Faculty Bibliography

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.

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.

OBJECTIVE/UNASSIGNED:Policy ramifications of the COVID-19 pandemic shape the concurrent housing and overdose crises in the USA. Housing insecurity is a known risk factor for overdose, yet how residential eviction may influence fatal overdose risk is understudied. We sought to evaluate the spatiotemporal relationship between neighbourhood-level residential eviction rates and overdose mortality in Rhode Island (RI) before and during a statewide eviction moratorium in response to COVID-19. METHODS/UNASSIGNED:We conducted an ecological study at the census tract level in RI (N=240) by modelling the association between quintiles of eviction rates and fatal overdose rates from 2016 to 2021. We applied a Bayesian spatiotemporal approach using an integrated nested Laplace approximation and adjusted for an a priori determined set of time-varying demographic and policy covariates. RESULTS/UNASSIGNED:Descriptively, we observed a direct, dose-response relationship between quintiles of eviction incidence rates over the full study period and fatal overdose. Prior to the implementation of a statewide eviction moratorium, census tracts in the highest eviction quintile had increased rates of overdose mortality, relative to those in the lowest quintile (posterior mean relative rate = 1.49, 95% credible interval: 1.05 to 2.13). Associations during the periods of eviction moratorium were non-significant. CONCLUSION/UNASSIGNED:This work highlights the neighbourhood-level relationship between residential eviction and fatal overdose risk in the absence of an eviction moratorium. Enhanced investment in eviction prevention policies, such as rent relief and limitations to the circumstances under which landlords can file for eviction, may complement harm reduction efforts to reduce neighbourhood-level overdose inequalities.

BACKGROUND:Drug overdose persists as a leading cause of death in the United States, but resources to address it remain limited. As a result, health authorities must consider where to allocate scarce resources within their jurisdictions. Machine learning offers a strategy to identify areas with increased future overdose risk to proactively allocate overdose prevention resources. This modeling study is embedded in a randomized trial to measure the effect of proactive resource allocation on statewide overdose rates in Rhode Island (RI). METHODS:We used statewide data from RI from 2016-2020 to develop an ensemble machine learning model predicting neighborhood-level fatal overdose risk. Our ensemble model integrated gradient boosting machine and Super Learner base models in a moving window framework to make predictions in 6-month intervals. Our performance target, developed a priori with the RI Department of Health, was to identify the 20% of RI neighborhoods containing at least 40% of statewide overdose deaths, including at least one neighborhood per municipality. The model was validated after trial launch. RESULTS:Our model selected priority neighborhoods capturing 40.2% of statewide overdose deaths during the test periods and 44.1% of statewide overdose deaths during validation periods. Our ensemble outperformed the base models during the test periods and performed comparably to the best-performing base model during the validation periods. CONCLUSIONS:We demonstrated the capacity for machine learning models to predict neighborhood-level fatal overdose risk to a degree of accuracy suitable for practitioners. Jurisdictions may consider predictive modeling as a tool to guide allocation of scarce resources.

Prior applications of machine learning to population health have relied on conventional model assessment criteria, limiting the utility of models as decision supports for public health practitioners. To facilitate practitioner use of machine learning as decision support for area-level intervention, this study developed and applied four practice-based predictive model evaluation criteria (implementation capacity, preventive potential, health equity, and jurisdictional practicalities). We used a case study of overdose prevention in Rhode Island to illustrate how these criteria could inform public health practice and health equity promotion. We used Rhode Island overdose mortality records from January 2016 to June 2020 (N=1,408) and neighborhood-level Census data. We learned two disparate machine learning models, Gaussian process and random forest, to illustrate the comparative utility of our criteria to guide interventions. Our models predicted 7.5-36.4% of overdose deaths during the test period, illustrating the preventive potential of overdose interventions assuming 5-20% statewide implementation capacities for neighborhood-level resource deployment. We described the health equity implications of predictive modeling to guide interventions along urbanicity, racial/ethnic composition, and poverty. In sum, our study discussed considerations to complement predictive model evaluation criteria and inform the prevention and mitigation of spatially dynamic public health problems across the breadth of practice.