Health out of foster care as young adults age out of foster care: A phenomenological exploration of seeking healthcare services after aging out of the US foster care system

Risk and Protective Factors for African American Kinship Caregiving: A Scoping Review

Background

African American children are disproportionately placed in kinship care, specifically with kin caregivers who have pressing service needs given their relatively poor physical and mental health, and low economic status. Kinship caregivers often face challenges, however, they also show resilience when raising relative children. Although the literature has examined risk and protective factors that affect kinship caregiving, no scoping review has been conducted to summarize these factors as they relate to African American kinship caregivers.

Objectives

This study aims to summarize the risk and protective factors that affect African American kinship caregiving.

Methods

Twenty-six studies were identified from this scoping review. A data template was used to extract information on data/sample, research methods, and risk and protective factors.

Results

This review found that the risk and protective factors in the identified studies can be summarized into four types: child, caregiver, relationship, and legal, cultural or social issues. Caregiver factors were examined from the perspective of physical and behavioral health, parenting, and resources. The most common risk factors were caregivers' low social economic status or great financial need, and caregivers' physical health. The most common protective factor was caregivers' spirituality or religion.

Discussion

This review helps to better understand the risk and protective factors for African American kinship caregivers. Policies should be informed by African American kinship caregivers' unique qualities to provide useful cultural solutions. Evidence based interventions should be developed and provided to African American kinship caregivers to improve their parenting abilities and to provide them with additional resources and supports. Implications for child welfare research are also presented.

Predicting youth at high risk of aging out of foster care using machine learning methods

BACKGROUND

Youth who exit the nation's foster care system without permanency are at high risk of experiencing difficulties during the transition to adulthood.

OBJECTIVE

To present an illustrative test of whether an algorithmic decision aid could be used to identify youth at risk of existing foster care without permanency.

METHODS

For youth placed in foster care between ages 12 and 14, we assessed the risk of exiting care without permanency by age 18 based on their child welfare service involvement history. To develop predictive risk models, 28 years (1991-2018) of child welfare service records from California were used. Performances were evaluated using F1, AUC, and precision and recall scores at k %. Algorithmic racial bias and fairness was also examined.

RESULTS

The gradient boosting decision tree and random forest showed the best performance (F1 score = .54-.55, precision score = .62, recall score = .49). Among the top 30 % of youth the model identified as high risk, half of all youth who exited care without permanency were accurately identified four to six years prior to their exit, with a 39 % error rate. Although racial disparities between Black and White youth were observed in imbalanced error rates, calibration and predictive parity were satisfied.

CONCLUSIONS

Our study illustrates the manner in which potential applications of predictive analytics, including those designed to achieve universal goals of permanency through more targeted allocations of resources, can be tested. It also assesses the model using metrics of fairness.

Life is a highway: Driver's license acquisition and social determinants for youth in foster care and their non-foster care peers

BACKGROUND

Data regarding the connection between driver licensure and social determinants for youth could provide insight into the impact of driver license acquisition. These relationships are important for youth overall and particularly for foster youth given that adolescents in foster care obtain driver's licenses less often than their non-foster care peers. This integrative review explores the association between driver licensure and social determinants.

METHODS

Whittemore and Knafl guidelines were used to conduct the integrative review. The articles were identified in collaboration with an expert in library science and public health. Results were organized by the Healthy People 2030 (HP 2030) Social Determinants of Health (SDOH) model.

RESULTS

Six studies were included. Social and community context included social support structures helping youth get driver's licenses. Economics, including income and education, influenced license acquisition. Driver license acquisition was associated with improved well-being, security, and mental health.

CONCLUSION

While fewer of those living in urban, walkable neighborhoods with access to public transportation were licensed, results from other studies suggest that car access is associated with psychological well-being. Further, licensure is disproportionately lower for populations historically marginalized from equal housing, education, and employment opportunities. Licensure plays a role in well-being.

Influence of social determinants of health and substance use characteristics on persons who use drugs pursuit of care for hepatitis C virus infection

BACKGROUND

Social determinants of health (SDOH), i.e., social, behavioral and environmental factors, are increasingly recognized for their important influence on health outcomes. Data are limited on the influence of SDOH, substance use characteristics, and their interactions on pursuit of hepatitis C virus (HCV) care among individuals with opioid use disorder (OUD). Linkage to HCV care remains low in this population despite high HCV prevalence and incidence.

AIMS

To investigate the influence of SDOH, substance use factors, and their interactions on HCV treatment uptake among OUD patients in a methadone treatment program.

METHODS

Information on patient demographics, SDOH, substance use characteristics, and co-morbid medical conditions were obtained from the paper and electronic medical records of OUD patients on methadone. We applied multiple correspondence analysis, k-means algorithm, and logistic regression with least absolute shrinkage and selection operator penalty to identify variables and clusters associated with pursuit of HCV care.

RESULTS

Data from 161 patients (57% male, 60% Caucasian, mean age 45 years) were evaluated. Being employed, the absence of support systems, and a history of foster care were the strongest positive predictors of treatment pursuit. The use of crack/cocaine as the initial illicit substance, criminal activity without incarceration, and the absence of a family history of chemical dependency were the strongest negative predictors. We identified clusters among persons with OUD based upon their likelihood to pursue HCV management.

CONCLUSION

Utilizing data from the medical record, we were able to identify factors positively and negatively associated with linkage-to-care for HCV. We were also able to divide patients into clusters of factors associated with linkage-to-care for HCV. These results could be used to identify individuals with OUD based upon their readiness for HCV care.