Public Health Dashboards in Overdose Prevention: The Rhode Island Approach to Public Health Data Literacy, Partnerships, and Action.

As the field of public health rises to the demands of real-time surveillance and rapid data-sharing needs in a postpandemic world, it is time to examine our approaches to the dissemination and accessibility of such data. Distinct challenges exist when working to develop a shared public health language and narratives based on data. It requires that we assess our understanding of public health data literacy, revisit our approach to communication and engagement, and continuously evaluate our impact and relevance. Key stakeholders and cocreators are critical to this process and include people with lived experience, community organizations, governmental partners, and research institutions. In this viewpoint paper, we offer an instructive approach to the tools we used, assessed, and adapted across 3 unique overdose data dashboard projects in Rhode Island, United States. We are calling this model the "Rhode Island Approach to Public Health Data Literacy, Partnerships, and Action." This approach reflects the iterative lessons learned about the improvement of data dashboards through collaboration and strong partnerships across community members, state agencies, and an academic research team. We will highlight key tools and approaches that are accessible and engaging and allow developers and stakeholders to self-assess their goals for their data dashboards and evaluate engagement with these tools by their desired audiences and users.

PROVIDENT: Development and Validation of a Machine Learning Model to Predict Neighborhood-level Overdose Risk in Rhode Island.

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 to 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.

Comprehensive testing and rapid dissemination of local drug supply surveillance data in Rhode Island.

BACKGROUND: The North American overdose crisis has continued at unprecedented rates with more than 100,000 overdose deaths estimated to have occurred in the United States in 2022. Regional variations in overdose rates signify differences in local drug supplies. State-level drug supply surveillance systems have been limited in their ability to document and communicate the rapidly changing drug supplies which can hinder harm reduction efforts at the community level. We sought to address by piloting a two-year, community-engaged local drug supply surveillance program in Rhode Island (RI). METHODS: The first set of samples (n = 125) were collected from May 2022 to January 2023 across RI and included used paraphernalia (e.g., cookers), refuse (e.g., baggies), and product. Samples were tested using comprehensive toxicology testing approaches via liquid chromatography quadrupole time-of-flight mass spectrometry (LC-QTOF-MS). Results were disseminated to participants and the broader public across platforms. RESULTS: Fentanyl was detected in 67.2% of all samples tested. 39.2% (n = 49) of samples were expected to be fentanyl. Xylazine was detected in 41.6% of all samples-always in combination with fentanyl-and no samples were expected to contain xylazine. In expected stimulant samples (n = 39), 10% contained fentanyl and/or analogues as major substances and 30.8% contained trace amounts of fentanyl and/or analogues. In expected stimulant samples, 15.4% contained xylazine with fentanyl. No opioids or benzodiazepines were detected in expected hallucinogen or dissociative samples (n = 7). In expected benzodiazepine samples (n = 8), no opioids were detected. CONCLUSIONS: Our results describe part of the local drug supply in Rhode Island, including a presence of NPS and adulterants (e.g., designer benzodiazepines, xylazine). Importantly, our findings underscore the feasibility of developing a community-driven drug supply surveillance database. Expanding drug supply surveillance initiatives is imperative for improving the health and safety of people who use drugs and informing public health approaches to addressing the overdose crisis.

Comparison of a national commercial pharmacy naloxone data source to state and city pharmacy naloxone data sources-Rhode Island, Massachusetts, and New York City, 2013-2019.

OBJECTIVE: Accurate naloxone distribution data are critical for planning and prevention purposes, yet sources of naloxone dispensing data vary by location, and completeness of local datasets is unknown. We sought to compare available datasets in Massachusetts, Rhode Island, and New York City (NYC) to a commercially available pharmacy national claims dataset (Symphony Health Solutions). DATA SOURCES AND STUDY SETTING: We utilized retail pharmacy naloxone dispensing data from NYC (2018-2019), Rhode Island (2013-2019), and Massachusetts (2014-2018), and pharmaceutical claims data from Symphony Health Solutions (2013-2019). STUDY DESIGN: We conducted a descriptive, retrospective, and secondary analysis comparing naloxone dispensing events (NDEs) captured via Symphony to NDEs captured by local datasets from the three jurisdictions between 2013 and 2019, when data were available from both sources, using descriptive statistics, regressions, and heat maps. DATA COLLECTION/EXTRACTION METHODS: We defined an NDE as a dispensing event documented by the pharmacy and assumed that each dispensing event represented one naloxone kit (i.e., two doses). We extracted NDEs from local datasets and the Symphony claims dataset. The unit of analysis was the ZIP Code annual quarter. PRINCIPAL FINDINGS: NDEs captured by Symphony exceeded those in local datasets for each time period and location, except in RI following legislation requiring NDEs to be reported to the PDMP. In regression analysis, absolute differences in NDEs between datasets increased substantially over time, except in RI before the PDMP. Heat maps of NDEs by ZIP code quarter showed important variations reflecting where pharmacies may not be reporting NDEs to Symphony or local datasets. CONCLUSIONS: Policymakers must be able to monitor the quantity and location of NDEs in order to combat the opioid crisis. In regions where NDEs are not required to be reported to PDMPs, proprietary pharmaceutical claims datasets may be useful alternatives, with a need for local expertise to assess dataset-specific variability.

Translating Predictive Analytics for Public Health Practice: A Case Study of Overdose Prevention in Rhode Island.

Prior applications of machine learning to population health have relied on conventional model assessment criteria, limiting the utility of models as decision support tools for public health practitioners. To facilitate practitioners' use of machine learning as a decision support tool for area-level intervention, we developed and applied 4 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-June 2020 (n = 1,408) and neighborhood-level US Census data. We employed 2 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 describe the health equity implications of use of predictive modeling to guide interventions along the lines of urbanicity, racial/ethnic composition, and poverty. We then discuss considerations to complement predictive model evaluation criteria and inform the prevention and mitigation of spatially dynamic public health problems across the breadth of practice. This article is part of a Special Collection on Mental Health.

Comparing Projected Fatal Overdose Outcomes and Costs of Strategies to Expand Community-Based Distribution of Naloxone in Rhode Island.

Importance: In 2021, the state of Rhode Island distributed 10 000 additional naloxone kits compared with the prior year through partnerships with community-based organizations. Objective: To compare various strategies to increase naloxone distribution through community-based programs in Rhode Island to identify one most effective and efficient strategy in preventing opioid overdose deaths (OODs). Design, Setting, and Participants: In this decision analytical model study conducted from January 2016 to December 2022, a spatial microsimulation model with an integrated decision tree was developed and calibrated to compare the outcomes of alternative strategies for distributing 10 000 additional naloxone kits annually among all individuals at risk for opioid overdose in Rhode Island. Interventions: Distribution of 10 000 additional naloxone kits annually, focusing on people who inject drugs, people who use illicit opioids and stimulants, individuals at various levels of risk for opioid overdose, or people who misuse prescription opioids vs no additional kits (status quo). Two expanded distribution implementation approaches were considered: one consistent with the current spatial distribution patterns for each distribution program type (supply-based approach) and one consistent with the current spatial distribution of individuals in each of the risk groups, assuming that programs could direct the additional kits to new geographic areas if required (demand-based approach). Main Outcomes and Measures: Witnessed OODs, cost per OOD averted (efficiency), geospatial health inequality measured by the Theil index, and between-group variance for OOD rates. Results: A total of 63 131 simulated individuals were estimated to be at risk for opioid overdose in Rhode Island based on current population data. With the supply-based approach, prioritizing additional naloxone kits to people who use illicit drugs averted more witnessed OODs by an estimated mean of 18.9% (95% simulation interval [SI], 13.1%-30.7%) annually. Expanded naloxone distribution using the demand-based approach and focusing on people who inject drugs had the best outcomes across all scenarios, averting an estimated mean of 25.3% (95% SI, 13.1%-37.6%) of witnessed OODs annually, at the lowest mean incremental cost of $27 312 per OOD averted. Other strategies were associated with fewer OODs averted at higher costs but showed similar patterns of improved outcomes and lower unit costs if kits could be reallocated to areas with greater need. The demand-based approach reduced geospatial inequality in OOD rates in all scenarios compared with the supply-based approach and status quo. Conclusions and Relevance: In this decision analytical model study, variations in the effectiveness, efficiency, and health inequality of the different naloxone distribution expansion strategies and approaches were identified. Future efforts should be prioritized for people at highest risk for overdose (those who inject drugs or use illicit drugs) and redirected toward areas with the greatest need. These findings may inform future naloxone distribution priority settings.

Identifying Predictors of Opioid Overdose Death at a Neighborhood Level With Machine Learning.

Predictors of opioid overdose death in neighborhoods are important to identify, both to understand characteristics of high-risk areas and to prioritize limited prevention and intervention resources. Machine learning methods could serve as a valuable tool for identifying neighborhood-level predictors. We examined statewide data on opioid overdose death from Rhode Island (log-transformed rates for 2016-2019) and 203 covariates from the American Community Survey for 742 US Census block groups. The analysis included a least absolute shrinkage and selection operator (LASSO) algorithm followed by variable importance rankings from a random forest algorithm. We employed double cross-validation, with 10 folds in the inner loop to train the model and 4 outer folds to assess predictive performance. The ranked variables included a range of dimensions of socioeconomic status, including education, income and wealth, residential stability, race/ethnicity, social isolation, and occupational status. The R2 value of the model on testing data was 0.17. While many predictors of overdose death were in established domains (education, income, occupation), we also identified novel domains (residential stability, racial/ethnic distribution, and social isolation). Predictive modeling with machine learning can identify new neighborhood-level predictors of overdose in the continually evolving opioid epidemic and anticipate the neighborhoods at high risk of overdose mortality.

Preventing Overdose Using Information and Data from the Environment (PROVIDENT): protocol for a randomized, population-based, community intervention trial.

BACKGROUND AND AIMS: In light of the accelerating drug overdose epidemic in North America, new strategies are needed to identify communities most at risk to prioritize geographically the existing public health resources (e.g. street outreach, naloxone distribution efforts). We aimed to develop PROVIDENT (Preventing Overdose using Information and Data from the Environment), a machine learning-based forecasting tool to predict future overdose deaths at the census block group (i.e. neighbourhood) level. DESIGN: Randomized, population-based, community intervention trial. SETTING: Rhode Island, USA. PARTICIPANTS: All people who reside in Rhode Island during the study period may contribute data to either the model or the trial outcomes. INTERVENTION: Each of the state's 39 municipalities will be randomized to the intervention (PROVIDENT) or comparator condition. An interactive, web-based tool will be developed to visualize the PROVIDENT model predictions. Municipalities assigned to the treatment arm will receive neighbourhood risk predictions from the PROVIDENT model, and state agencies and community-based organizations will direct resources to neighbourhoods identified as high risk. Municipalities assigned to the control arm will continue to receive surveillance information and overdose prevention resources, but they will not receive neighbourhood risk predictions. MEASUREMENTS: The primary outcome is the municipal-level rate of fatal and non-fatal drug overdoses. Fatal overdoses will be defined as unintentional drug-related death; non-fatal overdoses will be defined as an emergency department visit for a suspected overdose reported through the state's syndromic surveillance system. Intervention efficacy will be assessed using Poisson or negative binomial regression to estimate incidence rate ratios comparing fatal and non-fatal overdose rates in treatment vs. control municipalities. COMMENTS: The findings will inform the utility of predictive modelling as a tool to improve public health decision-making and inform resource allocation to communities that should be prioritized for prevention, treatment, recovery and overdose rescue services.

Toxicological and pharmacologic sex differences in unintentional or undetermined opioid overdose death.

INTRO: Understanding sex differences in toxicological etiologies of opioid-related drug overdose death could inform future sex- and gender-specific approaches to prevention and treatment. METHODS: A retrospective review of accidental or undetermined opioid-involved overdose deaths in Rhode Island 2016-2019 was performed using the Rhode Island Department of Health State Unintentional Drug Overdose Reporting System (SUDORS) database. Decedent toxicology data was linked with state Prescription Drug Monitoring Program (PDMP) records. RESULTS: Of 766 cases in the analytical sample, 568 cases were in men (74.2%) and 198 cases were in women (25.6%). Median age was 40.0 years for males and 42.0 years for females. Statistically significant sex-differences in drug exposures were found. Compared to men, women were more likely have exposure to benzodiazepine, antipsychotic, and antidepressant drug classes and less likely to have fentanyl and alcohol co-exposure. No sex differences were found in cocaine and amphetamine exposure. Female decedents were more likely than male decedents to have a prescription for benzodiazepines or opioids in the 30 days before death (40% vs 21%). The proportion of decedents with a benzodiazepine on post-mortem toxicology testing in combination with a benzodiazepine prescription (p < 0.001) or an opioid prescription (p = 0.005) was over two times higher in women than men. CONCLUSION: Higher rates of controlled substance prescription prior to death and prescription drug co-exposures suggest that female opioid-involved drug overdose decedents are often in contact with the health care system immediately preceding their death, presenting the opportunity to create patient-centric approaches for prevention, harm reduction, and substance use treatment.

Targeting community-based naloxone distribution using opioid overdose death rates: A descriptive analysis of naloxone rescue kits and opioid overdose deaths in Massachusetts and Rhode Island.

BACKGROUND: Rates of fatal opioid overdose in Massachusetts (MA) and Rhode Island (RI) far exceed the national average. Community-based opioid education and naloxone distribution (OEND) programs are effective public health interventions to prevent overdose deaths. We compared naloxone distribution and opioid overdose death rates in MA and RI to identify priority communities for expanded OEND. METHODS: We compared spatial patterns of opioid overdose fatalities and naloxone distribution through OEND programs in MA and RI during 2016 to 2019 using public health department data. The county-level ratio of naloxone kits distributed through OEND programs per opioid overdose death was estimated and mapped to identify potential gaps in naloxone availability across geographic regions and over time. RESULTS: From 2016 to 2019, the statewide community-based naloxone distribution to opioid overdose death ratio improved in both states, although more rapidly in RI (from 11.8 in 2016 to 35.6 in 2019) than in MA (from 12.3 to 17.2), driven primarily by elevated and increasing rates of naloxone distribution in RI. We identified some urban/non-urban differences, with higher naloxone distribution relative to opioid overdose deaths in more urban counties, and we observed some counties with high rates of overdose deaths but low rates of naloxone kits distributed through OEND programs. CONCLUSIONS: We identified variations in spatial patterns of opioid overdose fatalities and naloxone availability, and these disparities appeared to be widening in some areas over time. Data on the spatial distribution of naloxone distribution and opioid overdose deaths can inform targeted, community-based naloxone distribution strategies that optimize resources to prevent opioid overdose fatalities.

Machine learning takes a village: Assessing neighbourhood-level vulnerability for an overdose and infectious disease outbreak.

BACKGROUND: Multiple areas in the United States of America (USA) are experiencing high rates of overdose and outbreaks of bloodborne infections, including HIV and hepatitis C virus (HCV), due to non-sterile injection drug use. We aimed to identify neighbourhoods at increased vulnerability for overdose and infectious disease outbreaks in Rhode Island, USA. The primary aim was to pilot machine learning methods to identify which neighbourhood-level factors were important for creating "vulnerability assessment scores" across the state. The secondary aim was to engage stakeholders to pilot an interactive mapping tool and visualize the results. METHODS: From September 2018 to November 2019, we conducted a neighbourhood-level vulnerability assessment and stakeholder engagement process named The VILLAGE Project (Vulnerability Investigation of underlying Local risk And Geographic Events). We developed a predictive analytics model using machine learning methods (LASSO, Elastic Net, and RIDGE) to identify areas with increased vulnerability to an outbreak of overdose, HIV and HCV, using census tract-level counts of overdose deaths as a proxy for injection drug use patterns and related health outcomes. Stakeholders reviewed mapping tools for face validity and community distribution. RESULTS: Machine learning prediction models were suitable for estimating relative neighbourhood-level vulnerability to an outbreak. Variables of importance in the model included housing cost burden, prior overdose deaths, housing density, and education level. Eighty-nine census tracts (37%) with no prior overdose fatalities were identified as being vulnerable to such an outbreak, and nine of those were identified as having a vulnerability assessment score in the top 25%. Results were disseminated as a vulnerability stratification map and an online interactive mapping tool. CONCLUSION: Machine learning methods are well suited to predict neighborhoods at higher vulnerability to an outbreak. These methods show promise as a tool to assess structural vulnerabilities and work to prevent outbreaks at the local level.

Opioid Overdose Deaths with Buprenorphine Detected in Postmortem Toxicology: a Retrospective Analysis.

BACKGROUND: Buprenorphine is a unique µ-opioid receptor partial agonist with avid receptor binding, nominal euphoric reward, and a ceiling effect on sedation and respiratory depression. Despite a pharmacologic profile that enhances safety, cases of fatal opioid overdose with buprenorphine on postmortem toxicology are reported, but details of these cases in the literature are limited. METHODS: A retrospective review of opioid-involved drug overdose fatalities in Rhode Island (RI) from 2016 to 2018 using the RI Department of Health State Unintentional Drug Overdose Reporting System (SUDORS) database. Deaths with buprenorphine on toxicology testing versus opioid-involved overdose deaths without buprenorphine were compared to assess the type and number of co-exposures. RESULTS: Of 534 opioid-involved deaths, 29 (5.4%) included buprenorphine and/or norbuprenorphine on toxicology. Most frequent co-exposures are as follows: fentanyl (75.9%), norfentanyl (72.4%), cocaine (41.4%), benzoylecgonine (41.4%), cannabinoids (31.0%), ethanol (31.0%), levamisole (31.0%), and free morphine (31.0%). An average number of co-exposures for fatalities with buprenorphine were 9.24 versus 6.68 in those without buprenorphine. In one case buprenorphine was the only drug listed to cause death; all other fatalities with buprenorphine on toxicology reported additional drugs contributing to death. CONCLUSION: Decedents with buprenorphine detected on toxicology testing commonly had documented polysubstance use. Although data are limited, buprenorphine may provide some risk mitigation against full agonist opioid overdose including fentanyl. Further work should explore the use of postmortem concentrations of buprenorphine, norbuprenorphine, and other opioid metabolites to determine the role of buprenorphine in fatal overdose pharmacology.

US drug overdose mortality: 2009-2018 increases affect young people who use drugs.

PURPOSE: Drug overdose mortality remains a public health concern in many countries globally. In the US, overdoses involving synthetic opioids are the primary contributor to overdose mortality. We aimed to assess trends in overdose death due to synthetic opioids among young people and describe key demographic and temporal changes. METHODS: Data from the US National Vital Statistics System Multiple Cause of Death files for 2009-2018 were analysed to determine age-specific overdose death rates by region (i.e. east versus west of the Mississippi River). Age-adjusted overdose mortality rates were used to compare demographic differences in all drug and synthetic opioid overdose among young people (aged 15-34 years) using a joinpoint regression with Poisson-approximated standard errors. RESULTS: Driven by synthetic opioid overdose, the age burden of mortality shifted towards young people in eastern states and remained approximately constant in western states over the study period. The highest increases in drug overdose mortality rates were observed in young Black and Hispanic people and those living in large metropolitan areas. CONCLUSIONS: Rapid changes in the demographics of overdose demonstrate distinct but overlapping US overdose sub-epidemics, and highlight the need for targeted interventions to reduce overdose risk in young people.

Defining a recovery-oriented cascade of care for opioid use disorder: A community-driven, statewide cross-sectional assessment.

BACKGROUND: In light of the accelerating and rapidly evolving overdose crisis in the United States (US), new strategies are needed to address the epidemic and to efficiently engage and retain individuals in care for opioid use disorder (OUD). Moreover, there is an increasing need for novel approaches to using health data to identify gaps in the cascade of care for persons with OUD. METHODS AND FINDINGS: Between June 2018 and May 2019, we engaged a diverse stakeholder group (including directors of statewide health and social service agencies) to develop a statewide, patient-centered cascade of care for OUD for Rhode Island, a small state in New England, a region highly impacted by the opioid crisis. Through an iterative process, we modified the cascade of care defined by Williams et al. for use in Rhode Island using key national survey data and statewide health claims datasets to create a cross-sectional summary of 5 stages in the cascade. Approximately 47,000 Rhode Islanders (5.2%) were estimated to be at risk for OUD (stage 0) in 2016. At the same time, 26,000 Rhode Islanders had a medical claim related to an OUD diagnosis, accounting for 55% of the population at risk (stage 1); 27% of the stage 0 population, 12,700 people, showed evidence of initiation of medication for OUD (MOUD, stage 2), and 18%, or 8,300 people, had evidence of retention on MOUD (stage 3). Imputation from a national survey estimated that 4,200 Rhode Islanders were in recovery from OUD as of 2016, representing 9% of the total population at risk. Limitations included use of self-report data to arrive at estimates of the number of individuals at risk for OUD and using a national estimate to identify the number of individuals in recovery due to a lack of available state data sources. CONCLUSIONS: Our findings indicate that cross-sectional summaries of the cascade of care for OUD can be used as a health policy tool to identify gaps in care, inform data-driven policy decisions, set benchmarks for quality, and improve health outcomes for persons with OUD. There exists a significant opportunity to increase engagement prior to the initiation of OUD treatment (i.e., identification of OUD symptoms via routine screening or acute presentation) and improve retention and remission from OUD symptoms through improved community-supported processes of recovery. To do this more precisely, states should work to systematically collect data to populate their own cascade of care as a health policy tool to enhance system-level interventions and maximize engagement in care.

Suspected involvement of fentanyl in prior overdoses and engagement in harm reduction practices among young adults who use drugs.

Background: From 2011 to 2016, the United States has experienced a 55% increase in overall overdose deaths and a 260% increase in fatal fentanyl-related overdoses. Increasing engagement in harm reduction practices is essential to reducing the rate of fentanyl-related overdoses. This study sought to examine the uptake of harm reduction practices among young adults who reported recent drug use and who were recruited for a study to assess the utility and acceptability of rapid fentanyl test strips. Methods: Between May and October 2017, 93 young adults who reported drug use in the past 30 days were recruited through word of mouth, Internet advertising, and public canvasing. Participants completed an interviewer-administered survey that assessed participants' sociodemographic and behavioral characteristics, suspected fentanyl exposure, and overdose history. We assessed harm reduction practices and other correlates associated with experiencing a suspected fentanyl-related overdose. Results: Of 93 eligible participants, 36% (n = 34) reported ever having experienced an overdose, among whom 53% (n = 18) suspected having experienced a fentanyl-related overdose. Participants who had ever experienced a fentanyl-related overdose were more likely to keep naloxone nearby when using drugs compared with those who had never experienced an overdose and those who had experienced an overdose that they did not suspect was related to fentanyl (P < .001). Additionally, experiencing a suspected fentanyl-related overdose was associated with having previously administered naloxone to someone else experiencing an overdose (P < .001). Conclusion: Those who had experienced a suspected fentanyl-related overdose were more likely to carry and administer naloxone. Future overdose prevention interventions should involve persons who have experienced a suspected fentanyl overdose and/or responded to an overdose in order to develop harm reduction programs that meet the needs of those at risk of an overdose.

Perspectives on rapid fentanyl test strips as a harm reduction practice among young adults who use drugs: a qualitative study.

BACKGROUND: In 2016, drug overdose deaths exceeded 64,000 in the United States, driven by a sixfold increase in deaths attributable to illicitly manufactured fentanyl. Rapid fentanyl test strips (FTS), used to detect fentanyl in illicit drugs, may help inform people who use drugs about their risk of fentanyl exposure prior to consumption. This qualitative study assessed perceptions of FTS among young adults. METHODS: From May to September 2017, we recruited a convenience sample of 93 young adults in Rhode Island (age 18-35 years) with self-reported drug use in the past 30 days to participate in a pilot study aimed at better understanding perspectives of using take-home FTS for personal use. Participants completed a baseline quantitative survey, then completed a training to learn how to use the FTS. Participants then received ten FTS for personal use and were asked to return 2-4 weeks later to complete a brief quantitative and structured qualitative interview. Interviews were transcribed, coded, and double coded in NVivo (Version 11). RESULTS: Of the 81 (87%) participants who returned for follow-up, the majority (n = 62, 77%) used at least one FTS, and of those, a majority found them to be useful and straightforward to use. Positive FTS results led some participants to alter their drug use behaviors, including discarding their drug supply, using with someone else, and keeping naloxone nearby. Participants also reported giving FTS to friends who they felt were at high risk for fentanyl exposure. CONCLUSION: These findings provide important perspectives on the use of FTS among young adults who use drugs. Given the high level of acceptability and behavioral changes reported by study participants, FTS may be a useful harm reduction intervention to reduce fentanyl overdose risk among this population. TRIAL REGISTRATION: The study protocol is registered with the US National Library of Medicine, Identifier NCT03373825, 12/24/2017, registered retrospectively. https://clinicaltrials.gov/ct2/show/NCT03373825?id=NCT03373825&rank=1.

Use of rapid fentanyl test strips among young adults who use drugs.

BACKGROUND: The overdose epidemic has been exacerbated by a dramatic increase in deaths involving illicitly manufactured fentanyl (IMF). Drug checking is a novel strategy to identify IMF in illicit drugs. We examined the uptake and acceptability of rapid fentanyl test strips among young adults. METHODS: From May to September 2017, we recruited 93 young adults in Rhode Island who reported injecting drugs or using heroin, cocaine, or illicitly obtained prescription pills in the past 30 days. Participants were asked to test either their urine after drug use (post-consumption) or a drug sample prior to use (pre-consumption) using rapid fentanyl test strips. After a questionnaire and a brief training, participants received ten strips for their personal use and were asked to return for a one-month follow-up visit, which assessed the uptake and acceptability of the rapid strips tests and the behavioral outcomes associated with receipt of a positive test. RESULTS: Of the 81 (87%) participants who returned for follow-up and who had complete data, the mean age was 27, 45 (56%) were male, and 37 (46%) were non-white. A total of 62 participants (77%) reported using at least one test strip. Of these, 31 (50%) received at least one positive result. A positive result was associated with older age, homelessness, heroin use, injection drug use, ever witnessing an overdose, and concern about overdose or drugs being laced with fentanyl (all p < 0.05). Receiving a positive result was significantly associated with reporting a positive change in overdose risk behavior between baseline and follow-up (p ≤ 0.01). Among all participants, 79 (98%) reported confidence in their ability to use the test strips and 77 (95%) wanted to use them in the future. CONCLUSIONS: Young adults reported high uptake and acceptability of fentanyl test strips to detect IMF in illicit drugs.