Poor physical health: A major barrier to employment among individuals with serious mental illness

Depression and Cost-Related Health Care Utilization Among Persons with Diabetes

The presence of depression among people with diabetes can substantially increase health care costs and reduce health care utilization. This study aimed at further elucidating the factors underlying the relationship between depressive disorders and health care utilization among people with diabetes. Data were obtained from the 2019 Behavioral Risk Factor Surveillance System, and the sample was limited to people with diabetes (n = 22,642). The independent variable was assessed by a lifetime diagnosis of depressive disorder, including depression, major depression, dysthymia, or minor depression. The dependent variable was cost-related health care utilization assessed as a response (yes/no) to whether participants had not seen a doctor due to costs in the past year. Logistic regression models examined the association between depressive disorders and health care utilization, adjusting for covariates incorporating weighting to account for study design. Overall, 25.2% of the people with diabetes reported having had a depressive disorder in their lifetime. People with diabetes who had ever been diagnosed with a depressive disorder were more likely to have reported not seeing a doctor due to costs in the past year (adjusted odds ratio: 1.82 [1.49, 2.28]). Findings from this study suggest a need for further research regarding the relationship between depression and cost-related health care utilization among people with diabetes.

Predicting program outcomes for vocational rehabilitation customers: A machine learning approach

BACKGROUND: The Vocational Rehabilitation (VR) program provides support and services to people with disabilities who want to work. OBJECTIVE: Approximately one-third of eligible VR customers are employed when they exit the program. The remainder either exit without ever receiving services or without employment after receiving services. In this study, we explore how customer characteristics and VR services predict these outcomes. METHODS: We examined VR case level data from the RSA-911 files. Machine learning techniques allowed us to explore a large number of potential predictors of VR outcomes while requiring fewer assumptions than traditional regression methods. RESULTS: Consistent with existing literature, customers who are employed at application are more likely to exit with employment, and those with mental health conditions or low socioeconomic status are less likely to exit with employment. Some customers with low or no earnings at application who are not identified in prior studies are more likely than others to have poor program outcomes, including those with developmental disability who are under 18, customers without developmental or learning disabilities, and customers who do not receive employment or restoration services. CONCLUSIONS: VR counselors and administrators should consider implementing early, targeted interventions for newly identified at-risk groups of VR customers.

Testing Kumpfer’s Resilience Model Among Adults With Serious Mental Illness

Having a serious mental illness (SMI) is often associated with significant adversities, and people respond differently to adversities. The existing research supports that people with SMI can achieve and maintain positive life outcomes despite experiencing adversities. Resilience, the ability to cope with (or bounce back quickly from) crisis, can help buffer the negative effects of various types of adversities, including chronic illness and disability, and facilitate the psychosocial adaptation process to SMI. Kumpfer’s resilience model, a person-process-context framework, has been widely used to conceptualize, and assess for, resilience in various populations, including people with chronic illnesses and disabilities. However, the research in resilience among people with SMI is very limited. The purpose of this study was to empirically assess the utility of Kumpfer’s resilience model and its proposed predictive components for conceptualizing the adaptation process to SMI. One hundred forty-four participants completed a Qualtrics survey containing demographic questions and a series of validated instruments representing the major components of Kumpfer’s resilience model. Hierarchical regression analysis was used to analyze the data, and the final model explained 71% of the variance of the dependent variable—adaptation to disability. Avoidance coping, internalized stigma, and optimism were significant independent predictors of adaptation to disability. This study supports the utilization of Kumpfer’s resilience model to conceptualize the adaptation to disability process among people with SMI. Implications for rehabilitation counseling practices are discussed.