Section 1 Addiction Diagnosis

### **Chapter 1**

## Psychopharmacological Perspectives and Diagnosis of Substance Use Disorder

*Samson Duresso*

### **Abstract**

A considerable body of research has accumulated over several decades and altered the current understanding of substance use and its effects on the brain. This knowledge has improved the perception of the disease of addiction and has opened the door to new ways of thinking about diagnosis, prevention, and treatment of substance use disorders. The purpose of the current chapter is to briefly outline and summarize the major psychopharmacological framework underlying substance use disorder (SUD) and the factors that involve in the transformation of some people from recreational use or misuse of alcohol or drugs to SUD. The chapter explains the overall neurocircuitry theories of the addiction cycle: binge/ intoxication, withdrawal/negative affect, and preoccupation/anticipation. It briefly discusses how psychoactive substances produce changes in brain functioning that facilitate the development of addiction and contribute to craving which eventually leads to relapse. The chapter also deals with similarities and differences among various classes of addictive substances in their effects on the brain and behavior and briefly describes the main risk factors that involve SUD. Finally, an attempt is made to briefly discuss the major DSM 5 based behavioral criteria that involve SUD, corresponding to the most abused substances worldwide.

**Keywords:** Addiction, Substance use, addiction cycle, substance use disorder, Neurocircuitry, Reward system, Neuroadaptation, Incentive-Sensitization, Incentive-Salience, DSM-5 criteria

### **1. Introduction**

Addiction is the recurrent use of mood-altering substances, such as alcohol and other drugs, despite their adverse health and psychosocial consequences. The interplay among genetic, psychosocial, and environmental factors influences the development and manifestations of the disorder [1]. For some individuals, becoming physiologically dependent on a certain substance is likely to be a developmental process. Individuals may start with a positive attitude towards a substance, begin to practice using it, become regular users, progress to being heavy users and finally become dependent on it. While developing a positive attitude towards a certain substance, for instance, cigarettes and beginning to experiment with it, may be strongly related to exposures to smoking by other family members [2], becoming heavy smoker, on the other hand, is more strongly related to frequent exposures to peer smoking and being able to access cigarettes readily [3].

Addiction, which was once viewed mainly as a moral failing or character fault, is now considered as a chronic illness of the brain characterized by clinically significant impairments in health, social function, and involuntary control over drug use [4, 5]. Although the mechanisms may be different, addiction, like most physiological disorders, is chronic, subject to relapse, and influenced by genetic, developmental, psychosocial, and environmental factors. Addictive substances exert significant influences on the brain that may affect thoughts, emotions, and behaviors. Substances of abuse have powerful effects on the brain and produce euphoria (extreme pleasure) which provokes users to seek those substances repeatedly despite the risks for significant harms.

As individuals continue to misuse alcohol or other substances, neuroadaptations occur due to progressive changes in the structure and function of the brain. Neuroadaptation compromises brain function and eventually results in the transition of an individual from controlled, occasional substance use to chronic misuse, which can be difficult to control. These brain changes may persist long after an individual stops and produce a continuous or periodic craving for the substance that can lead to relapse: More than 60 percent of people treated for a certain substance use disorder experience relapse within the first year after completion of treatment [6], and some may remain at increased risk of relapse for many years.

Much of our knowledge about the effects of substance use and misuse on the brain as well as the development of addiction comes from the study of laboratory animals. Neurobiological studies in animals have examined both the immediate effects (acute impact) of addictive substance in the brain right after ingestion and the long term or chronic impact of drug use to understand, at the most basic level, the mechanisms through which substance use alters brain structure and function and facilitates the transition from occasional use to misuse, addiction, and relapse. Although the animal models do not fully reflect the human experience, animal studies help researchers investigate addiction and related behavioral changes under highly controlled conditions that may be difficult or unethical to replicate in humans [7].

To supplement the work in animals, a growing body of substance use research has been conducted with humans. The use of brain-imaging technologies, such as magnetic resonance imaging (MRI) and positron emission tomography (PET) scans for studying the effects of alcohol and drugs on the human brain have significantly advanced the current knowledge of SUD by allowing researchers to see inside the living human brain. Using these technologies, researchers can investigate and characterize the biochemical, functional, and structural changes in the brain which result from addictive substances and find out how such changes may ultimately contribute to substance use, misuse, and addiction [8]. Animal and human studies are integrated and inform each other for a more complete picture of the neurobiology of addiction [9].

The structural and functional changes caused by using drugs can be long-lasting and can lead to harmful behaviors seen in individuals who continued to abuse drugs. Although the initial decision to try a drug of choice is mostly voluntary, eventually, as drug abuse takes over or an individual transforms from occasional user to heavy user, his/her ability to exert self-control may become seriously affected. Brain imaging studies conducted on drug-addicted individuals typically revealed that physical changes have been observed in the areas of the brain that are critical to judgment, decision making, learning, memory, and self-control. Scientists believe that these changes alter the way the brain works and may help explain the compulsive and destructive behaviors of addiction [10].

Susceptibility for addiction varies from individual to individual as for any other diseases. This depends on the amount and level of risk factors a person has. The

### *Psychopharmacological Perspectives and Diagnosis of Substance Use Disorder DOI: http://dx.doi.org/10.5772/intechopen.99531*

more the risk factors the greater the chance that taking drugs will lead to abuse and addiction. No single factor determines whether a person will become addicted to drugs. The overall risk for addiction is influenced by the biological makeup of the individual including sex or ethnicity, developmental stage, and the surrounding social environment such as home, school, and the neighborhood.

Individuals with mental disorders are at greater risk of drug abuse and addiction than the general population [11]. A growing body of research reveals that SUDs and many psychiatric disorders share comparable neural mechanisms. There are several clinical resemblances and symptom overlaps between SUDs and affective disorders [12]. Epidemiological studies revealed that co-occurring substance abuse and psychiatric problems are common in clinical practice. SUDs and a variety of mental illnesses have similar changes in the dopamine-mediated reward system as well as different neurotransmitter systems such as GABA, and serotonin [10]. According to recent findings from neuroimaging research, similar abnormalities in frontal-limbic brain circuitry are implicated in SUDs and depressive disorders. Individuals with SUDs have been found to have lower frontal metabolism and anterior cingulate activation [13, 14]. Depressive symptoms are typically observed after acute and chronic drug withdrawal due to anomalies in the CRF and HPA axis, as well as alterations in catecholamines, serotonin, GABA, and glutamate systems [15]. Hence, chronic stress-induced neuroadaptations in brain stress system and reward pathways may increase the susceptibility to self-administer the substances of abuse and predispose or reveal a vulnerability to psychiatric conditions, SUDs, or both [16, 17].

The influence of home and family environment is usually most important in childhood. Children who are exposed to parental substance use early in life are more likely to develop SUD in their future life through behavioral modeling [18–20]. It is more likely that parents or older brothers or sisters who abuse alcohol or drugs can increase other children's risks of developing drug problems [21]. In addition, children with poor social skills and poor academic achievement may be at more risk of developing drug problems in school [22].

The route of administration of the substance being used is also a potential factor that may influence the progress of an individual from a regular user to a heavy user or abuse. For instance, as both smoked and injected drugs enter the brain fast and produce a powerful rush of pleasure, smoking a drug or taking it through the vein increases its addictive potential. This intense high feeling of pleasure (euphoria), however, can gradually fade away and a rebound effect of agitation or low feelings may occur. Then, these feelings develop into cravings and drive individuals to recurrent drug abuse to recapture the high pleasurable state which can worsen the risk of developing addiction [23].

The age of the onset of drug use is another potential factor that increases the likelihood of drug abuse by individuals. Although starting drugs at any age can eventually lead to addiction, early substance use is a strong indicator of problems ahead related to substance abuse and addiction [24]. Research findings have shown that people who start taking drugs at an early age are more likely to be at increased risk for adult substance dependence [25]. The part of the brain, the prefrontal cortex, which is responsible for assessing situations, making sound judgment and decisions, and keeping our emotions and desires under control is still maturing during adolescence. The fact that this critical part of an adolescent's brain is still a workin-progress puts young people at increased risk of making poor decisions regarding trying drugs for the first time and/or continuing drug abuse thereafter. Adolescents at this stage are developing judgment and decision-making skills which may limit their ability to assess risks accurately and make sound decisions about using drugs. Therefore, introducing drugs while the brain is still developing may have profound and long-lasting results concerning drug addiction. Early use of psychoactive

substances changes the structure and functions of the brain which can lead to addiction and other serious problems [26]. Thus, preventing early use of alcohol or other drugs may reduce the risk of progressing to later abuse and addiction.

Interindividual differences in addiction risk are mostly determined by genetic variation. The impact of genetic variation on total addiction risk has been estimated to be 50% in studies focusing on variability across identical and nonidentical siblings [27]. The largest study to date on 1.2 million people that looked at common genes in alcohol and nicotine use identified genes involved in dopaminergic and glutamatergic neurotransmission, transcription and translation, and brain development [28]. They also revealed that a crucial genetic component of SUDs appears to impact a vulnerability to disorders with pathological symptoms via a generalpurpose underlying mechanism.

Besides these common genetic characteristics, genetic variants that are mainly unique in a particular drug have been found. The most well-known genetic variants are those that code for the alcohol dehydrogenase (ADH) and aldehyde dehydrogenase (ALDH) enzymes, which cause poor alcohol metabolism and protect against alcoholism [28]. Like other complex biobehavioral disorders, addiction is a polygenic disease involving multiple genes and genetic networks [29, 30]. Genetic research has aided our understanding of addiction-related neurobiological processes. Addiction-related gene variations can increase the risk of drug misuse and addiction by altering neurotransmitter systems, drug metabolic pathways, and brain circuitry. For example, genetic polymorphisms in the nicotinic acetylcholine receptor subunits expressed in the medial habenula are linked to development of withdrawal symptoms [31] and are at least partially accountable for the genetic predisposition or vulnerability to tobacco addiction [32, 33]. This has demonstrated that the habenula is involved not just in nicotine addiction, but also in the unpleasant emotional states associated with the long-term use of numerous drugs of abuse [29], such as alcohol [30] and opioids [34].

Epigenetic factors are a diverse set of transcriptional tuning processes that generate and sustain gene expression–mediated physiological outcomes in response to environmental inputs [13]. Plasticity is at work in the transition to compulsive drug use, changing the physiology of the brain to produce addictive states. Physiologically, the rewiring of brain reward circuitries, particularly dopamine neurons in the ventral tegmental area (VTA), is thought to cause vulnerability to relapse after periods of attempted abstinence from drugs of abuse such as cocaine use. The same enzyme that attaches serotonin to Histone 3 can also catalyze the attachment of dopamine to H3 — a process, known as dopaminylation, which may control drug-seeking behavior [35]. Cocaine-induced transcriptional plasticity in the midbrain is mediated by histone H3 glutamine 5 dopaminylation (H3Q5dop). As a result, long-term cocaine use alters neuronal circuits in the brain's reward system, necessitating a consistent intake of the drug for the circuits to function normally. To make the proteins for those changes, certain genes must be turned on and off, and this is an epigenetic mechanism activated by dopamine acting on H3, not a change in DNA sequence. Epigenetic mechanisms provide a convergent regulatory framework within which the plasticity required to produce an addicted state can emerge and then remain long after drug use has stopped [36, 37].

### **2. The addiction cycle**

There are many theories and models of addiction. While some of the theories and models present their theoretical approaches at an individual level, some explain the addictive behavior in terms of population or group interaction and influence. It is also important to note that many theories of addiction are complex and have

### *Psychopharmacological Perspectives and Diagnosis of Substance Use Disorder DOI: http://dx.doi.org/10.5772/intechopen.99531*

evolved over time and therefore may overlap across one or more explanatory domains or perspectives.

The learning theory of addiction is relatively a broad category that emphasizes the importance of associations among cues, responses, and positive (pleasant) or negative (noxious) reinforcers. Among the specific models and theories under this category are operant learning, classical conditioning, and drug withdrawal theory [38, 39]. Moreover, the incentive sensitization theory of addiction also incorporates aspects of learning theory as it proposes that repeated exposure to drugs and drugassociated cues capitalizes on neuroplastic changes in mesocorticolimbic circuits and classical conditioning to make drugs and drug paired stimuli more motivationally salient [40, 41] .

The drive theories of addiction explain that addiction involves the development of powerful drives reinforced by homeostatic mechanisms. The drive theory is part of the disease model of addiction [42] and states that addiction is the result of pathological changes in the brain that produces overpowering urges. These changes that result in impairments may involve a structural or functional abnormality in the CNS [42]. Among the goal-focused theories of addiction are positive reward theories, acquired need theories, pre-existing need theories, and identity theories. These theories mainly explain addiction in terms of an individual's behavior that satisfies one's physiological or psychological needs, pleasure, and aspects of selfidentity [43–47].

Other theories such as cognitive control theory [48], executive dysfunction theory [49–51], self-regulation theory [52], self-determination theory [53, 54], and implementation intentions theory [55, 56], describe addiction as a failure of individual's strategies, ability or skills to self-control or to counteract impulses and motives underlying the addictive behavior. The biological theories describe that addiction involves specific neural circuitry or mechanisms [57–62] and is primarily a brain disease' characterized by dysfunction of neural pathways such as those that subserve executive function. The Reflective Choice Theories focus on the individual's decisions, preferences, and actions that are made based on reasons and analysis. Therefore, according to these theories, addiction is a rational choice made by individuals in favor of the benefits of the addictive behavior over the costs. The Inhibition dysfunction theories suggest that addiction is an ongoing dysregulation of the ability to inhibit a rewarded behavior due to impairment of impulse controlling mechanisms [63]. In other words, addiction involves impairment of the inhibitory system of the brain regions related to features of response selection, inhibition, and motivation of compulsive behaviors associated with drugs. The hedonic homeostatic dysregulation theory also states that addiction is a cycle of escalating dysregulation of the brain reward systems that gradually increases and facilitates the act of compulsive drug use and a loss of control over drug-taking behavior, which are the basic concepts that underlie the addiction cycle [64].

Drug addiction is a disorder that is chronically relapsing and manifested in terms of compulsive behavior to seek and consume the drug, significant loss of ability to control and limit drug intake, and the emergence of negative and distressful emotional states such as irritability, anxiety, and dysphoria. Drug addiction is conceptualized as a three-stage cyclic disorder that consists of impulsivity and compulsivity and involves neuroplastic changes in the brain reward, stress, and executive functions [64, 65]. Impulsivity often dominates the early stages of the cycle and compulsivity dominates the terminal stages. The three stages are considered to interact with each other, become more intense, and eventually lead to the pathological state known as addiction.

Drug addiction is an excessive drug-taking behavior in which individuals compulsively seek and take drugs and lose their ability to control in limiting their drug intake. One of the multiple motivational mechanisms that drive such behavior is the development of a negative emotional state when access to the drug is prevented or discontinued, which results in dysregulation of hedonic homeostasis [64]. At the neurobiological level, two neuroadaptive models, sensitization and counteradaptation, have been hypothesized to contribute to the changes in motivation for drug-seeking and compulsive use (hedonic homeostatic dysregulation) and the neurobiological mechanisms, such as the mesolimbic dopamine system, opioid peptidergic systems, and brain and hormonal stress systems [64]. While sensitization has been conceptualized to be a shift in an incentive-salience state since it involves a progressive rise in the effect of a certain drug due to its frequent administration (21), counteradaptation hypotheses (20), on the contrary, were closely related to hedonic tolerance and involve the reduction of dopaminergic and serotonergic neurotransmission in the nucleus accumbens during drug withdrawal (22). Critical neurobiological sites with specific neurotransmitters and hormones have been identified that may regulate the hedonic dysregulation and provide the substrates that convey both vulnerabilities to, and protection against drug addiction. Dysregulation of key neurochemical elements in both reward and stress systems, such as decreases in dopamine and opioid peptide functions in the ventral striatum and recruitment of brain stress systems known as corticotropin-releasing factor (CRF) in the extended amygdala is hypothesized to produce the negative emotional state that initiates a condition of negative reinforcement [66]. An individual who uses a drug impulsively starts using it compulsively when a shift has occurred from positive reinforcement driving the motivated behavior to negative reinforcement driving the motivated behavior. Impulsivity and compulsivity often coexist in the different stages of the addiction cycle. This occurrence reflects the role of dysregulated reward and stress systems in the negative emotional states associated with the withdrawal/negative affect and preoccupation/anticipation stages of the addiction cycle that drive drug-seeking behavior [67].

The addiction cycle becomes more severe and well-established as a person continues substance use and as it produces dramatic changes in the functioning of certain brain areas that reduce the person's ability to control his or her substance abuse. Three neurobiological circuits have been recognized to have empirical value for the study of the neurobiological changes associated with the development and persistence of drug addiction linked to the three-stage cycle (**Figure 1**). The key elements of the ventral tegmental area and ventral striatum are the focal points for the binge/intoxication stage and mediate the acute reinforcing effects of drugs of abuse. Acute withdrawal symptoms such as increased anxiety and dysphoria are associated with the second stage, the withdrawal/ negative affect stage, and are most likely the results of disruption in the function of the extended amygdala reward system and the recruitment of the brain stress neurocircuitry [68]. The preoccupation/ anticipation (craving) stage is a widely distributed network and involves the stressinduced processes at the central brain stress systems in the basolateral amygdala, the orbitofrontal cortex–dorsal striatum, prefrontal cortex, and the hippocampus. The prefrontal cortex is mainly responsible for executive functions like organizing thoughts and activities, prioritizing tasks, managing time, and making important decisions, including limiting substance consumption [65].

This three-stage model of addiction is largely attributed to the extensive works of George Koob [69] and his colleagues and is supported by several animal and human research findings which provide useful means to understand the aspects and symptoms of addiction and design prevention, intervention, and treatment strategies [70]. It is worth noting that the three stages of addiction being discussed do not have absolute temporal distinctions that some components of addiction such as craving, conditioned cued craving, incentive-salience related craving, craving linked to

*Psychopharmacological Perspectives and Diagnosis of Substance Use Disorder DOI: http://dx.doi.org/10.5772/intechopen.99531*

### **Figure 1.**

*A diagram depicting the spiralling distress – addiction cycle from social psychological and neurobiological viewpoints. NB. Stage 1: The reward circuit is overstimulated, resulting in a lack of control and bingeing. (A) Basal Ganglia refers to interconnected regions that are linked to learning, reward, and habit formation. (B) Nucleus accumbens (NAs) receives dopamine from Ventral Tegmental area, helps to control desire, satiation, and inhibition. (C) Thalamus is a centre that transmits sensory information and regulates arousal. (D) Ventral tegmental area: Dopamine is generated in this major structure near the top of the brain stem. Stage 2: After long-term exposure to addictive substances, the number of dopamine receptors in the nucleus accumbens decreases (B), requiring more of the addictive substance or action to feel the same. (B) The nucleus accumbens (NAs) is heavily implicated in the first and second stages of the addiction cycle. (E) The amygdala is linked to memory and emotions, particularly anxiety and dread. Stage 3: People who are addicted have a compulsive need to engage in the addictive behaviour again. The frontal cortex is thought to be affected by drug abuse. (F) The frontal cortex is in charge of ideas and actions. The orbitofrontal cortex is hypothesised to be involved in behaviour control. (G). Hippocampus: consolidated of memory. The three-stage addiction cycle incorporates some of the factors of possible self-regulation failure, such as under regulation and mis regulation, as well as the multiple DSM-5 criteria for substance dependence. The arrows depict the possible roles of several neurochemical and endocrine systems in the addiction cycle. Increased functional activity of DA, dopamine, Opioid Peptide, and CRF, corticotropin-releasing factor, is indicated by the arrows. It's worth mentioning that the addiction cycle is depicted as a circular pattern that rises in amplitude with repeated exposure, eventually leading to addiction as a pathological condition.*

withdrawal, etc. appear at many places in the addictive process and that the impacts of these aspects of craving and their neural basis remain to be further elucidated.

### **2.1 Stage 1: binge/intoxication**

In most cases, addiction is started by abusing substances that have hedonic properties. Many factors contribute to the transition from drug use to drug addiction, including availability (route of administration), genetics [71], prior drug use history, stress, and life events [72] . Initial; experimentation of drug abuse may be the result of peer pressure due to the rewarding effects of conforming to the peer

group. In some cases, the first use of a substance may be related to its therapeutic properties, such as opiate analgesics for the treatment of pain or stimulants such as amphetamine for the treatment of attention-deficit hyperactivity disorder [73].

In humans, the majority of drug users do not develop into drug abusers or drug addicts [74]. Similarly, even with intravenous drug administration in limited-access situations, stable drug intake can be observed in animals without pronounced signs of dependence. The current challenge is to discover the contribution of neurobiological factors to individual differences in drug addiction vulnerability [64]. It is broadly accepted that the key elements of the reinforcing effects of drugs of abuse involve their ability to activate large amounts of extracellular dopamine in limbic regions and the nucleus accumbens. Brain imaging studies in humans revealed that drug-induced increases in dopamine in the ventral striatum, where the nucleus accumbens is located, are significantly linked to subjective correlates of reward such as pleasure, high, and euphoria [27, 75].

The nucleus accumbens (NAs) and the dorsal striatum (DS) are the two sub-regions in the basal ganglia which are particularly important in substance use disorders. While the NAs is involved in motivation and the experience of reward the DS is responsible for forming habits and other routine behaviors [76]. The NAs is located strategically to collect important limbic information from the amygdala, frontal cortex, and hippocampus that could be transformed into motivated behavior (the acute reinforcing effects of drugs) through its connections with the extrapyramidal motor system [70]. It is the primary site mediating reward behavior and thought to directly involve in reinforcing addictive behaviors in response to drug use. It is hypothesized that the initial action of drug reward depends on dopamine release in the nucleus accumbens for cocaine, amphetamine, and nicotine; activation of the opioid peptide receptor in the VTA (dopamine activation) and nucleus accumbens (independent of dopamine activation) for opiates; and activation of the GABAA systems in the nucleus accumbens and amygdala for alcohol [70].

The dopaminergic projection from the VTA to the nucleus accumbens is known as the mesolimbic dopamine system (the mesolimbic pathway) and is strongly associated with the dependence-producing potential of addictive substances [77]. Many drugs of abuse directly or indirectly exert their powerful effects on this pathway and contribute to the development of dependence by signaling to the brain that addictive substances are especially important from a motivational perspective. The direct activation of dopamine, serotonin, opioid peptides, and GABA systems in the basal forebrain facilitates the acute reinforcing effects of drugs of abuse [74]. There are several supporting pieces of evidence for the hypothesis that addictive drugs dramatically activate the mesolimbic dopamine system and the serotonin systems, specifically, those involving 5-hydroxytryptamine-1B (5-HT1B) receptor activation in the nucleus accumbens, during limited-access self-administration [78, 79]. Several studies have shown that most addictive drugs including cocaine, amphetamine, and nicotine [79], either directly or indirectly, activate neurons that release dopamine. They produce their rewarding effects by stimulating the nucleus accumbens through the activation of the brain's dopamine and opioid signaling system. Brain imaging studies in humans who use alcohol, nicotine, and other substance have shown activation of dopamine and opioid neurotransmitters during use [80, 81]. In the same manner, the primary psychoactive component of marijuana, tetrahydrocannabinol (THC), targets the brain's endogenetic cannabinoid system and affects the reward system by influencing the function of dopamine neurons and the release of dopamine in the nucleus accumbens [30, 31].

Another key component of the binge/intoxication stage involves a second sub-region of the basal ganglia, the dorsal striatum. This part of the brain is

### *Psychopharmacological Perspectives and Diagnosis of Substance Use Disorder DOI: http://dx.doi.org/10.5772/intechopen.99531*

mainly related to habit formation [82]. With repeated drug use, the release of dopamine, glutamate (an excitatory neurotransmitter), and activation of brain opioid systems trigger changes in the dorsal striatum and strengthens substanceseeking and substance-taking habits. Studies on humans showed that the rise of DA level in the dorsal striatum was significantly correlated with the increase of craving for cocaine, [83, 84]. Likewise, pharmacological blockade of the dorsolateral striatum after forced abstinence resulted in the reduction of drug-seeking behavior in rats [85–87].

As addiction progresses compulsive drug use results and neuroadaptive changes in the structure and function of the brain occur. Neuroadaptations involve changes in the reward circuitry systems that promote compulsive drug use through sensitization and counteradaptation, by increasing a drug's positive and negative reinforcing effects, respectively [82]. Sensitization arises from repeated administration of addictive drugs and is mediated by the mesolimbic dopamine system [84]. It is an increased response to a drug effect which represents a with-in system mechanism of neuroadaptation [82]. Animal studies posit that direct injections of opiates or amphetamine into the ventral tegmental area which alter the function of the dopamine neurons ultimately produce sensitization to later injections of these drugs in the periphery [83]. However, like tolerance, sensitization may develop to a certain drug effect but not to another [82]. The second system that plays an important role in sensitization, representing a between-systems mechanism of neuroadaptation, is the CRF (corticotropin-releasing factor) mediated stress-response system [82]. The CRF is a hormone released by the hypothalamus and the amygdala in response to certain stressors. The CRF, in turn, stimulates the release of additional stress hormones into the bloodstream activating a stress response system called the hypothalamic–pituitary–adrenal (HPA) axis. Exposure to a variety of stressors implicates the CRF mediated stress-response system which promotes sensitization to the drug [82]. The role of sensitization in drug dependence is mainly linked to a motivational state described as "wanting" which progressively increases due to repeated exposure to drugs of abuse [85]. As "wanting" increases across repeated exposures to alcohol and drugs of abuse, the likelihood of relapse following periods of abstinence may increase, eventually leading to compulsive drug use [82].

Drug-associated stimuli raise dopamine levels in the dorsal striatum as addiction grows, leading to drug craving and reinforcing the habit of drug use [82]. In the new DSM-5 classification, drug craving as a motivational state for drug-seeking behavior is finally recognized as one of the main features of substance use disorders [88]. Cue reactivity and cue-elicited craving are both influenced by the process of "Positive Reinforcement," which involves learning to associate salient cues with drug-use rewards [89]. Drug craving is a complex neurocognitive emotional–motivational reaction to a variety of stimuli, ranging from internal to external settings, and from drug-related to stressful or affective experiences.

Substance craving, or "wanting" for a drug, is a common element in clinical definitions of addiction, and it appears to play a role in the maintenance of addictive behaviors [90]. According to Robinson and Berridge, sensitization processes which arise from neuroadaptations in brain reward pathways as a result of prolonged drug misuse are responsible for addicted animals' excessive "wanting" or drug-seeking behavior [91]. They propose that these neuroadaptations result in a rise in the motivational salience of drugs so that exposure to drugs and drug-associated cues causes excessive "wanting" or seeking, increasing the risk of relapse [92, 93]. Furthermore, environmental stimuli previously associated with drug use or internal cues such as stress responses, negative affect, and withdrawal-related states associated with drug abuse can function as conditioned stimuli capable of eliciting craving on their own [94, 95]. External drug-related stimuli, such as persons and places associated

with drug use, or drug paraphernalia such as needles, drug pipes, cocaine powder, or beer cans, as well as in vivo drug exposure, can increase drug craving and physiological reactivity [96].

In summary, in the nucleus accumbens of the basal ganglia, the "reward circuitry" along with dopamine and naturally occurring opioids play a vital role in the rewarding effects of alcohol and other substances. Furthermore, as the addiction progresses, stimuli or cues associated with that substance use can cause craving, substance seeking, and use. Chronic alcohol or substance use and frequent activation of the "habit circuitry" or the dorsal striatum of the basal ganglia significantly contributes to the compulsive substance seeking and taking that potentially lead to SUD. The involvement of the reward and habit neurocircuits play key roles in substance craving and compulsive substance seeking when addicted individuals are exposed to alcohol and/or other drug cues in their surroundings.

### **2.2 Stage 2: withdrawal/negative affect**

This stage is related to a neurocircuitry pathway known as the extended amygdala and its connections including the major components of the brain stress systems associated with the negative reinforcement [84]. This pathway is composed of the central amygdala (CeA), the bed nucleus of the stria terminalis (BNST), and the NAc shell. When DA is released, it intensively activates a number of areas that belong to the lateral subdivision of the extended amygdala such as the bed nucleus of stria terminalis, BSTM, and central amygdala) [97]. The extended amygdala also integrates brain arousal–stress systems with hedonic processing systems to produce unpleasant emotional states that promote negative reinforcement mechanisms linked to the development of addiction [98]. For example, hyperactivation of amygdala has been observed in individuals with SUDs, associated with cue-induced drug craving [99].

Two primary sources of reinforcement (positive and negative reinforcement) have been implicated to play significant roles in this allostatic process. Dysregulation of specific neurochemical mechanisms in the brain reward circuits (opioid peptides, γ-aminobutyric acid, glutamate and dopamine) and recruitment of brain stress systems (CRF), which are localized in the extended amygdala, are the reinforcing factors that provide the negative motivational state in the process of addiction [16, 100]. These allostatic changes in the reward and stress systems are assumed to maintain hedonic stability and as such contribute to the vulnerability for development of dependence and relapse in addiction [101].

An important component of this stage is the within-system neuroadaptations to chronic drug exposure which is characterized by a reduction in the function of the neurotransmitter systems in the neurocircuits implicated in the acute reinforcing effects of a drug of abuse [102]. One popular theory is that dopamine systems are negatively affected during key stages of the addiction cycle, such as withdrawal, resulting in decreased motivation for non-drug-related stimuli and increased sensitivity to the abused drug [103, 104]. In animal studies, acute drug withdrawal from all major drugs of abuse results in decreased activity of the mesolimbic dopamine system and decreased serotonergic neurotransmission in the nucleus accumbens [105]. Symptoms of withdrawal may occur with all addictive substances, but with varying intensity and duration depending on both the type of substance and the frequency and severity of use. Brain imaging studies have consistently revealed a long-lasting reduction of a particular type of dopamine receptor (the D2 receptor) in substance-addicted individuals compared to their counterparts. The same dose of stimulant causes a smaller release of dopamine in addicted persons than in non-addicted persons [83]. In addition, decreases in the activity of the dopamine

### *Psychopharmacological Perspectives and Diagnosis of Substance Use Disorder DOI: http://dx.doi.org/10.5772/intechopen.99531*

system have also been observed during withdrawal from stimulants such as opioids, nicotine, and alcohol [106].

Acute withdrawal mechanisms are likely to be drug-specific and reflect changes in the biological targets of these drugs. For example, during the first few days of cocaine withdrawal, the brain becomes more sensitive to the effects of GABAenhancing drugs, which may reflect the down regulation of this neurotransmitter in chronic cocaine users [107]. Brain imaging studies have shown decreased levels of endogenous opioids during cocaine withdrawal, which could explain the irritability, tiredness, and dysphoria experienced during the motivational phase of withdrawal [108]. Similarly, Imaging studies have documented hypofunction in dopamine pathways during protracted withdrawal, as evidenced by decreases in D2 receptor expression and decrease in dopamine release, which may contribute to the anhedonia (i.e., decreased sensitivity to rewarding stimuli) and motivation reported by drug-addicted subjects during protract withdrawal [109].

Typical neurochemical changes in these structures include not only reductions in reward system functioning, a within-system opponent processes, but also recruitment of the brain stress systems intermediated by corticotropin-releasing factor (CRF) and dynorphin- k opioid systems in the ventral striatum, extended amygdala, and frontal cortex known as a between-system opponent processes [80]. A between-system neuroadaptation is the second component of the withdrawal/ negative affect stage. This process involves the activation of stress neurotransmitters such as corticotropin-releasing factor (CRF), norepinephrine, and dynorphin in the extended amygdala [85]. Regardless of the presence of the drug, the neurochemical systems involved in stress modulation may be triggered within the neurocircuitry of the brain's stress systems to overcome the persistent effects of the distressing substance and restore normal function [110]. These neurotransmitters play a role in the development of negative feelings associated with withdrawal which leads to stress-triggered substance use. Chronic khat chewers attempting to quit chewing, for example, exhibited withdrawal symptoms that followed similar overall patterns, with notable elevations after the quit day. Most of the khat users relapsed within 11 days and very few maintained abstinences [111]. Negative affects including depression, nervousness, tiredness, restlessness, poor motivation, irritability, as well as craving substantially increased and reached their peak on the first week of khat cessation and remained higher there after indicating the persistence and severity of these symptoms over time [112].

The aforementioned phenomena have been well demonstrated both in animal and human studies [113]. Administration of antagonists for neurotransmitters significantly reduced substance intake in response to withdrawal and stress. Similarly stopping the activation of stress receptors in the brain lowered alcohol consumption in both alcohol-dependent rats and humans with alcohol use disorder [86, 87]. Hence, the desire to remove the negative feelings associated with withdrawal can be a strong driving force to continuous consumption of the substance since the taking of the substance at least momentarily relieves the negative feelings caused by the withdrawal. This process is, however, a vicious cycle as taking substances to reduce withdrawal symptoms during the period of abstinence makes it even more difficult to maintain abstaining the next time a person tries to quit the drug of abuse [56].

In summary, this stage of addiction involves a reduction in the function of the brain reward systems involving dopamine receptors and the activation of brain stress hormones and neuropeptide (CRF, dynorphin, and norepinephrine) in the extended amygdala. The combination of these events significantly contributes to providing a powerful neurochemical basis that produces a negative emotional state associated with drug abstinence or withdrawal. Increases in drug desire and physiological reactivity have also been linked to negative affect, stress, or

withdrawal-related suffering [114–117]. The strong desire to relieve these adverse feelings, in turn, drives additional sources of negative reinforcement in compulsive substance taking or drug addiction.

### **2.3 Stage 3: preoccupation/anticipation**

The preoccupation/anticipation (craving) stage mainly involves the Prefrontal Cortex, the region that controls executive functions like organizing thoughts and activities, task prioritization, time management, decision making, and regulation of one's actions, emotions, and impulses. Executive function is necessary for a person to make suitable choices about whether or not to submit to strong urges that may compel individuals to use substances, specifically, when the person encounters triggers and cues such as stimuli associated with that substance or stressful circumstances.

The preoccupation/anticipation stage, which is characterized by an increase in drug craving, is triggered by increased sensitivity to conditioned cues. An individual is driven to seek drugs of abuse again after a period of abstinence. Stress promotes relapse to drug-taking behaviors by activating brain circuits involved in reward processing as well as attentional and memory preferences for drug use cues [17, 118]. Chronic relapse is widely acknowledged as the most challenging obstacle in the fight against drug addiction. Long after encountering acute withdrawal symptoms, users are likely to return to compulsive drug use [119]. Chronic drug misuse is believed to cause a gradual restructuring of reward and memory circuits, which is thought to be critical to the mounting of these reactions. In clinical studies, both dopamine and glutamate have been identified as contributing to the neural alterations associated with conditioned responses [120].

The Incentive-Sensitization Theory of Addiction explains that drug-induced sensitization in the brain's mesocorticolimbic circuits, which assign incentive salience to reward-associated events, is the primary cause of addiction [41]. Incentive salience or "wanting," is generated by neural systems that comprise the mesolimbic dopamine. The role of this neural system is to attribute incentive salience or motivational importance to stimuli resulting their being viewed as highly salient, attractive, and "wanted" [40, 121]. Addictive drug administration, both continuous and occasional, causes incremental neuroadaptations in this neuronal system. Associative control of this sensitized neural system causes significantly increased incentive salience to be ascribed to the act of drug taking and stimuli related to drug-taking. This process is believed to occur due to hyperactivation of the dopamine system resulting in drugs and drug associated cues becoming pathologically more "wanted" by the drug user. Irrespective of other motivating variables such as the expectation of drug pleasure or the unpleasant aspects of withdrawal, sensitization of the neural system responsible for incentive salience can motivate addictive behavior such as compulsive drug seeking and drug-taking. The concomitant targeting of sensitized incentive salience to drug-related cues results in the recurrence of addictive behavior in the face of multiple barriers, including the loss of one's reputation, job, home, and family. This shows that drug addiction is motivated by a strong desire for drugs, often labeled as drug craving [122].

The Incentive-Sensitization Theory of Addiction offers a novel neuropsychological explanation for drug addiction. Due to dopamine system sensitization, drug craving is the subjective sensation that comes with the attribution of excessive amounts of incentive salience to drug-related stimuli or their mental images. This system causes pathological incentive motivation "wanting" for drugs that differ from both the unpleasant symptoms of withdrawal and drug pleasure. Although exposure to drug-related stimuli increased self-reported craving, as well as drug-opposite and drug-like effects, the cumulative effects of positive outcome

### *Psychopharmacological Perspectives and Diagnosis of Substance Use Disorder DOI: http://dx.doi.org/10.5772/intechopen.99531*

expectancies, cue-specific dysphoria, and cue-specific drug-positive reactions were able to predict 28 percent of the variance in cue-specific craving in a simple additive model [40, 123].

The changes that are taking place in the brain's reward and emotional circuits are followed by changes in the function of the cortical prefrontal cortex involved in the executive processes. Down-regulation of dopamine signals, which obscures the sensitivity of the reward circuits to pleasure, also occurs in the pre-frontal cortex and linked circuits, seriously affecting executive processes including the self-regulation, decision-making, flexibility in action selection, and initiation or assignment of salience (assignment of relativity) [124]. Neuroplastic changes in glutamatergic signals further disrupt the modulation of the reward and emotional circuits of the prefrontal regions. In people with drug addiction, the impaired signaling of dopamine and glutamate in prefrontal brain regions diminishes their ability to withstand strong urges or take strong decisions to stop taking the drug of abuse. These effects explain why people with addiction can be sincere in their desire and intention to stop using a substance while being impulsive and unable to follow through on their determination. Thus, changed signaling in prefrontal regulatory circuits, along with changes in rewards and emotional response circuits, causes an imbalance which is responsible for both progressive development of compulsive behavior in addictive diseases state and the related failure of individuals with addiction to voluntarily reduce the behavior [125].

Some scientists separate the functions of the prefrontal cortex into two opposing systems to better understand how this brain region is engaged in addiction: a "Go system" and a "Stop system" [80]. The Go system assists people in making decisions on topics that need a lot of thought and planning, as well as engaging in behaviors that are necessary for achieving life goals. When substance-seeking behavior is triggered by substance-related environmental cues (incentive salience), the Go circuits of the prefrontal cortex show significant increases in activity. As a result, the nucleus accumbens is stimulated to release glutamate, the brain's principal excitatory neurotransmitter [126]. In addition, the neurons in the Go circuits of the prefrontal cortex stimulate the habit systems of the dorsal striatum through connections that use glutamate and contribute to the impulsivity associated with substance-seeking behavior of a person [127].

Conversely, the Stop system primarily hinders the activity of the Go system [126]. The nucleus accumbens and habit responses driven by the dorsal striatum, that are areas of the basal ganglia implicated in the binge/intoxication stage of addiction, are controlled by Stop system. This system, according to researchers, helps to reduce incentive salience, or the ability of substance-related stimuli to trigger a relapse. The Stop system also plays an important role in relapse triggered by stressful life events or circumstances by exerting control on the brain's stress and emotional systems [126]. As explained above, the brain's stress and emotional systems involve the activation of stress hormones and neurotransmitters (CRF, dynorphin, and norepinephrine) in the extended amygdala caused by prolonged abstinence during the withdrawal/negative affect stage of addiction [109].

Imaging studies using laboratory animals revealed that lower activity in the Stop system of the prefrontal cortex is associated with increased activity of stress circuitry involving the extended amygdala, which increased substance-taking behavior and relapse [109]. Similar studies in humans with addiction show dysfunction of both the Go and Stop circuits [65, 109]. For example, people with alcohol, cocaine, or opioid use disorders exhibit significant deficiencies in executive functions such as impairments in the maintenance of spatial information, disruption of decisionmaking and behavioral inhibition. These executive function deficits are equivalent to the changes in the prefrontal cortex which suggest decreased activity in the Stop system and greater reactivity of the Go system in response to substance-related

stimuli. Moreover, research findings suggest that humans with post-traumatic stress disorder (PTSD), a syndrome that is usually accompanied by drug and alcohol use problems, have decreased prefrontal cortex control over the extended amygdala [75]. These findings add to the growing body of evidence supporting the importance of the prefrontal cortex-extended amygdala circuit in stress-induced relapse and imply that strengthening prefrontal cortex circuits may be crucial for the intervention and treatment of substance use disorders. The preoccupation/anticipation stage of the addiction cycle is characterized by a disruption of executive function caused by a compromised prefrontal cortex [128]. It is a key element of relapse in humans and the basis for defining addiction as a chronic relapsing disorder. While the over-activation of the Go system in the prefrontal cortex is related to habit-like substance seeking, the under-activation of the Stop system in the prefrontal cortex stimulates impulsive and compulsive substance seeking.

Overall, the study of neurobiological changes has identified three neurobiological circuits with heuristic value concerning the development and persistence of SUD. The three stages which involve different brain regions, neuro-circuits, and neurochemicals are interrelated to bring about specific kinds of changes in the brain. Activities in the nucleus accumbens-amygdala reward system, ventral tegmental dopamine input, and local opioid peptide and GABAergic circuits are among the acute reinforcing of drugs involved in the binge/intoxication stage addiction cycle. In contrast, acute withdrawal symptoms that are critical for addiction, such as dysphoria and heightened anxiety, are thought to be caused by reduced function of the extended amygdala reward system and activation of brain stress neurocircuitry during the withdrawal/ negative affect stage [78]. The preoccupation/anticipation (or craving) stage of addiction is characterized by the considerable increase of activities in the Go systems of the prefrontal cortex as incentive salience initiates substance seeking behavior. This results in major afferent projections to the nucleus accumbens and extended amygdala, in particular, the prefrontal cortex (for drug-induced reinstatement) and basolateral amygdala (for cue-induced reinstatement). Increased activities in this circuit further boost incentive salience and produces strong desire to use the substance in the presence of drug-related stimuli. The dorsal striatum's habit-response mechanisms are also activated by the Go system, which contributes to the impulsivity associated with substance seeking [126]. It's considered that the shift from ventral striatal-ventral pallidal-thalamic-cortical loops to dorsal striatal-pallidal-thalamiccortical loops is implicated in compulsive drug-seeking behaviour [129].

Molecular neuroadaptations start with the stage of binge/intoxication and as substance abuse progresses, transitions through the addiction cycle may bring about changes in long-term transcription which may convey a risk of relapse. A person may go through this cycle for weeks or months or progress through it several times in a day due to several factors including the type, amount, and frequency of substances used. There may also be a difference in how a person progresses through the cycle and the intensity with which he/she experiences each of the stages. In addition to this, it is to be noted that there are not absolute functional nor temporal boundaries that can be drawn between the stages in the process of addiction and therefore as the withdrawal/ negative affect aspects of addiction develop those effects from stage 1 persist (though reward may be attenuated) and those in stage 3 begin to emerge. Moreover, deficits in executive function are potential risk factors for developing addiction.

### **3. DSM-5 substance use disorder**

Drug abuse, substance-related problems, and substance use disorders (SUDs) have all been viewed in different ways throughout history and cultures. How drug

### *Psychopharmacological Perspectives and Diagnosis of Substance Use Disorder DOI: http://dx.doi.org/10.5772/intechopen.99531*

use and SUDs are conceptualized and how symptoms manifest and are interpreted are all influenced by culture. Sociocultural beliefs can influence how people approach and behave when it comes to substance use and misuse. Individuals' assumptions regarding potential drug-related problems are shaped in large part by their culture [130]. While in some cultures alcohol use was heavily controlled and was allowed only for ceremonial purposes [131], acculturation, on the other hand, has made significant contribution to accelerated abuse of alcohol and use of illegal drugs among certain groups of people [132, 133].

Although the characterization of substance problem syndromes as medical diseases or disorders has a long history, drug abuse and inebriety were historically regarded from a moralistic perspective [134]. Attempts to define and refine diagnostic criteria for SUDs began in the mid-twentieth century and are still ongoing. Research has identified some limitations in the existing diagnostic criteria for SUDs, which can help with the conception of future classification systems [135].

According to modern theories of substance dependence, chronic substance use can cause neuroadaptations in brain systems involved in reward, motivation, emotional regulation, inhibitory control, and tolerance/withdrawal, all of which can lead to compulsive drug use behavior [136]. Illicit drug usage has been documented all over the world with the highest estimates in Europe, North America, and Australasia. Regular use, "problem drug use," and drug addiction are less commonly measured, although they are critical to quantify in order to determine disease burden and risk factors for illicit drug use [137]. To better understand the harms associated with illicit drug dependency, future research should focus on collecting better estimates of mortality and morbidity.

Substance use and SUDs follow standard epidemiological age-related trends, with an onset often in late adolescence or early adulthood, showing peak prevalence in emerging adulthood, and then a decline. Although substance abuse is less common among older persons, often has a greater impact when it does occur, making it a public health concern [137]. A careful review of the population-based empirical literature reveals the importance of considering substance use in the context of development, with specific developmental aspects linked to the origin, course, and resolution of the problem.

SUDs are defined and thought of in a variety of ways. They have a lot in common with other chronic, recurrent diseases like diabetes or high blood pressure from a public health standpoint. Chronic diseases with significant behavioral health components may necessitate a lifelong commitment to manage and control. Many professionals believe that SUDs are inherently progressive. To put it another way, if these diseases are not treated, they tend to worsen over time. This chronic disease viewpoint has sparked significant controversy. For example, there is abundant evidence that many people, especially those suffering from the most severe kinds of addiction, follow a chronic, relapsing, escalating cycle. However, many people with SUDs seem to "recover" without undergoing professional therapy. This pattern, however, does not apply to everyone who has been diagnosed with a SUD. Many persons with milder, earlier-stage SUDs do not relapse and their substance-related problems do not progress [138]. Some experts, on the other hand, contend that, despite their history of chronic substance abuse, these individuals may not have been suffering from a severe SUD. Because of the great degree of individual variability in the course of addiction, it appears imprudent to employ a single treatment approach for all people diagnosed with the illness [138]. Despite age-related norms, significant individual course variability seems to be observed and contemporary statistical approaches have found numerous unique prototypic courses that appear to differ in their factors and outcomes. Research on substance use and misuse from a lifetime

perspective has significant implications for the design and implementation of successful, developmentally informed diagnosis, prevention, and intervention programs [136].

The Diagnostic and Statistical Manual of Mental Disorders (DSM) is a classification manual for mental illnesses. It establishes a classification system for clinicians, insurance providers, researchers, and policymakers to utilize during diagnosing, researching, and treating mental illness. The 4th edition (DSM-IV) of the DSM, which had been in use for almost a decade, was replaced in 2013 by the 5th edition (DSM-5) [134, 135]. This version included organizational modifications as well as significant revisions to the diagnostic criteria for nearly every DSM-IV disorder. Some SUDs had just minor wording modifications, while others had significant criterion revisions. Some disorders have been added to and some removed from the list [136].

The transition from DSM-IV to DSM-5 has relevance in a multitude of settings, and it is essential for diagnosing and treating mental illness and SUD, as well as medical billing procedures and mental health research. Furthermore, if the modifications result in significant changes in the estimate of illness burden in the United States, they may be relevant to policymaking [136]. Every year, the National Survey on Drug Use and Health (NSDUH) gathers data on substance abuse and mental health from roughly 70,000 occupants of households and noninstitutional group quarters (e.g., shelters, rooming houses, and dormitories) as well as civilians residing on military bases. The NSDUH data give current, relevant information on the nation's substance use and mental health status to the drug use and mental health prevention, treatment, and research communities. Stakeholders and policymakers can utilize this data to learn more about the disease burden, temporal patterns, and repercussions of substance abuse and mental illness, as well as identify high-risk groups [136]. Professionals in the United States currently rely extensively on the diagnostic method outlined in the DSM-5 for diagnosing substance use disorders [136]. Many nations throughout the world use the 11th edition World Health Organization's (WHO, 2016) International Statistical Categorization of Diseases and Related Health Problems version 10, (ICD-10).

According to the DSM-5, SUDs are a type of a class of disorder (substancerelated disorders) that are "associated to the use of a drug of abuse (including alcohol)". Although there are changes at various levels in the shift from DSM-IV to DSM-5 for SUDs, the core criteria stay the same [134, 135, 139]. However, there have been changes at the category/class level (see **Table 1** for the specific disorders considered within the overall group of disorders), substance level (which substances are considered "drugs of abuse"), disorder level (the template of criteria that is applied, with some deviations, across all substances), and individual criteria level (the number and types of symptoms needed to meet criteria for a disorder) [140]. Changes in relation to categorization refer to a disorder's "class," which is used in the DSM to designate groups of related disorders (e.g., personality disorders and anxiety disorders). The DSM-5 includes several revisions to the classification system, one of which is the classification of SUDs. SUDs were classified as substance-related disorders in the DSM-IV, which contained solely substance/drug-based illnesses. Gambling disorder has been added to this classification in DSM-5, and the section has been renamed Substance-Related and Addictive Disorders [136]. Changes from DSM-IV to DSM-5 in the types of substances assessed have been minor, but some reclassification has occurred. Based on empirical evidence since they have similar mechanisms of action (boosting synaptic dopamine), symptom profiles, consequences, and prognoses, cocaine (including crack) and amphetamines have been merged with other stimulants (except caffeine) into a distinct stimulant class [140].


### **Table 1.**

*Comparison of DSM-IV and DSM-5 Substance Use Disorder Assessment.*

The merging of substance abuse disorder and substance dependency disorder into a single SUD is a fundamental alteration in the criteria for substance use disorders from DSM-IV to DSM-5. DSM-5 has combined what had previously been considered as two distinct and hierarchical disorders (substance abuse and substance dependence) into a single construct, SUD, classifying it as mild, moderate, or severe, with the severity of an addiction based on how many of the established criteria are met. Diagnosis of SUD requires two out of eleven criteria to be met in a 12-month period. In addition, the DSM-5 has included a craving criterion to replace the abuse criterion associated with recurring substance-related legal difficulties (e.g., arrests for substance-related disorderly conduct). Due to low endorsement, poor fit with other items, and poor discrimination of this item (nearly everyone endorsing the legal criteria also endorsed other criteria), the

legal problems criterion was omitted (see **Table 2**) [136, 141]. The majority of cases that met DSM-IV abuse criteria will not receive a DSM-5 diagnosis since they do not meet the minimum two-criterion threshold for a mild SUD. Most DSM-IV abuse cases will now be classified as mild SUD if they also endorsed two dependence criteria, but those who endorsed multiple (i.e., two or three) abuse criteria and two to three dependence criteria will now be classified as moderate SUD [137]. DSM-IV dependence cases that met three dependence criteria and no more than two abuse criteria will be classified as moderate SUDs in DSM-5, whereas nearly all cases that met four or more dependence criteria will be classified as severe SUDs [140]. The withdrawal dependence criterion is another criterion that has undergone some changes in DSM-5. Unlike other criteria, withdrawal symptoms are unique to the substance's physiological action (see **Table 2**). Withdrawal is manifested by (1) a person experiencing the substance's characteristic withdrawal symptoms, or (2) a person taking the same or a closely comparable substance to avoid the substance's unique withdrawal symptoms in both DSM-IV and DSM-5. Except for cannabis, the DSM-IV and DSM-5 withdrawal criteria remain intact [136].

For that category of substances, the DSM-IV requires the endorsement of one or more symptoms (out of four, at any time) and no history of substance dependency (see **Table 2** for the specific criteria) [136]. In addition, in order to meet the substance dependence criteria, three or more symptoms (out of seven) have to be confirmed in 12-month period. According to DSM-IV diagnostic hierarchy standards, people who met both substance abuse and substance dependence criteria for a particular substance were labeled as having substance dependence alone. The objective of this was to highlight the severity of dependence as compared to the abuse diagnosis [136].

The separate abuse and dependence disorders have been eliminated from DSM-5 for several reasons: (1) the separation has little guidance for treatment; (2) the separation created "diagnostic orphans" (those who endorsed two dependence symptoms but no abuse symptoms and hence did not meet any diagnostic criteria); (3) the hierarchical structure did not follow the expected relationship between abuse and dependence (that abuse was largely a less severe symptom of dependence); and (4) the division caused the abuse diagnosis to suffer from substantial reliability problems [136, 140, 142, 143].

A cluster of symptoms related to cannabis withdrawal has been uncovered in research undertaken following the release of the DSM-IV, and this new information has been included in the DSM-5 [2]. The presence of three or more symptoms occurring within one week of stopping severe and persistent cannabis use is known as cannabis withdrawal syndrome. (1) irritability, anger, or depression; (2) nervousness or anxiety; (3) sleep problems (e.g., insomnia or unpleasant/vivid dreams); (4) decreased appetite or weight loss; (5) restlessness; (6) depressed mood; and (7) at least one physical symptom that causes considerable discomfort such as abdominal pain, shakiness/tremors, sweating, fever, chills, or headache, are all possible symptoms [136].

The severity Criteria were another significant change between the two versions of diagnostic criteria. The DSM-IV did not directly measure the severity of SUDs, though dependence was generally thought to be more severe than abuse, and patients who were diagnosed with dependence did not obtain an abuse diagnosis even if the criteria for abuse were met [136]. A symptom count-based severity indicator has been added to DSM-5, with two to three symptoms classified as mild, four to five symptoms categorized as moderate, and six or more symptoms categorized as severe. A study found that a simple symptom count was as successful at evaluating severity as more advanced algorithms [144], prompting the establishment of a severity index [136].


### *Psychopharmacological Perspectives and Diagnosis of Substance Use Disorder DOI: http://dx.doi.org/10.5772/intechopen.99531*


**Table 2.** *Comparison of DSM-IV and DSM-5 Substance Use Disorder Criteria.*

### **3.1 Criteria for diagnosing substance use disorders**

Drug addiction is a progressive and chronically relapsing disorder that develops from infrequent, limited, and regulated use of a substance to compulsive usage [145]. When people are unable to obtain the substance to which they are addicted, they suffer from unpleasant emotional states (dysphoria, anxiety, irritability, and other negative feelings) due to low level of reward, excessive stress, and compromised executive function. Eventually, the individual returns to excessive drug-seeking and excessive drug taking behavior. This, in turn, activates CRF in the medial PFC accompanied by executive function deficits that may aid the transition to compulsive like behavior [146].

The addiction process is cyclic and consists of three stages which are interconnected and can lead to a recurrent sequence of addictive behaviors characterized by increasing and persistent levels of psychological and physical problems over time [145]. Each of the components of the cycle involves a distinct psychosocial and behavioral manifestation relevant to the DSM-5 diagnostic criteria (see **Figure 1**).

The changes in various components of reward neurocircuitry may represent the different social psychological and behavioral components involved in the addiction cycle. The social-psychological components of lack of strength and self-regulation with regard to controlling substance use, for example, may reflect increased activity in the stress system and be linked to individuals' failures despite their persistent desire to limit or quit using addictive substances and using them in larger quantities than intended (refer to **Figure 1**). This process signifies the preoccupation/anticipation stage which eventually leads to a cycle of binge use and relapse [68]. In this stage, increased dopaminergic and opioidergic neurotransmission may be involved, resulting in sensitization. On the other hand, monitoring or attentional failures are linked to people's preoccupation with getting drugs, and they may reflect cognitive alterations impacted by broadly distributed brain monoamine systems. In this situation, counteradaptation is caused by compromised dopamine, serotonin, and opioidergic neurotransmission, as well as increased stress neurotransmitters, which may be responsible for the negative emotional state developed due to withdrawal. The combination of a change in the hedonic set-point caused by repeated counteradaptation and a different mechanism for sensitization would provide a powerful motivational drive for drug addiction to persist [68].

The DSM-5 states that a person must fulfill certain criteria to be diagnosed with a substance use disorder. Currently, there are 11 criteria (see **Table 2**) used to make such a diagnosis which can be divided into four categories [141].


result of prolonged substance use. (7) Giving up Important social, work, and recreational activities for the sake of substance use. Substance abuse may cause important and meaningful social and leisure activities to be abandoned or curtailed. A person may spend less time with his or her family or quit outdoor plays with his or her friends.

c.**Risky Use:** The central issue of this criterion is the failure to quit using the substance despite the harm it causes.

(8) Addiction may be indicated when someone takes substances in physically unsafe or dangerous conditions regularly. For instance, using alcohol or other drugs while operating machinery or driving a car. (9) Some people continue to use addictive substances even if they are aware that addictive substances are creating or exacerbating bodily and psychological problems. An individual may continue to smoke cigarettes despite having a respiratory condition such as asthma or chronic obstructive pulmonary disease (COPD), such as chronic bronchitis.

### d.**Pharmacological indicators: Tolerance and Withdrawal**

This criterion describes how the body adjusts or attempts to maintain homeostatic equilibrium to the sustained and frequent usage of a substance. For most people, tolerance and withdrawal are hallmark markers of progressing addiction. (10) Tolerance develops when people require a higher dose of a substance to obtain the same effect. To put it another way, it's when someone gets less of a result with the same amount of effort. Either the desire to avoid withdrawal symptoms or to become high could be the "desired effect." Tolerance is experienced differently by different people, i.e., people's sensitivities to different drugs differ. The rate at which tolerance develops and the dose required for tolerance to develop will differ depending on the substance [88]. (11) Withdrawal is the body's reaction to abrupt discontinuation of a drug once the body has built a tolerance to it. Each drug produces a distinct set of (sometimes unpleasant and lethal) symptoms (refer to the previous section for the unique symptoms in each category of substance). Although withdrawal is painful, it usually does not necessitate medical intervention. However, withdrawing from certain drugs, on the other hand, can be so deadly that medical advice may be critical before trying to quit them after a long period of use [88].

While an individual must meet at least two of the above criteria to be diagnosed with a SUD, the severity of the addiction is decided by the number of criteria met. A mild SUD might be the diagnosis, if two or three of these symptoms are present. A moderate SUD would be a more appropriate diagnosis if a person exhibits four or five of these symptoms. Ultimately, a severe SUD occurs when a person exhibits six or more of these symptoms. Substance withdrawal, however, is a distinct diagnosis that may or may not be associated with a substance use disorder diagnosis [88].

### **4. Conclusion**

Addiction is caused by the brain's gradual adaptation of neuronal activity to long-term drug exposure, which results in fundamental neuroplastic changes. The extended amygdala (EAc) is a network made up of the central amygdala and the stria terminalis' bed nucleus. This important location is in charge of controlling drug cravings and seeking behaviors [147]. Drug addiction signifies a three-stage intense dysregulation of motivational circuits due to a combination of excessive

### *Psychopharmacological Perspectives and Diagnosis of Substance Use Disorder DOI: http://dx.doi.org/10.5772/intechopen.99531*

incentive salience and habit formation [148], reward deficits and excessive stress, and impaired executive function [64, 80, 126].

Changes in dopamine and opioid peptides in the basal ganglia are involved in the rewarding effects of drugs of abuse, the development of incentive salience, and the development of drug-seeking habits in the binge/intoxication stage. In the withdrawal/negative affect stage, decreases in the function of the dopamine component of the reward system, as well as recruitment of brain stress neurotransmitters like corticotropin-releasing factor and dynorphin in the neurocircuitry of the extended amygdala, lead to an increase in negative emotional states and dysphoric and stresslike responses. The dysregulation of critical afferent projections from the prefrontal cortex and insula, particularly glutamate, to the basal ganglia and extended amygdala, causes craving and executive function deficiencies in the preoccupation/ anticipation stage [68].

Almost all addictive substances have the common property of increasing mesolimbic dopamine function. The mesolimbic dopamine (DA) pathway by which the DA cells in the ventral tegmental area (VTA) projecting into the nucleus accumbens (NAc) seems to be crucial for drug rewards. Most psychostimulants such as cocaine [149] amphetamine [150] narcotic analgesics [151], nicotine [152], alcohol [153], and phencyclidine [150] stimulate dopamine transmission in the nucleus accumbens [151], the main area of the ventral striatum. A couple of other dopamine pathways the mesostriatal (DA cells in substantia nigra [154] projecting into dorsal striatum) and the mesocortical (DA cells in VTA projecting into frontal cortex) are recently recognized to have a contribution to drug reward and addiction. Alcohol and other substances of abuse are fundamentally rewarding in that they are consumed by humans or self-administered by laboratory animals. As a result, individuals exposed to drugs, though small in percentage, will become addicted and move from controlled drug use to compulsive and uncontrolled drug consumption despite adverse consequences.

The three stages involving the different neurochemicals and regions of the brain are integrated and feed each other producing strong drives for substance seeking. The addiction cycle tends to intensify over time and progress to greater physical and psychological harms. People with such disorders may have distorted thinking and abnormal behaviors as a result of changes in the brain's structure and functions. Brain imaging studies on people who frequently use psychoactive substances show marked changes in the areas of the brain that are linked to judgment, decision making, learning, memory, and behavioral control, which can last long after the period of intoxication.

Even though there are no specific biological markers of substance abuse disorders currently on use, there are a number of intriguing neurobiological aspects of substance abuse disorders that can help in the diagnosis of substance use, misuse, and SUD. An impaired reward system, overactive brain stress systems, and compromised orbitofrontal/prefrontal cortex function are some of the major neurobiological changes in the brain revealed in both human and animal studies which are quite relevant for the diagnosis of substance use, misuse, and SUD [155]. Although addictive substances have common properties, there are still considerable variabilities among classes of drugs in terms of primary and long-term physical and psychological effects, mechanisms of action, development of tolerance, and withdrawal. Differences in the availability, cost, legality, marketing, and cultural attitudes towards addictive substances and their use also influence which substances are used, and the development of dependence upon them. Thus, the study of substance use disorder and addiction must take these factors into account, while at the same time noting the similarities across drug classes.

### **Conflict of interest**

The authors declare no conflict of interest.

### **Author details**

Samson Duresso University of Tasmania, Hobart, Australia

\*Address all correspondence to: samson.duresso@utas.edu.au

© 2021 The Author(s). Licensee IntechOpen. This chapter is distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/ by/3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

*Psychopharmacological Perspectives and Diagnosis of Substance Use Disorder DOI: http://dx.doi.org/10.5772/intechopen.99531*

### **References**

[1] Morse, R.M. and D.K. Flavin, *The definition of alcoholism. The Joint Committee of the National Council on Alcoholism and Drug Dependence and the American Society of Addiction Medicine to Study the Definition and Criteria for the Diagnosis of Alcoholism.* JAMA, 1992. **268**(8): p. 1012-4.

[2] Robinson, B.E. and P. Post, *Risk of addiction to work and family functioning.* Psychol Rep, 1997. **81**(1): p. 91-5.

[3] Wang, M.Q., et al., *Social influence on southern adolescents' smoking transition: a retrospective study.* South Med J, 1997. **90**(2): p. 218-22.

[4] Wise, R.A., *Addiction becomes a brain disease.* Neuron, 2000. **26**(1): p. 27-33.

[5] *Addiction and the brain--Part II.* Harv Ment Health Lett, 1998. **15**(1): p. 1-3.

[6] Friedman, D.P., *Drug addiction: a chronically relapsing brain disease.* N C Med J, 2009. **70**(1): p. 35-7.

[7] Wu, P.H. and K.M. Schulz, *Advancing addiction treatment: what can we learn from animal studies?* ILAR J, 2012. **53**(1): p. 4-13.

[8] Kramer, J., et al., *Mechanisms of Alcohol Addiction: Bridging Human and Animal Studies.* Alcohol Alcohol, 2020. **55**(6): p. 603-607.

[9] Cates, H.M., et al., *National Institute on Drug Abuse genomics consortium white paper: Coordinating efforts between human and animal addiction studies.* Genes Brain Behav, 2019. **18**(6): p. e12577.

[10] Markou, A., T.R. Kosten, and G.F. Koob, *Neurobiological similarities in depression and drug dependence: a self-medication hypothesis.* Neuropsychopharmacology, 1998. **18**(3): p. 135-74.

[11] Kreek, M.J., et al., *Genetic influences on impulsivity, risk taking, stress responsivity and vulnerability to drug abuse and addiction.* Nat Neurosci, 2005. **8**(11): p. 1450-7.

[12] Sentir, A.M., et al., *Polysubstance addiction vulnerability in mental illness: Concurrent alcohol and nicotine selfadministration in the neurodevelopmental hippocampal lesion rat model of schizophrenia.* Addict Biol, 2020. **25**(1): p. e12704.

[13] Volkow, N.D. and J.S. Fowler, *Addiction, a disease of compulsion and drive: involvement of the orbitofrontal cortex.* Cerebral cortex, 2000. **10**(3): p. 318-325.

[14] Davidson, R.J., et al., *Neural and behavioral substrates of mood and mood regulation.* Biological psychiatry, 2002. **52**(6): p. 478-502.

[15] Nemeroff, C.B., *The corticotropinreleasing factor (CRF) hypothesis of depression: new findings and new directions.* Mol. Psychiatry, 1996. **1**: p. 336-342.

[16] Sinha, R., *How does stress increase risk of drug abuse and relapse?* Psychopharmacology, 2001. **158**(4): p. 343-359.

[17] Sinha, R., *Chronic stress, drug use, and vulnerability to addiction.* Annals of the New York Academy of Sciences, 2008. **1141**: p. 105-130.

[18] Abasi, I. and P. Mohammadkhani, *Family Risk Factors Among Women With Addiction-Related Problems: An Integrative Review.* Int J High Risk Behav Addict, 2016. **5**(2): p. e27071.

[19] Ranjbaran, M., et al., *Risk Factors for Addiction Potential among College Students.* Int J Prev Med, 2018. **9**: p. 17.

[20] Zimic, J.I. and V. Jukic, *Familial risk factors favoring drug addiction onset.* J Psychoactive Drugs, 2012. **44**(2): p. 173-85.

[21] Cancrini, L., et al., *Social and family factors of teenager drug-addiction.* Eur J Toxicol, 1970. **3**(6): p. 397-401.

[22] Gjeruldsen, S., B. Myrvang, and S. Opjordsmoen, *Risk factors for drug addiction and its outcome. A follow-up study over 25 years.* Nord J Psychiatry, 2003. **57**(5): p. 373-6.

[23] Strang, J., et al., *Route of drug use and its implications for drug effect, risk of dependence and health consequences.* Drug Alcohol Rev, 1998. **17**(2): p. 197-211.

[24] Morales, A.M., et al., *Identifying Early Risk Factors for Addiction Later in Life: A Review of Prospective Longitudinal Studies.* Curr Addict Rep, 2020. **7**(1): p. 89-98.

[25] Odgers, C.L., et al., *Is it important to prevent early exposure to drugs and alcohol among adolescents?* Psychol Sci, 2008. **19**(10): p. 1037-44.

[26] Heilig, M., et al., *Developing neuroscience-based treatments for alcohol addiction: A matter of choice?* Transl Psychiatry, 2019. **9**(1): p. 255.

[27] Volkow, N.D., M. Michaelides, and R. Baler, *The Neuroscience of Drug Reward and Addiction.* Physiol Rev, 2019. **99**(4): p. 2115-2140.

[28] Lein, E.S., et al., *Genome-wide atlas of gene expression in the adult mouse brain.* Nature, 2007. **445**(7124): p. 168-176.

[29] Gardon, O., et al., *Expression of mu opioid receptor in dorsal diencephalic conduction system: New insights for the medial habenula.* Neuroscience, 2014. **277**: p. 595-609.

[30] Kang, S., et al., *Ethanol Withdrawal Drives Anxiety-Related Behaviors by Reducing M-type Potassium Channel* 

*Activity in the Lateral Habenula.* Neuropsychopharmacology, 2017. **42**(9): p. 1813-1824.

[31] Baldwin, P.R., R. Alanis, and R. Salas, *The Role of the Habenula in Nicotine Addiction.* Journal of addiction research & therapy, 2011. **S1**(2): p. 002.

[32] Mineur, Y.S. and M.R. Picciotto, *Genetics of nicotinic acetylcholine receptors: Relevance to nicotine addiction.* Biochemical pharmacology, 2008. **75**(1): p. 323-333.

[33] Shorey-Kendrick, L.E., et al., *Nicotinic receptors in non-human primates: Analysis of genetic and functional conservation with humans.* Neuropharmacology, 2015. **96**(Pt B): p. 263-273.

[34] Margolis, E.B. and H.L. Fields, *Mu Opioid Receptor Actions in the Lateral Habenula.* PLOS ONE, 2016. **11**(7): p. e0159097.

[35] Nestler, E.J. and C. Luscher, *The Molecular Basis of Drug Addiction: Linking Epigenetic to Synaptic and Circuit Mechanisms.* Neuron, 2019. **102**(1): p. 48-59.

[36] Lepack, A.E., et al., *Dopaminylation of histone H3 in ventral tegmental area regulates cocaine seeking.* Science, 2020. **368**(6487): p. 197-201.

[37] Wong, C.C.Y., J. Mill, and C. Fernandes, *Drugs and addiction: an introduction to epigenetics.* Addiction, 2011. **106**(3): p. 480-489.

[38] Eddy, N.B., et al., *Drug dependence: its significance and characteristics.* Psychopharmacol Bull, 1966. **3**(3): p. 1-12.

[39] Koob, G.F., R. Maldonado, and L. Stinus, *Neural substrates of opiate withdrawal.* Trends Neurosci, 1992. **15**(5): p. 186-91.

*Psychopharmacological Perspectives and Diagnosis of Substance Use Disorder DOI: http://dx.doi.org/10.5772/intechopen.99531*

[40] Berridge, K.C. and T.E. Robinson, *Liking, wanting, and the incentivesensitization theory of addiction.* American Psychologist, 2016. **71**(8): p. 670.

[41] Robinson, T.E. and K.C. Berridge, *The incentive sensitization theory of addiction: some current issues.* Philosophical Transactions of the Royal Society B: Biological Sciences, 2008. **363**(1507): p. 3137-3146.

[42] Gelkopf, M., S. Levitt, and A. Bleich, *An integration of three approaches to addiction and methadone maintenance treatment: the self-medication hypothesis, the disease model and social criticism.* Isr J Psychiatry Relat Sci, 2002. **39**(2): p. 140-51.

[43] Koob, G.F., *Hedonic Homeostatic Dysregulation as a Driver of Drug-Seeking Behavior.* Drug Discov Today Dis Models, 2008. **5**(4): p. 207-215.

[44] Solomon, R.L., *The opponent-process theory of acquired motivation: the costs of pleasure and the benefits of pain.* Am Psychol, 1980. **35**(8): p. 691-712.

[45] Solomon, R.L. and J.D. Corbit, *An opponent-process theory of motivation. II. Cigarette addiction.* J Abnorm Psychol, 1973. **81**(2): p. 158-71.

[46] Solomon, R.L. and J.D. Corbit, *An opponent-process theory of motivation. I. Temporal dynamics of affect.* Psychol Rev, 1974. **81**(2): p. 119-45.

[47] George, O., M. Le Moal, and G.F. Koob, *Allostasis and addiction: role of the dopamine and corticotropin-releasing factor systems.* Physiol Behav, 2012. **106**(1): p. 58-64.

[48] Miller, E.K. and J.D. Cohen, *An integrative theory of prefrontal cortex function.* Annu Rev Neurosci, 2001. **24**: p. 167-202.

[49] Fernandez-Serrano, M.J., et al., *Prevalence of executive dysfunction in*  *cocaine, heroin and alcohol users enrolled in therapeutic communities.* Eur J Pharmacol, 2010. **626**(1): p. 104-12.

[50] Hester, R. and H. Garavan, *Executive dysfunction in cocaine addiction: evidence for discordant frontal, cingulate, and cerebellar activity.* J Neurosci, 2004. **24**(49): p. 11017-22.

[51] Madoz-Gurpide, A., et al., *Executive dysfunction in chronic cocaine users: an exploratory study.* Drug Alcohol Depend, 2011. **117**(1): p. 55-8.

[52] Baumeister, R.F., *Self-regulation, ego depletion, and inhibition.* Neuropsychologia, 2014. **65**: p. 313-9.

[53] Patrick, H. and A. Canevello, *Methodological Overview of A Self-Determination Theory-Based Computerized Intervention to Promote Leisure-Time Physical Activity.* Psychol Sport Exerc, 2011. **12**(1): p. 13-19.

[54] Ryan, R.M. and E.L. Deci, *Selfdetermination theory and the facilitation of intrinsic motivation, social development, and well-being.* Am Psychol, 2000. **55**(1): p. 68-78.

[55] Gollwitzer, P.M. and B. Schaal, *Metacognition in action: the importance of implementation intentions.* Pers Soc Psychol Rev, 1998. **2**(2): p. 124-36.

[56] Sheeran, P., T.L. Webb, and P.M. Gollwitzer, *The interplay between goal intentions and implementation intentions.* Pers Soc Psychol Bull, 2005. **31**(1): p. 87-98.

[57] Brewer, J.A. and M.N. Potenza, *The neurobiology and genetics of impulse control disorders: relationships to drug addictions.* Biochem Pharmacol, 2008. **75**(1): p. 63-75.

[58] Everitt, B.J. and T.W. Robbins, *Neural systems of reinforcement for drug addiction: from actions to habits to compulsion.* Nat Neurosci, 2005. **8**(11): p. 1481-9.

[59] Schultz, W., *Potential vulnerabilities of neuronal reward, risk, and decision mechanisms to addictive drugs.* Neuron, 2011. **69**(4): p. 603-17.

[60] Hariri, A.R., *The neurobiology of individual differences in complex behavioral traits.* Annu Rev Neurosci, 2009. **32**: p. 225-47.

[61] Muller, D.J., O. Likhodi, and A. Heinz, *Neural markers of genetic vulnerability to drug addiction.* Curr Top Behav Neurosci, 2010. **3**: p. 277-99.

[62] Baker, T.E., et al., *Individual differences in substance dependence: at the intersection of brain, behaviour and cognition.* Addict Biol, 2011. **16**(3): p. 458-66.

[63] Lubman, D.I., M. Yucel, and C. Pantelis, *Addiction, a condition of compulsive behaviour? Neuroimaging and neuropsychological evidence of inhibitory dysregulation.* Addiction, 2004. **99**(12): p. 1491-502.

[64] Koob, G.F. and M. Le Moal, *Drug abuse: hedonic homeostatic dysregulation.* Science, 1997. **278**(5335): p. 52-8.

[65] Goldstein, R.Z. and N.D. Volkow, *Drug addiction and its underlying neurobiological basis: neuroimaging evidence for the involvement of the frontal cortex.* Am J Psychiatry, 2002. **159**(10): p. 1642-52.

[66] Koob, G., *Addiction is a Reward Deficit and Stress Surfeit Disorder.* Frontiers in Psychiatry, 2013. **4**(72).

[67] Berlin, G.S. and E. Hollander, *Compulsivity, impulsivity, and the DSM-5 process.* CNS Spectr, 2014. **19**(1): p. 62-8.

[68] Koob, G.F. and N.D. Volkow, *Neurobiology of addiction: a neurocircuitry analysis.* The lancet. Psychiatry, 2016. **3**(8): p. 760-773.

[69] George F. Koob, Ph.D., *Neurobiology of Addiction.* FOCUS, 2011. **9**(1): p. 55-65.

[70] Koob, G.F. and N.D. Volkow, *Neurocircuitry of addiction.* Neuropsychopharmacology, 2010. **35**(1): p. 217-38.

[71] Duncan, J.R., *Current perspectives on the neurobiology of drug addiction: a focus on genetics and factors regulating gene expression.* ISRN Neurol, 2012. **2012**: p. 972607.

[72] Nurco, D.N., et al., *Differential contributions of family and peer factors to the etiology of narcotic addiction.* Drug Alcohol Depend, 1998. **51**(3): p. 229-37.

[73] Harstad, E. and S. Levy, *Attention-Deficit/Hyperactivity Disorder and Substance Abuse.* Pediatrics, 2014. **134**(1): p. e293-e301.

[74] Koob, G.F. and M. Le Moal, *Drug addiction, dysregulation of reward, and allostasis.* Neuropsychopharmacology, 2001. **24**(2): p. 97-129.

[75] Volkow, N.D. and M. Morales, *The Brain on Drugs: From Reward to Addiction.* Cell, 2015. **162**(4): p. 712-25.

[76] United States. Department of Health and Human Services, *Facing addiction in America : the Surgeon General's report on alcohol, drugs and health*. HHS publication. 2016, Washington, D.C.: U.S. Department of Health & Human Services. 1 volume (various pagings).

[77] Wise, R.A., *Drug-activation of brain reward pathways.* Drug Alcohol Depend, 1998. **51**(1-2): p. 13-22.

[78] Koob, G.F., *The neurobiology of addiction: a neuroadaptational view relevant for diagnosis.* Addiction, 2006. **101 Suppl 1**: p. 23-30.

[79] Nestler, E.J., *Is there a common molecular pathway for addiction?* Nat Neurosci, 2005. **8**(11): p. 1445-9.

[80] Koob, G.F., M.A. Arends, and M. Le Moal, *Drugs, addiction, and the brain*.

*Psychopharmacological Perspectives and Diagnosis of Substance Use Disorder DOI: http://dx.doi.org/10.5772/intechopen.99531*

2014, Amsterdam ; Boston: Elsevier/AP, Academic Press is an imprint of Elsevier. viii, 342 pages.

[81] Clapp, P., S.V. Bhave, and P.L. Hoffman, *How adaptation of the brain to alcohol leads to dependence: a pharmacological perspective.* Alcohol Res Health, 2008. **31**(4): p. 310-39.

[82] Nestler, E.J., B.T. Hope, and K.L. Widnell, *Drug addiction: a model for the molecular basis of neural plasticity.* Neuron, 1993. **11**(6): p. 995-1006.

[83] Self, D.W., *Neural substrates of drug craving and relapse in drug addiction.* Ann Med, 1998. **30**(4): p. 379-89.

[84] Gilpin, N.W., *Brain reward and stress systems in addiction.* Front Psychiatry, 2014. **5**: p. 79.

[85] Koob, G.F. and M. Le Moal, *Plasticity of reward neurocircuitry and the 'dark side' of drug addiction.* Nat Neurosci, 2005. **8**(11): p. 1442-4.

[86] Vendruscolo, L.F., et al., *Glucocorticoid receptor antagonism decreases alcohol seeking in alcoholdependent individuals.* J Clin Invest, 2015. **125**(8): p. 3193-7.

[87] Dean, S.F., et al., *Addiction neurocircuitry and negative affect: A role for neuroticism in understanding amygdala connectivity and alcohol use disorder.* Neurosci Lett, 2020. **722**: p. 134773.

[88] Regier, D.A., E.A. Kuhl, and D.J. Kupfer, *The DSM-5: Classification and criteria changes.* World psychiatry : official journal of the World Psychiatric Association (WPA), 2013. **12**(2): p. 92-98.

[89] Jasinska, A.J., et al., *Factors modulating neural reactivity to drug cues in addiction: a survey of human neuroimaging studies.* Neurosci Biobehav Rev, 2014. **38**: p. 1-16.

[90] Dackis, C.A. and M.S. Gold, *Pharmacological approaches to cocaine addiction.* Journal of Substance Abuse Treatment, 1985. **2**(3): p. 139-145.

[91] Robinson, T.E. and K.C. Berridge, *The neural basis of drug craving: an incentive-sensitization theory of addiction.* Brain Res Brain Res Rev, 1993. **18**(3): p. 247-91.

[92] Robinson, T.E. and K.C. Berridge, *The psychology and neurobiology of addiction: an incentivesensitization view.* Addiction, 2000. **95 Suppl 2**: p. S91-117.

[93] Robinson, T.E. and K.C. Berridge, *Review. The incentive sensitization theory of addiction: some current issues.* Philosophical transactions of the Royal Society of London. Series B, Biological sciences, 2008. **363**(1507): p. 3137-3146.

[94] Stewart, J., H. de Wit, and R. Eikelboom, *Role of unconditioned and conditioned drug effects in the selfadministration of opiates and stimulants.* Psychological Review, 1984. **91**(2): p. 251-268.

[95] Rohsenow, D.J., et al., *Cue Reactivity in Addictive Behaviors: Theoretical and Treatment Implications.* International Journal of the Addictions, 1991. **25**(sup7): p. 957-993.

[96] Carter, B.L. and S.T. Tiffany, *Meta-analysis of cue-reactivity in addiction research.* Addiction, 1999. **94**(3): p. 327-340.

[97] Di Chiara, G., et al., *Drug addiction as a disorder of associative learning: role of nucleus accumbens shell/extended amygdala dopamine.* Annals of the New York Academy of Sciences, 1999. **877**(1): p. 461-485.

[98] Heimer, L., et al., *Specificity in the projection patterns of accumbal core and shell in the rat.* Neuroscience, 1991. **41**(1): p. 89-125.

[99] Kilts, C.D., et al., *The neural correlates of cue-induced craving in cocaine-dependent women.* American Journal of Psychiatry, 2004. **161**(2): p. 233-241.

[100] Kalivas, P.W. and N.D. Volkow, *The Neural Basis of Addiction: A Pathology of Motivation and Choice The American Journal of Psychiatry.*

[101] Walker, D.L. and M. Davis, *Role of the extended amygdala in short-duration versus sustained fear: a tribute to Dr. Lennart Heimer.* Brain Structure and Function, 2008. **213**(1): p. 29-42.

[102] Melis, M., S. Spiga, and *M. Diana*, *The dopamine hypothesis of drug addiction: hypodopaminergic state.* Int Rev Neurobiol, 2005. **63**: p. 101-54.

[103] LeDoux, J.E., *Emotion circuits in the brain.* Annu Rev Neurosci, 2000. **23**: p. 155-84.

[104] Neugebauer, V., et al., *The amygdala and persistent pain.* Neuroscientist, 2004. **10**(3): p. 221-34.

[105] Weiss, F., et al., *Ethanol Self-Administration Restores Withdrawal-Associated Deficiencies in Accumbal Dopamine and 5-Hydroxytryptamine Release in Dependent Rats.* The Journal of Neuroscience, 1996. **16**(10): p. 3474.

[106] Vengeliene, V., et al., *Neuropharmacology of alcohol addiction.* Br J Pharmacol, 2008. **154**(2): p. 299-315.

[107] Volkow, N.D., et al., *Association Between Decline in Brain Dopamine Activity With Age and Cognitive and Motor Impairment in Healthy Individuals.* American Journal of Psychiatry, 1998. **155**(3): p. 344-349.

[108] Zubieta, J.-K., et al., *Increased mu opioid receptor binding detected by PET in cocaine–dependent men is associated with cocaine craving.* Nature Medicine, 1996. **2**(11): p. 1225-1229.

[109] Volkow, N.D., et al., *Profound decreases in dopamine release in striatum in detoxified alcoholics: possible orbitofrontal involvement.* J Neurosci, 2007. **27**(46): p. 12700-6.

[110] Koob, G.F., *A role for brain stress systems in addiction.* Neuron, 2008. **59**(1): p. 11-34.

[111] Duresso, S.W., et al., *Stopping khat use: Predictors of success in an unaided quit attempt.* Drug Alcohol Rev, 2018. **37 Suppl 1**: p. S235-S239.

[112] Duresso, S.W., et al., *Khat withdrawal symptoms among chronic khat users following a quit attempt: An ecological momentary assessment study.* Psychol Addict Behav, 2018. **32**(3): p. 320-326.

[113] Koob, G. and M.J. Kreek, *Stress, dysregulation of drug reward pathways, and the transition to drug dependence.* Am J Psychiatry, 2007. **164**(8): p. 1149-59.

[114] Childress, A.R., et al., *Limbic Activation During Cue-Induced Cocaine Craving.* American Journal of Psychiatry, 1999. **156**(1): p. 11-18.

[115] Cooney, N.L., et al., *Alcohol cue reactivity, negative-mood reactivity, and relapse in treated alcoholic men.* J Abnorm Psychol, 1997. **106**(2): p. 243-50.

[116] Mantsch, J.R., et al., *Neurobiological mechanisms that contribute to stressrelated cocaine use.* Neuropharmacology, 2014. **76 Pt B**(0 0): p. 383-394.

[117] Sinha, R., D. Catapano, and S. O'Malley, *Stress-induced craving and stress response in cocaine dependent individuals.* Psychopharmacology (Berl), 1999. **142**(4): p. 343-51.

[118] Weiss, F., *Neurobiology of craving, conditioned reward and relapse.* Current opinion in pharmacology, 2005. **5**(1): p. 9-19.

*Psychopharmacological Perspectives and Diagnosis of Substance Use Disorder DOI: http://dx.doi.org/10.5772/intechopen.99531*

[119] Langleben, D.D., et al., *Acute effect of methadone maintenance dose on brain FMRI response to heroin-related cues.* American Journal of Psychiatry, 2008. **165**(3): p. 390-394.

[120] Everitt, B.J. and T.W. Robbins, *Neural systems of reinforcement for drug addiction: from actions to habits to compulsion.* Nature neuroscience, 2005. **8**(11): p. 1481-1489.

[121] Robinson, T.E. and K.C. Berridge, *The neural basis of drug craving: An incentive-sensitization theory of addiction.* Brain Research Reviews, 1993. **18**(3): p. 247-291.

[122] Robinson, T.E. and K.C. Berridge, *Incentive-sensitization and addiction.* Addiction, 2001. **96**(1): p. 103-114.

[123] Robinson, T.E. and K.C. Berridge, *Incentive-sensitization and drug 'wanting'.* Psychopharmacology, 2004. **171**(3): p. 352-353.

[124] Goldstein, R.Z. and N.D. Volkow, *Dysfunction of the prefrontal cortex in addiction: neuroimaging findings and clinical implications.* Nature Reviews Neuroscience, 2011. **12**(11): p. 652-669.

[125] Britt, J.P. and A. Bonci, *Optogenetic interrogations of the neural circuits underlying addiction.* Curr Opin Neurobiol, 2013. **23**(4): p. 539-45.

[126] Goldstein, R.Z. and N.D. Volkow, *Dysfunction of the prefrontal cortex in addiction: neuroimaging findings and clinical implications.* Nat Rev Neurosci, 2011. **12**(11): p. 652-69.

[127] Goodman, A., *Neurobiology of addiction. An integrative review.* Biochem Pharmacol, 2008. **75**(1): p. 266-322.

[128] Kalivas, P.W., *The glutamate homeostasis hypothesis of addiction.* Nat Rev Neurosci, 2009. **10**(8): p. 561-72.

[129] Koob, G.F. and N.D. Volkow, *Neurobiology of addiction: a* 

*neurocircuitry analysis.* Lancet Psychiatry, 2016. **3**(8): p. 760-773.

[130] Abbot, P. and D.M. Chase, *Culture and substance abuse: Impact of culture affects approach to treatment.* Psychiatric Times, 2008. **25**(1): p. 43-43.

[131] Paredes, A., *Social control of drinking among the Aztec Indians of Mesoamerica.* Journal of studies on alcohol, 1975. **36**(9): p. 1139-1153.

[132] Caetano, R., et al., *Acculturation, drinking, and alcohol abuse and dependence among Hispanics in the Texas–Mexico border.* Alcoholism: Clinical and Experimental Research, 2008. **32**(2): p. 314-321.

[133] Welte, J.W. and G.M. Barnes, *Alcohol and other drug use among Hispanics in New York State.* Alcoholism: Clinical and Experimental Research, 1995. **19**(4): p. 1061-1066.

[134] Shorter, E., *The history of nosology and the rise of the Diagnostic and Statistical Manual of Mental Disorders.* Dialogues in clinical neuroscience, 2015. **17**(1): p. 59-67.

[135] Robinson, S.M. and B. Adinoff, *The Classification of Substance Use Disorders: Historical, Contextual, and Conceptual Considerations.* Behavioral sciences (Basel, Switzerland), 2016. **6**(3): p. 18.

[136] Hasin, D.S., et al., *DSM-5 Criteria for Substance Use Disorders: Recommendations and Rationale.* American Journal of Psychiatry, 2013. **170**(8): p. 834-851.

[137] Hasin, D.S., et al., *DSM-5 criteria for substance use disorders: recommendations and rationale.* The American journal of psychiatry, 2013. **170**(8): p. 834-851.

[138] Cunningham, J.A. and J. McCambridge, *Is alcohol dependence best viewed as a chronic relapsing disorder?*

Addiction (Abingdon, England), 2012. **107**(1): p. 6-12.

[139] Hasin, D.S. and B.F. Grant, *The co-occurrence of DSM-IV alcohol abuse in DSM-IV alcohol dependence: results of the National Epidemiologic Survey on Alcohol and Related Conditions on heterogeneity that differ by population subgroup.* Arch Gen Psychiatry, 2004. **61**(9): p. 891-6.

[140] Kopak, A.M., S.L. Proctor, and N.G. Hoffmann, *The Elimination of Abuse and Dependence in DSM-5 Substance Use Disorders: What Does This Mean for Treatment?* Current Addiction Reports, 2014. **1**(3): p. 166-171.

[141] Substance, A. and A. Mental Health Services, *CBHSQ Methodology Report*, in *Impact of the DSM-IV to DSM-5 Changes on the National Survey on Drug Use and Health*. 2016, Substance Abuse and Mental Health Services Administration (US): Rockville (MD).

[142] Kahler, C.W. and D.R. Strong, *A Rasch Model Analysis of DSM-IV Alcohol Abuse and Dependence Items in the National Epidemiological Survey on Alcohol and Related Conditions.* Alcoholism: Clinical and Experimental Research, 2006. **30**(7): p. 1165-1175.

[143] Helzer, J.E., K.K. Bucholz, and M. Gossop, *A dimensional option for the diagnosis of substance dependence in DSM-V.* International Journal of Methods in Psychiatric Research, 2007. **16**(S1): p. S24-S33.

[144] Dawson, D.A., T.D. Saha, and B.F. Grant, *A multidimensional assessment of the validity and utility of alcohol use disorder severity as determined by item response theory models.* Drug and alcohol dependence, 2010. **107**(1): p. 31-38.

[145] Koob, G.F. and E.J. Simon, *The Neurobiology of Addiction: Where We Have Been and Where We Are Going.* J Drug Issues, 2009. **39**(1): p. 115-132.

[146] Koob, G.F., M.A. Arends, and M. Le Moal, *Chapter 2 - Introduction to the Neuropsychopharmacology of Drug Addiction*, in *Drugs, Addiction, and the Brain*, G.F. Koob, M.A. Arends, and M. Le Moal, Editors. 2014, Academic Press: San Diego. p. 29-63.

[147] Befort, K., et al., *Mu-opioid receptor activation induces transcriptional plasticity in the central extended amygdala.* European Journal of Neuroscience, 2008. **27**(11): p. 2973-2984.

[148] Robinson, M.J., T.E. Robinson, and K.C. Berridge, *Incentive salience and the transition to addiction.* Biological research on addiction, 2013. **2**: p. 391-399.

[149] Kuczenski, R. and D.S. Segal, *Differential effects of amphetamine and dopamine uptake blockers (cocaine, nomifensine) on caudate and accumbens dialysate dopamine and 3-methoxytyramine.* J Pharmacol Exp Ther, 1992. **262**(3): p. 1085-94.

[150] Carboni, E., et al., *Amphetamine, cocaine, phencyclidine and nomifensine increase extracellular dopamine concentrations preferentially in the nucleus accumbens of freely moving rats.* Neuroscience, 1989. **28**(3): p. 653-61.

[151] Di Chiara, G. and A. Imperato, *Preferential stimulation of dopamine release in the nucleus accumbens by opiates, alcohol, and barbiturates: studies with transcerebral dialysis in freely moving rats.* Ann N Y Acad Sci, 1986. **473**: p. 367-81.

[152] Imperato, A., A. Mulas, and G. Di Chiara, *Nicotine preferentially stimulates dopamine release in the limbic system of freely moving rats.* Eur J Pharmacol, 1986. **132**(2-3): p. 337-8.

[153] Imperato, A. and G. Di Chiara, *Preferential stimulation of dopamine release in the nucleus accumbens of freely*  *Psychopharmacological Perspectives and Diagnosis of Substance Use Disorder DOI: http://dx.doi.org/10.5772/intechopen.99531*

*moving rats by ethanol.* J Pharmacol Exp Ther, 1986. **239**(1): p. 219-28.

[154] Hassen, K., et al., *Khat as a risk factor for hypertension: A systematic review.* JBI Libr Syst Rev, 2012. **10**(44): p. 2882-2905.

[155] Volkow, N.D., G. Koob, and R. Baler, *Biomarkers in substance use disorders.* ACS Chem Neurosci, 2015. **6**(4): p. 522-5.

### **Chapter 2**

## The *Juramento*: Secondary and Tertiary Preventive Benefits of a Religious-Based Brief Alcohol Intervention in the Mexican Immigrant Community

*Victor Garcia, Katherine Fox, Emily Lambert and Alex Heckert*

### **Abstract**

Our chapter addresses the prevention benefits of the *juramento*, a grassroots religious-based brief intervention for harmful drinking practiced in Mexico and the Mexican immigrant community in the United States. With origins in Mexican folk Catholicism, it is a sacred pledge made to Our Lady of Guadalupe to abstain from alcohol for a specific time period; in most cases, at least six months. We draw on our data from a subsample of 15 Mexican workers who made *juramentos* and two priests who administered the *juramento* to the workers. The sample is from a larger qualitative study on the use of the *juramento* among Mexican immigrant and migrant workers in southeastern Pennsylvania. Our findings reveal that, in addition to serving as an intervention, the *juramento* results in secondary prevention—by identifying a harmful drinking before the onset of heavy drinking—and tertiary prevention—by slowing or abating the progression of heavy drinking.

**Keywords:** *juramentos*, alcohol interventions, prevention, Mexican immigrants, harmful drinking

### **1. Introduction**

In Latinx communities across the country, there are several grassroots interventions for harmful drinking, among them, the *juramento*—a religious-based, brief intervention with origins in Mexican folk Catholicism. Basically, it is a ritualized pledge, or vow, made to *Nuestra Señora de Guadalupe*, or Our Lady of Guadalupe, to abstain from alcohol use for a specific time period. Vows are also made to overcome other addictions, such as drug abuse, smoking, and gambling. The *juramento* arose naturally around the religious beliefs of the common people in Mexico, or *el pueblo*, as they say in Spanish, and as such, is not a formal intervention. It is "intrinsically organic and rooted in culture, which makes it familiar to participants and informs their understanding of the disorder and recovery" [1]. With origins outside of biomedicine, the *juramento* does not fall within the scope

of public health research, and as a result, it does not receive much attention in the alcohol intervention and treatment fields.

Although the *juramento* is based on religious traditions and practices, its possible contributions to these fields should not be underestimated. The aim of our book chapter is two-fold: (1) to discuss how the *juramento* is a brief intervention for different types of drinking, and (2) to examine how it offers secondary and tertiary prevention. In public health, secondary prevention refers to medical and public health efforts to catch diseases in their earliest stages, perhaps even before the appearance of signs or symptoms. This can include screening and routine checkups, identification of risk factors, and measures to stop the progression of asymptomatic or early-stage conditions [2]. Tertiary prevention aims to slow or stop progression after a diagnosis [3]. Though the disease is established, tertiary prevention seeks to minimize its damage to the suffer and to avoid the most serious of possible outcomes. Drawing on our ongoing research on the *juramento*, we argue that, although it is not a public health intervention and is not delivered by a medical or health care provider, but by a priest, the *juramento* results in these prevention benefits.

### **2. Background**

### **2.1 The** *juramento*

The *juramento* originated in Mexico and is centuries old, although exactly when the practice began remains unknown [4, 5]. It may be as old as the Shrine of Our Lady of Guadalupe itself, which was first constructed in present-day Mexico City nearly five hundred years ago, after the saint appeared several times at that location. Since then, Mexicans have made pilgrimages to the site, especially on her feast day of December 12. There, they make prayers, called *mandas*, for divine intervention for a problem; *mandas* are often made for a personal health ailment or on behalf of a family member or loved one who is suffering one. The *juramento* is based on this tradition but is solely for problems with alcohol and other substance use. Despite its tradition and popularity among certain segments of Mexican society, such as the poor, the *juramento* is not officially recognized by the Catholic Church. It is viewed as folk Catholicism, in which outdated beliefs and practices remain popular among parishioners and are tolerated by the Catholic Church even though they do not fall within current church doctrine. Nonetheless, parish priests familiar with the *juramento* process make *juramentos* upon request.

The particulars of this ritualized pledge vary according to local traditions. In the United States, especially in our research site in Pennsylvania, the *juramento* is a private affair that only involves the *jurado*, the individual making the pledge, and the priest. Close friends and family members may be present to give moral support. In Mexico, according to Cuadrado and Lieberman [5], *juramentos* are also made in group settings at a church and to other saints, such as *El Sagrado Señor de Chalma* (Sacred Lord of Chalma). At the Chapel of the Juramentos located on the grounds of the Shrine of Our Lady of Guadalupe, *juramentos* are made in groups every day of the week over the course of the year. The chapel can hold up to 50 people, and it is common for family members and friends of the *jurados* to attend.

Despite evidence of *juramento* use among impoverished groups in Mexico, especially in rural communities and regions with strong religious traditions, as well as in Mexican immigrant communities in the United States, it is understudied in both countries. Garcia, Lambert, Fox, Heckert, and Pinchi [1], only found four articles on the *juramento* in their literature review*.* It does not include a recent publication by some members of the same team, Garcia, Heckert, Lambert, and

*The* Juramento*: Secondary and Tertiary Preventive Benefits of a Religious-Based Brief Alcohol… DOI: http://dx.doi.org/10.5772/intechopen.95545*

Pinchi [6]. From the four articles discussed, they found that the *juramento* is practiced in Catholic parishes with large Latino populations in Pennsylvania, Florida, Texas, New Mexico, Arizona, and California. One binational study in the review, by Cuadrado & Lieberman [5], conducted in both the United States and Mexico documents its use among poor people in Mexico for whom formal alcohol treatment is unavailable. Like Mexican immigrants in the United States, they cannot always access biomedical resources, and turn to their faith, particularly the *juramento*, for help with their drinking. The number of *jurados* in the studies are not identified and as a result missing is information on their demographic characteristics and socioeconomic status.

The existing literature does document the many benefits of the *juramento* as an intervention. Researchers generally agree that the process allows individuals to explore the causes behind their drinking, to engage in a period of abstinence that sets up the right frame of mind for recovery, and to reconnect with estranged family and the larger community. Moreover, the *juramento* provides the *jurado* with church and community support essential for sobriety and recovery. Missing from this discussion is how the *juramento* also provides the individual with secondary and tertiary prevention. As will be discussed, secondary and tertiary prevention, respectively, can slow or stop the progression of drinking behaviors into dependence and mitigate the most serious harms of established alcohol use disorders.

### **2.2 Alcohol and substance use disorder prevention**

Prevention literature, especially in relation to alcohol and substance use disorders, can be a challenging body of scholarship to review. While it highlights the range of avenues through which harm reduction can be achieved, there is a lack of consistency in terms and definitions; what one article may describe as prevention may be characterized as treatment by another. Many studies raise the issue of prevention, but fail to explain exactly what is being prevented, or how the intervention can achieve such results. Furthermore, not all articles that discuss prevention classify their efforts into primary, secondary, or tertiary types. The following sections describe the types of efforts within alcohol and substance use research that are associated with a specific tier of prevention, in order to provide an overall sense of the work that occurs within each category.

### *2.2.1 Primary prevention*

The goal of primary prevention is to eliminate these disorders before they start. To that end, some studies seek to prevent substance abuse through research into underlying causes: Ridenour and Stormshak [7] advance an "Ontogenic Prevention" approach, which tailors prevention efforts to individual needs and characteristics; Moulahoum et al. [8] explore immunologically-based treatments and detection to understand sources of addiction; Volkow and Li [9] discuss addiction as a chronic brain disease that is affected by genetic, developmental, and environmental factors, and that such an understanding should inform prevention. Another significant body of literature focuses on psychological and psychosocial interventions. For example, Peterson and Reid [10] support interventions that promote empowerment to abstain from substance use, and Brooks et al. [11] find that hopefulness is an important component of primary prevention efforts. These approaches are especially common in programs aimed for adolescents, which attempt to deter or delay their engagement in substance use by building social, communication, and coping skills [12–14]. Recent literature also explores new delivery methods for prevention strategies. Through their review of motivational interviewing techniques, which

have proven to be beneficial in therapeutic settings, Jiang, Wu, and Gao [15] suggest that this prevention could be expanded to other media such as telephone calls and web-based interactions. Similarly, Hopson, Wodarski and Tang [16] discuss the use of video and online prevention modalities to reach adolescents. Additionally, Mutamba et al. [17] find that using lay community health workers (in place of medical providers) can extend the reach of primary prevention programs.

### *2.2.2 Secondary prevention*

According to the World Health Organization [3], secondary prevention for substance use disorders consists of interventions for individuals who are in early stages of substance use, so as to prevent them from developing problematic or harmful patterns. However, the literature on secondary prevention for SUDs demonstrates more varied goals. Nygaard [18] discusses the merits of screening and brief intervention (SBI) at length, and argues that this method should emphasize the motivational aspects of intervention, and should also include social contacts within the field of the intervention, particularly when individuals describe themselves as "social drinkers." As discussed by Trova et al. [19], secondary prevention included counseling and programming for high-risk groups, in order to help them develop alcohol refusal strategies and behavioral and social skills to resist alcohol use. Referrals to substance abuse treatment [20], as well as work with the close relatives of someone with a substance abuse problem [21], were also considered. For young drug users, effective secondary prevention interventions included behavioral therapy, family therapy, general drug treatment, and residential care, particularly when it included culturally sensitive counseling [22].

Secondary prevention can be particularly effective when expanded beyond behavioral health care providers. In their study of trauma patients, Fernandez Mondejar et al. [23] found that hospital emergency units could be important sources of secondary prevention, due to the frequency of serious accidents and injuries that occur to individuals when intoxicated. Another study advocates for secondary prevention in drug courts, as approximately one third of the clients were low risk offenders who did not demonstrate serious substance abuse and could benefit from early intervention [24].

### *2.2.3 Tertiary prevention*

In the literature on tertiary prevention for alcohol and substance use disorders, a few major themes emerge. One is the goal of maximizing normal life functioning for chronic sufferers of dependence; these therapeutic efforts may promote motivation to abstain from alcohol use or support acquisition of new behaviors to modify problem patterns of alcohol consumption. Such efforts may be offered in tandem with mental health treatment or as part of a case management plan [18, 25, 26]. McAnally [27] argues that tertiary prevention must include motivation to avoid substance use as well as reductions of underlying sources of distress in the sufferer's environment; pharmacotherapies for drug dependence are also included here. A few studies also include, as tertiary prevention, attempts to avoid other types of illness that can occur as a result of or in the process of substance use, such as bloodborne viruses among intravenous drug users [28].

Another group of studies describe tertiary prevention as promotion and support for sobriety. Here, the focus is on prevention of an initial relapse, and then on the management of any relapses that do occur [29] - though some question whether complete sobriety, with no relapses, is a realistic goal [30]. There are many different tactics for reducing relapses, including overdose education [27], peer support and

therapeutic groups [31], recovery housing [32], cognitive behavioral approaches [33] and mindfulness-based interventions [34–36]. However, not all relapse prevention studies position themselves as tertiary prevention for substance abuse; many treat relapses as a separate health problem.

### *2.2.4 Prevention in specific cultures and communities*

Present across all studies of prevention for alcohol and substance use disorders, whether they are primary, secondary, or tertiary, is a call to tailor interventions to specific populations and their cultural contexts. For example, Greenfield et al. [37] demonstrate that mindfulness-based relapse prevention varies in efficacy among different racial groups; for Whites, it is more effective in preventing heavy drinking relapse than drug use, but the opposite is true for racial and ethnic minorities. Walton, Blow, and Booth [38] found, among their study participants, that African Americans had greater coping skills and self-efficacy than other racial groups, but that their resource needs were greater; this necessitates different relapse prevention strategies. When dealing with diverse populations, intervention and marketing should be offered in multiple languages, and must also include steps to help marginalized communities feel safe from discrimination or criminalization, particularly for their citizenship status or illicit substance use [39]. Several studies address alcohol or substance abuse concerns within indigenous communities, and argue that they are best served by programs that incorporate cultural knowledge and values, and respond to therapeutics that emphasize connection to traditions ([40–42], among others). In summary, it is clear that successful prevention of alcohol and substance abuse requires careful attention to individual and community needs at every point of contact.

These considerations extend to research with Latinx communities, though less has been done to develop rigorous approaches to prevention. While several researchers investigate rates of and contributing factors to alcohol and substance use in Latinx and immigrant populations [43, 44], they do not offer explicit methods or interventions for prevention. Further, existing literature predominantly focuses on drinking and substance use patterns among Latinx adolescents ([45–47], for example). Unsurprisingly, the limited research on prevention also tends to address adolescents, with several that involve Latinx families as a unit [48, 49]. Indeed, those that deal explicitly with prevention in terms of the primary, secondary, and tertiary tiers all target adolescents [50–52]. There is little evidence with which to address these concerns among other Latinx populations, such as adults or migrant workers.

Moreover, though these studies discuss the importance of customizing formal prevention and treatment, none of them consider grassroots interventions. Grassroots interventions such as the *juramento* are culturally and linguistically specific from their inception and need no further adaptations. They are also familiar to the community and do not require any outreach campaigns. As such, they can bring much-needed therapeutic benefits to marginalized and underserved populations.

### **3. Methods**

We draw on our findings from an ongoing qualitative study on the use of the *juramento* among Mexican workers living and working in southeastern Pennsylvania. Scheduled for three years, the study started in 2017, but was interrupted because of COVID-19. This region is home to the country's largest mushroom production site and has experienced Mexican immigration since the mid-1970s. The subsample for this chapter consists of fifteen (15) Mexican immigrants or migrants, all males, who have made *juramentos*, and two priests at a new Catholic church in the region. The Mexican immigrants and migrants are part of a larger sample that includes individuals who have not made a *juramento* but are seeking help at a local Spanish language AA group. The *jurados* ranged in age from 25 to 55 years, with an average age of 34 years. Eight were married; five, single; and two, separated. All except one completed at least nine years of education. Most of the men in the sample are from small *ranchos* or towns in rural regions in the states of Guanajuato and southern states surrounding Mexico City. The Catholic Church has a strong presence spanning centuries in these regions; prominent religious practices in this area include the cargo system, or sponsoring of saint days, recognition of local saint days and Our Lady of Guadalupe on her feast day, and Passover events.

All the participants were selected using purposive sampling and were interviewed using semi-structured interviews. The two priests helped us recruit the fifteen *jurados*. The interviews with the priests centered around the *juramento* process, especially the counseling session; who makes *juramentos* and why; and the perceived benefits of the *juramento* for the individual and family. In keeping with their practice of confidentiality, the priests did not discuss specific *juramento* cases with us. The interviews with the *jurados* solicited basic demographic information, religious background and beliefs, drinking history, including problems associated with drinking, *juramento* history, reasons for making a *juramento* and the perceived benefits from making one, and information of other treatments pursued. Follow-up interviews were conducted if any of the interviews had missing or unclear information. The *jurados* were also administered the Duke Religious Index, or DUREL [53]. It is a five-item measure of religious involvement, developed for use in large crosssectional and longitudinal observational studies and designed to assess three major dimensions of religiosity: organizational religious activity, non-organizational religious activity, and intrinsic religiosity (or subjective religiosity).

The men's drinking behaviors prior to making their most recent juramento were self-reported, and as such, were not solicited or measured using a diagnostic nosology, such as the Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition (DSM-5). Self-reports have been found to be valid in assessing drinking practices [54, 55]. Based on their self-reports, we categorized their drinking according to one of the following drinking types: binge drinking, heavy drinking, problem drinking, alcohol dependence, and alcohol abuse, as defined by National Institute of Alcohol Abuse and Alcoholism (NIAAA). These are the five drinking types that increase one's risks for harmful consequences, including an alcohol use disorder, or AUD, such as alcohol dependence. In categorizing the men's drinking, we used one or more of the following criteria, as found in the definitions of the five drinking types: frequency of drinking, the amount of alcohol consumed, the intent behind the drinking, and the harm caused by the drinking. According to NIAAA, binge drinking is the consumption of alcohol with the intent to become inebriated. For the men, it was defined as at least five alcoholic drinks in two hours [56]. Heavy drinking is when an individual binge drinks five or more days in the last month [56]. Problem drinking may include binge drinking and heavy drinking and results in accidents, injuries, and other issues resulting from drinking alcohol [57]. Alcohol dependence, or alcoholism, is when an individual loses his or her control over alcohol intake [58]. It is characterized by intense alcohol cravings, high tolerance, and withdrawal symptoms when a person quits using alcohol. Alcohol abuse occurs when a person is not physically dependent upon alcohol, but drinking is resulting in serious health issues and problems at home or work [58]. Alcohol dependence and alcohol abuse are considered alcohol use disorders because they result in chronic

### *The* Juramento*: Secondary and Tertiary Preventive Benefits of a Religious-Based Brief Alcohol… DOI: http://dx.doi.org/10.5772/intechopen.95545*

relapsing brain disease characterized by compulsive drinking, loss of control over alcohol intake, and a negative emotional state when not using alcohol.

NVivo software was used to code text and to store, sort, and retrieve relevant text generated from questions in the priest and *jurado* semi-structured interviews. The data was coded by two members of the team, at first independently from each other, and later together for consistency. Inductive coding was used to identify themes, especially emerging themes. When coding, we assigned labels to words and phrases that represent important (and recurring) text in each response. A thematic analysis of the coded text was then performed, as is standard in qualitative research. Themes around drinking were developed around the quantity of alcohol consumed, especially prior to making a *juramento*, frequency, and drinking problems that emerged as a consequence. The drinking was then identified and categorized as binge drinking, heavy drinking, problem drinking, alcohol dependence, and alcohol abuse using standard definitions of each type of drinking. The core elements of the *juramento* were also identified and analyzed, as were ad hoc themes related to making a *juramento*, such as religious beliefs and family and community religious practices as shared by the *jurados*, the nature of religious catharsis of the pledge, and examples of the challenges and moral fortitude needed in keeping the promise of abstinence. Recurrent themes were identified in each category as they emerged through repeated review of the textual data. Descriptive reports were prepared around the different themes and subthemes that were used to prepare manuscripts for publication.

### **4. Findings**

Although records are not kept, the priests report that anywhere from 30 to over 50 *juramentos* are made in any given month at our research site. Despite this high number, the *juramento* is not part of the church's structure because, as discussed earlier, it is not recognized by Catholic Church doctrine. Instead, the *juramento* falls under pastoral care—the visiting, counseling, and helping parishioners who are experiencing a difficult hardship. The church where the *juramentos* are made is part of the first national parish for Hispanics of the region's archdiocese. Prior to its construction a decade ago, the Latinx community attended services at local church parishes, and the *juramentos* were made at a nearby Catholic mission for this community, which housed the offices of the priests who looked after the spiritual welfare of the Latinx population and offered social services. This national parish was established to accommodate the rapidly growing Latinx population in the region, which consists primarily of Mexican immigrants and their U.S.-born children, many of whom are now adults with their own children. Puerto Ricans and Guatemalans comprise other significant Latinx groups in the area. The parish serves an estimated 12,000 Latinx parishioners from several local communities.

### **4.1 The** *juramento* **process**

The *juramento*, as administered at our site, is a highly ritualized brief intervention around a counseling session and the pledge to Our Lady of Guadalupe to abstain from alcohol use. It is a private affair and only includes the priest and the *jurado*, and occasionally a family member who is present to provide support. Unlike a formal brief intervention, such as SAMSHA's Screening, Brief Intervention and Referral to Treatment, or SBIRT [59], it does not include a screening instrument to assess the drinking problem nor a referral to formal treatment [6]. Four general steps are involved in making a *juramento*. It starts with a counseling session with

a priest at his office or in private at the church. The purpose of this session is to discuss the individual's drinking and the problems that it creates for him and his family. The severity of drinking is identified through an informal discussion about his behaviors and any resulting problems. Additionally, they discuss the seriousness of making a pledge to Our Lady of Guadalupe, and the importance of seeking additional help. The second step involves determining an abstinence period, usually no more than one year, and preparing an *estampita*, or prayer card. On one side of the card is the image of Our Lady of Guadalupe, and on the other, a *juramento* prayer, which is written as a pledge with the name of the *jurado*, the abstinence period, and the signature of both the *jurado* and the priest. The prayer card is a symbolic contract to enter and keep a sacred pledge. The third step is the reciting of the *juramento* prayer, together with the priest, preferably in front of the image of Our Lady of Guadalupe. The fourth and last step is the *bendición*, the priest's blessing of the *jurado*. There is no follow-up after the intervention. During the abstinence period, the pledge is fortified with prayers to the saint and with regular attendance at mass. The *juramento* is renewable once it is completed and may be repeated as many times as deemed necessary.

The men made *juramentos* not only in southeastern Pennsylvania, but also in Mexico. While living in Mexico, one man traveled to Mexico City to make two *juramentos* at the Chapel of the Juramentos at the Shrine of Our Lady of Guadalupe. Regardless of where they were made, nearly all the *juramentos* were made to this saint. Only two of those made in Mexico were made to a different religious figure—one to El Señior de las Maravillas and the other to El Señor de Chalma. On average, the men each made two *juramentos*, but individually this number ranged from one to five per person. The abstinence periods were from forty days to five years; however, the majority were for a year. Most of the men remained abstinent after completing their *juramento*, some for years, and did not make another until they saw it necessary, either to fend off the temptation to drink or to stop drinking after having resumed. Nearly all completed their *juramentos* only two *juramentos*, both made in Mexico, were not fulfilled by the *jurados*. In these two cases, the men sought absolution from the priest who performed the *juramento* and made another.

### **4.2 Drinking behaviors and prevention**

The drinking behaviors of the fifteen men in our sample, prior to making a *juramento*, ranged from occasional binge drinking to heavy drinking. In all, three of the men were classified as occasional binge drinkers and three as binge drinkers, eight as heavy drinkers, and one as an alcohol abuser. None suffered from alcohol dependence. We also did not find any problem drinkers, as defined in the literature.

We divided the binge drinkers into two general types—occasional binge drinkers and binge drinkers. Occasional binge drinkers did not binge drink every week. They were social drinkers who engaged in casual drinking with friends and relatives on a weekly basis, usually on the weekends, and who only engaged in binge drinking once or twice a month without doing so again for weeks and months. Unlike the binge drinkers, their intent in drinking was not to get inebriated but to relax and enjoy the moment socially. However, during certain times of the year, such as Christmas or visits to the homeland, occasional binge drinkers would depart from their established drinking pattern and would binge drink in two or more weeks consecutively. Binge drinkers binged every weekend, with a few exceptions. The drinking would start on a Friday night, after work, and continued through Saturday night. The rest of the week they may have a beer or two after work.

*The* Juramento*: Secondary and Tertiary Preventive Benefits of a Religious-Based Brief Alcohol… DOI: http://dx.doi.org/10.5772/intechopen.95545*

### *4.2.1 Occasional binge drinkers and secondary prevention*

The three occasional binge drinkers only made one *juramento*, either for six months or a year. The men fulfilled their *juramentos* and afterwards remained abstinent. Although their binge drinking was infrequent in comparison to the other binge drinkers, occasional binge drinkers sought a *juramento* because they were concerned about their drinking getting out of hand and resulting in problems over time. There was no evidence that they were harming themselves or others with their drinking, but according to them, the potential to do so was there. Notably, the men did not want their drinking to interfere with work and the sending of remittances back home. These monies cover basic needs for their families, such as shelter, food, and medicine. The money transfers are also used to build a house or start a small business, such as a small general store in the neighborhood or a food eatery, and to invest in the education of their children.

Miguel (pseudonym), an immigrant worker in his thirties from the state of Mexico, is one of the occasional binge drinkers. He is married with four children, all of whom live with him locally. Miguel has 10 siblings: half live in Mexico, and half in the United States. Miguel left Mexico in his late twenties because he could not find gainful employment there. Although he lives in Pennsylvania with his immediate family, Miguel continues to send remittances to his parents on a regular basis.

Miguel only made one *juramento* and is currently fulfilling it. It was made in Pennsylvania. He made the *juramento* for a few reasons, not just one. One of them was his concern about his increased drinking over time. Although he did not drink every week, as do some of his co-workers, he felt that his drinking was getting out of hand. Every time he drank socially, his drinking was no longer limited to a beer or two, as it was when he first started drinking. Miguel had two other major concerns: one was his temptation to drink when he was around others who were indulging, and the other was his general health. He was a diabetic, and he knew that alcohol was not good for him.

Prior to making a *juramento*, Miguel tried several times to stop drinking on his own, but after two or three months, he would return to drinking. After making the *juramento*, this was no longer the case. He now is two months shy of completing it. Miguel credits Our Lady of Guadalupe for his abstinence. In his words,

*I have always believed in the Virgen of Guadalupe, since I was a child. I have faith in others, other saints, but since I can remember, my mother always had her virgincitas, as is customary. You can say that she is my preferred saint. I always entrust myself to the Virgen of Guadalupe.*

Our Lady of Guadalupe provided him with the necessary strength to abstain. She was present during temptation, as he shared. He used his *juramento* prayer card for strength and to ward off peer pressure to drink. As he puts it,

*Sometimes, I would have to tell them that I am jurado [under the pledge to Our Lady of Guadalupe] … If they did not believe me, I would show them my card. I always carry the virgincita with me.*

For Miguel and the two other occasional binge drinkers, it is possible that their *juramentos* contributed to prevention, although more research on the causality between making a *juramento* and prevention is needed. We discuss the need for additional research on this subject later in chapter. The men were already exhibiting early symptoms of harmful drinking, but not signs of heavy drinking or worse. No screening instrument is used in the *juramento* process to determine whether

their drinking is harmful or not; the two priests in our sample hold the *juramento* counseling sessions and employ a variation of motivational interviewing to get the men to reflect on their drinking and to consider making changes. The men reach their own conclusions about their drinking and choose what type of help they desire. Risk factors for more serious alcohol use were also identified in the session and discussed. The resulting *juramento* gives them strength to remain abstinent for months, if not years, which is a major goal in achieving sobriety and recovery. The abstinence period may serve to stop the progression of the men's drinking from occasional binge drinking to heavy drinking. During their hiatus from drinking, the men learn that there is another way of living. Instead of drinking or spending time fighting the urge to drink, they focus on themselves and their families, return to their religious convictions, and reaffirm their place in the larger community.

### *4.2.2 Heavy drinkers, alcohol abusers, and tertiary prevention*

The heavy drinkers in our sample made more than one *juramento*, and not always consecutively. Some made a *juramento* immediately after completing one, while others would remain abstinent for months or years before making another. *Juramentos* were made as needed. The heavy drinkers, including the one individual who was abusing alcohol, exhibited evidence of tertiary prevention benefits of the *juramento*—mainly slowing the progression of harmful drinking. The drinking of these men was beyond the level of the occasional binge drinkers, and they were no longer showing early signs of harmful drinking. They were already exhibiting a serious drinking problem, and as such they were outside of the realm of secondary prevention benefits.

Juan Manuel (pseudonym), a migrant in his fifties from the State of Hidalgo, near Mexico City, is one of the heavy drinkers in our sample. He is separated from his wife and has five adult children who live in the same community. Like the other *jurados* in our sample, Juan Manuel is from a rural region with a depressed economy, and like many men there, he migrates to the United States to work. He migrates as needed and periodically spends months and years away from Mexico.

Juan Manuel started drinking at a young age. By the age of 18, he was already binge drinking, and by his twenties, he was drinking heavily when he could afford to buy alcohol. Juan Manuel made his first *juramento* in his thirties, after seeking help with Alcoholics Anonymous in his hometown for five months. He wanted to stop drinking because he was having problems at home. The *juramento* was made at the Chapel of Juramentos at the Shrine of Our Lady of Guadalupe, during a pilgrimage on her feast day. He remembers the counseling session with the priest there, especially the questioning about his commitment to fulfilling the pledge. The second *juramento* was also made there after another pilgrimage, four months after he had completed the first. The abstinence period was for five years. This length of abstinence is unusual, but the priest at the chapel allowed him to make a *juramento* for this time period because Juan Manuel had successfully completed his first one. He chose to make a 5-year *juramento* because he was planning to migrate to the United States for work, and he did not want his drinking to interfere with his stay abroad. Juan Manuel completed the *juramento* while in the United States and did not drink again for another year. However, he resumed drinking again when he returned home and discovered that his wife had been cheating on him while he was away. Six months later, he made his third and last *juramento* in his hometown; it was for one year. Since his last *juramento* he has not consumed alcohol and has no desire to do so.

Luis (pseudonym), also a migrant worker in his fifties, and from a state adjacent to Juan Manuel's home state, is the only one in our sample whose drinking was

### *The* Juramento*: Secondary and Tertiary Preventive Benefits of a Religious-Based Brief Alcohol… DOI: http://dx.doi.org/10.5772/intechopen.95545*

determined to qualify as alcohol abuse. He is married with four children, all of whom live in Mexico. Luis is an experienced migrant worker who has spent many years working elsewhere in Mexico and the United States. Luis provides for his family by sending remittances on a regular basis.

Luis' drinking trajectory was like Juan Manuel's. He, too, started drinking at a young age and was drinking heavily in his 20s before making his first *juramento*. In all, he has made five: three in Mexico and two in Pennsylvania. Luis made his first *juramento* in his twenties, as Juan Manuel did. The pledge was for a year and a half and was made because his drinking was disrupting his home life. Luis would miss work and lose his employment, and consequently, his family was left in dire straits. He made four other *juramentos*—the second for one year, the third for five years, and the fourth for a year, as was his fifth and last. Like Juan Manuel, Luis made the five-year *juramento* because he was planning to migrate to the United States, and he did not want his alcohol consumption to interfere with work and with his ability to save enough money to construct a house for his family in Mexico. He completed this lengthy *juramento* in the United States, and a year later made another after he started drinking again. At the time of the interview for the study, he had just made his fifth *juramento*.

For Juan Manuel and the other heavy drinkers, including Luis, who were already engaged in harmful drinking and at risk for alcohol dependence, the *juramento* may have played a role in preventing it from occurring. It may have kept them from causing further harm to themselves and their families. In Luis's case, he made several *juramentos* to maintain sobriety over time. These men underwent the same counseling session with the priest as did Miguel and the other occasional binge drinkers. The session did not target them as heavy drinkers in danger of suffering from alcohol dependence, but it did focus on the particulars of their drinking. The priests are not familiar with the different types of harmful drinking, as defined by the NIAAA, and so do not apply such classifications. Like all the counseling sessions, the motivational interviewing centered around getting the men to think of the many perils of their drinking and to consider taking action to change their lives. Moreover, as in the case of Miguel and the other occasional binge drinkers, the abstinence made possible by the *juramento* may have kept the men's drinking from progressing during the *juramento,* and in some cases, for months or years of the post-*juramento* period. It provided them with the necessary time to reflect and work on their drinking.

### **5. Discussion**

Our study suggests that the *juramento* may only be beneficial to those who are religious and turn to their beliefs when troubled. The DUREL scores of all the men were high across the three major dimensions of religiosity: organizational religious activity, non-organizational religious activity, and intrinsic religiosity (or subjective religiosity). The DUREL has an overall score range from 5 to 27, and men's score ranged from 26 to 27. All are devout to their faith, and in particular, to Our Lady of Guadalupe. They practice a religion, Catholicism, that teaches that one is never alone in life and that the saints are always present to help. Crucially, sinners are not excluded from this divine assistance, which means that they can always turn to the saints, even if they feel ashamed or embarrassed to seek help from other people. They were taught this at an early age as children and are reminded of it daily in their communities in Mexico and the United States.

The men's religiosity is of no surprise when their religious backgrounds are considered. All were raised in a religious household where Catholicism was an integral part of daily life. Their childhood homes had altars devoted to Our Lady of Guadalupe and other saints. The altars, as they explained, were sacred spaces for reflection, prayer, and paying homage to saints. Their families regularly attended church and observed the different feast days of the saints and other religious events. As children and adolescents, the *jurados* attended catechism and learned about Catholic doctrine, and completed the Sacraments of Initiation, such as baptism, confirmation, and the Eucharist. The Sacrament of Penance, or confession, was also commonplace. Some were not always devoted to their faith during their adolescence or later in life, especially when drinking, but they never strayed far from their religious beliefs.

Although the *juramento* is not a formal public health intervention, it goes beyond just facilitating abstinence. It may also result in secondary and tertiary prevention, stopping the progression of the men's drinking. In our sample, the *juramento* may have kept occasional binge drinkers from becoming heavy drinkers and heavy drinkers from becoming alcohol dependent. None of the men were in this drinking category before making their *juramento*. Consequently, we did not get an opportunity to see if the *juramento* also served as an intervention with prevention benefits for those suffering from alcohol dependence, i.e., whether it kept men with alcohol dependence from continuing to drink. However, research shows that abstinence is unlikely to be successful for individuals who already meet the criteria for dependence without some type of formal treatment. This kind of drinker may need more attention than what the *juramento* can provide for achieving abstinence and preventing a return to drinking.

To grasp how the *juramento* intrinsically works and how it could result in secondary and tertiary prevention, you must understand the sacred pledge and dyadic relationship entered with Our Lady of Guadalupe. It starts early in the juramento process, during the making of the vow to Our Lady of Guadalupe. As a 34-year-old immigrant makes clear, she is at the center of the *juramento*:

*"You keep the pledge because you made it to the virgin, and for Mexicans and Catholics the virgin is like your mother. And not keeping a promise to the virgin is like not keeping a promise to your mother. You value and respect your mother". … "this is the power of the juramento, when you are truly committed to changing and have strength in your faith …".*

This saint is the symbolic mother of all Mexicans, including those living abroad. Mothers are revered in Mexican culture and society. In fact, the two most important celebrations in the country are December 12, the feast day of Our Lady of Guadalupe, and May 10th, Mother's Day. The importance of Our Lady of Guadalupe in Mexican life is obvious to the faithful and is not reserved for only the *juramento*. It is believed that she intercedes on their behalf and are close enough to God to prompt an intercession with a difficult problem, including granting a miracle when needed.

The vow made to Our Lady of Guadalupe reconnects the *jurados* to their faith and results in a powerful, religious-based catharsis. It sets the right frame of mind for recovery. The *juramento* creates a sense of hope in both the *jurado* and his family that the drinking will stop, as will the problems that come with it. The men shared that it releases them from shame of drinking and the harm committed to others, and in turn, gives them hope for turning their lives around and gaining lost respect in their families and communities. Further, this spiritual awakening allows them to reconcile with their church, religion, and ultimately God, which they were estranged from because of their drinking. Feeling unworthy of God's grace, they had distanced themselves from their religious beliefs. Now, the men have a renewed sense of worthiness in the eyes of God.

### *The* Juramento*: Secondary and Tertiary Preventive Benefits of a Religious-Based Brief Alcohol… DOI: http://dx.doi.org/10.5772/intechopen.95545*

The juramento also gives the *jurados* the necessary fortitude to keep their vows and abstain from alcohol use. The men are no longer alone in dealing with their drinking problem. Instead, the *jurado* now has divine support, and with it, the necessary fortitude to abstain from alcohol use and to work on his drinking problem. From this, he draws the strength to change his life and to atone for the harms committed to self and others. Once a juramento is made, there is an additional moral obligation to subordinate one's drinking indulgences to one's commitments to the saint and God. According to the men, not keeping a vow is a sin. A broken vow is an affront to Our Lady of Guadalupe and God. In light of this, the *jurado* tries his best to persevere in his abstinence because he fears committing an ultimate offense in his religion.

Some benefits are more tangible. Once a *juramento* is made, the *jurado's* reputation is at stake, as is his trustworthiness within his family, community, and church. He knows that he must keep the vow, or risk social as well as divine consequences. The *estampita*, or prayer card, contributes to his resolve: for example, during moments of weakness, reciting the *juramento* prayer on the card helps the men resist temptation. Prayer reminds the men that our Lady of Guadalupe is looking after them, and that they are not alone. It gives them strength. The *estampita* also helps to control peer pressure to drink. When friends offer them a drink, the men reply that they are not drinking because they have made a *juramento* and show their prayer card. In these situations, the *estampita* serves as a credential, a form of proof. It proves that the men are telling the truth and obligates others to respect the *jurado*'s promise.

There is a general debate in the substance abuse field regarding whether sobriety alone results in the life changes needed to stop drinking overtime or whether it needs to be combined with treatment to achieve recovery. Sobriety is often defined as the continued state of being sober, i.e., not drinking, while according to SAMHSA [60], recovery is "a process of change through which individuals improve their health and wellness, live self-directed lives and strive to reach their full potential." Some argue that without additional treatment and/or participation in 12-step meetings, such as those in Alcohol Anonymous, you will not be able to start the journey of recovery or develop a healthy mind, sound body, and supportive relationships. Treatment involves identifying and working on the causes of your harmful drinking. You need to know yourself, understand your true persona, and recognize behaviors that may have contributed to your drinking. Knowing who you are and understanding why you drank may also help you to discover personal strengths that can help in your recovery. Not addressing this will just make you a "dry drunk," an expression used in AA to refer to an individual who is not drinking but is not addressing behaviors and the problems of the past that contributed to harmful drinking.

Our study did not address recovery *per se*, but the findings indicate that the men are making changes in their lives, and they are doing this without treatment. This is distinctly significant to a population that has little or no access to formal health resources. None of the men in our study sought treatment or attended AA meetings after making the *juramento*, as the priest suggested during the *juramento*'s counseling session. While this may be the result of multiple contributing factors, one clear reason is that no alcohol treatment programs exist that are affordable, within a reasonable driving distance, and culturally and linguistically appropriate for Mexican and other Latinx immigrants. Formal treatment is simply not available to this population, in this region.

Nonetheless, there is evidence that, with the divine intervention of Our Lady of Guadalupe, the men are not only staying sober, they are also working on their recovery. The power of religion cannot be underestimated: research has found

that the lack of a spiritual or religious connection contributes to the escalation of substance misuse [61]. These men demonstrate some of the four indicators of recovery that accompanied the above definition: 1) addressing problems as they happen, without using, and without getting stressed out; 2) having at least one person he can be completely honest with; 3) having personal boundaries; and 4) taking time to restore physical and emotional states when not tired [60]. Their recovery is not based on a personal catharsis reached in treatment, but on a religious catharsis. This special relationship with Our Lady of Guadalupe enhances the men's coping, confers hope for the future, and provides a heightened sense of control, security, and stability. When asked about how the *juramento* has changed their lives, the men are quick to respond that it has improved their social ties and outlook on life; they no longer feel alone in their quest to live alcohol-free. The *jurados* severed unhealthy relationships, especially with those who continue to drink, and reconnected with family. These renewed bonds, in addition to newly established friendships with others in the community, become important sources of support. Healthier thinking and a new way of living prevails. For example, they try not to be consumed by problems that arise at home or at work, especially those that they cannot do anything about. Prayer helps. Some of the men returned to the church and are learning how to trust again and become part of a larger community. Reconnecting to their religion, through the *juramento*, makes a difference.

### **6. Limitations**

Despite its contributions, our chapter has limitations. The primary objective of our qualitative study was to examine the *juramento* as practiced in a single region to understand how it is used to curtail harmful drinking in the Latinx community. We generated data that allowed us to characterize the *juramento*, how it is made, who makes it, why, and how it works. Prevention was not one of the larger aims, but our data also allowed us to address its potential prevention benefits. More research is needed on this subject if we are to understand the *juramento's* contributions to prevention. In particular, we need to focus on some of the findings presented in the chapter. And as such, more attention needs to be paid to how the *juramento* prevents the men's drinking from progressing, by looking closer at the duration of the *juramentos*, the number of *juramentos* made and the reasons for making more than one, and the time period between the *juramentos*. Specifically, attention should be paid to the abstinence periods between *juramentos*, and how abstinence was achieved, including how the different components of the *juramento* contributed to abstinence. We also need to consider if the *juramento* alone is responsible for bringing about this change or if other social factors also contribute, such as the need to meet family responsibilities in the United States and Mexico. Additionally, our sample only included men who had just made a *juramento* or were abstinent after completing one. It did not include individuals who did not complete their juramento or were drinking again after completing one. These individuals must also be included in the research. Prevention benefits, such as stronger marital and family relations and gainful employment, should also be considered. These inquiries will require going beyond an ethnographic and cross-sectional study such as ours and launching a longitudinal study. Cross-sectional studies do not provide definite information about cause-and-effect relationships because such studies focus on a single moment in time; they do not consider what happens before or after that moment.

*The* Juramento*: Secondary and Tertiary Preventive Benefits of a Religious-Based Brief Alcohol… DOI: http://dx.doi.org/10.5772/intechopen.95545*

### **7. Conclusions**

For decades now, public health has called for the development of cultural and linguistically appropriate prevention and treatment programs for alcohol and substance use. It realizes that there is a need for these programs in the country's increasingly diverse population, especially in regions with concentrations of immigrants, migrants, and refugees. For the most part, this same campaign has overlooked grassroots interventions for these health problems. The *juramento* is one of several grassroots interventions for alcohol and substance use in Latinx communities, which include *anexos*, or 24-hour AA groups, *grupos de cuarto y quinto pasos*, or fourth and fifth step AA groups, and *curanderismo*, or traditional medicine [1]. Like the *juramento*, they are organic, arising from within the community, and they consider cultural traditions and beliefs and language as it is practiced daily. Latinx immigrants have introduced these grassroots interventions to the United States not necessarily because they are trying to recreate as much as possible of their homeland culture in new lands, but because there is a need for them in their U.S. communities. Because of their immigrant status in a country whose federal and state governments are increasing limiting services to all immigrants, they have limited government-sponsored health care and nearly no access to formal alcohol and substance use treatments [1, 62]. For Latinx immigrants, grassroots interventions such as the *juramento* are the only source of help for harmful drinking. Attention to such grassroots practices in public health can support the ways that they already provide care to marginalized populations and help connect the interventions with those who need them.

### **Author details**

Victor Garcia1,3\*, Katherine Fox2 , Emily Lambert1 and Alex Heckert1,4

1 Mid-Atlantic Research and Training Institute for Community and Behavioral Health, Indiana University of Pennsylvania, Indiana, PA, USA

2 Department of Anthropology, Southern Methodist University, Dallas, TX, USA

3 Department of Anthropology, Indiana University of Pennsylvania, Indiana, PA, USA

4 Department of Sociology, Indiana University of Pennsylvania, Indiana, PA, USA

\*Address all correspondence to: vgarcia@iup.edu

© 2021 The Author(s). Licensee IntechOpen. This chapter is distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/ by/3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

### **References**

[1] Garcia V, Lambert E, Fox K, Heckert D, Pinchi NH. Grassroots interventions for alcohol use disorders in the Mexican immigrant community: A narrative literature review. J Ethn Subs Abuse. 2020;6:1-20. Available from: https://doi.org/10.1080/15332640. 2020.1803781.

[2] Murrary DM, Villani J, Vargas AJ, Lee JA, Myles RL, Wu J, et al. NIH primary and secondary prevention research in humans during 2012-2017. Am J Prev Med. 2018;55(6):915-925.

[3] WHO, UNDCP. *Primary prevention of substance abuse: A workbook for project operators*. World Health Organization. 2000. Available from: https://www.who. int/substance\_abuse/activities/global\_ initiative/en/primary\_prevention\_17. pdf.

[4] Garcia V, Gonzalez L. Juramentos and mandas: Traditional Catholic practices and substance abuse in Mexican communities of Southeastern Pennsylvania. NAPA Bulletin. 2009;31(1):47- 63. Available from: https://doi. org/1111/j.1556-4797.2009.01018.x.

[5] Cuadrado M, Lieberman L. The Virgin of Guadalupe as an ancillary modality for treating Hispanic substance abusers: Juramentos in the United States. Journal of Religion and Health. 2011;50(4):922-930.

[6] Garcia V, Heckert DA, Lambert E, Hidalgo Pinchi N. Using the juramento as a brief AUD intervention for Mexican immigrant farmworkers. *Hispanic Health Care International*. 2020.

[7] Ridenour TA, Stormshak EA. Introduction and rationale for individualized substance abuse prevention from an ontogenetic perspective. Am J Drug Alcohol Abuse. 2009;35(4):206-208.

[8] Moulahoum H, Zihnioglu F, Timur S, Coskunol H. Novel technologies in detection, treatment and prevention of substance use disorders. J Food Drug Anal. 2019;27(1):22-31.

[9] Volkow ND, Li T. Drugs and alcohol: Treating and preventing abuse, addiction and their medical consequences. Pharmacol Ther. 2005;108(1):3-17.

[10] Peterson NA, Reid RJ. Paths to psychological empowerment in urban community: Sense of community and citizen participation in substance abuse prevention activities. Journal of Community Psychology. 2003;31(1).

[11] Brooks MJ, Marshal MP, McCauley HL, Douaihy A, Miller E. The relationship between hope and adolescent likelihood to endorse substance use behaviors in a sample of marginalized youth. Substance Use & Misuse. 2016;51(13):1815-1819.

[12] D'Amico EJ, McCarthy DM. Escalation and initiation of younger adolescents' substance use: The impact of perceived peer use. Journal of Adolescent Health. 2006;39(4):481-487.

[13] Salazar AM, Noell B, Cole JJ, Haggerty KP, Roe S. Incorporating self-determination into substance abuse prevention programming for youth transitioning from foster care to adulthood. Child & Family Social Work. 2018;23(2):281-288.

[14] Low NC, Lee SS, Johnson JG, Williams JB, Harris ES. The association between anxiety and alcohol versus cannabis abuse disorders among adolescents in primary care settings. Family practice. 2008;25(5):321-327.

[15] Jiang S, Wu L, Gao X. Beyond face-to-face individual counseling: A systematic review on alternative

*The* Juramento*: Secondary and Tertiary Preventive Benefits of a Religious-Based Brief Alcohol… DOI: http://dx.doi.org/10.5772/intechopen.95545*

modes of motivational interviewing in substance abuse treatment and prevention. Addict Behav. 2017;73:216-235.

[16] Hopson L, Wodarski J, Tang N. The effectiveness of electronic approaches to substance abuse prevention for adolescents. J Evid Inf Soc Work. 2015;12(3):310-322.

[17] Mutamba BB, van Ginneken N, Smith Paintain L, Wandiembe S, Schellenberg D. Roles and effectiveness of lay community health workers in the prevention of mental, neurological and substance use disorders in low and middle income countries: A systematic review. BMC Health Services Research. 2013;13(412):1-11.

[18] Nygaard P. Focus on secondary prevention: Implications of a study on intervention in social networks. Substance Use & Misuses. 2006;41(13):1719-1733.

[19] Trova AC, Paparrigopoulos T, Ginieri-Coccossis M. Prevention of alcohol dependence. Psychiatriki. 2015;26(2):131-140.

[20] Kagle JD. Secondary prevention of substance abuse. Social Work. 1987;32(5):446-448.

[21] Orford J. Empowering family and friends: A new approach to the secondary prevention of addiction. Drug and Alcohol Review. 1994;13(4):417-429.

[22] Elliott L, Orr L, Watson L, Jackson A. How effective are secondary prevention interventions for young drug users? Family Therapy. 2005;32(1):15-30.

[23] Fernandez Mondejar E, Guerrero Lopez F, Quintana M, Alted E, Minambres E, Salinas Gabina I, et al. Secondary prevention of alcohol and/or drug abuse in trauma patients: Results

of a national survey in Spain. Med Intensiva. 2009;33(7):321-326.

[24] DeMatteo DS, Marlowe DB, Festinger DS. Secondary prevention services for clients who are low risk in drug court: A conceptual model. Crime & Delinquency. 2006;52(1):114-134.

[25] DiClemente CC. Prevention and harm reduction for chemical dependency: A process perspective. Clinical Psychology Review. 1999;19(4):473-486.

[26] Carroll JFX, Tanneberger MA, Monti TC. A tertiary prevention strategy for drug-dependent clients completing residential treatment. Alcoholism Treatment Quarterly. 1998;16(3):51-61.

[27] McAnally HB. Addressing host factors: Primary, secondary, and tertiary prevention of opioid dependence. Opioid Dependence. 2018:265-290.

[28] Treloar C, Laybutt B, Carruthers S. Using mindfulness to develop health education strategies for blood borne virus prevention in injecting drug use. *Drugs: Education,* Prevention & Policy. 2010;17(4):431-442.

[29] Marlatt GA, Witkiewitz K. Relapse prevention for alcohol and drug problems. In: Marlatt A, Donovan DM. (eds.). *Relapse prevention: Maintenance strategies in the treatment of addictive behaviors*. New York, New York: The Guilford Press; 2005. p.1-44.

[30] Van Heeringen KC. The prevention of drug abuse – state of the art and directions for future actions. J Toxicol Clin Toxicol. 1995;33(6):575-579.

[31] Levy MS. Listening to our clients: The prevention of relapse. Journal of Psychoactive Drugs. 2008:167-172.

[32] Jason LA, Olson BD, Ferrari JR, Majer JM, Alvarez J, Stout J. An

examination of main and interactive effects of substance abuse recovery housing on multiple indicators of adjustment. Addiction. 2007;102(7):1114-1121.

[33] Schonfeld L, Dupree LW, Dickson-Fuhrmann E, Royer CM, McDermott CH, Rosansky JS, et al. Cognitive-behavioral treatment of older veterans with substance abuse problems. Journal of Geriatric Psychiatry and Neurology. 2000;13(3):124-129.

[34] Bowen S, Chawla N, Collins SE, Witkiewitz K, Hsu S, Grow J, et al. Mindfulness-based relapse prevention for substance use disorders: A pilot efficacy trial. Substance Abuse. 2009;30(4):295-305.

[35] Witkiewitz K, Bowen S. Depression, craving, and substance use following a randomized trial of mindfulnessbased relapse prevention. Journal of Consulting and Clinical Psychology. 2010;78(3):362-374.

[36] Amaro H. Implementing mindfulness-based relapse prevention in diverse populations: Challenges and future directions. Substance Use & Misuse. 2014;49(5):612-616.

[37] Greenfield BL, Roos C, Hagler KJ, Stein E, Bowen S, Witkiewitz KA. Race/ ethnicity and racial group composition moderate the effectiveness of mindfulness-based relapse prevention for substance use disorder. Addict Behav. 2018;81:96-103.

[38] Walton MA, Blow FC, Booth BM. Diversity in relapse prevention needs: Gender and race comparisons among substance abuse treatment patients. Am J Drug Alcohol Abuse. 2001;27(2):225-240.

[39] Kearney M, Reynolds L, Blitzstein S, Chapin K, Massey P. Primary prevention of prescription drug misuse

among culturally and linguistically diverse suburban communities. Journal of Community Health. 2019;44(2):238-248.

[40] Patchell BA, Robbins LK, Lowe JA, Hoke MM. The effect of a culturally tailored substance abuse prevention intervention with Plains Indian adolescents. Journal of Cultural Diversity. 2015;22(2):3-8.

[41] Kelley A, Witzel M, Fatupaito B. A review of tribal best practices in substance abuse prevention. Journal of Ethnicity in Substance Abuse. 2019;18(3):462-475.

[42] Straits KJ, deMaria J, Tafoya N. Place of strength: Indigenous artists and indigenous knowledge is prevention science. Am J Community Psychol. 2019;64(1-2):96-106.

[43] Cox RB, Roblyer MZ, Merten MJ, Shreffler KM, Schwerdtfeger KL. Do parent–child acculturation gaps affect early adolescent Latino alcohol use? A study of the probability and extent of use. Substance Abuse Treatment, Prevention, and Policy. 2013 Dec 1;8(1):4.

[44] Castro FG, Stein JA, Bentler PM. Ethnic pride, traditional family values, and acculturation in early cigarette and alcohol use among Latino adolescents. The Journal of Primary Prevention. 2009 Jul 1;30(3-4):265-292.

[45] Lac A, Unger JB, Basáñez T, Ritt-Olson A, Soto DW, Baezconde-Garbanati L. Marijuana use among Latino adolescents: Gender differences in protective familial factors. Substance Use & Misuse. 2011 Mar 15;46(5):644-55.

[46] Marsiglia FF, Miles BW, Dustman P, Sills S. Ties that protect: An ecological perspective on Latino/a urban preadolescent drug use. Journal of Ethnic

*The* Juramento*: Secondary and Tertiary Preventive Benefits of a Religious-Based Brief Alcohol… DOI: http://dx.doi.org/10.5772/intechopen.95545*

and Cultural Diversity in Social Work. 2002 Sep 1;11(3-4):191-220.

[47] Lorenzo-Blanco EI, Schwartz SJ, Unger JB, Zamboanga BL, Des Rosiers SE, Baezconde-Garbanati L, Huang S, Villamar JA, Soto D, Pattarroyo M. Alcohol use among recent immigrant Latino/a youth: acculturation, gender, and the Theory of Reasoned Action. Ethnicity & health. 2016 Nov 1;21(6):609-27.

[48] Cervantes R, Goldbach J, Santos SM. Familia Adelante: A multirisk prevention intervention for Latino families. The journal of primary prevention. 2011 Aug 1;32(3-4):225.

[49] Szapocznik J, editor. A Hispanic-Latino Family Approach to Substance Abuse Prevention. DIANE Publishing; 1998.

[50] Jurkovic GJ, Kuperminc G, Perilla J, Murphy A, Ibañez G, Casey S. Ecological and ethicalperspectives on filial responsibility: Implications for primary prevention with immigrant Latino adolescents. Journal of Primary Prevention. 2004 Sep 1;25(1):81-104.

[51] Marsiglia FF, Ayers S, Gance-Cleveland B, Mettler K, Booth J. Beyond primary prevention of alcohol use: A culturally specific secondary prevention program for Mexican heritage adolescents. Prevention Science. 2012 Jun 1;13(3):241-251.

[52] Shetgiri R, Kataoka S, Lin H, Flores G. A randomized, controlled trial of a school-based intervention to reduce violence and substance use in predominantly Latino high school students. Journal of the National Medical Association. 2011 Sep 1;103(9-10):932-940.

[53] Koenig, H.G.; Büssing, A. The Duke University Religion Index (DUREL): A Five-Item Measure for Use in

Epidemiological Studies. Religions. 2010, *1*, 78-85.

[54] Del Boca, F.K. and Darkes, J. The validity of self-reports of alcohol consumption: state of the science and challenges for research. Addiction. 2003; 98: 1-12. https://doi. org/10.1046/j.1359-6357.2003.00586.x

[55] Solbergsdottir, E., Bjornsson, G., Gudmundsson, L.S., Tyrfingsson, T., & Kristinsson, J. (2004) Validity of Self-Reports and Drug Use Among Young People Seeking Treatment for Substance Abuse or Dependence, Journal of Addictive Diseases, 23:1, 29-38, DOI: 10.1300/J069v23n01\_03

[56] National Institute on Alcohol Abuse and Alcoholism. *Drinking levels defined* [Internet]. (no date). Available from: https://www.niaaa.nih.gov/alcoholhealth/overview-alcohol-consumption/ moderate-binge-drinking

[57] Editorial Staff. *Problem drinking vs. alcoholism* [Internet]. 2020. Available from: https://www.alcohol.org/ alcoholism/or-is-it-just-a-problem/

[58] National Institute on Alcohol Abuse and Alcoholism. *Alcohol use disorder: A comparison between DSM-IV and DSM-5* [Internet]. 2020. Available from: https:// www.niaaa.nih.gov/publications/ brochures-and-fact-sheets/alcohol-usedisorder-comparison-between-dsm

[59] SAMHSA. *Screening, brief intervention, and referral to treatment (SBIRT)* [Internet]. 2017. Available from: https://www.samhsa.gov/sbirt/

[60] SAMHSA. *National and Regional Resources, Region VIII* [Internet]. 2014. Available from: https://www. samhsa.gov/sites/default/files/samhsarecovery-5-6-14.pdf

[61] National Center on Addiction and Substance Abuse. *So help me god: Substance abuse, religion and spirituality*. The National Center on Addiction and Substance Abuse (CASA) at Columbia University. 2001.

[62] Pagano A. Barriers to drug abuse treatment for Latino migrants: Treatment providers' perspectives*.* Journal of Ethnicity in Substance Abuse. 2014;13(3):273-287.

### **Chapter 3**

## Sex Differences between Young Adults in the Czech and Slovak Republics in the Relationship between Alcohol-Related Consequences and Depression

*Beata Gavurova, Martin Rigelsky and Viera Ivankova*

### **Abstract**

In general, the Czech and Slovak Republic are among the countries with increased alcohol consumption. It is clear that increased consumption can predict the occurrence of negative consequences that may subsequently be associated with various mental disorders. One of these mental disorders is depression, which is common in young adults and brings difficulties into their lives that can turn into problems in the future. The study examined the relationship between alcohol-related consequences and depressive symptoms in a sample of university students from the Czech and Slovak Republics in order to map the situation in these regions, where this problem is still ignored (n = 2514; CZE = 47.5%). The research included data from standardized questionnaires, namely the Young Adult Alcohol Consequences Questionnaire (YAACQ), which can predict alcohol use problems, and Health Questionnaire of depression (PHQ-9). The data was collected during the COVID-19 pandemic. Regarding sex differences, a higher YAACQ score was found in males and, conversely, a higher PHQ-9 score was identified in females. The results of correlation and regression analyses revealed significant associations between the scores in the individual YAACQ subscales and the PHQ-9 score, while low to moderate correlations were found in most cases. In all cases, positive trajectories were identified, meaning that the increased risk of depressive disorder can be associated with experience in selected dimensions of alcohol-related consequences. Stronger associations occurred in females than in males. In terms of practical implications, high priority was given to prevention programs and counseling. Professionals' efforts to help young people should be sex-oriented, while females were more vulnerable to depression, males were prone to the consequences of alcohol use.

**Keywords:** alcohol addiction, patterns of unhealthy behavior, depression, youth, sex, mental health inequalities, Czech and Slovak students

### **1. Introduction**

The importance of mental health is irrefutable in the lives of individuals as well as in society. Evidence supports the fact that there is no health without mental

health, as neuropsychiatric disorders such as depression, alcohol and substance use disorders significantly contribute to the global disease burden [1]. Many studies point to a considerable burden of these disorders in all aspects of society, including individuals, their families, workplaces and also the wider economy [2]. On this basis, mental health should not be overlooked, and it is considered important to know its main factors in different population groups. In this sense, depressive disorder and alcohol-related consequences can be considered as two of these risk factors for mental health. Depression is one of the most common mental disorders, and research into alcohol use and its consequences for depressive symptoms can provide useful information to clinicians and professionals. Therefore, the presented research examined the relationship between alcohol-related consequences and perceived depression in the Czech and Slovak young population with sex differentiation.

Alcohol problems at risk of addiction are considered a significant threat and need to be examined in the socio-economic dimension, including education. Sex differences are equally important in examining the community and its inclination to addictive substances. The present time has changed not only the economic but also the legal status of females, thus removing social barriers to their alcohol consumption. As a result, the number of females consuming alcoholic beverages as well as addicted females has significantly increased [3]. There are many reasons to take into account sex differences and specificities in services for addicted females or females at risk of addiction [4], as evidenced by the findings on hospitalizations of males and females in the member states of the European Union (EU) [3]. In any case, it can be considered important to examine this problem, it can also contribute to knowledge in the diagnostic issues of addiction.

There are many international studies examining the variables predicting alcohol use problems in young adults, while depression and sex differentiation have also played an important role in their research [5]. According to their findings, university students often reported depressed mood and alcohol problems. Attention should be paid to any signal that may be related to the problematic drinking and mental disorder of young people. In any case, Slovak and Czech students are no exception to this problem. The knowledge of this issue enables the implementation of active prevention programs that would eliminate the level of addiction in suffering people, or help prevent the emergence of others. The earlier the addiction is treated, the higher the chance of successful abstinence of the individual.

### **2. Alcohol-related consequences and depressive disorder as risk factors in the lives of young adults**

The presented study focused on alcohol-related consequences predicting alcohol use problems, which can lead to a depressed mood in university students. The purpose of the study was to present the alcohol-depression topic in specific geographical regions, to map the situation in these regions and to emphasize the problem at a professional and practical level.

The intensity of alcohol consumption in individual countries is often conditioned by several aspects, while it still remains true that the Slovak Republic is at the forefront of alcohol consumption among OECD countries and alcohol consumption in the Czech Republic is more moderate [6]. Alcohol consumption does not necessarily indicate directly the consequences of drinking, at least in the short term. At this point, it should be noted that the relationship between alcohol use and alcoholrelated consequences is not entirely trivial. In this regard, there is evidence of a similar likelihood of reporting negative alcohol-related consequences at both low

### *Sex Differences between Young Adults in the Czech and Slovak Republics… DOI: http://dx.doi.org/10.5772/intechopen.96469*

and higher levels of alcohol consumption [7]. There are many factors that can play an important role in drinking patterns with consequences. As stated by Rehm and Room [8], the cultural aspect is a determinant of alcohol consumption, while it is possible to speak of differences in the perception of acceptable level of alcohol consumption, up to the level of severity of negative alcohol-related behavior. This study focuses on the consequences of alcohol drinking in young adults, as they are a vulnerable group that may have very noticeable consequences. Several authors [9] attribute to this group an inclination to various types of risky behavior (consequences) associated with alcohol use, from reduced academic performance, through unprotected sex to violent and aggressive behavior. Alcohol consumption alone does not capture these important aspects that are part of problem drinking with the risk of addiction. On the other hand, it was supported that the instrument capturing the consequences of drinking (YAACQ) is also able to predict drinking patterns [10] and to assess the level of drinking risk in university students [11]. This is evidenced by the associations between the YAACQ outcomes and drinking outcomes [12]. These facts offer new opportunities for research into alcohol addiction across the population. The consequences of alcohol use can be a concomitant phenomenon of problem drinking and, at the same time, can indicate addictive behavior.

University students are not just young adults, they are a population group that is the expected driving force of the economy in the future, and they represent potential current and future health care, criminal justice, and social burdens as well. Therefore, it is important to pay special attention to them. In the context of drinking patterns, many university students drink a lot of alcohol and tend to drink more and more heavily than their non-university peers, which has countless negative consequences [13]. These habits of young people can be reflected in various aspects of their lives. Tembo et al. [14] revealed that high levels of alcohol consumption among university students are significantly associated with poor mental health outcomes. In addition, other risky behaviors, use of other addictive substances, psychological symptoms (depression, distress), or low interest in academic activities may prevail among university students with problem drinking at risk of addiction [15]. For all these reasons, many authors call for drinking prevention strategies and interventions in the university environment [16]. The importance of active counseling at universities was emphasized and social support represented a very important aspect [17]. The key factors were also participation in university activities, public discussions on the consequences of excessive alcohol consumption, motivation for healthy behaviors through academic and career success [18]. In other words, evidence from other geographical regions clearly supported effective prevention programs, which, however, have been implemented to a small extent in the Czech and Slovak Republics. Thus, it is essential that research efforts focus on this issue and its implications for the university student population.

It is not difficult to expect various negative alcohol-related problems in the lives of young people [19], while drinking motives play an important role in this issue [20]. The alcohol-related consequences include various negative experiences, such as embarrassing situations, problems with friends and family, problems at school or work, indecent (rude) and risky behavior, excessive drinking, physical symptoms, bad feelings, but also unpleasant sexual situations, physical attacks and blackouts [21]. In the university environment, one of the most appropriate measures to capture alcohol-related consequences is the 48-item Young Adult Alcohol Consequences Questionnaire (YAACQ), the great advantage of which is its subscales providing a method of aggregating the consequences of alcohol use that may be clinically useful [10, 21]. Moreover, its subscales show significant associations with other indices of alcohol involvement (such as drinking frequency or binge drinking frequency) [10], which is also considered important in the issue of alcohol addiction.

In terms of sex, Lemley et al. [22] revealed that the negative consequences of alcohol use differed between male students and female students, while males tended to acquire a higher YAACQ score. This is consistent with the results of Geisner et al. [23], who found more alcohol-related consequences for male students. In this regard, Merrill et al. [20] confirmed significant sex differences in the two dimensions of YAACQ, namely risky behaviors (RISK) and academic/occupational consequences (AC-OCC).

Regarding the results revealed in a study examining the consequences of alcohol use (YAACQ) in Spain, Argentina and the United States, it can be concluded that the obtained score may significantly differ from country to country, but also from subscale to subscale [24]. For example, students from Argentina and Spain acquired greater mean number of alcohol-related consequences than students from the United States [24]. Based on the results of another study from the United States, university students obtained the highest percentage to the maximum score in the subscales, such as blackouts, social interpersonal problems, impaired control and risky behaviors [21].

Depressive disorder is also a frequent psychological burden among university students, which may impair their interpersonal, social and work functioning [25]. Moreover, the prevalence of depression in students increases during their university studies [26]. Feelings of hopelessness and despondency are common to this disorder, which can affect their academic performance [27]. In fact, depressive symptoms along with alcohol use are a serious combination, as there is a risk of suicide proneness [28]. Based on these facts, it is necessary to examine these two critical disorders among university students.

Factors such as lack of social support, heavy alcohol consumption and traumatic experiences can be considered significant predictors [29]. Ibrahim et al. [25] conducted a systematic review of university students and it can be noted that the significant effect of alcohol use was not large. The high prevalence rate of depression among university students (ranging from 10% to 85%) contributes to the perception of students as a high-risk population [25], while females are at higher risk compared to male counterparts [30]. Leppink et al. [31] also revealed that females were significantly predominant in severe depression and this is in line with the findings that sex differences are greater in major depression than in minor depression [25].

One of the most commonly used screening instruments for depression is the Patient Health Questionnaire-9 (PHQ-9) [32, 33]. According to Kroenke and Spitzer [34], the PHQ-9 fulfills a dual purpose, namely to diagnose depression and to assess the severity of depression. In the Slovak Republic, the PHQ-9 measure was used by Hajduk et al. [35], who examined prevalence and correlations of depression and anxiety in university students, they revealed mild depression in their research sample. The authors also found a higher prevalence of depression among students compared to anxiety, and their results showed that students with a higher score tended to perceive their mental health as less satisfactory [35]. The Czech version of the PHQ-9 measure was successfully assessed in the general population by Dansova et al. [36].

Comparing the results in the international studies using the PHQ-9 measure, it can be concluded that 37.7% of students from the United States had mild to moderate depression, and 4.4% of students had severe depression [31]. In Croatia, 60.8% students suffered from depressive disorder, while 30.3% of students reported mild depression, 16.1% of students reported moderate depression, 7.2% of students reported severely moderate depression, and severe depression was identified in 0.2% of students [37]. Interesting results were provided by Honney et al. [38], who compared the prevalence of depression in medical and non-medical students from

*Sex Differences between Young Adults in the Czech and Slovak Republics… DOI: http://dx.doi.org/10.5772/intechopen.96469*

the United Kingdom. Their results revealed that 32.4%, 10.8%, and 5.6% of medical students suffered from mild, moderate and severe depression. Simultaneously, 28.7%, 17.7%, and 12.7% of non-medical students suffered from mild, moderate and severe depression [38]. This fact indicate that medical students were not at higher risk for moderate to severe depressive disorder than non-medical students.

All of these results suggest that depression is frequent in young people's lives and should not be overlooked; on the contrary, efforts should be made to help people overcome these difficulties. One way is to identify possible risk factors and try to eliminate them. It is the problem of alcohol use with consequences that appears to be an important factor in depressed people, who should be given early intervention. Early interventions in problematic drinking behavior could prevent depressive disorders, which can have other consequences. Every indication of problem drinking is crucial for further action to address and overcome these problems in young people's lives.

### **2.1 The relationship between alcohol-related consequences and depressive disorder**

With a focus on the mentioned behavioral and mental disorders in university students, the findings of several studies revealed that psychological symptoms are associated with drinking consequences and alcohol use [23, 39], while depression is no exception [40, 41]. In this regard, Martens [42] confirmed that depressive symptoms in university students were directly related to the negative consequences of alcohol drinking (using the Rutgers Alcohol Problem Index – RAPI), but not to alcohol consumption itself. This builds on the results of Park and Grant [43], in which the consequences of alcohol drinking were significantly associated with psychological risk as well as protective factors. These facts supported the assumption that alcohol-related consequences and mental disorders, such as depression, are closely linked.

The depression-drinking link among university students was examined in several other studies, in which various tools to measure depression and alcohol-related consequences were used. In any case, correlations were clearly found between depressive disorder and drinking consequences [44]. From the perspective of this study, the findings also showed that depressive symptoms were positively and significantly correlated with negative alcohol-related consequences, as measured by the YAACQ score [45]. Similarly, Ruiz et al. [46] confirmed a positive and significant correlation between psychological discomfort and the YAACQ score. Regarding the PHQ-9 measure, several studies can also be found that confirmed a positive association between depression and problem drinking, while Flesch et al. [47] noted that alcohol abuse can be considered a risk factor for a major depressive episode expressed in the PHQ-9 score.

There is also evidence to suggest that depressive symptoms may predict alcohol use and alcohol-related consequences [48, 49], while self-medication plays an important role in this association [50]. On the other hand, problems with alcohol can lead to an increased risk of depression [51, 52]. In this regard, Schutte et al. [53] found that alcohol-related consequences could lead to depression in males, but not in females. This can be explained by the fact that male university students are characterized by higher alcohol consumption and more negative consequences of drinking [23]. As a result, Geisner et al. [23] confirmed a stronger association between psychological symptoms and the negative consequences of alcohol use in males than in females.

On the other hand, Rosenthal et al. [54] used the Brief Young Adult Alcohol Consequences Questionnaire (BYAACQ) in a sample of female university students, and their findings revealed that experiencing negative alcohol-related consequences, regardless of the amount of alcohol consumption, could lead to a higher risk of depression (PHQ-9). With a focus on casual sex, one of the possible alcoholrelated consequences, this experience increased depressive symptoms in female students more than in male students [55]. Accordingly, it can be assumed that female students felt guilty or remorse in this situation and felt that they had violated social expectations [56].

Although indirect evidence has created expectations, the relationship between the YAACQ score and the PHQ-9 score has not yet been examined in some regions. The above-mentioned evidence has suggested that depressive disorder and alcoholrelated consequences may show interesting results in higher education, where the issue of mental health and unhealthy behavior is of undeniable importance. Insufficient examination of this issue can also be observed in the regions of the Czech and Slovak Republics. In these regions, the effects of alcohol-related consequences on depressive symptoms remain unknown. For this reason, the presented study filled this gap and provided public policy makers as well as experts with an inspiring perspective. At present, every country needs up-to-date information for responsible decision-making, the development of effective strategies and the implementation of interventions. In addition, at the time of the COVID-19 pandemic, it is necessary to monitor the patterns of behavior of vulnerable groups of the population, which are certainly also students. It has been shown that the COVID-19 pandemic can negatively contribute to students' psychological discomfort and unhealthy behavior [57, 58]. The main reasons for students' discomfort were worries about their health and the health of their loved ones, difficulty concentrating, sleep disorders, physical distancing and increased academic concerns [58]. These facts can lead to more serious consequences and a risk of substance abuse. In any case, all necessary measures to prevent an increase in alcohol-related problems should be adopted [59].

The purpose of the presented study was to assess the situation of alcohol-related consequences and depressive disorder in the Czech and Slovak Republics and to provide a valuable platform for the development of strategies and interventions in these regions. When developing and optimizing diagnostic procedures, prevention and treatment, it is desirable to specify potential patients. One way in the specification process is to differentiate according to sex characteristics.

### **3. Materials and methods**

The analyses included data obtained using the YAACQ [21] and PHQ-9 [33] measures. The PHQ-9 measure was successfully validated in several studies aimed at university students [60, 61]. Its reliability and psychometric properties are evidenced by the fact that this measure has been included in many studies on the mental health of university students from various countries, such as Australia [62], the United Kingdom [38], the United States [31] or Croatia [37]. In terms of crosscultural comparison, the usefulness of PHQ-9 was supported by a study at universities in Germany and China [63] and a study focusing on young adults from Poland and Korea [64]. The PHQ-9 consisted of nine survey items with a four-point scale (0 not at all; 1 several days; 3 more than half the days; 4 nearly every day) aimed at screening for depressive disorder among university students. This brief measure of depression could reach a score ranging from 0 to 27. On this basis, it was possible to know the probability of major or subthreshold depressive disorder at various cut points defining the lower limits of mild, moderate, moderately severe, and severe depression [32, 33]. The score was decisive in the assessment, while the higher the value, the more intense the depressive disorder. In assessing, several studies used

*Sex Differences between Young Adults in the Czech and Slovak Republics… DOI: http://dx.doi.org/10.5772/intechopen.96469*

score assigned at intervals (0–4 none; 5–9 mild; 10–14 moderate; 15–19 moderately severe; 20–27 severe). For the purposes of this research (i.e. application of regression and correlation analysis), it was more appropriate to use the gross score obtained by the students.

In general, the YAACQ measure covers from mild to more severe alcohol-related consequences and includes the following eight subscales: (1) social interpersonal problems – SOC, (2) impaired control – CONTR, (3) self-perception – SELF-P, (4) self-care – SELF-C, (5) risky behaviors – RISK, (6) academic/occupational consequences – AC-OCC, (7) physiological dependence – PHYS-DEP, and (8) blackout drinking – BLKOUT. Interestingly, two subscales (impaired control, self-care) take into account problem areas that are not fully assessed by other existing measures [10]. It can also be noted that the total YAACQ score correlated with another similar measure (RAPI), which supports the validity of this measure [21]. The advantages of YAACQ have been proven in several studies conducted among students [9, 20, 65] and the use of this eight-factor structure has been supported across countries and cultures [12, 21]. As in the previous case, the gross score was used for the YAACQ measure. A dichotomous scale is commonly used for this instrument (0 no; 1 yes), but in this research, the scale was extended (1 strongly disagree; 2 disagree; 3 undecided; 4 agree; 5 strongly agree). The total score of YAACQ or its individual subscales was formed by the sum of the individual items. The dichotomous scale had a number of benefits that were evident in the diagnostics, as the overall score reflected a number of consequences and it was not difficult to complete. The conversion to a 5-point scale could be more accurate and offer the use of more statistical methods. The use of an extended scale was more appropriate for research and academic purposes. There was some risk when comparing the results with the dichotomous scale, but this risk was minimal.

### **3.1 Research sample and data collection**

The data collection was performed in two parallel levels. First, university representatives as well as teachers were contacted by e-mail with a request to distribute the questionnaire to students. Second, the questionnaire was distributed through student groups on social networks (universities, faculties, dormitories, student communities). The research sample consisted of university students from the Czech and Slovak Republics, who stated that they had consumed alcohol in the last 3 months. Data collection was conducted in 2020, when it is necessary to take into account the coronavirus disease 2019 (COVID-19) pandemic. The questionnaire was distributed electronically. The total sample consisted of 2514 respondents (CZE: n = 1193, 47.5%). The data were cleaned up. First, respondents who answered doubtfully to the control question (in numerical terms, one million has six zeros – the scale of agreement/disagreement) were excluded (n = 179), then erroneous responses caused by the system (blank items, even if it was a mandatory item) were excluded (n = 27) and last, foreign students were excluded (n = 87).

The collection process can be characterized as quota sampling with a focus on the approximate proportionality of the responses in each country. The field of study can be considered as the main quota criterion (there was an effort to collect at least 30 responses per study field in each country). Efforts have also been made to include most universities (with the exception of foreign universities and foreign detached institutions). The quota sampling criteria were met and the research sample included the vast majority of all universities. **Table 1** provides the basic characteristics of the research sample.

The study and its concept were approved by the ethics committee of the General University Hospital in Prague as individual research (Ref. 915/20 S–IV). All


### **Table 1.**

*Characteristics of respondents.*

respondents who participated in the research confirmed their informed consent at the beginning of the questionnaire. All aspects in this research were conducted with respect to the seventh revision of the World Medical Association–Declaration of Helsinki [66] and the second revision of the Farmington Consensus [67].

### **3.2 Statistical analysis**

The statistical processing in this study consisted of two main parts, namely the first part focused on the statistical description of the data and the second part focused on the examined relationship. Descriptive statistics included the basic characteristics (mean (Mean), median (Median), standard deviation (SD), kurtosis (Kurt), skewness (Skew), minimum (Min), maximum (Max)), which were presented generally without classification of respondents as well as in the sex

*Sex Differences between Young Adults in the Czech and Slovak Republics… DOI: http://dx.doi.org/10.5772/intechopen.96469*

classification of respondents. Subsequently, Pearson's correlation coefficient (r) and Spearman's correlation coefficient (Spearman's ρ) were used to analyze the associations. Prior to this statistical procedure, normality (the Henze-Zirkler test for multivariate normality) and the presence of outliers (a quantile method based on Mahalanobis distance) were verified. The hypothesis of normality was rejected. However, it should be noted that due to the size of the sample, the normality tests may have been skewed and may have tended to reject the normality hypothesis. This was followed by regression analysis using a simple OLS model, before which the assumptions for use were verified (the Bonferroni outlier test, the Breusch-Pagan test). A simple quantile regression analysis (τ = 0.25, 0.50, 0.75) was applied to assess the effects, and the standard error was estimated by Powell's kernel version for the covariance matrix estimate on the commonly used Hall-Sheather bandwidth rule. IBM SPSS Statistic software (IBM Corp., Armonk, NY, USA) and R v 4.0.2 (RStudio, Inc., Boston, MA, USA) were used for statistical processing.

### **4. Results**

**Figure 1** shows the intensity of the total YAACQ and PHQ 9 scores. This visualization consists of the mean values of the percentage expression to the maximum value in terms of selected indicators. The presented figure provides some information, the most important of which is the low probability that the values of outputs in the Slovak Republic and the Czech Republic have acquired significant differences. This was evidenced by the result of the Wilcoxon nonparametric test, which in both indicators showed a p-value higher than 0.05 for the countries (p-value: YAACQ = 0.156; PHQ-9 = 0.137). On the other hand, it is possible to observe certain differences between regions in both indicators. Thus, a significant difference between the individual regions was found using the Kruskal Wallis test (YAACQ: χ<sup>2</sup> = 36.404, p-value = 0.020; PHQ-9: χ<sup>2</sup> = 33.311, p-value = 0.043). Sex characteristics were also taken into account in terms of differences, and the Wilcoxon test confirmed significant differences between males and females in both indicators at a level lower than 0.001 (mean YAACQ % to max: females = 32.7%, males = 36.8%; mean PHQ-9% to max: females = 23.1%, males = 20.2%). On this basis, the inclusion of sex characteristics in subsequent calculations were warranted. More intense color

### **Figure 1.**

*Relationship between the YAACQ score and the PHQ-9 score (% to max - mean) in the Czech Republic and the Slovak Republic. Note: Color shading of choropleth map – YAACQ (% to max - mean); circles in regions – PHQ-9 (% to max - mean); CZ – Czech Republic; SK – Slovak Republic.*

shading represents a higher value (in the case of the PHQ-9 indicator, the higher value is represented not only by color shading, but also by the size of the circle). In this context, a certain relationship could be observed between the YAACQ and PHQ-9 indicators, as in several regions with a higher intensity of the YAACQ indicator, a higher intensity of the PHQ-9 indicator can also be found. This fact was also supported by Spearman's correlation coefficient (ρ – 0.262; p-value <0.001). This secondarily declared the appropriateness of choosing a quantile regression model.

The first row of **Table 2** (TheoryM) shows the theoretical interval of the analyzed indicators, i.e. the lowest and highest value that the respondent could obtain. In this table, it is also possible to observe descriptive characteristics for all respondents (without classification), as well as separately for males and females. The significance of differences in all indicators was assessed using the Wilcoxon test. As the scales can take on different intervals of values (TheoryM), the results can be seen through the share of the maximum of the theoretical interval. Based on this, the highest score was found in the SOC subscale (mean = 12.376; 41.25% of the TheoryM maximum) and the BLKOUT subscale (mean = 14.585; 41.67% of the TheoryM maximum). On the other hand, the assessment of standard deviations (SD) has the highest added value in terms of comparing the values obtained for males and females. Apart from the PHQ-9 score, all of the cases showed lower values for females, meaning that the responses of females were more constant. As can be seen, the indicators associated with the negative consequences of alcohol use (YAACQ) were more common in males. On the other hand, the indicator of depressive disorder was more common in females.

In general, the PHQ-9 score indicates the level of intensity of perceived depression in five intervals. The results showed that exactly 50% of the students were identified in the "none" interval, a slightly increased depression in the "mild" interval was found in 29.8% of the students, the share of the "moderate" interval showed 11.6%, the share of the students with "moderately severe" depression was 5.5% and the group with the highest level of perception of depressive symptoms in the "severe" interval included 3.1% of the students.

The following part is devoted to the assessment of the associations between the selected indicators of alcohol-related consequences and depression in general, as well as in the sex classification. In the first step, the Henze-Zirkler test for multivariate normality was used, which showed significant deviations from the normal distribution in all of the analyzed cases. Also, the presence of outliers was assessed using a quantile method based on Mahalanobis distance, and a significant proportion of outliers were found. At this point, it should be noted that the research included a relatively large set of data, in which tests tend to reject the hypothesis of normality. For this reason, the results are provided in both parametric (Pearson's r) and nonparametric (Spearman's ρ) alternatives.

As mentioned above, **Table 3** shows both parametric and non-parametric alternatives to the bivariate analysis of associations. The double use of the correlation test minimized the statistical error resulting from the computational processes. In most cases, the parametric alternative of the test (Pearson's r) acquired slightly higher values than the nonparametric alternative (Spearman's ρ). Each analyzed association was significant at the level of α < 0.001. Focusing on the results in the given table, the strength of the association up to 0.30 can be interpreted as low to medium, up to 0.50 as medium to substantial and up to 0.70 as substantial to very strong. Accordingly, most of the associations between the selected YAACQ subscales could be considered as substantial to very strong. The associations between the score in the individual YAACQ subscales and the PHQ-9 score were shown to be low to medium. In general, SELF-P and SELF-C could be considered the most


*Sex Differences between Young Adults in the Czech and Slovak Republics… DOI: http://dx.doi.org/10.5772/intechopen.96469*

### **Table 2.** *Descriptive statistics.*

predictive subscales in terms of the PHQ-9 score. However, the differences in the correlation of individual scales were often very small. Due to the fact that the increased rate of associations between individual subscales was also confirmed, a simple regression model was applied in the following part focused on regression analysis.

The application of regression analysis (OLS) itself was determined by several tests of assumptions. The Bonferroni outlier test did not show any outliers at the level of α < 0.01, but the Breusch-Pagan test identified all the models as heteroskedastic, at the mentioned level of significance. Therefore, a white estimator of HC3 was used to assess the effect.

The results in **Table 4** show that all the associations were significant at the level of significance α < 0.001. Simultaneously, the coefficients were positive, which


*Note: Above the diagonal is a parametric alternative (Pearson's r) and below the diagonal is a nonparametric alternative (Spearman's ρ). The p-values are not shown, as all of the analyzed relationships were significant at the level of α < 0.001.*

### **Table 3.** *Correlation analyses.*

means that an increased risk of depressive disorder can be associated with experience in selected dimensions of alcohol-related consequences. The multiple R2 showed relatively low values, which can be considered a certain limitation.

The most important output of the presented study is shown in **Table 5**, which is devoted to the assessment of the effects of the score in the individual YAACQ


*Sex Differences between Young Adults in the Czech and Slovak Republics… DOI: http://dx.doi.org/10.5772/intechopen.96469*

*† p-value <0.001.*

### **Table 4.**

*Assessment of the effects of YAACQ on PHQ-9 (OLS model).*


*Note: SE – standard error;*

*\* p-value <0.1;*

*\*\* p-value <0.05;*

*\*\*\* p-value <0.01;*

*† p-value <0.00;*

*non-significant associations are highlighted in bold.*

### **Table 5.**

*Assessment of the effects of YAACQ on PHQ-9 (quantile regression).*

subscales on the PHQ-9 score. When assessing the results, it is most appropriate to focus on the β coefficients (independent variable). The standard error should also be taken into account when interpreting, while the lower the value, the more stable the model. This has the highest added value when comparing the β coefficients in males and females, while the results revealed lower values predominantly in the female models. Based on the results, the significant effects of alcohol-related consequences on depressive disorder can be clearly confirmed in the vast majority of analyzed cases. The associations that cannot be considered significant at the level of α < 0.05 were found only in ten cases (highlighted in bold). In all cases that supported to be significant, positive regression coefficients were found, which can be understood in the sense that alcohol-related consequences may be a risk factor for depressive disorder. In other words, an increased PHQ-9 score may be associated with an increased score in the YAACQ subscales. By focusing on the differences between females and males, the results suggested that females had a higher intensity of effects based on regression coefficients.

### **5. Discussion**

In order to develop successful addiction diagnosis programs and strategies, it is necessary to know the variables that predict the development of substance use problems. This study contributed to this knowledge, as diagnostic information on university students with alcohol problems in the Czech and Slovak Republics could be useful for the development of effective prevention strategies in addiction issue. Although many countries have effective strategies and can be an inspiration, the countries examined in this study implement active interventions and prevention aimed at students to a very small extent. This issue is little discussed at professional, political and social level, and therefore, the need to address it is overlooked. There is a lack of support for university counseling centres for students and, in addition, information on the current situation is insufficient.

The successful development of addiction prevention policy requires the availability of multidimensional analyses and the creation of specific databases that would make it possible to assess the effectiveness of policy in individual geographically defined areas [68]. Many national researches were initiated within international institutions [69]. These facts were the motivation for conducting research in national conditions. The main aim of the presented study was to assess the relationship between alcohol-related consequences and depressive disorder. This aim was met in a sample of university students from the Czech and Slovak Republics.

Based on the results of the descriptive analysis, it can be concluded that males acquired a higher score in all subscales of alcohol-related consequences. Similarly, Geisner et al. [23] revealed more alcohol-related consequences for male students and Merrill et al. [20] found significant sex differences in risky behaviors (RISK) and academic/occupational consequences (AC-OCC). This is consistent with the findings of Lemley et al. [22], who revealed that sex was a significant predictor for negative alcohol-related consequences, while male students tended to obtain a higher YAACQ score. This may be explained by the fact that male students are more prone to excessive alcohol consumption than female students [23], while their heavier drinking can be associated with alcohol-related consequences [70]. Males also perceived alcohol-related consequences less negatively than females [71]. In this regard, females were less likely to be exposed to risk factors and alcohol-related consequences. In contrast, protective factors against alcohol-related consequences predominated in females, as they perceived greater social sanctions for drinking and they were less likely to have characteristics associated with excessive drinking, such *Sex Differences between Young Adults in the Czech and Slovak Republics… DOI: http://dx.doi.org/10.5772/intechopen.96469*

as aggressiveness, uncontrollable behavior, sensation-seeking, and others [72]. On the other hand, there is also interesting evidence that although female students consumed less alcohol, they were more likely to experience negative consequences when drinking [73]. This can be explained by the fact that females have less alcohol dehydrogenase than males, they are less efficient at metabolizing alcohol and thus more vulnerable to its effects.

In the Czech and Slovak regions, students acquired the highest percentage to the maximum score in blackouts (41.67%), while different values could be found in other countries, such as the United States (56.71%) [21], Argentina (26%) and Spain (29,43%) [24]. In terms of impaired control, Czech and Slovak students obtained a comparable percentage to its maximum score (34.91%) as students from the mentioned countries (the United States = 35.33% [21]; Argentina = 35.8%; Spain = 28.8% [24]). Similar results with students from the United States (32%) [21] could be observed in risky behaviors (the Czech and Slovak Republics = 34.58%).

Regarding depression, female students acquired a higher PHQ-9 score than their male counterparts. This fact follows the evidence that depressive disorder (PHQ-9) is more common in females [74]. There are several other findings confirming that depression is a more frequent mental problem for females [30], while sex predispositions to mental disorders remain unclear. Albert [75] tried to explain this on the basis of biological factors that may contribute to a higher prevalence of depression in females as well as to their mental vulnerability. There are also insights into this issue that addressed sex differences in depression during adolescence, and their results showed that the causes of depression were more common in females, who were also more likely to develop risk factors for depressive disorder than males [76]. In the context of the main idea of this study, it should be noted that the main risk factors for depression among female students include low economic status, chronic illness, and unhealthy patterns of behavior [77].

In general, the results in this study also showed that 29.8%, 11.6%, 5.5% and 3.1% of students suffered from mild, moderate, moderately severe and severe depression. This can be compared with the findings of Hajduk et al. [35], who identified depression in 35.5% of Slovak students. For comparison, similar results were found in university students from the United Kingdom [38]. Croatian students also reported depressive disorder with a similar prevalence (mild = 30.3%, moderate = 16.1%, moderately severe = 7.2%, severe = 0.2%) [37]. Students from the United States had mild to moderate depression in a prevalence of 37.7%, and 4.4% of students suffered from severe depression [31]. This suggests that depression in Slovak and Czech students reached a comparable level with other countries even during the COVID-19 pandemic. By comparing depression, the measured scores did not differ much from the scores before the COVID-19 pandemic [35]. Due to the lack of information on the YAACQ score obtained by Czech and Slovak students, it was not possible to compare the values before the COVID-19 pandemic. At the same time, there is a need to compare the reported depression and the consequences of alcohol use after the COVID-19 pandemic.

The results also indicated the existence of significant and positive associations between all the examined indicators (individual alcohol-related consequences, depression), while the associations between the consequences of alcohol use and depressive disorder were identified in low to medium intensity. The findings in this study agree with the findings of other international studies that have revealed that depression is associated with alcohol-related consequences [42, 45]. This was also confirmed between psychological discomfort, such as distress, and the YAACQ score [46]. In this study, a stronger association was found in female students. Also, similar results were revealed in regression models, and the significant effects of alcohol-related consequences on depressive disorder were confirmed in the vast

majority of the analyzed cases. Correlation analysis as well as regression analysis provided an output with positive coefficients, meaning that the increased risk of depressive disorder can be associated with experience in selected dimensions of alcohol-related consequences. Specifically, the findings of Geisner et al. [23] showed a stronger association between psychological symptoms and alcohol-related consequences in males, who were also characterized by higher alcohol consumption and more negative alcohol-related consequences. The results presented in this study partially agree with the findings of Schutte et al. [53], who revealed that alcoholrelated consequences could cause depression in males, but not in females. On the contrary, the results of this study support the findings of Rosenthal et al. [54], who revealed that experiencing negative alcohol-related consequences (BYAACQ) may lead to a higher risk of depression (PHQ-9) among female students. The study conducted by Miller et al. [78] should also be emphasized, while their findings showed that blackouts were associated with other consequences of alcohol use (BYAACQ), which in turn were associated with depressive disorder (PHQ-8). In other words, blackouts showed direct and indirect effects on depression in young adults [78]. Focusing on other alcohol-related consequences, the experience of casual sex also increased depressive symptoms in females more than in males [55], while guilt, remorse, feelings that violated societal expectations played an important role in this situation [56]. Last but not least, the findings of this study are close to the knowledge that alcohol use disorder may increase the risk of subsequent depressive disorder [40, 41].

These findings could be useful to support the development of diagnostic variables for alcohol addiction in Czech and Slovak university students. Effective diagnostic measures and prevention programs exist in many other regions, which can be an inspiration. The examined regions should implement them in the university environment. It seems that strategies should also include monitoring the consequences of alcohol use and subsequent depressive symptoms, while experts should distinguish between the individual dimensions of the consequences. The idea is whether chronic alcohol use causes brain changes associated with depression [79], or whether there are genetic or other variables that could explain the relationship revealed in this study. The findings provide great potential for clinical and diagnostic research, that can build on this study.

### **6. Conclusion and implications**

The results of this study represent valuable outputs for national policy makers, as well as for national and international research communities, whose ambition is to examine psychological and behavioral predictors and sex differences in addictive behaviors. All of the above-mentioned findings suggested that the consequences of alcohol use should be an integral part of policy and professional discussions on young people's mental health, as these consequences can be an important factor in the increased risk of depressive disorder. Also, students represent a group of the population of society that is to become an active productive part of it, and they represent potential current and future health care, criminal justice, and social burdens as well. Therefore, it is necessary to point out these aspects and to support the development of prevention addiction programs for this population group as well.

For this reason, public health policies should be strengthened in order to raise people's awareness of the threats of alcohol use and other consequences that may affect the mental health of individuals. Policy makers should strive to integrate mental health into all aspects of social and health policies, strategies and interventions. This research supports the idea that alcohol use with a risk of addiction and

*Sex Differences between Young Adults in the Czech and Slovak Republics… DOI: http://dx.doi.org/10.5772/intechopen.96469*

consequences contributes negatively to the symptoms of depression in young people. Specifically, the dimensions of self-perception and self-care appear to be the most important among Slovak and Czech young adults. This fact requires a special attention in the development of diagnostic procedures and the implementation of interventions in the field of addiction in these geographical regions. As the YAACQ is able to predict drinking patterns [10] and the level of drinking risk [11], it is welcome if the dimensions of alcohol-related consequences are also taken into account in alcohol addiction policies. In fact, prevention and education should also play a key role in universities and counseling centres. It would be beneficial if prevention programs for female students focused on coping and overcoming depressive symptoms. On the other hand, there is a need to focus on helping male students with alcohol use disorders and the subsequent consequences of drinking. In general, there is a lack of university counseling centres to help students overcome difficult situations in their lives. Professionals providing adequate help should also focus on the individual alcohol-related consequences as an accompanying aspect of diagnosing alcohol addiction, which would also lead to the prevention of mental disorders such as depression. Promoting an active lifestyle will continue to play an important role in this issue.

In the regions of the Czech and Slovak Republics, alcohol use disorders, together with the consequences of drinking, do not have the necessary attention and are overlooked not only at universities, but also in society as a whole. It is important to be aware of the importance of this issue at both professional and political level. Interventions are urgently needed to prevent young people from becoming addicted.

Health literacy is also considered as an important part of prevention programs at various stages of addiction [80–82]. In the Slovak and Czech Republics, there is no health literacy system that would include specific programs for different population groups [83]. Therefore, it is very difficult to estimate the extent to which the emergence and development of addiction among university students is determined by an insufficient level of health literacy (or its complete absence) and the extent to which it is determined by socio-economic factors. The family and the quality of the previous educational process also play an important role in problem drinking.

In any case, regional differences in addiction need to be examined, while it is also important to examine the consequences of alcohol use, the level of addiction, the mental health of the population and the economic parameters of the region, which may provide greater opportunities for addiction [84]. The ambition in this differentiating perspective was to point out the sex-differentiated effects of problem drinking with consequences, specifically in terms of the higher prevalence of depression in females than in males. Taking these differences into account can also significantly support the development of sex-differentiated services and programs [4, 85, 86]. From an international perspective, the European Monitoring Centre for Drugs and Drug Addiction (EMCDDA) [87] also pointed to the limited nature of services specialized for females. Although there is at least one such facility in almost every EU country, the demand is much higher and it is therefore necessary to ensure greater availability of specialized health care.

One of the strengths of this study is the fact that the research was conducted in a relatively large geographically area. The sample size is not negligible, as the sample in this study covered the vast majority of Czech and Slovak universities. The study also has several weaknesses that need to be taken into account when interpreting the results. The limitations of the presented research may include the fact that the sample was not random, and thus certain questions may arise regarding the representativeness of the research sample. Given the size of the sample and the fact that quota sampling was used, it was not expected that the findings would be

significantly distorted due to the non-random sampling. Also, the questionnaire was distributed electronically and there is no guarantee that students have read all the attached information, which can be considered a limitation. There was also a possible limitation when comparing the results with the results of other authors, as a multilevel scale was used in this research, which is perceived as more accurate. It is not known how the COVID pandemic influenced access to alcohol, and this may be a weakness of the study.

As already emphasized, there is a need for scientific interest in the issue of addictive behavior and mental disorders. Future research will focus on uncovering other hidden differences in terms of health, mental disorders and alcohol use. In more detail, it is planned to compare these indicators between different fields of study. The ambition is to extend the research sample and to make a comparison between all countries belonging to the Visegrad Group. The time frame of data collection in this research also provides space for comparing the results aimed at evaluating changes in the addictive behavior of university students during the COVID-19 pandemic with the period after this pandemic situation. The international dimension of the research will provide an insight into the strength of the impact of the socio-economic changes caused by the COVID-19 pandemic on the emergence and development of the young generation's addiction.

### **Acknowledgements**

This research was supported by the Internal Grant Agency of FaME Tomas Bata University in Zlin: RVO/2020: "Economic quantification of marketing processes that focus on value increase for a patient in a process of system creation to measure and control efficiency in health facilities in the Czech Republic".

### **Conflict of interest**

The authors have no conflict of interest, financial or otherwise. The funders had no role in preparing the study; in data processing; in writing the manuscript or deciding on the publication of the results.

### **Notes/thanks/other declarations**

We thank the students for their participation in the research. We also thank the university representatives, scientific, pedagogical and administrative workers for their willingness to distribute the questionnaire.

*Sex Differences between Young Adults in the Czech and Slovak Republics… DOI: http://dx.doi.org/10.5772/intechopen.96469*

### **Author details**

Beata Gavurova<sup>1</sup> \*, Martin Rigelsky<sup>2</sup> and Viera Ivankova<sup>2</sup>

1 Center for Applied Economic Research, Faculty of Management and Economics, Tomas Bata University in Zlin, Zlin, Czech Republic

2 Faculty of Management, University of Presov in Presov, Presov, Slovakia

\*Address all correspondence to: gavurova@utb.cz

© 2021 The Author(s). Licensee IntechOpen. This chapter is distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/ by/3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

### **References**

[1] Prince M, Patel V, Saxena S, Maj M, Maselko J, Phillips MR, Rahman A. No health without mental health. The Lancet. 2007;370(9590):859–877. DOI: 10.1016/s0140-6736(07)61238-0

[2] Doran CM, Kinchin I. A review of the economic impact of mental illness. Australian Health Review. 2019;43(1): 42–48. DOI: 10.1071/ah16115

[3] Schnitzerova E, Antonicova L. Gender aspects in the context of cosial work and services for drug users. Alkoholizmus a drogové závislosti - Protialkoholický obzor. 2011;46(4):241– 251. Available from: https://www.adzpo. sk/images/articles/adzpo-2011-46- 4-241-251.pdf [Accessed: 2020-12-29]

[4] Kalina K. Ženy a muži jako specifické cílové skupiny. In: Kalina K, et al. editors. Základy klinické adiktológie. 1st ed. Praha: Grada; 2008. p. 265–274. ISBN 978–80–247-1411-0

[5] Kim SS, Lee HO, Kiang P, Kalman D, Ziedonis DM. Factors associated with alcohol problems among Asian American college students: gender, ethnicity, smoking and depressed mood. Journal of Substance Use. 2013;19(1–2):12–17. DOI: 10.3109/14659891.2012.709912

[6] OECD. Health at a Glance 2019: OECD Indicators [Internet]. 2019. Paris: OECD Publishing. Available from: h ttps://doi.org/10.1787/4dd50c09-en [Accessed: 2020-12-29]

[7] Skogen JC, Bøe T, Thørrisen MM, Riper H, Aas RW. Sociodemographic characteristics associated with alcohol consumption and alcohol-related consequences, a latent class analysis of The Norwegian WIRUS screening study. BMC Public Health. 2019;19(1). DOI:10.1186/s12889-019-7648-6

[8] Rehm J, Room R. The cultural aspect: How to measure and interpret

epidemiological data on alcohol-use disorders across cultures. Nordisk Alkohol- & Narkotikatidskrift. 2017;34 (4):330–341. DOI: 10.1177/ 1455072517704795

[9] Pilatti A, Read JP, Caneto F. Validation of the Spanish version of the Young Adult Alcohol Consequences Questionnaire (S-YAACQ). Psychological Assessment. 2016;28(5): e49–e61. DOI: 10.1037/pas0000140

[10] Read JP, Merrill JE, Kahler CW, Strong DR. Predicting functional outcomes among college drinkers: Reliability and predictive validity of the Young Adult Alcohol Consequences Questionnaire. Addictive Behaviors. 2007;32(11):2597–2610. DOI: 10.1016/j. addbeh.2007.06.021

[11] Read JP, Haas AL, Radomski S, Wickham RE, Borish SE. Identification of hazardous drinking with the Young Adult Alcohol Consequences Questionnaire: Relative operating characteristics as a function of gender. Psychological Assessment. 2016;28(10): 1276–1289. DOI: 10.1037/pas0000251

[12] Keough MT, O'Connor RM, Read JP. Replication and validation of the Young Adult Alcohol Consequences Questionnaire in a large sample of Canadian undergraduates. Alcoholism-Clinical and Experimental Research. 2016; 40(5):1093–1099. DOI: 10.1111/acer.13039

[13] Merrill JE, Carey KB. Drinking over the lifespan: Focus on college ages. Alcohol Research-Current Reviews. 2016;38(1):103–114.

[14] Tembo C, Burns S, Kalembo F. The association between levels of alcohol consumption and mental health problems and academic performance among young university students. Plos One. 2017;12(6):e0178142. DOI: 10.1371/journal.pone.0178142

*Sex Differences between Young Adults in the Czech and Slovak Republics… DOI: http://dx.doi.org/10.5772/intechopen.96469*

[15] dos Reis TG, de Oliveira LCM. Alcohol consumption among students of a Brazilian public university and consequences associated with this consumption. Bioscience Journal. 2017; 33(5):1371–1380.

[16] Larimer ME, Cronce JM. Identification, prevention, and treatment revisited: Individual-focused college drinking prevention strategies 1999–2006. Addictive Behaviors. 2007; 32(11):2439–2468. DOI: 10.1016/j. addbeh.2007.05.006

[17] Lamis DA, Ballard ED, May AM, Dvorak RD. Depressive symptoms and suicidal ideation in college students: The mediating and moderating roles of hopelessness, alcohol problems, and social support. Journal of Clinical Psychology. 2016;72(9):919–932. DOI: 10.1002/jclp.22295

[18] Murphy JG, Barnett NP, Goldstein AL, Colby SM. Gender moderates the relationship between substance-free activity enjoyment and alcohol use. Psychology of Addictive Behaviors. 2007; 21(2):261–265. DOI: 10.1037/0893-164x.21.2.261

[19] Bravo AJ, Pearson MR, Pilatti A, Read JP, Mezquita L, Ibanez MI, Ortet G. Impulsivity-related traits, college alcohol beliefs, and alcohol outcomes: Examination of a prospective multiple mediation model among college students in Spain, Argentina, and USA. Addictive Behaviors. 2018;81: 125–133. DOI: 10.1016/j. addbeh.2018.02.009

[20] Merrill JE, Wardell JD, Read JP. Drinking motives in the prospective prediction of unique alcohol-related consequences in college students. Journal of Studies on Alcohol and Drugs. 2014;75(1):93–102. DOI: 10.15288/ jsad.2014.75.93

[21] Read JP, Kahler CW, Strong DR, Colder CR. Development and

preliminary validation of the Young Adult Alcohol Consequences Questionnaire. Journal of Studies on Alcohol. 2006;67(1):169–177. DOI: 10.15288/jsa.2006.67.169

[22] Lemley SM, Kaplan BA, Reed DD, Darden AC, Jarmolowicz DP. Reinforcer pathologies: Predicting alcohol related problems in college drinking men and women. Drug and Alcohol Dependence. 2016;167:57–66. DOI: 10.1016/j. drugalcdep.2016.07.025

[23] Geisner IM, Larimer ME, Neighbors C. The relationship among alcohol use, related problems, and symptoms of psychological distress: Gender as a moderator in a college sample. Addictive Behaviors. 2004;29 (5):843–848. DOI: 10.1016/j. addbeh.2004.02.024

[24] Bravo AJ, Pilatti A, Pearson MR, Read JP, Mezquita L, Ibanez MI, Ortet G. Cross-cultural examination of negative alcohol-related consequences: Measurement invariance of the Young Adult Alcohol Consequences Questionnaire in Spain, Argentina, and USA. Psychological Assessment. 2019;31 (5):631–642. DOI: 10.1037/pas0000689

[25] Ibrahim AK, Kelly SJ, Adams CE, Glazebrook C. A systematic review of studies of depression prevalence in university students. Journal of Psychiatric Research. 2013;47(3):391– 400. DOI: 10.1016/j. jpsychires.2012.11.015

[26] Ludwig AB, Burton W, Weingarten J, Milan F, Myers DC, Kligler B. Depression and stress amongst undergraduate medical students. BMC Medical Education. 2015;15(1):141. DOI: 10.1186/s12909-015-0425-z

[27] Awadalla S, Davies EB, Glazebrook C. A longitudinal cohort study to explore the relationship between depression, anxiety and academic performance among Emirati university students. BMC Psychiatry. 2020;20(1):448. DOI: 10.1186/ s12888-020-02854-z

[28] Dvorak RD, Lamis DA, Malone PS. Alcohol use, depressive symptoms, and impulsivity as risk factors for suicide proneness among college students. Journal of Affective Disorders. 2013;149 (1–3):326–334. DOI: 10.1016/j. jad.2013.01.046

[29] Asante KO, Andoh-Arthur J. Prevalence and determinants of depressive symptoms among university students in Ghana. Journal of Affective Disorders. 2015;171:161–166. DOI: 10.1016/j.jad.2014.09.025

[30] Angst J, Gamma A, Gastpar M, Lepine JP, Mendlewicz J, Tylee A. Gender differences in depression: Epidemiological findings from the European DEPRES I and II studies. European Archives of Psychiatry and Clinical Neuroscience. 2002;252(5):201– 209. DOI: 10.1007/s00406-002-0381-6

[31] Leppink EW, Lust K, Grant JE. Depression in university students: Associations with impulse control disorders. International Journal of Psychiatry in Clinical Practice. 2016;20 (3):146–150. DOI: 10.1080/ 13651501.2016.1197272

[32] Kroenke K, Spitzer RL, Williams JBW. The PHQ-9: Validity of a brief depression severity measure. Journal of General Internal Medicine. 2001;16(9):606–613. DOI: 10.1046/ j.1525-1497.2001.016009606.x

[33] Kroenke K, Spitzer RL, Williams JBW, Lowe B. The Patient Health Questionnaire somatic, anxiety, and depressive symptom scales: A systematic review. General Hospital Psychiatry. 2010;32(4):345–359. DOI: 10.1016/j.genhosppsych.2010.03.006

[34] Kroenke K, Spitzer RL. The PHQ-9: A new depression diagnostic and

severity measure. Psychiatric Annals. 2002;32(9):509–515. DOI: 10.3928/ 0048-5713-20020901-06

[35] Hajduk M, Heretik A, Vaseckova B, Forgacova L, Pecenak J. Prevalence and correlations of depression and anxiety among Slovak college students. Bratislava Medical Journal. 2019;120(9): 695–698. DOI: 10.4149/BLL\_2019\_117

[36] Dansova P, Masopustova Z, Hanackova V, Kickova K, Korabova I. The Patient Health Questionnaire-9: the Czech version. 2016;60(3):468–481.

[37] Milic J, Skrlec I, Vranjes IM, Podgornjak M, Heffer M. High levels of depression and anxiety among Croatian medical and nursing students and the correlation between subjective happiness and personality traits. International Review of Psychiatry. 2019;31(7–8):653–660. DOI: 10.1080/ 09540261.2019.1594647

[38] Honney K, Buszewicz M, Coppola W, Griffin M. Comparison of levels of depression in medical and nonmedical students. The Clinical Teacher. 2010;7(3):180–184. DOI: 10.1111/ j.1743-498x.2010.00384.x

[39] Weitzman ER. Poor mental health, depression, and associations with alcohol consumption, harm, and abuse in a national sample of young adults in college. The Journal of Nervous and Mental Disease. 2004;192(4):269–277. DOI: 10.1097/01. nmd.0000120885.17362.94

[40] Boden JM, Fergusson DM. Alcohol and depression. Addiction. 2011;106(5): 906–914. DOI: 10.1111/ j.1360-0443.2010.03351.x

[41] Li J, Wang H, Li M, Shen Q, Li X, Zhang Y, Peng J, Rong X, Peng Y. Effect of alcohol use disorders and alcohol intake on the risk of subsequent depressive symptoms: a systematic review and meta-analysis of cohort

*Sex Differences between Young Adults in the Czech and Slovak Republics… DOI: http://dx.doi.org/10.5772/intechopen.96469*

studies. Addiction. 2020;115(7):1224– 1243. DOI: 10.1111/add.14935

[42] Martens MP, Martin JL, Hatchett ES, Fowler RM, Fleming KM, Karakashian MA, Cimini MD. Protective behavioral strategies and the relationship between depressive symptoms and alcohol-related negative consequences among college students. Journal of Counseling Psychology. 2008; 55(4):535–541. DOI: 10.1037/a0013588

[43] Park CL, Grant C. Determinants of positive and negative consequences of alcohol consumption in college students: alcohol use, gender, and psychological characteristics. Addictive Behaviors. 2005;30(4):755–765. DOI: 10.1016/j. addbeh.2004.08.021

[44] Kenney SR, Lac A, LaBrie JW, Hummer JF, Pham A. Mental Health, Sleep Quality, Drinking Motives, and Alcohol-Related Consequences: A Path-Analytic Model. Journal of Studies on Alcohol and Drugs. 2013;74 (6):841–851. DOI: 10.15288/ jsad.2013.74.841

[45] Preonas PD, Lau-Barraco C. Affective factors explaining the association between depressive functioning and alcohol outcomes among college students. Journal of American College Health. 2019. DOI: 10.1080/07448481.2019.1683565

[46] Ruiz P, Pilatti A, Pautassi RM. Consequences of alcohol use, and its association with psychological distress, sensitivity to emotional contagion and age of onset of alcohol use, in Uruguayan youth with or without college degree. Alcohol. 2020;82:91–101. DOI: 10.1016/j.alcohol.2019.09.001

[47] Flesch BD, Houvessou GM, Munhoz TN, Fassa AG. Major depressive episode among university students in Southern Brazil. Revista de Saude Publica. 2020;54:11. DOI: 10.11606/s1518-8787.2020054001540 [48] Dennhardt AA, Murphy JG. Associations between depression, distress tolerance, delay discounting, and alcohol-related problems in European American and African American college students. Psychology of Addictive Behaviors. 2011;25(4):595– 604. DOI: 10.1037/a0025807

[49] Wang J, Patten SB. A prospective study of sex-specific effects of major depression on alcohol consumption. The Canadian Journal of Psychiatry. 2001;46 (5):422–425. DOI: 10.1177/ 070674370104600507

[50] Kuo PH, Gardner CO, Kendler KS, Prescott CA. The temporal relationship of the onsets of alcohol dependence and major depression: using a genetically informative study design. Psychological Medicine. 2006;36(8):1153. DOI: 10.1017/s0033291706007860

[51] Fergusson DM, Boden JM, Horwood LJ. Tests of causal links between alcohol abuse or dependence and major depression. Archives of General Psychiatry. 2009;66(3):260. DOI: 10.1001/archgenpsychiatry.2008.543

[52] Hasin DS, Grant BF. Major depression in 6050 former drinkers: Association with past alcohol dependence. Archives of General Psychiatry. 2002;59(9):794. DOI: 10.1001/archpsyc.59.9.794

[53] Schutte KK, Hearst J, Moos RH. Gender differences in the relations between depressive symptoms and drinking behavior among problem drinkers: A three-wave study. Journal of Consulting and Clinical Psychology. 1997;65(3):392–404. DOI: 10.1037/ 0022-006x.65.3.392

[54] Rosenthal SR, Clark MA, Marshall BDL, Buka SL, Carey KB, Shepardson RL, Carey MP. Alcohol consequences, not quantity, predict major depression onset among first-year female college students. Addictive

Behaviors. 2018;85:70–76. DOI: 10.1016/ j.addbeh.2018.05.021

[55] Grello CM, Welsh DP, Harper MS. No strings attached: The nature of casual sex in college students. Journal of Sex Research. 2006;43(3),255–267. DOI: 10.1080/00224490609552324

[56] Foster C, Caravelis C, Kopak A. National College Health Assessment Measuring Negative Alcohol-Related Consequences among College Students. American Journal of Public Health Research. 2014;2(1):1–5. DOI: 10.12691/ ajphr-2-1-1

[57] Panno A, Carbone GA, Massullo C, Farina B, Imperatori C. COVID-19 related distress is associated with alcohol problems, social media and food addiction symptoms: Insights from the italian experience during the lockdown. Frontiers in Psychiatry. 2020;11:577135. DOI: 10.3389/fpsyt.2020.577135

[58] Son C, Hegde S, Smith A, Wang X, Sasangohao F. Effects of COVID-19 on college students' mental health in the United States: interview survey study. Journal of Medical Internet Research. 2020;22(9):e21279. DOI: 10.2196/21279

[59] Ramalho R. Alcohol consumption and alcohol-related problems during the COVID-19 pandemic: a narrative review. Australasian Psychiatry. 2020;28(5):524– 526. DOI: 10.1177/1039856220943024

[60] Adewuya AO, Ola BA, Afolabi OO. Validity of the patient health questionnaire (PHQ-9) as a screening tool for depression amongst Nigerian university students. Journal of Affective Disorders. 2006;96(1–2):89–93. DOI: 10.1016/j.jad.2006.05.021

[61] Du N, Yu K, Ye Y, Chen S. Validity study of Patient Health Questionnaire-9 items for Internet screening in depression among Chinese university students. Asia-Pacific Psychiatry. 2016;9 (3):e12266. DOI: 10.1111/appy.12266

[62] Farrer LM, Gulliver A, Bennett K, Fassnacht DB, Griffiths KM. Demographic and psychosocial predictors of major depression and generalised anxiety disorder in Australian university students. BMC Psychiatry. 2016;16(1). DOI: 10.1186/ s12888-016-0961-z

[63] Zhou Y, Xu J, Rief W. Are comparisons of mental disorders between Chinese and German students possible? An examination of measurement invariance for the PHQ-15, PHQ-9 and GAD-7. BMC Psychiatry. 2020;20:480. DOI: 10.1186/s12888-020-02859-8

[64] Zajenkowska A, Jasielska D, Melonowska J. Stress and sensitivity to frustration predicting depression among young adults in Poland and Korea - Psychological and philosophical explanations. Current Psychology. 2019; 38:769–774. DOI: 10.1007/s12144-017- 9654-0

[65] Corbin WR, Waddell JT, Ladensack A, Scott C. I drink alone: Mechanisms of risk for alcohol problems in solitary drinkers. Addictive Behaviors. 2020;102:106147. DOI: 10.1016/j.addbeh.2019.106147

[66] World Medical Association. Declaration of Helsinki—Ethical principles for medical research involving human subjects [Internet]. 2013. Available from: https://www.wma .net/policies-post/wma-declaration-ofhelsinki-ethical-principles-for-medica l-research-involving-human-subjects/ [Accessed: 2020-12-24]

[67] International Society of Addiction Journal Editors. The Farmington Consensus [Internet]. 2017. Available from: http://www.isaje.net/farmingtonconsensus.html [Accessed: 2020-12-24]

[68] Sopko J, Kocisova K. Key indicators and determinants in the context of the financial aspects of health systems in selected countries. Adiktologie. 2019;19

*Sex Differences between Young Adults in the Czech and Slovak Republics… DOI: http://dx.doi.org/10.5772/intechopen.96469*

(4):189–202. DOI: 10.35198/01-2019- 004-0003

[69] Megyesiova S, Gavurova B. Multivariate analysis of alcohol consumption and death rates resulting from alcohol consumption in EU and OECD member states. Adiktologie. 2019;19(4):179–187. DOI: 10.35198/ 01-2019-004-0002

[70] Foster DW, Young CM, Bryan J, Steers MLN, Yeung NCY, Prokhorov AV. Interactions among drinking identity, gender and decisional balance in predicting alcohol use and problems among college students. Drug and Alcohol Dependence. 2014;143:198–205. DOI: 10.1016/j.drugalcdep.2014.07.024

[71] Gaher RM, Simons JS. Evaluations and expectancies of alcohol and marijuana problems among college students. Psychology of Addictive Behaviors. 2007;21(4):545–554. DOI: 10.1037/0893-164x.21.4.545

[72] Nolen-Hoeksema S. Gender differences in risk factors and consequences for alcohol use and problems. Clinical Psychology Review. 2004;24(8):981–1010. DOI: 10.1016/j. cpr.2004.08.003

[73] Rose PA, Schuckman HE, Oh SS, Park EC. Associations between gender, alcohol use and negative consequences among Korean college students: A national study. International Journal of Environmental Research and Public Health. 2020;17(14):5192. DOI: 10.3390/ ijerph17145192

[74] Ngasa SN, Sama CB, Dzekem BS, Nforchu KN, Tindong M, Aroke D, Dimala CA. Prevalence and factors associated with depression among medical students in Cameroon: A cross-sectional study. BMC Psychiatry. 2017;17(1):216. DOI: 10.1186/s12888-017-1382-3

[75] Albert P. Why is depression more prevalent in women? Journal of

Psychiatry & Neuroscience. 2015;40(4): 219–221. DOI: 10.1503/jpn.150205

[76] Nolen-Hoeksema S, Girgus JS. The emergence of gender differences in depression during adolescence. Psychological Bulletin. 1994;115(3): 424–443. DOI: 10.1037/ 0033-2909.115.3.424

[77] Acikgoz A, Dayi A, Binbay T. Prevalence of depression among female university students and associated factors. Cukurova Medical Journal. 2018;43(1):131–140. DOI: 10.17826/ cumj.340629

[78] Miller MB, DiBello AM, Merrill JE, Neighbors C, Carey KB. The role of alcohol-induced blackouts in symptoms of depression among young adults. Drug and Alcohol Dependence. 2020;211: 108027. DOI: 10.1016/j. drugalcdep.2020.108027

[79] Shor C, Zuo W, Eloy JD, Ye J-H. The Emerging Role of LHb CaMKII in the Comorbidity of Depressive and Alcohol Use Disorders. International Journal of Molecular Sciences. 2020;21(21):8123. DOI: 10.3390/ijms21218123

[80] Degan TJ, Kelly PJ, Robinson LD, et al. Health literacy in substance use disorder treatment: A latent profile analysis. Journal of Substance Abuse Treatment. 2019;96:46–52. DOI: 10.1016/j.jsat.2018.10.009

[81] Aaby A, Friis K, Bo C, Rowlands G, Maindal HT. Health literacy is associated with health behaviour and self-reported health: A large populationbased study in individuals with cardiovascular disease. European Journal of Preventive Cardiology. 2017; 24(17):1880–1888. DOI: 10.1177/ 2047487317729538

[82] Bostock S, Steptoe A. Association between low functional health literacy and mortality in older adults: Longitudinal cohort study. BMJ-British Medical Journal. 2012;344:e1602. DOI: 10.1136/bmj.e1602

[83] Rolova G, Gavurova B, Petruzelka B. Exploring health literacy in individuals with alcohol addiction: A mixed methods clinical study. International Journal of Environmental Research and Public Health. 2020;17 (18):6728. DOI: 10.3390/ijerph17186728

[84] Gavurova B, Kovac V, Kulhanek A, Bartak M. Territorial distribution of alcohol and drug addictions mortality concerning regional disparities in the Slovak Republic from year 1996 to year 2015. Adiktologie. 2019;19(3):125–134. DOI: 10.35198/01-2019-003-0005

[85] Kuruc A, Smitkova H. Rod/gender ako sociálna kategória v psychoterapii a poradenstve. Československá Psychologie. 2007;51(3):269–278.

[86] Miovsky M. Evaluace adiktologických programu a služeb. In: Kalina K, et al. editors. Základy klinické adiktológie. Praha: Grada; 2008. p. 307– 315. ISBN 978–80–247-1411-0

[87] EMCDDA. 2005. Differences in patterns of drug use between men and women. Technical datasheet [Internet]. 2005. Lisbon: European Monitoring Centre for Drugs and Drug Addiction. Available from: https://www.emcdda. europa.eu/system/files/publications/ 387/TDS\_gender\_64783.pdf [Accessed: 2020-12-29]

### **Chapter 4**

## Leveraging Advanced Analytics to Understand the Impact of the COVID-19 Pandemic on Trends in Substance Use Disorders

*Ewa J. Kleczyk, Jill Bana and Rishabh Arora*

### **Abstract**

Coronavirus disease (COVID-19) caused an overwhelming healthcare, economic, social, and psychological impact on the world during 2020 and first part of 2021. Certain populations, especially those with Substance Use Disorders (SUD), were particularly vulnerable to contract the virus and also likely to suffer from a greater psychosocial and psychological burden. COVID-19 and addiction are two conditions on the verge of a collision, potentially causing a major public health threat. There is surge of addictive behaviors (both new and relapse), including use of alcohol, nicotine, and recreational drugs. This book chapter analyzed the bidirectional relationship between COVID-19 and SUD by leveraging descriptive summaries, advanced analytics, and machine learning approaches. The data sources included healthcare claims dataset as well as state level alcohol consumption to help in investigating the bi-directional relationship between the two conditions. Results suggest that alcohol and nicotine use increased during the pandemic and that the profile of SUD patients included diagnoses and symptoms of COVID-19, depression and anxiety, as well as hypertensive conditions.

**Keywords:** COVID-19, pandemic, addiction, smoking, alcohol, opioid addiction, public health, advanced analytics, linear regression, machine learning model

### **1. Introduction**

The coronavirus disease (COVID-19) has caused a large healthcare, economic, and psychosocial impact on communities in the United States and around the world in 2020 and first part of 2021. Many communities, especially those with low income and Substance Use Disorders (SUD), were particularly vulnerable to contract the infection and likely to suffered from a greater economic and psychosocial burden [1].

Addiction, characterized by a range of mental, physical, and behavioral symptoms, claims the lives of millions of people every year around the world [2]. In their National Survey on Drug Use and Health, Substance Abuse and Mental Health Services Administration estimated that 22.6 million Americans, 12 years of age or older (9.2% of the population), have SUD, including alcohol and tobacco use [3]. Furthermore, the long-term treatment has challenges to due frequent relapse [4]. Alcohol consumption and drug addiction cost around 1.5% of the global burden of disease, and it can be as high as 5% in some countries, according to recent data [2].

### *Addictions - Diagnosis and Treatment*

There are two basic settings to treat SUD: inpatient and outpatient. The primary goal is for patients affected by addiction to be in the most effective, yet least restrictive environment that allows them to move along a continuum of care, depending on their personal and medical needs. There are four phases of SUD care: outpatient treatment, intensive outpatient treatment, residential treatment, and inpatient hospitalization [5].

Furthermore, SUD treatment programs are often designed based on three basic models:


Many patients, receiving SUD treatments, may have also problems in other areas of their life, including but not limited to: physical and mental health issues, relationship problems, inadequate social and work skills, as well as legal or financial challenges. As a result, the treatment options should aim to address the entire spectrum of issues, and not only treat the addiction component [5].

Even with the variety of treatment options for SUD, more than 6,000 people a month died from overdosing before the pandemic started in the US [6]. In addition to the continued loss of lives due to addiction, the pandemic also added other challenges for those suffering from SUD, resulting in additional 2,000 individuals a month dying from SUD between March and August 2020 [6]. The government COVID-19 based restrictions, like home confinement, caused enormous economic burden to communities in the US as well as around the world. Individuals and their families faced various unwelcome emotional, psychological, and behavioral challenges, including excessive substance abuse and depression [4], which further increased the risk for addiction. The COVID-19 related restrictions caused individuals to turn to smoking, alcohol, drugs, including opioids and synthetic drugs like Fentanyl, as well as gaming activities to deal with the COVID-19 pandemic [6–8].

On the other hand, individuals suffering from addiction were often also part of low income communities that already faced many significant challenges related to access to healthcare, quality education, and unemployment. They also were also more prone to contract infection during the COVID-19 pandemic due to their underlying comorbid conditions and immune system deficiencies [8, 9].

In this book chapter, the bi-directional correlation between the COVID-19 diagnoses and SUD was investigated, and insights were provided to better understand the impact of the pandemic on addiction occurrence. The research leveraged multiple analytics methods from descriptive statistics, through a simple linear regression, and selected machine learning models to analyze this relationship. The data sources utilized for the analysis included healthcare claims dataset and the state level alcohol consumption.

### **2. Literature review**

The COVID-19 pandemic caused limited social interactions for individuals around the world due to the strict national, state, and local governmental

restrictions [10, 11]. As a result of the restrictions, many individuals started using tobacco, alcohol, and other substances to help with stress related symptoms. On the other hand, the increased restrictions and home confinement reduced the substance exposure, but also resulted in more pronounced cravings and withdrawal effects in current users. Selected articles have cited a substantially increased number of drug – and alcohol – withdrawal cases and hospitalizations, which were potentially putting burden on the already strained health care systems [12, 13].

Opioid addiction and its management was often discussed SUD type in the COVID-19 era. Opioid addicts particularly faced a challenge due to difficulty in accessing healthcare services, imposed restrictions on prescription and over-thecounter drugs, closures of rehabilitation centers, and an increased risk of lifethreatening withdrawals [14]. While loosening of restrictions were recommended for home-based self-injections and long-acting formulations of methadone and buprenorphine to mitigate these problems, there was also fear of overdosing and fatalities [15, 16].

Due to the financial burden and an uncertain future as a result of the pandemic, gambling activities also increased to unprecedented levels [17, 18]. Eating disorders and compulsive buying were progressively being reported [19, 20]. COVID-19 pandemic created a vicious cycle of stress, depression, social isolation, anxiety, excess leisure time that led to surge of behavioral addictions, resulting in mood alterations, irritability, anxiety, and stress [19, 20].

### **3. Data and methodology overview**

There were multiple types of data utilized for this research. The first source of the data is represented by the healthcare claims database with the study time period from January 31, 2019 to December 31, 2020. Patient cohorts: study target and control were established, using SUD and COVID-19 ICD 10 diagnosis codes. The diagnoses codes are listed in the appendix. The healthcare claims dataset included diagnosis codes, medical and surgical codes, therapeutics and treatments prescribed at the transactional level. In addition, socioeconomic variables, including age, gender, race, education and incomes levels were leveraged to provide additional insights into the characteristics of patients with SUD during COVID-19 pandemic [21].

The second dataset employed for the study represented the State Level Alcohol Consumption trends for 2019 and 2020. The 2020 data was available, however, only through end of September. For this analysis, alcohol consumption data on per capita, alcohol sales from 19 states (Alaska, Arkansas, Colorado, Connecticut, Delaware, Florida, Illinois, Kansas, Kentucky, Louisiana, Massachusetts, Missouri, North Dakota, Oregon, Tennessee, Texas, Utah, Virginia, and Wisconsin) by type of alcoholic beverage was leveraged. Only information from the states noted above was used due to limited availability of data from other states [22].

A number of analytical methods was employed for the analysis from the rulesbased patient qualification criteria, descriptive statistics, linear regression analysis to machine learning algorithms in order to understand the bi-directional relationship between SUD trends and the COVID-19 surge.

### **3.1 Healthcare claims patient level database**

The healthcare claims database is an anonymous longitudinal patient data set that can help researchers, healthcare providers, and pharmaceutical companies in the design of research studies in order to aid comparisons of diagnosis and

treatment outcomes that represent individual patient-based experiences and interactions with the US healthcare system [21].

The healthcare claims database leveraged for this study consisted of medical, hospital, and prescription claims across all insurance payment types. As shown in **Figure 1**, the database covers more than 317 million patients in the US, spans over more than 17 years of medical health history, and includes more than 1.9 million healthcare providers [21]. The data elements used for the study included diagnoses codes for SUD and other comorbid conditions, procedures and treatments, payment types: commercial, Medicaid, Medicare and cash, along with patient sociodemographic characteristics like age, gender, race, education and income levels, as well as geography [21].

### **3.2 Methodology overview: Linear regression introduction**

One of the methods utilized to analyze the relationship between addiction and the COVID-19 pandemic was a linear regression approach. In statistics, linear regression is a linear method to modeling the relationship between a scalar response (dependent variable – y) and one or more explanatory variables (independent variables – x):

$$\mathbf{y} = \mathbf{f}(\mathbf{x}) \tag{1}$$

When there is only one explanatory variable, the regression is called a simple linear regression. When there are more than one independent variables, the process is called a multiple linear regression [23].

In a linear regression, the relationships are modeled using linear predictor functions, whose model parameters are estimated from the data [24]. Linear regression focuses on the conditional probability distribution of the response given the values of the predictors, which is the domain of multivariate analysis [24].

There are several metrics often leveraged to evaluate the model performance: Rsquared and F-statistic. R-squared also called the coefficient of determination is the proportion of the variance in the dependent variable that can be explained by the variation in the independent variable(s). The value of the metric ranges between 0 and 1, and the higher value represents a better performance of the model [24]. An F-test represents a statistical test often used when comparing statistical models employed on the studied datasets to identify the model that best fits the population from which the data sample was drawn [24].

**Figure 1.** *Healthcare claims patient level database description.*

### **3.3 Machine learning introduction**

Machine learning is a subfield of the artificial intelligence area, which includes statistics, mathematics, computer algorithms, etc. focused on building applications that learn and improve their predictive capabilities automatically over time without being specifically programmed to do so. Machine learning models are built upon a statistical framework, since they involve data elements often described, using statistical distributions and assumptions. These algorithms gained in popularity in the recent years due the increased amounts of data availability and significant advancements in the computing power [25].

In this book chapter, selected algorithms were leveraged to analyze the relationship between SUD and COVID-19 diagnoses. The analysis identified factors beyond the pandemic, such as patient characteristics: age, race, education and income levels, comorbid conditions (example: diabetes, hypertension, mental health), concomitant treatments that increased the addiction diagnoses, including patients most likely to struggle with SUD, regions of greater prevalence, and comorbid conditions presented along with SUD and COVID-19 diagnoses.

### *3.3.1 Supervised learning algorithms*

Supervised learning is the process of training or building machine learning algorithms, in which algorithms learn to map from input space (X) to output space (Y) [26].

$$\mathbf{Y} = \mathbf{f}(\mathbf{X}) \tag{2}$$

The major objective is to approximate the mapping function (f) in order to predict (y) outcome when a new data point (x) is added [26]. Supervised learning algorithms are mainly used for classification and prediction problems [27]. The following are examples of supervised algorithms: logistic regression, decision trees (DTs), random forest (RF), extreme gradient boosting (XGBoost), support vector machines (SVMs), naïve bayes, adaptive boosting (AdaBoost), and artificial neural network (ANN) [28].

### *3.3.2 Unsupervised learning algorithms*

Unsupervised learning algorithms, on the other hand, learn the hidden patterns within the input dataset (X) [29]. These models are called unsupervised, because there is no supervision to guide them, and the algorithms learn, discover, and display the patterns in the input data (X) [30]. These algorithms are often employed to uncover the natural clusters, dimension reduction, anomaly detection, etc. Examples of unsupervised algorithms include: k-means clustering, principal component analysis (PCA), factor analysis (FA), singular value decomposition (SVD), apriori algorithm (association rule) [28].

Depending on the study objectives and the available data type, algorithms are tested for performance, data type fit, and are selected accordingly. A random forest and an extreme gradient boosting models were selected to explain the bi-directional relationships of the SUD trends and COVID-19 pandemic surge.

### *3.3.3 xExtreme gradient boosting*

Gradient boosting algorithm is an ensemble of weak prediction models, mostly decision trees [31]. XGBoost starts by creating a first simple tree [32, 33], which

than adds other trees, and builds upon the weaker learners. The model learns with each iteration and revises the previous tree until an optimal point is reached [34].

Feature importance is the value mostly generated by tree-based models like decision trees, random forest, XGBoost, etc. [31] and signifies the importance of features in the model in predicting the outcome. It represents how good the feature is at reducing node impurity. It is widely known as 'gini importance' or 'mean decrease impurity,' and is defined as the total decrease in node impurity averaged over all trees of the ensemble [32]. Importance is mostly calculated as: weight, gain and cover, where 'weight' is the number of times a feature is present in a tree, 'gain' is the average gain of splits, while 'cover' is the average coverage of splits, with 'coverage' being defined as the number of samples affected by the split [33].

### *3.3.4 Random forest*

Random forest or random decision forest is an ensemble learning method for classification and regression analysis that constructs an array of decision trees during the training timeframe. The output of the random forest for the classification task is the class selected by the majority of trees, while for the regression task, the output represents the mean or average prediction across individual trees [35, 36].

### *3.3.5 Chi-square test and p-value*

The Chi-square test is one of the most widely used non-parametric tests [37], often utilized to test the independence between observed and expected frequencies of one or more attributes in a contingency table, known as 'goodness of fit test' [38].

The p-value, also used in this study, evaluates the statistical significance of the predictor variables. The significance level was set at the 5% and 10% to aid the feature importance evaluation and statistical results' interpretation [24, 38].

### *3.3.6 Classification metrics*

The following classification metrics were leveraged to validate the machine learning models' performance. A confusion matrix is often generated from the predicted probability values with 0.5 as the classification threshold. Patients with probability value greater than or equal to 0.5 are noted as 1 and below 0.5 are noted as 0 [38].

Confusion matrix:


Model performance metrics:

• Accuracy: % of total patients correctly identified among total patients


### **4. Analysis results and discussion**

### **4.1 Substance usage disease trends overview**

This section provides an overview of SUD trends for 2019 and 2020 when leveraging the healthcare claims dataset that was discussed in the earlier section of the chapter. The summary includes information on the overall trends, patient demographics, and insights into the COVID-19 diagnosis rates. The first part of the analysis was to review and understand the SUD diagnoses trends as well as COVID-19 infection rates within the SUD population. The focus of the analysis was on the SUD population only to understand changes in trends during the pandemic.

The monthly trends of patients with SUD diagnoses presented that the addiction trends stayed consistent over 2019 and 2020, with the exception of April–May 2020 timeframe. The list of SUD diagnoses is presented in the appendix refers to **Tables 6**–**9**. At the beginning of the pandemic (April–May 2020), there was a decrease in the number of patients with addiction diagnoses. A two sample t-test that compared the SUD diagnosis counts between April–May 2019 and April–May 2020 revealed that the difference in counts was not significant at either the 5% or 10% significance level. However, the directional decline might have been a result of the state enacted restrictions, including home confinement as well as the inability to hold in-person HCP office visits and elective procedures (**Figure 2**).

The SUD diagnoses trend data also involved analyzing trends by splitting the patient cohort into newly diagnosed patients in the last 12 months as well as previously diagnosed patients within the same timeframe. The analysis presented that the share of newly diagnosed patients vs. previously diagnosed declined slightly between 2019 and 2020, but the difference was not statistically significant. In 2020, newly diagnosed patients accounted for 62% of all patients vs. 66% in 2019. In addition, patients diagnosed with addiction as well as COVID-19 represented 3% of the newly diagnosed patients and 4% of those with already a diagnosis.

Furthermore, several different types of SUD experienced a decline in the number of patients diagnosed at the start of the pandemic. Opioid dependence was the leading addiction type with alcohol dependence following as next most frequently diagnosed SUD (**Figure 3**). The counts of opioid dependence diagnosis were statistically different from the counts for other types of SUD. Statistically significant difference in trends at 5% significance level was also observed between opioid dependence and alcohol dependence, opioid dependence vs. cannabis dependence,

### **Figure 2.**

*Addiction monthly patient count trends, 2019–2020.*

### **Figure 3.**

sedatives dependance vs. cannabis dependance, and sedatives dependence vs. alcohol use. The addiction diagnosis codes are noted in the appendix. Patients with psychoactive type of addiction represented a higher share within the COVID-19 diagnosed population (19%) as compared to the overall share (3%). Psychoactive SUD is referred to as addiction type with hallucinatory symptoms. The COVID-19 patient distribution for other addictions was very similar to the overall addiction population.

An additional analysis of demographic and geographic attributes as presented in **Table 1** revealed that males presented a higher percentage of the SUD population compared to SUD and COVID-19 population, but the percentage was not statistically significantly different from the percentage of women SUD patients. On the other hand, patients using cannabis appeared younger compared to the rest of the SUD population. This finding was statistically significant based on a two sample ttest (p-value = 0.00, statistically significant at 5%).

Most of both SUD and COVID-19 patients had commercially provided insurance coverage (70%), while 30% of patients had a government provided healthcare insurance (p-value = 0.00, statistically significant at 5%). Inhalants and rehabilitation drug addiction represented the highest share of patients with commercial insurance with more than 75%.

*Addiction monthly patient count trends, 2019–2020.*


### **Table 1.**

*Patient demographic summary (age and gender).*

Furthermore, the North East and Midwest regions represented two main geographic areas of the United States with the highest level of patients diagnosed with SUD and covered more than 50% of the total addiction diagnosed patients. The share of patients in these two regions was statistically significantly higher compared to the rest of the US regions (p-value = 0.00, statistically significant at 5%). The West regions on the other hand covered approximately 20% of the addiction diagnosed population.

The SUD treatment pattern analysis revealed that the procedural services, including psychotherapy, recommended to treat SUD patients declined in April and May 2020 and then returned to similar levels before the pandemic and on par with

### *Addictions - Diagnosis and Treatment*

**Figure 4.**

*Procedural treatment trends, 2019–2020.*

**Figure 5.**

*Addiction Rx treatment monthly patient count trends, 2019–2020, by addiction type.*

the 2019 trends (**Figure 4**). The decline was statistically significant at the 10% significance level with a p-value = 0.07. The decline might have been related to the imposed country-wide lockdown during the two months on 2020.

On the other hand, the number of patients treated with prescription medications statistically significantly increased between 2019 and 2020 (p-value = 0.00, statistically significant at 5%), even during the pandemic, the trend continued to increase, implying that patients continuously were receiving patient care (**Figure 5**). The drugs names are presented in the appendix. Prescription treatments for drug related addiction had the highest share of the treatments, followed by addiction relapse treatments. The share of drug prescription treatments was statistically different from the other types of therapy, including relapse and alcohol treatments.

### **4.2 Alcohol consumption overview**

This section of the book chapter provides an overview of alcohol consumption trends for 2019 and 2020. For this analysis, alcohol consumption data on per capita, alcohol sales from 19 states (Alaska, Arkansas, Colorado, Connecticut, Delaware, Florida, Illinois, Kansas, Kentucky, Louisiana, Massachusetts, Missouri, North

**Figure 6.**

*Monthly avg. of pure alcohol (gallons of ethanol) across United States.*

Dakota, Oregon, Tennessee, Texas, Utah, Virginia, and Wisconsin) by type of alcoholic beverage was leveraged. The limited alcohol consumption information by state was due to the limited data availability at source [22].

The monthly trends of pure alcohol (gallons of ethanol) from 2019 and 2020 in **Figure 6** showed that the trend stayed nearly the same, with a only a directional increase in 2020 [22]. A two sample t-test did not present statistically significant differences between the yearly trends. On the other hand, it was observed that with the increase in COVID-19 cases in the middle of pandemic (June–August 2020), there was an associated increase in the consumption of pure alcohol, as evident from the high Pearson correlation coefficient of 0.87 between the alcohol consumption and COVID-19 diagnosed number of patients. The increase in the alcohol use might have been associated with individuals experiencing hardship due to the prolonged lockdowns, loss of job, and the overall changes in lifestyle as a result of pandemic, and alcohol being perceived as a way for coping with the changing environment.

The trends describing gallons of alcohol per capita for age 14 and older (**Figure 7**) showed a statistically significance increase (p-value = 0.04, statistically significant at 5%) in gallons per capita from mid-May 2020, which might be a result of the COVID-19 pandemic spread. This was also apparent from a strong a Pearson correlation coefficient of 0.91 between the pandemic outbreak as denoted by a volume of patients diagnosed with COVID-19 and gallons of alcohol per capita [22].

In order to understand the alcohol consumption over time, the percentage change in gallons of alcohol per capita from 2017 to 2019 (a 3-year average) to 2020

**Figure 7.** *Gallons of ethanol per capita age 14 and older (2019 and 2020).*

**Figure 8.**

*Percentage change in gallons of ethanol per capita age 14 and older from 2017 to 2019 (3-year average) to 2020. Note: Limited states shown due to unavailability of the data from the source.*

was analyzed (**Figure 8**). Overall, a visible increase in the trend was noticed; however, in May 2020, a statistically significant decline in the percentage in alcohol consumption (p-value = 0.06, statistically significant at 10%) was observed due to the COVID-19 imposed lockdowns and closures of liquor stores. From the month of June onwards, a statistically significant increase in the percentage change (p-value = 0.06, statistically significant at 10%) was noted, which might have been associated with the lockdown restrictions being partially lifted, leading to re-opening of liquor stores and increased purchasing levels [22].

**Figure 9** depicts a comparison of selected states' gallons of alcohol consumption per capita between 2019 and 2020. As noted earlier, only a few selected states were considered for the analysis due to the limited availability of the data at the source. For most of the states, an increase in alcohol (in gallons) per capita is noticeable in 2020. Delaware's excise tax on liquor of \$3.75 per gallon, lower than 72% of the other 50 states, led to the highest increase in per capita alcohol in 2020. Most of the states experienced an increase in per capita alcohol during the COVID-19 pandemic [22].

The alcohol consumption data was also analyzed for each alcohol type. **Figure 10** presents a yearly consumption comparison between 2019 and 2020 for beer, wine and spirits. It was observed that beer consumption was higher in January and February in 2020 compared to January and February in 2019, but after the COVID-19 pandemic started, the consumption decreased from March onwards until May 2020

### **Figure 10.**

*Average beverage consumption breakout by wine, beer and spirit from 2019 and 2020. Note: 2020 data is available only until Sep due to limited availability from the source.*

due to lockdowns and limited liquor facilities opened in each state. For wine and spirits, the trend of consumption showed an increase, starting from January 2020 onwards [21]. It was also evident that the increase in volume of beer, spirit, and wine consumption from 2019 to 2020 was statistically significant based on the two sample t-test, which resulted in a p-value < 0.02 for each alcohol type [22].

### **4.3 Effects of alcohol and substance use during COVID-19**

In this section of the book chapter, the effects of alcohol and SUD during the COVID-19 pandemic were analyzed. An ordinary least square linear regression model was used to investigate the correlation between these events. The dataset employed was a combination of the healthcare claims data and the alcohol consumption data, both aggregated at a state and monthly levels for comparison [21, 22].

The results of the ordinary least square regression are presented in **Table 2**. The analysis results showed that sedatives use, alcohol abuse, and beer consumption were the highly significant variables and positively correlated with the COVID-19 pandemic spread. Sedatives like benzodiazepines are often prescribed for anxiety and insomnia, confirming the finding [39]. Furthermore, an increased consumption of alcohol might have led to seeking treatments to manage the signs of frustration, sadness, mental health conditions, and stress [40], caused by the prolonged isolation during the pandemic.

Other parameters like opioid use, cannabis use/abuse were significant as well, but they were negatively correlated with the pandemic spread. These results were consistent with previous research articles, presenting that drug use declined during the pandemic while at the same time patients suffered from withdraws and other symptoms related to fewer substances available for consumption [12–14, 41, 42]. On the other hand, while beer consumption was positively correlated with COVID-19 pandemic trends, the consumption of spirits presented the reversed correlation, which was contrary to the findings of overall alcohol trend increases. The difference in correlation might have been related to the differences in states regulations of the different types of alcohol, which in turn might impact the alcohol type availability for consumption at the state level [43].

The model significance was evident from the F-statistic value of 14.7 with an adjusted R square value of 74%, which informed that a relatively high proportion of the variations in the data could be explained by the predictor variables.



*Note: Per capita alcohol sales from 19 states (Alaska, Arkansas, Colorado, Connecticut, Delaware, Florida, Illinois, Kansas, Kentucky, Louisiana, Massachusetts, Missouri, North Dakota, Oregon,Tennessee,Texas, Utah, Virginia, and Wisconsin) by type of alcoholic beverage.*

### **Table 2.**

*Ordinary least square regression analysis results.*

### **4.4 Machine learning: important features leading to addiction**

To understand the parameters associated with SUD and identify if the COVID-19 pandemic impacted the addiction diagnosis rate, supervised classification machine learning algorithms, including random forest and XGBoost were performed.

### *4.4.1 Dataset overview*

As a part of the analysis, two distinct patient cohorts: study target and control groups were developed to allow for analysis of the SUD and COVID-19 trends. The distinction between these two groups permitted the machine learning models to learn

the variations in the data and identify the important variables that best distinguished between both groups. The target group was defined by the patients in the data from October 2020 to December 2020, who had at least 2 addiction diagnoses, followed by a treatment after initial diagnosis, and the control group was defined by the patients from October 2019 to December 2019, having two addiction diagnoses, followed by a treatment after initial diagnosis. A sample of 20,000 records were randomly selected for the modeling exercise based on similar age and gender distribution as in the target group. Two months of historical claims data related to diagnosis, procedures, and pharmacological treatment were pulled from initial diagnosis event along with other demographic data elements like age, gender, income, education, etc. The healthcare claims level data was converted to patient level records, using data preprocessing steps, and a final data structure with 20,000 records and 15,000 features was created for machine learning modeling [21].

### *4.4.2 Feature selection*

The data elements used for the study included diagnosis, procedures, and pharmacological treatments along with other demographic features like age, gender, income, education, etc. Since the number of features was 15,000, the data element dimension needed to be reduced to a more manageable number.

In order to reduce the variables'space and select the top features, the LightGBM and Boruta algorithms were leveraged for the purposes of dimension reduction. LightGBM is a gradient boosting framework, which uses tree-based learning, whereas Boruta is a feature selection algorithm, a wrapper built around RF Classification algorithm. The top features from both the algorithms were selected for the machine learning algorithms development [44].

**Table 3** below represents a list of selected features important in the preliminary run of the models. Data elements related to the COVID-19 diagnoses and associated symptoms along with alcohol and nicotine use as well as major depressive disorder were noted as important variables, separating the 2020 and 2019 SUD patient cohorts.

### *4.4.3 Machine learning models overview*

In order to understand the underlying factors for the SUD 2020 and 2019 patient cohorts and investigate if there was an association with the COVID-19 pandemic, the following machine learning models were applied: random forest and XGBoost. Hyper tunning process was also performed to optimize the models. Below a brief methodology overview is presented.

Random forest is a classification algorithm, consisting of many decision trees that use bootstrap aggregation, bagging and feature randomness when building each individual tree. It creates an uncorrelated forest of trees whose prediction is more accurate than that of any individual tree. The model outcome provides estimates of variables important in the classification [45].

XGBoost is a decision-tree-based ensemble machine learning algorithm that uses a gradient boosting framework. XGBoost approaches the process of sequential tree building, using parallelized implementation. Each model run learns from the error of previous models and weak learners. It incorporates the error of weak learners in the ensemble model and re-runs the process. It uses bootstrap aggregating technique, also called as bagging, which implies diving the data into sub samples for each iteration of model training. For prediction purposes, the model chooses majority of the vote from all the learners [46].

Hyper parameter tuning is the optimization process of finding the model parameters to improve the model performance. The objective is to minimize the


### **Table 3.**

*Preliminary important data features.*

cost function, hence reduce the error caused by the model. It uses gradient descent algorithm, which initially randomly assigns the model parameter to calculate the cost function and later improves it at each step, so that the cost function assumes a minimum value. Mathematically, it takes the derivative of the sum of squared residuals and equates it to 0 to find a point where the function is changing [47].

### *4.4.4 Machine learning analysis details*

The machine learning algorithms were evaluated for the different performance metrics as noted in the earlier section of the book chapter. Initially, the models were overfitting, as they seemed to capture most of the variations from the training data, as well as at the same time captured the noise from the data. This resulted in many negative records misclassified as positive, which might have led to a considerably lower precision value.

As noted in **Table 4**, the baseline models, random forest resulted in AUC of 73% and XGBoost resulted in AUC of 74% with recall of 74.08% and 82.80% respectively. While working with healthcare claims data, a higher ratio of false negative records is perceived as a large problem. For example, predicting a sick patient as


**Table 4.**

*Machine learning model metrics.*

healthy, because ideally the patient should have received the treatment on time, may lead to health complications and even potentially death. Thus, it is advised to minimize the number of false negative observations, which will result in a higher recall, depicting an inverse relationship between the two, also called true positive rate or sensitivity. However, the initial baseline models resulted in a comparatively lower recall. To improve the model accuracy, the hyper parameter tuning was executed to find the best model parameters.

In addition, F1 score was defined as follows:

$$2\*(\text{Precision}\*\text{Recall})/(\text{Precision}+\text{Recall})\tag{3}$$

which is a function of precision and recall and should be maximized such that both precision and recall both are optimal [45–47]. Hyper parameter tuned models not only improved the recall, but also slightly improved the F1 scores for random forest and XGBoost to 69.4% and 69.57% respectively, implying a robust model sensitive to false negative observations.

The machine learning models were retrained, using a few model parameters, including mex=depth, min-samples\_split and max\_features. In order to obtain the optimal values of model parameters, k fold cross validation with 5 iterations were used in the hyper parameter tuning process [47].

Using the optimal values of the model parameters obtained from hyper parameter tuning, the models showed an improvement in the model performance, which was evident from the model metrics. As shown in **Table 4**, the recall significantly increased for both of the models and also, the F1 scores slightly improved compared to the baseline models. This resulted in a decrease in the false negatives count, which led to an increase in recall.

**Figure 11** depicts the final ROC AUC plot, which shows the relationship between the true positive rate and the false positive rate at different probability thresholds. A true positive rate also known as the sensitivity metric, which informs the proportion of positive records that were correctly classified over the total number of positive records. A false positive rate is the proportion of negative records misclassified over total number of negative records [38]. Both random forest and XGBoost resulted in AUC of 75% with recall of 94.7% and 85.6% respectively, which improved from the baseline models.

### *4.4.5 Machine learning model interpretation*

The random forest and XGBoost models identify features, which were a combination of SUD as well as COVID-19 data elements. **Table 5** presents the top

**Figure 11.** *ROC AUC curve of random forest and XGBoost.*

important features. The importance of the features was measured using the 'gini' importance metric, which calculated the impurity in the node. The metric measured how each feature decreased the impurity of the split, while making the decision tree in the algorithm and averaging it over all the trees in the forest, resulting in the measure of feature importance [32, 33].

Features like nicotine dependence, alcohol abuse, long term drug therapy, disulfiram [48], methadone [49] were presented as important in explaining the differences in SUD patient cohorts between 2019 and 2020. For example, the value 0.003 of nicotine dependence importance denoted that the impurity reduced in the node by adding the variable, which thus contributed to the model robustness and a higher accuracy level. The effects of pandemic on individual's lives were not only restricted to patients' physical health, but also affected their mental health, as noted by the major depressive, anxiety diagnoses, and suicidal tendencies as presented in the top most important healthcare data elements. The unexpected and unwanted change enforced on daily lives, drastically increased the stress levels. Difficulties in management of the changing environment and following preventive measures, such as undergoing lockdowns, fueled the stress levels even more. The economic downturn, leading to unemployment, and low consumer confidence played an imperative role in increasing the stress levels as well. As a result of the prolonged stress and anxiety due to the lockdowns, the consumption of alcohol, smoking, and other nicotinebased products increased [12, 13, 41, 42].




### **Table 5.**

*Top most important healthcare related features.*

It was also interesting to see features related to COVID-19 pandemic being noted as important differentiators between the 2019 and 2020 SUD patient cohorts. The features included COVID-19 diagnosis and related symptoms: headache, cough, acute upper respiratory infection, specimen collection for severe acute respiratory condition. Procedures noting HCP in-office and tele-visits along with in-patient hospital or ER visits were also noted as important variables, further highlighting that the amount of care might have increased as a result of SUD, but also due to COVID-19 diagnoses and related symptoms. In addition, medications often used to treat viruses and infections like Azithromycin were also presented as important data elements defining the 2020 SUD patient cohort.

Furthermore, the cohorts differed on the occurrence of the comorbid conditions, such as chronic kidney condition, hypertension, hyper lipidemia, and gastroesophagus conditions, which might inform a potential impact of a larger alcohol and other substance abuse activities during the pandemic or simply present that the patient profile changed during the pandemic, expanding the definition of the SUD patients group. There were also several data elements identifying SUD treatments such as Narcan, methadone to list a few and procedures related to drug testing, blood panels, and other related treatments, which present an increased rate of addiction testing and treatment between the two periods, confirming earlier findings of increased SUD treatment trends. The analysis also presented differences of the cohorts on a diagnoses for lower back pain and pain relieve medications use.

From the sociodemographic data elements, patients diagnosed with addiction or treated for addiction presented characteristics that can help further define the patient profiles for individuals that were likely for developing SUD during the pandemic. For example, the average age of 42 was observed for the impacted population. Ethnicity of Caucasian and Black/African American was also noted as prevalent. Patients with nicotine dependence, alcohol dependence, opioid use, and cannabis dependence were relatively more prevalent in the states of Florida and Texas. These states presented a relatively higher volume of patients with specific SUD diagnoses compared to other states. The impacted patients presented some college or achieved at least a high school diploma as well as were more likely to be associated with the lower economic status communities, with income level being less than \$30 K annually. The educational and economic levels were noted by other published articles, presenting the economic impact and increased risk for COVID-19 virus within low income population [2].

### **5. Conclusions and study limitations**

This book chapter investigated SUD and the resulting impact from the COVID-19 pandemic on the rate of diagnoses and treatment. Overall, the diagnoses rate of SUD was consistent over time in 2020 compared to 2019 (except for April and May); however, a statistically significant increase in treatment of different addiction types was noted during the pandemic. In 2020, newly diagnosed patients accounted for 62% of all SUD patients compared to 66% in 2019, but the difference was not statistically significant. Furthermore, the changes in procedures performed for addiction testing significantly declined at the beginning of the pandemic and then returned to normal levels in June of 2020, while the SUD treatment significantly increased between 2019 and 2020. In addition, patients diagnosed with addiction as well as COVID-19 represented 3% of the newly diagnosed patients and 4% of those with already a diagnosis. Patients using cannabis were found statistically significantly younger compared to the rest of the SUD population.

In 2020, a noticeable increase in alcohol consumption and drinking behaviors was observed compared to 2019, including an increase in the average gallons consumed by alcohol type: spirits, wine, and beer. Compared to the previous years, a statistically significant positive percentage change in gallons of alcohol per capita from 2017 to 2019 (a 3-year average) to 2020 was observed [22], which could be related to the increased stress levels due to the pandemic spread and prolonged lockdowns [50].

Machine learning analysis of SUD patient cohorts between 2020 and 2019 presented that the patients in the 2020 cohort who were diagnosed with SUD, were also often diagnosed with either COVID-19 or related symptoms, including headache, upper respiratory infection, and cough. Furthermore, it is likely that SUD patients with addiction to drugs and nicotine products were more likely to contract COVID-19, as a result of their weaker immune system due to lower white cells levels in the blood [51, 52]. The analysis also presented the importance of HCP in-office and tele-visits along with in-patient hospital visits that could be related to the increased level of SUD treatment, but also present the severity of COVID-19 related symptoms and the need for treatment.

Moreover, excess alcohol consumption identified as one of the important factors, differentiating between the two SUD patient cohorts could lead to immune deficiency, causing increased susceptibility to certain diseases. Prolonged alcohol abuse may cause disruptions to the digestive system and could result in liver failure. Alcohol use may also affect individual's ability to store adequate amounts of protein and nutrients. Most importantly drugs and alcohol affect white blood cells, which act as the defense system for the body. The weaker defense system can increase the risk of developing life-ending diseases [52].

Finally, based on the machine learning analysis, the SUD patient cohorts differed on occurrence of the comorbid conditions such as chronic kidney condition, hypertension, hyper lipidemia, and gastro-esophagus condition, which might present that the SUD patient profile changed during the pandemic due to the changes in the life style and increased consumption of alcohol and tobacco. Additional investigation should be conducted to further examine the patients' health history and understand the underlying reasons for the differences in the SUD patient cohort characteristics.

### **5.1 Study limitations**

Due to the timing of writing this book chapter, not all data was available for the entire year of 2020 to allow for a comprehensive analysis. Adding the additional data for alcohol consumption, as well as data for recreational drug use by state during the pandemic could enhance the analysis in presenting the SUD population characteristics including their health, mental, and economic state. Furthermore, it might also be helpful to add COVID-19 vaccination data by state in order to understand the effects of vaccinations and COVID-19 variants on general virus trends as well as SUD impacted populations.

Additionally, the healthcare patient level claims data, comprising of prescription, medical, and hospital claims can also observe gaps in the coverage of long-term care institutions, mental health hospitals, correctional facilities, and other institutions with a limited public reporting, and result in a potential bias in the studied population when compared to the entire US population. Furthermore, the COVID-19 related symptoms' diagnoses might also skew the analysis and overestimate the impact of COVID-19 on the SUD population. The statistical results could be

further enhanced and become more robust with the additional data availability and understanding of the diagnosis codes for the COVID-19 related symptoms.

Since the COVID-19 pandemic was a rare event, it became a new topic of interest for analysis. As a result, there was a limited number prior research studies conducted on this topic, which posed a challenge in creating a theoretical foundation for this book chapter's research questions and hypothesis. With little prior research, developing an entire new research typology was challenging.

The scope of the analysis can also be enhanced via adding additional data sources and having a longer timeframe to evaluate the impact of pandemic on addiction and health impact of those impacted by either condition. Furthermore, new set of advanced analytics, including deep learning and natural language processing (NLP) approaches, could be applied to create data driven evidence to confirm newly established hypothesis, research objectives, and results.

### **Acknowledgements**

Availability of data and materials: The healthcare claims dataset that supports the findings of this study are available from Symphony Health, ICON plc Organization, but restrictions apply to the availability of these data, which were used under a license for the current study, and so they are not publicly available.

The Alcohol Consumption Dataset is available from the National institute of Alcohol Abuse and Alcoholism [Online]. https://pubs.niaaa.nih.gov/publica tions/surveillance-covid-19/COVSALES.htm

### **Funding**

Authors work for Symphony Health, ICON plc Organization. The data used in the article is the property of Symphony Health, ICON plc Organization. Authors used the healthcare claims data for the sole purpose of publication of this article.

### **Conflict of interest**

The authors declare no conflict of interest.

### **Notes/Thanks/Other declarations**

The authors would like to thank Mike Byzon, Suzanne Rosado, Heather Valera, Lakshya Mandawat, and Koichi Iwata for their valuable feedback on earlier versions of this chapter.

### **Appendix**

(**Tables 6**–**9**).













**Table 6.**

*Substance abuse disorder diagnosis codes.*


### **Table 7.**

*Substance abuse disorder procedure codes.*















### **Table 8.**

*Substance abuse disorder treatment codes.*


**Table 9.**

*COVID-19 diagnoses codes.*

### **Author details**

Ewa J. Kleczyk1,2\*, Jill Bana3 and Rishabh Arora<sup>1</sup>

1 Symphony Health, ICON plc Organization, Blue Bell, PA, USA

2 School of Economics, The University of Maine, Orono, ME, USA

3 Symphony Health, ICON plc Organization, Phoenix, AZ, USA

\*Address all correspondence to: ewa.kleczyk@symphonyhealth.com

<sup>© 2021</sup> The Author(s). Licensee IntechOpen. This chapter is distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/ by/3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

### **References**

[1] SAMHSA. Leveraging Existing Health and Disease Management Programs to Provide Mental Health and Substance Use Disorder Resources During the COVID-19 Public Health Emergency (PHE). June 2020. [Online] https:// www.cms.gov/CCIIO/Programs-and-Initiatives/Health-Insurance-Marke tplaces/Downloads/Mental-Health-Sub stance-Use-Disorder-Resources-COVID-19.pdf. Accessed on July 22, 2021.

[2] GBD 2016 Alcohol and drug use collaborators the global burden of disease attributable to alcohol and drug use in 195 countries and territories, 1990-2016: A systematic analysis for the global burden of disease study 2016. Lancet Psychiatr. 2018;5:987–1012.

[3] SAMHSA. 2007. Results from the 2006 National Survey on Drug Use and Health: National Findings. (DHHS Publication no. SMA 07–4293, NSDUH series H-32) Office of Applied Studies: Rockville, MD.

[4] Feltenstein, M.W. and See, R.E. The neurocircuitry of addiction: an overview. British Journal of Pharmacology. 2008 154: 261-274. [Online] DOI:10.1038/bjp.2008.5. Accessed on July 15, 2021.

[5] Pedersen T. How Substance Use Disorders (SUD) are Treated? Psych Central. May 2021. [Online] https:// psychcentral.com/addictions/substanceuse-disorders-treatment#basic-princ iples. Accessed on July 15, 2021.

[6] Baumgartner, J. C. and Radley, D. C. The Spike in Drug Overdose Deaths During the COVID-19 Pandemic and Policy Options to Move Forward. March 2021. [Online] https://www. commonwealthfund.org/blog/ 2021/spike-drug-overdose-deathsduring-covid-19-pandemic-and-policyoptions-move-forward. Accessed on July 15, 2021.

[7] Dubey S., Biswas P., Ghosh R., Chatterjee S., Dubey M.J., Chatterjee S. Psychosocial impact of COVID-19. Diabetes Metab Syndrome. 2020 DOI: 10.1016/j.dsx.2020.05.035.

[8] Ornell F., Moura H.F., Scherer J.N., Pechansky F., Kessler F., von Diemen L. The COVID-19 pandemic and its impact on substance use: Implications for prevention and treatment. Psychiatr Res. 2020;289:113096.

[9] Columb D., Hussain R., O'Gara C. Addiction Psychiatry and COVID-19 – Impact on patients and service provision. Ir J Psychol Med. 2020 May 21:1–15. DOI:10.1017/ipm.2020.47.

[10] Hefler M., Gartner C.E. The tobacco industry in the time of COVID-19: Time to shut it down? Tobac Contr. 2020;29: 245–246.

[11] The Times of India. 2020. How to use lockdown as an opportunity to overcome alcohol addiction. [Online] https:// timesofindia.indiatimes.com/life-style/ health-fitness/health-news/how-to-uselockdown-to-de-addict-yourself-andways-to-manage-withdrawal-symptoms/ articleshow/75494592.cms. Accessed on February 1, 2021.

[12] News18. 2020. Amid coronavirus lockdown, states across India witness surge in suicide cases due to alcohol withdrawal symptoms. [Online] https:// www.news18.com/news/india/amidcoronavirus-lockdown-states-acrossindia-witness-surge-in-deaths-due-toalcohol-withdrawal-symptoms-2561191. html. Accessed on February 1, 2021.

[13] Rani S., Sahoo S., Parveen S., Mehra A., Subodh B.N., Grover S. Alcohol-related self-harm due to COVID-19 pandemic: Might be an emerging crisis in the near future: A case report. Indian J Psychiatr. 2020;62:333– 335.

[14] Sun Y., Bao Y., Kosten T., Strang J., Shi J., Lu L. Editorial: Challenges to opioid use disorders during COVID-19. Am J Addict. 2020;29:174–175.

[15] Becker W.C., Fiellin D.A. When epidemics collide: Coronavirus disease 2019 (COVID-19) and the opioid crisis. Ann Intern Med. 2020 Apr 2:M20– M1210. DOI:10.7326/M20-1210.

[16] Vecchio S., Ramella R., Drago A., Carraro D., Littlewood R., Somaini L. COVID19 pandemic and people with opioid use disorder: Innovation to reduce risk. Psychiatr Res. 2020;289: 113047.

[17] Håkansson A., Fernández-Aranda F., Menchón J.M., Potenza M.N., Jiménez-Murcia S. Gambling during the COVID-19 crisis – A cause for concern? J Addiction Med. 2020 May 18 DOI: 10.1097/ADM.0000000000000690.

[18] Schalkwyk M.C., Cheetham D., Reeves A., Petticrew M. Covid-19: we must take urgent action to avoid an increase in problem gambling and gambling related harms. BMJ Opinion. 2020. [Online] https://blogs.bmj.com/ bmj/2020/04/06/covid-19-we-musttake-urgent-action-to-avoid-anincrease-in-problem-gambling-andgambling-related-harms. Accessed on February 1, 2021.

[19] Touyz S., Lacey H., Hay P. Eating disorders in the time of COVID-19. Version 2. J Eat Disord. 2020;8:19.

[20] Watanabe M. 2020. Beyond retail therapy the case against pandemic shopping. [Online] https://www.bitch media.org/article/compulsive-onlineshopping-COVID-19. Accessed on February 1, 2021.

[21] Integrated Dataverse (IDV®). [Online] https://symphonyhealth.prahs. com/what-we-do/view-health-data. Accessed on October 1, 2020.

[22] National institute of Alcohol Abuse and Alcoholism. [Online] https://pubs. niaaa.nih.gov/publications/surveillancecovid-19/COVSALES.htm. Accessed on March 1, 2021.

[23] David A. Freedman (2009). Statistical models: Theory and practice. Cambridge university press. p. 26. "a simple regression equation has on the right hand side an intercept and an explanatory variable with a slope coefficient. A multiple regression e right hand side, Each with its Own Slope Coefficient."

[24] Hilary L. Seal (1967). "The historical development of the gauss linear model". Biometrika. 54 (1/2): 1–24. DOI: 10.1093/biomet/54.1-2.1. JSTOR 2333849.

[25] Sarker, I.H. Machine Learning: Algorithms, real-world applications and research directions. SN COMPUT. SCI. 2, 160. 2021. [online] DOI:10.1007/ s42979-021-00592-x. Accessed on July 15, 2021.

[26] Hastie, T., Tibshirani, R., And Friedman, J. Overview of Supervised Learning. The Elements of Statistical Learning. Springer. (2009). Pp. 9–39. Alpaydın, E. (2014).

[27] Introduction to machine learning. Cambridge, MA: MIT Press. Hosmer, D. W., Lemeshow, S. Applied Logistic Regression. New York: Wiley. (2013). ISBN 978-0-470-58247-3.

[28] Ballantyne Draelos, R.L. Best Use of Train/Val/Test Splits, with Tips for Medical Data. Glass Box Machine Learning and Medicine. [Online] https://glassboxmedicine.com/2019/09/ 15/best-use-of-train-val-test-splitswith-tips-for-medical-data. Accessed on October 5, 2020.

[29] Simeone, O. A very brief introduction to machine learning with applications to communication systems.

arXiv preprint arXiv:1808.02342v4. (2018).

[30] Hinton, G., Sejnowski, T. Unsupervised Learning: Foundations of Neural Computation. MIT Press. (1999). ISBN 978-0262581684.

[31] Friedman, J.H. Greedy function approximation: A gradient boosting machine. The Annals of Statistics 29: 1189-1232. (2001). [Online] DOI: 10.1214/aos/1013203451. Accessed on October 1, 2020.

[32] Extreme Gradient Boosting. [Online] https://xgboost.readthedocs.io/ en/latest/tutorials/model.html. Accessed October 1, 2020.

[33] Hastie, T., Tibshirani, R., Friedman, J. H. '10. Boosting and Additive Trees'. The Elements of Statistical Learning (2nd ed.). New York: Springer. (2009). pp. 337–384.

[34] Cochran, W.G. The chi-square test of goodness of fit. The Annals of Mathematical Statistics 23(3): 315–345. (1952).

[35] Ho, Tin Kam (1995). Random Decision Forests (PDF). Proceedings of the 3rd International Conference on Document Analysis and Recognition, Montreal, QC, 14–16 August 1995. pp. 278–282. Archived from the original (PDF) on 17 April 2016. Retrieved 5 June 2016.

[36] Ho TK (1998). "The random subspace method for constructing decision forests" (PDF). IEEE Transactions on Pattern Analysis and Machine Intelligence. 20 (8): 832–844. DOI:10.1109/34.709601.

[37] On the interpretation of χ2 from contingency tables, and the calculation of p. Journal of the Royal Statistical Society 85(1): 87-94. (1922). [Online] DOI:10.2307/2340521. Accessed on July 15, 2021.

[38] Manning, C.D., Raghavan, P., and Schütze, H. Introduction to Information Retrieval. Feature Selection, Chi-Square Feature Selection Cambridge University Press. (2008).

[39] Prescription Sedative Misuse and Abuse. [Online] https://www.ncbi.nlm. nih.gov/pmc/articles/PMC4553644/. Accessed on March 1, 2021.

[40] Centers for Disease Control and Prevention; Coping with stress. [Online] https://www.cdc.gov/corona virus/2019-ncov/daily-life-coping/ managing-stress-anxiety.html. Accessed on March 1, 2021.

[41] Narasimha V.L., Shukla L., Mukherjee D., Menon J., Huddar S., Panda U.K. Complicated alcohol withdrawal-an unintended consequence of COVID-19 lockdown. Alcohol Alcohol. 2020 May 13 agaa042.

[42] Varma R.P. Alcohol withdrawal management during the Covid-19 lockdown in Kerala. Indian J Med Ethics. 2020; V:105–106.

[43] List of alcohol laws of the United States. [Online] https://en.wikipedia. org/wiki/List\_of\_alcohol\_laws\_of\_the\_ United\_States

[44] Bhattacharyya, I. Feature Selection (Boruta/Light GBM/Chi Square)- Categorical Feature Selection. [Online] https://medium.com/@indreshbha ttacharyya/feature-selection-categorica l-feature-selection-boruta-light-gbmchi-square-bf47e94e2558. Accessed on March 1, 2021.

[45] Random Forest Algorithm. [Online] https://www.stat.berkeley.edu/breima n/RandomForests/cc\_home.htm; https://www.ibm.com/cloud/learn/rand om-forest; https://builtin.com/data-scie nce/random-forest-algorithm. Accessed on March 1, 2021.

[46] XGBoost Algorithm. [Online] https://www.mygreatlearning.com/ blog/xgboost-algorithm/. Accessed on March 1, 2021.

[47] Hyper Tunning [Online] https:// xgboost.readthedocs.io/en/latest/ parameter.html, https://scikit-learn.org/ stable/modules/generated/sklearn. ensemble.RandomForestClassifier.html. Accessed on March 1, 2021.

[48] MedlinePlus. [Online] https:// medlineplus.gov/druginfo/meds/ a682602.html. Accessed on March 1, 2021.

[49] HCPCS. Codes https://hcpcs.codes/ g-codes/G2067/. Accessed on March 1, 2021.

[50] Alcohol Consumption during the COVID-19 Pandemic: A Cross-Sectional Survey of US Adults. [Online] https:// www.ncbi.nlm.nih.gov/pmc/articles/ PMC7763183/. Accessed on March 1, 2021.

[51] Effect of alcohol on immune system [Online] https://vertavahealth.com/ blog/drugs-alcohol-affect-immunesystem. Accessed on March 1, 2021.

[52] COVID-19 and People who Use Drugs [Online] https://www.cdc.gov/ coronavirus/2019-ncov/need-extraprecautions/other-at-risk-populations/ people-who-use-drugs/QA.html. Accessed on March 1, 2021.

Section 2
