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Trajectories of Suicide Ideation and Attempts from Early Adolescence to Mid-Adulthood: Associations with Race/Ethnicity

  • Jennifer Toller ErausquinEmail author
  • Thomas P. McCoy
  • Robin Bartlett
  • Eunhee Park
Empirical Research

Abstract

Prior research has demonstrated that behavioral, demographic, and mental health characteristics are associated with suicide, particularly among youth and young adults. Although recent research has begun to explore developmental trajectories of suicide-related outcomes, few studies to date have extended beyond late adolescence. Understanding different trajectories of suicide-related thoughts and behaviors from adolescence through mid-adulthood has the potential to refine developmental perspectives on suicide risk and to inform prevention efforts. Using National Longitudinal Study of Adolescent to Adult Health data (n = 9421 respondents with data at all four waves), this study analyzed suicide-related outcomes across ages 12–31 years. Growth mixture modeling (GMM) was used to estimate trajectory classes for past-year suicide ideation and attempts, followed by multinomial logistic regression to explore the association between race/ethnicity and class membership. In weighted descriptive analyses, the sample was 50.0% female; it was 15.5% African American, 2.1% Asian/Pacific Islander, 12.0% Hispanic, 0.9% other, and 65.9% White. GMM results revealed three trajectory classes for ideation: sustained higher risk, sustained lower risk, and adolescent-limited risk. Two trajectory classes emerged for attempts: declining higher risk and sustained lower risk. For ideation, African Americans were less likely than Whites to be in either the sustained higher risk or the adolescent-limited risk trajectory. For attempts, African Americans had significantly lower odds than Whites and Asians/Pacific Islanders had nearly four times the odds of Whites of being in the sustained higher risk trajectory, though the latter was only marginally significant. The finding of associations between race/ethnicity and distinct patterns of suicide-related behavioral development from early adolescence into mid-adulthood suggests new directions for developmental research and provides evidence to inform future suicide prevention efforts.

Keywords

Suicide Health Race/ethnicity Longitudinal analysis Add health 

Notes

Acknowledgements

This research uses data from Add Health, a program project directed by Kathleen Mullan Harris and designed by J. Richard Udry, Peter S. Bearman, and Kathleen Mullan Harris at the University of North Carolina at Chapel Hill, and funded by grant P01-HD31921 from the Eunice Kennedy Shriver National Institute of Child Health and Human Development, with cooperative funding from 23 other federal agencies and foundations. Special acknowledgment is due Ronald R. Rindfuss and Barbara Entwisle for assistance in the original design. Information on how to obtain the Add Health data files is available on the Add Health website (http://www.cpc.unc.edu/addhealth). No direct support was received from grant P01-HD31921 for this analysis.

Authors’ Contributions

J.T.E conceptualized data analysis, summarized relevant extant empirical and theoretical research, interpreted results, and led the writing and revision of the manuscript; T.P.M. participated in the design, performed statistical analyses, participated in the interpretation of data, drafted the methods section of the manuscript, and participated in revisions; R.B. summarized relevant extant empirical and theoretical research, contributed to interpretation of results, drafted the discussion section, and participated in revisions; E.P. conceptualized data analysis, contributed to the introduction/background on theory, and participated in revisions. All authors read and approved the final manuscript.

Data Sharing and Declaration

The data that support the findings of this study are available from Add Health, a program project at the University of North Carolina at Chapel Hill. Restrictions apply to the availability of these data, which were used under license for the current study. Information on how to obtain the Add Health data files is available on the Add Health website (http://www.cpc.unc.edu/addhealth).

Funding

The authors received no funding support for this study.

Compliance with Ethical Standards

Conflict of Interest

The authors declare that they have no conflict of interest.

Ethical Approval

ll procedures performed in studies involving human participants were in accordance with the ethical standards of the institutional and/or national research committee and with the 1964 Helsinki declaration and its later amendments or comparable ethical standards. For this type of study formal consent is not required.

Informed Consent

Informed consent was obtained from all individual participants included in the study.

References

  1. Add Health. (2016). What is the best way to compute race in the Add Health Wave I in-home data? http://www.cpc.unc.edu/projects/addhealth/faqs/aboutdata/index.html#RACE. Accessed 20 Mar 2018.
  2. Aseltine, R. H., & DeMartino, R. (2004). An outcome evaluation of the SOS suicide prevention program. American Journal of Public Health, 94(3), 446–451.PubMedGoogle Scholar
  3. Asparouhov, T., & Muthén, B. (2014). Auxiliary variables in mixture modeling: three-step approaches using Mplus. Structural Equation Modeling: A Multidisciplinary Journal, 21(3), 329–341.  https://doi.org/10.1080/10705511.2014.915181.Google Scholar
  4. Balis, T., & Postolache, T. T. (2008). Ethnic differences in adolescent suicide in the United States. International journal of child health and human development, 1(3), 281–296.PubMedGoogle Scholar
  5. Bergman, L. R., & Lundh, L.-G. (2015). The person-oriented approach: Roots and roads to the future. Journal for Person-Oriented Research, 1(1–2), 1–6.Google Scholar
  6. Borges, G. (2012). Suicidality, ethnicity and immigration in the USA. Psychological Medicine, 42, 1175–1184.  https://doi.org/10.1017/S0033291711002340.PubMedGoogle Scholar
  7. Borges, G., Angst, J., Nock, M. K., Ruscio, A. M., & Kessler, R. C. (2008). Risk factors for the incidence and persistence of suicide-related outcomes: A 10-year follow-up study using the National Comorbidity Surveys. Journal of Affective Disorders, 105(1), 25–33.  https://doi.org/10.1016/j.jad.2007.01.036.PubMedGoogle Scholar
  8. Burnham, K. P., & Anderson, D. R. (2010). Model Selection and Multimodel Inference: A Practical Information-Theoretic Approach. 2nd ed. New York, NY: Springer. https://www.springer.com/us/book/9780387953649. Accessed 14 June 2019.
  9. CDC, & NCHS. (2017). National Vital Statistics System, United States, 2017. Web-based Injury Statistics Query and Reporting System [WISQARS]. https://webappa.cdc.gov/sasweb/ncipc/leadcause.html. Accessed 8 Jun 2019.
  10. Celeux, G., & Soromenho, G. (1996). An entropy criterion for assessing the number of clusters in a mixture model. Journal of Classification, 13(2), 195–212.Google Scholar
  11. Centers for Disease Control and Prevention (CDC). (2018). 1991–2017 High school youth risk behavior survey data. https://nccd.cdc.gov/Youthonline/App/Default.aspx. Accessed 8 Jun. 2019.
  12. Chen, P., & Chantala, K. (2014). Guidlines for Analyzing Add Health data. (pp. 53). Chapel Hill, NC: Carolina Population Center, University of North Carolina at Chapel Hill. http://www.cpc.unc.edu/projects/addhealth/documentation/guides/wt_guidelines_20161213.pdf.
  13. Colucci, E., Lester, D., Hjelmeland, H. & Park, B. C. B. (Eds.) (2013). Suicide and Culture: Understanding the Context. Cambridge, MA, US: Hogrefe Publishing.Google Scholar
  14. Curtin, S. C., Warner, M., & Hedegaard, H. (2016). Increase in suicide in the United States, 1999–2014. NCHS Data Brief, no. 241. Hyattsville, MD: National Center for Health Statistics. https://www.cdc.gov/nchs/products/databriefs/db241.htm. Accessed 3 Oct 2017.
  15. Dahl, R. E., Allen, N. B., Wilbrecht, L., & Suleiman, A. B. (2018). Importance of investing in adolescence from a developmental science perspective. Nature, 554(7693), 441–450.  https://doi.org/10.1038/nature25770.PubMedGoogle Scholar
  16. Eaton, W. W., Smith, C., Ybarra, M., Muntaner, C., & Tien, A. (2004). Center for Epidemiologic Studies Depression Scale: review and Revision (CESD and CESD-R). In M. E. Maruish (Ed.), The use of psychological testing for treatment planning and outcomes assessment: Instruments for adults (pp. 363–377). Mahwah, NJ, US: Lawrence Erlbaum Associates Publishers.Google Scholar
  17. Elgin, J. E. (2014, October 13). Examining the Relationships Between Suicidal Ideation, Substance Use, Depressive Symptoms, and Educational Factors in Emerging Adulthood. Thesis. https://digital.lib.washington.edu:443/researchworks/handle/1773/26285.
  18. Enders, C. K. (2010). Applied missing data analysis. New York, NY: Guilford Press.Google Scholar
  19. Galbraith, S., Bowden, J., & Mander, A. (2017). Accelerated longitudinal designs: an overview of modelling, power, costs and handling missing data. Statistical Methods in Medical Research, 26(1), 374–398.  https://doi.org/10.1177/0962280214547150.PubMedGoogle Scholar
  20. Goldman-Mellor, S. J., Caspi, A., Harrington, H., Hogan, S., Nada-Raja, S., Poulton, R., & Moffitt, T. E. (2014). Suicide attempt in young people: a signal for long-term health care and social needs. JAMA Psychiatry, 71(2), 119–127.  https://doi.org/10.1001/jamapsychiatry.2013.2803.PubMedGoogle Scholar
  21. Graubard, B. I., & Korn, E. L. (1996). Survey inference for subpopulations. American Journal of Epidemiology, 144(1), 102–106.PubMedGoogle Scholar
  22. Gutierrez, P. M., Muehlenkamp, J. J., Konick, L. C., & Osman, A. (2005). What role does race play in adolescent suicidal ideation? Archives of Suicide Research, 9(2), 177–192.  https://doi.org/10.1080/13811110590904025.PubMedGoogle Scholar
  23. Harris, K. M. (2009). The National Longitudinal Study of Adolescent to Adult Health (Add Health), Waves I & II, 1994–1996; Wave III, 2001–2002; Wave IV, 2007–2009 [machine-readable data file and documentation]. Chapel Hill, NC: Carolina Population Center, University of North Carolina at Chapel Hill. 10.3886/ICPSR27021.v9.Google Scholar
  24. Hottes, T. S., Bogaert, L., Rhodes, A. E., Brennan, D. J., & Gesink, D. (2016). Lifetime prevalence of suicide attempts among sexual minority adults by study sampling strategies: a systematic review and meta-analysis. American Journal of Public Health, 106(5), e1–e12.PubMedGoogle Scholar
  25. Joe, S., Canetto, S. S., & Romer, D. (2008a). Advancing prevention research on the role of culture in suicide prevention. Suicide and Life-Threatening Behavior, 38(3), 354–362.  https://doi.org/10.1521/suli.2008.38.3.354.PubMedGoogle Scholar
  26. Joe, S., Canetto, S. S., & Romer, D. (2008b). Advancing prevention research on the role of culture in suicide prevention. Suicide and Life-Threatening Behavior, 38(3), 354–362.  https://doi.org/10.1521/suli.2008.38.3.354.PubMedGoogle Scholar
  27. Kessler, R. C., Borges, G., & Walters, E. E. (1999). Prevalence of and risk factors for lifetime suicide attempts in the National Comorbidity Survey. Archives of General Psychiatry, 56(7), 617–626.PubMedGoogle Scholar
  28. Kim, J. L., Kim, J. M., Choi, Y., Lee, T.-H., & Park, E.-C. (2016). Effect of socioeconomic status on the linkage between suicidal ideation and suicide attempts. Suicide and Life-Threatening Behavior, 46(5), 588–597.PubMedGoogle Scholar
  29. Kim, Y. J., Moon, S. S., & Kim, M. J. (2011). Physical and psycho-social predictors of adolescents’ suicide behaviors. Child and Adolescent Social Work Journal, 28(6), 421–438.  https://doi.org/10.1007/s10560-011-0241-1.Google Scholar
  30. Lo, Y., Mendell, N. R., & Rubin, D. B. (2001). Testing the number of components in a normal mixture. Biometrika, 88(3), 767–778.Google Scholar
  31. Meyer, I. H. (2003). Prejudice, social stress, and mental health in lesbian, gay, and bisexual populations: conceptual issues and research evidence. Psychological Bulletin, 129(5), 674–697.  https://doi.org/10.1037/0033-2909.129.5.674.PubMedGoogle Scholar
  32. Moradi, B. (2013). Discrimination, Objectification, and dehumanization: toward a pantheoretical framework. In Objectification and (de)humanization (pp. 153–181). Springer, New York, NY.  https://doi.org/10.1007/978-1-4614-6959-9_7.
  33. Muthén, L., & Muthén, B. (2015). Mplus v7.3. Los Angeles, CA: Muthén & Muthén.Google Scholar
  34. Olfson, M., Blanco, C., Wall, M., Liu, S.-M., Saha, T. D., Pickering, R. P., & Grant, B. F. (2017). National trends in suicide attempts among adults in the United States. JAMA Psychiatry, 74(11), 1095–1103.PubMedGoogle Scholar
  35. Park, E., McCoy, T. P., Erausquin, J. T., & Bartlett, R. (2018). Trajectories of risk behaviors across adolescence and young adulthood: the role of race and ethnicity. Addictive Behaviors, 76, 1–7.PubMedGoogle Scholar
  36. Pirkis, J. E., Irwin, C. E., Brindis, C. D., Sawyer, M. G., Friestad, C., Biehl, M., & Patton, G. C. (2003). Receipt of psychological or emotional counseling by suicidal adolescents. Pediatrics, 111(4), e388–e393.PubMedGoogle Scholar
  37. Ram, N., & Grimm, K. J. (2009). Methods and measures: growth mixture modeling: a method for identifying differences in longitudinal change among unobserved groups. International Journal of Behavioral Development, 33(6), 565–576.PubMedGoogle Scholar
  38. SAS Institute. (2012). SAS. Cary, NC: SAS Institute.Google Scholar
  39. Séguin, M., Beauchamp, G., Robert, M., DiMambro, M., & Turecki, G. (2014). Developmental model of suicide trajectories. The British Journal of Psychiatry, 205(2), 120–126.  https://doi.org/10.1192/bjp.bp.113.139949.PubMedGoogle Scholar
  40. Sellers, R. M., Copeland‐Linder, N., Martin, P. P., & Lewis, R. L. (2006). Racial Identity Matters: the relationship between racial discrimination and psychological functioning in african american adolescents. Journal of Research on Adolescence, 16(2), 187–216.  https://doi.org/10.1111/j.1532-7795.2006.00128.x.Google Scholar
  41. Smedley, B. D., Myers, H. F., & Harrell, S. P. (1993). Minority-status stresses and the college adjustment of ethnic minority freshmen. The Journal of Higher Education, 64(4), 434–452.  https://doi.org/10.2307/2960051.Google Scholar
  42. Stack, S., & Wasserman, I. (2005). Race and method of suicide: culture and opportunity. Archives of Suicide Research: Official Journal of the International Academy for Suicide Research, 9(1), 57–68.  https://doi.org/10.1080/13811110590512949.Google Scholar
  43. Stevenson, H. C., Davis, G. Y., Herrero-Taylor, T., & Morris, R. (2003). Boys, not men: hypervulnerability in African American youth. In Playing with anger: teaching coping skills to African American boys through athletics and culture (3–20).Google Scholar
  44. Substance Use and Mental Health Services Administration. (2014). Results from the 2013 National Survey on Drug Use and Health: mental health findings (NSDUH Series H-49). Rockville, MD: Substance Abuse and Mental Health Services, 2014. http://www.samhsa.gov/data/sites/default/files/NSDUHmhfr2013/NSDUHmhfr2013.pdf. (No. HHS Publication No. (SMA) 14-4887).
  45. Supple, A. (2013). Ethnic, gender, and age differences in adolescent nonfatal suicide behaviors. Death Studies, 37, 830–847.  https://doi.org/10.1080/07481187.2012.699909.PubMedGoogle Scholar
  46. Walker, R. L., Wingate, L. R., Obasi, E. M., & Joiner, T. E. (2008). An empirical investigation of acculturative stress and ethnic identity as moderators for depression and suicidal ideation in college students. Cultural Diversity & Ethnic Minority Psychology, 14(1), 75–82.  https://doi.org/10.1037/1099-9809.14.1.75.Google Scholar
  47. Xu, J. (2017). QuickStats: average number of deaths from motor vehicle injuries, suicide, and homicide, by day of the week — National Vital Statistics System, United States, 2015. MMWR, 66.  https://doi.org/10.15585/mmwr.mm6622a5.

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2019

Authors and Affiliations

  1. 1.Department of Public Health EducationUniversity of North Carolina at GreensboroGreensboroUSA
  2. 2.School of NursingUniversity of North Carolina at GreensboroGreensboroUSA
  3. 3.School of NursingUniversity at BuffaloBuffaloUSA

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