This directory contains analytic code used to build cohorts, dependent variables, and covariates, and run all statistical analyses for the study, "Changes in care associated with integrating Medicare and Medicaid for dual eligible individuals: Examination of a Fully Integrated Special Needs Plan."The code files enclosed in this directory are:SAS_Cohorts_Outcomes 23-9-30.sas. This SAS code file builds study cohorts, dependent variables, and covariates. This code produced a person-by-month level database of outcomes and covariates for individuals in the integration and comparison cohorts.STATA_Models_23-6-5_weight_jama.do. This Stata program reads in the person-by-month level database (output from SAS) and conducts all statistical analyses used to produce the main and supplementary analyses reported in the manuscript.We have provided this code and documentation to disclose our study methods. Our Data Use Agreements prohibit publishing of row-level data for this study. Therefore, researchers would need to obtain Data Use Agreements with data providers to implement these analyses. We also note that some measures reference macros with proprietary code (e.g., Medispan® files) which require a separate user license to run. Interested readers should contact the study PI, Eric T. Roberts (eric.roberts@pennmedicine.upenn.edu) for further information.

These data are part of NACJD's Fast Track Release and are distributed as they were received from the data depositor. The files have been zipped by NACJD for release, but not checked or processed except for the removal of direct identifiers. Users should refer to the accompanying readme file for a brief description of the files available with this collection and consult the investigator(s) if further information is needed. The School Health Center Healthy Adolescent Relationship Program (SHARP) was a school health center (SHC) provider-delivered multi-level intervention to reduce adolescent relationship abuse (ARA) among adolescents ages 14-19 seeking care in SHCs. This study tested the effectiveness of a brief relationship abuse education and counseling intervention in SHCs. The SHARP intervention consisted of three levels of integrated intervention: A brief clinical intervention on healthy and unhealthy relationships for SHC (cisgender and transgender) male and female patients delivered by SHC providers during all clinic visits (evaluated via client pre- and post-surveys and chart review) Development of an ARA-informed SHC staff and clinic environment (evaluated via provider pre and post-training surveys and interviews) SHC-based youth-led outreach activities within the school to promote healthy relationships and improve student safety (evaluated by focus groups with youth leaders and measures of school climate) The collection consists of: 3 SAS data files sharp_abuse_data_archive.sas7bdat (n=1,011; 272 variables) sharp_blt2exit_long_data_archive.sas7bdat (n=1,949; 259 variables) sharp_chart_data_archive_icpsr.sas7bdat (n=936; 24 variables) 2 Stata data files SHARP_Provider Immediate Post_0829 and 0905 training_final-ICPSR.dta (n=38; 21 variables) SHARP_Provider Pre and Followup_final.dta-ICPSR.dta (n=66; 102 variables) 5 SAS syntax files NIJ SHARP - Analyses.sas NIJ SHARP - DataMgmt_Final.sas NIJ SHARP - Formats.sas SHARP - Chart Extraction Data-MASKED.sas SHARP - Chart Extraction Formats.sas 3 Stata syntax files code-for-SHARP-dating-violence-analyses-deidentified-MASKED.do SHARP_Provider Data to Archive-MASKED.do SHARP-analyses-deidentified-MASKED.do 3 PI provided codebooks SHARP Codebook_Client Chart Data.xlsx (1 worksheet) SHARP Codebook_Client Survey Data.xlsx (3 worksheets) SHARP Codebook_Provider Survey Data.xlsx (1 worksheet) For confidentiality reasons, qualitative data from focus groups are not currently available. Focus groups were conducted with each student outreach team following the conclusion of data collection. Discussions focused on awareness about ARA, the school-wide campaign, using the SHC as a resource, and what else can be done to prevent ARA in schools.

The SAS analysis code and dataset for the publication entitled, "Potential improvement in spatial accessibility of methadone treatment with integration into other outpatient substance use disorder treatment programs, New York City, 2024"

Thirteen young adults (YA group, mean age 25.5, SD 5.3, 4F/9M) and eleven elderly adults (EA group, mean age 64.2, SD 7.1, 4F/7M) were recruited for the study. All subjects had no medical history of the neural pathological conditions, i.e., stroke, head trauma or tumors.Each subject underwent the sensorimotor integration training (SIT) procedure which consisted in N repetitions of elementary sensorimotor integration task. The task required the participants to classify the duration of presented audio stimulus and execute of one of the motor actions (ME) depending on its duration. Specifically, the participant should clench left or right hand into a fist in response to short (SAS) or long (LAS) audio stimulus. The durations of SAS and LAS were 300 ms and 750 ms, respectively. During ME the hand should be held clenched until the next audio stimulus of the same duration, which informed the participant about the end of ME. The time interval required for SI and ME within single trail was chosen randomly in the range 4--5 s. The pause between the trials was also picked randomly within the range 6--8 s. See Fig. 1A for a single trial timeline. The audio stimuli were presented via audio speakers located on the table in front of the participant. During SIT each participant performed N=60 SI task repetitions (30 per stimulus). The overall duration of the experimental session was approximately 10 minutes per participant.The raw EEG recordings were filtered using the 50 Hz Notch filter. Additionally, the data were filtered using the 5th-order Butterworth filter in the range 1-100 Hz to remove low-frequency artifacts. The ocular and cardiac artifacts were removed using the independent component analysis (ICA). The filtered time-series were segmented into 60 epochs 6 s long each according to the experiment protocol. Each epoch included 3 s of prestimulus EEG and 3 s of poststimulus EEG centered at the presentation of the first audio stimulus. To evaluate the effect of SIT on cortical activation with trial progression the timeline of experimental session (N=60 epochs total, 30 per stimulus) was divided into four equal Intervals: T1 (epochs 1-15); T2 (epochs 16-30); T3 (epochs 31-45); T4 (epochs 46-60). Thus, the interval T1 represented early phase, while the interval T4 represented the last phase of the experiment. The data was then inspected manually and the epochs with the remaining artifacts were rejected. Finally, each interval contained 10 artifact-free epochs (5 epochs per stimulus), 40 total. See Fig. 1B for sampling epochs into sets.Individual epochs for each subject are stored in the attached ._epo.fif files for Python MNE package for M/EEG analysis. Prefix EA or YA in the name of the file identifies the age group, which subject belongs to. Postfix T1, T2, T3 or T4 in the name of the file indicates the interval of SIT.

In this paper, we investigate the use of Bayesian networks to construct large-scale diagnostic systems. In particular, we consider the development of large-scale Bayesian networks by composition. This compositional approach reflects how (often redundant) subsystems are architected to form systems such as electrical power systems. We develop high-level specifications, Bayesian networks, clique trees, and arithmetic circuits representing 24 different electrical power systems. The largest among these 24 Bayesian networks contains over 1,000 random variables. Another BN represents the real-world electrical power system ADAPT, which is representative of electrical power systems deployed in aerospace vehicles. In addition to demonstrating the scalability of the compositional approach, we briefly report on experimental results from the diagnostic competition DXC, where the ProADAPT team, using techniques discussed here, obtained the highest scores in both Tier 1 (among 9 international competitors) and Tier 2 (among 6 international competitors) of the industrial track. While we consider diagnosis of power systems specically, we believe this work is relevant to other system health management problems, in particular in dependable systems such as aircraft and spacecraft. Reference: O. J. Mengshoel, S. Poll, and T. Kurtoglu. "Developing Large-Scale Bayesian Networks by Composition: Fault Diagnosis of Electrical Power Systems in Aircraft and Spacecraft." Proc. of the IJCAI-09 Workshop on Self-* and Autonomous Systems (SAS): Reasoning and Integration Challenges, 2009 BibTex Reference: @inproceedings{mengshoel09developing, title = {Developing Large-Scale {Bayesian} Networks by Composition: Fault Diagnosis of Electrical Power Systems in Aircraft and Spacecraft}, author = {Mengshoel, O. J. and Poll, S. and Kurtoglu, T.}, booktitle = {Proc. of the IJCAI-09 Workshop on Self-$\star$ and Autonomous Systems (SAS): Reasoning and Integration Challenges}, year={2009} }

The hierarchical structure of SAs.