Several studies have reported positive correlations, such as drinks per week , monthly binge drinking days , and AUDIT scores . Although the exact reasons for this discrepancy are unclear, we speculate that two factors may be relevant. The first is that the APT used in our study is different from other studies in terms of its instruction about framing the hypothetical drinking context, which will be discussed more in the study limitations later. Briefly, our generic description of the drinking situations may be insufficient to allow participants to imagine their typical drinking scenarios thus that they could not accurately report their alcohol demand. The other possible reason might be differences between study populations. Unlike previous studies , our participants were treatment seeking, and thus their motivation of quitting/reducing drinking and smoking may have changed how they responded in these purchase tasks. Besides the difference of motivations, our participants were more dependent on alcohol than the undergraduate samples tested previously —the average AUDIT score in our sample was almost twice that of theirs. Similarly, all of our participants had a diagnosis of AUD, while only about 50% who were dependent or abusing alcohol in the study by Amlung et al. . Additionally, our participants were also heavy smokers and importantly, several studies found that smoking resulted in higher demand for alcohol than nonsmoking . Thus, smoking may have resulted in a higher and more uniform alcohol use demand, masking a possible linear relationship between dependence and demand. Consistent with this possibility, we did not find any relationships between alcohol demand and alcohol misuse diagnoses. Although this possibility exists,rolling grow table future studies evaluating this population will help address whether heavy smoking can indeed mask the relationship between alcohol dependence measures and alcohol demand indices. The positive correlations between alcohol and cigarette demand indices suggest that those who had higher demand for alcohol tended to have higher demand for cigarettes too.
This co-demand pattern is consistent with a recent study which revealed the same positive correlations among a similar sample of heavy drinking smokers . Moreover, by conducting hierarchical multiple regression analyses, their study found that smoking had a positive impact on the alcohol demand, but not the other way around . Their finding may help explain the relative higher demand for alcohol than for cigarettes among treatment-seeking smokers with AUD in the present study, because our participants were more dependent on nicotine than those non-treatment seeking heavy drinking smokers in their study —the relatively higher level of smoking in our sample may have resulted in greater alcohol demand in an asymmetric fashion. An important study factor that should be taken into account is the differential alcohol and smoking satiation statuses among the participants. Although our participants were instructed to complete the hypothetical purchase tasks in a general context, we cannot rule out the possibility that the reported demand patterns may have been influenced by their alcohol and smoking statuses. Previously, we speculated that the special characteristics may have caused the null correlations between alcohol demand and alcohol related measures. Unlike other alcohol-related measures, alcohol withdrawal scores were correlated with alcohol demand metrics, which support the possibility that alcohol deprivation status may have indeed increased the reported demand for alcohol among our participants who experienced more alcohol withdrawal, consistent with a previous study which showed the increased cigarette demand among nicotine-deprived smokers . In the current study, we also found that cigarette demand metrics were positively correlated with smoking withdrawal, which suggests an increased demand for cigarettes due to smoking deprivation. However, the exact effects of alcohol deprivation on alcohol demand are more speculative with the current study design , which can be examined in future studies that contrast the alcohol demand metrics between deprived and satiated patients with AUD.
The study has the following limitations. First, the APT and CPT were administered separately, with each having no assumption of allocating limited resources to the other. Although our findings suggested that alcohol had higher demand than cigarettes using the single-commodity tasks , we do not have direct evidence that alcohol is preferred if both drugs are considered in the same context. Such relative preference between two co-used drugs can be best captured by a cross-commodity task wherein the consumption patterns for both drugs are examined simultaneously. Using the cross-commodity paradigm, researchers have found a complex interplay between cannabis and alcohol use with nontrivial proportions of the study sample showing patterns of complementarity, substitution, and independence . However, in a different cross-commodity study involving marijuana and tobacco cigarettes, researchers found an independent demand pattern between these two drugs . These studies suggest the manipulation robustness of using the cross-commodity paradigm in substance use research to simultaneously study couse of drugs. More importantly, this paradigm provides a better ecological validity by placing participants in a more realistic context with their access to both drugs while having limited shared resources. Future studies should consider using this cross commodity paradigm to better capture the demand for alcohol and cigarettes among smokers with AUD, which may shed light on developing personalized treatments based on relative demand patterns between alcohol and cigarettes. Second, to make the participants have similar contexts for the APT and CPT, the APT’s instruction used the same contextual description as the CPT’s, and differences in the current APT’s instructions from previous studies may have affected participants’ ability to report their alcohol demand with ecological validity. Previous studies have generally assessed alcohol demand under contexts in which alcohol is likely to be consumed . Similarly, time parameters such as duration of access and weekend vs. weekday have been shown to impact alcohol demand. Third, per protocol requirements, participants were abstinent from alcohol to have proper cognitive functionality to complete the visits, but they could smoke ad libitum.
Thus, differences in alcohol deprivation and smoking satiation may have affected the demand for alcohol and cigarettes. Alcohol appeared to have higher relative reinforcing efficacy than cigarettes among adult smokers with alcohol use disorder, as evidenced by their greater demand for alcohol than for cigarettes, although it is possible that acute substance status may play a role in modulating the demand for alcohol and cigarettes. A two-factor structure was identified for both alcohol and cigarette demand curves, and the differential loadings of demand indices in the current population of heavy drinking smokers and other less dependent younger samples assessed previously suggest a distinct demand pattern for smokers with AUD. As an important future direction of the present study,indoor plant table hierarchical multiple regressions analyses of multiple purchase tasks should be conducted to provide a deeper understanding of cross substance demand for alcohol and cigarettes among treatment seeking smokers with AUD.Health care reform in the United States has had major implications for people with substance use disorders , including greater opportunities to enroll in private insurance coverage, increased access to services, and changes in health care costs . The Affordable Care Act established state insurance exchanges to promote and offer health coverage, and mandated SUD and psychiatric disorder treatment as essential benefits. Practitioners expected these ACA mandates, implemented in 2014, to increase access to care . Following ACA implementation in 2014, the overall number of individuals living without insurance dropped . Evidence suggests a positive impact of the ACA on both SUD and psychiatry coverage , including an increase in insurance choices . The number of individuals with identified SUDs enrolled in health plans increased . But access to services remains a major concern , and much is still unknown regarding how ACA-associated enrollment through insurance exchanges and cost-sharing structures are associated with access to and use of SUD treatment and other health services in this complex patient population. SUD treatment initiation and retention are key clinical goals for SUD patients . Specific characteristics of the ACA, such as enrollment via new state insurance exchanges and increased patient cost sharing via higher deductibles, may influence treatment differentially for people with SUDs who may be new enrollees . Patient cost sharing may adversely impact both initiation and retention. If SUD treatment and psychiatry services are viewed as discretionary and less essential than primary care, they may be especially vulnerable to cost-sharing mechanisms . A previous evaluation of SUD patients enrolled in the same California healthcare system found that compared to a pre-ACA enrollment cohort with SUDs, post-ACA SUD patients had more psychiatric and medical conditions and greater enrollment in high-deductible plans. Although this prior work did not examine patterns of health service utilization, the findings suggest that newly enrolled patients post-ACA may have greater clinical needs as well as increased financial obstacles to accessing services . It is important to not only evaluate SUD treatment initiation and retention over time following implementation of the ACA, but also to evaluate how factors related to the ACA may influence utilization of other health services. The current study aimed to extend what is currently known about the consequences of healthcare reform by examining the potential relationship of ACA exchange enrollment and high deductible health plans to trends in health service utilization in a cohort of individuals who were newly enrolled in a healthcare system and had a documented SUD. We examined factors associated with utilization as conceptualized by the Andersen model of healthcare utilization , which proposes that utilization is determined by predisposing need and enabling factors .
We hypothesized that psychiatric comorbidity would be associated with greater use of health services, and that members with higher deductibles would be less likely to initiate SUD and psychiatry treatment but would have higher emergency department and inpatient utilization than those without deductibles. As with earlier studies , which indicate that SUD diagnosis is often precipitated by a critical event such as an ED visit, we expected that post diagnosis utilization would be highest in the period immediately following diagnosis but would likely decrease over time, although trajectories would vary by type of utilization. Knowing how these factors are associated with use of healthcare can be highly informative to future healthcare reform and behavioral health services research. Kaiser Permanente Northern California is an integrated healthcare system serving approximately 4 million members . The membership is racially and socioeconomically diverse and representative of the demographic of the geographic area . SUD treatment is provided in specialty clinics within KPNC, which patients can access directly without a referral.Treatment sessions take place daily or four times a week, depending on severity, for nine weeks . Treatment in psychiatry includes assessment, individual and group psychotherapy, and medication management . KPNC is not contracted to provide SUD care or intensive psychiatry treatment for Medicaid patients and those patients are referred to county providers. The University of California, San Francisco and Kaiser Permanente Northern California Institutional Review Boards approved the study and approved a waiver of informed consent. We identified common chronic medical conditions , many of which are known to be associated with SUDs using ICD-9/10 codes recorded within the first year after initial enrollment. Conditions included asthma, atherosclerosis, atrial fibrillation, chronic kidney disease, chronic liver disease, chronic obstructive pulmonary disease, coronary disease, diabetes mellitus, dementia, epilepsy, gastroesophageal reflux, heart failure, hyperlipidemia, hypertension, migraine, osteoarthritis, osteoporosis and osteopenia, Parkinson’s disease or syndrome, peptic ulcer, and rheumatoid arthritis. Patients with chronic medical conditions utilize more health services than patients without such conditions , which may influence their decision to choose a plan with a lower deductible if given an option , so we included this covariate to control for confounding. Deductibles are features across different benefit plans, including commercial plans, but are more common in ACA benefit plans. The individual deductible limit is the amount the individual must pay out of-pocket for health expenses before eligibility for health plan benefits. At KPNC, there are many types of benefit plans that include deductibles. Patients with deductible plans that do not include SUD as a covered benefit are responsible for bearing the cost of those services until their deductible is reached, and/or the accumulating cost of copays for multiple visits as part of the SUD care model. We did not include type of insurance as a covariate due to its collinearity with deductible limits and enrollment via the ACA exchange .