ABSTRACT
Introduction: College students represent an important subpopulation of the United States, with over 19 million college students in the U.S. enrolled yearly. Methods: Descriptive analysis of the causes of death for all deceased students reported by the UW Dean of Students Office (DSO) between 2004 and 2018. We analyzed frequencies and yearly rates. Results: Our analysis shows that contrary to published data and national statistics for the relevant age groups, intentional by self-harm deaths lead causes of death in enrolled students from 2004 to 2018. Intentional by self-harm is the main cause of death in male students, younger students, and white students. "Other" causes of death is the main cause in female students, older students, and students of color. Conclusions: These results must be shared with different stakeholders across campus as well as with other universities in order to support and evaluate campus-wide prevention strategies for means restriction and environmental safety.
Introducción: Los estudiantes universitarios representan una subpoblación importante de los Estados Unidos, con más de 19 millones de matriculados anualmente. Sin embargo, hay pocos datos publicados sobre la mortalidad y causas de muerte en la población universitaria. El propósito de este estudio fue analizar las causas de muerte, basadas en datos de certificados de defunción, de estudiantes matriculados en University of Winconsin- Madison desde 2004 hasta 2018. Métodos: Análisis descriptivo de las causas oficiales de muerte de todos los estudiantes fallecidos reportados por la Oficina del Decano de Estudiantes entre 2004 y 2018. Se analizaron frecuencias y tasas anuales. Resultados: El análisis muestra que, contrariamente a los datos publicados y las estadísticas nacionales para los grupos de edad relevantes, las muertes intencionales por autolesión lideran las causas de muerte en los estudiantes matriculados entre esos años. Las autolesiones intencionales son la principal causa de muerte en los estudiantes varones, en los estudiantes más jóvenes y en los estudiantes blancos. Las causas incluidas en la categoría indicada como Otras son las principales en las estudiantes mujeres, en estudiantes mayores y en estudiantes de color. Conclusiones: Los resultados de este estudio deben compartirse con las diferentes áreas interesadas en todo el campus universitario y con otras instituciones universitarias, para apoyar y evaluar las estrategias de prevención, la aplicación de los medios de restricción y la seguridad ambiental.
Subject(s)
Mortality , Students , Suicide , Universities , Accidents, Traffic , Cause of Death , Self-Injurious BehaviorABSTRACT
BACKGROUND: Translational research is a key area of focus of the National Institutes of Health (NIH), as demonstrated by the substantial investment in the Clinical and Translational Science Award (CTSA) program. The goal of the CTSA program is to accelerate the translation of discoveries from the bench to the bedside and into communities. Different classification systems have been used to capture the spectrum of basic to clinical to population health research, with substantial differences in the number of categories and their definitions. Evaluation of the effectiveness of the CTSA program and of translational research in general is hampered by the lack of rigor in these definitions and their application. This study adds rigor to the classification process by creating a checklist to evaluate publications across the translational spectrum and operationalizes these classifications by building machine learning-based text classifiers to categorize these publications. METHODS: Based on collaboratively developed definitions, we created a detailed checklist for categories along the translational spectrum from T0 to T4. We applied the checklist to CTSA-linked publications to construct a set of coded publications for use in training machine learning-based text classifiers to classify publications within these categories. The training sets combined T1/T2 and T3/T4 categories due to low frequency of these publication types compared to the frequency of T0 publications. We then compared classifier performance across different algorithms and feature sets and applied the classifiers to all publications in PubMed indexed to CTSA grants. To validate the algorithm, we manually classified the articles with the top 100 scores from each classifier. RESULTS: The definitions and checklist facilitated classification and resulted in good inter-rater reliability for coding publications for the training set. Very good performance was achieved for the classifiers as represented by the area under the receiver operating curves (AUC), with an AUC of 0.94 for the T0 classifier, 0.84 for T1/T2, and 0.92 for T3/T4. CONCLUSIONS: The combination of definitions agreed upon by five CTSA hubs, a checklist that facilitates more uniform definition interpretation, and algorithms that perform well in classifying publications along the translational spectrum provide a basis for establishing and applying uniform definitions of translational research categories. The classification algorithms allow publication analyses that would not be feasible with manual classification, such as assessing the distribution and trends of publications across the CTSA network and comparing the categories of publications and their citations to assess knowledge transfer across the translational research spectrum.
