The Spatial Relationships among Neurotoxicant Exposure, Child Admissions, and Mental Health Assessment Scores: How do they Interact in the State of Ohio?

In 2007, billions of pounds of known and suspected neurological toxicants were released into the air and water of the United States. Ohio had the dubious distinction of being one of the worst offenders because it was in the top-five emitters nationally (U.S. EPA, 2009a). While environmental toxicants are harmful to everyone, children are especially vulnerable because of the rapid growth and development that occurs throughout infancy and childhood (NRC, 1993). Acute or chronic exposure to these chemicals can cause a variety of psychiatric symptoms in children because they have the capacity to bypass or degrade the blood-brain barrier (Gallo, 2008). This ecologic study investigated spatial relationships between neurotoxicant exposure and mental illness. Theories related to deep ecology and children’s increased vulnerability informed the research agenda and suggested that children would be negatively impacted by neurotoxicants. Two basic questions guided the study: 1) How do neurotoxicant hazard quotients/indices impact child admissions, and 2) How do neurotoxicant hazard quotients/indices impact mental health assessment scores? The research design controlled for several macro-level (e.g., child poverty) and micro-level (e.g., race) variables based upon prior research. Data from the Ohio Department of Mental Health, the National-scale Air Toxics Assessment, and American Community Survey were united and modeled with geostatistical software packages (e.g., ArcMap 10.0). Address data were successfully geocoded for 93 percent of all child records. Almost 68,000 admission records for youth, ages 5-17, were aggregated to the census tract level. After developing census tract admission rates, Empirical Bayes smoothing was used to correct for census tract with small numbers of admissions. Ordinary least squares regression was used to develop global parsimonious models and geographic weighted regression (GWR) was used to examine local relationships.GWR models explained a large percentage of the variance in child admissions (46%) and attention deficit-hyperactivity disorder (AD-HD) admissions (40%); however, the total hazard quotient for neurotoxicants was not statistically significant in either model. The GWR model for pervasive developmental disorder only explained five percent of the variation in admissions, and the model for AD-HD with the manganese hazard index only explained 15 percent of the variation in admissions. GWR models associated with mental health assessment scores explained a moderate percentage of the variance in hopefulness (39%) and problem severity (39%) assessment scores. Child functioning was the only outcomes variable associated with a neurotoxicant (i.e., solvents). While this model explained 58 percent of the variation in child functioning, it must be interpreted with caution because of the high degree of local multicollinearity on the western edge of the state.Findings from this study suggest neurotoxicants may play a role in child AD-HD admissions and child functioning scores, but more research needs to be conducted to validate these findings. Future researchers could focus on smaller geographic areas instead of the state or use different statistical techniques like spatial dependence modeling or hierarchical linear modeling. Finally, they could conduct similar research other populations to expand the generalizability of the findings.

Name thesis:degree_name

Doctor of Philosophy

Level thesis:degree_level

doctoral

Discipline thesis:degree_discipline

Social Work

Grantor dc:publisher

The Ohio State University

Year dc:date

2013

Author dc:creator

• Massatti, Richard Roland

Contributors dc:contributor

• Early, Theresa

• Social Work
• Geography
• Public Health
• Mental Health
• Child Admissions
• Assessment
• Hopefulness
• Functioning
• Problem Severity
• Attention Deficit Hyperactivity Disorder
• Pervasive Developmental Disorder
• Neurotoxicant Exposure
• Geographic Weighted Regression

Statement dc:rights

• unrestricted
• This thesis or dissertation is protected by copyright: some rights reserved. It is licensed for use under a Creative Commons license. Specific terms and permissions are available from this document's record in the OhioLINK ETD Center.

Language dc:language

English

Repository record dc:identifier

http://rave.ohiolink.edu/etdc/view?acc_num=osu1365542600

OAI identifier oai:identifier

oai:etd.ohiolink.edu:osu1365542600