Abstract
Substance use disorder (SUD) and drug overdose deaths represent major economic, health, and safety issues in the United States. The psychology of uncertainty provides a mechanism for understanding, reducing, and controlling the damage from substance misuse. Illicit drugs (such as heroin or cocaine) are uncertain in their availability, quality, and acquisition (the time and effort required to obtain them) compared with nondrug-related alternatives (such as consumable goods, hobbies, or paychecks). Furthermore, the severity and likelihood of negative outcomes associated with drug use likewise are uncertain. Such uncertainties worsen substance use outcomes. The current review conveys what is known about the impact of uncertainty on substance use: laboratory investigations of uncertain time and effort required to obtain a substance and uncertain drug quality show uncertainty exacerbates harm. Furthermore, uncertain negative outcomes are not likely to deter substance use in individuals with a SUD. Finally, several policy implications include access to agonist medications; creating a safer drug supply; access to clean syringes/needles, naloxone, and safe-injection sites; and ending incarceration for substance use.
Keywords: substance use disorder, uncertain outcomes, variable outcomes, reinforcement, punishment
Tweet
Environmental uncertainty related to substance use exacerbates harm and does not deter drug-taking. Policies should increase predictability and end incarceration for substance use.
Introduction
Substance use disorder (SUD) and overdose deaths have been major economic, health, and safety issues in the United States for decades. However, the opioid crisis, recent COVID-19 pandemic, and continued “War on Drugs,” among other factors, have contributed to perhaps the worst crisis related to substance use in recent history. Drug overdose deaths reached the highest level ever recorded at 93,655 in 2020, and 107,622 are estimated for 2021 (Centers for Disease Control and Prevention [CDC], 2022; U.S. Department of Health and Human Services [HHS]). The estimated economic impact of illicit substance use is $193 billion annually (HHS), but with the rising rate of opioid misuse and overdose deaths during the COVID-19 pandemic, the Joint Economic Committee (JEC) estimated that the opioid crisis alone cost $1.47 trillion in 2020 (JEC Democrats, 2022). While effective treatments exist, few individuals with a SUD receive treatment. Results from the 2020 National Survey on Drug Use and Health indicate only 6.5% of individuals with a SUD reported receiving treatment (SAMHSA, 2021).
Several genetic, environmental, and societal factors contribute to SUDs. This review focuses on environmental uncertainty. Illicit drugs such as heroin or cocaine are uncertain in their availability, quality, and the time and effort required to obtain them, compared to nondrug alternatives such as consumable goods, hobbies, or paychecks (Huskinson, 2020). Furthermore, substance use also creates uncertainty in the severity and likelihood of negative outcomes. The current review conveys what is known about two types of environmental uncertainty and their impact on substance use. The first section defines SUD and key behavior principles. The next section focuses on laboratory investigations of drugs’ uncertainty, in time and effort required to obtain them and in drug quality, followed by uncertain negative outcomes that result from drug use. Finally, some policy implications follow from the reviewed research.
Behavior Principles: Reinforcement and Punishment
Understanding substance-related reinforcers and punishers should guide policies to prevent and treat SUDs. The Diagnostic and Statistical Manual of Mental Disorders (DSM; American Psychiatric Association [APA], 2013) defines SUD according to 11 criteria related to escalated use, physical dependence and withdrawal, and continued use despite negative consequences associated with drug use. Although the field is divided about whether SUD is a brain or behavioral disorder (e.g., Heilig et al., 2021; Heyman, 2009; Lamb & Ginsburg, 2018), a primary premise of either position is that individuals should have access to treatment, as one would for other behavioral, physiological, or brain illnesses, such as diabetes, hypertension, anxiety, depression, or others. Both perspectives argue that SUD is not a moral failing or reflective of a weak character and not to punish or incarcerate individuals because of substance use. Punishing an individual for their SUD would be akin to punishing someone for failing to appropriately control their hypertension or symptoms of depression. This review’s research is rooted in behavioral principles, and therefore focuses on a behavioral perspective of SUD.
A behavioral perspective posits that behavior is a result of its consequences. This pairing of behaviors with consequences is a crucial aspect in the development and maintenance of SUDs. Some consequences strengthen or maintain behavior, increasing the likelihood that the behavior will be repeated, and these are reinforcers. Conversely, consequences that weaken behavior and reduce the likelihood that the behavior will be repeated are punishers. Reinforcement and punishment are further divided into positive and negative reinforcement (or punishment). Rather than conceptualizing positive as good and negative as bad, these terms are simply meant to describe whether the consequence is presented (positive) or removed (negative). Therefore, positive reinforcement is the presentation of a consequence, contingent on a behavior, that increases the likelihood of that behavior reoccurring. For example, an individual may use a substance because it produces pleasurable effects that increase the likelihood of taking the substance again. As drug-taking persists, a tolerance to the substance develops, resulting in a need to use more to produce the same positive reinforcing effects that were produced initially. Negative reinforcement is the removal or avoidance of a consequence, contingent on a behavior, that increases the likelihood of that behavior reoccurring. In our example, as the frequency and quantity of substance use increases, one can become physically or psychologically dependent and may require the substance to feel normal or avoid withdrawal symptoms. The individual’s behavior that was once governed by positive reinforcement is now governed by negative reinforcement. This is a simplified example of one of many ways that positive or negative reinforcing effects can strengthen substance use. Other examples of positive or negative reinforcing effects can include taking substances because of peer influences, to alleviate symptoms of anxiety or depression, or to cope with trauma.
Conversely, positive punishment is the presentation of a consequence, contingent on a behavior, that decreases the likelihood of that behavior reoccurring. For example, if a person develops a hangover after a night of heavy alcohol drinking, they may be less likely to drink alcohol or may drink less in the future. Other examples can include aversive effects such as nausea or toxicity, the latter sometimes resulting in overdose. Negative punishment is the removal of a stimulus, contingent on a behavior, that decreases the likelihood of that behavior reoccurring. For example, an individual may lose certain rights/privileges, relationships, or social/recreational activities as a result of their substance use. Because most substances are illegal in the United States, other aversive consequences can include trouble with law enforcement or exposure to crime-related violence. For many, these consequences may act as punishers and deter substance use. For others, substance use persists despite the presence of such consequences, perhaps because the reinforcing effects outweigh potential and uncertain punishing effects. Understanding how reinforcement and punishment contribute to SUDs is essential in developing policies to help in their prevention and treatment. Here, this review focuses on uncertainty in the reinforcing (uncertain time and effort or quality of drugs) and punishing (uncertain negative outcomes) processes of substance use and how such uncertainty may contribute to worsened substance-use outcomes.
Uncertain Time and Effort or Quality of Drugs
Access to illicit drugs involves uncertainty in the time and effort required to obtain them and in the quality of the drug supply compared with nondrug-related alternatives. The relative uncertainty of illicit substances lies on a continuum, anchored by individuals in resource-rich environments, who may have ready and predictable access to drugs. Those with relatively higher incomes who use cocaine or heroin report higher amounts and frequencies of use (Greenwald & Steinmiller, 2014; Roddy & Greenwald, 2009; Roddy et al., 2011). On the other hand, in those experiencing low incomes, unemployment, or homelessness, access to illicit substances may be available under relatively large time and effort requirements, for relatively small amounts, and under uncertain conditions. These individuals do not have the financial resources to purchase large amounts of drugs on demand, and those with relatively lower incomes who use cocaine or heroin report lower amounts and frequencies of use (Greenwald & Steinmiller, 2014; Roddy & Greenwald, 2009; Roddy et al., 2011). Under such scarce conditions, those with SUD may spend the majority of their time engaging in a variety of behaviors to earn sufficient funds to purchase drugs. This cycle of drug procurement may repeat daily or multiple times a day because small quantities are purchased at a time. On the other hand, some aspects of drug procurement may not be particularly uncertain. Those who use heroin report living near and having a relatively reliable supplier (Roddy & Greenwald, 2009; Roddy et al., 2011). Therefore, large and uncertain cost requirements seem to be related to the time and effort required to earn sufficient funds to purchase drugs and not necessarily the monetary cost.
Additionally, drug producers and dealers frequently use cutting agents (e.g., Broséus et al., 2016; Fiorentin et al., 2019), creating uncertain drug quality. Individuals with SUDs have reported getting “blanks” or receiving poor-quality drugs from dealers (Szalavitz, 2017) and estimate a wide range of purity of their heroin supply (2%–100%; Roddy & Greenwald, 2009; Roddy et al., 2011). Seized drugs vary widely in purity (e.g., Fiorentin et al., 2019). In addition to cutting agents, fentanyl and fentanyl analogs are increasingly reported in illicit opioids sold as heroin as well as in illicit stimulants (e.g., Ciccarone, 2017; Ciccarone et al., 2017; DEA, 2021; Zibbell, 2019). Adding fentanyl to the drug supply creates a particularly dangerous aspect of uncertain drug quality that has resulted in a marked increase in the toxicity of illicit-drug sources. The recent emergence of highly potent synthetic opioids like fentanyl has become a major driver of overdose deaths (e.g., Ciccarone, 2019; CDC). Furthermore, synthetic stimulants, cannabinoids, and benzodiazepines regularly appear in the illicit market to circumvent drug laws, creating an unknown illicit-drug supply with understudied properties and toxicities.
Laboratory Investigations of Drug-Taking
Drug self-administration in nonhuman subjects is one way to evaluate effects of uncertainty on drug-taking behavior. In drug self-administration, a subject such as a rodent or nonhuman primate can respond by pressing a lever to earn drug deliveries. Typically, the drug is delivered intravenously via a catheter, among other modes of delivery (e.g., oral). Drug self-administration is the gold-standard model for studying drug-taking and translates with validity to human behavior (e.g., Huskinson et al., 2014; Platt & Rowlett, 2012). However, many drug self-administration studies involve only small, predictable response requirements, and predictable drug quality.
Uncertain Response Requirements
One way to model uncertain time and effort required to obtain a substance is to use variable-ratio (VR) schedules of reinforcement. This type of schedule delivers reinforcers at unpredictable response requirements, and is remarkably effective at maintaining behavior. VR schedules require a variable number of responses for each reinforcer. Under this schedule, a reinforcer may be delivered after a single response, after several responses, or anywhere between the two extremes, but the average number of responses per reinforcer is equal to a predetermined value. For example, a VR 200 schedule may require 1 response or 399 responses per reinforcer, but the average requirement equals 200. VR schedules can be compared with fixed-ratio (FR) schedules that always require the same number of responses per reinforcer. In this example, the FR comparator would always require 200 responses per reinforcer. A VR schedule mirrors uncertain access in that drug-seeking sometimes results in immediate access to drugs, but in other cases may require a large amount of time and effort, resulting in delayed acquisition of the drug effect.
Known differences in behavior result from VR versus FR schedules of reinforcement. In nonhuman animals, VR schedules result in high-rate behavior that occurs with little pausing after reinforcer delivery or between response bouts. One can think of behavior that occurs when gambling on a slot machine; the schedule on a slot machine is purposely variable, with the intention of creating high-rate behavior. FR schedules also can result in high-rate behavior, but a key difference is that relatively long pauses in behavior occur after each reinforcer delivery. That is, once the reinforcer is delivered, the subject does not engage in the behavior that produced the reinforcer for a period of time. When applying this comparison between VR and FR schedules to drug-seeking, if illicit substances are available after relatively uncertain amounts of time and effort, this behavior is likely to occur at a relatively high rate and is likely to continue with little pausing, even after drug procurement. Conversely, if drugs are available after relatively certain amounts of time and effort, a pause in drug-seeking is likely to occur after drug procurement. From a translational perspective, VR schedules should result in much more time and effort dedicated to drug-seeking and drug-taking at the expense of nondrug-related activities, whereas FR schedules may allow one to engage in nondrug-related activities during pauses in drug-related behaviors.
Decades of research support the behavioral outcomes described above with VR versus FR schedules with nondrug reinforcers, such as food, and some investigators have begun to expand what is known about these schedules to drug reinforcers. In nonhuman primates, when responding was maintained by cocaine, remifentanil, or ketamine, greater drug intake was obtained under a variable schedule compared with a fixed schedule (Lagorio & Winger, 2014). This effect was particularly pronounced at relatively large response requirements and low drug doses (i.e., relatively scarce conditions). That is, conditions of scarce and uncertain drug availability resulted in greater behavioral output compared with scarce but certain drug availability. If these results generalize to humans, it would indicate that scarce and uncertain access to illicit drugs is not likely to reduce drug-seeking behavior. In fact, drug-seeking may persist or even be enhanced during periods of scarce and uncertain availability.
When subjects can choose between nondrug reinforcers associated with VR versus FR schedules, they generally choose the reinforcer associated with the VR schedule more often than the same reinforcer associated with an FR schedule (e.g., Fantino, 1967; Madden & Hartman, 2006). In some cases even when the VR is larger, on average, than the FR (e.g., Ahearn et al., 1992; Johnson et al., 2011; 2012). In two recent investigations, cocaine associated with a VR schedule was chosen more often than cocaine associated with an equal, on average, FR schedule (Huskinson et al., 2017; Zamarripa et al., 2022), and the effects were more robust with larger average response requirements compared with smaller requirements. Taken together, these experiments indicate that VR schedules, particularly in scarce conditions, result in high-rate behavior and greater distribution of behavior toward the variable outcome compared with the fixed outcome.
Thus far, VR schedules might seem to result in excessive drug-related behaviors at the expense of more certain, nondrug-related alternatives. However, such a conclusion would require the use of drug versus nondrug choice procedures. While choice between two options is a simplified version of the choices made by individuals in the real world, drug versus nondrug choice studies have good predictive validity (see Banks et al., 2015 for a review) in that outcomes obtained with drug versus food choice in nonhuman subjects align with outcomes in humans (e.g., Higgins et al., 1994; Nader & Woolverton, 1991). Recently, choice between cocaine and food was evaluated under different VR and FR schedules (Zamarripa et al., 2022): cocaine choice was greatest when available under a VR and food under an FR schedule. Similarly, cocaine choice was reduced when available under an FR and food was available under a VR schedule. Again, the effect was most robust with larger average requirements compared with smaller requirements. If translated to the natural environment, this could indicate that for individuals with scarce resources, uncertain access to illicit substances may result in excessive allocation of behavior toward drug-related activities at the expense of nondrug-related activities. Furthermore, when substances are relatively certain, overall drug intake was reduced.
Uncertain Drug Quality
One way to model uncertain drug quality in the laboratory is to vary the amount or dose of each drug delivery. Again, few researchers have done these experiments with drug reinforcers. However, in one experiment, nonhuman primates chose between fixed and variable doses of cocaine (Huskinson et al., 2017). When total possible intake was held constant by maintaining the same average dose on both options, subjects chose the variable-dose option more than the fixed-dose option, suggesting that uncertain drug quality garnered more behavior compared with certain drug quality. To our knowledge, no other study with nonhuman subjects has evaluated how uncertain drug quality affects self-administration behavior, but two studies with human participants involved choosing between fixed and variable hypothetical heroin amounts and fixed and variable delays to hypothetical heroin delivery. In these studies, individuals who were opioid dependent chose the variable heroin option more than the fixed option under a simulated state of opioid withdrawal, and this effect was most robust with larger average amounts, longer average delays, and in intravenous compared with intranasal users (Bickel et al., 2004; Kirshenbaum et al., 2006). Uncertain drug quality does not appear to deter drug-related behavior and perhaps could worsen this behavior during periods of withdrawal.
Uncertain Negative Outcomes
Similar to uncertainty in the reinforcing aspects of drugs, negative outcomes or potentially punishing effects associated with substance use also are uncertain. A number of diagnostic criteria for SUD pertain to persistent substance use despite negative consequences (APA, 2013). From a behavioral perspective, such negative consequences should suppress substance use. In individuals with SUD, however, the maintaining consequences of substance use outweigh the aversive consequences that might otherwise suppress such use. One potential reason for the ineffectiveness of negative consequences in reducing drug-maintained behavior in those with a SUD is that these consequences are uncertain in terms of the regularity or magnitude with which they occur. For example, using a substance may result in a hangover, which would be considered mild compared with toxicity that results in hospitalization or death. This concept applies in parallel to legal issues. Every occurrence of substance use does not result in legal trouble. Periodically, however, one may receive a citation or be arrested and incarcerated. Unfortunately, very few investigators have evaluated how uncertain negative consequences (i.e., punishers) affect behavior in nonhuman subjects compared with more certain negative consequences.
Uncertain Regularity or Magnitude of Negative Outcomes
To study negative outcomes (i.e., punishment) in nonhuman animals, a target behavior (e.g., lever pressing) must be maintained by a reinforcer (e.g., a drug). Once behavior is maintained under a reinforcement schedule, a punisher (e.g., shock) is introduced to reduce the reinforced behavior. We are unaware of any laboratory investigations of certain versus uncertain punishment of drug-maintained behavior, but there are some examples of certain versus uncertain punishment of food-maintained behavior. Using this approach, uncertain shock frequency was a weaker deterrent of food-maintained behavior than predictable shock frequency (e.g., Deluty, 1976; Seligman & Binik, 2021), and uncertainty in the likelihood of a negative consequence resulted in less effective punishment. However, the uncertain shock conditions arranged fewer overall shock deliveries compared with predictable shock conditions. Thus, we are unable to conclude whether weaker suppression of behavior was due to an uncertain presentation or simply a lower frequency of punishment.
In contrast, VR and FR punishment schedules did not differentially suppress food-maintained behavior (Boe, 1971). A limitation of this study is that the punishment schedule was denser than the reinforcement schedule, a relation opposite of what occurs in real-world scenarios where drug-related behavior can occur for long periods of time without any negative outcomes. Finally, variable-shock intensities (50–110V/delivery) were more effective at suppressing food-maintained behavior compared with a fixed-shock intensity (80 V; Boe, 1971). This latter finding could indicate that uncertainty in the magnitude of negative outcomes is more effective at reducing behavior than more certain magnitudes. Given the mixed outcomes and limitations described, more research is needed to determine how uncertain negative consequences vary in their ability to deter drug-taking. Future research should aim at understanding how uncertain negative consequences affect substance use.
Policy Implications
Overall, precarity multiplies the harm of illicit drugs. Uncertainty in the time and effort required to obtain drugs and drug quality makes drug-seeking and drug-taking worse when compared with more certain conditions of drug access. This suggests that policy should find ways to reduce environmental uncertainty related to drug access. Furthermore, uncertain negative outcomes do not appear to deter continued drug use, at least for individuals with a SUD. Several policy and treatment implications follow from these general conclusions.
Agonist Medications Increase Predictability
One way to reduce environmental uncertainty related to drug access is through agonist medications, that is, drugs that alleviate withdrawal and craving for the sought-after drug’s reinforcing effects. Currently, methadone and buprenorphine are the only two Food and Drug Administration (FDA)-approved agonist medications for opioid use disorder (OUD). FDA-approved agonist medications are not currently available for stimulants, cannabinoids, or benzodiazepines. Methadone is a mu-opioid receptor full agonist, meaning it binds to the same receptor and with full efficacy as traditional opioids. Compared with commonly used illicit opioids, however, methadone has a longer duration of action and can be given once daily. Buprenorphine is a long-acting, mu-opioid receptor partial agonist and similar to methadone, binds to the same receptor as illicit opioids. Because buprenorphine is a partial agonist, it has lower efficacy compared with heroin or methadone, resulting in a safer profile. The major assumption behind an agonist-medication approach is that the medication will alleviate withdrawal symptoms and prevent craving. Buprenorphine has the added benefit of blocking other opioids from binding to the mu-opioid receptor and, therefore, prevents drugs like heroin from exerting their effects. Both agonist medications are effective treatments for OUD, and these medications improve treatment retention and reduce mortality, infectious disease transmission, and drug-related crime (e.g., Kakko et al., 2003; Mattick et al., 2009; NIDA, 2021a). In a recent blog post, the Director of the National Institute on Drug Abuse (NIDA) indicated that agonist medications do not require further research to know that they are effective treatments for OUD (NIDA, 2022).
An agonist-medication approach also could be effective at reducing illicit substance misuse because it makes access to a pharmacologically similar substance predictable, both in terms of the time and effort required to obtain it and in quality/dose of the medication. Once available in a predictable way, an individual can dedicate more time to nondrug-related activities. Because these medications are legal and regulated, other environmental uncertainties also are eliminated. Overdose rates and emergency room visits are much lower with agonist medications compared with illicit drugs (Hedegaard et al., 2018, 2020; NIDA, 2021b). It is not illegal to be maintained on methadone or buprenorphine with a prescription, and individuals may maintain employment and social relationships while taking them. Perhaps agonist medications for other illicit substances would be similarly effective. For example, agonist medications have been proposed for stimulant-use disorder (e.g., Negus & Henningfield, 2015; Stoops & Rush, 2013).
A Safer Drug Supply and Legal Regulation Would Increase Predictability
Another way to reduce environmental uncertainty related to drug access is admittedly controversial. The “War on Drugs” and strategies aimed at eliminating substances from the market does not reduce drug consumption; rather, when it becomes more difficult or impossible to procure drugs, dealers resort to synthetic analogs in an attempt to evade drug laws (e.g., Tamama, 2021; Tyndall, 2020). The result of such evasion is emergence of synthetic substances with unknown health risks. Such uncertainty could be reduced or eliminated by creating a safer drug supply: providing substances with known contents to individuals with SUDs or through legal regulation. A safer supply reduces crime and improves health outcomes (e.g., reduction in abscesses, infectious disease transmission, and mortality) in individuals with a SUD (e.g., Bernstein et al., 2020; Fairgrieve et al., 2018; Fleming et al., 2020). Finally, if results from laboratory investigations described in previous sections translate to humans, a more predictable supply could even reduce overall drug intake.
Syringe-Exchange Programs, Access to Naloxone, and Safe-Injection Sites All Increase Predictability
Two uncertain negative outcomes associated with substance use are drug overdoses and the transmission of infectious diseases (e.g., hepatitis C, human immunodeficiency virus [HIV]) via needle sharing/reusing. Fortunately, a well-supported combination—syringe-exchange programs that offer clean needles and syringes to injection-drug users, ready access to naloxone, and implementation of safe-injection sites—all reduce transmission of infectious diseases and overdose deaths. In analyzing the HIV outbreak from 2011 to 2015 in Scott County, Indiana (Gonsalves & Crawford, 2018), earlier, more efficient implementation of a syringe-exchange program could have decreased the number of infections by as much as 95%.
Narcan® contains the opioid antagonist, naloxone, and can be safely delivered to individuals with known or suspected opioid overdose and can reverse the overdose if delivered early enough. Because naloxone is safe, effective, and easily administered by anyone, regardless of health-related experience, it should be readily available to anyone who wishes to have it. At the very least, naloxone should be readily available to first responders, those who use opioids, and their family and friends. In a country that is experiencing an overdose crisis, ready access to naloxone will save lives. As with agonist medications, this has been highlighted by the Director of NIDA as an area that we do not need further research to know access to naloxone will help curb the opioid crisis (NIDA, 2022).
Finally, safe-injection sites can reduce infectious disease transmission while also significantly decreasing risk of overdose. These sites provide safe locations, clean equipment, and typically are staffed with health professionals with access to naloxone. Implementation of safe-injection sites has been effective at decreasing opioid overdoses in other countries, with up to a 70% reduction in opioid-related ambulance calls and deaths (NCHECR, 2007).
Incarceration
Incarceration for symptoms of a disorder should not occur for several moral and ethical reasons, but these comments focus solely on the effectiveness of punishment in deterring substance use: incarceration is not likely to be effective. In fact, postincarceration outcomes are poor. While over 80% of incarcerated people report illicit substance use (Chamberlain et al., 2019), only 20% report receiving treatment while incarcerated (Fazel et al., 2017), and rates of drug overdose mortality in previously incarcerated individuals is approximately 10 times greater than the general population (Hedegaard et al., 2018; O’Connor et al., 2022). This outcome speaks to the ineffectiveness of incarceration as a deterrent of drug-taking upon release and to the higher risk of negative health outcomes after incarceration. All these factors support the implementation of policies that decriminalize drug use and support access to evidence-based treatment over incarceration as means of deterring drug-taking.
Highlights.
Uncertain time and effort required to obtain a substance and uncertain drug quality exacerbates harm.
Uncertain negative outcomes are not likely to deter drug-taking in those with substance use disorder (SUD).
Policies should improve access to agonist medications and create a safer drug supply.
Access to clean syringes/needles, naloxone, and safe-injection sites reduce uncertain negative outcomes.
Policies should end incarceration for substance use.
Acknowledgments
The authors would like to thank Kevin Freeman, Ph.D. and Jake Valeri for their feedback on prior versions of this manuscript. Manuscript preparation was supported by National Institute on Drug Abuse grants R01 DA045011 and R01 DA054177 to SLH. The funding source had no role in the writing of this review.
Funding
The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This work was supported by the National Institute on Drug Abuse (grant number DA045011, DA054177).
Footnotes
Declaration of Conflicting Interests
The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.
References
- Ahearn W, Hineline PH, & David FG (1992). Relative preferences for various bivalued ratio schedules. Animal Learning & Behavior, 20(4), 407–415. 10.3758/BF03197964 [DOI] [Google Scholar]
- American Psychiatric Association (2013). Diagnostic and statistical manual of mental disorders (5th ed.). 10.1176/appi.books.9780890425787 [DOI] [Google Scholar]
- Banks ML, Hutsell BA, Schwienteck KL, & Negus SS (2015). Use of preclinical drug vs. food choice procedures to evaluate candidate medications for cocaine addiction. Current Treatment Options in Psychiatry, 2(2), 136–150. 10.1007/s40501-015-0042-9 [DOI] [PMC free article] [PubMed] [Google Scholar]
- Bernstein SE, Amirkhani E, Werb D, & MacPherson D (2020). The regulation project: Tools for engaging the public in the legal regulation of drugs. International Journal of Drug Policy, 86, 102949. 10.1016/j.drugpo.2020.102949 [DOI] [PMC free article] [PubMed] [Google Scholar]
- Bickel WK, Giordano LA, & Badger GJ (2004). Risk-sensitive foraging theory elucidates risky choices made by heroin addicts. Addiction, 99(7), 855–861. 10.1111/j.1360-0443.2004.00733.x [DOI] [PubMed] [Google Scholar]
- Boe EE (1971). Variable punishment. Journal of Comparative and Physiological Psychology, 75(1), 73. 10.1037/h0030696 [DOI] [PubMed] [Google Scholar]
- Broséus J, Gentile N, & Esseiva P (2016). The cutting of cocaine and heroin: A critical review. Forensic Science International, 262, 73–83. 10.1016/j.forsciint.2016.02.033 [DOI] [PubMed] [Google Scholar]
- Centers for Disease Control and Prevention (CDC). Understanding the Opioid Overdose Epidemic. Retrieve from https://www.cdc.gov/opioids/basics/epidemic.html#three-waves.
- Centers for Disease Control and Prevention (CDC; 2022, May 11). U.S. Overdose deaths in 2021 increased half as much as in 2020 – but are still up 15%. Retrieved from https://www.cdc.gov/nchs/pressroom/nchs_press_releases/2022/202205.htm.
- Chamberlain A, Nyamu S, Aminawung J, Wang EA, Shavit S, & Fox AD (2019). Illicit substance use after release from prison among formerly incarcerated primary care patients: A cross-sectional study. Addiction Science & Clinical Practice, 14(1), 1–8. 10.1186/s13722-019-0136-6 [DOI] [PMC free article] [PubMed] [Google Scholar]
- Ciccarone D (2017). Fentanyl in the US heroin supply: A rapidly changing risk environment. The International Journal on Drug Policy, 46, 107–111. 10.1016/j.drugpo.2017.06.010 [DOI] [PMC free article] [PubMed] [Google Scholar]
- Ciccarone D (2019). The triple wave epidemic: Supply and demand drivers of the US opioid overdose crisis. International Journal on Drug Policy, 71, 183–188. 10.1016/j.drugpo.2019.01.010 [DOI] [PMC free article] [PubMed] [Google Scholar]
- Ciccarone D, Ondocsin J, & Mars SG (2017). Heroin uncertainties: Exploring users’ perceptions of fentanyl-adulterate and substituted ‘heroin’. International Journal of Drug Policy, 46, 146–155. 10.1016/j.drugpo.2017.06.004 [DOI] [PMC free article] [PubMed] [Google Scholar]
- Deluty MZ (1976). Choice and the rate of punishment in concurrent schedules 1. Journal of the Experimental Analysis of Behavior, 25(1), 75–80. 10.1901/jeab.1976.25-75 [DOI] [PMC free article] [PubMed] [Google Scholar]
- Fairgrieve C, Fairbairn N, Samet JH, & Nolan S (2018). Nontraditional alcohol and opioid agonist treatment interventions. Medical Clinics, 102(4), 683–696. [DOI] [PMC free article] [PubMed] [Google Scholar]
- Fantino E (1967). Preference for mixed- versus fixed-ratio schedules 1. Journal of the Experimental Analysis of Behavior, 10(1), 35–43. 10.1901/jeab.1967.10-35 [DOI] [PMC free article] [PubMed] [Google Scholar]
- Fazel S, Yoon IA, & Hayes AJ (2017). Substance use disorders in prisoners: An updated systematic review and metaregression analysis in recently incarcerated men and women. Addiction, 112(10), 1725–1739. 10.1111/add.13877 [DOI] [PMC free article] [PubMed] [Google Scholar]
- Fiorentin TR, Krotulski AJ, Martin DM, Browne T, Triplett J, Conti T, & Logan BK (2019). Detection of cutting agents in drug-positive seized exhibits within the United States. Journal of Forensic Sciences, 64(3), 888–896. 10.1111/1556-4029.13968 [DOI] [PubMed] [Google Scholar]
- Fleming T, Barker A, Ivsins A, Vakharia S, & McNeil R (2020). Stimulant safe supply: A potential opportunity to respond to the overdose epidemic. Harm Reduction Journal, 17(1), 1–6. 10.1186/s12954-019-0351-1 [DOI] [PMC free article] [PubMed] [Google Scholar]
- Gonsalves GS, & Crawford FW (2018). Dynamics of the HIV outbreak and response in Scott County, IN, USA, 2011–15: A modelling study. The Lancet HIV, 5(10), e569–e577. 10.1016/S2352-3018(18)30176-0 [DOI] [PMC free article] [PubMed] [Google Scholar]
- Greenwald MK, & Steinmiller CL (2014). Cocaine behavioral economics: From the naturalistic environment to the controlled laboratory setting. Drug and Alcohol Dependence, 141, 27–33. 10.1016/j.drugalcdep.2014.04.028 [DOI] [PMC free article] [PubMed] [Google Scholar]
- Hedegaard H, Bastian BA, Trinidad JP, Spencer M, & Warner M (2018). Drugs most frequently involved in drug overdose deaths: United States, 2011–2016. [PubMed]
- Hedegaard H, Miniño AM, & Warner M (2020). Drug overdose deaths in the United States, 1999–2019. NCHS Data Brief , no 394. National Center for Health Statistics. [PubMed] [Google Scholar]
- Heilig M, MacKillop J, Martinez D, Rehm J, Leggio L, & Vanderschuren LJ (2021). Addiction as a brain disease revised: Why it still matters, and the need for consilience. Neuropsychopharmacology, 46(10), 1715–1723. 10.1038/s41386-020-00950-y [DOI] [PMC free article] [PubMed] [Google Scholar]
- Heyman GM (2009). Addiction: A disorder of choice. Harvard University Press. [Google Scholar]
- Higgins ST, Bickel WK, & Hughes JR (1994). Influence of an alternative reinforcer on human cocaine self-administration. Life Sciences, 55(3), 179–187. 10.1016/0024-3205(94)00878-7 [DOI] [PubMed] [Google Scholar]
- Huskinson SL (2020). Unpredictability as a modulator of drug self-administration: Relevance for substance-use disorders. Behavioural Processes, 178, 104156. 10.1016/j.beproc.2020.104156 [DOI] [PMC free article] [PubMed] [Google Scholar]
- Huskinson SL, Freeman KB, Petry NM, & Rowlett JK (2017). Choice between variable and fixed cocaine injections in male rhesus monkeys. Psychopharmacology, 234, 2353–2364. 10.1007/s00213-017-4659-9 [DOI] [PMC free article] [PubMed] [Google Scholar]
- Huskinson SL, Naylor JE, Rowlett JK, & Freeman KB (2014). Predicting abuse potential of stimulants and other dopaminergic drugs: Overview and recommendations. Neuropharmacology, 87, 66–80. 10.1016/j.neuropharm.2014.03.009 [DOI] [PMC free article] [PubMed] [Google Scholar]
- Johnson PS, Madden GJ, Brewer AT, Pinkston JW, & Fowler SC (2011). Effects of acute pramipexole on preference for gambling-like schedules of reinforcement in rats. Psychopharmacology, 213(1), 11–18. 10.1007/s00213-010-2006-5 [DOI] [PMC free article] [PubMed] [Google Scholar]
- Johnson PS, Madden GJ, & Stein JS (2012). Effects of acute pramipexole on male rats’ preference for gambling-like rewards II. Experimental and Clinical Psychopharmacology, 20(3), 167–172. 10.1037/a0027117 [DOI] [PMC free article] [PubMed] [Google Scholar]
- Joint Economic Committee (JEC) Democrats (2022, September 28). The economic toll of the opioid crisis reached nearly $1.5 trillion in 2020. Retrieved from https://www.jec.senate.gov/public/index.cfm/democrats/issue-briefs?ID=CE55E977-B473-414F-8B88-53EB55EB7C7C.
- Kakko J, Svanborg KD, Kreek MJ, & Heilig M (2003). 1-year retention and social function after buprenorphine-assisted relapse prevention treatment for heroin dependence in Sweden: A randomised, placebo-controlled trial. The Lancet, 361(9358), 662–668. 10.1016/S0140-6736(03)12600-1 [DOI] [PubMed] [Google Scholar]
- Kirshenbaum AP, Bickel WK, & Boynton DM (2006). Simulated opioid withdrawal engenders risk-prone choice: A comparison of intravenous and intranasal-using populations. Drug and Alcohol Dependence, 83(2), 130–136. 10.1016/j.drugalcdep.2005.11.002 [DOI] [PubMed] [Google Scholar]
- Lagorio CH, & Winger G (2014). Random-ratio schedules produce greater demand for IV drug administration than fixed-ratio schedules in rhesus monkeys. Psychopharmacology, 231(15), 2981–2988. 10.1007/s00213-014-3477-6 [DOI] [PMC free article] [PubMed] [Google Scholar]
- Lamb RJ, & Ginsburg BC (2018). Addiction as a BAD, a behavioral allocation disorder. Pharmacology Biochemistry and Behavior, 164, 62–70. 10.1016/j.pbb.2017.05.002 [DOI] [PMC free article] [PubMed] [Google Scholar]
- Madden GJ, & Hartman EC (2006). A steady-state test of the demand curve analysis of relative reinforcer efficacy. Experimental and Clinical Pyschopharmacology, 14(1), 79–86. 10.1037/1064-1297.14.1.79 [DOI] [PubMed] [Google Scholar]
- Mattick RP, Breen C, Kimber J, & Davoli M (2009). Methadone maintenance therapy versus no opioid replacement therapy for opioid dependence. Cochrane Database of Systematic Reviews, (3). 10.1002/14651858.CD002209.pub2 [DOI] [Google Scholar]
- Nader MA, & Woolverton WL (1991). Effects of increasing the magnitude of an alternative reinforcer on drug choice in a discrete-trials choice procedure. Psychopharmacology, 105(2), 169–174. 10.1007/BF02244304 [DOI] [PubMed] [Google Scholar]
- National Centre in HIV Epidemiology and Clinical Research. (2007). Sydney Medically Supervised Injecting Centre evaluation report no. 4: Evaluation of service operation and overdose-related events.
- Negus SS, & Henningfield J (2015). Agonist medications for the treatment of cocaine use disorder. Neuropsychopharmacology, 40(8), 1815–1825. 10.1038/npp.2014.322 [DOI] [PMC free article] [PubMed] [Google Scholar]
- NIDA (2021a, December 3). How effective are medications to treat opioid use disorder? Retrieved from https://nida.nih.gov/publications/research-reports/medications-to-treat-opioid-addiction/efficacy-medications-opioid-use-disorder.
- NIDA (2021b, April 13). What is the treatment need versus the diversion risk for opioid use disorder treatment? Retrieved from https://nida.nih.gov/publications/research-reports/medicationsto-treat-opioid-addiction/what-treatment-need-versus-diversion-risk-opioid-use-disorder-treatment.
- NIDA (2022, August 31). Five areas where “More Research” isn’t needed to curb the overdose crisis. Retrieved from https://nida.nih.gov/about-nida/noras-blog/2022/08/five-areas-where-more-research-isnt-needed-to-curb-overdose-crisis.
- O’Connor AW, Sears JM, & Fulton-Kehoe D (2022). Overdose and substance-related mortality after release from prison in Washington State: 2014–2019. Drug and Alcohol Dependence, 241, 109655. 10.1016/j.drugalcdep.2022.109655 [DOI] [PubMed] [Google Scholar]
- Platt DM, & Rowlett JK (2012). Nonhuman primate models of drug and alcohol addiction. In Abee CR, Mansfield K, Tardif S, & Morris T (Eds.), Nonhuman primates in biomedical research: Diseases (Vol. 2, pp. 817–839). Elsevier Inc. [Google Scholar]
- Roddy J, & Greenwald M (2009). An economic analysis of income and expenditures by heroin-using research volunteers. Substance Use & Misuse, 44(11), 1503–1518. 10.1080/10826080802487309 [DOI] [PMC free article] [PubMed] [Google Scholar]
- Roddy J, Steinmiller CL, & Greenwald MK (2011). Heroin purchasing is income and price sensitive. Psychology of Addictive Behaviors, 25(2), 358–364. 10.1037/a0022631 [DOI] [PMC free article] [PubMed] [Google Scholar]
- Seligman ME, & Binik YM (2021). The safety signal hypothesis. In Operant-Pavlovian interactions (pp. 165–187). Routledge. [Google Scholar]
- Stoops WW, & Rush CR (2013). Agonist replacement for stimulant dependence: A review of clinical research. Current Pharmaceutical Design, 19(40), 7026–7035. [DOI] [PMC free article] [PubMed] [Google Scholar]
- Substance Abuse and Mental Health Services Administration (SAMHSA) (2021). Key substance use and mental health indicators in the United States: Results from the 2020 National Survey on Drug Use and Health (HHS Publication No. PEP21-07-01-003, NSDUH Series H-56). Rockville, MD: Center for Behavioral Health Statistics and Quality, Substance Abuse and Mental Health Services Administration. Retrieved from https://www.samhsa.gov/data/. [Google Scholar]
- Szalavitz M (2017). Unbroken brain: A revolutionary new way of understanding addiction. Picador St. Martin’s Press. [Google Scholar]
- Tamama K (2021). Synthetic drugs of abuse. Advances in Clinical Chemistry, 103, 191–214. 10.1016/bs.acc.2020.10.001 [DOI] [PubMed] [Google Scholar]
- Tyndall M (2020). A safer drug supply: A pragmatic and ethical response to the overdose crisis. Canadian Medical Association Journal, 192(34), E986–E987. 10.1503/cmaj.201618 [DOI] [Google Scholar]
- U.S. Department of Health and Human Services. Addiction and substance misuse reports and publications. Retrieved from https://www.hhs.gov/surgeongeneral/reports-and-publications/addiction-and-substance-misuse/index.html.
- U.S. Department of Health and Human Services. Overdose prevention strategy. Retrieved from https://www.hhs.gov/overdose-prevention/.
- U.S. Drug Enforcement Administration, Special Testing and Research Laboratory (2021). Emerging threat report: Mid-year 2021. Retrieved from https://cesar.umd.edu/sites/cesar.umd.edu/files/pubs/DEA-Emerging-Threat-Report-2021-Mid-Year.pdf.
- Zamarripa CA, Doyle WS, Freeman KB, Rowlett JK, & Huskinson SL(2022).Choice between food and cocaine reinforcers under fixed and variable schedules in female and male rhesus monkeys. Experimental and Clinical Psychopharmacology, Advance online publication. 10.1037/pha0000547 [DOI] [PMC free article] [PubMed] [Google Scholar]
- Zibbell JE (2019). The latest evolution of the opioid crisis: Changing patterns in fentanyl adulteration of heroin, cocaine, and methamphetamine and associated overdose risk. RTI International. https://www.rti.org/insights/latest-evolution-opioid-crisis-changing-patterns-fentanyl-adulteration-heroin-cocaine-and. [Google Scholar]
