Exploration of Risk Factors Associated with Adolescent Drug Use through Cutting Edge Recursive Partitioning Techniques

The purpose of this article is to explore and identify risk factors influencing drug use in school going adolescents aged 10 to 19 in a hilly state in the North-Eastern part of India. This article will explore the data collected from the National Institute of Health and Family Welfare, New Delhi, by using cutting edge Recursive Partitioning techniques such as Discriminant Analysis, Decision Tree Method, Artificial Neural Network etc to build a predictive model. Out of 3069 randomly selected participants who undertook the Adolescent Reproductive and Sexual health (ARSH) questionnaire a subset have been used to form this data set. Statistical techniques like Independent T-Tests, Chi Square test for independence, Logistic Regression, Discriminant Analysis, Artificial Neural Networks (ANN) were used for the exploration of data. These techniques were found to be extremely useful in the prediction of associated risk factors that contribute to consumption of banned drugs among adolescents. The recursive techniques addressed in this article are becoming useful predictive instruments not only in the context of drug misuse; however, for other socio-health problems such as alcohol consumption, adolescent sex behaviour and burden of disease.