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Objectives. To develop statewide estimates of known victims and individuals at risk of human trafficking in Ohio.
Methods. We collected information from 12 state and local sources including child welfare, legal services, and law enforcement data. We collected the data from agency records dating 2013 to 2018. However, the majority of data were from calendar years 2014, 2015, and 2016 (roughly 95% across individual and aggregate sources). We used probabilistic matching to estimate victim and at-risk cases-accounting for duplicates.
Results. According to available data, there were 1032 known victims during the study time frame. We identified approximately 4209 at-risk individuals based on youths presenting with common risk factors for trafficking victimization.
Conclusions. Estimating the prevalence of human trafficking is an important public health research priority. As the first "cataloging" of existing record systems in Ohio to our knowledge, this study provided a comprehensive overview of the number of victims and the type of information that is available in the state.This study highlights the importance of moving toward the use of epidemiological approaches to measure the prevalence of human trafficking. (Am J Public Health. 2019;109:1396-1 399. doi:10.2105/ AJPH.2019.305203)
Measuring human trafficking is a complex challenge. As such, it is a priority to estimate the prevalence of human trafficking more precisely while also identifying the relevant data limitations.1-3 To extend previous research in line with this objective,4,5 in this study, we estimated the prevalence of minors and young adults who are known victims and at risk for trafficking in Ohio by using existing agency record data. This article describes these estimates, discusses data challenges identified, and makes recommendations for future research.
METHODS
We identified and analyzed 12 state and local data sources, including 8 from agencies with individual-level information and 4 aggregate reports of victimization without individual-level information. The data represent cases identified between 2013 and 2018-with a majority of cases from 2014, 2015, and 2016 (95.3%). Table 1 summarizes the characteristics of each individual-level data source including definitions used to define known victims and at-risk individuals.
Descriptive statistics for variables that are consistent across these sources are listed in Table 2. Across the 8 individual-level sources, there were 486 known victims (32.9%); more than half of the individuals were labeled as at risk (67.1%). Systems...