Faculty Bibliography

CONTEXT/BACKGROUND:Predictive modeling can identify neighborhoods at elevated risk of future overdose death and may assist community organizations' decisions about harm reduction resource allocation. In Rhode Island, PROVIDENT is a research initiative and randomized community intervention trial that developed and validated a machine learning model that predicts future overdose at a census block group (CBG) level. The PROVIDENT model prioritizes the top 20th percentile of CBGs at highest risk of future overdose death over the subsequent 6-month period. In CBGs assigned to the trial intervention arm, these predictions are then displayed for partnering community organizations via an interactive mapping dashboard. OBJECTIVE:To evaluate whether CBGs prioritized by the PROVIDENT model were associated with increased user engagement via an online dashboard for fatal overdose forecasting and resource planning. DESIGN/METHODS:We estimated prevalence ratios using modified Poisson regression models, adjusted for CBG-level characteristics that may confound the relationship between model predictions and dashboard engagement. SETTING/METHODS:We used CBG-level data in Rhode Island (N = 809) from November 2021 to July 2024. INTERVENTION/METHODS:Our exposure of interest was whether each CBG was prioritized by the PROVIDENT model and shown as prioritized on the interactive mapping dashboard. MAIN OUTCOME MEASURE/METHODS:Our primary outcome was whether a dashboard user from any partnering community organization engaged (eg, clicked, interacted with dashboard elements, or completed assessment or planning surveys) with each CBG on the interactive mapping dashboard. RESULTS:After adjusting for previous model predictions and dashboard engagement, nonfatal overdose counts, and distribution of race and ethnicity, poverty, unemployment, and rent burden, dashboard users were 1.0 to 2.4 times as likely to engage with CBGs prioritized by the PROVIDENT model that were shown as prioritized on the dashboard as compared to CBGs that were prioritized by the PROVIDENT model that were blinded on the dashboard. CONCLUSIONS:Interactive mapping tools with predictive modeling may be useful to support community-based harm reduction organizations in the allocation of resources to neighborhoods predicted to be at high risk of future overdose death.

More than one million people have died from drug overdose in the United States in the past 20 years. The overdose crisis started in the late 1990s with the proliferation of overdoses involving prescription opioids, transitioned to heroin-involved overdoses in 2010, and is currently driven by illegally manufactured synthetic opioids such as fentanyl. In response to this crisis, New York City implemented two publicly recognized overdose prevention centers (OPCs) in the nation in November 2021. Rhode Island became the first US state to authorize OPCs through state legislation and will open a site in Fall 2024. We are conducting a rigorous, multi-site, multi-component evaluation of OPCs in New York City and Rhode Island. At the individual level, we assess whether a cohort of 500 persons utilizing OPCs experience lower rates of overdose, other health problems (e.g., hepatitis C, skin infections), and emergency department use, and a higher rate of substance use treatment initiation, compared to a cohort of 500 persons who use drugs but do not utilize OPCs. At the community level, we examine whether neighborhoods surrounding the OPCs experience a greater change in overdose, measures of drug-related public disorder, and acute economic conditions following the opening of OPCs, compared to neighborhoods with no OPCs. Third, we delve into the role that the operational context, including neighborhood location, program models, and operating procedures, plays in shaping the effectiveness of OPCs using qualitative and ethnographic approaches. Fourth, we estimate the costs and cost savings associated with starting up and operating OPCs. In this paper, we: (1) present the study design and harm reduction framework which is used to evaluate the impact of OPCs in New York City and Rhode Island; (2) share the types of assessment instruments and data sources used to measure changes at the individual and community level; and (3) discuss the strengths and limitations associated with the planned approach to evaluate the health and community effects of OPCs.

As the United States grapples with an ongoing overdose crisis, states and jurisdictions are adopting novel approaches to reduce overdose mortality. In one novel approach, public health researchers and leaders in Rhode Island leveraged the state's robust surveillance data and collaborations between government, academic, and community-based organizations (CBOs) to launch the PROVIDENT (PReventing OVerdose using Information and Data from the EnvironmeNT) project, a population-based randomized controlled research trial (NCT05096429) in December 2019. The PROVIDENT trial utilizes machine learning (ML) methods to identify neighborhoods at risk of future overdose deaths at the census-block-group level to inform community-level overdose prevention resource distribution. To disseminate the ML model predictions, our research team developed an interactive, online mapping dashboard in close collaboration with three statewide CBOs. We measured whether these organizations utilized the PROVIDENT dashboard to allocate harm-reduction services based on ML model predictions and collected information about their data-driven decision-making processes. This case study describes how we assembled and piloted this overdose forecasting dashboard for use by CBOs between November 2021 and August 2024. By measuring dashboard logins, completed surveys, and engagement with ongoing training, we illustrate how organizations utilized ML and surveillance data to inform their outreach efforts and generate valuable insights at a neighborhood level.

To meet the needs of diverse communities, public health authorities are increasingly reliant on hyperlocal interventions targeting specific health issues and distinct populations. To facilitate epidemiological evaluation of hyperlocal interventions on community-level outcomes, we developed a framework of six practice-based considerations for researchers: spatial zone of impact, temporal resolution of impact, outcome of interest, definition of a plausible comparison group, micro vs. macro impacts, and practitioner engagement. We applied this framework to a case study of an impact evaluation of the New York City (NYC) overdose prevention centers (OPCs) on neighborhood-level drug-related arrests. We used drug arrest data from NYC from January 1, 2014, to September 30, 2023 and US Census data to conduct synthetic control modeling, comparing pre- and post-OPC arrests in the neighborhoods surrounding the two NYC OPCs (East Harlem and Washington Heights). We conducted sensitivity analyses to validate our results and compare our findings with those from a prior published study. Our findings indicate no significant change in drug-related arrests following the OPC openings. The mean absolute differences in daily drug-related arrests between the OPCs and their synthetic controls were 0.63 (p = 0.19) in East Harlem and 0.14 (p = 0.22) in Washington Heights. Sensitivity analyses corroborated our main results. Overall, findings demonstrate how our framework can be used to guide future epidemiological evaluations of diverse, hyperlocal public health interventions.

Following federal regulatory changes during the COVID-19 pandemic, Rhode Island expanded methadone access for opioid treatment programs (OTPs) in March 2020. The policy, which permitted take-home dosing for patients, contrasted with longstanding restrictions on methadone. This study used patient-level OTP admission and discharge records to compare six-month retention before and after the policy change. We conducted a retrospective cohort study of 1,248 patients newly admitted to OTPs between March 18 and June 30 of 2019 (pre-policy) and 2020 (post-policy). We used logistic regression to estimate associations with retention before and after the policy and used a machine learning approach, the Heterogeneous Treatment Effect (HTE)-Scan, to explore heterogeneity in retention across subgroups. Overall, we found no change in retention following the policy, with an adjusted OR of 1.08 (95% CI: 0.80-1.45) and adjusted RR of 1.03 (0.90-1.18). Using HTE-Scan, we identified two subgroups with significantly increased retention above the overall cohort: (1) patients with below high school education and past-month arrest and (2) male, non-Hispanic white or Hispanic/Latino patients reporting heroin or fentanyl use with past-month arrest. We identified no subgroups with significantly decreased retention. Collectively, findings suggest that expanded methadone access may benefit vulnerable populations without harming overall retention.

BACKGROUND:Concerned significant others (CSOs) play a significant role in supporting individuals with substance use disorders. There is a lack of tailored support services for these CSOs, despite their substantial contributions to the well-being of their loved ones (LOs). The emergence of helplines as a potential avenue for CSO support is outlined, culminating in the focus on the Partnership to End Addiction's helpline service, an innovative public health intervention aimed at aiding CSOs concerned about an LO's substance use. OBJECTIVE:The article analyzes the demographics and use patterns of the Partnership to End Addiction's helpline service, highlighting the critical role of such services, and advocating for expanded, tailored support models. METHODS:This observational study draws data from 8 data platforms spanning April 2011 to December 2021, encompassing 24,096 client records. Surveys were completed by helpline specialists during synchronous telephone calls or self-reported by CSOs before helpline engagement. Collected information encompasses demographics, interaction language, substance of concern, CSO-LO relationship, and the LO's "use state," that is, their location on the continuum of substance use. RESULTS:CSOs primarily comprised women (13,980/18,373, 76.1%) seeking support for their children (1062/1542, 68.9%). LOs were mostly male (1090/1738, 62.7%), aged 18-25 years (2380/7208, 33%), with primary substance concerns being cannabis (5266/12,817, 40.9%), opioids (2445/12,817, 19%), and stimulants (1563/12,817, 12.1%). CSOs primarily sought aid for LOs struggling with substances who were not in treatment (1102/1753, 62.9%). The majority of CSOs were looking for support in English (14,738/17,920, 82.2%), while the rest (3182/17,920, 17.8%) preferred to communicate in Spanish. Spanish-speaking CSOs were significantly more likely to call about cannabis (n=963, 53.7% vs n=4026, 38.6%) and stimulants (n=304, 16.9% vs n=1185, 11.3%) than English-speaking CSOs (P<.001). On the other hand, English-speaking CSOs were more likely to be concerned about opioids than Spanish-speaking CSOs (n=2215, 21.3% vs n=94, 5.2%; P<.001). CONCLUSIONS:The study illuminates the helpline's pioneering role in aiding CSOs grappling with an LO's substance use. It highlights helplines as crucial resources for CSOs, revealing key demographic, substance-related, and use-state trends. The dominant presence of women among users aligns with other helpline patterns and reflects traditional caregiving roles. While parents form a significant percentage of those reaching out, support is also sought by siblings, friends, and other family members, emphasizing the need for assistance for other members of an LO's social network. Spanish-speaking individuals' significant outreach underscores the necessity for bilingual support services. Substance concerns revolve around cannabis, opioids, and stimulants, influenced by age and language preferences. The helpline serves as an essential intermediary for CSOs, filling a gap between acute crisis intervention services and formalized health care and treatment services. Overall, the study highlights this helpline's crucial role in aiding CSOs with tailored, accessible support services.

OBJECTIVES/OBJECTIVE:To evaluate the predictive performance of different data sources to forecast fatal overdose in Rhode Island neighborhoods, with the goal of providing a template for other jurisdictions interested in predictive analytics to direct overdose prevention resources. METHODS:We evaluated seven combinations of data from six administrative data sources (American Community Survey (ACS) five-year estimates, built environment, emergency medical services non-fatal overdose response, prescription drug monitoring program, carceral release, and historical fatal overdose data). Fatal overdoses in Rhode Island census block groups (CBGs) were predicted using two machine learning approaches: linear regressions and random forests embedded in a nested cross-validation design. We evaluated performance using mean squared error and the percentage of statewide overdoses captured by CBGs forecast to be in top percentiles from 2019 to 2021. RESULTS:Linear models trained on ACS data combined with one other data source performed well, and comparably to models trained on all available data. Those including emergency medical service, prescription drug monitoring program, or carceral release data with ACS data achieved a priori goals for percentage of statewide overdoses captured by CBGs prioritized by models on average. CONCLUSIONS:Prioritizing neighborhoods for overdose prevention with forecasting is feasible using a simple-to-implement model trained on publicly available ACS data combined with only one other administrative data source in Rhode Island, offering a starting point for other jurisdictions.

OBJECTIVES/OBJECTIVE:The escalating overdose crisis in the United States points to the urgent need for new and novel data tools. Overdose data tools are growing in popularity but still face timely delays in surveillance data availability, lack of completeness, and wide variability in quality by region. As such, we need innovative tools to identify and prioritize emerging and high-need areas. Forecasting offers one such solution. Machine learning methods leverage numerous datasets that could be used to predict future vulnerability to overdose at the regional, town, and even neighborhood levels. This study aimed to understand the multi-level factors affecting the early stages of implementation for an overdose forecasting dashboard. This dashboard was developed with and for statewide harm reduction providers to increase data-driven response and resource distribution at the neighborhood level. METHODS:As part of PROVIDENT (Preventing OVerdose using Information and Data from the EnvironmeNT), a randomized, statewide community trial, we conducted an implementation study where we facilitated three focus groups with harm reduction organizations enrolled in the larger trial. Focus group participants held titles such as peer outreach workers, case managers, and program coordinators/managers. We employed the Exploration, Preparation, Implementation, Sustainment (EPIS) Framework to guide our analysis. This framework offers a multi-level, four-phase analysis unique to implementation within a human services environment to assess the exploration and preparation phases that influenced the early launch of the intervention. RESULTS:Multiple themes centering on organizational culture and resources emerged, including limited staff capacity for new interventions and repeated exposure to stress and trauma, which could limit intervention uptake. Community-level themes included the burden of data collection for program funding and statewide efforts to build stronger networks for data collection and dashboarding and data-driven resource allocation. DISCUSSION/CONCLUSIONS:Using an implementation framework within the larger study allowed us to identify multi-level and contextual factors affecting the early implementation of a forecasting dashboard within the PROVIDENT community trial. Additional investments to build organizational and community capacity may be required to create the optimal implementation setting and integration of forecasting tools.

There is an urgent need to ensure the opioid overdose reversal agent naloxone is available to protect youth given the increasing rates of overdose among this population. Through a public-academic partnership, overdose education and naloxone distribution training were provided to nonmedical public school staff in New York City. School staff were invited to a 90-minute in-person training. Consented participants took a pre- and post-survey to assess their overdose knowledge, confidence, and substance use stigma. A majority of respondents had never received training on how to identify an opioid overdose (70.7%) or how to administer naloxone (73.5%). Participants' overdose knowledge, including recognition of the signs of an overdose, response actions, and confidence to respond, significantly increased pre- to post-training. Participants' stigmatization of drug use significantly decreased following the training. Naloxone access and opioid overdose response training for nonmedical school staff is an acceptable and effective solution to expand overdose response. The significant reduction in participants' stigmatization of drug use suggests overdose education and naloxone training that address stigma may help prevent unnecessary mortality among youth.

This pilot study explores the utilization of the Overdose Detection Mapping Application Program (ODMAP) as a tool for enhancing collaboration between the public health and public safety sectors to address the overdose epidemic in the United States. Through qualitative interviews with ODMAP users, key themes emerged, including the role of data sharing in facilitating collaboration, challenges posed by divergent data privacy standards, and the need for clearer guidance on cross-sector data sharing. Findings highlight ODMAP's potential to integrate data for targeted interventions at individual and population levels. Future research directions include overcoming data sharing barriers, strategically utilizing data across sectors, and rigorously evaluating the impact of cross-sector partnerships on overdose morbidity and mortality. Overall, this study underscores the importance of ODMAP in fostering coordinated responses to the overdose crisis and provides valuable insights for improving overdose surveillance and intervention efforts.