MENTOR: A Bayesian Model for Prediction and Intervention in Mental Retardation

Mental Retardation(MR) or mental deficiency is a complex medical and social problem. The prevalence is estimated to be about $2.5$ per cent of the population [Bats93], [StSu92]. Various studies have reported somewhat different figures(between $2 %$ and $5 %$ ) depending on the definition of MR adopted and the age group surveyed [StSu92]. It is a developmental disability with a complex etiology. The causative factors and mechanisms are not well understood. According to the American Association on Mental Retardation (AAMR), "Mental Retardation is characterized by significantly subaverage intellectual functioning" [AAMR92, page 5]. The AAMR has recommended that people scoring below two Standard Deviations (SD) in a standardized IQ test be classified as retarded [AAMR92, page 5]. These tests are normalized to a mean of 100 with a SD of 15 . Those with scores below 50 are considered severly retarded. Scores in the category of $50-69$ fall in the classification of Mild Mental Retardation (MMR). Though AAMR suggests inclusion of limitation of adaptive skills also [AAMR92, page 6], many studies have used cognitive tests (IQ scores) for classification [StSu92], [McDe93]. A category called Borderline Mental Retardation (BMR)-scores falling between one and two standard deviations, was in vogue previously. But due to the social stigma attached to MR and concerns about test errors, it was de-emphasized subsequently. We shall go by IQ scores and keep the category of BMR for understanding causal mechanisms. For severe MR a cause can be found in the majority of cases. In MMR, which forms $85 %$ of MR, a cause cannot be put down in half the cases [Bats 93 ]. So here we have a complex web of unknown causal mechanisms, disagreement among experts, controversies (the large literature of nature versus nurture) and serious gaps in the experts’ understanding of the etiological factors.