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. Author manuscript; available in PMC: 2023 Jun 1.
Published in final edited form as: J Subst Abuse Treat. 2021 Dec 24;137:108713. doi: 10.1016/j.jsat.2021.108713

The impact of social network dynamics on engagement in drug use reduction programs among men and women who use drugs

Oluwaseun Falade-Nwulia a, Marisa Felsher a, Michael Kidorf a, Karin Tobin b, Cui Yang b, Carl Latkin b
PMCID: PMC9086095  NIHMSID: NIHMS1767491  PMID: 34969578

Abstract

Background.

Cross-sectional studies have shown strong relationships between social network characteristics and substance use disorder (SUD) treatment engagement. The current study examined associations between longitudinal changes in egocentric social networks of male and female people who use drugs (PWUD) and engagement in drug use reduction programs, broadly defined as either formal SUD treatment or self-help groups.

Method.

Using data from an HIV prevention and care study in Baltimore, MD, this study categorized PWUD into those who engaged and did not engage in any drug use reduction programs over two follow-ups during a one-year observation window. The study used multivariate logistic generalized estimating equations (GEE) to examine associations between network composition and stability measures and drug use reduction program engagement, stratified by gender.

Results.

Of the 176 subjects participating in drug use reduction programs at baseline, 56.3% remained engaged at one year. Among both male and female respondents, higher turnover into non-kin networks was associated with increased odds of engagement in drug use reduction programs (AOR 1.4; 95% CI: 1.1-1.9, AOR 1.3; 95% CI: 1.0–1.8, respectively). For males, retention of intimate partner networks was associated with increased odds of program engagement (AOR 2.9; 95% CI: 1.1–7.6); for females, higher turnover into kin networks was associated with decreased odds of engagement (AOR 0.8; 95% CI: 0.5–1.0).

Conclusion.

Evaluation of associations between social network characteristics and drug use reduction program engagement appears to benefit from longitudinal analyses that are stratified by gender. Efforts to improve retention in formal SUD treatment or self-help groups might consider intervening through social networks, perhaps by increasing overall levels of social support.

Keywords: People who use drugs, Social network, Treatment, Care engagement, gender

1. Introduction

In 2018, an estimated 8.1 million people in the United States aged 12 or older, representing 3.0% of the population, met the criteria for a substance use disorder (SUD) in the previous year (Bose et al., 2019). SUDs negatively impact the health and well-being of individuals, families, and communities through fatal and nonfatal overdose (Bohnert et al., 2012); interpersonal violence (Chermack et al., 2010); increased risk of human immunodeficiency virus (HIV), hepatitis C virus (HCV), and other infectious diseases (Wang & Maher, 2019); and are a significant burden on hospital emergency departments (Cherpitel & Ye, 2008).

The prevailing conceptualization of SUD as a chronic, relapsing condition suggests that its treatment requires sustained and coordinated treatment, sometimes referred to as continuing care (Proctor & Herschman, 2014). Treatments include both inpatient and outpatient levels of care, with and without medication (NIDA, 2020). Self-help programs like Alcoholics Anonymous (AA) and Narcotics Anonymous (NA) provide important levels of support independent of and augmenting SUD treatment, and an increasing amount of research shows that participation in these services is associated with reductions in drug use (Humphreys, 2003; Humphreys & Moos, 2001; Humphreys & Moos, 1996; McAuliffe, 1990; Moos, 2008; Moos & Timko, 2008; Weiss et al., 2005; Weiss et al., 1996). Continuing recovery support care over a protracted period of at least 12 months appears to be essential if an individual desires an outcome of sustained recovery (Proctor & Herschman, 2014).

Despite the well-documented benefits of prolonged engagement in SUD treatment and recovery support, many people with SUD often leave care programs before drug use cessation or control (Dakof et al., 2001; Deane et al., 2012; Lin et al., 2013; McHugh et al., 2013; McKay et al., 2004; Samuel et al., 2011; Smyth et al., 2012). While considerable research has addressed program and individual-based factors associated with retention (drug use severity, proper use of medications, program satisfaction (Arfken et al., 2001; Hser et al., 2004; Siqueland et al., 2002; Zhang et al., 2008), less study has occurred of the social environment where PWUDs spend most of their time. Yet emerging research shows that the composition and size of social networks impact retention in both formal treatment programs and self-help groups (I. Arnaudova et al., 2020; Tracy et al., 2016; William Best & Ian Lubman, 2017).

An important limitation to this literature is that most studies are cross-sectional in design and characterize social network variables as static, with associations between network variables and drug use outcomes assessed at a single time point. Yet social relationships among people with SUD are often transient and influenced by many factors, including treatment participation (John P. Hoffmann et al., 1997; Neaigus et al., 1995). Another limitation is that studies among PWUD routinely choose not to disaggregate data by gender (El-Bassel & Strathdee, 2015; Iversen et al., 2015; Springer et al., 2015), an important drawback due to research showing that women’s close social relationships are often more closely tied to their substance use than men (Brown et al., 2002; Ellis et al., 2004).

The current study addresses these limitations using data from a longitudinal study of individuals recruited in Baltimore, Maryland, who have a history of drug use and recent engagement in SUD treatment or self-help groups. Over a one-year observation window, the study examines associations between changes in social network characteristics and retention in drug use reduction programs. The study also evaluates whether these associations differ by gender.

2. Methods

2.1. Study design and sample

Data come from a prospective HIV prevention and care intervention study that enrolled 568 participants between May 2014 and June 2017 (Rudolph et al., 2018). The study team used street outreach to recruit initial participants, who were required to be at least 18 years of age, HIV positive (validated with documentation or OraQuick rapid HIV testing), and have a history of injection drug use. Initial participants recruited network members who were drug using and/or sexual partners to join the study. Participants completed three waves of surveys at baseline and 6- and 12-month follow-up. The research team restricted the current analyses to participants who completed baseline, 6- and 12-month follow-up surveys, and who at baseline reported both drug use (operationalized as having used any drug to get high in the past six months) and engagement in drug use reduction programs over the previous six months (n = 176).

2.2. Measures

2.2.1. Drug use reduction program engagement over one year

The study defined the outcome of drug use reduction program engagement as answering affirmatively at baseline, 6-month, and 12-month follow-ups to the question: In the past 6 months have you gone for any kind of drug treatment, meetings, or groups to help you stop using drugs (not including alcohol treatment)? Broadly defining engagement across standard treatment and self-help groups provides a strong measure of any continuing care directed toward illicit drug use over the observation period.

2.2.2. Social network items

2.2.2.1. Social network measures.

The study elicted information about personal social networks by asking participants to define their social support network, which is the network of family, friends, neighbors, and community members that is available in times of need to give psychological, physical, and financial help (Lin et al., 1979). The study used the following items to elicit networks: 1) During the last 6 months, if you needed someone to talk to, who are the people that you could talk to about things that were personal and private?; 2) During the last 6 months, who pitched in to help you do things that you needed some help with, such as running errands, giving you a ride, etc?; 3) During the last 6 months, if you needed some money, who would loan or give you some money?; 4) Other than a doctor or healthcare provider, who could you talk to if you were having a hard time coping with HIV medication side effects?; and 5) Who could you take with you to an HIV medical appointment?

At the beginning of the follow-up network survey, network elicitation began with the following question, “First, I am going to read the names of the people you listed on your [previous] survey. Please tell me if you have spoken to any of these people in the past 6 months.” The study retained network members if respondents indicated affirmatively and did not include them if respondents indicated negatively. Once participants completed the list of names, participants provided information about each network member, such as whether they use drugs and the relationship type. The study categorized relationship types based on previous work (Yang et al., 2013) and used these types to create subnetworks: the kin subnetwork included any family member, such as parent, child, or sibling. The nonkin subnetwork included friends, acquaintances, NA/AA program sponsors, and spiritual leaders. The intimate partner subnetwork included spouse, boy/girlfriend, fiancé, and partner. Network members belonged to a drug network if the respondent believed they injected drugs, or if the respondent used drugs with them. Respondents also reported the length of time that they have known each network member in years, and which of their network members knew one another (density). We compared baseline and follow-up network inventories to identify which network members were renamed at the follow-up visits, with name, age, gender, and type of relationship (e.g. friend, spouse, etc.) used to confirm matches.

2.2.2.2. Individual and network properties.

Surveys collected the following demographic and drug behavior data from each respondent: sex at birth (male or female); race (White, Black, or Other); age (in years); and whether they are currently homeless, unemployed, or uninsured. The Center for Epidemiologic Studies Depression Scale (CESD-20) assessed depression, with a score of 16 or more indicating depression (Radloff, 1991). Participants described their overall physical health (poor, fair, good, excellent, dichotomized to poor/fair vs good/excellent due to skewed data). Participants at baseline also described the last time they used the following drugs: crack, cocaine, heroin, and injection drug use (never In your life, more than 5 years ago, 1–5 years ago, 6–12 months ago, 4–6 months ago, In the past 3 months; dichotomized to within 6 months or longer than 6 months ago due to skewed data).

We calculated two network measures that research has shown to have an effect on drug use behaviors in other network studies of PWUD: network density and network size (De et al., 2007; Tracy et al., 2012). The study calculated density as the total number of connections between network members divided by the total possible number of connections, assessed at baseline and 12-month follow-up. Network size was the total number of network members listed by survey respondents at baseline and at 12-month follow-up. The study measured network stability as the average length of network relationships (in years) within each subnetwork at baseline. We also calculated multiplexity at baseline, which is when people fill more than one relationship role and belong to multiple subnetworks, which research has shown is associated with increased intimacy and trust (Wasserman & Faust, 1994) and behaviors that elevate HIV risk, such as syringe sharing and condomless sex (Felsher & Koku, 2018). The study calculated multiplexity as the average number of network members belonging to the drug and sex, drug and kin, drug and nonkin, and nonkin and sex subnetworks.

In addition to cross-sectional network measures of density and size, we also measured network change between baseline and 12-month follow-up by subtracting the measure at follow-up from the measure at baseline. To assess network turnover, we adapted measures developed by Costenbader et al. (2006) and Hoffman et al. (1997) (Costenbader et al., 2006; John P. Hoffmann et al., 1997). Hoffman et al. developed two separate indicators for movement into and out of a network over time: turnover-in and turnover-out. Costenbader et al. further divided the counts of turnover-in and turnover-out into turnover-in and turnover-out of the drug and nondrug networks, respectively. Building off these two measures, we further divided the counts of turnover-out, retention, and turnover-in to the drug, intimate partner, kin, and nonkin networks, respectively.

2.3. Analytic approach

The study used descriptive statistics to describe the sample. Study staff used logistic generalized estimating equations (GEE) models with ‘xtgee’ in STATA (Rabe-Hesketh & Skrondal, 2008) to examine the bivariate relationship between each predictor variable and the primary outcome. The GEE approach is a general modeling strategy to adjust for the correlated (i.e., dependent) structure of the sample, which included indexes and their network members recruited into the study. Last, we conducted separate multivariable models using each network stability measure as the sole predictor, adjusting for baseline network size, respondent gender, age, relationship status, insurance, and depression due to statistical significance at the bivariate level as well as being conceptually relevant (Arfken et al., 2001; Vaughn et al., 2002). To identify differences in network dynamics by gender, we disaggregated the dataset by gender and re-ran multivariable models.

3. Results

3.1. Participant characteristics.

Table 1 shoes participants’ characteristics. Program engagers were more likely than program disengagers to be insured at baseline.

Table 1.

Participant characteristics by drug use reduction program engagement at 1 year (n=176)

Total (n=176) Disengaged
from drug
use
reduction
programa
(n=77)		 Engaged in drug
use reduction
program at 1
year(n=99)b
N (%) N (%) N (%) UOR (95%CI)
Sex at birth
 Male 94 (53.4) 42 (54.6) 52 (52.5) REF
 Female 82 (46.4) 35 (45.5) 47 (47.5) 1.1 (0.6-2.0)
Sexual Orientation
 Straight 132 (75.0) 54 (70.1) 78 (78.8) REF
 Other 44 (25.0) 23 (29.9) 21 (21.2) 0.6 (0.3-1.3)
In Committed Relationship 94 (53.4) 39 (50.7) 55 (55.6) 0.8 (0.5-1.5)
Race
 Black 155 (88.1) 67 (87.0) 88 (88.9) REF
 White 19 (10.8) 9 (11.7) 10 (10.1) 0.8 (0.3-2.2)
 Other 2 (1.1) 1 (1.3) 1 (1.0) 0.8 (0.0-12.5)
Age (mean, std) 50.9 (8.3) 50.3 (8.6) 51.4 (8.0) 1.0 (1.0-1.1)
Homeless1 29 (16.5) 12 (15.6) 17 (17.2) 1.1 (0.5-2.5)
Unemployed 156 (88.6) 68 (88.3) 88 (88.9) 1.1 (0.4-2.7)
Insured2 158 (89.8) 65 (84.4) 93 (93.9) 2.9 (1.0-8.0) *
Depressed3 63 (35.8) 24 (31.2) 39 (39.4) 1.4 (0.8-2.7)
Good physical health4 108 (61.4) 51 (66.2) 57 (57.6) 0.7 (0.4-1.2)
HIV positive 96 (54.6) 40 (52.0) 56 (56.6) 1.2 (0.7-2.1)
Drug use in 6 months preceding baseline
 Heroin 131 (74.4) 56 (72.7) 75 (75.8) 1.2 (0.6-2.3)
 Crack 144 (81.8) 64 (83.1) 80 (80.8) 0.9 (0.4-2.0)
 Cocaine 85 (48.2) 39 (50.7) 46 (46.4) 0.9 (0.5-1.6)
 Injection drug use 74 (42) 30 (39.) 44 (44.4) 1.3 (0.7-2.3)
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a

Of the 176 participants who participated in drug treatment at baseline, 40/176 were in residential treatment, 49/176 were in drug detox, 80/176 were in outpatient treatment, 94/176 were on methadone maintenance, and 155/176 attended self-help meetings

b

of the 99 participants who remained engaged in drug treatment at follow up, 20/99 were in residential treatment, 23/99 were in drug detox, 45/99 were in outpatient treatment, 57/99 were on methadone maintenance and 87/99 attended self-help meetings

1

Comparison group is “not homeless”

2

Comparison group is “uninsured”

3

Comparison group is “no indication for depression using CESD20”

4

Comparison group is “poor/fair health5”

+

p<0.10

*

p<0.05

**

p<0.01

3.2. Drug use reduction program participation.

Of the 176 respondents who reported drug use in the preceding 6 months and had engaged in a drug use reduction program at baseline, 56.3% reported that they remained engaged in a program at each of the follow-ups (n=99). Across all study respondents (n = 176), program enrollment included: residential treatment (n = 40), detoxification (n = 49), outpatient treatment (n = 80), methadone maintenance (n = 94), buprenorphine treatment (n = 70), and self-help meetings (n = 155). For those who remained engaged in treatment (n = 99), program enrollment included residential treatment (n = 20), detoxification (n = 23), outpatient treatment (n = 45), methadone maintenance (n = 57), buprenorphine treatment (n = 22), and self-help meetings (n = 87). Related to prior 6 months drug use reported at baseline, 144/176 reported crack use, 131/176 reported heroin use, 77/176 reported cocaine use, and 74/167 reported injection drug use.

The average network size at baseline was 4.7 (std. 3.0) and 6.0 (std. 3.3) at 12-month follow-up. The average duration of network relationships was 24.1 years (std. 11.8) overall, 13.5 years for drug relationships (std. 13.4), 14.5 years for intimate partner relationships (std. 12.2), 13.3 years for nonkin relationships (std. 10.8), and 39.6 years for kin relationships (std. 13.0; table 2). Within kin relationships, 20% of network members enumerated were parents, 28% were children, 33% were siblings; and 19% other kin relationships including uncles, aunts, cousins and in-laws. We report network dynamics related to overall size, drug, intimate partner, nonkin, and kin networks in Table 2. Related to multiplexity: participants had on average 0.5 (std. 0.8) network members at baseline and 0.3 (std. 0.4) network members at follow up who were a member of both the drug and sex subnetworks; an average of 0.1 (std. 0.4) network members at both baseline and follow up who were a member of both the drug and kin subnetworks; an average of 0.6 (std. 1.2) network members at both baseline and follow up who were a member of the both the nonkin and drug subnetworks; and an average of 0.3 (std. 0.9) network member at baseline and 0.1 (std. 0.3) network member at follow up who were a member of both the intimate partner and nonkin networks. The distribution of kin enumerated varied by gender. Kin relationships enumerated by men at baseline included 22% parents, 17% children, 40% siblings, and 21% other. At 6 month follow-up, kin relationships remained similar, with 20% parents, 17% children, 40% siblings, and 22% other kin relationships. Among women, kin relationship types enumerated at baseline were 18% parent, 39% children, 27% sibling, and 16% other. At 6 month follow-up, an increase occurred in the proportion of children reported to 43%, with 17% parent, 24% sibling, and 17% other kin relationship type.

Table 2.

Social network characteristics by drug use reduction program engagement at 1 year (n=176)

Total (n=176) Disengaged
from drug
use
reduction
program
(n=77)		 Remained
engaged in
drug use
reduction
program
(n=99)
Mean (SD)
range		 N (%) N (%) UOR
(95%CI)		 AOR (95%
CI)1
Network Dynamics, mean (std) range
TO network size 4.7 (3.0) 1-18 4.5 (3.1) 4.9 (2.9) 1.0 (0.9-1.2) NA
T2 network size 6.0 (3.3) 1-18 5.4 (3.2) 6.6 (3.3) 1.1 (1.0-1.2) * 1.1 (1.0-1.3) *
Change in network size 1.3 (3.2) −9-15 0.9 (3.1) 1.7 (3.2) 1.1 (1.0-1.2) + 1.1 (1.0-1.2) +
TO Density 0.7 (0.3) 0-1 0.7 (0.4) 0.7 (0.3) 1.2 (0.5-3.1) --
T2 Density 0.7 (0.3) 0-1 0.7 (0.3) 0.7 (0.3) 0.4 (0.1-1.6) --
Change in density 0.02 (0.0) −1-1 0.06 (0.4) −0.01 (3.1) 0.6 (0.2-1.5) --
Duration of network relationship at follow-up, in years
 Overall 24.1 (11.8) 1.0-65 25.5 (10.76) 23.1 (12.6) 1.0 (1.0-1.0) --
 Drug 13.5 (13.4) 0.3-51 16.6 (13.3) 11.8 (11.3) 1.0 (1.0-1.0) --
 Intimate partner 14.5 (12.2) 0.3-56 13.1 (10.7) 15.4 (13.0) 1.0 (1.0-1.0) --
 Non-kin 13.3 (10.8) 0.3-56 13.6 (12.9) 13.1 (13.8) 1.0 (1.0-1.0) --
 Kin 39.6 (13.0) 1-66 38.9 (11.6) 40.2 (14.0) 1.0 (1.0-1.1) --
Average Movement out of networks
 Total drug dropped 0.5 (0.9) 0-5 0.5 (0.8) 0.6 (1.0) 1.1 (0.8-1.5) 1.2 (0.9-1.7)
 Total intimate partner dropped 0.6 (1.2) 0-10 0.7 (1.5) 0.5 (1.0) 0.9 (0.7-1.1) 0.9 (0.7-1.2)
 Total non-kin dropped 0.8 (1.3) 0-7 0.6 (0.9) 0.9 (1.5) 1.2 (1.0-1.5) + 1.3 (1.0-1.7) *
 Total kin dropped 0.2 (0.6) 0-4 0.2 (0.6) 0.3 (0.7) 1.0 (0.7-1.7) 1.0 (0.6-1.6)
Average Retention within networks
 Total drug retained 0.4 (0.6) 0-3 0.3 (0.6) 0.5 (0.7) 1.7 (0.9-3.0) + 1.6 (0.9-2.8)
 Total intimate partner retained 0.7 (0.8) 0-7 0.6 (0.5) 0.8 (0.9) 2.0 (1.2-3.4) * 2.1 (1.2-3.8) *
 Total non-kin retained 1.0 (1.4) 0-6 0.9 (1.5) 1.1 (1.3) 1.1 (0.9-1.4) 1.1 (0.9-1.5)
 Total kin retained 1.5 (1.4) 0-6 1.5 (1.5) 1.5 (1.2) 1.0 (0.8-1.3) 1.0 (0.8-1.2)
Average Additions to networks
 Total drug added 0.5 (1.1) 0-9 0.4 (0.8) 0.6 (1.3) 1.2 (0.9-1.5) 1.1 (0.8-1.5)
 Total intimate partner added 0.3 (0.6) 0-2 0.3 (0.1) 0.3 (0.6) 1.2 (0.7-1.9) 1.2 (0.7-2.1)
 Total non-kin added 1.5 (1.9) 0-10 1.0 (1.4) 1.9 (2.2) 1.4 (1.1-1.7) * 1.4 (1.1-1.7) *
 Total kin added 1.0 (1.4) 0-8 1.0 (1.4) 0.9 (1.4) 1.0 (0.8-1.2) 1.0 (0.8-1.2)
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1

Each separate multivariable model adjust for respondent baseline network size, gender, age, insurance, relationship status and depression

+

p<0.10

*

p<0.05

**

p<0.01

3.3. Predictors of drug use program engagement

In univariable analysis (Table 1 and Table 2), being insured versus being uninsured (UOR 2.9; 95% CI: 1.0-8.0) and larger network size at follow-up (i.e., increase in network size) (UOR 1.1: 95% CI: 1.0-1.2) were significantly associated with drug use reduction program engagement. Between baseline and follow-up, a higher total of nonkin network members who left the network was associated with 1.2 times higher odds of drug use reduction program engagement (95% CI: 1.0–1.5). A higher total of drug and intimate partner network members retained was associated with increased odds of drug use reduction program engagement (UOR 1.7, 95% CI: 0.9–3.0; UOR 2.0, 95% CI: 1.2–3.4, respectively). A higher total of nonkin added to the network (turnover) was associated with drug use reduction program engagement (UOR 1.4, 95% CI: 1.1-1.7). After controlling for baseline network size, respondent’s gender, age, insurance, relationship status, and depression in the separate multivariable models, higher turnover out of the nonkin networks and higher retention within intimate partner networks remained significantly associated with drug use reduction program engagement (AOR 1.3, 95% CI: 1.0-1.7; AOR 2.1, 95% CI: 1.2-3.8;, respectively). Additionally, higher turnover into nonkin networks was significantly associated with drug use reduction program engagement AOR 1.4, 95% CI: 1.1–1.7) suggesting that nonkin network members are simultaneously being dropped and added.

Table 3 presents multivariable models disaggregated by respondents’ gender. Among men, but not women, larger network size at follow-up was associated with increased odds of drug use reduction program engagement (AOR 1.2, 95% CI: 1.0–1.4). Among men, higher turnover out of the nonkin network, higher retention of intimate partner networks, and higher turnover into nonkin networks remained significantly associated with increased odds of care engagement (AOR 1.6 95% CI: 1.0–2.4; AOR 2.9 95% CI: 1.1–7.6; AOR 1.4; 95% CI: 1.1–1.9 respectively). Among female respondents, similar to men, higher total turnover into women’s nonkin networks at follow-up was associated with increased odds of engagement in care (AOR 1.3; 95% CI: 1.0–1.8). Unlike male respondents, higher turnover into kin networks at follow-up was marginally associated with decreased odds of drug use reduction program engagement for women (AOR 0.8; 95% CI: 0.5–1.0).

Table 3.

Adjusted Odds Ratios (AOR)a of Network Dynamics Associated with Drug use reduction program engagement at 1 year, stratified by sex

Characteristic Men (n=94) Women (n=82)
AOR (95% CI) AOR (95%CI)
Network Dynamics
T2 network size 1.2 (1.0-1.4) * 1.1 (0.9-1.3)
Change in network size 1.1 (1.0-1.3) 1.1 (0.9-1.2)
Average Movement out of networks
 Total drug dropped 1.4 (0.9-2.2) 1.0 (0.6-1.7)
 Total intimate partner dropped 0.9 (0.7-1.2) 0.9 (0.6-1.6)
 Total non-kin dropped 1.6 (1.0-2.4) * 1.2 (0.8-1.6)
 Total kin dropped 2.0 (0.8-5.6) 0.5 (0.2-1.4)
Average Retention within networks
 Total drug retained 1.2 (0.6-2.5) 2.0 (0.7-6.1)
 Total intimate partner retained 2.9 (1.1-7.6) * 1.7 (0.7-3.8)
 Total non-kin retained 1.1 (0.8-1.6) 1.1 (0.8-1.6)
 Total kin retained 1.0 (0.7-1.4) 1.0 (0.7-1.3)
Average Additions to networks
 Total drug added 1.4 (0.9-2.2) 0.8 (0.5-1.4)
 Total intimate partner added 0.8 (0.3-2.1) 1.4 (0.7-3.0)
 Total non-kin added 1.4 (1.1-1.9) * 1.3 (1.0-1.8) *
 Total kin added 1.2 (0.9-1.5) 0.8 (0.5-1.0) +
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a

adjusted for baseline network size, age, insurance, depression and relationship status

+

p<0.10

*

p<0.05

**

p<0.01

4. Discussion

Almost half of the participants receiving some form of SUD care in drug use reduction programs at baseline reported that they were no longer engaged in care after one year, replicating previous studies demonstrating low rates of treatment and self-help group retention in this population (Manhapra et al., 2018; Morgan et al., 2018). The current study shows that some of the variability in ongoing care retention can be understood through social network variables. For both men and women, higher turnover into nonkin networks at follow-up was associated with one-year retention. A possible interpretation of this is that new nonkin network members represent peers gained while in drug use reduction programs, including NA/AA program sponsors, who are supportive of participants’ drug use reduction program engagement. The positive association between drug use reduction program engagement and network growth is supported by other studies that show that drug use treatment and self-help group participation can facilitate social network change (Zywiak et al., 2009). Previous literature has documented connections between social support and positive SUD treatment outcomes, such as enrollment, satisfaction, and abstinence, and may be the mechanisms through which an increase in network size is associated with increased engagement in drug use reduction programs (Cavaiola et al., 2015; Flynn et al., 2003; Wasserman et al., 2001; Yang et al., 2019).

For men, but not women, reduction of nonkin relationships was associated with one-year retention. A process of nonkin network turnover may occur for men, such that respondents may replace nonsupportive nonkin network members with more supportive nonkin network members, a strategy that could benefit recovery efforts. Such nonkin network replacement may be less common among women, as some research has shown that women are more likely than men to maintain social relationships, including friends, over time (Ogolsky & Bowers, 2013; Stevens & Van Tilburg, 2011). Nevertheless, that turnover into nonkin networks was associated with retention in care for both genders signals the importance of adding new nonkin network members regardless of whether they replace previous nonkin. Also men seemed to derive more continuing care benefits from enduring intimate partner relationships than did women. This finding is supported by previous literature among patients in SUD treatment, demonstrating that patients whose intimate partnerships lasted through the first year post-treatment had better outcomes than patients whose relationships ended (Tracy et al., 2005). Research has also shown that support from primary partners can be instrumental in medication adherence, such as adherence to antiretroviral viral therapy (ART) for people living with HIV (Conroy et al., 2017; Conroy et al., 2019; Knowlton et al., 2011). That women failed to derive similar benefits from intimate relationships dovetails with studies showing that women are more likely than men to have partners who use drugs and enable their drug use post-treatment (Falkin & Strauss, 2003; Riehman et al., 2003). More frequent deployment of partner interventions, including behavioral couples therapy, might be helpful in stabilizing intimate relationships and improving long-term outcomes (Powers et al., 2008). An alternative explanation may be linked to the greater stigma that women who use drugs face compared to men who use drugs, including from health care providers and drug use program staff due to societal expectations of the role of women in society (El-Bassel et al., 2014; Gibson & Hutton, 2021; Shirley-Beavan et al., 2020). This structural violence and stigma enacted against women may be such a significant deterrent to accessing treatment that cannot be as easily overcome by supportive intimate partners.

Our finding that for women, but not men, the total number of kin added at follow-up was marginally associated with decreased odds of drug treatment engagement also deserves some attention. This finding is supported by a vast literature demonstrating that women have decreased access to health care, including SUD treatment, compared to men due to women’s greater family related responsibilities, such as caring for family members, that restrict women’s ability to manage time for their health (Avotri & Walters, 1999; Grella & Stein, 2013; Heise et al., 2019; Roberts et al., 2010; Slaunwhite, 2015). Our finding that children compose a higher proportion of kin relationships for women compared to men is supportive of previous findings that women have increased child care responsibilities that may negatively impact engagement in SUD treatment (Chatterjee et al., 2018; Feder et al., 2018). One implication of this finding is that women may benefit from SUD interventions that strengthen supports for family-related responsibilities and address practical concerns such as family care provision. For example, child-friendly residential services may be of particular benefit to women in recovery. More research should identify how gender-related network dynamics related to kin network membership impact retention in drug treatment over time. This research is particularly critical in light of the narrowing gender gap in the prevalence of SUDs, characterized by an increasing representation of women (Keyes et al., 2008; Seedat et al., 2009).

That changes in the drug network were not correlated with drug use reduction program engagement conflicts with previous research showing that larger drug network size is associated with decreased treatment retention and poor treatment outcomes (Inna Arnaudova et al., 2020; Lloyd et al., 2008; Tracy et al., 2016; William Best & Ian Lubman, 2017). The lack of association may be attributed to the low frequency of turnover in and out of the drug subnetworks, perhaps due to the long-standing nature of network relationships reported by PWUD in our study. Additionally, in urban inner-city populations with a high prevalence of substance use, PWUDs may find it challenging to expand networks to include new members without a history of drug use. These data suggest the importance of supporting PWUD in garnering support from their network members to maintain their goals related to drug use. Engagement in treatment and self-help groups might, at times, bring new people who use drugs into the PWUD’s social network, including people engaged in recovery activities but who are not yet abstinent. Finally, because respondents had already initiated some form of drug use reduction program at baseline, change to the drug subnetworks may have already occurred prior to study entry.

Several important caveats accompany our findings. First, we used an inclusive measure to classify drug use reduction program engagement, encompassing both formal treatment and self-help groups. We favored this approach because both interventions are frequently used by people with SUD and associated with good outcomes over time (NIDA, 2020). Second, by focusing on changes in membership within subnetworks, we chose not to pursue other ways in which networks might change over time (e.g., changes in relationship strength, social support, etc.), areas that would benefit from future research. Third, we tended to interpret correlations based on the impact of network change on drug use reduction program participation. While the study design makes it difficult to infer causality, the temporal associations provide good preliminary data to explore more explicit causal hypotheses. For example, future studies could test the hypothesis that increases in nonkin support improve engagement in SUD treatment through the mediation of social support. Also, while the study examined some forms of ongoing care engagement, it could not quantify the intensity of engagement nor its impact on drug use or other outcome variables—important areas of research for future studies. Finally, our study population represented an inner-city, impoverished population. As such, our findings may not be generalizable to other communities. Taken together, the study demonstrates multiple relationships between social network characteristics and ongoing care, and the benefits of exploring these relationships using gender-disaggregated analyses.

Highlights.

  • Social network member changes influence drug use treatment engagement

  • An increase in non-kin social network members facilitated engagement

  • Among males, retention of intimate partners facilitated engagement

  • Among females an increases in kin network members was a barrier to engagement

Acknowledgements

This work was supported by National Institutes of Health [grant numbers K23DA041294 (to OFN), R01 DA040488 (to CL, KT). This research was facilitated by the infrastructure and resources provided by the Johns Hopkins University Center for AIDS Research, a National Institutes of Health funded program [grant number P30AI094189], which is supported by the following National Institutes of Health Co-Funding and Participating Institutes and Centers: National Institute of Allergy and Infectious Diseases, National Cancer Institute, National Institute of Child Health and Human Development, National Heart, Lung, and Blood Institute, National Institute on Drug Abuse, National Institute of Mental Health, National Institute on Aging, Fogarty International Center, National Institute of General Medical Sciences, National Institute of Diabetes and Digestive and Kidney Diseases, and Office of AIDS Research. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health.

Footnotes

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