scholarly journals Heroin Overdose-Related Child and Adolescent Hospitalizations: Insight on Comorbid Psychiatric and Substance Use Disorders

2019 ◽  
Vol 9 (7) ◽  
pp. 77 ◽  
Author(s):  
Uwandu Queeneth ◽  
Narmada N. Bhimanadham ◽  
Pranita Mainali ◽  
Henry K. Onyeaka ◽  
Amaya Pankaj ◽  
...  
Keyword(s):  
Substance Use ◽  
Substance Use Disorders ◽  
Odds Ratio ◽  
Nationwide Inpatient Sample ◽  
Child And Adolescent ◽  
Prescription Opioids ◽  
Demographic Trend ◽  
Heroin Overdose ◽  
Older Adolescents ◽  
Study Population

Objective: To evaluate the association between psychiatric comorbidities, substance use disorders and heroin overdose-related hospitalizations (HOD). Next, to understand the demographic trend of HOD hospitalizations and comorbidities. Methods: Using the Nationwide Inpatient Sample (NIS), we included 27,442,808 child and adolescent hospitalizations, and 1432 inpatients (0.005%) were managed primarily for HOD. The odds ratio (OR) of the association of variables in HOD inpatients were measured using a logistic regression model. Results: Adolescents had 56 times higher odds (95% CI 43.36–73.30) for HOD-related hospitalizations compared to 4.6% children under 11 years. About three-fifth of the HOD inpatients were male, and they had 1.5-fold higher odds (95% CI 1.30–1.64) compared to 43% females in the study population. Whites were considerably higher in proportion (81%) than other race/ethnicities. A greater portion of HOD inpatients (40%) were from high-income families. Most common comorbid psychiatric disorders were mood (43.8%) and anxiety (20.4%). The prevalent comorbid substance use disorders were opioid (62.4%), tobacco (36.8%) and cannabis (28.5%) use disorders. Conclusion: HOD-related hospitalizations were predominant in males, White and older adolescents (12–18 years). Prescription opioids are the bridge to heroin abuse, thereby increasing the vulnerability to other substance abuse. This requires more surveillance and should be explored to help reduce the heroin epidemic in children.

Risk for Substance Use Disorders in Youths With Child- and Adolescent‐Onset Bipolar Disorder

1999 ◽  
Vol 38 (6) ◽  
pp. 680-685 ◽  
Author(s):  
TIMOTHY E. WILENS ◽  
JOSEPH BIEDERMAN ◽  
RACHAEL B. MILLSTEIN ◽  
JANET WOZNIAK ◽  
AMY L. HAHESY ◽  
...  
Keyword(s):  
Bipolar Disorder ◽  
Substance Use ◽  
Substance Use Disorders ◽  
Child And Adolescent

scholarly journals The International ADHD in Substance Use Disorders Prevalence (IASP) study: background, methods and study population

2013 ◽  
Vol 22 (3) ◽  
pp. 232-244 ◽  
Author(s):  
Geurt van de Glind ◽  
Katelijne Van Emmerik-van Oortmerssen ◽  
Pieter Jan Carpentier ◽  
Frances R. Levin ◽  
Maarten W.J. Koeter ◽  
...  
Keyword(s):  
Substance Use ◽  
Substance Use Disorders ◽  
Study Population

scholarly journals 4317 Using Failure Modes and Effects Analysis to Guide Adaptation of an Evidence-Based Parenting Program for Mothers with Substance Use Disorders

2020 ◽  
Vol 4 (s1) ◽  
pp. 151-152
Author(s):  
Elizabeth Peacock-Chambers ◽  
Peter Friedmann ◽  
Nancy Byatt ◽  
Nancy Suchman ◽  
Emily Feinberg
Keyword(s):  
Substance Use ◽  
Substance Use Disorders ◽  
Home Visiting ◽  
Failure Modes ◽  
Parenting Program ◽  
Advisory Panel ◽  
Evidence Based ◽  
Intervention Delivery ◽  
Potential Development ◽  
Study Population

OBJECTIVES/GOALS: To identify possible failures that could occur in the delivery of an evidence-based parenting program for mothers with substance use disorders (SUD) through existing home-visiting services, and to develop solutions to the most significant failures. METHODS/STUDY POPULATION: Using failure modes and effects analysis (FMEA) methodology, we conducted two 2-hour advisory panel discussions with 15 people from a variety of disciplines and life experiences related to SUDs. The intervention delivery process included five steps: (1) Recruitment, (2) Screening, (3) Matching, (4) Enrollment in person, and (5) Intervention delivery. Participants collectively determined possible failures, causes, and consequences. Participants then agreed on three scores (Likert Scale 0-10) for the likelihood of occurrence, detection, and severity of the failure, with 10 being the highest likelihood, difficulty detecting, or severity. A risk priority number (RPN) was calculated as the product of the 3 scores (maximum RPN = 1,000). The group then identified possible solutions for failures with higher RPNs. RESULTS/ANTICIPATED RESULTS: For each step in the process we identified the following number of failure nodes and RPN scores: (1) recruitment: 13 failures; RPN = 800, (2) screening: 102 failures; RPN = 10, (3) matching: 4 failures: RPN = 490, (4) enrollment: 6 failures; RPN = 80, (5) delivery: 11 failures; RPN = 80. The most critical failures related to recruitment and were perceived as being caused by potential development of mistrust in the community. Participants strongly encouraged the use of “strengths-based language,” clear referral plans for mothers that did not qualify, and inclusion of mothers that did not have custody of their children. These findings resulted in changes to the screening script, enrollment procedures, and inclusion criterial for the program. DISCUSSION/SIGNIFICANCE OF IMPACT: FMEA methodology was particularly effective in identifying possible failures for the integration of an evidence-based parenting program into existing home-visiting services as they related to the psychological safety of mothers with SUDs. The process resulted in direct changes to procedures for the anticipated program integration and study.

scholarly journals Conduct Disorder-Related Hospitalization and Substance Use Disorders in American Teens

2019 ◽  
Vol 9 (7) ◽  
pp. 73
Author(s):  
Anum Masroor ◽  
Rikinkumar S. Patel ◽  
Narmada N. Bhimanadham ◽  
Sanjeetha Raveendran ◽  
Naveed Ahmad ◽  
...  
Keyword(s):  
Substance Use ◽  
Alcohol Use ◽  
Conduct Disorder ◽  
Substance Use Disorders ◽  
Nationwide Inpatient Sample ◽  
Cannabis Use ◽  
Psychiatric Illnesses ◽  
Related Quality ◽  
Health Related

Objective: Our study aimed to compare the demographic characteristics of conduct disorder (CD) inpatients versus other psychiatric inpatients in children and adolescents, and assess the association between conduct disorder patients and the spectrum of substance use disorders (SUD). Methods: We included 800,614 psychiatric adolescent (12–18 years) inpatients, and this included 8885 inpatients (1.1%) primarily for conduct disorder in the Nationwide Inpatient Sample (2010–2014). ICD-9 codes were used to detect SUD, and a logistic regression model was used to evaluate the odds ratio (OR) for SUD in conduct disorder inpatients. Results: A higher proportion of conduct disorder inpatients were of 12–15 years of age (62.6%), male (64.4%), and White (45.7%). The lower median household income was correlated with a higher prevalence of conduct disorder (36.4%). Among SUD, cannabis use (23.7%) was most prevalent in conduct disorder inpatients followed by tobacco and alcohol use (10.1% each). Conduct disorder inpatients have 1.7-fold higher odds (95% confidence interval (CI) 1.52–1.82) for alcohol use and 1.4-fold higher odds (95% CI 1.31–1.49) for cannabis use compared to the non-conduct disorder inpatients. Cannabis use was seen significantly in adolescents (49.1%, 12–15 years), male (75.6%), and African Americans (45.6%). Conclusion: Conduct disorder inpatients have a higher risk of comorbid SUD compared to other psychiatric illnesses. The most common substance to be abused is cannabis followed by tobacco and alcohol. Varying pattern of substance use was seen by demographics and these predictors may help the clinicians for early diagnosis and treatment to improve overall health-related quality of life.

scholarly journals Adolescent nonmedical users of prescription opioids: Brief screening and substance use disorders

2012 ◽  
Vol 37 (5) ◽  
pp. 651-656 ◽  
Author(s):  
Sean Esteban McCabe ◽  
Brady T. West ◽  
Christian J. Teter ◽  
James A. Cranford ◽  
Paula L. Ross-Durow ◽  
...  
Keyword(s):  
Substance Use ◽  
Substance Use Disorders ◽  
Prescription Opioids ◽  
Brief Screening
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