Abstract
The evolving opioid epidemic in the United States has increased drug-related overdose rates exponentially (Centers for Disease Control and Prevention, 2020c). Fentanyl, a synthetic opioid, has recently fueled the epidemic, increasing overdose death rates (Centers for Disease Control and Prevention, 2019a). Harm reduction strategies (drug checking, naloxone administration, etc.) are at the forefront of preventing opioid-related overdoses in high-risk populations (Kennedy et al., 2018; Laing, Tupper, & Fairbairn, 2018). Little is known, however, about how people who inject drugs (PWID) may modify their drug use behaviors after suspected fentanyl contamination in their drugs. We conducted a cross-sectional survey among 105 opioid-dependent PWID enrolled in a methadone maintenance program. We assessed their willingness to engage in various harm reduction methods (i.e., slowing down drug use, not using drugs, carrying naloxone, using with someone who has naloxone) after suspected fentanyl contamination of their drugs. In a multivariable analysis, participants who were white, low-income, polysubstance users, and had previously experienced an overdose or had previously administered naloxone were more likely to report a willingness to engage in harm reduction measures. These findings provide an evidence-based understanding of PWID’s engagement in harm reduction behaviors after suspecting potential fentanyl exposure as well as a basis for tailoring intervention strategies in the context of fentanyl-adulterated markets.
Keywords: people who inject drugs, opiate agonist therapy, fentanyl, overdose, methadone maintenance treatment, harm reduction strategies
1. Introduction
The United States has experienced an exponential increase in the rate ofdrug overdose in the past 20 years, with the opioid epidemic contributing to nearly half a million overdose deaths since 1999 (Centers for Disease Control and Prevention, 2020a). The beginning of the epidemic stemmed from a rise in prescription opioid overdose deaths in the late 1990s. Since then, the epidemic has evolved, fueled by illicit heroin and synthetic opioid use (Centers for Disease Control and Prevention, 2020d). The most common synthetic opioids used, including fentanyl and its illicitly manufactured fentanyl analogs, are over 50 times more potent than illicit heroin (Centers for Disease Control and Prevention, 2020a). As a result, nearly two-thirds of all opioid-related overdose deaths involved synthetic opioids in 2017 (Centers for Disease Control and Prevention, 2020a).
Important trends related to opioid-related deaths and racial disparities have been observed. While both opioid-related and synthetic opioid-related overdose death rates increased across all racial/ethnic groups from 2015-2017, subgroups of the black population in large metro areas saw the highest increase, from 19.3 to 41.9 per 100,000 (Centers for Disease Control and Prevention, 2019b). Synthetic opioid-related deaths in subpopulations of Hispanics in large metro areas also increased by 433% (from 1.5 to 9.0 per 100,000) in those two years. Historically, minority populations have lower opioid-related overdose death rates compared to White people due to differences in opioid prescribing rates; however, the increase in synthetic opioid use has changed the racial demographics of the opioid overdose epidemic (Centers for Disease Control and Prevention, 2019b).
An additional factor influencing the rise in overdoses is polysubstance use (including various opioids and non-opioid substances), particularly when combined with illicit fentanyl. A recent study of 10 states identified more than half of overdose deaths involving fentanyl to also include cocaine, methamphetamine, or heroin (Centers for Disease Control and Prevention, 2020c). The rise in overdoses and its association with specific drug use behaviors has caused increased concern among healthcare workers and people who inject drugs (PWID) (National Institute on Drug Abuse, 2020). Given the highly addictive qualities of opioid use and concurrent polysubstance use often observed among opioid users, the likelihood of experiencing multiple overdoses is common in individuals with opioid use disorder (OUD) (Olfson, Wall, Wang, Crystal, & Blanco, 2018). To reduce the harmful risks associated with opioid use, including drug overdose, public health researchers support the use of harm reduction strategies (Centers of Disease Control and Prevention, 2020).
Given the unknown purity and content of illicit drugs and uncertainty of PWID’s exposure to fentanyl before use, many have called for a harm reduction approach to address this crisis. Some examples of harm reduction strategies specific to this crisis include using clean needles to prevent the spread of infections (Centers of Disease Control and Prevention, 2020), naloxone administration to prevent fatal overdose (National Institutes on Drug Abuse, 2019), and using fentanyl testing strips (FTS) to test drugs for fentanyl contamination (Krieger, Goedel, et al., 2018; Tupper, McCrae, Garber, Lysyshyn, & Wood, 2018), though FTS are not yet FDA approved (Harm Reduction Coalition, 2020). These harm reduction strategies, often implemented in Syringe Service Programs (SSP), have been found to reduce the risks associated with injection drug use and potential overdose (Centers of Disease Control and Prevention, 2020). Despite the demonstrated benefits of such strategies, little is known about how PWID, particularly those at an increased risk for a drug overdose, respond to suspected fentanyl contamination in their drugs. The purpose of this study was to assess whether and how opioid-dependent PWID change their drug use behaviors after suspected fentanyl exposure.
2. Methods
Study Design and Participants
A cross-sectional survey was administered to 234 opioid-dependent individuals to assess their preference for various modalities of HIV prevention approaches. Eligibility criteria included: 1) 18 years or older; 2) HIV-uninfected or status unknown (self-reported); 3) reported drug- (i.e., sharing of injection equipment) or sex-related (i.e., condomless sex) HIV risk (past month); 4) met DSM-V criteria for OUD; 5) enrolled in methadone maintenance program (MMP); and 6) able to understand, speak, and read English. The analytical sample for the current study included 105 participants who responded to the outcome variables.
Study Procedures
Participants were recruited from APT Foundation, Inc., an addiction treatment center in New Haven, Connecticut. The treatment center is the largest in Connecticut, with over 7,000 patients on medication-assisted treatments for OUD. Clinic-based advertisements and flyers, word of mouth, and referrals from counselors were used as recruitment methods. Trained research assistants conducted all screenings and interviews in a private room at APT Foundation. Upon informed consent, participants were asked to complete a 45-minute survey using audio computer-assisted self-interview (ACASI). Participants were compensated $25 for their time. The study protocol was approved by the Institutional Review Board at the University of Connecticut and received board approval from the APT Foundation Inc.
Measures
The primary outcomes were four self-reported harm-reduction behaviors participants would utilize in response to the question, “What would you do if you knew prior to using/purchasing that your drugs contained fentanyl?” The four non-mutually exclusive responses included “Not use the drugs,” “Use drugs more slowly,” “Have naloxone supply ready in case overdose happens,” and “Use drugs with others who have naloxone.”
Participant demographics were collected, including age, self-reported gender, ethnicity, marital status, education status, employment status, living status, and income. Data were also obtained on drug use characteristics (e.g., substance use history and knowledge and experience with fentanyl test strips and naloxone) and clinical history (e.g., methadone dose, access to healthcare, overdose history, depressive symptoms, and social support). HIV risk was measured through participants’ engagement in transactional sex, condomless sex, number of sexual partners, and sharing of injection equipment.
Data Analysis
Descriptive statistics were computed using frequencies and percentages for categorical variables and means and standard deviations for continuous variables. Chi-square/Fisher’s exact tests and Student’s T-tests were used to identify bivariate associations with harm-reduction outcomes. Variables with bivariate associations of p<0.1 were included in multivariable logistic regression models. The backward selection was used to arrive at the most parsimonious model. When applicable, a Firth logistic regression was used to obtain unadjusted odds ratios and multivariable models that account for small sample size and paucity of outcome events (Heinze & Schemper, 2002). Model fit was assessed using the area under the receiver operating curves (ROC) and the Hosmer and Lemeshow Test (Hosmer, Hosmer, Le Cessie, & Lemeshow, 1997). Statistical significance was evaluated from 95% confidence intervals and defined as p<0.05. All statistical analyses were performed using SAS 9.4.
3. Results
One-hundred and five opioid-dependent individuals enrolled in the MMP completed the survey (Table 1). The mean age of the sample was 40.8 (SD= +/−9.5 years old. Just over half (55.2%) of the sample identified as male, and a majority reported being White (78.1%) and with a high school degree (70.5%). Polysubstance use was high among the sample, as 84.8% reported polysubstance use in the past 30 days, and 69.5% of the sample were concerned about fentanyl contamination in their drug supply. Less than half (45.7%) of the sample had ever administered naloxone, though 82.9% of the sample reported witnessing an overdose. Almost the entirety of the sample (91.4%) knew someone who died from an overdose in the past 12 months. Willingness to engage in harm reduction behaviors was high among the sample, including 84.8% of participants reported a willingness to use FTS.
Table 1.
Participant characteristics stratified by harm reduction behaviors (N=105)
| Total (N=105) |
Not use drugs (n=39) |
Use drugs slowly (n=42) |
Carry naloxone (n=33) |
Use drugs with someone who has naloxone (n=18) |
|
|---|---|---|---|---|---|
| Demographic Characteristics | |||||
| Age | 40.8 ± 9.5 | 43.8 ± 9.9a | 38.7 ± 8.9 a | 38.7 ± 8.6 | 37.0 ± 8.2 a |
| Gender | |||||
| Female | 47 (44.8) | 19 (48.7) | 18 (42.9) | 19 (57.6) a | 10 (55.6) |
| Male | 58 (55.2) | 20 (51.3) | 24 (57.1) | 14 (42.4) a | 8 (44.4) |
| Heterosexual Orientation | |||||
| No | 18 (17.1) | 6 (15.4) | 10 (23.8) | 7 (21.2) | 4 (22.2) |
| Yes | 87 (82.9) | 33 (84.6) | 32 (76.) | 26 (78.8) | 14 (77.8) |
| Ethnicity | |||||
| Non-White | 23 (21.9) | 13 (33.3) a | 3 (7.1) a | 5 (15.2) | 1 (5.6) |
| White | 82 (78.1) | 26 (66.7) a | 39 (92.9) a | 28 (84.8) | 17 (94.4) |
| Currently married/living with partner | |||||
| No | 78 (74.3) | 32 (82.1) | 30 (71.4) | 25 (75.8) | 12 (66.7) |
| Yes | 27 (25.7) | 7 (18.0) | 12 (28.6) | 8 (24.2) | 6 (33.3) |
| High school graduate | |||||
| No | 31 (29.5) | 13 (33.3) | 13 (31.0) | 9 (27.3) | 7 (38.9) |
| Yes | 74 (70.5) | 26 (66.7) | 29 (69.1) | 24 (72.7) | 11 (61.1) |
| Income level | |||||
| < $10,000 | 77 (73.3) | 33 (84.6) a | 28 (66.7) | 26 (78.8) | 13 (72.2) |
| ≥ $10,000 | 28 (26.7) | 6 (15.4) a | 14 (33.3) | 7 (21.2) | 5 (27.8) |
| Homeless in past 12 months | |||||
| No | 49 (46.7) | 21 (53.9) | 20 (47.6) | 15 (45.5) | 6 (33.3) |
| Yes | 56 (53.3) | 18 (46.2) | 22 (52.4) | 18 (54.6) | 12 (66.7) |
| Drug Use Characteristics | |||||
| Heroin in past 30 days | |||||
| No | 16 (15.2) | 4 (10.3) | 6 (14.3) | 4 (12.1) | 3 (16.7) |
| Yes | 89 (84.8) | 35 (89.7) | 36 (85.7) | 29 (87.9) | 15 (83.3) |
| Cocaine in past 30 days | |||||
| No | 23 (21.9) | 7 (17.9) | 11 (26.2) | 6 (18.2) | 2 (11.1) |
| Yes | 82 (78.1) | 32 (82.1) | 31 (73.8) | 27 (81.8) | 16 (88.9) |
| Fentanyl use past 30 days | |||||
| No | 84 (80.0) | 33 (84.6) | 32 (76.2) | 28 (84.9) | 13 (72.2) |
| Yes | 21 (20.0) | 6 (15.4) | 10 (23.8) | 5 (15.2) | 5 (27.8) |
| Marijuana past 30 days | |||||
| No | 57 (54.3) | 24 (61.5) | 17 (40.5) a | 12 (36.4) a | 6 (33.3) a |
| Yes | 48 (45.7) | 15 (38.5) | 25 (59.5) a | 21 (63.6) a | 12 (66.7) a |
| Daily injection drug use | |||||
| No | 85 (81.0) | 34 (87.2) | 34 (81.0) | 25 (75.6) | 13 (72.2) |
| Yes | 20 (19.1) | 5 (12.8) | 8 (19.1) | 8 (24.2) | 5 (27.8) |
| Polysubstance use | |||||
| No | 16 (15.2) | 6 (15.4) | 6 (14.3) | 1 (3.0) a | 0 (0.0) a |
| Yes | 89 (84.8) | 33 (84.6) | 36 (85.7) | 32 (97.0) a | 18 (100.0) a |
| Clinical Characteristics | |||||
| Methadone | 84.6 ± 31.4 | 83.4 ± 31.1 | 81.3 ± 36.2 | 85.8 ± 37.4 | 86.8 ± 39.2 |
| Alcohol use disorder | |||||
| No | 65 (61.9) | 26 (66.7) | 23 (54.8) | 19 (57.6) | 7 (38.9) a |
| Yes | 40 (38.1) | 13 (33.3) | 19 (45.2) | 14 (42.4) | 11 (61.1) a |
| Depression | |||||
| No | 23 (21.9) | 11 (28.2) | 9 (21.4) | 4 (12.1) | 2 (11.1) |
| Yes | 82 (78.1) | 28 (71.8) | 33 (78.6) | 29 (87.9) | 16 (88.9) |
| Social Support | 84.6 ± 31.4 | 59.1 ± 31.2 | 60.5 ± 24.0 | 51.3 ± 23.8 | 54.2 ± 23.2 |
| Visited primary care physician in past 12 months | |||||
| No | 13 (12.4) | 2 (5.1) | 6 (14.3) | 6 (18.2) | 1 (5.6) |
| Yes | 92 (87.6) | 37 (94.9) | 36 (85.7) | 27 (81.8) | 17 (94.4) |
| Visited psychiatrist in past 12 months | |||||
| No | 37 (35.2) | 11 (28.2) | 17 (40.5) | 12 (36.4) | 6 (33.3) |
| Yes | 68 (64.8) | 28 (71.8) | 25 (59.5) | 21 (63.6) | 12 (66.7) |
| HIV Risk Behaviors | |||||
| Number of sexual contacts | 3.1 ± 5.3 | 3.7 ± 5.2 | 2.3 ± 3.5 | 3.7 ± 6.8 | 2.4 ± 2.6 |
| Consistent condom use* | |||||
| No | 82 (78.1) | 30 (96.8) | 6 (14.3) | 24 (72.7) | 17 (5.6) |
| Yes | 4 (3.8) | 1 (3.2) | 33 (78.6) | 1 (3.0) | 0 (0.0) |
| Shared injection equipment in past 30 days | |||||
| No | 62 (59.1) | 25 (64.1) | 28 (66.7) | 22 (66.7) | 10 (55.6) |
| Yes | 43 (41.0) | 14 (35.9) | 14 (33.3) | 11 (33.3) | 8 (44.4) |
| Engaged in transactional sex | |||||
| No | 79 (75.2) | 28 (71.8) | 36 (85.7) a | 27 (81.8) | 14 (77.8) |
| Yes | 26 (24.8) | 11 (28.2) | 6 (14.3) a | 6 (18.2) | 4 (22.2) |
| Fentanyl Perceptions & Knowledge | |||||
| Concerned about fentanyl contamination | |||||
| No | 32 (30.5) | 7 (17.9) a | 11 (26.2) | 9 (27.3) | 5 (27.8) |
| Yes | 73 (69.5) | 32 (82.1) a | 31 (73.8) | 24 (72.7) | 13 (72.2) |
| Perceived past fentanyl contamination | |||||
| No | 14 (35.9) a | 7 (16.7) | 3 (9.1) a | 0 (0.0) a | |
| Yes | 25 (64.1) a | 35 (83.3) | 30 (90.9) a | 18 (100.0) a | |
| Desire to know about contamination prior to drug use | |||||
| No | 24 (22.9) | 6 (15.4) | 7 (16.7) | 5 (15.2) | 3 (16.7) |
| Yes | 81 (77.1) | 33 (84.6) | 35 (83.3) | 28 (84.8) | 15 (83.3) |
| Prior knowledge of fentanyl strips | |||||
| No | 56 (53.3) | 20 (51.3) | 21 (50.0) | 15 (45.5) | 6 (33.3) a |
| Yes | 49 (46.7) | 19 (48.7) | 21 (50.0) | 18 (54.5) | 12 (66.7) a |
| Ever used fentanyl testing strips | |||||
| No | 87 (82.9) | 34 (87.2) | 35 (88.3) | 29 (87.9) | 15 (83.3) |
| Yes | 18 (17.1) | 5 (12.8) | 7 (16.7) | 4 (12.1) | 3 (16.7) |
| Willingness to use fentanyl testing strips | |||||
| No | 16 (15.2) | 4 (10.3) | 4 (9.5) | 2 (6.1) a | 1 (5.6) |
| Yes | 89 (84.8) | 35 (89.7) | 38 (90.5) | 31 (93.9) a | 17 (94.4) |
| Access to fentanyl testing strips | |||||
| No | 90 (85.7) | 34 (87.2) | 35 (83.3) | 27 (81.8) | 15 (83.3) |
| Yes | 15 (14.3) | 5 (12.8) | 7 (16.7) | 6 (18.2) | 3 (16.7) |
| Overdose experiences | |||||
| Experienced overdose in past 12 months | |||||
| No | 84 (80.0) | 33 (84.6) | 34 (81.0) | 22 (66.7) a | 12 (66.7) |
| Yes | 21 (20.0) | 6 (15.4) | 8 (19.0) | 11 (33.3) a | 6 (33.3) |
| Ever witnessed overdose | |||||
| No | 18 (17.1) | 10 (25.6) a | 7 (16.7) | 2 (6.1) a | 1 (5.6) |
| Yes | 87 (82.9) | 29 (74.4) a | 35 (83.3) | 21 (93.9) a | 17 (94.4) |
| Ever administered naloxone | |||||
| No | 57 (54.3) | 25 (64.1) | 23 (54.8) | 12 (36.4) a | 4 (22.2) a |
| Yes | 48 (45.7) | 14 (35.9) | 19 (45.2) | 21 (63.6) a | 14 (77.8) a |
| Knows someone who has died from overdose in past 12 months | |||||
| No | 9 (8.6) | 5 (12.8) | 1 (2.4) a | 1 (3.0) | 0 (0.0) |
| Yes | 96 (91.4) | 34 (87.2) | 41 (97.6) a | 32 (97.0) | 18 (100.0) |
p-value <0.1 in bivariate analysis
Some missing data due to questionnaire skip pattern
Would not use the drugs, if they learned supply was contaminated with fentanyl
Thirty-nine (37.1%) participants reported that they would not use the drugs if they were to learn their supply was contaminated with fentanyl. In a multivariable analysis, we found significant differences based on income (aOR 0.30; 95%CI= 0.10-0.89; p=0.030), concern about fentanyl contamination (aOR 2.89; 95%CI= 1.04-8.03; p=0.042), and perceived past fentanyl contamination (aOR 0.33; 95%CI= 0.12-0.92; p=0.035) for reporting not using the drugs if they were contaminated with fentanyl.
Would use more slowly, if they learned supply was contaminated with fentanyl
Forty-two (40.0%) participants reported they would use the drugs more slowly if they were to learn their supply was contaminated with fentanyl. In a multivariable analysis, we found ethnicity (aOR 4.39; 95%CI= 1.25-15.39; p=0.021) and marijuana use (aOR 2.44; 95%CI= 1.05-5.66; p=0.037) to be significantly different in willingness to use the drugs more slowly upon learning of fentanyl contamination.
Would carry naloxone, if they learned supply was contaminated with fentanyl
Thirty-three (31.4%) participants reported they would carry naloxone if they were to learn their supply was contaminated with fentanyl. In a multivariable analysis, we found a significant difference between participants who ever experienced an overdose (aOR 3.18; 95%CI= 1.05-9.67; p=0.041) on reporting willingness to carry naloxone, upon learning of fentanyl contamination in drug supply. Additionally, participants who had witnessed someone else overdose tended to be more willing to carry naloxone in case of fentanyl contamination (aOR 4.95; 95%CI= 1.00-24.50; p=0.050).
Would use with someone who has naloxone, if they learned supply was contaminated with fentanyl
Eighteen (17.1%) participants reported they would use with someone who had naloxone if they were to learn their supply was contaminated with fentanyl. In a multivariable analysis, we found a significant difference between participants having AUD (aOR 3.45; 95%CI= 1.16-10.23; p=0.026) and participants who reported prior administration of naloxone on someone overdosing (aOR 5.45; 95%CI= 1.68-17.75; p=0.005) on willingness to use drugs with someone who had naloxone.
4. Discussion
This study identified factors associated with willingness to engage in a range of harm reduction strategies relevant to fentanyl contamination among opioid-dependent PWID. Several important findings were gleaned from this study that have important implications for future research and public health interventions.
First, we found interesting trends when analyzing demographic variables in relation to PWID’s willingness to change drug use behaviors after suspecting fentanyl exposure. Participants with lower income (<$10,000) were more likely to report they would not use the drugs, upon learning their drug supply was contaminated with fentanyl. This may be due to low-income participants’ acknowledgment of their risk for overdose and the ability to take prevention measures into their own hands, given greater challenges in accessing treatment (Priester et al., 2016); though there is little research to support this mechanism of harm reduction, indicating an area for future research. We also found that White participants were more likely to practice harm reduction behaviors and non-whites were less likely to engage in these behaviors in response to suspected fentanyl exposure. These findings fit our understanding of racial disparities within the opioid epidemic, where we have seen a disproportionate increase in opioid-related overdoses among minority populations (Alexander, Kiang, & Barbieri, 2018; Centers for Disease Control and Prevention, 2019b). White populations have traditionally had greater access to and trust in opioid agonist treatment (OAT) (Kilaru et al., 2020) and SSP (Centers of Disease Control and Prevention, 2020) compared to people of color, thus contributing to disparities in treatment engagement, retention, and health education among racial and ethnic minority populations. Efforts should focus on outreach to communities of color and having culturally competent treatment providers in harm reduction settings.
Second, we found that PWID who had previously experienced an overdose or had administered naloxone in the past were more likely to report carrying a supply of naloxone or use drugs with someone carrying naloxone, respectively, in anticipation of their drugs possibly being contaminated with fentanyl. Individuals who have experienced a non-fatal overdose are at higher risk of future overdose (Centers for Disease Control and Prevention, 2020b), and our findings suggest a heightened risk perception among participants who have overdosed, as well as willingness to reduce the chances of overdose-related death with naloxone. Past overdose experiences may also have important implications for future drug use and harm reduction behaviors and highlight a unique time point for intervention. In a qualitative analysis by Elliott et al. (2019), people who recently experienced an overdose identified it as an opportunity to engage in harm reduction behavior change(Elliott, Bennett, & Wolfson-Stofko, 2019). Emergency departments can be a crucial stage of intervention to link an individual to care (i.e., Substance Abuse Treatment, OAT, SSP) to reduce the chances of multiple overdoses (Centers for Disease Control and Prevention, 2020b). Effective strategies for linking PWID to SSP and OAT services, immediately after hospitalization for overdose, such as mobile harm reduction service delivery modality, may provide a more feasible intervention approach for PWID post overdose (Nuamah, Mehta, & Sasangohar, 2019).
Finally, we found that PWID who engaged in polysubstance use (alcohol and marijuana), were more willing to engage in harm reduction behaviors (i.e., use drugs with someone carrying naloxone and use drugs slowly). These findings are consistent with the literature reporting willingness to engage in overdose prevention among polysubstance users (Lagu, Anderson, & Stein, 2006; Olfson et al., 2018; Riggs et al., 2020; Schiavon et al., 2018). Although a high willingness to use harm reduction is present among polysubstance users, Schneider et al. (2019) found polysubstance drug users, who ingest drugs via multiple methods, to be less likely to have received overdose training and naloxone than individuals who use drugs via injection alone (Schneider, Park, Allen, Weir, & Sherman, 2019). With up to 80% of opioid-related overdose involving other substances (Centers for Disease Control and Prevention, 2020b), interventions are needed that focus on increasing the accessibility of naloxone among people who engage in polysubstance use.
Integrated care models implemented in primary care facilities are a strategy to encourage the use of naloxone and other harm reduction strategies among polysubstance users. Such models may increase positive experiences in treatment, influencing greater knowledge of harm reduction behaviors in this subpopulation (Longabaugh et al., 2005; National Institute of Mental Health, 2017). Our findings add to the literature by identifying a subset of polysubstance users, those using marijuana and alcohol, who may be receptive to further life-saving overdose prevention interventions since they appear to already be engaging in harm reduction. The greater social acceptability of alcohol and marijuana and their widespread use may explain these findings, though the mechanism is unclear for why these particular polysubstance users are likely engaging in harm reduction. Drug treatment clinics and SSPs should continue to engage and educate these individuals, as polysubstance users are at increased risk for overdose (Centers for Disease Control and Prevention, 2020a). Future harm reduction interventions should focus efforts on polysubstance users to reduce the overall risk of overdose-related death (Centers for Disease Control and Prevention, 2020a; Centers of Disease Control and Prevention, 2020).
Fentanyl testing strips (FTS), for example, is a new and efficacious tool to inform users if their drug supply contains fentanyl (Harm Reduction Coalition, 2020). A high willingness to use FTS among PWID has been supported by recent studies (Kennedy et al., 2018; Krieger, Yedinak, et al., 2018; Peiper et al., 2019), though without FDA approval, researchers are limited in their ability to identify if the use of FTS can directly influence harm reduction behaviors (Alexander et al., 2018; Krieger, Goedel, et al., 2018; McGowan, Harris, Platt, Hope, & Rhodes, 2018). Our findings indicate that nearly a third of PWID would engage in some type of harm reduction behavior if they possessed knowledge of fentanyl contamination prior to substance use. Results from this study support the urgency for FDA approval and implementation and accessibility of FTS in drug treatment settings and SSP. An increase in funding and availability and accessibility of SSP and OAT shows promise at increasing the effectiveness of these programs in reducing the adverse effects of injection drug use (Centers of Disease Control and Prevention, 2020; Lister, Weaver, Ellis, Himle, & Ledgerwood, 2020). Findings from this study can be used to inform future interventions and harm reduction policies. While SSP and OAT are effective at reducing harm among PWID, novel strategies for encouraging engagement in harm reduction behaviors should be further explored.
Limitations
Limitations are present with the current cross-sectional, observational study. The nature of the study, while administered in a methadone treatment clinic, may present bias of participants who are already engaging in care; the results lack generalizability to the community-wide population of PWID not receiving methadone treatment. The questions asked were also posed as a hypothetical scenario, and are therefore not necessarily indicative of actual engagement in harm reduction behaviors. The small sample size may have also limited our ability to identify other important correlates associated with willingness to use harm reduction behaviors.
Conclusion
While treatment approaches for OUD are a trending area of research and health care, opioid addiction is a complicated matter, with a high likelihood of relapse and overdose (Centers for Disease Control and Prevention, 2020d), justifying the need for a multidimensional approach to limit harm. Harm reduction strategies have been shown to reduce the economic, physical, and emotional toll of the opioid epidemic, thus reinforcing the need for scale-up of harm reduction services and PWID engagement in harm reduction behaviors (Centers for Disease Control and Prevention, 2020b; Centers of Disease Control and Prevention, 2020; National Institutes on Drug Abuse, 2019). Interventions to encourage engagement in harm reduction behaviors should target PWID who have previous overdose experiences and/or engage in concurrent polysubstance use (Centers for Disease Control and Prevention, 2020b; Centers of Disease Control and Prevention, 2020; National Institutes on Drug Abuse, 2019; Schneider et al., 2019). Novel approaches, including FTS, may further improve engagement in harm reduction behaviors (Nuamah et al., 2019; Peiper et al., 2019). This study provides an evidence-based understanding of PWID’s engagement in harm reduction behaviors after suspecting potential fentanyl exposure and insight into tailoring of intervention strategies in the context of fentanyl-adulterated markets.
Table 2:
Bivariate and independent correlates of not using drugs among PWID (N=105)
| Unadjusted OR (95% CI) |
p-value | aOR (95% CI) |
p-value | |
|---|---|---|---|---|
| Age | 1.06 (1.01 – 1.11) | 0.012 | 1.05 (1.00 – 1.10) | 0.061 |
| White | 0.36 (0.14 – 0.92) | 0.030 | -- | -- |
| Income ≥ $10,000 | 0.36 (0.13 – 1.00) | 0.045 | 0.30 (0.10 – 0.89) | 0.030 |
| Concern about fentanyl contamination | 2.79 (1.07 – 7.26) | 0.032 | 2.89 (1.04 – 8.03) | 0.042 |
| Perceived past fentanyl contamination | 0.319 (0.13 – 0.82) | 0.014 | 0.33 (0.12 – 0.92) | 0.035 |
| Ever witnessed an overdose | 0.400 (0.14 – 1.12) | 0.076 | -- | -- |
Table 3:
Bivariate and independent correlates of using drugs slowly (N=105)
| Unadjusted or (95% CI) |
p-value | aOR (95% CI) |
p- value |
|
|---|---|---|---|---|
| Age | 0.96 (0.92 – 1.00) | 0.063 | -- | -- |
| White | 6.19 (1.71 – 22.48) | 0.003 | 4.39 (1.25 – 15.39) | 0.021 |
| Marijuana use | 2.67 (1.19 – 5.99) | 0.020 | 2.44 (1.05 – 5.66) | 0.037 |
| Transactional sex | 0.38 (0.14 – 1.05) | 0.042 | 0.46 (0.16 – 1.30) | 0.143 |
| Knows someone who has died from overdose | 5.22 (0.62 – 44.08) | 0.082 | -- | -- |
Table 4:
Bivariate and independent correlates of carrying naloxone (N=105)
| Unadjusted OR (95% CI) |
p-value | aOR (95% CI) |
p-value | |
|---|---|---|---|---|
| Female | 2.13 (0.92 – 4.93) | 0.074 | 2.14 (0.85 – 5.42) | 0.108 |
| Polysubstance | 8.42 (1.06 – 66.7) | 0.020 | 7.50 (0.91 – 61.87) | 0.061 |
| Perceived past fentanyl contamination | 4.12 (1.13 – 14.97) | 0.025 | -- | -- |
| Willingness to use fentanyl strips | 3.74 (0.80 – 17.52) | 0.088 | 4.27 (0.87 – 21.08) | 0.075 |
| Experienced non-fatal overdose | 3.10 (1.16 – 8.30) | 0.021 | 3.18 (1.05 – 9.67) | 0.041 |
| Ever witnessed someone else overdose | 4.43 (0.96 – 20.54) | 0.051 | 4.95 (1.00 – 24.50) | 0.050 |
| Ever administered naloxone | 2.92 (1.24 – 6.86) | 0.020 | -- | -- |
Table 5:
Bivariate and independent correlates of using drugs with someone carrying naloxone (n=105)
| Unadjusted OR (95% CI) |
p- value |
aOR (95% CI) |
p-value | |
|---|---|---|---|---|
| Age | 0.95 (0.89 – 1.00) | 0.063 | -- | -- |
| Polysubstance use | 8.54 (0.45 – 162.61) | 0.068 | -- | -- |
| Alcohol use disorder | 3.04 (1.09 – 8.52) | 0.027 | 3.45(1.16 – 10.23) |
0.026 |
| Perceived fentanyl contamination | 14.28 (0.78 – 261.00) | 0.011 | -- | -- |
| Administered naloxone to someone* | 5.46 (1.66 – 17.97) | 0.004 | 5.45 (1.68 – 17.75) | 0.005 |
| Prior knowledge of fentanyl strips | 2.70 (0.93 – 7.87) | 0.062 | -- | -- |
Acknowledgments:
This work was supported by grants from the National Institute on Drug Abuse for research (R01 DA032290 to MMC), for career development (K01DA051346 to RS; K24 DA051344 to MMC; K01 DA038529 to JAW), and from the National Institute of Mental Health (T32MH074387-14) to CBM.
Footnotes
Publisher's Disclaimer: This Author Accepted Manuscript is a PDF file of an unedited peer-reviewed manuscript that has been accepted for publication but has not been copyedited or corrected. The official version of record that is published in the journal is kept up to date and so may therefore differ from this version.
Conflict of interest: The authors declare that they have no conflict of interest.
Human participants: The study protocol was approved by the Institutional Review Board at the University of Connecticut and received board approval from the APT Foundation Inc.
Informed consent: The interviewees provided written informed consent before participating in the study.
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