As is noted in Table 5, the demographic variables significantly differentiate OUDs from non-OUDs, though the effect sizes for these variables are quite small. Diagnostic data, particularly variables of barbiturate abuse/dependence, unspecified drug abuse/dependence, and poly substance drug dependence had strong effect sizes in differentiating OUDs from non-OUDs. The amount of short-acting opioid, measured in morphine equivalent units, dispensed was a better predictor than the amount of long-acting opioid. It should be noted that the magnitude and the directionality of the odds ratios in Table 5 differ from the bivariate comparisons in Table 3; in modeling multiple variables simultaneously, bivariate relationships are subject to change. Finally, ten interactions remained in this model, primarily involving the aforementioned variables of short-acting opioids dispensed, unspecified drug dependence, poly substance dependence, and barbiturate dependence. Participant age, inpatient mental health admissions, and mental health inpatient days were also present in the significant interaction variables.The detection of opioid misuse is an important step in addressing the public health problems of prescription drug abuse, dependence, diversion, and overdose. Although previous studies have identified some of the factors that place individuals at greater risk for misuse of opioids, this investigation benefits from a comprehensive database that has illuminated more differences between those who develop opioid use disorders and those who receive an initial prescription but do not develop a diagnosis of opioid dependence or abuse. Additionally, this study may be useful in providing health plans with a method for monitoring claims data that may assist in detecting members who are at risk for substance misuse, potentially providing relevant feedback to medical providers. The current study replicates the findings of previous studies that being male and younger are associated with increased risk of becoming an OUD; an additional significant difference captured in this dataset is that those who were OUDs are less likely to be the primary insured individual,shelf grow light and are more likely to be a dependent or spouse/partner of the primary insured. OUDs significantly differed from non-OUDs in a number of other areas, as well.
The prescription patterns for opioids were quite different between these groups, with OUDs receiving a larger supply of opioids, paying a significantly higher copayment for opioids, and receiving more short-acting opioids than non-OUDs. The directionality of this relationship is unclear from this study; it is possible that particular prescribing patterns place individuals at greater risk for developing a problem with opioids, but it is also possible that OUDs are more likely to request short-acting, and a greater number of, medications from a health care provider. This finding was present among inpatient and outpatient clinics, emergency department, general medical care, and mental health specialty care visits. As with the relationship between opioid prescribing and misuse, the directionality of this relationship is also unclear. OUDs are likely to be at risk for other health problems that may co-occur with their opioid misuse; depression, anxiety, infections, metabolic difficulties, and injuries are all possible correlates of opioid misuse. Conversely, individuals who have other health problems may start to use opioids, and to misuse them, as a means of coping with their difficulties, such as chronic pain or mental health difficulties. The patterns of medication usage help to clarify, to some extent, the differences between OUDs and non-OUDs. OUDs are more likely to be receiving treatment for anxiety, depression, chronic pain, and many other conditions than non-OUDs. The mathematical modeling of opioid misuse, and the resultant predictors of misuse that were identified in the final model, underscore the relationship between mental health, other substance misuse, and opioid abuse/dependence. It is noteworthy that of the different models that were tested to identify OUDs, diagnostic and mental health care variables rose to be among the most robust predictors. This finding has implications for future research and practice. In settings that serve individuals at high risk for opioid misuse, collecting data on co-occurring mental health conditions, mental health treatment history, and psychotropic medication usage is imperative in identifying those who may be at risk for developing an opioid use disorder. Those identified as at-risk may benefit from indicated prevention programs that educate individuals about signs of prescription drug misuse and the relationship between opioid use and mental health conditions.
Treating co-occurring mental health difficulties is an important part of addressing the health of individuals who are prescribed opioids. Variables that significantly predicted OUDs must, in some cases, be interpreted within the context of significant interactions that were identified through CHAID analysis. Due to the atheoretical nature of CHAID analysis, the significant interactions were not anticipated prior to the analytic process; however, several variables frequently appeared in the significant interaction terms. Implications of these interactions include, for example, the finding that the impact of receiving short-acting opioids depends on co-occurring substance use diagnoses when predicting OUDs. These interactions may be of clinical utility in identifying individuals, through data readily available to health plans, who are at risk for OUDs and may benefit from prevention efforts. The model developed in this study was designed for use in the entire population of patients in the database, regardless of where they live. Given the significant regional differences in the distribution of diagnosed OUDs, future studies should test the model at the regional level to determine whether location impacts model performance. This investigation has a number of limitations that prevent broader conclusions from being drawn about opioid abuse and dependence. The key limitations are the use of an existing data set and the reliance on a physician’s diagnosis of abuse and dependence. Many individuals may develop an opioid use disorder that does not come to the attention of their physician. Those who have a diagnosis of abuse or dependence may represent an unusual opioid using population, in that they may have either talked with their physician directly about a potential problem or have such florid difficulties with misuse that it is evident to their health care provider or providers. The operationalization of cooccurring mental health and other substance use disorders as any lifetime diagnosis is also a limitation of this study, as important temporal relationships between opioid misuse and other mental health problems cannot be established. Given the possible bidirectional development of such difficulties, the research team did not specify a priori any time frame for co-occurring disorders, though such analysis could be an important line of future research in this area.
The primary strengths of this study are the large sample size, the comprehensive number of variables regarding study participants, and the use of claims data, the likes of which may be generally available to health plans for use in their own risk stratification and intervention. Those interested in the prediction of opioid misuse may not have all of the significant variables present in their data sets, and thus may not be able to directly apply the particular mathematical model created here. To summarize, the detection of opioid misuse has important implications for public health; better identification of individuals at risk may help to reduce morbidity and mortality that is often associated with opioid use disorders. The current study made use of a large, comprehensive data set that may aid researchers and clinicians in their attempts to address this important issue. For decades it was believed that the effects of the main active ingredient in cannabis, delta-9-tetrahydrocannabinol ,hydroponic shelf were due to alterations of cellular membrane structure. However, in the late 1980s, due to the availability of new synthetic CB receptor agonists, it was first suggested that specific CB receptors exist . Soon after, the first CB receptor was sequenced and cloned . This receptor, named CB1, is highly expressed in the brain and mediates most, if not all, of the psychoactive/central effects of cannabis. A short time later, a second CB receptor, named CB2, was discovered . Until recently it was thought that CB2 receptors were present only in the periphery and did not mediate any central effect of CBs, but recent findings suggest that CB2 receptors are present at low levels in some areas of the brain . On the basis of studies showing certain behavioural and pharmacological effects of CB ligands that could not be explained exclusively by CB1 and CB2 receptors, it has also been hypothesized that additional non-CB1 and non-CB2 receptors might exist . The potential involvement of CB2 and non-CB1 and non-CB2 receptors in central effects of CBs needs further investigation and is not discussed in the present review. CB1 receptors are the most abundant G-protein-coupled receptors found in the brain . They are metabotropic receptors coupled to Gi/o proteins, whose activation results in inhibition of adenylyl cyclase activity and in a consequent decrease in cytosolic cAMP content, closure of Ca2 þ channels, opening of K þ channels and stimulation of kinases that phosphorylate tyrosine, serine and threonine residues in proteins . CB1 receptors are localized preferentially at the presynaptic level and, thus, it is believed that they inhibit the release of glutamate, GABA and other neurotransmitters . The localization of CB1 receptors in the brain is consistent with the known central effects of CBs, with highest concentrations in areas involved in memory , motor coordination and emotionality .
In the dopaminergic mesolimbic system, the best known circuit involved in motivational processes , average to high concentrations of CB1 receptors are found in the terminal region, the striatum, whereas low concentrations of CB1 receptors are found in the origin, the ventral tegmental area . These relatively low concentrations in the VTA do not necessarily indicate that CBs do not have important actions in this area. Several lines of evidence indicate that CB1 receptor agonists have strong modulating effects on VTA neuron activity and that CBs can produce rewarding effects when directly injected into this structure . It should be noted that anandamide, along with a variety of other lipids, can also activate transient receptor potential vanilloid type 1 vanilloid receptors . However, the role of these receptor channels in the behavioural and neurochemical effects of anandamide in brain reward processes remains largely undefined .In the early 1990s, anandamide and 2-arachidonoylglycerol were discovered and characterized as the first endogenous ligands for CB receptors. Subsequently, other possible endocannabinoids have been proposed, such as noladin ether , virodhamine and arachidonoyldopamine , but their natural occurrence and their roles are still unclear. Anandamide and 2-AG have different structures, different biosynthesis and degradation pathways and, in addition, appear to be formed under different conditions and to be differently affected by several manipulations, including pharmacological stimulation, as reviewed elsewhere . In addition, a recent paper has shown that anandamide inhibits the metabolism and the effects of 2-AG levels in the stiratum . Thus, it has been proposed that anandamide and 2-AG might play different roles in physiological and pathophysiological conditions . A peculiarity of the endocannabinoids, which makes them an interesting target for the discovery of new drugs, is that they are not present in vesicular stores but instead, are formed ‘on demand’ and undergo rapid metabolic deactivation, so that drugs that target this system would act predominantly when and where altered levels of endocannabinoids are present .CB1 receptors appear to play an important role in brain reward processes. One long-standing line of evidence for the role for CB1 receptors in brain reward processes is that CB1 receptor agonists, such as the active ingredient in cannabis, THC, have rewarding effects in humans and animals . The reinforcing effects of THC have been extensively reviewed elsewhere . Here, we focus on recent evidence for a modulatory role of endocannabinoids on the rewarding effects of drugs of abuse, food and electric brain stimulation.CB1 receptor agonists, such as THC, WIN 55,212-2 and HU-210, can facilitate the rewarding effects of drugs. For example, administration of THC or WIN 55,212-2 increases the reinforcing effects of heroin , nicotine and alcohol . Concerning psychostimulants, one study in rats has shown that administration of WIN 55,212-2 decreased self-administration of cocaine under a fixed-ratio schedule . However, as a decrease in the number of drug injections self-administered under a FR1 schedule can be interpreted either as a decrease or an increase in reinforcing efficacy , definitive conclusions cannot be drawn from these experiments.