Literature Collection

Background: Opioids accounted for 75% of drug overdoses in the United States in 2020, with rural states particularly impacted by the opioid crisis. While medication assisted treatment (MAT) with Suboxone remains one of the more efficacious treatments for opioid use disorder (OUD), approximately 40% of people receiving Suboxone for outpatient MAT for OUD (MOUD) relapse within the first 6 months of treatment. We developed the smartphone app-based intervention OptiMAT as an adjunctive intervention to improve MOUD outcomes. The aims of this study are to (1) evaluate the efficacy of adjunctive OptiMAT use in reducing opioid misuse among people receiving MOUD; and (2) evaluate the role of specific OpitMAT features in reducing opioid misuse, including the use of GPS-driven just-in-time intervention. Methods: We will conduct a two-arm, single-blind, randomized controlled trial of adults receiving outpatient MOUD in the greater Little Rock AR area. Participants are English-speaking adults ages 18 or older recently enrolled in outpatient MOUD at one of our participating study clinics. Participants will be allocated via 1:1 randomized block design to (1) MOUD with adjunctive use of OptiMAT (MOUD+OptiMAT) or (2) MOUD without OptiMAT (MOUD-only). Our blinded research statistician will evaluate differences between the two groups in opioid misuse (as determined by quantitative urinalysis conducted by clinical lab staff blinded to group membership) during the 6-months following study enrolment. Secondary analyses will evaluate if OptiMAT-usage patterns within the MOUD+OptiMAT group predict opioid misuse or continued abstinence. Discussion: This study will test if adjunctive use of OptiMAT improve MOUD outcomes. Study findings could lead to expansion of OptiMAT into rural clinical settings, and the identification of OptiMAT features which best predict positive clinical outcome could lead to refinement of this and similar smartphone appbased interventions. Trial registration: ClinicalTrials.gov identifier: NCT05336188, registered March 21, 2022, https://clinicaltrials.gov/ct2/show/NCT05336188.

BACKGROUND: Opioids accounted for 75% of drug overdoses in the USA in 2020, with rural states particularly impacted by the opioid crisis. While medication-assisted treatment (MAT) with Suboxone remains one of the more efficacious treatments for opioid use disorder (OUD), approximately 40% of people receiving Suboxone for outpatient MAT for OUD (MOUD) relapse within the first 6 months of treatment. We developed the smartphone app-based intervention OptiMAT as an adjunctive intervention to improve MOUD outcomes. The aims of this study are to (1) evaluate the efficacy of adjunctive OptiMAT use in reducing opioid misuse among people receiving MOUD and (2) evaluate the role of specific OptiMAT features in reducing opioid misuse, including the use of GPS-driven just-in-time intervention. METHODS: We will conduct a two-arm, single-blind, randomized controlled trial of adults receiving outpatient MOUD in the greater Little Rock AR area. Participants are English-speaking adults ages 18 or older recently enrolled in outpatient MOUD at one of our participating study clinics. Participants will be allocated via 1:1 randomized block design to (1) MOUD with adjunctive use of OptiMAT (MOUD+OptiMAT) or (2) MOUD without OptiMAT (MOUD-only). Our blinded research statistician will evaluate differences between the two groups in opioid misuse (as determined by quantitative urinalysis conducted by clinical lab staff blinded to group membership) during the 6-months following study enrolment. Secondary analyses will evaluate if OptiMAT-usage patterns within the MOUD+OptiMAT group predict opioid misuse or continued abstinence. DISCUSSION: This study will test if adjunctive use of OptiMAT improve MOUD outcomes. Study findings could lead to expansion of OptiMAT into rural clinical settings, and the identification of OptiMAT features which best predict positive clinical outcome could lead to refinement of this and similar smartphone app-based interventions. TRIAL REGISTRATION: ClinicalTrials.gov identifier: NCT05336188 , registered March 21, 2022.

OBJECTIVE: This study examined trends in receipt of smoking cessation medications among smokers with and without mental illness, including serious mental illness, from 2005 to 2019 and characterized physician attitudes and practices related to tobacco screening and cessation treatment. METHODS: Medical Expenditure Panel Survey (MEPS) data (2005-2019) were examined for receipt of cessation medication prescriptions for bupropion, varenicline, and nicotine replacement therapy (NRT) among 55,662 smokers-18,353 with any mental illness and 7,421 with serious mental illness. Qualitative interviews with 40 general internists and psychiatrists between October and November 2017 used a semistructured guide. MEPS data were analyzed with descriptive statistics, and interviews were analyzed with hybrid inductive-deductive coding. RESULTS: From 2005 to 2019, at least 83% of smokers with or without mental illness did not receive varenicline, NRT, or bupropion. Over 14 years, the proportion of smokers receiving varenicline peaked at 2.1% among those with no mental illness, 2.9% among those with any mental illness, and 2.4% among those with serious mental illness. The respective peak proportions for NRT were 0.4%, 1.1%, and 1.6%; for bupropion, they were 1.2%, 8.4%, and 16.7%. Qualitative themes were consistent across general internists and psychiatrists; providers viewed cessation treatment as challenging because of the perception of smoking as a coping mechanism and agreed on barriers to treatment, including lack of insurance coverage and contraindications for people with mental illness. CONCLUSIONS: System- and provider-level strategies to support evidence-based smoking cessation treatment for people with and without mental illness are needed.

INTRODUCTION: Smoking is a significant modifiable risk factor for mortality for persons with serious mental illness (SMI), who have a life expectancy 15-20 years shorter than the general population. Individuals with SMI and comorbid diabetes who are smokers face an even higher risk of cardiovascular complications and early death. Yet despite high rates of smoking among people with SMI, tobacco cessation interventions have not been broadly offered to this population. METHODS: We conducted a retrospective cohort study using electronic health records from 2014 in a large integrated care delivery system to examine whether use of smoking cessation pharmacotherapy among smokers with type 2 diabetes varies by serious mental illness (SMI) diagnosis. We analyzed smoking cessation medication prescription fills among adult smokers with diabetes, comparing those with SMI (N = 634) and without SMI (N = 18,021). Risk ratios were adjusted for age, gender, race, urban area type, and medical facility. RESULTS: Of the SMI group, 19.09% filled at least one smoking cessation prescription compared to 9.73% of the non-SMI group (adjusted risk ratio 1.80 [95% CI 1.52-2.13]; p < .001). For the SMI group, primary care providers wrote 80.24% of prescriptions, while psychiatrists wrote 8.81% of prescriptions. CONCLUSIONS: These findings offer an example of a delivery system with higher uptake of smoking cessation pharmacotherapy among people with SMI than without SMI, and highlight the opportunity to provide more smoking cessation interventions in mental health care settings.

OBJECTIVES: Factors associated with treatment retention on medications for opioid use disorder (MOUD) in rural settings are poorly understood. This study examines associations between social determinants of health (SDoH) and MOUD retention among patients with opioid use disorder (OUD) in rural primary care settings. METHODS: We analyzed patient electronic health records from 6 rural clinics. Participants (N = 575) were adult patients with OUD and had any prescription for MOUD from October 2019 to April 2020. MOUD retention was measured by MOUD days and continuity defined as continuous 180 MOUD days with no more than a 7-day gap. Mixed-effect regressions assessed associations between the outcomes and SDoH (Medicaid insurance, social deprivation index [SDI], driving time from home to the clinic), telehealth use, and other covariates. RESULTS: Mean patient MOUD days were 127 days (SD = 50.7 days). Living in more disadvantaged areas (based on SDI) (adjusted relative risk [aRR]: 0.98; 95% confidence interval [CI], 0.98-0.99) and having more than an hour (compared with an hour or less) driving time from home to clinic (aRR: 0.95; 95% CI, 0.93-0.97) were associated with fewer MOUD days. Using telehealth was associated with more MOUD days (aRR: 1.23; 95% CI, 1.21-1.26). In this cohort, 21.7% of the participants were retained on MOUD for at least 180 days. SDoH and use of telehealth were not associated with having continuity of MOUD. CONCLUSIONS: Addressing SDoH (eg, SDI) and providing telehealth (eg, improvements in public transportation, internet access) may improve MOUD days in rural settings.

Opioid use and misuse are a widespread problem across the United States. Identifying and targeting social determinants of opioid use may help to identify predictive factors to influence intervention and policy. The purpose of this study was to identify social determinants of opioid use frequency among patients seeking primary care in rural Alabama healthcare facilities. This survey-based study focused on a patient population located in rural west Alabama surveyed for a screening, brief intervention, and referral to treatment program. The screening tool contained demographic information and questions regarding the social determinants of health and opioid use, among others. Adjusted incidence rate ratios (IRRs) for the relationship between social determinants of health and opioid use frequency (in days/month) were estimated in Poisson regression models. Eleven percent of the population self-reported opioid use in the past 30 days. Three social determinants of health measured (level of education, housing stability, and employment status) were identified as having a significant association with the frequency of opioid use. Targeting certain social determinants of health may allow for further predictive interventions to mitigate opioid misuse and potential fatality or mortality.