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Cluster randomized trials, or CRTs, are studies that compare treatments across different groups of patients, or clusters. An example of a cluster is people who receive care at one clinic.

To reduce bias in CRT results, researchers assign clusters by chance to different treatments. But what happens after they assign treatment can lead to differences across clusters and bias the results. For example, patients who visit clinics assigned to a treatment may be older than patients who visit clinics not assigned to that treatment. Current statistical methods for analyzing data from CRTs don't work well to account for these differences.

In this study, the research team developed new methods to account for differences across clusters after treatment assignment.

Clinical notes in electronic health records, or EHRs, can help researchers study treatments. For example, EHR notes may contain information about whether patients take their medicines as directed. But it takes researchers a lot of time to find this information.

Natural language processing, or NLP, methods can help researchers find information in EHR notes. With NLP, computer programs read and identify written language to make it easier to sort and study. But current NLP methods don't work well to find and label text about medicine use.

In this study, the research team created and tested a new NLP method to find and label EHR notes on patients' medicine use.

The overall goal for this project was to reduce the incidence of COVID-19, hospitalization, and mortality among adults with serious mental illness (SMI) and intellectual disabilities/developmental disabilities (IDD) in congregate living settings (i.e., group homes) in Massachusetts, as well as to reduce COVID-19 incidence among staff who work in these settings. The research team was guided by two comparative effectiveness questions:

1. With the goal of prioritizing and making actionable best practices available as resources, what is the comparative effectiveness of various types and intensities of preventative interventions (e.g., screening, isolation, contact tracing, hand hygiene, physical distancing, use of face masks) in reducing rates of COVID-19, related hospitalizations, and related mortality in this population?
2. With the goal of effectively implementing best practices, what is the most effective implementation strategy to reduce rates of COVID-19 in this population: using tailored best practices (TBP) with SMI/IDD residents and staff of group homes in mind, or general best practices (GBP) from state and federal standard guidelines for all congregate care settings?

The specific aims of this study were as follows:

Aim 1a. Synthesize existing baseline data collected by 6 state behavioral health agencies on COVID-19 rates, hospitalization, mortality, and use of infection prevention practices.

Aim 1b. Collect stakeholder input via surveys and virtual focus groups on staff and resident experiences and on barriers/facilitators to implementing recommended preventative practices.

Aims 2a and 2b. Determine the comparative effectiveness of various COVID-19 preventative practices by (Aim 2a) using a validated simulation model to estimate COVID-19 spread in group homes and (Aim 2b) obtaining stakeholder input on prioritizing and defining tailored best practices for implementation.

Aim 3. Compare the effectiveness of TBPs with GBPs by using a hybrid effectiveness-implementation cluster randomized controlled trial.

Data collected to answer Aims 1 and 2 served as the foundation for designing the Aim 3 trial. Data for the trial were collected in 3-month intervals beginning January 2021 (baseline) until October 2022 (15-month follow-up). Residents and staff were sampled from approximately 400 group homes. Primary implementation outcome measures were COVID-19 vaccination rates and fidelity scores. The primary effectiveness outcome measure was COVID-19 infection.

Notes: This collection contains only data from Aim 1a and Aim 3. Throughout the data and documentation, "intellectual and/or developmental disabilities" is abbreviated as both IDD and ID/DD.

Using a pragmatic trial design to limit exclusions, the investigators conducted a 36-month multi-center randomized effectiveness trial to compare the impact of an enhanced usual care (control) intervention, with exercise coaching (exercise), on fragility fractures and serious fall-related injuries (FF/SFRI) in patients with a previous fragility fracture. Specifically, the investigators examined the impact of the intervention on social loneliness, physical function, and bone strength. 1,139 individuals over 65 with a history of fragility fractures and/or osteoporosis were recruited over two years across three regions of Pennsylvania and randomized into either the enhanced usual care control group or exercise with coaching treatment group, where in-person exercise activities were led by trained volunteers.

Dataset (DS) 1 contains the following data used for analysis: participant characteristics at baseline by study group (referred to as Table 5 in the documentation), intervention participant characteristics at baseline based on exercise session type (referred to as Table 6), cumulative incidences of first serious fall-related injury compared by study group (referred to as Figure 3), cumulative incidence for first serious fall-related injury by age, gender, race, and osteoporosis medication (referred to as Table 8 and Figure 4), and cumulative incidence for first series fall-related injury by tertile of average intervention sessions per month (referred to as Figure 5). Other datasets used for analysis are fall injury data (DS2), monthly workout sessions data (DS3), secondary outcomes data (DS4, referred to as Table 7), and adverse events data (DS5, referred to as Table 9). DS6 includes markers designating before and after the start of the COVID-19 pandemic (March 11, 2020), allowing for analyses of participants who experienced fall-related injuries relative to COVID-19.

Datasets labeled "Miscellaneous" were not used in any analysis. These datasets contain extra measures from screening (DS7), baseline assessments (DS8), 4-month check-in visits (DS9), participant's distance to study site (DS10), coaching check-ins for weeks 1-12 (DS11), exercise sessions by month (DS12), adverse events (DS13), and end of study information (DS14).

The main objective of this pragmatic randomized clinical trial (PCT) is to identify the optimal dose of aspirin for secondary prevention in atherosclerotic cardiovascular disease (ASCVD). A total of 15,076 high-risk patients with a history of a myocardial infarction (MI) or documented ASCVD were randomized in a 1:1 ratio to receive 81 mgs versus 325 mgs of aspirin every day. This trial addressed the following specific aims:

1. To compare the effectiveness of two doses of aspirin (81 mg and 325 mg) in reducing a composite of all-cause mortality and hospitalization for nonfatal MI, or nonfatal stroke, and the primary safety endpoint of major bleeding. Secondary endpoints include the components of the primary endpoint and hospitalization for transient ischemic attack, unstable angina, or coronary revascularization procedures.
2. To compare the effects of aspirin in selected subgroups of patients by sex, age, race, Internet users vs. non-users, and those with diabetes or advanced chronic kidney disease (CKD).
3. To develop and refine the infrastructure for PCORnet to conduct multiple comparative effectiveness trials in the future.
4. To explore biological mediators of heterogeneity of response to aspirin and of impact on clinical events.