Call for Papers
NOTE: Online abstract submission site will open 9/13.
Submission Deadline: October 31, 2024
Prevention Science for Action: Leveraging Data Science & Technology to Enhance Research, Practice, & Policy
New and innovative opportunities for prevention science research, practice, and policy are emerging rapidly with advances in data science and technology. Collaboration across diverse disciplines and partners, including prevention scientists, community and practice partners, data scientists, methodologists, statisticians, engineers, computer programmers, and policymakers, is necessary to reveal the knowledge contained in complex data sets. Prevention scientists are poised to lead multidisciplinary, collaborative, and international teams that advance the development and application of data-analytic and technological innovations to benefit all individuals and communities. These innovations have the potential to improve the development, testing, implementation, and scale-up of effective prevention programs and services. For prevention science, the term “data science” includes “big data analytics” (i.e., advanced analytics for very large, intensively collected, and/or multimodal and integrated data sets) and artificial intelligence (AI; e.g., machine learning algorithms for prediction and/or clustering). Prevention scientists are leveraging data science techniques, and the technologies that make them possible, to advance and accelerate real-time data collection (e.g., ecological momentary assessment), analysis, prediction, and automation and to create interactive tools for tailored prevention and treatment.
Examples of Leveraging Data Science & Technology in Prevention Science
- Machine learning techniques are being used to anticipate trajectories of substance use disorder risk, from onset through recovery, while incorporating feedback from prevention scientists to ensure applicability and relevance of results.
- Clustering algorithms are being used to identify groups of individuals who might benefit most from tailored preventive intervention strategies based on genetics, comorbidities, and psychosocial, behavioral, and environmental factors.
Importantly, prevention scientists have a responsibility to critically evaluate both the potential benefits and unintended negative consequences to society of AI and related data and technologies. Overall, advances in AI and related data and technologies provide unprecedented opportunities to advance prevention science across diverse settings, including justice systems, schools, primary care, and treatment centers. However, the collection, analysis, interpretation, and quality control of data present challenges that require the development of new ethical paradigms guiding infrastructure, training, team science, and data sharing that prioritize equity.
Representative Ethical Challenges of AI in Prevention Science
- How do we not cause further harm by oversurveillance? What is ethical use of real-time data, particularly when surveying populations that are minoritized and historically oppressed?
- Who owns the data and how do we ethically address concerns from individuals and communities about data ownership and access?
- How do AI and data science innovations affect the prevention science workforce?
- How do we prevent exacerbation of the “digital divide,” especially for populations that are minoritized and historically oppressed?
SPR is committed to ensuring that prevention science promotes a healthy and equitable society through research-informed and socially just programs, practices, and policies. This year’s conference theme complements the last four themes focused on equity and social justice: The Role of Prevention Science in Achieving Social Justice and Health Equity for All, Realizing the Power of Prevention Through Equitable Dissemination and Implementation Science, Addressing Racism and Disparities When Considering Biology and Context, Advancing Partnerships and Collaborative Approaches in Prevention Science. Technological tools (e.g., smartphones, ambulatory assessment devices, social media) expand our data collection and program/service design capabilities. Integrating advanced quantitative methods and data science strategies (e.g., generative AI, machine learning algorithms, natural language processing) expand the ability of prevention scientists to examine complex, intersecting effects of social and structural determinants of health, tailor program/service delivery, and accelerate health equity. However, as our opportunities grow, so do our ethical and practical challenges (e.g., algorithmic bias, data sharing, education and training) and the need to address equitably these challenges and their implications for individuals, families, communities, practices, and policies. Multidisciplinary partnerships, collaborative approaches, and ongoing, open dialogue are cornerstones of SPR’s continued focus on equity and imperative to address the challenges of the rapidly changing landscape of data and technology in prevention science. Submissions with diverse teams, particularly those including community, practice, or policy-making partners, are strongly encouraged.
2025 Special Conference Themes
Each year, SPR selects three special themes designed to highlight specific areas of research relevant to prevention science and the overall conference theme. The SPR Conference committee encourages basic, epidemiological, etiological, intervention, and dissemination and implementation research submissions across these special themes. Consistent with this year’s conference theme, Leveraging Data Science & Technology, the SPR Conference Committee encourages special conference theme submissions related to pressing needs and the role of prevention science in three areas:
- Collecting, integrating, and understanding data using innovative study designs, collection modalities, and integration and analysis approaches.
- Developing, implementing, evaluating, and disseminating tailored prevention programs and intervention services.
- Advancing equity and action when using AI in prevention science.
Special Theme #1. Leveraging Data Science & Technology: Collecting, integrating, and understanding data using innovative study designs, collection modalities, and analysis approaches.
- Devices such as smartphones and ambulatory assessment devices (e.g., Fitbits, alcohol monitors) have made real-time data collection a daily fact of life.
- Tools such as social media, community-driven data dashboards, and online data warehouses have made data integration across multiple sources a daily possibility.
- Methods such as daily diaries and ecological momentary assessment have become routinely possible in prevention science.
Volumes of data on momentary and longer-term behaviors, real-time and historical locations, and biological processes can be collected efficiently and with limited intrusion. These data can then be integrated with community-based contextual data (e.g., regional socioeconomic deprivation, migration patterns, large-scale weather events), neurobiological and genetic data, and/or administrative data obtained from local, state, or national resources.
As new data collection modalities come online and need to be integrated with existing modalities, it is critical that high-quality, methodologically sound study designs, data integration techniques, and analysis approaches are developed and applied.
- Submissions that are examples of successful quantitative, qualitative, and mixed-method designs, techniques, and approaches, as well as lessons learned and best practices, are welcomed.
- Submissions with community-based and/or policy-based focuses across all areas of prevention science, especially those focused on health disparities and promoting health equity, are particularly encouraged.
Special Theme #2. Leveraging Data Science & Technology: Developing, implementing, evaluating, and disseminating tailored prevention programs and intervention services.
- Prevention scientists have long been interested in program/service adaptation and tailoring.
- Mobile and online technologies provide a wealth of new opportunities to design, implement, evaluate, and disseminate programs and services with increasing levels of precision.
- The ability to deliver intervention content in real-time via mobile technologies (e.g., via ecological momentary interventions) has the potential to exponentially increase decision-making about delivering which content, to whom, when, and where.
New opportunities for decision-making based on environmental context and based on information across multiple levels (e.g., family, peer, school, community) are available. For example, experimental designs to build adaptive interventions and just-in-time adaptive interventions (e.g., sequential, multiple-assignment, randomized trials, microrandomized trials) are in active development to help prevention scientists determine how to deliver intervention content.
- Submissions that are examples of successful program/service development, implementation, evaluation, and dissemination, as well as lessons learned and best practices, across all areas of prevention science are welcomed.
- Submissions focused on multilevel programs/services developed, implemented, and evaluated in collaboration with community, practice, and/or policy-making partners across all areas of prevention science, especially those focused on promoting health equity, are particularly encouraged.
Special Theme #3. Leveraging Data Science & Technology: Advancing equity and action when using AI in prevention science.
- Scientific and financial investment in data science and related technological tools have rapidly increased the pace at which data are collected and significant technical advancements have been made by statisticians, engineers, and programmers.
- However, the pace at which new knowledge gained has impacted practice and policy has been slower.
There is increasing recognition of bias within data sets and the algorithms applied to them. Detecting and mitigating bias is a growing area of research critical to prevention science.
- Bias potentially can contribute to continued stigma and discrimination, promotion of inequities, and exacerbation of disparities.
- Moreover, there is a risk of an increased digital divide, with more complex AI models requiring expertise and skills for developing, implementing, monitoring, and evaluating them that are not widely accessible.
Despite concerns, AI-based content generation presents a growing opportunity to create and tailor program/service content and to return clear and relevant take-home messages to individuals, families, schools, communities, and policymakers. Content generation combined with high-quality, user-friendly, attractive, and exciting data visualizations have the potential to revolutionize the way prevention scientists translate their work and increase its impact. Critically, different user experiences can be developed to address the needs of a variety of audiences. For example, individual data dashboards might be used to facilitate participant engagement or behavior change, whereas community-level data dashboards or other visuals might be used to guide agencies and policymakers.
- Submissions that are examples of bias detection and mitigation, AI-based content generation, and data visualization for translation of knowledge from algorithmic-based studies, including successful designs, techniques, and approaches, as well as lessons learned and best practices, are welcomed.
- In addition, building AI literacy among preventionists and the public is essential. Submissions focused on translational research and translational science, especially those focused on health disparities and promoting health equity, as well as those in collaboration with community, practice, and/or policymaking partners, are particularly encouraged.
General Conference Themes
Each year, the SPR Conference committee encourages basic, epidemiological, etiological, intervention, and dissemination and implementation research submissions across key themes that promote advances in prevention research.
Epidemiology and Etiology: Submissions focused on describing distributions and patterns of health (including but not limited to, e.g., anxiety, cancer, cardiovascular disease, depression, HIV/AIDS, injury, substance use disorders, violence) and on identifying risk and protective targets of preventive interventions are welcomed. Submissions with a developmental and/or life-course approach, or that include genetic, neurobiological, equity, and/or contextual factors, are particularly encouraged.
Development and Testing of Interventions: Preventive interventions can be tested for efficacy under conditions of high quality-assurance and strong research designs and tested for effectiveness under real-world conditions in settings and systems and with diverse populations. Submissions reporting findings from efficacy or effectiveness trials (including pilot studies with preliminary outcome data) are welcomed. Submissions that combine the findings of such trials with one or more of the special conference themes are particularly encouraged.
Dissemination and Implementation Science: Dissemination, implementation, and translational science bridge the gap between research and everyday practice through a dynamic, collaborative process between the public health community and researchers. Submissions advancing scientific understanding of dissemination, implementation, and translation, including cost-efficient sustainability of preventive interventions into systems, are welcomed. Submissions that focus on program dissemination and implementation outcomes, dissemination and implementation processes, individual-, provider-, organizational-, and/or system-level factors, and community- and system-collaborator and decision-maker engagement are particularly encouraged.
Research, Policy, and Practice: Decision-makers around the world emphasize evidence-based policy reform. New policy initiatives at local, state, and national levels require scientific evidence to guide further policy change. Submissions that evaluate or estimate the effects of planned, new, or existing policies, examine the impact of efficacious programs in emerging policy contexts, or demonstrate how empirical research has been used to inform and guide new policies are welcomed. A wide variety of content areas are welcomed, including both emergent areas (e.g., cannabis legalization, immigration policy, climate change impacts) and ongoing areas (e.g., anti-bullying laws/policies, cancer screening, education policy, firearm policy, medication adherence, mental and physical health parity, obesity prevention) of concern. Submissions that describe and evaluate processes by which policies have been formed, developed, and implemented are encouraged. Submissions focused on international research or comparative research across policy contexts and submissions that combine findings of such research with one of the special conference themes are particularly encouraged.
Innovative Methods and Statistics: Submissions focused on “leading-edge” study designs and analytical approaches that address challenges to unlocking information contained in prevention science data, including studies on quantitative, qualitative, and mixed methods approaches, are welcomed. Submissions that use advanced methods and statistics but do not study directly a novel methodological or statistical question should be submitted to one of the other themes. Submissions addressing novel methodological or statistical challenges in studies promoting health equity are particularly encouraged. Submissions should highlight the prevention science challenges that these innovative designs and approaches can address, as well as the benefits gained by using these techniques.
NIDA International SPR Poster Session
The SPR International Program and the Division of Epidemiology, Services, and Prevention Research of the National Institute on Drug Abuse (NIDA) will host their annual NIDA International SPR Poster Session. Posters should highlight research on the prevention of drug use, prevention of drug use in combination with alcohol use, or prevention of HIV/AIDS in the context of drug use or drug and alcohol use. See their separate call for poster abstracts here or PDF.
All abstracts must be submitted online at www.preventionresearch.org.
Submission Deadline: October 31, 2024
For questions regarding online abstract submissions, the peer review process, or other details, please contact Jennifer Lewis by email at jenniferlewis@preventionresearch.org or 703-934-4850, ext. 1.