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. Author manuscript; available in PMC: 2020 Nov 20.
Published in final edited form as: Am J Drug Alcohol Abuse. 2020 Apr 3;46(4):485–497. doi: 10.1080/00952990.2020.1725032

Risk factors for heroin use following release from jail or prison in adults in a Central Appalachian state between 2012–2017

Kirsten Elin Smith a,b, Adrian Archuleta b, Michele Staton a,c, Erin Winston a
PMCID: PMC7678949  NIHMSID: NIHMS1619277  PMID: 33223579

Abstract

Background:

Corrections-involved adults with a history of opioid use disorder are at elevated risk of opioid overdose following release from correctional settings. Increased opioid prescribing restrictions and monitoring during a time when heroin is becoming cheaper and ubiquitous means that adults who misused prescription opioids prior to incarceration may be reentering communities at greater risk for heroin exposure and use.

Objectives:

Determine risk factors of post-release heroin use among a sample of adults who participated in corrections-based drug treatment in Kentucky released between 2012 and 2017.

Methods:

Survey data obtained as part of an ongoing evaluation of corrections-based drug treatment were examined.

Results:

The final sample (N = 1,563) was majority male (80.9%). Nearly 11.0% reported past-year heroin use following their release. Depressive symptoms, polydrug use, and urban proximity were more common among participants reporting post-release heroin use. Heroin use 30 days prior to incarceration was associated with a 432.1% increase in odds of heroin use subsequent to incarceration. Post-release suicidal ideation increased odds of heroin use by 154.2%, whereas reporting satisfaction from social interactions decreased odds of use by nearly 60%. Post-release use of cocaine and diverted buprenorphine were associated with increased likelihood of heroin use during this time period, increasing odds by 469.1% and 265.9%, respectively. Residing in Central Appalachia subsequent to incarceration was associated with decreased likelihood of use.

Conclusions:

In this sample, post-release heroin use was associated with concerning features, such as polydrug use, lack of social satisfaction, and suicidal ideation. These features can serve as clear targets for clinical intervention.

Keywords: Heroin, suicidal ideation, community reentry, polydrug use, opioids, criminal justice

Introduction

Opioid misuse entails risk. Compared to other drugs, opioids are more likely to result in overdose and to also be perceived by survivors as comparatively worse overdose experiences (1,2). Heroin, in particular, entails extreme risk. Still, in 2017, nearly half a million persons aged 12 or older reported past-month heroin use (3). As opioid-related fatalities have increased in the United States, no group has been spared and no region unaffected (4–6). However, corrections-involved adults are overrepresented among opioid overdose fatalities, and criminal justice involvement is a risk factor for opioid overdose (7–10). Residents of rural, Central Appalachian regions have also been uniquely impacted by the opioid epidemic, with high per capita opioid-related overdose fatalities (11–15). Accordingly, corrections-involved adults with a history of drug use residing in Central Appalachia are a population that warrants further investigation and intervention.

The Kentucky context

Kentucky, a state that comprises part of Central Appalachia, has high rates of non-medical prescription opioid (NMPO) and heroin misuse (3,16). Though historically heroin use in the US has been more endemic in urban/metro areas, compared to rural and Appalachian regions where NMPO use is a perineal public health concern, this is gradually changing (17–22). Continued NMPO use in rural and Appalachian Kentucky is attributable to high regional rates of opioid prescribing and diversion and to a geography that can make accessing heroin in urban markets more resource- and time-intensive (23–26). Additionally, a large proportion of Kentucky adults are incarcerated or on communitysupervision (e.g., probation, parole), many with opioid use disorder (OUD) (27–32). Though this population is provided with substance use intervention by the Kentucky Department of Corrections (KY DOC), use of opioid agonist therapies (OAT), such as buprenorphine and methadone, are not yet commonplace in Kentucky correctional facilities, similar to most other state and federal corrections systems (33–35). Because of this, incarcerated adults in Kentucky with OUD may be at elevated risk for overdose subsequent to release, should opioid use resume.

Post-release risk may be greater if heroin, rather than NMPO, is used, given that heroin is readily injectable and increasing adulterated with fentanyl (36–39). Psychiatric issues or polydrug use may increase the risk further (9,40). More broadly, community reentry entails significant stress and reintegration into environments where opioids were previously used or into relationships in which use was a feature (41). This means that the reentry process, a time of high stress and exposure to old environments, has the potential to contribute to the development or compounding of existing psychiatric symptoms, as well as to cue-induced craving (42–45). Because recently incarcerated adults with OUD are socially stigmatized and may have fewer supports and less access to rewarding non-drug alternatives following their release, stress may be exacerbated, apparent opportunity costs of use fewer, and perceived utility of opioids greater (46,47). Thus, reentry can be conceptualized as an intersection of risk among socially marginalized people who used opioids prior to incarceration (48–50). Because Central Appalachia is economically distressed, has less population density, greater rurality, and is associated with “deaths of despair,” residence in this region may also reflect greater difficulty accessing potentially rewarding non-drug alternatives, such as education, career, and positive social enrichment (51,52). It is unclear, however, if heroin use is becoming more common among corrections-involved people who reside in rural and Appalachian areas, rather than urban/metro areas outside of Appalachia.

Study aims

This study sought to determine risk factors of post-release heroin use among a sample of adults who participated in corrections-based drug treatment in Kentucky using institutional survey data. It was hypothesized that: (1) opioid use prior to incarceration would increase odds of post-release heroin use; (2) a greater proportion of participants who reported post-release heroin use would report psychiatric issues, compared to those who did not report heroin use; (3) potentially rewarding non-drug alternatives present during the post-release period would be associated with decreased odds of heroin use; (4) odds of post-release heroin use would decrease as a function of Central Appalachian residence.

Methods

Survey data collected as part of an ongoing evaluation of the KY DOC Substance Abuse Treatment Program (SAP) were examined for this secondary analysis. For this study, baseline and follow-up data for participants released between 2012 and 2017 were examined. During follow-up tracking, research staff used information provided by participants at baseline and institutional records to target flyers, calls, and social media (53,54). Methods were approved by the university Institutional Review Board (see supplementary materials for additional information about SAP and data collection).

Measures

The baseline survey was developed by the university in partnership with KY DOC. A large portion of both baseline and follow-up surveys were comprised of questions adapted from the Addiction Severity Index (ASI) (5th edition) (55), a public domain instrument that has been used among a variety of populations to assess drug use, drug-related problems, and prior treatment (56–60).

Dependent variable

Post-release heroin use was measured at follow-up by dichotomizing participant responses to the question, “During the past 12 months, how many months did you use did you use heroin?” (0 months = none vs. ≥1 = “post-release heroin use”).

Independent variables

Please refer to Table 4, provided in the supplementary materials, for additional description of the independent and control variables examined in this study outlined below.

Demographic data examined include age, sex, race, and education.

Urban proximity was measured using the 2013 Urban Influence Codes, a 12-part categorization scheme designating counties by metropolitan (i.e., counties in metro areas with 20,000 to >1 million residents) and non-metropolitan (i.e., counties adjacent/not adjacent to metro areas, but not in metro areas) status into two metro and ten nonmetro classifications (61).

Appalachian residence was also measured using participant county of residence. Cases were coded “Appalachia” if they were one of 54 Kentucky counties within the Central Appalachian region (62).

Psychiatric indicators for the post-release period included past-year anxiety symptoms, depressive symptoms, and suicidal ideation. Abstinence self-efficacy was also measured at baseline.

To investigate potential non-drug alternatives available to participants following their release, responses for follow-up survey items pertaining to close relationships, social interaction, social satisfaction, and educational/vocational involvement were examined.

Drug use history included examining age of drug use initiation, intravenous drug use (IDU) history, drug use prior to incarceration (drug use that occurred 30 days prior to arrest), and post-release drug use (drug use that occurred during the year subsequent to release).

Control variables

Prior drug treatment episodes, nights spent in a controlled environment post-release, SAP completion status, post-release correctional status (e.g. probation/parole), and having been mandated to SAP were examined.

Analysis

Descriptive statistics were used to characterize the sample. Between-group differences were examined using t-test and chi-square test for independence. Binary logistic regression was used to examine the relationships between heroin use and independent variables. Variables for which significant difference was observed (p < .05) in bivariate analyses were included in the models. Data were analyzed using Stata/SE version 15 (63).

Results

Participants who did not reside in Kentucky for the majority of months prior and subsequent to incarceration (n = 102) were removed, resulting in a final sample size of 1,563. Table 1 displays summary statistics of participants who reported using heroin during the one-year post-release period. For this time period, 10.7% (n = 168) of the sample reported past-year heroin use.

Table 1.

Summary statistics and differences between participants who reported post-release heroin use and those who did not.

N (%) Sample 1,563 Heroin 168 10.7 No Heroin 1,395 89.3 p-Value
Age 33.9 31.5 34.9 .001
Male 80.9% 76.8% 81.4% .179
White 81.5% 96.4% 79.7% .001
High school diploma/GED 71.2% 76.2% 70.6% .161
Central Appalachia 30.3% 17.3% 31.9% .001
Urban influence 4.5 2.7 4.7 .001
Anxiety symptoms 40.7% 45.8 40.1 .180
Depressive symptoms 32.7% 45.2 31.2 .001
Suicidal ideation 4.5% 9.5% 3.9% .002
Abstinence self-efficacy prior to treatment 81.5% 73.2% 82.5% .005
Close relationships 6.1 5.8 6.2 .379
Social interaction (vs. alone) 88.4% 84.5% 88.9% .122
Social satisfaction 88.9% 76.2% 90.5% .001
Educational/vocational involvement 67.1% 65.5% 67.3% .705
Age of drug use initiation 14.7 13.6 14.8 .010
IDU history 42.9% 66.1% 39.9% .001
Past 30-day drug use prior to incarceration Alcohol 49.5% 44.6% 50.1% .209
Cannabis 52.5% 50.6% 52.8% .653
Sedatives 32.3% 45.2% 30.8% .001
Hallucinogens 3.2% 5.4% 2.9% .147
Synthetics 7.7% 10.1% 7.4% .269
Cocaine/crack cocaine 23.2% 32.1% 22.2% .005
Amphetamines 31.3% 31.0% 31.3% .992
Heroin 17.7% 54.2% 13.3% .001
Prescription opioids 50.7% 71.4% 48.2% .001
Diverted Buprenorphine 18.7% 25.0% 17.9% .034
Past-year drug use, post-release Alcohol 31.5% 42.3% 30.3% .002
Cannabis 25.5% 45.8% 23.0% .001
Sedatives 8.0% 25.6% 5.9% .001
Hallucinogens 1.2% 5.4% 0.6% .001
Synthetic drugs 7.0% 8.9% 6.8% .393
Cocaine/crack cocaine 7.4% 28.6% 4.8% .001
Amphetamines 15.2% 37.5% 12.5% .001
Prescription opioids 14.3% 38.7% 11.3% .001
Buprenorphine 9.4% 33.3% 6.5% .001
Lifetime drug treatment episodes 2.2 3.3 2.1 .002
Completed SAP 78.3% 3.8% 78.9% .160
Nights controlled environment 43.6 68.7 40.6 .001
On community supervision 87.0% 88.1% 86.8% .732
Mandated to SAP 73.0% 67.9% 73.6% .133
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SAP = Substance Abuse Treatment Progam

Demographics

The heroin-use group was slightly younger than the nonuse group (31.5, Standard Deviation [SD] = 7.3 vs. 34.9, SD = 9.1, t = 5.4, p < .001), though did not differ significantly from the nonuse group in the proportion of males (76.8% vs. 81.4%, X2 = 1.81, p = .179). Compared to the nonuse group, the majority of the heroin-use group was White (96.4%, X2 = 26.86, p < .001). Groups were similar for HSD/GED (76.2% vs. 70.6%, X2 = 2.21, p =.161).

Central Appalachia and urban influence

The heroin-use group had a smaller proportion of participants who resided in Central Appalachia post-release (17.3% vs. 31.9%, X2 = 15.21, p < .001). The mean urban influence designation for the heroin-use group was lower, indicating greater urban/metro area influence among this group (2.7 vs. 4.7, p < .001).

Psychiatric indicators and abstinence self-efficacy

Just under half of the heroin-use group reported experiencing past-year anxiety, similar to the rates among the nonuse group (45.8% vs. 40.1%, X2 = 1.78, p = .180). Groups differed for past-year depressive symptoms (45.2% vs 31.2%, X2 = 12.68, p < .001) and suicidal ideation, the latter nearly three-times higher among the heroin-use group (9.5% vs. 3.1%, X2 = 9.92, p = .002). This group also had lower rates of “moderately-very good” abstinence self-efficacy prior to treatment (73.2% vs. 82.5%, X2 = 79.9, p = .005).

Potential non-drug alternatives

Groups were similar for average number of close relationships (5.8, SD = 6.6 vs. 6.2, SD = 5.3, t = 0.87, p = .379), having past-year social interaction (84.5% vs. 88.9%, X2 = 2.30, p = .122), and educational/vocational involvement (65.5% vs. 67.3%, X2 = 0.14, p = .705). However, groups differed for social satisfaction. Specifically, the heroin-use group had significantly lower rates of receiving satisfaction from social interaction, with 76.2% of the heroin-use group reporting satisfaction compared to 90.5% of the nonuse group (X2 = 29.61, p < .001).

Age of drug use initiation and IDU history

On average, the heroin-use group was younger at the time they first used alcohol or illicit drugs compared to the nonuse group (13.5, SD = 4.1 vs. 14.8, SD = 5.1, t = 2.6, p = .010). IDU history was greater among the heroin-use group (66.1% vs. 39.9%, X2 = 41.12, p < .001).

Past 30-day drug use prior to incarceration

Rates of pre-incarceration alcohol use were similar across groups, with 44.6% of the heroin-use group and 50.1% of the nonuse group reporting use (X2 = 1.58, p = .209), whereas rates of pre-incarceration cannabis use did not differ between groups (X2 = 0.20, p = .653). The heroin-use group reported higher rates of sedative (45.2% vs. 30.8%, X2 = 13.73, p < .001), hallucinogen (5.4% vs. 2.9%, X2 = 2.10, p = .147), and synthetic drug use prior to incarceration (10.1% vs. 7.4%), though groups did not differ significantly for the latter (X2 = 1.22, p = .269). Rates of cocaine/crack cocaine use prior to incarceration were higher among the heroin-use group (32.1% vs. 22.2%, X2 = 7.85, p = .005), though for amphetamines, rates were nearly equivalent at approximately 31.0% (X2 = 0.10, p = .922). For opioids, rates of heroin use prior to incarceration were higher among the heroin-use group (54.2% vs. 13.3%, X2 = 168.67, p < .001). Similarly, the heroin-use group reported using NMPO (71.4% vs. 48.2%, X2 = 31.52, p < .001) and diverted buprenorphine at higher rates (25.0% vs. 17.9%, X2 = 4.49, p = .034).

Post-release drug use

Just over 40% of the heroin-use group reported past-year alcohol use compared to 30.3% of the nonuse group (X2 = 9.47, p = .002). The heroin-use group also had higher rates of cannabis (45.8% vs. 23.0%, X2 = 39.96, p < .001), sedative (X2 = 76.57, 25.6% vs. 5.9%, p < .001), hallucinogen (5.4% vs. 0.6%, X2 = 25.25, p < .001), and synthetic drug use (8.9% vs. 6.8%), though groups did not significantly differ for the latter (X2 = 0.73, p = .393). Cocaine/crack cocaine was used during the post-release period at a far higher rate by the heroin-use group (28.6% vs. 4.5%, X2 = 120.81, p < .001), as were amphetamines (37.5% vs. 12.5%, X2 = 70.42, p < .001). Nearly 39% of the heroin-use group reported using NMPO post-release compared to 11.3% of the nonuse group (X2 = 89.57, p < .001). Sample-wide, diverted buprenorphine was used post-release by 9.4%. Among the heroin-use group, however, 33.3% reported diverted buprenorphine use, nearly five times that of the nonuse group (6.5%, X2 = 123.36, p < .001).

Control variables

The heroin-use group reported a greater average number of lifetime drug treatment episodes (3.3, SD = 4.8 vs. 2.1, SD = 3.7, t = −3.10, p = .002). Groups were similar for SAP completion (73.8% vs. 78.9%, X2 = 1.96, p = .160). During the post-release period, the heroin-use group spent more nights on average in a controlled environment (68.7, SD = 78.0 vs. 40.6, SD = 77.0, t = −4.40, p < .001). Being on community supervision did not differ between groups (88.1% vs. 86.8%, X2 = 0.11, p = .732). Additional analyses revealed no statistically significant difference between groups for rates of being mandated to SAP, versus voluntary enrollment.

Binary logistic regression results

Due to the number of variables that differed significantly in bivariate analyses, two models were used to examine relationships of interest in order to avoid model overfitting. Model 1 examined associations between heroin use and variables hypothesized to correlate with use. Model 2 examined associations between heroin use and other drug use during the post-release period. Collinearity was assessed via variance inflation factor and was not an issue, with the exception of urban influence and Central Appalachia, attributable to the overall greater rurality of Appalachia. Central Appalachia was retained in model 1 as it reflects less urban influence in addition to residence within a distressed region, both of which are of interest.

Model 1

Model 1 results are presented in Table 2. Figure 1 displays the percent change in odds of post-release heroin use. Participants who reported post-release heroin use were more likely to be younger (Adjusted Odds Ratio [AOR] = 0.96, p = .002) and White (AOR = 4.84, p < .001), but less likely to have resided in Central Appalachia for the majority of time post-release (AOR = 0.38, p < .001). Though experiencing any past-year depressive symptoms were not associated with a statistically significant increase in odds of heroin use (AOR = 1.42, p = .089), suicidal ideation was associated with a 154.2% increase in odds (AOR = 2.55, p = .017). Receiving social satisfaction was associated with a 58.7% decrease in odds (AOR = 0.42, p < .001). Among drugs used during the 30-day period prior to incarceration, heroin (AOR = 5.32, p < .001) and NMPO (AOR = 2.05, p = .002) were associated with an increased likelihood in post-release heroin use, with the former increasing odds by 432.1%. Amphetamine use prior to incarceration was associated with decreased odds of post-release heroin use (AOR = 0.58, p = .008). Use of sedatives (AOR = 0.95, p = .812), cocaine/crack cocaine (AOR = 1.43, p = .80), and diverted buprenorphine (AOR = 0.72, p = .154) prior to incarceration were not associated with a change in odds. Those reporting post-release heroin use were more likely to have spent a greater number of nights in a controlled environment (AOR = 1.00, p = .005).

Table 2.

Model 1 results of binary logistic regression examining post-release heroin use.

Adjusted Odds Ratio Standard Error 95% Confidence Interval p-Value
Age 0.96 0.011 0.94–0.99 .002
White 4.84 2.177 2.00–11.06 .001
Central Appalachia 0.38 0.091 0.24–0.60 .001
Past-year depressive symptoms 1.42 0.290 0.95–2.11 .089
Past-year suicidal ideation 2.55 0.996 1.17–5.42 .017
Abstinence self-efficacy prior to treatment 0.90 0.192 0.60–1.37 .654
Social satisfaction 0.42 0.101 0.25–0.67 .001
Age of drug use initiation 0.99 0.022 0.95–1.04 .903
Ever IDU 1.34 0.296 0.87–2.07 .180
30-day drug use prior to incarceration Prescription sedatives 0.95 0.203 0.62–1.44 .812
Cocaine/crack cocaine 1.43 0.297 0.96–2.15 .080
Amphetamines 0.58 0.121 0.38–0.87 .008
Heroin 5.32 1.191 3.54–8.25 .001
Prescription opioids 2.05 0.468 1.31–3.21 .002
Buprenorphine 0.72 0.168 0.46–1.13 .154
Lifetime drug treatment episodes .1.02 0.022 0.98–1.06 .285
Nights spent in controlled environment 1.00 0.001 1.00–1.01 .005
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Intercept-only: −533.336/Model: −405.742 Pseudo R2 = 0.239.

Wald Chi2 (df = 18) 220.306 Cox-Snell/ML = 0.151.

Hosmer-Lemeshow Chi2 = 2.39, p = .966 Cragg-Uhler/Nagelkerke = 0.305.

Figure 1.

Figure 1.

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Model 1 percent change in odds of post-release heroin use. *Denotes statistical significance p < .05.

Model 2

Results of model 2 are presented in Table 3. Percent change in odds are displayed in Figure 2. Sedatives (AOR = 1.82, p = .043), cocaine/crack cocaine (AOR = 5.70, p < .001), amphetamines (AOR = 1.71, p = .029), NMPO (AOR = 1.72, p = .030), and diverted buprenorphine (AOR = 3.66, p < .001) used post-release were all associated with an increased likelihood of heroin use. Post-release cocaine/crack cocaine and diverted buprenorphine use increased odds by 469.1% and 265.9%, respectively. Alcohol (AOR = 0.85, p = .446), cannabis (AOR = 1.40, p = .119), and hallucinogens (AOR = 1.61, p = .397) were not associated with a change in odds, though these findings and others should be considered in light of the fact that this model controlled for all other substance use.

Table 3.

Model 2 results of binary logistic regression examining post-release heroin and post-release other drug use.

Adjusted Odds Ratio Standard Error 95% Confidence Interval p-Value
Alcohol 0.85 0.179 0.56–1.28 .446
Cannabis 1.40 0.302 0.92–2.13 .119
Prescription sedatives 1.82 0.540 1.01–3.22 .043
Hallucinogens 1.61 0.908 0.53–4.66 .397
Cocaine/crack cocaine 5.70 1.44 3.48–9.34 .001
Amphetamines 1.71 0.421 1.06–2.76 .029
Prescription opioids 1.72 0.431 1.06–2.81 .030
Buprenorphine 3.66 0.907 2.25–5.91 .001
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Intercept-only: −533.336/Model: −436.689 Pseudo R2 = 0.181.

Wald Chi2 (df = 8) 189.581 Cox-Snell/ML = 0.116.

Hosmer-Lemeshow Chi2 = 15.56, p = .082 Cragg-Uhler/Nagelkerke = 0.235.

Figure 2.

Figure 2.

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Model 2 percent change in odds of post-release heroin use. *Denotes statistical significance p < .05.

Discussion

This study examined post-release heroin use among a sample of adults who participated in corrections-based drug treatment in Kentucky and were released between 2012 and 2017.

Findings show a relationship between any opioid use prior to incarceration and heroin use subsequent to incarceration. That opioid use prior to incarceration was associated with heroin use subsequent to incarceration is both unsurprising and worth emphasizing. It is unsurprising because lapses and relapses subsequent to intervention or periods of abstinence are well documented (64–66). It bears emphasis because this datum provides a clear risk indicator that is easy to assess among adults entering correctional systems and is actionable as a target for institutional authorities and clinicians (67–69).

Heroin use prior and subsequent to incarceration may signify that heroin was a preferred drug for some, but more work is needed to explore this possibility. Upon reentry, other participants may have transitioned from prescription opioids to heroin due to increases in abuse-deterrent prescription opioid formulations, decreased prescription opioid supply, and commensurate increased barriers to and costs of accessing these drugs illicitly (70–73). In another study, heroin use among corrections-involved adults in Kentucky was found to have increased 204% between 2008 and 2016, a time when opioid prescribing was becoming more restrictive and surveilled (74). Some participants may have also been influenced by proximity to, and differing characteristics of, illicit drug markets. This is evidenced by findings which support the hypothesis that post-release heroin use would be negatively correlated with residence in Central Appalachia. Between 2012 and 2017, prescription opioids were more readily accessible in rural and Appalachian parts of Kentucky, whereas heroin availability and use in urban/metro areas was increasing (26,75–77). While heroin availability and use continue to increase in rural and Appalachian regions, during the time data were collected it was less available than other drugs (12,78). That cocaine accessibility and use is often associated with urban/metro areas contextualizes the observation that post-release cocaine use was significantly associated with heroin use (76,79,80); however, cocaine use is also documented across rural and urban areas (81–83). Sedatives, NMPO, and amphetamines, while available in urban/metro areas, are more ubiquitous and culturally normed in rural and Appalachian regions, due in part to greater prescribing density in these areas and subsequent diversion potential (11, 24, 84–86). The positive correlation between post-release diverted buprenorphine and heroin use is less clear, as per capita buprenorphine prescriptions are higher in Appalachian Kentucky, meaning diversion potential is possibly greater (25). Indeed, other studies have documented widespread diverted buprenorphine use in this region (30,87).

Ultimately, that polydrug use was greater among the heroin-use group and that post-release use of multiple drugs was associated with increased odds of using heroin means that many who reported using a high-risk drug like heroin following their release were also possibly using other drugs contemporaneously with heroin, increasing risk further. Opioids, when co-used with stimulants or benzodiazepines, can significantly increase the likelihood of overdose and adverse events (88–91).

Findings also support the idea that psychiatric indicators would be greater among those who reported post-release heroin use, compared to participants who did not use heroin following their release. Incarceration is commonly associated with deterioration of psychiatric and physical health (92–94). This, coupled with the difficulties inherent to community reentry and belonging to a marginalized group, helps contextualize the fact that 32.7% of the sample reported experiencing depressive symptoms following their release and that 4.5% of the sample reported suicidal ideation. It does less to articulate the relationships between depressive symptoms and heroin use and suicidal ideation and heroin use, which are intriguing but cannot be fully explicated here. It may be that heroin use begat depressive symptoms, including suicidal ideation, or that the converse occurred. It is also possible that, for some, heroin use was a means to an end, as intentional drug overdoses are a real but underexplored phenomenon (95–98). For participants, risk may have been well known and possibly even embraced (99–101).

Finally, the hypothesis that potentially rewarding non-drug alternatives would be associated with decreased likelihood of post-release heroin use was partially supported by findings. Post-release close relationships, social interaction, and educational/vocational involvement did not differ between groups; however, receiving satisfaction from social interaction was associated with decreased odds. One straightforward interpretation is that the presence of alternatives with the potential to be rewarding means less than their actually being rewarding or satisfying, or at least perceived as such. Lack of satisfaction with post-release social interaction may reflect many conditions, such as depressive symptoms or strained relationships. More work is needed to better understand social contexts associated with high-risk opioid use following incarceration. That satisfaction with post-release social interaction decreased odds of heroin use does reinforce the importance of moving beyond traditional conceptualizations of “risk” and “protective” factors to instead prioritize understanding people’s perceptions, such as the degree to which they feel vested in or rewarded by social and other non-drug alternatives (102). For example, “being employed,” traditionally considered a “protective” factor, may indeed indicate the presence of a rewarding non-drug alternative, but it may also indicate a source of stress, fatigue, or disillusionment (103,104). Due to low rates of post-secondary education in this sample, career options may be limited. Some of the work that participants in this sample may have engaged in could be considered labor-intensive, exploitative, dangerous, or tedious, meaning that employment could be conceptualized as punishing rather than rewarding (105–108), counterintuitively reinforcing rather than decreasing use.

Limitations

This study has several limitations. Participants were asked to report past-year heroin use, but dosing frequency was not measured, meaning that some participants may have used heroin the majority of days every month subsequent to release or used far less frequently. Although findings suggest that participants used multiple drugs contemporaneously, it was not possible to determine if heroin and other drugs were used concomitantly in real-time. That post-release cocaine and amphetamine use were associated with heroin use may indicate that some participants preferred to use these drugs concomitantly. For instance, speedballing (i.e., co-using cocaine and opioids) or goofballing (i.e., co-using amphetamines and opioids). However, preferences and patterns were not reported and thus cannot clarify this finding. Nor can the relationship between post-release diverted buprenorphine and heroin use be clarified, given that temporal order was not established. It may be that diverted buprenorphine use occurred subsequent to heroin discontinuation as a form of self-medication, as this has been observed elsewhere (109–111). Participants were also not asked to report known fentanyl use. Such information would have helped to characterize post-release risk further. Depressive symptoms were higher among participants who reported post-release heroin use; however, psychiatric indicators, such as depression, were not assessed for severity and may have been underreported. Though past-year depressive symptoms were not associated with increased odds of heroin use this may be an artifact of statistical analyses as this variable also controlled for suicidal ideation. Although secondary data analyses of institutional survey data provide one step in the formative research process, future work will need to use validated measures for constructs such as social satisfaction, depression, self-efficacy, etc. Moreover, non-drug alternative reinforcers available to participants will need to be conceptualized and operationalized with greater specificity; temporal relation and physical proximity of these to drug availability and use will also need to be determined. Because the sample is corrections-involved and resided in one southeastern state limits generalizability. Lastly, self-report introduces potential threats to validity (e.g., poor recall, mendacity, social desirability), though this method is established as valid for data collection among drug-using populations (112–114).

Conclusion

Among adults reentering the community from correctional settings, prior heroin use should be considered as possible predictor of continued heroin use following release. As the availability of heroin increases further, it may be that any opioid use prior to incarceration be treated as a risk factor for post-release heroin use. Because adults exiting jails and prisons often have poorer psychiatric health and social supports during a time of increased stress and exposure to drug-related cues, the risk of relapse and overdose among people with OUD should be anticipated by healthcare and corrections officials. It is important, then, not only that OUD severity and psychiatric symptoms be assessed promptly after a period of incarceration begins (in jail prior to final sentencing or any corrections-based treatment), but also prior to release and as part of a coordinated reentry preparation that includes harm-reduction education and referrals to community-based OAT providers. Decreases in opioid prescribing and increases in prescription monitoring and NMPO street prices as heroin continues to become cheaper and more available means that the opioid misuse landscape in rural and Appalachian Kentucky will likely continue to shift toward heroin, with younger users the vanguard (73,115–117). More work is needed to better measure and understand the roles of drug market dynamics within and across locations (118). Future studies should also work toward real-time measurement of craving, stress, and drug use, and the ecological conditions in which they occur following prolonged confinement in controlled environments. Such work might help elucidate patterns of behavior with greater contextualization and accuracy (119). In the interim, correctional settings should prioritize implementation of scientifically informed OUD interventions, like OAT, that includes post-release OAT continuity of care. Such intervention has been shown to be life-saving for corrections-involved people with OUD, who are at increased risk of overdose and death in the initial weeks and months following their release (10,120–122).

Supplementary Material

Risk factors for heroin use

Footnotes

Disclosure of Interest

The authors declare that there is no conflict of interest.

Supplemental data for the article can be accessed on thepublisher’s website

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Supplementary Materials

Risk factors for heroin use
NIHMS1619277-supplement-Risk_factors_for_heroin_use.pdf (195.6KB, pdf)

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