issue: Drug-related death is the leading cause of death among homeless New Yorkers, with the vast majority resulting from accidental overdose, which increased 39% from 2016 to 2017. In response, the New York City (NYC) Department of Homeless Services (DHS), which provides shelter and services to New Yorkers experiencing homelessness, developed and implemented a comprehensive, mandatory, multi-faceted Opioid Overdose Prevention Program (OOPP).

description: In 2016, DHS became a New York State-certified OOPP and started mandatory, systematic intranasal naloxone administration training at DHS sites. The expansion of naloxone administration training was supported by a formal DHS substance use and overdose response policy. NYCDHS’ OOPP is built on three key pillars: train-the-trainer model; overdose Champions; and data monitoring. Every trainer is provided with goals for training clients and staff. OOPP data are reviewed in-depth by the NYCDHS Office of the Medical Director to monitor program performance and identify areas for improvement.

results: In 2018, DHS trained 7,222 individuals, including 3,480 shelter staff, 136 champions, and 3,606 clients—a three-fold increase from 2017 when 2,323 individuals were trained. NYC DHS distributed 5.5 times more naloxone kits in 2018 (15,975) than in 2017 (2,861). As a result, the number of naloxone administrations for presumed overdoses increased by 417%, from 112 in 2016 to 579 in 2018. To further target these efforts, NYCDHS added overdose-risk questions to shelter intake, developed alerts about individuals who may need overdose prevention services, and used monitoring data to target sites for naloxone dispensing drives.

lessons learned: The DHS OOPP has been successful at extending naloxone administration training throughout NYC’s shelter system. Naloxone administration training helped increase effectiveness of staff response to overdoses, with increases in naloxone administration and overdoses reversed.

recommendation: A robust substance use policy and rigorous monitoring of the intervention are critical anchors of a successful program.

Mental health diagnoses (MHD) are common among those with opioid use disorders. Methadone/buprenorphine are effective medication-assisted treatment (MAT) strategies; however, treatment initiation and adherence are low in those with MHDs. There may exist racial/ethnic and gender disparities in who receives MAT, regardless of chronicity of use. Little is known regarding how MHDs may affect the likelihood of receiving MAT between races/ethnicities and genders. A tailored approach to MAT in clinical settings may benefit some underrepresented groups.

Episodes of MAT among treatment seekers indicating addiction to heroin or other opioids were identified using the Treatment Episodes Dataset–Discharges (TEDS-D; 2015-2016) (n=339,188). We used multiple logistic regression to test for associations between co-occurring MHD and whether a treatment client received MAT. We used an interaction term to explore moderation by race/ethnicity and gender. Discussion was framed within Intersectionality Theory.

Among treatment participants with opioid abuse or dependence, within-group proportions of co-occurring MHDs varied widely, highest among White, Hispanic, and African American women (52%, 51%, and 50%, respectively) (x²=6328.83, p<0.0001). In our adjusted model, an MHD was associated with lower odds of receiving MAT among Native American women (aOR=0.53, 95% CI=0.42-0.66, p<0.0001) and African American women (aOR=0.86, 95% CI=0.79, 0.95, p=0.001), and a higher odds of receiving MAT among Hispanic men and women (aOR=1.50, 95% CI=1.42-1.58, p<0.0001; aOR=1.35, 95% CI=1.23-1.48, p<0.0001, respectively) and White men and women (aOR=1.38, 95% CI=1.34-1.42, p<0.0001; aOR=1.29, 95% CI=1.24-1.33, p<0.0001, respectively).

The association between co-occurring MHDs and the receipt of MAT varied by race/ethnicity and gender, indicating moderation. African American women demonstrated a high prevalence of MHDs, but MHDs were associated with a lower likelihood of MAT utilization. Racial/ethnic minority groups and women undergoing MAT would likely benefit from integrated mental health services. Future clinical research is needed to demonstrate the efficacy of tailored MAT approaches.

Background: Text message (TM) interventions have demonstrated promising clinical outcomes in primary care yet integration in office- based opioid treatment (OBOT), HIV, and HCV care remains limited.

Objectives:
1) Design a mHealth prototype for office-based treatment with buprenorphine (OBOT) based on qualitative interviews (patients, providers, and administrators) addressing medication adherence, patient-provider communication, self-management, abstinence,
and counseling/self-help group participation

2) Conduct a pilot randomized controlled trial evaluating linkage and retention to OBOT among inpatient detoxification program patients transitioning to OBOT

Methods:

This is an ongoing study being conducted at Bellevue Hospital’s Adult Primary Care Clinic in NYC. mHealth design was based on the medical management model, the service oriented architecture model, and evaluated per the technology acceptance model. During Aim 1, prototype mock-ups and ‘test’ messages were provided to participants and elicited feedback on content and design features (version 1.0). During Aim 2, a randomized controlled trial of the refined prototype (version 2.0) is being conducted among newly enrolled OBOT program patients assessing linkage and retention in primary care compared to treatment as usual among inpatient detoxification program patients with opioid use disorder.

Results: Adoption of the TM intervention was high (n=23/28, 82%) among a mostly underserved sample of OBOT program patients [African-American (42%), Hispanic/Latino (23%), medicaid (62%), unemployed (34%), street homeless (45%)]. Clinical characteristics were representative of the general clinic population, including HIV+ (22%) and HCV+ (48%) status, and depression (21%). Usability testing revealed delays in software programming, de-bugging, dashboard design, inability to deliver algorithmic or sequential messages during induction, inability to link with the hospital EMR or appointment scheduling software, and need for real-time feedback to patient queries by providers. At 8 weeks, most were highly satisfied with the intervention (93%) and reported no adverse events (privacy, cost, increased cravings). Suggestions also included personalizing the frequency and timing of messages, providing more CBT-
based content, and peer support. Linkage to OBOT among inpatient detoxification program patients receiving the mHealth intervention was increased to 34% versus treatment as usual 23%. Retention among participants enrolling in OBOT was 65% at 8 weeks compared to 52% retention among participants randomized to treatment as usual.

Conclusions: TM based interventions are a feasible approach for enhancing linkage and retention in OBOT, HIV, and HCV care. Increased linkage and retention in primary care among participants randomized to the mHealth intervention suggest potential efficacy compared to treatment as usual. This is an ongoing study and require additional participants prior to any significance testing and confirmation of potential effect.

Background. The number of private, for-profit drug and alcohol treatment facilities in the US increased between 2002-2010, whereas the number of public/non-profit facilities declined; poorer communities were less likely to gain a private facility and more likely to lose a public facility. This analysis uses more recent data to assess associations between census block group (CBG) poverty and presence of drug and alcohol treatment facilities accepting Medicaid within close proximity.

Methods. This analysis includes all metropolitan CBGs in the US (n=173,261). We geocoded addresses of drug and alcohol treatment facilities in the National Survey of Substance Abuse Treatment Services Directory to CBGs for 2010, 2013, and 2017. We counted total facilities accepting Medicaid within 3 miles of the population density-weighted centroid of each CBG and created a binary dependent variable denoting whether there was >1 facility in each CBG. American Community Survey 5-year estimates for 2010, 2013, and 2017 were used to create measures capturing sociodemographic characteristics of CBGs (e.g., unemployment, race). Generalized estimating equations examined associations between CBG percent families below poverty and presence of treatment facilities accepting Medicaid, controlling for mean-centered CBG sociodemographic factors, census tract urbanicity, and state.

Results. In 2010, 54% of CBGs had >1 treatment facility within 3 miles accepting Medicaid; this increased to 55% in 2013 and 59% in 2017. The mean percent of families in poverty was 11%, 13%, and 12% in 2010, 2013, and 2017, respectively. In the multivariable model, a one standard deviation increase in the percent of families in poverty was significantly associated with increased odds of >1 treatment facility accepting Medicaid within 3 miles (AOR 1.17, 95% CI 1.15, 1.18, p< .0001).

Conclusions. Gains in treatment facilities were modest; nearly half of metropolitan CBGs did not have a treatment facility accepting Medicaid within 3 miles. Controlling for multiple covariates, the adjusted odds were greater in higher poverty CBGs. Future research should explore potential drivers, including policies, of geographic accessibility of drug and alcohol treatment facilities in communities with higher concentrations of poverty.