Executive Summary

If, based on the evidence I collected, I had to choose one parent report, one teacher report, and one diagnostic interview upon which to base a diagnosis of child or adolescent ADHD, I would choose the following: Parent Report Form: CBCL, specifically the CBCL Attention Problems Scale Teacher Report Form: Conners Teacher Rating Scale Revised-Long Form (CTRSR-L), though it should be noted that there is not a lot of evidence that any teacher rating scale is really very effective in informing one's diagnosis of ADHD, and teacher report adds questionable incremental validity in terms of ADHD diagnosis over and above parent-report (Pelham, Fabiano, & Massetti, 2005; Shemmassian & Lee, 2011) Diagnostic Interview: MINI-KID, though others are also acceptable.

Recommended Diagnostic Interviews: MINI-KID : MINI-KID has good to excellent AUC values for most individual disorders, and good AUC values for ADHD diagnosis. MINI-KID also provides both positive and negative likelihood ratios that are helpful in determining changes in probability that clients has the disorder. Finally, the MINI-KID takes 68% less time (33 minutes versus 103 minutes) than the K-SADS-PL (Sheehan et. al, 2009).

Other recommended structured diagnostic interviews: Diagnostic Interview Schedule for Children Version IV (DISC-IV; Jensen et al., 1996): Moderate to high test-retest reliability for the parent version (.79), adequate interrater reliability (0.70), demonstrated that children classified using the DISC had higher risk on indexes of child impairment, sensitive to behavioral and pharmacological treatment effects (Pelham et. al, 2005). Diagnostic Interview for Children and Adolescents-Revised (DICA-R; Boyle et. al, 1993): High reliability scores for the parent version, parent assessment of ADHD tended to be more reliable for older children, stability of diagnosis demonstrated over 1 to 3 years. Good sensitivity and specificity of assessment and diagnosis reported (Pelham et al., 2005).

Other recommended semi-structured diagnostic interviews: Kiddie Schedule for Affective Disorders and Schizophrenia (K-SADS; Biederman et al., 1993): Interrater reliability of К = .56, but demonstrated excellent convergence with CBCL Attention Problems Scale, used as the "gold-standard" against which numerous ADHD screening instruments and diagnostic interviews are compared (Pelham et al., 2005).

Recommended Screening Measures: Parent Report: CBCL Attention Problems Scale: The CBCL Attention Problems Subscale was the parent-reported subscale that had been analyzed using ROC methodology in multiple studies, resulting in AUC values ranging from 0.84-0.90. Those AUC values were the highest observed in my search of the literature. Additionally, in different studies the CBCL Attention Problems Scale produced LR+ values that ranged 6.92 to 47, which means they ranged from helpful to clinically decisive, though LR- ratios produced by the scale were less helpful (the lowest negative likelihood ratio value found was 0.19 and LR- values ranged from 0.19 to 0.66). Other Recommended Measures: Disruptive Behavior Disorder Rating Scale-Parent Report : Very similar results to CBCL. Slightly smaller AUC, (AUC = .78), and LR+ value (5.06), but more useful LR- value (0.20). Teacher Report: Conners Teacher Rating Scale Revised-Long Form (CTRS-R-L): There were no AUC values reported for the CTRS-R-L but the CTRS-R-L was the only scale to demonstrate a LR+ value (8.66) and a LR- value (0.24) that were both in the range of values that were clinically helpful. Other (very tenatively) Recommended Measures: TRF Attention Problems Subscale: No AUC value reported, somewhat clinically helpful LR+ value (LR+ = 3.66), not a clinically helpful LR- value (=0.73)

Setting Reference Base Rate (Female) Basic Rate (Male) Demography Diagnostic Method
Military Antczak & Brininger, 2008[1] .04% (combined) .04% (combined) US Military ICD codes from electronic records
Non-clinical; Military Beekley et al., 2009 .02% (7 years) 0.0% (7 years) US Military Academy cadets EAT-26
Non-clinical; Military McNulty, 1997 1.1% (current & past) N/A US Navy female nurses DSM-III
Non-clinical; Military Striegel et al., 2008 .04% .005% US veterans ICD-9-CM
Non-clinical; Military McNulty & Fisher, 1997 N/A 2.5% Active duty males in US Navy N/A
Clinical; Collaborative Study on the Genetics of Alcoholism (COGA) Schuckit et al., 1996[2] 1.41% (lifetime) .00% (lifetime) US alcohol-dependent adults from San Diego, St. Lois, Iowa City, Farmington, New York, & Indianapolis SSAGA
Non-clinical; healthcare members Striegel-Moore et al., 2008 .0269% (current) Members of a large US healthcare organization in Portland, Oregon Healthcare provider records
Non-clinical; high school students Lewinsohn et al., 1993 . 00% (point), .45% (lifetime) .00% (point), .00% (lifetime) US high school students in west central Oregon DSM-III-R4
Clinical; substance users Ross et al., 1988 .4% (lifetime), .3% (current) .4% (lifetime), .3% (current) Canadian treatment-seeking substance users DIS9
Europe
Non-clinical; adolescents Lahortiga et al., 2005 .3% -- Adolescent females residing in Navarra, Spain EAT-403
Non-clinical; adolescents Kjelsås et al., 2004 .7% (lifetime) .2% (lifetime) Adolescents in secondary schools in Sør-Trøndelag, County in Norway SEDs10
Non-clinical; adolescents Isomaa et al., 2009 .7% (point; age 15), 1.8% (lifetime, age 15), .00% (point, age 18), 2.6% (lifetime, age 18), .9% (3 years) .00% (point & lifetime) Adolescents in a comprehensive school in Ostrobothnia district in Finland RAB-T11 & RAB-R12
Australia
Non-clinical; adolescents Patton et al., 2003 .00% (full), 1.8% (partial) -- Adolescent females residing in Victoria, Australia BET13
Central & South America
Non-clinical; college students Mancilla-Diaz et al., (2007) .00% -- Mexican first & second year college females EAT-403
East Asia
Clinical; eating disorder patients Nadaoka et al., 1996 .53% -- Adolescent and adult Japanese patients at a university hospital DSM-III-R4
Non-clinical; Korean Epidemiologic Catchment Area (KECA) Study Je Cho et al., (2007) .1% (lifetime), .1% (12 months) .2% (lifetime), .00% (12 months) Korean adults K-CIDI15 2.1
Centers participating in the Collaborative Study on the Genetics of Alcoholism in San Diego; St. Louis; Iowa City; Farmington, CN; New York; & Indianapolis Schuckit et al., 1996 1.41% N/A Alcohol-dependent adults Semi-Structured Assessment for the Genetics of Alcoholism; criteria based on DSM-III-R

1World Health Organization Composite International Diagnostic Interview

2International Statistical Classification of Diseases and Related Health Problems

3Eating Attitudes Test

4Diagnostic and Statistical Manual of Mental Disorders

5Eating Disorder Inventory

6Semi-Structured Assessment for the Genetics of Alcoholism

7Eating Disorders Diagnostic Interview

8Questionnaire for Eating Disorder Diagnoses

9National Institute of Mental Health Diagnostic Interview Schedule

10Modified Survey for Eating Disorders

11Interview Rating of Anorexia and Bulimia - Teenager Version

12Interview Rating of Anorexia and Bulimia - Revised Version

13Branched Eating Disorders Test

14Eating Disorder Examination

15Korean Version of Composite International Diagnostic Interview

16Structured Clinical Interview for DSM Disorders

Search terms: [Anorexia Nervosa OR anorexia OR eating disorder OR disordered eating] AND [prevalence OR base rate OR epidemiology] in Google Scholar

  1. ^ Antczak, Amanda J.; Brininger, Teresa L. (2008-12-01). "Diagnosed eating disorders in the U.S. Military: a nine year review". Eating Disorders. 16 (5): 363–377. doi:10.1080/10640260802370523. ISSN 1532-530X. PMID 18821361.
  2. ^ Schuckit, M. A.; Tipp, J. E.; Anthenelli, R. M.; Bucholz, K. K.; Hesselbrock, V. M.; Nurnberger, J. I. (1996-01-01). "Anorexia nervosa and bulimia nervosa in alcohol-dependent men and women and their relatives". The American Journal of Psychiatry. 153 (1): 74–82. doi:10.1176/ajp.153.1.74. ISSN 0002-953X. PMID 8540597.