Characteristics and outcomes of hospitalizations and readmissions for opioid dependence and overdose: nationally representative data.

Background: Despite the increasing rates of morbidity, mortality, and costs from the opioid addiction crisis, there is a paucity of literature on nationwide patterns of opioid abuse and dependence admissions and readmissions. We sought to investigate common comorbidities, readmission rates, and variables associated with readmission following index admission for opioid overdose or dependence. Methods: The 2013 Nationwide Readmission Database is a national database including data on more than 14 million US admissions. We used International Classification of Disease, Ninth Revision, Clinical Modification codes to identify index opioid abuse or dependence admissions, readmissions, and medical co-morbidities. We summarized all-cause readmission rates and reasons for readmission following index opioid dependence or overdose admission. We performed multivariable logistic regression, testing the association between characteristics of index admission and readmission. Results: 64,426 individuals were admitted for drug overdose or dependence during 2013. Of those, 30.1% were readmitted for all causes within one year and 8.7% were readmitted for opioid overdose or dependence within that year. The most common primary diagnoses on the readmission record were infection, kidney failure, drug related admission, and psychiatric admission. Predictors of readmission were smoking, male sex, younger age, alcohol, bipolar disorder, non-opioid drug use, admission to teaching hospitals in metropolitan areas, and discharge against medical advice. Conclusion: There is a high all-cause readmission rate following index admission for opioid overdose or dependence and a greater likelihood of readmission among young males with psychiatric comorbidities in metropolitan areas. Targeted interventions to address psychiatric comorbidities and transitions of care may be needed for the high-risk opioid dependence and overdose population.

Educational Research: Why Medical Students Choose Neurology: A Computational Linguistics Analysis of Personal Statements.

OBJECTIVE: To understand medical students' motivations for choosing neurology and how applicants conceptualize the field, as this information can be used to enhance interest in neurology and develop educational programs to help identify, support, and recruit future neurologists. BACKGROUND: Applicants to neurology residencies submit personal statements describing themselves and their motivations. Textual analysis of personal statements has been performed in internal medicine and general surgery, but never before in neurology. We hypothesized that specific words and themes would be mentioned in residency personal statements with high frequencies indicating students' motivations. METHODS: We used computational linguistics software to assess key words and thereby study motivations, expectations, and themes present among neurology applicants. A total of 2,405 personal statements submitted over 5 years to our institution were de-identified and compiled into a database for evaluation through 3 computational linguistics software programs. We performed calculations of term frequencies (TF) and TF-inverse document frequencies and performed K-means clustering to identify unique words and common themes. RESULTS: Specific disease states were discussed. For example, stroke (TF 2,178), epilepsy (TF 970), and dementia (TF 944) were referenced more often than amyotrophic lateral sclerosis (TF 220) and carpal tunnel (TF 10). The most common proper names cited were Oliver Sacks (TF 94) and Sherlock Holmes (TF 41). Common themes included fascination with the brain, interest in research, desire to help patients, early interests in neurology, continued pursuit of learning, appreciation for time with patients, family history with neurologic illness, and intellectual curiosity. CONCLUSIONS: This first computational linguistic analysis of neurology personal statements provides understanding into medical students' motivations and interests. Ongoing subgroup and thematic analyses may inform educational strategies and enhance recruitment to our field.