A “pharmacy data only” model included utilization variables related to medications and opioid use data, similar to what might be available to a pharmacy benefit manager researcher. The CHAID procedure identified a number of significant interactions that met the criteria outlined in the Methods section. These interactions were added to the core variables for each model, but only if the variables involved in the interaction were also included in the model individually. The model that was selected as the best fit for the data was comprised of mental health variables; specifically, this model included the diagnostic status for ICD-9 mental disorders and the health service utilization variables that focused on mental health . This model provided the best fit for the data, as defined by AIC values and by overall parsimony. The log likelihood ratio test for the selected model was 12,695 . The remaining models had LR values ranging from 5,785 to 13,095 ; all of these ratios were also statistically significant . Likewise, the results for the Wald and Score tests were significant across all models. In comparison to the selected model, the only other model with a lower AIC value contained all of the variables presented in the bivariate comparisons above, as well as all significant interactions involving these variables, while only providing a modest decrease in AIC. The resulting model is presented in Table 5. This predictive model was 79.5% concordant with actual OUDs in the validation data set, meaning that almost four-fifths of the OUDs were correctly identified when the model was applied to a different sample of participants. As is noted in Table 5,indoor farming systems 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, 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. Health service utilization was also significantly greater among OUDs than among nonOUDs. 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,vertical farming equipment suppliers 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.
Anxiety disorders are associated with significant quality-of-life impairments, carry an economic burden of billions of dollars in the United States, and are one of the top ten leading causes of disability globally . When anxiety disorders co-occur with substance use disorders, such co-occurrence is associated with markedly worse outcomes such as increased rates of drug-related problems, unemployment, and poorer treatment outcomes . The onset of both anxiety and substance use disorders often occurs during childhood and adolescence and understanding the potential shared etiological mechanisms underlying both anxiety and substance use disorders may help improve assessment and treatment of these disorders and their co-occurrence. Gray’s reinforcement sensitivity theory offers explanations of risk for both disorders and possibly also for their co-occurrence. RST describes two major neurophysiological motivational systems that differ in their responsivity to reward and punishment. The behavioral inhibition system is activated when punishment or non-reward conflicts with a goal or reward and results in worry, risk assessment, and increased attention to threat. In contrast, the behavioral approach/activation system is activated in response to rewarding stimuli and results in impulsivity, goal pursuit, and increased attention to reward. The biologically based temperamental style of BI is similar to the BIS in that it is characterized by anxious, fearful, and vigilant reactions to novel stimuli . BI is identifiable as early as infancy and has demonstrated trait-like stability . Several studies have shown BI to be stable across early and middle childhood and possibly through late adolescence and early adulthood . However, the stability and predictive validity of BI varies across individuals. For example, BI is more stable for girls and those who are initially highest in BI as compared to their peers . Further, many of the infants and toddlers that are initially high in behaviorally assessed BI no longer display such sensitivity to novel stimuli as they age . Degnan et al. showed that only 15% of toddlers displayed initially high levels of BI and continued to display such high levels of BI at age 5. Identification of children with high and stable levels of BI is particularly important as they are at increased risk for experiencing symptoms of anxiety and the development of anxiety disorders in adolescence . In fact, nearly half of children high in BI develop social anxiety disorder in adolescence . The BIS may be related to and influenced by BI. While the BIS is assessed by self-report and at later stages of childhood through adulthood, as compared to the behavioral assessment of BI, scores on the BIS/BAS scale may yield improved reliability and generalizability in its assessment of BI . The relationship between BI and substance use is much more mixed than that of BI and anxiety. BI may be a protective factor against substance use as the conflicting rewarding and punishing outcomes associated with substance use may activate the BIS and lead to worry and increased attention to the potential negative consequences of use. However, it is also possible that BI may increase the likelihood of substance use through coping motives for use . Individuals with alcohol use disorder as well as individuals with co-occurring anxiety and alcohol dependence evidence greater levels of sensitivity to uncertain threat , a trait associated with, and predicted by, high levels of BI .