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Recent increases in depressive symptoms among US adolescents: trends from 1991 to 2018

Abstract

Background

Mental health problems and mental health related mortality have increased among adolescents, particularly girls. These trends have implications for etiology and prevention and suggest new and emerging risk factors in need of attention. The present study estimated age, period, and cohort effects in depressive symptoms among US nationally representative samples of school attending adolescents from 1991 to 2018.

Methods

Data are drawn from 1991 to 2018 Monitoring the Future yearly cross-sectional surveys of 8th, 10th, and 12th grade students (N = 1,260,159). Depressive symptoms measured with four questions that had consistent wording and data collection procedures across all 28 years. Age–period–cohort effects estimated using the hierarchical age–period–cohort models.

Results

Among girls, depressive symptoms decreased from 1991 to 2011, then reversed course, peaking in 2018; these increases reflected primarily period effects, which compared to the mean of all periods showed a gradual increase starting in 2012 and peaked in 2018 (estimate = 1.15, p < 0.01). Cohort effects were minimal, indicating that increases are observed across all age groups. Among boys, trends were similar although the extent of the increase is less marked compared to girls; there was a declining cohort effect among recently born cohorts, suggesting that increases in depressive symptoms among boys are slower for younger boys compared to older boys in recent years. Trends were generally similar by race/ethnicity and parental education, with a positive cohort effect for Hispanic girls born 1999–2004.

Conclusions

Depressive symptoms are increasing among teens, especially among girls, consistent with increases in depression and suicide. Population variation in psychiatric disorder symptoms highlight the importance of current environmental determinants of psychiatric disorder risk, and provide evidence of emerging risk factors that may be shaping a new and concerning trend in adolescent mental health.

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References

  1. Angold A (1988) Childhood and adolescent depression: I. Epidemiological and aetiological aspects. Br J Psychiatry 152:601–617

    CAS  Article  Google Scholar 

  2. Costello EJ, Erkanli A, Angold A (2006) Is there an epidemic of child or adolescent depression? J Child Psychol Psychiatry 47(12):1263–1271

    Google Scholar 

  3. Mojtabai R, Olfson M, Han B (2016) National trends in the prevalence and treatment of depression in adolescents and young adults. Pediatrics. https://doi.org/10.1542/peds.2016-1878

    PubMed Central  Article  PubMed  Google Scholar 

  4. Substance Abuse and Mental Health Services Administration (2017) Key substance use and mental health indicators in the United States: Results from the 2016 National Survey on Drug Use and Health. Rockville, MD Cent Behav Heal Stat Qual Subst Abus Ment Heal Serv Adm. https://www.samhsa.gov/data/sites/default/files/NSDUH-FFR1-2016/NSDUH-FFR1-2016.htm. Accessed 23 Mar 2019

  5. Curtin SC, Warner M, Hedegaard H (2016) Increases in suicide in the United States, 1999–2014. NCHS data brief, no 241. National Center for Health Statistics, Hyattsville, MD

  6. CDC (2016) Trends in the prevalence of suicide–related behavior national YRBS: 1991–2015. https://www.cdc.gov/healthyyouth/data/yrbs/pdf/trends/2015_us_suicide_trend_yrbs.pdf. Accessed 23 Mar 2019

  7. Cloninger CR (2012) Healthy personality development and well-being. World Psychiatry 11(2):103–104

    PubMed Central  Article  Google Scholar 

  8. Olfson M, Marcus SC (2009) National patterns in antidepressant medication treatment. Arch Gen Psychiatry 66(8):848. https://doi.org/10.1001/archgenpsychiatry.2009.81

    Article  PubMed  Google Scholar 

  9. Marcus SC, Olfson M (2010) National trends in the treatment for depression from 1998 to 2007. Arch Gen Psychiatry 67(12):1265–1273. https://doi.org/10.1001/archgenpsychiatry.2010.151

    Article  PubMed  Google Scholar 

  10. Olfson M, Marcus SC, Druss B, Elinson L, Tanielian T, Pincus HA (2002) National trends in the outpatient treatment of depression. JAMA 287(2):203–209

    Article  Google Scholar 

  11. Lewinsohn PM, Rohde P, Seeley JR, Fischer SA (1993) Age-cohort changes in the lifetime occurrence of depression and other mental disorders. J Abnorm Psychol Abnorm Psychol 102(1):110–120. https://doi.org/10.1037/0021-843x.102.1.110

    CAS  Article  Google Scholar 

  12. Kessler RC, Berglund P, Demler O et al (2003) The epidemiology of major depressive disorder: results from the National Comorbidity Survey Replication (NCS-R). JAMA 289(23):3095–3105. https://doi.org/10.1001/jama.289.23.3095

    Article  PubMed  Google Scholar 

  13. Klerman GL, Lavori PW, Rice J et al (1985) Birth-cohort trends in rates of major depressive disorder among relatives of patients with affective disorder. Arch Gen Psychiatry 42(7):689–693. https://doi.org/10.1001/archpsyc.1985.01790300057007

    CAS  Article  PubMed  Google Scholar 

  14. Klerman GL (1989) Increasing rates of depression. JAMA 261(15):2229. https://doi.org/10.1001/jama.1989.03420150079041

    CAS  Article  PubMed  Google Scholar 

  15. Eaton WW, Kalaydjian A, Scharfstein DO, Mezuk B, Ding Y (2007) Prevalence and incidence of depressive disorder: the Baltimore ECA follow-up, 1981–2004. Acta Psychiatr Scand 116(3):182–188. https://doi.org/10.1111/j.1600-0447.2007.01017.x

    PubMed Central  CAS  Article  PubMed  Google Scholar 

  16. Murphy JM, Laird NM, Monson RR, Sobol AM, Leighton AH (2000) Incidence of depression in the Stirling County Study: historical and comparative perspectives. Psychol Med 30(3):505–514. https://doi.org/10.1017/s0033291799002044

    CAS  Article  PubMed  Google Scholar 

  17. Keyes KM, Nicholson R, Kinley J et al. Age, period, and cohort effects in psychological distress in the United States and Canada. Am J Epidemiol. 2014. https://doi.org/10.1093/aje/kwu029

    Article  PubMed  PubMed Central  Google Scholar 

  18. Murphy JM, Laird NM, Monson RR, Sobol AM, Leighton AH (2000) A 40-year perspective on the prevalence of depression: the Stirling County Study. Arch Gen Psychiatry 57(3):209–215. https://doi.org/10.1001/archpsyc.57.3.209

    CAS  Article  PubMed  Google Scholar 

  19. Simpson KR, Meadows GN, Frances AJ, Patten SB (2012) Is mental health in the Canadian population changing over time? Can J Psychiatry 57(5):324–331. https://doi.org/10.1177/070674371205700508

    Article  PubMed  Google Scholar 

  20. Polanczyk GV, Salum GA, Sugaya LS, Caye A, Rohde LA (2015) Annual research review: a meta-analysis of the worldwide prevalence of mental disorders in children and adolescents. J Child Psychol Psychiatry Allied Discip. https://doi.org/10.1111/jcpp.12381

    Article  Google Scholar 

  21. Levenson JC, Shensa A, Sidani JE, Colditz JB, Primack BA (2016) The association between social media use and sleep disturbance among young adults. Prev Med (Baltim) 85:36–41. https://doi.org/10.1016/j.ypmed.2016.01.001

    Article  Google Scholar 

  22. Rosenthal SR, Buka SL, Marshall BDL, Carey KB, Clark MA (2016) Negative experiences on facebook and depressive symptoms among young adults. J Adolesc Heal 59(5):510–516. https://doi.org/10.1016/j.jadohealth.2016.06.023

    Article  Google Scholar 

  23. Shensa A, Escobar-Viera CG, Sidani JE, Bowman ND, Marshal MP, Primack BA (2017) Problematic social media use and depressive symptoms among US young adults: a nationally-representative study. Soc Sci Med 182:150–157. https://doi.org/10.1016/j.socscimed.2017.03.061

    PubMed Central  Article  PubMed  Google Scholar 

  24. Twenge JM, Joiner TE, Rogers ML, Martin GN. Increases in depressive symptoms, suicide-related outcomes, and suicide rates among US adolescents after 2010 and links to increased new media screen time. Clin Psychol Sci. 2017. https://doi.org/10.1177/2167702617723376

    Article  Google Scholar 

  25. Keyes KM, Maslowsky J, Hamilton A, Schulenberg J. The great sleep recession: changes in sleep duration among US adolescents, 1991–2012. Pediatrics. 2015. https://doi.org/10.1542/peds.2014-2707

    Article  PubMed  PubMed Central  Google Scholar 

  26. Robinson WR, Utz RL, Keyes KM, Martin CL, Yang Y. Birth cohort effects on abdominal obesity in the United States: the silent generation, baby boomers and generation X. Int J Obes. 2013. https://doi.org/10.1038/ijo.2012.198

    Article  Google Scholar 

  27. Messias E, Kindrick K, Castro J (2014) School bullying, cyberbullying, or both: correlates of teen suicidality in the 2011 CDC youth risk behavior survey. Compr Psychiatry 55(5):1063–1068. https://doi.org/10.1016/j.comppsych.2014.02.005

    PubMed Central  Article  PubMed  Google Scholar 

  28. Holfeld B, Sukhawathanakul P (2017) Associations between internet attachment, cyber victimization, and internalizing symptoms among adolescents. Cyberpsychol Behav Soc Netw 20(2):91–96. https://doi.org/10.1089/cyber.2016.0194

    Article  PubMed  Google Scholar 

  29. Patchin JW, Hinduja S (2017) Digital self-harm among adolescents. J Adolesc Health 61(6):761–766. https://doi.org/10.1016/j.jadohealth.2017.06.012

    Article  PubMed  Google Scholar 

  30. Bottino SMB, Bottino CMC, Regina CG, Correia AVL, Ribeiro WS (2015) Cyberbullying and adolescent mental health: systematic review. Cad Saude Publica 31(3):463–475. https://doi.org/10.1590/0102-311x00036114

    Article  PubMed  Google Scholar 

  31. Holt MK, Vivolo-Kantor AM, Polanin JR et al (2015) Bullying and suicidal ideation and behaviors: a meta-analysis. Pediatrics 135(2):e496–e509. https://doi.org/10.1542/peds.2014-1864

    PubMed Central  Article  PubMed  Google Scholar 

  32. Miech RA, Johnston LD, O’Malley PM, Bachman JG, Schulenberg JE, Patrick ME (2018) Monitoring the future national survey results on drug use, 1975–2017: volume I, secondary school students. The University of Michigan Institute for Social Research, Ann Arbor, MI. http://monitoringthefuture.org/pubs.html#monographs. Accessed 6 Aug 2018

  33. Keyes KM, Jager J, Hamilton A, O’Malley PM, Miech R, Schulenberg JE (2015) National multi-cohort time trends in adolescent risk preference and the relation with substance use and problem behavior from 1976 to 2011. Drug Alcohol Depend. https://doi.org/10.1016/j.drugalcdep.2015.06.031

    Article  PubMed  PubMed Central  Google Scholar 

  34. Keyes KM, Utz RL, Robinson W, Li G (2010) What is a cohort effect? Comparison of three statistical methods for modeling cohort effects in obesity prevalence in the United States, 1971–2006. Soc Sci Med. https://doi.org/10.1016/j.socscimed.2009.12.018

    Article  PubMed  PubMed Central  Google Scholar 

  35. Reither ENEN, Land KCKC, Jeon SYSY et al (2015) Clarifying hierarchical age-period-cohort models: a rejoinder to bell and jones. Soc Sci Med 145:125–128. https://doi.org/10.1016/j.socscimed.2015.07.013

    PubMed Central  Article  PubMed  Google Scholar 

  36. Yang Y, Land K (2013) Age-period-cohort analysis: new models, methods, and empirical applications. Chapman & Hall/CRC, Boca Raton

    Google Scholar 

  37. Merikangas KR, He JP, Burstein M et al (2010) Lifetime prevalence of mental disorders in US adolescents: results from the National Comorbidity Survey Replication–Adolescent Supplement (NCS-A). J Am Acad Child Adolesc Psychiatry 49(10):980–989. https://doi.org/10.1016/j.jaac.2010.05.017

    PubMed Central  Article  PubMed  Google Scholar 

  38. Cantwell DP, Baker L (1991) Manifestations of depressive affect in adolescence. J Youth Adolesc 20(2):121–133

    CAS  Article  Google Scholar 

  39. Dohrenwend BP, Shrout PE, Egri G, Mendelsohn FS (1980) Nonspecific psychological distress and other dimensions of psychopathology: measures for use in the general population. Arch Gen Psychiatry. https://doi.org/10.1001/archpsyc.1980.01780240027003

    Article  PubMed  Google Scholar 

  40. Miech R, Johnston LD, O’Malley PM, Bachman JG, Schulenberg J (2016) Monitoring the future national survey results on drug use, 1975–2015, secondary school students. Ann Arbor Inst Soc Res Univ Michigan 1:636

    Google Scholar 

  41. Bachman JG, Johnston LD, O’Malley PM, Schulenberg J, Miech R (2015) The monitoring the future project after four decades: design and procedures. Institute for Social Research, University of Michigan, Ann Arbor, MI

  42. Maslowsky J, Schulenberg JE, O’Malley PM, Kloska DD (2013) Depressive symptoms, conduct problems, and risk for polysubstance use among adolescents: results from US national surveys. Ment Heal Subst Use 7(2):157–169. https://doi.org/10.1080/17523281.2013.786750

    Article  Google Scholar 

  43. Maslowsky J, Schulenberg JE, Zucker RA (2014) Influence of conduct problems and depressive symptomatology on adolescent substance use: developmentally proximal versus distal effects. Dev Psychol 50(4):1179–1189. https://doi.org/10.1037/a0035085

    Article  PubMed  Google Scholar 

  44. Merline A, Jager J, Schulenberg JE (2008) Adolescent risk factors for adult alcohol use and abuse: stability and change of predictive value across early and middle adulthood. Addiction 103:84–99. https://doi.org/10.1111/j.1360-0443.2008.02178.x

    PubMed Central  Article  PubMed  Google Scholar 

  45. Schulenberg J, Zarrett N (2006) Mental health during emerging adulthood: continuity and discontinuity in courses, causes, and functions. In: Arnett JJ, Tanner JL (eds) Emerging adults in America: coming of age in the 21st century. American Psychological Association, Washington, pp 135–172

    Chapter  Google Scholar 

  46. American Psychiatric Association (2013) Diagnostic and statistical manual of mental disorders: DSM-5. American Psychiatric Association. https://doi.org/10.1176/appi.books.9780890425596.744053

  47. Radloff LS. The CES-D, Scale (1977) A self-report depression scale for research in the general population. Appl Psychol Meas. https://doi.org/10.1177/014662167700100306

    Article  Google Scholar 

  48. Yang Y, Land K (2013) Mixed effects models: hierarchical APC-cross-classified random effects models (HAPC-CCREM), Part II: Advanced analyses. In: Yang Y, Land K (eds) Age-period-cohort analysis: new models, methods and empirical applications. Chapman & Hall/CRC Interdisciplinary Statistics, Boca Raton, pp 231–284

    Chapter  Google Scholar 

  49. Yang Y, Land KC (2006) A mixed models approach to age-period-cohort analysis of repeated cross-section surveys: trends in verbal test scores. In: Stolzenberg RM (ed) Sociological methodology, vol 36. Blackwell Publishing, Boston

    Google Scholar 

  50. Olfson M, Druss BG, Marcus SC (2015) Trends in mental health care among children and adolescents. N Engl J Med 372(21):2029–2038. https://doi.org/10.1056/NEJMsa1413512

    Article  PubMed  Google Scholar 

  51. CDC (2016) Trends in the prevalence of alcohol use national YRBS: 1991–2015. https://www.cdc.gov/healthyyouth/data/yrbs/pdf/trends/2015_us_alcohol_trend_yrbs.pdf. Accessed 5 Oct 2017

  52. CDC (2016) Trends in the prevalence of marijuana, cocaine, and other illegal drug use national YRBS: 1991–2015. https://www.cdc.gov/healthyyouth/data/yrbs/pdf/trends/2015_us_drug_trend_yrbs.pdf. Accessed 6 Oct 2017

  53. Martins SS, Segura LE, Santaella-Tenorio J et al (2017) Prescription opioid use disorder and heroin use among 12–34 year-olds in the United States from 2002 to 2014. Addict Behav. https://doi.org/10.1016/j.addbeh.2016.08.033

    PubMed Central  Article  PubMed  Google Scholar 

  54. Twenge JM, Park H (2017) The decline in adult activities among US adolescents, 1976–2016. Child Dev. https://doi.org/10.1111/cdev.12930

    Article  PubMed  Google Scholar 

  55. Figueredo A, Vasquez G, Brumbach BH et al (2006) Consilience and life history theory: from genes to brain to reproductive strategy. Dev Rev 26(2):243–275

    Article  Google Scholar 

  56. Mittal C, Griskevicius V (2014) Sense of control under uncertainty depends on people’s childhood environment: a life history theory approach. J Pers Soc Psychol 107(4):621–637. https://doi.org/10.1037/a0037398

    Article  PubMed  Google Scholar 

  57. Twenge JM (2017) Have smartphones destroyed a generation? The Atlantic. https://www.theatlantic.com/magazine/archive/2017/09/has-the-smartphone-destroyed-a-generation/534198/. Accessed 23 Mar 2019

  58. Przybylski AK, Weinstein N (2017) A large-scale test of the goldilocks hypothesis: quantifying the relations between digital-screen use and the mental well-being of adolescents. Psychol Sci. https://doi.org/10.1177/0956797616678438

    Article  PubMed  Google Scholar 

  59. Odgers C (2018) Smartphones are bad for some teens, not all. Nature. https://doi.org/10.1038/d41586-018-02109-8

    PubMed Central  Article  PubMed  Google Scholar 

  60. Kessel Schneider S, O’Donnell L, Smith E (2015) Trends in cyberbullying and school bullying victimization in a regional census of high school students, 2006–2012. J Sch Health 85(9):611–620. https://doi.org/10.1111/josh.12290

    Article  PubMed  Google Scholar 

  61. Bauman S, Toomey RB, Walker JL (2013) Associations among bullying, cyberbullying, and suicide in high school students. J Adolesc 36(2):341–350. https://doi.org/10.1016/j.adolescence.2012.12.001

    Article  PubMed  Google Scholar 

  62. Bonanno RA, Hymel S (2013) Cyber bullying and internalizing difficulties: above and beyond the impact of traditional forms of bullying. J Youth Adolesc 42(5):685–697. https://doi.org/10.1007/s10964-013-9937-1

    Article  PubMed  Google Scholar 

  63. Tsuno N, Besset A, Ritchie K (2005) Sleep and depression. J Clin Psychiatry 66(10):1254–1269. https://doi.org/10.4088/JCP.v66n1008

    Article  PubMed  Google Scholar 

  64. Johnson MK, Staff J, Patrick ME, Schulenberg JE (2017) Adolescent adaptation before, during and in the aftermath of the Great Recession in the USA. Int J Psychol 52(1):9–18. https://doi.org/10.1002/ijop.12389

    Article  PubMed  Google Scholar 

  65. Elder GH (1999) Children of the great depression: social change in life experience. Westview, Boulder

    Google Scholar 

  66. Krieger N, Kiang MV, Kosheleva A, Waterman PD, Chen JT, Beckfield J (2015) Age at menarche: 50-year socioeconomic trends among US-born black and white women. Am J Public Heal 105(2):388–397. https://doi.org/10.2105/AJPH.2014.301936

    Article  Google Scholar 

  67. Hasin DS, Goodwin RD, Stinson FS, Grant BF (2005) Epidemiology of major depressive disorder: results from the National Epidemiologic Survey on Alcoholism and Related Conditions. Arch Gen Psychiatry 62(10):1097–1106. https://doi.org/10.1001/archpsyc.62.10.1097

    Article  PubMed  Google Scholar 

  68. WHO (2008) The global burden of disease: 2004 update, Table A2: burden of disease in DALYs by cause, sex and income group in WHO regions, estimates for 2004. The World Health Organization, Geneva. http://www.who.int/healthinfo/global_burden_disease/GBD_report_2004update_AnnexA.pdf. Accessed 23 Mar 2019

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Acknowledgements

Monitoring the Future study is funded by National Institute on Drug Abuse Grant R01001411.

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Keyes, K.M., Gary, D., O’Malley, P.M. et al. Recent increases in depressive symptoms among US adolescents: trends from 1991 to 2018. Soc Psychiatry Psychiatr Epidemiol 54, 987–996 (2019). https://doi.org/10.1007/s00127-019-01697-8

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Keywords

  • Depression
  • Age–period–cohort
  • Adolescent
  • Suicide
  • Time trend