National Institute on Drug Abuse

RESEARCH

MONOGRAPH SERIES

Social Networks,

Drug Abuse, and

HIV Transmission

U.S. Department of Health and Human Services • Public Health Services • National Institutes of Health

151

Social Networks, Drug Abuse, and

HIV Transmission

Editors:

Richard H. Needle, Ph.D., M.P.H.

Susan L. Coyle, Ph.D.

Division of Epidemiology and Prevention Research

National Institute on Drug Abuse

Sander G. Genser, M.D., M.P.H.

Division of Clinical and Services Research

National Institute on Drug Abuse

Robert T. Trotter II, Ph.D.

Northern Arizona University

NIDA Research Monograph 151

1995

U.S. DEPARTMENT OF HEALTH AND HUMAN SERVICES

Public Health Service

National Institutes of Health

National Institute on Drug Abuse

5600 Fishers Lane

Rockville, MD 20857
ACKNOWLEDGMENT

This monograph is based on the papers from a technical review on

“Social Networks, Drug Abuse, and HIV Transmission” held on August

19-20, 1993. The review meeting was sponsored by the National

Institute on Drug Abuse.

COPYRIGHT STATUS

The National Institute on Drug Abuse has obtained permission from the

copyright holders to reproduce certain previously published material as

noted in the text. Further reproduction of this copyrighted material is

permitted only as part of a reprinting of the entire publication or chapter.

For any other use, the copyright holder’s permission is required. All

other material in this volume except quoted passages from copyrighted

sources 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.

The U.S. Government does not endorse or favor any specific commercial

product or company. Trade, proprietary, or company names appearing in

this publication are used only because they are considered essential in the

context of the studies reported herein.

National Institute on Drug Abuse

NIH Publication No. 95-3889

Printed 1995

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.

ii
Contents

Introduction: The Social Network Research Paradigm . . . . . . . . . . . . . 1

Richard H. Needle, Susan L. Coyle, Sander G. Genser, and

Robert T. Trotter ZZ

Social Networks in Disease Transmission:

The Colorado Springs Study . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3

Richard B. Rothenberg, Donald E. Woodhouse, John J. Potterat,

Stephen Q. Muth, William W. Darrow, and Alden S. Klovdahl

Using Dyadic Data for a Network Analysis of HIV Infection and

Risk Behaviors Among Injecting Drug Users . . . . . . . . . . . . . . . . . . .

Alan Neaigus, Samuel R. Friedman, Marjorie Goldstein,

Gilbert Zldefonso, Richard Curtis, and Benny Jose

Injecting Drug Use, Characteristics of Significant Others, and

HIV Risk Behaviors . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

Rumi K. Price, Linda B. Cottler, Doug Mager, and

Keith S. Murray

Sibling Homophily in HIV Infection: Biopsychosocial Linkages

in an Urban African-American Sample . . . . . . . . . . . . . . . . . . . . . . . .

Ann F. Brunswick, Peter A. Messeri, Jay Dobkin,

Mary T. Flood, and Arthur Yang

Focal Networks and HIV Risk Among African-American Male

Intravenous Drug Users . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

Frederick W. Frey, Elias Abrutyn, David S. Metzger,

George E. Woody, Charles P. O’Brien, and Paul Trusiani

A Comparison of Drug Use Networks Across Three Cities . . . . . . . . .

Mark L. Williams, Zhangqing Zhuo, Harvey A. Siegal,

Rafaela R. Robles, Robert T. Trotter ZZ, and Adelbert Jones

Ethical and Legal Issues in Social Network Research:

The Real and the Ideal . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

Donald. E. Woodhouse, John J. Potterat, Richard B. Rothenberg,

William W. Darrow, Alden S. Klovdahl, and Stephen Q. Muth

20

38

60

89

109

131

iii
Network Models for HIV Outreach and Prevention Programs

for Drug Users. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 144

Robert T. Trotter II, Anne M. Bowen, and James M. Potter, Jr.

A Personal Network Approach to AIDS Prevention:

An Experimental Peer Group Intervention for Street-Injecting

Drug Users: The SAFE Study . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 181

Carl A. Latkin

Promising Social Network Research Results and Suggestions

for a Research Agenda . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 196

Samuel R. Friedman

iv
Introduction: The Social Network

Research Paradigm

Richard H. Needle, Susan L. Coyle, Sander G. Genser, and

Robert T. Trotter II

On August 19-20, 1993, the National Institute on Drug Abuse (NIDA)

held its first meeting to explore the social network research paradigm and

its application to the study of drug use and human immunodeficiency

virus (HIV) transmission. The meeting was jointly sponsored by NIDA’s

Community Research Branch (in the Division of Epidemiology and

Prevention Research) and Clinical Medicine Branch (in the Division of

Clinical and Services Research) and included participants from the United

States, Puerto Rico, and Australia. By the conclusion of the meeting, it

was evident that the study of drug user networks was a promising

theoretical and methodological complement to the more heavily relied

upon and empirically established paradigm that focuses on individual risk

behaviors. Network analysis offers a unique opportunity to examine

nonrandom patterns of risk behaviors and HIV transmission, the potential

of HIV spread, and opportunities for developing and implementing

strategies to prevent drug use and the transmission of disease, including

drug treatment linkages to nondrug-using networks.

Presentations at the technical review meeting examined the intertwined

epidemics of drug abuse and acquired immunodeficiency syndrome

(AIDS), with attention to HIV transmission in the context of a variety of

networks-networks of drug injectors, sex workers, siblings and other

relatives, and sexual partners or significant others. The studies reviewed

at the meeting indicated that network characteristics did indeed affect

behavioral practice as well as the probability of viral transmission. For

example, seroincidence is affected by such structural characteristics of

networks as their basis of affiliation, their density (the proportion of

direct ties among network members out of all possible ties), and their

reachability (proportion of network members connected by indirect as

well as direct paths). Mixing strategies also affect seroincidence, as

migration or other outgroup contacts constitute bridges to other networks,

where viral infection may or may not be already seeded. What is more,

network membership characteristics (e.g., an actor’s centrality [an

indexed number of his or her aggregate ties within a network]) and

network norms (e.g., drug injection frequency, sexual solicitation,

1
abstinence) appeared to influence the adoption of individual risk

behaviors. Finally, data presented at the technical review meeting

demonstrated that network-oriented interventions aimed at diffusing

information about HIV and at changing transactional patterns have been

successful at introducing behavioral change among network members,

reducing high-risk behaviors, accelerating readiness for treatment, and

limiting the spread of HIV.

Many of the presenters to NIDA’s technical review have contributed the

chapters contained in this research monograph, including a summary

review that reflects the discussions among investigators that took place at

the technical review meeting. NIDA staff is enthusiastic about publishing

these chapters and hopes that this monograph will stimulate greater

interest and involvement in network research related to drug abuse and

HIV infection.

AUTHORS

Richard H. Needle, Ph.D., M.P.H.

Chief

Community Research Branch

Susan L. Coyle, M.A., Ph.D.

Health Scientist Administrator

Community Research Branch

Division of Epidemiology and Prevention Research

Sander G. Genser, M.D., M.P.H.

Acting Chief

Clinical Medicine Branch

Division of Clinical and Services Research

National Institute on Drug Abuse

Parklawn Building

5600 Fishers Lane

Rockville, MD 20857

Robert T. Trotter II, Ph.D.

Department of Anthropology

Northern Arizona University

Flagstaff, AZ 86011

2
Social Networks in Disease

Transmission: The Colorado

Springs Study

Richard B. Rothenberg, Donald E. Woodhouse, John J. Potterat,

Stephen Q. Muth, William W. Darrow, and Alden S. Klovdahl

INTRODUCTION

Colorado Springs, a community of 400,000 located 65 miles south of

Denver, has reported 740 occurrences of human immunodeficiency virus

(HIV) through 1992. The number of HIV-positive cases, the number of

acquired immunodeficiency syndrome (AIDS) cases, and the number of

AIDS deaths have been fairly constant since 1986 (Potterat et al. 1993).

More than 60 percent of infected persons have resided in Colorado

Springs for less than a year. Intensive case investigation and partner

notification have failed to reveal significant endogenous transmission.

Yet a substantial, well-described nexus of prostitutes, persons who use

injectable drugs, and their associates-all of whom practice behaviors

that can transmit HIV-exists in Colorado Springs. Unlike the major

epicenters, for which the question is why so much transmission is

occurring, the question of concern in Colorado Springs is why there is so

little transmission. With this concern in mind, a study of people who

might transmit HIV by heterosexual or drug-related routes was initiated,

and an attempt was made to add a social network perspective to

traditional epidemiologic approaches (Woodhouse et al. 1994). This

chapter uses network-analytic methods to examine the relative

prominence of persons in this setting and its possible implications for

HIV transmission.

METHODS

Study Design

This study of prostitutes, the paying and nonpaying partners of

prostitutes, persons who use injectable drugs, and the sexual partners of

drug users, was begun in Colorado Springs in 1988. Participants, all

presumably at high risk of acquiring and transmitting HIV and other

3
organisms, were enrolled through the sexually transmitted disease (STD)

clinic, a drug clinic, self-referral, and street outreach; persons named by

two or more respondents were also sought as participants. Because

random sampling is not an option with a mobile and elusive population

(Johnson 1990), purposive enrollment was used to construct an

interacting group of persons representative of the general milieu. The

population is not representative of that of Colorado Springs in general.

All persons enrolled in the study were asked to participate in a series of

yearly interviews. Of the original 595 respondents, 278 (47 percent)

completed a second interview, and 100 (17 percent) completed a third;

fewer than 20 were interviewed a fourth or fifth time (Johnson 1990).

During each interview, which usually lasted over 1 hour, respondents

were asked about demographic characteristics; knowledge, attitudes, and

practices with regard to HIV/AIDS; past medical history; and

self-perception of risk for HIV/AIDS. They were also asked for a

complete enumeration of their personal contacts. Contacts were defined

as social (specifically, sharing meals or lodging), sexual, using injectable

drugs, or using noninjectable drugs. For each contact in the previous 6

months, the respondent was asked (as appropriate) if needles were shared,

what specific sexual practices took place, and if a condom was used.

Respondents were asked to identify the strength of their relationship with

the contact and to indicate the types of relationships that existed among

their contacts. Respondents were counseled about HIV and asked to

undergo a blood test for HIV, hepatitis B virus, and syphilis. (Many

participants already avail themselves of the routine counseling and testing

services offered in Colorado Springs.)

Network Analysis

Graph Definition and Analysis Package (GRADAP), a multipurpose

network analysis program that provides information on groups of

interconnected persons (Inter-University Project Group of the

Universities of Amsterdam, Groningen, Nijmegen, and Twente 1989),

was used for the analysis. Such a group, known as a connected

component, comprises persons who are associated with each other either

directly or indirectly. In network terms, there exists a path of some

length from one person to any other person in the connected component.

Each person in the component can be connected to one or many; each

relationship may be of a single type (exclusive: for example, only sexual)

or of several types (multiple: for example, sexual and needle sharing). If

X persons are sampled, the number of connected components can vary

4
from 1 (all persons are connected to each other) to Y (no one is connected

to anyone else).

To analyze connected components, the first interviews for the 595

respondents were used (table 1). Using GRADAP, connected

components were created in three ways: (1) using all possible

relationships between people; (2) using only exclusive relationships

(e.g., only sexual alone was counted; sexual in conjunction with other

relationships was not counted); and (3) using multiple relationships

(e.g., sexual was counted whether or not other relationships were present

within the respondent-contact pair). The position of the 17 HIV-positive

persons within the components of this network was then identified.

TABLE 1. Enumeration of interviews, respondents, and contacts in the

social network: Colorado Springs, 1988-1991.

Respondents Contacts Contacts Persons

Plus Only

Associates

All interviews 990 10,033 9,050 6,995

First interview 595 6,474 5,894 5,162

First interview

(complete)

559 6,474 5,819 5,090

Connected

component

of respondents

341 3,942 3,818 3,016

Measurement of Centrality

In 1989, Stephenson and Zelen (1989) described an approach for

measuring centrality that was based on the analogy between the matrix of

persons in a social network and the theory of statistical estimation in the

design of experiments; this approach was applied to the Colorado Springs

network data. This method creates a measurement for each person in a

connected component that represents the average distance from that

person to all other persons in the network. The measurement, called

5
information centrality, incorporates every possible pathway from one

person to all other persons.

Unweighted information centrality scores were calculated for 341

respondents in the network who were interconnected (that is, who

constituted the largest connected component among respondents) (table

1) and compared the unweighted score with scores derived from two

methods for weighting. First, a risk-weighted centrality measure was

created by assigning values to the types of connections between any two

persons: 0.5 for injecting drug use, 0.3 for sexual contact, and 0.1 for

noninjecting drug use and for social contact. These arbitrary values sum

to 1 and were chosen to give relative weights to the probability for

transmission. This weighting scheme was an attempt to capture

information about the implications of multiple connections for disease

transmission.

The second weighting scheme-relationship weighting-used a variable

measurement that asked respondents to provide an index of the strength

of their relationship with the contact (in this matrix, the contact is another

respondent). Respondents were asked to grade their relationship with a

named contact on a scale of 1 (casual acquaintance) to 10 (best friend).

The responses ranged over the entire scale, with peaks at 1, 5, and 10, and

they generally were evenly distributed over the range. This score was

multiplied by 0.1 to provide a range from 0.1 to 1.0. Relationshipweighted centrality attempts to capture information about the strength of

social interactions.

Though the information centrality score has a specific statistical

definition, its absolute value does not convey immediate meaning, and

small differences between centrality scores are hard to interpret.

Centrality ranks, reflecting a person’s position in the network, may be of

more use. The persons who are most central (in the highest decile) were

compared to those who are least central (in the lowest decile) with regard

to behaviors and practices.

RESULTS

Over the 4 years of the study (1988 through 1991), 1,078 people were

identified as eligible for the study, and 595 agreed to participate

(Woodhouse et al. 1994). Nonparticipation was primarily related to

mobility (63 percent could not be located) and refusal (28 percent). The

6
595 respondents named a total of 10,033 contacts (persons who have a

direct relationship with the respondent) and associates (persons known to

the respondent through someone else), but that simple enumeration

requires further specification. In their first interview, the 595 respondents

named 6,474 contacts and associates, of whom 5,894 were contacts

(table 1). Because some respondents are also contacts, and because some

contacts are named by more than one respondent, these 5,894

respondent-contact pairs comprised 5,162 different persons.

Characteristics of Persons in the Network

Descriptive information about the respondents confirms the

appropriateness of the convenience sample and the respondents’ potential

to be involved in HIV transmission (Woodhouse et al. 1994). Of the 595

respondents, 133 were prostitutes, roughly one-half of the estimated

number of prostitutes prevalent in Colorado Springs (Potterat et al. 1990).

Two hundred persons who used injectable drugs were enrolled as

respondents, but close to 600 additional persons were identified through

the interview process (if the rate of use of injectable drugs in the United

States is roughly 1 in 250, about 1,500 such persons would be expected

in Colorado Springs) (Anderson and Way 1992). The prostitutes were

the youngest group and their paying partners, the eldest. Whites

predominated, except among the nonpaying partners of prostitutes. Most

persons had slightly less than a high school education.

Most of the respondents had been in the Colorado Springs area for 3 to

5 years, but the time they had lived at their current residence was

considerably less, particularly among prostitutes. The subgroup of

HIV-infected persons—and prior experience in Colorado Springs

(Potterat et al. 1993)—suggests that infected persons had been in

residence for a shorter time, possibly contributing to their position in the

networks. The overall impression is that this group consists of persons in

early adulthood who are predominantly white, undereducated, and

relatively mobile.

Information about risky behaviors is derived from two sources: the

reports of actual interactions between respondents and their contacts, and

the indepth interview information on respondents. Both sources indicated

that risky behaviors were common. Among the 5,894 respondent-contact

pairs, very few used condoms, and needles were shared in 13 percent of

the events involving injecting drug use. Focusing on a subset of respondents for whom detailed information is complete (536 out of 595), the

7
substantial frequency of risky behaviors can be contrasted with the

relative self-perception of low risk. For example, 17 percent stated that

they rarely or never cleaned their works; 67 percent had sex with a person

who used injectable drugs; and 30 percent said that they had exchanged

sex for drugs or money. Perhaps even more important is the observation

that 60 to 73 percent of people with these characteristics considered

themselves at low risk of acquiring HIV. Similarly, the overall frequency

both for sexual contacts (inflated because of the large number of

prostitute contacts) and for taking drugs is high, but there does appear to

be a difference in the behavior of those who consider themselves at high

risk and those who do not. Interestingly, needle sharing is reported

infrequently, a finding at variance not only with self-reported behaviors

but with the actual reported occurrence described later in this chapter.

Interrelationships of Risk

The simple categorization of respondents does not capture the complexity

of personal activities in this group. If viewed from a multifactorial rather

than hierarchical perspective, one can see that risks occur simultaneously

and that more categories exist than the six devised for enrollment. For

example, 59 of the 133 prostitutes were also users of injectable drugs, as

were 28 of their paying partners.

As the analysis moves toward a more network-oriented approach-one

that looks simultaneously at multiple factors and multiple

interrelationships-the richness of the human interchange and its

potential impact on disease transmission become more evident. Thus,

when the nature of the interactions of the 595 respondents with the 5,894

contacts named in the first interview is examined, 15 categories of

overlapping risk that characterize the respondent-contact pairs are found

(table 2). In social network terms, the uniplex relationships are found

along the diagonal of the upper box. Duplex relationships are counted to

the left and below the diagonal, and multiplex relationships are presented

in the box below. Relationships involving social interactions and

noninjecting drug use predominate. It is of interest that use of injectable

drugs seems to occur less often in isolation than do the other forms of

connection. Only 3 1 percent (147 of 475) of the relationships involving

the use of injectable drugs occur in isolation. Overall, 29 percent (1,709

of 5,894) of all relationships involve interactions with the direct potential

for HIV transmission.

8
TABLE 2. Relationships among 595 respondents and their 5,894

contacts: Colorado Springs, 1988-1991.

Social Drug Sexual IDU Total

Social 1,648

Drug 823 767

Sexual 521 178 804

IDU 137 112 31 147

TOTAL 3,129 1,057 835 147 5,168

Sexual, IDU, drug

Social, IDU, drug

Social, sexual, drug

Social, sexual, IDU

Social, sexual, IDU, drug

17

176

398

53

82

TOTAL 5,894

KEY: IDU = injecting drug user.

SOURCE: Woodhouse et al. (1994).

Connected Components

Using single relationships only, the pattern was found in each instance to

be a large, single, connected component with many components of much

smaller size. In general, the HIV-positive persons are found in the

smaller components, peripheral, as it were, to the major group of

interconnected persons (table 3). A similar pattern emerges from

examining multiple components. Most HIV-positive persons appear in

smaller, unconnected groups. For example, only 4 of 10 HIV-positive

9
TABLE 3. Summary of connected components in the network of 595 respondents and 5,894 contacts using

exclusive (uniplex) and multiple (multiplex) relationships: Colorado Springs, 1988-1991.

Type of

Connection

Total Total No. of No. of Persons Number of HIV+ Size Range No. of HIV+

Number of Persons in in the Largest Persons in of Smaller Persons in

Components Components Single Largest Single Components Smaller

Component Component Components

Exclusive

Sexual 171 1,109 406 0 2-19 5

IDU 39 176 64 1 2-6 2

Non-IDU 126 945 230 5 2-92 3

Social

Multiple

226 2,733 977 3 2-41 11

Sexual 299 2,233 965 5 2-29 12

IDU 97 766 365 4 2-21 6

Non-IDU 139 2,336 1,253 5 2-66 7

Social 198 3,603 1,910 5 2-54 12

All 147 5,162

KEY: HIV+= HIV positive.

SOURCE Woodhouse et al. (1994).

3,658 9 2-40 9
persons are in the large connected component of 356 who use injectable

drugs. When all forms of contact are considered simultaneously, about

half of the HIV-infected persons are in the large component of 3,658, and

the other half are in the remaining small components.

Centrality Scores

Unweighted, risk-weighted, and relationship-weighted centrality scores

each have a different scale, but all have similar distributions (table 4). All

three distributions are slightly skewed to the left (mean is less than

median) but are nearly normally distributed. There is a high but

imperfect correlation among the three (table 5). Both product-moment

and rank-order correlation coefficients are similar and exhibit a lower

correlation between risk-weighted and relationship-weighted scores than

between each of these with the unweighted score.

In addition to its role in network analysis, the centrality score may

function as a variable for descriptive purposes. Within this matrix,

prostitutes and their nonpaying partners had the highest mean centrality

scores (table 6). Though the differences were small, the unweighted

score placed prostitutes first, and the risk-weighted score placed

nonpaying partners first. All three measures put persons who use

injectable drugs in a less central position.

Centrality Ranks

The upper and lower deciles for centrality appear to be similar in their

self-perception of AIDS risk (about 40 percent believe themselves to be

at high risk), and two-thirds of each group state that they have changed

their behavior since the advent of AIDS (table 7). More than 90 percent

of each group state that they clean their works, and the majority say that

they do it most of the time. More than 70 percent of both groups have

had sex with a person who uses injectable drugs. There are, however,

some important differences. Sixty-five percent of the top decile have

exchanged sex for money or drugs, compared with 27 percent of the

lowest decile. Similarly, 77 percent of the former group have had active

anal sex, compared to 19 percent of the latter group. About 1.5 times as

many people in the highest decile than in the lowest decile claim to know

someone with AIDS.

11
TABLE 4. Information centrality scores for the matrix of 341

interconnnected respondents: Colorado Springs, 1988-1993.

Unweighted Risk- RelationshipWeighted Weighted

Mean 0.604 0.144 0.222

Standard deviation 0.207 0.050 0.078

Median 0.613 0.152 0.237

Maximum 0.978 0.222 0.334

90th percentile 0.888 0.203 0.314

10th percentile 0.317 0.222 0.106

Minimum 0.154 0.025 0.047

TABLE 5. Correlation among centrality scores using unweighted, riskweighted, and relationship-weighted measures: Colorado

Springs, 1988-1991.

Unweighted RiskWeighted

RelationshipWeighted

P

*

 S+

P S P S

Unweighted

Risk-weighted

Relationshipweighted

1 1 0.903 0.914 0.877 0.910

1 1 0.809 0.859

1 1

KEY: P

*

 = Pearson’s r.

S

†

 = Spearman’s r.

12
TABLE 6. Mean centrality scores associated with behavioral categories

of respondents: Colorado Springs, 1988-1991.

Centrality Score (Mean)

Category Sex N Unweighted

Prostitute Female 97

Paying Male

partner

Nonpaying Male

partner

IDU Female

Male

Sex

partner

of IDU

Female

Male

Other Female 21 0.602 0.132 0.245

Male 8 0.486 0.120 0.181

58

39 0.687 0.164 0.257

43

53

3

4

0.714

0.599

0.505 0.131 0.189

0.488 0.126 0.185

0.490 0.099 0.209

0.438 0.103 0.137

Riskweighted

0.161

0.145

Relationshipweighted

0.258

0.207

KEY: IDU = injecting drug user.

Similarly, the highest and lowest decile are not uniformly distinguished

by the frequency and intensity of certain behaviors (table 8). Judged by

risk-weighted centrality, the highest decile of this matrix has a substantially larger overall number of sexual contacts (142 in 6 months, compared with 30 in 6 months for the lowest decile) and a much greater

frequency of anal sex since the advent of AIDS. The highest decile has

also had substantially larger numbers of contacts with whom noninjectable drugs were used. On the other hand, persons at both ends of the

centrality spectrum have similar experience with sharing needles.

When relationship-weighted centrality was used, the results were found to

be similar except for two activities. First, the extraordinary difference in

the frequency of anal sex apparent in risk-weighted centrality is reversed:

Those in the lowest decile have a larger mean number of persons with

13
TABLE 7. Percent of persons with selected characteristics in the

highest and lowest deciles for risk-weighted centrality:

Colorado Springs, 1988-1991.

Highest Decile Lowest Decile

High chance of getting AIDS 43 42

Changed behavior since AIDS 67 65

Clean works 93 90

Clean works > 75 percent of time 71 67

Had sex with an IDU 73 71

Had passive anal sex without a

condom

20 19

Had active anal sex 77 19

Exchanged sex for money/drugs

Know someone with AIDS

KEY: IDU = injecting drug user.

65 27

60 39

whom they have had anal sex when relationship-weighted centrality is

used. Second, those in the lowest decile also have a larger mean number

of persons with whom they have shared needles during the past 5 years.

Finally, if one looks closely at the 8 HIV-positive persons in this network

of 341 respondents, the predominance of persons who inject drugs

(paralleling their predominance among all HIV-positive heterosexuals in

Colorado Springs) is noted, and all but one are noncentral in the matrix

(table 9). Their centrality diminishes even further (again with only one

exception) when relationship-weighted centrality is examined. The one

person who is central by the risk-weighted criterion moves out of the

highest decile for relationship-weighted centrality.

14
TABLE 8. Values for selected characteristics of persons in the highest

and lowest deciles of risk-weighted centrality: Colorado

Springs, 1988-1991.

Risk-Weighted Relationship-Weighted

Centrality Centrality

Highest Lowest Highest Lowest

10 Percent 10 Percent 10 Percent 10 Percent

Lived at current

address (mo)

Lived in region

(mo)

Highest grade

completed

Frequency of

anal sex (10 yr)

Social contacts

(no., 6 mo)

Sexual contacts

(no., 6 mo)

Shared needles

(no., 6 mo)

Shared needles

(no., 5 yr)

Used drugs

(no., 6 mo)

Used drugs

(no., 5 yr)

20 21 18

45 42 43

12 11 12

264 1 3

8 6 8

142 163

2

5

20

511

30

1

7

12

100

1

4

24

542

20

39

13

14

5

54

3

12

11

91

15
TABLE 9. Characteristics of HIV-positive persons: Colorado Springs,

1988-1992.

Category Sex Race

RiskWeighted

Centrality

RelationshipWeighted

Centrality

IDU

IDU

Prostitute

(IDU)

Other

(IDU)

IDU

Nonpaying

partner

(IDU)

Prostitute

F White, nonHispanic

M

F

M 215 249

M

M Black, nonHispanic

F White,

Hispanic

Nonpaying M Black,

partner Hispanic

227 286

241

293

196 211

129 90

222 236

29 88

319

302

KEY: IDU = injecting drug user.

DISCUSSION

These network results, including the size of personal networks (on

average, about 10) and the number and distribution of connected

components, cannot be compared directly to most other networks

analyses because of inherent differences in target populations. Further

experience with such personal networks is needed for empiric validation

of the completeness of network ascertainment.

Nonetheless, these data suggest that one possible reason for the absence

of significant transmission of HIV in Colorado Springs is the position of

16
infected people within the larger social network. If the personal networks

of infected people are small and not connected to larger, more interacting

components, transmission may be impeded. The presence of transmitting

behaviors alone may not be sufficient to produce significant spread of

disease.

This potential explanation was examined further through a preliminary

analysis of centrality within the network. The meaning of the term

“centrality,” as applied to persons within a network, is both intuitively

obvious and elusive. The term attempts to define members of a network

who are important, but importance itself is vague and requires

considerable specification. Centrality, by default, is often defined by the

tool developed to measure it. Freeman, for example, describes three such

tools: degree, or the number of points that are directly adjacent to a given

point; closeness, a measure of the distance between a given point and all

points on the shortest paths (the geodesic) that touch the given point; and

betweenness, which examines the frequency with which a given point

falls on the geodesic that connects pairs of points (Freeman 1979).

Information centrality, a relatively new measure, offers some promise for

providing insights into the structure of networks and the relative

importance of people within them.

Results using information centrality suggest that the central persons in

this network participate in greater sexual risk taking than do noncentral

persons, a finding that is in keeping with the high mean centrality score

among prostitutes. The highest and lowest deciles are similar with regard

to injectable drug use, however. In fact, on the the basis of relationshipweighted centrality, persons in the lowest decile may actually share

needles more than members of the highest decile. This observation is

consonant with the low mean centrality scores among injectable drug

users. Finally, the comparison of risk- and relationship-weighingparticularly as applied to HIV-positive persons-may provide a measure

of the degree to which people are socially marginalized. Taken together,

observations on connected components and centrality suggest

mechanisms by which social network structure may impede the

transmission of HIV.

These conclusions must be viewed as tentative, since they are based on a

subset of a nonrandom sample, involve data collection from an elusive

population, depend to some extent on self-reported behaviors and

practices, and do not fully explore the potential of network analysis.

They do, however, suggest that such analysis may make a contribution to

17
understanding-and perhaps altering—the dynamics of disease

transmission. Future research might focus on the hypothesis generated by

this study that network structure may serve as barrier or facilitator in the

transmission of disease. In addition, it is clear that the effort required to

describe networks is considerable, and an important focus for future work

will be the rapid and inexpensive delineation of network structure.

REFERENCES

Anderson, R.M., and May, R.M. Understanding the AIDS pandemic.

Sci Am 266:58-66, 1992.

Freeman, L.C. Centrality in social networks: Conceptual clarification.

Soc Netw 1:215-239, 1979.

Inter-University Project Group of the Universities of Amsterdam,

Groningen, Nijmegen, and Twente. GRADAP: Graph Definition and

Analysis Package. Computer program. Groningen, The Netherlands:

ProGAMMA, 1989.

Johnson, J.C. Selecting ethnographic informants. In: Johnson, J.C., ed.

Sage University Paper Series on Qualitative Research Methods.

No. 22. Newbury Park, CA: Sage Publishing Inc., 1990.

Potterat, J.J.; Woodhouse, D.E.; Muth, J.B.; and Muth, S.Q. Estimating

the prevalence and career longevity of prostitute women. J Sex Res

27:233-243, 1990.

Potterat, J. J.; Woodhouse, D.E.; Rothenberg, R.B.; Muth, S.Q.; Dar-row,

W.W.; Muth, J.B.; and Reynolds, J.U. AIDS in Colorado Springs: Is

there an epidemic? AIDS 7:1517-1521, 1993.

Stephenson, K., and Zelen, M. Rethinking centrality: Methods and

examples. Soc Netw 11:1-37, 1989.

Woodhouse, D.E.; Rothenberg, R.B.; Potterat, J.J.; Dar-row, W.W; Muth,

S.Q.; Klovdahl, A.S.; Zimmerman, H.P.; Rogers, H.L.; Maldonado,

T.S.; Muth, J.B.; and Reynolds, J.U. Mapping a social network of

heterosexuals at high risk for human immunodeficiency virus

infection. AIDS 8:1331-1336, 1994.

18
AUTHORS

Richard B. Rothenberg, M.D., M.P.H.

Department of Family and Preventive Medicine

Emory University School of Medicine

60 Butler Street, S.E.

Atlanta, GA 30303

Donald E. Woodhouse, J.D.

John J. Potterat, B.A.

Stephen Q. Muth, B.A.

El Paso County Department of Health

and Environment

301 South Union Boulevard

Colorado Springs, CO 80910

William W. Darrow, Ph.D.

Department of Public Health

Florida International University

North Campus

3000 N.E., 145th Street

North Miami, FL 33181-3600

Alden S. Klovdahl, Ph.D.

Department of Sociology

The Faculties

The Australian National University

Canberra, A.C.T.

Australia 0200

19
Using Dyadic Data for a Network

Analysis of HIV Infection and Risk

Behaviors Among Injecting Drug

Users

Alan Neaigus, Samuel R. Friedman, Marjorie Goldstein,

Gilbert Ildefonso, Richard Curtis, and Benny Jose

INTRODUCTION

The emphasis in much contemporary network analysis is on investigating

and analyzing networks as structures of links between network members.

Analyzing simpler network structures-dyads within egocentric

networks-may also yield useful results and, under certain circumstances,

may be a feasible way to conduct a network-informed study among

hidden populations such as injecting drug users (IDUs). This chapter is

based on a study of HIV risk among IDUs in a high-risk neighborhood in

Brooklyn (the Social Factors and HIV Risk [SFHR] project). The chapter

discusses some of the methodological and practical issues involved in

establishing links and recruiting IDU networks. Issues of network data

validation are also covered. The chapter reviews some analyses of the

relationships between the character of dyadic links in egocentric networks

of IDUs and the likelihood that subjects are infected with HIV and

engage in high-risk behaviors. Finally, the chapter considers network

turnover among IDUs.

METHODOLOGICAL ISSUES IN RECRUITING IDU NETWORKS

AND ESTABLISHING LINKS

The primary unit in developing network data is the dyadic link.

Networks are the arrangement of these links. Thus, developing methods

to nominate, recruit, and validate network links is fundamental to

analyzing social networks. This process is never an easy one. For

example, both the characteristics of network members elicited from index

subjects and the size of the network depend on the naming stimulus

(Campbell and Lee 1991; van der Poel 1993), how many names a subject

wishes to reveal, variations in the skill of interviewers to elicit names, and

the content of the relationship being studied (Milardo 1992). In

20
particular, researchers studying networks within populations engaging in

illicit or stigmatized activities may find index subjects extremely reticent

about revealing the names of network members. In the case of

populations such as IDUs, who may have anonymous relationships with

those with whom they engage in illicit or stigmatized activities, subjects

may not know (with sufficient detail to enable identification) who is in

their network. In addition to a description of the project, this chapter

explains how the SFHR project obtained the names of the IDU network

members of index subjects, how these members were recruited, how the

links were validated, and some of the issues that arose during this

process.

The SFHR project was conducted in the Bushwick neighborhood in

Brooklyn. This is a neighborhood with a large number of poor people

(income below $10,000) and is predominantly Latino. The neighborhood

borders on other areas with populations that are predominantly white

(neighborhoods in Queens such as Ridgewood and Maspeth) and

predominantly black (Bedford-Stuyvesant, which is also in Brooklyn).

Bushwick has large concentrations of drug injectors, many of whom are

HIV positive. There are also many crack users in the neighborhood, as

well as an active trade by women in exchanging sex for money and drugs.

The aims of the study, in part, are to determine what factors are

associated with a lower risk of HIV infection among particular groups of

injectors, especially new injectors. In the first phase of the study,

ethnographic interviews were conducted over several days with

individual injectors, and ethnographic observations were undertaken in

the study area. These methods were used to understand the varieties of

drug-injector networks and social patterns in the study area and to

develop a structured survey instrument and sampling plan for a survey of

drug injectors’ risk networks in the second phase. Subjects for the

structured survey were street recruited and were eligible for the study if

they were 18 years of age or older and had injected drugs within the last

12 months. Both in- and out-of-treatment subjects were interviewed.

Informed consent was obtained for the interviews and for blood tests for

HIV, hepatitis B, and syphilis.

Data for the structured survey were collected from July 1991 through

January 1993. In the structured interviews, subjects provided data on

their sociodemographic characteristics, biography, current and historical

drug- and sex-risk behaviors, medical history, health beliefs, and peer

culture. Subjects also provided information on up to 10 members of their

social networks. This information included how long subjects had known

21
their social network members, the nature of their relationships, their risk

and other behaviors with their network members, and other risk behaviors

by network members.

The sampling objective was to obtain a representative sample of IDUs in

Bushwick, although the authors wanted to oversample new IDUs (defined

as those who had been injecting for 5 or fewer years), as well as to obtain

a sufficient number of white injectors and female injectors in order to

make racial/ethnic and gender comparisons with sufficient statistical

power. To achieve these sampling goals, the authors adopted an

ethnographically directed, purposive sampling strategy. The project’s

ethnographer, assisted by two recruiters who were trained to understand

IDU networks in terms of the needs of the authors’ research design,

carried out systematic observation in the target neighborhood. In

addition, the project ethnographer conducted indepth interviews with

IDUs to find out about the drug-using patterns and social networks of

IDUs. To recruit IDU network members, chain-referral techniques were

used, with recruiters enlisting the support of index IDUs who were asked

to refer to the research storefront IDU members of their social network

whom they had nominated in response to a naming stimulus during the

structured interview.

Initial index subjects for the study were IDUs who were identified

through ethnographic observation and qualitative interviews as having

contacts with several other IDUs. In order to lessen bias in the sample

that may have developed from relying on IDUs who were well connected

to other IDUs, the authors also included as index subjects IDUs who were

less cooperative and those who appeared to be “loners.”

In the survey interview, subjects were asked to nominate people with

whom they had more than casual contact in the last 30 days. The naming

stimuli concentrated on people they used drugs with (the first naming

stimulus) and people they had sex with (the second naming stimulus) and

then, through a variety of other naming stimuli, other people they had

more than casual contact with (e.g., people they lived with who had not

been mentioned before). The 10-name limit was, except in a few

instances, sufficient to handle the nominated list.

To maximize recruitment capability and the validity of the links among

network members, the authors adopted two methods that relied on the

index subjects’ participation. One method was the use of auxiliary

recruiters, who were index IDUs paid a small finder’s fee for helping to

22
locate members of their network. The index subjects were asked to locate

and recruit network members and to physically accompany them to the

storefront. This type of link was called a storefront link. A second

method of recruiting subjects and also a way of validating a link was

what was termed a field link. Because it was not always possible for an

index IDU to take time out from procuring and using drugs and other

aspects of “taking care of business” to accompany a network member to

the storefront, the recruiting team contacted interviewed IDUs in the field

where they were asked if they would assist in identifying and recruiting

members of their network.

Two other kinds of links were also established, but these were not

directly verified through the joint appearance of the index subject and the

network member. These linkages are the ethnographic link and the

database link. In the former, a link is established when a nominated

subject is observed in the field by the project’s ethnographic team with

the index IDU in a social or drug-related interaction. In the latter, a link

is established by matching identifiers of interviewed subjects in a

database with identifiers in index subjects’ lists of network members.

When there was a tie (which occurred with only a few respondents), the

authors utilized ethnographic information to confirm the database link.

Even given these efforts to recruit network members, there were mixed

results. Recruitment of IDU network members was hampered by a high

degree of turnover among IDUs in the neighborhood. Of the 102 IDUs

who lived in Bushwick at the time of the interview and who had

nominated IDU network members, 45 percent had lived in Bushwick for

less than 1 year and 35 percent for less than 6 months. A number of

factors may have contributed to this turnover in the IDU population in

Bushwick, including police sweeps (which may have led to the

incarceration of IDUs or their decision to “hide out” during the sweeps),

attrition through sickness and death, entry into treatment, and the constant

flow of IDUs in and out of the neighborhood resulting from, for example,

urban dislocation and the vagaries of the drug market.

In this environment of high turnover of IDUs in the neighborhood, some

networks were recruited in depth while others were only partially

recruited. Of those IDUs in index subjects’ networks who were

nominated and eligible for recruitment, the mean percent actually

recruited was 61 percent. Also, some index IDUs turned out to have

small networks. This may have occurred because some index IDUs,

though observed to have contacts with other IDUs, may have held

23
themselves back from revealing the full extent of their network.’ Others

were “loners” and some, particularly those who injected in relatively

anonymous multiuser settings such as shooting galleries and outside

locations, were unable to recall exactly with whom they injected. The

effect of turnover among nominated IDU network members, which

reduces recruitment and underreporting of IDU networks, is that many

IDU members of egocentric networks were not interviewed. These

missing IDU links may limit researchers’ ability to understand the size of

and interconnections within egocentric networks and the structure of

larger order networks. This chapter includes analysis of dyadic

relationships as reported by IDU subjects. Issues encountered in

validating data about linked pairs of subjects and examples of analyses

using dyadic data are also discussed.

SAMPLE CHARACTERISTICS

Of 767 subjects who were interviewed, 70 percent were male. There was

racial/ethnic heterogeneity, with 26 percent of subjects black, 34 percent

Latino (nonblack), 8 percent Latino (black), and 32 percent white. The

mean and also the median age were 35. The mean number of years

injecting was 14 and the median 13. Sixty-nine percent had been in drug

abuse treatment at one time or another. Of 687 subjects who were tested

for HIV, 40 percent were seropositive.

The 767 subjects nominated a total of 3,175 network members.

Excluding the subject, the mean number of nominees was 4.14 (standard

deviation [SD] = 3.0). The mean number of nominees with whom

subjects had at some time in the past injected was 2.51 (SD = 2.31), and

the mean number of nominees who were non-IDUs was 1.42

(SD = 2.05).

VALIDATION OF DYADIC DATA

There were 5 11 linked pairs comprised of unidirectional and bidirectional

links. When only one member of the pair was linked to his or her

network nominee, there was a unidirectional link. If each member in a

pair nominated and was linked to the other, there was a bidirectional link.

Of the 5 11 linked pairs, 145 were bidirectional, and 366 were

unidirectional. In total there were 656 dyads2 available for analysis,

24
comprising the sum of the unidirectional links and both halves of the

bidirectional links.

The authors examined 3 types of questions to evaluate the reliability and

validity of the data obtained from the linked pairs. These were: (1)

knowledge that each member in the dyad had about the other member’s

age, race/ethnicity, and gender (these analyses used all dyads, including

unidirectional links and both halves of bidirectional links); (2) agreement

on activities that both members engaged in together, for example,

injecting together and having sex together (only both halves of

bidirectional links were used); and (3) agreement by one member on the

behavior of another member, for example, going to shooting galleries (as

in question type 1, all dyads are used).

In establishing reliability of the dyadic data, Pearson correlation

coefficients were used for continuous data and percent agreement for

dichotomous and other categorical variables. The results are shown in

table 1.

Subjects were reliable in reporting on the age, sex, and race/ethnicity of

their network members. The Pearson correlation coefficient for a

subject’s estimation of a member’s age by that member’s self-reported

age is 0.79 (p < 0.001). There is 100 percent agreement between a

subject’s report about a network member’s gender and the member’s selfreported gender, and 92 percent agreement between a subject’s report

about a network member’s race/ethnicity and the member’s self-reported

race/ethnicity. (Percent agreement on race/ethnicity is calculated by

adding agree-agree responses for four racial/ethnic categories-black,

Latino, white, and other-and dividing by all responses.)

Subjects’ reports on some shared behaviors were very reliable. There

was 93 percent agreement on ever having injected drugs with each other

and 99 percent agreement on ever having had sex with each other.

Among those who had sex together, there was 84 percent agreement on

whether condoms had been used. Subjects were quite reliable on

reporting whether they had injected with a syringe after their IDU partner

had used it first (receptive syringe sharing). In an analysis of 79 linked

pairs of IDUs who had injected with each other (generating 158 injecting

dyads), there was 69 percent (109 of 158) agreement on whether subjects

engaged in receptive syringe sharing.

25
TABLE 1. Reliability of index IDUs’ reports about their network

members {linked-pairs analysis).

Variables Statistic

Sociodemographics

Age

Gender

Race/ethnicity

(Categories are black, Latino,

white, and other)

R = 0.79 (p < 0.001)

100 percent agreement

92 percent agreement

Mutual Behaviors (Last 30 Days)

Injected drugs with each other

Had sex together

Condom use

Receptive syringe sharing

93 percent agreement

99 percent agreement

84 percent agreement

69 percent agreement

Network Member’s Behavior (Last 30 Days)

Injected at shooting gallery 74 percent agreement

Subjects were accurate in reporting on whether their IDU partner had

injected at a shooting gallery. There was 74 percent agreement as to

whether an IDU partner had injected in a shooting gallery.

In the examples given above, reliability was good. Thus, at least for

some network data among street-recruited IDUs, there is reliable and

26
accurate reporting by IDUs about the characteristics and behaviors of

IDU members of their social networks.

THE EFFECT OF DYADIC NETWORK CHARACTERISTICS ON

HIV INFECTION, SYRINGE SHARING, AND CONSISTENT

CONDOM USE

Although many IDU members of egocentric networks were not

interviewed, a large amount of data was collected about network

members and their relationships with index subjects that are based on the

self-reports by index subjects. These data, with appropriate caveats, were

used to examine the influence of egocentric networks on risks for HIV

infection, syringe sharing, and consistent condom use. In the first

example, the network variables qualitatively characterize index subjects’

egocentric networks as high risk if they contain IDU members with highrisk behaviors or characteristics. The unit of analysis here is the

individual subject. The latter two examples are based on the content of

the dyadic relationship between the index subject and his or her network

members, as self-reported by the index subject. One is an analysis of the

characteristics of dyads in which the index subject engaged in receptive

syringe sharing. The other is an analysis of dyads in which index

subjects engaged in consistent condom use. In both of these analyses, the

unit of analysis is the dyadic relationship.

Risk Factors for HIV Infection Among New IDUs

To a large extent, viruses are transmitted dyadically, although they can

also be transmitted via “anonymous” networks, such as those which are

mediated through renting used injecting equipment in shooting galleries.

Thus, risk-factors-for-infection studies may fruitfully be done on the

dyadic level. The present study examines factors associated with being

infected with HIV among IDUs who had been injecting for 6 or fewer

years.

The sample of new IDUs included 174 current drug injectors who started

injecting 6 or fewer years ago.3 Because the data used for individual risk

behaviors were for the 2 years prior to the interview, three subjects who

reported that they were diagnosed with acquired immunodeficiency

sydrome or HIV more than 2 years prior to being interviewed were

excluded from the analysis.

27
In addition to standard drug- and sex-HIV-risk behaviors (during the

2 years prior to the interview) and sociodemographic variables, the

analysis also included network variables (based on subjects’ social

networks in the 30 days prior to the interview) that were dichotomized

and treated as indicators of high-risk networks. Separate network

variables measured whether the subject had any drug-injecting network

member who:

1. Was 5 or more years older than the subject (an indicator of a greater

likelihood of past exposure to HIV);

2. The subject had known 1 year or less (an indicator of greater network

turnover);

3. Injected in shooting galleries in the prior 30 days (an indicator of

potential exposure to a larger HIV-seropositive IDU network); or

4. Injected more than once daily (an indicator of an IDU network with

greater injecting risk behavior).

The sample was 58 percent male, 19 percent black, 53 percent Latino,

and 28 percent white. The mean age was 30 (SD = 5.7 years), mean

years since first injection was 2.6 (SD = 1.7 years), and the median was

3 years. HIV seroprevalence among the new injectors was 20 percent

(versus 45 percent among 494 subjects injecting more than 6 years,

p < 0.0001).

The authors analyzed whether the probability of being infected with HIV

among new injectors was higher among those who engaged in high-risk

behaviors in high-risk networks. To do this, they examined whether

network variables interacted with high-risk behaviors and modified their

association with HIV serostatus. The high-risk behaviors that were

analyzed for their interaction with high-risk networks were selected by

separate multiple logistic regression analyses within categories of

variables which, in addition to high-risk behaviors, also measured other

subject characteristics. The categories of variables included

sociodemographics, other social background, drug use frequencies,

injection settings and needle use, sexual behaviors, individual biography,

and medical and sexually transmitted disease history.

Four interaction terms were statistically significant (p < 0.05 or lower).

The contingency table analyses of these terms are displayed in table 2.

28
TABLE 2. HIV seropositivity by sociodemographic, behavioral, and

settings variables and IDU network variables.

(Table entries are % HIV seropositive.)

Sociodemographic, Behavioral, and

Settings Variables

IDU Network Variables P(x2)<

Any IDU network member

5 or more years older

than the subject

No

Yes

Subject’s gender

Male Female

18 % 17 % 0.888

11 % 41 % 0.004

Any IDU network member

who injects > 1 per day

0.0001†

Subject injects

speedball > 1 per day

No Yes

No 16 % 0 %** 0.586

Yes 12 % 54 %

Any IDU network member

who injected in shooting

galleries in prior 30 days Subject injects

speedball > 1 per day

No Yes

No 16 % 26 % 0.278

Yes 5 %* 69 % 0.0001†

Any IDU network member

whom subject has known

1 year or less Subject injects in outside settings

No Yes

No 10 % 25 % 0.059

Yes 0 %** 44 % 0.0001

KEY: † Fisher’s Exact Test; * cell contains 1; ** cell contains 0.

NOTE: IDU = injecting drug user.

29
(The multivariate odds ratios for three of these terms were indeterminate

since the tables contain cells with frequencies of 1 or 0.)

These analyses indicate that the probability that new injectors who

engage in risk behavior are infected with HIV is increased if they are

members of high-risk injector networks. Thus, the likelihood of being

infected with HIV is greater among new injectors who:

1. Inject speedball more than once per day and have IDU networks that

include members who are either (a) high-frequency injectors or (b)

who inject in shooting galleries; or

2. Inject in outside settings (many of which are multiuser settings) and

have IDU networks with a greater turnover in membership.

In addition, IDU women with older injectors (who may be more likely to

be infected) in their IDU network are more likely to be infected with HIV

than other IDU women.

In a stepwise multiple logistic regression analysis that included variables

selected from the within-category multiple logistic regression analyses,

the interaction term for being a female subject and having an injector 5 or

more years older in one’s egocentric network (odds ratio [OR] = 8.07,

95 percent confidence interval [CI] = 1.13, 57.64) remained significant,

along with injecting at an outside setting (OR = 7.32, 95 percent

CI = 2.66, 20.19), and Latino race/ethnicity (OR = 4.30, 95 percent

CI = 1.55, 11.94).

Dyadic Relationships and Predictors of Receptive Syringe

Sharing

This analysis sought to determine whether the social characteristics of

drug-injecting dyads (index subjects and their IDU nominees who had

injected together in the last 30 days) were associated with receptive

syringe sharing occurring within the dyad. Of 583 respondents,

28 percent were black, 37 percent Latino, and 35 percent white;

71 percent male; and 41 percent HIV positive. The unit of analysis is the

dyadic relationship; there were 1,713 injecting dyads (mean = 2.94 per

subject). Index subjects were asked whether they injected with a syringe

after their partner used it first (receptive sharing). The dependent variable

was index subjects’ reports of receptive sharing with their partners.

30
Receptive sharing occurred in 26 percent of the injecting dyads (448 of

1,713) . In stepwise (backward and forward) multivariate logistic

regression, receptive sharing was significantly associated with dyad social

characteristics (daily contact with partner, OR = 1.6; injecting together

for more than a year, OR = 2.0; having a “very close” relationship,

OR = 1.5; having a sexual relationship, OR = 2.1), as well as with subject

characteristics (injecting speedball or cocaine, OR = 1.5; engaging in sex

for money or drugs, OR = 1.9; not being black, OR = 1.6; not having

been in drug abuse treatment, OR = 1.6). These variables all remained

significant when HIV status, gender, and subjects’ nominated injector

network size were separately added to the final equation.

Receptive syringe sharing in dyads is affected by the character of the

social relationships between injectors. These results indicate that it is

more likely to occur in relationships between IDUs who have strong

social ties.

Dyadic Relationships and Predictors of Consistent Condom

Use

Condom use, because it usually requires mutual consent, is a characteristic of relationships (Sibthorpe 1992). Thus, studying the extent of

condom use within relationships as the unit of analysis may add to the

knowledge obtained from other studies that focus on the individual as the

unit of analysis.

In the study reported here, the unit of analysis is the dyadic relationship

of an IDU subject and his or her nominated heterosexual sex partner with

whom sexual behavior occurred in the 30 days prior to the interview.

The 3 17 subjects generated 421 dyadic heterosexual relationships,

34 percent of whom were HIV seropositive, 68 percent were men,

44 percent were Latino/a, 32 percent were white, and 24 percent were

black. Subjects who had only one relationship comprised 60 percent of

the relationships, those with two or three relationships comprised

29 percent, and those with four or more relationships were 11 percent. Of

the 421 heterosexual relationships, 202 (48 percent) were with sexual

partners who injected drugs, and 219 (52 percent) were with noninjectors.

The dependent variable is IDU subjects’ reports of consistent condom use

with partners of the opposite sex and is specific to the dyadic relationship.

The behavior reported is for the 30-day period prior to the interview and

31
describes whether a condom was used every time IDU subjects and their

nominated sex partners had sex together.

The validation of condom use among linked pairs (see “Validation of

Dyadic Data,” above) may be limited because the linkages are among

IDUs and do not include non-IDU sex partners.

Always using condoms is reported in 33 percent of relationships (141 of

421). There is a greater proportion of consistent condom use in relationships with sex partners who have never injected drugs (43.8 percent) than

in relationships with IDU sex partners (22.3 percent; p < 0.001), as well

as in relationships of HIV seropositive subjects (46 percent) than in

relationships of HIV seronegative subjects (27 percent; p < 0.001).

Consistent condom use is less common in more intimate relationships:

26 percent in relationships with sex partners who are spouses, ex-spouses,

or lovers versus 53 percent (p < 0.001) with friends or acquaintances; and

24 percent with sex partners with whom subjects feel “very close” versus

43 percent (p < 0.001) with other sex partners.

As shown in table 3, consistent condom use is particularly widespread

(68 percent) in relationships in which the drug-injecting respondent is

seropositive and the sex partner of the respondent is reported not to be an

IDU. This finding holds when controlling for the closeness of the

relationship. Thus consistent condom use is particularly likely to occur in

relationships between seropositive IDUs and noninjecting partners even

when these relationships are or are not “very close,” or even when they

are with a spouse, lover, ex-spouse, or with a friend or acquaintance (data

not shown).

Consistent condom use in relationships between seropositive IDUs and

non-IDU sex partners may help to reduce the spread of HIV to non-IDU

heterosexuals. If this pattern in New York City, where approximately

half of IDUs are infected with HIV, is found in other areas of the United

States, then it may account for the relatively slow spread of HIV in

heterosexuals from IDUs to noninjectors. Why this pattern of condom

use occurs may be due to sex partner pressure or to IDU altruism (i.e., the

desire to prevent others from becoming infected with HIV). This may

also be a factor in the voluntary participation of IDUs as lay outreach

workers and syringe exchange participants in many projects in the United

States. However, since the main focus of the interviews in this project

was on drug injectors’ social networks and behaviors, data were

32
TABLE 3. Proportion of relationships in which condoms were always

used in the prior 30 days.

Does Sex Partner Inject Drugs?

No Yes p (row comparison)

Respondent is:

Seronegative

Seropositive

31% 23%

(43 of 141) (30 of 133)

68% 22%

(53 of 78) (15 of 69)

0.137

0.001

p (column

comparison) 0.001 0.895

not gathered on subjects’ altruism or sex partner pressure to help explain

this pattern of condom use.

NETWORK TURNOVER

The authors analyzed the extent of short-term and long-term relationships

with network members. These relationships included the length of time

subjects injected with their drug-injecting network members, the length of

time they knew their drug-injecting members, and the length of time they

had been having sex with their sex partners. As shown in table 4, there

was a large proportion of short-term injecting relationships, with fully

44 percent of relationships existing in which subjects injected with their

network members for 1 year or less. However, there was also a substantial proportion of long-term injecting relationships, with 23 percent

existing for more than 5 years. Subjects’ relationships with their druginjecting network members tended to precede the initiation of injecting

with them, since only 28 percent of subjects had known their druginjecting network member for 1 year or less, while fully 42 percent had

known them for more than 5 years. Sexual relationships with network

members were as likely to be long term as short term. Just over one-third

(36 percent) of sexual relationships with network members had existed

for 1 year or less, while similarly just over one-third (35 percent) had

lasted for more than 5 years.

33
TABLE 4. Duration of drug injectors’ relationships.

Length of time they have: 1 Year > 1 Year,

5 years > 5 Years

Injected with their druginjecting network members

Known their drug-injecting

network members

44% 33% 23%

28% 30% 42%

Been having sex with their sex

partners

36% 29% 35%

Network turnover has methodological and substantive implications for

conducting network-informed research on HIV risk and prevention

among IDUs. Short-term relationships can affect the ease with which

IDU networks can be recruited, since it may be more difficult to identify

and locate short-term network members. In addition, short-term

relationships imply that network structures are subject to substantial

change, particularly on their peripheries. At the same time, to the extent

that long-term relationships also characterize IDU networks, it may be

easier to recruit network members who have long-term relationships and

to identify the structure of relationships among these members.

Substantively, short-term relationships may affect the rapidity with which

HIV spreads among IDUs, while long-term relationships may be a basis

for mobilizing social influence for HIV prevention.

CONCLUSIONS

Dyads derived from IDUs’ egocentric networks can be used for analyzing

network influences on HIV infection and risk behavior. The present

analysis of the reliability of subjects’ reports about each other, based on

linked-pairs data, indicates that IDUs, with appropriate caveats, give

reliable information about members of their IDU networks. Thus, IDUs’

self-reports about their relationships with members of their egocentric

networks and the characteristics and behaviors of these members can,

even when linked-pairs data are unavailable, be a useful resource for

analysis.

34
As discussed above, the qualitative characterization of IDUs’ egocentric

networks by virtue of the kinds of dyads and members they contain can

be used to characterize IDU egocentric networks qualitatively as being

high-risk if they contain IDU network members with high-risk behaviors

or characteristics. High-risk networks were shown to be important in

predicting HIV infection among new injectors. Dyadic data can also be

used to specify the content of dyadic relationships, such as the closeness

of the relationship. HIV-risk behaviors were more likely in dyads with

close ties, and consistent condom use was more likely in relationships

between HIV-positive IDUs and non-IDU sex partners.

Many of the predictors of risks for becoming infected with HIV, for

syringe sharing, and for consistent condom use are variables that measure

characteristics of the social relationships within IDUs’ first-order

networks. Risk behavior and risk reduction are clearly affected by the

social relationships in which behaviors occur. Thus, there is a need to

understand how social relationships affect risk behaviors and risk

reduction and for prevention programs to focus on social relationships, as

well as on individual characteristics.

Dyads may, therefore, be useful for analyzing certain features of drug

injectors’ networks that can affect the likelihood of IDUs’ becoming

infected with HIV and engaging in risk behaviors. Nevertheless, in order

to more fully understand how HIV spreads through IDU populations and

from IDUs to non-IDU populations, and how reductions in HIV-risk

behaviors can be disseminated in the IDU subculture, the analysis of

larger IDU network structures is necessary. Under certain societal

conditions, however, the very nature of IDU networks makes this task

difficult. The difficulty resides not only in devising the most appropriate

method for analyzing the data but also in the initial steps in which index

subjects are chosen, in the nomination of network members by index

subjects, and in the recruiting and linking of network members, as

discussed above.

Networks of street drug injectors may not have a stable structure, and

many relationships can be short term. The nature of street life among

drug injectors is that, in certain neighborhoods characterized by a “war on

drugs” environment involving police sweeps, urban desertification, and

urban gentrification, stable drug-injector networks can be fragmented.

Some of them are thus emergent, setting-specific, and transient, although

many dyads are, nevertheless, long term. Other networks, through

attrition, may be shrinking. Thus, on a large scale, the structure of these

35
drug-injector networks is a mixture of stable, long-term relationships and

of transient, short-term relationships.

The unstable relationships in IDU networks have implications for the

spread of HIV and for prevention. To the degree that these unstable

relationships represent weak ties to other IDU networks, they may

increase the probability of exposure to HIV. High-turnover networks

may increase the rapidity with which HIV spreads. Thus, short-term

relationships may carry HIV across long-term networks and, thereby,

across social categories.

The instability of IDU networks may increase the difficulty with which

they can be used as channels of peer influence and pressure to reduce

HIV risk. Under such conditions, network interventions among IDUs

may need to first assist in the formation and stabilization of drug

injectors’ networks. Since IDU networks also contain longer term and

more stable relationships, these relationships may be a better starting

point from which to initiate efforts to utilize IDU social networks for HIV

prevention. These more stable parts of scenes may become an initial

focus for efforts to organize the more unstable segments of IDU networks

and can be used as a base from which to create and expand those links

through which peer influence and pressure may flow to reduce HIV-risk

behaviors and control the spread of AIDS.

NOTES

1. IDUs were reluctant, even though they were given full assurance

(which was backed up by a Federal Certificate of Confidentiality) that

everything they reported was confidential.

2. The actual number of dyads used in any analysis varies depending on

the type of characteristic or behavior analyzed.

3. In order to increase the sample size, new injectors were redefined from

injecting 5 or fewer years ago to 6 or fewer years ago.

REFERENCES

Campbell, K.E., and Lee, B.A. Name generators in surveys of personal

networks. Soc Netw 13(3):203-221, 1991.

36
Milardo, R.M. Comparative methods for delineating social networks.

J Soc Pers Relatsh 9:447-461, 1992.

Sibthorpe, B. The social construction of sexual relationships as a

determinant of HIV risk perception and condom use among injection

drug users. Med Anthropol Q 6:255-270. 1992.

van der Poel, M.G.M. Delineating personal support networks. Soc Netw

15(1):49-70, 1993.

AUTHORS

Alan Neaigus, Ph.D.

Project Director and Co-Investigator

Samuel R. Friedman, Ph.D.

Principal Investigator

Marjorie Goldstein, Ph.D.

Principal Research Associate

Gilbert Ildefonso, A.S.

Research Associate

Richard Curtis, M.A.

Principal Research Associate

Benny Jose, M.S.

Principal Research Associate

National Development and Research Institutes, Inc.

11 Beach Street

New York, NY 10013

37
Injecting Drug Use,

Characteristics of Significant

Others, and HIV-Risk Behaviors

Rumi K. Price, Linda B. Cottler, Doug Mager, and

Keith S. Murray

INTRODUCTION

The proportion of acquired immunodeficiency syndrome (AIDS) cases

attributable to injecting drug use is growing rapidly (Allen et al. 1991;

Barker et al. 1989). In 1987, approximately 17 percent of all U.S. cases

of AIDS had been attributed to injecting drug use, excluding cases

attributed to both injecting drug use and homosexual contacts. By

November 1991, however, the proportion of AIDS cases among

heterosexual injecting drug users (IDUs) exceeded the rate of AIDS

among noninjecting drug-using homosexual men in some cities in the

United States (Centers for Disease Control 1989). Also of pivotal

importance is the risk of human immunodeficiency virus (HIV)

transmission that IDUs present to their sexual partners. Heterosexual

IDUs serve as “bridges” of infectivity to the heterosexual community at

large (Des Jarlais and Friedman 1987; Fordyce et al. 1991; Murphy

1988). Forecasting the future trend of seroconversion among IDUs, as

well as timely prevention and intervention efforts, are thus critical to

minimize the spread of HIV in the IDU population and, ultimately, in the

heterosexual population at large.

Projecting the time trend of HIV/AIDS, however, has proven difficult.

Different methods have produced wide ranges of estimates for the future

trajectory (Bregman and Langmuir 1990; Lemp et al. 1990; MacDonald

1986; Rosenberg et al. 1991). Currently, estimates of the HIV

seropositivity in the IDU population vary markedly from community to

community (Allen et al. 1991; Berkelman et al. 1989; Chaisson et al.

1989; Des Jarlais et al. 1988; Quinn et al. 1989; Siegal et al. 1991). The

differences, however, do not appear to be a simple consequence of the

size of the IDU pool in specific communities. As seen most dramatically,

the seroprevalence rate among IDUs in Manhattan (New York City)

increased from 9 percent to 50 percent in the 1978 to 1983 period (Des

Jarlais et al. 1989). On the other hand, the rates among IDUs in San

38
Francisco, another HIV epicenter, remained in the mid-teens range until

1987 (Chaisson et al. 1989).

There are a number of plausible explanations for uneven prevalence and

spread of HIV/AIDS across cities and regions in the United States. One

obvious explanation is that the differences merely reflect the time lag due

to the long incubation period of HIV, because HIV was introduced to

each specific community at different points in time. A second

explanation is that the current low-prevalence areas have benefited from

the experience of the coastal cities. High-risk individuals in the current

low-prevalence areas are taking more measures to prevent HIV infection

than those in the high-prevalence areas did at the time the disease was

still not well known. Reduction in high-risk drug-using behaviors among

IDUs has been noted in response to the AIDS epidemic (Ottomanelli et

al. 1990). It has not yet been determined if fear of eventual, possible

infection is sufficient to reduce risk behaviors among those living in areas

not currently plagued with the epidemic (Calsyn et al. 1992).

A third explanation is the possibility that the social structure of the highrisk groups is unique to each specific community. This argument is

acknowledged in the epidemiological literature in forecasting the

incidence of infectious diseases. The random mixing assumption, in

which the probability of being contacted by infected persons is presumed

to be constant across social groups, is violated in the transmission of HIV

(Koopman et al. 1991). However, a particular local structure that faults

random mixing is difficult to capture mathematically (Gupta et al. 1989).

Descriptive studies, nevertheless, have shown that IDUs are not a

homogeneous group even within a geographically limited area.

Furthermore, they have found that the social milieu of drug-injecting

behaviors is an important predictor of HIV transmission (Des Jarlais et al.

1986, pp. 111-125; Schoenbaum et al. 1989; Watters and Cheng 1987).

These findings point to the research need for understanding local social

relations in order to forecast the future spread of the disease within the

community.

The present chapter explores this last question using dyadic data obtained

from a seroepidemiological study of St. Louis drug users. Unlike some

coastal cities, St. Louis is still in the low-prevalence phase of the

HIV/AIDS epidemic. There are, however, indications that the rates for

St. Louis may soon rise sharply. For example, the St. Louis rate of

hepatitis-B, which is known to parallel the rate of AIDS, was until

recently substantially lower than that of coastal cities such as New York

39
City and San Francisco. However, St. Louis’ hepatitis-B rate is now

comparable to rates in these cities (Storch et al. 1992, p. 302). Thus, data

on social networks of HIV high-risk groups obtained prior to a rapid

spread of HIV would be useful to determine if relational factors may be

an element for slow spread.

The application of the social network approach to drug abuse is not new,

but the focus has been primarily on issues of social control (Fraser and

Hawkins 1984) or of social support (Saltz and Smith-Donals 1984;

Westermeyer and Neider 1988; Wills 1990). The network approach is

also particularly suitable for transmission of some concrete objects such

as innovation, information, illicit substances, and infectious diseases.

Thus, it was natural to extend the network approach to the AIDS

epidemic (Klovdahl 1985; Laumann et al. 1989). More recently, a

number of HIV projects in drug abuse were targeted specifically at

capturing the effects of social networks on HIV transmission among drug

users (see other chapters in this volume).

The networks of relations examined in this chapter are based on three

significant others reported by a sample of drug abusers recruited from six

drug abuse treatment centers in St. Louis. Drug abusers were first

classified into three groups using their injecting drug use history and that

of their reported partners and steady friends. Correlates of injecting drug

use concordance of the proband (index subject) and his significant others

were then examined, with several relational characteristics between them

and high-risk behaviors and HIV seropositivity of probands. Multivariate

categorical regression analyses were performed to identify composite

characteristics of these subgroups with respect to high-risk sexual and

drug-using behaviors, demographic characteristics, and drug abuse

history.

The results of this chapter should be viewed cautiously because the

network analysis included in it was not intended as a major aim of the

original study. Some of the methodological limitations are discussed

later.

40
METHODS

Ascertainment

The data were derived from the first longitudinal HIV epidemiological

study of drug abusers and their sexual partners in St. Louis, known as

Substance Abuse and Risks for AIDS study (SARA). The probands

ascertained for this study consisted of a total of 5 14 persons admitted to

six drug abuse treatment centers in St. Louis within a maximum of

6 months prior to the enrollment in the SARA study. The centers were

selected on the basis of HIV risk assessment carried out in collaboration

with the Division of Alcohol and Drug Abuse of the Missouri

Department of Mental Health. These centers differed somewhat in

treatment methods, socioeconomic status, gender composition of patients

and the types of primary substances abused. The probands were enrolled

in the study between October 1989 and December 1990. Further details

can be found in Cottler and colleagues (1991).

The ascertained proband was given a baseline face-to-face interview. At

its beginning, the proband was asked to name “his/her most steady

girlfriend/boyfriend-the person who means the most to you,” so that he

or she could be included in the study as well. A total of 91 “partners”

named by probands were subsequently ascertained. These partners were

excluded in the analysis included in this chapter because partner

ascertainment was found to be correlated with the injecting drug use

concordance type.

Assessment

The National Institute on Drug Abuse (NIDA) high-risk assessment

questions were adapted to examine high-risk sexual and drug-using

behaviors for the 6 months prior to the interview. The National Institute

of Mental Health (NIMH) Diagnostic Interview Schedule (DIS) III-R

(Robins et al. 1989) was used to assess current and past symptoms and

diagnoses of each of 10 classes of substance dependence and abuse

(alcohol, amphetamines, cannabis, cocaine, hallucinogens, inhalants,

opioids, phencyclidine [PCP], sedatives, and nicotine) and other major

psychiatric disorders, as defined by Diagnostic and Statistical Manual of

Mental Disorder (DSM)-III-R (American Psychiatric Association 1987).

In the brief “significant other” section of the interview, the proband was

asked to provide information about the partner the proband named at the

41
beginning of the interview, as well as the proband’s “most steady friend”

and the “next most steady friend.” Thus, a maximum of the proband’s

three significant others were assessed. The questions about these three

persons included race, injecting drug use history (i.e., ever use), treatment

and arrest records in the past 6 months, and proximity to the proband.

These questions were modified from the questionnaires used in a study of

recovering heroin abusers (Minor 1983, pp. 89-99; Price 1988). This

social network assessment was limited to the alter-level, and no attempt

could be made to link alters. Therefore, the study attempted to capture

social relations at the dyadic (proband-alter) level only.

HIV antibody tests were performed using licensed Enzyme-Linked

Immuno Sorbent Assay tests with confirmed Western Blots at the Core

Retrovirology Laboratory of the Washington University School of

Medicine. Information on significant others, HIV high-risk behaviors,

and demographic characteristics for this chapter were derived from the

baseline interviews, which took place from October 1989 to December

1990. In this chapter, HIV serostatus was followed up in the second

wave of data collection, which ended in December 1991.

Injecting Drug Use Concordance

Altogether, 864 significant others were identified by the probands

through the significant other section, and further information about them

and the proband’s relationships with them was obtained for 824

(95.3 percent) of them (figure 1). Although three significant others at

maximum were allowed in this section, only a small portion of significant

others appear to have been missed: across three groups, 79.0 percent

gave names and other location information of their partners; 57.6 percent

identified a second-most steady friend; but only 29.4 percent identified a

third-most steady friend.

The initial intention of the significant other section was to identify sexual

partners for subsequent interviews, rather than measuring the HIV

transmission between the proband and the significant others of the

proband. As a result, it was not possible to create a variable that would

assess the extent of HIV transmission risk potential between the proband

and his significant other. The proband was, however, asked to report his

or her significant other’s injecting drug use as well as the proband’s own

use. In this way, probands were classified based on the concordance of

injecting drug use between probands and their significant others:

42
FIGURE 1. Reporting pattern of three significant others by

probands. #I: Reported “partner”; #2: “most steady

friend”; #3: Reported “next steady friend.”

• The “injecting drug use concordant” (hereafter denoted as IDU

concordant) was defined as a pair in which both the proband and all

significant others were current or past IDUs;

• The “injecting drug use discordant” (hereafter denoted as IDU

discordant) was a pair where the proband was an IDU but at least one

partner or steady friend was not, or the proband was a non-IDU but at

least one significant other was an IDU.

• The “noninjecting drug use concordant” (hereafter denoted as nonIDU concordant) was defined as a pair in which neither the proband

nor any of his significant others were current or past IDUs.

It was thought that injecting drug use concordance could be used as an

indicator of HIV high-risk dyadic relation since injecting drug use is a

well-established HIV-risk factor among drug users. The concordance

status was cross-examined with the subject’s self-report on the number of

the subject’s injecting drug-using sexual partners and with the subject’s

interviewed partner’s response about his or her own injecting drug use

status (Price et al. 1992, p. 434). The concordance status was changed in

43
only a few cases as a result of the cross-examination. Thus, while this

procedure might have increased the magnitude of correlation between the

number of sexual partners and the concordance status, the net effect was

considered negligible.

The two variables used for cross-examination-number of sexual

partners, and number of IDUs who were sexual partners-were obtained

on aggregate. Because there was no way of identifying who these

partners and IDUs were, it was not possible to use these variables alone

for dyadic classification.

Analysis

It has been long known that statistical analysis applied to alter-level

network data is subject to sampling bias because alters nominated by

probands are not randomly drawn from a population (Dow et al. 1982).

The authors have thus chosen to present the results using measures at the

individual level (i.e., the proband is the unit of analysis). Bivariate

analyses were used to examine the association of the injecting drug use

concordance typology with probands’ demographics, characteristics of

significant others, and probands’ self-reported sexual and drug-using

behaviors during the past 6 months.

Using the categorical data modeling procedure in the computer program

SAS (Statistical Analysis System 1988, pp. 188-282) multivariate

polychotomous and dichotomous categorical regression analyses were

applied to examine demographic and risk factors which would

differentiate the injecting drug use concordance type. The categorical

regression analysis is an extension of dichotomous logistic regression in

which the dependent variable consists of more than two categories.

Although making causal inference was not intended, this method was

judged advantageous because statistical power is increased and

presentation is more parsimonious due to simultaneous estimation of the

magnitude of effects of all variables on two categories of injecting drug

use concordance type in comparison to the referent group. Dichotomous

categorical regression analyses were also performed on the injecting

drug-using proband subsample (N = 27 1) because a few variables were

applicable only to IDUs (e.g., frequency of injection, sharing works).

44
RESULTS

Injecting Drug Use Concordance Type

When probands were classified according to injecting drug use

concordance status of all of their pairs, 64 probands (12.5 percent) fell

into the category of the IDU concordant. Because the definition of

injecting drug use discordance required only one discordant pair, the IDU

discordant group was the largest: It included 236 probands

(45.9 percent), 87.7 percent of whom were IDUs. The proportion of the

non-IDU concordant was similarly large, totaling 214 probands

(41.6 percent).

Inspection of demographic characteristics stratified by injecting drug use

concordance type revealed substantially different representations of

gender, race, age, and other demographics (table 1). The IDU concordant

TABLE 1. Demographic characteristics by injecting drug use

concordance type (N = 514).

IDU IDU Non-IDU

Concordant’ Discordant* Concordan?

(N = 64) (N = 236) (N = 214)

Male (%)a,b 48.4 67.0 67.3

Black (%) a,b’

c

34.4 54.2 75.2

Age (mean years)b,c 34.3 33.9 29.9

Never married (%)b,c 42.2 42.8 62.6

No employment (%)a,b 48.4 26.2 18.2

KEY: 1Proband is an IDU, and all significant others are IDUs.

2

Proband is an IDU, and at least one significant other is a

non-IDU, or vice versa.

3

Proband is a non-IDU, and all significant others are nonIDUs.

a

P < 0.05, IDU concordant versus IDU discordant.

b

P < 0.05, IDU concordant versus non-IDU discordant.

c

P < 0.05, IDU discordant versus non-IDU concordant.

45
was the oldest group (mean age = 34.3 years), with the least

representation of males (48.4 percent) and blacks (34.4 percent), as well

as the most unemployed (48.4 percent). The non-IDU concordant, on the

other hand, was the youngest group (mean age = 29.9 years), with

overrepresentation of blacks (75.2 percent) and those who reported they

had never married (62.6 percent). Over 81 percent of this group was

employed. The IDU discordant was similar to the IDU concordant with

respect to age (mean age = 33.9 years) and the proportion never married

(42.8 percent). However, they were similar to the non-IDU concordant

with respect to gender composition (67.0 percent male).

Characteristics of Significant Others

The mean number of reported significant others was 1.1 among the IDU

concordant, 1.8 among the IDU discordant, and 1.6 among the non-IDU

concordant, corresponding to the size of each subtype. This is not

surprising because those with more significant others have an increased

chance to be placed into the discordant category.’ However, the number

of significant others in drug abuse treatment during the past 6 months was

highest among the IDU concordant (mean = 0.6), next highest among the

IDU discordant (mean = 0.3), and the lowest among the non-IDU

concordant (mean = 0.1). The number arrested in the last 6 months

showed the same descending pattern (means = 0.4, 0.2, 0.2, respectively)

(table 2). These could not be an artifact, and the difference might

actually be underestimated.

While the IDU-concordant probands reported that 39.7 percent of their

significant others were living with them, the probands in the other two

groups reported smaller proportions of their significant others living with

them (25.7 percent in the IDU discordant and 20.7 percent in the nonIDU concordant). Moreover, 27.4 percent of the significant others

reported by the IDU concordants were living with their families in

comparison to 46.5 percent of IDU discordants and 47.0 percent of nonIDU concordants.

Across the three groups, only 6.7 percent of the relationships between

probands and their significant others were interracial. Proportions of

interracial relationships were the lowest in the IDU-concordant

(3.2 percent) and highest in the IDU-discordant group (7.5 percent).

However, these differences were not statistically significant.

46
TABLE 2. Characteristics of reported significant others by injecting

drug use concordance type *‘† (N = 14).

IDU IDU Non-IDU

Concordant Discordant Concordant

(N = 64) (N = 236) (N = 214)

Mean number of significant

others in drug treatmentl123

Mean number of significant

others arrested1,2

0.6 0.3 0.1

0.4 0.2 0.2

Significant others living with 39.7 25.7 20.7

proband (%)1,2

Significant others living with 27.4 46.5 47.0

their family (%)1,2

Interracial significant

others (%)‡

3.2 7.5 5.9

KEY: * Includes significant others probands identified and provided

information (NS = 824).

† Assessed for the past 6 months.

‡ Pairs other than white-white, black-black, and Hispanic-Hispanic.

1

P < 0.05, IDU concordant versus IDU discordant.

2

P < 0.05, IDU concordant versus non-IDU concordant.

3

P < 0.05, IDU discordant versus non-IDU concordant.

HIV High-Risk Behaviors and Seropositivity

IDU discordants reported the highest number of sexual partners

(mean = 8.0) in the past 6 months, 3 times higher than the non-IDU

concordant group (mean = 2.7) and more than 10 times higher than the

IDU concordant group (mean = 0.7) (table 3). Because the number of

47
TABLE 3. HIV high-risk behaviors* and seropositivity by injecting drug

use concordance type (N = 514).

IDU

Concordant

(N = 64)

IDU

Discordant

(N = 236)

Non-IDU

Concordant

(N = 214)

Number sexual partners

(mean number)1,2,3

Sexual partners’ injecting

drug use (%)

Sexual solicitation (%)

Prostitution (%)1,3

No condom use (%)‡1

Number of drug partners works

shared (mean number)§

Always clean needle (%)

HIV positive (%)

As of Dec. 1990

Incidence Dec. 1990

to Nov. 1991

0.7 8.0 2.7

100.0†10.0 0.0†

3.1 8.1 8.9

1.6 18.2 8.9

85.0 67.6 70.2

1.1

50.0

4.7

0.0

0.8 NA

22.8 NA

1.3

1.3

0.5

0.0

KEY: * Assessment for the past 6 months.

† True by definition.

‡ Among sexually active (N = 425).

§ Injecting drug use probands (N = 271).

Among active IDUs (N = 152).

1

2

3

4

P < 0.05, IDU concordant versus IDU discordant.

P < 0.05, IDU concordant versus non-IDU concordant.

P < 0.05, IDU discordant versus non-IDU concordant.

T-ratio tests performed on the log score.

48
sexual partners is not totally independent of the number of significant

others, the mean number of sexual partners would predictably be highest

among the IDU-discordant group. Nevertheless, the difference seems

much larger than expectations based on differences in the number of

significant others alone.

Only 10 percent of the IDU discordant probands reported that their sexual

partners injected drugs during the past 6 months. Prostitution and sexual

solicitation (i.e., giving money or drugs for sex) during the past 6 months

were high among the IDU discordant (18.2 percent for prostitution and

8.1 percent for solicitation), as well as among the non-IDU concordant

(8.9 percent and 8.9 percent, respectively). These behaviors were more

than five times more frequent in the IDU discordant than the IDU

concordant group (combined 26.3 percent versus 4.9 percent). Further

inspection showed that the high mean number of sexual partners in the

IDU discordant group was in part attributable to high levels of

prostitution and sexual solicitation in this group.

While the IDU discordant group reported the highest number of sexual

partners, they also appeared to be more cautious about sex; nonetheless,

67.6 percent still reported no condom use during the past 6 months. A

higher percentage (85.0 percent) was observed in the IDU concordant.

Among the IDU probands, the number of needle-sharing drug partners

during the past 6 months was higher in the IDU concordant than the IDU

discordant group (mean number 1.1 versus 0.8). However, 50 percent of

the IDU concordant probands reported that they always cleaned needles,

whereas only 22.8 percent of the IDU discordant probands reported the

same. On balance, HIV transmission risk from unsafe needle sharing

among concordant IDUs does not appear greater than that among the IDU

discordant probands.2

At the end of the baseline data collection (December 1990), the HIV

seropositivity rate was highest (4.7 percent) among the IDU concordant,

lower (1.3 percent) among the IDU discordant, and lowest (0.5 percent)

among the non-IDU concordant. The ranking of the seropositivity rates

stratified by injecting drug use concordance type corresponded to that of

the number of needle-sharing partners rather than of the number of sexual

partners. At the end of the followup period (December 1991), three

incident cases were found-all in the IDU discordant group, thus raising

the prevalence of HIV seropositivity at that time to 2.6 percent in that

group.

49
Multivariate Analysis

The foregoing bivariate analyses illuminated the differences in high-risk

sexual and drug-using behaviors among the three dyadic groups. These

analyses, however, do not reveal the relative importance of the variables

that were found to differentiate the three groups. To assess the relative

importance of sexual and drug-using behaviors, these variables were

simultaneously controlled for in the multivariate categorical modeling

analyses available in SAS. In addition to HIV high-risk behaviors,

demographic characteristics (age, sex, race), drug abuse history (lifetime

opiate or cocaine dependence or abuse), antisocial personality symptoms,

and relational variables used in the previous analyses (significant others

living with proband, number of significant others in treatment or arrested

in the past 6 months) were also included in the models. The number of

significant others was included to account for the effect of the size of the

significant others on the injecting drug use concordance category, which

may have inflated the statistical significance of the difference in the

number of sexual partners among the three groups in the previous

bivariate analyses. The injecting drug use probands were also analyzed

only for models that included injecting behaviors (e.g., sharing works

most often with spouse or partners, frequency of injection).

Several variables differentiated the three IDU concordant groups (table 4,

left and middle columns). The IDU concordant, when contrasted with the

other two groups, was characterized by “monogamous-like” relationships,

indicated by the smaller number of sexual partners (OR = 0.7) and the

living arrangements of significant others with probands (OR = 2.9). This

group had a larger number of significant others with recent arrest records

(OR = 3.8). Both the IDU concordant and IDU discordant had higher

numbers of significant others recently in drug treatment in comparison to

the non-IDU concordant. However, significant others’ treatment status

was more predictive of the IDU concordant (OR = 9.2) than of the IDU

discordant (OR = 2.0). These results, consistent with the bivariate

analyses, held up even when controlling for demographics, history of

drug abuse, and antisocial behaviors. Further, the number of significant

others was controlled for in this analysis; thus, the magnitude of the effect

of the number of sexual partners is not overestimated.

Histories of opiate or cocaine dependence or abuse and of antisocial

behaviors were predictive of both the IDU concordant and the IDU

discordant group in comparison to the non-IDU concordant group.

Among St. Louis drug abusers in the present study, blacks were less

50
TABLE 4. Composite characteristics differentiating injecting drug use

concordance groups (odds ratios).*

All Probands IDU Probands

(N = 514)† (N = 271)‡

Age¶

Female

Black

Number sexual partners

(LOG)¶

Number of significant others¶

Significant others living with

proband

Number significant others in

drug treatment¶

Number significant others

arrested¶

Opiate/dependence/abuse§,

Cocaine/dependence/abuse§,

Number antisocial behaviors¶

Sharing works most often with

spouse/partner

IDU

Concordant

(N = 64)

1.1

NS

0.3

0.7

0.2

2.9

9.2

3.8

17.6

3.4

1.3

—

IDU

Discordant

(N = 236)

1.1

NS

0.5

NS

NS

NS

2.0

NS

9.5

1.7

1.2

—

IDU

Concordant

(N = 64)

NS

3.0

NS

0.5

0.2

—

5.5

—

—

—

—

21.1

Injection 1+ times/day — — 2.4

KEY: * NS: p > 0.05; : p = 0.053; —: not included in the model.

† Polychotomous categorical regression: the non-IDU concordant, referent

group; L2 = 702, df = 996, Prob.= 1.00, where the probability refers to the

likelihood of “correct” specification.

‡ Dichotomous categorical regression: the IDU discordant, referent group;

L

2

= 145, df = 246, Prob.=1.00.

§ Lifetime DSM-III-R diagnosis.

Dummy variable.

¶ Effect is on per-unit increase of the measure.

51
likely to be IDUs, irrespective of their significant others’ injecting drug

use.

Overall, the polychotomous model fitted the data very well. The

likelihood ratio (L2 = 702, d.f. = 996, Prob. = 1.00) indicates that the set

of variables chosen in this model sufficiently described the characteristics

of the three subtypes of drug abusers in St. Louis, so that the probability

that the model is “correctly” specified is close to unity.

A striking result, although intuitively clear, is that paraphernalia sharing

with a spouse or a sexual partner was found to be a powerful predictor of

the IDU concordant (OR = 21.1), compared with the IDU discordant,

when the analysis was applied to the injecting drug use probands only

and controlled for the frequency of injection (OR = 2.4) as well as

demographics (table 4, right column). Past or current opiate and cocaine

dependence or abuse or antisocial behaviors were all insignificant,

indicating that they were risk factors of injecting drug use itself but were

not associated with injecting drug use of significant others.

DISCUSSION

Early prevention and intervention efforts are more hopeful in the low

prevalence areas such as the metropolitan St. Louis area than in areas

already experiencing high prevalence. Present results could be useful in

pointing out a direction for targeted prevention efforts before the

epidemic really begins to escalate.

The SARA study was the first seroepidemiological study of drug abusers

in St. Louis. It was not aimed at studying the association of social

networks with HIV transmission, and there are several limitations in this

chapter. The network assessment of the SARA study was not exhaustive.

It was limited to assessing information about a maximum of three

significant others. Present data suggest, nevertheless, that the majority of

sample respondents did not have large networks of significant others; a

sufficient pool of significant others appears to have been assessed.

Another limitation is the modest number of network questions asked

about significant others. Present analyses, however, indicate that even a

few questions can unveil some important aspects of drug abusers’

networks in relation to HIV-risk behaviors.

52
Data analysis at the individual level was chosen for two reasons. The

limitation of three significant others per proband made an alter-level

analysis less meaningful, and, as mentioned earlier, special statistical

treatment would have been necessary to control for the effect of

differential probability of selection at the alter level. Injecting drug use

concordance topology was chosen as the main focus of analyses. While

the topology was found to be useful, it may not be the best way to

describe the heterogeneity within the sample.

The probability of being classified into a specific injecting drug use

concordance category depended on the number of significant others

reported. As a result, the odds ratios and T-ratio statistics for metric

variables such as the number of sexual partners may have been inflated.

Analyses not shown here indicated that the number of significant others

was only modestly correlated with the number of sexual partners and not

with any other metric variables. Moreover, in the multivariate analysis,

the two metric variables, the number of significant others, and the number

of sexual partners were simultaneously controlled for. Thus, results

appear valid despite this concern. A procedure to adjust for the statistical

significance according to the size of network members is necessary for

future network analyses that attempt to infer statistical significance of

network variables on outcome variables.

Perhaps the biggest concern with respect to HIV is that the significant

others were not identified on the basis of the proband’s joint behaviors

with them. Instead, they were identified in terms of relational descriptors

such as “most steady girlfriend or boyfriend” and “next steady friend.”

Field reports indicated that respondents understood these relational

descriptions to mean sexual partners. Nonetheless, this assessment

strategy, while suited for the purpose of the original study, has made it

difficult to make definite inferences about HIV transmission in the

population based on findings.

These limitations considered, this chapter nevertheless has documented a

relationship between dyadic topology with HIV high-risk behaviors. The

three categories of drug abusers, classified according to the injecting drug

use concordance with significant others, differentiated demographic

characteristics, sexual and drug-using behaviors, and characteristics of

significant others and their relationships with probands. The two main

findings are:

53
A minority of IDUs in this sample appeared to maintain intimate

contacts only with IDUs. They were not sexually promiscuous. The

HIV seroprevalence rate was the highest in this group, and the HIV

transmission appears to be mainly through paraphernalia sharing with

their significant others.

A majority of IDUs in the sample were involved in intimate contacts

with non-IDUs, consistent with an earlier finding (Des Jarlais et al.

1984). This mixed group was found to be highly promiscuous,

which appears to be inconsistent with reporting from a New York

sample (Neaigus et al. 1994). HIV seroprevalence had been lower in

this group, but the incidence rate increased in 1991.

These findings may shed some light on the course of HIV spread in St.

Louis and other cities with similar social stratification of the drug-abusing

population. Apart from the late introduction of HIV to the city, the

current low HIV rate in St. Louis might be in part due to relative isolation

of a segment of IDUs who are more “deviant” than other IDUs. HIV

appears to have begun spreading first among them. It is plausible that

their relative isolation prevented HIV from spreading quickly to other

IDUs.

HIV is already spreading within the mixed group of IDUs and non-IDUs,

however. Because the pool of noninjecting drug use heterosexuals linked

to IDUs is much larger in this group, the impact of the spread on the

community at large could become more devastating.

While plausible, present inferences about HIV are highly speculative.

Studies using specific behavior-based alter identification are required to

produce more definitive findings that can test the IDU isolation

hypothesis as an explanation for low prevalence and slow HIV spread in

some areas of the nation (see Rothenberg, this volume).

Effective intervention and prevention strategies should target different

risk groups with different strategies (Watters and Cheng 1987). Increased

education on paraphernalia cleaning, reduction of sharing, and reduction

of injecting drug use through treatment would be appropriate for

paraphernalia-sharing IDUs. For drug abusers who maintain wider social

networks of IDUs and non-IDUs, a rigorous approach to reducing highrisk sexual behaviors would yield better results to prevent the further

spread of HIV.

54
NOTES

1.

2.

3.

4.

The authors thank an anonymous reader of an earlier version for

making this point.

As one reviewer pointed out, the mean number of “unsafe” contact

would be 1.1(1-0.5) = 0.55 for the IDU concordant probands and

0.8(1-0.228) = 0.70 for the IDU-discordant probands.

Preliminary results in this chapter were presented at the annual

meeting of the College of Problems on Drug Dependence, Palm

Beach, FL, June 16-20, 1991.

Direct all correspondence to Rumi K. Price, Ph.D., Department of

Psychiatry, Medical Box 8134, Washington University School of

Medicine, St. Louis, MO, 63 110.

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ACKNOWLEDGMENTS

This work was supported in part by NIDA grant numbers DA05619,

DA06163, and DA05585 and NIMH grant number MH17014.

Acknowledgment is also due to Susan Keating for instrumentation and

implementation of the SARA project.

58
AUTHORS

Rumi K. Price, Ph.D., M.P.E.

Research Assistant Professor of Epidemiology in Psychiatry

Linda B. Cottler, Ph.D., M.P.H.

Associate Professor of Epidemiology in Psychiatry

Doug Mager, M.A.

Statistical Data Analyst

Keith S. Murray, B.A.

Coordinator

Department of Psychiatry

Washington University School of Medicine

St. Louis, MO 63110

59
Sibling Homophily in HIV

Infection: Biopsychosocial

Linkages in an Urban AfricanAmerican Sample

Ann F. Brunswick, Peter A. Messeri, Jay Dobkin, Mary T. Flood,

and Arthur Yang

INTRODUCTION

With the exception of studies that have looked at human immunodeficiency virus (HIV)-discordant couples and mother-to-infant vertical

transmission, and some early work that disproved the possibility of casual

transmission among family members, no research has been reported about

familial clusters of infection. Similarly absent are reports concerning the

range of biopsychosocial factors that might influence vulnerability to

HIV infection among family members who are not known to be members

of a procreational set.

One type of relational group or network, siblings, is examined in the

study reported in this chapter. The study was undertaken to see if

influences that go beyond individual-level or individually occurring

determinants might account for differential distribution of HIV in sibling

groups. A quarter-century study of African-American individuals who

were adolescents growing up in Harlem during the late 1960s (Brunswick

and Josephson 1972) provided an opportunity to explore whether

historical as well as more recent contextual and intraindividual attributes

shared by siblings changed their likelihood of HIV infection. The present

study is essentially a descriptive one, undertaken in response to the

unanticipated observation of sibling agreement or concordance in HIV

serostatus.

The study cohort, furthermore, represents a population that is recognized

to be at elevated risk of HIV infection. Initial data were collected from

this group 25 years ago between 1968 and 1970. All children between

the ages of 12 and 17, inclusive, who resided at that time in a probability

sample of households in Central Harlem (New York City) were included

in the sample. As a consequence of this design feature, a substantial

60
number of respondents in the study can be linked to their sibs who met

the study’s age criterion.

Sib-set membership is construed to be a proxy for a wide range of

individual and social features sibs share that might increase their

vulnerability to HIV infection. The possibility that infection among

siblings was a consequence of direct infection or mutual infection from

another person cannot be ruled out. Neither would it impact on the

purposes or findings of this investigation. The study cohort is too old to

have been exposed to vertical transmission from their mothers. With or

without a mutual source of infection, common physical and social

environment could endow them with shared risk, and that is the issue

explored in this study.

Identifying Sibling Influence

Eight of the 55 sib sets in this study group included one or more members

who were seropositive for HIV. The sibling sample (numbering 124 in

all) showed an HIV seroprevalence rate of 9.7 percent (not significantly

different from the 8.4 percent prevalence in the full assayed sample of

287) (Brunswick et al. 1993).

Gender-specific infection rates within the sib sample were 6.5 percent for

females and 12.9 percent for males, once again not significantly different

from the full assayed sample rates of 6 percent and 10.8 percent for

females and males, respectively.

The kappa statistic, an intraclass correlation coefficient (Cohen 1960;

Fleiss 1981; Hale and Fleiss 1993), was used to measure the concordance,

or agreement, in serostatus within sib sets compared with that observed

between sib sets.’ The observed intraclass coefficient was 0.433771,

standard error (s.e.) = 0.118422. A coefficient of this magnitude carries a

probability of less than 0.001 that the observed concordance in sib results

could have arisen by chance. This finding means that if one sib was

positive, there was the likelihood of one or more other siblings also being

infected. This also means that, when one sib was negative, the likelihood

that others also were negative was well in excess of what could have

arisen by chance.

In concrete terms, what was observed was that 7 of 12 seropositives in the

sibling sample clustered in only 3 multiply infected sib sets. This was

highly significant, not only as determined by the kappa test of

61
concordance, but also when replicated in a probability analysis of paired

seropositive siblings. This procedure yielded a 0.009 probability of

selecting a seropositive dyad at random from the overall sibling sample.

Finding seven seropositives clustered in three sets had a probability that

was ninetyfold greater than this overall probability. Some nonrandom

processes were inferred to be at work to explain why close to 60 percent

of all HIV-infected individuals from 55 sib sets were found in just 3 sib

sets.

The obvious first step was to test the hypothesis that this finding resulted

from shared patterns of injecting drug use. To test whether the observed

sibling concordance was, in fact, capturing homophily in injecting drug

use behavior, the same statistical analysis was repeated, with injecting

drug use replacing HIV status as the dependent variable. This analysis

yielded a kappa coefficient of 0.026755, s.e. = 0.1153 18 and a clearly

nonsignificant z = 0.232. Without any scientific literature to suggest

alternative hypotheses, the present exploratory study was designed to

generate hypotheses to explain the observed familial concordance in HIV

and, more specifically, to seek alternate explanations to direct sib-to-sib

transmission.

Guided by an ecological model of behavioral influences (Bronfenbrenner

1979; Brunswick 1985, in press-u), 10 potential domains of influence

were selected for testing, domains that captured social and physical as

well as distal and proximal influences, in addition to intraindividual

attributes. Distributions on these variables were examined to see if they

correlated with the sibling clustering in HIV infection. In conceptual and

methodological terms, this is an exploratory and descriptive investigation

that hopefully will be followed by more purposively designed and causal

analyses of factors that influence sibling homophily in HIV infection.

(Note that this study was completed before the New Jersey health

department reported two cases of brother-to-brother transmission of HIV

infection in the absence of the commonly defined risk exposures

[Brownstein and Fricke 1993].)

METHODS

Survey data covering a broad range of biopsychosocial domains have

been collected from this uniformly African-American panel at four

periods between 1968 and 1990. For the present analysis, the sample has

been restricted to a subset (numbering 124) of respondents who were

62
interviewed in the fourth study wave (conducted between August 1989

and December 1990), who both agreed to have their blood drawn for

research purposes only to test for the presence of HIV antibodies and who

had at least one other sibling in the study who also was interviewed and

had blood drawn in the fourth study wave. General procedures followed

in the fourth study wave have been described in Brunswick and

colleagues (1993).

To test for differential distribution on the 10 domains of direct and

indirect risk factors, the sibling sample was subdivided into four HIV

relational groups: (1) clustered or multiply infected sibs (those with at

least one other seropositive sibling in the sample); (2) discordant or

isolated seropositive sibs (positives from sib sets where all others were

seronegative); (3) isolated or discordant seronegative sibs who had one or

more seropositive siblings; or (4) clustered seronegatives who had only

uninfected siblings. With the exception of one individual in group 3 who

had two infected sibs included in group 1, groups 2 and 3 came from the

same sib sets, of which group 2 represented the seropositive members and

group 3 the seronegative.

Presumptive evidence for a shared sibling vulnerability to HIV infection

was examined for each variable in a three-step fashion. First, reports

were compared of siblings grouped into the above classification to see if

the clustered positives were markedly different from the other three

groups. In such a case, a variable was interpreted as exercising a

synergistic effect on infection. Lacking this synergistic effect, the same

data were examined to see if the group with homogeneous negative

siblings was markedly different from others, in which case that factor’s

protective influence might be inferred.

The differentiation provided by the sib typology was then compared to

the distribution obtained when the sibling sample was simply divided into

seropositive and negative individuals. When the sib relational typology

yielded stronger differentiation than simply sorting the sample into those

HIV positive and negative, the inference that some shared experience or

attribute was at work seems justified. In such a case, that variable’s

relationship to HIV was considered to be mediated by sibling factors.

Otherwise, individually distributed conditions were presumed to be

operating. When neither a sib-relational nor a simple HIV-positive

versus negative difference appeared, that variable was considered

unrelated to HIV infection in the sample.

63
A third analytic procedure compared the distribution of HIV positives

and negatives in the nonsibling sample (N = 163) to that observed in the

sibling sample. This was done to assess the reliability of observations

from the sibling sample regarding linkages to HIV serological status

(i.e., the extent to which the sib sample might be taken as representative

of the full urban African-American community sample of which it was a

part).

The reader is reminded again of the essentially exploratory and

descriptive nature of this study. The restricted size of three of the four

groups in the sibling typology mitigated against multivariate analysis.

Instead, each of the 10 variable domains was examined, one at a time, for

sibling mediation in the measures within it. Tests of significance, chi

square and Fisher exact test (Fleiss 1981) for categorical variables and

t-test for interval measures, were performed to guide the interpretation of

results. Their inclusion does not imply a hypothesis-testing level of

investigation. These tests were used as indicators of the magnitude of

observed differences. (More detailed specification of the independent

variables and results of significance tests are available from the chapter

authors.)

Finally, the homogeneous African-American sample that contributed

these results limits generalizing findings to other groups before

appropriate replication is performed on samples of those populations.

FINDINGS

Analysis of Correlates of Seropositivity

The disposition of sibs into the four-group relational typology is shown in

table 1. Table 2 presents results of the analysis of influences on serostatus. The first four columns show the results of the test for mediation

effects using the sibling typology. Results are shown for significance

tests that were run on all 62 independent variables comparing group 1 to

all others or comparing group 4 to all others. Special attention should be

given to those measures where clustered positives (group 1) or clustered

seronegative respondents (group 4) differed from the remaining groups.

Variables in table 2 are grouped into 10 domains: (1) HIV-risk practices

and drug use; (2) macrosocial influences and early socialization

exposures; (3) sib proximity and cohesion; (4) nonsib social supports;

(5) risk networks; (6) role attainment/social position; (7) psychosocial

64
TABLE 1. Distribution of siblings in HIV typology.

Positive Negative

Two or More All Other Sibs One or More All Sibs

Sibs Positive Negative Sibs Positive Negative

(++) (+-) (-+) (--)

Sibsets

Size No. of Sets

4 (N = 3) 1

2

1 3

1

3 (N = 8) 1

6

1

1

2 (N = 44) 1

1

1

39

3

2 1

8

18

2

1 1

1 1

1 1

1 1

78

Total 55 7 5 8 104

outlooks; (8) HIV perceptions, knowledge, and attitudes; (9) long-term

somatic and psychological health; and (10) blood serology.

The next section of table 2, headed “All siblings,” shows each variable’s

relation to serological status, independent of the type of sibship.

Significance tests were run on these for gender and also for total positives

versus negatives.

Table 3 repeats the sib findings classified by gender and seropositivity,

and alongside them are data from “nonsiblings” similarly classified.

Overall Patterns

In brief, statistically significant associations (at p = 0.10 or less) were

found for 37 of the 62 variables. All but six of these (i.e., 3 1 variables)

65
TABLE 2. Sibling influences on HIV-Z (in percents, unless otherwise

noted).1

1

2

3

All results reflect a small sample adjustment-a weighting-for differential ratios in

initial recruitment into sample of younger adolescents (ages 12-15), who were twice

as likely to be selected as older adolescents (ages 16 and 17). Fractional weights

were applied to avoid inflating the aggregate numbers (see Brunswick and Josephson

1972).

Conventional significance notations (p) are included to indicate the magnitude of

intergroup differences. Their purpose is descriptive: # < 0.10 > 0.05; * 0.05;

** 0.01; *** 0.001. Notation alongside group 1 indicates difference tested

against all other individuals. Similarly, when significance notation appears for group

4. it refers to the contrast with all other groups. NS = not significant

Significance of all siblings within gender and total group. Seropositive versus

seronegative are indicated in “Total” column. Gender-specific significance is

indicated by a superscript in the “HIV Pos” column for appropriate gender.

66
TABLE 2. Sibling influences on HIV-i (in percents, unless otherwise

noted) (continued).

met the p 0.05 significance criterion. This number clearly is in excess

of what would have occurred by chance, even if one acknowledges the

likely collinearity of certain measures within domains. The predominant

pattern of association supported a synergistic effect where clustered

seropositive sibs were distinguished from the other three groups. Almost

three-quarters (73 percent) of all significant effects (27 of 37) and 44

percent of all 62 tested variables conformed to this pattern. Ten

additional variables exhibited a significant individual-level effect only for

one or both genders. On these 10 variables, the two seropositive groups

67
TABLE 2. Sibling influences on HIV-I (in percents, unless otherwise

noted) (continued).

were distinguished from the two seronegative groups without

demonstrating any synergistic sibling effect.

Risk Factors

Clustered seropositives scored higher on five of six HIV-risk exposures.

A larger percentage of that group reported a history of intravenous drug

use, risky needle practices, had exchanged money or drugs for sex

(female linked), had engaged in male-to-male sex (obviously male

linked), and reported multiple sex partners during the prior 5 years. In

contrast, the isolated seropositives had risk behavior profiles that were not

noticeably different from the seronegative groups except for an elevated

report of intravenous drug use (but not of poor needle practices). The

concordant negative group was noticeable for the absence of male

homosexual risk, an observation that was not fully accounted for by the

68
TABLE 2. Sibling influences on HIV-i (in percents, unless otherwise

noted) (continued).

a

b

c

Empirical classification of ZIP Codes was based on proportions of subsumed census

tracts in poverty. Proportions shown are in ZIP Codes where all subsumed census

tracts are poor.

HIV neighborhoods defined as in NRC 1993. Classified high if 401 or more HIV

cases per 100,000 population in 1991.

Data obtained through New York State AIDS Institute, cases as of 1990. Classified

high if 401 + HIV cases per 100,000 population.

69
TABLE 2. Sibling influences on HIV-I (in percents, unless otherwise

noted) (continued).

a. Empirical classification of ZIP Codes was based on proportions of subsumed census

tracts in poverty. Proportions shown are in ZIP Codes where all subsumed census

tracts are poor.

b. HIV neighborhoods defined as in NRC 1993. Classified high if 401 or more HIV

cases per 100,000 population in 1991.

c. Data obtained through New York State AIDS Institute, cases as of 1990. Classified

high if 401 + HIV cases per 100,000 pop,

d. Summed across five potential problem areas.

e. Mean number of problems, summed over five, for which no help at all was available.

70
TABLE 2. Sibling influences on HIV-I (in percents, unless otherwise

noted) (continued).

71
TABLE 2. Sibling influences on HIV-I (in percents, unless otherwise

noted) (continued).

72
TABLE 2. Sibling influences on HIV-1 (in percents, unless otherwise

noted) (continued).

73
TABLE 3. Influences on HIV: Siblings and nonsiblings1

 (in percents,

unless otherwise noted).

1. Conventional significance notations are included to indicate the magnitude of

intergroup differences. Their purpose is descriptive: # < 0.10 > 0.05; * 0.05;

** 0.01; *** 0.001.

2. Significance tested positives versus negatives within gender and total seropositive

versus total seronegative.

74
TABLE 3. Influences on HIV: Siblings and nonsiblings1

 (in percents,

unless otherwise noted) (continued).

1. Conventional significance notations are included to indicate the magnitude of

intergroup differences. Their purpose is descriptive: # < 0.10 > 0.05; * 0.05;

** 0.01; *** 0.001.

2. Significance tested positives versus negatives within gender and total seropositive

versus total seronegative.

75
TABLE 3. Influences on HIV: Siblings and nonsiblings1

 (in percents,

unless otherwise noted) (continued).

a. Empirical classification of ZIP Codes based on proportions of subsumed census tracts

in poverty. Proportions shown are in ZIP Codes where all subsumed census tracts

are poor.

b. HIV neighborhoods defined as in NRC 1993. Classified high if 401 or more HIV

eases per 100,000 pop. in 1991.

c. Data obtained through New York State AIDS Institute, cases as of 1990. Classified

high if 401 + HIV cases per 100,000 population.

76
TABLE 3. Influences on HIV: Siblings and nonsiblings1

 (in percents,

unless otherwise noted) (continued).

77
TABLE 3. Influences on HIV: Siblings and nonsiblings1

 (in percents,

unless otherwise noted) (continued).

78
TABLE 3. Influences on HIV: Siblings and nonsiblings1

 (in percents,

unless otherwise noted) (continued).

79
TABLE 3. Influences on HIV: Siblings and nonsiblings’ (in percents,

unless otherwise noted) (continued).

somewhat higher proportion of women in that group (table 2).

Confirming their reduced risk exposure, concordant negatives scored

lowest (or shared lowest position) on all risk factors. Consistent with

this, they scored highest on a computed scale of behaviors protective

against HIV (e.g., monogamy, condom use, clean needles). The clustered

positives clearly scored lower than others on the protective behavior

scale.

Drug Use Patterns

Excepting alcohol, the clustered seropositives scored highest on all drug

consumption measures. The isolated positives reported a smaller amount

80
of illicit drug use than the discordant negatives (i.e., little crack use and a

somewhat lower lifetime average number of drugs). The clustered

negative sibs reported the lowest amount of use of all recreational

substances.

Macrosocial Influences

Gender and Cohort. As has been true generally in the United States,

males in this study cohort evidenced higher infection rates than females

(Brunswick et al. 1993). Accordingly, males outnumbered females in all

but the clustered negative group within the sibling typology. The ratio of

men was highest in the two discordant groups. It would seem that, once

at risk, being part of a multiply infected set reduced the gender gap and

increased female risk.

Regarding birth cohort, given the diminished heroin use among younger

cohorts that long has been observed in this study panel (Brunswick and

Boyle 1979), the fact was not surprising that a higher proportion of

younger cohort members (i.e., those born in the late 1950s) appeared in

the clustered negative sib sets than in the others. Examined by gender,

however, the cohort effect was substantial only for women. This was a

likely consequence of selection factors arising from the excess mortality

experienced among older male heroin users in the study cohort.

Historic (Background) Socioeconomic Status. Even within this

relatively homogeneous cohort of urban African Americans, background

characteristics sharply differentiated the sibling typology: Little

education for the mother (elementary school only) as well as reduced

geographic mobility characterized the multiply positive sib sets. Of

particular note, all HIV-infected individuals in this sample (nonsibs as

well as sibs) were northern born, as were discordant negatives (those who

had one or more positive siblings)2. This compared to three-quarters of

the full sample who were northern born.

Geographic Context (Current). Neither the sib types nor individually

classified positives and negatives showed significant geographic variation

as measured by poverty status of area of residence. When the sib sample

was grouped according to gender and serostatus, however, an area

difference appeared for women only: The small group of infected female

sibs were concentrated in census tracts where greater than 35 percent of

households were at or below the poverty level (i.e., lived in the poorest

areas).

81
When census tracts were reassembled into broader neighborhood districts

similar to those that had been used to classify HIV rates in a recent

National Research Council report (National Research Council 1993),

neighborhood infection rate did not differentiate positives and negatives

in this sample: 100 percent of clustered positives versus 94 percent of

concordant negative sib sets lived in high infection areas (classified as

401 and over HIV cases per 100,000 persons).

Sibling and Other Social Support

Sibs in the clustered seropositive sets reported greater proximity to one

another—both spacial and social-than was seen in other groups. They

all lived in the same or an adjacent ZIP Code, compared to 49 percent of

discordant infecteds who lived near their sibs. Multiply infected sets also

were more likely to live with relatives who were not spouses or children

(taken as a possible proxy for living with sibs). Concordant infected sibs

received less help for their life problems (money, health, etc.) from

family members (other than sibs). The deficit in social support extended

across infected females as well as males where support networks of the

uninfected were clearly larger than for infected. Furthermore, clustered

and isolated seropositives (i.e., all infected sibs) were more likely to

report receiving help and advice from their siblings than seronegative

respondents. Clustered and isolated seropositives, however, parted

company with respect to general social supports: Sibs from multiply

infected sets received less help from other family members, from peers,

and from professional sources than did sibs in the other three groups.

Risk Networks

Increased reports of risky sexual networks (i.e., having friends who are

prostitutes, bisexuals, or homosexuals) differentiated the clustered

seropositives from the other sib types. Meanwhile, better than half the

respondents in all sib types had drug-using friends, a clearly nondistinguishing measure in this sample.

Social Attainment

To be congruent with findings already reported, those in multiply infected sib sets might be expected to show reduced social attainment and the

weaker ties to conventional institutions that are associated with it.

Clustered positives did score most poorly on five of the six indicators in

this domain, particularly on early parenting, never having been married,

82
and failure to complete high school. Gender, however, was a more

powerful mediator than sib status in these relationships. Education and

marriage discriminated between infected and uninfected women but not

men, as did unemployment and welfare. Early parenting showed less

distinct gender variation.

That these variables discriminated more strongly among women than men

is consistent with other analyses of this longitudinal cohort that have

shown African-American women to be better integrated into dominant

culture norms than their masculine counterparts (Brunswick et al. 1992,

pp. 4 19-472). This suggests that gender predominates over sibling

context in the relationship between social attainment and HIV infection.

Psychosocial Attitudes

Psychosocial measures were examined both from the prior study wave

6 years earlier and also from subjects’ adolescence 22 years earlier. The

mixed results led to the conclusion that, whatever their etiological role in

HIV, effects of psychosocial factors were not mediated by sibling status.

HIV Knowledge, Attitudes, and Beliefs

The few differences in this domain, perceived vulnerability and worrying

about getting or having AIDS, were differentiated better by serological

status than by sibship (i.e., sibling context did not influence these

individual-level, HIV-centered attitudes and beliefs).

Health and Medical Care

At all study times (t2 at ages 18-23 in 1975-76, t3 at ages 26-31 in 1983-

84, and concurrently t4 at ages 32-38 in 1989-90), those in multiply

infected sibsets reported more health problems than the concordantly

negative and isolated positives. Isolated negatives were sicker than

isolated positives at all study times. (General health was associated with

serostatus in the nonsib sample as well.)3 Conditions that might be

associated with HIV infection, expectedly, were reported more often by

those infected than those not infected, regardless of sibship. A history of

sexually transmitted diseases showed no clear association with HIV in

this cohort.

Clustered seropositives also gave evidence of greater psychological and

emotional burden than did other sib groups. In both the past and present,

83
they reported more psychological (affective) symptoms and strain

(dissatisfaction with varied aspects of their lives) (see also Brunswick et

al. 1992). Isolated seropositives were more similar to the clustered

seronegatives on these measures. The former reported fewer psychological as well as health problems (somatic) than were reported by isolated

seronegatives.

Despite their high number of health problems, the clustered positive sibs,

along with the discordant negative sibs, least often had a regular doctor in

private practice or a health maintenance organization. The isolated

positives reported the highest rates of regular physician care.

Serology

In this particular study, serology analysis (for markers of hepatitis B and

C and syphilis) was performed as a validity check on self-reported risk

behavior. Hepatitis markers were consistent with the greater risk exposure reported by clustered infected sibs (see section on “Risk Factors,”

above). Less clear, however, was why-on the hepatitis markers, which

could be linked to parenteral drug use-discordant seronegative sibs were

more similar to clustered positives than negatives. The reverse was

observed for discordant positives, whose hepatitis marker levels were

closer to those observed in the clustered negative sib group.

CONCLUSION

The study discussed here was exploratory in the true sense of the word. It

represents an attempt to identify influences that might be linked to the

unexpected sibling concordance in HIV infection that was observed in

this uniform African-American community sample, a concordance that

well exceeded what could have been expected by chance. No corresponding sibling concordance appeared in injecting drug use. The findings,

overall, implicate historical factors (i.e., family background) and biological ones (i.e., earlier health status) as synergistic factors associated with

increased HIV seropositivity risk. As such, these findings fit in with

other analyses of the study cohort that demonstrate the enduring effects of

early poor health status and of social factors in increasing vulnerability to

HIV infection (Brunswick, in press-b; Brunswick and Titus, unpublished

manuscript).

84
In addition, this analysis of sibling sets who were concordant and

discordant with respect to HIV infection has suggested two pathways of

susceptibility to infection. One is systematic and can be linked to an

array of historical, social, and biological factors that appear synergized in

sibling sets. This pathway applied to better than half (58 percent) of the

infecteds within the sibling sample. This finding carries an implication

for the utility of determining sib-set membership in infected persons and

targeting familial prevention or intervention efforts when an HIV

seropositive is diagnosed within a sibling network.

Discordant or isolated seropositives, who lacked most of the biopsychosocial detriments that were in evidence among those in multiply infected

sib sets, suggest the other, more random trajectory or pathway to

infection. This group, indeed, was offset by the random or discordant

negatives whose biological deficits exceeded their randomly infected

counterparts and who, as yet, had remained seronegative. Whether the

latter’s absence of infection was simply a matter of timing will be tested

when the 3-year followup data, currently being collected, are analyzed.

In addition to the insights gained here about sibling correspondence in

biopsychosocial risk factors, a good correspondence between blood

serology and self-reported risk factors and health status was demonstrated. The differential in hepatitis markers between discordant seropositives and seronegatives, however, where seronegatives evidenced

higher rates than seropositives, was counterintuitive and puzzling

although not contradictory to the self-reported health data. Together with

the random pathway logic discussed above, might these be a reflection of

variations in vulnerability? That is, the social and biological correlates of

HIV seropositivity that predominated in the condordant seropositive

sibling group also were in evidence in the discordant seronegatives but

absent among the discordant seropositives, leading the authors to call one

a random pathway to infection versus unidentified or random protection

from infection in the discordant seronegatives.

Returning to the main questions that motivated this analysis, at a

minimum these findings demonstrate a congruence in biopsychosocial

risks and multiply infected sib networks. While knowledge of risk

exposures is a necessary precondition to understanding these phenomena,

it seems far from a sufficient one. The study discussed here provides

further evidence of the need for taking contextual social factors into

account in attempts to modify HIV-infection risk and HIV-risk behaviors.

85
NOTES

1. Appreciation is expressed to Cecilia Hale, Ph.D., of the Biostatistics

Division, School of Public Health, Columbia University, for

performing the sibling concordance analysis.

2. Reduced mobility may be the explanatory link to the strong

association between HIV infection and northern birthplace. Since

Harlem historically was peopled by African-American migrants from

the South, the less mobile northern-born would have remained after

the more upwardly mobile of the southern migration wave(s) moved

out. This “winnowing out” process had not yet occurred among the

new first-generation wave from the South.

3. For reasons not readily seen, prior and concurrent general health

disadvantage in the nonsib sample was stronger for women than men

in the sib sample, it was stronger for men. This conceivably could

relate to the diminished gender advantage in HIV-infection status

when women had infected sibs. (See section on “Gender and

Cohort,” second paragraph.)

REFERENCES

Bronfenbrenner, U. The Ecology of Human Development. Cambridge,

MA: Harvard University Press, 1979.

Brownstein, A., and Fricke, W. HIV transmission between two

adolescent brothers with hemophilia. Morb Mortal Wkly Rep

42(49):948-951, 1993.

Brunswick, A.F. Health services for adolescents with impairment,

disability, and/or handicap: An ecological paradigm. J Adolesc Health

Care 6(2):141-151, 1985.

Brunswick, A.F. Bringing the context in from the cold: Substantive,

technical and statistical issues for AIDS research in the second decade.

In: Battjes, R.J.; Sloboda, Z.; and Grace, W.C., eds. The Context of

HIV Risk Among Drug Users and Their Sexual Partners. National

Institute on Drug Abuse Research Monograph 143. NIH Pub. No.

94-3750. Washington, DC: Supt. of Docs., U.S. Govt. Print. Off., in

press-a.

86
Brunswick, A.F. Issues in HIV-AIDS natural history research

methodology. In: Davenny, K., and Sloboda, Z., eds. Natural

History/Longitudinal Studies of HIV in Drug Users. National Institute

on Drug Abuse Research Monograph 143. NIH Pub. No. 94-3750.

Washington, DC: Supt. of Docs., U.S. Govt. Print. Off., in press-b.

Brunswick, A.F., and Boyle, J. Patterns of drug involvement:

Developmental and secular influences on age at initiation. Youth Soc

11(2):139-162, 1979.

Brunswick, A.F., and Josephson, E. A study of adolescent health in

Harlem. Supplement. Am J Public Health October: 1-62, 1972.

Brunswick, A.F.; Aidala, A.A.; Dobkin, J.; Howard, J.; Titus, S.P.; and

Banaszak-Holl, J. HIV-l seroprevalence and risk behaviors in an

African American community cohort. Am J Public Health

83(10):1390-1394, 1993.

Brunswick, A.F.; Lewis, C.; and Messeri, P. A life span perspective on

drug use and affective distress in an urban African American sample.

J Community Psychol 19:123-135, 1992.

Brunswick, A.F.; Messeri, P.A.; and Titus, S.P. Predictive factors in adult

substance abuse: A prospective study of African American

adolescents. In: Glantz, M.D., and Pickens, R.W., eds. Vulnerability to

Drug Abuse. Washington, DC: APA Press, 1992.

Brunswick, A.F., and Titus, S.P. “Differential Vulnerability: Poor Health

as an Antecedent of HIV Infection.” Unpublished manuscript.

Cohen, J. A coefficient of agreement for nominal scales. Educ Psychol

Meas 20:37-46, 1960.

Fleiss, J.L. Statistical Methods for Rates & Proportions. 2d Ed. New

York: Wiley, 1981.

Hale, C.A., and Fleiss, J.L. Interval estimation for kappa with binary

classification under two study designs. Biometrics 49:523-534, 1993.

National Research Council. The Social Impact of AIDS in the United

States. Washington, DC: National Academy Press, 1993.

AUTHORS

Ann F. Brunswick, Ph.D.

Senior Research Scientist

Division of Sociomedical Sciences

School of Public Health

Columbia University

60 Haven Avenue, Box 394

New York, NY 10032

87
Peter A. Messeri, Ph.D.

Associate Clinical Professor of Public Health

Division of Sociomedical Sciences

School of Public Health

Columbia University

600 West 168th Street

New York, NY 10032

Jay Dobkin, M.D.

Director, AIDS Program

Mary T. Flood, Ph.D., M.D.

Infectious Disease Specialist, AIDS Program

Columbia-Presbyterian

Medical Center

630 West 168th Street

New York, NY 10032

Arthur Yang, Ph.D.

Consultant

AT&T/Bell Labs

2333 Husdon Terrace, Apartment A 1

Fort Lee, NJ 07024

88
Focal Networks and HIV Risk

Among African-American Male

Intravenous Drug Users

Frederick W. Frey, Elias Abrutyn, David S. Metzger,

George E. Woody, Charles P. O’Brien, and Paul Trusiani

INTRODUCTION

The prospects appear poor for the development of an effective vaccine to

prevent the number of those afflicted with the human immunodeficiency

virus (HIV) from rising to perhaps 30 million by the year 2000. As a

result, the “one clear message” sounded at the Ninth International

Conference on AIDS in Berlin in 1993, according to many observers, was

that “prevention is the only way to stop the alarming spread of the virus,

HIV, throughout the world” (Altman 1993).

Prevention fundamentally involves either (1) reducing the risk associated

with the well-known drug-taking and sexual activities that spread the

virus or (2) reducing the frequency of the activities themselves. The

former might be accomplished, for example, by the distribution of sterile

syringes or condoms and the latter by efforts to reduce drug use, the

sharing of infectious paraphernalia, or dangerous sexual relations.

The predominant approach toward influencing key groups to change their

high-risk behaviors has been education about the causes and

consequences of the disease (Koop 1987). Much research indicates,

however, that individual awareness of consequences is often not

enough-that the causal link between awareness of risks and actual

change in high-risk behaviors is tenuous and uncertain (Flynn et al. 1987,

p. 93; Jain et al. 1987, p. 42; Kelly et al. 1987, p. 39; Kleinman et al.

1987, p. 196; McAuliffe et al. 1987, p. 40; Solomon and DeJong 1986;

Williams 1986). Intervention to change what are usually habitual,

intimate, socially embedded behaviors may require more than merely

increasing awareness of risks.

89
Just as education may not play its common role in this pandemic, so also

the usual individualized conception of disease and treatment may be

inappropriate. Individualized clinical treatment may have limited effects

if social influences are ignored (Gottlieb 1981, p. 229; Pesce et al. 1987,

p. 60; Schuster 1989, pp. 12-13; Yancovitz et al. 1991). It is crucial to

appreciate that the high-risk behaviors associated with the spread of HIV

are social phenomena. Other people are involved, both directly as

coparticipants and indirectly in that social supports and pressures either

encouraging or opposing risky behaviors may have great influence

(Antonucci and Depner 1982, pp. 233-254; Bartlett et al. 1987, p. 41;

Cleary et al. 1986; Emmons et al. 1986; Leventhal and Cleary 1980;

Stanton and Todd 1982; Sterk and French 1987, p. 196).

Research generally seems to suggest significant associations between

social network features, on one hand, and a wide range of health-related

behaviors and problems, on the other (Berkmen and Syme 1979;

DiMatteo and Hays 1981, pp. 117-148; Romano et al. 1991). Social

network characteristics are apparently predictive of such addictive

behaviors as smoking and drinking and even the tendency for drug users

to seek or benefit from treatment (Caplan et al. 1976; Chassin et al. 1984;

Earp 1979; Emmons et al. 1986). Therefore, it is not surprising that

network characteristics are one of the six basic elements shared by all of

the dozen or more health models reviewed by Ostrow (Ostrow 1986).

Problems also exist with the relevant corpus of research on social

networks. Most such research has been very limited in approach and

oriented mainly to positive effects. Three of the five literature reviews on

networks and health lament the lack of “fine-grained analyses” (Barrera

1981, pp. 69-96). Network research on African Americans, especially

males, is particularly lacking. Finally, the current invigoration of

network research related to the acquired immunodeficiency syndrome

(AIDS) pandemic has tended to stress “risk networks”-chains of persons

linked by drug behaviors (or high-risk sexual behaviors). Such research

is essential. No less essential is research into the close networks of the

focal, potentially high-risk individual.

From this perspective, one can ascertain the relative influences, positive

and negative, of the various people in the focal individual’s entourage,

the positive and negative substructures. The structural configuration of

such forces may be decisive in shaping the risk or resistance behaviors of

that individual.

90
The study discussed in this chapter provides data that begin to address the

designated needs. It focuses on the social networks of male AfricanAmerican injecting drug users (IDUs) currently in treatment. Its

objective is to determine the significance of their close, focal networks

for their high-risk behaviors related to HIV/AIDS. In contrast to previous

studies, this work provides data on a crucial but relatively neglected set of

respondents, interviewing not only the focal IDU but also those persons

he identified as most influential in his life, that is, his “referrals” (RFs).

METHODS

Networks

A social network is a set of actors (individual people in the present case)

together with a mapping of one or more sets of social relations onto those

actors (e.g., communication, power, kinship, support, or drug sharing).

Graphically, these relations are usually indicated by lines or arrows

connecting appropriate actors; mathematically, they are represented by

matrices. Focal networks consist of a designated focal respondent (FR)

of prime reference and the RFs named by that FR. Apart from the

defining focal links, focal networks can vary in the degree to which their

members interact. Unlike many previous studies, in this research an

effort was made to interview all referrals. A close network is one for

which the network-defining relationship is relatively intimate and

significant, such as kinship, strong friendship, or important influence.1

Study Population

The research subjects consisted of two groups: (1) 20 male AfricanAmerican IDUs in clinical treatment in Philadelphia for opioid dependence (FRs) and (2) 85 of the 106 persons these FRs identified as

members of their close social networks (RFs).

All FRs were part of a larger project that was studying the spread of HIV

among IDUs in and out of treatment (Metzger et al., in press). This larger

project included interviews with, among others, a random sample of

152 IDUs selected from the clients in the aforementioned treatment

program. The sample of 152 IDUs was stratified by race and gender, and

a simple random subsample of 32 male African-American respondents

was drawn. Four of these became unavailable (due to death, illness, or

imprisonment), leaving 28 who gave informed consent and were

91
interviewed from 1990 to 1992 along with as many of their RFs as

possible, also with informed consent. For 8 of these 28 FRs researchers

interviewed less than 60 percent of their total network, so these subjects

were dropped from subsequent analyses. For the remaining 20 networks,

83 percent of all network members were interviewed. Comparison of the

basic characteristics of the 20 networks that were included and the 8 that

were dropped revealed no significant differences in either network or

demographic attributes.

Instruments and Procedures

All respondents were interviewed by one of three specially trained

interviewers using the same interview schedules and format. The

instruments included items from previous public health research and

items developed specially for this study.

High-risk drug-taking and sexual behaviors were the criterion

(dependent) variables. Three types of predictor (independent) variables

were used: (1) the FR’s personal demographic characteristics, (2) general

network features, and (3) network drug use patterns.

To obtain network information, all respondents were asked to “think

about the main people in your life . . . the people who most affect you (be

it good, bad, or mixed) and also the people you most affect. They can be

relatives, friends, enemies, neighbors, people at work, people you do

things with, and so on-anyone who importantly affects you or whom

you affect.” No limit was placed upon the number of referrals that could

be named.

For each referral named, the interviewer inquired about the connection to

the respondent, demographic characteristics, communication, influence,

satisfaction, conflict, and other matters. A special format was used to

expedite recording such information. Attempts were made to interview

all referrals of the focal respondents. Respondents were paid $20 for

participating and came to the clinic for most interviews.

Criterion Variables: Drug and Sex Risk

Both drug- and sex-risk variables related to HIV transmission were

investigated. Drug risk was assessed via three main criterion variables:

(1) whether the respondent injected drugs, (2) whether the respondent

shared “works” (i.e., needles, syringes, and other paraphernalia), and

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(3) the total number of different drugs the respondent had used during the

previous 30 days. Moreover, all three variables were combined with

essentially equal weights into a summary drug-risk index.

Sex risk was assessed by four criterion variables: (1) the number of

different sex partners during the previous 6 months, (2) having had sex

with a prostitute during the same period, (3) having had sex with an IDU

during this period, and (4) whether the respondent reported always

wearing a condom during sex. These four items were also combined with

essentially equal weights (valence reversed for item 4) into a summary

sex-risk index.

Predictor Variables

Three types of predictor variables, as noted, were examined: (1) the FR’s

demographic characteristics, (2) general network features, and (3) the

drug use patterns of the RFs.

The demographic factors considered were age, education, employment,

household size, marital status, number of children, number of dependents,

time at current residence, subjective general health, and time spent

watching television.

General network features included network size, percent female, percent

relatives, percent of the FR’s network claims that were reciprocated by

the RFs, percent of reciprocal relations among the RFs, total density,

referral density, kin (relatives) density, nonkin referral density, percent of

claimed network links that were internal to the network, and the mean

number of extra-network links per referral. Reciprocity refers to relations

in which both individuals have the given relationship to each other (e.g.,

A influences B, and B influences A). Density refers to the number of

actual links in the network divided by the maximum number of possible

links in a network of that size. It expresses the proportion of the possible

links in the network that is actually present. It is thus a rough measure of

the degree of integration of the network for the given type of link. It

should be noted that focal networks have a minimal density of N-I/N,

where N equals the number of people in the network, which is not

necessarily true for other types of networks.

The RF drug use patterns examined were: (1) the percent of network RFs

using drugs, (2) the percent of RFs who inject drugs, and (3) the mean

number of different drugs used by the RFs (excluding alcohol and

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methadone), plus a summary index of these three variables. Note that

these variables concerned only RFs and were conceptually independent of

the FR’s risk behaviors.

RESULTS

General Network Features

The basic initial question is whether the focal networks of these 20 male

African-American IDUs in treatment are significantly different from most

social networks. If not, further analysis would not seem promising.

On initial inspection, the 20 focal networks appear quite normal. The

networks of most Americans and Canadians average from 4 to 8 persons

in size; the focal networks of 20 IDU respondents averaged 6.3 persons.

Most focal networks are racially homogenous; these 20 FR networks

were virtually 100 percent African American. Most close focal networks

are relatively evenly mixed by gender; in these networks, 51 percent of

the RFs were female, and 49 percent were male. Most focal networks

have a majority of relatives (i.e., kin); in these 20 networks, 61 percent

were relatives. Two-thirds of those relatives (68 percent) were female.

The nonkin RFs were thus predominantly male (74 percent).2

Most American and Canadian networks have average densities of from

about 0.3 to 0.6. In other words, they have roughly 30 percent to

60 percent of the maximal number of links. For the 20 networks in the

present study, the average density was 0.456, thus in the middle of the

normal range.

There is, however, more to the picture; the superficial perspective is

inadequate. When the data are inspected more closely, it appears that the

normal density level of these 20 networks comes mainly from the dyadic

(two-person) links radiating out from the FR like spokes on a wheel,

while linkages among the RFs are unusually meager. The density among

the RFs alone, excluding the FR, is but 0.13 1. Three-quarters of the

reciprocal links involved the FR, and only one-quarter involved the much

more numerous RFs by themselves. This does not mean, however, that

reciprocal designation of the FR by his RFs was especially high. Only

43 percent of the FRs’ RFs in turn named their FR. In particular, it is

interesting to note that although nearly two-thirds of the FRs named their

mothers, half of the mothers interviewed did not name their FR sons. In

94
general, these 20 networks, compared to those of most Americans, appear

quite Hypercentric about the IDU focal respondent.

Criterion Risk Variables

Despite being in a treatment program forbidding such behavior,

60 percent of the FRs injected drugs, 25 percent shared works, and the

average number of different drugs used during the preceding 30 days was

2.3, usually heroin, cocaine or crack, speedball, or marijuana. On the

average, the FRs also had 1.75 different sex partners during the preceding

6 months. Over the same period, some 30 percent had sex with a

prostitute, 10 percent reported having had sex with another IDU, and

30 percent claimed they always used a condom.

These various drug-risk measures are all significantly related to one

another. Injecting drugs and sharing works were positively correlated

(r = 0.47 1, p = 0.036), as were injecting and the number of different

drugs used (r = 0.755, p < 0.001) and sharing works and the number of

different drugs used (r = 0.452, p = 0.046). One type of drug risk

predicts the other types rather well.

The interrelations among the sex-risk indicators are more varied.

Significant correlations exist only between the number of sex partners

and having sex with a prostitute (r = 0.494, p = 0.027) and between sex

with a prostitute and sex with an IDU (r = 0.509, p = 0.022), presumably

often the same person.

Perhaps surprisingly, for this sample the correlations between individual

drug risk and sex risk were not significant. On the contrary, 9 of the

12 correlations were negative (p = 0.07, sign test) though not individually

significant. In fact, the correlation between the summary drug-risk and

sex-risk indices was r = -0.190, also negative though not significant.

Therefore, the subsequent analysis will concentrate primarily upon drug

risk.

Predictor Risk Variables and FR Drug Use

Demographic Characteristics. The demographic characteristics of the

FRs, such as age and education, were the first type of predictor variables

examined. Most of the correlations between the FR’s demographic

characteristics and the four criterion drug use variables (i.e., injecting,

sharing works, using a wider variety of drugs, and the summary index)

95
were insignificant (see figure 1[a]). Overall, the FR’s demographic

characteristics were poor predictors of drug use.

Network Features. Limited but important associations were found

between the general features of the FR’s network and drug use (see figure

1[b]). The percent of the FR’s relatives in the network was significantly

negatively correlated with the FR’s sharing works (r = -0.470, p = 0.037)

and with his using a wider variety of drugs (r = -0.466, p = 0.038), while

the density of interaction among his nonkin referrals was positively

correlated with sharing works (r = 0.429, p = 0.165). This was not

statistically significant since only 12 cases were available, the other 8

having too few nonkin RFs for meaningful calculation.

Referral Drug Use Patterns. In contrast to the exiguous associations

between the FRs’ demographic characteristics and drug risk or the

moderate associations between general network features and drug risk, a

very broad and strong set of associations existed between the network

RFs’ drug use and that of the FR. Every presumably independent referral

drug use variable was significantly and positively correlated with every

presumably dependent FR drug use variable (figure 1[c]). These

correlations range from a low of 0.349 (p < 0.10, one-tail) for that

between the percent of RFs who inject and whether the FR injects to a

high of 0.697 (p = 0.001) for that between the two summary indices, one

for drug use by the FR and the other for drug use by his RFs. More than

two-thirds of the correlations are at the 0.500 (p = 0.02) level or higher,

although there is obviously some collinearity. The RFs’ summary drug

use indices alone “explain” nearly half (48.6 percent) of the variation in

the FRs’ summary drug use indices.

A majority of the FRs (55 percent) used drugs with at least one of their

network RFs. That majority averaged more than two members of their

network with whom they used drugs. Of the 27 RFs with whom the FRs

did drugs, 41 percent were relatives, and 59 percent were nonkin. Since

the RFs in general were at least three-fifths relatives, this means that the

FRs who did drugs did so with 17 percent of their relative referrals and

with 38 percent of their nonkin referrals-more than double

proportionately.

No FR did drugs with parents, grandparents, or children. Spouses,

siblings, and cousins made up 91 percent of the kin with whom the FRs

did drugs. For the nonkin, 81 percent of the RF drug partners were male

96
FIGURE 1(a). Personal demographic characteristics by FR’s drug risk

variables (p < 0.10).

FIGURE 1(b). Network structural features by FR’s drug risk variables

(p < 0.10).

FIGURE 1(c). Referral drug use patterns by FR’s drug risk (p < 0.05).

97
friends. Of the kin RFs who did drugs with the FR, 36 percent injected;

of the nonkin RFs who did the same, 8 1 percent injected.

The correlation of what is perhaps the key criterion variable, FRs sharing

works, with the summary drug-risk index for the RFs is 0.650

(p = 0.002). More than 42 percent of the variation in sharing works by

FRs is “explainable” solely in terms of the drug use behaviors of their

RFs (not including the RFs’ sharing works). The best single predictor of

the FR’s sharing works is the percent of his RFs who inject (r = 0.553,

p = 0.011).

Separating the FRs into two groups, those who share works and those

who do not, yields yet another perspective. Kin averaged 43 percent of

the RFs of those who shared, compared with 66 percent of those who did

not (T = 2.256, 18 df, p < 0.05, two-tail). On the average, 85 percent of

the RFs of those FRs who shared works used drugs, compared with only

43 percent of the RFs of those who did not share (T = 2.763, 18 df,

p < 0.02). Similarly, on the average, 45 percent of the RFs of those FRs

who shared works injected drugs in contrast to only 13 percent of the RFs

of those FRs who did not share (T = 2.814, 18 df, p c 0.02). Finally, the

average density among nonkin RFs of those FRs who shared was four

times the density of the nonkin RFs of those who did not share

(T = 1.501, 10 df, p < 0.10, one-tail).

If the best set of proximate explanatory factors for the FR’s drug risk is

the pattern of drug use among his network RFs, what factors in the

present data best explain that RF drug use? Answer: These explanatory

factors are general network characteristics, especially the separate

densities of the kin and nonkin RFs in the network, working in opposite

directions. In multiple linear regression, with the network RFs’ summary

drug use index as the criterion variable, these two predictor variables

together produced an adjusted r2 of 0.429, p = 0.07. Thus, the separate

densities of the relatives and the nonrelatives among the FRs’ RFs

account for more than 40 percent of the variation in the RFs’ summary

drug use index, with more density among relatives being associated with

less RF drug use and more density among nonrelatives being associated

with more RF drug use.

Stepwise Regression Analysis. More than 60 predictor variables were

examined, and more than a dozen of these displayed several statistically

significant correlations with criterion variables. Therefore, stepwise

regression analyses were also performed to sort out their relative

98
importance. In each analysis, all variables that were conceptually

independent of the criterion variable and correlated with it beyond the

0.400 level were taken as predictors.

Focal Respondent’s Drug Use

Using the FR’s summary drug use index as the criterion variable, three

significant predictor variables emerged (minimum tolerance for

entry = .01). Most significant was the percent of his network for whom

the FR would “get drugs if they needed it” (beta = 0.412), next was the

network RFs’ summary drug-use index (beta = 0.369), and third was the

degree to which the FR approved of drug use (beta = 0.244). Together,

these three best predictors “explained” more than 60 percent of the

variation in FR drug use (r2 = 0.606, F-ratio = 10.735, p < 0.001). Two

of these three predictors are network-related features.

Focal Respondent’s Sharing Works

Using the FR’s sharing works as the criterion variable, four significant

best predictors emerged: (1) the FR’s felt risk of getting AIDS

(beta = -0.442); (2) the network RFs’ summary drug use index once more

(beta = 0.412); (3) the FR’s marital status (beta = -0.322); and (4) the

ratio of what support the FR expected from his network RFs compared to

what he would give (beta = -0.291). These four predictors together had

an adjusted multiple r2 of 0.738, thus accounting for nearly three-quarters

of the variance in the sharing of works by FRs (F-ratio = 14.372,

p< 0.001). Three of the four best predictors were network-related

features.

Intervening and Elaborative Variables

From an intervention perspective, it is not enough to know that network

features strongly affect our criterion drug use behaviors. Usually, one

also needs to know how those network effects are produced (i.e., what the

specific mediating processes are).

Close networks affect the behaviors of their members in at least two basic

ways: (1) by interactions that directly impinge upon members’ behavior

(e.g., procuring drugs, occupying a person’s time, providing material

support), and (2) indirectly by affecting members’ attitudes and

orientations that, in turn, lead them to behave in certain ways.

99
Especially in the early stages of network research, it is important to

discover the psychological factors that presumably mediate between

network characteristics, on one hand, and the behaviors of interest on the

other. In this way, greater confidence is generated in the validity of a

causal interpretation of the network characteristics. The causal chain

becomes more explicit.

Among the FRs, knowledge of how HIV is spread was not associated

with reduced drug use and was negatively correlated with felt risk of

getting the disease, although such felt risk was indeed linked to reduced

sharing of works.

Two basic attitudes were, however, conspicuously and significantly

associated with lower drug use-namely, happiness and felt social

support (i.e., having others one can depend upon). Felt social support,

moreover, is the kind of psychological factor that should plausibly

mediate the found association between kin referral density and reduced

drug use. Kin obligations provided general social support, whereas the

interactions with nonkin referrals were more likely to primarily involve

drugs. Thus, nearly two-thirds of FRs involved in the study expected at

least some of their RFs to get drugs for them, usually nonkin RFs, and

would do the same in return. These two variables (i.e., expectations of

getting and receiving drugs from network members if needed) together

explained nearly 60 percent of the variation in the FRs’ drug use indices

and were also strongly related to the referrals’ drug use indices.

Along with associated psychological factors, another dimension of

networks lies in the discussions and activities that typify them. In

general, the FRs’ level of discussion of family, work, money, and basic

health with their RFs was unrelated to their drug-taking and sexual

behaviors. Discussion of sex with RFs was, however, significantly and

positively related to the number of different sex partners, having sex with

a prostitute, and the overall sex-risk index. Discussion of drugs was

significantly and positively related to sharing works and positively but

not significantly (given the small subsample size) to injecting and the

number of different drugs used. Discussion of AIDS, on the contrary,

was significantly negatively related to injecting and the number of

different drugs used, but, oddly, not with sharing works.

Finer analysis with a larger sample of networks is obviously necessary.

Initially, however, it seems likely that sex and drugs are primarily

discussed with sympathetic network members, often copractitioners,

100
while AIDS is a more likely topic with RFs opposed to high-risk

behaviors. Again, the relative balance of forces within a conflicted

network may be crucial for the FR’s behavior.

Examination of network activities also provides some insight into the

social processes involved. Lending or borrowing money and talking

frequently with network members were not significantly related to risky

behaviors. Watching television, though not significant, was consistently

negatively correlated with all drug use dependent variables, perhaps as an

alternative activity, especially for family members. Drinking together

was not related to drug use but was significantly linked to the number of

sex partners and having sex with a prostitute. The acknowledged drug

activities were, of course, strongly linked to the drug use responses but,

interestingly, not to sex risk.

DISCUSSION

General Network Characteristics

On initial examination, the close focal networks of these male, urban,

African-American IDUs who are in methadone maintenance treatment

appear quite similar to those found for other nondrug-using groups in the

United States and Canada. Such networks have been described as “small,

centered on kin, comparatively dense, and homogeneous by comparison

to the respondent population . . . ” (Marsden 1987). Closer scrutiny of

the data, however, leads to quite different conclusions. These superficially normal networks are in certain fundamental respects not “normal”

or typical at all. On the contrary, they appear quite distinctive if not

pathological. They are relatively hypercentered upon the FR (perhaps

deliberately structured that way by him) rather than being broadly

interactive and integrated. Their density comes mainly from dyadic links

radiating out like bicycle spokes from the FR, while the linkages among

other network members are unusually sparse.

In an important sense, these hypercentric networks seem mainly those of

the FRs alone rather than being those of most of the other members as

well. The more linkages among referrals, the fewer linkages there are

with the FR. Possibly, this is the only type of network that IDUs can

maintain-separating their close relations from one another as much as

possible to minimize criticism or influence or because they are

incompatible. Perhaps such networks tend to generate or reflect the

101
problems that led to drug use. Comparative research on other groups is

needed to confirm this finding and to fathom the causes and effects of

such distinctive network patterns.

HIV-Related Risk Taking: Drugs and Sex

Although participating in a methadone maintenance program, many of

the FRs appeared to be at considerable risk from their drug use and sexual

activity. However, drug risk and sex risk were usually not correlated

with each other. The primary focus of this analysis was on drug risk.

Demographic Characteristics and Drug Risk. In general, the

demographic attributes of the FRs were not significantly linked to their

drug use. Although a few scattered correlations were significant, the

personal characteristics of the FRs usually were not very helpful in

predicting drug use.

General Network features and Drug Risk. Certain general network

characteristics, however, were significantly related to drug use. The

primary finding of this analysis reveals a tendency toward a fundamental

bifurcation of these networks along two axes: (1) relatives (kin) of the

FR, mainly female, and usually antipathetic toward his drug use, and

(2) male friends (nonkin), who differentially tend to support and

participate in his drug use.

Since roughly three-fifths of the referrals are kin, it is not surprising to

find that most general measures of the degree of integration (density) of

the network are consistently negatively correlated with drug use by the

FR. More specifically, kinship density in the network is negatively

linked to drug use, while density among his nonkin RFs, on the contrary,

is positively associated with drug use. The FRs displaying greater drug

use seem to have assembled a largely male and nonkin subnetwork that

supports and participates in their drug activities. Sometimes this pattern

spreads to their kin-based subnet. When this happens, drug use is

significantly greater and reduction of their risky behavior would appear

especially difficult.

Network Drug Patterns and the FR’s Drug Use. The patterns of drug

use among network RFs were strong predictors of the FR’s drug use. The

percent of referrals who inject, who use drugs, the average number of

drugs they use, and the summary index of these variables were all

significantly related to all of the FRs’ drug use measures. The composite

102
index of RF drug use plus the percent of relatives in the network together

explain more than half of the variation in the FRs’ drug use index, each

predictor variable working in an opposite direction.

Despite the risk of being dropped from treatment, a majority of the FRs

used drugs with at least one of their network RFs. Those who did so had,

on the average, more than two RFs with whom they used drugs. The

percentage of nonkin referrals who did drugs with the FR (38 percent)

was more than double that of the kin who did drugs with him

(17 percent).

The more his network RFs used drugs, the more the FR did so, and vice

versa. Stopping or markedly reducing drug use by the FR therefore will

often have major implications for his closest personal relations. It is in

this sense that the risky drug use behaviors so significant for HIV

transmission are strongly socially embedded and appear unlikely to yield

to pressures for change that do not take this fact into account. Drug use

relevant to the spread of HIV is clearly a social (network) problem rather

than simply an individual one.

Finally, stepwise multiple regression indicated that the three best

predictors of the FR’s drug use were the percentage of his network for

whom he would get drugs, the network RFs’ composite drug use index,

and the degree to which the FR approved of drug use. These three

variables accounted for more than three-fifths of the variation in the FRs’

drug use. Similarly, the four best predictors of the FR’s sharing works

were his felt risk of getting AIDS (negative), the RFs’ composite drug use

index (positive), the FR’s being married (negative), and how much

support he expected from his network compared to how much he

expected to give (negative). These four predictor variables “explained”

nearly three-quarters of the variation in the FRS’ sharing works. Three of

the four are network related.

CONCLUSION

This general pattern of significant relationships between drug use by FRs

and by their network RFs provides substantial support for the assertion

that the high-risk behaviors associated with HIV/AIDS tend to be strongly socially embedded. If the IDU is heavily involved with drug-using

friends or if the female and kin-based links of his network are themselves

drug involved, the prospects for successful intervention to alter drug use

103
seem markedly reduced. Other network patterns, however, seem more

favorable and provide potential leverage for risk reduction.

Serious attention should be given to strengthening the interactions

between the focal IDU and that subset of his network that works against

drug use while correspondingly weakening the dysfunctional relations.

The specific mechanisms for achieving these objectives must be

developed. They would clearly include regular clinical collection of

essential network information. Interventions that directly address the

social network aspects of drug use should improve treatment outcomes

and reduce the spread of HIV in this population.

NOTES

1. General introductions to social network analysis can be found in

the following, inter alia: Berkowitz, S.D. An Introduction to

Structural Analysis. Toronto: Butterworths, 1982; Burt, R.S.,

and Minor, M.J. Applied Network Analysis. Beverly Hills, CA:

Sage Publications, 1983; Knoke, D., and Kuklinski, J.H. Network

Analysis. Beverly Hills, CA: Sage Publication, 1982; Marsden,

P.V., and Lin, N. Social Structure and Network Analysis.

Beverly Hills, CA: Sage Publications, 1982; and the journal

Social Networks.

2. For these and related data see, for example, Marsden 1987 and

Wellman 1979.

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Sterk, C., and French, J. An ethnography of needle sharing. In:

Proceedings of the Third International Conference on AIDS (THP

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Wellman, B. The community question: The intimate networks of East

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Williams, L.S. AIDS risk reduction: A community health education

intervention for minority high risk group members. Health Educ Q

13:407-421, 1986.

106
Yancovitz, S.R.; DesJarlais, D.C.; Peyser, N.P.; Friedman, P.; Trigg,

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AUTHORS

Frederick W. Frey, Ph.D.

Professor of Political Science

University of Pennsylvania

Philadelphia, PA 19 104

Elias Abrutyn, M.D.

Chief of Infectious Diseases

Philadelphia Veterans Administration

Medical Center (PVAMC)

Philadelphia, PA 19 104

David S. Metzger, Ph.D.

Clinical Assistant Professor of Social Work in Psychiatry

School of Medicine

University of Pennsylvania

and

Associate Director of Drug Research

PVAMC

Philadelphia, PA 19 104

George E. Woody, M.D.

Clinical Professor

Department of Psychiatry

School of Medicine

University of Pennsylvania

Philadelphia, PA 19 104

and

Chief, Substance Abuse Treatment Unit

PVAMC

Philadelphia, PA 19 104

107
Charles P. O’Brien, M.D., Ph.D.

Professor and Vice Chair

Department of Psychiatry

University of Pennsylvania

Philadelphia, PA 19 104

Paul Trusiani, B.A.

Research Associate

Political Science Department

University of Pennsylvania

Philadelphia, PA 19 104

108
A Comparison of Drug Use

Networks Across Three Cities

Mark L.. Williams, Zhangqing Zhuo, Harvey A. Siegal, Rafaela

R. Robles, Robert T. Trotter II, and Adelbert Jones

INTRODUCTION

The risk of acquired immunodeficiency syndrome (AIDS) among

injecting drug users (IDUs) and their sexual partners remains one of the

most critical health problems facing the United States. The proportion of

the national AIDS caseload composed of individuals who have acquired

the human immunodeficiency virus (HIV) through the use of

contaminated syringes or as the result of having sex with an IDU

continues to grow. In some major metropolitan areas, the number of

AIDS patients who have acquired the disease through behaviors

associated with intravenous drug use now exceeds the number of

individuals who became infected as the result of male-to-male sexual

transmission. The risk of HIV infection either directly or indirectly as the

result of intravenous drug use is greatest among those in American

society least able to cope with the consequences of disease. Minority

poor residing in the Nation’s urban centers are most at risk of

succumbing to AIDS (Curran et al. 1988; Hahn et al. 1989; Lange et al.

1988; Newmeyer 1988). A number of behavioral or sociodemographic

variables have been found to be associated with a higher risk of HIV

infection due to drug injection. Among the behaviors and conditions

implicated are frequency of drug injection, years of injection, use of only

injected drugs, use of a shooting gallery, the use of cocaine, and

homelessness (Chaisson et al. 1989; Chitwood et al. 1990; Des Jarlais

1992; Kahlsa et al. 1992; Marmar et al. 1987; McCusker et al. 1992;

Schoenbaum et al. 1989; Siegal et al. 1991).

Variation in the risk of HIV infection associated with injecting drug use

demonstrates that not all IDUs are at equal risk of infection. Some

injectors are more likely to become infected than others. Drug injection

is a not a simple act. Studies have found that the injection of drugs, not

to mention the lifestyle of an IDU, is a complex set of behaviors and

interactions (Grund et al. 1991; Singer et al. 1992; Watters 1988, 1989).

The circumstances of the drug injection scene and those who are present

when drugs are injected can influence the dynamics of disease

109
transmission (Battjes et al. 1989; Grund et al. 1991; van den Hoek et al.

1992). If the act of injecting involves more than one person, there are a

number of behavioral norms and customs associated (Williams and

Johnson 1993). Who injects, how much each participant injects, the

order of injection, and whether a needle is cleaned are regulated by a

complex set of rules recognized by most injectors. Each can have an

effect on the transmission of HIV.

Increasing knowledge of the factors and circumstances associated with

HIV infection does not increase the macrolevel understanding of the

dynamics of transmission or of risk (Samuels et al. 1992). Lack of

understanding is, to a large degree, due to the absence of a strong

theoretical perspective from which to evaluate knowledge of microlevel

factors associated with HIV infection and to understand the macrolevel

dynamics of the epidemic. Without a macrolevel theory that can put

microlevel behaviors and circumstances of injecting drug use into a

meaningful perspective, researchers are unable to evaluate the

relationships among behaviors and circumstances or to explain how

variations in these factors influence the rate of infection for a population.

A theory of social networks holds some promise as a theory that can

accommodate the translation of microlevel behaviors into macrolevel

understanding. A social network is the sum of interpersonal linkages

within a population. Put another way, a social network is the sum of

personal network interactions (Klovdahl 1985). Linkages among people

can vary in length of interaction, frequency of contact, number of

contacts, heterogeneity of contacts, and strength of emotional ties

(Auslander and Litwin 1987; Granovetter 1973; Pilisuk and Froland

1978; Saulnier and Rowland 1985). The sum of linkages across these

network variables produce patterns of social networks that can have

consequences for the transmission of disease within individual network

structures.

The purpose of this chapter is to present the result of an investigation of

the drug use network structures of intravenous drug users in two cities in

the United States and one in Puerto Rico. One city involved in the study

had a high rate of HIV infection among intravenous drug users and, by

comparison, the other two cities had low rates of HIV infection (Robles et

al. 1992; Siegal et al. 1991; Williams 1990). The drug use networks in

each of the three cities were evaluated for differences in number of

contacts, frequency of contacts, length of interactions, heterogeneity of

interactions, and strength of emotional ties. Only intravenous drug use

linkages between individuals were investigated in the analysis. Although

110
other interpersonal linkages may be significant in the transmission of HIV

infection from one individual to another, the focus of this study was on

the structures of social networks related to intravenous drug use only. It

was expected, given the differences in rates of HIV transmission among

the three sites, that there would be significant differences among the

network structures.

METHODOLOGY

Data Collection

Data for this analysis were abstracted from a larger data set of

out-of-treatment drug users collected in the United States and Puerto

Rico. Data were collected in Dayton/Columbus, OH, Houston, TX, and

Rio Piedras, PR. To be eligible to participate in data collection activities,

respondents were required to have injected a drug or smoked crack

cocaine at least once during the 30 days before participation, have a

positive urine screen for cocaine or opiates or show evidence or recent

track marking, and not have been in drug treatment during the month

preceding participation. In addition to these requirements, participants

were required to be 18 years of age, recruited from selected targeted

geographic areas, and to have signed an informed consent form (see

Kaplan et al. 1987; Lee 1993). All study participants were provided the

opportunity to participate in HIV prevention programs and to be tested

free of charge for HIV infection.

Data used for analysis were collected using the Risk Behavior

Assessment (RBA) and the Social Network Questionnaire (SNQ). The

RBA was developed for the National Institute on Drug Abuse as a

method for collecting HIV-infection risk data related to drug abuse and

sexual behaviors at the community level. The instrument was designed to

collect demographic, drug use, needle-sharing, sexual behaviors, and

medical and drug treatment history data. The reliability and validity of

the RBA has been found to be quite high (see Needle et al., unpublished

manuscript). The SNQ was developed as a supplement to the RBA to

assess the strength and characteristics of drug use linkages among chronic

drug users and the context in which their drug use occurs. Questions

developed to assess the linkages among drug users include the number,

characteristics, and types of drug use relationships of the questionnaire

respondent. To be consistent with the RBA, the timeframe of the SNQ

was limited to the 30 days before the interview. Since the SNQ was

111
developed as a supplement to the RBA, data about networks were

collected from the perspective of the respondent. No attempt was made

to link data collected from respondents or to data collected from all

members of a network. The SNQ also asked respondents about their drug

use relationships and the context of drug use the last time that they used

drugs, but those data are not presented in this study.

All data for this study were collected in private settings by trained

interviewers. Most interviews took place at locations convenient to the

respondent. Interviewers were trained to use both the RBA and the SNQ.

Although the SNQ was developed as a supplement to the RBA, time of

administration varied from site to site. Some sites chose to administer the

SNQ immediately after the RBA. Others chose to collect data using the

SNQ during a separate session, usually 1 week after the administration of

the RBA. It is not known whether the timing of administration had any

consequence for the data being collected. However, any effect of the

timing of data collection using the SNQ is likely to be small. Respondents were paid for the time spent responding to both questionnaires,

although method and amount of payment varied from site to site.

Data Analysis

Data collected for this study were unlinked; that is, data collected from

one respondent were not linked to data collected from other respondents.

Unless otherwise indicated, the level of analysis for this study was the

network. Data presented by the respondent on drug use contacts during

the previous 30 days were aggregated to the network level. Specifically,

the analysis sought to characterize the number and frequency, strength,

and heterogeneity of linkages within networks. Only drug use relationships are presented. The data do not include relationships the respondent

may have with others with whom he or she did not use drugs in the

30 days before data collection. Heterogeneity was assessed by gender,

age groups, race/ethnicity, and drug use. Number and frequency of

linkages was assessed by the number of drug use contacts reported by the

respondent and the frequency of drug use within the network. Strength of

linkages was measured by the length of time that members of the network

had used with each other. In addition to these variables, the level of

needle sharing and sex within the drug use network was measured.

Four variables were constructed to investigate the heterogeneity of the

networks within the data set. Networks were assessed to determine if

members of a network were the same gender, within one or two age

112
groups, in the same racial/ethnic group, and used only intravenous drugs

together. Gender of a network was measured by composition: all male,

all female, or male and female members. Age groups were categorized

into broad groups: 30 years and younger, 31 to 40, 41 to 50, and

5 1 years or older. Because the exact ages of a respondent’s drug use

contacts were not requested, a network’s age group was assessed to be the

same if the age groups of the members were within one group. Race/

ethnicity of a network was measured to be the same if all members

belonged to the same racial/ethnic group. Drug use of the network was

measured by the drugs, either intravenous drugs or crack cocaine, used by

the respondent with other members of the network. Variables measuring

the homogeneity of networks were measured by nominal variables where

the network either matched or did not match a criterion.

The number and frequency of drug use linkages were measured by two

variables. The number of linkages within a network was measured by the

number of contacts reported by a respondent plus one. The number of

network members was measured by a categorical variable ranging from

one, indicating a drug use network of only the respondent, to five or more

members. The frequency of drug use linkages was measured by the

average frequency that the respondent reported using drugs with each

contact within the network. Frequency of drug use contact was measured

by a categorical variable ranging from less than or equal to three times a

month to four or more times daily. Frequency of drug use also was

measured by the absolute frequency of drug use between the respondent

and each network contact. (Absolute frequency of use was measured by a

categorical variable ranging from three or fewer times per month to twice

or more daily.) Strength of drug use links was measured by the average

duration of time the respondent reported having used drugs with each

network member. Duration of time was measured by a categorical

variable ranging from 6 or fewer months to greater than 4 years.

Three HIV-risk variables were also analyzed for the networks: receiving

a used needle from a network contact, giving a needle to a network

contact, and having sex with a network contact. Unfortunately, the

questionnaire asked only if the respondent had given or received a needle

or had sex with a network member. Although a respondent reporting a

risk behavior does provide evidence of the behavior occurring within a

network, it is an inadequate measure of the true level of a behavior within

a network. All three risk variables were measured as categorical variables

ranging from zero to greater than or equal to two contacts within the

network.

113
All sample characteristic and network variables were investigated,

controlling for site. Preliminary analysis had shown that there were

significant variations in the data by site where the data were collected.

Sample and network characteristic data were analyzed using contingency

tables and chi-square tests of significance. The interrelationship of

network characteristics was investigated using Pearson correlation

coefficients. The site where data were collected was not controlled for in

the correlational analysis.

Respondent Characteristics

The sample used for this analysis was limited to respondents who had

injected a drug at least once during the previous 30 days. The sample

included 192 respondents reporting 275 drug use contacts. As shown in

table 1, demographic characteristics of the respondents in the sample

varied across most variables by site. There was no statistically significant

differences among the sites as to the gender of the respondents. Thirty

percent of the sample was female, and 70 percent was male. On the

remainder of the demographic variables presented in table 1 (race/

ethnicity, age, education, marital status, current living arrangement, and

injected drug use), the sample varied depending on the city where the

data were collected. All respondents in Rio Piedras were Hispanic.

Two-thirds of the sample collected in Dayton/Columbus were African

American and one-third was white. Sixty-four percent of the Houston

respondents were African American, 19 percent Hispanic, and 17 percent

white.

The average age of respondents in the study was 34.2 years in Rio

Piedras (standard deviation [SD] = 7.0), 38.2 years in Houston

(SD = 7.8), and 39.8 years in Dayton/Columbus (SD = 8.7). The largest

number of respondents in Rio Piedras and Dayton/Columbus were

between the ages of 3 1 and 40 years. The greatest proportion of

respondents in Houston were between the ages of 41 and 50 years. The

level of educational attainment was highest among participants in

Dayton/Columbus. Almost two-thirds of the Dayton/Columbus sample

had completed high school, a GED program, or continued their education

after high school. Forty-six percent of the Houston sample, and

40 percent of the Rio Piedras sample reported having a high school

education or greater. Forty-six percent of the Houston sample reported

less than a high school but more than an eighth-grade education. Forty

percent of the Rio Piedras sample had less than an eighth-grade

education.

114
TABLE 1. Demographic characteristics.

D/C H RP

Gender

Male

Female

Race/Ethnicity

African American

Hispanic

White

Age

30

31-40

41-50

51

Education

<8th grade

<High school

High school

> High school

Marital Status

Single

Married

Separated

Divorced

Widowed

Current Living Arrangement

Own house or apartment

Someone else’s

house/apartment

.74

.26

p<0.13

.67

—

.31

p<0.000

.13

.47

.29

.11

p<0.000

.06

.31

.30

.33

p<0.000

.33

.30

.10

.24

.03

p<0.000

.47

.37

.62 .76

.38 .24

.64

.19

.17

—

1.0

—

.19 .34

.38 .50

.40 .13

.04 .03

.08 .40

.46 .20

.31 .27

.15 .13

.44 .26

.20 .20

.18 .43

.12 .10

.06 .01

.24

.57

.57

.34

115
TABLE 1. Demographic characteristics (continued).

D/C H RP

Current Living Arrangement

(continued)

Hotel, halfway house,

shelter

Streets

Homeless

No

Yes

Injected Drug Use

Cocaine

Heroin

Heroin/cocaine

.13

.03

p<0.000

.77

.23

p<0.051

.18

.27

.55

p<0.000

.15 —

.05 .07

.79 .91

.21 .09

.70 .04

.13 .09

.17 .87

KEY: D/C = Dayton/Columbus; H = Houston; RP = Rio Piedras.

Most respondents at all three sites reported that they were not married at

the time of the interview. However, how respondents classified their

marital status varied significantly among the three cities. The greatest

number of respondents in Houston and Dayton/Columbus reported that

they were single at the time of the interview. The largest proportion of

respondents in Rio Piedras reported that they were separated. The

majority of respondents in Dayton/Columbus and Rio Piedras reported

living in their own house or apartment, 47 percent and 57 percent,

respectively. The majority of respondents in Houston (57 percent)

reported living in someone else’s house or apartment. The Rio Piedras

sample had the greatest proportion of participants reporting that they

lived on the streets (7 percent). Yet, only 9 percent of the Rio Piedras

sample considered themselves homeless, compared to 21 percent in

Houston and 23 percent in Dayton/Columbus.

116
Injecting drug use in the 30 days before the study was fairly limited to

either heroin, cocaine, or heroin and cocaine in all three cities. However,

the patterns of cocaine or heroin injection varied significantly between

the sites. The primary drug injected in Houston was cocaine, used by

70 percent of participants. About one-fifth of the respondents in Houston

reported injecting heroin and cocaine mixed together, and approximately

one-tenth reported injecting heroin by itself. Few respondents in Rio

Piedras reported injecting heroin (9 percent) or cocaine (9 percent). Most

respondents in the Puerto Rico sample reported injecting heroin and

cocaine mixed together. Twenty-seven percent of the sample in Dayton/

Columbus reported injecting heroin. Yet, like the sample in Rio Piedras,

the majority of respondents in Ohio (55 percent) reported injecting heroin

and cocaine mixed together. Seventeen percent reported injecting

cocaine.

ANALYSIS

Network Characteristics

The average size of the networks reported by participants in the 30 days

before being interviewed was 2.43 persons. As shown in table 2, network

size did not vary among the sites (p c 0.066). Approximately 15 percent

of the sample reported injecting drugs with no other persons and,

therefore, had a network size of one. Twenty-eight percent reported a

network size of two people, 20 percent a network size of three people,

17 percent a network size of four to five people, and 17 percent a network

size of six or more people. Individuals who reported a network size of

one were deleted from the remainder of the analysis.

The homogeneity of networks did vary significantly by gender,

race/ethnicity, and drug use by site. Only the age groups of those

involved in the networks did not vary significantly between the samples

(p < 0.370). Approximately one-third of those interviewed reported

networks composed of individuals within the same age groups. The

remainder reported that the network was composed of members from at

least two age categories. A higher proportion of networks in Dayton/

Columbus and Houston were composed of both men and women than in

the Rio Piedras sample (p c 0.001). Fifty-three percent of the networks in

Rio Piedras were single-gender networks, compared with 22 percent in

the Ohio sample and 30 percent in the Texas sample. Networks in which

both intravenous drug and crack cocaine use was reported were high in

117
TABLE 2. Characteristics of drug use contacts.

D/C H RP

Network Size

1

2

3

4-5

6

Age

Same age group

Mixed age group

Gender

Single gender

Mixed gender

Race/Ethnicity

Same racial group

Mixed racial group

Drug Use

IV only

IV and crack

0.16

0.21

0.17

0.24

0.21

p<0.066

0.20

0.80

p<0.37

0.22

0.78

p <0.001

0.78

0.22

p<0.001

0.31

0.70

p<0.000

0.11 0.19

0.24 0.39

0.18 0.21

0.27 0.11

0.20 0.10

0.25 0.32

0.75 0.68

0.33 0.47

0.67 0.53

0.76 0.98

0.24 0.02

0.25 0.51

0.75 0.49

118
TABLE 2. Characteristics of drug use contacts (continued).

D/C H RP

Average Time Respondent

Used With Others

6 Months 0.22 0.20 0.40

7-12 Months 0.15 0.13 0.14

1-2 Years 0.12 0.17 0.11

Average Time Respondent

Used With Others

2-4 Years

> 4 Years

Average Time

(as a dichotomous variable)

6 Months

>6 Months

Average Frequency of

Drug Use With Network

Monthly

Weekly

Daily

2-3 times daily

4 times daily

0.12

0.39

p<0.21

0.18 0.12

0.32 0.23

0.22

0.78

p<0.018

0.20 0.40

0.80 0.60

0.15

0.46

0.22

0.10

0.02

p<0.000

0.17 0.02

0.50 0.14

0.24 0.28

0.07 0.39

0.03 0.18

119
TABLE 2. Characteristics of drug use contacts (continued).

D/C H RP

Absolute Frequency of

Interactions With Others

Monthly

Weekly

Daily

>Daily

0.29

0.39

0.18

0.14

p<0.001

0.26 0.04

0.41 0.18

0.20 0.24

0.13 0.54

Received a Needle From a

Network Member

No

1

2

Gave a Needle to a

Network Member

No

1

2

0.71

0.17

0.12

p<0.092

0.68 0.70

0.11 0.23

0.21 0.07

0.66 0.65 0.63

0.19 0.11 0.26

0.15 0.25 0.11

p<0.066

Sexual Relationships

With Network Members

None

1

2

0.46

0.49

0.05

p<0.000

0.49 0.84

0.38 0.12

0.13 0.04

KEY: D/C = Dayton/Columbus; H = Houston; RP = Rio Piedras.

120
Dayton/Columbus (70 percent) and Houston (75 percent), compared to

49 percent in the Rio Piedras sample (p < 0.000). The proportion of

networks reporting only intravenous drug use in Rio Piedras was

5 1 percent.

There was no statistically significant difference among the three sites in

the average time that respondents reported using drugs with their

networks (p < 0.210). Twenty-seven percent of respondents reported

using with network members an average of 6 months or less. Fourteen

percent reported using with their network 7 to 12 months, 14 percent 1 to

2 years, and 15 percent an average of 2 to 4 years. Thirty-one percent of

respondents reported using with their network for more than 4 years.

Although there was no statistically significant difference in the average

time a respondent reported using with his or her network among the three

sites when average time was investigated as a categorical variable, when

average time was coded as a dichotomous variable ranging from

6 months or less to greater than 6 months, a significant difference among

the sites did emerge (p < 0.000). Forty percent of the network members

in Rio Piedras reported using drugs with their networks an average of

6 months or less, compared to 22 percent in Dayton/Columbus and

20 percent in Houston.

The average frequency of drug use within a network varied significantly

by site (p < 0.000). The majority of networks in Rio Piedras (57 percent)

reported using together an average of two or more times daily. The

majority of networks in Dayton/Columbus and Houston reported using

together an average of weekly or less, 61 percent and 67 percent,

respectively. The differences in average frequency of network drug use is

reflected in the frequency of drug use interaction between the respondent

and individual members of the network. The majority of interactions in

Rio Piedras occurred twice or more daily. The majority of interactions

between respondents and individuals within their networks in the Ohio

and Texas samples occurred weekly or less, 68 percent and 67 percent,

respectively.

There was no statistically significant difference among networks at the

three sites in regard to the respondent reporting that he or she received a

needle in the previous 30 days from another network member

(p < 0.092). Approximately 30 percent of respondents reported receiving

at least one needle from another network member. There was no

statistically significant difference in the number of networks in which the

respondent reported giving a needle to another network member

121
(p < 0.066). About two-thirds of the networks did not involve the

respondent giving a needle to another network member. Seventeen

percent of the networks involved the respondent giving a needle to at

least one other network member and 17 percent to two other network

members. There was a statistically significant difference among the sites

in regard to the number of networks where the respondent reported

having sex with one or more network members (p < 0.018). The majority

of networks in Dayton/Columbus (54 percent) and Houston (51 percent)

involved a sexual relationship between the respondent and at least one

other network member. Only 16 percent of the networks in Rio Piedras

involved a reported sexual relationship between the respondent and

another network member.

As shown in table 3, a number of network characteristics investigated

were intercorrelated. A network of mixed gender was positively

correlated with a network of members from more than one age group

(r = 0.22, p < 0.01) and more than one racial/ethnic group (r = 0.19,

p < 0.01). However, a network with both men and women was

negatively correlated with network size (r = -0.19, p < 0.01). Mixedgender networks were positively correlated with all three HIV-risk

behaviors investigated, the respondent giving needles to another network

member (r = 0.19, p < 0.01), the respondent receiving needles from

another network member (r = 0.27, p < 0.001), and the respondent having

a sexual relationship with another member of the network (r = 0.51,

p < 0.001). Network size was negatively correlated with networks

composed of more than one age group (r = 0.24, p < 0.001). Networks

composed of more than one racial/ethnic group were positively correlated

with a respondent reporting he or she gave needles to (r = 0.26,

p c 0.001) and had a sexual relationship with another network member

(r = 0.20, p < 0.01). Giving and receiving needles within a network were

very highly interrelated. Giving a needle to another network member and

receiving a needle from another network member had a correlation

coefficient of 0.82 (p < 0.001). Although having a sexual relationship

with a network member was not correlated with giving a needle to

another network member, having a sexual relationship and receiving

needles from another network member were positively correlated

(r = 0.23, p c 0.001). Two variables, the average time network members

had been using with each other and the average frequency network

members used drugs together, were not found to be significantly related

to any other variable in the analysis.

122
TABLE 3. Correlation of network characteristics.

G ARE NS AT AF GN RN

Gender

Age 0.22*

Race/ethnicity 0.19* .14

Network size -0.19* -.24** -.06

Average time -0.08 .06 .09 -.11

Average frequency -0.17 .04 -.10 -.08 .01

Gave needles 0.19* .17 .26** -.07 .04 -.01

Received needles 0.27** .18 .17 -.06 .04 -.06 .82**

Sexual relationship 0.51** .17 .20* -.10 -.03 -.14 .12 .23**

KEY: * p<0.01;** p < 0.001; G = Gender; A = Age;

R/E = Race/Ethnicity; NS = Network Size; AT = Average Time;

AF = Average Frequency; GN = Gave Needles;

RN = Received Needles; SR = Sexual Relationship.

SUMMARY AND DISCUSSION

This study presented drug use network data collected in three cities in the

United States and Puerto Rico. Respondents in the study were out-oftreatment intravenous drug injectors at risk for HIV infection. All

respondents in the study had injected at least once during the previous

30 days. There were significant differences among the respondents

related to the site where the data were collected. The racial/ethnic

composition of the samples varied according to the city in which

respondents were interviewed. Not surprisingly, all respondents in the

Rio Piedras sample reported that they were Hispanic. About two-thirds

of the respondents in the Houston and Dayton/Columbus samples were

African American. In addition, respondents in the Puerto Rico sample

were more likely to have been younger and to have injected heroin and

cocaine mixed together. Respondents from the Houston site were more

likely to have been older, considered themselves homeless, and to have

injected cocaine. Given these significant differences in the samples, the

number, heterogeneity, strength, and frequency of network linkages were

investigated, controlling for site.

The number of drug use linkages reported by study participants did not

vary significantly by site. Respondents at the three sites reported having

123
slightly over two drug use linkages or, put another way, a drug use

network size of slightly over three people. However, there were

significant differences in network characteristics among the sites in

regard to heterogeneity, strength of linkages, and frequency of linkages.

Respondents in Puerto Rico were more likely to be involved in networks

that were relatively homogeneous, with relatively weak ties and frequent

drug use interactions. Networks in Rio Piedras were much more likely to

be all Hispanic, all male, use only injectable drugs, to have used drugs

with each other a short period of time, and to use together multiple times

daily. Injectors in the Dayton/Columbus and Houston samples, on the

other hand, were more likely to be involved in heterogeneous networks,

with comparatively stronger ties and less frequent drug use interactions.

Networks in Houston and Dayton/Columbus were much more likely to be

of mixed gender or race/ethnicity, to use both injectable drugs and crack

cocaine, to have used drugs together a comparatively long time, and to

use together once a week or less. Although these differences in network

characteristics did emerge, there was no signiftcant difference between

the sites in a respondent reporting that he or she received or gave a needle

to another member of the network. There was a significant difference,

however, in reported sexual relationships. Respondents in Dayton/

Columbus and Houston were more likely to have reported a sexual

relationship with one or more network members.

Some network characteristics were found to be significantly correlated.

Networks with mixed genders were related mixed-age groups, with the

respondent giving needles to or receiving needles from one or more

network contacts and the respondent reporting having sex with one or

more network contacts. However, networks with mixed genders were

negatively related to the size of the network. In addition, network size

was negatively related to more than one age group in a network. Networks with members of more than one racial/ethnic group were significantly related to the respondent reporting that he or she gave one or more

network contacts a needle and had a sexual relationship with one or more

contacts. Networks in which the respondent reported giving a needle

were highly correlated with the respondent receiving a needle. Receiving

a needle from a network member was correlated with reported sex with a

network member.

There are a number of limitations to this study and, as a result, the

findings must be interpreted with caution. The sample used was not

derived using a random sampling procedure. The degree to which the

sample is representative of drug injectors in the three cities can be only

124
estimated vaguely. Therefore, the generalizability of the findings is

unknown. The requirements of the larger study to which this study was a

supplemental effort, especially the need to relocate study participants

after 6 months, may have served to bias the sample toward individuals

who were less likely to be involved in risky behaviors. The relative age

of the respondents in this sample would suggest that participants were

more likely to have been more settled than younger drug injectors. Since

the respondents in the study were unlinked, the degree to which the

respondents were reporting the same networks is unknown. However, it

is possible individuals may be reporting data on the same networks.

Relying on egocentric unlinked data may also present another bias.

Individuals included in the sample may be limited to those who are

relatively stable in their living arrangements and lifestyle. Others with

whom the respondents may be linked, but who were not recruited to

participate in the study, may be less stable and far more likely to have

drug use interactions with a larger number of more varied people. The

timeframe of data collection, 30 days before the interview, may have had

the effect of obscuring relationships or behaviors that are relatively rare.

However, given these limitations, the study does provide some worthwhile information and suggest some potentially meaningful avenues for

further investigation.

The differences in network characteristics between the Rio Piedras and

the other two samples, given the significantly different rates of HIV

infection among drug injectors in Puerto Rico, Ohio, and Texas, would

suggest that network structures are related to rates of infection (Robles et

al. 1992; Siegal et al. 1991; Williams 1990). The homogeneity of

networks in Rio Piedras may have the effect of removing barriers that

inhibit forming weak drug use linkages. The heterogeneity of networks

in Texas and Ohio may have the opposite effect. African Americans may

be reluctant to inject with whites or Hispanics. Hispanics may be

reluctant to inject with whites or African Americans. Men and women,

particularly if they have a sexual relationship, may be reluctant to inject

with someone who is not a current network member (see Williams and

Johnson, in press). For example, it would be expected that there would

be far less sexual tension in single-gender than in mixed-gender drug use

networks. The lack of sexual tension within single-gender networks may

act to facilitate the formation of new drug use contacts outside the

network. The larger number of drug use relationships of 6 months or less

in the Rio Piedras sample would support such a speculation. In addition,

the frequency of drug use interaction within the network in Rio Piedras

would also tend to support the formation of new, weak-link relationships.

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Injecting one or more times daily may present a situation in which it is

much more difficult to coordinate drug use activities with others and far

easier to use with whomever is present. Although it requires more

analysis to assess, homogeneity of network structures may actually

facilitate the formation of weak drug use relationships and, thereby, once

introduced into a social network, the transmission of HIV.

The greater proportion of weak contacts and frequency of drug-using

interactions in the Puerto Rico sample suggest that there may be a

multiplicative effect among these two network measures. The chances

for the transmission of HIV are increased if the number of contacts

between drug injectors who have used only a short amount of time

together is high, as suggested by the number of short-term relationships

in the data from Puerto Rico. The chances for transmission would also

seem to be increased if the frequency of injections is high. If duration is

short and frequency high, the effect of each would seem to be multiplied

by the other. There are a number of ways to assess this possibility

controlling for the duration of drug use among injectors within a network

and the frequency of injection using a multisite sample. The use of a

multisite sample would be necessary to control for the effect of local

circumstances and conditions.

Intercorrelation among some of the network measures suggests that some

types of risk behaviors may be interrelated and related to network

structure. For example, respondent reports of receiving and giving

needles and a sexual relationship between network members were

intercorrelated with measures of network heterogeneity. This would

suggest that the risks of infection in a heterogeneous network may be

more related to the strength of the relationships in the network as

measured by duration of drug use rather than weakness. For example, it

is not uncommon for those investigating risk behaviors to hear of needle

sharing between sexual intimates. Such sharing, because of the emotional

bond involved, is not usually considered sharing by the respondent

(Williams and Johnson, in press). Therefore, risk in this situation would

be related to the strength of the relationship. In addition, the intercorrelation between giving and receiving needles and sexual relationships

would suggest that sex may be an equally, if not more, important means

of HIV transmission in strong-link networks.

A social network theory of HIV infection may prove a useful mechanism

for understanding rates of infection in different populations. Although

respondents in all samples reported sharing needles, the rate of HIV

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infection is far higher in Rio Piedras than in either Houston or Dayton/

Columbus. One place to begin looking for differences, if behaviors are

the same, is in the structure of the networks. Several significant differences were found. Although linked HIV data were not available for this

study, an analysis of linked HIV social network data would be the next

logical step. Such an analysis could show that, rather than one social

network structure evident at a site, multiple structures related to HIV

serostatus are evident. Multiple structures are possible. For example, in

Houston, independent social network structures were found to be related

to race/ethnicity (Williams and Johnson, in press). Whatever avenue of

research is followed, social network theory does seem to have promise as

a means for understanding the macrolevel implications of microlevel

behaviors.

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Drugs and Society 7:65-90, 1993.

AUTHORS

Mark L. Williams, Ph.D.

Principal Investigator

NOVA Research Company

Bethesda, MD 208 14

Zhangqing Zhuo, Ph.D.

Research Scientist

University of Minnesota

Minneapolis, MN 55455

129
Harvey A. Siegal, Ph.D.

Principal Investigator

Wright State University

3640 Colonel Glen Highway

Dayton, OH 45435

Rafaela R. Robles, Ed.D.

Principal Investigator

Department of Anti-Addiction Services

P.O. Box 21414, Rio Piedras Station

Rio Piedras, PR 00917

Robert T. Trotter II, Ph.D.

Principal Investigator

Northern Arizona University

Campus Box 15200

Flagstaff, AZ 86011

Adelbert Jones, Ph.D.

Principal Investigator

Desire Narcotic Rehabilitation Center

2037 Orleans Drive

New Orleans, LA 70126

130
Ethical and Legal Issues in Social

Network Research: The Real and

the Ideal

Donald E. Woodhouse, John J. Potterat, Richard B. Rothenberg,

William W. Darrow, Alden S. Klovdahl, and Stephen Q. Muth

INTRODUCTION

The sine qua non of social network research is identifying “nodes” and

describing the connections between them. When the nodes are people

and the connections are intimate activities, ethical questions are

inevitable. Ideal answers are relatively easy and emanate from the

general principles of bioethics. The reality is more complex: When the

people are “criminals” and their activities are illegal (such as injecting

narcotics or buying and selling sex), legal and ethical dilemmas

frequently arise and practical solutions are elusive.

To describe social network structures and their influence on the spread of

human immunodeliciency virus (HIV), it is necessary to collect detailed

information about people at risk, their behaviors, and how they connect to

others. There are many obstacles to collecting information about sexual

activity, drug use, and social connections. Beyond social and political

resistance to investigating intimate aspects of human behavior, a

fundamental obstacle that remains is ignorance about how to measure

such things. These are methodological problems. More pressing issues

concern researchers’ obligations to society at large and to the people they

study.

Behavioral researchers in the United States are guided both by express

requirements for protecting human subjects in federally funded research,

promulgated by the Office for Protection from Research Risks (OPRR),

Department of Health and Human Services (DHHS) (45 CFR 46), and by

general principles of biomedical ethics. These principles include respect

for persons (a duty to respect others, to respect their autonomy, and to

protect those with diminished autonomy), beneficence (the duty to

maximize benefits to the subjects of research and to society at large),

nonmaleficence (the duty to prevent harm to subjects), and justice

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(the obligation to distribute equally both the benefits and burdens of

research) (Department of Health, Education, and Welfare 1979).

These principles (the ideal) rarely constitute prescriptive measures to

guide decisionmaking (Mulvey and Phelps 1988). In fact, they often

conflict with each other or with the study design. Evaluating HIV

seroepidemiologic research, Avins (1989) concluded: “All study design

options result in some undermining of research ethics” (emphasis

supplied). An ad hoc approach is thus required; just as judges must

weigh opposing principles of law in making decisions, researchers must

weigh conflicting ethical principles and consider the tradeoffs inherent in

any course of action (Mulvey and Phelps 1988).

If collecting and using sensitive information about social connections is

justified by the potential benefits, and if methodological hurdles are

surmounted, the foremost problem network researchers face is protecting

the data they gather. Investigators entrusted with information about

intimate aspects of people’s lives are ethically bound to protect the

confidentiality and the privacy of their subjects.

Offered here is a description of ethical issues likely to arise in network

research, as well as some observations based on the authors’ experience

studying social networks of injecting drug users and prostitute women in

Colorado Springs, CO (Rothenberg, this volume).

HIV TESTING AND NOTIFICATION

Testing research participants for antibodies to HIV is necessary to map

the loci of infection within specific networks and can directly benefit the

subjects themselves. Uninfected participants can initiate or continue

preventive measures, while infected subjects can seek medical care and

avoid exposing others. Some people, however, are unwilling to learn

their serostatus, and unauthorized disclosure of test results can have

adverse psychological, social, financial, or legal consequences. All States

require reporting of diagnosed acquired immunodeficiency syndrome

(AIDS) cases to health authorities, and about half require the reporting of

people with HIV infection.

Some investigators fear that HIV testing deters potential subjects or limits

participation to those willing to be tested and learn their results (with

implications for sampling and recruitment strategies). Others wish to

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avoid the responsibility of informing subjects of their serostatus and

offering appropriate posttest counseling and followup, or they reason that

subjects themselves do not wish to know their serostatus. In such cases,

HIV testing has been omitted from study designs.

Where knowledge of seroprevalence is desirable, however, various

methodologies are available, ranging from “blind” testing to mandatory

notification. Each raises ethical questions about whether and how to

inform the participants of their serostatus.

As public health workers and advocates of partner notification (Potterat et

al. 1989), the authors opted to treat HIV-infected participants identified

through the present study in the same manner as those identified by other

means. The consent form used in the study stated that individuals who

tested positive for HIV would be informed of their results and that the

results would be reported to health authorities in accordance with State

law, All infected subjects were offered counseling, medical referral, and

assistance with notifying partners. Positive test results did not preclude

continued participation in the prospective study, and all participants were

followed regardless of serostatus.

This approach-mandatory notification-is now public health policy;

individuals whose results can be linked to personal identifiers must be

informed of their results and offered appropriate counseling. Research

subjects may no longer be given an option “not to know” (optional

notification). This raises ethical questions, however, as it means that,

once tested, it is impossible for a subject to “withdraw” from the study, an

important element of respect for personal autonomy and an OPRR

requirement.

Alternatives to mandatory notification include anonymous testing and

blind testing (Avins 1989). In “anonymous” designs, participants may

obtain their own results, but the investigators remain unaware of who was

tested or of the test outcomes. Responsibility for obtaining results lies

solely with the subject. With blind testing, all identifiers are deleted from

specimens, rendering it impossible to link tests to individuals or to inform

subjects of their results. Such approaches are of limited value in network

research, where infection status and the location of infected people within

networks are important outcome variables.

The effects of testing and procedures for notifying subjects of their results

must be carefully considered in adopting a research design. To the extent

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that testing discourages or limits participation, it may detrimentally affect

sampling strategies and limit generalizability of the findings. Research

that includes interventions aimed at reducing subjects’ risk of infection

may be enhanced by testing, which helps “personalize” that risk. If

testing is incorporated in the study design, every effort should be made to

inform infected subjects of their results. Experience suggests that, even

in studies with elaborate mechanisms to shield researchers from

knowledge of subjects’ identity and serostatus, many participants have

the misperception (despite detailed informed consent forms and

instructions) that someone will notify them if they are infected. This

assumption that “no news is good news” may generate a false sense of

security. If testing is performed, local reporting requirements must be

clearly stated in the informed consent.

INFORMED CONSENT

It has been said that informed consent is a moral prerequisite to

enrollment of research subjects. For research funded by DHHS, it is also

a legal prerequisite. Federal guidelines specify the basic elements of

informed consent: a statement of the research purpose, reasonably

foreseeable risks, reasonably expected benefits, alternate procedures, a

statement concerning confidentiality of records, whom to contact for

additional information, and a statement that participation is voluntary and

that consent may be revoked at any time without penalty (45 CFR 46).

In medicine, the general rule is that adults (over 18 years of age) or

emancipated minors who are conscious, competent, and uncoerced can

consent to (or refuse) medical care, and this also applies to participation

in research. Unfortunately, the people of greatest interest to AIDS

researchers often fail to meet these criteria simultaneously. Studies of the

etiology of substance abuse, adolescent prostitution, and the manner in

which adolescent users connect to adult networks may necessitate

recruiting people below the age of 18. Can they legally consent? The

authors reviewed Colorado law, which permits minors (whether

emancipated or not) to consent to diagnosis and treatment for sexually

transmitted diseases, substance abuse treatment, and birth control

counseling; they concluded that those who could legally consent to such

treatment could also consent to participate in related research. Similar

conclusions have been reached by other researchers.

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The issue of competence is more challenging: Does a chronic substance

abuser who is rarely, if ever, sober have capacity to consent? To provide

informed consent, subjects must have the mental ability to make choices

and understand their consequences. Researchers studying drug users, the

homeless, or similar populations must consider the extent to which

intoxication, mental illness, or long-term substance abuse affect their

subjects’ capacity to consent. They must determine on a case-by-case

basis whether these conditions interfere with a potential subject’s ability

to comprehend the information provided and to decide whether to

participate.

Consent should be obtained under circumstances that provide the

prospective subject with sufficient opportunity to consider whether or not

to participate, as well as minimize the possibility for coercion or undue

influence. Federal guidelines expressly limit research conducted in

prisons and similar settings where subjects are especially vulnerable.

Opportunities for coercion in prison are obvious, and respondents may

perceive (correctly or incorrectly) that some benefit might accrue from

participation. Incarceration is a fact of life, however, for many of the

people studied (such as prostitutes and drug users), and jails are often

good settings for locating and interviewing these people. While

incarcerated, subjects are free from the influence of alcohol or drugs, and

there are few interruptions. For these reasons, the authors reconducted

some interviews in jails, but recruitment from jails was not undertaken.

The prospective nature of the present study made it difficult to locate

people for annual followup; by reviewing jail rosters, the authors often

found people with no fixed address who had otherwise disappeared.

When conducting interviews in jail, they explained that researchers had

no connection with the jailers, that participation conveyed no benefits,

and that the jailers had no access to study notes. Every detained subject

was offered the opportunity to be interviewed after release.

Another potential source of coercion involves paying subjects for

participating. Payment of research participants is common and, for many

potential subjects from low-income populations, may constitute a

powerful incentive. Ideal ethnographic research requires establishing

trust and rapport between researcher and subject over a long period of

time. Few researchers have this luxury, and payment may be a

convenient substitute: subjects share some aspects of their lives with

observers simply because the price is right. While it is appropriate to

reimburse subjects for actual expenses and inconvenience, there is a fine

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line between reasonable reimbursement and payment for participation.

To the extent that payment is a monetary incentive, it may compromise

the integrity of the research (Feinleib 1991).

Subjects may believe that participation increases their chances for

obtaining treatment or that they will be denied treatment or access to

services if they refuse. In such cases, consent is not voluntary.

Researchers must also evaluate the extent to which their own tenacity,

and the means they use to persuade or “convert” subjects who initially

refuse, may constitute coercion.

What about subjects who cannot read a consent form or who choose not

to? The authors attempted to ascertain whether subjects could read (few

who could not volunteered this information), and when necessary the

investigators read the form verbatim or paraphrased it. Some participants

elected not to read the two-page, Institutional Review Board (IRB)-

approved consent form, so the authors designed a “checklist” containing

the essential elements. When interviews were conducted in homes, cars,

bars, parks, or places of employment, this checklist was used, and the

researchers offered individuals a copy of the full form. Research

participants can waive rights to fully informed consent just as medical

patients can. In such instances, however, it is important to document that

the information was offered and that the patient declined to read it.

Participants cannot, however, waive any legal rights or release

investigators from negligence.

DISCLOSURE

The greatest potential threat to participants in network research may be

disclosure of the information they provide. Information about their drug

use, sexual behavior, and illegal activities can harm respondents and their

partners, and may be of interest to police and others. Because

confidentiality can never be guaranteed, consent forms should not

mislead respondents. To obtain truly informed consent, as well as to

minimize their own liability, researchers must accurately inform subjects

of potential risks of disclosure. Even if disclosure is compelled,

investigators might incur liability under a variety of legal theories,

including invasion of privacy, breach of contract, or misrepresentation

(Teitelbaum 1983, pp. 11-47). Threats to confidentiality include

inadvertent disclosure, public health laws, subpoenas, and criminal

reporting laws.

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INADVERTENT DISCLOSURE

Inadvertent disclosure can result from negligence or accidents. An

investigator injured in an accident, for example, could lose control of

interview records. Precautions involve minimizing the amount of

information carried by investigators, requirements for prompt return of

data collection instruments to secure settings, limiting personal identifiers

on instruments, and using codes to link records. Other forms of “inadvertent” disclosure may take place due to program audits and evaluations,

secondary analyses, selection of data entry clerks, and access to records

by computer technicians.

PUBLIC HEALTH LAWS

Many States require HIV reporting; if serology results are obtained,

researchers may be required, in the absence of a “research exception”

(see, for example, Colorado Revised Statute 25-4-1402.5), to report

infected people to local or State health authorities. While these people

may already be known to health authorities, or are likely to be reported

by other entities, the consent form should nevertheless describe reporting

requirements.

SUBPOENAS

Another threat to confidentiality is subpoena power. Information

collected by network researchers is of potential interest to police. The

trend in both criminal and civil cases is to make sweeping requests for

documents, and subpoenas may be issued for reasons unrelated to the

subject of the research or simply for prosecutorial “fishing expeditions”

(see In re Grand Jury Subpoena, 750 F.2d 223 [2nd Cir. 1984]). While

the most obvious threats involve criminal prosecution of respondents

themselves, investigations of agency improprieties, fiscal mismanagement, or alleged illegal acts by researchers could result in subpoenas for

“any and all records” or, if a Government agency, in a request under the

Freedom of Information Act. Similarly, participants in civil actions (such

as divorce) might believe research data to be of value and attempt to

obtain it.

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CRIMINAL REPORTING

One of the thorniest problems facing those who work with drug users, sex

workers, street gangs, and similar populations are the legal and ethical

duties concerning observed criminal behavior. Ethnographic research

frequently entails observation of illegal drug activity, ranging from

individual use to the manufacture and sale of drugs. Generally, private

citizens have no duty to report criminal activity they observe, so long as

they neither assist nor encourage it.’ There are statutory exceptions,

however. Law enforcement officers, for example, must report observed

criminal activity, and a much wider range of people are mandated to

report known or suspected child abuse.

CHILD ABUSE REPORTING

Researchers who observe drug users in private homes, “crack houses,” or

“shooting galleries” frequently see instances of child abuse or neglect.

State laws vary in their delineation of those required to report, ranging

from physicians and nurses to, in some jurisdictions, anyone with

knowledge or suspicion that abuse is occurring. Employees of local or

State agencies (including universities) are often included among those

mandated to report. A duty to report may even exist where the subjects

themselves confide that they have been victims or perpetrators of child

abuse. Some studies of adolescents include an express warning that, if

the subjects reveal themselves to be victims of ongoing abuse, the

researcher must report this information.

Even in the absence of formal legal requirements, researchers clearly

have ethical obligations to prevent harm to children. These obligations

can and do conflict with research goals of learning about “hidden”

activities and gaining the trust and cooperation of subjects. At minimum,

field investigators must be trained about what constitutes reportable abuse

or neglect in their jurisdiction, as well as their legal responsibilities when

abuse is observed. If these include reporting any observed or suspected

abuse, potential participants must be so informed.

DUTY TO WARN

A more complex problem is the researcher’s “duty to warn.” By virtue of

the information most valuable to a network researcher-the identity of

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sexual or needle partners-investigators are likely to learn of people

directly at risk of infection from an HIV-infected subject. What is the

researcher’s responsibility to those people? Can someone bring an action

against a researcher who knew that a subject was infected, and that the

subject failed to inform partners, if one of those partners subsequently

became infected?

Current case law generally limits liability to those in a therapeutic

relationship, primarily medical providers or mental health therapists.

Even for physicians, the duty is less than clear; many believe that their

sole obligation is to the patient, that the sanctity of the physician-patient

relationship is inviolate, and that any breach undermines the therapeutic

relationship. Others maintain that privacy is not an absolute right:

Potentially fatal harm to third parties overrides the physician-patient

privilege (Bayer and Toomey 1992).

Most (but not all) cases impose a duty where the third parties at risk are

“identifiable.” In the case of infected prostitutes or injectors who

patronize “shooting galleries,” it may be impossible to identify specific

“partners.” Yet the circumstances may be such that potential transmission

to someone is high. With identifiable partners, physicians and others

apply various standards, often considering the extent to which partners

“should” be aware of their risk. Those who work exclusively with gay

men, for example, might conclude that every gay man in this era knows

of AIDS and knows how to protect himself from it, and the corresponding need to intervene may be small or nonexistent. Research with

“heterosexual” populations, on the other hand, could uncover bisexual

men whose female partners are ignorant of their risk, or men who

patronize prostitutes unbeknownst to their wives. In such cases, or in the

case of a secretly bisexual man whose wife is pregnant, a higher level of

duty might be found (to prevent eventual infection of the wife, for

example, or, if already infected, to permit consideration of terminating

the pregnancy).

Confidentiality commitments to subjects must be weighed against the

magnitude of harm which could result and the extent to which third

parties are likely to be aware of their risk. Researchers should, at

minimum, counsel HIV-infected subjects about how to prevent

transmission of the virus and strongly encourage them to inform past,

present, and prospective partners.

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SAFEGUARDS

A variety of mechanisms exist to protect research subjects from

unauthorized disclosures and other forms of harm. These mechanisms

are statutory, procedural, and technical in nature.

Statutory

Although several States have considered “researcher shield statutes,” not

one has enacted them (Melton and Gray 1988). Neither do any States

recognize a “researcher-subject” privilege analogous to physician-patient

privilege (although the existence of such a privilege might be successfully

argued in response to a subpoena).

The Public Health Service does, however, offer some advance protection

for research records (42 U.S.C. 242). “Certificates of confidentiality,”

which originally applied only to studies involving substance abuse or

mental health, now cover more varied research. They provide protection

from subpoenas but must be applied for on a case-by-case basis (i.e., they

do not automatically cover all federally funded research).

Procedural

Federally funded research involving human subjects must be reviewed by

an IRB to ensure that subjects are not exposed to unreasonable harm

(45 CFR 46). IRBs vary in composition and sophistication. While most

members are capable of judging the ethical propriety of research, they

may lack the expertise to evaluate the potential benefits of sophisticated

research such as social network analysis. IRB members may have little

insight into the practical problems of conducting “street” research with

drug users or other alienated populations. Some IRBs are much more

sensitive than others to the issues of privacy and confidentiality that arise

in network research.

Technical

Technical protections include common-sense limitations on transporting

and storing sensitive data, as well as sophisticated measures such as

randomized responses or “error inoculation” to limit the value of data to

others (Steinberg 1983, pp. 249-26 1). Consent forms and data collection

140
instruments may explicitly instruct participants not to reveal information

about crimes they participated in for which they have not been arrested or

prosecuted.

Ideally, personal identifiers should be removed as soon as possible and

before data are shared with others. In longitudinal studies, however,

where identifying and linking data are collected over time, this may be

impossible. In such cases, data encryption and the use of linking files are

recommended. Some commentators go so far as to suggest that such files

be physically stored beyond the control of the researchers, perhaps in a

foreign country. Careful consideration should be given to the ultimate

disposition or destruction of data containing personal identifiers.

CONCLUSION

All research occurs within a social context (Hurley and Pinder 1992).

Research involving human subjects entails some ethical and legal

obligations. Network research, which requires identifying the

participants and elucidating connections between them, poses special

problems that are compounded when the subjects are drug users and the

focus is on AIDS.

Legal guidelines and ethical principles provide a framework for designing

such research but rarely provide prescriptive solutions to the complex and

subtle problems that arise in conducting it. Awareness of these principles, however, and ongoing concern for the dignity and well-being of

subjects are essential to resolving the inevitable ethical dilemmas that

arise. Informed consent, respect for subjects’ privacy, data confidentiality, sound and valid research methodology (to ensure research results are

of genuine benefit to society), and a cognizance of the researchers’ own

values are all essential to the design and implementation of social network studies. Greater attention must be focused on the practical problems of studying high-risk populations, both to guide investigators and to

inform future research.

NOTE

1. A number of researchers report subjects who express willingness to

allow their drug use to be observed but who request payment in

advance. When the researcher knows that the purpose of the advance

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payment is to permit the subject to “cop” the drugs he or she will use,

does this constitute “assisting or encouraging”? Are researchers who

provide sterile injection equipment to injectors they are observing

“assisting”?

REFERENCES

Avins, A.L., and Lo, B. To tell or not to tell: The ethical dilemmas of

HIV test notification in epidemiological research. Am J Public Health

79(11):1544-1548, 1989.

Bayer, R., and Toomey, K.E. Health law and ethics: HIV prevention and

the two faces of partner notification. Am J Public Health

82(8):1158-1164, 1992.

Department of Health and Human Services, Office of the Secretary.

Title 45 Code of Federal Regulations Part 46.

Department of Health, Education, and Welfare. “The Belmont Report:

Ethical Principles and Guidelines for the Protection of Human

Subjects of Biomedical and Behavioral Research.” Federal Register

April 18, 1979.

Feinleib, M. The epidemiologist’s responsibilities to study participants.

Supplement 1. J Clin Epidemiol 44:735-795, 1991.

Hurley, P., and Pinder, G. Ethics, social forces, and politics in

AIDS-related research: Experience in planning and implementing a

household HIV seroprevalence survey. Milbank Q 70(4):605-628,

1992.

Melton, G., and Gray, J.N. Ethical dilemmas in AIDS research. Am

Psychol 43(1):60-64, 1988.

Mulvey, E.P., and Phelps, P. Ethical balances in juvenile justice research

and practice. Am Psychol 43(1):65-69, 1988.

Potterat, J.J.; Spencer, N.E.; Woodhouse, D.E.; and Muth, J.B. Partner

notification in the control of human immunodeficiency virus infection.

Am J Public Health 79:874-876, 1989.

Steinberg, J. Social research use of archival records: Procedural solutions

to privacy problems. In: Boruch, R.F., and Cecil, J.S., eds. Solutions

to Ethical and Legal Problems in Social Research. New York:

Academic Press, 1983.

Teitelbaum, L. A positivist approach to law and social science research.

In: Boruch, R.F., and Cecil, J.S., eds. Solutions to Ethical and Legal

Problems in Social Research. New York: Academic Press, 1983.

142
AUTHORS

Donald E. Woodhouse, J.D., M.P.A.

Research Coordinator

John J. Potterat, B.A.

STD/HIV Program Manager

Stephen Q. Muth, B.A.

STD/HIV Information Systems Manager

El Paso County Department of Health and Environment

301 South Union Boulevard

Colorado Springs, CO

Richard B. Rothenberg, M.D., M.P.H.

Department of Family and Preventive Medicine

Emory University School of Medicine

69 Butler Street, SE.

Atlanta, GA 30303

William W. Darrow, Ph.D.

Department of Public Health

North Miami Campus

Florida International University

North Miami, FL 33181

Alden S. Klovdahl, Ph.D.

Department of Sociology

Faculty of Arts

Australian National University

GPO Box 4

Canberra, A.C.T. Australia A2601

143
Network Models for HIV Outreach

and Prevention Programs for

Drug Users

Robert T. Trotter II, Anne M. Bowen, and James M. Potter, Jr.

INTRODUCTION

Most drug abuse and human immunodeficiency virus (HIV) prevention

programs rely on individually oriented models of change, or they are

conducted at the mass media level. The media approach assumes that the

mass transmission of information, embedded in emotionally sculptured

scenarios, will spur individual behavioral change through social diffusion

theory effects. Prevention programs focused on the individual are

constructed around competing or complementary assumptions about

individual behavior. They assume that people lack accurate knowledge

about the targeted problem (health beliefs model), that the problem

occurs because situational intensity interferes with the individual’s ability

to negotiate a favorable situation (self-efficacy model), that the individual

lacks the decision-making models necessary to protect oneself (theory of

reasoned action), that individuals are not at an appropriate state of

readiness to change their behavior (stages of change theory), or that

people are in need of motivational support for change (motivational

counseling approach) (Bandura 1986, 1990, pp. 128-141; LaFromboise

and Rowe 1983; Tyler and Holsinger 1975).

Both individual and mass media programs attempt to strengthen the

probability that individuals will overcome risks through increased

knowledge, improved self-efficacy, and modeling alternative or resistance

behaviors (May 1992). These approaches have had an important impact

on HIV and drug prevention programs, but their cumulative effect falls

short of a complete elimination of risk-taking behavior.

The most significant element missing from these prevention efforts is an

accommodation of the effects of basic units of human interaction: the

networks of family, friends, work environments, and the other small

human groups that produce the key social contexts for people’s daily

lives. This chapter describes a model that combines social network

considerations with psychosocial approaches to HIV-risk reduction.

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The authors propose that the conditions found in group contexts directly

affect HIV transmission and drug use. These conditions demand the use

of specific social interventions (change in group norms, consensus-based

problem solving, improved social dynamics) in prevention programs.

This new paradigm should be added to individually targeted culturally

competent interventions in order to successfully reduce the overall risk of

HIV infection and drug abuse in the United States.

The model being promoted has been constructed from three types of

network analytical approaches, used in a complementary fashion. The

first is an ethnographic exploration of drug-using networks. The authors

employ open-ended questions and conduct direct observations of drug

group activities in order to acquire descriptive and typological data on

drug networks. The second approach is an ego-centered (i.e., single

person-oriented) attributional data collection process that relies on a

standardized questionnaire to determine the characteristics of individuals’

networks. The final strategy is a network relationship analytical approach

that includes both qualitative and quantitative elements for analysis and

interpretation.

This multicultural acquired immunodeficiency syndrome (AIDS)-risk

reduction program, still ongoing, is focused on the development and

testing of culturally sensitive outreach interventions for injecting drug

users (IDUs) and crack smokers in small towns and rural areas.’ Efforts

focus on the use of both network and individually based interventions in

four cultural groups: African Americans, Anglo Americans, Hispanics,

and Native Americans in the Southwest. The authors’ objective is to

demonstrate the effectiveness of two prevention approaches: a standard

approach used at 20 cooperating sites and an enhanced intervention

developed locally. The aims are: (1) to define the cultural and

psychosocial parameters of HIV/AIDS-risk behaviors in medium-sized

multiethnic towns; (2) to develop models for understanding and

preventing risk behavior cross-culturally; and (3) to develop relevant

network and individual approaches to HIV/AIDS prevention for each

cultural group.

The project is being conducted in Strip Town’, a town of approximately

45,000 people. It resembles other Southwestern towns that stretch along

the railroad tracks that bisect them, forming neighborhoods on “both

sides of the track.” Strip Town is slightly more than 100 years old and

contains considerable cultural diversity. The largest population is the

Anglo-American community (29,647). The second largest population is

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Hispanic (6,972), and the third group is Native American (4,210). (There

are several federally recognized Native-American reservations within 100

miles of the town.) The fourth Strip Town cultural community is African

American (1,135). The community residents feel that the town is

relatively isolated. However, the community is linked to numerous

metropolitan areas by more than 3 million tourists who travel through the

town annually. These individuals are a potential source of HIV infection,

as are local visits to metropolitan areas.

ETHNOGRAPHIC OVERVIEW OF DRUG USE

IN SMALL TOWNS

In contrast to urban areas, drug use is rarely visible on the streets of Strip

Town. However, ethnographic research has determined that the town is

not exempt from any of the drugs found in urban centers in the United

States. Heroin, cocaine, methamphetamines, marijuana, hallucinogens,

and a variety of prescription drugs are all readily available for illegal

consumption. Some drugs are grown or manufactured within the

community, but more often drugs are obtained from metropolitan areas

within 3 to 5 hours of driving time from Strip Town.

The drug-purchasing sites are bars and individual homes or apartments,

complemented by a home delivery service available to some drug

networks. Drug users utilize motel rooms scattered around town for

occasional drug deals. The program has identified a number of “party

houses” where people go to use drugs. These are not commercial

enterprises but are a part the social aspect of existing drug networks.

Finally, a number of sites called “rock houses” were identified. They are

“mom and pop” operations for relatively small drug networks. They

appear to be different from crack houses in urban settings and do not

involve a significant sex-for-drugs trade. “Shooting galleries” do not

exist in town.

Locally Produced Drugs

The most common locally grown drug is marijuana. Respondents have

reported that psilocybin mushrooms, jimson weed (Datura), and poppy

bulbs grow in the area, although these are drugs that are more often used

by “drug experimenters” rather than the drug users who are the focus of

this project. Peyote is locally legally used by members of the NativeAmerican Church in religious ceremonies, but it is illegally used as a

146
recreational drug by some Native Americans, Anglo Americans, African

Americans, and Hispanics. Peyote must be imported from other locations

since it does not grow locally. Marijuana is grown both in and out of

doors, with the most successful production systems being elaborate

indoor operations that provide controlled lighting, moisture, and

fertilization.

Drugs that require a modest degree of processing, such as crack cocaine

and crystal meth, are produced locally. These operations are small

enterprises that can be set up in homes or apartments without being

highly visible. “Cooking” methamphetamine to produce “crystal meth” is

done locally. Another common local production involves “rocking

cocaine.” This is carried out in multiple rock houses. A rock house will

typically serve one or two drug networks of 10 to 15 users. The process

utilizes baking soda in place of ether and can be easily and safely done in

most settings. People who belong to these rock house circles use the drug

in the home of the supplier or carry it away to use elsewhere.

Importing Drugs From Urban Areas

The most sophisticated processes for creating drugs from raw products

are beyond the expertise of local groups and occur in urban or

international locations that have access to raw materials and necessary

production facilities. Crack and powder cocaine (cocaine hydrochloride),

heroin, and methamphetamine are usually imported from urban areas.

Crack “rocks,” or the “tar” form of heroin, are acquired by small-town

middlemen who break or cut the drugs into locally usable sizes. The

street value of these larger purchases is normally $2,000 to $3,000, which

is within the economic range of small operators. The sale of “eight balls”

is also common. This amount of money will provide about a week’s

worth of drugs for the buyer and provide cash for another buy when the

part that is not personally used is sold.

The local price of drugs varies in terms of available supply but also varies

according to the buyer’s social relationship with the seller. If a drug user

has a strong social relationship with a dealer, the dealer will sell drugs for

a lower price than is charged to people with a more casual relationship

with the dealer. Kinsmen tend to get the best price, friends next, longterm buyers, and so on down the line.

The main local crack supply is obtained by dealers purchasing it in an

urban area and bringing it back to town. Some drugs are also brought in

147
by traveling dealers who follow regular routes through the State or

through several States. They are the modem-day equivalent of the rural

pack peddlers of the past. Crack is commonly distributed by a home

delivery system or an individual “pickup” system. Once a dealer is

known to have a renewed supply of a drug, regular customers contact him

or her; the dealer then takes it to customers’ homes, or they come to his or

her home and purchase it. There is also at least one “opportunistic

scoring location” in town. If someone wants drugs, they go to this

location and hang around. Dealers drive by on an irregular basis, and if

they recognize the person standing there, they will ask the person if they

are “looking, ” “buying,” or “scratching,” code words for wanting to buy

drugs. These “drive-by” dealers will not normally sell to individuals who

they do not recognize in order to avoid local law enforcement undercover

agents.

Heroin is imported in two different forms, the traditional white powder

called “china white” and a substance locally called “Mexican tar.” The

tar, or “tootsie roll,” form is the most common. At the present time,

heroin and cocaine are similar in price. Smalltown heroin-using groups

often pool their money and send one of their members to an urban area to

score their heroin. This generates a considerable need for trust on the part

of group members since the purchases tend to be infrequent and to

involve large sums of money in relation to the wealth of the group.

In some cases, one of the network members is a local primary supplier of

drugs on a permanent basis. Drug suppliers make trips to urban centers

as entrepreneurs. A project respondent who has assumed this supplier

position displayed his products recently. They included an “eight ball” of

heroin “tar,” a small bag of powdered cocaine, a baseball-size crack rock,

and several boxes of prescription drugs. This individual is primarily a

heroin user but supplies other users a wide range of drugs he obtains in

urban areas.

Smalltown HIV Intervention

The U.S. national AIDS effort concentrates on urban systems in which

the HIV prevalence is very high. Recently, the Centers for Disease

Control and Prevention (1992) has identified a growing HIV risk for

nonmetropolitan areas, where there is more limited knowledge of beliefs

and attitudes toward AIDS (Estrada, unpublished data; Estrada et al.

1989) and far fewer intervention and education programs. The recent

HIV infection surveillance report for the State3 in which Strip Town is

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located indicates a total of 1,900 AIDS cases (52.19 per 100,000) and

3,285 HIV-infected individuals (121 per 100,000). The AIDS infection

rate for the predominantly rural county of which Strip Town is a part is

10.35 per 100,000, and the HIV infection rate is 20.71 per 100,000.

However, the local drug-using population has an HIV prevalence rate of

3 percent (3,000 per 100,000), based on 470 active local cocaine, heroin,

and other IDUs tested by the project in the past 22 months. This differential in HIV rates indicates that the project is targeting many if not most of

the highest risk individuals in the area.

Some of the locations where activities that present HIV risks occur in

urban areas are missing in Strip Town; others are not. The town does not

have prostitution strolls where street-based sex workers are available. It

lacks abandoned buildings with crack houses and has no shooting

galleries for sharing needles. Most of the HIV high-risk locations for

drug users are in homes or bars, and the risks come primarily from sex

and needle sharing in defined drug networks. In one park, there is a male

homosexual “pickup” area, unobserved by most of the people who take

their children there to play. The majority of the activity initiated in the

park is not prostitution; it is casual sex for bisexual males in town who

are married and want to make homosexual contacts. In addition, a

number of bars have been identified where there are drugs available for

sale, as well as a high rate of sexual pickups and a high level of sexually

transmitted disease contact referrals.

The primary objective of this project is to reduce HIV risks in drug-using

networks. The characteristics of local drug use must be taken into

account in the intervention, since the size of the town impacts on peoples’

attitudes toward AIDS. Some of the conditions ethnographers are

investigating act to reduce HIV risk-taking behavior; others create an

environment that will support rapid spread of the disease from any node

where it enters the community. The protection derives from the fact that

it is difficult to hide persistent behaviors in a small town. The anonymity

of urban settings is lacking for anyone who has lived in the area for any

length of time. This condition helps to reduce behaviors that place people

at risk, especially sexually related risks, because the people in the town

have a generally conservative orientation toward sexual activities with

strangers. On the other hand, the density of the social networks sets up a

condition in which the disease, once it penetrates a network, is likely to

spread rapidly to a significant portion of the population.

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The perceived isolation of the town has an effect on the local

population’s assessment of their risk of HIV infection. Some people

believe that isolation provides immunity; they believe that AIDS is an

urban problem. However, since the drugs must at some point be procured

from an urban center, someone must take risks in an urban environment.

These “drug runs” carry primary risks for rural drug users. The buyers

travel to poorly known territory where they must negotiate as outsiders.

Respondents report numerous risk-taking activities on these trips,

including testing drugs with borrowed “works” (drug paraphernalia) and

casual sexual activities. When asked how they try to reduce these risks,

the first response is usually “I never share needles when I go to score.”

However, with further inquiry, most will report that there was “that one

time” for either sharing works or casual sex. When enough of these “one

time” events are aggregated, the risk becomes significant.

ETHNOGRAPHIC NETWORK APPROACHES TO HIV- AND

DRUG-RISK REDUCTION

The project collects baseline and ongoing ethnographic data (Bernard

1986; Trotter 1991) in conjunction with quantitative data collection on

psychosocial variables (Bandura 1986; Mays et al. 1990, pp. 128-141;

Prochaska et al. 1992). The ethnographic data collected has two purposes: It is used to define the sociocultural elements of drug use and HIV

risks in small towns and to develop models for preventing HIV and drug

risks cross-culturally. The general ethnographic data and the qualitative

network analysis is complemented by quantitative forms of network

analysis (Fraser and Hawkins 1984; Klovdahl 1985; Knoke and

Kuklinski 1982), as well as other systematic data collection procedures.

Ethnographic Network Data

A network can be defined as “a specific type of relation

linking a defined set of persons” (Knoke and Kuklinski

1982, p. 12).

The authors began exploring rural drug networks at a community level by

conducting ethnographic interviews with in-treatment and active drug

users. The questions asked included how long the respondent had used

drugs, what drugs they used, why they use drugs, which drugs they prefer

and why they prefer them, how they get their drugs, how many people

they know use drugs, and the characteristics of their own drug-using

150
network. The purpose of these questions was to establish baseline data

and to provide a preliminary overview of the local drug networks.

Heroin addicts reported that there were only a few users in the area

shooting heroin, often estimating that they knew of 10 to 15 people in

this situation. The same was true of most cocaine users, crystal meth

injectors, and others. However, it became clear that these individuals, for

the most part, did not know each other. They were describing a

potentially large number of strongly bounded and mutually exclusive

groups. This information was supported by a noticeable lack of

consensus between these users on where and how they scored and on the

type of membership (family, friends, strangers) in their drug network.

Discovering numerous small drug networks that do not have knowledge

of one another appears somewhat counterintuitive given the smalltown

nature of Strip Town, where everyone feels they know everyone else. It

would be reasonable to assume that most drug users would have common

social connections, scoring locations, or long-term associations from

grade school and high school. However, a countervailing problem exists

that changes the local ecology of drug networks. Drug arrests commonly

take place at the user (not dealer) level. Using drugs with anyone but a

small circle of well-known acquaintances is hazardous. Therefore, the

authors have repeatedly confirmed that there are many more drug

networks in town than are assumed by the drug users themselves and that

these networks only minimally overlap, being linked by one or two

people at the most.

Developing a Rural Typology of Drug Networks. The authors felt

that using an ethnographic approach to developing a drug network

classification was an important first step for this intervention.

Ethnographers found there was no clear local “folk” typology of drugusing groups. People do not have clear labels or descriptions for

networks. Yet, the actual networks that have been observed form and

maintain stable social relationships. In the absence of a preexisting

classification, the authors decided to create a typology that emphasizes

the social and cultural variables that appear to be the most promising for

the development of the intervention strategies. These variables define

differences in risk taking and risk protection for the groups, which would

subsequently assist in improving the efficacy of the authors’ network

intervention programs.

Ethnographic interviews indicated there are three major variables that

permit construction of a typology of drug groups in small towns. The

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first is the relative degree of openness of the network, measured by the

level of recruitment of new members over time. An open network is one

that has a high percentage of newly recruited members, and a closed

network is one that does not allow the recruitment of new members to

any significant degree. This is a continuous variable, from networks that

are completely open to new membership at any time (a risky situation

given the illegal nature of drug use), to those that never recruit new

members after their initial formation. There is nothing, other than mutual

agreement, that prevents networks from changing from open to closed

and back again over time, depending on internal and external

circumstances. However, these networks have not been observed long

enough to determine whether or not these cyclical changes occur.

The second set of classification variables includes the types and the

number of social bonds that predominate in the group. At present, there

are four conditions associated with this set of variables in the project

classification system: (1) kinship relationships, (2) long-term friendships,

(3) shorter term acquaintanceships, and (4) weak or virtually anonymous

relationships. All or only a few of each type of relationship may occur in

a single drug-using group. Among the long-term and well-established

drug networks, kinship and very long-term friendship are the core

structural elements. One particular drug network the project is studying

consists of a three-generation family of more than 10 IDUs. At the other

extreme, acquaintance based on convenience and almost random

association may predominate in a network.

The third variable used to construct this classification is the type and level

of social activities or interactions that exist within the drug network, such

as group drug use, joint recreational activities, or work-related

associations. Joint drug use, in particular, is a key variable. One anchor

for this variable is the absence of any of these activities for the group as a

whole. The next observable level is face-to-face activities limited to

dyads or triads in the group. The other end of this activity spectrum

includes a high level of social interaction (e.g., parties, participation in

softball leagues, other recreational activities) involving the entire group.

These activities are generally associated with the social structure that

exists in a network but are not correlated to them in a one-to-one

relationship. Even a family-based network may decide not to engage in

face-to-face activities due to hostility or conflict in the group. There is

some covariation between levels and types of activities in a group and the

types of social bonds that predominate in that network, but it is not a

perfect relationship.

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Another variable was considered in creating this drug network typology:

the type of drug used. This variable did not produce a significant

improvement in the identification of network types. Most groups prefer a

single drug or specific drug combinations. Most groups also use other

drugs, especially marijuana and alcohol, when the primary drug is not

available. However, there are networks of each type that use each of the

drugs available. So, while networks tend to be single drug-oriented, the

other relationships (openness, types of relationships, and social activities)

determine the actual network structure.

Analyzing the ethnographic network data produced a typology with four

distinct (i.e., internally consistent, externally divergent) classes of drug

networks. They have been labeled Types A, B, C, and D. Type A

(mature injector networks) is a closed system in which members allow

virtually no new recruitment. Group size ranges from approximately 5 to

10 individuals. Type A networks often include individuals from a variety

of social, economic, and ethnic backgrounds. The most commonly

encountered drug of use for this type of group is heroin, although other

drug preferences were found in Type A groups. The primary purpose of

the group is to pool resources for the acquisition of drugs. Joint drug use

activities do not extend beyond scoring for the most part. The group has

social bonds based on kinship and very long-term friendship that help to

maintain the group, but they socialize less than the other groups. The

socialization occurs as dyads or triads and does not involve the whole

group. A respondent described scoring, the group’s primary activity:

Somebody in the group will get a hold of the others

when they want to score or when they are going to score.

Who ever wants some will put their money together and

someone will go to . . . (major city) . . . usually, and get

the stuff and bring it back and call the others. The others

will come and get their part and go home and use.

Type A drug use tends to be very secretive. Most of the members are

married or in monogamous relationships. They are employed at various

economic levels. They may use on a maintenance level during the week

and get “loaded” on weekends or special occasions. The major area of

risk for HIV transmission is from contact with persons outside the group

(weak ties), for example, when they come in contact with outsiders whose

HIV status is unknown. For the most part, this type of network does not

153
involve sex for drugs, although it is not completely avoided. A respondent belonging to a Type A group states:

A couple of girls I know up here wanted to work

something out for some chiva [heroin] . . . but I

didn’t. . . coke users do more of that.

Type B drug networks (kin-based groups) are semi-closed and are

predominantly kinship groups (family, in-laws, or fictive kinship such as

compadrazgo relationships in the Hispanic community). One is either

born into these groups, marries in, or has a steady sexual partner in the

group (with rare exceptions). The members have gone to school together

and were often raised together. The groups tend to be homogeneous in

terms of socioeconomic status (SES) and ethnic identification. Drug use

within these groups could be considered a family tradition, a special case

of peer pressure. The individual has very strong pressures to conform to

group norms. The nonuser is considered to be sending a message

condemning the group’s behavior. An example of this was reported by a

Navajo respondent who was attempting to abstain from drug use:

They called me names, they said that I was too good for

them.. . I fought with them. . . I beat two of them up

but I still had to go to the hospital.

These groups form a contrast with Type A groups, where the social

relationships surrounding drug abuse are minimal. The HIV-risk areas

for this group include the sharing of works between family and friends.

This activity is often not even labeled sharing and may actually be a part

of the bonding process that occurs within the group; refusal to share can

be considered a distancing from the group’s social norms. For the most

part, these networks involve individuals with longstanding monogamous

partnerships, and there is not a significant amount of exchange of sex for

drugs, although there is co-use of sex and drugs in some of the

partnerships. These groups can have any one of several drugs as the drug

of choice for the group, with the most common being cocaine, crack,

rock, crystal meth, marijuana, and alcohol.

Type C networks (friendship-based networks) are semi-open systems

whose members score together and are socially bonded by drug use. The

majority of these networks are relatively homogeneous in terms of SES

and ethnicity, but they are more mixed than Type B groups. The

predominant social bonds in the group are long-term friendships,

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although some kinship relationships normally are present. Individuals in

these networks involve one another in both drug use and in other types of

social activities. The members are often connected through work as well

as their social activities. These groups are somewhat open to recruitment

of new members, although it takes time. “Good friends” may be invited

to “party” (to use drugs) with the group, but it is very common for the

group to take from 12 to 18 months of feeling people out before they are

recruited. Multiple drugs are used in this type of group, including

heroine, cocaine, crack, speed, and alcohol. The groups also tend to

include both injectors and noninjectors in the same network. A

respondent describes a night of mixed drugs:

People will be drinking or doing coke and those who

want to shoot up go in the other room.

The risk areas for this group include the sharing of works “among

friends.” Sexual activity may also be present within the group, with

multiple sexual partners a possibility, and with some changing sexual

relationships within the group over time. There appears to be some

exchange of sex for drugs, although this seems to involve ongoing social

relationships rather than commercial transactions.

Type D (acquaintance) networks are the most open of the four types.

They often include polydrug users who bridge or skip from group to

group. The most common drug used is crack cocaine. The crack dealers

operate more openly than most of the other suppliers, and profit is a

major condition for establishing a relationship with recruits. Introduction

into the group can be accelerated if an individual has become a known

buyer. Others will introduce that person to the group’s dealer, saying

“He’s OK, he’s buying.” This indicates that the existing group member

has seen and has been with the new person when they were scoring.

Having a known supply of money is a significant credential for entry into

these networks and can expedite the process of acceptance. Individuals in

these groups regularly exchange sex for drugs, and there are far more

“impersonal” exchanges of this type than in the other groups, including a

considerable power differential between the person giving the sex for the

drug and the person in control of the drug (and consequently in control of

the sexual activity).

A long-time drug user derogatorily referred to members of this type of

group as “trash can addicts. ” They will use anything. These groups are

normally heterogeneous in terms of ethnicity. The SES of group

155
members can also vary to a considerable degree. These networks tend to

consist of users who are new in the area and are looking for contacts,

people who have progressed to a drug use stage that makes them

unattractive to members of the more closed groups, users in transition

between groups, or young drug users who have not been recruited to a

stable network. As a respondent indicated:

I was here chipping, running back and forth to [nearby

metropolitan area] for a year before I finally ran into one

person and from them I met about 10 others.

This person then moved into a more stable and less visible Type C

network once he gained acceptance. The Type D groups appear to be at

the highest risk for HIV infection, due to a full range of sex-for-drug

activities (commercial and noncommercial) and needle sharing with

strangers. These groups also include numerous individuals who are

highly mobile and who are likely to move back and forth to nearby urban

areas during the year, increasing local risks due to contact with higher

HIV prevalence sites.

The majority of the networks tend toward the closed end of the spectrum.

Of the 23 networks the authors currently have data on in Strip Town, five

are Type A, five are Type B, eight are Type C, and five are Type D. This

creates a 4: 1 ratio of closed to relatively more open networks.

In order to confirm the validity of this general network typology, a small

cross-validation study was conducted using the quantitative data collected

on the drug use and HIV-risk patterns of project clients. Each client is

assigned membership in an existing or a new drug network when they are

identified as meeting three of seven assignment criteria. Individuals who

do not meet those criteria are considered isolates, peripheral members, or

unassigned. Each identified network is classified, using the qualitative

criteria described above. The authors then used one-way analysis of

variance to compare selected drug- and HIV-risk variables from the Risk

Behavior Assessment questionnaire, with the variable identifying the type

of network membership (A, B, C, D, or none) for each client.

It was hypothesized that there would be differences in intravenous drug

use across the groups, but not general drug use, following the use patterns

found in the community. Significant differences were seen for days using

intravenous drugs in the last 30 days (F(4,171) = 9.16, p = 0.0000), with

the Type A network reporting most frequent use in the last 30 days

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(M = 13.85, standard deviation [SD] = 16.63), and the remaining four

networks seldom using intravenous drugs (isolates, M = 2.04, SD = 6.67;

Type B, M = 2.52, SD = 6.9; Type C, M = 4.57, SD = 6.90; Type D,

M = 1.79, SD 6.09). Post-hoc Tukey-HSD multiple range significance

tests indicated that members of Type A networks used drugs significantly

more than any of the other four networks. There were no differences

across the network types in days using nonintravenous (non-IV) drugs

during the previous 30 days (F(4,171) = 0.78, p = 0.54). This result was

expected since the measure of non-IV drugs combines alcohol, marijuana,

and cocaine. Almost all subjects report alcohol use, and many report

crack use in addition to other IV drugs. The range of use was so large

that differences would be difficult to detect.

Sexual risk and HIV testing were also examined across the five groups.

Frequency of unprotected sex was expected to differ among the groups.

This hypothesis was supported for males (F(4,90) = 3.93, p = 0.006) but

not females (F(4,49) = .16 p = 0.95) with the kinship network (M = 1.00,

SD = 0.00 and the isolates (M = 0.96, SD = 0.12) engaging in the most

frequent unprotected sex with Type A (M = 0.70, SD = 0.44) and Type C

(M = 0.76, SD = 0.41) less frequent and the type D the least frequent

(M = 0.5 1, SD = 0.46). The family-based network members have many

socially negative connotations associated with using condoms with

regular partners. These results match well with the ethnographic data,

including the lack of difference for females. The rate of unprotected sex

was uniformly high across all five groups for females, although the

smaller group sizes collected for them may obscure some actual

differences. Alternatively, insisting on condom use may be a more

complicated behavior for women than men, and the power differentials

associated with it may be unrelated to the criteria used to assess

membership in this social network typology.

Intercourse with IV drug users was also expected to vary across the

groups, with Type B networks engaging in the least amount of “safe sex”

(intercourse with non-IDUs). This hypothesis was supported

(F(4,139) = 3.06, p <0.02), with the Type A networks engaging in

significantly less safe sex (M = 0.54, SD = 0.48) than the younger, Type

D networks (M = 0.87. SD = 0.31). The remaining three groups were

between these two in frequency of safe sex (isolates, M = 0.83,

SD = 0.36; Type B, M = 0.73, SD = 0.40; Type C, M = 0.74, SD = 0.41).

The frequency of HIV testing was not significantly different

(F(4,172) = 1.96, p = 0.10) across the five groups. Although this was not

157
significant, there was a trend in the data for network Type B to be tested

more often with an average of 1.22 (SD = 1.62) tests per person, with

Type C (M = 0.70, SD = 0.95) and D (M = 0.73, SD = 1.3) slightly less.

Network Type A (M = 0.38, SD = 0.67) and the isolates (M = 0.36,

SD = 0.79) had been tested the least. The finding that Type B networks

have been tested most frequently may be related to social norms about the

need to protect other family members, a consistent theme in the ethnographic interviews conducted with these individuals. The low rate of testing

for isolates again may reflect either the social ecology of being an isolate,

including a differential access to resources, or may be related to

psychosocial conditions of isolation.

Finally, two variables that were identified were not expected to vary

across the groups: income in the last 30 days and amount of time spent in

jail. Neither days in jail [(F(4,169) = 1.30, p = 0.27)] nor income in the

last month [(F(4,172) = 1.23, p = 0.30)] were found to be significantly

different across the groups. The lack of variation in income may be due

to that targeted sampling strategy. The lack of variation in jail time may

or may not be an important condition for network-based interventions.

Since the authors hypothesize that the local networks may provide several

forms of social protection, it would be interesting to have comparable

data for networks in metropolitan areas to determine whether the size and

composition of smalltown networks provide a differential amount of

protection from jail time when compared to other locations.

These data are useful for targeting intervention and education activities

for the highest risk group, IDUs, based on multiple-risk criteria. They

also contain important information about the subepidemics that are likely

to be part of HIV transmission in rural areas. The authors believe that the

overall effort of their ethnographic network data collection is well

justified in terms of the advantages it provides in prevention and

intervention efforts. It also creates a mechanism to help validate the

utility of their network approach beyond its ability to describe drug use in

a small town.

Using Ethnographic Network-Based HIV Intervention

in Small Towns

The present network typology meets several needs for understanding

important social relationships among drug users. One primary use of this

ethnographic network approach is to create an effective outreach system

for contacting high-risk individuals. Network-associated outreach

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follows existing social relationships, and recruitment can be initiated

within the context of the same unit that will either reinforce or act as a

barrier to program objectives. Once the first few individuals in the

network have been recruited, the group itself can provide impetus for

other members to participate. Rather than relying on individual-byindividual recruitment, group dynamics are in force beyond individual

motivations. Once the group is assessed, the prevention or intervention

program can be transmitted to a central individual in the network, with a

good chance that it will subsequently be transmitted to part or all of the

rest of the network. This makes the prevention effort more effective. For

example, there is now one woman, the central person in a kinship-based

network, who uses her kin relationships to assure that all of her children,

nieces, and nephews have condoms and clean needles before they go out

to party on the weekends. The typology also makes it possible to engage

in interventions that focus on both group and individual behavior or that

pursue only individual-level intervention for isolates and type D network

members. The majority of the authors’ smalltown drug-using networks

show a strong tendency for tight communication and reinforcement of the

group’s norms. This means that if the network is currently “clean” of

HIV infection, the group itself can become an excellent focal point for

developing social norms that promote remaining HIV free. Prevention or

intervention efforts are enhanced by knowing the variables that cause

these differences.

Network-informed outreach has additional advantages. Keeping track of

network members is a natural, ongoing function of the gatekeepers of the

network. This condition can greatly assist the followup phase of any

project. If the core or most influential members of the network can be

identified and tracked, then they can act as primary links to the other

members of the group 6 months, 12 months, or even longer into the

future, reducing the disadvantages of followup that must track every

single individual.

An additional benefit to network interventions is related to the condition

that drug users in small towns rarely receive positive services from the

community. Many programs available in cities do not exist in rural areas

or are not accessible to the drug-using population. Drug users are a

stigmatized population, and they do not want to become more visible than

they already are. Drug users often feel that it is unlikely that anyone

outside their group would be concerned about their well-being. This

causes them to reject participation in programs if they are approached

anonymously or through normal communication channels. Outreach

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workers are often confronted with questions like “Who are you really

working for?” Participants have expressed fears of being “busted”

following interviews. These problems are greatly reduced by networkbased recruitment since the first person to participate tests out the system

to see if the researchers are doing what they say they will do and to see if

they get busted. These drug users tend to become strong advocates of the

program when they discover it has value to them. These individuals then

help recruit the rest of the network, short-circuiting the suspicion that

would be caused by a one-to-one “cold” contact by outreach workers.

The need to remain hidden, due to the illegality of drug-related behavior,

can be accommodated by normal networked entry into these groups.

EGO-CENTERED NETWORK DATA

The second component of this network approach to risk reduction is also

informed by ego-centered network analysis. An ego-centered network

consists of a single individual and all of the persons that he or she

recognizes as being connected in terms of some specified social relation.

The attributional data associated with ego networks (i.e., size, gender and

ethnic composition, retrospective conditions) can be identified and

described as a “typical” network profile. These data can be further

compared with other variables of interest, such as level of HIV risk,

choice of drugs, unprotected sex, or any other variable potentially

associated with network social relationships. This approach has both

advantages and disadvantages for constructing intervention programs.

The basic data collection instrument can be administered as a

standardized questionnaire, using sampling approaches that provide a

reliable communitywide view of ego-centered networks. This allows for

much larger samples of networks than other approaches, and the results

can be subjected to the same types of analytical procedures as any

questionnaire. This is an excellent method for gaining a rapid overview

of the networks in a large population.

The disadvantages of this approach stem from the lack of ability to

determine connections or overlaps between networks and the missing

reciprocal data from the individual’s ego discusses, especially,

relationships that may be directional (i.e., stronger in one direction than in

the other, such as the level of trust between two people). The data are

collected from ego’s perspective but are not checked by asking the

individuals named by the ego to comment on the relationships to the ego

or to each other.

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The quantitative baseline data for the authors’ AIDS prevention project is

an attributional survey of HIV and drug risks, using a form called the

Risk Behavior Assessment (RBA). Collecting ego-centered network data

from informants was managed as a cost effective add-on and provided

additional variables that can be analyzed in conjunction with the survey

data. The ego-centered network instrument was developed as part of the

National Institute on Drug Abuse’s (NIDA’s) Cooperative Agreement

Project and has been tested in five sites. The primary purpose of the

instrument is to describe the ego-centered networks of active drug users

not in treatment and their risk-taking behavior in relation to possible HIV

infection. The primary questions in the instrument ask about the size,

composition (age, sex, ethnicity), drug use, and sexual relationships of

the ego’s network.

The authors collected ego-centered network data from 52 active drug

users to provide a statistical overview of the drug networks. The data

include general information about the ego networks, information about

ego network drug activities in the last 30 days, and information about the

ego’s last episode of drug use. Table 1 illustrates the gender, age, and

ethnic distributions of respondents.

The number of people each ego reports “spending time with” ranges

from 0 to more than 25, with 76.3 percent responding that they spend

time with 0 to 10 other people. The composition of these networks

includes between 1 and 10 family members for all but 16 of the

respondents. These findings support other network data indicating that

the majority of these individuals belong to relatively small drug-using

networks that commonly include both users and nonusers, some kin

relations, and close friends. Only 25 percent responded that all of the

people they “spend time with” use drugs, and 13 percent reported that

none of the people they “spend time with” use drugs. Of those alters who

used drugs, 25 percent injected drugs, 69 percent smoked crack, and the

rest used some other drug and method of administration.

Respondents were asked to think about and list (first names only) up to

six people they had used drugs with in the last 30 days. They reported

the size of these networks as follows: 25 percent denied injecting drugs

or smoking crack with anyone else; 17 percent identified one person;

11 percent identified two people; 13 percent identified three people;

11 percent identified four people; 9 percent identified five people; and

11 percent identified six people. There were a maximum of six slots

available on the questionnaire; some respondents would have added more

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TABLE 1. Gender, age, and ethnic distributions of respondents, and

respondents’ 30-day and recent-use networks.

Respondent

N=52 (%)

Alters in Alters in

30-Day Network Most Recent Use

N=127 (%) N=90 (%)

Gender

Male 34 (67) 81 (63) 62 (68)

Female 18 (33) 46 (37) 28 (31)

Age

10-195

20-29

30-39

40-49

50-59

12 (23) 36 (28) 28 (31)

14 (26) 38 (29) 25 (27)

23 (44) 43 (33) 30 (33)

3 (5) 9 (7) 7 (7)

0 (0) 1 (1) 0 (0)

Ethnicity

African

American 10 (19) 22 (17) 16 (17)

Hispanic 19 (36) 64 (50) 42 (46)

Anglo 18 (34) 35 (27) 27 (30)

Native

American 5 (9) 6 (4) 5 (5)

people if they had been allowed. Examination of the ethnic mixture of

these 52 networks showed that 48.8 percent were confined to a single

ethnic group, 46.5 percent included representatives from two ethnic

groups, and two networks (3.8 percent) included three ethnic groups.

The risk factors assessed by the questionnaire included needle sharing

and sexual relations with network members. Table 2 illustrates drug use

and HIV-risk patterns for the respondents’ 30-day alter network and for

their most recent drug use episode.

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TABLE 2. HIV- and drug-risk patterns for 30-day and most recent use

networks.

30-Day Network Most Recent Use Network

N (%) N (%)

Drug Use

Crack

IV

Crack

and IV

Frequency

Daily

Weekly

Monthly

<1x/mo

Missing

Needle Sharing

Gave needles

Got needles

Sexual Activity

Sexual

relationship

Sex with drugs

66 (76) n/a6

9 (10) 19 (26)

21 (24) n/a

6 (6) n/a

34 (39) n/a

21 (24) n/a

24 (27) n/a

1 (1) n/a

16 4

11 6

20 15

16 n/a

Needle-sharing episodes occurred between 12 egos and 30 alters who

injected drugs. These included 11 mentions of needles obtained by the

egos from alters and 16 given to alters by egos. Most needles were

shared between the ego and the first person identified in his or her matrix.

Sexual activity was reported with 20 of the possible 127 alters

(15 percent). Sixteen of the relationships were with the first person

identified on the ego’s list, and 14 of these included sex with drugs. All

four of the sexual relationships with other alters included sex during drug

use, probably with casual partners.

The authors have identified a number of additional risks that were present

in the 30-day ego-centered networks, using a linked network question163
naire and RBA data set. The numbers of respondents were insufficient to

look at trends in this sample, but the following risks were listed by at

least one individual as occurring in the past 30 days: not cleaning shared

needles with bleach, using the same cooker as someone else, using the

same rinse water, and having sex during drug use. A larger sample

would be required to determine how these risks were distributed through

the various networks, but their presence indicates that the egos are

definitely at risk for HIV infection from drug use or sexual activities

associated with drug use.

The networks for the latest drug use episode show similar results. Thirtyfive respondents (67.3 percent) reported using drugs with between 1 and

11 people, for a total of 116 alters. The most frequent was use with 2

others (10 respondents), with 3 others a close second (9 respondents).

The egos’ perception of their relationship with the alters was assessed.

For the people named in the first position, 35 percent were relatives or in

the husband-wife-lover category; 48 percent were considered very good

friends; 12 percent were friends; and only 3 percent were acquaintances.

Of the 26 people in the second position in the matrix, 15 percent were

identified as acquaintances or friends of friends. These data support the

drug network descriptions and typologies created through this

ethnographic research and provide risk-related data for comparison with

the full network data below.

The drug use- and sexual-risk factors assessed for these individuals

included needle sharing and sexual relations with network members.

Nineteen alters were identified as using intravenous drugs during the

most recent drug-using episode. Needle sharing between the egos and

alters included six mentions of needles obtained by the egos from the

alters and four given to alters by egos. All needles were shared between

the ego and the first or second person identified in the matrix. Sexual

activity was fairly limited, with relationships reported with 15 of the

possible 90 alters. Eleven of the relationships were with the first person

identified on the ego’s list, three with the second person and one with the

sixth person on his/her list. Sex during drug use was not assessed for this

episode.

The demographic composition of the 30-day network and the “last use”

networks are similar, supporting the validity of the ego’s responses. The

gender and age distributions are representative of the larger sample of

drug users in the project, as well as the hypothesized gender breakdown

for the drug-using community as a whole. The ethnic distribution is

164
skewed toward Hispanics more than would be predicted by the total

project client population. This is an artifact that is primarily due to the

composition of the networks that were being interviewed at that time.7

Drug-related risks in the form of both needle sharing and sexual activity

during drug use occur relatively frequently. Due to the questionnaire’s

construction of ego-centered instruments, it was impossible to assess the

needle sharing and sexual activity of the alters. If their rate of activity is

similar to that reported by the egos, the level of network risk could

increase exponentially. The finding that most of the sexual activity

occurs between close friends, spouses, or lovers can be taken as an

indicator of potentially heightened risk. Research findings from within

this study and others (Prochaska et al., unpublished data) suggests that the

use of condoms with “main” partners is very low and difficult to initiate.

As a result, risk of infection from sexual activity may be very high if

anyone in the group becomes infected.

These ego-centered data both confirm and advance the present

ethnographic data. The data demonstrate that the majority of drug

networks in Strip Town are small (2 to 10 individuals), are based on close

friendship or kinship ties, and are relatively stable in their composition.

The data also indicate that the majority of needle-sharing activities occur

with the first three people named by the ego as members of their network

and that sexual activities occur predominantly with the first person named

by the ego. This finding has potential importance in targeting prevention

information using parts of the ego-centered data. A smaller portion of the

needle sharing and sexual encounters occur with people outside of the

ego’s network, but the data also indicate that it is exactly these

encounters, called “weak ties,” that are the highest risk contacts for the

majority of drug users. Based on these data, part of the present HIV

prevention and education effort has been directed at making

recommendations that would help these individuals break, reduce, or

decrease the risks associated with “weak tie” types of relationships.

FULL NETWORK (RELATIONAL) DATA

The ethnographic and ego-centered network approaches described here

are providing valuable baseline data for the authors’ intervention

strategies, but they do not provide all of the information needed about the

actual type, strength, or direction of the relationships within drug

networks. Nor do they allow comparison of differences in relationships

based on diverse kinds of interactions, such as drug use, social activities,

165
or intimate topics. Therefore, the authors decided to conduct full network

analysis on a subsample of the local drug networks in order to collect

information about the interactive affiliations of each individual in the

network.

The authors currently are working with 23 clearly defined groups of

active drug users in Strip Town, as well as a number of individuals who

have no known network connections (isolates). The size of these groups

varies from 2 (usually couples) to 42 or more. Full network relationship

data have been obtained on a total of 10 of the active networks in this

study. During the full network data collection process, the group is

brought together and asked to rate their relationship to each member of

the network based on a structured set of questions about their social

relationships, their drug use patterns, and communication about intimate

subjects such as sex. The authors also record focus group discussions

about the ways in which new members are recruited and the norms the

group holds in relation to HIV risks.

These network group interviews identify the perspectives that members

have on the social and drug-using characteristics of the group. They

provide information on how the group perceives its need to protect itself

from HIV risks by either reinforcing or changing group norms about

needle sharing or unprotected sexual relations with main and casual

partners. The process also includes a network problem-solving

intervention that allows the group to identify risks to the group as a whole

(without blaming or identifying members). Feedback from these sessions

indicates that the sessions are successful in initiating communication

within the group on topics that were not formerly discussed. The analysis

of the relational network data collected in these sessions provides the

opportunity to identify network characteristics that exist in these drug

networks (Glover 1989, 1990; Kilworth and Bernard 1974; Knoke and

Kuklinski 1982; Panning 1982; Scott 1991). Knowledge of the

communication patterns of networks allows a much more focused

approach for carrying out the educational and intervention objectives of

this project.

One network has been chosen to illustrate the types of information used

to analyze relational aspects of the AIDS risk reduction program. This

network, labeled “N1” was chosen as an example of a multigeneration,

family-based drug network. There also are networks with virtually no

family ties or centralized leadership, which demand different intervention

approaches. Network Nl contains members from two Hispanic kinship

166
groups, one of which includes an Anglo-American spouse. The group

has both male and female membership, as can be seen from the following

kinship diagrams (see figure 1).

The drugs of choice for the group are cocaine and crystal meth, and the

group includes both IDUs and non-IDUs. The members of the group are

“in the system,” living in local project housing. Three undocumented

individuals from Mexico are members of the group. The core group has

been using drugs together since high school; some of the members are

now in their forties. The network is relatively closed, with membership

being restricted to kin and sexual partners of kin.

The structural relationships identified in this network (or any network)

include different types of connections between actors, the centrality or

influence of individual actors as subgroups within the larger group, and

as roles or “positions” within the network (Knoke and Kuklinski 1982).

The authors are currently studying both the connections and the

substructures of the networks. They are analyzing the patterns of

FIGURE 1. Network N1 kinship charts.

167
information flow within networks, sometimes called connectivity

(Doreian 1974). This flow can be characterized by several measures,

including the amount of information that passes through a particular

individual, the length of time it takes information to reach each person in

the network, identification of the people who are gatekeepers of the

information flow, measures of differential influence in the group, and

measures of the probability that someone can or cannot receive

information that is injected into the network (Ford and Fulkerson 1956;

Gomory and Hu 1964; Katz 1953; Taylor 1969). Issues of subgroups and

positions in the networks are also being investigated. Network

researchers have created two methods of identifying key structural

elements of groups. One is based on the idea of social cohesion, where

cliques or circles of social actors are identified by the aggregate bonds

that link them together (Bron and Kerbosch 1973; Mokken 1979). The

other is based on the idea of structural equivalence, where people who are

similarly connected (have the same types of links to others) are thought to

be more similar to each other than people in the same subset who have

different types of links to others (Kilworth and Bernard 1974). Both the

structural and connection data provide information that can increase the

effectiveness of HIV education and prevention efforts directed either at

individuals or the whole group by identifying the most effective targets

for the messages and skills training.

The authors’ full network questionnaire is a matrix consisting of 27

questions that allow each individual to define his or her relationships to

the other members of their network. The questionnaire includes social

relationship questions (e.g., How much do you hang out with X?); drug

relationship questions (e.g., How willing are you to share needles with

X?), and HIV- or intimacy-related questions (e.g., How willing would

you be to tell X you have AIDS?). These are aggregated and analyzed to

provide a picture of the social, drug, and other intimate communication

relationships in the network. The following diagram provides a

sociogram model of the relationships in network N1 , derived from the

social relationship questions on the matrix (figure 2).

The connecting lines between individuals (identified by a number)

indicate the existence of a strong connection between two people. All

individuals in this group have some weak interactions with each other,

but the influence or communication between some is minimal; the

diagram concentrates on the strong ties. Females are represented by a

number in a circle and males by a number in a square. An arrowhead

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FIGURE 2. Network N1 sociogram of social relations.

indicates a one-way connection between two people, while a solid line

indicates a two-way connection. The width of the line indicates the

strength of the connection. The larger, lighter circle around Anita, #13,

indicates she is the central person in terms of influence measures. She is

also the most central communication node in the network. The core of

the network is comprised of Anita (#13), Lydia (#6), Adelita (#4),

Marcos (#5), Jaime (#9), and Josepha (#3). All of these individuals have

close kinship ties, and communication between them is strong. Miguel

(#11) and Dolores (#12) are living as married, and Miguel is the first

cousin of Maria (#1) and Lydia (#6). Aida (#7) is Lydia’s (#6) niece and

Josepha’s (#3) first cousin.

The drug network characteristics (derived from analysis of the drug

questions on the network matrix) are an interesting contrast to the social

relationships. Several people change position, from peripheral to more

strongly connected, or vice versa, as can be seen in the following diagram

(figure 3).

There is a change in the information flow and influence patterns of the

network in relation to drug issues. Aida (#7) is a nonuser, which is

clearly represented in her lack of connections on the drug questions.

Anita (#13) shares the influence on drug relationships with her son,

Marcos (#5), and with Jaime (#9), who is a central member because he is

a bilingual communication bridge between the Spanish (only) and

English (only) speakers in the network. Marcos (#5) scores drugs for this

169
FIGURE 3. Network N1 sociogram of drug relations.

network, keeps track of drug-related conditions, and influences the

network through his mother’s close connections with everyone else.

Some of the individuals who were strongly connected by social

relationships are connected by weak ties or are no longer directly

connected in terms of their drug relations. For example, Adelita (#4) and

Marcos (#5), who are married to each other, are strongly tied in the

diagram on social relationships, but Adelita (#4) does not communicate

much about drugs with her husband, only with her mother-in-law, Anita

(#I 3). The kinship ties between Jaime (#9) and Maria Elena (#2) (living

as married) are not visible in the drug relationship diagram, nor is the

aunt-niece connection between Lydia (#6) and Aida (#7). This indicates

that some people are reachable for the HIV/drug-risk reduction

information but are less accessible to the social-risk-reduction

information, such as sexual risk, unless the information is brokered by

different individual connections.

As a final note on the relationships displayed by these three

representations, the shape of the drug relations network diagram is similar

to a classic problem-solving configuration for networks. It is called a star

pattern, in which one person acts as a center in direct communication

with the rest of the network through dyadic relationships, with relatively

few interconnections. This allows rapid input on any issue and facilitates

problem solving for the group as a whole. The social network diagram is

170
a classic communication configuration where there are multiplex ties

within the group. This structure ensures that communication will not

break down with the loss of one member of the network since everyone

in the core group is tied to multiple individuals.

These findings are parallel to those for other networks this project has

investigated. The majority of drug-using networks in Strip Town, like

N1 , are small and relatively tight. They depend on kinship and long-term

friendship for entry, and they show a strong tendency for tight

communication and reinforcement of the group’s norms. This means that

if the network is currently “clean” of HIV infection, the group can

become an excellent focal point for developing or reinforcing social

norms that promote remaining HIV free. These norms can support the

elimination of risks through the elimination of ties that produce HIV

risks, such as needle sharing with strangers or unprotected sex with casual

partners. In addition, the existing boundaries can be reinforced, and some

assessment of HIV risk can be added to the trust issues that already affect

new recruitment into the group. New recruits could be sought only from

low-risk categories of drug abuse or sexual behavior.

The authors have also analyzed several other classic measures of network

connections and network structure’ in the 10 networks for which they

have collected relational data in order to inform their education and

prevention efforts. These can be taken as a general model of the

measures found to be useful for these purposes. The geodesic distance

measures for the network show that information flow is tight: The

general distances between individuals are small even though there are

quite a few people in the group (Doreian 1974). A second network

measure, Freeman Betweenness Centrality (Freeman 1979), indicates that

there are two persons who are the most central in the social relationships,

Anita and Jaime. They act as brokers for information flowing through

the network for the social relations. Most social information (such as

sexual education information) must flow through one or both of these

individuals if it is to reach everyone in the network. In contrast, there are

three brokers for the drug relations: Marcos, Jaime, and Anita, in that

order. Marcos is Anita’s son, which decreases his social centrality. In

both sets of relationships, Jaime holds a central position in the network

and is a critical person to recruit for both information exchange and

behavioral change.

A further analysis of the network structures, of factions and cliques,

(Borgatti et al. 1990; Bron and Kerbosch 1973; Seidman and Foster

171
1978) indicates there are only a small number of people who are marginal

to the core of the group. The factions within the group change

significantly when the drug relation questions are analyzed. These data

divide the core drug group into two subsystems. This information has

been used to identify boundaries where information may be blocked if at

least one individual from each subgroup is not involved in the

intervention. If a network is badly fissured, information must be

provided to multiple individuals. Centralized networks like this one, with

few marginals, can be provided education through fewer contacts and

through a smaller number of individuals than in diffuse, less tightly

constructed networks.

STRATEGIES FOR NETWORK-BASED DRUG AND HIV

INTERVENTION

Current evaluations indicate there are numerous advantages in using a

multiple-method network approach in HIV- and drug-risk reduction

programs. Ego-centered data collection, especially early in the program

cycle, provides excellent baseline data for understanding the general

network characteristics in a population. Ethnographic network data

collection combined with relational data can provide critical information

throughout the project by identifying the most effective recipients of

prevention and intervention actions; this type of data collection can also

act as an effective evaluation tool to determine the impact of interventions

at a level above individual measures of change.

Network-based outreach is an effective mechanism for establishing the

contacts and relationships necessary to conduct effective HIV prevention

programs in hidden or hard-to-reach populations. The most difficult part

of the process is often the initial contact or entry into a new network.

Network-based outreach follows existing social relationships. Finding

the first individual who will provide access is challenging. However,

with the sponsorship of that person, the remainder of the network can be

contacted without violating social taboos surrounding the necessary

secrecy of the group’s membership. Any gatekeeper is a natural gobetween who can reduce barriers to participation by endorsing the

program to others in the network. Recruitment of individuals into

programs can be made within the context of the same social group that

will reinforce program objectives or oppose them. Once the network has

been recruited, the group itself can provide the impetus to participate

rather than having to rely on individual-by-individual motivational

172
techniques. Group dynamics are in force that can reduce barriers to

prevention and education.

Network-based intervention has additional advantages. Keeping track of

network members is a natural function of the gatekeepers of the network.

Doing this can greatly assist the followup phase of any project. If the

core or most influential members of the network are identified and

tracked, they can act as primary links to the other members of the group,

reducing the disadvantages of individually based followup by providing

assistance to outreach workers who cannot spend as much time following

the whereabouts of network members as the gatekeepers can.

Once effective outreach is established, networks with strong group norms

can be approached differently from those with predominantly weak ties

and variable norms. Strong group norms can be helped to adopt or

maintain norms that reduce HIV risks with strangers or outsiders and

reinforce protective behavior (needle cleaning, safe sex) as appropriate

behaviors within the group. The intervention can also support increased

communication between members of these groups. If the initial

prevention or intervention message is successfully transmitted to a central

or core individual in the network, there is a good chance that person will

subsequently transmit it to part or all of the rest of the network.

The network approach can also identify individuals who are peripheral to

the network, those people intervention will reach only if they are

individually educated. It can identify differences in the ease of

communication across various topics. The network members may speak

openly about cleaning needles but restrict conversations on intimate

subjects. Using network techniques to identify these areas of low or

nonexistent communication can lead to a more clearly targeted

intervention directed toward lifting communication taboos.

In the case of drug networks where members interact as short-term

acquaintances and operate in a loosely structured group, the intervention

may have to be conducted on an individual, dyadic, or triadic basis. The

network structure prevents a synergistic effect beyond anything more

than small segments of the group. The intervention is also most likely to

consist of reducing the risks associated with sexual and drug interactions

among ties that create high risks within the group; it does so by

encouraging people to break those ties and reduce their risk or to become

associated with a group that has more protective boundary mechanisms.

The standard intervention employed by the project described in this

173
chapter assumes clients are capable of interacting assertively within their

peer group and with sexual partners. Since this may not be the case, the

enhanced intervention adds the opportunity for the whole network to

discuss these issues and to establish group norms that may protect the less

powerful group members and reduce the chance of HIV infection for the

group as a whole.

Data collected during this project also explain some of the failures of the

classic strategy used in both drug rehabilitation and in HIV-risk

reduction, which is to move an individual away from high-risk personal

social networks into lower risk relationships (peer cluster theory).

Smalltown drug networks are frequently kinship based or based on longterm friendship. There are relatively few choices for making friends in a

small town; for example, there is a restricted pool to choose from,

compared with an urban area with more groups and associations. In a

small town, if a person does not like someone in his or her Narcotics

Anonymous (NA) group, “that’s tough,” because it is the only one in

town. People might have to leave town or even leave the State to

accomplish the classic goal of changing friends and networks. Data show

this is an unlikely event for most of the people interviewed. On the other

hand, these network data indicate it is possible to change the norms and

risk-taking patterns of networks, as a whole, by reinforcing positive risk

reduction behaviors. For this reason, natural network-based approaches

to risk reduction are a highly desirable adjunct to individual intervention

strategies.

Beyond these basics, a number of approaches could be introduced to

enhance network-based HIV interventions. Ethnographic network

analysis has identified individuals who could become key players at the

between-network intervention and outreach levels. These individuals act

as a bridge for HIV infection entering or leaving a network through their

drug or sexual activities outside of the core group. Once identified, these

individuals could be recruited for a number of intervention-related roles.

They could become key players by reducing the chances of HIV bridges

being activated. These bridge individuals could also potentially be

assembled into a grassroots organization to assist in forming a drug

community effort to confront the spread of HIV infections. Since they

are already backed by social groups, they could form the nucleus for

“street-level” community development efforts. This type of organizational intervention provides some opportunity for creating a self-help

structure that will last beyond the end of the Federal funding of HIV

prevention projects, allowing the effects to continue on their own.

174
FUTURE DIRECTIONS FOR NETWORK-BASED PREVENTION

The authors’ approach to risk reduction in smalltown drug networks has

proven to be valuable in the identification, location, and recruitment of

hidden or difficult-to-access populations. Ethnographic network analysis

has led to a series of suggestions combining qualitative and quantitative

approaches to increasing knowledge about HIV and drug intervention in

“not-in-treatment” drug users. At the simplest level, network data

identify the presence or absence of communication between individuals

and between sets of individuals on particular topics. At the next level,

network information data can identify the central person or persons who

exhibit the most influence on the group, the nodes in the network that act

as gatekeepers for interaction, or the subsets of individuals who interact

more among themselves than they do with others in the larger network.

Each of these conditions can suggest processes for direct and indirect

intervention and provide outcome measures of the efficacy of both

preventions and interventions.

The authors hypothesize that they will be able to measure both the

individual effects of interventions and the cumulative network effects

using combined analytical tools. Ethnographic findings demonstrate that

qualitative descriptions of network conditions can be used as a direct

adjunct to this prevention program. The ethnographic data can also act as

an important theory generation bridge into quantitative measures of the

impact of social networks on HIV and drug risk-taking processes.

The next step in the present research will be to look at the relationships

among aggregated variables associated with individuals in each network

in order to test hypotheses on the information obtained about the various

groups. , For example, there should be additional linkages between

network types and the presence or absence of risky conditions. The

presence of HIV infection (or percentage of infected individuals) should

vary among the networks. The size of the network should have some

impact on the ease or difficulty of changing norms that will protect

members from risks in the community. The authors should be able to

measure differential effects, if there are any, on HIV risk and the type of

drug used, holding the type of network constant. They should also be

able to measure differences in the overall risk to individuals and to the

group as a whole, based on the ratio of kin to nonkin membership, or the

ratio of strong to weak ties, or on the basis of group norms that favor or

that interfere with positive attitudes toward drug treatment programs.

175
Knowing the network membership of participants should allow

measurement of peer influence on attempts to enter or avoid drug

rehabilitation programs.

The authors are hypothesizing that both individual effects of interventions

and cumulative network effects using network analysis tools can be

measured. For example, those conditions that require increased

communication can be measured by increased information flow within

the network (Hubbell 1965; Taylor 1969). They are also measurable in

terms of reduced geodesic distances among all network members or some

portions of the network (Doreian 1974). It should be possible to identify

risk reduction in the network, between time 1 and time 2 if high-risk

elements of the network have been segmented off and the interactions

with those cliques are reduced or eliminated (Glover 1989, 1990).

Factions within the network should show either risk reduction or risk

concentration with increased distance to the risky parts of the network.

Centralization is a measure of the way that information is being

controlled by individuals (Stephenson and Zelen 1991), and for some

networks, a reduction of centralization should correlate with risk

reduction through the creation of more communication linkages between

noncentral individuals.

The authors also should be able to detect changes in influence, both in

drug and socially related issues, where individuals take on new roles

within the group to reinforce protective behaviors and reduce risks

(Bonacich 1987). When these data are correlated with qualitative and

attributional data sets, it provides an important set of tools to measure

HIV-risk reduction in a high-risk population. In sum, network analysis in

its various forms appears to be a highly desirable and productive tool for

the reduction of HIV hazards in hard-to-reach populations.

The authors have also hypothesized that network relational and structural

analysis can identify individuals who should become key players in

network-level intervention and outreach, as adjuncts to project staff for a

particular network. Once identified, these individuals can be recruited

specifically for intervention-related roles. Central individuals from

different groups could also be assembled into a grassroots organization to

assist in forming a drug community effort to confront the spread of HIV

infection. Since these leaders are already backed by social groups, they

could form the nucleus for street-level community development efforts.

This type of organizational intervention provides some opportunity for

176
creating a self-help structure that will last beyond the end of the Federal

funding for this project, allowing the effects to continue on their own.

An individual’s potential for treatment success may be directly related to

network variables that can then be converted to a group “willingness to

change” measure. The authors should also be able to measure the impact

of splits in the group, as well as differences in within-group associations

on risk taking and the effects of those risks on both individuals and the

groups. They may also be able to detect changes in influence, both in

drug and socially related issues, where individuals take on new roles

within the group to reinforce protective behaviors and reduce risks.

The authors believe there are numerous other qualitative and quantitative

measures that will allow identification of the effects of this program

beyond the individual level. Many are yet to be discovered, but efforts at

the network level appear to be invaluable in helping researchers address

the key HIV and drug risks of hidden populations.

NOTES

1.

2.

3.

4.

This is a 5-year project funded by the National Institute on Drug

Abuse (NIDA) grant #U01-DA07295. It is part of the NIDA

Community Research Branch’s Cooperative Agreement program.

The project principal investigator is Robert T. Trotter II, and the coprincipal investigators are Laurie J. Price and Anne M. Bowen.

Following the general ethical guidelines for projects like this one, the

names of the communities involved in the project have been replaced

by pseudonyms or generic terms to meet privacy and confidentiality

conditions.

The citation for this report has been omitted to protect the anonymity

of the community.

The instrument Assessment of Drug Use Social Networks was

developed by Dr. Mark Williams, Dr. Richard Needle, and Dr.

Harvey Siegal in cooperation with other researchers from

Cooperative Agreement sites. Correspondence should be directed to

Dr. Williams, Affiliated Systems Corporation, 3104 Edloe, Suite 330,

Houston, TX 77027-6022.

177
5.

6.

7.

8.

In order to participate, respondents must be 18 years of age or older,

must not have been in treatment for at least the past 12 months, and

must have a positive urine test for either cocaine or heroine use (or

have fresh needle tracks and test positive for other injectable drugs) at

the time of the RBA interview. Therefore, this category for the

clients includes only 18- and 19-year-olds. The nonclient (alter)

categories may include younger individuals since no age restrictions

were placed on naming the people in these categories.

The n/a designation indicates that this variable was not assessed for

the designated group.

Recruitment is conducted on a network-by-network basis, with an

overall targeted sampling plan in effect over a 12-month period. For

any given shorter length of time, subsections of the targeted sample

may be overrepresented.

All of the calculations were conducted using the program UCINET

4.0.

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AUTHORS

Robert T. Trotter II, Ph.D.

Professor and Chair

Department of Anthropology

Anne M. Bowen, Ph.D.

Assistant Professor of Psychology

James M. Potter, Jr., M.A.

Flagstaff Multicultural AIDS Prevention Project

Northern Arizona University

Campus Box 5615

Flagstaff, AZ 86011

180
A Personal Network Approach to

AIDS Prevention: An

Experimental Peer Group

Intervention for Street-Injecting

Drug Users: The SAFE Study

Carl A. Latkin

INTRODUCTION

The Stop AIDS For Everyone (SAFE) study is a social network-oriented

experimental intervention designed to reduce the human immunodeficiency virus (HIV)-risk behaviors in injecting drug users (IDUs). The

outcome of the study has been presented elsewhere (see Latkin et al., in

press; Mandell et al., submitted).

In a 3-month followup survey in the initial phase of the study, significant

differences were found between the experimental group and the control

group regarding changes in the self-reported HIV-risk behaviors of

needle sharing, attending shooting galleries, cleaning injection equipment

with bleach, and carrying bleach. Although at baseline the experimental

group reported higher levels of injection-related HIV-risk behaviors, at

the 5-month followup study the experimental group reported significantly

greater risk reduction. Individuals in the experimental condition who

reported lower levels of risk behavior at baseline demonstrated

significantly greater risk reduction, as compared with the control group,

suggesting that these results are likely to be a product of a regression to

the mean phenomenon. At baseline, personal network characteristics of

size of drug network and density of network predicted at followup the

risk behavior of needle sharing, and a smaller material aid subnetwork

predicted attendance in shooting galleries.

This chapter will first describe the intervention; second, examine

evidence of strengths of a social network approach for IDUs not in

treatment; and third, examine evidence of the social influence of drugsharing subnetworks on the HIV-risk behaviors of their members.

181
The strategy of using naturally occurring drug-sharing subnetworks as a

vehicle for behavior change is based on theories of social influence.

Several studies have indicated that perceived peer norms and peer

pressure are determinants of HIV-related risky injection practices and

unprotected sexual activity (Des Jarlais et al. 1985; Friedman et al. 1986,

1987; Magura 1989; Mandell et al., in press; Murphy 1987). Ethnographic studies have identified social pressures associated with greater

involvement in drug-using social groups as promoting needle-sharing

behavior (Des Jarlais et al. 1985; Murphy 1987).

One method of studying social influence is through social and personal

network analysis. Analysis of networks allows for the study of both

individuals’ behaviors and their social environments. Investigators have

used social and personal network analysis to examine socialization,

diffusion of innovation, and norm formation (Marsden 1990). Social

networks are theorized to influence members’ behaviors through social

comparison processes, fear of social sanctions, information exchange, and

socialization of new members (Fisher 1988; Fisher and Fisher 1992; Hall

and Wellman 1985). Previous research indicates that social network

characteristics are associated with adoption of health behaviors (Gottlieb

1985; Hunter et al. 1991), buffer against psychological stress, and

differentiate drug use behaviors (Fraser and Hawkins 1984; Hunter et al.

1991).

There is little systematic information on the role of social influence in

adopting or maintaining acquired immunodeficiency syndrome (AIDS)-

related behaviors among IDUs. In the present study, IDUs’ drug-sharing

subnetworks were the focus of intervention and analysis. The study

design included random assignment to an intervention or control group.

METHODS

Recruitment

Respondents were recruited from the AIDS Linked to Intravenous

Experiences (ALIVE) study, a longitudinal study of the natural history of

HIV infection and disease. Approximately 3,000 IDUs in Baltimore

enrolled in ALIVE (Vlahov et al. 1991a, 1991b). The majority of

participants (85 percent) were recruited by word of mouth, which in itself

is a network recruitment technique. At the ALIVE clinic, participants

who were 18 years old and at their regular 6-month followup visit

182
reported that they had shared drugs in the preceding 6 months and had

shared needles were asked to participate in the SAFE study. At the SAFE

clinic, potential participants were again screened for eligibility. Those

who were eligible were interviewed for 1 to 11⁄2 hours on their

background, HIV-risk behaviors, and personal networks.

Before entering the SAFE study, ALIVE participants were given

information on risk reduction and were counseled and tested for HIV.

Many of the ALIVE study participants had already reported HIV-risk

reduction related to injecting practices, yet many injectors continued to

report risky injection practices (Vlahov et al. 1991 c). After the baseline

interview, participants were randomly assigned to a control or experimental condition. Criteria for enrollment included injecting within the

last 6 months, sharing injection equipment, having a drug-sharing

subnetwork, and willingness to bring network members into the clinic

and participate in six group sessions. Individuals who were a member of

the index’s drug network or were isolates or dyads were not eligible for

the intervention.

Participants who were assigned to the experimental condition (called

indexes) were asked to bring in their drug-sharing subnetwork for the

intervention. The individuals with whom they reported that they “did

drugs” and were brought into the clinic were called members of the

index’s drug network. After the random assignment, the indexes met

with one of the group facilitators, who were former drug-dependent

individuals, to receive information about the study and to discuss ways

the indexes could talk to members of their drug-sharing networks about

the intervention and encourage them to participate.

All potential participants were invited to the study on a fully informed

consent basis. They were told that the research staff would provide study

participants with information and the opportunity to practice skills to

prevent contracting and transmitting HIV. The indexes were given a

financial incentive ($25) for bringing at least two members of their drugsharing subnetwork into the clinic.

Measures

All participants were administered a personal network interview, a

demographic survey, and a survey on drug use and HIV-risk behaviors.

The personal network survey asked participants to list, by giving the first

name and the first letter of the last name or nickname, members of their

183
social network. They were first asked to list individuals who could

provide support in the domains of intimate interactions, material

assistance, socializing, physical assistance, positive feedback, and health

information (Mitchell 1982; Mitchell and Trickett 1980). Participants

also were asked to list individuals with whom, in the last 6 months, they

have had sex and shared drugs.

Criteria for Completion

For a discussion session to occur, the index and the majority of the group

members needed to be present. If they did not meet these conditions, the

session was rescheduled. Indexes who attended three or more sessions

were considered to have completed the program. The followup interview

occurred 3 months after completion of the final intervention session.

Participants in the control group were reinterviewed within the same

timeframe.

Intervention

The experimental groups received up to six sessions. Each session was

1% hours in length. The group intervention utilized the self-help peer-led

group techniques and cognitive-behavioral approaches to behavior

change. A primary goal of the intervention was to provide a social

context for the members of drug-sharing networks to choose methods of

reducing their HIV-related drug-injecting and sexual practices and

increase their health-promoting behaviors. The group facilitator

emphasized that each group was to decide how it would address the

issues of HIV and AIDS and how it would support individual members’

decisions to alter their risk behaviors.

The intervention strategy was focused on all network members, identifying the risk of HIV to the drug network as a whole and to each member.

The facilitators, who were former drug users, helped the groups to

identify risky behaviors and make decisions to protect their health. Seven

impediments to HIV/AIDS-risk behavior change were identified: (1)

denial of risk, (2) belief that safe partners can be identified, (3) inadequate information about risk reduction techniques, (4) inadequate

decision-making procedures, (5) lack of detailed behavior-planning skills,

(6) failures in managing relapse, and (7) failure of social network

responses to risky behaviors.

184
The first group session began with a 15-minute videotaped discussion of

HIV/AIDS and the risk of becoming infected from contaminated needles,

followed by a video segment of an IDU with AIDS describing the illness

symptoms. Personal risk information was communicated using strategies

suggested by Slovic (1987) for enhancing meaningfulness and a sense of

personal identification. The sessions were structured to lead from

recognition of the general risk of HIV/AIDS to recognition of personal

risk of infection. The network was then involved in a group decision

process about reducing behavior and preventing relapses for those risky

behaviors that they had already reduced or eliminated. This was followed

by step-by-step planning of safer behaviors and identifying methods by

which the group could monitor and reinforce safer behaviors of each

member.

At each session, the facilitator employed cognitive and/or behavioral

techniques related to drug use and HIV. Role playing of problem

situations was also used. Topics for session discussions included:

1. Cognitive distortions (e.g., denial, avoidance, hopelessness)

regarding use of unhygienic needles;

2. Communication skills, in particular rejecting high-risk injection

practices in a socially acceptable manner;

3. Degree of identity and self-esteem attached to needle sharing;

4. Alternative activities to visiting shooting galleries;

5. Control and management of emotions through cognitive-behavioral

techniques; and

6. Methods of relapse control,

Group members were then asked to list target situations that they felt

might be most likely to interfere with their achieving safer drug use.

They were asked to make a commitment to risk reduction in front of their

peers. Different techniques were discussed for assuring the availability of

hygienic needles, such as purchase of sterile needles or proper use of

alcohol and bleach as disinfectants, and their relative advantages in

different situations. The participants were asked to identify strategies

they would use in different settings. Groups would then discuss potential

impediments to altering the identified risky behaviors and methods of

185
addressing these impediments. Several portions of the counseling

sessions were devoted to assisting participants in developing the skills to

implement their decisions. On completion of the six sessions,

participants were given a certificate of completion.

Analyses

The unit of analysis was the drug-sharing subnetworks, and the number

of “subjects” in the trial was the number of indexes.

RESULTS

Social Networks as a Method of Recruitment

In the first set of analyses the overlap between participants in the ALIVE

and SAFE was examined. Out of 348 network members recruited,

16 percent reported that they were in the ALIVE study. Few network

members reported recent participation in drug treatment programs. Only

10 percent reported that in the last 6 months they had been in a methadone maintenance program; 15 percent reported that within the prior

6 months they had been in a detoxification program, and 5 percent

reported involvement in both types of programs.

In the next analyses, the feasibility of using personal networks as a

method of recruitment was assessed. Of the 189 individuals who had

been randomly assigned as indexes, 70 individuals did not return with

drug network members (table 1). Another 12 indexes brought in only

one member of their drug-sharing network to be interviewed. Thus, of

the 189 assigned indexes, 107 returned with two or more members of

their drug-sharing network, bringing in a total of 348 network members

(table 1).

Drug-Sharing Personal Networks as an Intervention Target

The next analyses examined the study attrition rate. Out of the

189 potential indexes, 78 (41 percent) indexes completed at least one

session, and 66 indexes and 192 network members completed three or

more sessions. The mean and median number of network members in

groups who attended the sessions was three.

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TABLE 1. Number of drug subnetwork members brought into the clinic

by probands in the SAFE study, Baltimore, MD, 1990-1992.

Frequency of Drug Frequency

Subnetwork Members of Probands

Number of

Subnetwork

Members

0

1

2

3

4

5

6

7

8

70 0

12 12

40 80

36 108

19 76

6 30

1 6

4 28

1 8

Total Network

Probands = 189 Members = 348

The followup interview occurred 3 months after the completion of the

sessions. The followup rate was 94 percent for the indexes who had

completed the program and 88 percent for the controls. There was a

significant difference in average size of drug networks between indexes

who completed the program and those who did not (mean of 6.4 versus

5.2, respectively; t = 2.31, df = 1,186, p < 0.05).

Description of Participants

The respondents were predominantly low-income, African-American

males (table 2). The majority had received public assistance within the

last 6 months and were unemployed. Within the last 6 months,

approximately one-third had been homeless. Injectable cocaine and

heroin were the drugs of choice in this sample (table 3).

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TABLE 2. Demographic characteristics and life events at baseline of

IDUs enrolled in the SAFE study, Baltimore, MD,

1991-1992.

Variables Network

Members

Probands

Gender: Male

Race: African-American

Education: > 12th grade

Married

Currently employed

Arrested within last 6 months

Homeless within last 6 months

On public assistance now

75.3 348 85.7 189

98.3 348 97.4 189

45.0 347 42.6 188

4.6 347 4.8 187

11.8 339 10.8 185

30.0 320 29.4 163

29.1 320 41.5 164

62.5 347 60.6 188

Mean and median year of birth 1953 1953

Indexes’ and Drug Subnetwork Members’ Risk Behaviors

To assess the relationship between indexes’ and their drug-sharing

subnetwork’s HIV-risk behaviors, the associations between the index’s

risk behaviors and those of the drug-sharing subnetwork’s were

examined. A network risk level was defined as the group mean,

excluding the index, on each of the risk variables. Each group’s risk

score was then correlated with the index’s score on the five HIV-risk

variables of frequency of injecting cocaine, attending shooting galleries,

always cleaning injection equipment with bleach, frequency of sharing

injection equipment, and number of sharing partners. There were

12 drug-sharing networks that only had 2 members (i.e., the index and

1 network member). As these 12 were dyads, not groups, they were

removed from the analyses. As presented in table 4, there was a strong

188
association between indexes’ risk behaviors and the mean of the drugsharing network. Four out of the five Spearman’s correlation coefficients

were statistically significant.

TABLE 3. Self-reported drug use for the last 6 months at baseline

interview of 189 probands and 348 networks members in the

SAFE study, Baltimore, MD, 1990-1992. *

Variables Probands

(%)

Controls

(%)

Any use of cocaine 93.1

Injecting cocaine once a day

or more 47.6

Any use of heroin 87.3

Injecting heroin once a day

or more 47.1

Use of crack more than

once a month

Always clean used needles

Injected in a shooting gallery

14.3

34.9

29.6

Frequency of sharing needles

Not at all

Less than once a month

Once a month or more

19.8 43.8

24.1 18.3

56.1 47.9

93.5

48.1

86.5

53.9

17.7

54.8

27.6

KEY: * Missing data on four participants,

189
TABLE 4. The association between the probands’ risk behavior and the

mean of the probands’ drug-sharing network on five HIV-risk

behaviors among 107 drag-sharing networks, Baltimore, MD,

1990-1992.

Spearman’s Number of

Correlation Drug-Sharing P

Coefficient Subnetworks

Attending shooting

gallery in prior

6 months 0.24 107 0.007

Frequency of

injecting cocaine 0.25 107 0.004

Always clean used

needles 0.21 107 0.014

Frequency of

sharing injection

equipment 0.06 106 0.273

Number of individuals

with whom share

injection equipment 0.18 102 0.039

DISCUSSION

In this preventive intervention aimed at drug-sharing subnetworks,

significant differences between the control group and the experimental

group in the HIV-related injecting behaviors were found at a 3-month

followup. Differences were found in the HIV-risk behaviors of cleaning

used needles, always carrying bleach, frequency of needle-sharing

partners, and use of shooting galleries (see Latkin et al., in press; Mandell

et al., submitted).

190
As the ALIVE study, from which indexes and controls were recruited,

obtained the majority of their participants by word of mouth, the indexes’

network members were examined to see if they were also ALIVE

participants. The social network approach to recruitment brought into the

clinic a group of 348 IDU network members, few of whom had been in

the ALIVE study. The low percentage of overlap among SAFE and

ALIVE participants suggests that such a social network approach to

recruitment may reach individuals who have not been involved or

exposed to other public health programs. The lack of overlap between

participants in these two studies also may be partially explained by the

turnover rate in drug-sharing subnetworks. Cochran and colleagues

(1990) found a 3-year turnover rate of 23 percent among network ties of

two-parent mothers in Syracuse, NY. The rate of turnover was much

higher (39 percent) for single mothers. The authors suggest that single

mothers in their study were often subject to stressful changes and abrupt

moves that disrupted their social relations. The lives of IDUs are also

subject to numerous stresses that disrupt their social ties, and therefore

IDUs also may have high rates of network turnover.

The network approach to recruitment of IDUs may also have other

applications. By examining the overlap among drug-sharing networks

within and between samples, network analysis could be used to estimate

the number of IDUs in a given geographic area.

The results from this study demonstrate that a social network approach is

a viable method for recruiting and imparting skills relevant to HIV

prevention in IDUs, and perhaps the approach may facilitate enrollment

in treatment programs. The significant statistical association between the

indexes’ levels of risk behavior and the mean risk behavior of the

indexes’ drug-sharing network suggests that high-risk groups of IDUs

may be recruited into preventive interventions by first identifying highrisk individuals and then using these individuals to recruit their drugsharing networks into treatment and prevention programs.

There are several explanations for the significant association between the

groups’ behaviors and the behaviors of the indexes. One explanation is

that of differential association; that is, those who engage in high risk may

associate with other high-risk individuals, or low-risk groups may shun

high-risk individuals. Among adolescents, research on drug use patterns

has verified the relationship between drug usage and the use pattern of

friends (Brooks et al. 1989; Elliott et al. 1985). Several social factors

have been identified as influencing HIV-risk behaviors. Friedman and

191
colleagues (1987) found that perceptions of friends’ HIV-related risky

behaviors were the strongest predictors of behavioral change in IDUs.

Perceptions of friends’ risk behaviors were a stronger determinant of risk

reduction than was knowledge about AIDS, education level, or personal

knowledge of somebody with AIDS. Magura and colleagues (1989) also

report that friends’ attitudes are a strong determinant of needle sharing.

Friedman and colleagues (1986), reporting on an IDU subculture in New

York City, describe the existence of small friendship groups that work

together in pairs or small groups to obtain money, drugs, and protection

against assault. In Baltimore the organized group of IDUs known as

Street Voice, which publishes a monthly newsletter, has emphasized the

importance of a “walking partner” for help and protection on the street

(Street Voice 1993).

Group norms for IDUs are hypothesized to include sharing of injection

equipment. Group norms have been shown to influence members’

behaviors (Myers and Bishop 1970; Newcomb 1958). Group norms have

been characterized as conservative factors, working against the initiation

of new behaviors and may account for the resistance of many IDUs to

adopt HIV-risk protection (Nadler and Fisher, unpublished manuscript).

However, if groups do adopt new norms, it is theorized that the new

norms will maintain themselves and reward members who comply with

the new norms (Zucker 1977).

The authors found that most members of drug-sharing networks returned

as a group to the clinic and completed the program as long as the clinic

was sufficiently flexible in operating hours and rescheduling appointments. The greatest impediment to participation was an index’s inability

to bring in his or her drug-sharing network. However, once the groups

attended the first session, it was highly probable that they would return.

The generalizability of the findings may be limited. Explicit criteria for

enrollment included injecting drugs within the last 6 months and having a

drug-sharing network; implicit criteria were the ability and motivation to

bring members of one’s drug-sharing network into the clinic and to

participate in at least three intervention sessions. This was primarily a

sample of unemployed individuals who were or had been on public

assistance and had a history of incarceration. The cohort was comprised

of volunteers from the ALIVE study. It is plausible that volunteers differ

from those who did not volunteer or those who withdrew from the study.

Type of drug use, age, and duration of acquaintances with drug-sharing

network members also may be factors in the generalizability of these

192
results. As the intervention was intended to capitalize on the social

influence factors occurring in groups, isolates and dyads were not

included in the intervention, further limiting the generalizability of the

results.

In summary, the findings from this study suggest that a personal network

approach may be a powerful method for recruiting IDUs into preventive

interventions and other treatment programs, that drug-sharing personal

networks vary by their level of risk behavior, and that a personal network

approach may be used to alter high-risk behaviors through methods of

social influence. Future research may examine how personal networks

link to form social networks and how social networks could be used for

augmenting HIV prevention.

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ACKNOWLEDGMENTS

This research was supported by grants DA06326 and DA04334 from the

National Institute on Drug Abuse.

AUTHOR

Carl A. Latkin, Ph.D.

Research Associate

Stop AIDS for Everyone (SAFE)

Department of Mental Health

Johns Hopkins University

624 North Broadway

Hampton House

Baltimore, MD 21205

195
Promising Social Network

Research Results and

Suggestions for a Research

Agenda

Samuel R. Friedman

INTRODUCTION

Previous research indicates that social network characteristics and

processes affect human immunodeficiency virus (HIV) risk and

protective behaviors. In a comparison of a “standard” education and

HIV-testing intervention with a social network intervention, which

promoted mutual concern about risk reduction through peer-led group

exercises and discussions among members of drug injector networks,

Latkin and colleagues (1994) have provided preliminary evidence that

those in the social network intervention, compared to the standard

intervention control group, were more likely to have reduced their

frequency of sharing needles and injecting in shooting galleries as well as

always carrying bleach and cleaning their needles before injecting.

Zapka and colleagues (1993) reported that, in an intervention targeting

clients in a short-term residential detoxification program, decrease in the

level of drug use-related HIV-risk behavior was related to reducing the

number of drug-injecting friends in subjects’ social networks. Neaigus

and colleagues (1994) found that the relative proportion of interaction

with drug injectors and with noninjectors in drug injectors’ egocentric

networks was related to syringe sharing, shooting gallery use, renting

syringes, borrowing syringes, and sharing cookers.

The spread of HIV is shaped by the networks of those engaging in risk

behaviors. Klovdahl (1985) has argued that the “structure of a network

has consequences for its individual members and for the network as a

whole, over and above effects of characteristics and behavior of the

individuals involved” (p. 1204). Auerbach and colleagues (1984), in a

study of HIV infection among homosexual men during the early 1980s,

found that a cluster of 40 patients with acquired immunodeficiency

syndrome in 10 cities were linked directly or indirectly (via index

patient 0) through sexual contact. In New York City, HIV and hepatitis

196
B core antibody seroprevalence vary by sociometric network location

(Friedman et al., in press).

The importance of research into the social networks of drug injectors

flows from the above discussion. HIV and other blood-borne pathogens

are, in many cases, transmitted among drug injectors who are acquainted

with each other-that is, through their social networks-and social

influence that affects the extent of risk behavior and of deliberate risk

reduction is exerted by individual or group processes through social

networks.

The chapters in this monograph, as well as the discussions at the

Technical Review on “Social Networks, Drug Abuse, and HIV

Transmission” (August 19-20, 1993), support this initial research

conducted on HIV and social networks. Social network research is a

promising approach for understanding HIV-risk behaviors and

epidemiology among drug injectors. Social network approaches also

offer fruitful lines of research for understanding the spread of other

infectious agents that are transmitted sexually or parenterally. In

addition, social network research may offer important insights into how

to design more effective interventions to reduce the spread of infectious

diseases and, as well, to assist drug users to stop using drugs.

This chapter reviews evidence from prior studies and from the studies in

this volume indicating that social networks are important in the lives and

fates of drug injectors. This chapter also presents some ideas (based on

the work in this volume and on the discussions at the Technical Review)

about some of the research that is needed for: (1) improving the

methodology of research on the social networks and risk networks of

drug users and (2) understanding these networks, their implications, and

the interventions that can be based on them.

EVIDENCE THAT SOCIAL NETWORKS MATTER

More specifically, the chapters presented in this volume provide prima

facie evidence that the social networks to which drug users belong have a

wide variety of impacts on their behaviors and on their likelihood of

becoming infected with HIV, although the evidence here might also be

explained in terms of differential association. As the research on which

these chapters are based matures, the author strongly suspects that the

positive promise of the (mainly) preliminary results reported here will be

197
confirmed. The data presented here suggest but do not prove that drug

injectors’ social networks have effects on a number of key variables, as

discussed in the three following sections.

Who Gets Infected

Rothenberg and colleagues (this volume) show that the seropositive drug

users in Colorado Springs are not members of the core of their large

connected component. Instead, seropositives who are members of this

component are comparatively weakly connected to the rest of the

component, and other seropositive drug injectors are members of smaller

components. They suggest that the low seroprevalence in Colorado

Springs might result from the failure of HIV to be introduced into the

core of the large component. Price and colleagues (this volume), on the

other hand, present egocentric data that suggest that HIV is concentrated

in St. Louis among drug injectors whose close social connections are

exclusively with other drug injectors; they suggest that this concentration

of virus among this “isolated” group of injectors may have reduced viral

spread. The egocentric data available in the St. Louis study do not allow

determination of the sociometric structure of the “isolated” group of drug

injectors, and thus it cannot be ascertained whether the network structure

of this group would be conducive to rapid epidemic spread among this

isolated group. Recent analyses from the New York City group have

shown that (1) members of the Seidman 2-core of a large connected

component are more likely to be infected with HIV and to have hepatitis

B core antibody than are peripheral members of the large connected

component, members of smaller components, or unlinked subjects

(Friedman et al., in press), and (2) that an ethnographically defined core

(whose members interact frequently with each other and who often refer

to each other in conversations as being insiders) has higher HIV seroprevalence than either those injecting drug users (IDUs) with links to core

members (an inner periphery) or an outer periphery who lack such ties

(Curtis et al., in press). The New York City and Colorado Springs

findings at least suggest that the early entry of HIV into the cores of large

components might be an important part of the process whereby HIV

spreads rapidly in some cities.

Williams and colleagues (this volume) study egocentric networks in three

cities-Rio Piedras (Puerto Rico), which has a high seroprevalence rate,

and Houston and Dayton/Columbus, which have low seroprevalence.

They find that network turnover is considerably higher in Rio Piedras, as

is the frequency of drug injecting with network members. The networks

198
in Rio Piedras are more homogeneous, including gender and race/

ethnicity membership. They suggest that homogeneous networks may be

more prone to involve short-term relationships, which seems to be

somewhat at variance with the analysis in Williams and Johnson (1993,

p. 83) and that this higher turnover, in combination with the higher

frequency of reported injections with network members, could lead to

rapid HIV spread among the drug-injecting population.

Neaigus and colleagues (this volume) study risk factors for HIV infection

at an individual level among new injectors. Their findings indicate that

new drug injectors who engage in frequent behavioral risk (such as

injecting speedball more than once a day or injecting in outdoor settings)

and have high-risk egocentric networks are more likely to be infected, as

are new women injectors who have older drug injectors in their networks.

Brunswick and colleagues (this volume) study one particular form of

network tie-sibling relationships. They find that HIV infection is more

likely among subjects who have a sibling who is infected than among

those who do not have an infected sibling. These findings suggest that

perhaps the siblings of infected persons should receive HIV counseling

and testing and other prevention efforts and that family-targeted interventions might be developed. Sets of siblings who were concordantly

seropositive differed both in social background and in (non-HIV-related)

health markers from other sets of siblings, which suggests that contextual

social factors need to be taken into account both in epidemiologic

research and in prevention efforts.

Individual Risk Behaviors

A number of the studies reported in this volume report that network

variables are related to risk behaviors cross-sectionally. In some cases,

regression techniques have been used to study whether these relationships

meet cross-sectional tests for potential causality. Such techniques, of

course, can provide only provisional evidence about causation.

One behavior of considerable importance for HIV spread is injecting with

a syringe others have used. Frey and colleagues (this volume) present

data on 20 African-American male IDUs in methadone treatment and on

85 of 106 of the “most important” persons in their lives. They find that

the drug-using behaviors of methadone clients’ egocentric network members are predictors of the drug-using behaviors of the client, including

syringe sharing. Neaigus and colleagues (this volume) find that the

199
characteristics of subjects’ dyadic relationships with alters are related to

the probability that the subject will inject with a syringe that the alter has

used. Closer relationships (such as those in which ego and alter have

daily contact, have injected together for more than a year, have a “very

close” relationship, or have a sexual relationship) are most likely to

involve such receptive syringe sharing. On the other hand, Williams and

colleagues (this volume) find that in data from three citiesreceptive

syringe sharing is not related to duration of the injection relationship

(although it is more common in relationships that are also sexual relationships). Latkin (this volume) finds that subjects’ frequency of sharing

injection equipment is not associated with that of their egocentric network

members, but that there were significant relationships between the ego’s

scores and the mean scores of his or her network members for (1) the

number of persons they share injection equipment with, (2) shooting

gallery use, (3) cocaine injection frequency, and (4) always cleaning used

needles. Frey and colleagues (this volume), similarly, found that

subjects’ drug use behaviors are associated with those of their egocentric

network members.

Trotter and colleagues (this volume) used ethnography to define four

types of networks: (1) closed long-term groups whose interaction is

focused almost solely on drugs; (2) semiclosed kin networks; (3) semiopen networks based on long-term friendships and sexual partnerships,

with recreational drug use as one of several important foci for group

activities; and (4) open networks where membership is based on

acquaintance or willingness to purchase drugs. Network type was

significantly related to the number of days per month the subject injected,

the proportion of sex acts using condoms (for men-but not for women),

and the proportion of sex acts with drug injectors.

Price and colleagues (this volume) find that the nature of egocentric

networks is associated with a number of sexual risk behaviors, including

the number of sex partners, soliciting sex from commercial sex workers,

working as a commercial sex worker, and ever using condoms. Neaigus

and colleagues (this volume) report that condom use in dyadic relationships is associated with relationship characteristics such as closeness and

with peer norms (see also Friedman et al. 1994).

Intervention Efficacy

In addition, these chapters provide preliminary evidence that interventions that target social networks may be effective in reducing risk

200
behavior. Latkin (this volume) indicates that having drug injectors recruit

their network members to come together in groups for sessions aimed at

furthering risk reduction may be more effective in encouraging needle

cleaning and the carrying of bleach and in discouraging sharing needles

and attending shooting galleries than risk reduction sessions that are

aimed at individual drug injectors in isolation from their networks. Their

network intervention also reached “new people” who had not been

reached by another project in Baltimore from which the index subjects

had been recruited-which suggests that network recruitment for

interventions might be a valuable supplement to other approaches.

Wiebel and colleagues (1993) used ethnographic research techniques to

study networks and to implement an intervention in the streets; this was

associated with declines in both high-risk behavior and in HIV

seroconversion. Moreover, those individuals who stopped “sharing dirty”

due to this network-based intervention were less likely to seroconvert

than those who continued to inject with syringes that others had used

without bleaching them in between.

SETTlNG A RESEARCH AGENDA: METHODOLOGICAL

ISSUES THAT NEED TO BE STUDIED

As a relatively new framework for investigation, social network research

still poses a wide range of different kinds of methodological issues.

Some of these involve basic issues of how networks should be conceived

of and how research on networks should be integrated with other

frameworks; other issues are far more specific.

Networks as Formal Linkage Structures, as Sociocultural

Communities, and as Historically Embedded

Both the conceptions of networks and their operationalization vary

between studies. One notable difference is that the more quantitatively

oriented investigators seem to focus on networks as formal structures of

dyadic linkages among people, whereas the ethnographers see networks

more as groups of persons who interact together in a cultural context. A

third perspective looks at networks as historically embedded and thus as

both changing over time in their own right and being shaped (and helping

to shape) larger scale changes in neighborhoods or even cities. Further

conceptual and methodological development is needed to clarify these

differences, how the different perspectives can strengthen each other, and

how to develop research designs that fruitfully combine the different

201
perspectives. In addition, it will be useful, as studies accumulate, to

commission review articles that attempt to update categorizations of what

kinds of conceptions of “networks” have been developed and which of

these conceptions seems to be most useful for research on problems of

different kinds.

Several studies have combined ethnographic field observation with

survey techniques to study networks (Curtis et al., in press; Neaigus et al.,

this volume; Trotter et al., this volume; Vera et al. 1993). These seem to

offer several possible suggestions for how to combine different

perspectives on networks. First, preliminary ethnography helps in

winning community acceptance and cooperation, in understanding the

social environment so as to be able to plan sampling processes, and in

writing a meaningful interview instrument. Later, ethnographic data

provide cultural and social contexts for the network data, and continuing

ethnography allows understanding of how the drug scene, its environment, and its networks change during the course of the study. In addition, the ethnography provides the opportunity for direct observation of

interactions between network members. Neaigus and colleagues (this

volume) and Trotter and colleagues (this volume), for example, discuss

how one project attempted to use such field observations to confirm and

to supplement the survey-derived data on which subjects were linked to

each other. Further research is needed on how to systematize such field

observation to make it more compatible with survey network assumptions

and also to test out how best to use such field links in modeling networks.

Network Population Dynamics

Surveys of networks among drug users can be viewed as gathering

information about networks that may be “fuzzy” since they contain many

short-term associations that are hard to conceptualize, much less to

measure-although a comparison of the Neaigus and colleagues (this

volume) and Trotter and colleagues (this volume) chapters suggests that

the extent of such short-term associations varies between localities, with

it being possible that there are fewer short-term associations among drug

injectors in small cities than in large ones. In addition, the data that are

gathered about these networks tend to be somewhat “fluffy” (i.e., to

involve only partial reporting of links) due to a combination of reticence

on the part of some subjects to name their associates, subjects’ difficulty

in recalling whom they have interacted with, and the problems resulting

from still-being-developed name-elicitation techniques. A situation of

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“fluffy” data about “fuzzy” networks poses a number of difficult issues

for analysis:

1.

2.

Many links among subjects will not be ascertained. It is possible that

missing data in network studies may make analysis much more

difficult than it does in studies of individual attributes. Some

questions that need to be resolved include: What are the effects of

“missing links” on the ability to understand social influence within

networks? Disease transmission? How are various measures of

distance among specific network members affected by missing links?

One potentially serious problem that needs to be studied is whether

missing links pose the threat of sizable discontinuities in estimates of

key parameters. That is, under what circumstances can one missing

link lead to large-scale errors in parameter estimates rather than small

errors?

A related issue is how to develop techniques that either minimize the

number of missing links or that at least increase the probability that

missing links will have only minimal impacts on parameter estimates.

Methods need to be developed and tested to ascertain the validity and

reliability of network data. Goldstein and colleagues (1993) have

compared what different members of a network say about each other

and their mutual behaviors-which, to some extent, provides some

understanding of the validity of data about the content of network

ties. They conclude that network data about other persons’

demographic characteristics and behaviors-and about mutual

behaviors transacted by a subject and another network member-are

reasonably accurate. Further research is needed to confirm this

finding and, in addition, to further quantify the degree of error in

different kinds of such data.

3. A related issue that needs research is the validity and biases of data

about the existence or lack of ties among specific network members.

What are the impacts of such error on measures of network

properties? Similarly, to what extent are the links that are established

(as compared to those that are missed) socially or statistically

distinct? That is, to what extent is the observed sample of links a

biased one? If it is biased, how do these biases affect results? How

can biases be minimized, or else estimated, and thus controlled for?

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4. The changing nature of networks means that several kinds of

modeling issues need development beyond that incorporated in the

chapter in this volume. Modeling techniques are needed that can

incorporate data about the time duration of linkages among members

or about changes in the content of such linkages (as, for example,

when persons who inject drugs together start-or stop-having sex

together). Such models should be able to help develop understanding

of a number of social processes, including:

(a) the population dynamics of the system of networks as

individuals move across networks. For example, researchers

need to be able to model how the volume of movement across

networks affects the rate of viral spread and also the diffusion

of risk reduction norms and practices. Here, of course, the

models need to be able to incorporate the ways in which

movement of persons among networks is not random but will

more normally involve selective mixing (Morris 1993); and

(b) biographical patterns of movement of drug users and their

nonusing associates as they age and as the drug users become

more experienced users. One issue raised by Neaigus and

colleagues (this volume), for example, is whether women who

begin to inject drugs may enter into the networks of long-term

drug injectors earlier in their drug careers than men do-and

whether they may thus get infected with HIV sooner (see also

Friedman et al. 1993).

Social Settings as Supra-Personal Nodes

Drug users’ networks often include several kinds of social “nodes” that

may be crucial in the transmission of infectious agents (Neaigus et al.

1994). Shooting galleries and other locations in which persons from

different networks may use “anonymous paraphernalia” can provide

settings for the transmission of HIV and other pathogens across networks

without direct face-to-face contact among any of their members.

Network studies should include ways to measure the potential for such

anonymous transmission, a task which is complicated by the following

facts: (1) shooting galleries form and dissolve quite frequently in many

neighborhoods, as do anonymous “outside settings”; and (2) attempts to

elicit precise and unambiguous specifications about the settings at which

a drug user injects involve considerable measurement error due to a

combination of reticence to disclose some locations as well as the

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inherent difficulties of describing social settings that may move in space

on a daily basis.

Sampling

Further research is needed on how to sample the networks of drug users.

Issues include the relative utility of targeted sampling, snowball, and

random walk designs-which may well vary depending both on the

research question and on the city or other location where the study is

being conducted-and the representativeness of data about drug users’

networks. In terms of representativeness, network studies share a

problem that bedevils all research on drug users: the impossibility of

developing a method to draw a random sample of drug users in the first

place. Thus, given present research methods, researchers are unable to

draw a random sample of “initial index cases.” To understate the

problem considerably, this poses difficulty for the statistical characterization of the nominees of the index cases, and then of the nominees of

the nominees; likewise, this poses difficulty for the extent to which the

measured characteristics of large-scale drug-user networks can be said to

be representative of those in a neighborhood or city drug scene. On the

other hand, the author suggests that the concern expressed by Price and

colleagues (this volume) about the need for special statistical treatment in

using dyadic relationships obtained from egocentric data might lead

researchers to needlessly eschew important analyses. Given the lack of

probability sampling of index cases in network studies, the reasons to be

concerned about nonrandom selection of alters may be of secondary

importance.

Boundaries-Who Should Be Studied?

A related issue is how to study network boundaries. Drug injectors and

other drug users have social ties with nonusers, and these ties can have

important influences on all parties. For what kinds of studies should

researchers concentrate on the characteristics of the networks of drug

users with each other, with their sexual partners, or both-that is, with

persons likely to be in their risk networks-and for what kinds of studies

should researchers investigate their total social networks (including,

therefore, the nature and implications of ties between drug users and other

persons)? Most of the studies discussed in this volume have focused on

samples of drug users alone. In a study of the social networks of young

adolescents in high-risk neighborhoods, on the other hand, Lovely and

colleagues (1993) took a different approach. Starting with census tract205
delimited area probability samples of youth (aged 11-15) in Atlanta and

San Juan, Puerto Rico, additional subjects (11-29 years of age) were

sampled by random selection from lists they provided of their social

contacts, including those with whom they had sex or engaged in drug use.

The random walk continued with these additional subjects (“second

nodes”) being asked similar questions, a random selection being made

among their contacts to pick a third subject; and, then, a fourth node was

similarly selected and interviewed. Data were collected about their

relationships and behaviors together. These data were collected, in part,

in order to assess whether the structure of subjects’ sociometric networks

was related to their risk behaviors, with an underlying perspective that

social networks are vehicles for social influence that can shape and

change both peer norms and individual beliefs, values, and behaviors

(Fischer et al. 1992). For prevention purposes, they suggest that studying

social networks in high-risk neighborhoods provides data on how to

shape risk behaviors by understanding and changing social influences.

There are benefits and disadvantages to studying risk networks without

broader social networks. The benefits inhere largely in being able to

concentrate on the dyadic relationships in which HIV and other bloodborne or sexually transmitted infectious agents can be transmitted. The

disadvantage to this approach is that patterns of social influence are not

fully ascertained and that risk networks are by and large only a subset of

the total web of relationships in which their members are enmeshed.

Focusing on social networks will capture most of what is studied by

focusing on risk networks1, but unless the sampling design ensures that

risk network members are selected for interview, important epidemiologic

data may be lost.

Issues of Categorization in Analysis

Turning now to methods of analysis, network data pose additional

questions for methodological research. One of these involves methods of

categorization of networks, ways of separating out sections of networks

in useful ways, and ways of describing individual locations in sociometric

networks (Friedman et al., in press; Klovdahl 1985; Knoke and Kuklinski

1982; Rothenberg et al., this volume; Scott 1991; Seidman 1983;

Seidman and Foster 1978; Stephenson and Zelen 1989; Trotter et al., this

volume). Examples of ways to define subsets of networks include both

those relying on formal network properties (e.g., groups composed of

members, all of whom name each other as contacts) and others that are

based on more substantive grounds (e.g., women as a subset, or drug

206
users who inject with anonymous works at a particular shooting gallery).

The issue, then, is to find typologies that help researchers to detect and

explain differences in viral transit times within sections of networks,

across sections of networks, and among separate networks; to determine

the relative risks of persons in different sections and in different

categories of networks; and to determine which categorizations allow

most meaningfully the study of differences in beliefs, behaviors, peer

cultures, multiplexity of ties, and solidarity among drug users.

Statistical Issues

Network data also pose questions for statistical modeling. First, if data

on large-scale network structures are developed from data provided by

subjects about their nominees and then nominees are interviewed (or

perhaps multiple levels of nominees), it is clear that the set of persons

studied cannot in any meaningful way be viewed as statistically

independent. Furthermore, there are several levels of analysis, and

researchers have to develop ways to model the influence of these different

levels upon each other. To mention some relatively easy examples, how

do researchers model data about subjects as functions of data about the

set of their nominees? Or as functions of data about the sections of largescale networks to which they belong? How do researchers model the

relationships of egocentric network properties to sociometric network

properties-particularly since egocentric data often are available for all

the alters whom the ego names (and about the characteristics of their

social and risk relationships)-whereas sociometric data in communitybased studies typically are available only (Friedman et al., in press) or

primarily (Klovdahl et al. 1994; Rothenberg et al., this volume) for egos

and alters who are interviewed.

Human Subjects Issues

Woodhouse and colleagues (this volume) raise a number of issues

important to network researchers. They start with a fundamental point:

All research designs in HIV epidemiologic research require compromise

among ethical principles. In network research, these difficulties may be

magnified by the existence of conflicting potential threats and benefits to

different social entities: (1) the individual research participant; (2) the

persons whom participants name as “alters” who are not themselves (yet)

participants; (3) the social groups whose activities, interrelationships, and

other interests might be enhanced or hurt by the research (even though

the ethical rights of such social groups-particularly among drug

207
injectors-might not be recognized by current human subjects laws); and

(4) those individuals and groups who might benefit or lose from the new

scientific knowledge generated by the project.

One of the issues Woodhouse and colleagues (this volume) discuss is the

ethical and legal “duty to warn.” Given the uncertainty about how courts

will rule in this evolving area of case law, the authors are somewhat

limited in what they conclude. “Duty to warn” issues pose a serious

dilemma for network research in which data on HIV or other infectious

agents are collected, since this research simultaneously provides

information about which participants are likely to be infectious and about

with whom they engage in high-risk drug injection and sex. It may be

useful to ruminate briefly about the implications of this fact for several

kinds of network research.

If the research team retains identifiers for a given participant (the ego),

then they are likely to know: (1) certain egos are infectious; (2) certain

alters are uninfected; and (3) which seronegative alters a given seropositive ego engages in high-risk behaviors with. Thus, the researcher is

confronted with a situation in which failure to warn such alters of their

specific threats from having unprotected sex with a named ego, or from

sharing injection equipment with that ego, might put the alter’s life in

danger. (Although this situation can be mitigated by general warnings to

both ego and alter to avoid high-risk activity and may be ambiguous

because the alter may engage in high-risk behaviors with other potentially

infected persons, there may nonetheless be a legal or ethical liability from

failure to warn.)

On the other hand, specific disclosure is also dangerous. The alter may

blame the ego if both are seropositive, and seronegative alters may blame

seropositive egos for endangering their lives. This may disrupt marriages, friendships, or other valuable social relationships. In a group

context, it may lead to splits or feuds; such feuds might lead to potentially

fatal violence (particularly, perhaps, if splits disrupt existing drug-dealing

organizations or if some members of the network are either emotionally

unstable or become so as a result of the interaction of stress and drugtaking patterns.) Thus, disclosure could lead to fatal consequences.

Considerable discussion occurred at the Technical Review meeting on

which this monograph is based about the implications of the evolving

case law on “duty to warn.” Proponents of contact notification argued

that if researchers know that somebody is infected and that they have

208
relationships with others that might transmit HIV to these others, the

investigator should ensure that these others are notified that they are at

risk. Other participants argued that confidentiality guarantees around

HIV testing at least provided considerable public-health benefit that

would be lost if subjects knew that their contacts would learn that they

are infected. Several other research issues were raised:

1.

2.

Whether the knowledge that contacts would be notified might lead

some potential participants to refuse to take part (sample bias), lead

some subjects to reduce the extent to which they would name their

contacts, or lead subjects to name some kinds of contacts but not

others (linkage bias). This could lead both to major underestimation

of the size and density of networks and perhaps to serious biases in

these data. For example, if drug injectors were to be more concerned

about the repercussions of such contact notification than other

subjects in a study of persons at risk of HIV through different modes

of transmission, researchers might be led to methodologically

induced “findings” that drug injectors are of low centrality and have

small egocentric network sizes and that their networks are sparse

rather than dense, even if this is not the case in reality.

In addition, such duty to warn might make the cost of network

studies prohibitive in locales such as New York and Puerto Rico

where a considerable proportion of drug injectors are HIV infected.

For example, if a study samples 500 drug injectors, 250 of whom are

HIV-seropositive, these 250 persons might nominate 1,000 or more

other persons as people with whom they have injected drugs or had

sex in the last 30 days (and many more if the naming prompt is for

6 months or longer). The resources required to locate these persons,

confirm that they are indeed the persons nominated, and then to

provide them with appropriate contact notification messages (and,

presumably, with HIV counseling and testing if they want it) would

probably surpass the costs of gathering study data many times over.

Clearly, social and risk network research on HIV poses many difficult

issues about the interactions of ethics, legal requirements, and research.

At the present time, researchers do not yet know enough about how

different network research designs affect the different levels of social

entities that are involved in research to provide fully adequate guidance

about the necessary tradeoffs. Thus, one priority for the National

Institutes of Health should be to fund research that addresses the

consequences for human subjects of different network research designs.

209
SETTING A RESEARCH AGENDA: SUBSTANTIVE

QUESTIONS TO BE RESEARCHED

The chapters in this volume present data that indicate that the fate of a

drug injector is shaped in many ways by the network to which she or he

belongs. This monograph, however, just begins what may well become

an extensive body of research. The chapters open up areas for research

that need considerable further development before researchers can answer

with assurance any of the following key questions:

1. One set of key questions concerns further specification of the effects

of networks in terms of how the structure or content of drug injectors’

networks of or their links to non-IDUs in a city or neighborhood,

perhaps in interaction with, or mediated by, their personal

characteristics or neighborhood characteristics, affect:

(a) The rate and patterns of spread of HIV and other parenterally

and sexually transmitted infectious agents.

(b) The rate and patterns of spread of tuberculosis.

(c) The extent to which these agents spread outside of the

immediate neighborhood; for example, how do the network

structures of IDUs in New York City affect the patterns of HIV

seroprevalence among youth in the suburbs?

(d) The kinds and extent of risk reduction efforts by individual

IDUs.

(e) The lives of drug injectors who become infected with HIV. Do

networks affect their overall quality of life? The extent and

kind of social or material support they receive from other drug

injectors or other persons? As their disease progresses, do the

characteristics of their network affect whether the network

abandons them or becomes an increasing source of support?

How do the processes discussed in this paragraph affect the

extent to which they can resist or avoid opportunistic infections

and death?

(f) The ability of IDUs to work together collectively in drug users’

organizations to prevent or retard the spread of HIV or to care

for those who become infected. Here, network characteristics

210
2.

parallel the kinds of variables that resource mobilization theory

suggests are important determinants of the extent to which

people can mobilize for collective action around shared needs

(Gamson 1975; Lo 1992; Morris 1984; Tilly 1978), although

efforts to establish such organizations also need to incorporate

theories that make solidarity, strategy, and micromobilization

problematic (Friedman and Des Jarlais 1993; Gamson 1992).

(g) The extent to which IDUs know about, seek, and use medical

services to obtain treatment for HIV-related or other medical

problems.

(h) The extent to which drug users know about, seek, and enter

drug abuse treatment.

(i) The success rates of drug abuse treatment (as networks provide

support or opposition to clients’ efforts to stop using drugs, or

as networks differ in their hold on clients’ loyalties).

(j) The recruitment of new persons to use drugs and to inject

drugs.

(k) The degree of stability in the drugs that are used in a local area.

That is, are some network patterns more prone to take up new

drug fads and others more resistant?

Networks can also be investigated as dependent variables. Several

major areas for research in this regard include:

(a) How do the networks of men and women who inject drugs

differ? Of new and long-term injectors? Of drug-injecting

members of different racial/ethnic groups? To what extent do

these different networks overlap?

(b) How does knowledge of a drug injector’s HIV serostatus affect

his or her network?

(c) What variables affect the rate of formation and disappearance

of drug injector networks in a neighborhood or city? The rate

at which individuals move between networks?

211
(d) How are the various formal and substantive characteristics of

network structure or content associated with each other?

3. To the extent that networks affect behaviors or the probability that a

given level of taking risks leads to infection by a pathogen, interventions that target networks may have positive effects. Latkin (this

volume) and Wiebel and colleagues (1993) present evidence that

such interventions can be efficacious. Network interventions might

target networks at the level of the personal networks of individuals,

neighborhood network structures, or city network structures. Further

research and development are needed to design and evaluate

network-based interventions to:

(a) Decrease individual risk.

(b) Change network structures or population dynamics in ways that

should reduce viral transmission.

(c) Change the cultures of networks. Ways to do this might

include: introducing positive role models from outside the

network; promoting communication and normative

reinforcement for risk reduction within a network; or

encouraging the formation of formal organizations of some or

all network members (perhaps with memberships that cross

network boundaries) that could try to change their beliefs,

norms, and practices in ways that would reduce risks.

Research should also consider what level of network or social processes

that embed the network should be the target of intervention. That is, for

each of the three kinds of outcomes just discussed, is the appropriate

target for the intervention the individual? The individual’s risk network?

The individual’s social network? The neighborhood network? The local

drug culture? Large-scale characteristics of local urban development or

policy (such as police patterns or gentrification that disrupt existing drug

users’ networks-and therefore, perhaps, lead pathogens to be spread to

new neighborhoods or weaken the strength of preexisting network

controls over drug injectors’ behavior)? Further discussion of network

intervention issues appears in Friedman and colleagues (in press).

4. Very little is currently known about the extent of variation in drug

injectors’ networks across cities or about how these affect the rates of

spread of HIV and other agents. Egocentric data could be collected

212
in conjunction with other research projects and, perhaps, in a

relatively routine way by drug abuse treatment clinics. Data on larger

scale network structures might require more resources. If they can be

obtained, baseline and followup data on large-scale and egocentric

network structures, peer cultures, and larger social contexts in a large

number of cities could be useful for understanding both the spread of

pathogens among drug injectors and from them to other persons, as

well as how local drug scenes respond to the threat of HIV and,

indeed, why patterns of drug use and drug treatment outcomes vary

across cities. The payoff for such research is hard to calculate at this

time, but clearly there is sufficient potential for fundamentally new

interventions to make it well worth considering.

NOTE

1. One complication should be mentioned: To the extent to which

infectious agents are transmitted through injecting with syringes or

other anonymous equipment at shooting galleries or other

multiperson settings, probes to elicit social network members may

well fail to elicit parts of the risk networks. The complexities of

“anonymous settings” such as these (or locations where anonymous

sex may occur, whether in crack houses, commercial sex work, or

bathhouses) require further study and the development of additional

research techniques. See Neaigus and colleagues (1994) for further

discussion.

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AUTHOR

Samuel R. Friedman, Ph.D.

Principal Investigator

National Development and Research Institutes, Inc.

11 Beach Street

New York. NY 10013

215
National

Institute on

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THE BEHAVIORAL ASPECTS OF SMOKING.

Norman A. Krasnegor, Ph.D., ed. (Reprint from 1979 Surgeon General’s

Report on Smoking and Health.)

NCADI#M26 NTIS PB #80-118755/AS (A09) $27.00

THE ANALYSIS OF CANNABINOIDS IN BIOLOGICAL FLUIDS.

Richard L. Hawks, Ph.D., ed.

NCADI #M42 NTIS PB #83-136044/AS (A07) $27.00

COCAINE: PHARMACOLOGY, EFFECTS, AND TREATMENT OF

ABUSE. John Grabowski, Ph.D., ed.

NCADI #M50 NTIS PB #85-150381/AS (A07) $27.00

TESTING DRUGS FOR PHYSICAL DEPENDENCE POTENTIAL

AND ABUSE LIABILITY. Joseph V. Brady, Ph.D., and

Scott E. Lukas, Ph.D., eds.

NCADI #M52 NTIS PB #85-150373/AS (A08) $27.00

PHARMACOLOGICAL ADJUNCTS IN SMOKING CESSATION.

John Grabowski, Ph.D., and Sharon M. Hall, Ph.D., eds.

NCADI #M53 NTIS PB #89-123186/AS (A07) $27.00

MECHANISMS OF TOLERANCE AND DEPENDENCE.

Charles Wm. Sharp, Ph.D., ed.

NCADI #M54 NTIS PB #89-103279/AS (A19) $52.00

ETIOLOGY OF DRUG ABUSE: IMPLICATIONS FOR

PREVENTION. Coryl LaRue Jones, Ph.D., and

Robert J. Battjes, D.S.W., eds.

NCADI #M56 NTIS PB #89-123160/AS (A13) $36.50

COCAINE USE IN AMERICA: EPIDEMIOLOGIC AND CLINICAL

PERSPECTIVES. Nicholas J. Kozel, M.S., and

Edgar H. Adams, M.S., eds.

NCADI #M61 NTIS PB #89-131866/AS (A11) $36.50

NEUROSCIENCE METHODS IN DRUG ABUSE RESEARCH.

Roger M. Brown, Ph.D., and David P. Friedman, Ph.D., eds.

NCADI #M62 NTIS PB #89-130660/AS (A08) $27.00

PREVENTION RESEARCH: DETERRING DRUG ABUSE AMONG

CHILDREN AND ADOLESCENTS. Catherine S. Bell, M.S., and

Robert J. Battjes, D.S.W., eds.

NCADI #M63 NTIS PB #89-103287/AS (A11) $36.50

PHENCYCLIDINE: AN UPDATE. Doris H. Clouet, Ph.D., ed.

NCADI #M64 NTIS PB #89-131858/AS (A12) $36.50

WOMEN AND DRUGS: A NEW ERA FOR RESEARCH.

Barbara A. Ray, Ph.D., and Monique C. Braude, Ph.D., eds.

NCADI #M65 NTIS PB #89-130637/AS (A06) $27.00

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OPIOID PEPTIDES: MEDICINAL CHEMISTRY.

Rao S. Rapaka, Ph.D.; Gene Bamett, Ph.D.; and

Richard L. Hawks, Ph.D., eds.

NCADI #M69 NTIS PB #89-158422/AS (A17) $44.50

OPIOID PEPTIDES: MOLECULAR PHARMACOLOGY,

BIOSYNTHESIS, AND ANALYSIS. Rao S. Rapaka, Ph.D., and

Richard L. Hawks, Ph.D., eds.

NCADI #M70 NTIS PB #89-158430/AS (A18) $52.00

RELAPSE AND RECOVERY IN DRUG ABUSE.

Frank M. Tims, Ph.D., and Carl G. Leukefeld, D.S.W., eds.

NCADI #M72 NTIS PB #89-151963/AS (A09) $36.50

NEUROBIOLOGY OF BEHAVIORAL CONTROL IN DRUG ABUSE.

Stephen I. Szara, M.D., D.Sc., ed.

NCADI #M74 NTIS PB #89-151989/AS (A07) $27.00

THE ROLE OF NEUROPLASTICITY IN THE RESPONSE TO

DRUGS. David P. Friedman, Ph.D., and Doris H. Clouet, Ph.D., eds.

NCADI #M78 NTIS PB #88-245683/AS (A10) $36.50

STRUCTURE-ACTIVITY RELATIONSHIPS OF THE

CANNABINOIDS. Rao S. Rapaka, Ph.D., and

Alexandros Makriyannis, Ph.D., eds.

NCADI #M79 NTIS PB #89-109201/AS (A10) $36.50

NEEDLE SHARING AMONG INTRAVENOUS DRUG ABUSERS:

NATIONAL AND INTERNATIONAL PERSPECTIVES.

Robert J. Battjes, DSW., and Roy W. Pickens, Ph.D., eds.

NCADI #M80 NTIS PB #88-236138/AS (A09) $36.50

OPIOIDS IN THE HIPPOCAMPUS. Jacqueline F. McGinty, Ph.D., and

David P. Friedman, Ph.D., eds.

NCADI #M82 NTIS PB #88-245691/AS (A06) $27.00

HEALTH HAZARDS OF NITRITE INHALANTS.

Harry W. Haverkos, M.D., and John A. Dougherty, Ph.D., eds.

NCADI #M83 NTIS PB #89-125496/AS (A06) $27.00

LEARNING FACTORS IN SUBSTANCE ABUSE.

Barbara A. Ray, Ph.D., ed.

NCADI #M84 NTIS PB #89-125504/AS (A10) $36.50

EPIDEMIOLOGY OF INHALANT ABUSE: AN UPDATE.

Raquel A. Crider, Ph.D., and Beatrice A. Rouse, Ph.D., eds.

NCADI #M85 NTIS PB #89-123178/AS (A10) $36.50

OPIOID PEPTIDES: AN UPDATE. Rao S. Rapaka, Ph.D., and

Bhola N. Dhawan, M.D., eds.

NCADI #M87 NTIS PB #89-158430/AS (A11) $36.50

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MECHANISMS OF COCAINE ABUSE AND TOXICITY.

Doris H. Clouet, Ph.D.; Khursheed Asghar, Ph.D.; and

Roger M. Brown, Ph.D., eds.

NCADI #M88 NTIS PB #89-125512/AS (A16) $44.50

BIOLOGICAL VULNERABILITY TO DRUG ABUSE.

Roy W. Pickens, Ph.D., and Dace S. Svikis, B.A., eds.

NCADI #M89 NTIS PB #89-125520/AS (A09) $27.00

TESTING FOR ABUSE LIABILITY OF DRUGS IN HUMANS.

Marian W. Fischman, Ph.D., and Nancy K. Mello, Ph.D., eds.

NCADI #M92 NTIS PB #90-148933/AS (A17) $44.50

PHARMACOLOGY AND TOXICOLOGY OF AMPHETAMINE AND

RELATED DESIGNER DRUGS. Khursheed Asghar, Ph.D., and

Errol De Souza, Ph.D., eds.

NCADI #M94 NTIS PB #90-148958/AS (A16) $44.50

PROBLEMS OF DRUG DEPENDENCE, 1989. PROCEEDINGS OF

THE 51st ANNUAL SCIENTIFIC MEETING. THE COMMITTEE ON

PROBLEMS OF DRUG DEPENDENCE, INC.

Louis S. Harris, Ph.D., ed.

NCADI #M95 NTIS PB #90-237660/AS (A99) $67.00

DRUGS OF ABUSE: CHEMISTRY, PHARMACOLOGY,

IMMUNOLOGY, AND AIDS. Phuong Thi Kim Pham, Ph.D., and

Kenner Rice, Ph.D., eds.

NCADI #M96 NTIS PB #90-237678/AS (A11) $36.50

NEUROBIOLOGY OF DRUG ABUSE: LEARNING AND MEMORY.

Lynda Erinoff, Ph.D., ed.

NCADI #M97 NTIS PB #90-237686/AS (A11) $36.50

THE COLLECTION AND INTERPRETATION OF DATA FROM

HIDDEN POPULATIONS. Elizabeth Y. Lambert, M.S., ed.

NCADI #M98 NTIS PB #90-237694/AS (A08) $27.00

RESEARCH FINDINGS ON SMOKING OF ABUSED SUBSTANCES.

C. Nora Chiang, Ph.D., and Richard L. Hawks, Ph.D., eds.

NCADI #M99 NTIS PB #91-141119 (A09) $27.00

DRUGS IN THE WORKPLACE: RESEARCH AND EVALUATION

DATA. VOL II. Steven W. Gust, Ph.D.; J. Michael Walsh, Ph.D.;

Linda B. Thomas, B.S.; and Dennis J. Crouch, M.B.A., eds.

NCADI #M100 GPO Stock #017-024-01458-3 $8.00

RESIDUAL EFFECTS OF ABUSED DRUGS ON BEHAVIOR.

John W. Spencer, Ph.D., and John J. Boren, Ph.D., eds.

NCADI #M101 NTIS PB #/91-172858/AS (A09) $27.00

ANABOLIC STEROID ABUSE. Geraline C. Lin, Ph.D., and

Lynda Erinoff, Ph.D., eds.

NCADI #M102 NTIS PB #91-172866/AS (A11) $36.50

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IMPROVING DRUG ABUSE TREATMENT. Roy W. Pickens, Ph.D.;

Carl G. Leukefeld, D.S.W.; and Charles R. Schuster, Ph.D., eds.

NCADI #M106 NTIS PB #92-105873(A18) $50.00

DRUG ABUSE PREVENTION INTERVENTION RESEARCH:

METHODOLOGICAL ISSUES. Carl G. Leukefeld, D.S.W., and

William J. Bukoski, Ph.D., eds.

NCADI #M107 NTIS PB #92-160985 (A13) $36.50

CARDIOVASCULAR TOXICITY OF COCAINE: UNDERLYING

MECHANISMS. Pushpa V. Thadani, Ph.D., ed.

NCADI #M108 NTIS PB #92-106608 (A11) $36.50

LONGITUDINAL STUDIES OF HIV INFECTION IN INTRAVENOUS

DRUG USERS: METHODOLOGICAL ISSUES IN NATURAL

HISTORY RESEARCH. Peter Hartsock, Dr.P.H., and

Sander G. Genser, M.D., M.P.H., eds.

NCADI #M109 NTIS PB #92-106616 (A08) $27.00

MOLECULAR APPROACHES TO DRUG ABUSE RESEARCH:

RECEPTOR CLONING, NEUROTRANSMITTER EXPRESSION, AND

MOLECULAR GENETICS: VOLUME I. Theresa N.H. Lee, Ph.D., ed.

NCADI #M111 NTIS PB #92-135743 (A10) $36.50

EMERGING TECHNOLOGIES AND NEW DIRECTIONS IN DRUG

ABUSE RESEARCH. Rao S. Rapaka, Ph.D.;

Alexandros Makriyannis, Ph.D.; and Michael J. Kuhar, Ph.D., eds.

NCADI #M112 NTIS PB #92-155449 (A15) $44.50

ECONOMIC COSTS, COST EFFECTIVENESS, FINANCING, AND

COMMUNITY-BASED DRUG TREATMENT.

William S. Cartwright, Ph.D., and James M. Kaple, Ph.D., eds.

NCADI #M113 NTIS PB #92-155795 (A10) $36.50

METHODOLOGICAL ISSUES IN CONTROLLED STUDIES ON

EFFECTS OF PRENATAL EXPOSURE TO DRUG ABUSE.

M. Marlyne Kilbey, Ph.D., and Khursheed Asghar, Ph.D., eds.

NCADI #M114 NTIS PB #92-146216 (A16) $44.50

METHAMPHETAMINE ABUSE: EPIDEMIOLOGIC ISSUES AND

IMPLICATIONS. Marissa A. Miller, D.V.M., M.P.H., and

Nicholas J. Kozel, M.S., eds.

NCADI #M115 NTIS PB #92-146224/ll (AO7) $27.00

DRUG DISCRIMINATION: APPLICATIONS TO DRUG ABUSE

RESEARCH. R.A. Glennon, Ph.D.; Toubjörn U.C. Järbe, Ph.D.; and

J. Frankenheim, Ph.D., eds.

NCADI #M116 NTIS PB #94-169471 (A20) $52.00

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METHODOLOGICAL ISSUES IN EPIDEMIOLOGY, PREVENTION,

AND TREATMENT RESEARCH ON DRUG-EXPOSED WOMEN

AND THEIR CHILDREN. M. Marlyve Kilbey, Ph.D., and

Kursheed Asghar, Ph.D., eds.

NCADI #M117

GPO Stock #017-024-01472-9 $12.00

NTIS PB #93-102101/LL (A18) $52.00

DRUG ABUSE TREATMENT IN PRISONS AND JAILS.

Carl G. Leukefeld, D.S.W., and Frank M. Tims, Ph.D., eds.

GPO Stock #017-024-01473-7 $16.00

NCADI #M118 NTIS PB #93-102143/LL (A14) $44.50

BIOAVAILABILITY OF DRUGS TO THE BRAIN AND THE

BLOOD-BRAIN BARRIER. Jerry Frankenheim, Ph.D., and

Roger M. Brown, Ph.D., eds.

NCADI #M120

GPO Stock #017-024-01481-8 $10.00

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BUPRENORPHINE: AN ALTERNATIVE TREATMENT FOR

OPIOID DEPENDENCE. Jack D. Blaine, Ph.D., ed.

GPO Stock #017-024-01482-6 $5.00

NCADI #M121 NTIS PB #93-129781/LL (A08) $27.00

ACUTE COCAINE INTOXICATION: CURRENT METHODS OF

TREATMENT. Heinz Sorer, Ph.D., ed.

GPO Stock #017-024-01501-6 $6.50

NCADI #M123 NTIS PB #94-115433/LL (A09) $27.00

NEUROBIOLOGICAL APPROACHES TO BRAIN-BEHAVIOR

INTERACTION. Roger M. Brown, Ph.D., and

Joseph Fracella, Ph.D., eds.

GPO Stock #017-024-01492-3 $9.00

NCADI #M124 NTIS PB #93-203834/LL (A12) $36.50

ACTIVATION OF IMMEDIATE EARLY GENES BY DRUGS OF

ABUSE. Reinhard Grzanna, Ph.D., and Roger M. Brown, Ph.D., eds.

GPO Stock #017-024-01503-2 $7.50

NCADI #M125 NTIS PB #94-169489 (A12) $36.50

MOLECULAR APPROACHES TO DRUG ABUSE RESEARCH

VOLUME II: STRUCTURE, FUNCTION, AND EXPRESSION.

Theresa N.H. Lee, Ph.D., ed.

NCADI #M126 NTIS PB #94-169497 (A08) $27.00

PROGRESS AND ISSUES IN CASE MANAGEMENT.

Rebecca Sager Ashery, D.S.W., ed.

NCADI #M127 NTIS PB #94-169505 (A18) $52.00

STATISTICAL ISSUES IN CLINICAL TRIALS FOR TREATMENT

OF OPIATE DEPENDENCE. Ram B. Jain, Ph.D., ed.

NCADI #M128 NTIS PB #/93-203826/LL (A09) $27.00

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INHALANT ABUSE: A VOLATILE RESEARCH AGENDA. Charles

W. Sharp, Ph.D.; Fred Beauvais, Ph.D., and Richard Spence, Ph.D., eds.

GPO Stock #017-024-01496-6 $12.00

NCADI #M129 NTIS PB #93-183119/LL (A15) $44.50

DRUG ABUSE AMONG MINORITY YOUTH: ADVANCES IN

RESEARCH AND METHODOLOGY. Mario De La Rosa, Ph.D., and

Juan-Luis Recio Adrados, Ph.D., eds.

GPO Stock #017-024-01506-7 $14.00

NCADI #M130 NTIS PB #94-169513 (A15) $44.50

IMPACT OF PRESCRIPTION DRUG DIVERSION CONTROL

SYSTEMS ON MEDICAL PRACTICE AND PATIENT CARE.

James R. Cooper, Ph.D.; Dorynne J. Czechowicz, M.D.;

Stephen P. Molinari, J.D., R.Ph.; and Robert C. Peterson, Ph.D., eds.

GPO Stock #017-024-01505-9 $14.00

NCADI #M131 NTIS PB #94-169521 (A15) $44.50

PROBLEMS OF DRUG DEPENDENCE, 1992: PROCEEDINGS OF

THE 54TH ANNUAL SCIENTIFIC MEETING OF THE COLLEGE ON

PROBLEMS OF DRUG DEPENDENCE. Louis Harris, Ph.D., ed.

GPO Stock #017-024-01502-4 $23.00

NCADI #M132 NTIS PB #94-115508/LL (A99)

SIGMA, PCP, AND NMDA RECEPTORS. Errol B. De Souza, Ph.D.;

Doris Clouet, Ph.D., and Edythe D. London, Ph.D., eds.

NCADI #M133 NTIS PB #94-169539 (A12) $36.50

MEDICATIONS DEVELOPMENT: DRUG DISCOVERY,

DATABASES, AND COMPUTER-AIDED DRUG DESIGN.

Rao S. Rapaka, Ph.D., and Richard L. Hawks, Ph.D., eds.

GPO Stock #017-024-01511-3 $11.00

NCADI #M134 NTIS PB #94-169547 (A14) $44.50

COCAINE TREATMENT: RESEARCH AND CLINICAL

PERSPECTIVES. Frank M. Tims, Ph.D., and

Carl G. Leukefeld, D.S.W., eds.

GPO Stock #017-024-01520-2 $11.00

NCADI #M135 NTIS PB #94-169554 (A13) $36.50

ASSESSING NEUROTOXICITY OF DRUGS OF ABUSE.

Lynda Erinoff, Ph.D., ed.

GPO Stock #017-024-01518-1 $11.00

NCADI #M136 NTIS PB #94-169562 (A13) $36.50

BEHAVIORAL TREATMENTS FOR DRUG ABUSE AND

DEPENDENCE. Lisa Simon Onken, Ph.D.; Jack D. Blaine, M.D., and

John J. Boren, Ph.D., eds.

NCADI #M137

GPO Stock #017-024-01519-9 $13.00

NTIS PB #94-169570 (A15) $44.50

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IMAGING TECHNIQUES IN MEDICATIONS DEVELOPMENT:

CLINICAL AND PRECLINICAL ASPECTS. Heinz Sorer, Ph.D., and

Rao S. Rapaka, Ph.D., eds.

NCADI #M138

SCIENTIFIC METHODS FOR PREVENTION INTERVENTION

RESEARCH. Arturo Cazares, M.D., M.P.H., and

Lula A. Beatty, Ph.D., eds.

NCADI #M139

PROBLEMS OF DRUG DEPENDENCE, 1993: PROCEEDINGS OF

THE 55TH ANNUAL SCIENTIFIC MEETING, THE COLLEGE ON

PROBLEMS OF DRUG DEPENDENCE, INC. VOLUME I: PLENARY

SESSION SYMPOSIA AND ANNUAL REPORTS.

Louis S. Harris, Ph.D., ed.

NCADI #M140

PROBLEMS OF DRUG DEPENDENCE, 1993: PROCEEDINGS OF

THE 55TH ANNUAL SCIENTIFIC MEETING, THE COLLEGE ON

PROBLEMS OF DRUG DEPENDENCE, INC. VOLUME II:

ABSTRACTS. Louis S. Harris, Ph.D., ed.

NCADI #M141

ADVANCES IN DATA ANALYSIS FOR PREVENTION

INTERVENTION RESEARCH. Linda M. Collins, Ph.D., and

Larry A. Seitz, Ph.D., eds.

NCADI #M142

THE CONTEXT OF HIV RISK AMONG DRUG USERS AND THEIR

SEXUAL PARTNERS. Robert J. Battjes, D.S.W.; Zili Sloboda, Sc.D.;

and William C. Grace, Ph.D., eds.

NCADI #M143

THERAPEUTIC COMMUNITY: ADVANCES IN RESEARCH

AND APPLICATION. Frank M. Tims, Ph.D.;

George De Leon, Ph.D.; and Nancy Jainchill, Ph.D., eds.

NCADI #M144

NEUROBIOLOGICAL MODELS FOR EVALUATING

MECHANISMS UNDERLYING COCAINE ADDICTION.

Lynda Erinoff, Ph.D., and Roger M. Brown, Ph.D., eds.

NCADI #M145

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146 HALLUCINOGENS: AN UPDATE. Geraline C. Lin, Ph.D.,

and Richard A. Glennon, Ph.D., eds.

NCADI #M146

147 DISCOVERY OF NOVEL OPIOID MEDICATIONS.

Rao S. Rapaka, Ph.D., and Heinz Sorer, Ph.D., eds.

NCADI #M147

148 EPIDEMIOLOGY OF INHALANT ABUSE: AN INTERNATIONAL

PERSPECTIVE.

Nicholas J. Kozel, M.S.; Zili Sloboda, Sc.D.;

and Mario R. De La Rosa, Ph.D., eds.

NCADI # M148

149 MEDICATIONS DEVELOPMENT FOR THE TREATMENT OF

PREGNANT ADDICTS AND THEIR INFANTS.

C. Nora Chiang, Ph.D., and Loretta P. Finnegan, M.D., eds.

NCADI # M149

150 INTEGRATING BEHAVIORAL THERAPIES WITH MEDICATIONS

IN THE TREATMENT OF DRUG DEPENDENCE.

Lisa Simon Onken, Ph.D., Jack D. Blaine, M.D., and

John J. Boren, Ph.D., eds.

NCADI # M150

*U-S. GOVERNMENT PRINTING OFFICE: 1995 386-912/00017

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NIH Publication No. 95-3889

Printed 1995