Background: The public health significance of the opioid epidemic is well-established. However, few states collect data on opioid problems among families involved in child welfare services. The absence of data creates significant barriers to understanding the impact of opioids on the service system and the needs of families being served.
Objective: This study sought to validate binary and count-based indicators of opioid-related maltreatment risk based on mentions of opioid use in written child welfare summaries.
Data and procedures: We developed a comprehensive list of terms referring to opioid street drugs and pharmaceuticals. This terminology list was used to scan and flag investigator summaries from an extensive collection of investigations (N = 362,754) obtained from a state-based child welfare system in the United States. Associations between mentions of opioid use and investigators' decisions to substantiate maltreatment and remove a child from home were tested within a framework of a priori hypotheses.
Results: Approximately 6.3% of all investigations contained one or more opioid use mentions. Opioid mentions exhibited practically signficant associations with investigator decisions. One in ten summaries that were substantiated had an opioid mention. One in five investigations that led to the out-of-home placement of a child contained an opioid mention.
Conclusion: This study demonstrates the feasibility of using simple text mining procedures to extract information from unstructured text documents. These methods provide novel opportunities to build insights into opioid-related problems among families involved in a child welfare system when structured data are not available.
Keywords: Child welfare; Named entity recognition; Natural language processing; Opioid; Text analysis.
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