Prevention Science

, Volume 13, Issue 6, pp 562–573 | Cite as

Identifying a High-Risk Cohort in a Complex and Dynamic Risk Environment: Out-of-bounds Skiing—An Example from Avalanche Safety

  • Pascal HaegeliEmail author
  • Matt Gunn
  • Wolfgang Haider


The development of effective prevention initiatives requires a detailed understanding of the characteristics and needs of the target audience. To properly identify at-risk individuals, it is crucial to clearly delineate risky from acceptable behavior. Whereas health behavior campaigns commonly use single conditions (e.g., lack of condom use) to identify high-risk cohorts, many risk behaviors are more complex and context dependent, rendering a single condition approach inadequate. Out-of-bounds skiing, an activity associated with voluntary exposure to avalanche hazard, is an example of such a multifaceted risk-taking activity. Using a dataset from an extensive online survey on out-of-bounds skiing, we present an innovative approach for identifying at-risk individuals in complex risk environments. Based on a risk management framework, we first examine risk-taking preferences of out-of-bounds skiers with respect to exposure and preparedness—the two main dimensions of risk management—separately. Our approach builds on existing person-centered research and uses Latent Class Analysis to assign survey participants to mutually exclusive behavioral classes on these two dimensions. Discrete Choice Experiments are introduced as a useful method for examining exposure preferences in the context of variable external conditions. The two class designations are then combined using a risk matrix to assign overall risk levels to each survey participant. The present approach complements existing person-centered prevention research on the antecedents of risk-taking by offering a process-oriented method for examining behavioral patterns with respect to the activity itself. Together, the two approaches can offer a much richer perspective for informing the design of effective prevention initiatives.


High-risk cohort identification Avalanche safety Out-of-bounds skiing Visual discrete choice experiment Latent class analysis Person-centered research 



This project was part of the ADFAR2 initiative of the Canadian Avalanche Centre, which was funded by the Government of Canada through the Search and Rescue New Initiatives Fund (SAR-NIF). We would like to thank the anonymous reviewers and the editor David MacKinnon for their constructive comments on earlier drafts of this manuscript.

Supplementary material

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  1. Adamowicz, W. L., Boxall, P. C., Williams, M., & Louviere, J. J. (1998). Stated preference approaches for measuring passive use values: Choice experiments and contingent valuation. American Journal of Agricultural Economics, 80, 64–75.CrossRefGoogle Scholar
  2. Agrawal, A., Lynskey, M. T., Madden, P. A. F., Bucholz, K. K., & Heath, A. C. (2007). A latent class analysis of illicit drug abuse/dependence: Results from the National Epidemiological Survey on Alcohol and Related Conditions. Addiction, 102, 94–104.PubMedCrossRefGoogle Scholar
  3. Akaike, H. (1974). A new look at the statistical model identification. IEEE Transactions on Automatic Control, 19, 716–723.CrossRefGoogle Scholar
  4. Bergman, L. R., & Magnusson, D. (1997). A person-oriented approach in research on developmental psychopathology. Development and Psychopathology, 9, 291–319.PubMedCrossRefGoogle Scholar
  5. Bergman, L. R., Magnusson, D., & El-Khouri, B. (2003). Studying individual development in an interindividual context: A person-oriented approach. Mahwah, NJ: Lawrence Erlbaum Associates.Google Scholar
  6. Bouter, L. M., Knipschild, P. G., Feij, J. A., & Volovics, A. (1988). Sensation seeking and injury in downhill skiing. Personality and Individual Differences, 9, 667–673.CrossRefGoogle Scholar
  7. Boxall, P. C., & Adamowicz, W. L. (2002). Understanding heterogeneous preferences in random utility models: A latent class approach. Environmental and Resource Economics, 23, 421–446.CrossRefGoogle Scholar
  8. Canadian Standards Association. (1997). Risk management: Guideline for decision-makers - a national standard of Canada (No. CAN/CSA-Q850-97). Ottawa: Canadian Standards Association.Google Scholar
  9. Chung, Y.-S., & Wong, J.-T. (2011). Beyond general behavioral theories: Structural discrepancy in young motorcyclist’s risky driving behavior and its policy implications. Accident Analysis & Prevention. doi: 10.1016/j.aap.2011.04.021
  10. Chung, T., Maisto, S. A., Cornelius, J. R., & Martin, C. S. (2004). Adolescents’ alcohol and drug use trajectories in the year following treatment. Journal of Studies on Alcohol, 65, 105–114.PubMedGoogle Scholar
  11. Coffman, D., Patrick, M., Palen, L., Rhoades, B., & Ventura, A. (2007). Why do high school seniors drink? Implications for a targeted approach to intervention. Prevention Science, 8, 241–248.PubMedCrossRefGoogle Scholar
  12. Colder, C. R., Campbell, R. T., Ruel, E., Richardson, J. L., & Flay, B. R. (2008). A finite mixture model of growth trajectories of adolescent alcohol use: Predictors and consequences. Journal of Consulting and Clinical Psychology, 70, 976–985.CrossRefGoogle Scholar
  13. Collins, L. M., & Lanza, S. T. (2010). Latent class and latent transition analysis: With applications in the social, behavioral, and health sciences. Hoboken, NJ: Wiley.Google Scholar
  14. Collins, L. M., Murphy, S. A., & Bierman, K. L. (2004). A conceptual framework for adaptive preventive interventions. Prevention Science, 5, 185–196.PubMedCrossRefGoogle Scholar
  15. Cronbach, L. (1951). Coefficient alpha and the internal structure of tests. Psychometrika, 16, 297–334.CrossRefGoogle Scholar
  16. Croon, M. (2002). Ordering the classes. In A. L. McCutcheon & J. A. Hagenaars (Eds.), Advances in latent class models (pp. 137–162). New York: Cambridge University Press.CrossRefGoogle Scholar
  17. Dillman, D. A. (2007). Mail and internet surveys: The tailored design method (2nd ed.). Hoboken, NJ: Wiley.Google Scholar
  18. Donohew, R. L., Zimmerman, R., Cupp, P. S., Novak, S., Colon, S., & Abell, R. (2000). Sensation seeking, impulsive decision-making, and risky sex: Implications for risk-taking and design of interventions. Personality and Individual Differences, 28, 1079–1091.CrossRefGoogle Scholar
  19. Glanz, K., Yaroch, A. L., Dancel, M., Saraiya, M., Crane, L. A., Buller, D. B., et al. (2008). Measures of sun exposure and sun protection practices for behavioral and epidemiologic research. Archives of Dermatology, 144, 217–222.PubMedCrossRefGoogle Scholar
  20. Goma-i-Freixanet, M. (2004). Sensation seeking and participation in physical risk sports. In R. Stelmack (Ed.), On the psychobiology or personality: Essays in honour of Marvin Zuckeman (pp. 185–201). New York: Elsevier.Google Scholar
  21. Goodman, L. A. (1974). Exploratory latent structure analysis using both identifiable and unidentifiable models. Biometrika, 61, 215–231.CrossRefGoogle Scholar
  22. Grijalva, T. C., Berrens, R. P., Bohara, A. K., & Shaw, W. D. (2002). Testing the validity of contingent behavior trip responses. American Journal of Agricultural Economics, 84, 401–414.CrossRefGoogle Scholar
  23. Gunn, M. (2010). Out-of-bounds skiers and avalanche risk: High-risk cohort identification and characterisation (Masters thesis). Burnaby, BC: Simon Fraser University.Google Scholar
  24. Haegeli, P., Haider, W., Longland, M., & Beardmore, B. (2010). Amateur decision-making in avalanche terrain with and without a decision aid - a stated choice survey. Natural Hazards, 52, 185–209.CrossRefGoogle Scholar
  25. Haider, W. (2002). Stated preference and choice models—A versatile alternative to traditional recreation research. Paper presented at the International Conference on Monitoring and Management of Visitor Flows in Recreational and Protected Areas, Vienna, Austria.Google Scholar
  26. Hausman, J., & McFadden, D. (1984). Specification tests for the multinomial Logit model. Econometrica, 52, 1219–1240.CrossRefGoogle Scholar
  27. Hoyle, R. H., Stephenson, M. T., Palmgreen, P., Lorch, E. P., & Donohew, R. L. (2002). Reliability and validity of a brief measure of sensation. Personality and Individual Differences, 32, 401–414.CrossRefGoogle Scholar
  28. International Commission for Alpine Rescue. (2004–2010). People rescued from snow avalanches, alive or dead 2004/05 to 2009/10, from
  29. Jonah, B. A., Thiessen, R., & Au-Yeung, E. (2001). Sensation seeking, risky driving and behavioral adaptation. Accident Analysis and Prevention, 33, 679–684.PubMedCrossRefGoogle Scholar
  30. Langeheine, R., Pannekoek, J., & van de Pol, F. (1996). Bootstrapping goodness-of-fit measures in categorical data analysis. Sociological Methods and Research, 24, 492–516.CrossRefGoogle Scholar
  31. Lanza, S. T., & Rhoades, B. (2011). Latent class analysis: An alternative perspective on subgroup analysis in prevention and treatment. Prevention Science. doi: 10.1007/s11121-011-0201-1
  32. Larimer, M. E., & Cronce, J. M. (2007). Identification, prevention, and treatment revisited: Individual-focused college drinking prevention strategies 1999–2006. Addictive Behaviors, 32, 2439–2468.PubMedCrossRefGoogle Scholar
  33. Laska, M., Pasch, K., Lust, K., Story, M., & Ehlinger, E. (2009). Latent class analysis of lifestyle characteristics and health risk behaviors among college youth. Prevention Science, 10, 376–386.PubMedCrossRefGoogle Scholar
  34. Lazarsfeld, P. F., & Henry, N. W. (1968). Latent structure analysis. New York: Houghton Mifflin.Google Scholar
  35. List, J. A., & Gallet, C. A. (2001). What experimental protocol influence disparities between actual and hypothetical stated values? Environmental and Resource Economics, 20, 241–254.CrossRefGoogle Scholar
  36. Louviere, J. J., Hensher, D. A., & Swait, J. D. (2000). Stated choice methods: Analysis and application. New York: Cambridge University Press.CrossRefGoogle Scholar
  37. Lundgren, R. E., & McMakin, A. H. (2009). Risk communication: A handbook for communicating environmental, safety, and health risks (4th ed.). Hoboken, NJ: Wiley.Google Scholar
  38. McClung, D. M. (2002). The elements of applied avalanche forecasting - Part I: The human issues. Natural Hazards, 25, 111–129.CrossRefGoogle Scholar
  39. McFadden, D. (1974). Conditional logit analysis of qualitative choice behaviour. In P. Zarembka (Ed.), Frontiers in econometrics (pp. 105–142). New York: Academic Press.Google Scholar
  40. Schwarz, G. (1978). Estimating the dimension of a model. The Annals of Statistics, 6, 461–464.CrossRefGoogle Scholar
  41. Schweizer, J., Kronholm, K., Jamieson, J. B., & Birkeland, K. W. (2008). Review of spatial variability of snowpack properties and its importance for avalanche formation. Cold Regions Science and Technology, 51, 253–272.CrossRefGoogle Scholar
  42. Slanger, E., & Rudestam, K. E. (1997). Motivation and disinhibition in high risk sports: Sensation seeking and self-efficacy. Journal of Research in Personality, 31, 355–374.CrossRefGoogle Scholar
  43. Statham, G., Haegeli, P., Birkeland, K. W., Greene, E., Israelson, C., Tremper, B., et al. (2010). The North American public avalanche danger scale. Paper presented at the International Snow Science Workshop, Lake Tahoe, CA.Google Scholar
  44. Syvertsen, A., Cleveland, M., Gayles, J., Tibbits, M., & Faulk, M. (2010). Profiles of protection from substance use among adolescents. Prevention Science, 11, 185–196.PubMedCrossRefGoogle Scholar
  45. Train, K. (2003). Discrete choice methods with simulation. New York: Cambridge University Press.CrossRefGoogle Scholar
  46. Tremper, B. (2008). Staying alive in avalanche terrain (2nd ed.). Seattle, WA: The Mountaineers.Google Scholar
  47. UNISDR (2009). UNISDR Terminology on Disaster Risk Reduction. Retrieved Jan. 22, 2009, from
  48. Vermunt, J. K., & Magidson, J. (2002). Latent class cluster analysis. In A. L. McCutcheon & J. A. Hagenaars (Eds.), Advances in latent class models (pp. 89–106). New York: Cambridge University Press.CrossRefGoogle Scholar
  49. Vermunt, J. K., & Magidson, J. (2005a). Latent gold 4.0: User’s guide. Belmont, MA: Statistical Innovations Inc.Google Scholar
  50. Vermunt, J. K., & Magidson, J. (2005b). Latent GOLD choice 4.0 user’s manual. Belmont MA: Statistical Innovations Inc.Google Scholar
  51. Vermunt, J. K., & Magidson, J. (2008). LG-syntax user’s guide: Manual for latent GOLD 4.5 syntax module. Belmont, MA: Statistical Innovations Inc.Google Scholar
  52. Weinstein, N. D., & Sandman, P. M. (2002). The precaution adoption process model. In K. Glanz, B. K. Rimer & F. M. Lewis (Eds.), Health behavior and health education. San Francisco: Jossey-Bass.Google Scholar
  53. Whitehead, J. C. (2005). Environmental risk and averting behavior: Predictive validity of jointly estimated revealed and stated behavior data. Environmental and Resource Economics, 32, 301–316.CrossRefGoogle Scholar
  54. Zuckerman, M. (2006). Sensation seeking and risky behaviour. Washington, DC: American Psychological Association.Google Scholar

Copyright information

© Society for Prevention Research 2012

Authors and Affiliations

  1. 1.School of Resource and Environmental ManagementSimon Fraser UniversityBurnabyCanada
  2. 2.Avisualanche ConsultingVancouverCanada

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