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Prospective Relationship Between Obsessive–Compulsive and Depressive Symptoms During Multimodal Treatment in Pediatric Obsessive–Compulsive Disorder

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Abstract

The present study examined the prospective relationship between obsessive–compulsive and depressive symptoms during a multimodal treatment study involving youth with obsessive–compulsive disorder (OCD). Participants included fifty-six youth, aged 7–17 years (M = 12.16 years) who were enrolled in a two-site randomized controlled pharmacological and cognitive behavioral therapy treatment trial. Obsessive–compulsive severity was measured using the Children’s Yale-Brown Obsessive–Compulsive Scale, and depressive symptoms were rated using the Children’s Depression Rating Scale-Revised. Multi-level modeling analyses indicated that, on average over the course of treatment, variable and less severe obsessive–compulsive symptoms significantly predicted a decrease in depressive symptoms. Additionally, week-to-week fluctuations in OCD severity did not significantly predict weekly changes in depressive symptom severity. Level of baseline depressive symptom severity did not moderate these relationships. Findings suggest that when treating youth with OCD with co-occurring depression, therapists should begin by treating obsessive–compulsive symptoms, as when these are targeted effectively, depressive symptoms diminish as well.

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References

  1. Douglass HM, Moffitt TE, Dar R, McGee R, Silva P (1995) Obsessive-compulsive disorder in a birth cohort of 18-year-olds: prevalence and predictors. J Am Acad Child Adolesc Psychiatry 34:1424–1431

    Article  PubMed  Google Scholar 

  2. Grenier S, Préville M, Boyer R, O’Connor K (2009) Prevalence and correlates of obsessive–compulsive disorder among older adults living in the community. J Anxiety Disord 23:858–865

    Article  PubMed  Google Scholar 

  3. Abramowitz JS, Franklin ME, Schwartz SA, Furr JM (2003) Symptom presentation and outcome of cognitive-behavioral therapy for obsessive-compulsive disorder. J Consult Clin Psychol 71:1049–1057

    Article  PubMed  Google Scholar 

  4. Lack CW, Storch EA, Keeley ML, Geffken GR, Ricketts ED, Murphy TK et al (2009) Quality of life in children and adolescents with obsessive-compulsive disorder: base rates, parent–child agreement, and clinical correlates. Soc Psychiatry Psychiatr Epidemiol 44:935–942

    Article  PubMed  Google Scholar 

  5. Piacentini J, Bergman RL, Keller M, McCracken J (2003) Functional impairment in children and adolescents with obsessive-compulsive disorder. J Child Adolesc Psychopharmacol 13:61–69

    Article  Google Scholar 

  6. Murray CJ, López AD (1996) The global burden of disease: a comprehensive assessment of mortality and disability from diseases, injuries, and risk factors in 1990 and projected to 2020. Published by the Harvard School of Public Health on behalf of the World Health Organization and the World Bank

  7. Geller D, Biederman J, Griffin S, Jones J, Lefkowitz TR (1996) Comorbidity of juvenile obsessive-compulsive disorder with disruptive behavior disorders. J Am Acad Child Adolesc Psychiatry 35:1637–1646

    Article  PubMed  Google Scholar 

  8. Geller D, Biederman J, Faraone S, Agranat A, Cradock K, Hagermoser L et al (2001) Developmental aspects of obsessive compulsive disorder: findings in children, adolescents, and adults. J Nerv Ment Dis 189:471–477

    Article  PubMed  Google Scholar 

  9. Swedo SE, Rapoport JL, Leonard H, Lenane M, Cheslow D (1989) Obsessive-compulsive disorder in children and adolescents. Clinical phenomenology of 70 consecutive cases. Arch Gen Psychiatry 46:335–341

    Article  PubMed  Google Scholar 

  10. Storch EA, Lewin AB, Larson MJ, Geffken GR, Murphy TK, Geller DA (2012) Depression in youth with obsessive-compulsive disorder: clinical phenomenology and correlates. Psychiatry Res 196:83–89

    Article  PubMed  Google Scholar 

  11. Storch EA, Merlo LJ, Larson MJ, Geffken GR, Lehmkuhl HD, Jacob ML et al (2008) Impact of comorbidity on cognitive-behavioral therapy response in pediatric obsessive-compulsive disorder. J Am Acad Child Adolesc Psychiatry 47:583–592

    Article  PubMed  Google Scholar 

  12. Peris TS, Bergman RL, Asarnow JR, Langley A, McCracken JT, Piacentini J (2010) Clinical and cognitive correlates of depressive symptoms among youth with obsessive compulsive disorder. J Clin Child Adolesc 39:616–626

    Article  Google Scholar 

  13. Murphy TK, Segarra A, Storch EA, Goodman WK (2008) SSRI adverse events: how to monitor and manage. Int Rev Psychiatry 20:203–208

    Article  PubMed  Google Scholar 

  14. Dopheide JA (2006) Recognizing and treating depression in children and adolescents. Am J Health-Syst Ph 63:233–243

    Article  Google Scholar 

  15. Stengler-Wenzke K, Trosbach J, Dietrich S, Angermeyer MC (2004) Experience of stigmatization by relatives of patients with obsessive compulsive disorder. Arch Psychiatr Nurs 18:88–96

    Article  PubMed  Google Scholar 

  16. Gazelle H, Ladd GW (2003) Anxious solitude and peer exclusion: a diathesis-stress model of internalizing trajectories in childhood. Child Dev 74:257–278

    Article  PubMed  Google Scholar 

  17. Reimold M, Knobel A, Rapp MA et al (2011) Central serotonin transporter levels are associated with stress hormone response and anxiety. Psychopharmacology 213:563–572

    Article  PubMed Central  PubMed  Google Scholar 

  18. Morilak DA, Frazer A (2004) Antidepressants and brain monoaminergic systems: a dimensional approach to understanding their behavioural effects in depression and anxiety disorders. Int J Neuropsychopharmacol 7:193–218

    Article  PubMed  Google Scholar 

  19. Morein-Zamir S, Papmeyer M, Gillan CM, Crockett MJ, Fineberg NA, Sahakian BJ et al (2012) Punishment promotes response control deficits in obsessive-compulsive disorder: evidence from a motivational go/no-go task. Psychol Med 1–10

  20. Miller WR, Seligman MEP (1976) Learned helplessness, depression and the perception of reinforcement. Behav Res Ther 14:7–17

    Article  PubMed  Google Scholar 

  21. Anholt GE, Aderka IM, Van Balkom AJLM, Smit JH, Hermesh H, De Haan E et al (2011) The impact of depression on the treatment of obsessive–compulsive disorder: results from a 5-year follow-up. J Affect Disord 135:201–207

    Article  PubMed  Google Scholar 

  22. Williams TI, Salkovskis PM, Forrester EA, Allsopp MA (2002) Changes in symptoms of OCD and appraisal of responsibility during cognitive behavioural treatment: a pilot study. Behav Cogn Psychother 30:69–78

    Google Scholar 

  23. Farrell L, Barrett P, Piacentini J (2006) Obsessive–compulsive disorder across the developmental trajectory: clinical correlates in children, adolescents and adults. Behav Change 23:103–120

    Article  Google Scholar 

  24. Abramowitz JS, Franklin ME, Street GP, Kozak MJ, Foa EB (2000) Effects of comorbid depression on response to treatment for obsessive-compulsive disorder. Behav Ther 31:517–528

    Article  Google Scholar 

  25. Kaufman J, Birmaher B, Brent D, Rao U, Flynn C, Moreci P et al (1997) Schedule for affective disorders and schizophrenia for school-age children-present and lifetime version (K-SADS-PL): initial reliability and validity data. J Am Acad Child Adolesc Psychiatry 36:980–988

    Article  PubMed  Google Scholar 

  26. Poznanski EO, Mokros HB (1996) Children’s depression rating scale-revised (CDRS-R) manual. Western Psychological Services, Los Angeles

    Google Scholar 

  27. Scahill L, Riddle MA, McSwiggin-Hardin M, Ort SI, King RA, Goodman WK et al (1997) Children’s yale-brown obsessive compulsive scale: reliability and validity. J Am Acad Child Adolesc Psychiatry 36:844–852

    Article  PubMed  Google Scholar 

  28. Gallant J, Storch EA, Merlo LJ, Ricketts ED, Geffken GR, Goodman WK et al (2008) Convergent and discriminant validity of the children’s yale-brown obsessive compulsive scale-symptom checklist. J Anxiety Disord 22:1369–1376

    Article  PubMed  Google Scholar 

  29. Storch EA, Murphy TK, Adkins JW, Lewin AB, Geffken GR, Johns NB et al (2006) The children’s yale-brown obsessive–compulsive scale: psychometric properties of child- and parent-report formats. J Anxiety Disord 20:1055–1070

    Article  PubMed  Google Scholar 

  30. Storch E, Murphy TK, Geffken GR, Soto O, Sajid M, Allen P et al (2004) Psychometric evaluation of the children’s yale-brown obsessive-compulsive scale. Psychiatry Res 129:91–98

    Article  PubMed  Google Scholar 

  31. Jain S, Carmody TJ, Trivedi MH, Hughes C, Bernstein IH, Morris DW et al (2007) A psychometric evaluation of the CDRS and MADRS in assessing depressive symptoms in children. J Am Acad Child Adolesc Psychiatry 46:1204–1212

    Article  PubMed Central  PubMed  Google Scholar 

  32. Keller F, Grieb J, Ernst M, Spröber N, Fegert JM, Kölch M (2011) Children’s Depression Rating Scale–Revised (CDRS-R): entwicklung einer deutschen version und psychometrische gütekriterien in einer klinischen stichprobe. Z Kinder Jugendpsychiatr Psychother 39:179–185

    Article  PubMed  Google Scholar 

  33. Mayes TL, Bernstein IH, Haley CL, Kennard BD, Emslie GJ (2010) Psychometric properties of the children’s depression rating scale–revised in adolescents. J Child Adolesc Psychopharmacol 20:513–516

    Article  PubMed  Google Scholar 

  34. Birmaher B, Ehmann M, Axelson DA et al (2009) Schedule for affective disorders and schizophrenia for school-age children (K-SADS-PL) for the assessment of preschool children: a preliminary psychometric study. J Psychiatr Res 43:680–686

    Article  PubMed Central  PubMed  Google Scholar 

  35. Lauth B, Arnkelsson GB, Magnússon P, Skarphéðinsson GÁ, Ferrari P, Pétursson H (2010) Validity of K-SADS-PL (Schedule for affective disorders and schizophrenia for school-age children–present and lifetime version) depression diagnoses in an adolescent clinical population. Nord J Psychiatry 64:409–420

    Article  PubMed  Google Scholar 

  36. Singer JD, Willett JB (2003) Applied longitudinal data analysis: modeling change and event occurrence. Oxford University Press, Oxford

    Book  Google Scholar 

  37. Laird NM, Ware JH (1982) Random-effects models for longitudinal data. Biometrics 38:963–974

    Article  PubMed  Google Scholar 

  38. Bryk AS, Raudenbush SW (1992) Hierarchical linear models: applications and data analysis methods. SAGE, Newbury Park

    Google Scholar 

  39. Goldstein H (1995) Multilevel statistical models, 2nd edn. Oxford Press, New York

    Google Scholar 

  40. Snijders TAB, Bosker RJ (1999) Multilevel analysis: an introduction to basic and advanced multilevel modeling. Sage Publ, London

    Google Scholar 

  41. Burr JA, Nesselroade JR (1990) Change measurement. In: von Eye A (ed) New statistical methods in developmental research. Academic Press, New York, pp 3–34

    Google Scholar 

  42. Willett JB (1990) Measuring change: the difference score and beyond. In: Walberg HJ, Haertel GD (eds) The international encyclopedia of education evaluation. Pergamon, Oxford, pp 632–637

    Google Scholar 

  43. Lin H, Yeh CB, Peterson BS, Scahill L, Grantz H, Findley DB et al (2002) Assessment of symptom exacerbations in a longitudinal study of children with Tourette’s syndrome or obsessive-compulsive disorder. J Am Acad Child Adolesc Psychiatry 41:1070–1077

    Article  PubMed  Google Scholar 

  44. Cole DA, Nolen-Hoeksema S, Girgus J, Paul G (2006) Stress exposure and stress generation in child and adolescent depression: a latent trait-state-error approach to longitudinal analyses. J Abnorm Psychol 115:40–51

    Article  PubMed  Google Scholar 

  45. Cahill SP, Foa EB, Hembree EA, Marshall RD, Nacash N (2006) Dissemination of exposure therapy in the treatment of posttraumatic stress disorder. J Trauma Stress 19:597–610

    Article  PubMed  Google Scholar 

  46. Olatunji BO, Deacon BJ, Abramowitz JS (2009) The cruelest cure? Ethical issues in the implementation of exposure-based treatments. Cogn Behav Pract 16:172–180

    Article  Google Scholar 

  47. Ford JG, Howerton MW, Lai GY et al (2008) Barriers to recruiting underrepresented populations to cancer clinical trials: a systematic review. Cancer 112:228–242

    Article  PubMed  Google Scholar 

  48. Hall WD (1999) Representation of blacks, women, and the very elderly (aged > or = 80) in 28 major randomized clinical trials. Ethn Dis 9:333–340

    PubMed  Google Scholar 

  49. Heiat A, Gross CP, Krumholz HM (2002) Representation of the elderly, women, and minorities in heart failure clinical trials. Arch Intern Med 162:1682–1688

    Article  PubMed  Google Scholar 

  50. Sateren WB, Trimble EL, Abrams J et al (2002) How sociodemographics, presence of oncology specialists, and hospital cancer programs affect accrual to cancer treatment trials. J Clin Oncol 20:2109–2117

    Article  PubMed  Google Scholar 

  51. Swanson GM, Ward AJ (1995) Recruiting minorities into clinical trials: toward a participant-friendly system. J Natl Cancer Inst 87:1747–1759

    Article  PubMed  Google Scholar 

  52. Swanson GM, Bailar JC 3rd (2002) Selection and description of cancer clinical trials participants–science or happenstance? Cancer 95:950–959

    Article  PubMed  Google Scholar 

  53. Williams M, Powers M, Yun Y-G, Foa E (2010) Minority participation in randomized controlled trials for obsessive-compulsive disorder. J Anxiety Disord 24:171–177

    Article  PubMed Central  PubMed  Google Scholar 

  54. Taylor S, Asmundson GJG (2008) Internal and external validity in clinical research. In: McKay D (ed) Handbook of research methods in abnormal and clinical psychology. Sage Publications, Los Angeles, pp 23–34

    Google Scholar 

  55. Almeida OP, Pirkis J, Kerse N, Sim M, Flicker L, Snowdon J et al (2012) Socioeconomic disadvantage increases risk of prevalent and persistent depression in later life. J Affect Disord 138:322–331

    Article  PubMed  Google Scholar 

  56. Walsh SD, Levine SZ, Levav I (2011) The association between depression and parental ethnic affiliation and socioeconomic status: a 27-year longitudinal US community study. Soc Psychiatry Psychiatr Epidemiol 47:1153–1158

    Article  PubMed  Google Scholar 

  57. Wang JL, Schmitz N, Dewa CS (2010) Socioeconomic status and the risk of major depression: the Canadian National Population Health Survey. J Epidemiol Community Health 64:447–452

    Article  PubMed  Google Scholar 

  58. Türksoy N, Tükel R, Özdemir Ö, Karali A (2002) Comparison of clinical characteristics in good and poor insight obsessive–compulsive disorder. J Anxiety Disord 16:413–423

    Article  PubMed  Google Scholar 

Download references

Acknowledgments

This research was supported by grant 5UO1 MH078594 from the NIMH. The authors thank the study coordinators, all staff members who contributed to data collection, the families for their participation, and Drs. Ayesha Lall, Jane Mutch and Omar Rahman for their contribution to the study interventions.

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Authors and Affiliations

  1. Division of Medical Psychology, Department of Psychiatry, University of Florida, P.O. Box 100234, 1600 S Archer Rd, Gainesville, FL, 32611, USA

    Johanna M. Meyer, Joseph P. H. McNamara, Adam M. Reid, Gary R. Geffken, Dana M. Mason & Regina Bussing

  2. Department of Clinical & Health Psychology, University of Florida, P.O. Box 100165, Gainesville, FL, 32610, USA

    Adam M. Reid, Gary R. Geffken & Regina Bussing

  3. Department of Pediatrics, University of South Florida, 880 6th Street South 4th Floor, Box 7523, St. Petersburg, FL, 33701, USA

    Eric A. Storch & Tanya K. Murphy

  4. Department of Psychiatry & Behavioral Neurosciences, University of South Florida, 3515 E. Fletcher Ave., Tampa, FL, 33613, USA

    Eric A. Storch & Tanya K. Murphy

  5. Department of Pediatrics, University of Florida, P.O. Box 100296, Gainesville, FL, 32610, USA

    Regina Bussing

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  1. Johanna M. Meyer
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Correspondence to Adam M. Reid.

Appendix: Additional Description of MLM design

Appendix: Additional Description of MLM design

Each model throughout the analysis produces a −2 log likelihood (−2LL). This is a nonstandardized number that shows the amount of overall variance there is to be explained. With each additional model, the −2LL is expected to decrease, which means the model is improving. The Bayesian Information Criterion (BIC) serves a similar purpose as the −2LL, but in addition to denoting the amount of overall variance to be explained, it also takes into account the number of parameters that are being utilized in the model. The BIC is also expected to decrease with each subsequent model. Standardized (Z-score) parameterizations were used for all models, enabling comparison between predictors. This puts each parameter in a standard deviation scaling, where an estimate of 1 is interpreted as a one standard deviation change in the dependent variable (depressive symptoms).

Unconditional Means Model (UMM)

The Unconditional Means Model (UMM) is the worst fitting model, which is used to investigate whether there is significant between and within subject variance to be explained in depression severity. The between intercept and within residual were .650 (p < .001) and .361 (p < .001) respectively, denoting additional variability to be explained by further analysis. The −2 Log Likelihood (−2LL) for the unconditional means model (null model) was 1216.868.

Model A- Linear Time

Depressive symptoms over the course of treatment were estimated to identify any significant trajectories of depression that need to be controlled for in any subsequent modeling. When testing a linear trend in time, the model improved with a −2LL decreasing from 1216.868 in the null model to 1160.300 in the new model (χ² (1, N = 55) = 56.57, p < .001). Due to the uniformity of the way time was modeled across subjects, the average linear growth trend could still be modeled but not appropriately tested for effect size (i.e., no between subject variability). However, to enhance interpretation, a between-subjects linear growth model was still estimated in a separate analysis. On average, a significant negative slope in depressive symptomology was observed (p < .001), with significant positive variability in this slope (p < .001) that suggests a portion of the sample saw a much smaller decrease in depressive symptoms over time than on average. Linear time explained a substantial amount of the Random Effects (21.1 %). Table 1 does not include the between-subjects linear growth model that was estimated to enhance interpretability.

Model B- Quadratic Time

The next model tested a quadratic trend in depressive symptoms after orthogonalizing from any linear trends, which was done in order to minimize multicollinearity by controlling for linear time. The −2LL decreased once again from 1160.300 in the linear model to 1144.758 in the quadratic model (χ² (1, N = 55) = 15.54, p < .001). Similarly to the linear model, a between-subject quadratic growth model (nested within the negative linear slope described above) was estimated to increase interpretability but found a non-significant positive quadratic growth trend (p = .650). However, significant positive quadratic variability in slope was observed (p < .01). The quadratic model explained an additional 10.9 % of the within subject variance. However, after the addition of the best fitting error structure in Model F, the quadratic effect was no longer significant and thus will not be interpreted further. To summarize the growth modeling described above, on average, depressive symptom severity decreased uniformly across treatment (between subject linear slope). A few subjects saw much shallower linear decrease in depressive symptoms over time (within subject linear).

Model C- Age

A static covariate of age was nested within the linear and quadratic models and the age model improved the −2LL from 1144.758 to 1132.401 (χ² (1, N = 55) = 12.36, p < .001). For the Fixed Effects, age accounted for an additional 22.6 % of the variance. The beta weight for this model after the adjusted error structure was .339 (p < .001) indicating that a one standard deviation increase in age predicted a .339 standard deviation increase in depressive symptoms on average.

Model F- Best Fitting Error Structures

The initial six models were conducted under the SPSS default assumptions for Random and Repeated Error Structures (independence of errors and variance components). The final −2LL for Model E was 1015.693 and thus, all possible error structure combinations, which theoretically were appropriate for this study design, were tested. The best fitting random error structure identified was variance components (assumes homoscedasticity of the intercept-slope relationship throughout the study). The best fitting repeated measures error structure identified was autoregressive error (AR1), which assumes homoscedasticity, and inter-occasion correlations that are reduced, exponentially, by the time lag between them. Using these two error structures, our final −2LL was reduced to 1007.113, which represented the best fitting model compared to any other combination tested. The final model with the best fitting error structures did not alter the pattern of significance (with the exception of the random effect of quadratic time which became insignificant), but was a significant improvement from the unadjusted final model (χ² (1, N = 55) = 8.58, p < .01).

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Meyer, J.M., McNamara, J.P.H., Reid, A.M. et al. Prospective Relationship Between Obsessive–Compulsive and Depressive Symptoms During Multimodal Treatment in Pediatric Obsessive–Compulsive Disorder. Child Psychiatry Hum Dev 45, 163–172 (2014). https://doi.org/10.1007/s10578-013-0388-4

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