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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.

In randomized controlled trials, or RCTs, researchers assign patients by chance to different treatments to compare the benefits and harms. In RCTs, researchers have a high level of control over how patients receive treatment. RCTs often take place in research clinics with staff who monitor how patients follow treatment plans.

Pragmatic RCTs, or pRCTs, take place where patients typically receive treatment, such as a regular clinic. pRCTs can help capture the real-world effects of treatment but determining whether a treatment works can be hard in pRCTs. Also, no clear guidance exists about how to collect and analyze data from pRCTs. Some kinds of analysis are better for helping researchers focus on what's important to patients.

In this study, the research team created guidance for collecting and analyzing data in pRCTs so that results reflect what matters to patients and researchers.

To access the methods and software, please visit the following Github repositories:

• CDP-analysis-2018
• GFORMULA-RCT-SAS
• IV-Bounds
• CHARM_reanalysis
• Adherence_LRCCPPT

Based on the state of the scientific evidence, the Sequenced Treatment Effectiveness for Posttraumatic Stress (STEPS) Trial was designed to address three specific aims. The first aim was to quantitatively compare engagement, self-reported PTSD symptom severity (primary outcome), quality of life, and recovery outcomes of primary care patients randomized to initially receive brief psychotherapy (Written Exposure Therapy - WET) or their choice of the three selective serotonin reuptake inhibitors (SSRIs). The second aim was, among patients not responding to initial treatment, to quantitatively compare outcomes of primary care patients randomized to: 1) augment the SSRI with WET, 2) switch from the SSRI to another class of antidepressants (serotonin-norepinephrine reuptake inhibitors - SNRI), or 3) switch from WET to the choice of the three SSRIs. The third specific aim was to quantitatively examine treatment heterogeneity among subgroups of primary care patients receiving pharmacotherapy and psychotherapy, including veterans, women, and those using cannabis.

According to the World Health Organization, palliative care is "an approach that improves the quality of life of patients and their families facing the problems associated with life-threatening illness, through the prevention and relief of suffering by means of early identification and impeccable assessment and treatment of pain and other problems, physical, psycho-social and spiritual." The goal of the study was to generate comparative effectiveness research evidence to support the delivery of coordinated, community-based palliative care that effectively implements care plans consistent with the goals and preferences of older adults with advanced illness and their caregivers.

This study included a pragmatic, two-arm, multi-site randomized controlled trial of older adults (50+ years) with either poor prognosis cancer or end-stage organ failure who were recruited during an emergency department (ED) visit, along with their informal caregivers, to compare nurse-led telephonic case management to facilitated, outpatient specialty palliative care on: 1) quality of life in patients, 2) loneliness, 3) healthcare use in the 12 months following enrollment, 4) symptom burden, 5) caregiver strain, 6) caregiver quality of life, and 7) bereavement.

During care transitions, patients move from one care setting to another, such as from the hospital to home. If not done well, these care transitions can result in health problems for patients and the need for them to return to the hospital.

Healthcare organizations can use patient surveys to measure the success of care transitions. One survey about the quality of care transitions already exists. The survey was created with input from patients but with no input from caregivers and healthcare providers. In addition, the survey doesn't ask about topics that patients may find important, such as caregiver involvement and the time after care transitions.

In this study, the research team created and tested a new survey. To create the survey, the team asked for input from patients, caregivers, and healthcare providers. The team tested whether the survey was

• Valid, or able to correctly capture what it intends to measure
• Reliable, or able to get consistent answers

Researchers can use data from patient registries to look at which medicines or other treatments work best. Registries store data about people with a specific health problem. The data may include the health care and medicines patients receive over time and patient reports of their health status.

To find out patients' health status, registries ask patients to fill out surveys at different times during treatment. Researchers can compare survey results from when patients first take the survey with results from surveys taken after treatment. They can then find out how well a medicine works. But patients may not always take the first survey before they start a new medicine. Sometimes, they don't take the first survey until after starting treatment. When this happens, it is hard to know how well the medicine works.

In this study, the research team looked at different ways to use data from patient surveys in registries. The team wanted to learn which way would give the most accurate understanding of the effects of a new medicine. The study also looked at patients' views on taking part in registries.

In the United States, tobacco smoking is associated with significant morbidity and premature mortality for individuals with serious mental illness (SMI) (e.g., schizophrenia, post-traumatic stress disorder, bipolar disorder, major depressive disorder). While many smokers with SMI wish to quit smoking, few are offered advice or treatments with demonstrated effectiveness in reducing tobacco dependence, primarily medication-assisted treatments. The overall aim of this randomized controlled trial was to test the effects of provider education (PE) (i.e. provider-level educational intervention focused on evidence-based smoking cessation treatment for those with SMI) and community health worker (CHW) support on the provision and utilization of smoking cessation treatment to those with SMI, and cessation rates for adults with SMI who smoke or use tobacco over a 2-year period. The objectives of this trial were to:

1. Examine whether an intervention combining PE and CHW support would increase prescriber provision of advice and assistance to quit smoking, and improve tobacco cessation rates in smokers with SMI compared to usual care/treatment as usual (TAU) and compared to PE-only treatment
2. Determine the effect of the combined PE+CHW intervention on patient-reported overall health compared to TAU and PE-only treatment

Eligible individuals were recruited from two outpatient psychiatric service providers in the Boston, Massachusetts metropolitan area. Clinics where individuals received services were randomized into either the TAU condition or into the PE condition, where health care providers would receive additional education on first-line medications used to treat tobacco use disorder. Within clinics in the PE arm, individuals were further randomized into the community health worker (CHW) support condition (PE+CHW), where CHWs would assist participants with smoking cessation care access and provide community outreach and education, or no CHW support (PE-only). Enrolled participants (n=1,010) completed surveys on smoking/tobacco use at 3 timepoints: study baseline, 1 year post-randomization, and 2 years post-randomization.

A mixed-methods evaluation of the trial was also conducted post-intervention, using an interactive convergent design. The aims of the evaluation were to identify barriers and facilitators to effective implementation; examine how primary care providers differed by performance and engagement level, and how experiences with the intervention compared across these groups; and identify anticipated barriers to implementing the intervention as discussed by stakeholders. Quantitative outcome and visit data from the trial were used in the evaluation. For the evaluation's qualitative component, interviews were conducted with purposively sampled community health workers, smoker participants, primary care providers, and other stakeholders in policy, payor, and clinical administration. Please note that the qualitative evaluation data are not available for this collection.

Medical devices, such as pacemakers or stents, can help diagnose, treat, or prevent health problems. Companies that make medical devices label them with unique device identifiers, or UDIs. UDIs contain data about a device, such as the make, model, and expiration date. Healthcare providers can scan UDIs when they use the devices and record UDI data in patients' health records.

Right now, UDI data can only be accessed by the health systems that use the devices. Having the UDI data in insurance claim forms, instead of only in patients' health records, would mean that researchers could look at data over time and across health systems. They could then use these data to help monitor devices for safety or study questions like how well devices are working.

In this study, the research team created ways to send UDI data from health systems to insurance claims forms.

As people age, medical decisions become more complex, including conversations about cancer screening. For patients aged 76-85, the United States Preventive Services Task Force (USPSTF) advises clinicians that decisions about colorectal cancer (CRC) screening should be individualized based on overall health and prior screening history (C recommendation). However, studies find that many older adults are not well-informed about, nor meaningfully engaged in, decisions about whether to continue CRC screening. Shared decision making (SDM) has been shown to improve the quality of decisions about initiating cancer screening but little is known about its effectiveness for decisions about stopping interventions. This study addresses a gap in the understanding of how to support clinicians and older patients in making good decisions about whether to continue CRC screening or not.

The researchers conducted a comparative effectiveness trial that randomly assigned clinicians at participating academic and community practices to one of two different decision support strategies. The first strategy (Registry arm) took a population health management (PHM) approach and used a patient registry to identify and track use of CRC screening among older adults for each clinician. The second strategy enhanced the registry by adding a multi-faceted SDM training program for clinicians (SDM Skills arm). The researchers enrolled patients of participating primary care providers (PCPs), aged 76-85, who are due or overdue for CRC screening, and survey them shortly after an office visit to determine the impact of the two strategies on outcomes of importance to patients. The study randomly assigned about 60 participating PCPs to the SDM skills or Registry arms, and enroll about 500 of their eligible patients.

Chronic low back pain is a leading cause of disability and reduced quality of life for nearly 100 million individuals in the United States. Approximately 5 to 10 percent of these individuals are prescribed opioids for treating their chronic low back pain. Limited data exists on the long-term efficacy of opioid treatment while much concern exists regarding the harm they can do to a person. Despite medication treatment patients still suffer from inadequate pain relief, impaired function, and quality of life.

This study sought to compare the long-term effectiveness of two types of alternative therapy approaches - mindfulness based therapy (MBT) and cognitive behavioral therapy (CBT) - for treating chronic low back pain especially those being treated by opioids.

One way to see if a treatment works is to compare data from people who received the treatment with data from those who didn't or who received a different treatment. But sometimes the ways that people differ, such as their age or other health problems, can bias results. For example, if the people who didn't get the treatment are older or sicker than people who did get the treatment, results could suggest that the treatment works better than it really does.

One way to avoid this type of bias is to use case-only study designs. Case-only studies compare each patient's health before and after treatment. But case-only studies often report the relative risk of a health event, such as stroke, among two groups of patients, instead of the absolute risk. For example, relative risk can show how the risk of stroke differs between patients who smoke and those who do not. Absolute risk would give the percentage of patients having a stroke among all patients. Absolute risk can help inform treatment decisions. But methods to measure absolute risk in case-only studies are limited. Also, clear guidance is lacking on how to best design and analyze a case-only study.

In this study, the research team created a guide and new methods for designing and analyzing case-only studies.

The TARGET (Targeted Interventions to Prevent Chronic Low Back Pain in High-Risk Patients) Trial was a primary care-based, multisite, cluster randomized, pragmatic trial comparing guideline-based care (GBC) to GBC + referral to Psychologically Informed Physical Therapy (PIPT) for patients presenting with acute lower back pain (LBP) and identified as high risk for persistent disabling symptoms. Chronic lower back pain (LBP) is defined as a response of "more than three months" to question 1, and a response of "half the days or more than half the days" in the past 6 months to question 2. See Appendix 1 for the LBP Questionnaire in the Protocol report.

Study sites included primary care clinics within each of four geographical regions in the United States, with clinics randomized to either GBC or GBC+PIPT. Acute LBP patients at all clinics were risk stratified (high, medium, low) using the STarT Back Tool. The primary outcomes were the presence of chronic LBP and LBP-related functional disability determined by the Oswestry Disability Index at 6 months. Secondary outcomes were LBP-related processes of health care and utilization of services over 12 months, determined through electronic medical records.

Study enrollment began in May 2016 and concluded in June 2018. The trial was powered to include at least 1,860 high-risk patients in the cluster-randomized controlled trial cohort. A prospective observational cohort of approximately 6,900 low and medium-risk acute LBP patients was enrolled concurrently.

This data collection contains a single data file with 223 variables and 9,730 cases. The number of respondents at each of the study locations were:

• Boston Medical Center: 997 respondents
• Intermountain Health (Salt Lake City): 2,094 respondents
• Johns Hopkins University (Baltimore): 1,615 respondents
• University of Pittsburg Medical Center: 5,024 respondents

Clinical notes in electronic health records, or EHRs, may contain information that can help researchers study and compare treatments. But it takes researchers a lot of time to find information in EHR notes.

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 in EHR notes, some sentences may contain more than one topic. Also, EHR notes may discuss a single topic over many sentences. In these cases, current NLP methods don't work well to find complete and accurate information about a specific topic.

In this study, the research team developed and tested new NLP methods to identify topics from EHR notes.