TY - JOUR
T1 - Predicting type of psychiatric disorder from Strengths and Difficulties Questionnaire (SDQ) scores in child mental health clinics in London and Dhaka
AU - Goodman, R
AU - Renfrew, D
AU - Mullick, M
PY - 2000
Y1 - 2000
N2 - A computerised algorithm was developed to predict child psychiatric diagnoses on the basis of the symptom and impact scores derived from Strengths and Difficulties Questionnaires (SDQs) completed by parents, teachers and young people. The predictive algorithm generates "unlikely", "possible" or "probable" ratings far four broad categories of disorder, namely conduct disorders, emotional disorders, hyperactivity disorders, and any psychiatric disorder. The algorithm was applied to patients attending child mental health clinics in Britain (N = 101) and Bangladesh (N = 89). The level of chance-corrected agreement between SDQ prediction and an independent clinical diagnosis was substantial and highly significant (Kendall's tau b between 0.49 and 0.73; p <0.001). A "probable" SDQ prediction for any given disorder correctly identified 81-91% of the children who definitely had that clinical diagnosis. There were more false positives than false negatives, i.e. the SDQ categories were over-inclusive. The algorithm appears to be sufficiently accurate and robust to be of practical value in planning the assessment of new referrals to a child mental health service.
AB - A computerised algorithm was developed to predict child psychiatric diagnoses on the basis of the symptom and impact scores derived from Strengths and Difficulties Questionnaires (SDQs) completed by parents, teachers and young people. The predictive algorithm generates "unlikely", "possible" or "probable" ratings far four broad categories of disorder, namely conduct disorders, emotional disorders, hyperactivity disorders, and any psychiatric disorder. The algorithm was applied to patients attending child mental health clinics in Britain (N = 101) and Bangladesh (N = 89). The level of chance-corrected agreement between SDQ prediction and an independent clinical diagnosis was substantial and highly significant (Kendall's tau b between 0.49 and 0.73; p <0.001). A "probable" SDQ prediction for any given disorder correctly identified 81-91% of the children who definitely had that clinical diagnosis. There were more false positives than false negatives, i.e. the SDQ categories were over-inclusive. The algorithm appears to be sufficiently accurate and robust to be of practical value in planning the assessment of new referrals to a child mental health service.
UR - http://www.scopus.com/inward/record.url?scp=0033933759&partnerID=8YFLogxK
U2 - 10.1007/s007870050008
DO - 10.1007/s007870050008
M3 - Article
SN - 1435-165X
VL - 9
SP - 129
EP - 134
JO - European Child and Adolescent Psychiatry
JF - European Child and Adolescent Psychiatry
IS - 2
ER -