Hostname: page-component-cd9895bd7-gxg78 Total loading time: 0 Render date: 2024-12-23T00:57:31.142Z Has data issue: false hasContentIssue false
  • English
  • Français

The causal effect of resilience on risk for drug abuse: a Swedish national instrumental variable, co-relative and propensity-score analysis

Published online by Cambridge University Press:  07 January 2020

Kenneth S. Kendler Open the ORCID record for Kenneth S. Kendler [Opens in a new window] ,
Henrik Ohlsson ,
Sean Clouston ,
Abigail A. Fagan ,
Jan Sundquist  and
Kristina Sundquist
Kenneth S. Kendler*
Affiliation:
Virginia Institute for Psychiatric and Behavioral Genetics, Virginia Commonwealth University, Richmond, VA, USA Department of Psychiatry, Virginia Commonwealth University, RichmondVA, USA
Henrik Ohlsson
Affiliation:
Center for Primary Health Care Research, Lund University, Malmö, Sweden
Sean Clouston
Affiliation:
Department of Family, Population, and Preventive Medicine, Program in Public Health, Stony Brook University Health Sciences Center, Stony Brook, NY, USA
Abigail A. Fagan
Affiliation:
Department of Sociology, Criminology & Law, University of Florida, Gainesville, FL, USA
Jan Sundquist
Affiliation:
Center for Primary Health Care Research, Lund University, Malmö, Sweden Department of Family Medicine and Community Health, Department of Population Health Science and Policy, Icahn School of Medicine at Mount Sinai, New York, USA Department of Functional Pathology, Center for Community-based Healthcare Research and Education (CoHRE), School of Medicine, Shimane University, Japan
Kristina Sundquist
Affiliation:
Center for Primary Health Care Research, Lund University, Malmö, Sweden Department of Family Medicine and Community Health, Department of Population Health Science and Policy, Icahn School of Medicine at Mount Sinai, New York, USA Department of Functional Pathology, Center for Community-based Healthcare Research and Education (CoHRE), School of Medicine, Shimane University, Japan
*
Author for correspondence: Kenneth S. Kendler, E-mail: [email protected]
Get access Rights & Permissions [Opens in a new window]

Abstract

Background

We sought to quantify and investigate the causal nature of the association between resilience at age 18 and future drug abuse (DA).

Method

In a national sample of Swedish men (n = 1 392 800), followed for a mean of 30.3 years, resilience was assessed during military conscription and DA defined from medical, criminal and pharmacy registers. For causal inference, we utilized three methods: (i) instrumental variable analyses with the month of birth as the instrument; (ii) co-relative analyses using the general population, cousins, siblings and monozygotic twins; and (iii) propensity scoring on a subsample (n = 48 548) with strong resilience predictors. Cox proportional hazards models were utilized to examine survival time till DA diagnosis.

Results

Low resilience was most robustly predicted from internalizing symptoms. Lower levels of standardized resilience strongly predicted the risk for DA (HR = 2.31, 95% CIs 2.28–2.33). In instrumental, co-relative, and propensity score analyses, the association between resilience and DA was estimated at HR = 3.06 (2.44–3.85), 1.34 (1.28–1.39), and 1.40 (1.28–1.53), respectively. Sensitivity analyses suggested that our instrument was weak and, despite our large sample, likely under-estimated confounding.

Conclusions

Low resilience strongly predicts DA risk. Three different causal analysis methods, with divergent assumptions, concurred in estimating that an appreciable proportion of this association was causal, probably around 40%, with the remainder arising from confounding variables many of which are likely familial. Consistent with prior interventions focused on substance use prevention, our results suggest that prevention programs that increase resilience in adolescence should meaningfully reduce the long-term risk for DA.

Keywords

Causalitydrug abuseresilience
Type
Original Article
Information
Psychological Medicine , Volume 51 , Issue 5 , April 2021 , pp. 846 - 852
Check for updates
Copyright
Copyright © The Author(s) 2020. Published by Cambridge University

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

Akaike, H. (1987). Factor analysis and AIC. Psychometrika, 52, 317–332.CrossRefGoogle Scholar
Armstrong, S., & Oomen-Early, J. (2009). Social connectedness, self-esteem, and depression symptomatology among collegiate athletes versus nonathletes. Journal of American College Health, 57(5), 521–526. doi:10.3200/JACH.57.5.521-526.CrossRefGoogle ScholarPubMed
Association, A. P. (2013). Diagnostic and statistical manual of mental disorders: Fifth edition, DSM-5. Washington, DC: American Psychiatric Association.Google Scholar
Austin, P. C. (2011). An introduction to propensity score methods for reducing the effects of confounding in observational studies. Multivariate Behavior Research, 46(3), 399–424. doi:10.1080/00273171.2011.568786.CrossRefGoogle ScholarPubMed
Bell, J. F., Massey, A., & Dexter, T. (1997). Birthdate and ratings of sporting achievement: Analysis of physical education GCSE results. European Journal of Physical Education, 2(2), 160–166.CrossRefGoogle Scholar
Boef, A. G., Dekkers, O. M., Vandenbroucke, J. P., & Le, C. S. (2014). Sample size importantly limits the usefulness of instrumental variable methods, depending on instrument strength and level of confounding. Journal of Clinical Epidemiology, 67(11), 1258–1264. doi:10.1016/j.jclinepi.2014.05.019.CrossRefGoogle ScholarPubMed
Botvin, G. J., Baker, E., Dusenbury, L., Botvin, E. M., & Diaz, T. (1995). Long-term follow-up results of a randomized drug abuse prevention trial in a white middle-class population. Journal of the American Medical Association, 273(14), 1106–1112. Retrieved from http://www.ncbi.nlm.nih.gov/pubmed/7707598CrossRefGoogle Scholar
Bound, J., Jaeger, D. A., & Baker, R. M. (1995). Problems with instrumental variables estimation when the correlation between the instruments and the endogenous explanatory variable is weak. Journal of the American Statistical Association, 90(430), 443–450.Google Scholar
Brown, E. C., Catalano, R. F., Fleming, C. B., Haggerty, K. P., & Abbott, R. D. (2005). Adolescent substance use outcomes in the raising healthy children project: A two-part latent growth curve analysis. Journal of Consulting and Clinical Psychology, 73(4), 699–710. doi:10.1037/0022-006X.73.4.699.CrossRefGoogle ScholarPubMed
Carlstedt, B. (1999). Validering av inskrivningsprövningen mot vitsord från den militära grundutbildningen: Försvarshögskolan, Klara AB Tryckeri i Karlstad.Google Scholar
Catalano, R. F., Berglund, L., Ryan, J. A., Lonczak, H. S., & Hawkins, J. D. (2004). Positive youth development in the United States: Research findings on evaluations of positive youth development programs. The Annals of the American Academy of Political and Social Science, 591, 98–124.CrossRefGoogle Scholar
Cingolani, L., & de Crombrugghe, D. (2012). Techniques for dealing with reverse causality between institutions and economic performance (#2012-034). Retrieved from Maastricht: http://collections.unu.edu/view/UNU:45Google Scholar
Crane, J., & Temple, V. (2015). A systematic review of dropout from organized sport among children and youth. European Physical Education Review, 21(1), 114–131.CrossRefGoogle Scholar
Crouter, A. C., & Head, M. R. (2002). Parental monitoring and knowledge of children. In Bornstein, M. (Ed.), Handbook of parenting: Becoming and being a parent (2nd ed., Vol. 3, pp. 461–483). Mahwah, NJ: Erlbaum. (Reprinted from: Not in File).Google Scholar
Delorme, N., Chalabaev, A., & Raspaud, M. (2011). Relative age is associated with sport dropout: Evidence from youth categories of French basketball. Scandinavian Journal of Medicine & Science in Sports, 21(1), 120–128. doi:10.1111/j.1600-0838.2009.01060.x.CrossRefGoogle ScholarPubMed
Dixon, J., Horton, S., & Weir, P. (2011). Relative age effects: Implications for leadership development. International Journal of Sport and Society, 2(2), 1–15. Retrieved from http://sportandsociety.com/journal/CrossRefGoogle Scholar
Dodge, K. A., Bierman, K. L., Coie, J. D., Greenberg, M. T., Lochman, J. E., McMahon, R. J., Pinderhughes, E. E. (2015). Impact of early intervention on psychopathology, crime, and well-being at age 25. American Journal of Psychiatry, 172(1), 59–70. doi:10.1176/appi.ajp.2014.13060786.CrossRefGoogle ScholarPubMed
Dunning, T. (2012). Natural experiments in the social sciences: A design-based approach. Cambridge, UK: Cambridge University Press.CrossRefGoogle Scholar
Durlak, J. A., Weissberg, R. P., Dymnicki, A. B., Taylor, R. D., & Schellinger, K. B. (2011). The impact of enhancing students' social and emotional learning: A meta-analysis of school-based universal interventions. Child Development, 82(1), 405–432. doi:10.1111/j.1467-8624.2010.01564.x.CrossRefGoogle ScholarPubMed
Durlak, J. A., Weissberg, R. P., & Pachan, M. (2010). A meta-analysis of after-school programs that seek to promote personal and social skills in children and adolescents. American Journal of Community Psychology, 45(3–4), 294–309. doi:10.1007/s10464-010-9300-6.CrossRefGoogle ScholarPubMed
Fagan, A. A., Bumbarger, B. K., Barth, R. P., Bradshaw, C. P., Cooper, B. R., Supplee, L. H., Walker, D. K. (2019). Scaling up evidence-based interventions in US public systems to prevent behavioral health problems: Challenges and opportunities. Prevention Science, 20(8), 1147–1168. doi:10.1007/s11121-019-01048-8.CrossRefGoogle ScholarPubMed
Fergus, S., & Zimmerman, M. A. (2005). Adolescent resilience: A framework for understanding healthy development in the face of risk. Annual Review of Public Health, 26, 399–419. doi:10.1146/annurev.publhealth.26.021304.144357.CrossRefGoogle ScholarPubMed
Foxcroft, D. R., & Tsertsvadze, A. (2011). Universal multi-component prevention programs for alcohol misuse in young people. Cochrane Database System Reviews, (9), CD009307. doi:10.1002/14651858.CD009307. PMID:21901732.Google Scholar
Greenberg, M. T., Weissberg, R. P., O'Brien, M. U., Zins, J. E., Fredericks, L., Resnik, H., Elias, M. J. (2003). Enhancing school-based prevention and youth development through coordinated social, emotional, and academic learning. American Psychologist Journal, 58(6–7), 466–474. Retrieved from http://www.ncbi.nlm.nih.gov/pubmed/12971193CrossRefGoogle ScholarPubMed
Hahn, J., & Hausman, J. (2003). Weak instruments: Diagnosis and cures in empirical econometrics. American Economic Review, 93(2), 118–125.CrossRefGoogle Scholar
Hawkins, R., & Mulkey, L. M. (2005). Athletic investment and academic resilience in a national sample of African American females and males in the middle grades. Education and Urban Society, 38(1), 62–88. doi:10.1177%2F0013124505280025CrossRefGoogle Scholar
Kendler, K. S., Ohlsson, H., Fagan, A. A., Lichtenstein, P., Sundquist, J., & Sundquist, K. (2018). Academic achievement and drug abuse risk assessed using instrumental variable analysis and co-relative designs. JAMA Psychiatry, 75(11), 1182–1188. doi:10.1001/jamapsychiatry.2018.2337.CrossRefGoogle ScholarPubMed
Kringlen, E., Torgersen, S., & Cramer, V. (2001). A Norwegian psychiatric epidemiological study. American Journal of Psychiatry, 158(7), 1091–1098. Retrieved from http://www.ncbi.nlm.nih.gov/pubmed/11431231CrossRefGoogle ScholarPubMed
Lemez, S., Baker, J., Horton, S., Wattie, N., & Weir, P. (2014). Examining the relationship between relative age, competition level, and dropout rates in male youth ice-hockey players. Scandinavian Journal of Medicine & Science in Sports, 24(6), 935–942. doi:10.1111/sms.12127.CrossRefGoogle ScholarPubMed
Luthar, S. S., Cicchetti, D., & Becker, B. (2000). The construct of resilience: A critical evaluation and guidelines for future work. Child Development, 71(3), 543–562. Retrieved from http://www.ncbi.nlm.nih.gov/pubmed/10953923CrossRefGoogle ScholarPubMed
Meschke, L. L., & Patterson, J. M. (2003). Resilience as a theoretical basis for substance abuse prevention. Journal of Primary Prevention, 23(4), 483–514.CrossRefGoogle Scholar
Munafo, M. R., & Davey, S. G. (2018). Robust research needs many lines of evidence. Nature, 553(7689), 399–401. doi:10.1038/d41586-018-01023-3.CrossRefGoogle ScholarPubMed
Ohlsson, H., & Kendler, K. S. (2019). Applying causal inference methods in psychiatric epidemiology: A review. JAMA Psychiatry, Published online December 11, 2019. doi:https://doi.org/10.1001/jamapsychiatry.2019.3758.Google Scholar
Rudzinski, K., McDonough, P., Gartner, R., & Strike, C. (2017). Is there room for resilience? A scoping review and critique of substance use literature and its utilization of the concept of resilience. Substance Abuse Treatment, Prevention and Policy, 12(1), 41. doi:10.1186/s13011-017-0125-2.Google Scholar
Rutter, M. (1985). Resilience in the face of adversity. Protective factors and resistance to psychiatric disorder. British Journal of Psychiatry, 147, 598–611. doi:S0007125000208775.CrossRefGoogle ScholarPubMed
SAS Institute, I. (2012). SAS/STAT® Online Documentation, Version 9.4. Cary, N.C.: SAS Institute, Inc. In.Google Scholar
Stattin, H., & Kerr, M. (2000). Parental monitoring: A reinterpretation. Child Development, 71(4), 1072–1085. Retrieved from <Go to ISI>://000089443600023CrossRefGoogle ScholarPubMed
Stein, B. D., Jaycox, L. H., Kataoka, S. H., Wong, M., Tu, W., Elliott, M. N., Fink, A. (2003). A mental health intervention for schoolchildren exposed to violence: A randomized controlled trial. Journal of the American Medical Association, 290(5), 603–611. doi:10.1001/jama.290.5.603.CrossRefGoogle ScholarPubMed
Taylor, D. L. (1995). A comparison of college athletic participants and nonparticipants on self-esteem. Journal of College Student Development, 36(5), 444–451.Google Scholar
Thompson, A. H., Barnsley, R. H., & Battle, J. (2010). The relative age effect and the development of self-esteem. Educational Research, 46(3), 313–320.CrossRefGoogle Scholar
Wagnsson, S., Lindwall, M., & Gustafsson, H. (2014). Participation in organized sport and self-esteem across adolescence: The mediating role of perceived sport competence. Journal of Sport and Exercise Psychology, 36(6), 584–594.CrossRefGoogle ScholarPubMed
Weiss, M. R., & Ebbeck, V. (1996). Self-esteem and perceptions of competence in youth sport: Theory, research, and enhancement strategies: The child and adolescent athlete. In Bar-Or, O. (Ed.), The encyclopaedia of sports medicine (Vol. VI, pp. 364–382). Oxford: Blackwell Science, Ltd. (Reprinted from: Not in File).Google Scholar
Werner, E. E. (1993). Risk, resilience, and recovery: Perspectives from the Kauai longitudinal study. Development and Psychopathology, 5(4), 503–515.CrossRefGoogle Scholar
Windle, G., Bennett, K. M., & Noyes, J. (2011). A methodological review of resilience measurement scales. Health and Quality of Life Outcomes, 9, 8. doi:10.1186/1477-7525-9-8.CrossRefGoogle ScholarPubMed
Yu, P. (2018). Chapter 7. Endogeneity and Instrumental Variables. Retrieved from http://web.hku.hk/~pingyu/6005/LN/LN7_Endogeneity%20and%20Instrumental%20Variables.pdfGoogle Scholar
Supplementary material: File

Kendler et al. supplementary material

Kendler et al. supplementary material

Download Kendler et al. supplementary material(File)
File 21.7 KB