Evidence-based assessment/Preparation phase

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The preparation phase is a great opportunity to work smarter, and not harder. We can think about what are the most common issues among the reasons people come to the clinic, and use that perspective as a way to filter and organize our assessments and actions.

In the preparation phase, we:

  • Identify and plan for the most common issues,
  • Make sure that we have the best cost-effective assessments for them ready to use (and often built into our clinic's core battery), and
  • Benchmark prevalence rates so that we have a sense of what is typical, and whether our local pattern differs in any interesting ways.

Identifying and planning for the most common issues allow us to avoid cognitive heuristics and errors that may cause us to miss out important diagnoses.

Benchmarking prevalence rates allows us to get a sense of how often we are seeing a particular disorder in this setting. Second, by comparing the local estimates to external benchmarks helps us to get a sense of whether we might want to invest in additional assessment methods, or reflect on reasons why our practice might be different from other settings.

Tables and Figures[edit | edit source]

Base rates for transdiagnostic comparison[edit | edit source]

Prevalence Table
Condition Chapter CDC DAU SDI General Population
ADHD 4 11%[1] or 6.8%[2] 23%[3] 38%[3]
Conduct Problems 5 3.5%[2] 17% CD[3], 37% ODD[3] 25% CD[3], 38% ODD[3] 1.5-3.2% antisocial[4]
Mood Disorders 17% MDD[3], 10% dysthymia[3] 26% MDD[3], 8% dysthymia[3]
Pediatric Depression 6 2.1%[2] --
Adult Depression 7 --
Late Life Depression 8 -- -- --
Adult Bipolar 9 -- -- -- 2.9%[5], 1% bipolar I[6], 2-4% spectrum[6]
Pediatric Bipolar mean= 1.8% (95% CI, 1.1%–3.0%)[7], bipolar I (mean =1.2%; 95% CI, 0.7%–1.9%)[7]
Self-Injurious Thought and Behavior 10
Anxiety 3.0%[2]
Child and Adolescent 11 8%(separation anxiety)[3] 18%(separation anxiety)[3]
Specific & Social Phobia 12 -- 6% (social)[3], 6% (specific)[3] 20% (social)[3], 15% (specific)[3] 9% social [5]& 19% specific[5]
Panic & Agoraphobia 13 -- 12% (panic)[3] 11% (panic)[3] 2.4% agora[5] & 2.3%panic[5]
Generalized Anxiety Disorder 14 -- 5%[3] 10%[3] 2.2%[5]
Obsessive-Compulsive Disorder 15 -- 9%[3] 12%[3] 1-2%(Ch. 15)[8]
Post-Traumatic Stress Disorder 16 -- 3%[3] 9%[3] 5%[5], 3.6%[9]
Substance Use Disorders 17 4.7%[2] 14%[3] 17%[3]
Alcohol Use Disorder 18 4.2%[2] 10%[3] 13%[3]
Gambling Disorders 19 -- -- --
Schizophrenia 20 -- -- -- 0.014% child (Thomsen 1996)[8]
Personality Disorders 21 -- -- --
Cluster A 3-6% life[4]
Cluster B (antisocial separate) 1-5%% life[4]
Cluster C 2-4% life[4]
Couple Distress 22 -- -- -- ~50% divorce rate(Ch. 22)[8]
Sexual Dysfunction 23 -- -- --
Eating Disorders 24 -- -- --
Sleep/Wake Disorders 25 -- -- --
Pain in Children and Adolescents 26 -- -- --
Chronic Pain in Adults 27 -- -- --


  • Table from Hunsley GATW 2nd edition (Youngstrom & Van Meter 2016)
  • (Perou et al., 2013) is epidemeological
  • (Rettew et al., 2009) is derived from an outpatient clinic
  • (Roth & Fonagy, 2005)
  • Epidemiological rates refer to general population, not treatment seeking samples, and so often represent a lower bound of what might be expected at a clinic.
  • DAU= diagnosis as usual
  • SDI= standard diagnostic interview

Notes[edit | edit source]

What is the right level of detail for for the list of topics? There is a recurring tension between "lumping" -- bunching things into fewer groups to keep things simple, at the expense of ignoring differences -- versus "splitting" and focusing more on the features that are distinctive.

At one extreme is the view that at heart, it's all one issue: General psychopathology or emotional distress. At the other extreme are classification systems like the DSM, which literally include several hundred different categories, or other systems that carve off subgroups based on etiology, treatment response, or some other factor.

There is not a clear "right answer" yet.

Our guiding principle is start simple, and add complexity when it clearly could inform clinical decision-making. The 3 Ps (Prediction, Prescription, Process) is a simple heuristic for quickly checking whether this is a distinction that is likely to matter clinically.

The big differences in treatment prescription are between externalizing and internalizing disorders[10], or perhaps between subtypes of conduct disorder versus other disruptive behavior disorders[11], or unipolar versus bipolar mood disorders[12][13]. The distinction between oppositional defiant disorder or disruptive behavior disorder not otherwise specified does not change the choice of treatment, so we do not lose predictive or prescriptive information by lumping them together[14][15]. If there is no electronic medical record, then some sort of random sampling of charts or cases can provide a good snapshot of services – perhaps drawing 5 or 10 percent of cases at a clinic from each month to get a picture of services throughout the year including the variation in referral patterns between the school year and summer.

Annotated bibliography[edit | edit source]

Click here for annotated bibliography
  • Gray, G. E. (2004). Evidence-based psychiatry. Washington, D.C.: American Psychiatric Publishing, Inc.
  • Meehl, P. E. (1954). Clinical versus statistical prediction: A theoretical analysis and a review of the evidence. Minneapolis, MN: University of Minnesota Press.
  • Straus, S. E., Glasziou, P., Richardson, W. S., & Haynes, R. B. (2011). Evidence-based medicine: How to practice and teach EBM (4th ed.). New York, NY: Churchill Livingstone.

References[edit | edit source]

  1. "ADHD Data & Statistics". Centers for Disease Control and Prevention. 2018-03-21. Retrieved 2018-06-25.
  2. 2.0 2.1 2.2 2.3 2.4 2.5 Perou, Ruth; Bitsko, Rebecca H.; Blumberg, Stephen J.; Pastor, Patricia; Ghandour, Reem M.; Gfroerer, Joseph C.; Hedden, Sarra L.; Crosby, Alex E. et al. (2013-05-17). "Mental health surveillance among children--United States, 2005-2011". MMWR supplements 62 (2): 1–35. ISSN 2380-8942. PMID 23677130. https://www.ncbi.nlm.nih.gov/pubmed/23677130. 
  3. 3.00 3.01 3.02 3.03 3.04 3.05 3.06 3.07 3.08 3.09 3.10 3.11 3.12 3.13 3.14 3.15 3.16 3.17 3.18 3.19 3.20 3.21 3.22 3.23 3.24 3.25 3.26 3.27 Rettew, David C.; Lynch, Alicia Doyle; Achenbach, Thomas M.; Dumenci, Levent; Ivanova, Masha Y. (2009-09). "Meta-analyses of agreement between diagnoses made from clinical evaluations and standardized diagnostic interviews". International Journal of Methods in Psychiatric Research 18 (3): 169–184. doi:10.1002/mpr.289. ISSN 1049-8931. http://dx.doi.org/10.1002/mpr.289. 
  4. 4.0 4.1 4.2 4.3 Anthony., Roth (2005). What works for whom? : a critical review of psychotherapy research. Fonagy, Peter, 1952- (2nd ed.). New York: Guilford Press. ISBN 1572306505. OCLC 55877858. https://www.worldcat.org/oclc/55877858. 
  5. 5.0 5.1 5.2 5.3 5.4 5.5 5.6 Kessler, R. C., Avenevoli, S., Costello, E. J., Georgiades, K., Green, J. G., Gruber, M. J., . . . Merikangas, K. R. (2012). Prevalence, persistence, and sociodemographic correlates of DSM-IV disorders in the National Comorbidity Survey Replication Adolescent Supplement. Archives of General Psychiatry, 69(4), 372-380. doi:10.1001/archgenpsychiatry.2011.160
  6. 6.0 6.1 Merikangas, Kathleen R.; Akiskal, Hagop S.; Angst, Jules; Greenberg, Paul E.; Hirschfeld, Robert M. A.; Petukhova, Maria; Kessler, Ronald C. (2007-05-01). "Lifetime and 12-Month Prevalence of Bipolar Spectrum Disorder in the National Comorbidity Survey Replication". Archives of General Psychiatry 64 (5): 543. doi:10.1001/archpsyc.64.5.543. ISSN 0003-990X. PMID 17485606. PMC PMC1931566. http://archpsyc.jamanetwork.com/article.aspx?doi=10.1001/archpsyc.64.5.543. 
  7. 7.0 7.1 Van Meter, Anna R.; Moreira, Ana Lúcia R.; Youngstrom, Eric A. (2011-05-31). "Meta-Analysis of Epidemiologic Studies of Pediatric Bipolar Disorder". The Journal of Clinical Psychiatry 72 (09): 1250–1256. doi:10.4088/jcp.10m06290. ISSN 0160-6689. http://article.psychiatrist.com/?ContentType=START&ID=10007438. 
  8. 8.0 8.1 8.2 "Hunsley, Lee: Introduction to Clinical Psychology: An Evidence-Based Approach, 2nd Edition - Student Companion Site". bcs.wiley.com. Retrieved 2018-07-02.
  9. (US), Office of the Surgeon General; (US), Center for Mental Health Services; (US), National Institute of Mental Health (August 2001). "[Table], Table 1-1. Prevalence rates (1-year) of mental disorders: Best estimates for adults, ages 18-54". Retrieved 2018-06-25.
  10. Chorpita, B. F., Rotheram-Borus, M. J., Daleiden, E. L., Bernstein, A., Cromley, T., Swendeman, D., & Regan, J. (2011). The Old Solutions Are the New Problem: How Do We Better Use What We Already Know About Reducing the Burden of Mental Illness? Perspectives on Psychological Science, 6, 493-497. doi:10.1177/1745691611418240
  11. Frick, P. J. (2012). Developmental pathways to conduct disorder: implications for future directions in research, assessment, and treatment. Journal of Clinical Child and Adolescent Psychology, 41, 378-389. doi:10.1080/15374416.2012.664815
  12. Fristad, M. A., & Macpherson, H. A. (2014). Evidence-based psychosocial treatments for child and adolescent bipolar spectrum disorders. Journal of Clinical Child and Adolescent Psychology, 43, 339-355. doi:10.1080/15374416.2013.822309
  13. Stark, K. D., Swearer, S., Delaune, M., Knox, L., & Winter, J. (1995). Depressive Disorders. In R. T. Ammerman & M. Hersen (Eds.), Handbook of child behavior therapy in the psychiatric setting (pp. 269-300). New York: Wiley.
  14. Eyberg, S. M., Nelson, M. M., & Boggs, S. R. (2008). Evidence-based psychosocial treatments for children and adolescents with disruptive behavior. Journal of Clinical Child and Adolescent Psychology, 37, 215-237. doi:10.1080/15374410701820117
  15. Steiner, H., Remsing, L., & Work Group on Quality, I. (2007). Practice parameter for the assessment and treatment of children and adolescents with oppositional defiant disorder. Journal of American Academy of Child and Adolescent Psychiatry, 46, 126-141. doi:10.1097/01.chi.0000246060.62706.af