Skip to main content
SpringerLink
Log in

A Call to Address Complexity in Prevention Science Research

  • Published:
Prevention Science Aims and scope Submit manuscript

Abstract

The problems targeted by preventive interventions are often complex, embedded in multiple levels of social and environmental context, and span the developmental lifespan. Despite this appreciation for multiple levels and systems of influence, prevention science has yet to apply analytic approaches that can satisfactorily address the complexities with which it is faced. In this article, we introduce a systems science approach to problem solving and methods especially equipped to handle complex relationships and their evolution over time. Progress in prevention science may be significantly enhanced by applying approaches that can examine a wide array of complex systems interactions among biology, behavior, and environment that jointly yield unique combinations of developmental risk and protective factors and outcomes. To illustrate the potential utility of a systems science approach, we present examples of current prevention research challenges, and propose how to complement traditional methods and augment research objectives by applying systems science methodologies.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Similar content being viewed by others

References

  • Bronfenbrenner, U. (1979). The ecology of human development: Experiments by nature and design. Cambridge, MA: Harvard University Press.

    Google Scholar 

  • Brody, G. H., Chen, Y., Beach, S. R. H., Philibert, R. A., & Kogan, S. M. (2009). Participation in a family-centered prevention program decreases genetic risk for adolescents’ risky behaviors. Pediatrics, 124, 911–917.

    Article  PubMed  Google Scholar 

  • Brown, C. H., & Liao, J. (1999). Principles for designing randomized preventive trials in mental health: An emerging developmental epidemiology paradigm. American Journal of Community Psychology, 27, 673–710.

    Article  PubMed  CAS  Google Scholar 

  • Brown, C. H., Wang, W., & Sandler, I. (2008). Examining how context changes intervention impact: The use of effect sizes in multilevel mixture meta-analysis. Child Development Perspectives, 2, 198–205.

    Article  PubMed  Google Scholar 

  • Brown, C. H., Wang, W., Kellam, S. G., Muthén, B. O., Petras, H., Toyinbo, P., Poduska, J., Ialongo, N., Wyman, P. A., Chamberlain, P., Sloboda, Z., MacKinnon, D. P., Windham, A., & Prevention Science Methodology Group. (2008). Methods for testing theory and evaluating impact in randomized field trials: Intent-to-treat analyses for integrating the perspectives of person, place, and time. Drug and Alcohol Dependence, 95, S74–S104.

    Article  PubMed  Google Scholar 

  • Cohen, I. R., & Harel, D. (2006). Explaining a complex living system: Dynamics, multi-scaling and emergence. Journal of the Royal Society, Interface, 4, 175–182.

    Article  Google Scholar 

  • Collins, L. M., & Horn, J. L. (Eds.). (1991). Best methods for the analysis of change: Recent advances, unanswered questions, future directions. Washington, DC: American Psychological Association.

    Google Scholar 

  • Collins, L. M., & Lanza, S. T. (2010). Latent class and latent transition analysis: With applications in the social, behavioral, and health sciences. New York: Wiley.

  • Collins, L. M., & Sayer, A. G. (Eds.). (2001). New methods for the analysis of change. Washington, DC: American Psychological Association.

  • Collins, L. M., & Seitz, L. A. (Eds.). (1994). Advances in data analysis for prevention intervention research. Washington, DC: National Institute on Drug Abuse.

    Google Scholar 

  • Coronges, K., Stacy, A. W., & Valente, T. W. (2011). Social network influences of alcohol and marijuana cognitive associations. Addictive Behaviors, 36, 1305–1308.

    Article  PubMed  Google Scholar 

  • Epstein, J. (2007). Generative social science: Studies in agent-based computational modeling. Princeton, NJ: Princeton University Press.

    Google Scholar 

  • Epstein, J. (2008). Why model? Journal of Artificial Societies and Social Simulation, 11, 12.

    Google Scholar 

  • Flay, B. R., Biglan, A., Boruch, R. F., Castro, F. G., Gottfredson, D., Kellam, S., Moscicki, E. K., Schinke, S., Valentine, J. C., & Ji, P. (2005). Standards of evidence: Criteria for efficacy, effectiveness and dissemination. Prevention Science, 6, 151–175.

    Article  PubMed  Google Scholar 

  • Gallagher, R., & Appenzeller, T. (1999). Beyond reductionism. Science, 284, 79.

    Article  CAS  Google Scholar 

  • Harris, J. K., Luke, D. A., Zuckerman, R. B., & Shelton, S. C. (2009). Forty years of secondhand smoke research: The gap between discovery and delivery. American Journal of Preventive Medicine, 36, 538–548.

    Article  PubMed  Google Scholar 

  • Hartmann, S. (1996). The world as a process: Simulations in the natural and social sciences. In R. Hegselmann, U. Mueller, & K. G. Troitzsch (Eds.), Modelling and simulation in the Social sciences: From the philosophy of science point of view. Norwell, MA: Kluwer Academic Publishers.

    Google Scholar 

  • Hirsch, G., Homer, J., Evans, E., & Zielinski, A. (2010). A System Dynamics model for planning cardiovascular disease interventions. American Journal of Public Health, 100, 616–622.

    Article  PubMed  Google Scholar 

  • Hoffer, L. D., Bobashev, G., & Morris, R. J. (2009). Researching a local heroin market as a complex adaptive system. American Journal of Community Psychology, 44, 273–286.

    Article  PubMed  Google Scholar 

  • Homer, J., & French, M. (2009). Motorcycle helmet laws in the United States from 1990 to 2005: Politics and public health. American Journal of Public Health, 99, 415–423.

    Article  PubMed  Google Scholar 

  • Homer, J., Milstein, B., Wile, K., Pratibhu, P., Farris, R., & Orenstein, D. (2008). Modeling the local dynamics of cardiovascular health: Risk factors, context, and capacity. Preventing Chronic Disease, 5, 1–6.

    Google Scholar 

  • Homer, J., Milstein, B., Wile, K., Trogdon, J., Huang, P., Labarthe, D., & Orenstein, D. (2010). Simulating and evaluating local interventions to improve cardiovascular health. Preventing Chronic Disease, 7, 1–11.

    Google Scholar 

  • Hovmand, P. S., & Ford, D. N. (2009). Sequence and timing of three community interventions to domestic violence. American Journal of Community Psychology, 44, 261–272.

    Article  PubMed  Google Scholar 

  • Hovmand, P. S., & Gillespie, D. F. (2010). Implementation of evidence-based practice and organizational performance. Journal of Behavioral Health Services and Research, 37, 79–94.

    Article  PubMed  Google Scholar 

  • Hovmand, P. S., Ford, D. N., Flom, I., & Kyriakakis, S. (2009). Victims arrested for domestic violence: Unintended consequences of arrest policies. System Dynamics Review, 25, 161–181.

    Article  Google Scholar 

  • Jippes, E., Achterkamp, M. C., Brand, P. L. P., Kiewiet, D. J., Pols, J., & van Engelen, J. M. L. (2010). Disseminating educational innovations in health care practice: Training versus social networks. Social Science & Medicine, 70, 1509–1517.

    Article  Google Scholar 

  • Kellam, S., & Langevin, D. (2003). A framework for understanding “evidence” in prevention research and programs. Prevention Science, 4, 137–153.

    Article  PubMed  Google Scholar 

  • Kellam, S. G., Koretz, D., & Moscicki, E. (1999). Core elements of developmental epidemiologically based prevention research. American Journal of Community Psychology, 27, 463–483.

    Article  PubMed  CAS  Google Scholar 

  • Mabry, P. L., Marcus, S. E., Clark, P. I., Leischow, S. J., & Méndez, D. (2010). Systems science: A revolution in public health policy research. American Journal of Public Health, 100, 1161–1163.

    Article  PubMed  Google Scholar 

  • MacKinnon, D. P. (2008). Introduction to statistical mediation analysis. Mahwah, NJ: Lawrence Erlbaum Associates.

    Google Scholar 

  • Meadows, D., Richardson, J., & Bruckmann, G. (1982). Groping in the dark: The first decade of global modelling. New York: Wiley.

    Google Scholar 

  • Midgley, G. (2003). Systems thinking. Thousand Oaks, CA: Sage Publications.

    Google Scholar 

  • Milstein, B., Homer, J., & Hirsch, G. (2010). Analyzing national health reform strategies with a dynamic simulation model. American Journal of Public Health, 100, 811–819.

    Article  PubMed  Google Scholar 

  • Moody, J., Brynildsen, W. D., Osgood, D. W., Feinberg, M. E., & Gest, S. (2011). Popularity trajectories and substance use in early adolescence. Social Networks, 33, 101–112.

    Article  PubMed  Google Scholar 

  • Morris, M., Kurth, A. E., Hamilton, D. T., Moody, J., & Wakefield, S. (2009). Concurrent partnerships and HIV prevalence disparities by race: Linking science and public health practice. American Journal of Public Health, 99, 1023–1031.

    Article  PubMed  Google Scholar 

  • Morrissey, J. P., Hassmiller Lich, K., Anhang Price, R., & Mandelblatt, J. (2012). Computer simulation models and multilevel cancer control interventions. Journal of the National Cancer Institute. 44, 55–66.

  • National Research Council and Institute of Medicine. (2009). Preventing mental, emotional, and behavioral disorders among young people: Progress and possibilities. Washington, DC: National Academies Press.

    Google Scholar 

  • Neal, J. W., Neal, Z. P., Atkins, M. S., Henry, D. B., & Frazier, S. L. (2011). Channels of change: Contrasting network mechanisms in the use of interventions. American Journal of Community Psychology, 47, 277–286.

    Article  PubMed  Google Scholar 

  • Oesterle, S., Hawkins, J. D., Fagan, A. A., Abbott, R. D., & Catalano, R. F. (2010). Testing the universality of the effects of the Communities That Care prevention system for preventing adolescent drug use and delinquency. Prevention Science, 11, 411–423.

    Article  PubMed  Google Scholar 

  • Okamoto, J., Johnson, C. A., Leventhal, A., Milam, J., Pentz, M. A., Schwartz, D., & Valente, T. W. (2011). Social network status and depression among adolescents: An examination of social network influences and depressive symptoms in a Chinese sample. Research in Human Development, 8, 67–88.

    Article  PubMed  Google Scholar 

  • Provan, K. G., Beagles, J. E., & Leischow, S. J. (2011). Network formation, governance, and evolution in public health: The North American Quitline Consortium case. Health Care Management Review, 36, 315–326.

    Article  PubMed  Google Scholar 

  • Schaefer, D. R., Light, J. M., Fabes, R. A., Hanish, L. D., & Martin, C. L. (2010). Fundamental principles of network formation among preschool children. Social Networks, 32, 61–71.

    Article  PubMed  Google Scholar 

  • Sterman, J. D. (1992). Flight simulators for management education. OR/MS Today, 19, 40.

    Google Scholar 

  • Stormont, M., Reinke, W. M., & Herman, K. C. (2010). Introduction to the special issue: Using prevention science to address mental health issues in schools. Psychology in the Schools, 47, 1–4.

    Google Scholar 

  • Sun, W., Skara, S., Sun, P., Dent, C. W., & Sussman, S. (2006). Project Towards No Drug Abuse: Long-term substance use outcomes evaluation. Preventive Medicine, 42, 188–192.

    Article  PubMed  CAS  Google Scholar 

  • Sussman, S., Dent, C. W., & Stacy, A. W. (2002). Project Towards No Drug Abuse: A review of the findings and future directions. American Journal of Health Behavior, 26, 354–365.

    Article  PubMed  Google Scholar 

  • Tengs, T., Osgood, N., & Chen, L. (2001). The cost-effectiveness of intensive national school-based anti-tobacco education: Results from the tobacco policy model. Preventive Medicine, 33, 558–570.

    Article  PubMed  CAS  Google Scholar 

  • Thompson, K. M., & Duintjer Tebbens, R. J. (2007). Eradication versus control for poliomyelitis: An economic analysis. Lancet, 369, 1363–1371.

    Article  PubMed  Google Scholar 

  • Urban, J. B., Osgood, N. D., & Mabry, P. L. (2011). Developmental systems science: Exploring the application of systems science methods to developmental science questions. Research in Human Development, 8, 1–26.

    Article  Google Scholar 

  • Valente, T. W. (2010). Social networks and health: Models, methods, and applications. New York: Oxford University Press.

    Book  Google Scholar 

  • Valente, T. W., Chou, C. P., & Pentz, M. A. (2007a). Community coalitions as a system: Effects of network change on adoption of evidence-based substance abuse prevention. American Journal of Public Health, 97, 880–886.

    Article  PubMed  Google Scholar 

  • Valente, T. W., Ritt-Olson, A., Stacy, A., Unger, J. B., Okamoto, J., & Sussman, S. (2007b). Peer acceleration: Effects of a social network tailored substance abuse prevention program among high-risk adolescents. Addiction, 102, 1804–1815.

    Article  PubMed  Google Scholar 

  • Vickers, D., & Osgood, N. (2007, December 20). A unified framework of immunological and epidemiological dynamics for the spread of viral infections in a simple network-based population. Theoretical Biology and Medical Modeling, 4. Retrieved December 20, 2007 from http://www.tbiomed.com/content/4/1/49

  • Wipfli, H. L., Fujimoto, K., & Valente, T. W. (2010). Global tobacco control diffusion: The case of the framework convention on tobacco control. American Journal of Public Health, 100, 1260–1266.

    Article  PubMed  Google Scholar 

  • Yang, L., & Gilbert, N. (2008). Getting away from numbers: Using qualitative observation for agent-based modeling. Advances in Complex Systems, 11, 175–185.

    Article  Google Scholar 

Download references

Acknowledgments

Effort by KHL was partially supported by Award Number KL2RR025746 from the National Center for Research Resources at the National Institutes of Health (NIH). NDO gratefully acknowledges support of the National Science and Engineering Research Council’s (NSERC’s) Discovery Grant RGPIN-327290-20. The content of this paper is solely the responsibility of the authors and does not necessarily represent the official views of the National Center for Research Resources, the Office of Behavioral and Social Sciences Research, the National Cancer Institute, or the National Institutes of Health.

Conflict of Interest

None of the authors have any conflicts of interest to disclose.

Author information

Authors and Affiliations

  1. Department of Health Policy and Management, Gillings School of Global Public Health, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA

    Kristen Hassmiller Lich

  2. National Cancer Institute, National Institutes of Health, Bethesda, MD, USA

    Elizabeth M. Ginexi

  3. University of Saskatchewan, Saskatoon, Canada

    Nathaniel D. Osgood

  4. Office of Behavioral and Social Sciences Research, National Institutes of Health, Bethesda, MD, USA

    Patricia L. Mabry

Authors
  1. Kristen Hassmiller Lich
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Elizabeth M. Ginexi
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Nathaniel D. Osgood
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Patricia L. Mabry
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Kristen Hassmiller Lich.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Lich, K.H., Ginexi, E.M., Osgood, N.D. et al. A Call to Address Complexity in Prevention Science Research. Prev Sci 14, 279–289 (2013). https://doi.org/10.1007/s11121-012-0285-2

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11121-012-0285-2

Keywords

Search

Navigation