Risk, resilience, and thriving among racial/ethnic minorities and underserved populations at-risk for substance use disorders

Author(s):  
Christopher P. Salas-Wright
2013 ◽  
Vol 60 (4) ◽  
pp. 610-616 ◽  
Author(s):  
Christoph Flückiger ◽  
Aaron C. Del Re ◽  
Adam O. Horvath ◽  
Dianne Symonds ◽  
Michael Ackert ◽  
...  

2021 ◽  
Author(s):  
Joanne Weinreb ◽  
Penina Gavrilova ◽  
Jonathan Avery ◽  
Sean M. Murphy ◽  
Jyotishman Pathak

Abstract BackgroundRacial and ethnic health disparities have been linked with inequalities in access to health care and outcomes. The present study considers whether inequalities persist between racial/ethnic groups among patients with mental health or substance use disorders who visit the emergency department (ED). MethodsWe analyzed data from the National Hospital Ambulatory Medical Care Survey (NHAMCS) from 2012-2018, assessing health disparities among patients diagnosed with mental health or substance use disorders by observing whether significant differences exist in ED wait time and length of visit (LOV) for patients of different races/ethnicities. Stratified models were performed to further understand the impact of regions across the U.S., year, and triage level on the association analysis. ResultsFrom 2012-2018, non-Hispanic Black and Hispanic patients experienced significantly longer ED wait times and LOV as compared to White patients. Patients with private insurance experienced significantly shorter wait times compared to patients with self-pay, and shorter LOV than those with Medicaid/ Children’s Health Insurance Program, or Medicare. Male patients had significantly longer LOV compared to female patients. We observed year by year differences in wait times of non-Hispanic Black patients with improvement appearing between the years 2013 to 2016, while LOV remained consistently longer. We observed both regional and triage level differences, with the U.S. Northeast presenting with the most disparities. Additionally, we noted a general upward trend of SUD diagnoses. Conclusion Our analysis suggests that while there has been an overall improvement in median ED wait time through the years, non-Hispanic Black and Hispanic patients experience significantly longer ED wait time compared to non-Hispanic White patients. Additionally, non-Hispanic Black and Hispanic patients have a significantly longer ED LOV compared to non-Hispanic White patients.


2020 ◽  

The first study to examine the potential of machine learning in early prediction of later substance use disorders (SUDs) in youth with ADHD has been published in the Journal of Child Psychiatry and Psychology.


2019 ◽  
Author(s):  
Yanli Zhang-James ◽  
Qi Chen ◽  
Ralf Kuja-Halkola ◽  
Paul Lichtenstein ◽  
Henrik Larsson ◽  
...  

AbstractBackgroundChildren with attention-deficit/hyperactivity disorder (ADHD) have a high risk for substance use disorders (SUDs). Early identification of at-risk youth would help allocate scarce resources for prevention programs.MethodsPsychiatric and somatic diagnoses, family history of these disorders, measures of socioeconomic distress and information about birth complications were obtained from the national registers in Sweden for 19,787 children with ADHD born between 1989-1993. We trained 1) cross-sectional machine learning models using data available by age 17 to predict SUD diagnosis between ages 18-19; and 2) a longitudinal model to predict new diagnoses at each age.ResultsThe area under the receiver operating characteristic curve (AUC) was 0.73 and 0.71 for the random forest and multilayer perceptron cross-sectional models. A prior diagnosis of SUD was the most important predictor, accounting for 25% of correct predictions. However, after excluding this predictor, our model still significantly predicted the first-time diagnosis of SUD during age 18-19 with an AUC of 0.67. The average of the AUCs from longitudinal models predicting new diagnoses one, two, five and ten years in the future was 0.63.ConclusionsSignificant predictions of at-risk co-morbid SUDs in individuals with ADHD can be achieved using population registry data, even many years prior to the first diagnosis. Longitudinal models can potentially monitor their risks over time. More work is needed to create prediction models based on electronic health records or linked population-registers that are sufficiently accurate for use in the clinic.


Author(s):  
JOHN D. CORRIGAN ◽  
RACHEL SAYKO. ADAMS ◽  
KRISTEN DAMS-O’CONNOR

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