Identifying perinatal risk factors for infant maltreatment: an ecological approach.
BACKGROUND
Child maltreatment and its consequences are a persistent problem throughout the world. Public health workers, human services officials, and others are interested in new and efficient ways to determine which geographic areas to target for intervention programs and resources. To improve assessment efforts, selected perinatal factors were examined, both individually and in various combinations, to determine if they are associated with increased risk of infant maltreatment. State of Georgia birth records and abuse and neglect data were analyzed using an area-based, ecological approach with the census tract as a surrogate for the community. Cartographic visualization suggested some correlation exists between risk factors and child maltreatment, so bivariate and multivariate regression were performed. The presence of spatial autocorrelation precluded the use of traditional ordinary least squares regression, therefore a spatial regression model coupled with maximum likelihood estimation was employed.


RESULTS
Results indicate that all individual factors or their combinations are significantly associated with increased risk of infant maltreatment. The set of perinatal risk factors that best predicts infant maltreatment rates are: mother smoked during pregnancy, families with three or more siblings, maternal age less than 20 years, births to unmarried mothers, Medicaid beneficiaries, and inadequate prenatal care.


CONCLUSIONS
This model enables public health to take a proactive stance, to reasonably predict areas where poor outcomes are likely to occur, and to therefore more efficiently allocate resources. U.S. states that routinely collect the variables the National Center for Health Statistics (NCHS) defines for birth certificates can easily identify areas that are at high risk for infant maltreatment. The authors recommend that agencies charged with reducing child maltreatment target communities that demonstrate the perinatal risks identified in this study.
Zhou
Yueqin
Y
Department of Geosciences, Georgia State University, 340 Kell Hall, Atlanta, GA 30303, USA. yzhou10@student.gsu.edu
Hallisey
Elaine J
EJ
Freymann
Gordon R
GR
D002649:Child Abuse / Q000517:prevention & control* / Q000706:statistics & numerical data; D004463:Ecology; D005191:Family Characteristics; D005260:Female; D005845:Georgia; D006801:Humans; D007223:Infant; D016013:Likelihood Functions; D008484:Medicaid; D008962:Models, Theoretical; D009035:Mothers; D015999:Multivariate Analysis; D011247:Pregnancy; D011295:Prenatal Care; D012044:Regression Analysis; D012307:Risk Factors; D012959:Socioeconomic Factors; D014481:United States