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Factors, such as personal traits, behaviors, or the environment, can affect a person's risk of getting an illness. Doctors can use risk models, which account for these factors, to predict a person's chance of getting an illness. The risk models group patients into different levels for certain illnesses, such as high risk or low risk.

Most risk models look at only a small number of factors, which affects how well the models can separate patients into different levels. Combining factors from different studies into a single risk model may improve how well the model works. Researchers can use statistical methods to combine data from different studies. But current methods don't work when the studies look at different traits or other factors.

In this study, the research team developed a new method for combining data from studies that have information on different risk factors. The new method is called Generalized Meta-Analysis, or GENMETA.

To access the R package, please visit the Implements Generalized Meta-Analysis Using Iterated Reweighted Least Square Algorithm CRAN webpage.

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.

Organizations that fund cancer research need to decide which studies to fund. Value of information (VOI) is a way to help rank research studies. VOI estimates the value of research by looking at the impacts on health and on healthcare that could result from the research.

SWOG (formerly the Southwest Oncology Group) is a network of cancer researchers funded by the National Cancer Institute. SWOG leaders review and score new study proposals based on the scientific value of the studies and their potential impact. Based on a study's score, SWOG's leadership committee decides whether to send the study to the National Cancer Institute for funding review.

In this study, the research team wanted to learn if giving VOI data to the committee affected its scoring of proposals. The team also wanted to see if providing VOI data was helpful in deciding which studies to fund. The study had two parts. The research team created a process to quickly estimate VOI. Then, the team tested the process on nine study proposals that the SWOG committee reviewed.

Data collected from interviews and group discussions, called qualitative data, can help researchers understand people's experiences, values, and cultures. But large amounts of qualitative data can be hard to show in a way that's easy for people to understand.

In this study, the research team created charts called ethnoarrays. These charts use color coding to show individual stories and overall patterns in qualitative data. The team wanted to learn whether ethnoarrays were useful and easy to understand.

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:

1. Determine whether integrating electronic patient-reported outcomes (ePRO) in cancer care improves patient-centered outcomes;
2. Elicit perspectives about benefit burden tradeoffs for integrating patient-reported outcomes into clinical workflow; and
3. 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.

Patients are often more satisfied with care when it matches their goals. To provide care that matches what matters to patients, doctors and other clinical staff need to communicate well with patients so they can learn about their needs and concerns. Healthcare organizations can use patient surveys to measure how well clinicians communicate with patients. To be useful, a survey needs to be

• Valid, or correctly capturing how well doctors communicate
• Reliable, or getting the same results over time

In this study, the research team created and tested a survey for patients getting care for colorectal cancer that asked about communication between the patient and their care team.

Many research studies seek to learn how treatments affect patients' quality of life. Quality of life includes mood and energy. It also includes how people view their roles in their families or communities and whether they can perform those roles. Researchers use surveys to ask about patients' quality of life. But patients may answer the same question differently depending on different characteristics, such as their age or where they live. Some patients may think about their work roles while others may think about their families or social lives. How patients think about quality of life can affect what researchers learn about the effects of treatment.

In this study, the research team tested two surveys they created to measure differences in how patients think about their quality of life. The first, long survey had 74 questions, and the second, short survey had 23 questions.

The Broadening the Reach, Impact, and Delivery of Genetic Services (BRIDGE) randomized controlled trial included 3,073 eligible patients between 2020-2023. The trial examined whether chatbot and standard of care approaches are equivalent in completion of pre-test cancer genetic services and genetic testing.

The rate of cancer screening is generally increasing in the United States. In Minnesota, the statewide average rate of screening for colorectal cancer (CRC) is 73 percent. However, screening completion is relatively low among Somali men; overall, only 27 percent of Somali immigrants have been screened for CRC. Factors contributing to this disparity have not been well researched.

The purpose of this pilot study was to employ focus group methodology to describe and advance understanding of the barriers and enablers associated with CRC screening among Somali men ages 50-74 in Minnesota. Three focus groups were conducted among 27 Somali men in Minnesota. A 9-question, semi-structured interview guide was used. The sessions were audio recorded, transcribed verbatim, and checked for accuracy by research staff prior to data analysis. Three research team members utilized the constant comparative method and NVivo to conduct data analysis.

Five barriers to CRC screening emerged from the analyses:

1. Lack of knowledge
2. Emotional barriers
3. Acculturation
4. Accountability
5. Fatalistic beliefs

In addition, two factors enabling CRC screening and prevention emerged: the need for tailored interventions and preventive lifestyle behaviors. The insights gained from this research will assist in developing health promotion and education-focused interventions that encourage Somali immigrants in Minnesota and beyond to seek early detection screening for CRC.

These data are being released in BETA version to facilitate early access to the study for research purposes. This collection has not been fully processed by NACDA or ICPSR at this time; the original materials provided by the principal investigator were minimally processed and converted to other file types for ease of use. As the study is further processed and given enhanced features by ICPSR, users will be able to access the updated versions of the study. Please report any data errors or problems to user support and we will work with you to resolve any data related issues.

The National Health Interview Survey (NHIS) is conducted annually and sponsored by the National Center for Health Statistics (NCHS), which is part of the U.S. Public Health Service. The purpose of the NHIS is to obtain information about the amount and distribution of illness, its effects in terms of disability and chronic impairments, and the kinds of health services people receive across the United States population through the collection and analysis of data on a broad range of health topics. The redesigned NHIS questionnaire introduced in 1997 (see National Health Interview Survey, 1997 [ICPSR 2954]) consists of a core that remains largely unchanged from year to year, plus an assortment of supplements varying from year to year.

The 2010 NHIS Core consists of three modules: Family, Sample Adult, and Sample Child. The datasets derived from these modules include Household Level, Family Level, Person Level, Injury/Poison Episode Level, Injury/Poison Verbatim Level, Sample Adult Level, and Sample Child level.

The 2010 NHIS supplements consist of stand alone datasets for Cancer Level and Quality of Life data derived from the Sample Adult core and Disability Questions Tests 2010 Level derived from the Family core questionnaire. Additional supplementary questions can be found in the Sample Child dataset on the topics of cancer, immunization, mental health, and mental health services and in the Sample Adult dataset on the topics of epilepsy, immunization, and occupational health.

Part 1, Household Level, contains data on type of living quarters, number of families in the household responding and not responding, and the month and year of the interview for each sampling unit. Parts 2-5 are based on the Family Core questionnaire. Part 2, Family Level, provides information on all family members with respect to family size, family structure, health status, limitation of daily activities, cognitive impairment, health conditions, doctor visits, hospital stays, health care access and utilization, employment, income, participation in government assistance programs, and basic demographic information. Part 3, Person Level, includes information on sex, age, race, marital status, education, family income, major activities, health status, health care costs, activity limits, and employment status. Parts 4 and 5, Injury/Poisoning Episode Level and Injury/Poisoning Verbatim Level, consist of questions about injuries and poisonings that resulted in medical consultations for any family members and contains information about the external cause and nature of the injury or poisoning episode and what the person was doing at the time of the injury or poisoning episode, in addition to the date and place of occurrence.

A randomly-selected adult in each family was interviewed for Part 6, Sample Adult Level, regarding specific health issues, the relation between employment and health, health status, health care and doctor visits, limitation of daily activities, immunizations, and behaviors such as smoking, alcohol consumption, and physical activity. Demographic information, including occupation and industry, also was collected. The respondents to Part 6 also completed Part 7, Cancer Level, which consists of a set of supplemental questions about diet and nutrition, physical activity, tobacco, cancer screening, genetic testing, family history, and survivorship. Part 8, Sample Child Level, provides information from an adult in the household on medical conditions of one child in the household, such as developmental or intellectual disabilities, respiratory problems, seizures, allergies, and use of special equipment like hearing aids, braces, or wheelchairs.

Parts 9 through 13 comprise the additional Supplements and Paradata for the 2010 NHIS. Part 9, Disability Questions Tests 2010 Level, is a supplemental set of six questions asked at the end of the Family Core questionnaire about sensory, mobility, self-care, cognition, and independent living issues. Part 10, Paradata Level, does not contain health related information, but rather data which are related to the interview process, including measures of time, contact-ability, and cooperation. Please see the User Guide for additional information and details. Part 11, Quality of Life Level, was asked to a randomly selected subsection of the Sample Adult questionnaire. Respondents were asked about participation in society, degree of difficulty and functioning in activity domains including vision, hearing, mobility, upper body, learning, cognition, affect, pain, fatigue, and communication. Part 12, Special Sample Adult Disability Weights Level, contains weights for use with an analysis of the merged data from the Sample Adult Level and Disability Questions Tests 2010 Level. Part 13, Sample Child Birth Weights Level, contains corrected birth weight data for 2010. Please see the Survey Description files for additional information and details.

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.