Request for Information: The HEALing Communities Study

Executive Summary


  • On June 29, 2018, The National Institute on Drug Abuse and the Substance Abuse and Mental Health Services Administration released a Request for Information on a potential new research initiative called the HEALing Communities Study, which is part of NIH’s Helping to End Addiction Long-term (HEAL) Initiative.
  • Input and ideas were requested on:
    • Study design, including selecting communities for study, baseline data, reasonable duration of interventions, confounding variables, and collaboration, coordination, and data sharing
    • Outcomes and data sources, including disease burden and implementation and utilization
    • Evidence-based interventions to be included in the study
    • Additional study components, including health economics questions, implementation research metrics, and research, prevention, and treatment infrastructure


  • 46 responses were received in total from 42 unique respondent organizations (in a few cases, several individual responses were received from the same organization).
  • Respondents were classified by institution and institution type as described below:
    • Non-profit/Advocacy: 19
    • Academic: 18
    • Consulting Corporation: 6
    • Local Government Health Dept: 1
    • Healthcare Provider: 1
    • Pharmaceutical Company: 1
    • TOTAL: 46
  • Responses were sorted to correspond with the four topic areas described above (see Attachment A).
  • Within each topic area, responses were further subdivided to align specific questions posed in the RFI related to each topic area.

Major Themes

The following key themes were identified based on an analysis of the responses:

  • Incorporate measurable outcomes appropriate to the community being studied, including rates of overdose, opioid use disorder (OUD) prevalence, access to care, service utilization, and attitudes toward treatment.
  • Engage key stakeholders in the planning and implementation of the study, including OUD and chronic pain patients, caregivers, and local public health and law enforcement officials.
  • Ensure diversity of participants and use diverse criteria for designating “heavily affected communities”, including gender, ethnicity/tribal affiliation, socioeconomic determinants (e.g., homelessness); health disparities, and variations in severity level (OUD).
  • Build in flexibility and variability in study design to include pragmatic methods in addition to randomized controlled trials (e.g. natural experiments, stepped wedge, randomized clustering, difference-in-differences, pretest-posttest, mixed qualitative and quantitative methods)
  • Include variability in data sources by collecting community, treatment program, and patient-level information (e.g., medical history, screening, referral to treatment, prescription monitoring data, and naloxone prescribing and distribution).
  • Be transparent, consistent, and practical regarding data collection and analysis by establishing common data elements and standards for data compilation, synthesis, storage and analysis.
  • Examine effects of interventions across multiple levels including patient, provider, payer, policy and community levels.
  • Consider a broad range of factors such as socioeconomic determinants, genetic factors, chronic pain symptoms, infectious diseases, mental illness, and other substance use.
  • Incorporate a variety of interventions (and combinations) into study design including pharmacological and nonpharmacological treatment, pain management, harm reduction, lifestyle changes, primary care, interdisciplinary care models, recovery support services, pharmacies, and acute/perioperative care.
  • Assess how integrated interventions can be practically implemented in the long term, including costs and financing, who to treat and when, and how to engage and retain patients.

Appendix A

Detailed Responses by Topic

This appendix contains a condensed inventory of selected specific suggestions and comments provided by respondents, organized according to the four thematic areas upon which the RFI was based. Items included are meant to be representative and not comprehensive.

Study Design

Many responses suggested ways to select communities for study; estimate reasonable duration of interventions; collect baseline data; think about confounding variables; and encourage collaboration, coordination, and data sharing.

Defining “heavily affected communities” for study

  • Must include out-of-care populations
  • Include online communities
  • A level granular enough to account for such divergence, such as at the level of contiguous zip codes or census tracts 
  • Diversity in age and ethnicity, as well as rural and urban
  • Three communities may not be sufficient
  • A community that statistically ranked above average for OUD across the continuum of prescription opioids, heroin and synthetics 
  • Focus on specific populations, such as incarcerated individuals, veterans, and seniors
  • CDC’s “high risk” counties could be used
  • Include minorities and disadvantaged communities in the context of this crisis, and be mindful of disparities in resource allocation, contact with law enforcement, and medicalization vs. criminalization of substance use
  • Using county-level data, it is possible to extract expected opioid death rates given the quantifiable aspects of a community.  From this, counties can be classified by the relationship between expected and observed death rates:
    • stability counties, where the overdose rate approaches the expected level,
    • momentum counties, where the overdose rate is above the expected level,
    • powder keg counties, where the overdose rate is below the expected level with reason to expect a precipitous increase, and
    • lessons-learned counties, where the overdose rate is below the expected level due to efficacious response & prevention. 
    • Both momentum counties and powder keg counties may be considered "heavily affected," though they will appear quite distinct.  They will require distinct intervention approaches.

Study designs for real-world care settings

  • RCTs, cluster randomized designs, and natural experiments
  • Mixed methods should be applied to collect quantitative and qualitative data, including secondary data analysis, longitudinal comparative data analysis, surveys and interviews
  • Research designs should be community-informed/engaged, preferably multi-organization
  • Use the PCORI Engagement Rubric
  • Harm reduction providers will drive specific outcomes, while treatment/healthcare providers will drive others. These two groups of providers must be coordinated and have equal seats at the design table
  • Include sex, gender, and gender identity as variables
  • Don’t exclude pregnant women
  • Children deserve to be a focus of the study with an eye towards implementing new models of care that support the whole family in myriad dimensions
  • Include the chronic pain community
  • People who use drugs can and should be incorporated as paid research assistants and have the ability to generate productive research questions and data collection.
  • A difference-in-differences model may be appropriate, provided comparison data are available from non-intervention communities or key metrics can be tracked retrospectively
  • Agent based modelling could be considered
  • Qualitative methods are needed to understand the data in context
  • Use adaptive and pragmatic evaluation methods that can assess comparative delivery strategies with real-time feedback loops to make mid-course corrections
  • Focus not on what works, but rather on how to best deliver an intervention that works. Focusing on real-world effectiveness and sustainability, combined with study designs that permit attribution of observed outcomes to the program or intervention of interest, will improve the dissemination and uptake of effective interventions
  • A fluid, mixed model evaluative and longitudinal approach to test integrated approaches
  • Include qualitative inquiries, including those related to law, education, and public health along with environmental scans to identify existing interventions and metrics
  • A cascade of analysis studies, starting with descriptive and inferential statistics, moving to relational and, finally, predictive statistics to associate predictors, with outcomes
  • Use predictive models to predict outcomes, such as successful cure of OUD, and use those predictions to risk-adjust specific patients for those specific outcomes – to fairly compare the effectiveness of various treatments for OUD. Estimate the risk of a specific outcome among a specific cohort of patients before judging the effectiveness of a treatment intervention in producing that treatment outcome
  • The proposed study needs millions of patients, standardized data collection and analysis to calculate their rates and ratios, and risk adjustment for the outcomes of interest. Some of the requirements, such as percent of patients screened for opioid misuse, will depend on how screening is captured in the billing system and the medical record. This study should call for a large consortium of health systems that already share a vast database of patients, clinical findings, medications prescribed and services rendered.
  • Researchers can create hypotheses through retrospective analysis of tens of millions of people, and a survey of the published literature. This would eliminate the need to start from scratch in three cities with a patient population too small for predictive modeling.
  • Study design can be left to the researcher but should be some type of staggered design with some randomization/stepped wedge, smart design, experiments (policy comparison) description, and natural. Not just RCT.
  • Use RCTs and Pretest-posttest (pre-post) designs
  • Quasi-experimental interrupted time series with matched comparison communities, in which outcomes are measured repeatedly in the intervention and comparison communities. One county gets the intervention first and the other waits until later in the program to get it if it is effective.

Estimating effect size

  • Comprehensive metrics should leverage some rational weighting among outcomes to both establish baseline and evaluate success of integrated interventions over time.
  • Rates of non-fatal and fatal OD, and prevalence and incidence of opioid misuse can be calculated on small population size – though to make the findings generalize accurately to many different communities, patient populations in the hundreds of thousands is needed. The Effect size depends on the number of predictors used to calculate the probabilities of specific outcomes.
  • We recommend a national scope and data on tens of millions of people to generate and test hypotheses.
  • A measure of reporting fidelity will help to characterize the otherwise unobserved effects of improved overdose reporting during the study period.
  • Measuring outcomes in small communities is difficult. Year-to-year results in such regions are thus not particularly meaningful. Given the need for rapid development and deployment of interventions, it would make the most sense to include primarily larger regions, where intervention effects are more rapidly detectable. With a sufficiently large and well-resolved community, 5-7 months of post-deployment data should be sufficient to detect any findings.
  • The effect size should be assessed through percentage-based cascades of care for OUD and related conditions (e.g., neonatal opioid withdrawal syndrome [NOWS]). Cascades of care are models of treatment that define sequential stages of interventions leading from detection of the problem through enrollment and delivery of treatments to final desired outcomes; a critical goal of these cascades is maintaining enrollment/compliance as patients progress from one stage to the next.

Confounding variables to consider when planning the study

  • Environmental effects such as county or organizational policies, fidelity of interventions and variance in practices, and unmeasured endogeneity of clusters
  • Legal and policy barriers. Consider adding an ethical, legal and social implications (ELSI) component, in much the same way related issues were integrated into Human Genome Project.
  • State-, clinic-, provider- and patient-level variables.
  • Race and ethnicity
  • Community characteristics
  • Access to treatment
  • Diagnosis and comorbidities are essential to consider, along with individual social determinants of health.
  • Data for helping metrics are prescribing, diagnosis, and if possible, inpatient data. Also EMS responses, police responses, arrests/criminal justice statistics, social services access, and births or deaths.
  • Efforts should be made to control for geographic characteristics like employment rates, income level, and level of educational attainment.
  • Rates of incarceration/institutionalization, housing stability and wealth/income disparity.
  • State-mandated provider training

Potential threats to internal and external validity

  • Many potential threats can be mitigated by employing near real-time data sets from large populations of patients from many locations of care, rather than data on patients from only three cities.
  • Threats to internal and external study validity include the sheer overwhelming number of new studies and policies concerning opioids.
  • Co-occurring interventions, such as changes in state policies. Acknowledging and accounting for these factors within the study analysis could help mitigate threats to study validity.

Coordination across research centers and data sets

  • Consider a role for an unbiased and independent entity, not affiliated with the research org, as the data integrator and manager.
  • Common data elements must be developed to enhance robust secondary and meta-data analysis, as well as individual participant meta-analysis. Secondary data analysis from judicial sources, treatment facilities, and clinical settings can be used to enhance evaluation and monitoring.
  • Developing a data governance framework to determine data sharing processes, protect the needs of data stakeholders, and ensure transparency through a robust data use and reciprocal support agreement.
  • Projects to facilitate data storage in such a way that reduces barriers for analytics while also maintaining security/reducing chance of data breach.
  • Development of statistical, data scientific, and software engineering methods that integrate heterogeneous data: very structured medical outcomes, clinician reports, unstructured digital and ecological data.
  • ADVANCE or PCORI model data standardization and datasets, and interventions across multiple industries. There should also be better models for data governance or linkage for unique encounters and modeling like the metric development (CMS).
  • Collaborate with analytics organization to perform data collection and analysis.

Outcomes and Data Sources

Multiple responses included suggestions for outcomes to be measured and related data sources.

Possible outcomes to measure

  • Specific metrics assessing harm reduction practices and presence of harmful or risky behaviors
  • Wellness data beyond urine or oral toxicology including metrics such as amount of engagement with primary care or a buprenorphine prescriber or other positive indicators of improved well-being
  • Overdose death, neonatal abstinence syndrome, HIV, hepatitis, drug-related mood and anxiety disorders.
  • Priority outcomes must be determined through cross-sector consultation 
  • Increased access to treatment and services.
  • Improved health and quality of life measures among people who use drugs. 
  • Rates of non-fatal and fatal overdose.
  • Repeat prescriptions for naloxone.
  • Prevalence and incidence of opioid misuse, OUD and Hepatitis C
  • Percent of patients screened for opioid misuse and OUD and who received a brief intervention or were referred to treatment
  • Percent of patients initiated on medication assisted treatment (MAT) and retained in medication treatment beyond 6 months
  • Rates of naloxone distribution and overdose reversals
  • Opioid analgesic and benzodiazepine prescription rates
  • Implementation of prevention programs
  • Explore outcomes that reflect a patient’s complete medical history as it relates to the source of their chronic or acute pain and/or subsequent opioid use disorder
  • What are priority patient-centered outcomes?
  • Impact of incarceration on the trajectory of infections caused by illicit drug use
  • Effective delivery of informed consent: this study could provide data that would support guidance regarding obtaining fully informed consent through educating patients on the distinct features of all three FDA-approved forms of medication for OUD, ensuring that they have a genuine choice to access directly the medication they choose, and that there is documentation of their active participation in the selection of their treatment.
  • Knowledge and attitudes related to opioid use, such as knowledge of prescription drop off locations or how to get naloxone.
  • Other social determinants of health such as housing and homelessness.
  • Opioid prescribing patterns; opioid consumption; perioperative days of therapy; average daily dose at the hospital; post-discharge prescriptions (quantity and duration); quantity consumed or number of refills; and information about how the patient stored and disposed of medications.
  • Time to relapse and subsequent healthcare costs, social service, criminal justice, child welfare, and emergency service utilization
  • Economic implications of each OUD medication and adherence/time on therapy.

Potential data sources

  • Include out-of-care populations and include non-institutional sources of data as well as institutional data. This can include street-based qualitative data collection by individuals with access to high-risk populations.
  • Clinical data and social and behavioral data, geospatial data, and data on social determinants of health should be included.
  • All projects should incorporate characteristics of the individual, basic demographics, and other standardized measures—mental health, depression, anxiety, trauma, etc.
  • Secondary data analysis from judicial sources, treatment facilities, and clinical settings can enhance evaluation and monitoring.
  • Need data on the migration of pain medicine from primary care doctors to pain clinics. People are not getting the pain medications they really need.
  • Rates of OUD, rates of overdose, a record of any treatment already received, etc. Data could be captured from the patients’ electronic health record, Medicaid/Medicare data, other claims data, vital records data, etc. Challenges include:
    • gaining access to certain data sets
    • completeness of EHR data
    • does the data set have a mechanism for capturing data that may not be located in standardized fields?
    • linking criminal justice and other records that are not managed by health systems
  • Rates of non-fatal and fatal overdose should be captured via National Vital Statistics System (NVSS), Enhanced State Opioid Overdose Surveillance (ESOOS) and State Unintentional Drug Overdose Reporting System (SUDORS) data.
  • Prevalence and incidence of opioid misuse and OUD should be captured via NVSS and Behavioral Risk Factor Surveillance System (BRFSS).
  • Prevalence and incidence of Hepatitis C captured through the National Notifiable Diseases Surveillance System (NNDSS), along with data on the scope and reporting fidelity of local Hepatitis C testing programs to refine local estimates.
  • Percent of patients screened for OUD, referred to treatment, and maintained in treatment, respectively, may be available via the Treatment Episode Data Set (TEDS) or retrospective analyses of local electronic health record (EHR) data, but data will be of variable quality and could be better captured through implementation of blockchain-based referral and care tracking systems.
  • Data on naloxone distribution and overdose reversals may be available through pharmacy surveys or pharmacy benefits manager (PBM) claims, but would be better captured via localized OEND program records as captured in the Harm Reduction Coalition’s National Naloxone Survey dataset. Additional data on the scope and scale of existing local opioid overdose education and naloxone distribution (OEND) programs will help to support effect size estimates for interventions in study communities.
  • Develop baseline measures to consider how a patient’s pain interferes with specific physical functions and daily activities.
  • Opioid and benzodiazepine prescription rates should be captured via Prescription Drug Monitoring Program (PDMP) data, along with data on the scope of mandated reporting for PDMPs in study communities.
  • Data on the implementation of prevention programs should be collected via a survey of local drug education and messaging interventions, prescription take-back programs, and other interventions identified in a formative, community-based service mapping process.
  • Additional measures should include the price per 40mg MME for diverted prescription opioids as captured nationally by, rates of opioid counterfeiting and adulteration as captured in local medicolegal forensic testing programs, and rates of opioid-injection-related skin and soft tissue infection as identified in localized EHR data.
  • Rates and consistency of payer-induced delays in accessing prescribed MAT
  • ER and inpatient data should be included

Evidence-Based Interventions

Many responses contained suggestions for which evidence-based interventions should be incorporated into the integrated care model being tested in the study.

  • Follow-up with non-fatal overdoses.
  • Incorporate technology: social media, Pulse Point app, fit-bit sensors.
  • Incorporate non-pharmacological treatment of pain, including dissemination of information and education.
  • Consider projects that have the capability to include programs that include two or more intervention approaches, for example, social recovery and MAT, Harm Reduction and MAT, etc.
  • Establish a behavioral surveillance initiative aimed at gaining an understanding of illicit drug users’ behaviors, resembling the National HIV Behavioral Surveillance program created by CDC
  • Pilot communities/counties with high opioid misuse vs low use, matched on characteristics
  • Design the study to enable the agency to examine the course of treatment for patients with chronic and acute pain, beginning with the patients’ initial encounter with a physician or other clinician. Examine patients’ behavior when they are prescribed an opioid. For example, if it was prescribed, was it filled? If so, how much was used? The study also should monitor patients’ level of pain, change in condition, and continued need for prescription opioids during their course of treatment, up to the point at which they were diagnosed with an OUD.
  • Recommend that the Collaborative Care Model (CoCM) be among the integrated evidence-based interventions that the study focuses on. Also recommend further research into effective-ness of pharmacy-based interventions as well as the general public’s understanding of naloxone and the impact of standing orders.
  • Utilize pharmacist-provided services.
  • Develop OUD surveillance system: convene expert panel to assess and utilize existing databases, develop a framework for federally-operated county-level system to yield annual estimates, and partner with one or more counties to develop and implement the pilot as proof of concept.
  • Include acute care settings, such as the perioperative or other hospital settings.
  • In evidence review, include studies of abuse and addiction of other substances.  
  • Root-cause analysis to understand why individuals begin or continue to take opioids. The causal factors that present the greatest opportunity for change should be prioritized.
  • Interventions include coaching, educating providers and supplemental staff on opioid management and prescribing reduction, and referral and waivers, as well as stigmas. Access to naloxone, needle exchange, and safe-injection locations could also provide an impact.
  • Prioritize community-based naloxone distribution in the study.
  • Novel interventions might include flexible care teams that work outside of traditional clinic or hospital spaces, including mobile teams, to help increase access to MAT and naloxone as well as harm reduction supplies. Interventions that increase access to MAT and naloxone across a variety of spaces including the criminal justice system, traditional detox/treatment spaces, homeless shelters, and non-traditional community gathering places is imperative.
  • In the wake of police/fire responses to overdose and individuals receive naloxone but might refuse to go to the hospital for further care, these patients should be offered MAT as a strategy to prevent ongoing use.
  • Examine interdisciplinary care models that offer unrestricted access to non-pharmacological and non-opioid treatments for pain. Such models would need to eliminate or streamline the referral process, eliminate multiple copays, streamline interoperability among the various clinicians, and include any necessary waivers to applicable state law on reimbursement, therapy visits, prior authorization, and similar policies that unintentionally delay and hinder access to care.
  • Include standard prescribing procedures, requiring physicians to refer all patients with chronic pain to physical therapists, and prescribe alternative treatments before opioids.
  • Professional education and training on OUD and other substance use disorders, disease state awareness for non-specialists, evidence-based information on all OUD medications, sources for additional training and support, informed consent procedures for all medications for OUD, OUD medication induction strategies, collaborative care model to promote appropriate duration of treatment, community outreach, treatment services, recovery housing, peer support services, and vocational training and job placement services.
  • Hospital-based professional training should also include emergency department-based interventions following naloxone reversals, and linkage to care based on patient preference.
  • Community Outreach to schools, faith communities, workplace, and law enforcement.
  • Quantitative and qualitative multi-center systematic evaluation of detox programs that are transforming their services by beginning medication assisted treatment prior to discharge.
  • Survey emergency department directors to assess their interest in implementing ED-based buprenorphine induction programs.
  • Allocate a minimum of 10% of study funds to build community capacity to choose and implement effective universal and selective prevention in neighborhoods, schools and families in all test sites.
  • Include tested and effective programs for families of children entering adolescence proven to reduce the risks that they will initiate tobacco or alcohol or marijuana use during adolescence and thereby shown to reduce later misuse of opioids.
  • Focusing on treatment retention > 6 months as an outcome measure underscores the clinical reality that a solution with demonstrated efficacy is often available but adhered to only temporarily and is thus, alone, not effective.
  • Hybrid designs that understand clinical interventions as well has the effectiveness of implementation strategies.
  • Use of Community Health Workers (CHWs) to reach people where they are; ongoing surveillance
  • Using the ABCD Study as a good way to study neighborhood effects
  • Drop boxes, deterra bags
  • Systems approach to opioid use disorders: CHWs distributing pouches efficiently in communities
  • Health care teams; CHW, Nurse, Emergency Responder
  • Systems approach-police, community, DOH, Health care, faith-based
  • People already working with people using drugs (not just people in recovery) must be included at every level of decision-making and compensated for their input.

Additional Study Components

Many responses contained suggestions for how to ensure that the study will be able to incorporate additional questions of interest that could be helpful for scaling up the care model or implementing it in other locations in the future. These took the form of questions from the fields of health economics and implementation research, as well as questions about how to use existing infrastructure and partnerships.

Health economics research questions

  • If the research study is not inclusive of developing predictive models, which are the foundation of risk-adjustment for each patient, it will not be possible to judge effectiveness of evidence-based interventions.
  • What changes in provider- and systems-level policies, within and across agencies, are needed to facilitate prevention and early intervention; treatment entry, engagement and retention; effective overdose prevention; and adoption of best practices?
  • How effective is involuntary treatment in efforts to prevent opioid-related mortality?
  • How is the inherent tension between enforcement of drug laws and the health and safety of people with OUD optimally resolved in practice?
  • What is the optimal pathway to economic sustainability for best practices?
  • The test approach will need to address reimbursement policies to ensure that physical therapy and other alternative treatments are a viable option for patients, and that providers receive adequate reimbursement when furnishing these services.
  • Study the health economics (e.g., cost-benefit analysis, cost-effectiveness analysis, and/or cost utility analysis) of different pharmacists’ services, such as: medication management services; evaluation of chronic pain treatment plans; naloxone training and referral; education on opioid safety and overdose prevention; tapering; physician-pharmacist buprenorphine/naloxone
    maintenance practice; screening; and academic detailing.
  • Economic analysis should consider the costs of opioid misuse, addiction and overdose from the diverse perspectives of different kinds of stakeholders, and should include disability and loss of productivity due to pain conditions. Use cost-consequences analysis (CCA).
  • The key economic questions pertain to costs of diagnosis, treatment and recidivism. Assigning patients to causes and calculate rates of OUD by geography, biology and socio-economics of the patients. Might find that rates of OUD vary by employment rates, population density, socio-economic factors as well as genetics.
  • Test innovations in provider payment, along with the other initiatives aimed at improving access and quality of care. Also consider the role of economic incentives in the interactions among payers, providers, patients and other stakeholders.
  • Patient income and insurance (churn impact); employment (infrastructure for employers); housing; cost of treatment vs. the cost of lives vs. their societal contribution; the medication costs; the behavioral health costs; inpatient and outpatient treatment and access to treatment; and the social support costs.
  • Build the logic for outcomes-based financing: value-based payments, alternative payment models, etc.
  • Novel approaches to enabling providers to access medication should be given immediate consideration, including point-of-care access models.

Implementation research questions

  • What is the optimal time to begin treatment?
  • What are the best methods for identifying those who are eligible for treatment?
  • Which models of service delivery are most cost-effective?
  • What constitutes the essential suite of laboratory tests necessary for monitoring clinical outcomes?
  • How effective are combination strategies in changing user behavior?
  • Which components of a combination are most effective?
  • How do we assess efficiency and effectiveness of specific interventions?
  • How do we transfer interventions from one setting or population to another?
  • Documenting the impact of stigma.
  • How to tailor effective interventions to patient characteristics and preferences, health system structures, processes and settings; create toolkits; and, provide guidance on best practices for how to implement effective strategies.
  • What interventions, or combinations of interventions, are most effective in preventing opioid addiction at the individual and population levels? By whom are these interventions optimally delivered, and to whom are they targeted?
  • How, and in what settings, should people at risk of opioid addiction be screened, identified, and referred to the most appropriate level of care? 
  • What individual-level barriers exist to accessing the care to which patients are referred? How can these barriers best be surmounted, and by whom?
  • What follow-up is needed for people referred for additional evaluation and treatment? How can needed communications within and across systems be facilitated?
  • How, if at all, can people with OUD who don’t perceive a need for treatment be motivated to enter care?
  • What individual-, provider-, and systems-level factors discourage people from seeking and/or engaging in treatment for OUD? How, and by whom, can these factors be addressed?
  • What factors discourage patients with OUD from accessing MAT? How, and by whom, can these factors be addressed?
  • What are the attitudinal, philosophical, educational/training, organizational, policy and barriers and facilitators to implementation of best practices—including MAT--within various systems? How can barriers be minimized and facilitators maximized?
  • What are the regulatory and legal barriers to implementing an integrated, cross-systems public response to the opioid epidemic involving all involved sectors?  How can these barriers best be surmounted?
  • What interventions, or combinations of interventions, are most likely to motivate, engage and retain patients in MAT after a nonfatal overdose? By whom are these interventions delivered, and to whom are they targeted?
  • What heterogeneity of treatment effects are related to factors such as age, race/ethnicity, gender, treatment history, incarceration history, co-occurring mental disorders, and other factors?
  • Grow the evidence base for peer support specialists and services.
  • Use Socio-economic, clinical, genetic and organizational data
  • Botvin’s Life Skills Training. Family and school based preventive interventions work to prevent opioid misuse. They establish family and peer supported norms, self-control and social skills and health promoting behaviors within the primary social units of development, families and schools at a key point of development, entry to adolescence.  NIDA should allocate a minimum of 10% of study funds to build community capacity to choose and implement effective universal and selective prevention in neighborhoods, schools and families in all test sites as part of the study.
  • An action research network could promote and evaluate adoption of new practices quickly.

Infrastructure, partnerships, and collaboration

  • Partnership with governmental organizations in conjunction with collecting participant personal identifiers would allow linkage of data to systems such as homeless shelter and criminal justice system records.
  • Many smaller or minority communities are suspicious of larger research institutions. Suggest that a P20 funding mechanism be developed that stresses Diversity, Equity and Inclusion in Drug Abuse research. The goal would be to promote partnerships between larger research institutions and smaller, or rural institutions.
  • Encourage partnerships and infrastructure that can support the most vulnerable users who often do not access health care or traditional health care services.
  • Successful implementation of the study will require collaboration and consensus on topics such as software development approach, governance, policy, communications, and branding.
  • Modeling the effects of a “whole-system” approach allows stakeholders to assess the impact of changes in factors such as treatment choices, law enforcement actions, budgeting, resources allocation, leadership strength, and collaborative partnerships on key outcomes.
  • Patients, families and community stakeholders should be engaged in the study design.
  • Current funding vehicles create a challenge to meaningful collaborations: channeling funding to local authorities puts resources in the hands of those closest to the local problem but it limits coordinated efforts.
  • Needs an integrated team of IT experts, medical researchers, and data scientists from leading organizations.
  • Platforms such as NIH NIAID enable the meaningful sharing of clinical research data worldwide by fostering collaboration and participation through secure access to data following existing data standards.
  • To enable infrastructure, partnership and collaboration, we recommend using the OTA, to enable grants, contracts and flexibility to integrate from small business to national organizations.
  • An optimal research initiative needs to be EHR-based with aligned incentives. If it’s clinical, ensure the tools and workflows make clinician work more efficient to reduce provider burnout and ensure participation.
  • Partnerships for a community-based program should include shelters, jails, and addiction treatment facilities.