Predicting childhood sexual or physical abuse: a logistic regression geo-mapping approach to prevention.

Authors

Roland K. Tadoum, University of Texas at Houston – School of Health Information Sciences, Baylor College of Medicine, Section of Pediatric Emergency Medicine
Kamila Smolij, University of Texas at Houston – School of Health Information Sciences
Michelle A. Lyn, Baylor College of Medicine, Section of Pediatric Emergency Medicine
Craig W. Johnson, University of Texas at Houston – School of Health Information Sciences

Publication Date

1-1-2005

Journal

AMIA Annu Symp Proc. 2005; 2005: 1130.

Abstract

This study investigates the degree to which gender, ethnicity, relationship to perpetrator, and geomapped socio-economic factors significantly predict the incidence of childhood sexual abuse, physical abuse and non- abuse. These variables are then linked to geographic identifiers using geographic information system (GIS) technology to develop a geo-mapping framework for child sexual and physical abuse prevention.

Keywords

Child, Child Abuse, Child Abuse, Sexual, Family Characteristics, Female, Geographic Information Systems, Humans, Logistic Models, Male, Retrospective Studies, Risk Factors, United States