Funding Opportunity Number: RFA-DA-26-056
Deadline: July 17, 2025
Purpose
NIH and other funders have invested billions of dollars in the collection of data to inform our understanding of substance use and disorder etiology, neurodevelopmental risk, epidemiology, prevention, treatment, and services, as well as associated HIV risk behaviors and outcomes. Yet much of these data have not been analyzed to their full potential, and further investigation provides opportunities to answer novel research questions at relatively low cost. Existing data provide unique opportunities to better understand interactions between intrapersonal and environmental factors and substance use and disorder (defined as alcohol, tobacco, prescription, and other substance use and disorder), as well as factors related to disease patterns and progressions, and variations in response to preventive interventions and substance use disorder (SUD) treatment and HIV services utilization. Studies using these data might better describe, discriminate, and predict the complex, relapsing nature and course of substance use and disorder, as well as further understanding of factors predicting substance use and disorder trajectories, prevention program effects, and how services can be organized and delivered to improve enrollment, participation, retention, engagement, adherence and health outcomes.
Specific Areas of Research Interest include but are not limited to the following approaches or topic areas:
- Estimate the magnitude, impact, and risk of substance use and disorder in a population and provide direction for developing strategies to prevent or treat substance use and disorder, plan and evaluate SUD services, and suggest new areas for basic, clinical, and treatment research;
- Identify state-level factors or policies that contribute to or prevent overdose mortality;
- Describe, discriminate, and predict the complex nature and course of substance use and disorder, elucidate factors predicting substance use and disorder trajectories such as the impact of psychiatric comorbidity, environmental or contextual influences, or gene X environment interplay;
- Identify risk factors and consequences of substance use or use disorder associated with health disparate populations, such as those with high prevalence and/or intensity of use or who experience disparate outcomes from use (e.g., race/ethnicity, sex, sexual identity, age, disability, socioeconomic status, geographic location, comorbid mental health or SUD diagnoses, individuals with physical disabilities, veterans/military, and criminal justice populations), analyses on these characteristics or other social determinants of health that affect development and could lead to interventions to ameliorate the impact. For more information on these topics, see NIDA’s Office of Diversity and Health Disparities;
- Examine gender differences in the nature and extent of substance-using behaviors, in the pathways and determinants of initiation, progression and maintenance of alcohol and other SUD, differential responsivity to preventive interventions, and in the utilization of SUD treatment services. For more information on these topics, see NOT-OD-15-102 “Consideration of Sex as a Biological Variable in NIH-funded Research”;
- Characterize through model-based simulations or combinations of multiple data sources, the differential trajectories among groups of individuals who initiate and use drugs, including recovery trajectories (both with and without access to formal treatment and informal supports), and structural, systemic, or individual-level barriers to implementation and utilization of prevention, treatment, and other life-saving services;
- Analyses of the organizational and system contexts that improve the accessibility, utilization, efficiency, effectiveness, and quality of prevention intervention, treatment implementation, and service delivery, including variation in factors such as organizational structure, manpower characteristics, training, policy context, shifting attitudes towards drug use, and drug availability;
- Identify effective clinical shared decision-making factors in the prevention and treatment of SUD and related disorders;
- Examine morbidity (e.g., disability status) and mortality (suicide and other causes of death) outcomes of substance use and disorder behaviors and potential mitigating interventions at the state and local level to improve public health impact;
- Understand how structural interventions, including policies at the federal, state, and local level influence initiation of use, progression to misuse, addiction, and recovery from disordered use of substances such as cannabis, tobacco, prescription drugs, and other substances. Studies may consider unintended consequences of interventions and other health outcomes. Analysis of differential effects of these interventions on various health disparity groups (racial, ethnic, gender minorities) are encouraged;
- Analyses of individual developmental trajectories using brain, cognitive, emotional, academic, and/or other data, including examination of associations among measures of neurocognition, language use, brain structure and function.
- Analyses of developmental trajectories and their variability resulting from exposure to substance use, including in utero, and identification of risk or ameliorating factors that impact those trajectories.
- Identification of reliable biobehavioral signatures and/or cultural and familial factors that predict risk to or protection from SUD, mental illness, and other health outcomes;
- Analyses of neurobiological mechanisms underlying real-world complexities associated with SUD – e.g., polysubstance use, complex morbidity involving SUD and other neuropsychiatric disorders, transdiagnostic risk factors;
- Development and application of whole-brain computational models that integrate multimodal data (e.g., structural imaging, fMRI, PET) towards understanding mechanisms underlying SUD and the impact of behavioral/pharmacological interventions;
- Development of innovative analytical and/or visualization tools for complex datasets (e.g., large-scale longitudinal data; multimodal data including brain, behavior, and genetics) including the use of artificial intelligence approaches (e.g., machine learning, natural language processing, neural networks, deep learning, large language models).
For more information, please see the opportunity webpage.