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Asthma is one of the nation's most common and costly chronic conditions, affecting over 38 million Americans at some time in their lives. Managing asthma requires a long term, multifaceted approach, including patient education, behavior changes, asthma trigger avoidance, pharmacological therapy, and frequent medical follow-up. This study provides asthma data available at the state and local level to direct and evaluate interventions undertaken by asthma control programs located in the state health departments. Improved tracking for asthma is critical for planning and evaluating efforts to reduce the health burden from the disease.

The Behavioral Risk Factor Surveillance System (BRFSS) is a state-based system of health surveys that collects information on health risk behaviors, preventive health practices, and health care access primarily related to chronic disease and injury. For many states, the BRFSS is the only available source of timely, accurate data on health-related behaviors. BRFSS was established in 1984 by the Centers for Disease Control and Prevention (CDC); currently data are collected monthly in all 50 states, the District of Columbia, Puerto Rico, the United States Virgin Islands, and Guam. More than 350,000 adults are interviewed each year, making the BRFSS the largest telephone health survey in the world. States use BRFSS data to identify emerging health problems, establish and track health objectives, and develop and evaluate public health policies and programs. The BRFSS is a cross-sectional telephone survey conducted by state health departments with technical and methodological assistance provided by CDC. States conduct monthly telephone surveillance using a standardized questionnaire to determine the distribution of risk behaviors and health practices among adults. Responses are forwarded to CDC, where the monthly data are aggregated for each state, returned with standard tabulations, and published at the year's end by each state. The BRFSS questionnaire was developed jointly by CDC's Behavioral Surveillance Branch (BSB) and the states. Data derived from the questionnaire provide health departments, public health officials, and policymakers with necessary behavioral information. When combined with mortality and morbidity statistics, these data enable public health officials to establish policies and priorities and to initiate and assess health promotion strategies. Demographic variables include race, age, sex, education level, marital status, employment status, and income level.

Many people have health problems that affect how well they can do normal activities, either for a short time or for their lifetime. These problems may be present from birth or result from illness, injury, or aging. Rehabilitation, or rehab, can help patients regain the ability to do normal activities. Rehab providers include doctors, nurses, psychologists, and physical, occupational, speech, or language therapists.

Rehab treatments often lack a common definition. Rehab providers often name treatments by the type of professional who delivers them or the problem they treat, rather than by the content of the treatment. Also, treatments can vary across rehab providers. Using a standard way to define rehab treatments may help researchers compare these treatments.

In this study, the research team created and tested a manual to help rehab providers use standard ways to define rehab treatments.

CCPC's long term vision is to use pragmatic comparative effectiveness methods, linked to an extensive primary care practice data repository, to establish evidence about best practices for complex real world patients and deliver appropriate, real-time decision support at point of service for primary care practitioners (PCPs) in a way that will account for individualized management of conditions and choice of treatments in order to provide optimal care.

The primary aim of BICEP was to advance analytical methods of observational Comparative Effectiveness Research (CER) to support evidentiary needs of primary care practitioners in answering important questions related to care of patient populations with Multiple Complex Conditions (MCCs).

The secondary aim of BICEP was to conduct a pilot study to demonstrate the feasibility and value of using the analytic methods for conducting CER among complex patients.

BICEP sought to answer the following clinical research questions: In adults with Type 2 Diabetes Mellitus (T2DM) coupled with additional chronic diseases,

1. What is the comparative effectiveness of T2DM medications in achieving glycemic control?
2. What is the comparative effectiveness of T2DM medications on intermediate outcomes, adverse events, side effects, tolerability?
3. Does the effectiveness and safety of the diabetic treatment options differ across subgroups of patients based on patient demographic characteristics, complex co-morbidities, or the use of other concurrent therapies?

Patients with chronic health problems, such as diabetes, often need to change treatment plans over time to improve their health. To help with this process, doctors can monitor patients' health through follow-up clinic visits and lab tests. Doctors may also suggest changing a treatment plan in response to visits or lab test results. When a treatment plan changes in this way, it's called a dynamic treatment plan. In this study, the research team developed and tested new statistical methods to learn how dynamic treatment plans and choices about follow-up care affect patients' health. These methods use electronic health records, or EHRs. Using EHRs is helpful because they have data on

• What treatments patients have received over time
• How treatments have affected patients' health
• Follow-up information such as lab test results

But the data may differ for patients based on when and why they go to the doctor. These differences make it hard for researchers to accurately know the effect of dynamic treatment plans across many patients.

To access the methods and software, please visit the simcasual R Package.

Comparative effectiveness research compares two or more treatments to see which one works better for which patients. Electronic healthcare data are useful for this type of research. These data come from medical records and insurance claims. The data include information about how well patients respond to treatments. But many things--not just treatments--affect whether a patient's health improves.

How well a patient responds to a treatment may depend on the patient's age or what medicines the patient takes. It could also depend on what other health problems a patient has and how severe those problems are. Or a doctor may suggest one treatment instead of another because of a patient's personal situation and health. Researchers need ways to determine whether changes in a patient's health result from a certain treatment or something else.

Different statistical methods help researchers account for the various things that can affect treatment results. But researchers don't know which methods work best. This study compared several methods. The team looked at how well the methods worked to predict patients' responses to treatment, taking into account their personal situations and health. The team then created a computer program to help researchers use the methods.

To access the methods and software, please visit the Hdps GitHub and TargetedLearning GitHub.

Institutional misconduct, especially violent misconduct, poses a problem for all prisons. To address the misconduct concern, this study tested whether an evidence-based, cognitive behavioral treatment (CBT) program would reduce misconduct, including incidents of violent misconduct, and post-release arrests compared to non or less intensive CBT programming. The Delaware Department of Correction (DOC) implemented CBT-based programs in their institutions, which included the Cognitive Behavioral Interventions - Core Curriculum (CBI-CC) developed at the University of Cincinnati. The three programs addressed in this study include Thinking Things Through (TTT), Road to Recovery (R2R), and Reflections. TTT consisted of the instruments and materials of the CBI-CC, whereas R2R and Reflections focused on CBT skills and techniques. The Center for Drug and Health Studies (CDHS) in collaboration with DOC evaluated the impact of the program using administrative records and surveys with program participants.

Results indicated that all treatment groups performed better than the control group in terms of rearrest and incarceration. The group who received the intensive CBT treatment performed significantly better than all other groups. Intensive CBT treatment was thus effective in reducing recidivism. In terms of CBI-CC programming, participants had the highest rates of misconduct but saw a significant decrease after completing programming. In addition, both R2R and TTT have the lowest rates of rearrest after completing treatment programming compared to all other groups. This can be credited to the length and intensity of programming, as well CBT implemented within the programs.

Purpose. The CoNNECT Project enables comparative effectiveness research on mental health, behavioral health, and substance use in primary care. CoNNECT tracked two main elements: (1) the number of patients identified with a comorbid mental health and physical health diagnosis; (2) the number of patients who initiate treatment secondary to a mental health diagnosis. CoNNECT created the capacity to build a base for mental health in primary care comparative effectiveness research using electronic connectivity to generate retrospective and in time prospective clinical data.

Data Access. CoNNECT data are not available from ICPSR. The data from this study are hosted at DARTNet.

Antibiotics are considered a feasible treatment for appendicitis, yet appendectomy remains the treatment standard in the United States. Previous randomized trials comparing these treatments excluded important subgroups and recruited small sample sizes but questions remain about the applicability of these previous findings. This study conducted the Comparison of Outcomes of antibiotic Drugs and Appendectomy (CODA) randomized clinical trial to compare antibiotics with appendectomy among adults with appendicitis, including those with appendicolith. Those recruited comprised a diverse population, compared an overall measure of health status as the primary outcome, and included several secondary clinical and patient-reported outcomes, complications, and measures of healthcare utilization.

The Comprehensive Post-Acute Stroke Services (COMPASS) Study is a pragmatic cluster-randomized clinical trial that evaluated the real-world effectiveness of the COMPASS transitional care (COMPASS-TC) model compared to usual care among adult stroke and transient ischemic attack (TIA) patients discharged home between 2016 and 2018. In Phase 1, 40 North Carolina hospital units were randomized 1:1 to the COMPASS-TC intervention or usual care, stratified by stroke patient volume and stroke center certification. In Phase 2, hospitals randomized to usual care crossed over to implement COMPASS-TC, and hospitals randomized to the intervention sustained COMPASS-TC. The intervention was patient-centered and assessed social and functional determinates of health to inform individualized care plans for secondary prevention, recovery, and referrals to services and community-based resources. COMPASS-TC was consistent with Centers for Medicare and Medicaid Services (CMS) TC management reimbursement requirements.

The primary outcome was functional status (Stroke Impact Scale-16; SIS-16) at 90 days; secondary outcomes were mortality, disability, medication adherence, depression, cognition, self-rated health, fatigue, care satisfaction, home blood pressure monitoring, falls, and caregiver strain. Telephone interviewers, blinded to treatment assignment, assessed these outcomes at 90 days.

Research tools available to help advance knowledge of effective community-based elder mistreatment (EM) interventions are limited. The field lacks an understanding of what success means in EM response program (EMRP) interventions, which work directly with victims to reduce the risk of re-victimization. Without establishing indicators of EMRP success, it is not possible to develop valid intervention outcome measures to compare different EMRP models toward the development of evidence-based practice. Informed by the EMRP practice principle of older adult self-determination, this study developed a victim-centric taxonomy of case outcomes that indicate EMRP success.

This study drew on two sources of data, including interviews with EM victims and a scoping review to inform taxonomy development. Prioritizing the perspective of victims, this study conducted interviews with 27 victims involved in EMRP services who vary in EM subtype, gender, and race/ethnicity.

The taxonomy of successful EMRP outcomes will serve as important research infrastructure to support the development of EMRP intervention outcome measurement in future research.

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 purpose of this study was to investigate violence and aggression committed by nursing home residents that is directed toward other residents, referred to here as resident-to-resident elder mistreatment (R-REM). Resident-to-resident mistreatment (R-REM) was defined as: negative and aggressive physical, sexual, or verbal interactions between long term care residents, that in a community setting would likely be construed as unwelcome and have high potential to cause physical or psychological distress in the recipient.

The goals of this project were to: enhance institutional recognition of R-REM; examine the convergence of R-REM reports across different methodologies; identify the most accurate mechanism for detecting and reporting R-REM; develop profiles of persons involved with R-REM by reporting source; investigate existing R-REM policies, and; develop institutional guidelines for reporting R-REM episodes. Also, the project team sought to answer the following research questions: (1) Will the reporting of R-REM differ by source? (2) Which reporting methods will show the highest level of convergence and accuracy in reporting? (3) What resident characteristics or profiles will predict R-REM across the differing reporting sources? (4) What are the existing guidelines and/or institutional policies for reporting R-REM? To achieve these goals, the researcher conducted this study over a two week period in five urban and five suburban New York City facilities. Resident-to-resident abuse information was derived from five sources: (1) resident interviews (2) staff informants (3) observational data (behavior sheets) (4) resident chart reviews (5) incident and accident reports.

This study evaluated program design, quality of treatment delivery, and program effectiveness of three separate state sponsored alcohol specific treatment programs in prisons located in Montana, Ohio, and Texas from 2006 to 2012.

Overview

The Florida Department of Health and the Florida Cancer Data System (FCDS) collaborated with a hospital network composed of nine clinical facilities, to capture electronic medical records (EMR) data of patients who were diagnosed with or treated for invasive breast cancer from 2007 to 2010. Certain hospital data elements were available throughout 2006. An additional year of 2011 follow-up data was also available for a subset of patients receiving medication treatment. The purpose of the data capture was to advance patient-centered outcomes research to reduce the morbidity and mortality of cancer and other comorbidities.

A breast cancer pilot study was also conducted from a subset of all transmitted EMR records, consisting of admission records with a principal and/or secondary ICD-9-CM diagnosis between 174.0 and 174.9. The subset dataset was then linked to the central cancer registry using patient social security number, first and last name, and date of birth. Using a deterministic matching algorithm a total of 11,506 unique patients were matched to a patient in the FCDS database, resulting in 12,804 primary tumors and 53,940 unique hospital admission records. While the hospital EMR defined the patient dataset, all registry records for that patient were included in the final breast cancer pilot database, regardless of the reporting hospital or the date of diagnosis. This was to ensure capture of the entire diagnostic and treatment profile for each breast cancer patient.

Data Access

These data are not available from ICPSR. The data contain confidential information that can directly identify a patient. There are also reporting facility data. Therefore, to obtain these data, researchers will need to follow the Florida Cancer Data System data-sharing agreement process, as outlined on the FCDS data sharing request.

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.

This study, with assistance from the National Institute of Justice's Data Resources Program (FY2012), is a reanalysis of data from the national evaluation of the federal Serious and Violent Offender Reentry Initiative (SVORI). SVORI provided funding to 69 agencies across the United States to enhance reentry programs and coordination between corrections and community services. The national evaluation covered 16 of these sites, twelve of which provided services to the 2,054 adult ex-prisoners who are the focus of the present study.

The purpose of this study is to understand whether or not offenders receive the services they say they need, and whether the degree of 'fit' between this self-reported criminogenic need and services received is related to recidivism. This study analyzes data from the SVORI multisite evaluation to assess the potential explanations for the mixed effectiveness of reentry programs. The goal is to understand whether or not service-risk/need fit is related to successful reentry outcomes, or whether the needs of returning prisoners are unrelated to their risk of recidivism regardless of how well they are addressed. For the present study researchers obtained the SVORI (ICPSR 27101) outcome evaluation datasets from the National Archive of Criminal Justice Data (NACJD). The archive holds four separate datasets from the evaluation: Adult Males Data (Part 1, N=1,697), Adult Females Data (Part 2, N=357), Juvenile Males Data (Part 3, N=337) and official recidivism and reincarceration data (Part 4, N=35,469), which can be linked on a one-to-many basis with the individual-level data in the other three datasets. To prepare the SVORI data for analysis researchers merged Datasets 1 and 2 (Adult Males and Adult Females) and created seven separate datasets containing Waves 1 through 4 survey data, National Crime Information Center (NCIC) crime data, administrative data, and sampling weights.

This deposit to NACJD is intended to complement the existing SVORI dataset (ICPSR 27101). It contains an R syntax file to be used with the datasets contained in the ICPSR 27101 collection.

A rare disease is one that affects fewer than 200,000 people in the United States. Because few people have these diseases, clinical studies on treatments can be hard to conduct. One way to study rare disease treatments is with an small n sequential multiple assignment randomized trial (snSMART) study.

snSMART studies have two stages. In the first stage, researchers assign patients to a treatment by chance. In the second stage, patients may stay with the same treatment or switch treatments. Patients stay on the same treatment if it's working well. If the treatment isn't working, researchers assign patients by chance to a new treatment.

snSMARTs can help researchers learn more from a smaller number of patients than a standard clinical study. But most current methods for analyzing snSMARTs use data only from the first stage, which can lead to inefficient results.

In this project, the research team developed and tested new methods that use data from both stages to analyze snSMARTs. The team compared results from the new methods to actual treatment effectiveness to see:

• Bias, or whether results are too high or too low
• Efficiency, or how big the difference is between the results and actual treatment effectiveness

This study contains two supplementary documentation files. There is no data included in this release.

Patients may respond differently to the same treatment due to individual traits such as age or gender. Knowing how different traits can affect a patient's response to treatment can help doctors and patients make better treatment decisions. For example, this information can help doctors know what types of cancer medicines work better for certain patients. This project focuses on improving the methods that researchers use to compare how treatments work for different patients.

In this project, the research team developed and tested a statistical method called random forests, or RF. RF is a way to analyze data using a technique called machine learning. In machine learning, computers use data to learn how to perform different tasks with little or no human input. Many types of RF methods exist. The team compared multiple RF methods to learn how well the methods would work to find out how patients with different traits respond to the same treatment.

To access the R package, please visit the randomForestSRC CRAN webpage.

Since 2001, Vermont Department of Corrections (DOC) sex offender treatment providers and probation and parole officers have scored every adult male sex offender under community supervision on three measures of static (unchangeable) risk at intake (i.e., Static-99, RRASOR-Rapid Risk Assessment for Sex Offence Recidivism, and VASOR-Vermont Assessment of Sex Offender Risk) and one measure of dynamic (changeable) risk (i.e, SOTNPS) at intake and then every six months thereafter. This project conducted record reviews to verify the accuracy of the risk assessment scores and examined how scores on the Sex Offender Treatment Needs and Progress Scale (SOTNPS) and one or more of these static risk instruments can be combined into an overall model of risk assessment. An empirically derived decision-making model was created to assist correctional administrators, probation and parole officers, and treatment providers in allocating and delivering supervision and treatment services based on an individual's treatment needs and risk to sexually re-offend.

Three hypotheses were tested. First, it was expected that one or more static risk measures (Static-99R, Static-2002R and VASOR) would predict sexual recidivism with moderate accuracy in the sample. Second, a dynamic risk measure, Sex Offender Treatment Needs and Progress Scale (SOTNPS), or a subset of risk factors contained in this measure would also predict sexual recidivism with moderate accuracy and be sensitive to the changes in dynamic risk over time. Third, a combined static and dynamic risk measure would predict sexual recidivism more accurate than either measure alone.