Bridget Freisthler1, Nichole Michaels2, Jennifer Price Wolf3. 1. College of Social Work, The Ohio State University, Columbus, Ohio. 2. Center for Injury Research and Policy, The Research Institute at Nationwide Children's Hospital, Columbus, Ohio. 3. Prevention Research Center, Pacific Institute for Research and Evaluation, Berkeley, California.
Abstract
OBJECTIVE: The increase in the use of opioids in Ohio is believed to have contributed to a crisis within county child welfare agencies throughout the state. Prior research has found a connection between opioid use and child abuse and neglect, but no previous studies have examined the relationship between opioid overdoses and child maltreatment rates at the neighborhood level. METHOD: The sample is 9,231 Census block groups in Ohio for 2015. Bayesian conditionally autoregressive models were used to examine the relationship between naloxone administrations (as a proxy for overdose) and child maltreatment. We controlled for variables representing social disorganization characteristics including unemployment, racial/ethnic heterogeneity, and vacant housing rates. We specifically examined child maltreatment referrals per child population and child maltreatment substantiations per child population. RESULTS: Higher rates of naloxone administration by emergency medical services were related to higher rates of referrals for child welfare investigations (relative risk = 1.0026) and substantiations (relative risk = 1.0027) at the block group level. Neighborhoods located in Appalachia with higher rates of overdoses were at greater risk for experiencing more referrals for child welfare investigations (relative risk = 1.0043). CONCLUSIONS: As communities continue to struggle with containing opioid misuse and reducing opioid overdose deaths, they must also contend with addressing problems that may arise from overdoses, including child abuse and neglect. Our findings suggest that the relationship between overdoses and maltreatment occurs at a much smaller spatial scale than has previously been observed. This may allow resources to be targeted more effectively within counties and communities.
OBJECTIVE: The increase in the use of opioids in Ohio is believed to have contributed to a crisis within county child welfare agencies throughout the state. Prior research has found a connection between opioid use and child abuse and neglect, but no previous studies have examined the relationship between opioid overdoses and child maltreatment rates at the neighborhood level. METHOD: The sample is 9,231 Census block groups in Ohio for 2015. Bayesian conditionally autoregressive models were used to examine the relationship between naloxone administrations (as a proxy for overdose) and child maltreatment. We controlled for variables representing social disorganization characteristics including unemployment, racial/ethnic heterogeneity, and vacant housing rates. We specifically examined child maltreatment referrals per child population and child maltreatment substantiations per child population. RESULTS: Higher rates of naloxone administration by emergency medical services were related to higher rates of referrals for child welfare investigations (relative risk = 1.0026) and substantiations (relative risk = 1.0027) at the block group level. Neighborhoods located in Appalachia with higher rates of overdoses were at greater risk for experiencing more referrals for child welfare investigations (relative risk = 1.0043). CONCLUSIONS: As communities continue to struggle with containing opioid misuse and reducing opioid overdose deaths, they must also contend with addressing problems that may arise from overdoses, including child abuse and neglect. Our findings suggest that the relationship between overdoses and maltreatment occurs at a much smaller spatial scale than has previously been observed. This may allow resources to be targeted more effectively within counties and communities.
Authors: Stephen M Davis; Danielle Davidov; Alfgeir L Kristjansson; Keith Zullig; Adam Baus; Melanie Fisher Journal: PLoS One Date: 2018-10-12 Impact factor: 3.240