Comparing Patient-reported Impact of COVID-19 Shelter-in-place Policies and Access to Containment and Mitigation Strategies Overall and in Vulnerable Populations, United States, 2020-2022 (ICPSR 39218)
The COVID-19 Citizen Science (CCS) Study was launched early in the pandemic to collect patient-reported information about exposures, risk behaviors and outcomes relevant to the pandemic. The Patient-Centered Outcomes Research Institute (PCORI) funded the research team to expand recruitment into CCS using PCORnet, the National Patient-Centered Clinical Research Network, and to use the resulting data to compare the patient-reported impact of pandemic associated policies. The research team systematically collected pandemic-associated policies enacted by counties across the United States (focusing in areas where there were many CCS participants), and to do so on a weekly basis from the beginning of the pandemic using publicly available sources.
Researchers combined data from various sources to answer two primary research questions (RQ):
- What is the comparative impact of different shelter-in-place/reopening policies, overall and in vulnerable populations, on patient-reported financial insecurity, mental health, and other subjective outcomes important to patients?
- What is the comparative effectiveness of county-level containment and mitigation strategies at achieving timely access to COVID-19 vaccination, testing, healthcare, information and contact tracing?
The research team collected patient-reported data from the CCS study and policy data from the U.S COVID-19 County Policy (UCCP) database. Electronic health record (EHR) data were also available from some participants recruited from health systems located across 7 U.S. states who consented and authorized use of these data for the study. Data for these participants were extracted from the PCORnet Common Data Model (CDM). Additional county-level contextual variables were included in analysis.
This collection contains CCS survey data on patient-reported anxiety with county-level policies data (DS1), respondent demographics (DS2), baseline survey results (DS3), daily (DS4) and weekly (DS5) COVID-19 symptoms reports, COVID-19 vaccination surveys repeated monthly (DS6) as well as a one-time vaccination survey (DS7), and pandemic impacts check-in surveys (DS8). CDM datasets include logistic regression model outcomes to predict study enrollment among all invited participants (DS9), codes for immunizations (DS10), laboratory tests (DS11), and procedures (DS12). County-level variables are also available for years 2021 (DS13) and 2023 (DS14).
Comparing Ways to Monitor Patients with COVID-19 at Home (COVID Watch), New Jersey, Pennsylvania, Delaware, 2020-2021 (ICPSR 38951)
The University of Pennsylvania Health System (Penn Medicine) developed COVID Watch, an automated text message-based, remote monitoring program with 24/7 clinical support. Remote outpatient monitoring of patients with COVID-19 became needed because patients with SARS-CoV-2 infection can decline rapidly and unpredictably, and because of their own limited capacity to manage acute symptoms and concerns about staff safety, office-based outpatient practices often redirect patients with confirmed or suspected COVID-19 to hospitals. As a result, emergency departments (EDs) and hospitals became overwhelmed during surge periods of high community incidence rates and prevalence. Remote monitoring has the potential to facilitate ED- and hospital-level care for patients who require it while supporting access to care for patients who can safely remain at home.
This study compared outcomes for patients enrolled in COVID Watch with those of patients who were eligible to enroll but received usual care, with the hypothesis that enrollment in COVID Watch was associated with reduced mortality. The present research examined whether patients with COVID-19 who were enrolled in COVID Watch experienced better health outcomes compared with usual care (Aim 1) and whether augmenting COVID Watch with at-home monitoring of SpO2 (blood-oxygen saturation) improves patient outcomes (Aim 2).
PRO-TECT: Electronic Patient Reporting of Symptoms During Outpatient Cancer Treatment, United States, 2017-2022 (ICPSR 39449)
Patients treated for metastatic cancer, or cancer that has spread to another part of the body, often have symptoms from cancer and its treatment. They may feel tired, depressed, or nauseated. They may find it hard to do their usual activities. Better symptom tracking may help improve patients' care. For example, symptom tracking could quickly alert doctors when a patient may need a different medicine. In this study, the research team compared use of a weekly electronic symptom tracking system versus usual care for patients with cancer. Patients receiving usual care could report their symptoms to their care team during regular clinic visits. The research team wanted to see if the tracking system helped patients live longer, have better quality of life, or go to the hospital or emergency room less often. The aims of this study were as follows:
- Determine whether integrating electronic patient-reported outcomes (ePRO) in cancer care improves patient-centered outcomes;
- Elicit perspectives about benefit burden tradeoffs for integrating patient-reported outcomes into clinical workflow; and
- Identify barriers, facilitators, and strategies used by practices to integrate patient-reported outcomes into clinical workflow.
A total of 1,191 patients were enrolled from 52 U.S.-based community oncology practices. Randomization into intervention and control conditions occurred at the site level. Data collected as part of this study included patient clinical information; weekly symptom surveys, quality of life surveys, and cancer care surveys completed by patients; feedback on the ePRO intervention from patients, clinical research associates, nurses, and physicians; and symptom alerts sent to nursing staff. Please note that while qualitative data were collected as part of this study, they are not available.
Incomplete Stepped Wedge Designs: Methods for Study Planning and Analysis [Methods Study], United States, 2007-2023 (ICPSR 39743)
In a stepped-wedge cluster randomized trial, or SW-CRT, researchers compare new treatments to standard treatments in groups of patients, such as patients at different clinics, to look at the treatments' effectiveness. They assign groups by chance to switch from the standard to new treatment at different time points until all groups have received the new treatment. The different time points to switch treatments are called steps.
SW-CRTs take time and resources. If researchers know they can't collect data on all groups and all steps in a SW-CRT, they can plan to use an incomplete SW-CRT design. In incomplete SW-CRTs, researchers plan the study knowing that some clinics or steps will have missing data. But researchers need better guidance for planning incomplete SW-CRTs that still get accurate results.
Also, current methods for planning how many patients and groups should take part in SW-CRTs don't work well for large studies. They also don't work well with certain types of outcomes, like yes or no outcomes; outcomes that have counts, like number of hospital visits; or continuous outcomes, like a score from 0 to 100.
In this study, the research team developed and tested new methods to design and analyze SW-CRTs with different patterns of planned missing data, large data sets, and different types of outcomes.
Integrating Causal Inference, Evidence Synthesis, and Research Prioritization Methods [Methods Study], United States, 2013-2018 (ICPSR 39489)
Comparative effectiveness research compares two or more treatments to see which one works better for certain patients. For example, research can see if medicines or stents work better for people with heart problems. Such research may include:
- Observational studies. A research team studies what happens when patients and their clinicians choose the treatments. Traits, such as age or health, may affect patients' treatment choices. These traits may also affect patients' responses to treatments. It may be hard for the team to tell if a patient's traits, the treatment, or a mix of the two affected how well the treatment worked.
- Clinical trials. The team assigns patients to a treatment by chance. Traits may affect a patient's ability to join a clinical trial.
In this study, the team tested ways to improve understanding of which treatment works better. First, the team compared different methods that account for things, such as patients' traits, that could affect results of observational studies. In the second part of the study, the team worked on ways to use all available data with a method called meta-analysis. This method combines data from both study types.
Building Data Registries with Privacy and Confidentiality for Patient-Centered Outcomes Research (PCOR) [Methods Study], 2020 (ICPSR 39579)
Researchers can use patient health data to compare treatments. But these data may include information, like names or social security numbers, that could identify patients. Researchers use different methods to remove such information and protect patients' privacy. Some methods work well to protect privacy but may make data less useful for research. Other methods don't protect privacy well enough.
Current methods for protecting privacy don't work well when:
- The number of patients in the data set is smaller than the number of data fields, such as patient traits or health conditions, and data are updated many times
- Patients' health and treatments are measured at more than one point in time
- Data are displayed as a graph to better capture some types of content
In this study, the research team created three new methods. The team wanted to see if the new methods better protect patient privacy but also make sure data remain useful for research.
To access the methods and software, please visit the AIMS Group at Emory University.