Integrating human services and criminal justice data with claims data to predict risk of opioid overdose among Medicaid beneficiaries: A machine-learning approach.

Health system data incompletely capture the social risk factors for drug overdose. This study aimed to improve the accuracy of a machine-learning algorithm to predict opioid overdose risk by integrating human services and criminal justice data with health claims data to capture the social determinants of overdose risk. This prognostic study included Medicaid beneficiaries (n = 237,259) in Allegheny County, Pennsylvania enrolled between 2015 and 2018, randomly divided into training, testing, and validation samples. We measured 290 potential predictors (239 derived from Medicaid claims data) in 30-day periods, beginning with the first observed Medicaid enrollment date during the study period. Using a gradient boosting machine, we predicted a composite outcome (i.e., fatal or nonfatal opioid overdose constructed using medical examiner and claims data) in the subsequent month. We compared prediction performance between a Medicaid claims only model to one integrating human services and criminal justice data with Medicaid claims (i.e., integrated model) using several metrics (e.g., C-statistic, number needed to evaluate [NNE] to identify one overdose). Beneficiaries were stratified into risk-score decile subgroups. The samples (training = 79,087, testing = 79,086, validation = 79,086) had similar characteristics (age = 38±18 years, female = 56%, white = 48%, having at least one overdose = 1.7% during study period). Using the validation sample, the integrated model slightly improved on the Medicaid claims only model (C-statistic = 0.885; 95%CI = 0.877-0.892 vs. C-statistic = 0.871; 95%CI = 0.863-0.878), with small corresponding improvements in the NNE and positive predictive value. Nine of the top 30 most important predictors in the integrated model were human services and criminal justice variables. Using the integrated model, approximately 70% of individuals with overdoses were members of the top risk decile (overdose rates in the subsequent month = 47/10,000 beneficiaries). Few individuals in the bottom 9 deciles had overdose episodes (0-12/10,000). Machine-learning algorithms integrating claims and social service and criminal justice data modestly improved opioid overdose prediction among Medicaid beneficiaries for a large U.S. county heavily affected by the opioid crisis.

Assessment of Integration of Disability Content into Social Work Education.

Three hundred members of the Council on Social Work Education (CSWE) responded to a survey regarding the inclusion of disability content in social work courses and supports needed to increase disability content. Although respondents generally agreed that disability content is important in social work education, its inclusion is inconsistent, with most frequent inclusion in courses on diversity and least frequent inclusion in courses on research. Respondents identified barriers to increasing disability content, including lack of resources for teaching, lack of relevant faculty expertise, and an overcrowded curriculum. Strategies and resources for infusing disability content into social work education are discussed.

Inclusion for People with Developmental Disabilities: Measuring an Elusive Construct.

The philosophy of inclusion for people with intellectual and developmental disabilities (IDD) has evolved over the last 50 years. Over time, inclusion research has shifted from a focus on deinstitutionalization to understanding the extent to which individuals with IDD are meaningfully involved in the community and social relationships. Yet, there has been no agreed on way to measure inclusion. Many different measurement and data collection techniques have been used in the literature. This study proposes a brief measure of inclusion that can be used with family members and on survey instruments.

Exploring parent-sibling communication in families of children with sickle cell disease.

BACKGROUND: Communication within families of children with sickle cell disease is important yet has not been adequately investigated. METHODS: Focus group interviews were conducted with parents of children with sickle cell disease to explore parent-sibling communication about sickle cell disease. RESULTS: Communication was influenced by attributes and behaviors of the parent, the child with sickle cell disease, and the sibling; extended family, neighbors, friends, and church members or social networks; and available, accessible resources related to the child's health, child's school, and parent employment. Outcomes that influenced and were influenced by factors within and outside the parent-sibling dyad and nuclear family included parent satisfaction, parent roles, family intactness, and status attainment. CONCLUSIONS: These findings support previous research with African-American families and expand our views of the importance of educating parents, family members, and others about sickle cell disease. The findings suggest a need to explore sibling perception of this communication, parent and sibling perception of the impact of frequent hospitalizations and clinic visits on the sibling and family, and variations within families of children with sickle cell disease.