Unjust: the health records of youth with personal/family justice involvement in a large pediatric health system

Pediatric Health and System Impacts of Mass Incarceration, 2009-2020: A Matched Cohort Study

OBJECTIVE

The US has the highest incarceration rate in the world; incarceration's direct and indirect toll on the health and healthcare use of youth is rarely investigated. We sought to compare the health of youth with known personal or family justice involvement and a matched cohort of youth without known personal/family justice involvement.

METHODS

A cross-sectional matched parallel cohort study was conducted. We queried electronic health records on youth (< 21 years) with a visit in a large Midwestern pediatric hospital-based institution from January 2009 to December 2020. Youth were located by searching for justice-related (e.g. prison, jail) keywords within all clinician notes. Health diagnostic profiles were measured using ICD 9/10 codes. Healthcare use included total admissions, inpatient days, emergent and urgent visits, and outpatient visits.

RESULTS

Across all youth at one institution over an 11-year period, 2.2% (N = 38,263) were identified as having probable personal or family justice-involvement. Youth with personal or familial justice involvement had 1.5-16.2 times the prevalence of mental health and physical health diagnoses across all domain groupings compared to a matched sample and the total population sample. From 2009-2020, approximately two-thirds of behavioral healthcare and nearly a quarter of all hospital inpatient days were attributed to the 2.2% of youth with probable personal or familial justice system involvement.

CONCLUSION

The study illuminates the vast disparities between youth with indirect or direct contact with the criminal legal system and matched youth with no documented contact. Better investment in monitoring and prevention efforts are needed.

Health, behavior, and social outcomes among offspring of parents with criminal convictions: a register-based study from Sweden

BACKGROUND

There is currently insufficient understanding of the health and behavior of children whose parents engage in criminal behavior. We examined associations between parental criminal convictions and wide range of offspring health, behavioral, and social outcomes by age 18 in a large, national sample, aiming to get a comprehensive picture of the risks among children of offending parents.

METHODS

We studied 1,013,385 individuals born in Sweden between 1987 and 1995, and their parents. Using data from several longitudinal nationwide registers, we investigated parental convictions and 85 offspring outcomes until the end of 2013, grouped into birth-related conditions, psychiatric and somatic disorders, accidents and injuries, mortality, school achievement, violent victimization, and criminality. Cox proportional hazards regression and logistic regression models were used to examine the associations. The role of genetic factors in intergenerational associations was studied in children-of-siblings analyses. We also examined the co-occurrence of multiple outcomes using Poisson regression.

RESULTS

A total of 223,319 (22.0%) individuals had one parent convicted and 31,241 (3.1%) had both parents convicted during the first 18 years of their life. The strongest associations were found between parental convictions and offspring behavioral problems, substance use disorders, poor school achievement, violent victimization, and criminality, with an approximately 2 to 2.5-fold increased risk in children with one convicted parent and 3- to 4-fold increased risk in children with two convicted parents. The risks were particularly elevated among children of incarcerated parents with a history of violent convictions. The associations appeared to be at least partly explained by genetic influences. Parental convictions were also associated with an increased likelihood of experiencing multiple outcomes.

CONCLUSIONS

Our findings help to calibrate the risks of a wide range of adverse outcomes associated with parental convictions and may be used to guide prevention efforts and identify key areas for future research.

How legal problems are conceptualized and measured in healthcare settings: a systematic review

Legal problems encompass issues requiring resolution through the justice system. This social risk factor creates barriers in accessing services and increases risk of poor health outcomes. A systematic review of the peer-reviewed English-language health literature following the PRISMA guidelines sought to answer the question, how has the concept of patients' "legal problems" been operationalized in healthcare settings? Eligible articles reported the measurement or screening of individuals for legal problems in a United States healthcare or clinical setting. We abstracted the prevalence of legal problems, characteristics of the sampled population, and which concepts were included. 58 studies reported a total of 82 different measurements of legal problems. 56.8% of measures reflected a single concept (e.g., incarcerated only). The rest of the measures reflected two or more concepts within a single reported measure (e.g., incarcerations and arrests). Among all measures, the concept of incarceration or being imprisoned appeared the most frequently (57%). The mean of the reported legal problems was 26%. The literature indicates that legal concepts, however operationalized, are very common among patients. The variation in measurement definitions and approaches indicates the potential difficulties for organizations seeking to address these challenges.

Identification of Child Survivors of Sex Trafficking From Electronic Health Records: An Artificial Intelligence Guided Approach

Survivors of child sex trafficking (SCST) experience high rates of adverse health outcomes. Amidst the duration of their victimization, survivors regularly seek healthcare yet fail to be identified. This study sought to utilize artificial intelligence (AI) to identify SCST and describe the elements of their healthcare presentation. An AI-supported keyword search was conducted to identify SCST within the electronic medical records (EMR) of ∼1.5 million patients at a large midwestern pediatric hospital. Descriptive analyses were used to evaluate associated diagnoses and clinical presentation. A sex trafficking-related keyword was identified in .18% of patient charts. Among this cohort, the most common associated diagnostic codes were for Confirmed Sexual/Physical Assault; Trauma and Stress-Related Disorders; Depressive Disorders; Anxiety Disorders; and Suicidal Ideation. Our findings are consistent with the myriad of known adverse physical and psychological outcomes among SCST and illuminate the future potential of AI technology to improve screening and research efforts surrounding all aspects of this vulnerable population.

Locating Youth Exposed to Parental Justice Involvement in the Electronic Health Record: Development of a Natural Language Processing Model

BACKGROUND

Parental justice involvement (eg, prison, jail, parole, or probation) is an unfortunately common and disruptive household adversity for many US youths, disproportionately affecting families of color and rural families. Data on this adversity has not been captured routinely in pediatric health care settings, and if it is, it is not discrete nor able to be readily analyzed for purposes of research.

OBJECTIVE

In this study, we outline our process training a state-of-the-art natural language processing model using unstructured clinician notes of one large pediatric health system to identify patients who have experienced a justice-involved parent.

METHODS

Using the electronic health record database of a large Midwestern pediatric hospital-based institution from 2011-2019, we located clinician notes (of any type and written by any type of provider) that were likely to contain such evidence of family justice involvement via a justice-keyword search (eg, prison and jail). To train and validate the model, we used a labeled data set of 7500 clinician notes identifying whether the patient was ever exposed to parental justice involvement. We calculated the precision and recall of the model and compared those rates to the keyword search.

RESULTS

The development of the machine learning model increased the precision (positive predictive value) of locating children affected by parental justice involvement in the electronic health record from 61% (a simple keyword search) to 92%.

CONCLUSIONS

The use of machine learning may be a feasible approach to addressing the gaps in our understanding of the health and health services of underrepresented youth who encounter childhood adversities not routinely captured-particularly for children of justice-involved parents.