Faculty Bibliography

New instruments are needed to assist substance use disorder (SUD) clinics in monitoring client treatment progress. This paper describes the development of an 8-item monitoring tool and results from a pilot to understand the tool's structure and reliability. 393 clients completed the tool upon treatment entry. A factor analysis resulted in a 2-factor solution: SUD symptoms and treatment progress. Multiple regression analyses showed that clients in inpatient detoxification had lower scores than those in less intensive levels of care. Acceptable internal consistency reliability was found. This study shows promise for the tool and sets the stage for future validation work.

Healthcare systems are implementing value-based payment (VBP) arrangements in efforts to incentivize cost-effective, high quality of care. These arrangements represent a major shift for substance use disorder (SUD) treatment providers who may need to make changes to their clinical and business operations to meet new demands for quality under value-based contracts. This qualitative study was conducted in the context of New York State's efforts to implement VBP among SUD treatment providers to understand their experiences, challenges, and needs. Five focus groups were conducted across the State with a total of 68 treatment professionals. Content analysis was conducted and five themes emerged. First, competing demands, limited workforce and technology infrastructure, and perceived lack of information were leading to overwhelmed administrators. Second, confusion and financial fear was being driven by the need for new clinical roles, business practices, and external partnerships. Third, providers were undertaking a number of measures to address workforce needs. Fourth, providers were building new business models and clinical practices. Fifth, providers desired more support and information. As VBP models are being adopted, healthcare systems should identify ways to mitigate challenges and support SUD treatment providers that may have limited resources to address complex workforce, client, and infrastructure needs.

Two studies examined inter-rater reliability and content-related validity of an addiction treatment level of care determination tool currently in use in New York, the LOCADTR 3.0. The studies occurred after tool implementation. In study 1, 139 providers used the LOCADTR 3.0 to determine level of care for four case vignettes. Inter-rater reliability coefficients were calculated. In study 2, 387,338 state records from existing data were analyzed to determine how often providers opted to override the LOCADTR 3.0 level of care determination by choosing an alternative level of care. In study 1, an acceptable inter-rater reliability (IRR = .57-.59) was found. Good indication of content-related validity was also found; participants chose the same level of care the study team chose for each vignette 80% of the time. In study 2, the override option was selected only 10% of the time, further establishing the content validity of the tool. These studies provide evidence for acceptable preliminary reliability and validity of the LOCADTR 3.0.

BACKGROUND:Medication for addiction treatment (MAT) has received much attention in recent years for treating individuals with opioid use disorders (OUD). However, these medications have been significantly underused among particular subgroups. In this paper, we describe the age distribution of treatment episodes for substance use disorder among Medicaid beneficiaries in New York and corresponding MAT use. METHODS:Using New York Medicaid claims, we identified individuals with OUD that received treatment for substance use disorder in 2015. The type of substance use treatment is the primary outcome measure, which includes methadone, buprenorphine, naltrexone or other non-medication treatment. RESULTS:A total of 88,637 individuals were diagnosed with OUD and received treatment for substance use disorder and 56,926 individuals received some type of MAT in 2015, with 40.2% receiving methadone, 21.9% receiving buprenorphine and 2.2% receiving naltrexone while 21.9% received non-medication based treatment. Young adults (ages 18-29) were a large proportion (25%) of individuals in treatment for OUD yet were the least likely to receive MAT. Relative to young adults, 30-39 year olds (adjusted odds ratio [AOR] = 1.62, 95% CI = 1.56-1.68), 40-49 year olds (AOR = 1.90, 95% CI = 1.82-1.99), 50-59 year olds (AOR = 2.65, 95% CI = 2.52-2.78), and 60-64 year olds (AOR = 5.03, 95% CI = 4.62-5.48) were more likely to receive MAT. CONCLUSIONS:These preliminary findings highlight high numbers of young adults in treatment for OUD and low rates of MAT, which is not consistent with treatment guidelines. Significant differences exist in the type of medication prescribed across age. More attention is needed to address the treatment needs among individuals of different age, notably young adults.

RATIONALE, AIMS, AND OBJECTIVES/UNASSIGNED:There are well-documented barriers that have limited widespread, sustained adoption of screening and brief intervention for risky substance use in health care settings. In order to better inform implementation efforts, this study evaluates whether patient characteristics, screening results, and implementation success indicators differed between two clinical setting types: primary care and emergency. METHODS:Patients presenting to an emergency or primary care setting were screened for risky substance use (n = 41 567). Patients with a positive screen were further assessed for psychosocial, health, and substance use problems (n = 1604). Differences in patient characteristics between primary care and emergency settings were examined using chi-square and t tests. Multilevel logistic regression was used to examine whether setting type predicted screening results. Site-level indicators of implementation success were calculated (percentage prescreens completed, percentage full screens completed, and percentage refused services) for all patient visits (n = 78 656). RESULTS:As compared with primary care patients, emergency patients had more severe substance use patterns and screening scores, were more likely to use a variety of illicit drugs, and reported more psychosocial issues. In logistic regression models, setting type did not predict whether patients screened positive; however, it did predict screening into a higher vs lower risk category such that emergency patients were more likely to be in a higher risk category. Emergency settings had lower indicators of implementation success (eg, 14% lower prescreen completion rate) as compared with primary care settings on some implementation measures. CONCLUSIONS:This evaluation found important differences in patient characteristics and screening and implementation results between primary care and emergency settings. Health care organizations and administrators implementing screening and brief intervention should attend to setting differences that could affect implementation and clinical care.

Reducing repeat use of costly inpatient services, such as inpatient withdrawal management, among Medicaid members is a target of healthcare reform. However, characteristics of frequent users of inpatient withdrawal management are understudied. We described the characteristics, service utilization, and costs of New York Medicaid clients who use withdrawal management services by analyzing data from Medicaid records from 2008. We examined follow-up care for individuals with different levels of repeat withdrawal management. We found 32,196 Medicaid withdrawal management patients with a total of 67,073 episodes and we divided patients into low (1 episode, n = 19,602), medium (2-3 episodes, n = 8619) and high (≥4 episodes, n = 3978) use categories. High users had almost 8 times the withdrawal management cost of low users. Similarly, they had 5 times more emergency department visits than low users. High users had high levels of homelessness (75%), 20% had HIV/AIDS, and 40% had Hepatitis. High withdrawal management users were less likely than low users to receive any follow-up treatment services. Medicaid clients with high utilization of inpatient withdrawal management are a small but costly population with poor follow-up rates to subsequent treatment services. They are a socially disenfranchised group that may benefit from targeted services to address their complex clinical needs.

Brazil has a sizable alcohol and substance use disorder (ASUD) population, yet there are considerable gaps in treatment access and retention. ASUD, a chronic medical condition, is highly comorbid with medical and behavioral health disorders. This indicates a need for more targeted interventions in order to achieve health care integration (a major goal of Brazil's health care system). Care management - that is, the organization of patient care by an institution - is a viable strategy to engage individuals with ASUD who might benefit from treatment but are not aware of or do not use the available resources, as well as to help maintain patients in treatment. Care management is considered an essential supplement to the treatment of chronic disease. The objective of this article is to discuss the applicability of care management for the treatment of ASUD within the public health care system in Brazil. We describe models of care management that have been adopted internationally and identify the feasibility and advantages for its adoption in Brazil.

BACKGROUND:Screening and brief intervention (SBI) for unhealthy drinking has not been widely implemented in primary care partly due to reliance on physicians to perform it. METHODS:We implemented a model of nursing staff-delivered SBI for unhealthy drinking for adult patients receiving primary care at an academically-affiliated Federally Qualified Health Center in the Bronx, NY. Our model consisted of nursing staff screening all patients with the alcohol use disorders identification test consumption questions (AUDIT-C) and, if screening positive, providing BI or referral to specialty services. We developed a clinical decision support tool integrated into the electronic health record to guide nursing staff and record SBI provision. To evaluate this model, we determined overall SBI delivery to patients and factors associated with receiving SBI. RESULTS:Between October 2013 and September 2014, 9119 unique adult patients made 24,285 visits. Patients were majority women (67.5%) and Hispanic/Latino (54.5%). Overall, 46.2% were screened, with 19.0-35.8% of eligible patients screened in each month. Increasing age (OR: 0.82 [95% CI 0.80-0.85] for a 10-year increase), female sex (OR: 0.83 [95% CI 0.77-0.91]), and chronic conditions like hypertension (OR: 0.62 [95% CI 0.56-0.70]) and diabetes (OR: 0.66 [95% CI 0.58-0.75]), among others, were associated with a lower odds of being screened. Of all patients screened, 225 (5.3%) screened positive and of those patients, 122 (54.2%) received a BI. Patients with higher AUDIT-C scores were more likely to receive a BI (OR: 1.24 [95% CI 1.04-1.47] for a 1-point increase) and non-English speaking patients were less likely to receive a BI than those who spoke English (OR: 0.42 [95% CI 0.18-0.97]). CONCLUSIONS:Our model of SBI resulted in screening of nearly half of all eligible patients and BI provision to over half of those screening positive. Future efforts to improve SBI delivery should focus on groups such as older adults, women, and those with chronic medical conditions.

BACKGROUND:The transitional period from late adolescence to early adulthood is a vulnerable period for weight gain, with a twofold increase in overweight/obesity during this life transition. In the United States, approximately one-third of young adults have obesity and are at a high risk for weight gain. PURPOSE:To describe the design and rationale of a National Institute of Diabetes and Digestive and Kidney Diseases (NIDDK) sponsored randomized, controlled clinical trial, the Healthy Body Healthy U (HBHU) study, which compares the differential efficacy of three interventions on weight loss among young adults aged 18-35years. METHODS:The intervention is delivered via Facebook and SMS Text Messaging (text messaging) and includes: 1) targeted content (Targeted); 2) tailored or personalized feedback (Tailored); or 3) contact control (Control). Recruitment is on-going at two campus sites, with the intervention delivery conducted by the parent site. A total of 450 students will be randomly-assigned to receive one of three programs for 18months. We hypothesize that: a) the Tailored group will lose significantly more weight at the 6, 12, 18month follow-ups compared with the Targeted group; and that b) both the Tailored and Targeted groups will have greater weight loss at the 6, 12, 18month follow-ups than the Control group. We also hypothesize that participants who achieve a 5% weight loss at 6 and 18months will have greater improvements in their cardiometabolic risk factors than those who do not achieve this target. We will examine intervention costs to inform implementation and sustainability other universities. Expected study completion date is 2019. CONCLUSIONS:This project has significant public health impact, as the successful translation could reach as many as 20 million university students each year, and change the current standard of practice for promoting weight management within university campus communities. ClinicalTrial.gov: NCT02342912.