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OBJECTIVES/GOALS: Medications to treat opioid use disorder (mOUD) are available and can save lives, but are underutilized. We hypothesize that the rate of prescribing varies by treatment facility and these differences will shed light on barriers and facilitators to mOUD utilization. METHODS/STUDY POPULATION: We performed an exploratory analysis in MD Clone, a platform which generates non-identifiable synthesized data based on real patient data in the electronic health record (EHR) of St. Louis based hospitals. Our query included adults aged 18-70 with an OUD diagnosis using ICD-9 of -10 codes (opioid abuse, opioid dependence, opioid poisoning, opioid withdrawal) occurring between 2013 and 2022 along with prescriptions for buprenorphine, methadone, or naloxone within 7 days of the condition being entered in the record. We compared the rate of medication prescription within 7 days across settings and facilities where the patients were seen. We propose to replicate this analysis in actual patient records from the EHR following IRB approval. RESULTS/ANTICIPATED RESULTS: Our synthetic data comprised 24600 patient diagnoses. After filtering for patients seen in the ER or inpatient 16235 patients remained in the data set. Of these, 4376 fell into one of the categories that clearly warrant treatment with medication. Out of 4376 patients with a qualifying OUD related condition, only 815 (18.6%) received a prescription for any of the medications. Rates of prescribing within facilities varied between 67.2% of eligible patients receiving a prescription at a rural location to 0% at some urban centers. We anticipate similar findings from analysis of patient records obtained from the EHR. We will extend our analysis to explore factors which may be driving the wide difference in prescribing to better understand barriers and facilitators to mOUD utilization. DISCUSSION/SIGNIFICANCE: We identify under-utilization with differences across facilities in prescribing mOUD based on preliminary work in synthetic data. If true, this represents a gap in care and opportunity for intervention. By replicating the MD Clone results in patient data from the EHR we will confirm this finding and increase acceptability to clinicians.
Alcohol use disorder (AUD) and schizophrenia (SCZ) frequently co-occur, and large-scale genome-wide association studies (GWAS) have identified significant genetic correlations between these disorders.
We used the largest published GWAS for AUD (total cases = 77 822) and SCZ (total cases = 46 827) to identify genetic variants that influence both disorders (with either the same or opposite direction of effect) and those that are disorder specific.
We identified 55 independent genome-wide significant single nucleotide polymorphisms with the same direction of effect on AUD and SCZ, 8 with robust effects in opposite directions, and 98 with disorder-specific effects. We also found evidence for 12 genes whose pleiotropic associations with AUD and SCZ are consistent with mediation via gene expression in the prefrontal cortex. The genetic covariance between AUD and SCZ was concentrated in genomic regions functional in brain tissues (p = 0.001).
Our findings provide further evidence that SCZ shares meaningful genetic overlap with AUD.
This chapter reviews the family studies supporting the role of genetics and recent molecular genetic results. Mapping studies using linkage and association methods have had modest success to date despite difficulties in replication between studies. Linkage studies have shown the best support for chromosomal regions: 6q, 8q, 9p, 13q, 14q, and 22q. Several candidate genes first identified in studies of schizophrenia have shown reproducible association in bipolar disorder. Genome-wide association studies (GWAS) have been successful in identifying a few genes with small effects on risk. The data overall suggest a high level of both genic and allelic heterogeneity, as well as, a complex mode of inheritance. The coming availability of economical whole genome sequencing promises availability of complete genomic information. This, and large samples now being collected, may provide the datasets necessary to unravel the genetic complexities of this illness.
Louis A. Roussos, Senior Psychometrician, Measured Progress,
Louis V. DiBello, Research Professor of Psychology, University of Illinois at Chicago,
William Stout, Professor of Statistics, University of Illinois at Urbana-Champaign,
Sarah M. Hartz, Resident, Department of Psychiatry, University of Iowa,
Robert A. Henson, Assistant Professor of Education Research and Methodology, University of North Carolina at Greensboro,
Jonathan L. Templin, Assistant Professor of Psychology, University of Kansas
There is a long history of calls for combining cognitive science and psychometrics (Cronbach, 1975; Snow & Lohman, 1989). The U.S. standards movement, begun more than 20 years ago (McKnight et al., 1987; National Council of Teachers of Mathematics, 1989), sought to articulate public standards for learning that would define and promote successful performance by all students; establish a common base for curriculum development and instructional practice; and provide a foundation for measuring progress for students, teachers and programs. The standards movement provided the first widespread call for assessment systems that directly support learning. For success, such systems must satisfy a number of conditions having to do with cognitive-science–based design, psychometrics, and implementation. This chapter focuses on the psychometric aspects of one particular system that builds on a carefully designed test and a user-selected set of relevant skills measured by that test to assess student mastery of each of the chosen skills. This type of test-based skills level assessment is called skills diagnosis. The system that the chapter describes in detail is called the Fusion Model system.
This chapter focuses on the statistical and psychometric aspects of the Fusion Model system, with skills diagnosis researchers and practitioners in mind who may be interested in working with this system. We view the statistical and psychometric aspects as situated within a comprehensive framework for diagnostic assessment test design and implementation.
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