Alcohol Use Behaviors across Countries and Cultures (2021-2022)

Background 

The 2016 Alcohol Collaborators’ Global Burden of Disease report estimated that no amount of alcohol consumption could be considered “safe.” Alcohol use is among the top ten global risk factors for death and disability. The impact and manifestation of alcohol use disorders (AUD) are highly heterogeneous across populations. Current screening and diagnostic tests for AUD are severely limited by a number of factors.

With the growing AUD burden in low- and middle-income countries, it is important to systematically measure alcohol use and AUD risk using valid and reliable tools that are empirically justified and interpretable across diverse cultures. The Research Domain Criteria (RDoC) framework developed by the National Institute of Mental Health (NIMH) permits transdiagnostic assessment of dimensions and stages in mental disorders where behaviors are measured using paradigms (tasks and games) instead of direct questions. While behavioral paradigms speak to the universal determinants of global mental health, their use has been limited to U.S. research labs.

Project Description

This project team will develop and implement the Alcohol Use Behavioral Phenotyping Test (AUBPT) and assess its transcultural validity and feasibility in Tanzania. The purpose of this tool is to understand differences among drinking behaviors based on constructs like reward valuation, relative reward efficacy and proportionate reinforcement due to alcohol. AUBPT’s parameters will be used to create predictive models for deployment in research and clinical settings. The open-source app-based design will ensure accessibility and cost-effective deployment in low-resource settings.

The team will conduct a cultural adaptation and psychometric evaluation of AUBPT across U.S.- and Tanzania-based samples that are culturally and clinically different. Components of the project include app development, transcultural adaptation, data collection and predictive modeling.

The selected tasks in AUBPT will be coded and assorted into modules designed to offer the optimal task components. The modules will be developed using agent-based modeling with deep reinforcement learning to feed an adaptive module sequencing of task delivery. The team will pilot these modules with participants to confirm the utility or to reinforce the module’s adaptive behavior.

Building on a collaboration with the Kilimanjaro Christian Medical Centre in Moshi, Tanzania, the team will draw on partners’ expertise for translations, content validation and cultural adaptation. Iterative feedback through virtual meetings and interviews with research and clinical staff will be used for the Swahili translation of task instructions and determining the cultural appropriateness of stimuli. Pilot data from cognitive interviewing with stakeholders at partnering sites will be used to assess AUBPT’s content validity.

The team will collect data through a study design involving clinical and general population samples across the U.S. and Tanzania. For the first phase, team members will recruit young adults in two distinct samples. In the next phase, the team will recruit two samples from Moshi. Finally, the team will create predictive models for participants’ data using machine learning.

Learn more about this project team by viewing the team's video.

Anticipated Outputs

Open-source computer application for AUBPT in English and Swahili; open-source machine-learning optimization and predictive models; research papers; presentations at international conferences; preliminary data for grant application

Student Opportunities

Ideally, this project team will be comprised of 3 graduate student and 9 undergraduate students. Team leaders highly value a diverse student group and will prioritize varied backgrounds, education, and racial and cultural experiences.

Graduate students from global health, public policy, interdisciplinary data science, computer science, cognitive science and biostatistics are encouraged to apply. Undergraduate students with a major or minor in global health will be preferred for selection to the global subteam. For the app subteam, students should have prior web development experience in open-source languages (Python, R, Java, etc.). For the stats subteam, students should have aptitude and coursework in data analysis and modeling. Premed students could contribute to and benefit from several aspects of the project.

All student team members should have a strong interest in global mental health, aptitude for quantitative and qualitative research methods and openness to work with international collaborators.

Students will gain core interdisciplinary human participants research competencies, such as study design and planning, working with international collaborators, scientific literature synthesis, quantitative and qualitative data collection, statistical techniques, contributing to research papers and presenting at conferences.

Students will also learn the use of internet-based study samples, working with international partners for translations and cultural adaptation, and implementing deep learning and other predictive models. Major practice components include application development and deployment and the opportunity to work with clinical and research staff at KCMC in Tanzania.

Graduate students will lead the subteams, draft research papers as first/second authors and work independently on advanced data analyses and predictive modeling.

The optional Summer 2021 component will take place from June 5 to August 20, 2021, involving approximately 20 hours of work per week. Four or five students will be selected to work on literature review, application development and participant recruitment strategies.

Siddhesh Zadey will serve as project manager.

Timing

Summer 2021 –Summer 2022 

  • Summer 2021 (optional): Literature review; data collection planning; computer application development template
  • Fall 2021: App development and testing; meetings and interviews with KCMC staff for translation and content validation; recruitment of internet study samples (partial phase 1 data collection)
  • Spring 2022: Analysis of phase 1 data app optimization, onsite (Duke and KCMC) samples recruitment (phase 2 data collection)
  • Summer 2022 (optional): Analysis of phase 2 data; manuscripts; presentations at conferences

Crediting 

Academic credit available for fall and spring semesters; summer funding available

 

Image: Sake wall, by Rick, licensed under CC BY 2.0

Sake wall.

Team Leaders

  • Catherine Staton, School of Medicine-Surgery
  • Joao Vissoci, School of Medicine-Surgery: Emergency Medicine
  • Siddhesh Zadey, Duke Global Health Institute

/yfaculty/staff Team Members

  • Eric Green, Duke Global Health Institute
  • Ashley Phillips, School of Medicine-Surgery: Emergency Medicine
  • Eve Puffer, Arts & Sciences-Psychology and Neuroscience
  • Thiago Rocha, School of Medicine-Surgery: Emergency Medicine
  • Anna Tupetz, School of Medicine-Surgery: Emergency Medicine

/zcommunity Team Members

  • Judith Bosche, Kilamanjaro Region, Tanzania
  • Blandina Mmbaga, Kilimanjaro Christian Medical Centre