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Given the disproportionate impact of substance use on individuals, families, and communities from populations underrepresented in clinical trials, increasing their enrollment in treatment research is critical for ensuring that the findings inform policies and programs that are inclusive of all communities, thereby advancing health equity. However, since underrepresented groups continue to be underenrolled in clinical trials testing the efficacy and effectiveness of psychosocial treatments for substance use disorder, substance use researchers are still grappling with this challenge.

In this commentary, we describe rigorous methodological approaches, such as integrative data analysis (IDA) and related methods (e.g., moderated nonlinear factor analysis and propensity score weighting), that can help address the challenges posed by the underrepresentation of certain populations. By combining individual-level data from multiple studies into a pooled dataset, these methods increase sample size and statistical power while addressing covariate imbalance across treatment groups. We describe how we employed these methods to address the aims of our recently completed secondary data analysis project conducted within the National Drug Abuse Treatment Clinical Trial Network (CTN-0125; Integrative Data Analysis of CTN Studies to Examine the Impact of Psychosocial Treatments for Black People Who Use Cocaine and/or Opioids). Our study used these methods to pool and analyze data from nine completed CTN trials to assess the comparative effectiveness of psychosocial treatments for Black adults who use cocaine and/or opioids, a group underrepresented in registered trials of the NIDA. We illustrate the application of these methodological approaches in CTN-0125 and demonstrate how they complement each other to address unique analytic challenges. We describe how we addressed data harmonization challenges due to variations in data formats and inconsistencies or gaps in the supportive documentation available on the NIDA Data Share website. We conclude with recommendations for the research field on how to further address sample size and data integration challenges.

Related protocols: CTN-0125

There is an urgent need within the substance-use-disorders (SUD) treatment field to develop and implement consensus-based common core data elements (CDEs) with standardized vocabularies relevant to drug addiction treatment that could be incorporated and widely adopted into harmonized electronic medical record systems (EMRs). This will benefit patients by improving the quality of care and will assist in integration of specialty addiction treatment into disciplines of mainstream medicine. To achieve these aims, the NIDA Clinical Trials Network (CTN) has collected and collated dozens of treatment-form-related information and standardized instruments to develop a treatment-relevant set of CDEs. These CDEs were refined following a consensus-based meeting of federal, state, and community-based treatment stakeholders and providers. This poster describes the collaborative “Mind Map” used for developing and implementing core questions as CDEs for EMRs on SUD in primary care and SUD specialty treatment settings. Current progress in developing EMR core questions as CDEs for use in those settings is also provided, as well as implications of this project for the future of drug abuse treatment. NIDA is especially interested in input from College on Problems of Drug Dependence (CPDD) members on data collection hierarchy and core data elements and on the overall strategy in regards to other sources of input, other stakeholders who should be consulted, and other “next steps” as this project moves forward.

The goal of Project CODE-PRO (Capturing Opioid Use Disorder Electronically and Patient Reported Outcomes) was to build clinical data research infrastructure that will begin to enhance capacity to use electronic health record (EHR) data and patient reported outcomes measures (PROs) to conduct opioid related research in emergency departments (EDs). The project builds data capacity for research in four main areas, including standardized collection of standardized clinical data, collection of participant provided information, linking of clinical and other data for research, and use of clinical data for research in ED settings. As such, the project included three distinct tasks and objectives, including:

1. Identifying existing or potential common data elements (CDEs) for OUD that are relevant to the ED setting (Task 1)
2. Demonstrating that CDEs from the EHRs can be transmitted or integrated into the American College of Emergency Physicians (ACEP) Clinical Emergency Data Registry (CEDR) (Task 2)
3. Exploring the feasibility and acceptability of collecting PRO measures electronically in patients with non-medical opioid use in the ED setting (Task 3)

Together, these tasks were designed to improve interoperability and linkages between EHRs, research networks and registries for research relevant to the opioid epidemic. Enhanced clinical data research infrastructure in ED settings has the potential to facilitate research to address key strategic priorities, such, as improving access to treatment and recovery services, promoting use of overdose-reversing drugs, providing support for cutting edge research on addiction and pain, and advancing better practices for management of OUD and pain.

Related protocols: CTN-0081

The emergency department (ED) offers an important opportunity to identify patients with opioid use disorder (OUD) and initiate treatment. However, post-ED follow-up is challenging, and novel approaches to enhance care transitions are urgently needed. Outcomes following ED visits have traditionally focused on overdose, treatment engagement, and mortality with an absence of patient reported outcomes (PROs), for example patient ability to schedule follow-up OUD treatment appointments or pick up a prescription medication, that may better inform evaluation of treatment pathways and near-term outcomes after acute events. In the context of increasing novel secure mobile health (mHealth) platforms, researchers explored the feasibility and acceptability of electronically collecting PROs from ED patients with non-medical opioid use to enhance care in the ED and transitions of care.

ED patients with non-medical opioid use or opioid overdose who endorsed willingness and ability to complete electronic surveys after discharge were enrolled from a tertiary, urban academic ED. Participants were enrolled in an mHealth platform, shared electronic health records with researchers, and completed electronic surveys of PROs at baseline, three- and thirty-days post discharge from the hospital, including questions about ability to schedule a follow-up appointment, pick up a prescription medication and overdose risk behaviors. Primary outcomes were measures of feasibility and acceptability of electronic PRO collection among ED patients with non-medical opioid use.

Among 1,808 patients assessed for eligibility between June-December 2019, 101 of 130 (78%) eligible adult patients consented to participate. Ninety-six (95%) of 101 patients completed registration in the mHealth platform, and 77/96 (80%) were successful in sharing their electronic health data. Completion rates for the baseline, three-day and thirty-day surveys were 97% (93/96), 49% (47/96) and 42% (40/96). Implementation challenges included short engagement window during ED visit, limited access to smartphones/computers, insufficient battery life of participant phone to access email and password, forgotten emails and passwords, multi-step verification processes for account set-up, and complaints about hospital care, most of which were effectively addressed by study personnel.

Conclusions: ED patients with OUD were willing to share electronic health information and PROs, although implementation challenges were common, and more than half of participants were lost-to-follow-up after hospital discharge at 30 days. Efforts to streamline communication and remove barriers to engagement are needed to improve the collection of PROs and pathways of care in ED patients with OUD.

Related protocols: CTN-0081

This guide was developed by investigators at Yale University as part of a project designed to build data capacity for patient-centered outcomes research (CTN-0081).

It provides an overview of the collection of patient reported outcomes (PROs) from emergency department (ED) patients with nonmedical opioid use using a novel mobile health (mHealth) platform and is designed to aid in the design and conduct of similar projects at other institutions.

Related protocols: CTN-0081

This is the primary outcomes paper for CTN-0081. Gaps in electronic health record (EHR) data collection and the paucity of standardized clinical data elements (CDEs) captured from electronic and digital data sources have impeded research efforts aimed at understanding the epidemiology and quality of care for opioid use disorder (OUD). As part of CTN-0081, researchers aimed to identify existing CDEs and evaluate their validity and usability, which is required prior to infrastructure implementation within EHRs.

Investigators conducted (a) a systematic literature review of publications in Medline, Embase and the Web of Science using a combination of at least one term related to OUD and EHR and (b) an environmental scan of publicly available data systems and dictionaries used in national informatics and quality measurement of policy initiatives. Opioid-related data elements identified within the environmental scan were compared with related data elements contained within nine common health data code systems and each element was graded for alignment with match results categorized as “exact”, “partial”, or “none.”

The literature review identified 5186 articles for title search, of which 75 abstracts were included for review and 38 articles were selected for full-text review. Full-text articles yielded 237 CDEs, only 12 (5.06%) of which were opioid-specific. The environmental scan identified 379 potential data elements and value sets across 9 data systems and libraries, among which only 84 (22%) were opioid-specific. We found substantial variability in the types of clinical data elements with limited overlap and no single data system included CDEs across all major data element types such as substance use disorder, OUD, medication and mental health. Relative to common health data code systems, few data elements had an exact match (<1%), while 61% had a partial match and 38% had no matches.

Conclusions: Despite the increasing ubiquity of EHR data standards and national attention placed on the opioid epidemic, we found substantial fragmentation in the design and construction of OUD related CDEs and little OUD specific CDEs in existing data dictionaries, systems and literature. Given the significant gaps in data collection and reporting, future work should leverage existing structured data elements to create standard workflow processes to improve OUD data capture in EHR systems.

Related protocols: CTN-0081

Alcohol and drug use are leading causes of morbidity and mortality that frequently go unidentified in medical settings. As part of a multi-phase study to implement electronic health record-integrated substance use screening in primary care clinics (the CTN-EHR-PC study, CTN-0062-Ot), researchers interviewed key clinical stakeholders to identify current substance use screening practices, barriers to screening, and recommendations for its implementation. Focus groups and individual interviews were conducted with 67 stakeholders, including patients, primary care providers (faculty and resident physicians), nurses, and medical assistants, in two urban academic health systems affiliated with the NIDA Clinical Trials Network. Themes were identified using an inductive approach, revised through an iterative process, and mapped to the Knowledge to Action (KTA) framework, which guides the implementation of new clinical practices.

Factors affecting implementation based on KTA elements were identified from participant narratives:

• Identifying the problem: Participants consistently agreed that having knowledge of a patient’s substance use is important because of its impacts on health and medical care, that substance use is not properly identified in medical settings currently, and that universal screening is the best approach.
• Assessing barriers: Patients expressed concerns about consequences of disclosing substance use, confidentiality, and the individual’s own reluctance to acknowledge a substance use problem. Barriers identified by providers included individual-level factors such as lack of clinical knowledge and training, as well as systems-level factors including time pressure, resources, lack of space, and difficulty accessing addiction treatment.
• Adapting to the local context: Most patients and providers stated that the primary care provider should play a key role in substance use screening and interventions. Opinions diverged regarding the optimal approach to delivering screening, although most preferred a patient self-administered approach. Many providers reported that taking effective action once unhealthy substance use is identified is crucial.

Conclusions: Participants expressed support for substance use screening as a valuable part of medical care, and identified individual-level as well as systems-level barriers to its implementation. These findings suggest that screening programs should clearly communicate the goals of screening to patients and proactively counteract stigma, address staff concerns regarding time and workflow, and provide education as well as treatment resources to primary care providers.

Related protocols: CTN-0062-Ot

The agreement between self-reported cannabis abstinence with urine cannabinoid concentrations in a clinical trials setting is not well characterized. This study assessed the agreement between various cannabinoid cutoffs and self-reported abstinence across three clinical trials, one including contingency management for abstinence. All three of the trials included both participant self-report and weekly urine samples for cannabis and creatinine concentration measurements. Bootstrapped data were assessed for agreement between self-reported 7+ day abstinence and urine cannabinoid tests using generalized linear mixed effects models for clustered binary outcomes. One study implemented contingency management for cannabis abstinence. Four hundred and seventy-three participants with 3787 valid urine specimens were included. Urine was analyzed for 11-nor-9-carboxy-delta-9-tetrahydrocannabinol and creatinine using immunoassay methods Biological cutoffs of 50, 100, and 200 ng/ml, as well as changes in CN normalized THCCOOH (25%/50% decrease), were assessed for agreement with self-reported abstinence during the three clinical trials.

Results found that agreement between measured THCCOOH and self-reported abstinence increases with increasing cutoff concentrations, while the agreement with self-reported non-abstinence decreases with increasing cutoff concentrations. Combining THCCOOH cutoffs with recent changes in CN-THCCOOH provides a better agreement in those self-reporting abstinence. Participants in the studies that received CM for abstinence had a lower agreement between self-reported abstinence and return to use than those in studies that did not have a contingency management component.

Conclusions: Using a combination of both concurrent THCCOOH and recent changes in CN-THCCOOH, the agreement between self-reported cannabis abstinence initiation and measure agreement is shortened significantly.

Related protocols: CTN-0053

Quantifying cannabis use is complex due to a lack of a standardized packaging system that contains specified amounts of constituents. A laboratory procedure has been developed for estimating physical quantity of cannabis use by utilizing a surrogate substance to represent cannabis, and weighing the amount of the surrogate to determine typical use in grams. This secondary analysis used data from a multi-site, randomized, controlled pharmacological trial for adult cannabis use disorder (N=300), sponsored by the National Drug Abuse Treatment Clinical Trials Network (protocol CTN-0053), to test the incremental validity of this procedure. In conjunction with the Timeline Followback, this physical scale-based procedure was used to determine whether average grams per cannabis administration predicted urine cannabinoid levels (11-nor-9-carboxy-delta-9-tetrahydrocannabinol) and problems due to use, after accounting for self-reported number of days use (in the past 30 days) and number of administrations per day in a 12-week clinical trial for cannabis use disorder.

Likelihood ratio tests suggest that model fit was significantly improved when grams per administration and relevant interactions were included in the model predicting urine cannabinoid level and in the model predicting problems due to cannabis use, relative to a model that contained only simpler measures of quantity and frequency.

Conclusions: This study provides support for the use of a scale-based method for quantifying cannabis use in grams. This methodology may be useful when precise quantification is necessary, for example, for researchers to begin to establish meaningful cut-offs for high-risk cannabis use. Researchers may use grams per episode to determine clinical cut-offs for high-risk episodic use in terms of “standard joints,” similar to cut-offs developed in the alcohol literature. Precise quantification of cannabis use also offers some advantages over urine cannabinoid biomarker data, as it can be adapted for remote data collection and is better suited to detect variability in use patterns.

Related protocols: CTN-0053

The use of prescription opioids has increased dramatically in the past 2 decades, with associated increases in opioid misuse/abuse and opioid overdose. These are among the most commonly prescribed medications, with 259 million prescriptions written for opioid pain relievers in the U.S. in 2012.

This study aimed to establish a prescription opioid registry protocol in a large health system, Kaiser Permanente Northern California (KPNC), and to describe algorithms to characterize individuals using prescription opioids, opioid use episodes, and concurrent use of sedative/hypnotics.

Using KPNC electronic health record data, the investigators selected patients using prescription opioids in 2011. Opioid and sedative/hypnotic fills, and physical and psychiatric comorbidity diagnoses were extracted for years 2008 to 2014. Algorithms were developed to identify each patient’s daily opioid and sedative/hypnotic use, and morphine daily-dose equivalent. Opioid episodes were classified as long-term, episodic, or acute. Logistic regression was used to predict characteristics associated with becoming a long-term opioid user.

Results found that in 2011, 18% of KPNC adult members filled at least 1 opioid prescription. Among those patients, 25% used opioids long term and their average duration of use was more than 4 years. Sedative/hypnotics were used by 76% of long-term users. Being older, white, living in a more deprived neighborhood, having a chronic pain diagnosis, and use of sedative/hypnotics were predictors of initiating long-term opioid use.

Conclusions: This study established a population-based opioid registry that is flexible and can be used to address important questions of prescription opioid use. It will be used in future studies to answer a broad range of other critical public health issues relating to prescription opioid use.

Related protocols: CTN-0061-Ot

This webinar provided an update on CTN-0062-Ot, a phased feasibility and proof-of-concept study seeking to incorporate addiction-specific screening and assessment of common data elements (CDEs) into a widely used electronic health record (EHR), explore the logistics and time required to do this, and assess impacts on the frequency of identification, diagnosis, and referral to treatment in large healthcare organizations.

The webinar included these components (click each title for the individual slides):

• A Phased-Implementation Feasibility and Proof-of-Concept Study to Assess Incorporating the NIDA CTN Common Data Elements into the Electronic Health Record in Large Primary Care Settings (CDE-EHR-PC Study, CTN-0062-Ot). Jennifer McNeely, MD, MS, New York University School of Medicine
• Usability: An Introduction. Joseph Kannry, MD, Mount Sinai Health System
• Usability in Healthcare IT: Data Collection and Analysis Approaches. Andrew Kushniruk, PhD, School of Health Information Science, University of Victoria
• Lessons Learned and Conclusion.

Related protocols: CTN-0062-Ot

Clinical trials have been slow to incorporate e-technology (digital and electronic technology that utilizes mobile devices of the Internet) into the design and execution of studies. In the meantime, individuals and corporations/organizations are relying more on electronic platforms and most have incorporating such technology into their daily lives. This paper, written by five members of the NIDA Clinical Trials Network, provides a general overview of the use of e-technologies in clinical trials research, specifically within the last decade, marked by rapid growth of mobile and Internet-based tools. Benefits of and challenges to the use of e-technologies in data collection, recruitment and retention, delivery of interventions, and dissemination are provided, as well as a description of the current status of regulatory oversight of e-technologies in clinical trials research. As an example of ways in which e-technologies can be used for intervention delivery, a summary of e-technologies for the treatment of substance use disorders is presented, including the Therapeutic Education System studied in protocol CTN-0044 of the NIDA Clinical Trials Network, making trials more efficient while also reducing costs. However, researchers should be cautious when adopting these tools given the many challenges in uses new technologies, as well as threats to participant privacy/confidentiality.

Conclusions: Challenges of using e-technologies in each stage of a clinical trial can be overcome with careful planning, useful partnerships, and forethought. The role of web- and smartphone-based applications is expanding, and the increasing use of those platforms by scientists and the public alike make them tools that cannot be ignored.

Related protocols: CTN-0044

Good Clinical Practice guidelines provide ethical and scientific standards for the design, quality assurance, data collection, analyses, and reporting for clinical trials. GCP standards ensure that research staff protect the rights, safety, well-being, and confidentiality of trial participants as well as comply with best practices in their conduct of clinical investigations.

This 90-minute presentation focused on the critical aspects of ICH GCP Principles and the Code of Federal Regulations (CFR) for clinical research trials. Presenters Denise King, MS and Lauren Yesko, both of The Emmes Corporation, also discussed Good Clinical Practice (GCP) and Good Clinical Data Management Practice (GCDMP) trends in the National Drug Abuse Treatment Clinical Trials Network (CTN).

Learning objectives included:

• Review principles and regulatory requirements for GCP.
• Discuss staff roles and responsibilities, protocol compliance, and other criteria for conducting quality trials.
• Examine best practices, examples of GCP non-compliance, and corrective actions for protocol or procedural deviations.
• Identify significant GCP/GCDMP trends in the CTN, such as informed consent, safety, documentation, drug management, and data management.

Additional Resources:

• Download slides (pdf)
• Download handout (pdf)

The National Drug Abuse Treatment Clinical Trials Network (CTN) of the National Institute on Drug Abuse (NIDA) recently launched a public portal which provides a single-source repository for CTN-recommended common data elements (CDEs) for substance use disorders (SUD) for use in electronic health record systems (EHRs) and clinical research. A CDE in this context is a data element consisting of a question and enumerated set of possible values for responses precisely defined by standardized metadata descriptors. CDEs consisting of individual question/answer pairs can be combined into more complex questionnaires and case report forms or used when gathering medical information in the context of providing clinical care. Thus, CDEs describe semantic characteristics for a discrete piece of data, which will be collected, stored, or exchanged during the course of a study or health examination. This will facilitate exchange of standardized data because of the use of CDEs. In this manner, NIDA CDEs can be commonly applied to multiple data collection systems whether in research or clinical care and across different institutions, such that their intentional commonality with use of common data standards can improve data quality, facilitate data repurposing, and promote data sharing.

This paper describes objectives and importance of the CTN CDEs initiative and portal to translational psychiatric research: To support harmonized use of EHR-compatible common data elements to enable exchange and integration of data to answer clinical meaningful questions of broad interest to SUD treatment research, thereby facilitating big-data biomedical science crossing boundaries between research and clinical care.

Use of psychosocial measures with different conceptual meanings across cultural groups may render treatment outcome analyses invalid in social work research. Determining measurement invariance allows researchers to assess whether the construct of a measure is similarly comprehended and measured across participant groups (e.g., based on race, ethnicity, gender, age, etc.). Nonequivalence is introduced when groups of participants experience or conceptualize a construct differently, or use distinctive criteria to describe the concept. Measurement nonequivalence across cultural groups is posited to occur due to (a) cultural differences in norms and relevance of the constructs being assessed; (b) language of assessment; or (c) potential differences in participants’ environments and opportunity structures to engage in certain behaviors or develop beliefs due to contextual differences, racism, or other forms of discrimination.

To illustrate this statistical procedure, this poster presents measurement invariance properties across racial groups for two commonly used instruments in social work and substance abuse treatment research (the Revised Helping Alliance Questionnaire (HAq-II) and the Short Inventory of Problems (SIP-R)), using data from the National Drug Abuse Treatment Clinical Trials Network protocol CTN-0004 (“Motivational Enhancement Treatment to Improve Treatment Engagement and Outcome in Subjects Seeking Treatment for Substance Abuse”). Analysis shows that use of measures with different conceptual meaning across racial and ethnic groups may render invalid analyses comparing such groups. Conclusions drawn from invalid findings can lead to ineffective treatments and policy initiatives.

Conclusions: Findings support the comparable understanding of therapeutic alliance and consequences of substance as measured by the HAq-II and SIP-R in African American and non-Latino white participants. Difference in reliability caused by the identified items needs verification in future studies to ensure use of reliable HAq-II and SIP-R latent factors.

Related protocols: CTN-0004