key: cord-301300-nfl9z8c7
authors: Slavova, Svetla; LaRochelle, Marc R.; Root, Elisabeth; Feaster, Daniel J.; Villani, Jennifer; Knott, Charles E.; Talbert, Jeffrey; Mack, Aimee; Crane, Dushka; Bernson, Dana; Booth, Austin; Walsh, Sharon L.
title: Operationalizing and selecting outcome measures for the HEALing Communities Study
date: 2020-10-02
journal: Drug Alcohol Depend
DOI: 10.1016/j.drugalcdep.2020.108328
sha: 
doc_id: 301300
cord_uid: nfl9z8c7

BACKGROUND: The Helping to End Addiction Long-term (HEALing) Communities Study (HCS) is a multisite, parallel-group, cluster randomized wait-list controlled trial evaluating the impact of the Communities That HEAL intervention to reduce opioid overdose deaths and associated adverse outcomes. This paper presents the approach used to define and align administrative data across the four research sites to measure key study outcomes. METHODS: Priority was given to using administrative data and established data collection infrastructure to ensure reliable, timely, and sustainable measures and to harmonize study outcomes across the HCS sites. RESULTS: The research teams established multiple data use agreements and developed technical specifications for more than 80 study measures. The primary outcome, number of opioid overdose deaths, will be measured from death certificate data. Three secondary outcome measures will support hypothesis testing for specific evidence-based practices known to decrease opioid overdose deaths: (1) number of naloxone units distributed in HCS communities; (2) number of unique HCS residents receiving Food and Drug Administration-approved buprenorphine products for treatment of opioid use disorder; and (3) number of HCS residents with new incidents of high-risk opioid prescribing. CONCLUSIONS: The HCS has already made an impact on existing data capacity in the four states. In addition to providing data needed to measure study outcomes, the HCS will provide methodology and tools to facilitate data-driven responses to the opioid epidemic, and establish a central repository for community-level longitudinal data to help researchers and public health practitioners study and understand different aspects of the Communities That HEAL framework.

The Helping to End Addiction Long-term (HEALing) Communities Study (HCS) is a multisite, parallel-group, cluster randomized wait-list controlled trial evaluating the impact of the Communities That HEAL intervention to reduce opioid overdose deaths and other associated adverse outcomes (Walsh et al., in press) . The intervention includes three components:

(1) a community-engaged and data-driven process to assist communities in selecting and implementing evidence-based practices to address opioid misuse and opioid use disorder (OUD), and reduce opioid overdose deaths (Martinez et al., in press) ;

(2) the Opioid Reduction Continuum of Care Approach which contains a compendium of evidence-based practices and strategies to expand opioid overdose education and naloxone distribution, medications for opioid use disorder (MOUD), and safe opioid prescribing (Winhusen et al., in press) ; and

(3) community-based health communication campaigns to increase awareness and demand for the evidence-based practices and reduce their stigma (Lefebrve et al., in press) .

A total of 67 communities across four highly affected states (Kentucky, Massachusetts, New York, Ohio) were recruited to participate in the HCS and randomized to one of two waves in a wait-list, controlled design. The communities were randomized to receive either the intervention (referred to as Wave 1 communities) or a waitlist control (referred to as Wave 2 communities). The HCS has one primary hypothesis (H1) and three secondary hypotheses (H2, H3, H4) (Walsh et al., in press) . It is hypothesized that during the evaluation period (January 1, 2021 to December 31, 2021), Wave 1 communities compared with Wave 2 communities, will: H1: reduce opioid overdose deaths (primary outcome); H2: increase naloxone distribution; J o u r n a l P r e -p r o o f H3: expand utilization of MOUD; and H4: reduce high-risk opioid prescribing.

Quality data are needed to measure the study outcomes and assess the impact of the integrated intervention and the specific evidence-based practices. Data are also an important component of the intervention because communities can use data on opioid overdose mortality and morbidity supplemented with data on community resources and needs to develop a datadriven action plan to expand the utilization of evidence-based practices. Communities also need timely and accurate data for visualization in data dashboards designed to monitor the uptake and success of the selected evidence-based practices and strategies, and respond to emerging challenges and community needs (Wu et al., in press) .

This article describes the process for using administrative data to develop the HCS outcome measures aligned with the primary and three secondary hypotheses of the study.

Each research site developed collaborations and partnerships with state agencies and other data owners to understand the regulations and policies governing the use of administrative data for research. An HCS Data Capture Work Group was formed and included representatives from the four research study sites, the data coordinating center at the RTI International, and the sponsors (the National Institute on Drug Abuse and the Substance Abuse and Mental Health Services Administration [SAMHSA]). A structured consensus decision-making strategy was used to:

A. Identify data sources to measure the primary, secondary, and other study outcomes; C. Develop data governance strategy and data use agreements; D. Develop study measure definitions, technical specifications, programming code, procedures for data quality control, common data model, and schedule for data transfer to the data coordinating center;

During development, priority was given to use of existing state-level administrative data sources with regulated and sustained data collections and established infrastructures for quality assurance and control. This is an efficient and cost-effective way to study community-level changes, capitalizing on the federal and state investments for collecting standardized surveillance data, and adopting, when possible, validated surveillance definitions. In addition, using multiple administrative data sources allowed for the construction of measures at the community/population level (i.e., unit of analysis being HCS community) by aggregating individual-level data (e.g., unit of measurement being a community resident or a provider practicing within an HCS community) that best matched HCS outcomes.

Priority also was given to data sources with timely reporting, preferably with less than a 6-month lag between the occurrence of events and data availability. Timeliness and near-realtime access to data was critical for three reasons:

(1) the community engagement component of the intervention is data-driven and dependent on providing ongoing data feedback to community partners throughout the process (Walsh et al., in press); J o u r n a l P r e -p r o o f

(2) it is imperative that the study results are made publicly available quickly because of the magnitude and impact of the opioid crisis on US communities; and

(3) the HCS was designed as a four-year study.

This study protocol (Pro00038088) was approved by Advarra Inc., the HEALing Communities Study single Institutional Review Board. The study is registered with

This section presents the results from the selection and operationalization of administrative data measures for study hypotheses testing (Table 1) , as well as study measures for secondary analyses and monitoring the progress in implementing evidence-based practices ( 

The primary HCS outcome is the number of opioid overdose deaths among residents in HCS communities. The traditional data source for capturing drug overdose deaths are death certificate records (ISW7, 2012; Hedegaard et al., 2020; Warner et al., 2013) . Suspected drug overdose deaths are considered unnatural deaths and are subject to medicolegal death J o u r n a l P r e -p r o o f investigation before the death is certified by a coroner or a medical examiner (Hanzlick, 2014; Hanzlick and Combs, 1998) , and a completed death certificate is filed with the office of vital statistics in the state where the death occurred (NCHS, 2003a, b) . Selected fields from the death certificate record are then sent to the National Center for Health Statistics where the cause-ofdeath information is coded with one underlying and up to 20 multiple (i.e., supplementary)

cause-of-death codes using the International Classification of Diseases, Tenth Revision (ICD-10)

coding system (WHO, 2016) . The CDC definition for identifying drug overdose deaths with opioid involvement in ICD-10-coded death certificate records is commonly accepted by researchers and public health agencies. Using ICD-10-coded death certificate data, drug overdose deaths are identified as deaths with an underlying ICD-10 cause-of-death code X40- Previous research has identified several methodological challenges for identification of opioid involvement in drug overdose deaths (e.g., lack of routinely performed postmortem toxicology testing, especially for fentanyl and designer opioids; challenges to detection and quantification of new designer opioids; variation in jurisdictional office policy in completion of drug overdose death certificates; differences in the proportion of drug overdose death certificates completed by different jurisdictions that do not list the specific contributing drugs) (Buchanich et al., 2018; Ruhm, 2018; Slavova et al., 2015; Slavova et al., 2019; Warner and Hedegaard, 2018;  J o u r n a l P r e -p r o o f Warner et al., 2013) . Prior to the evaluation period, the research sites are administering surveys among the coroners, medical examiners, and toxicology labs serving both Wave 1 and Wave 2 communities to collect information related to death investigations of suspected drug overdose deaths (including postmortem toxicology testing, timelines for death certificate completion, and possible COVID-19-related changes in these processes that could have lasting effects during the HCS evaluation period) in order to understand possible limitations and changes in the completeness and accuracy of the primary outcome measure.

Each HCS research site will use death certificate records from their state office of vital statistics to identify HCS resident deaths with opioid contribution. One challenge in using death certificate data for the primary study outcome is the lag between the death date and the date when death certificate records are available for analysis (Rossen et al., 2017) . Sites have been working with local coroners, medical examiners, and state vital statistics offices to improve the timeliness of data availability across all HCS communities. In 2019, almost all the death certificate records in Kentucky, Massachusetts, New York, and Ohio were available for analysis within 6 months after the overdose death (CDC, 2020).

The following steps describe the HCS operational definition for capturing opioid overdose deaths for testing the primary study hypothesis:



Step 1: All sites will use state death certificate files captured 6 months after the end of the evaluation period to identify the death certificate records for residents of HCS communities with a date of death within the evaluation period, an underlying cause-of- This process will ensure a quality harmonized measure that is captured consistently across the four research sites.

Number of naloxone units distributed in an HCS community as measured by the sum of the naloxone units (1) 

The US Surgeon General's advisory on naloxone emphasized that expanding naloxone availability in communities is a key public health response to the opioid crisis (HHS, 2018) .

Research has shown that opioid overdose death rates were reduced both in communities that implemented overdose education and naloxone distribution programs (Walley et al., 2013) and in jurisdictions enacting laws allowing direct pharmacist dispensing of naloxone (Abouk et al., 2019) . There are three limitations of this data source: (1) no information is provided about the number of pharmacies dispensing naloxone prescriptions;

(2) suppression rules preclude reporting of data for geographic areas with fewer than four pharmacies; and (3) prescriptions are assigned to communities based on the location of the pharmacy rather than the customer's residence. Suppression rules impacted three communities in Massachusetts; this was resolved by requesting the total for the three communities and dividing it relative to the community populations. The assignment of a pharmacy to a community based on pharmacy address may result in an overcount of naloxone in a community with pharmacies that serve residents of non-HCS communities or an undercount if a pharmacy is just outside an HCS community but serves HCS residents.

A limitation of the measure is that it may not capture naloxone distributed in hospitals, correctional facilities, or other venues when the naloxone is purchased with support from private donations, foundations, or locally awarded federal funding.

The number of HCS residents receiving buprenorphine products approved by the Food 

There are three FDA-approved MOUD products: buprenorphine, methadone, and naltrexone. Multiple randomized controlled trials (Krupitsky et al., 2011; Mattick et al., 2009 Mattick et al., , 2014 have demonstrated that MOUD can reduce cravings and illicit opioid use. Observational studies have identified that buprenorphine and methadone are associated with reduced mortality Sordo et al., 2017) . Thus, as part of the Opioid Reduction Continuum of Care Approach, communities are required to expand MOUD with buprenorphine and/or methadone (Winhusen et al., in press) . Access to MOUD is geographically heterogeneous and differs by patient population (Haffajee et al., 2019; Pashmineh Azar et al., 2020) . For example, Opioid Treatment Programs providing methadone are less common in rural than urban areas (Joudrey et al., 2019) . Criminal justice-involved populations, where there has been a historical J o u r n a l P r e -p r o o f preference toward naltrexone (Krawczyk et al., 2017) , are less likely to receive buprenorphine and methadone. There also is a great deal of variation in billing and documentation of the type of MOUD, administration modality (e.g., office-based administration as compared with prescription filled at pharmacy by patient), provider type, state policies, and insurance coverage.

Accurate estimation on the prevalence of OUD in HCS communities is important for planning and scaling of the MOUD uptake. However, estimating the population at need for MOUD is a challenge for the HCS. The HCS team is working on developing improved estimations for OUD prevalence in each HCS community using a capture-recapture statistical methodology previously applied by Barocas et al. (Barocas et al., 2018) .

Five potential sources for measurement of MOUD were identified: Medicaid claims, allpayer claims databases, PDMPs, Opioid Treatment Program Central Registries, and pharmacy dispensed prescriptions (IQVIA). The disparate data sources vary in completeness and timeliness. 

All-payer claims databases are large state databases that typically include medical claims across multiple settings (e.g., hospitalizations, emergency departments visits, outpatient visits), pharmacy claims, and eligibility and provider files. Data are collected from both public and private payers and reported directly by insurers to a state repository. All-payer claims databases are structured similarly to Medicaid claims data and allow for linking of individuals across claims to identify individuals with OUD and their treatments. The key advantage is the inclusion of private insurance, allowing more accurate estimation of prevalence of individuals with diagnosed OUD and treatment with MOUD in a state.

All-payer claims have been used previously in OUD-related research (Burke et al., 2020; Freedman et al., 2016; LeBaron et al., 2019; Saloner et al., 2017) . Seventeen states have all- (Grecu et al., 2018) .

Opioid Treatment Programs are the only facilities allowed to deliver methadone for OUD but may also offer buprenorphine and naltrexone along with behavioral therapy. They must be certified by SAMHSA and an independent, SAMHSA-approved accrediting body to dispense MOUD (SAMHSA, 2020). They also must be licensed by the state in which they operate and must be registered with the Drug Enforcement Administration. The registries are established to prevent patient's simultaneous enrollment in multiple locations (e-CFR, 2020a). Number of enrolled patients, aggregated at the HCS community level, as permitted by section §2.52

Research, 42 CFR Part 2 (e-CFR, 2020a) can be used as a measure for methadone treatment uptake, but central registries were not available in all four research sites.

IQVIA data capture pharmacy dispensed naltrexone. However, naltrexone is indicated for treatment of OUD and for alcohol use disorder. Because pharmacy records do not include diagnose-related information for making this distinction, this data source may overestimate the uptake of naltrexone for OUD.

defined as ≥90 mg MME over 3 calendar months; or (4) incident overlapping opioid and benzodiazepine prescriptions greater than 30 days over 3 calendar months.

High opioid dosages, co-prescribing opioids with benzodiazepines or other sedative hypnotics, and receipt of opioid prescriptions from multiple providers or pharmacies are associated with opioid-related harms (Bohnert et al., 2011; Cochran et al., 2017; Dunn et al., 2010; Rose et al., 2018) . Characteristics of opioid initiation are also important. For example, initiating opioid treatment with extended-release/long-acting opioids (Miller et al., 2015) is associated with increased risk of overdose, and longer prescription duration is associated with transition to long-term opioid use (Shah et al., 2017) .

Based on available evidence, in 2016 the CDC published guidelines (Dowell et al., 2016) for prescribing opioids for chronic pain. Numerous quality measures have been developed to encourage and measure progress toward improving the safety of opioid prescribing. After decades of increases, rates of opioid prescribing peaked and are now declining, although they remain historically high (Guy et al., 2017; Schieber et al., 2020) .

Developing safe and patient-centered approaches for individuals receiving long-term opioid therapy has been a challenge to address in underlying evidence or guidelines. Increasing Two constructs with the best available evidence to support decreases in opioid-related harms were targeted with the intention of reducing the number of individuals initiating high-risk opioid prescribing and the likelihood that new opioid prescribing episodes develop into longterm episodes (Shah et al., 2017) .

State PDMPs were identified as the best available data source for these measures across all four research sites. A limitation of these data is the lack of clinical context-such as diagnostic codes for disease or condition-associated with the prescribed medication. As a result, at the patient level, it is difficult to assess the appropriateness of a high-dose opioid prescribing episode, such as that needed for management of severe pain for patients with cancer or end-of-life care. Another limitation of this measure is the lack of automated data sharing among state PDMPs on prescriptions filled across state boundaries. A benefit of the PDMP data source is that it is timely and captures dispensed prescriptions for controlled substances paid for by both insurance and cash.

Medicaid claims and all-payer claims databases are alternative data sources. The main advantage of claims data compared with PDMP data is the clinical context. However, claims data lack information on prescriptions paid by cash or alternative insurance coverage, which is associated with increased risk of opioid-related harms (Becker et al., 2017) . Medicaid claims are common across the sites, whereas all-payer claims databases exist in only two of the four states.

Claims data lag by at least 6 months, making them less useful for timely monitoring of progress.

Existing measures were identified through a review of the literature, including existing measures from CDC and National Quality Forum, and National Committee for Quality Alliance, which were subsequently adapted to the constructs identified above. All opioid agonist medications, including tramadol, were included, with the exception of antitussive codeine formulations and buprenorphine formulated for pain. The reasons for their exclusion are a lack of clear guidance for conversion to morphine milligram equivalents and a lack of evidence that buprenorphine, a partial opioid agonist, conveys the same risk as this of full opioid agonists.

To maintain consistency across sites, the team developed a standardized list of national drug codes for opioids, benzodiazepines, and MOUD using the MEDI-SPAN ELECTRONIC DRUG FILE (MED-File) V2 and the Drug Inactive Data File (Wolters Kluwer, 2020) . The standardized study drug list is updated quarterly. The MED-File includes product names, dosage forms, strength, the NDC, and generic product identifier (GPI). The GPI is a 14-digit number that allows identification of drug products by primary and secondary classifications and simplifies identification of similar drug products from different manufacturers or different packaging. Because our study requires baseline data on opioid utilization, the inactive date file is used to include drugs that may be currently inactive but were used during the baseline period.

All GPIs beginning with the classification "65"-which identifies any drug product containing an opioid or combination-are included in the opioid list. Next, opioid products that are not likely to be used in the outpatient/ambulatory pharmacy setting-such as bulk powder, bulk chemicals, and dosage forms typically used in hospitals or hospice settings (e.g., epidurals, IVs)-are excluded. Products classified as cough/cold/allergy combinations, cough medications, J o u r n a l P r e -p r o o f antidiarrheal/probiotic agents, buprenorphine products used for OUD and pain, and methadone products used for OUD were also excluded. The CDC file that identifies oral MMEs (CDC, 2019b) was used to add MMEs to each opioid product and to identify products as long-acting or short-acting. To ensure the HCS list includes all current and inactive products, the CDC list was cross-referenced with the list of all GPI products.

The benzodiazepine products are identified using the GPI classification "57", which identifies any drug product containing a benzodiazepine or combination. Products that are not likely to be used in the outpatient/ambulatory pharmacy setting-such as bulk powder, bulk chemicals, and dosage forms typically used in hospitals or hospice settings-were excluded. The full list of GPIs for opioids, benzodiazepines, and buprenorphine are included in the appendix.

The success of the intervention relies on the community's ability to assess the complexity and specifics of the local opioid epidemic and identify the best ways to implement and promote evidence-based practices locally. A set of additional measures was developed, to be shared with the intervention communities as counts and/or rates over time and visualized as trends on community-tailored dashboards (Wu et al., in press) . These measures monitor the complexity of the opioid-related harms as well as the progress in the three main evidence-based practices from the Opioid Reduction Continuum of Care Approach (Winhusen et al., in press) . A list of selected study measures is provided in Table 2 Working closely with state stakeholders, the research sites also developed standard operating procedures for data quality assurance and control, and improved data collection (e.g., improved timelines of an existing data sources or development of new administrative data collections).

The HCS data coordinating center created a common data model to match the complexity and scale of the clinical trial design and measures and the conditions of the data use agreements.

The common data model consisted of (1) an internal identification number for each HCS measure outcome;

(2) frequency of reporting (i.e., daily, monthly, quarterly, semi-annually, or annually);

(3) display features for dashboards and visualization (i.e., display date, display value, research cite/research community identification number, label); and J o u r n a l P r e -p r o o f

(4) internal usage information (i.e., is estimate, is suppressed [per data use agreement suppression requirements], notes, stratification, and version number).

The common data model allows coordinated presentation of data to communities to aid with decision making and monitoring of progress and allows the HCS consortium and trial sponsors to routinely monitor progress.

During the first year of the HCS, the Data Capture Work Group evaluated more than 15 administrative data sources across the four states for their ability to support study measures in multiple relevant domains. The research site teams established multiple data use agreements with data owners to support the calculation for more than 80 study measures based on administrative data collections, such as death certificates, emergency medical services data, inpatient and emergency department discharge billing records, Medicaid claims, syndromic surveillance data, PDMP data, Drug Enforcement Administration data on drug take back collection sites and events, DATA 2000 waivered prescriber data, HIV registry, naloxone distribution and dispensed prescription data.

There were many challenges related to state variations in data timeliness and content that needed to be addressed, and compromises were made to achieve harmonization across research sites. The harmonization on Medicaid measure specifications required participation from the state partners because individual states have some unique codes or code bundles for capturing specific services. Collaborative workgroups with participation from state partners were formed with specific focus on Medicaid data, PDMP data, and emergency medical services data.

Another challenge is the lack of quality validation studies for many of the measures, so the degree of possible misclassification of diagnosis or service codes used in some specifications J o u r n a l P r e -p r o o f is unknown. One example is attempting to identify OUD prevalence using diagnosis codes in medical claims or other administrative data sources knowing that OUD is often underdiagnosed. 

Massachusetts also is seeking to partner with emergency medical services agencies to improve timeliness of data reporting and completeness of race/ethnicity data. New York developed a cloud-based application to facilitate data aggregation and sharing both for HCS and future research projects. In Ohio, the HCS team partnered with the InnovateOhio Platform, which was established by executive order a few weeks prior to the HCS project start date. The HCS has been a highly successful "test case" for how a single technology platform could be leveraged to provide necessary data quickly and efficiently for a large study involving multiple state agencies.

The platform facilitated a multi-agency data use agreements, and curates, cleans, and links data sets across multiple Ohio state agencies monthly. This allowed the Ohio HCS team to sign one data use agreement to cover all project data activities.

The HCS will provide methodology and tools to facilitate data-driven responses to the opioid epidemic at the local, state, and national levels. 

Number of opioid overdose deaths among HCS residents during the evaluation period as measured by deaths with an underlying cause-of-death being drug overdose (i.e. an underlying cause-of-death ICD-10 code in the range X40-X44, X60-X64, X85, Y10-Y14) where opioids, alone or in combination with other drugs (i.e. a multiple cause-of-death ICD-10 code in the range T40.0-T40.4, or T40.6), were determined to be contributing to the drug overdose death. Data source: Drug overdose deaths are captured by death certificate records; additional medicolegal death investigation records can be used (per established protocol) to determine opioid involvement when specific drugs contributing to the overdose deaths are not listed on the death certificate.

Number of naloxone units distributed in an HCS community during the evaluation period as measured by the sum of (1) the naloxone units distributed to community residents by overdose education and naloxone distribution programs with support from state and federal funding, including dedicated HCS funding, and (2) the naloxone units dispensed by retail pharmacies located within HCS communities.

Data source: Data are captured from state administrative records and supplemented by study records to include naloxone funded through HCS, as well as IQVIA Xponent® database.

Number of HCS residents receiving buprenorphine products approved by the Food and Drug Administration for treatment of opioid use disorder as measured by the number of unique individuals residing in an HCS community who had at least one dispensed prescription for these products during the evaluation period.

Data source: State prescription drug monitoring program data.

Number of HCS residents with new incidents of high-risk opioid prescribing during the evaluation period as measured by the number of residents in an HCS community who met at least one of the following four criteria for a new high-risk opioid prescribing episode after a washout period of at least 45 days: (1) Incident opioid prescribing episode greater than 30 days duration (continuous opioid receipt with no more than a 7day gap); (2) Starting an incident opioid prescribing episode with extended-release or long-acting opioid formulation; (3) Incident high-dose opioid prescribing, defined as ≥90 mg morphine equivalent dose over 3 calendar months; or (4) Incident overlapping opioid and benzodiazepine prescriptions greater than 30 days over 3 calendar months. 

Association between state laws facilitating pharmacy distribution of naloxone and risk of fatal overdose

The Medicaid Outcomes Distributed Research Network (MODRN)

Innovative solutions for state medicaid programs to leverage their data, build their analytic capacity, and create evidence-based policy

Claims Database Council

Estimated Prevalence of Opioid Use Disorder in Massachusetts

Multiple sources of prescription payment and risky opioid therapy among veterans

Association between opioid prescribing patterns and opioid overdose-related deaths

The effect of incomplete death certificates on estimates of unintentional opioid-related overdose deaths in the United States

Trends in opioid use disorder and overdose among opioid-naive individuals receiving an opioid prescription in Massachusetts from

Annual surveillance report of drugrelated risks and outcomes-United States

Opioid overdose. Data resources. Analyzing prescription data and morphine milligram equivalents (MME)

Centers for Disease Control and Prevention (CDC), 2020. NCHS Data Quality measures

An examination of claims-based predictors of overdose from a large Medicaid program

KASPER controlled substance reporting guide, . Kentucky Cabinet for Health and Family Services

Nonfatal opioid overdose standardized surveillance case definition

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Characteristics of US counties with high opioid overdose mortality and low capacity to deliver medications for opioid use disorder

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Only one in twenty justice-referred adults in specialty treatment for opioid use receive methadone or buprenorphine

Injectable extended-release naltrexone for opioid dependence: a double-blind, placebocontrolled, multicentre randomised trial

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Opioid epidemic or pain crisis? Using the Virginia all payer claims database to describe opioid medication prescribing patterns and potential harms for patients with cancer

Health communication campaigns to drive demand for evidence-based practices and reduce stigma in the HEALing Communities Study

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Methadone maintenance therapy versus no opioid replacement therapy for opioid dependence

Buprenorphine maintenance versus placebo or methadone maintenance for opioid dependence

Prescription opioid duration of action and the risk of unintentional overdose among patients receiving opioid therapy

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Prescription Drug Monitoring Program Training and Technical Assistance Center

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Method to adjust provisional counts of drug overdose deaths for underreporting. Division of Vital Statistics

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Patterns of Buprenorphine-Naloxone Treatment for Opioid Use Disorder in a Multistate Population

Variation in adult outpatient opioid prescription dispensing by age and sex -United States

Drug and opioid-involved overdose deaths -United States

Characteristics of initial prescription episodes and likelihood of long-term opioid use -United States

Methodological complexities in quantifying rates of fatal opioid-related overdose

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Community dashboards to support datainformed decision making in the HEALing Communities Study

Table 1. HEALing Communities Study primary and secondary outcome measures for hypothesis testing

All authors contributed to the development of the HCS measures, the development of the framework for the manuscript, and the editing of the manuscript. S. Slavova, J. Villani, and S.L.Walsh drafted the introduction, S. Slavova drafted the methods, M.R. LaRochelle developed the table, and each author participated in drafting parts of the results or discussion sections.