The statistical significance of privacy (after adjusting for age) was

evaluated using partial F-tests (H0:B2 = 0, given B0, B1).

Once regression equations were obtained, expected values for the

dependent variable were computed under each of two assumptions:

1) Assume every respondent had complete privacy

(privacy = 1)

2) Assume every respondent had no privacy

(privacy = 9)

The percent difference between these two expected values provides

a standardized measure of the effect of privacy (vs. no privacy)

on reported use of various drugs. The percent difference is computed by subtracting the estimated prevalence assuming no privacy

from the estimated prevalence assuming complete privacy, and then

by dividing by the estimated prevalence assuming complete privacy.

It can be interpreted as an estimate of the proportion of lifetime

users of the specific drug who would not report their use when

interviewed with no privacy. The expected prevalence, assuming

complete privacy, can also be compared with the actual prevalence

obtained in the survey to estimate the degree to which drug use is

underreported by youths in the National Survey as a result of a

lack of complete privacy in every household.

RESULTS

Table 1 shows the proportion of youth interviews conducted at each

privacy level in 1979 and 1982. It illustrates two conclusions.

25
First, interviewers in the survey have been successful in obtaining privacy. Well over half of all interviews had complete

privacy, and over 80 percent of interviews were in the first three

categories, which include "minor distractions." Second, the degree of privacy obtained in 1982 was very similar to that obtained

in 1979. Thus, any reporting bias resulting from lack of privacy

can be assumed to have little or no impact on trend analysis of

National Survey data, assuming the effect of privacy is nearly

constant.

TABLE 1. Percent distributions of 1979 and 1982 National Household Survey on Drug Abuse sample of youths by privacy

during interview

Percent Distribution

Privacy Level

1979 1982

(n=2,148) (n=1,538)

1 (Completely private) 60.8 57.9

2 8.6 9.6

3 (Minor distractions) 13.7 15.8

4 1.9 2.0

5 (Parent 1/3 time) 7.3 6.1

6 0.9 1.1

7 (Serious Interruptions) 0.5 1.1

8 0.3 0.7

9 (Constant parent) 6.1 5.7

Table 2 shows the results of the regression analyses. Values for

R

2

 ranged from .35 to .86. These high R2 values were primarily

due to the age variable in the models. However, privacy was significant at the .1 level in 8 of the 12 regressions and at the .05

level in 4 of those 8. While the statistical significance of privacy is not overwhelming for the 1982 data, the consistency of the

effect cannot be ignored. As table 3 shows, for 11 of the 12

regressions, privacy had a positive effect on reported drug use.

The one case where the effect was negative was nonsignificant.

Furthermore, the estimated effect of privacy is very large in some

cases, indicating that if sufficient privacy is not achieved when

conducting the interviews, drug use could be severely underreported. However, the high levels of privacy achieved in the

National Survey (table 1) result in a minimal overall impact of

this bias on that survey, as is indicated by the small differences

between the first two columns of table 3.

26
TABLE 2. Results of regression analysis of privacy and age with

reported lifetime prevalence of drug use

Year of Survey

and Drug

1979

R

2

Significance Level

of Privacy In Model

(H0:B2 = 0, Given B0, B1,)

Cigarettes*

Alcohol

Marijuana

Cocaine

Hallucinogens

Psychotherapeutics*

1982

Cigarettes*

Alcohol

Marijuana

Cocaine

Hallucinogens

Psychotherapeutics

.60

.73

.86

.55

.66

.49

.61

.74

.69

.51

.35

.48

.032

.019

.063

.098

.002

.061

.002

.293

.208

.700

.627

.084

*Self-administered form not used.

TABLE 3. Estimates of lifetime prevalence of drug use for youths

and the effect of privacy in interviews

Percent of Youths Ever Used Percent Difference

Between CompIete

Year of Survey

and Drug

Assuming Assuming Privacy and

Reported compIete No No Privacy

in Survey Privacy Privacy (Privacy Effect)

1979

Cigarettes* 54.2 56.0 44.9 19.8

Alcohol 70.3 72.0 61.4 14.7

Marijuana 30.8 32.0 24.5 23.4

Cocaine 5.3 5.9 2.3 61.0

Hallucinogens 7.0 7.9 2.2 72.2

Psychotherapeutics* 7.3 8.2 2.4 70.7

1982

Cigarettes* 49.6 52.8 31.3 40.7

Alcohol 65.0 65.9 60.4 8.3

Marijuana 26.8 28.1 20.4 27.4

Cocaine 6.5 6.7 5.7 14.9

Hallucinogens 5.0 4.7 6.5 -38.3

Psychotherapeutics 10.3 11.3 5.1 54.9

*Self-administered form not used.

Although comparisons of the estimated effect of privacy between

drugs and years may be outside the limits of this analysis, a few

interesting differences are worth noting. The estimated effect of

27
privacy was greater for cigarettes in 1982 than it was in 1979

(40.7 percent vs. 19.8 percent). The reverse was true for cocaine

and hallucinogens, for which the privacy effect was greater in

1979. If these changes in the effect of privacy on reporting have

really occurred, then trend analysis of National Survey data may

be slightly biased. Adjustment for the bias results in a slightly

smaller increase in cocaine prevalence between 1979 and 1982. The

increase is statistically nonsignificant without this adjustment,

and remains so after the adjustment. The decrease in cigarette

prevalence, significant at the .05 level, becomes smaller after

adjustment, and becomes nonsignificant. The nonsignificant decrease in hallucinogen prevalence becomes larger and significant

at the .05 level after adjusting for the bias.

Cigarette questions in 1979 and 1982 and psychotherapeutic questions in 1979 were answered verbally by respondents, while all

other drug questions (including psychotherapeutics in 1982) were

answered using self-administered answer sheets. This should be

kept in mind when comparing the estimated effect of privacy among

the drugs and years, since underreporting would be expected to be

greater without the self-administered form. While the estimated

effect of privacy for psychotherapeutics was greater in 1979 using

the verbal responses than it was in 1982 using the self-administered form, the difference is not large and could certainly result

from random variation or error in the regression estimation. Of

more interest is the overall increase in the percentage of youths

using psychotherapeutics, which is quite likely to be due to the

change in methodology, as suggested by studies showing that selfadministered questionnaires produce higher reported prevalence of

drug use than personal interviews (Hochstim 1967; Krohn et al.

1975).

DISCUSSION

The estimation procedure used here is admittedly imprecise.

Nevertheless, the consistency of the direction of the privacy

effect provides strong evidence that privacy is important in youth

drug surveys, even when self-administered answer sheets are used.

This result contradicts findings from an earlier study (Zanes and

Matsoukas 1979), which concluded that the presence of parents in

the room had no effect on reporting of drug use by youths.

Different sample populations may be the cause of this difference.

The National Survey is a nationally representative sample of

youths ages 12 to 17 years, while the earlier study included only

eleventh qraders in a single high school located in a stable.

mostly white middle- and upper-middle-class neighborhood. Also,

the studies were conducted in different years.

One other issue must be mentioned regarding this study. Respondents were not randomly allocated to different levels of privacy.

This raises the possibility that there is some other variable that

is related both to privacy and to drug use.

28
This was investigated to the extent possible by analyzing the

relationship between privacy and several variables, including sex,

family size, race, geographic area, type of housing, and occupation of the head of the household, and then repeating the analysis

using reported drug use with each of the variables. In general,

most population groups that reported higher prevalence of drug use

did not have significantly more privacy than lower prevalence

groups. One exception was whites, who had slightly higher

reported prevalence of cigarette and alcohol use than other races

and also had more privacy during interviews. To investigate this

relationship, the regressions were rerun on whites only. The

privacy effect remained consistent, indicating that the privacy

effect is independent of race. In some cases, population groups

with higher reported prevalence of drug use actually had been

interviewed with less privacy. For example, while males reported

higher prevalence of drug use, they had been interviewed with less

privacy than females. Thus, it appears that the relationship

between privacy and reported drug use demonstrated by the

regressions is not the result of the variables included in the

survey.

In conclusion, it does appear that reporting of drug use by youths

is affected by the degree of privacy during the interview, even

when a self-administered answer sheet is used by the respondent.

This underscores the importance of achieving maximum privacy when

conducting drug surveys and raises questions regarding the

validity of data from surveys in which adequate privacy was not

obtained.

REFERENCES

Draper, N.,and Smith, H. Applied Regression Analysis. New York:

John Wiley and sons, 1966. 407 pp

Fishburne, P.M.; Abelson, H.I.; and Cisin, I. National Survey on

Drug Abuse: Main Findings: 1979. Prepared for the National Institute Drug Abuse by Response Analysis Corp. and the Social

Research Group of the George Washington Univ. Washington, D.C.:

Supt. of Docs., U.S. Govt. Print. Off., 1980. 169 pp.

Hochstim, J.R. A critical comparison of three strategies of

collecting data from households. J Am Stat ASSOC, 62:976-989,

1967.

Hubbard, R.C.; Eckerman, W.C.; and Rachal, J.V. Methods of

validating self-reports of drug use: A critical review. Proc

Am Stat Assoc : 406-409, 1976.

Johnston, L.D. Techniques for reducing measurement error in surveys of drug use. In: Robins, L.N., ed. Studying Drug Use and

Abuse. New Brunswick, N.J.: Rutgers University Press, in

press.

King, F.W. Anonymous vs. identifiable questionnaires in drug

usage surveys. Am Psychol, 25:982-985, 1970.

Krohn, M.; Waldo, G.P.; and Chiricos, T.G. Self-reported

delinquency: A comparison of structured interviews and selfadministered checklists. J Crim Law Criminol, 65:545-553, 1975.

29
Luetgert, M.J., and Armstrong, A.H. Methodological issues in drug

usage surveys: Anonymity, recency and frequency. Int J Addict,

8:683-689, 1973.

Miller, J.D.; Cisin, I.H.; Gardner-Keaton, H.; Harrell, A.V.;

Wirtz, P.W.; Abelson, H.I.; and Fishburne, P.M. National Survey

on Drug Abuse: Main Findings: 1982. Prepared for the National Institute on Drug Abuse by the Social Research Group of The George

Washington Univ. and Response Analysis Corp. Washington, D.C.:

Supt. of Docs., U.S. Govt. Print. Off., 1983. 141pp.

Needle, R.; McCubbin, H.; Lorence, J.; and Hochhauser, M.

Reliability and validity of adolescent self-reported drug use in

a family-based study: A methodological report. Int J Addict,

18:901-912, 1983.

O'Malley, P.M.; Bachman, J.G.; and Johnston, L.D. Reliability and

consistency in self-reports of drug use. Int J Addict, 18:805-

824, 1983.

SAS Institute, Inc. SAS User's Guide: Statistics. 1982 Edition.

Cary, N.C.: SAS Institute, Inc., 1982. 584 pp.

Single, E.; Kandel, D.; and Johnson, B.D. The reliability and

validity of drug use responses in a large-scale longitudinal

survey. J Drug Issues, 5:426-443, 1975.

Smart, R.G. Recent studies of the validity and reliability of

self-reported drug use, 1970-1974. Can J Criminol, 17:326-333,

1975.

Smart, R.G., and Jarvis, G.K. Do self-report studies of drug use

really give dependable results? Can J Criminol, 23:83-92, 1981.

Sudman, S., and Bradburn, N. Response Effects in Surveys: A

review and synthesis. Chicago: Aldine, 1974

Zanes, A., and Matsoukas, E. Different settings, different

results? A comparison of school and home responses. Public

Opin Q, 43:550-557, 1979.

AUTHOR

Joseph Gfroerer, B.A.

Division of Epidemiology and

Statistical Analysis

National Institute on Drug Abuse

5600 Fishers Lane

Rockville, MD 20857

30
ISSUES OF VALIDITY AND

POPULATION COVERAGE IN

STUDENT SURVEYS OF DRUG USE

Lloyd D. Johnston

Patrick M. O’Malley

As with any survey technique, surveys of young people in school

have both advantages and disadvantages for the quantification and

study of drug abuse in the population.

ADVANTAGES OF STUDENT SURVEYS

In an earlier review of the use of survey methods in the drug

field (United Nations 1980), Johnston listed the following advantages of surveys of special populations in institutions, including

student surveys: First, there are considerable economies involved

in being able to take clustered samples and in administering the

instruments in groups. The lowered cost per respondent permits

the collection of data from larger numbers of respondents,

yielding more accurate estimates of prevalence and trends and a

greater ability to conduct subgroup analyses. It also results in

the collection of data on more users of the various substances,

thus permitting the characterization and study of the users of

more rare substances, such as cocaine or PCP, and even heavy users

of certain substances, for example, daily marijuana users.

Insofar as drug use is particularly concentrated in the population

of interest--which is certainly the case of people of secondary

school and college age in contemporary North America--student surveys are capable of yielding a fairly high cost-benefit ratio by

focusing on those more at risk and/or involved. Again, more users

will be identified for study, given a particular sample size, than

in a survey of the population at large. Finally, the population

under study is already in identified institutions, giving them a

particular accessibility for planned interventions.

Two other advantages, mentioned by Smart et al. (1980) in a review

of student survey methodology, are that nonresponse rates tend to

be low in school settings, and that the degree of anonymity

obtainable is likely to be much greater than in other methods.

The rate of nonresponse to the questionnaire among those present

in the classroom is often under 1 percent (e.g., Johnston et al.

31
1982a), undoubtedly due in large part to the demand characteristics of the situation. This compares very favorably to the noncooperation rates usually experienced in household or telephone

surveys.

While household interviews sometimes involve the use of private

answer sheets, which the interviewer promises not to read (Miller

et al. 1983), obviously the interviewer knows the name and address

of the person and, from the respondents' perspective, might

abridge confidentiality by examining the answer sheets.

(Similarly, in a telephone survey, the respondents' phone numbers

are known, and for all they know, their names and addresses as

well.) In a school survey, nearly complete anonymity can be

convincingly given by the group collection of unidentified

questionnaires. And if confidentiality, but not anonymity, is to

be offered, this still can be done in a convincing way (Johnston

1980; Johnston et al. 1982a).

A related advantage, not mentioned in either of the two previous

reviews, is that the young people are answering the sensitive

questions about illicit behaviors without the proximity of their

parents or other family members. While we know of no empirical

research to date demonstrating a suppression effect of such

proximity in household surveys, we would certainly hypothesize

that there is one. A corollary hypothesis is that the effect

would be greater if an adult member of the household is also being

interviewed on the same topic--as happens in some household

surveys (Miller et al. 1983)--since we think the young person

would be concerned about being questioned later by that adult

about what his or her answers were.

Another advantage that derives from the large sample sizes

economically possible in student surveys is that the sample may be

broken down into subsamples, each of which receives a partially

different questionnaire. This permits the inclusion of many more

variables in the study. Examples may be found in the Monitoring

the Future survey and the more recent national student survey in

Greece (Kokkevi 1984).

Finally, insofar as student surveys tend to make use of selfadministered questionnaires, composed of precoded items, they also

have the technical advantage over household interviews of using

machine-readable forms, which considerably reduce the time and

costs required to generate a computer-readable data file. This

method can also reduce to near zero the error rate in data

handling and processing, since it eliminates the human error

involved in coding and keypunching (or otherwise entering) the

data into a computer-readable form.

32
LIMITATIONS OF STUDENT SURVEYS

Having cited some of their virtues, let us hasten to add that they

have their limitations, as well. Among the limitations of student

surveys cited in those previous reviews (Johnston 1980; Smart et

al. 1980) were the following: The population under study by definition excludes those out of the age range to be students, as well

as those of equivalent age who already have left school. Those

who tend to be absent from school more than average are another

group that would be proportionally underrepresented--though not

totally excluded from the sample --assuming there are no procedures

to obtain absentees' participation after the time of the initial

survey administration.

Other potential problems mentioned were the possible inadequacy of

lists from which to draw a proper sample of schools from the

larger universe being represented and the ever-present need to

secure sufficient cooperation from schools to yield a reasonably

representative sample of schools. In the United States and a

number of other countries there are sufficient listings of schools

from which to draw samples, but the autonomy of local school

systems makes actually securing a representative sample a

formidable task. (Of course, to the extent that representativeness is not a crucial feature of the research design--as is the

case in some relational and evaluation design--this obstacle is

far less serious, because researchers are free to "shop around"

until they find enough schools willing to cooperate.)

Another limitation of school surveys, to the extent that they tend

to use self-administered questionnaires, is that they cannot have

as complex a branching of questions (that is, a branching in the

sequence which is determined by the respondent's answer to prior

questions). Some branching can be included in self-administered

questionnaires, of course (Bachman et al. 1984), but too elaborate

a sequence carries the danger that a respondent will become lost.

However, it is worth noting that even in interviews complex

branching sequences cannot be used in those segments to be

answered by the respondent on private answer sheets, because the

interviewer has to know the respondent's answers in order to do

any branching.

THE “MONITORING THE FUTURE” STUDY

The remainder of this paper will be devoted to a more in-depth

look at certain of these areas--in particular, the definition of

the universe covered by student surveys, sampling issues raised in

attempts to sample that universe accurately, the effects of omitting the same-age segment of the population not covered in the

student universe (dropouts) as well as those absent on the day of

the administration, and some issues having to do with the validity

of the data gathered in student surveys. Many of the examples

used will derive from the Monitoring the Future study, which is an

ongoing series of annual surveys of high school seniors, with

33
samples drawn to be representative of all seniors in public and

private high schools in the coterminous United States in a given

year (Bachman and Johnston 1978; Johnston et al. 1984b). One of

the purposes of this series is to provide an accurate estimation

of the prevalence and trends in the use of various substances

(both licit and illicit) in this population. Estimates are also

developed for high school graduates at later ages, up to age 28,

using followup panels from each of the previously participating

senior classes (O'Malley et al. 1984). However, because this

cohort-sequential design feature is atypical of student surveys,

we will not be discussing in this paper any of the issues it may

raise.

DEFINITION OF THE UNIVERSE IN STUDENT SURVEYS

Student surveys usually have their universe defined in terms of

students registered in particular kinds of schools at a particular

grade level in a particular year; for example, seniors in academic

high schools in 1984. Several different age levels are usually

contained in varying proportions at any given grade, which makes

comparisons with census and household data sometimes more difficult than might be supposed.

Grade Levels Encompassed

The grade level(s) chosen have methodological importance for

several reasons, since grade level tends to be correlated with

reading skills and the ability to follow directions, as well as

with the proportion of the age group likely to be in school. In

the developed countries, relatively large proportions remain

enrolled even at the level of secondary school, though there are

still substantial differences in school enrollment rates among

countries. In the developing countries, the majority of a class

cohort is likely not to be in school by the secondary level.

In Monitoring the Future we chose to focus on senior year, which

corresponds approximately to ages 17 and 18, because: 1) it represents the end point in universal public education and the great

majority of a class cohort are still in school; 2) it represents a

"jumping off" point from which students make a great many different changes in environment and role status--ones which we want to

study; 3) the students are old enough that the schools are less

protective of them on sensitive subjects such as drug use, which

increases our chances of securing a high enough school cooperation

rate to develop a representative sample; 4) it represents an ideal

point at which to take stock of the cumulative influences of

family and school; and 5) it represents a good "check point" in

the development process at which to measure drug involvement of

various sorts. While there is good evidence to suggest that the

incidence and prevalence of certain drugs continue to rise into

the early twenties (O'Malley et al. 1984, Kandel, in press), the

fact is that by the end of senior year nearly two-thirds of

seniors in recent class cohorts have already had their initial

34
experience with illicit drug use, and an even greater proportion

have had their initial experiences with alcohol and cigarettes

(Johnston et al. 1984b).

Types of Schools Encompassed

There tend to be a number of different types of schools in most

countries that could be included in the sampling universe.

Obviously, the more comprehensive the inclusion, the more generalizable are the findings. The Monitoring the Future study encompasses both private and public schools, as well as academic high

schools and vocational high schools. A nationwide survey of drug

use among secondary school students in Greece, currently underway,

does the same (Kokkevi 1984). Seeking inclusiveness is particularly important if the student survey is to be one in a series

intended to measure trends, since there is always the possibility

that the distribution of students enrolled across the different

types of schools may shift over time.

SAMPLING ISSUES

For the purpose of selecting samples that are representative of a

given universe, a multistage sample is often used in school

surveys. While the first stage might be the selection of schools,

in a survey of a large geographical area, such as a country, there

may be cost advantages to first selecting a set of primary

sampling areas (PSA's) that usually correspond to counties, and

which contain a population which is itself representative of the

general population in the country. This is the procedure we use

in Monitoring the Future--the reason being that the University of

Michigan's Survey Research Center retains a permanent staff of

interviewers who live in a fixed set of PSA's (which are used for

various national surveys), thus making a field operation confined

to those areas far less costly than one in which we would have to

send our field teams to far-flung areas around the country.

Little sampling accuracy is lost by using this first stage.

Selecting Schools

The second stage involves selecting schools within those areas,

which requires as an initial step obtaining an enumeration of all

schools within those areas from which to make a random selection.

In the Monitoring the Future study we select the schools with

probability proportionate to their size--that is, proportionate to

the estimated number of seniors in the school. When schools are

selected proportionate to size, the optimal sampling algorithm

calls for equal-sized samples of students in each school in order

for all students in the universe to have equal probability of

selection. This method increases the number of large schools in

the sample; that is important because otherwise only a few large

schools in the sample would represent all students from large

schools, and thus most students from large cities, since community

size and school size are fairly highly correlated.

35
The alternative sample design is to give all schools equal probability of selection and then to take all students (or some fixed

proportion of all students) in every school. The fact that a sizable portion of the sample would then come from just a few schools

reduces sampling accuracy in general, even if school size were not

confounded with degree of urbanicity.

In Monitoring the Future we draw schools with probability proportionate to size, but then, in those schools having more seniors

than the number specified in the optimal sample design, we take

most or all of those seniors anyway. We do this simply because it

is often administratively more convenient for us, and for the

schools, to take all seniors than to have to sample them, and the

marginal cost of the extra cases tends to be very low since the

interviewers are already in these schools. The extra cases

increase the accuracy of the sample, but not as much as if they

were being drawn from additional schools. If the school is very

large (400 seniors), we will randomly sample classrooms, being

sure that we have picked a set of classroom periods that contain

all seniors and each senior only once. This is the third stage of

sampling.

Corrective weighting must be introduced to correct for unequal

probabilities of being chosen that are known to have been introduced at the different stages of sampling (Kish 1965). Assuming

that proper randomization procedures are followed, and proper corrective weighting is introduced in the analyses of the data that

eventually result, the resulting sample should be representative

of the universe (leaving aside for the moment the nonresponse

issue).

However, statistics based on the assumptions of a simple random

sampling procedure cannot be applied to the results from such a

sample. Corrections must be introduced to take into account the

effects of clustering in the sample, that is, the effects of

having drawn whole groups of students (in schools, and also within

schools, in classrooms), rather than having drawn each student

totally independently of all the others. Procedures exist for

adjusting statistical tests for the loss of accuracy introduced by

the clustered sample approach (Kish 1965).

Examples of the adjustments resulting in the Monitoring the Future

study may be found in appendices on this subject in Bachman et al.

1984 and Johnston et al. 1981. In essence one can find a reduced

"effective N"--that is, a sample size that, if based on a simple

random sample, would yield sampling accuracy equivalent to the

clustered sample actually obtained. The smaller the average cluster size, the less will be the proportional downward adjustment.

In any case, the very large sample size in the Monitoring the

Future study (N is approximately 17,000 cases per year) still

yields a very respectable "effective N" for nearly all purposes.

36
THE EFFECTS OF SCHOOL NONPARTICIPATION

The study is designed in such a way that each year (after the

first) the sample of schools consists of half participating for

the first time, and half participating for the second time. In

1976 and subsequent years, participation rates for the new halfsamples of schools have ranged from 66 percent to 80 percent,

Half of the sample in each of these years consisted of repeat

schools, ones that had participated in the previous year. The

rates of repeat (i.e., second-year) participation range from 95 to

100 percent. Any schools that dropped out were replaced with

substitute schools. These substitute schools were from the same

geographic areas, from similar neighborhoods, and of similar size

and racial composition. In the event of a refusal by the substitute school, a second (and if necessary, a third or fourth)

substitute school was selected and invited to participate.

Cooperation was obtained from an original or a substitute school

in all but one or two instances each year. In the very few cases

where no school was obtained, compensatory weighting of the data

from similar participating schools was used to improve the

population estimates.

It is reasonable to ask whether nonparticipation of some of the

originally sampled schools is likely to have a significant effect

on the findings. Insofar as population estimates of drug use and

attitudes are concerned, the answer depends on two factors: the

size of the refusal rate and the similarity of the substitute

schools to the original schools they are replacing. With respect

to the first factor, only between one-fifth and one-third (in

early surveys) of the schools have been substitutes during any

given year. With respect to the second factor, the substitutes

are chosen to be as similar as possible to the original school.

There is no particular reason to expect that the students in

schools that refuse are greatly different from those in schools

that agree to participate, since the reasons for school nonparticipation are based primarily on general policy issues and/or on

somewhat happenstance events that are not likely to relate

systematically to student drug use. In sum, the school refusal

rate is not excessively high compared with other school-based

studies, and the substitute schools seem likely to be quite

similar to the refusal schools.

There is one additional point to be considered. Insofar as monitoring change is concerned, the effects of school nonparticipation

should be minimal. Any systematic biases that might emerge (say,

underrepresenting politically conservative districts) should be

approximately replicated from year to year, so the trend data

should still accurately reflect any major changes in drug use that

might be occurring. A partial check on the adequacy of the sample

schools can be made by comparing trend data based on the total

sample with trend data based only on the half-sample that remains

constant from one year to the next. Since this half-sample consists of the same set of schools, the trends cannot be affected by

37
schools' participation or refusal. We have examined drug-use

trend estimates, comparing the data from all schools with the data

from only the matched half-samples. These estimates were extremely similar, suggesting that any error due to sampling of schools

is constant.

THE EFFECTS OF OMITTING ABSENTEES

The proportion of students in the classroom at the time of

administration who decline to participate generally tends to be

extremely low (less than 1 percent) in Monitoring the Future.

Thus, the only segment of the student universe omitted consists of

those who are absent from school or class at the time of the

administration. In Monitoring the Future, absentee rates range

from 17 to 23 percent of the enrolled students (depending on the

year), based on data from the teacher's class register from each

classroom on the day of the survey.

Of course, it would be possible to try to collect data on subsequent occasions from the missing students, but we have judged this

effort not worth the financial and administrative difficulty for

both our interviewers and the schools. To be able to assess the

effects on our estimates of drug use of omitting the absentees, we

included a question in the study that asks students how many days

of school they missed in the previous 4 weeks. Using this

variable, we can place individuals into different strata as a

function of how often they tend to be absent. For example, all

students who been absent 50 percent of the time could form one

stratum. Assuming that absence on the day of the administration

is a fairly random event, we can use the respondents in this

stratum to represent all students in the stratum, including the

ones who happen to be absent that particular day. By giving them

a double weight, they can be used to represent both themselves and

the other 50 percent of their stratum that was absent that day.

Those who say they were in school only one-third of the time would

get a weight of three to represent themselves plus the two-thirds

in their stratum who were not there, and so forth.

Table 1 shows the lifetime prevalence rates for the different

drugs that result from the Monitoring the Future study, with and

without this special weighting to correct for omitting the

absentees. It also gives the prevalence rates deduced in this

manner to exist among the absentees and the biases in the overall

estimates that result from missing them. Tables 2 through 4 do

the same things using annual, 30-day, and daily prevalence.

38
TABLE 1. Lifetime drug use estimates for absentees and all high

school seniors, 1981

Seniors Absentees Seniors &

Present Only* Absentees* Bias

Cigarettes 92.6 80.0 72.6 1.6

Alcohol 92.6 95.4 93.1 0.5

Marijuana 59.5 74.8 62.2 2.7

LSD 9.8 17.1 11.1 1.3

Other Psychedelics 9.1 17.0 10.5 1.4

Cocaine 16.5

32.2

27.8 18.5 2.0

Amphetamines 45.2 34.5 2.3

Quaaludes 10.6 17.9 11.9 1.3

Barbiturates 11.3 18.1 12.5 1.2

TranquiIizers 14.8 21.6 16.0 1.2

Heroin 1.1 2.2 1.3 0.2

Other Narcotics 10.1 16.3 11.2 1.1

Inhalants 12.3 17.9 13.3 1.0

*Estimates based on seniors’ self-reported absences from school.

TABLE 2. Annual drug use estimates for absentees and all high

school seniors, 1981

Seniors Absentees Seniors &

Present Only* Absentees* Bias

Alcohol 87.0 91.5 81.8

Marijuana 46.1 62.5 49.0

LSD 6.5

Other Psychedelics 5.6

Cocaine 12.4

Amphetamines

Quaaludes

Barbiturates

TranquiIizers

Heroin

Other Narcotics

Inhalants

11.6 7.4 10.1 6.4

21.4 14.0

26.0 38; 4 28.2

7.6 13.8 8.7

6.6 12.2 7.6

8.0 13.1 8.9

0.5 1.6

10.5

0.7

6.0 6.8

4.1 6.4 4.5

*Estimates based on seniors’ self-reported absences from school.

0.8

2.9

0.9

0.8

1.6

2.2

1.1

1.0

0.9

0.2

0.8

0.4

39
TABLE 3. 30-day drug use estimates for absentees and all high

school seniors, 1981

Seniors

Present

Absentees Seniors &

Only* Absentees* Bias

Cigarettes

Alcohol

Marijuana

LSD

Other Psychedelics

Cocaine

Amphetamines

Quaaludes

Barbiturates

Tranquilizers

Heroin

Other Narcotics

Inhalants

29.4

70.7

31.6

2.5

2.1

5.8

15.8

3.1

2.7

2.7

0.2

2.1

1.5

41.3

80.3

46.9

4.8

4.4

11.4

24.8

6.5

5.5

6.1

0.8

4.4

3.2

31.5 2.1

72.4 1.7

34.3 2.7

2.9

2.9

0.4

6.8

0.4

1.0

17.4 1.6

3.7 0.6

3.2 0.5

3.3 0.6

0.3

2.5

0.1

0.4

1.8 0.3

*Estimates based on seniors’ self-reported absences from school.

TABLE 4. Daily drug use estimates for absentees and all high

school seniors, 1981

Seniors Absentees Seniors &

Present Only* Absentees* Bias

Cigarettes 20.3 31.0 22.2 1.9

1/2 Pack or

More per Day 13.5 22.0 15.0 1.5

Alcohol 6.0 11.1 6.9 0.9

Marijuana 7.0 13.8 8.2 1.2

*Estimates based on seniors’ self-reported absences from school.

It can be seen in these four tables that, while absentees as a

group are deduced to have appreciably higher than average usage

levels for all licit and illicit drugs, their omission does not

depress any of the prevalence estimates in any of the tables by

more than 2.7 percent because they represent such a small proportion of the total sample. Considering that a substantial portion

of those who are absent are likely to be absent for reasons

unrelated to drug use-- such as illness and participation in extracurricular activities --it may be surprising to see the extent of

the differences. In any case, from the point of view of instructing policy or public perceptions, the small "corrections" in

tables 1 through 4 appear to be of little or no significance.

(The correction across all 13 drugs in the lifetime prevalence

table averaged only 1.4 percent.) Further, such corrections

should have virtually no effect on cross-time trend estimates

unless the rate of absenteeism were changing, and we find no evidence in our data that it is. Put another way, the presence of a

fairly slight underestimate that is constant across time should

not influence trend results. Should absentee rates change

40
appreciably, then it could be argued more convincingly that such

corrections should be presented routinely.

THE EFFECT OF OMITTING DROPOUTS

While school studies do not purport to represent those not in

school, the concern is still raised about how accurate the estimates would be if taken to represent the entire class cohort, both

those still in and those now out of school. Unfortunately, we

cannot derive corrections from data gathered from seniors to

impute the prevalence rates for dropouts, as we did for absentees,

since we have no completely appropriate stratum from which to

"sample." We do know from our own previous research (Johnston

1973). as well as the work of others (Kandel 1982). that dropouts

have prevalence rates for all classes of drugs that are substantially higher than the in-school students. In fact, the dropouts

may not be too dissimilar to the absentees. But again, because

dropouts represent a fairly limited proportion of the age group,

we would expect their omission to have relativelv little effect on

the overallestimates--even assuming they have substantially

higher than average rates of use.

This is particularly true when one considers the range of reasons

for becoming a dropout. In a report based on a recent NIDA technical review on the effect of omitting dropouts, Clayton and Voss

(1982) note the range of reasons why young people leave school

early, a number of which would not be expected to relate to drug

use. Citing a followup study of ,600 ninth-graders in California

who were followed through their high school years, they note that

of the 19 percent who were known to have dropped out, 2 percent

left as the result of serious illness or accidents and 32 percent

were categorized as educationally handicapped (Elliot and Voss

1974). Economic hardship and pregnancy also undoubtedly account

for some additional proportion.

However, as with absentees, there remains little doubt that dropouts as a group have higher than average involvement in drugs.

The question, then, is how to estimate the effect of their omission on the overall usage rate. To do this, two parameters must

first be estimated: the proportion of the class cohort that is

missing from school, and the estimated rate of use for the various

drugs in that missing segment.

The proportion who fail to complete secondary school is about 15

percent based on Census data published for 1977 (U.S. Bureau of

the Census 1978), which showed that the proportion of 20- to 24-

year-olds who were not high school graduates was 15.4 percent.

(Younger age brackets are more difficult to use because they include some who are still enrolled in high school.) Monitoring the

Future probably covers some small proportion of the 15 percent,

since it takes place a few months before graduation, and not

everyone will graduate. On the other hand, perhaps 1 percent to 2

percent of the age group Census shows as having a diploma get it

41
through a General Equivalency Degree and thus would not be covered

in Monitoring the Future. (Elliot and Voss report this result for

less than 2 percent of their sample.) So these two factors probably cancel each other out. Thus, we use 15 percent as our

estimate of the proportion of a class cohort not covered.

Extrapolating To Dropouts From Absentees

To estimate the drug-use prevalence rates for this group we used

two methods. One is based on extrapolations in which we assume

that the difference between dropouts and the seniors who participated in the study is equivalent to 1) the difference between

absentees and participating seniors, 2) one and one-half times

that difference, and 3) twice that difference. The last we would

consider a rather extreme assumption.

The second method involves using the best recent national data on

drug use among dropouts--namely the National Household Surveys on

Drug Abuse (Fishburne et al. 1980; Miller et al. 1983). While

these surveys have rather small samples of dropouts in the relevant age range in any given year, they should at least provide

unbiased estimates for dropouts still in the household population.

Using the first method of correction, tables 5 through 8 again

give adjusted prevalence estimates for lifetime, annual, 30-day,

and daily use respectively, this time correcting simultaneously

for both absentees and dropouts. The three different assumptions

about how different dropouts are from participating seniors are

included in the table along with the resulting bias under each

assumption.

Several things should be noted in these tables. First, under the

assumption that dropouts are just like absentees, no prevalence

rate is changed by more than 5 percent over the estimate based on

seniors only, even with the simultaneous correction for both

absentees and dropouts. The largest correction involves marijuana, with lifetime prevalence rising from just under 60 percent

to 64 percent. The second point to note is that even under the

most extreme assumption--which results in extremely high prevalence rates for dropouts on all drugs, for example 90 percent

lifetime prevalence for marijuana --the overall correction in any

of the prevalence figures for any drug remains less than 7.5 percent. Again, marijuana shows the biggest correction (7.5 percent

in annual prevalence, raising it from 46 percent uncorrected to 54

percent corrected). As we would have expected, the biggest proportional change occurs for heroin, since it represents the most

deviant end of the drug-using spectrum and thus would be most

associated with truancy and dropping out.

42
TABLE 5. Lifetime drug use estimates for dropouts and high school seniors, 1981
TABLE 6. Annual drug use estimates for dropouts and high school seniors, 1981
TABLE 7. 30-day drug use estimates for dropouts and high school seniors, 1981
TABLE 8. Daily drug use estimates for dropouts and high school seniors, 1981
Extrapolating From Household Surveys

The second method of estimating drug use among dropouts was by

using data from household surveys on dropouts versus those remaining in school. We conducted secondary analyses of the archived

data from the 1977 and 1979 National Household Surveys. Analyses

were restricted to the age range 17- to 19-years-old, since about

95 percent of the Monitoring the Future respondents fall in this

range. Of course, the numbers of cases were small. In the 1977

survey there were only 46 dropouts and 175 enrolled seniors in

this age group. In the 1979 survey 92 dropouts and 266 seniors

were included.

Table 9 shows the differences observed between these dropouts and

enrolled seniors for the lifetime prevalence and monthly prevalence of marijuana in both 1977 and 1979. Also presented in the

same table for comparison purposes are the estimated differences

between dropouts and seniors (including absentees) that were generated under each of the three different assumptions used in the

method just discussed. As can be seen in table 9, the estimated

differences from the household survey data come out at a level at

or below the least extreme assumption made in the previous method

(i.e., where dropouts are assumed to have the same drug use levels

as absentees).

TABLE 9. Differences between dropouts and high school seniors in

prevalence of marijuana

Time

Seniors Survey, 1979

Household Survey Differences Estimated Differences

Seniors Dropouts Difference A B C

Lifetime Prevalence

1977 46.9 58.7 11.0 12.6 20.3 27.9

1979 53.0 66.6 12.8 12.6 20.3 27.9

Monthly Prevalence

1977 31.0 30.4 -0.6 12.6 20.3 27.9

1979 30.9 41.8 10.9 12.6 20.3 27.9

Numbers of Cases

1977 175 46

1979 266 92

Note: The estimated differences for both lifetime and monthly prevalence in the

seniors survey happen to coincide by chance.

47
While this may be comforting to the authors of the present paper,

we must admit that we believe the household sample underrepresents

the more drug-prone dropouts to some degree. Those without permanent residence and those in the prison population, to take perhaps

the two most important examples, would be excluded from the sample

coverage in a household survey. Thus, we are inclined to think

that estimates closer to those made under assumption B--that

dropouts are one and one-half times more likely to be users than

absentees--may be closer to reality.

Again, we emphasize that there are a number of reasons for dropping out, many of which bear no relationship to drug use,

including economic hardship in the family and certain learning

disabilities and health problems. The extreme groups, such as

those in jail or without a permanent place of residence, are undoubtedly a very small proportion of the total age group and

probably even a small proportion of all dropouts. Thus, dropouts'

prevalence rates would not change the prevalence estimates by much

except in the case of the most rare events--in particular, heroin

use. We do believe that, in the case of heroin use--particularly

regular use--we are unlikely to get a very accurate estimate even

with the corrections used in this paper. For the remaining drugs,

we conclude that our estimates based on participating seniors,

though somewhat low, are not a bad approximation for the age group

as a whole.

Effects of Omitting Dropouts on Trend Estimates

Whether the omission of dropouts affects the estimates of trends

in prevalence rates is another question, however. The relevant

issues parallel those discussed earlier regarding the possible

effects on trends of omitting the absentees. Most important is

the question of whether the rate of dropping out has been changing

in the country, since a substantial change would mean that seniors

studied in different years would represent noncomparable segments

of the whole cohort. Fortunately for the purposes of this study,

the data published by the National Center for Educational Studies

(NCES 1982) show that dropout rates stabilized in about 1968,

following a period of slow decline, and have remained essentially

stable up through 1980, which is the most recent year for which we

have been able to locate published data. NCES projects the dropout rate to remain constant, as figure 1 illustrates. The reader

should note that the statistic being traced in figure 1 is more an

indicator of the dropout rate than a literal estimate, since it is

based on how many 17- and 18-year-olds have completed school

(excluding GED recipients) in each year. Clearly more of those

two birth cohorts eventually will complete school as they become

18 (in the case of the 17-year-olds), 19, and so on.

48
Secondary schools graduated about 74 percent of the relevant age group, a proportion that has remained unchanged since the mid-1960s and Is expected to be stable

throughout the 1980s.

FIGURE 1. Percent of 17- to 18-year-olds who have completed high

school (excluding GED recipients)

SOURCE: National Center for Educational Statistics. The

Condition of Education, 1982 Edition, Chart 1.6, p. 19.

Given that there appears to be no sound evidence of a change in

the dropout rate, the only reason that trend data from seniors

would deviate from trends for the entire class cohort (including

dropouts) would be if the constant proportion of those who have

been dropping out for some reason showed trends contrary to those

observed among seniors; even then, because of their small numbers,

they would have to show dramatically different trends to be able

to change the trend "story" very much.

There has been no convincing hypothesis offered for such a differential shift among dropouts. One hypothesis occasionally heard

is that more youngsters are being expelled from school, or voluntarily leaving school, because of their drug use and that this

explains the recent downturn in the use of many drugs being reported by the study (Johnston et al. 1984b). However, it is hard

to reconcile this hypothesis with the virtually flat dropout rates

over a 15-year period (through 1980), unless one posits a perfectly offsetting tendency for more completion among those who are

less drug prone--hardly a very parsimonious set of explanations.

Further, the reported prevalence of some drugs has remained

49
remarkably stable throughout the life of the study, e.g., alcohol,

opiates other than heroin, and the prevalence of some has risen,

e.g., amphetamines, cocaine. These facts are not very consistent

with the hypothesis of a recent increased rate of departure by the

most drug prone. Certainly more youngsters leaving school in the

1980s have drug problems than was true in the 1960s. (So do more

of those who stay in.) However, they still seem likely to be very

much the same segment of the population, given the degree of association that exists between drug use and deviance and problem

behaviors of various sorts.

In sum, while we believe there is some underestimation of the

prevalance of drug use in the cohort at large as a result of the

dropouts' being omitted from the universe of the study, we think

the degree of underestimation is rather limited for all drugs

(with the possible exception of heroin) and, more important, that

trend estimates have been rather little affected. Short of having

good trend data gathered directly from dropouts, we cannot close

the case definitively. Nevertheless, we think the available evidence argues strongly against alternative hypotheses--a conclusion

that was also reached by the members of the NIDA technical review

on this subject held in 1982:

...the analyses provided in this report show

that failure to include these two groups

(absentees and dropouts) does not substantially affect the estimates of the incidence

and prevalence of drug use (Clayton and Voss

1982 abstract).

VALIDITY ISSUES

Since the issue of validity is dealt with at some length in other

chapters, we will address the subject only briefly here, and confine our comments to the Monitoring the Future study. By way of

historical perspective, it should be said that we have come into

this field with some skepticism about whether honest reporting

could be secured through self-report on a topic as sensitive as

illicit drug use. Some 15 years later we are firm believers that

it can, at least in certain populations and with appropriate

procedures. Certainly our extensive experience with the data from

Monitoring the Future has convinced us of its fundamentally high

quality.

To begin with, we have adopted a set of procedures that we think

meets some necessary conditions for securing high cooperation and

validity: namely, by convincing respondents that 1) there is a

legitimate scientific or other reason for gathering the data,

2) they can answer in a situation that provides suitable privacy,

3) there are adequate procedures for continuing to protect confidentiality, and 4) those responsible for gathering and handling

the data can be trusted. We will not go into detail regarding

those procedures, since they have been described elsewhere

50
(Johnston et al. 1982a) and will be discussed at greater length in

a forthcoming chapter (Johnston, in press) that reviews methods for

increasing the validity of drug-use self-report data in general.

Debriefing interviews in several high schools with some 100 students who had completed the questionnaire in the first year of the

study convinced us that a high level of trust had been obtained.

But the most compelling evidence for us comes from the actual data

generated. First, we have recently reported that, based on a

series of three-wave panel analyses, the reliability and stability

of our drug use measures tend to be quite high (O'Malley et al,

1983). Reliability estimates ranged in the eighties and nineties

for cigarettes, alcohol, and marijuana lifetime prevalence

measures, and in the seventies and eighties for illicit drugs

other than marijuana taken as a group--certainly very respectable

levels by most research standards.

In addition, because underreporting is the potential source of

error of primary concern to us, we find it persuasive that in all

classes surveyed so far, the majority of respondents have admitted

using an illicit drug and fully two-thirds have made such an

admission in the last couple of years, with 40 percent admitting

to using an illicit drug other than marijuana. (In fact, the

estimate rate for the absentee group exceeds 75 percent lifetime

prevalence for illicit drug use based on respondents weighted

according to their absenteeism (see table 1).) While troublesome

for society, these exceptionally high proportions provide a kind

of compelling evidence that, if there is systematic concealment,

it is occurring only among a small minority (Johnston et al.

1982a, 1984b). The data from other questions on personal disapproval of drugs, and on the proportion of friends who use

various drugs, give results that are highly consistent with the

proportion self-reporting use. In fact, the aggregate level

trends in friends' use and personal exposure to use--about which

there is presumably much less motivation to lie--tend to parallel

very closely the trends in self-reported use for the various

drugs.

There is also strong evidence of construct validity to be found at

the individual level of analysis in the relationships between use

and a host of other variables that would be expected to relate in

predictable ways to self-reported use. Among these are attitudes

and beliefs about drugs, perceived availability of drugs, selfreported delinquency, truancy, religiosity, grades in school,

evenings spent out of the house, etc. (Bachman et al. 1981;

Johnston 1973). Most of these relationships are strong and

replicable across graduating classes.

Finally, we have found that the missing data rates on the drug

questions are only very slightly above normal for that point in

the questionnaire where they occur, even though we have just

instructed the respondents to skip the drug questions if they do

not feel they can answer them honestly. For example, on the

51
questions immediately preceding the drug section, the missing data

rate averages about 2 percent, while for all of the drugs except

marijuana the missing data rate runs from 2.5 percent to 3.0

percent (Johnston et al. 1982a). For marijuana, the missing data

rate is slightly higher, at 4.5 percent, suggesting that marijuana

use may be underestimated by about 2.5 percent. But overall, we

find the absence of any substantial amount of skipping very

reassuring.

CONCLUSIONS

The available evidence suggests to us that the noncoverage of

absentees and dropouts has only modest implications for the estimation of overall prevalence rates and rather little implication

for the estimation of trends in prevalence. Regarding the validity of self-report data in student surveys on drug use, we conclude

that in the United States population, at least, it is possible

with the proper procedures to secure data that have high reliability and show strong evidence of high validity as well. That is

not to say that the data in such surveys are perfect, nor necessarily valid--since we think validity is highly dependent on the

construction of the questions, the procedures under which they are

administered, and the perceived intentions of the investigators.

Further, we think that the validity of the trend data from such

studies is dependent on the constancy of methods across time--

particularly in question content, question context, field procedures, and timing of the survey during the year. But with the

right procedures, and with the proper care given to keeping them

constant across time, we believe such surveys can and do generate

valid findings--on prevalence, trends, risk factors, and effects--

which have considerable significance for the formulation and evaluation of policy, and for the advancement of scientific knowledge

in this area.

REFERENCES

Bachman, J.G., and Johnston, L.D. The Monitoring the Future

Project Design and Procedures. Ann Arbor: Institute for Social

Research, 1978.

Bachman, J.G.; Johnston, L.D.; and O'Malley, P.M. Smoking,

drinking, and drug use among American high school students:

Correlates and trends, 1975-1979. Am J Public Health 71(1):59-

69, 1981.

Bachman, J.G.; Johnston, L.D.; and O'Malley, P.M. Monitoring the

Future: Questionnaire Responses from the Nation's High School

Seniors, 1982. Ann Arbor: Institute for Social Research, 1984.

Clayton, R.R., and Voss, H.L. Technical Review on Drug Abuse and

Dropouts. National Institute on Drug Abuse Washington,D.C.:

Supt. of Docs., U.S. Govt. Print. Off., 1982.

Elliott, D., and Voss, H.L. Delinquency and Dropout. Lexington,

MA: D.C. Heath-Lexington Books, 1974.

52
Fishburne, P.M.; Abelson, H.I.; and Cisin, I. National Survey on

Drug Abuse: Main Findings 1979. National Institute on Drug

Abuse. DHEW Pub. No. ADM) 80-976. Washington, D.C.: Supt. of

DOCS., U.S. Govt. Print. Off., 1980.

Johnston, L.D. Drugs and American Youth. Ann Arbor: Institute

for Social Research, 1973.

Johnston, L.D. Review of General Population Surveys of Drug

Abuse. WHO Offset Pub. No. 52. Geneva: World Health

Organization, 1980. 60 pp.

Johnston, L.D. The epidemiology of drug use and abuse: An overview of the research/policy questions and the methods appropriate for answering them. Paper distributed by Council of

Europe's Pompidou Group. Document P-PG (82) 38. December 1982.

21 pp.

Johnston, L.D. Techniques for reducing measurement error in

surveys of drug use. In: Robins, L.N., ed. Studying Drug Use.

New Brunswick, NJ: Rutgers Universitv Press. in press.

Johnston, L.D.; Bachman, J.G.; and O'Malley, P.M. Student Drug

Use in America, 1975-1981. National Institute on Drug Abuse.

DHEW Pub. No. (ADM) 82-1221. Washington, D.C.: Supt. of DOCS.,

U.S. Govt. Print. Off., 1982(a).

Johnston, L.D.; Bachman, J.G.; and O'Malley, P.M. Student Drug

Use, Attitudes and Beliefs: National Trends 1975-1982.

National Institute on Drug Abuse. DHEW Pub. No. (ADM) 83-1260.

Washington, D.C.: Supt. of DOCS., U.S. Govt. Print. Off.,

1982(b).

Johnston, L.D.; Bachman, J.G.; and O'Malley, P.M. Monitoring the

Future: Questionnaire Responses from the Nation's High School

Seniors, 1983. Ann Arbor: Institute for Social Research,

1984a.

Johnston, L.D.; O'Malley, P.M.; and Bachman, J.G. Highlights from

Drugs and American High School Students, 1975-1983. National

Institute on Drug Abuse. Washington, D.C.: Supt. of DOCS.,

U.S. Govt. Print. Off., 1984(b).

Kandel, D.B.; Logan, J.A.; and Raveis, V.H. A comparison of

former regular students, former school absentees, and school

dropouts in a follow-up cohort of young adults. Unpublished

manuscript, 1982.

Kandel, D.B. Developmental patterns of the use of legal, illegal,

and medically prescribed psychotherapeutic drugs from adolescence to young adulthood. In: Jones, C., and Battjes, R., eds.

The Etiology of Drug Abuse: Implications for Prevention.

National Institute on Drug Abuse. Research Monograph, in

press.

Kish,L. Survey Sampling. New York: John Wiley & Sons, 1965.

Miller,J.D., et al. National Survey on Drug Abuse: Main

Kokkevi, A. Personal communication, 1984.

Findings 1982. National Institute ofDrug Abuse DHEW Pub. No.

(ADM) 83-1263. Washington, D.C.: U.S. Govt. Print. Off., 1983.

National Center for Educational Statistics. The Condition of Education: 1982 Edition. DHEW Pub. No. NCES-82-400. Washington,

D.C.: Supt. of, U.S. Govt. Print. Off., 1982.

53
O'Malley, P.M.; Bachman, J.G.; and Johnston, L.D. Reliability and

consistency in self-reports of drug use. Int J Addict, 19:805-

824, 1983.

O'Malley, P.M.; Bachman, J.G.; and Johnston, L.D. Period, age,

and cohort effects on substance use among American youth. Am J

Public Health, 74:682-688, 1984.

Smart, R.G.; Hughes, P.H.; Johnston, L.D.; et al. Drug Use Among

Non-Student Youth. Geneva: World Health Organization, 1980.

United Nations Divsion on Narcotic Drugs. Manual on Drug Abuse

Assessment: Part II, the Use of POpulation Surveys. 1980.

U.S. Bureau of the Census. School Enrollment--Social and Economic

Characteristics of Students, October 1976. Current Population

Report Series P. 20, No. 319. Washington, D.C.: Supt. of

DOCS., U.S. Govt. Print. Off., 1978.

AUTHORS

Lloyd D. Johnston, Ph.D.

Patrick M. O'Malley, Ph.D.

Institute for Social Research

University of Michigan

Ann Arbor, MI 48106

54
SAMPLING AND COVERAGE

DIFFICULTIES IN CANADIAN

DRUG USE SURVEYS AND

EFFORTS TO AVOID THEM

Reginald G. Smart

In Canada, surveys of illicit drug use and abuse have been

conducted since the late 1960s. The first surveys were of school

populations; not until about 1973 were any general population

surveys done. Since then, much experience has been gained with a

variety of assessments and survey techniques for drug abuse. We

continue to have problems with sampling and with coverage of the

whole population of students and adults. We have made some

efforts to develop methods of dealing with some of the problems.

Total coverage, however, remains elusive.

SCHOOL SURVEYS IN ONTARIO: SAMPLING AND COVERAGE PROBLEMS

The first drug use surveys in Ontario were studies of the Toronto

school population (Smart and Jackson 1969). These were done every

2 years from 1968 to 1974. In 1977, it was decided to expand the

sample to include all school districts in Ontario (Smart et al.

1983). This made it much more difficult to do the sampling and to

collect the data, since there were 200 rather than only 5 school

boards involved, and they extended over a wide geographic area.

Currently, data are collected every 2 years, with the last survey

in 1983.

Ontario is a very large province; it measures 700 miles from east

to west and 1,200 miles from north to south. The northern parts

have severe winters. Unfortunately, we determined early in the

surveys that the best month for surveys is February. It avoids

all holidays, school events, and examinations but is the worst

month for travel. Originally we had the survey data collected by

people travelling from Toronto. We now use a survey research

center that has a local field staff, so there are fewer problems

with survey staff not arriving. Snowstorms still reduce the "at

school" population on the day of the survey, so we try to reschedule the survey if the weather is bad. Also, we do not

include small, remote boards of education in the sampling frame.

Those northerners left out constitute only 7 percent of the target

student population.

55
Our coverage is incomplete in other ways. The school system in

Ontario is very complex. There are both Ontario Public and

Separate (Roman Catholic) School Boards in most areas. The separate boards include only students in grades 1 to 10. Those in

grades 11 to 13 are "private school" students. We include public

and separate schools up to grade 10, but only public schools in 11

to 13. This probably increases our reported illicit drug use

rates since students in Catholic schools have somewhat lower rates

of use. We also leave out all other private and church-related

schools, but they educate a very small segment of the young

population. It is not possible to say how this affects results.

A variety of types of schools and classes have been excluded from

the provincial survey because of expected problems, such as

special education classes, those students institutionalized for

health or correctional reasons, and schools on Indian

reservations. Schools on Canadian Forces (Army and Airforce)

establishments also are excluded, but there are only a few of

these. Probably, these exclusions tend to leave out heavierdrinking and drug-using populations. There are indications that

those in our prisons and youth correctional institutions do have

higher rates of drug abuse. The same is true of Indians living on

reservations. However, about half of the Indians in Ontario do

not live on reservations, and they would be included in the

sample. Probably, our exclusions from total coverage serve to

decrease the reported rates of drug and alcohol use.

Our sample is based on a stratified single-stage cluster probability sample design. The data are weighted to take into account

variable sampling fractions and nonresponse by selected classes

and students. The target population is students in regular programs in public and separate school boards in grades 5,7,9,11, and

13. We require a sample of 8,000 in order to get a minimum sample

of 5,000 students. The sample is stratified into 4 regions and 5

grades, resulting in 20 strata or area/grade clusters. We used

projected enrollment figures for 1983 based on those for 1980 to

1983 because we could not get the up-to-date figures early enough.

Usually the projections are good. A probability sample is

independently selected from within each of the 20 strata.

The sampling units are "homerooms" either as they actually exist

in schools or on the basis of average size in the relevant

stratum. In anticipation of refusals to participate we selected

additional homerooms in each stratum.

When we started our provincial survey, we estimated, from Toronto

data, class sizes as 25 for grade 13 and 30 for other grades.

These estimates were too high, especially in the north, and probably resulted in some undersampling there (this area has many

drinking problems). We now base the estimate of homeroom size on

what we found last time in each stratum; there is less guesswork

and it works out more accurately. In a few schools there are no

homerooms, in that students have each class with a different set

56
of students. We constructed some so that students could complete

the questionnaire in groups of the right size--usually 25 to 30

students.

Our study depends greatly on cooperation by school boards and

principals. In Ontario the principal has absolute power over such

things as external surveys, even if the board approves them.

Sometimes we have trouble getting the cooperation of school

boards. It seems that the level of cooperation is falling. In

our 1981 survey, 91 percent of boards approached agreed to the

survey, but only 82 percent agreed in 1983. There is an increasing tendency for boards to say there are too many surveys and that

drug use surveys tend to promote drug use. Others complain about

the specifics of the questionnaire (too long, too difficult). In

our 1983 study there were 10 boards that refused and 31 that cooperated. We replace boards that refuse but concern is growing

that perhaps the cooperative boards are interested in the drug

problem because they have an abnormally large number of student

users. Sometimes boards will approve the survey overall but

refuse the participation of grade 5 or 7 students on the basis

that they are too young to be using any drugs and that use should

not be encouraged.

In the past we did not replace boards. In the 1981 survey, a

board in Western Ontario refused too late to allow replacement,

and this resulted in loss of an entire stratum. Since then we

have always allowed board replacement and we ask more boards than

we will need so refusals are less important.

In Ontario, principals or headmasters can refuse to allow their

school or certain classes to participate. In our early surveys,

we allowed principals to designate classes to participate, but for

some time we have insisted that we select at random. If principals do not agree, we drop the school and replace it. In fact,

problems with individual schools are decreasing. In 1981, 75

percent of selected classes participated in the survey, but in

1983 some 97.8 percent did. We think the improvement occurred

because we do more followup work with schools. We send schools

copies of the survey report and offer to make community development help available to improve drug education programs. In the

past, schools refused because they saw no benefit to the school

from the survey. Others complained that there were too many

surveys. We have tried to be helpful to schools by giving them

advice about drug education programs. Also, we have explained

that our survey typically requires few students per board from

each grade level (one to five classes in all). In contrast, most

surveys require large numbers of students.

A further problem with coverage of our target sample involves

parental consent forms. Almost all of our students are below the

age of 18 and hence, legal minors. By law, schools stand "in loco

parentis" but some schools are not willing to mandate a drug use

survey for younger students. Older students do not need parental

57
consent. It is difficult to predict whether boards will require

consent forms; if they do the forms are sent home with students

and are to be returned in a few days. They add to the time and

expense of the study; students without forms cannot participate.

An earlier comparison indicated that schools with a low rate of

forms returned had lower rates of reported illicit drug use. We

make special efforts such as frequent reminders and detailed

explanations of the study to get the forms returned. About 95

percent of them are approved and returned. Fewer are returned in

metropolitan Toronto and for students in grades 5 and 7. The low

rate of return in Toronto probably reflects the large population

of parents who do not read English or French very well. Younger

students probably lose the forms or parents think drug use surveys

are inappropriate for them. Parents are more likely to refuse

participation if the student is using drugs; there may be some

concern that their children will be identified by school

authorities.

Some of these results are similar to those of Kearney et al.

(1983), who found that requiring parental consent forms caused a

large reduction in sample size with an overrepresentation of

whites and an underrepresentation of blacks and Asian Americans.

Kearney et al. (1983) reported a sample reduction of 50 percent in

their Seattle and Portland studies. However, this must reflect

very inadequate followup by school officials since we find no more

than a 5 percent reduction because of consent forms. Almost all

parents in our studies who are asked for their consent give it.

We send our consent forms home with students and they return them,

whereas in Kearney et al.'s study, the consent forms were mailed.

A last problem concerns students who are not at school on the day

of the survey. About 7 to 10 percent of students are away on any

given day. The rate is higher in bad weather, especially during

snowstorms and near holidays. Students may be away because of

illness, family responsibilities, part-time work, or a dislike of

school. Dropouts usually develop a pattern of infrequent attendance before actually leaving school completely. School absences

are higher among males, those with lower grades, and those in the

higher grade levels. Much research (e.g., Haberman et al. 1972)

shows that students not at school on the survey day have relatively high rates of drug abuse. We have not done any studies of

those not at school for the drug survey, because absenteeism rates

are not very high and the results of such studies are predictable.

We have studied early school dropouts. In 1978, we studied 292

young people ages 14 to 18 during a household survey of adults.

The young people were inhabitants of the same house where an adult

was interviewed. We left a questionnaire to be filled in and

returned later. Almost all were returned on time and complete.

Table 1 shows some of the results. In the household survey

students more often reported somewhat more cannabis use than did

nonstudents (36.6 percent compared to 32.9 percent). However,

both reported far more cannabis use than did students in a school

58
survey a few months before. Results were different for tranquilizers; students in the school survey reported more use. These

results were different from those in Mexico City and Chandigarh,

where nonstudents reported more drug use (Smart et al. 1981). It

appears that students identified in a household survey report more

cannabis use than those in a school survey. Probably we got some

students in the household survey who would have been absent on a

survey day.

TABLE 1. Comparison of school and nonschool populations in

reported drug use (percent using in past 12 months)

Drug

Percent Users

Household Survey School Survey

Students Nonstudents Students

(N=257) (N=173) (N=4,794)

Cannabis 36.6 32.9 25.1

Tranquilizers 2.7 1.7 8.9

In summary, most but not all sources of noncoverage in our school

survey probably lead to an underreporting of the true use of drugs

such as cannabis. Probably our results are conservative underreports and we miss a number of heavier drug users. Our surveys

do give a very good sampling of the school population by grade and

geographic area (within 4 percent of expectancy), based on known

demographic characteristics of the school population. Over 85

percent of students selected participated in 1983. If any students are missed, they are more likely to be drug users. Of

course we would like to be completely accurate, but it is far

better to be able to describe our drug use data as "underreports"

than "overreports" or exaggerations. If we exaggerate the extent

of drug use it is likely to be seen as self-serving and alarmist.

Hence, the message would more likely be discounted. Frequently it

is very difficult to know what problems a particular survey has

had in sampling or other coverage problems. Details of such

matters are often left out of published reports, but no real

assessment of the reported drug use rates can be made without

them.

Some types of noncoverage can be reduced by increasing staff time

and commitment, e.g., to get consent forms returned. Other kinds

can be reduced by increasing the survey's value to schools and

school boards. We are concerned that in the long run refusals by

boards will make studies difficult; hence we now offer consultation from Addiction Research Foundation community consultants. We

also make the data for each board available to the trustees, but

each board gets only its own data.

59
SANPLING PROBLEMS IN HOUSEHOLD SURVEYS

Similar problems to those in school surveys arise with surveys of

general populations. Most general population surveys--our own

included--are of people living in houses and apartments. In some

cases, even apartments cannot be included because of management's

security concerns. The surveys also leave out people in prisons,

hospitals, old age homes, hostels for the mentally ill, halfway

houses for alcoholics, therapeutic communities for drug addicts,

and many other institutions. Transients, street people, and skid

row habitues are also missed, as are students in residence and

people living on military bases.

These exclusions almost certainly reduce our reported drug use

figures from their true value. Among those approached, there are

problems in gaining completed interviews. Noncooperation rates in

our unpaid surveys are 20 to 25 percent. We get the least cooperation from wealthy, upper-class residents who wish to protect

their privacy and from the foreign-born, who often worry about

surveys and government snooping into their affairs. We can

improve the rates for foreign-born respondents by having interviewers who can speak different languages, but covering all of the

possibilities is very difficult.

Many of our drug-use surveys have produced interview samples that

are somewhat overweighted with older people. Older persons tend

to be more often at home and more tolerant of long interviews, as

they seem to have time on their hands. We have also found it

difficult to interview sufficient numbers of young males without a

large number of call-backs. Most of our interviewing is done in

the evenings when young men are often not at home. We know that

young males are more likely than others to be heavy drinkers and

users of illicit drugs. Also, we expect that many of those who

are out are heavier drinkers and drug users. Perhaps they are at

bars, pubs, or parties where drugs are being used. In our survey

(Smart and Goodstadt 1978) we have oversampled young males (aged

18 to 20) and have done the interviewing on Saturday morning.

This is the best time to find young males at home, especially in

the colder times of the year.

The results are shown in table 2. We cannot make an exact

comparison between the expanded young male sample and the adult

sample. However, it is obvious that the young males are a group

containing many users of marijuana (41 percent) and stimulants (6

percent) and that virtually all are drinkers. If this group is

missed in surveys, we will have underestimated drug use figures

and lost contact with a special target population for drug

education efforts.

60
TABLE 2. Percentage of Ontario adult and expanded male samples

using various drugs in past 12 months (1977 data)

Percent Users by Type of Drugs

Sleeping

Age Pills Stimulants Tranquilizers Marijuana Alcohol

18 to 20 3.0 6.0 8.0 41.0 93.0

(males)

18 to 29 4.8 2.9 8.3 23.7 87.5

30 to 49 5.0 0.9 13.9 3.5 88.6

50+ 16.4 1.3 17.3 0.7 69.8

Poor coverage in drug and alcohol surveys has been attributed to

sampling problems and nonresponse. However, selective reporting

and forgetfulness by respondents are also difficulties that cannot

be easily overcome. We know that heavy drinkers are very likely

to underreport their consumption in household surveys. Such

surveys do not account for more than 40 to 60 percent of the known

consumption of alcoholic beverages when sales figures are used.

We have experimented with an informant method (Smart and Liban

1982) to try to improve the estimates from surveys. In the

informant method, selected individuals report the drinking practices (and amounts) for groups known to them. They do not report

on their own drinking as in the usual surveys. This avoids the

problem of respondents being asked to admit to overdrinking about

which they probably feel guilty.

The participants or informants are selected to reflect the major

occupational and geographic strata in a society. For example, in

our study there were 30 groups including managerial/professional

categories, secretarial/sales, industrial workers, housewives,

students, farmers, and retired persons, with the number of groups

of each in proportion to their representation in the population.

The groups met once for about 2-1/2 hours to discuss answers to a

56-item questionnaire on drinking practices and attitudes. Each

group reported only on drinking in the occupation stratum that

they represented. Results are then aggregated for the 30 groups

to provide a picture of drinking in the society as a whole.

The results indicated that, compared with a standard household

survey done in the same area, the informant method gave better

results. It reported higher rates of drinking and heavy drinking

and gave per capita consumption figures close to those from

alcohol sales (8.26 liters of absolute alcohol per year compared

to 10.23 in sales figures). The survey method, as expected, gave

poor estimates of per capita consumption (only 3.94 liters).

61
Because the method is cheap and more accurate than survey methods,

it could be used in a variety of ways. It could be used to study

heavy-using populations and to study drinking in developing countries without the resources for surveys. We have not applied it

to drug-use surveys, but it should be useful in school studies

where there is concern about noncoverage. Results from the

informant method and a standard school survey could be compared.

Unfortunately there are no good sales figures, as there are with

alcohol, with which to validate the method.

In summary, coverage and sampling problems still plague school and

general population surveys. Most of the problems work to decrease

the estimates of illicit drug use and heavy drinking. We need

continued research on new methods of reducing coverage problems.

We also need more reporting of the details of sampling plans and

how well they were actually achieved in practice.

REFERENCES

Haberman, P.W.; Josephson, E.; Zanes, A.; and Elinson, J. High

school drug behavior: A methodological report on pilot studies.

In: Einstein, S., and Allan, S., eds. Proceedings of the First

International Conference on Student Drug Surveys. New York:

Baywood Publishing Co., 1972. 346 pp.

Kearney, K.A.; Hopkins, R.H.; Mauss, A.L.; and Weisheit, R.A.

Sample bias resulting from a requirement for written parental

consent. Public Opin Q, 47:96-102, 1983.

Smart, R.G., and Jackson, D. A Preliminary Report on the Attitude

and Behavior of Toronto Students in Relation to Drugs. Toronto:

Addiction Research Foundation, 1969. 71 pp.

Smart, R.G., and Goodstadt, M.S. Alcohol and Drug Use Among

Ontario Adults: Report of a Household Survey, 1977. Substudy

No. 957. Toronto: Addiction Research Foundation, 1978. 11 pp.

Smart, R.G., and Liban, C.B. Alcohol consumption as estimated by

the informant method, a household survey and sales data. J Stud

Alcohol, 43:1020-1027, 1982.

Smart, R.G.; Mora, M.E.; Terroba, G.; and Varma, V.K. Drug use

among non-students in three countries. Drug Alcohol Depend,

7:125-132, 1981.

Smart, R.G.; Goodstadt, M.S.; Adlaf, E.M.; Sheppard, M.A.; and

Chan, G.C. Preliminary Report of Alcohol And Other Drug Use

Among Ontario Students in 1983, and Trends Since 1977.

Toronto: Addiction Research Foundation, 1983. 23 pp.

AUTHOR

Reginald G. Smart, Ph.D.

Addiction Research Foundation

33 Russell Street

Toronto, Ontario

Canada

62
DYNAMIC SIMULATION MODELS:

HOW VALID ARE THEY?

Raymond C. Shreckengost

VALIDITY, PURPOSE, AND CONFIDENCE

In a strict sense, the subject of dynamic simulation model validity can be treated throughly and quickly: there are no fully valid

models because all models are something less than the object, or

system, being modeled. For example, millions of people have a

conceptual model of the President, but, like fingerprints, no two

of these models is exactly the same. Further, none matches

precisely every detail of the real system. The same reasoning

applies to all types and kinds of models of drug abuse.

In a practical sense, we are concerned with usefulness rather than

validity. Does the model serve the purpose for which it was

intended? Is it helpful? Thus, the developer's or user's

purposes must be kept in mind in evaluating a model's usefulness,

or validity. Criticisms of models also should reflect this

perspective.

Much depends on the purpose for which the model is developed--for

example, the choice of the level of detail used in the model.

Just as micrometers are not used to measure intercity distances to

a tenth of a mile, explicit modeling of each household would be

equally absurd in a model treating the gross behavior of drug

abuse systems. The selection of an appropriate level of detail,

problem boundaries, and similar considerations constitute the

"art" aspect of dynamic simulation model development.

Validity, or usefulness, lies in the subjective view of the user.

We think of models as valid when they can be used with confidence.

So, this paper focuses on how we can gain confidence in dynamic

simulation models. In particular, it considers confidence or

validity tests as they relate to a particular dynamic simulation

method, System Dynamics (Forrester 1961, 1975; Roberts 1978).

63
These tests, however, are equally valid for other simulation

techniques. They have evolved from nearly 30 years' experience of

the inventor of System Dynamics, Jay W. Forrester (Bell and Senge

1980; Forrester and Senge 1980).

MODEL STRUCTURE TESTS

Because the foundation for model behavior is the model's structure, the first test in validating a model is whether the

structure of the model matches the structure of the system being

modeled. Every element of the model should have a real-world

counterpart, and every important factor in the real system should

be reflected in the model. Although this may seem like a simple,

obvious test, it may not be so. For example, descriptions of how

all of the structural parts of real systems are tied together

rarely exist. More often than not, such descriptions must be

based on the concepts, or mental models, of people familiar with

the system. Further, important parts of some systems may lie

unrecognized prior to modeling. During the development of a model

dealing with the effects of heroin imports into the United States,

for example, the key factor in the system, the relative abundance

of heroin, was not immediately identified (Gardiner and

Shreckengost this volume). Thus, the art of model building may,

at times, entail discovery and invention.

This approach differs strongly from "Let's collect lots of data

and then see what they tell us." Structure, like many other

System Dynamics model elements, exploits judgment, experience, and

intuition. Data play a secondary role.

Model Parameter Tests

The model's parameter values are a specific area for testing.

Parameter values in a model often may be tested in a straightforward manner, e.g., against historical data. However, in

dynamic simulation models of social systems the desired data may

be unavailable, in an inappropriate form, or incorrect. There may

be elements that are not usually quantified, but that are critical

to the system being modeled. These elements must be included in

the model. If prejudice, for example, is an important element, it

must be included in the model, and its relationship to other

pertinent parts of the system must be specified quantitatively.

Many required parameter values may not exist and must be

developed. In the heroin model, data and descriptions relating to

heroin's relative abundance were initially absent. On the other

hand, some available, apparently reasonable and acceptable, data

on heroin imports turned out to be unreasonable and unacceptable

when employed in the model. The point is that dynamic simulation

model parameter values, from whatever source they may be derived,

are subject to a rigorous and demanding environment. These values

contribute significantly to confidence in the model when the

specified parameter values are reasonable and consistent with

whatever supporting data might exist.

64
Boundary Adequacy Test

If a model is focused on the heroin system in New York City, it

will not generate national behavior. Conversely, a national

heroin model is not likely to replicate the behavior of local

systems. Model boundaries must match the purpose for which the

model is designed, if the model is to be used with confidence:

that is, the model must include all of the important factors

affecting the behavior of interest. In practice, boundaries tend

to shift as the developers' and users' understanding of a problem

evolves with the model's development. As model purpose shifts,

changes in the model's boundaries may be required.

In many problems, a simple model with limited boundaries may be

expanded, or disaggregated, from time to time, as the model is

used to address problems in greater detail. When this occurs,

careful attention must be given to indirect effects, which may not

be obvious. Suppose, for example, a model treating United States

heroin users as a homogeneous group is disaggregated to identify

users of small, medium, and large amounts. This will change the

user boundaries, and associated changes will be needed in the

consumption boundaries.

If the model boundaries are improper, or inadequate, the model's

validity is degraded. However, criticism of dynamic simulation

models aimed at boundary issues frequently reflects different

notions about the model's intended use or purpose. For example,

criticism of the user boundary in a model treating users as a

homogenous group may ignore the fact that the grouping is consistent with the purpose of the model. But, as explained above, if

the purpose is to account for different classes of users, a boundary change is required to account for the change in purpose.

Extreme Conditions Test

A less obvious test relating to model structure involves the

effects of extreme conditions. The ability of a model to function

properly under extreme conditions contributes to its utility as a

policy evaluation tool as well as user confidence. Testing to

extreme conditions may easily be overlooked or brushed aside in

the hectic environment of early model development. Subsequently,

this oversight may degrade model performance: subtly under normal

conditions and significantly when the model is used to answer

"What if?" questions that fall outside the operating regions

emphasized in early development.

Again. the heroin imoort model provides a good example. In the

past, heroin imports have been, roughly, 5 metric tons per year.

Thev have not fallen to zero, nor have thev soared to 10 or 20

tons. Consequently, during the model's development parameter

values covered the range of import variations that were of immediate interest, say, 3 to 7 tons. If these initial values only were

retained in later versions of the model, the model would show a

65
residual, sizable user population even if imports were reduced to

zero. At the other extreme, the number of users would reach an

understated upper limit in the presence of a very large heroin

supply surplus. The point is that model validity is enhanced if

the region within which the model was originally designed to

operate is extended so the model generates plausible behavior

conditions outside the initial region. For example, the user

population should be zero when imports are zero.

Tests under extreme conditions may also expose structural faults

or inadequacies and incomplete or erroneous parameter values.

MODEL BEHAVIOR TESTS

Behavior Replication Test

The tests relating to model behavior are less technical and, for

many users, more appealing and convincing than the structural

tests. Foremost among these tests is the comparison of model

behavior with the behavior of the system being modeled. A model

whose behavior has little, or nothing, in common with that of the

system of interest generates little, or no, confidence.

Where historical time series data are available, the model must be

capable of producing similar data. That is, if the model's initial conditions are matched to the state of the system being

modeled at some time in the past, the model's behavior should

parallel the historical data from that time to the present. In

this test, it is again important to keep in mind the purpose of

the model--including the time span of the areas of behavior that

are of interest. Further, judgment must be exercised about how

closely the model's behavior should match the historical data,

since historical data are less than perfect, and, sometimes, far

from perfect. It is not at all uncommon for models to illuminate

erroneous data. Where historical data are very poor or nonexistent, the test may be one of reasonableness.

Given the imports over 10 years or so, the heroin model (Gardiner

and Shreckengost, this volume) generates heroin purity and price

values that match well with the historical data for these

parameters. Further, it also produces heroin-related death

figures that match the historical data closely. The closeness of

the model-historical correspondence is quite surprising, given the

difficulties inherent in collecting and processing the data that

the historical time series represents.

Purity, price, and deaths can be defined and measured with relative ease compared to the heroin user population. The model

generates user population values against a strict, limited definition of a heroin user. Here, no parallel historical data exist,

and the test becomes one of reasonableness considering the purpose

for which the model was developed. Subsequent to its initial

development, this sector of the model has been detailed to

66
accommodate users with different consumption habits and varying

responses to the abundance of heroin. Although this is intuitively more satisfying, there is still no opportunity for a confirming

historical test.

Anomalous Behavior Test

When model behavior does not replicate the behavior of the real

system, model structure, parameter values, boundaries, or similar

factors are suspect. Something may have been omitted, improperly

specified, or assigned incorrect values. In addition to being a

powerful tool during model development, tests of anomalous behavior may contribute convincingly to model validity. For example,

if a model behaves well except for, say, a limited period of time,

and no faults can be found in the model, the error may lie in the

data with which the model behavior is being compared. Or, matching the real system's purported behavior may require the inclusion

of implausible structure, or parameter values, in the model. In

the heroin model, the import data for 1 year were revised downward, because the consumption required to match that import level

could be achieved only by an unrealistic increase in heroin user

population. Whether due to faults in the model or in the real

system, the resolution of the discrepancies found through the

anomalous behavior test bolsters confidence and validity.

Behavior Sensitivity Test

Most, but certainly not all, social systems are stable--

bureaucracies, in particular, are frequently lampooned for their

very, very stable behavior. Small, reasonable changes in a

model's parameter values, then, should normally not produce

radical behavior changes. If the model's behavior is not seriously affected by plausible parameter variations, confidence in

the model is increased. On the other hand, dynamic simulation

models are often used to search for parameters that can effect

behavior changes. The criterion in the sensitivity test is that

any sensitivity exhibited by the model should not only be plausible, but also consistent with observed, or likely, behavior in the

real system.

Behavior Prediction Test

Dynamic simulation models are especially useful in predicting how

a system would behave if various policies of interest were

implemented. Dynamic simulation models offer significant advantages when used in this role; they provide a consistent basis for

the predictions. This basis is a consolidation of judgment,

experience. and intuition that has been tested aaainst historical

evidence, and the predicted effects of implementing alternative

policies are promptly available. Confidence in the model is

reinforced if the model not only replicates long-term historical

behavior, but also responds similarly to existing systems in which

various policies have been implemented. For example, over the

67
years many treatment policies have been followed in drug abuse

treatment centers. A generic model of such a system, tailored to

match any particular center of interest, should replicate the

effects produced by the policies implemented in that center.

Family Member Test

Dynamic simulation models acquire added value and confidence when

they are generic, i.e., applicable to a family of similar situations, as in the case of treatment centers mentioned above. Drug

abuse treatment centers have common basic features, so any one

facility may be thought of as a particular case of the basic model

embodying these common features. The same is true of payroll,

retirement, university, village, city, region, and many other

social systems or organizations.

Under these conditions, confidence is enhanced not only because

the complementary systems can contribute to the robustness of the

model developed for a particular member of the family, but also

because the differences among the members can be explicitly

identified and defined.

Some family member applications of the heroin model, for example,

are readily apparent. The structure is equally applicable to

subdivisions of the United States, such as regions or cities.

Further, it appears that it is also directly applicable for

cocaine, and, possibly, other illegal drug systems.

Behavioral Boundary Test

Exploiting generic models, behavior prediction, and tests of

extreme policies may impinge on the model boundary. Is the

boundary still adequate for excursions that may extend beyond the

region of operation initially envisioned for the model? In

prediction, for example, the basic model may have to be revised,

so that policy alternatives, or events, such as the impact of

discoveries in research programs, can be introduced. In drug

abuse models, the inclusion of social trends, or new domestic or

foreign policies, may require boundary modifications. The

behavioral boundary test is an important step in determining

whether the model includes the necessary modifications.

OTHER TESTS

A third class of test--policy implication tests--which includes

system improvement, changed behavior prediction, boundary

adequacy, and policy sensitivity tests, deals with whether a real

system's response to a policy change would replicate the response

to the policy change predicted by a model. These tests reflect a

different perspective in the application of some of the tests discussed earlier. For example, if real system behavior improves as

predicted when tested in a model, was the policy change responsible for the improvement, or were other factors responsible?

68
This test builds confidence only after numerous real life tests

have been completed. The boundary question is inverted: how

would boundary changes alter the evaluation of policies and the

selection of policies for implementation? These tests tend to be

long term and to contribute to confidence and validity most importantly by enlarging the scope of congruence between dynamic

simulation models and the systems they represent.

Checking the dimensional consistency of model equations is an

additional structural test that may be ignored as trivial, or

obvious, but at some peril. For example, if a model contains an

equation with heroin expressed in grams on the left side of the

equal sign, heroin in grams, and heroin in grams only, must fall

out from the right side of the equation. Errors in dimensional

consistency can easily creep into model equations during model

development and, subsequently, during revisions,

Additional behavioral tests, surprise behavior, and extreme policy

behavior can also contribute to confidence and validity. Surprise

behavior relates to the recognition of behavior in the real system

that was there all along, but not noticed until the system was

modeled. Because of its emphasis on identifying the causes underlying observed behavior, System Dynamics readily leads to such

discoveries. For example, in the heroin system model, such a

surprise was the identification of the relative abundance of

heroin as a key parameter influencing the purity, price, and

heroin-related deaths that occurred in the real system. In retrospect, like many inventions and discoveries, the relationships may

seem very obvious. Such new-found perspectives, of course, contribute significantly to confidence in the model. Extreme policy

tests introduce radical policies into the model to see if the

behavior of the model is consistent with what would be expected

under these conditions. This helps affirm the model's robustness.

COMMON TESTS NOT USED

Paralleling the development of the tests described above has been

a growing body of evidence and opinion that many tests commonly

associated with model testing are inappropriate, inadequate, or

even dysfunctional. In part, these changes derive from the philosophy underlying the System Dynamics method of dynamic simulation modeling, particularly, the notion that all important factors

in the real system exerting an influence on the behavior of the

system must appear in the model--whether these factors are

normally modeled or not. Further, all factors in the model must

have a counterpart in the real system. Together with the dynamic,

rather than static, nature of the simulation, these characteristics have shifted emphasis from more traditional, statistical

tests to the kinds of tests described in this paper--whole model

tests that engage all the model variables and their relationships

in the testing process.

69
The t-test, for example, has been shown to be of little use, and

possibly misleading, in several studies (Johnson 1980, Mass and

Senge 1978).

Briefly, the tests can lead to the exclusion of factors that are

important to a model's behavior. Although the tests may be helpful in detecting structural flaws, they are insufficient in the

absence of whole model tests. Recently, statistical tests employing Kalman filtering principles have been developed. These tests

may be more useful in the development of dynamic simulation models

(Peterson 1979). The greater power of these tests stems from

their ability to eliminate the effects of measurement error in

hypothesis testing.

REFERENCES

Bell, J.A., and Senge, P.M. Enhancing refutability. In:

Legasto, A.A. Jr.; Forrester, J.W.; and Lyneis, T.M., eds.

System Dynamics. New York: Elsevier North-Holland, 1980.

pp. 61-73.

Forrester, J.W. Industrial Dynamics. Cambridge, Mass.: MIT

Press, 1961. 464 pp.

Forrester, J.W. Collected Papers of Jay W. Forrester. Cambridge,

Mass.: MIT Press, 1975. 284 pp.

Forrester, J.W., and Senge, P.M. Tests for building confidence in

System Dynamics models. In: Legasto, A.A. Jr.; Forrester,

J.W.; and Lyneis, T.M., eds. System Dynamics. New York:

Elsevier North-Holland, 1980. pp. 209-228.

Johnson, C.B. Some effects of data error on econometric models.

In: Legasto, A.A. Jr.; Forrester, J.W.; and Lyneis, T.M.,

eds. System Dynamics. New York: Elsevier North-Holland,

1980. pp. 143-159.

Mass, N.J., and Senge, P.M. Alternative tests for the selection

of model variables. IEEE Systems, Man, and Cybernetics,

8(6):450-459, 1978.

Peterson, D.W. Statistical tools for System Dynamics. In:

Randers, J., ed. Element of the System Dynamics Method.

Cambridge, Mass.: MIT Press, 1979. pp. 224-245.

Roberts, E.B., ed. Managerial Applications of System Dynamics.

Cambridge, Mass.: MIT Press, 1978. 699 pp.

AUTHOR

Raymond C. Shreckengost

Central Intelligence Agency

Washington, DC 20505

70
TELEPHONE SURVEYING FOR

DRUG ABUSE:

METHODOLOGICAL ISSUES AND

AN APPLICATION

Blanche Frank

INTRODUCTION

In the past decade telephone surveying has grown in popularity.

Not only have marketing firms used this medium, but so have

government agencies, notably the National Center for Health

Statistics and the Census Bureau. Although telephone interviewing

has limitations, its advantages are making it the dominant method

of survey research.

This paper highlights methodological issues in the use of

telephone surveys, generally, and for drug abuse, specifically.

First, some major issues in telephone surveying are discussed,

including sampling, questionnaire design, response rates, data

validity, and the management of research using this mode of

administration. Then, a New York State telephone survey of drug

abuse is described, with emphasis on these methodological issues.

GENERAL METHODOLOGICAL ISSUES

Sampling

A major concern in telephone surveying is the exclusion of nontelephone households. In 1936, when the Literary Digest's telephone survey erroneously predicted that Alf Landon would defeat

Franklin D. Roosevelt in the presidential election--at a time when

35 percent of the households had telephones--it was not surprising

that much bias was introduced in the sampling (Dillman 1978). By

1981, however, 97 percent of the households in America had telephone service (Census Bureau 1982). Nevertheless, the 3 percent

of the households that do not have telephones are surely of

interest. They are more likely to be in the South and West, black

than white, and in non-Standard Metropolitan Statistical Areas

(SMSA) and rural areas (Tyebjee 1979). Some researchers found

that households with telephones available were more likely to have

"white, male heads of higher average age, income and educational

level and to have the spouse present than those households with no

71
telephone available" (Tull and Albaum 1977, p. 394). Thus, given

the concentration of these characteristics in the population of

interest, a sample can under-represent certain population

segments. Some researchers have circumvented this bias by augmenting the telephone sample with personal interviews in census

tracts with low telephone penetration.

Sampling for telephone households, however, has essentially relied

on two strategies: telephone directories and random digit dialing

(RDD). Both have advantages and limitations. Telephone directories are convenient listings of sample units from which a household sample may be drawn, minimizing the nonhousehold units.

Sources of bias, however, exist in the use of directories. First,

they are out-of-date the moment they are issued because directories cannot include numbers issued in the interim. Given the

residential mobility of many Americans, this is surely a

consideration. The second and more serious objection is that

telephone directories do not include unlisted numbers. About 20

percent of all telephone households are unlisted. The rate of

unlisted households varies geographically, with the highest in the

Pacific and mid-Atlantic regions and lowest in the South. Urban

households with younger heads, fewer children over 12 years old,

and more persons between 18 and 34 years old are likely to have

unlisted numbers (Tyebjee 1979).

RDD, on the other hand, is a strategy that avoids the sampling

biases of telephone directories, and has been shown to produce

results akin to areal probability sampling (Klecka and Tuchfarber

1978). Nevertheless, RDD has its own problems. These problems

concern the high probability of getting a nonworking or nonhousehold number. Unrestricted random sampling for RDD becomes

extremely costly because approximately 80 percent of the numbers

in the sampling frame are not assigned to households (Waksberg

1978). Most are either unused, assigned to nonhouseholds, or have

some technical difficulty.

Some knowledge of the telephone system is essential. First, depending on the geographic areas of interest, a list of appropriate

telephone area codes and existing three-digit working central

offices is generally available. Second, it is the practice of

telephone companies to assign the four remaining digits in

clusters. With this information, a multistage sampling scheme is

generally employed in RDD for geographic areas of interest. The

scheme admits a cluster of numbers if a first try in a selected

series yields a household number (Waksberg 1978, Cummings 1979).

Of course, once a cluster of numbers is selected, there are bound

to be numbers whose eligibility cannot be determined immediately.

Followup calls are necessary. For instance, business telephone

numbers can be eliminated quickly by making initial calls during

the daytime. Checking with the telephone company about numbers

suspected of having technical difficulties will probably clear up

those problems.

72
Another consideration with RDD is the chance of selecting a household with more than one telephone number. About 3 percent or more

of telephone households have multiple numbers. These households

are more likely to be urban and Eastern and to have more teenagers

(Glasser and Metzer 1972). This bias can be corrected by asking

respondents the number of telephone numbers reaching the household

and weighting the responses by the inverse of this number.

In summary, although nontelephone households obviously are not

covered in telephone sampling, RDD can ultimately provide coverage

of the sampling frame of households with working telephone

numbers. Telephone sampling is frequently compared with sampling

for personal interviews. Both types involve a degree of noncoverage. According to one researcher, "Even careful face-to-face

surveys probably cannot locate 5 percent or more of all households, although not necessarily the same ones" (Sudman 1981,

p. 1).

Questionnaire Design

In general, survey researchers believe that most questions asked

in personal interviews may be asked on the telephone. In fact,

questions on sensitive topics can move almost freely from one mode

to the other (Aneshensel et al. 1982; Freeman et al. 1982).

Furthermore, the telephone interview need not be shorter than the

face-to-face interview. Depending on the salience of the topic to

the respondent, telephone interviews have lasted for an hour or

more (Sudman 1981).

There are, however, some caveats in item construction. Because

visual aids cannot be used unless special arrangements are made,

and respondents can remember only a small number of alternatives,

questions should not offer more than three or four alternatives.

Questions asking for "yes-no" responses and using branching techniques should be used. Numerical scales are probably preferable

to Likert-type scales.

Some items of interest have shown more respondent resistance than

others on the telephone. Income is one such item. Many respondents are particularly suspicious when income questions are asked.

These are often placed at the end of the interview after there has

been an opportunity to build rapport. Race is another item that

sometimes meets resistance. This question should be asked directly on the phone or omitted. In any case, a simple but meaningful

introduction is important to "grab" the respondent in a telephone

interview.

Response Rates

In discussing response rates, it is important to distinguish

between refusals and "not-at-homes." Both are extremely important

in probability surveys.

73
In general, telephone surveying has recorded a higher refusal rate

than face-to-face interviewing. The refusal rates in national

telephone surveys, for instance, are at least 5 percentage points

higher than those recorded for personal interviews (Groves and

Kahn 1979). A variety of factors may contribute to refusals, such

as characteristics of the respondents, characteristics of the

interviewer that are detectable in speech, and the conditions of

the interview, such as the time of the day.

Little investigation has been conducted into these factors,

although some findings indicate that refusals are more likely to

occur among the poorest households, underprivileged minorities,

households with an older head, and households with a head having

little education (O'Neil 1979). A recent study has shown some

interesting interviewer effects on response rates. When

interviewing experience is controlled, older interviewers and

interviewers with optimistic expectations achieved higher response

rates (Singer et al. 1983). In addition, a study found that the

timing of the call affected response rates. Refusals were higher

on weekends than weekdays, highest in the evenings, and lowest in

the mornings (Falthzik 1972). These factors in telephone surveying should be explored further.

Several techniques, however, have been used to minimize refusals.

One technique referred to as "foot-in-the-door" relies on a miniinterview of five questions to get initial compliance and to

schedule a longer interview later. This technique used in an

experimental group received a higher response rate than the

control group, where the technique was not used (Reingen and

Kernan 1977).

Another technique used mixed-mode surveying. The telephone

respondents who refused cooperation were followed up with a selfadministered mail questionnaire, a personal interview, or both to

obtain cooperation. Response rates ultimately increased to about

90 percent (Siemiatycki 1979).

An interesting observation about refusals is that telephone

screening methods can reach respondents who are more difficult to

locate in face-to-face interviewing, but obtain a lower response

rate from respondents who are located, i.e., those who reside in

security-conscious, high-rise apartments (Sudman 1981).

The second source of nonresponse--the not-at-homes--can create a

systematic bias in the sample. Empirical evidence indicates that

those at home are overrepresented by respondents in the over-64-

years age group, those with low education and low income, those

with home-related occupations, and those who reside in rural

places (Dunkelberg and Day 1973).

There are essentially two ways of dealing with the not-at-home

nonresponse problem. One is to use a correction factor that

weights each completed interview by the inverse of the probability

74
of finding the respondent at home, such as the Politz-Simmons

correction. The factor is derived from a question that asks, for

instance, on how many nights in the past week the respondent was

at home (Politz and Simmons 1949).

Another approach to minimizing this problem is to employ a

rigorous policy of call-backs at various times during the day and

week. Although a policy of three or four call-backs is often

used, residents of larger SMSAs--especially in the Northeast--

require more than four call-backs, compared with two in smaller

areas (Tyebjee 1979).

Validity of Data Collected

The literature is replete with studies that find results for

telephone surveying not significantly different than data

collected from personal interviewing (Coombs and Freedman 1964;

Hochstim 1967: Klecka and Tuchfarber 1978). Many of these studies

have randomly assigned respondents to one mode of interviewing or

the other, and then compared the findings. Nevertheless, as

already indicated, different interview methods tend to cover somewhat different segments of the population, data on income are not

easily shared over the telephone,and telephone interviewers may

affect outcomes in ways that are not entirely understood.

Some evidence is offered that respondents do not put as much

effort into a telephone interview as they do into a face-to-face

interview (Groves and Kahn 1979). Phone interviews are generally

shorter, and studies have found a lack of the richness of data

collected by other modes of administration (Siemiatycki 1979).

Management of Telephone Surveying

Some of the major advantages of telephone surveying come from

management considerations such as cost, quality control, and time.

As far as cost is concerned, it is generally held that telephone

interviewing costs about 50 percent or less of the cost of personal interviewing (Siemiatycki 1979; Sudman 1981). Given the

centralization of telephone facilities and the computerization of

many procedures in current telephone surveying compared to travel

costs and data processing costs in personal surveying, it is not

difficult to understand the economies. According to one researcher, more than half of the costs of face-to-face interviewing

involve interviewer travel to locate respondents (Sudman 1981).

A second consideration is the quality control that can be

exercised in the several steps of the surveying process. With

centralized facilities, telephone interviewing can be monitored

easily and problems can be detected very early in the data

collection. Furthermore, the use of computer-assisted telephone

interviewing, known as CATI, minimizes error on the part of the

interviewer and inconsistent responses on the part of the

respondent. It allows the use of a complicated questionnaire

75
design with elaborate skip instructions and forward feedback

instructions.

Finally, analyses may be performed even before all the interviews

are completed. For instance, analyses of respondents' profiles

can indicate whether subsequent sampling may have to concentrate

on underrepresented segments of the population (Tyebjee 1979).

Time is also an important consideration. The interval between

data collection and the reporting of findings is shortened since

steps in the process such as coding, keypunching, and verifying

are eliminated in CATI surveys. Soon after the last interview is

completed, a computer tape of responses checked for reliability is

ready for analysis.

AN APPLICATION OF TELEPHONE SURVEYING IN A STUDY OF DRUG ABUSE

Given these general methodological considerations in telephone

surveying, how do they come into play in a household survey of

medical, non-medical, and illicit substance use? The remaining

discussion deals with New York State's experience with telephone

surveying.

The New York State Division of Substance Abuse Services periodically conducts a household survey of drug use across the State.

The purpose of the survey is to monitor the drug abuse problem

among household residents and to estimate the number of drug

abusers in this population. With the rising cost of face-to-face

interviewing and the reluctance of respondents to open their

doors--especially in New York City--a telephone survey seemed a

probable alternative to a face-to-face survey. In order to determine the feasibility of this mode of administration in a survey of

sensitive, stigmatizing, and illicit behavior such as drug abuse,

it was decided to conduct a pilot study first. In 1980, the pilot

study was conducted. The results did show that such a survey was

feasible. In 1981, a full-scale computer-assisted telephone

survey was conducted in the State. The discussion that follows

highlights the methodological issues and the ways in which these

were handled in the pilot telephone survey and the full-scale

survey.

Sampling

New York State's telephone survey tried sampling nontelephone

residents as well as telephone households. The nontelephone segment included the residents of Single Room Occupancy (SRO) hotels

in New York City. This subgroup had never been included in a

probability sample studying drug abuse, and it is a population

that has been of particular interest in the city. The approximately 200 SRO hotels with their 23,000 occupants provided a

fairly well-defined frame from which residents could be sampled.

76
Based on experience gathered from the pilot study, a sampling

scheme was worked out. A sample of 47 SRO hotels was selected

from the universe, which was stratified by borough and price

range. Each building manager of a selected hotel received a

letter explaining the study and requesting cooperation. Each

manager was paid $25 for assistance in sharing information about

the number of rooms, the layout of the hotel, and the availability

of a pay telephone. State field workers were able to sample the

rooms systematically with a different random start for each hotel.

The field workers would knock at the door of a selected room and

try to elicit cooperation from the resident. If and when the

resident agreed, he/she was walked to the phone, a phone call was

made to the central telephone facility, and the respondent was

interviewed out of earshot of the fieldworker. When the respondent said the interview was completed, the fieldworker would speak

to the interviewer to determine whether the interview was in fact

completed. At that time the respondent received $10 for his/her

time and effort. This combination of field worker and telephone

interviewing yielded a sample of 236 respondents from 43 SRO

hotels.

The sampling of the telephone households eventually used RDD.

During the pilot study, however, the use of the telephone directory was also attempted with an advance letter sent to the

selected households explaining the study, stating that a call

would be made to them at a future date, and offering $10 for

completing the interview. The check could be donated to a charity

or mailed directly to the respondent. An attempt was also made

with a similar advance letter that asked the respondent to call

the contractor using an 800 toll-free telephone number. Response

rates based on completed interviews as a proportion of eligible

households contacted were calculated for each strategy. As the

pilot study turned out, the RDD sample yielded a response rate of

69 percent, the advance letter indicating that the contractor

would call yielded 72 percent, and the advance letter asking the

respondent to call yielded a 9 percent response rate. Since the

response rates for the RDD sample and the telephone directory

sample with the first advance letter were not significantly different, and since the RDD sample assures inclusion of nonlisted

households and the ability to maintain the anonymity of the

respondent, the RDD strategy for sample generation was used for

the full-scale study.

Although RDD can be a costly effort, the accumulated knowledge of

the telephone research organization allowed efficient sampling in

the multistage design. First, the research organization had

available a listing for each of the approximately 2,000 telephone

exchanges or central offices in use in New York State. Each

exchange with its six-digit identifier had associated geographic

information, including county, and city within county, which was

important to the stratification design used in the study. A

strictly proportional sample of exchanges was selected to represent the metropolitan and nonmetropolitan counties in each region

77
of the State. Then, using a random digit generating formula

calculated by the computer, an equal or known number of four-digit

combinations was selected within each central office. Using the

cluster sampling described earlier, a sample was generated. Since

a larger proportion of the nonworking and nonhousehold numbers in

each telephone exchange was identified using prior knowledge,

approximately 70 percent of these numbers were identified and

eliminated before the interviewing got started. This hurried the

sampling process along and cut costs. An RDD-generated sample of

3,251 householders participated in the survey, representing the

demographic and educational characteristics of the total household

population as well as their metropolitan and nonmetropolitan

location in the State.

An effort was also made in the pilot study to sample two "rare

trait" subgroups: American Indians and high school dropouts. The

literature does speak of RDD as useful in locating rare populations, especially if a large sample is generated (Waksberg 1978).

Using network sampling--where respondents were asked to provide

names of such individuals--for both subgroups, and the telephone

directory for surnames that might be native American, some interviews were conducted. Nevertheless, this type of network and

telephone directory sampling for subgroups was not pursued in the

full-scale study.

Questionnaire

The questionnaire included numerous items on the use of prescription drugs and illicit substances, the possible consequences of

that drug use in terms of problems encountered in everyday life,

self-perception of drug dependence, and the need for treatment.

Despite the sensitive nature of many questions, the number of

respondents having any knowledge of the subject matter who refused

to complete the interview was minimal.

Some of the drawbacks considered earlier in questionnaire design

did not present a problem in the survey. For instance, in faceto-face drug use surveys, visual aids are often used to help

respondents identify prescription drugs that they have used. In a

telephone survey, however, having such stimuli available would

require some prearrangements. The contractor did suggest that if

telephone directory sampling was used, a lockbox might be mailed

to each household prior to the telephone interview. When the

respondent is reached on the phone, the combination to open the

lockbox is given, and within the box would be the visual aids

required. The curiosity aroused not only increases the interest

in the interview but provides the necessary materials. Since the

use of directories was not planned and the respondents' anonymity

was important, this device was not used. Instead, to help the

respondents remember the names of the drugs they used, the interviewer had a comprehensive list of drug names by category of drug

programmed into the computer. This list was referred to when

questions of drug identification arose.

78
The household income question yielded better results than

expected. The question was among the last in the interview and

the missing data were minimal. Recently, the findings for drug

use by income were analyzed. The findings showed that the distribution of the weighted sample by household income was almost

identical to the 1980 Census distribution of New York State

households by family income.

Response Rates

To encourage participation, respondents were offered compensation

for their time. Respondents in New York City were offered $10 for

completing the interview, and $5 was offered in the rest of the

State. This money could be sent to the respondent, another individual selected by the respondent, or to a charity of the

respondent's choice. Most respondents opted to receive the money.

The response rate for the RDD portion of the survey was 66

percent --a rate similar to the face-to-face administered survey

the State conducted in 1976. In the telephone survey, there were

3,251 completed interviews of 4,956 eligible households. These

eligible households include refusals where eligibility was established (429) and refusals where eligibility was not established

(estimated 1,276). It was assumed from prior experience that 74.3

percent of the refusals with undetermined eligibility were in fact

eligible. The calculation excludes troublesome categories that

could not be resolved despite five callbacks and some nonresponse

followup where refusals were not received. These categories

include: no answer/busy (829), callbacks (263), and "language

barriers" (454).

What contributed to the refusal rate was the need to oversample in

five central cities--where refusal rates were the highest--and to

oversample youth between the ages of 12 and 17 years, who required

parental permission for participation.

Validity of the Data

One of the purposes of the pilot study was to determine whether

the telephone as a mode of data collection could yield valid rates

of drug use. The findings from the 1980 pilot study were compared

to rates obtained in the 1979 National Household Survey conducted

by the National Institute on Drug Abuse (Fishburne et al. 1979).

The rates proved very similar. Compared to New York State's 1976

household survey, the rates for the 1980 pilot study and the 1981

full-scale study were higher for several drugs, which reflected

trend data from a variety of indirect indicators.

A question about validity, however, may pertain to the findings

for respondents 12 to 17 years of age. Since these respondents

needed parental permission for participation, it was entirely

possible that someone else was present during the interview,

interfering with the candor of responses. In general, this

79
obstacle to participation may have acted as a biasing factor.

Compared to self-administered school survey findings for this age

group, the telephone survey found very low rates of drug use.

Perhaps the most interesting experience in this project was the

attempt to improve the validity of the findings by use of the

randomized response technique on the telephone. This technique

was first developed in the mid-60s to enhance the privacy of the

respondent's answers to sensitive items (Warner 1965). The technique usually involved a randomizing device, such as a pair of

dice, and two statements--one about a sensitive attribute, such

as, "I have used heroin," and one about an innocuous attribute,

such as, "I have watched television." Based on some rules, the

respondent uses the randomizing device to determine the statement

to which the "Yes" or "No" response is given. The interviewer

simply records the response without knowing the statement to which

it applies. Based on the probabilities associated with the

results of the game, and the assumption that the respondent is

playing according to the rules, estimators of the proportion of

the population having the sensitive characteristics may be

determined. This technique, however, was designed for use in

personal interview surveys. Thus, its use on the telephone in the

pilot study was a new undertaking for the contractor.

After much discussion, two variations of the randomized response

technique were incorporated into the interview. First, rather

than two statements, one question was employed so that the

respondent would not have to remember too much. Second, the

randomizing device was something that was readily available in the

home with known probability that would optimize the percentage of

response based on the truth. The randomizing device was three

coins. Each of the selected respondents was asked to toss the

coins before each question and to answer based on certain rules.

Several questions using this randomized response technique were

asked about each of four drugs: cocaine, LSD, Angel Dust, and

heroin.

Of the sample of 203 household respondents in the pilot study, 115

were randomly selected for randomized response and 88 were asked

the questions directly. Of the 115, only 60 agreed to use the

technique. Of the 55 who rejected its use, 33 claimed they never

used the drugs and it would be a waste of time, and 22 wanted to

tell the truth without playing games. In general, there were no

significant differences in findings between the 88 respondents

originally selected to answer by direct questioning and the 66 who

participated in the randomized response technique.

This technique was not used in the full-scale study for several

reasons. The technique, by definition, prevents knowledge of

individual behavior and only allows findings for the group. Thus,

this drawback inhibits the data analyses that may be performed.

Second, the technique reduces the effective size of the sample and

80
thus the power of the statistical techniques. Third, the additional complexity increases the length of the interview, which has

implications for cost. Finally, it became clear from the pilot

study that many respondents did not want to be bothered with the

technique. On the other hand, certain people seemed to be at ease

with it; these were mainly the younger adults. Although our

experience indicates that the technique may not be suitable for a

total sample, it might be offered to those who feel uneasy about

answering certain questions. Nevertheless, the telephone turned

out to be a more flexible medium than had been expected.

Management of the Survey

Some of the obvious advantages of the telephone survey were the

computer-assisted capabilities, the ease in monitoring the interview, and the timeliness of the process. The use of the computer

in programming the questionnaire and its complex skip patterns, in

customizing the questionnaire to reflect answers already given, in

recording the responses, and in flagging inconsistencies minimized

interviewer error and editing problems. A clean data tape was

delivered soon after the last interview was completed.

Being able to monitor the interviews was a reassuring experience.

It became clear that sensitive drug use questions could be asked

on the telephone, and that interviewers were very adept in asking

the questions, including the use of the randomized response procedures in the pilot study. The ease in supervision surely enhanced

the quality of interviewing.

Finally, the timeliness was most remarkable. Data collection

started in the beginning of March 1981; the first report of

prevalence and incidence was issued in September 1981. Six

months was the fastest turnaround for any of the population

surveys this agency had conducted.

SUMMARY AND CONCLUSIONS

In light of New York State's experience, it is probable that

future household drug use surveys will use telephone administration. Drug use questions are not as sensitive as had been

thought, and are easily administered by telephone. In addition,

the lower costs, the computer-assisted capabilities, and the

saving in time are some of the advantages in comparison to faceto-face surveying. In order to address the nontelephone segments

of the household population--despite their declining proportion--

and to improve response rates, mixed-mode interviewing may have to

be considered. Given a better understanding of telephoneassociated behavior and the increasing popularity of technological

advances, such as the portability and mobility of phones, telephone surveying may become even more attractive in the future.

81
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Coombs, L., and Freedman, R. Use of telephone interview in a

longitudinal fertility study. Public Opin Q, 28:112-117, 1964.

Cummings, K.M. Random digit dialing. A sampling technique for

telephone surveys. Public Opin Q, 43:233-244, 1979.

Dillman, D.A. Mail and Telephone Surveys: The Total Design

Method. New York: John Wiley & Sons, 1978. 325 pp.

Dunkelberg, W.C., and Day, G.S. Non-response bias and call-backs

in sample surveys. J Market Res, 10:160-168, 1973.

Falthzik, A. When to make telephone interviews. J Market Res,

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Fishburne, P.; Abelson, H.; and Cisin, I. National Survey on Drug

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Washington, D.C.: U.S. Govt. Print. Off., 1980. 169 pp.

Freeman, H.E.; Kiecolt, K.J.; Nichols, W.L. II; and Shanks, J.M.

Telephone sampling bias in surveying diability. Public Opin Q,

46:392-407, 1982.

Glassner, G., and Metzger, G.D. Random digit dialing as a method

of telephone sampling. J Market Res, 9:59-64, 1972.

Groves, R.M., and Kahn, R.L. Surveys by Telephone: A National

Comparison With Personal Interviews. New York: Academic Press,

1979. 358 pp.

Hochstim, J.R. A critical comparison of three strategies of

collecting data from households, J Am Stat Assoc, 62:976-989,

1967.

Klecka, W.R., and Tuchfarber, A.J. Random digit dialing: A comparison to personal surveys, Public Opin Q, 42:105-114, 1978.

O'Neil, M.J. Estimating the nonresponse bias due to refusals in

telephone surveys. Public Opin Q, 43:218-232, 1979.

Politz, A., and Simmons, An attempt to get the not-at-homes

into the sample without call-backs. J Am Stat Assoc, 44:9-31,

1949.

Reingen, P.H., and Kernan, J.B. Compliance with an interview

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Siemiatycki J. A comparison of mail, telephone, and home interview strategies for household health surveys. Am J Public

Health, 69(3):238-245, 1979.

Singer, E.; Frankel, M.R.; and Glassman, M. The effect of interviewer characteristics and expectations on response. Public

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Sudman, S. Telephone methods in survey research: The state of

the art. Paper read at American Educational Research

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Tull, D.S., and Albaum, G.S. Bias in random digit dialed surveys.

Public Opin Q, 41:389-395, 1977.

Tyebjee, T.T. Telephone survey methods: The state of the art.

J Market Res, 43:68-78, 1979.

82
U.S. Bureau of the Census. Statistical Abstract of the United

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Waksberg, J. Sampling methods for random digit dialing. J Am

Stat Assoc, 73:40-46, 1978.

Warner, S.L. Randomized response: A survey technique for eliminating evasive answer bias. J Am Stat Assoc, 60:63-69, 1965.

AUTHOR

Blanche Frank, Ph.D.

New York State Division of Substance Abuse Services

2 World Trade Center

New York, NY 10047

83
A PILOT STUDY ASSESSING

MATERNAL MARIJUANA USE BY

URINE ASSAY DURING

PREGNANCY

Barry S. Zuckerman, Ralph W. Hingson, Suzette Morelock,

Hortensia Amaro, Deborah Frank, James R. Sorenson,

Herbert L. Kayne, Ralph Timperi

INTRODUCTION

Approximately 10 percent of childbearing-age women in the United

States smoke marijuana (Fried 1980). Studies of both middle- and

low-income women in the Boston area indicate that 10 to 15 percent

of pregnant women acknowledge smoking marijuana during pregnancy

(Hingson et al. 1982, Wilner 1981). Because many women do not

realize they are pregnant until at least 1 or 2 months into their

pregnancy, it is possible that the proportion of women who consume

marijuana during the first trimester of pregnancy may be even

higher.

The main ingredient of marijuana, delta-9-THC, is known to cross

the placental barrier; in early pregnancy the transfer is higher

than in late pregnancy (Harbison and Mantillaplata 1972). Smoking

marijuana during pregnancy raises the possibility of fetal toxicity through placental transfer.

Some studies with animals demonstrate an association between marijuana exposure and intrauterine growth retardation (Abel 1980).

Conclusions from these studies are limited because of lack of an

inhalation model for animals and lack of pair-fed controls.

Studies on humans also demonstrate conflicting findings about the

relationship between marijuana use during pregnancy and adverse

perinatal outcome. Associations have been observed between marijuana use and a greater likelihood of meconium staining and

precipitative labor (Greenland et al. 1982). Another study

(Hingson et al. 1982) demonstrated that marijuana use was

independently associated with lower birthweight when other maternal characteristics that might influence fetal growth were

controlled. Results of that study indicated that women who used

marijuana fewer than three times per week during pregnancy delivered infants who were 95 grams smaller than infants of nonusers.

Women who used marijuana three or more times per week delivered

babies 139 grams smaller than infants of nonusers. In comparison,

84
women who smoked one pack or more of cigarettes daily delivered

infants who were 83 grams smaller than infants of nonusers.

Another study (Linn et al. 1983) did not demonstrate an association between marijuana use during pregnancy and low birthweight

when possible confounding variables were controlled.

Possible explanations for these conflicting findings include the

following: 1) the different studies represent different populations of pregnant women; 2) the dependent variables of growth were

assessed differently (record review vs. examination); and 3) the

independent variable of drug use was assessed differently

(prospective vs. retrospective self-report).

A prospective study using urine assays to detect the presence of

marijuana has been designed to overcome some of the limitations in

the previous studies, including our own. A prospective design

should diminish recall bias of self-reported drug use. The urine

assay will be used to confirm the validity of reported marijuana

use. The main hypothesis of the prospective study, to be conducted by the authors, is: When potentially confounding factors

are analytically controlled, mothers who smoke marijuana during

pregnancy will deliver infants who are significantly smaller,

exhibit more congenital anomalies, and demonstrate greater neurobehavioral dysfunction than infants of mothers who do not smoke

marijuana. An important component of the study will be the

measurement of marijuana in urine during pregnancy. This information will supplement interview results and will address the inherent difficulty in obtaining valid and reliable interview data.

Because the proposed study includes the novel feature of requesting a prenatal urine sample to assess drug use, a pilot study was

needed to determine whether these women would be willing to

participate.

This report presents the results from the pilot study. The aims

of this pilot study were:

1) to assess study participation rates among pregnant

women informed that their drug consumption would be

assessed by urine assay;

2) to compare rates of marijuana consumption as determined by self-report versus urine assay among

participating women;

3) to obtain more contemporary data than in our 1977-

79 sample (Hingson et al. 1982) on the levels of

marijuana, alcohol, psychoactive drug, and cigarette use during pregnancy among women who receive

prenatal care at Boston City Hospital (BCH).

85
PROCEDURE

Women registering for prenatal care at BCH Prenatal Clinic from

June 13, 1983 through July 14, 1983 were requested to participate

in the study. The study was conducted on 16 of the 21 clinic days

during the study period. All pregnant women who spoke English or

Spanish were eligible.

Interviews were conducted by a bilingual female psychologist.

Data on the frequency and quantity of marijuana use as well as a

variety of other health habits and characteristics thought to

influence fetal development were gathered through a close-ended

questionnaire. The interview time ranged from 20 to 60 minutes.

Women were asked whether they had ever used marijuana, and, if so,

whether they had used it during pregnancy. Women who reported

using marijuana during pregnancy were asked about the frequency of

use during each trimester and during the past week. This last

time period was requested in order to compare self-reported

findings to the results of the urine assay. The 1-week period

represents a conservative estimate of the assay sensitivity.

During the pilot month, 269 women visited the prenatal clinic.

Our interviewer asked 81 of those women to participate. Selection

of subjects was based on interviewer availability. Those asked to

participate did not differ in age, race, marijuana use, or other

drug use, as reported to the clinic staff, from those not asked to

participate.

Upon arrival at the interviewer's office, women were informed of

the nature of the study and provided with both a verbal description and written informed consent form that described the study in

detail and the use of both interview and urine samples in detail.

They were also informed that they would be paid $5.00 for their

time. The interviews were conducted immediately after consent was

obtained.

After the interview, participants provided a urine specimen, which

was immediately labeled with a subject identification number and

refrigerated in the clinic. At the end of each day, urine specimens were sealed inside foam coolers filled with ice packets.

Urine samples remained refrigerated inside these coolers for 12-24

hours (depending on the time of collection) before being collected

by the clinic courier service for transportation to the

Massachusetts State Laboratory.

Seventy-five urine samples were received in the laboratory and

frozen at -20 degrees C in plastic containers until analyses were

performed. Detection of cannabinoid metabolites in urine was done

using the enzyme-mediated immunoassay technique (EMIT) (Rowley et

al. 1976). Three known calibrator samples were used in the

assays: a negative calibrator containing no metabolite

(0.0 mg/ml), a low calibrator containing 20 mg/m of the 11-nordelta8

-THC-9 carboxylic acid derivative of delta9-THC, and a

86
medium calibrator containing 75 mg/ml of the derivative. The low

calibrator (20 mg/ml) is used as the cutoff value for

positive/negative interpretation and ensures at least 95 percent

confidence in the positive/negative classifications (DeLaurentis

et al. 1982). Levels of urinary metabolites are detectable within

a few hours after exposure to marijuana (Rodgers et al. 1978) and

remain detectable 7 to 10 days after smoking (Clark et al. 1980).

Frequent users often have continually detectable baseline levels.

Usually as much as 50 percent of an initial dose is excreted

within 72 hours.

Samples were run in duplicate. Positive samples were tested again

in a separate assay, and distilled water blanks were used as

spacers in this repeat assay to ensure against the possibility of

carryover. Confirmation of 13/18 EMIT THC-positive urine samples

was done by gas chromatography (GC) (Whiting et al. 1982). There

was an insufficient volume of urine to do a confirmatory analysis

by GC for five samples. The confirmatory procedure only measures

the primary urinary metabolite of THC and would be expected to

agree with EMIT THC-positive results in 90 percent of the samples.

RESULTS

The results will be presented in the order of the goals of the

pilot study.

Of the 81 pregnant women asked to participate, 6 (7 percent)

refused. Three of them refused because they did not have adequate

uninterrupted time to stay for the interview. Following establishment of a uniform procedure for interviewing women during the

second week of the study, there were no refusals for this particular reason. To our knowledge, no women cited the urine assay

explicitly as a reason for nonparticipation. The refusal rate of

7 percent compares favorably with that in our previous study (14

percent) (Hingson et al. 1982) and suggests that women are willing

to participate in a study that tests their urine for marijuana and

psychoactive drugs.

Of the study sample of 75 women, 38 (51 percent) reported smoking

marijuana at some time in their lives. Of these, 23 women (31

percent) reported smoking marijuana during their pregnancy, and 11

women (15 percent) reported smoking in the week prior to the

interview (table 1).

87
TABLE 1. Self-reported marijuana use

Marijuana Use Number of Women (%)

(N=75)

Never smoked marijuana 37 (49%)

Ever smoked marijuana: 38 (51%)

Prior to but not during pregnancy 15 (20%)

During pregnancy 23 (31%)

During week prior to interview 11 (15%)

Of the 75 women in the study, 18 (24 percent) had urine samples

that were positive for the presence of marijuana metabolites

(table 2). Sixteen of the positive samples had greater than 75

mg/ml of delta9-THC metabolite when assayed by the EMIT method.

The other two EMIT-positive samples had 45 and 60 mg/ml of the

metabolite. Ninety-two percent of the EMIT-positive urine tests

were confirmed by a blindly conducted gas chromatography test

(table 3). This is consistent with rates reported in the literature (CDC 1983). Calculations from the data in table 2 indicate

the sensitivity of self-report is 56 percent with a specificity of

98 percent. The low sensitivity focuses on the large numbers of

false negatives while the high specificity demonstrates the small

number of false positives. Eight women (12.5 percent) reported

not having smoked marijuana in the previous week but had positive

urine findings. While all eight women reported having used marijuana at some point, four of them reported using marijuana prior

to but not during their pregnancy. The other four acknowledged

using marijuana during pregnancy, but not during the week before

the interview. Had we relied on self-report alone, we would have

missed 15 percent (4/27) of the women who used marijuana during

pregnancy and 44 percent (8/18) who used it in the previous week.

One woman who reported smoking marijuana in the past week had a

negative urine test.

88
TABLE 2. Marijuana use in the previous week assessed by

self-report and cannabinoid assay

Cannabinoid Assay

(+) (-)

Total

Self-report

(+) 10 1 11

(-) 8 56 64

Total 18 57 75

TABLE 3. Confirmatory tests of urine samples found to

contain cannabinoid metabotites by EMIT

assay

Sample Number

G C

EMIT THC RRT*

1

2

3

4

5

6

7

8

9

10

11

12

13

14

15

16

17

18

positive 1.29

positive 1.29

positive QNS**

positive 1.37

positive 1.29

positive 1.36

positive QNS

positive 1.29

positive 1.31

positive 1.35

positive 1.37

positive QNS

positive 1.37

positive negative

positive 1.29

positive QNS

positive 1.31

positive QNS

*Relative retention time (RRT) of THC-COOH compared to

oxyphenbutazone by gas chromatography (GC).

**Quantity not sufficient for analysis.

89
In the pilot sample a greater percentage of pregnant women reported smoking marijuana, smoking cigarettes, and drinking during

pregnancy than had reported such behavior during our 1977-79 study

at Boston City Hospital (table 4). The knowledge that urine tests

would assess the use of these substances in our pilot study may

have prompted a greater proportion of women to report such use in

our pilot study than in our earlier study, in which urine samples

were not collected. However, other possible reasons for the

differences can also be hypothesized, e.g., differences in the

characteristics of women using the hospital prenatal clinic now

compared to 1977-79, changes in maternal habits now compared to

the late 197Os, or greater accuracy of data on such habits collected during pregnancy as opposed to after delivery, as in our

earlier study.

TABLE 4. Self-reported substance use any time during

pregnancy

Boston City

Substance Use Hospital Study Pilot Project

During Feb. 1977 - Oct. 1979* June 1983

Pregnancy Percent (N=1,690) Percent (N=75)

Marijuana 14 31

Cigarettes 48 60

Psychoactive Drugs 1 11

Alcohol 38 64

*Hingson et al. 1982.

Whatever the reasons for the higher levels of marijuana use,

psychoactive drug use, smoking, and drinking during pregnancy

reported in our pilot sample, they suggest an increased ability

for the proposed study to detect whether an association exists between maternal marijuana use and adverse fetal development. On

the basis of cross-tabulations between self-reported marijuana use

and other substance use in our pilot data, we project that in the

proposed sample of 1,500 women, 240 will smoke marijuana but not

take other psychoactive drugs during pregnancy, 60 will smoke

marijuana but not cigarettes, and 40 will smoke marijuana but not

drink at all during pregnancy. If marijuana use during pregnancy

is identified by either self-report or urine assay, 540 women will

be identified as using marijuana. Of these, 320 will not use

other psychoactive drugs, 60 will not smoke cigarettes, and 60

will not drink during pregnancy. These numbers are considerably

larger than those in our earlier study, in which we were still

90
able to identify an independent association between maternal

marijuana use and reduced fetal growth using multiple regression

techniques.

The markedly higher proportion of women who reported other psychoactive drug use during pregnancy than in our earlier study (11

percent vs. 1 percent) may, if it persists, also afford us a

greater opportunity to assess whether use of other psychoactive

substances may relate to adverse fetal development.

DISCUSSION

The testing of urine for marijuana and other drug use resulted in

the identification of 15 percent more women who used marijuana

during pregnancy than by use of self-report alone. This pilot

study reassures us that our proposed prospective study utilizing a

urine assay for marijuana use will provide more reliable answers

to the questions about the possible effects of maternal marijuana

use on human fetal growth than any research conducted to date.

The data from this pilot study further suggest that we will be

able to obtain a sufficiently high participation rate to ensure

recruitment of a sample of at least 1,500 mothers. The logistical

and practical problems of conducting the proposed project are

manageable. Finally, we will have an adequate number of cases of

individuals with and without marijuana and other drug exposure

during pregnancy to assess whether or not marijuana has an

independent effect on fetal outcome.

The differences observed in our pilot project in self-reported

marijuana use in the week prior to interview and urine assays

raise important questions about the validity of using only selfreports of marijuana use to assess the possible effects of maternal substance use during pregnancy on fetal development. One

might speculate that in the absence of urine samples, people might

systematically underreport drug use and other habits. If the

underreported habits are also associated with adequately reported

other habits (e.g., nutrition), then the effects of the underreported factors on infant outcome may be underestimated. Whether

self-reports consistently produce underreporting of marijuana (and

use of other substances as well) should be a fundamental concern

for researchers in this area.

The effect of testing urine for drugs on women's self-report of

drug use is also an important methodologic question. As we have

demonstrated, reported use of all drugs is much higher in the

present pilot study than in our previous study, in which there was

no urine testing for drug use. We are undertaking a randomized

control study evaluating the effect of urine assays on self-report

of drug use. Our hypothesis is that women will more often report

drug use when they know their urine will be tested. The results

of this study will be important to future studies assessing drug

use.

91
REFERENCES

Abel, E.L. Prenatal exposure to cannabis: A critical review of

effects on growth development and behavior. Behav and Neural

Biol, 29:137, 1980.

Center for Disease Control. Morbidity and Mortality Weekly

Report, 32(36):469-471, 1983.

Clark, S.; Turner, J.; and Bastiani, R. EMIT(R) Cannabinoid

Assay: Clinical Study No. 74 Summary Report. Palo Alto, CA:

Syva, 1980. 28 pp.

DeLaurentis, M.J.; McNeil, K.; Mann, A.J.; Clark, S.; and

Greenwood, H.M. An EMIT assay for cannabinoid metabolites in

urine. In: Hawks, R.L., ed. The Analysis of Cannabinoids in

Biological Fluids. National Institute on Drug Abuse Research

Monograph 42. DHHS Pub. No. (ADM) 82-1212. Washington, D.C.:

Supt. of Docs., U.S. Govt. Print. Off., 1982. pp. 69-84.

Fried, P.A. Marijuana use by pregnant women: Neurobehavioral

effects in neonates. Drug Alcohol Depend, 6:415-424, 1980.

Greenland, S.; Staish, D.; Brown, N.; and Gross, S. The effects

of marijuana use during pregnancy. Am J Obstet Gynecol,

143:408, 1982.

Harbison, R.D., and Mantillaplata, B. Prenatal toxicity, material

distribution and placental transfer of tetrahydrocannabinol. J

Pharmacol Exp Ther, 180:446, 1972

Hingson, R.; Alpert, J.; Day, N.; Dooling E.; Kayne, H.;

Morelock,S.; Oppheimer,E.; and Zuckerman, B. Effects of

maternal-drinking and marijuana use on fetal-growth and

development. Pediatrics, 70:539, 1982.

Idanpaan-Haikkila, J. Placental transfer of tritiated I-delta

9 tetrahydrocannabinol. N Eng J Med, 281:330, 1969.

Linn, S.; Schoenbaum, S.C.; Monson, R.R.; Rosner, R.;

Stubblefield, P.C.; and Ryan, K.J. The association of marijuana

use with outcome at pregnancy. Am J Public Health, 70:1161,

1983.

Rodgers, R.; Crowl, C.P.; Eimstad, W.M.; Hu, M.W.; Kam, J.K.;

Ronald, R.C.; Rowley, G.L.; and Ullman, E.F. Homoaeneous enzyme

immunoassay for cannabinoids in urine. Clin Chem, 24:95-100,

1978.

Rowley, G.L.; Armstrong, T.A.; and Crowl, C.P. Determination of

THC and its metabolites by EMIT homogeneous enzyme

immunoassay: A summary report. In: Willette, R.E., ed.

Cannabinoid Assays in Humans. National Institute on Drug Abuse

Research Monograph 7. DHEW Pub. No. (ADM) 78-339. Washington,

D.C.: Supt. of DOCS., U.S. Govt. Print. Off., 1976. pp. 28-32.

Whiting, J.D., and Manders, W.W. Confirmation of tetrahydrocannabinol metabolite in urine by gas chromatography. J Anal

Toxicol, 6:49-52, 1982.

Wilner,. A comparison of smoking and other maternal behaviors

during pregnancy in a health maintenance organization and fee

for services practices. In: Wilner, S. Maternity Care in Two

Health Care Systems. Doctoral Dissertation, Harvard University,

June, 1981.

92
AUTHORS

Barry S. Zuckerman, M.D.

Department of Pediatrics

Boston City Hospital

818 Harrison Avenue

Boston, Massachusetts 02118

Ralph W. Hingson, Sc.D.

School of Public Health

Boston University School of Medicine

80 East Concord Street

Boston, Massachusetts 02118

Suzette Morelock, Ed.M.

School of Public Health

Boston University School of Medicine

80 East Concord Street

Boston, Massachusetts 02118

Hortensia Amaro, Ph.D.

School of Public Health and Department

of Pediatrics

Boston University School of Medicine

80 East Concord Street

Boston, Massachusetts 02118

Deborah Frank, M.D.

Department of Pediatrics

Boston City Hospital

818 Harrison Avenue

Boston, Massachusetts 02118

James R. Sorenson, Ph.D.

School of Public Health

Boston University School of Medicine

80 East Concord Street

Boston, Massachusetts 02118

Herbert L. Kayne, Ph.D.

School of Public Health

Boston University School of Medicine

80 East Concord Street

Boston, Massachusetts 02118

Ralph Timperi, M.P.H.

State Laboratory Institute

Massachusetts Department of Public Health

305 South Street

Jamaica Plain, Massachusetts 02130

93
HISTORY OF HEROIN

PREVALENCE ESTIMATION

TECHNIQUES

Marc D. Brodsky

INTRODUCTION

Methods for estimating the number of heroin users have changed in

response to changes in the nature of the opiates of addiction and

their legality as well as to changes in types of available data.

Greene (1974) and Rittenhouse (1977) have outlined some recent

methods used to estimate the prevalence of heroin use. The part

of the population affected by opiate addiction changed from the

users of legal patent medicines to illegal opiates. The progression is traced by Austin (1979a and b) in a history of opiates in

the United States from 1840 to 1930, and by Musto (1973). This

paper reviews the techniques used to estimate the number of heroin

users in the United States.

HEROIN PREVALENCE ESTIMATION TECHNIQUES PRIOR TO 1928

Several State- or community-based estimates of the number of

opiate-dependent persons were made prior to 1928. Several sources

of data were used other than interviewing addicts themselves. The

techniques generally used were based on data provided by such

sources as physicians, druggists, and treatment clinics. Terry

and Pellens (1970) used these estimates to make national projections of the numbers of addicts for the prevailing period. They

extended the following State- or community-based rates to the

Nation:

Marshall (1878) surveyed physicians in 96 small towns in Michigan

in 1877 and found 1,313 addicts. Terry and Pellens (1970) determined that if this survey in Michigan were representative of the

United States, the national number of addicts would have been

251,936 in 1877.

Hull (1885) surveyed 123 druggists in Iowa in 1884, asking how

many of their customers were opium addicts, and found 235 opium

addicts. Again, Terry and Pellens (1970) found that if this

94
survey in Iowa were representative of the United States, there

would have been 182,215 addicts in the Nation in 1884.

Terry (1927) studied a treatment clinic population and the distribution of drug prescriptions in Jacksonville, Florida in 1913.

Terry and Pellens (1970) stated that if the rate for Jacksonville,

Florida based on the 541 opiate addicts found by Terry were to be

extrapolated to the population of the entire country it would

result in an estimate of 782,118 addicts.

Brown (1915) used State registration data in 1915 of 2,370 addicts

in Tennessee to estimate the addict population. Terry and Pellens

(1970) used Brown's Tennessee data to produce an estimate of

269,000 addicts in 1915.

The Secretary of the Treasury of the United States (1919) made an

estimate of the number of addicts in treatment, based on a survey

of 3,023 district, county, and municipal health officers. In this

survey, there were 983 replies reporting a total of 73,150

addicts. Terry and Pellens (1970) found that if all physicians in

the United States could be characterized by those who responded to

this survey, the national estimate for the number of addicts in

1918 would have been 237,655.

Terry and Pellens' (1970) national projections for 1919 were based

on the study by Hubbard, who made an estimate based on clinic data

of 7,464 addicts in New York City. If this New York City data

were representative of the country then, according to Terry and

Pellens, the national estimate in 1919 would be 140,554 addicts.

Terry and Pellens' (1970) estimate for 1920 is based on the number

of cases recorded at the Shreveport, Louisiana, treatment clinic

over a period of 4 years. The Shreveport clinic director, W.P.

Butler, recorded a count of 211 individual addicts in 1 year. The

ratio of this number to the population of Caddo Parish, where the

clinic was located, gives a rate of 0.25 percent. Terry and

Pellens (1970) found that this rate, if representative of the

United States, would imply 264,276 addicts in 1920.

METHODS DEVELOPED AFTER THE LATE 1960s

Between 1928 and the late 196Os, few estimates of the number of

narcotic users were made. It was not until the number of heroin

users increased in the late 1960s and early 1970s that estimates

were again attempted.

Multiplier Methods for Estimating the Number of Heroin Addicts

A "multiplier" ratio relates some known estimate to the unkown

desired estimate. Typically a multiplier is developed in a particular time period, then used without considering the potential

for a change over time.

95
In 1969, Englander (Ball and Chambers 1970) established a multiplier based on the ratio of the number of addicts reported to the

Bureau of Narcotics and Dangerous Drugs (BNDD) and those on the

New York City narcotics register. This multiplier was then

applied to the number of heroin addicts reported to the BNDD in 38

"heroin" states. A slightly different procedure was developed

using BNDD and National Institute of Mental Health (NIMH)

Lexington and Fort Worth treatment data for 12 southern States.

The national total, 108,424 for 1967, was the sum of the estimate

based on the 38 "heroin" States and the 12 southern States.

Friedman (1972) estimated the total number of habitual heroin

users in the United States in 1970 to be 712,793. Friedman's

method was based on an extrapolation of an estimate of the number

of heroin users (230,000) in New York City. This number (230,000)

is based on the number on the NYC narcotics register (150,000),

corrected for the ratio of the number of reported narcotics deaths

to cases known on the register (2.0), and several other factors:

emigration (0.9), remission or death not noted on the register or

duplicates on the register (0.9), and register underrepresentation

of gainfully employed people with habits (1.05). The 712,793 is

the sum of four parts: 1) the estimate for New York City; 2) the

estimate for the total of all cities with a population of 500,000

to 5,000,OOO; 3) the estimate for the total for all cities with a

population of 50,000 to 500,000, and 4) the estimate for the total

of all cities with populations up to 50,000. Each of these parts

is composed of three multiplied factors: the ratio of narcotics

users in that area to New York by area size--1) 1.0, 2) 0.4,

3) 0.2, and 4) 0.1 respectively; the ratio of the total population

in that area to the population of New York City-- 1) 1.0, 2) 3.02,

3) 5.25, 4) 16.47 respectively; and a heroin supply and criminal

financial support factor of 1) 0.8, 2) 0.7, 3) 0.6, 4) 0.5

respectively.

Baden (1970) formulated the number of heroin addicts in a city by

simply multiplying the number of heroin-related overdose deaths in

a year by 100. Baden (1970) initially estimated that the overdose

death records in New York City constituted approximately 1 percent

of the known heroin addicts on the New York City narcotics

register. The death rate among heroin addicts varies widely due

to factors such as variations in the percentage of heroin and contaminants in retail packages, the user's physical condition, and

the type and quantity of drugs used in combination with heroin. A

national estimate of heroin addicts could not be made at that time

because a national estimate of the number of heroin-related deaths

was not available.

Survey Methods for Estimating Heroin Prevalence

The low rate of heroin addiction makes general household surveys

impractical to use for estimating the number of heroin addicts

(Harrell, Gfroerer, and Frank, this volume). Crider (this volume)

has shown that the household population and the population of

96
treated heroin addicts show similar trends in the year of first

use of heroin. Data about the number of new heroin users might be

useful in a model for calculation of the number of heroin addicts.

Survey methodology relevant to heroin users has been advanced by

Rittenhouse (1979), Lipton (1981, 1982, 1983), and Miller (1984).

Rittenhouse (1979) described an application of the randomized

response technique. The respondent is asked to flip a coin or

perform some other random choice for one of two questions. One of

the questions concerns the respondent's heroin use. The other

question concerns some nonthreatening behavior for which the

response rate in the sampled population is known.

Fishburne (1979) developed the nominative technique, described

also by Miller (this volume). This technique was established

because it was believed that heroin use may be underreported in

face-to-face household interviews. Application of this technique

is done by asking respondents how many of their close friends who

live in a household have used heroin. In order to remove duplicates from the count, information is also obtained regarding how

many other close friends of a particular user also know about this

person's heroin use. These data, in addition to the reported

number of close friends, can generate an estimate of the number of

heroin users in the sampled population.

Based on the 1982 household survey, it was estimated that 1.8

million people in the United States have ever used heroin in their

lifetime (Miller et al. 1983). Sometimes prevalence estimates are

reported as a percent of the population. For example, in a survey

of high school seniors and followup cohorts, 1.2 percent of

seniors were reported as having ever used heroin (Johnston 1982).

Issues of validity and population coverage are described by

Johnston and O'Malley (this volume). Reliability and consistency

in self-reports of drug use are described by O'Malley et al.

(1983). The effect of truancy and high school dropouts on the

estimation of the number of heroin users from high school surveys

is discussed by Clayton and Voss (1982) and Kandel et al. (in

press). The rate of heroin use is much higher among high school

dropouts than among the sampled high school population (Robins and

Murphy 1967). This fact must be considered when interpreting

heroin use surveys in high school populations.

Miller (1984) has developed the Item Count Technique. Two random

unique samples of the population are presented with a list of

deviant behaviors. The difference between the two lists is that

one list includes heroin use. The respondent simply states how

many of the behaviors on the list he or she has done. The prevalence of heroin use is calculated from a function of the resultant

probabilities.

97
Mathematical Models for Estimating Heroin Prevalence

The estimates of the number of heroin addicts made by Greenwood

(1976), Person et al. (1977), Demaree et al. (1981), and

Shreckengost (1983) cover the period from 1970 to 1982. These

estimates are shown in table 1. Greenwood's (1971) indicatordilution method extended the capture-recapture method for estimating the size of a hidden population. A "capture" is an

occurrence of a name on the BNDD file of known heroin users. A

"recapture" is a recurrence in a successive year. Greenwood's

model describes the mathematical adjustment for the number of

deaths and the increased probability of a recapture once a capture

has taken place. The results are then multiplied by the proportion of addicted narcotic arrestees, assumed to be the same as the

proportion of addicted narcotic treatment admissions, i.e., 77

percent. Greenwood's method results in an estimate of 546,000

heroin users in 1975. A multiple recapture model applied to

repeated admissions to treatment for heroin addiction is described

by Woodward (this volume). Heroin addiction recidivism and treatment readmission must be considered when applying such a model.

TABLE 1. Estimates of number of heroin addicts United States 1969

through 1982

Year Greenwood Person et al. Demaree et al.Shreckengost

1969

1970

1971

1972

1973

1974

1975

1976

1977

1970

1979

1980

1981

1982

SOURCES:

242,000

403,000 - -

-

430,000 - - -

482,000 - - -

464,000 573,000 - 434,000

558,000 584,000 - 460,000

546,000 540,000 - 475,000

-

- 523,000 473,000

-

- 495,000 475,000

-

- 471,000 471,000

-

- 420,000 463,000

-

-

492,000 478,000

-

- - 488,000

- - - 496,000

Greenwood 1976, Person et al. 1977, Demaree et al. 1981, Shreckengost

1983.

Levin et al. (1975) give a computer source code for a Systems Dynamics model of "The Heroin System" at the community level called

the "Persistent Poppy Model." Their Persistent Poppy Model does

not produce a national estimate but does provide considerable

insight on the dynamic relations among the social parameters

describing its operation. The nature of the heroin system depends

upon characteristics of the local community. This may require

that any national estimate based on models be composed of estimates that represent regions. The development of regional models

98
may benefit from synthetic estimation techniques reviewed by

Steinberg (1979).

Cooley et al. (1977) developed a Monte Carlo population simulation

model for estimating the number of heroin users; the number of

frequent heroin users; and the number in various states such as

treatment, prison, or arrest. The prevalence estimates for 1976

were 1,796,OOO ever used; 218,000 frequently use; 21,000 in

treatment; 176,000 in prison; and 42,000 in arrest. This model

helps tie together estimates from the household population survey

and the estimates of frequent users from other techniques.

Several attempts have been made to extend the application of the

indicator-dilution method to more than two samples in local estimates (Brazie 1978; Streeter 1981). Woodward and Doscher (1979)

have used narcotic addict treatment data to make Standard

Metropolitan Statistical Area (SMSA) estimates of the number of

treated heroin addicts. Woodward and Ng (1979) have compared the

result of their log-linear based capture-recapture estimates to

the estimates made by Person et al. (1977) and found them to be

highly correlated.

Person et al. (1977) developed a method based on the Heroin

Problem Index (HPI) (Person et al. 1976). Five indicators were

chosen to form a basis for an index of the heroin problem:

1) treatment program admissions per 100,000 population; 2) narcotic analgesic hospital emergency room episodes per 100,000

population; 3) heroin-related deaths per 100,000 population;

4) retail heroin price; 5) retail heroin purity. Rank order

scores of 24 SMSAs were developed for each of these measures. A

weight was assigned to each SMSA based on the sum of the rank

order scores. This sum of rank order scores was called the HPI

index value. The critical assumption at this point was that the

HPI index value was a linear correlate of the rate of heroin use

in each SMSA. The estimating method was as follows. An independent estimate of the heroin use prevalence rate for at least

one SMSA with a large HPI index and at least one SMSA with a small

HPI index was obtained. These two or more SMSAs were called

anchor cities. A linear relationship was assumed to exist between

the prevalence of heroin addiction in an SMSA and the value of the

SMSA's HPI index. This linear relationship and the estimate of

heroin prevalence for the two or more anchor cities determined the

estimated value for the heroin prevalence for all the other SMSAs

for which HPI index values were computed. The estimate for the

United States was made based on a population extrapolation from

the estimate for the 24 SMSAs. Person et al.'s method resulted in

an estimate of 540,000 heroin addicts in 1975.

Systems Analysis Models, Dynamic Simulation

Shreckengost (1983) developed a Systems Dynamics model of the

heroin supply/demand market based on international supply data and

the number of susceptible individuals in the 14- to 34-year-old

99
age group. The user population, as a portion of this susceptible

population, was shown to change in response to changes in the

availability of heroin in the United States. Shreckengost (1984)

has developed a model that explains in part the relation of the

numbers of small, medium, and large heroin users in terms of

milligrams of pure heroin consumed per day. Shreckengost's method

results in an estimate of 475,000 heroin addicts in 1975.

SUMMARY

Historical methods for estimating the number of heroin addicts

were based on extrapolation from local surveys. More recent estimation methods have attempted to use data collected from special

sources, such as records of narcotic arrests and treatment program

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1979. 14 pp.

AUTHOR

Marc D. Brodsky, M.S.

Division of Epidemiology and

Statistical Analysis

National Institute on Drug Abuse

5600 Fishers Lane

Rockville, MD 20857

103
THE NOMINATIVE TECHNIQUE: A

NEW METHOD OF ESTIMATING

HEROIN PREVALENCE

Judith Droitcour Miller

The nominative technique is a relatively new method of indirect

survey-based estimation that is being developed expressly for the

purpose of estimating heroin prevalence in the general population.

This new technique, which involves asking respondents to report on

their close friend's heroin use, is essentially an attempt to reap

the benefits of survey research, while at the same time avoiding

some of the major problems of the self-report method. The primary

purpose of the nominative technique is to minimize respondent

denial of socially undesirable behavior. Another possible advantage is achieving coverage of "hard-to-reach" deviant population

groups. The nominative question series has been inserted in the

1977, 1979, and 1982 National Surveys on Drug Abuse (Miller et al.

1983). The resulting nominative estimates of heroin prevalence

are presented here and contrasted with corresponding self-report

estimates.

BACKGROUND

As delineated in other papers in this volume, valid self-report

data on serious forms of deviance and drug use are difficult to

obtain via conventional forms of survey research. Of course, as

in any survey, there are bound to be problems of respondent

recall, incomplete knowledge concerning specific drugs used, etc.,

as well as some differences due to question wording. But these

relatively routine difficulties represent far less serious sources

of bias than two special problems faced by deviance researchers:

First, the most deviant persons may not be captured by conventional methods of survey sampling. Second, persons who have

engaged in serious forms of deviance, such as heroin use, have a

very clear motive for distorting the facts of their experience in

a survey interview.

104
Of these two problems, respondent denial has usually received the

greater amount of attention, perhaps because each of us can

identify, to some extent, with the situation of the deviant respondent. We know that if we were asked to disclose "undesirable

facts" about ourselves, we would--consciously or unconsciously--

anticipate some degree of negative consequence, perhaps merely the

lowering of our self-image in the eyes of another person. An even

stronger motive for denial would seem to exist whenever there is

any possibility of more severe consequences, such as unemployment,

job loss, or even prosecution or incarceration.

Cisin and Parry (1980) report a study of the validity of selfreported drug use, including heroin use. This work indicates that

some heroin users do reveal the true facts of their experience,

but many others do not--even if interviewers provide them with

secret answer sheets, sealed envelopes, and sincere promises of

confidentiality. Other empirical studies of response validity, as

well as theories of social desirability and self-disclosure that

are reviewed by Harrell (this volume) also suggest respondent

denial of stigmatized behaviors.

Despite problems of denial of deviant behaviors such as heroin

use, investigators have been reluctant to give up the possibility

of obtaining valid survey data even in very sensitive question

areas. As a result, over the past 20 years, a variety of novel

survey-based estimation techniques have been devised with the

purpose of encouraging deviant respondents to tell the truth. The

first of these was the randomized response method, which dates

from 1965 (Warner 1965; Folsom et al. 1973). Under the randomized

response condition, a respondent might be shown two questions,

each printed on a card. One card would display the sensitive

research question, such as "Have you used heroin during the past

month?" The other card would display an innocuous question, such

as, "Were you born in April?" The respondent is instructed to

answer only one of these questions and to determine which question

by a secret randomizing device (such as flipping a coin in

private). Then he (or she) merely says yes or no, and no one

else, not even the interviewer, can ever know which of the two

questions is being answered.

Randomized response is a form of self-reporting that avoids complete disclosure. Interviewers and researchers never know whether

a particular respondent actually engaged in the deviant behavior

that is being studied. Of course, once the results are combined

for all respondents, it is possible to estimate the overall prevalence of the deviant behavior, based on prior knowledge of the

prevalence of the innocuous behavior as well as the probability of

selecting the deviant item. Unfortunately, there have been various problems reported by those who have implemented randomized

105
response in the field. Apparently, some respondents do not comprehend why they are being asked to "play the game" and therefore

may be suspicious and confused (Wiseman et al. 1975-76). Moreover, once the question of one's own deviant behavior has been

raised, some innocent respondents apparently do not like being

forced to remain ambiguous on that point. They would prefer to go

on record with a statement that they have not engaged in it. Some

of these people would say no regardless of the question drawn

(Shimizu and Bonham 1978; Miller 1981). Another drawback of the

randomized response method is a very high variance cost, i.e., the

variance of a randomized response estimate is, in most cases, at

least four times as high as the variance of a corresponding

conventional estimate (Miller and Cisin 1980).

Because of the disadvantages of the randomized response approach,

other methods of indirect self-reporting have been developed.

These include the aggregated response method (Warner 1971), in

which the respondent adds a random number to his (or her) quantitative answer. Another version of aggregated response features

two subsamples; in one subsample respondents add responses to two

(or more) questions, while respondents in the other are instructed

to subtract responses (Boruch and Cecil 1979). The most recent

version of indirect self-reporting, the "item-count/paired lists"

technique (Miller 1983), is completely unobtrusive. Respondents

in one subsample are shown a short list of behavior items (including the deviant behavior) and are asked how many of these

categories apply to them; respondents in the other subsample are

treated in exactly the same way, except that the deviant item is

omitted from the list.

The nominative technique, described in the following section, is a

rather different approach, for it involves reporting not on one's

own behavior, but the behavior of other persons that one knows.

This completely avoids the issue of whether or not the respondent

has engaged in the behavior. The underlying premise is that respondents will be more truthful in reporting the deviance of

anonymous others than they are in reporting their own deviant

behavior. The nominative approach also has a unique potential for

increasing coverage of deviant population groups.

THE NOMINATIVE METHOD: STATISTICAL LOGIC

The nominative technique is a variant of the multiplicity methods

of survey research pioneered by Monroe Sirken, originally for the

purpose of studying rare diseases and conditions (Sirken 1975).

These multiplicity methods require the respondents to serve as

informants reporting on the illness, behavior, or experience of

(anonymous) other persons, such as their friends or relatives.

106
As applied to socially acceptable illnesses, the multiplicity approach asks about the respondent's own experience as well as that

of close relatives, e.g., brothers and sisters. Knowing the number of brothers and sisters that a respondents has is the equivalent of knowing the number of siblings that can report each of the

other's illnesses; this allows the investigator to "correct" for

the duplication of reports of a single case, as occurs, for

example, when two siblings both report that a third has a rare

disease or condition.

In applying multiplicity methods to socially unacceptable or

deviant behaviors, it is preferable to avoid requiring reports of

the respondent's own behavior as well as that of close relatives.

Instead, the focus in studies of deviance has been on the anonymous friends, or close friends, of the respondent. The version

that was first applied to deviant behavior asked respondents, for

example, "What proportion of your friends have used heroin?" In

order to achieve a prevalence estimate, the investigator simply

averaged the percentage of user-friends across all respondents.

(Sudman et al. 1977). This approach assumes that deviants and

nondeviants have equal numbers of friends--and that persons who

know deviants have the same number of friends as persons who do

not know deviants. It also assumes that the respondent knows

whether each has engaged in the deviant behavior.

The newer version of this approach, which has been termed the

"nominative technique," is a statistically defensible method of

obtaining indirect estimates of deviant behavior. The technique

was developed expressly for the study of heroin use, under

contract to the National Institute on Drug Abuse. With this

technique, the reference group is close friends.

The nominative technique is based upon the following proposition:

If each member of the population reports the number (0, 1, 2,

3...) of close friends who have used heroin, then, with appropriate

correction for duplication, it is possible to derive an accurate

count of the number of heroin users in the population.

The two key items in the nominative question series are (in

essence):

A. So far as you know, how many of your close friends

have ever used heroin? Just count the ones that

you know for sure have used it.

Then for each of the interviewee's heroin-using close friends:

B. How many of this person's other close friends (besides yourself) know that he (or she) has used

heroin?

107
The information gathered in Question B allows an appropriate

"weight" (or correction for duplication) to be attached to each

report of a heroin-using friend. This weight is the inverse of

the total number of persons in the population who are eligible to

report that particular heroin user.

Specifically, the fractional weight that must be attached to the

j

th interviewee's report of the ith heroin user is:

1/(1+Bij)

This weight corrects for the fact some heroin users will be reported by two, three, four, or more close friends. To grasp this

point, suppose for a moment that I am a heroin user in a population where a nominative census is being taken. Further, suppose

that 10 of my close friends--also in this population--know that I

have used heroin, and that each of them reports this in the

interview. This would add up to a duplicated count of 10 heroin

users instead of 1.

But now suppose that each of my friends tells the interviewer that

nine others also know about my heroin use. Then the investigator

would realize that each of the reports of my heroin use that would

be included in the data would really be 1 of 10 reports of the

same thing, and so each separate report should received a weight

of 1/10. In other words, for each report of my heroin use, the

investigator should count one-tenth of a heroin user. Then, when

all 10 of these reports are counted together, they sum up to a

total of 1 heroin user, which is what we started with. Taking one

more example, suppose that only two people know about your heroin

use. If each of these reports is counted as one-half of a heroin

user, then the total of the two reports is simply 1 heroin user,

which again is what we started with. And, of course, what can be

done for each individual user can be done for all users in the

population, so that the grand total of weighted counts of users

would be the same as the total number of users in the population.

The algebra of the nominative count in a population census is presented in appendix 1 of this paper. As described in a subsequent

section and detailed in appendix 2, sample estimates are easily

obtained.

THE NOMINATIVE TECHNIQUE: FIELD EXPERIENCE

Are people willing and able to provide answers to Question A and

Question B with some degree of accuracy?

Prior to pretesting and field work, it was anticipated that Question A might be somewhat sensitive, that respondents might not

want to talk about the heroin use of their close friends. Pretests revealed that people were quite willing, perhaps even eager,

108
to talk about their friends' heroin use (Fishburne 1980). Moreover, when respondents were asked how they knew that their close

friends had used heroin, they usually said that the friend himself

(or herself) had told them. Sometimes, they said they had actually seen the person take heroin. In at least one case, the

respondent said that he and his friend had "shot up" together.

Roughly 10 percent of the total (nominative-form) sample in the

1977, 1979, and 1982 National Surveys on Drug Abuse reported that

one or more of their close friends had used heroin.

Quantitative analyses of the National Survey data revealed that

drug users were much more likely than nonusers to report having

close friend(s) who had used heroin (Fishburne 1980). Thus, there

have been several indications of valid responses to Question A.

Question B (How many of a particular heroin user's other close

friends also know about his/her heroin use) is much more

difficult. In each of the three surveys, 15 percent to 20 percent

of the respondents who had a close-friend user could not estimate

the number of that person's other close friends who also know.

For this group of respondents, imputation procedures have been

employed in data analysis. Specifically, based on the notion that

all users known by a single respondent probably know each other,

the rule for cases where Question B is not answered is to set the

answer to Question B equal to the answer to Question A minus one.

(The user himself/herself is subtracted out, since the counting

rules for the nominative estimate do not include self-reports.)

Applying this conservative rule ought not distort the data, but

the fact that so many persons were unable to provide an estimate

of B casts doubt on the accuracy of the responses that were given.

THE PRACTICAL VERSION OF THE NOMINATIVE TECHNIQUE

The foregoing discussion has emphasized that each heroin user in

the population may be known to several interviewees. The other

side of the coin is that any one respondent may report several

heroin users. From a practical point of view, it can be awkward

to ask Question B separately for each heroin user that a particular respondent knows. Of course, many respondents don't know any

users at all and many others would know only one (Miller 1983).

But for those who do know two or more heroin users, Question B

must be asked separately for each user. For example, if a respondent tells the interviewee that he (she) knows five heroin

users, then the interviewer must ask first for the first person,

how many of his other close friends also about his heroin use;

then, for the second person, how many of his friends know; and so

forth for all five users. Moreover, it is necessary to ask about

the age, sex, and recency of heroin use for each heroin user, so

that estimates can be derived for age-sex subgroups and for current use. Obviously, this can become burdensome, especially if

imbedded in an already lengthy interview.

109
For practical purposes, an optional version of the nominative

question series was developed for use in the National Surveys on

Drug Abuse. This limits the number of nominees that are followed

up with Question B and the questions on sex, age, and recency of

use. Specifically, in the National Survey, only one nominee (per

respondent) is referenced in the followup questions. Appendix 3

of this paper presents copies of the exact questions and procedures that were used in the 1982 National Survey. Briefly, whenever a respondent reported knowing two or more heroin users, one

of these persons was randomly selected in the following way.

Interviewers gave respondents a folded card, so they could list

the initials of their close friends who had used heroin--and

number these persons. The interviewer then consulted a random

number table and told the respondent that the rest of the questions would be about "Person Number ." Very little difficulty

has been encountered in implementing these procedures in the

field. In the first (1977) National Survey to include the nominative question series, a few respondents objected to writing their

friends' initials on the card; in subsequent surveys, such persons

were urged to number their friends "in their heads."

Statistically, the fact that a single nominee is selected from all

those mentioned in Question A means that this single user--this

"random one" --must stand for the total number of users the respondent knows. For example, if a respondent knows three heroin

users (Aj = 3) and we randomly select one of them, then that one

must stand for all three of them. This is accomplished in data

analysis simply by "weighting up" the "random one." In other

words, for the jth interviewee, the random one must be counted Aj

times. This procedure is analogous to the conventional practice

of randomly selecting one adult respondent per household and then

weighting up that lone respondent to stand for the entire group of

adult residents in that household.

Thus, in the practical version of the nominative technique, each

interviewee or respondent has either a "0" score for no users

known or a "1" score for a report of a particular heroin user

known. This particular user is either the only user that the

respondent knows or is the "random one" selected from all users

known to that respondent. This particular user, who has a number

of characteristics such as sex, age, and recency of use, bears two

data weights:

Aj which "weights up" the j

th respondent's

report of this heroin user to stand for the

total number of users that he or she has

mentioned.

1/(1+Bj) which is the correction for duplicate reports

of the same user described above.

110
The combined weight factor for each score of "1" (i.e., for each

respondent who knows a heroin user) is:

Aj (1/(1+Bj))

(Note: The i subscript has been dropped, since in this version

there is only one user referenced per respondent.)

Using this combined weight factor--and summing reports across all

respondents in a complete census of a given population--yields an

estimate of the total number of heroin users in that population.

OBTAINING NOMINATIVE PREVALENCE ESTIMATES FOR SUBGROUPS AND

SAMPLES

Once the total nominative count of heroin users for a population

is obtained, dividing that figure by the total population size

produces a prevalence rate or percentage. For a subgroup, such as

females, the analyst would simply sum those reports in which the

random heroin user is a female--across both male and female

respondents (for it is the characteristics of the nominated users

that we are interested in, not the characteristics of the respondents who are nominating them.) Once a nominative count of all

female users in the population has been obtained, this number may

be divided by the total number of females in the population to

produce a prevalence rate.

To this point, the statistical discussion has assumed a complete

census of the population. Actually, in most research (as, of

course, in the National Surveys in which the nominative question

series has been inserted), sample data are used to estimate population prevalence figures. When using nominative data from a

sample of respondents, the analyst would simply take the sample

count of nominated heroin users, weighting each report with the

combined weight factor shown on the previous page. Then, to obtain a population projection, an inflation factor would be used to

raise the nominative sample count to the population level. The

inflation factor is simply N/n where N refers to the population

size and n is the sample size; this is the inverse of the sampling

fraction. This procedure is shown in appendix 2 of this paper.

For a prevalence estimate based on sample data, the analyst simply

divides the total nominative count for the sample by the total

sample size (appendix 2). When deriving a subgroup prevalence

estimate from nominative sample data, one would simply count all

(weighted) reports in which the nominee is a member of the relevant subgroup, e.g., is a female; then, this count would be

divided by the number of respondents in the sample who are members

of that subgroup, e.g., the number of female respondents (appendix

2). Throughout, the usual data weights used for conventional

analyses of the sample data would be applied.

111
The variance of a sample-based nominative estimate depends upon

variability in the nominative weight, i.e., Aj(1/(1+Bj)). Thus,

it is not possible to express the variance associated with the

nominative estimator as a function of the size of the variance of

a corresponding direct estimator. Empirical variance estimates

have been calculated via random subsampling, using data from the

pilot test of the nominative technique (in application to heroin

estimation) in the 1977 National Survey on Drug Abuse. Cisin

(1980) has reported these nominative variances in comparison to

comparable direct-report variances. The preliminary results

indicate that, as applied to the estimation of heroin use, the

nominative technique does not, in general, carry a high variance

cost. However, given the practical version of the nominative

approach that has been used in the National Survey (followup

questions for only one nominee per respondent), even a few very

high Aj values could raise the variance substantially. In order

to discourage overreporting of heroin-using close friends, the

nominative question series used in the 1979 and 1982 surveys features the introductory question: "How many close friends do you

have?" This tends to limit the number of close-friend heroinusers that will be subsequently reported. In general, the

nominative approach carries much lower variance costs than the

methods of indirect self-reporting described in an earlier section

of this paper.

NOMINATIVE HEROIN ESTIMATES FROM THE 1982 NATIONAL SURVEY AND TWO

PREVIOUS NATIONAL SURVEYS

Table 1 shows nominative and self-report estimates of lifetime

heroin prevalence from the 1982 National Survey on Drug Abuse.

Clearly, the nominative estimates are higher for young adults than

for other age groups, and within each age group, the estimates are

higher for males than for females. These basic patterns follow

expected distributions, based on a variety of data sources.

Moreover, table 1 indicates that for each sex-age group, the nominative estimates are higher than the corresponding self-report

estimates. Notably, for young males, the nominative estimate

indicates that 9.4 percent have at least tried heroin at some

point in their lives, whereas the self-report estimate is only 1.4

percent. These are the kinds of patterns that were expected if

people were, in fact, more likely to tell the truth about their

friends' heroin use than about their own heroin use.

112
TABLE 1. Nominative and self-report estimates from the 1982

National Survey on Drug Abuse: Lifetime prevalence of

heroin use by sex and age

Population by

(Age) and Sex

Estimate of Ever Used Heroin

by Percent

Nominative Self-report

Youth (12-17)

Males 4.5 0.6

FemaIes 1.8 *

Young Adults (18-25)

Males 9.4 1.4

FemaIes 5.6 0.9

Older Adults (26+)

Males

FemaIes

2.7

0.9

2.0

*

*Less than one-half of one percent; estimate not shown.

Note: Self-reports of heroin use are based on the entire 1982 survey sample of

1,581 youth; 1,283 young adults; and 2,750 older adults. Nominative estimates are based on the answers of the 2,852 respondents who were assigned

the N-form questionnaire, which included the nominative question series;

211 of these respondents reported having one or more close friends who had

used heroin. Of these 211 reports, 47 referred to a youth nominee, 85 to a

young adult nominee, and 79 to an older adult nominee.

Table 2 provides nominative estimates of past-year and past-month

heroin use for three age groups. Self-report data are not shown,

because estimates were less than one-half of one percent in all

age categories. Thus, the nominative technique produces higher

estimates for current use than does the conventional self-report

method. Population projections for current heroin users were not

derived from self-reports in the 1982 National Survey, because of

the low prevalence of self-reported current use. Population

projections based on the nominative technique are, however, shown

in table 2. The total population estimate for past-year users is

nearly two million.

113
TABLE 2. Nominative estimates from the 1982 National Survey on

Drug Abuse: Past-year and past-month prevalence of

heroin use and population projections for three age

groups

Percent Used Heroin In:

Past Year Past Month

Number Used

Heroin In

Past Year

Youth 12-17 2.0 1.0 450,000

Young adults 18-25 2.6 1.0 840,000

Older adults 26+ 0.5 * 590,000

Total number used 1,880,000

*Less than one-half of one percent; estimate not shown.

Note: Number of respondents providing self-report and nominative data Is given in

table 1 on the previous page. The number of nominative reports referencing

a past-year heroin user was 29 for youth nominees, 43 for young adult

nominees, and 21 for older-adult nominees; the corresponding flgures for

past-month use are 18, 19, and 9, respectively.

Table 3, on the following page, shows trends in nominative estimates of the lifetime prevalence of heroin use, from the 1977,

1979, and 1982 National Surveys on Drug Abuse. Self-report trends

are also shown for purposes of comparison.

Perhaps the most striking feature of table 3 is that, consistently, across all 3 survey years, nominative estimates have been

higher than corresponding self-report estimates.

Overall, the nominative data (part A of table 3) suggest a downward trend in the percentage of the population that has ever tried

heroin. The downward trend apparently began with a slight decrease between 1977 and 1979 and then continued with a somewhat

more marked decrease between the 1979 and 1982 studies. The

apparent decrease in older adult's heroin use, as measured by the

nominative method, should be viewed with caution. Based on

patterns of illicit drug use in various age groups during recent

years, the former young adults who recently moved into the older

adult category are more likely to have tried illicit drugs than

are elderly persons who "drop out" of the population because of

death or institutionalization in nursing homes. The only alternative explanation would be untimely deaths or institutionalization

of persons who tried heroin.

114
TABLE 3. Nominative and self-report trends, based on the 1977,

1979, and 1982 National Surveys on Drug Abuse: Lifetime prevalence of heroin use in three age groups

A. Nominative Trends: Percent Ever Used Heroin

1977 1979 1982

Youth (12-17) 8.3 7.5 3.1

Young Adults (18-25) 10.7 9.1 7.3

Older Adults (26+) 4.0 3.6 1.7

B. Self-report Trends: Percent Ever Used Heroin

1977 1979 1982

Youth (12-17) 1.1 0.5 *

Young Adults (18-25) 3.6 3.5 1.2

Older Adults (26+) 0.8 1.0 1.1

*Less than one-half of one percent; estimate not shown.

Note: For numbers of respondents on which self-report and nominative estimates

from the 1977 and 1979 surveys are based, see Miller (1983).

Part B of table 3 also indicates that self-reports of heroin use

based on the same three surveys also suggest a downward trend in

heroin prevalence among young persons--but not among older adults.

For reasons just stated, the self-report for older adults seems

more reasonable than the nominative trends for this age group.

The possible anomaly in older-adult ever-use trends, based on the

nominative method, serves as a reminder that no strict tests of

validation have been performed for the nominative approach.

SUMMARY

Over the years, nominative estimates of heroin prevalence have

been consistently higher than self-reports of heroin use. During

this time, nominative data have generally followed mainstream

patterns of drug use: nominative estimates for young adults and

for males are higher than nominative estimates for older persons,

youth, and females; moreover, the recent downward trends in drug

use have been replicated by the nominative heroin data. Thus, the

overall picture presented by the nominative data--similar patterns

but higher levels of prevalence-- seems to support the validity of

the new approach.

Nevertheless, considerable caution should be exercised in interpreting nominative data. This is chiefly because a substantial

minority of nominators cannot report the number of other close

friends of the heroin user who also "know." While missing data

has been handled by a conservative imputation rule, the fact that

115
so many persons are unable to provide an answer to this key question casts doubt on the accuracy of the answers that were given.

In fact, the nominative approach might tend to produce overestimates, because of the potential for undercounts of the numbers

of others who "know."

Additional tests of validity should be performed, such as application of the nominative approach to nonsensitive behaviors or

minimally sensitive behaviors, such as marijuana use or perhaps

cocaine use. Certainly, the overall validity of the nominative

heroin data would be supported if in future surveys new nominative

heroin estimates for relatively unstigmatized forms of drug use

proved to be similar to self-reported levels of use, thus pointing

to the unique difference in estimates that might be observed for

heroin.

Finally, in interpreting the heroin estimates presented here, it

should be remembered that both the nominative and self-report

estimates refer to heroin use in the household population of the

United States. Thus, many heroin addicts and other users who

reside in various unconventional living arrangements would not be

included in the counts presented here. Among the excluded groups

are transients residing in rooming houses or "crashing" in the

home of one "friend" after another or who are incarcerated in

jails or confined to residential drug treatment centers. This is

a caution for interpreting the estimates presented in this paper,

not a criticism of the nominative technique itself. In fact,

since many persons in the household population undoubtedly have

friends who live in nonconventional residences, the nominative

question series could be revised to include respondent reports of

heroin users who presently reside in group quarters or who lack a

fixed address.

To begin to reap the potential benefits of the nominative

approach, further research is needed. First, investigations into

the validity of nominative estimates are necessary, if these data

are to be interpreted with confidence. Second, preliminary studies of revised question wording that would extend the reference

population beyond the household population would greatly enhance

the utility and unique advantages of this approach.

REFERENCES

Boruch, R.F., and Cecil, J.S. Method for Assuring Privacy and

Confidentiality of Social Research Data. Philadelphia:-

University of Pennsylvania Press, 1979. 312 pp.

Cisin, I.H. The variance of prevalence estimates. In:

Rittenhouse, J.D., ed. Developmental Papers: Attempts to

Improve the Measurement of Heroin Use. Unpublished report.

Rockville, Md.: National Institute on Drug Abuse, Rockville,

Md., 1980. pp. 65-72.

116
Cisin, I.H., and Parry, H.J. Sensitivity of survey techniques in

measuring illicit drug use. In: Rittenhouse, J.D., ed.

Developmental Papers: Attempts to Improve the Measurement of

Heroin Use. Unpublished report. Rockville, Md.: National

Institute on Drug Abuse, 1980. pp. 3-46.

Fishburne, Patricia M. Survey techniques for studying threatening

topics: A case study on the use of heroin. Doctoral

Dissertation, New York University, 1980.

Folsom, R.E.; Greenberg, B.G.; Horvitz, D.G.; and Abernathy,

J.R. The two alternate questions randomized response model for

human surveys. J Am Stat ASSOC, 68:525-530, 1973.

Miller, J.D. Complexities of the randomized response solution.

(Comment on Tracy and Fox, ASR, April, 1981.) Am Socil Rev,

46:928-930, 1981.

Miller, J.D. A new survey technique for studying deviant

behavior. Doctoral Dissertation, The George Washington

University, 1984.

Miller, J.D. The nominative technique: Method and heroin

estimates from the 1982 National Survey on Drug Abuse.

Unpublished report. National Institute on Drug Abuse,

Rockville, Md., October, 1983.

Miller, J.D., and Cisin, I.H. Measuring the hidden prevalence of

heroin use. In: Rittenhouse, J.D., ed. Developmental

Papers: Attempts to Improve the Measurement of Heroin Use.

Unpublished report. National Institute on Drug Abuse,

Rockville, Md., 1980. pp. 74-170.

Miller, J.D.; Cisin, I.H.; Gardner-Keaton, H.; Wirtz, P.W.;

Abelson, H.I.; and Fishburne, P.M. National Survey on Drug

Abuse: Main Findings 1982. DHHS Pub. No. (ADM) 83-1263

Washington, D.C.: Supt. of DOCS., U.S. Govt. Print. Off., 1983.

141 pp.

Shimizu, I.M., and Bonham, G.S. Randomized response technique in

a national survey. J Am Stat ASSOC, 73:35-39, 1978.

Sirken. M.G. Network surveys of rare and sensitive conditions.

In: Advances in Health Survey Research Methods. National

Center on Health Statistics Research Proceedings Series. 1975.

pp. 31-32.

Sudman, S.; Blair, E.; Bradburn, N.M.; and Stocking, C. Estimates

of threatening behavior based on reports of friends. Public

Opin Q, 41:261-264, 1977.

Warner, S.L. Randomized response: A survey technique for

eliminating evasive answer bias. J Am Stat ASSOC, 60:63-69,

1965.

Warner, S.L. The linear randomized response model. J Am Stat

ASSOC, 60:63-69, 1971.

Wiseman, F.; Moriarty, M.; and Schafer, M. Estimating public

opinion with the randomized response mode. Public Opin Q,

39:507-513, 1975-76.

117
AUTHOR

Judith Droitcour Miller, Ph.D.

Social Research Group

The George Washington University

2000 L Street Suite 305

Washington, D.C. 20036

118
APPENDIX 1

The statistical logic of the nominative count is presented

below. The notation to be used is as follows:

X = a sensitive attribute, such as heroin use; Xi = 1 if the ith

person in the population possesses the attribute (e.g., has

used heroin); Xi = 0 if the ith person does not possess the

attribute.

TX= the total number of persons in the population who possess the

attribute; i.e., TX= ΣXi.

F = an attribute involving an interviewee's ability to report that

another person possesses attribute X. Fij refers to the j

th

interviewee's report of the ith person is a heroin use. Thus,

Fij= 0 if the j

th person does report that the ith person

is a heroin user. Fij = 1 if the jth person does report that

the ith person is a user. (Note: i j; i.e., no selfreports are included.)

N N

Σ Σ Fij =

i j

the total number of reports that persons possess

attribute X. (Obviously, if uncorrected, this would

be an overcount--unless each user is known to only one

close friend.)

N

ΣFij =

j= 1

the number of times the ith person is reported as a

heroin user or the number of interviewees who report

this particular heroin user.

N

Σ Fij =

i=1

the number of "nominees" (possessors of attribute X)

reported by the jth person. In other words, this term

represents a sum across heroin-user nominees (i's) for

the jth nominator.

The total number of heroin users in the population can be

expressed as follows:

Since:

is, each user in the population,

times the number of others who report him, summed across all

users, totals to the total number of reports of heroin users.

119
Therefore:

Thus, in the population, the total number of persons possessing

attribute X (i.e., TX) is equal to: the sum of the nominations

(reports of other persons possessing attribute X), where each

individual report of a user is divided by (weight by the inverse

of) the number of persons reporting that particular user. In

other words, each report of a user is weighted by the inverse of

the number of persons reporting that particular heroin user.

120
APPENDIX 2

In a survey in which a sample of size n is drawn from a population

of size N, the sampling fraction is n/N. Any direct count of

persons in the sample who possess a certain characteristic, i.e.:

where Cj = 1 if the jth person possesses the

characteristic and otherwise is equal to 0,

can be raised or "projected" to a population value via applying

the inverse of the sampling fraction.

The same reasoning holds for the nomin tive count. Letting Zj =

the weighted nominative score of the j

th respondent:

In order to obtain a prevalence estimate based on a nominative

count, one could divide the population projection by the total

population size, i.e.:

However, this expression can be reduced as follows:

Thus, the mean nominative score for the entire sample can be used

as an unbiased estimate of heroin prevalence in the population

from whih sample was drawn.

121
APPENDIX 3

CLOSE FRIENDS (FORM N ONLY)

39. Now, we would like you to think about people you know who

live in regular households. Please do not include those

people who live in a college dormitory, on a military base,

jail, in a drug rehabilitation center, or have no definite

address. Ready?

Most of us know many people. But, usually only some of

these, if any, are people that we consider to be close

friends. About how many close friends would you say that you

have? Remember, we are only interested in those close

friends who live in regular households.

NUMBER OF CLOSE FRIENDS ( ACCEPT ONLY

LIVING IN REGULAR HOUSEHOLDS A NUMBER)

342-

343

0 NO CLOSE FRIENDS LIVING IN SKIP TO PAGE 34

REGULAR HOUSEHOLDS

40. This next question is about your (INSERT NUMBER

FROM Q. 39) close friends who live in regular households.

Keep the names of these people to yourself. We want to know

about them, but we do not want to know who they are.

About how many of these close friends can you say for sure

have ever used heroin? We want to know about them, but we do

not want to know who they are, because we are going to ask

you about their drug use.

NUMBER OF CLOSE FRIENDS (ACCEPT ONLY

LIVING IN REGULAR HOUSEHOLDS A NUMBER) 344-

WHO EVER USED HEROIN 345

0 NO CLOSE FRIENDS LIVING IN

REGULAR HOUSEHOLDS WHO EVER -- SKIP TO PAGE 34

USED HEROIN

INTERVIEWER: IF RESPONDENT HAS ONLY ONE CLOSE FRIEND WHO HAS USED

HEROIN, GO TO Q. 41, TOP OF PAGE 33. IF MORE THAN

ONE FRIEND, GO TO TOP OF NEXT PAGE.

122
CLOSE FRIENDS (FORM N ONLY)

32

INTERVIEWER: IF MORE ONE THAN ONE CLOSE FRIEND WHO USED HEROIN,

READ THE FOLLOWING:

GIVE RESPONDENT A SMALL BLANK WHITE CARD

On the card I gave you, I would like you to list the initials of

your (INSERT FROM Q 40) close friends who live in regular

households who you know for sure have ever used heroin. No one

but you will ever see these initials. (WAIT UNTIL RESPONDENT

MAKES LIST. IF RESPONDENT REFUSES TO USE CARD, HE/SHE MAY DO THIS

PART IN HIS/HER HEAD.)

Now, please number the people on your list. Put the number "one"

next to the initials of the first person on your list. Then put

the number "two" next to the initials of the second person on your

list, and so on until everyone on your list has a different

number. (WAIT UNTIL RESPONDENT FINISHES NUMBERING.)

I only want to ask you about one of the persons on your list.

(INTERVIEWER: USE TABLE BELOW TO SELECT CORRECT INDIVIDUAL.)

INTERVIEWER: CIRCLE NUMBER OF PERSON YOU ARE

GOING TO ASK ABOUT. THAT IS THE ONLY PERSON TO

ASK ABOUT. NO SUBSTITUTES.

IF THE NUMBER ASK ABOUT

OF CLOSE FRIENDS PERSON

IN Q. 40 IS: NUMBER

2

3

4 2

5 1

6+ 6

2

3

Please draw a circle around the

initials of the person number

(INSERT FROM TABLE); the

remaining questions will be about

this person.

Rotation 6 of 6

123
41.

42.

43.

44.

45.

46.

Is this person male or

female?

How old is this person now?

Is he/she 12-17 years

old, 18-25 years old, 26-34

years old, or more than 34

years old? 8 NOT SURE

As far as you know, how long

ago was the first time

this person tried heroin?

As far as you know, when was

the most recent time this

person used heroin?

CLOSE FRIENDS (FORM N ONLY)

3 3

1 MALE

2 FEMALE 346

1 12-17 YEARS OLD

2 18-25 YEARS OLD

3 26-34 YEARS OLD 347

4 35+ YEARS OLD

1 WITHIN THE PAST

MONTH

2 WITHIN THE PAST

YEAR 348

3 MORE THAN A YEAR

AGO

8 NOT SURE

1 WITHIN THE PAST

MONTH

2 WITHIN THE PAST

YEAR 349

3 MORE THAN A YEAR

AGO

8 NOT SURE

There are many different ways of knowing that another person

has used heroin. Please tell me how you know for sure that

this person has used heroin. (WRITE EXACTLY WHAT RESPONDENT

SAYS. IF RESPONDENT SAYS "SOMEONE ELSE TOLD ME" OR

"EVERYBODY KNOWS," RECORD VERBATIM, THEN PROBE: How do they

know?)

350

Now, we would like you to think about this person's other

close friends, besides yourself.

As far as you know, how many of this person's other close

friends, besides yourself, know for sure that this person has

ever used heroin? Remember, we are only interested in

his/her close friends who live in regular households. (IF

RESPONDENT FINDS QUESTION HARD TO ANSWER OR SAYS "ALL" OR

"MANY OF HIS/HER CLOSE FRIENDS," SAY: We need to have a

number; please give us your best estimate.)

NUMBER OF CLOSE FRIENDS LIVING 352-

IN REGULAR HOUSEHOLDS WHO KNOW 353

NO OTHER CLOSE FRIENDS LIVING IN

REGULAR HOUSEHOLDS WHO KNOW

COULD NOT MAKE AN ESTIMATE

124
HEROIN INCIDENCE: A TREND

COMPARISON BETWEEN

NATIONAL HOUSEHOLD SURVEY

DATA AND INDICATOR DATA

Raquel A. Crider

INTRODUCTION

A commonly held belief is that respondents in a face-to-face

survey will underreport the extent of their involvement in deviant

behavior (Sirkin 1975, Fishburn 1980, Miller this volume). To

test this assumption, a time series of self-reported year of first

use of heroin was compared to a time series of heroin indicator

data, such as heroin-related emergencies and deaths, treatment

admissions, hepatitis B cases, etc. Although there is a general

belief that self-reported heroin use would be underreported, no

test of that assumption has been conducted comparing the time

series trend of heroin incidence based on survey data to the time

series trends of heroin indicators.

PROCEDURE

Seven drug indicators are compared: 1) the number of heroin

initiates based on the face-to-face National Household Survey on

Drug Abuse (National Survey); 2) the number of heroin initiates

voluntarily entering a panel of consistently reporting federally

funded treatment programs (Client Oriented Data Acquisition

Process, CODAP) for the first time by the year of first use;

3) the residual number of hepatitis B cases per year; 4) the

percent of high school seniors ever using heroin (Johnston et al.

1982); 5) the number of heroin-related emergency room visits

reported to the Drug Abuse Warning Network (DAWN); 6) the number

of heroin-related deaths reported to DAWN; and 7) the average

street level heroin purity (DEA 1984).

The analytic technique used in this paper produces numbers to

examine the relationship between the National Household Survey

trends and other indicators of new heroin users. The generated

numbers are not to be construed as actual numbers of new heroin

users in any particular year.

125
The household survey year-of-first-use data, the number of hepatitis B cases, and the number of heroin users in high school

(recent initiates based on their age) are considered incidence

indicators. The number of emergency room visits and heroinrelated deaths are considered prevalence indicators. Heroin purity is not considered to be either an incidence or prevalence

indicator, but is presented as a correlate of heroin incidence and

prevalence.

Data from the 1977, 1979, and 1982 National Household Surveys on

Drug Abuse were pooled for the analysis presented in this report.

The total pooled sample consisted of 17,000 interviews. A total

of 274 heroin users were found (1.6 percent of the pooled sample).

The data from the national surveys are presented in 2-year moving

averages for purposes of data smoothing and all discussion is

based on these averages. Additional data smoothing is based on

spline interpolation (Integrated Software Systems Corporation

1983).

While the purpose of this paper is to show a relationship between

the number of self-reported heroin users over time and the number

of hepatitis B cases, the number of treated initiates, and other

heroin indicators, the purpose is not to make a statement about

the number of heroin initiates in any particular year. With

17,000 interviews upon which to base the trend data and a total of

274 heroin users, it is thought that the trends over time can be

interpreted and a correspondence between epidemic periods in the

various indicators can be observed.

The epidemic periods discussed are those reported in the Heroin

Work Group Report (NIDA 1984), i.e., the 1968-72 period (the early

1970s epidemic), the 1974-76 period (the mid-1970s epidemic), and

the increase starting in approximately 1979-80 (the recent

epidemic). For purposes of this report, an epidemic period is

defined as those years in which several heroin indicators simultaneously increased and remained at elevated levels for 2 or more

years.

Household Survey-Based Incidence Curve

Each of the three national household survevs contain items relating to the year of first heroin use and the age of first heroin

use. The "age of first use" (AFU) item was used rather than the

"year of first use" (YFU) item because it is believed that an age

at which an event occurred is easier to remember than the year in

which it occurred.

The AFU was converted to YFU by combining AFU with the year of the

survey (YOS) and the age at the survey (AAS) using the equation:

YFU=YOS-AAS+AFU.

Since the data were pooled over three surveys, the data were first

weighted based on the survey sampling plan. Then each observation

126
was weighted in accordance with the population represented. The

population projections for 1977, 1979, and 1982 were then

averaged. This pooling procedure takes into account the aging of

the population from one survey to another and the differing sample

sizes in each of the surveys, but does not attempt to minimize the

variance estimates since sample variance estimates were approximately equal.

For the years of first use in 1978 through 1982, weighting factors

were needed, since the 1977 survey could only include respondents

who began heroin use prior to 1978, while the 1979 and 1982

surveys could only include respondents for whom heroin initiation

preceded or occurred during those survey years. Thus, weighting

factors of 1.5 and 3.0 were used for recent initiates in the 1979

and 1982 surveys respectively to adjust respondent rates to the

1977 base. The factor of 1.5 serves to weight two surveys as if

they were three surveys for recent initiates. The factor 3.0

weights one survey as if it were three surveys for initiates

starting after 1979.

Two-year moving averages were used throughout the period of analysis, i.e., for 1965 through 1980. The greatest variance about the

P-year moving mean occurred for years prior to 1973. The variance

is thought to be due primarily to the difficulty of remembering

the age of first use for an event occurring several years prior to

the surveys.

Treatment-Based Incidence Curve

The year-of-first-use distributions from treatment admissions to

federally-funded treatment programs are available through CODAP

(1977-81 data were used in this report). The number of new users

per year entering treatment for the first time is obtained from

the year-of-first-use question on the treatment admission form.

These data, when tabulated by year of first use, produce an incidence curve. Because recent initiates will wait for some time

before entering treatment, a correction to the treatment data is

needed. This correction procedure is based on the distribution of

the lag time between first use and treatment for 1977 admissions

(NIDA 1984b).

A recurring question is "What proportion of all heroin users enter

treatment?" To address this question, a regression analysis was

applied between the number of heroin initiates in households (2-

year moving averages) and the 2-year moving average of the number

of heroin initiates in treatment by year of first use, fitting the

model:

Number of initiates in number of initiates

households starting in = BO + B1 x in treatment starting in

a particular year a particular year

127
where BO is the number of initiates in households when the number

of treatment initiates is zero. The B1 is the slope of the

regression line. This slope is the factor by which the number of

treated initiates would be multiplied to obtain the number of

household initiates.

Hepatitis-Based Incidence Curve

The number of hepatitis B cases for 1975-83 was obtained from the

Centers for Disease Control. Hepatitis B is often contracted by

new users of heroin since needle-sharing frequently occurs. After

contracting the disease, the second occurrence is rare. Thus, the

number of hepatitis B cases may serve as an indicator of the

number of heroin initiates (Schreeder 1978).

The number of hepatitis B cases per year has been reported by the

Centers for Disease Control since 1966. A trend towards an increasing number of reported cases is thought to be due to several

factors, among which are the improved reporting of hepatitis B

cases, the increasing number of male homosexuals (a group thought

to be especially susceptible to hepatitis B), and the linearly

increasing trend of the number of intravenous cocaine users (Kozel

et al. 1982).

Assuming that the improved reporting and the increase in the

number of male homosexuals with hepatitis B and in the number of

intravenous cocaine users follow a linear temporal trend, that

trend can be removed. The residuals are thought to represent the

number of hepatitis B cases associated with intravenous heroin

use.

High School Senior Incidence Curve

High school senior heroin use is considered to be an incidence

indicator because of the proximity of the year of first use to the

year of the survey. Drug abuse treatment admission data from

1977-81 show that the mean age at first use for heroin is age 18

and that most users start between the ages of 16 and 20 (Johnston

et al. 1982). Thus, a high school senior having used heroin is

likely to have begun within a few years prior to the high school

survey. Because of the close proximity of the year of first use

to the date of the survey, the distribution of lifetime prevalence

vs. the year of the survey can be considered an incidence curve

with a 0 to 2-year lag. All high school data are taken from

Johnston et al. 1982.

Heroin-Related Emergency Room Visits Reported to DAWN

The number of heroin-related emergency room visits is based on

reports submitted to DAWN through 1982. Because DAWN is not a

saturated sample of all hospitals in all cities in the United

States, but is based on a percentage of the hospitals in 26

metropolitan areas plus a sample in other areas, a national

projection is used (Hinkley and Greenwood 1982).

128
Heroin-Related Deaths Reported to DAWN

The number of heroin-related deaths reported to DAWN is also presented as a prevalence indicator. No attempt is made to project

the number of heroin-related deaths to the nation, based on the

number of cases occurring in the 26 DAWN metropolitan areas,

because DAWN does not have a sample of medical examiners outside

the major metropolitan areas.

Average Heroin purity

Heroin purity as reported by the Drug Enforcement Administration

(DEA) is found to be correlated with the number of emergencies and

deaths related to heroin. Purity reported in the System to

Retrieve Information from Drug Evidence (STRIDE) through 1978 and

to the Domestic Monitor Program after 1978 was used.

FINDINGS AND DISCUSSION

The number of cases of heroin use combined from the three National

Surveys on Drug Abuse is shown in table 1 by year of first use,

1965 through 1980. While the number of heroin users in any one

survey is small, the pooled data produce frequencies large enough

to establish trends. These data are not used to make estimates of

the number of heroin initiates in any particular year, but are

used to show a changing pattern over a several-year period.

TABLE 1. Actual number of cases in pooled National Surveys on

Drug Abuse (1977, 1979, 1982) reporting heroin use in

lifetime by year of first use

Year of Number Year of Number Year of Number

First Use of cases First Use of cases First Use of cases

1965

1966

1967

1968

1969

2 1970 24 1975 24

2 1971 20 1976 28

6 1972 23 1977 21

13 1973 22 1978 20

11 1974 26 1979 7

1980 6

Figure 1 shows the population projections based on 2-year moving

averages for all 3 surveys combined. The estimated number of

initiates increased throughout the late 1960s and early 1970s from

approximately 30,000 new users per year in 1965 to an average of

approximately 180,000 new users per year between 1968 and 1972.

Based on this analytic procedure, by 1978 and 1979, the estimated

number of new cases declined to an average of approximately 80,000

per year.

129
The data in figure 1 show an elevated level in the estimated number of initiates between 1968 and 1976, with fluctuations between

those years. Other data, such as heroin purity and treatment

incidence curves, show the period between 1968 and 1976 actually

consisted of two epidemics (DEA 1984; NIDA 1984). The two

epidemic periods, i.e., the early 1970s and the mid-1970s,

corresponded to the period of relatively plentiful heroin from the

"French Connection" and Mexico. After approximately 1972, when

the French Connection was broken, and after approximately 1976,

when the Mexican poppy eradication program was fully operational,

the heroin supply was reduced. The current supply appears to be

from Southeast and Southwest Asia and Mexico, although the recent

epidemic period is not evident in the household survey data.

FIGURE 1. Two-year moving averages of the estimated number of

heroin initiates in thousands by year of first use

Trends over time appear within subcategories of age and frequency

of use. Figures 2A and 2B show the estimated number of new users

by age groups 12 to 25 years, and 26 years and older. The number

of initiates age 26 and older at the survey was highest in the

early 1970s. The findings for persons age 26 and older at the

survey (figure 2b) are consistent with the time series for treated

initiates (figure 4), which shows a predominant peak in 1969 and

smaller peaks thereafter (NIDA 1984). The average age of treated

heroin users in 1981 was over age 26 (NIDA 1981).

130
FIGURE 2A. Two-year moving averages of the estimated number of

heroin initiates in thousands by year of first use--

ages 12-25 at the survey

FIGURE 2B. Two-year moving averages of the estimated number of

heroin initiates in thousands by year of first use--

age 26 and older at the survey

The number of new users age 26 and older in households has shown a

recent leveling or increasing trend. A similar recent leveling or

increasing pattern can be seen for treated initiates by 1979 and

1980 (NIDA 1984).

The time series of young initiates shows the mid-1970s epidemic

and no other. Persons ages 12 to 25 years interviewed in a survey

conducted in 1977, 1979, or 1982 would be too young to be susceptible in the 1968 to 1972 epidemic period. Therefore, there

appears to be an interaction between age and epidemic period.

131
Data from the three pooled National Household Surveys were used to

examine the relationship between year of new, or first, use and

"type" of heroin user. Two "types" of heroin users are defined.

Persons who used heroin only one or two times in a lifetime were

considered experimenters and those who used three or more times in

a lifetime were considered continuing users. The estimated number

of new heroin users in households by year of first use is shown in

figure 3A for experimenters and in figure 3B for the continuing

users.

Figure 3A shows the 1968-72 and the 1974-76 peaks of initiates.

In 1974-76 the number of experimenters is greater than in 1968-72.

In contrast, as shown in figure 3B, the number of initiates who

became continuing users from 1968-72 is greater than from 1974-76.

FIGURE 3A. Two-year moving averages of the estimated number of

heroin initiates in thousands by year of first use--

for persons who used 1-2 times in lifetime

FIGURE 3B. Two-year moving averages of the estimated number of

heroin initiates in thousands by year of first use--

for persons who used 3 or more times in lifetime

132
Users who progress to frequent use of heroin usually do so within

a short interval, 1 to 3 years, of their first exposure to heroin

(Harding 1984). Therefore the distribution of frequency of use of

heroin in 1974-76 as shown by the National Surveys is not a

temporal artifact of the data collected.

The trends noted for epidemic periods of heroin initiation based

on household survey self-reports were compared to trends based on

periods of initiation reported by heroin users in treatment. The

number of new heroin users who continue heroin use, in households,

parallels the trends in the number of heroin users that eventually

enter treatment. The estimated number of new heroin users entering a consistently reporting panel of federally funded treatment

programs for the first time in a 5-year period by year of first

use is shown in table 2 and figure 4. Table 2 and figure 4 show

an epidemic period between 1968 and 1972. During the 1974-76

period the number of initiates was level compared to the earlier

declining trend and was not as high as the 1968-72 peak. The

larger 1968-72 peak is followed by a smaller 1974-76 epidemic

period for initiates in treatment, a pattern similar to that noted

for continuing heroin users.

The national distribution shown in figure 4 is the sum of two

different regional distributions. The first is the late '60s and

early '70s epidemic in most regions of the country and the second

is the mid '70s epidemic occurring primarily in the western United

States. The sum of the two makes the composite trend line appear

level in figure 4 in the 1974-76 period.

TABLE 2. Number of heroin users entering treatment by year of

first use (corrected for treatment lag)

Year of Number of Year of Number of Year of Number of

First Use lnitiates First Use lnitiates First Use lnitiates

1965

1966

1967

1968

1969

4,920

5,739

8,459

12,080

15,085

1970

1971

1972

1973

1974

14,413

12,584

11,081

10,864

10,818

1975

1976

1977

1978

1979

1980

10,357

8,818

7,310

6,829

7,519

8,080

SOURCE: National Institute on Drug Abuse. Client Oriented Data Acquisition

Process. Data files for 1977 through 1981.

133
FIGURE 4. Number of primary treatment admissions to federally

funded programs by year of first use

SOURCE: National Institute on Drug Abuse, Client Oriented Data

Acquisition Process. 1977 through 1981 data files.

Using the function "number of household heroin initiates = BO + B1

x the number of treatment heroin initiates" by year of first use,

yields a B1 of 16.6 (F=38.13, n1=1, n2=13, p less than .OO1,

R

2

=0.74). Thus, it is estimated that 1 in 17 household resident

heroin users will enter a panel of 402 consistently reporting

federally funded treatment programs for the first time in a 5-year

period (treatment data 1977 through 1981). The ratio of heroin

initiates in households to all treated initiates would be much

smaller.

The high R2 value implies that the trends in periods of initiation

based on self reports from continuing users in households were

similar to those based on self-reports from users in treatment.

Trends based on self-reported drug use were compared with other

indicators, such as hepatitis B cases, high school survey data,

heroin-related emergencies, heroin-related deaths, and average

heroin purity.

The comparison of trends in heroin initiates to trends in hepatitis B cases required that a linear trend be removed. The linear

trend is thought to be related to increasing numbers of male homosexuals, improved reporting, and an increase in the number of

intravenous cocaine users. Table 3 and figure 5 show the residuals after the removal of the linear trend. The years in which

the number of residual cases peaked were 1971 and 1977, approxi134
mately 1 to 2 years later than the year of the maximum number of

initiates for household residents (i.e., in 1970 and 1975).

Schreeder (1978) observed that 75 percent of the heroin users

entering treatment contracted hepatitis B within 5 years of

initiation. Thus, a lag between the number of household resident

heroin initiates and a number of hepatitis B cases would be

expected.

TABLE 3. Residual number of hepatitis B cases after removal of

linear trend

Year

1965

1966

1967

1968

1969

1970

Residual

Number of

Cases

-416

-667

490

358

1546

Year

1971

1972

1973

1974

1975

1976

Residual

Number of

Cases

1580

213

-1950

-982

294

934

Year

1977

1978

1979

1980

1981

1982

Residual

Number of

Cases

1579

-1448

-2224

125

1050

217

FIGURE 5. Residual nwnber of hepatitis B cases after removal of

the linear trend

SOURCE: Centers for Disease Control, 1975 through 1982.

The percent of high school seniors having ever used heroin, displayed in figure 6 by year of the survey, show a trend similar to

135
that of other incidence indicators. In 1975, 2.2 percent of the

high school seniors had ever used heroin. By 1980, the percent

had declined to 1.1 percent. The relatively high prevalence among

high school seniors in the mid-1970s and subsequent sharp decline

correspond to the decline in incidence shown in the national

household survey data for 12- to 25-year-olds.

FIGURE 6. Percent of high school seniors using heroin at least

once in lifetime

SOURCE: Johnston et al. 1983, p. 32.

Figure 7 shows the number of heroin-related emergency room visits

by month from 1973 through 1982 reported to DAWN and projected to

the nation. These data are based on a national projection from

DAWN data for that period (Hinkley and Greenwood 1982). In figure

7, the mid-1970s epidemic period can be seen, along with the

stabilizing trend in the late 1970s after a decline following

1976. Recent increases can be seen in the emergency room data,

although these data are too recent to allow comparison to national

survey data.

136
FIGURE 7. Number of heroin-retated emergency room visits

projected to Nation

SOURCE: National Institute on Drug Abuse 1984, p. 9.

Figure 8 shows the number of heroin-related deaths reported to

DAWN. The data show the mid-1970s epidemic period and the recent

increase. The peak in the 1974 through 1976 period corresponds to

the increase in the number of heroin initiates in that period.

FIGURE 8. Number of heroin-retated deaths reported to DAWN

SOURCE: National Institute on Drug Abuse 1984b.

137
Average heroin purity January 1973 through September 1983, based

on data provided by the DEA (1983), is shown in figure 9. As was

noted for the other heroin indicators, the purity was elevated

during the mid-1970s epidemic period, declined between 1976 and

1979, and shows a recent increase.

FIGURE 9. Average heroin purity reported to STRIDE and Domestic

Monitor Program

SOURCE: Drug Enforcement Administration 1984.

SUMMARY

Because of the small proportion of the population reporting ever

having used heroin, the year of first use data from NIDA's

National Household Surveys on Drug Abuse conducted in 1977, 1979,

and 1982 were pooled to show the number of new users in the

household population by year of first use. In addition, the data

were "smoothed" by using a 2-year moving average. The early 1970s

and the mid-1970s epidemics were evident. These epidemic periods

occurred at the same periods reported by high school seniors and

by heroin users in treatment. The household self-report data

trends based on age and frequency of use were also consistent with

the trends in periods of initiation reported by heroin users in

treatment as noted in drug abuse treatment admission data for year

of first heroin use.

Trends in indicators of heroin epidemics were compared with trends

based on self-report data from the National Household Surveys.

The trends in hepatitis B cases, heroin-related emergency room

visits, heroin-related deaths, and the average retail heroin

purity were consistent with the epidemic periods suggested by the

household data.

This consistency among the three sources of self-reported data on

trends in year of first heroin use combined with the consistency

138
of these self-reported data with the trends based on the

indicators of heroin epidemics offers some validation to the use

of retrospective direct questions concerning age of first use of

heroin to monitor heroin incidence in the household population.

REFERENCES

Centers for Disease Control. Mortality and Morbidity Weekly

Report Annual Summary 1975, 1976, 1977, 1978, 1979, 1980, 1981.

Data for 1982 and 1983 were from computer printouts that form

the basis of the Mortality and Morbidity Weekly Report Annual

Summary.

Drug Enforcement Administration. Domestic Monitor Program, JulySeptember 1983. Washington, D.C.: Supt. Docs., U.S. Govt.

Print. Off., 1984. 34 pp.

Fishburn, P.M. Heroin estimator development: The nominative

technique for heroin prevalence estimation. In: Rittenhouse,

J.D., ed. Development Papers: Attempts to Improve the

Measurement of Heroin Use in the National Survey. National

Institute on Drug Abuse Administrative Report. 1980. 170 pp.

Harding, W.M. Can compulsive opiate users establish stable

patterns of controlled use? In: Epidemiology of Drug Abuse:

Research and Treatment Issues. Volume II. Community

Epidemiology Work Group Proceedings. Rockville, Md.: National

Institute on Drug Abuse, June 1984. pp. 172-190.

Hinkley, S., and Greenwood, J. Computer Printouts, National

Projections of Emergency Room Visits, data through September

1982. Washington. D.C., Drug Enforcement Administration, 1983.

Integrated Software Systems Corporation. TELLAGRAF Version 4.5.

San Diego. 1983. 40 pp.

Johnston, L.D.; Bachman, J.G.; and O'Malley, P.M. Student Drug

Use, Attitudes, and Beliefs, National Trends 1975-1982.

Prepared for the National Institute on Drug Abuse by the University of Michigan Institute for Social Research. Washington,

D.C.: Supt. of Docs., U.S. Govt. Print. Off., 1982. 134 pp.

Kozel, N.J.; Crider R. A.; and Adams, E.H. National surveillance

of cocaine use and related health consequences. Morbidity and

Mortality Weekly Report, 31(20):265-273, 1982.

National Institute on Drug Abuse. Client Oriented Data

Acquisition Process. Data files for 1977 through 1981.

National Institute on Drug Abuse. Drug Abuse Warning Network.

Data files through 1982.

National Institute on Drug Abuse. Heroin Work Group Preliminary

Report, Rockville, Md., 1984a.

National Institute on Drug Abuse. Heroin Work Group Report,

Rockville, Md., 1984b.

National Institute on Drug Abuse. National Survey on Drug

Abuse. Data files for 1977, 1979, and 1982.

139
National Institute on Drug Abuse. Statistical Series, Trend

Report January 1978-September 1981. data from the Client

Oriented data Acquisition Process (CODAP). Rockville, Md.,

Series E, Number 24, 1981. p. 36.

Schreeder, M.T. CDC hepatitis laboratory presentation. In:

Proceedings Community Correspondents Group Meeting Five.

National Institute on Drug Abuse Research Monograph.

Washington, D.C., 1978. pp. 35-38.

Sirken, M.G. Network surveys of race and sensitive conditions.

In: Advances in Health Survey Research Methods. NCHSR Research

Proceedings Series Washington, D.C.: Supt. of Docs., U.S.

Govt. Print. Off., 1975. 15 pp.

AUTHOR

Raquel A. Crider, Ph.D.

Division of Epidemiology and

Statistical Analysis

National Institute on Drug Abuse

5600 Fishers Lane

Rockville, MD 20857

140
ESTIMATING HEROIN IMPORTS

INTO THE UNITED STATES

Keith L. Gardiner

Raymond C. Shreckengost

INTRODUCTION

One problem that plagues strategic narcotics analysis--analysis of

major trends in the illegal production, trafficking, and consumption of assorted dangerous drugs-- is the poor quality of data or

lack of it on crucial aspects of what is grown, manufactured,

moved, and consumed in the international illegal drug world.

There is no fully adequate substitute for data, but there are

useful analytical aids that help get around the problem by making

better use of the data that are available.

This paper describes the development of one such aid, a dynamic

simulation model implemented on a computer. The method used in

designing the model, System Dynamics, has several features that

are highly advantageous in this sort of analysis--including, for

example, the use of expert opinion to identify the critical

factors that influence the behavior of the system and to see how

a change in one factor affects the others (Forrester 1961, 1975;

Roberts 1978).

This paper illustrates how the fundamental factors affecting the

behavior of the heroin system are interrelated, and it provides

several examples of how estimates, or predictions, produced by the

model have spotlighted erroneous data, supplied missing data, and

anticipated data that become available only later. Most important, the model provides a general structure not necessarily

restricted to heroin; it may be applicable to other illegal drug

systems.

Model development was initiated during the summer of 1983 to

reduce the time required to estimate the amounts of heroin being

imported into the United States from foreign sources. Data

supplied by the Office of Intelligence of the Drug Enforcement

Administration (DEA) and the National Institute on Drug Abuse

(NIDA) were essential to the development of this model (Greenwood

and Crider 1978; Rosenquist 1983).

The views expressed in this paper are those of the authors and do

not necessarily reflect those of the Central Intelligence Agency.

141
THE MODEL AND HOW IT WORKS

The key to the design of the model was the notion that the

behavior of the heroin system in the United States would be

affected most importantly by how much heroin is available at any

time (Inventory) in comparison to the amount the addicts want

(Desired Inventory). This ratio is referred to in the model as

the Relative Abundance Measure. This indicates the surplus,

adequacy, or shortage of the heroin supply at any time. This, in

turn, affects directly such things as the price of heroin, its

purity, and the number of heroin users. These influences are

depicted in figure 1.

FIGURE 1. Supply ratio influences in the heroin system,

United States

As is often the case, these relationships appear simple and

obvious once described, but they are not so apparent beforehand.

In addition, to get to the essence of what makes the system work,

some of the clutter of conflicting data must be resolved. In this

model only one factor was troublesome--the definition of a heroin

user. There are heavily addicted users (who spend substantial

amounts of time in jail); light, sporadic users; and moderate

users who fall between these extremes. The heavy users are comparatively few, the occasional users comparatively many. For the

purposes of this model, using NIDA data, we created an "average"

user who consumes a package of heroin a day that contains 22.6

milligrams of pure heroin when the United States inventory is at

the desired level. As will be seen later, the purity of the

heroin consumed and the number of users both increase when supply

142
increases more than consumption, and they decrease when there is

a shortage of heroin. Total consumption is thus affected by both

the number of users at any time and the purity of the heroin in

the package they buy.

In the heroin problem, as in most other social problems, the

important factors in the system are numerous and highly

interrelated, and this makes analysis based on pure reasoning

(that is, without the use of analytical aids) difficult, tiring,

uncertain, and almost impossible to retrace. One function of the

model is to make the relationships clear and explicit, and then

let a computer perform the tedious processing. We do this by

creating a "decision rule" about, for example, how much on the

average the purity in a package of heroin will rise as the

available quantity of heroin increases; we express this rule as

a mathematical reference in the model, and then let the computer

calculate purities as the imports of heroin rise and fall. The

multiple effects of the factors (variables) influencing the

behavior of this model are shown in figure 2.

FIGURE 2. Complete influence diagram of the heroin system,

United States

143
Notes on the Model

Details of our assumptions about the variables in the heroin

system model are as follows (letters below correspond to those

in figure 2):

a. At the upper left-hand corner is the Susceptible

Age Group. This group consists of the 14- to

34-year-olds in the population, as derived from

census data. The User Population is a fraction

of this group, which varies with the availability

of heroin--the Relative Abundance Measure. If

desired, the User Population could be shown as

responding to Price, in which case Price would

reflect supply/demand and the User Population

would change with Price. As will be explained

later, Price is not directly affected as it is

in normal marketing systems, and the Relative

Abundance Measure was selected as the more

appropriate factor to use.

b. The size of the User Population, and the Purity

of what addicts buy, determine Consumption. The

various classes of users and the amounts they

normally use per day are not treated separately,

but they could be if desired.

c. Sales to support Consumption reduce the U.S.

Heroin Inventory.

d. The four sources of heroin imports since 1973 for

the United States heroin market are arrayed at the

bottom of the figure in the order in which they

first delivered supplies to the United States.

e. The Desired Inventory is taken as five times the

weekly Perceived Consumption. This provides a

slight buffer against small variations in overall

supply, but holds down risks of loss. Perceived

Consumption serves to represent the lag in the

response of the operators in the system to the ups

and downs in Consumption in the short term.

f. The Inventory Gap is merely the difference between

the Desired Inventory and the actual inventory,

the U.S. Heroin Inventory.

g. The Relative Abundance Measure provides a single

index for the actual/desired inventory status.

This ratio affects both the heroin User Population

and the Purity of the heroin it buys.

144
h. As distinct from purchases of other things, such

as food, the addict does not buy heroin directly

at so many dollars per milligram but buys instead

a package in which the amount of pure heroin is

small and variable. Thus, price per milligram is

indirectly derived.

i. In the model, Price (per milligram pure) is

related to the Relative Abundance Measure and

Inflation. The values for inflation are taken

from the Commodity Price Inflation Index.

j.

k.

The relationships among the Relative Abundance

Measure and Purity, Price, and the User Population

are of particular interest. Purity and the User

Population are parts of loops--that is, the

Relative Abundance Measure affects Consumption,

which affects the U.S. Heroin Inventory, which

affects the Relative Abundance Measure, which

affects Purity, and so on. A similar situation

exists with the User Population: changes in

Purity and User Population affect Consumption

and the U.S. Heroin Inventory, and these are

influenced by the Relative Abundance Measure.

Over the 10-year span, the ratio of the U.S.

Heroin Inventory to the Desired Inventory ranges

from a low of .8 (undersupply) to 1.8

(oversupply). Purity bottoms out at about 3.5

percent when it is in short supply, rises rapidly

until the inventory overage amounts to about 50

percent, and then begins to taper off slightly.

The variation in User Population is much less

dramatic, falling a bit when heroin is in short

supply and increasing significantly only when the

excess is greater than 20 percent. Prices come

down as heroin becomes more abundant, but seem to

bottom out at $0.90 per milligram of pure heroin.

l. As described in this paper, the effect of the

Relative Abundance Measure on Deaths is more

complex than the effect of the ratio on Purity,

Price, and User Population. The system exhibits

two modes of behavior, one when the ratio is

rising and the other when it is falling.

Considering these influences, and given the heroin imports

described below, the model forecasts what the User Population,

Purity, Prices, and Deaths will be. These predictions are

generally referred to as the model's behavior, and the way the

model behaves is determined by the influences, or structure,

145
described above. Whether the model can be used confidently is

largely dependent on how closely the predictions of the model

match their real-life counterparts: for example, at any given

moment, do the model's predictions for Purity match independently

measured national averages for purity? The independently measured

values for purity, price, and heroin-related deaths are not direct

inputs for the model. They are used only for comparison with the

model-generated estimates to assist in evaluating how well the

model behavior matches real-life behavior. The particular

question here is whether the model will provide good predictions

using only the fluctuating heroin imports over the last 10 years

as the input to the model.

MODEL INPUTS

The sources and amounts of heroin imports used in the model

correspond, with a single exception discussed later, to the

sources and amounts commonly accepted by the various agencies

concerned with drug abuse. These inputs are shown in tabular form

in table 1 and graphically for the same 10-year period in figure 3.

TABLE 1. United States imports of heroin in pure metric tons

Country of

Origin 1973 1974 1975 1976 1977 1978 1979 1980 1981 1982 1983*

Turkey 10 0.5

Mexico 3.7 5.0 5.6 4.0 3.1 19 1.0 1.7 1.8 1.8 -

Southeast Asia 1.0 2.0 2.0 1.7 1.2 0.4 0.4 0.7 -

Southwest Asia 0.4 0.8 1.4 2.1 2.2 2.7 -

Total 4.7 5.5 6.6 6.0 5.5 4.4 3.6 4.2 4.4 5.2 5.4

*The 1983 estimate procedure is described later.

The total heroin imports are plotted again in figure 4, which

shows how these imports relate to the Relative Abundance Measure.

Using expert opinion, we built into our model the assumption that

heroin traffickers attempt to hold 5 weeks' supply (at the current

consumption rate) as a buffer against surges in demand and delays

in supply. This provides a degree of inertia in the system and

causes changes in the Relative Abundance Measure to lag behind the

changes in Imports. The relatively high amount of heroin on hand

when the model begins in 1973 results from a sustained growth in

heroin imports from Mexico prior to 1973.

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FIGURE 3. Imports

FIGURE 4. Imports and relative abundance measures

147
MODEL VALIDITY

There are no absolutely valid models because such models

would have to replicate any "real-world" system faithfully and

exhaustively in even the most trivial detail; all models,

including all mental models, are thus simplifications. However,

as described earlier (Shreckengost, this volume), if the key

factors influencing the system are identified and correctly

interrelated, the behavior, or predictions, of the model will

parallel the behavior of the real system closely enough to

validate, or provide confidence in, the model. In addition,

confidence in the accuracy of the model is enhanced when its

behavior, using inputs from the real world, does not violate

common sense. The following discussion deals with one dimension

of the model --its ability to predict the size of the addict

population--for which there are no usable matching independent

data by which to show the model's validity, but where the value of

the model is demonstrated by its ability to spot a possible error

in the data supplied to the model. Three areas in which data are

available for comparison with numbers the model generates--Purity,

Price, and Deaths--are then discussed. By matching the figures

that the model generates on these three variables with the

independently measured numbers, we can determine how accurately

the behavior of the model parallels the behavior of the real

system.

User Population

Figure 5 shows the decision rule stating how the Relative

Abundance Measure affects the size of the User Population. The

effect of the Relative Abundance Measure on the User Population is

quite constant when the Inventory and Desired Inventory are nearly

balanced, but lowers the population slightly when shortages occur

and increases the population slowly with increases in supply--that

is, an 80 percent oversupply produces only a 10 percent rise in

the population. Our model shows only how changes in the Relative

Abundance Measure affect the percentage of change in the User

Population. The actual number of addicts added or subtracted

depends on the size of the 14- to 34-year-old age group. As the

size of this group increases, logically the user population will

also increase for any given Relative Abundance Measure.

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FIGURE 5. Effect of relative abundance on user population

The addict population numbers the model produces do not violate

common sense and are consistent with other estimates available

from various surveys and analyses. However, there are no good

data on the actual size of the addict population in the United

States. Consequently, the validity of the model cannot be

checked by comparing the numbers it produces with independent user

population measurements. The model-estimated User Population is

shown in figure 6. As a baseline from which to observe

fluctuations in the size of the addict population that are

independent of the normal growth that occurs as the 14- to

34-year-old age group increases, we derived an estimate using

population data that the addict population equals 0.65 percent of

the 14- to 34-year-old population when supply and consumption are

in balance (Greenwood and Crider 1978). The natural demographic

increase in that percentage is shown in the figure as a "reference

group" against which the size of the addict population predicted

by the model rises and falls in relationship to how much heroin is

available. The size of the population swelled with the growing

availability of heroin through 1975, and then remained fairly

constant through 1978 even though the amount of heroin available

dropped, because the growing age group population offset the

decline in the percentage of the age group addicted to heroin.

149
FIGURE 6. Comparison of growth in heroin user population with

growth in reference population (14-34-year-old age

group)

Even though we cannot independently verify that the model's user

population projections are accurate, we were able to use them to

reveal a logical inconsistency between one of the import figures

used as an input to the model and the size of the user population

that the model indicates would be required to consume that amount

of heroin. Originally, the estimate for heroin imports for 1975

supplied to the model included 6.5 tons from Mexico instead of the

5.6 tons shown in table 1. This was inconsistent with the size of

the User Population predicted by the model for that year. In

particular, the rate of increase in the size of the population

would have had to have jumped considerably to produce a User

Population large enough to consume so much heroin. This would run

contrary to the commonsense notion that, since there is probably

an upper limit on the fraction of the total population that might

become heroin users (as there is for cigarette smokers), the rate

of increase should tend to subside as heroin becomes more

abundant. In addition, that much surplus heroin would have had

effects on Purity and Price that were not borne out by the data

available for that year. Because of these inconsistencies, we

double-checked the import figures and discovered that 5.6 tons

provides a better "fit" and is consistent with other independently

available information. This is one illustration of the way in

which dynamic simulation models can highlight logical inconsistencies, which may not be apparent until otherwise plausible data

are viewed against a comprehensive and consistent framework.

150
Heroin Purity

The way the Relative Abundance Measure affects Purity in the model

is shown in figure 7. When heroin is in relatively short supply,

the amount of pure heroin in the package the user buys is also

low. When heroin supplies are abundant, the amount of pure heroin

increases. Unlike the User Population, Purity changes quite

dramatically with changes in the Relative Abundance Measure. As

with the User Population, however, the amounts of change are not

constant but depend on the adequacy of heroin supply that exists

at any particular time.

FIGURE 7. Effect of relative abundance on purity

Samples of heroin have been analyzed by the DEA for purity over

the years, and the comparison of these data with the modelgenerated Purity values is shown in figure 8. In view of the

simplicity of the model and the sampling and statistical problems

involved in determining purity, the correspondence between the

purity measured in the heroin samples and the Purity estimated by

the model over the 10-year period is surprisingly good. Only

annual average purity values are used for 1973, 1974, and 1975,

after which quarterly values are available. Since Purity figures

in the model are derived in part from known imports, and because

the correspondence between sample and model Purity data is so

close, we can reverse the procedure and use known purity figures

to estimate what imports were at a given time. We have used this

151
procedure three times to produce "missing" import figures. In the

first instance, we used sample purity data from earlier years to

estimate what the residual imports from Turkey must have been in

the 1973-74 period following the halt in Turkish production in

1972. We have no figures from other sources on Turkish imports

for these years. With data only for Mexican imports, the modelgenerated Purity values fall well below the 5.1 percent and 5.8

percent found in sample data. The amount of imports from Turkey

that we have inserted in table 1 and figure 3 are those required

to cause model Purity to be consistent with the observed system

values. These imports are intuitively reasonable and "fill in the

blanks."

FIGURE 8. Actual purity vs. model purity

The second case in which we used purity figures to derive imports

was in attempting to estimate overall imports for 1982. Our

original information was that imports for 1982 were about the same

as in 1981. This, however, resulted in model Purity values for

1982 that were quite a bit lower than the measured purity values.

Here, again, comparison of model and sampled purity suggested that

the imports were understated. Subsequently, estimates of the

imports from Southeast and Southwest Asia were revised upwards by

the DEA. While this brings the model and real-world data into

good correspondence, the total imports still appear to be slightly

underestimated.

In addition to using purity figures to double-check available

import statistics, we have used them to produce an estimate of how

much heroin was imported into the United States in 1983. Using

the DEA-measured values for purity for 1983, the model indicates

152
that some 5.4 metric tons of heroin would have had to be imported

into the United States in that year to generate model Purity

values that correspond to the measured numbers. To check the

validity of this import figure, we also generated model Prices for

1983 to see if they would correspond to available measured street

prices. The correspondence was good: street prices for the first

three quarters of 1983 (the fourth-quarter prices are not yet

available) vary from $2.28 to $2.43 per milligram, and the model

Prices range from $2.22 to $2.45.

Heroin Prices

Price moves opposite to Purity with changes in the Relative

Abundance Measure. Also, as shown in figure 9, the prices fall in

an almost straight line as the Relative Abundance Measure rises,

but then level off rather suddenly (in constant 1970 dollars) as a

price floor is reached below which it does not make economic sense

for a trafficker to continue to deal in heroin (in part because of

the personal risk involved).

This lower limit on prices influences prices strongly during the

buildup of imports, and accompanying surpluses, into 1975. With

a growing surplus of heroin supply over consumption, a continuing

drop in price might have been expected, but instead the price

floor was reached and, in fact, nominal prices rose slightly

because of inflation. Notice, as shown in figure 10, that the

model Price estimates are higher than the observed prices in

1982--another indicator that the estimated imports may be low.

FIGURE 9. Effect of relative abundance on price

153
FIGURE 10. Comparison of model price and actual price

Heroin-Related Deaths

Figure 11 shows how the relative abundance or shortage of heroin

affects the number of heroin-related deaths. It is more complex

than the Purity and Price relationships because two distinct modes

operate--one when the amount of heroin is increasing and the other

when the supply is falling. This is required because when the

supply of heroin is falling, the death rate does not retreat along

the same path that it followed when supplies were increasing. Use

of only the rising relationship results in major departures

between model-produced Death estimates and the measured values.

These two modes are believed to reflect the effect of Purity

changes as the Relative Abundance Measure rises and falls. When

supply is increasing, purity is also increasing, and the addict is

subject to unexpectedly high doses, which may be fatal. On the

other hand, when supplies and purity are falling, the likelihood

of overdosing, either accidentally or deliberately, is reduced.

Figure 12 compares the heroin deaths reported from 1973 through

1981 with the model's predictions. It is important to appreciate

that the definition of what constitutes heroin-caused or heroinrelated deaths is neither precise nor uniform in the United States.

Contributing factors, such as alcohol, are often present, so the

need for subjective judgments is understandable and unavoidable.

154
FIGURE 11. Effect of relative abundance on deaths

FIGURE 12. Comparison of model deaths and actual deaths

155
The reason 1982 sample data are incomplete illustrates, in part,

the problems encountered in obtaining "hard data." For example,

the number of heroin-related deaths in New York City is

problematic because such deaths are recognized only when confirmed

by a toxicological examination. Even if a victim is found in

surroundings where the cause of death seems apparent--for example,

heroin scattered about and a syringe in hand--if a toxicological

examination is not done, the death is not tallied as heroinrelated. The model indicates, however, that Deaths probably rose

in 1982; we await confirming data.

IMPLICATIONS AND POSSIBLE FUTURE APPLICATIONS

This modeling effort is still in a developmental phase. We need

additional time and research to determine how robust it is and

whether it can be applied with equal facility to other aspects of

the heroin market in the United States, whether it can be extended

to non-United States markets, and whether it can be adapted to

deal with illegal drugs other than heroin. In pursuing possible

future applications of this model to the United States heroin

market, of the various relationships demonstrated among elements

of the heroin situation by this model, the one that stands out as

potentially most exploitable involves purity. The notion that

purity measurements of seized or purchased samples tend to rise

and fall as heroin supplies grow and shrink has long been known.

However, the relative precision with which the model predicted

changes in heroin purity over a 10-year period indicates a

relationship between Purity and such other factors as Imports and

Consumption that is sufficiently strong to suggest the use of

purity figures alone as a powerful, timely indicator of the state

of heroin abundance in the United States at any given moment. In

particular, it may be possible to use purity figures to estimate

whether, and how much, imports of heroin and the size of the

United States heroin addict population are expanding, holding

steady, or declining. The advantage of using Purity as an

indicator for these other factors is that purity measurements are

usually available on a fairly "real-time" basis.

One problem with using purity measurements other than for what

they intuitively suggest is that purity often varies sharply among

daily samples obtained in different regions of the United States

and among individual samples within a region. The large

variations in purity at the local level have tended to mask the

relationship with the other dimensions of heroin supply and

consumption that are revealed when averages are used. The model

uses national averages, thus smoothing out the momentary peaks and

valleys and providing data that can be compared with other

aggregate figures such as total imports and changes in the

national user population.

156
Another possible use for the model would be to apply the same

basic structure that was used on national level factors to monitor

heroin supply and consumption at the regional/city level of the

heroin system. If, for example, variations in purity from region

to region were detected and the source countries were known, it

might be possible to define and monitor lines of supply and

communication in the national system. The time element involved

in moving heroin supplies within the United States could be

explicitly represented in a national heroin model, thereby adding

to the possibility of predicting changes in the system.

A further interesting application of the model would be to attempt

to use it to understand supply and demand relationships for other

illegal drugs. To the extent that such drugs as cocaine,

marijuana, and certain synthetics follow the same basic dynamics

as heroin supply and demand, they could be modeled in the same

fashion. Much more ambitious, but theoretically equally feasible,

would be to design and develop a comprehensive, integrated

national drug model, using the System Dynamics "loom" to weave the

various individual drug systems together. A key advantage that

might be afforded by such a model would be a better understanding

of the addicts who use multiple drugs--the polyusers--and the

tradeoffs that might occur in concentrating intelligence and law

enforcement resources against particular drugs in the system.

REFERENCES

Forrester, J.W. Industrial Dynamics. Cambridge, Mass.: MIT

Press, 1961. 464 pp.

Forrester, J.W. Collected Papers of Jay W. Forrester. Cambridge,

Mass.: MIT Press, 1975. 284 pp.

Greenwood, J., and Crider, R. Estimated Number of Heroin Addicts,

1977. National Institute on Drug Abuse. Rockville,Md.: 1978.

Roberts, E.B., ed. Managerial Applications of System Dynamics.

Cambridge, Mass.: MIT Press, 1978. 699 pp.

Rosenquist, E. Private communication. 1983

AUTHORS

Keith L. Gardiner, M.A.

Raymond C. Shreckengost, M.S., M.A., M.P.A.

Central Intelligence Agency

Washington, DC 20505

157
ESTIMATING THE SIZE OF

A HEROIN-ABUSING

POPULATION USING

MULTIPLE-RECAPTURE CENSUS

J. Arthur Woodward, Douglas G. Bonett,

M.L. Brecht

INTRODUCTION

During the last 10 years, the Federal Government has attempted to

monitor trends in heroin abuse prevalence to develop appropriate

drug prevention and control activities, determine research and

training priorities, and allocate treatment and rehabilitation

resources. In this period, the epidemiology of drug abuse has

assumed great importance, and a variety of research approaches

have been devised to aid in the study of heroin abuse patterns.

The most prominent and traditional of these approaches is the

National Household Survey (Cisin et al. 1978) that applies standard survey research and sampling theory to the study of drug abuse

prevalence in a sample of U.S. households. Because of the great

cost of a National Household Survey, it cannot be implemented at

(brief) regular time intervals (e.g., quarterly periods) for each

of the 25 or 30 major metropolitan communities of the nation. In

addition, traditional survey research methods are difficult to

apply to criminal activities such as heroin abuse because of

denial of the activity and the fact that the proportion of heroin

users in the general population is relatively small (Hunt and

Chambers 1976).

In order to supplement the national surveys with communityspecific and (regular) quarterly estimates of heroin abuse prevalence, a number of researchers have adapted approaches to

population size estimation used in biology and paleobiology. One

such method is the so-called multiple-recapture census. In the

remainder of this paper we will describe two different varieties

of the multiple-recapture census and provide examples of how it

can be applied to the study of heroin abuse.

THE MULTIPLE-RECAPTURE CENSUS

When applied by the biologist to study the size of animal populations, the multiple-recapture census can be described in the

158
following way. The population under study is sampled k times. In

each sample, every unmarked animal is marked uniquely; previously

marked animals have their previous captures recorded; and then all

animals are released back into the population. At the end of the

k sampling trials, the complete capture history of every captured

animal can be constructed.

Mathematical models have been formulated to estimate the size of

the population from the multiple-recapture record of population

members recorded on the k sampling trials. The mathematical

models of this sampling process require certain technical assumptions about the nature of the sampling; for example, about the

sampling probabilities for members of the population and the stability of the population during the multiple-recapture experiment.

Multiple-recapture models often are classified according to their

assumptions about whether the population is changing ("open") or

nonchanging ("closed"). Because both the open and closed population models have potential for heroin abuse research, we present a

brief description of each model, followed by applications to data

from the Client Oriented Data Acquisition Process (CODAP).

Open Population Models

Open population models are useful not only to estimate population

size at specific time periods, but also to estimate the number of

new members entering the population between time periods and the

probability of remaining in the population during a succeeding

time interval (survival probability). When an extensive amount of

recapture information can be constructed for the members of an

open population, the Jolly-Seber model is probably of greatest

potential interest to drug abuse epidemiologists (Jolly 1965;

Seber 1965, 1973). When only limited recapture information is

available, the more restrictive band-recovery model can be used.

The Jolly-Seber Model. In the Jolly-Seber open-population model,

samples are drawn from the population at k successive time

periods. At the ith time period (sample) there are Nj members in

the population, of which ni were observed in the sample. To apply

the Jolly-Seber model, the following recapture history must be

constructed:

1) ni = number of members in ith sample

2) mi = number of marked members in ith sample

3) ri = number of marked members released after the ith

sample and subsequently recaptured

4) zi = number of members captured before the ith sample

that are not caught in ith sample, but are caught

subsequently

159
When certain assumptions (to be stated later) are true, then the

above observed information can be used to estimate the number of

members in the population at each time period, the survival probability, and the number of members entering (or leaving) the

population between time periods. Computer programs now exist to

compute the maximum likelihood (ML) estimates and standard errors

of the above unknowns, and certain intuitively appealing estimators can be used to increase intuitive understanding of the

model (or used as intermediate values in computing the ML

estimates). The intuitive estimators can be explained as follows.

Suppose Mi is the unknow number of marked animals in the population just prior to the ith sample. Then mi is the number of these

actually captured in sample i. When certain sampling assumptions

are met, the proportion of members marked in the sample (mi/ni)

will equal the proportion of marked members in the population

(Mi/Ni) and

Since Mi is unknown, it must be estimated from the recapture

history of the members captured at each of the k samples. To

estimate Mi, note that when the ni members of the ith sample are

released, there are two groups of marked members. There are the

marked members not captured in the ith sample, (Mi - mi), of which

zi subsequently are caught, and there are ni just released members

of which ri subsequently are caught. Under the sampling assumptions, the chances of recapture are assumed identical for both

groups and

This yields

as the required estimator of Mi. Then

160
The estimator of also is used to obtain the estimator of φ

i, the

survival probability, as

Finally, the number of members entering the population between

sample i and i+1 can be estimated by

Additional discussions of these estimators and their asymptotic

variances can be found in Seber (1973).

The assumptions required for the application of the Jolly-Seber

model are summarized below:

1) each member of the population has the same probability of

being captured in the i

th: sample

2) every marked member has the same probability (φi) of

surviving from the ith to the (i+1)th sample

3) every member caught has the same probability (vi) of

being returned to the population

4) marked members retain their marks

The effects of departure from the above assumptions have been well

documented. Heterogeneous encounter probabilities have been shown

to produce negative bias in the estimates of Ni (Carothers 1973;

Gilbert 1973). When the population is heterogeneous, grouping

into more homogenous subgroups (e.g., heavy vs. light users) will

help alleviate the problem. Heterogeneity of survival probabilities will result in a negative bias on Nj. Several methods now

exist for assessing the reasonableness of the assumptions of the

model. Specific tests are available for heterogeneity of encounter probability (Leslie 1958; Carothers 1971) and heterogeneous

survival probabilities (Balser 1981). Computer programs exist for

computing the maximum likelihood estimates and standard errors

from the open population study (Davies 1971; White 1971; Arnason

and Baniuk 1980).

Closed Population Models

When the population under study is constant, then simpler

multiple-recapture designs can be used to estimate the size of the

closed population. These designs are popular because of their

simplicity and because the estimates still may be valid even when

the population changes. For example, if the population is

changing between two time periods as a result of random mortality,

Robson (1969) shows that certain estimators are suitable for

estimating the population size at the time of the first sample.

When there is an increase in the population size between samples,

161
Seber (1973) shows that the estimate is suitable for the time of

the second sample. If a high degree of temporal precision is not

required (e.g., time of first sample vs. time of second sample),

then these violations of the closed population assumption may

present no problem. The assumption of the closed population

models are:

1) the population is closed

2) all members of the population have the same probability

of being captured

3) each sample is a representative sample of the population

4) the multiple-recapture history of each captured member is

accurate

As in the open population models, heterogeneity of capture

probabilities can be handled by grouping into homogeneous groups.

There have been a number of applications of the closed population

model to the study of heroin abuse (Greenwood 1971; French 1977;

Woodward et al. 1984). Most frequently a simple, two-sample

design has been used that requires the additional assumption of

independence among the samples. In the second example provided

here, a three-sample design will be used to avoid the overly

restrictive assumption of independent samples.

The three-sample multiple-recapture census for the closed

population can be represented as a three-way contingency table as

shown in table 1.

TABLE 1. Schematic representation of the three-sample multiple

recapture experiment

The frequency f111 represents the number of individuals captured

on all three samples. The other cells represent different

combinations of captured and not captured members on the three

162
sampling trials. The unobserved frequency f222 represents the

number of persons not captured on any sampling trial.

An estimate of the total population size and its standard error

can be obtained from a log-linear model where the missing cell

(f222) is treated as a structural zero (Bishop et al. 1975). In

the three-sample capture-recapture design, the three-way interaction must be assumed to be zero; however, the samples may be

pair-wise dependent.

Recently, a new approach to estimating the size of the closed

population has been developed using closed-form, constrained estimators of the parameters of the log-linear model (Bonett et al., in

press). This approach has several new features that will be

important in drug abuse applications:

1) all estimators and standard errors are in closed form and

thus can be computed easily, even in the microcomputer

2) the models can be fit under exact linear and stochastic

constraints on the parameters of the log-linear model and

on the unobserved population sizes, thus greatly reducing

standard errors in many applications

3) the population can be divided into strata that are

homogeneous in the capture-probabilities, so that the

assumption of equal capture-probabilities can be met

4) hypothesis testing can be carried out on unobserved

population sizes, so that differences among communities

and differences across time can be studied simultaneously

in a statistical framework

Application to Heroin Abuse Research

In applying the multiple-recapture census to heroin abuse, some

method of sampling from the population of heroin abusers must be

devised, as must a way of recording their "capture" history. Such

sampling cannot be so straightforward as it is in the study of

wildlife populations. In the case of heroin abuse, the sampling

will be more similar to paleobiology applications where naturalistic samples of extinct animal fossils are collected from

different strata of rock.

In application to heroin abuse, admissions to federally funded

treatment programs during a specific time period represent a sample from the population of heroin abusers. Because the sampling

is naturalistic, the population being studied consists of all heroin abusers who have a nonzero probability of entering federally

funded treatment programs. This definition of the population may

be more restricted than if random sampling were involved, but the

population of persons who are likely to enter a federally funded

treatment program is a large and important population to study.

163
In order to construct the multiple-capture history for heroin

abusers, a computerized matching procedure was developed for the

anonymous CODAP files (Woodward et al. 1984). Using 2-month

periods to accumulate admissions, the computerized matching

program was used to identify readmissions. A number of practical

issues concerning the feasibility and accuracy of this method of

constructing the multiple-recapture table are discussed elsewhere

(Woodward et al. 1984).

The admission to treatment could be used as a basis for either an

open or closed population model. At the local level of the treatment program, accurate records of readmission to treatment facilities could be accumulated so that an open or closed population

model could be applied. In the closed population applications

presented here, inaccuracies exist because a matching procedure

had to be devised to recognize readmissions of anonymous clients

to treatment programs, using the aggregate CODAP files.

Example 1: Homogeneity of capture probabilities. If the assumption of homogeneous capture probabilities is violated, then it is

recommended that the population be stratified into groups more

homogeneous in the capture probabilities. By including a relevant

blocking variable in the capture-recapture design as an additional

factor, the assumptions of the model may be satisfied. A variable

hypothesized to be related to the probability of admission to a

drug treatment program is the level of heroin use. Heroin users

were identified each time they were admitted to drug treatment

programs using the computerized matching procedure applied to the

CODAP files. At the time of first admission, users were classified as being either heavy or moderate users of heroin. The data

for a single Standard Metropolitan Statistical Area (SMSA) appear

in table 2 (Doscher and Woodward 1983).

TABLE 2. Multiple-recapture census with heavy and moderate heroin

users

This table contains two unobservable cells that correspond to the

number of heavy and moderate heroin users who were not admitted

during either of two admission periods. In this design the

presence of two latent cells renders unestimable the Admission 1

164
by Admission 2 interaction effect and the Admission 1 by Admission

2 by Use interaction. An exploratory analysis revealed four

constraints consistent with the data:

1) the main effect of Admission 1 equals the main effect of

Admission 2

2) the Use by Admission 1 interaction effect is zero

3) the Use by Admission 2 interaction effect is zero

4) the number of moderate heroin users not admitted on

either occasion is three times larger than the number of

heavy heroin users not admitted on either occasion

The constrained closed-form estimators and associated test statistic of Bonett et al. (in press) yielded an excellent fit to the

data (X2= 5.73 on 4 degrees of freedom) Given this model, it

can be seen that the heavy-moderate stratification did not reveal

heterogeneous capture probabilities as evidenced by the zero Use

by Admission interactions. In this case, the obtained estimate of

8,152 with standard error 934 is not substantially different from

the value that would have been obtained from an analysis of the

design collapsing across the stratification Use factor. Although

it is possible that other stratification factors may reveal

heterogeneous capture probabilities, they must be discovered

empirically through a study analogous to the one presented above.

For the moment, the hypothesis that heterogeneous capture probabilities arise because of differences among heavy and moderate

heroin users is rejected.

Example 2: A cross-sectional, longitudinal study of heroin

abuse. Using the general statistical framework of the Bonett et

al. constrained closed-form estimators, it is possible to carry

out a larger study that addresses simultaneously differences among

communities and differences across time. Twelve major metropolitan areas (SMSAS) were studied at each of three time periods.

For each community, a three-sample multiple-recapture census

(table 1) was conducted at each of 3 years--1977, 1978, and 1979--

using the computerized matching procedure applied to the CODAP

files. The 12 communities were grouped into 3 heroin supplysource clusters: 1) Mexican; 2) Southeast/Southwest Asian , and

3) mixed (both of the above). The grouping is, of course, approximate and represents current knowledge of supply derived from

extensive drug enforcement activities. The complete design for

the multiple-community multiple-time study is represented

schematically in table 3.

1

These sources include Pakistan, Afghanistan, and the Golden

Triangle.

165
TABLE 3. Schematic representation of the cross-sectional,

longitudinal multiple-recapture study

Within each of the 36 cells of the design was a 3-sample multiplerecapture table like the one in table 1. Now a single statistical

model was fit to the entire design, so that there is a simultaneous estimation of the population sizes for all 12 communities at

all 3 time periods. This provides for a statistical framework

within which hypotheses can be tested about differences across

communities, across time, and about the interaction of communities

and time. The simultaneous estimation also permits constraints to

be imposed across similar communities and across time in order to

reduce the standard errors of the resulting population size

estimates. This is an important contribution of the Bonett et al.

methodology since a typical multiple-recapture estimation procedure (Bishop et al. 1975) may yield extremely large standard

errors. By constraining certain parameters to be equal across

time, and across similar communities, the standard errors were

reduced substantially in the simultaneous analysis of the data.

166
Without such a procedure, many standard errors are as large as the

estimates themselves, indicating an unacceptable degree of precision in the single-community single-time multiple-recapture census

approach.

The simultaneous population size estimates and their standard

errors are shown in table 4. Since the purpose of these analyses

is to illustrate the methodology and to compare time trends in the

different supply-source groupings, the SMSAs are referred to by

arbitrary numbers. Even though a very substantial reduction in

standard error size was achieved with the current methodology,

some of the standard errors still are quite large. Using the

estimates and their standard errors in table 4, 95 percent confidence intervals were computed for each community at each year and

are presented in table 5. As can be seen, in certain cases, the

precision of the multiple-recapture census is low.

TABLE 4. Capture-recapture heroin abuse estimates and standard

errors (in parentheses)

Source City 1977 1978 1979

1 66,077 (10,909) 39,678 (6,582) 25,601 (6,297)

Mexican

2 5,628 (907) 4,599 (968) 12.226 (2,955)

3 20,043 (7,527) 11,475 (4,017) 18,770 (3,681)

4 30,404 (7,163) 44,269 (7,481) 28,396 (4,834)

5 48,745 (10,662) 15.863 (3,601) 15,646 (3,590)

Southeast/

Southwest

Asian

6 14,579 (3,251) 46,586 (7,476) 41,071 (6,827)

7 65,044 (14,495) 4,235 (564) 29,289 (5,710)

8 104,601 (44,837) 40,748 (10,961) 50,443 (8,728)

Mixed

9 20,302 (6,207) 17,321 (5,220) 12,948 (4,152)

10 43,351 (6,186) 22,539 (3,313) 40,062 (6,466)

11 63,636 (7,926) 55,672 (13,138) 27,562 (4,345)

12 27,374 (6,651) 49,018 (12,181) 29,766 (7,334)

167
TABLE 5. 95 percent confidence intervals for capture-recapture

heroin abuse estimates

Source City 1977 1978 1979

1

2

Mexican

3

4

5

Southeast/ 6

Southwest

Asian 7

8

44,695-87,458 26,777-52,579 13,258-37,943

3,850-7,405 2,702-6,496 6,434-18,017

5,290-34,795 3,602-19,348 11,555-25,984

16,364-44,443 29,606-58,932 18,921-37,870

27,847-69,642 8,805-22,921 8,609-22,682

8,207-20,950 31,933-61,239 27,690-54,451

36,633-93,454 3,130-5,340 18,097-40,480

16,720-192,481 19,264-62,232 33,336-67,549

Mixed

9 8,136-32,467 7,090-27,552 4,810-21,086

10 31,226-55,475 16,046-29,032 27,389-52,735

11 48,101-79,170 29,922-81,422 19,046-36,078

12 14,338-40,409 25,143-72,893 21,271-38,261

Based on the estimates and their standard errors, it now is possible to test hypotheses about population size across communities

and across time. First, we tested the (null) hypothesis that the

patterns across time are equal in the population for the three

source groups. This hypothesis was rejected (χ

2

 = 20.52, df = 4).

The conclusion is that heroin abuse population sizes show

different time trends for the three groups of communities (i.e.,

Mexican, Southeast/Southwest Asian, and mixed). The average

sample values for the source time trends appear in table 6.

TABLE 6. Average time trends in population size estimates for

Mexican, Southeast/Southwest Asian, and mixed heroin

supply communities

Source

Mexican

Southeast/

Southwest Asian

Mixed

1977 1978 1979

30,538 25,005 21,248

58,242 26,858 34,112

38,665 36,137 27,584

Because the time trends differ across groups, we tested the

hypothesis of zero change across time for each group. For the

168
Mexican-supplied communities there is a significant linear

decrease across the three years, 1977, 1978, and 1979 (χ

2

 = 5.12,

df = 1); for the Southeast/Southwest Asian, there is a highly

significant "U" shaped pattern (χ

2

 = 89.3, df = 2). In the mixed

group of communities there was a significant decrease between 1978

and 1979 (χ

2

 = 6.6, df = 2). Even though there are differences in

time trends for the Mexican, Southeast/Southwest Asian, and mixed

communities during 1977, 1978, and 1979, the overall pattern is

one of a decrease in heroin abuse population size.

DISCUSSION

The multiple-recapture census can provide a practical method of

estimating the size of a population when standard survey research

methods are difficult to apply. Open population models deal with

populations that are changing. The major purposes of the census

are to estimate the population size at each of several time

periods, the probability of remaining in the population between

each time interval, and the number of persons entering or leaving

the populations between each time period. Such information would

be useful to the heroin abuse researcher, and practical ways of

conducting the open population study of the heroin abusing

population do exist.

The simpler closed population multiple-recapture census is appropriate when the population is not changing or when temporal

precision is not of great importance. When the population is decreasing, for example, closed population estimates are appropriate

for the time period of the first sample.

Several applications of the multiple-recapture census to heroin

abuse are presented. In the first example, the issue of equal

capture probabilities is examined. In the second example, a

cross-sectional longitudinal design is used to test for population

size differences among communities and across time. The time

trends for Mexican, Southeast/Southwest Asian, and mixed supplysource cities differed in their time trends on heroin abuse

population size. The overall trend across the years 1977, 1978,

and 1979 was seen to be decreasing.

The applications presented here used admissions to treatment as

the sampling procedure. This kind of naturalistic sampling is not

inconsistent per se with the statistical assumptions of the

multiple-recapture census, but it does restrict the definition of

the population to those heroin abusers that have a nonzero probability of entering treatment. While this is a large and important

population to study, this procedure does not yield what can be

considered estimates of heroin abuse prevalence in a strict sense

of the word.

One difficulty with the multiple-recapture census is the magnitude

of the standard errors that result from this kind of sampling.

Even the newly developed constrained closed form estimators of

169
Bonett et al. (in press) did not reduce all standard errors in the

examples to acceptable levels. In spite of this, however, the degree of precision was sufficient to detect significant differences

in population sizes across time. In addition, the time trends for

communities believed to have different sources of heroin were

significantly different. The overall trend during the 1977-1979

period was significantly decreasing; thus, the findings of this

application are consistent with the findings of many other studies

based on different sampling methodologies (DEA 1983a, 1983b; NIDA

1983).

REFERENCES

Arnason, A.N., and Baniuk, L.A. Computer system for markrecapture analysis of open populations. J Wildl Management,

44:325-332, 1980.

Balser, J.P. Confidence interval estimation and tests for

temporary outmigration in tag-recapture studies. Doctoral

Dissertation, Cornell University, 1981.

Bishop, Y.M.M.; Fienberg, S.E.; and Holland, P.W. Discrete

Multivariate Analysis: Theory and Practice. Cambridge,

Mass.: MIT Pres, 1975.

Bonett, D.G.; Woodward, J.A.; and Bentler, P.M. A linear model

for estimating the size of a closed population. In press.

Carothers, A.D. An examination and extension of Leslie's test of

equal catchability. Biometrics, 27:615-630, 1971.

Carothers, A.D. The effects of unequal catchability on JollySeber estimates. Biometrics, 29:79-100, 1973.

Cisin, L.H.; Miller, J.D.; and Harrell, A.V. Highlights from the

National Survey on Drug Abuse: 1977. Prepared for the National

Institute on Drug Abuse by the Social Research Group of The George

Washington Univ. and Response Analysis Corp. Washington, D.C.:

Supt. of Docs., U.S. Govt. Print. Off., 1978.

Davies, R.G. Computer Programming in Quantitative Biology.

London: Academic Press, 1971

Doscher, M.L., and Woodward, J.A. Estimating the size of

subpopulations of heroin users: Applications of log-linear

models to capture/recapture sampling. Int J Addict, 18:167-182,

1983.

Drug Enforcement Administration. Monitor Program Report.

Washington, D.C., 1983a.

Drug Enforcement Administration. Signature Report. Washington,

D.C., 1983b.

French, J.F. Prevalence of Heroin Use in New Jersey. Unpublished

report. Department of Health; alcohol; Narcotic, and Drug Abuse

Unit, State of New Jersey. Trenton, 1977.

Gilbert, R.O. Approximations of the bias in the Jolly-Seber

capture-recapture model. Biometrics, 29:501-526, 1973.

Greenwood, J.A. Estimating Relative Incidence of Narcotic

Addiction Using Treatment Facility Admissions, DEA Report RTPTR11 June, 1974

Hunt, L.G., and Chambers, C.D. The Heroin Epidemics. New York:

Spectrum, 1976.

170
Jolly, G.M. Explicit estimates from capture-recapture data with

both death and immigration-stochastic model. Biometrika,

52:225-247, 1965.

Leslie, P.H. Statistical appendix. J Anim Ecol, 27:84-86, 1958.

National Institute on Drug Abuse. Heroin Work-Group. Unpublished

report. Washington, D.C., 1983.

Robson, D.S. Mark-recapture methods of population estimation.

In: Johnson, N.L., and Smith, H., Jr., eds. New Developments

in Survey Sampling. New York: Wiley and Sons, 1969.

Seber, G.A.F. A note on the multiple-recapture census.

Biometrika, 52:225-247, 1965.

Seber, G.A.F. The Estimation of Animal Abundance and Related

Parameters. London: Griffin, 1973.

White, E.G. A Computer Program for Capture-Recapture Studies of

Animal Populations: A FORTRAN Listing for the Stochastic Model

of G.M. Jolly. Tussock Grasslands and Mountain Lands

Institute. Spec. Publ. No. 8. 1971.

Woodward, J.A.; Retka, R.L.; and Ng, L. Construct validity of

heroin abuse estimators. Int J Addict, 19:93-117, 1984

AUTHORS

J. Arthur Woodward, Ph.D.

M.L. Brecht, Ph.D.

Department of Psychology

University of California, Los Angeles

Los Angeles, CA

Douglas G. Bonett, Ph.D.

University of Southern California

171
PARTICIPANTS

Work Group on Validity issues in Self-Reported Drug Use

May 8-9, 1984

Edgar Adams, M.S., Chief, SEAB

Division of Epidemiology and

Statistical Analysis, NIDA

5600 Fishers Lane, Room 11A56

Rockville, MD 20857

(301) 443-6637

Michael Backenheimer, Ph.D.

Division of Epidemiology and

Statistical Analysis, NIDA

5600 Fishers Lane, Room 11A56

Rockville, MD 20857

(301) 443-2974

Catherine Bell, M.S.

Division of Clinical Research,

NIDA

5600 Fishers Lane, Room 10A20

Rockville, MD 20857

(301) 443-1514

Marc Brodsky, M.S.

Division of Epidemiology and

Statistical Analysis, NIDA

5600 Fishers Lane, Room 11A55

Rockville, MD 20857

(301) 443-6637

Reuben Cohen, M.A.

Senior Vice-President

Response Analysis Corporation

Research Park, Route 206

Princeton, NJ 08540

(609) 921-3333

Raquel Crider, Ph.D.

Division of Epidemiology and

Statistical Analysis, NIDA

5600 Fishers Lane, Room 11A55

Rockville, MD 20857

(301) 443-6637

Blanche Frank, Ph.D., Chief

Evaluation, Research,

and Planning

New York State Division of

Substance Abuse Services

2 World Trade Center

New York, NY 10047

(212) 488-3967

Joseph Gfroerer, B.A.

Division of Epidemiology and

Statistical Analysis, NIDA

5600 Fishers Lane, Room 11A55

Rockville, MD 20857

(301) 443-6637

Bernard A. Gropper, Ph.D.

Office of Research Programs

National Institute of Justice

Washington, DC 20531

(202) 724-7631

Adele Harrell, Ph.D.

Institute for Social Analysis

1625 "K" Street

Washington, DC 20006

(202) 728-1059

172
Richard Hawks, Ph.D.

Chief, Research Technology

Division of Preclinical

Research, NIDA

5600 Fishers Lane, Room 10A

Rockville, MD 20857

(301) 443-5280

Lloyd Johnston, Ph.D.

Institute for Social Research

The University of Michigan,

Box 1248

Ann Arbor, MI 48106

(313) 763-5043

Nicholas Kozel, M.S.

Division of Epidemiology and

Statistical Analysis, NIDA

5600 Fishers Lane, Room 11A40

Rockville, MD 20857

(301) 443-6637

Douglas Lipton, Ph.D.

Deputy Director for Substance

Abuse Research and

Evaluation

State of New York Division of

Substance Abuse Services

2 World Trade Center,

67th Floor

New York, NY 10047

(212) 488-3969

Jacqueline P. Ludford, M.S.

Chief, Research Analysis Branch

Office of Science, NIDA

5600 Fishers Lane

Rockville, MD 20857

(301) 443-1056

Judith Miller, Ph.D.

Social Research Group

George Washington University

2000 L Street, Northwest

Washington, DC 20036

(202) 275-1910

David Nurco, D.S.W.

Research Professor

Department of Psychiatry

University of Maryland

1229 West Mount Royal Avenue

Baltimore, MD 21217

(202) 621-4208

Louise Richards, Ph.D.

Division of Epidemiology and

Statistical Analysis, NIDA

5600 Fishers Lane, Room 11A56

Rockville, MD 20857

(301) 840-9348

Beatrice Rouse, Ph.D.

Division of Epidemiology and

Statistical Analysis, NIDA

5600 Fishers Lane, Room 11A56

Rockville, MD 20857

(301) 443-2974

Neil H. Sampson, M.B.A.,

M.P.H., Director

Division of Epidemiology and

Statistical Analysis, NIDA

5600 Fishers Lane, Room 11A56

Rockville, MD 20857

(301) 443-6504

Ray Shreckengost, M.S.,

M.A.. M.P.A.

CIA

Washington, DC 20505

(703) 351-6024

Monroe Sirken, Ph.D.

National Center for

Health Statistics

Prince George's Center

Building, Room 2-12

3700 East-West Highway

Hyattsville, MD 20782

(301) 436-7110

173
Reginald Smart, Ph.D.

Director, Program Development

Research Department

Addiction Research Foundation

of Ontario

33 Russell Street

Toronto, Ontario

CANADA M5S 2S1

(416) 595-6017

J. Arthur Woodward, Ph.D.

Department of Psychology, UCLA

Los Angeles, CA 10024

(213) 825-6403

Barry Zuckerman, M.D.

Department of Pediatrics

Boston University School

of Medicine

Boston City Hospital

818 Harrison Avenue

Boston, MA 02118

(617) 424-4234

174
monograph series

While limited supplies last, single copies of the monographs may be

obtained free of charge from the National Clearinghouse for Drug

Abuse Information (NCDAI). Please contact NCDAI also for

information about availability of coming issues and other

publications of the National Institute on Drug Abuse relevant to

drug abuse research.

Additional copies may be purchased from the U.S. Government Printing

Office (GPO) and/or the National Technical Information Service

(NTIS) as indicated. NTIS prices are for paper copy. Microfiche

copies, at $4.50, are also available from NTIS. Prices from either

source are subject to change.

Addresses are:

NCDAI

National Clearinghouse for Drug Abuse Information

Room 10A-43

5600 Fishers Lane

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GPO NTIS

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1 FINDINGS OF DRUG ABUSE RESEARCH. Not available from NCDAI.

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4 NARCOTIC ANTAGONISTS: THE SEARCH FOR LONG-ACTING PREPARATIONS.

Robert Willette, Ph.D., ed.

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5 YOUNG MEN AND DRUGS: A NATIONWIDE SURVEY. John A. O'Donnell,

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12 PSYCHODYNAMICS OF DRUG DEPENDENCE. Jack D. Blaine, M.D., and

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15 REVIEW OF INHALANTS: EUPHORIA TO DYSFUNCTION. Charles Wm.

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16 THE EPIDEMIOLOGY OF HEROIN AND OTHER NARCOTICS. Joan Dunne

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176
17 RESEARCH ON SMOKING BEHAVIOR. Murray E. Jarvik. M.D., Ph.D.,

et al., eds. Includes epidemiology, etiology, consequences of

use, and approaches to behavioral change. From a NIDA-supported

UCLA conference.

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18 BEHAVIORAL TOLERANCE: RESEARCH AND TREATMENT IMPLICATIONS.

Norman A. Krasnegor. Ph.D., ed. Theoretical and empirical studies

of nonpharmacologic factors in development of drug tolerance.

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19 THE INTERNATIONAL CHALLENGE OF DRUG ABUSE. Robert C. Petersen,

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20 SELF-ADMINISTRATION OF ABUSED SUBSTANCES: METHODS FOR STUDY.

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21 PHENCYCLIDINE (PCP) ABUSE: AN APPRAISAL. Robert C. Petersen,

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22 QUASAR: QUANTITATIVE STRUCTURE ACTIVITY RELATIONSHIPS OF

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24 SYNTHETIC ESTIMATES FOR SMALL AREAS: STATISTICAL WORKSHOP

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27 PROBLEMS OF DRUG DEPENDENCE, 1979: PROCEEDINGS OF THE 41ST

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28 NARCOTIC ANTAGONISTS: NALTREXONE PHARMACOCHEMISTRY AND

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Gene Barnett, Ph.D., eds. Papers report research on sustainedrelease and long-acting devices for use with the narcotic antagonist naltrexone. Not available from NCDAI.

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30 THEORIES ON DRUG ABUSE: SELECTED CONTEMPORARY PERSPECTIVES.

Dan J. Lettieri, Ph.D.; Mollie Sayers; and Helen Wallenstein

Pearson, eds. Volume presents summaries of major contemporary

theories of drug abuse by each of 43 leading theorists.

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32 GC/MS ASSAYS FOR ABUSED DRUGS IN BODY FLUIDS. Rodger L. Foltz,

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collection of methods for quantitative analysis of several

important drugs of abuse by gas chromatography- mass spectrometry.

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33 BENZODIAZEPINES: A REVIEW OF RESEARCH RESULTS, 1980. Stephen

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A RAUS (Research Analysis and Utilization System) Review Report

on the abuse liability of the benzodiazepine "tranquilizers."

GPO Stock #017-024-01108-8 $5.00 NTIS PB #82-139106 $13

34 PROBLEMS 0F DRUG DEPENDENCE, 1980: PROCEEDINGS OF THE 42ND

ANNUAL SCIENTIFIC MEETING, THE COMMITTEE ON PROBLEMS OF DRUG

DEPENDENCE, INC. Louis S. Harris, Ph.D., ed. Not available from

NCDAI.

GPO Stock #017-024-01061-8 $8.00 NTIS PB #81-194847 $34

35 DEMOGRAPHIC TRENDS AND DRUG ABUSE, 1980-1995. Louise G.

Richards, Ph.D., ed. Estimates of probable extent and nature of

nonmedical drug use, 1980-1995, based on age structure and other

characteristics of U.S. population.

GPO Stock #017-024101087-1 $4.50. NTIS PB #82-103417 $13

178
36 NEW APPROACHES TO TREATMENT OF CHRONIC PAIN: A REVIEW OF MULTIDISCIPLINARY PAIN CLINICS AND PAIN CENTERS. Lorenz K.Y. Ng, M.D.,

ed. Discussions by active practitioners in the treatment of pain.

GPO Stock #017-024-01082-1 $5.50. NTIS PB #81-240913 $19

37 BEHAVIORAL PHARMACOLOGY OF HUMAN DRUG DEPENDENCE. Travis

Thompson, Ph.D., and Chris E. Johanson, Ph.D., eds. Presents a

growing body of data, systematically derived, on the behavioral

mechanisms involved in use and abuse of drugs.

GPO Stock #017-024-01109-6 $6.50 NTIS PB #82-136961 $25

38 DRUG ABUSE AND THE AMERICAN ADOLESCENT. Dan J. Lettieri,

Ph.D., and Jacqueline P. Ludford, M.S., eds. A RAUS Review

Report, emphasizing use of marijuana: epidemiology, sociodemographic and personality factors, family and peer influence,

delinquency, and biomedical consequences.

GPO Stock #017-024-01107-O $4.50 NTIS PB #82-148198 $14.50

39 YOUNG MEN AND DRUGS IN MANHATTAN: A CAUSAL ANALYSIS.

Richard R. Clayton, Ph.D., and Harwin L. Voss, Ph.D. Examines

the etiology and natural history of drug use, with special focus

on heroin. Includes a Lifetime Drug Use Index.

GPO Stock #017-024-01097-9 $5.50 NTIS PB #82-147372 $19

40 ADOLESCENT MARIJUANA ABUSERS AND THEIR FAMILIES. Herbert

Hendin, M.D., Ann Pollinger, Ph.D., Richard Ulman, Ph.D., and

Arthur Carr, Ph.D. A psychodynamic study of adolescents

involved in heavy marijuana use, to determine what interaction between family and adolescent gives rise to drug abuse.

GPO Stock #017-024-01098-7 $4.50 NTIS PB #82-133117 $13

41 PROBLEMS OF DRUG DEPENDENCE, 1981: PROCEEDINGS OF THE 43RD

ANNUAL SCIENTIFIC MEETING, THE COMMITTEE ON PROBLEMS OF DRUG

DEPENDENCE, INC. Louis S. Harris, Ph.D., ed. Not available from

NCDAI.

Not available from GPO NTIS PB #82-190760 $41.50

42 THE ANALYSIS OF CANNABINOIDS IN BIOLOGICAL FLUIDS. Richard L.

Hawks, Ph.D., ed. Varied approaches to sensitive, reliable, and

accessible quantitative assays for the chemical constitutents of

marijuana, for researchers. Not available from NCDAI.

GPO Stock #017-024-01151-7 $5 NTIS PB #83-136044 $1643

43 PROBLEMS OF DRUG DEPENDENCE, 1982: PROCEEDINGS OF THE 44TH

ANNUAL SCIENTIFIC MEETING, THE COMMITTEE ON PROBLEMS OF DRUG

DEPENDENCE, INC. Louis S. Harris, Ph.D., ed. Not available from

NCDAI.

GPO Stock #017-024-01162-2 $8.50 NTIS PB #83-252-692/AS $40

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44 MARIJUANA EFFECTS ON THE ENDOCRINE AND REPRODUCTIVE SYSTEMS.

Monique C. Braude. Ph.D., and Jacqueline P. Ludford, M.S., eds.

A RAUS Review Report of animal studies and preclinical and

clinical studies of effects of cannabinoids on human endocrine

and reproductive functions.

GPO Stock #017-024-01202-5 $4. NTIS PB #85-150563/AS $14.50

45 CONTEMPORARY RESEARCH IN PAIN AND ANALGESIA, 1983. Roger M.

Brown, Ph.D.; Theodore M. Pinkert, M.D., J.D.; and Jacqueline P.

Ludford, M.S., eds. A RAUS Review Report on the anatomy,

physiology, and neurochemistry of pain and its management.

GPO Stock #017-024-01191-6 $2.75 NTIS PB #84-18467O/AS $11.50

46 BEHAVIORAL INTERVENTION TECHNIQUES IN DRUG ABUSE TREATMENT.

John Grabowski, Ph.D.; Maxine L. Stitzer, Ph.D., and Jack E.

Henningfield, Ph.D., eds. Reports on behavioral contingency

management procedures used in research/treatment environments.

GPO Stock #017-024-01192-4 $4.25 NTIS PB #84-184688/AS $16

47 PREVENTING ADOLESCENT DRUG ABUSE: INTERVENTION STRATEGIES.

Thomas J. Glynn, Ph.D.; Carl G. Leukefeld. D.S.W.; and

Jacqueline P. Ludford, M.S., eds. A RAUS Review Report on a

variety of approaches to prevention of adolescent drug abuse, how

they can be applied, their chances for success, and needed future

research.

GPO Stock #017-024-01180-1 $5.50 NTIS PB #85-159663/AS $22

48 MEASUREMENT IN THE ANALYSIS AND TREATMENT OF SMOKING

BEHAVIOR. John Grabowski, Ph.D., and Catherine S. Bell, M.S..

eds. Based upon a meeting cosponsored by NIDA and the National

Cancer Institute to delineate necessary and sufficient measures

for analysis of smoking behavior in research and treatment

ettings.

GPO Stock #017-024-01181-9 $4.50 NTIS PB 84-145-184 $14.50

49 PROBLEMS OF DRUG DEPENDENCE, 1983: PROCEEDINGS OF THE 45TH

ANNUAL SCIENTIFIC MEETING, THE COMMITTEE ON PROBLEMS OF DRUG

DEPENDENCE, INC. Louis S. Harris, Ph.D., ed. A collection of

papers which together record a year's advances in drug abuse

research; also includes reports on tests of new compounds for

efficacy and dependence liability.

GPO Stock #017-024-01198-3 $12 NTIS PB 85-159663/AS $22.

50 COCAINE: PHARMACOLOGY, EFFECTS, AND TREATMENT OF ABUSE. John

Grabowski, Ph.D., ed. Content ranges from an introductory

overview through neuropharmacology, pharmacology, animal and

human behavioral pharmacology, patterns of use in the natural

environment of cocaine users, treatment, through commentary on

societal perceptions of use.

GPO Stock #017-020-01214-9 $4 NTIS PB 85-150381/AS $14.50

180
51 DRUG ABUSE TREATMENT EVALUATION: STRATEGIES, PROGRESS, AND

PROSPECTS. Frank M. Tims, Ph.D., ed. A state-of-the-art review

of drug abuse treatment evaluation, identifying research needs,

promising approaches, and emerging issues.

GPO Stock #017-020-01218-1 $4.50 NTIS PB 85-150365/AS $17.50

52 TESTING DRUGS FOR PHYSICAL DEPENDENCE POTENTIAL AND ABUSE

LIABILITY. Joseph V. Brady, Ph.D., and Scott E. Lukas, Ph.D.,

eds. Describes animal and human test procedures for assessing

dependence potential and abuse liability of opioids, stimulants,

depressants, hallucinogens, cannabinoids, and dissociative

anesthetics.

GPO Stock #017-024-0204-1 $4.25 NTIS PB 85-150373/AS $16

54 MECHANISMS OF TOLERANCE AND DEPENDENCE. Charles Wm. Sharp,

Ph.D., ed. Review of basic knowledge concerning the mechanism of

action of opiates and other drugs in producing tolerance and/or

dependence.

GPO Stock #017-024-01213-1 $8.50 NTIS PB No. to be assigned.

55 PROBLEMS OF DRUG DEPENDENCE, 1984. PROCEEDINGS OF THE 46TH

ANNUAL SCIENTIFIC MEETING, THE COMMITTEE ON PROBLEMS OF DRUG

DEPENDENCE, INC. Louis S. Harris, Ph.D., ed.

IN PRESS OR PREPARATION

53 PHARMACOLOGICAL ADJUNCTS IN SMOKING CESSATION.

John Grabowski, Ph.D., ed.

56 ETIOLOGY OF DRUG ABUSE: IMPLICATIONS FOR PREVENTION. Coryl

LaRue Jones, Ph.D., and Robert J. Battjes, D.S.W., eds.

58 PROGESS IN THE DEVELOPMENT 0F COST-EFFECTIVE TREATMENT FOR

DRUG ABUSERS. Rebecca S. Ashery, D.S.W., ed.

59 CURRENT RESEARCH ON THE CONSEQUENCES OF MATERNAL DRUG ABUSE.

Theodore M. Pinkert, M.D., J.D., ed.

60 PRENATAL DRUG EXPOSURE: KINETICS AND DYNAMICS. C. Nora

Chiang, Ph.D., and Charles C. Lee, Ph.D., eds.

* U.S. GOVERNMENT PRINTING OFFICE: 1985–478-257

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