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Researchers often use surveys to learn about what patients prefer. The wording of survey questions may affect how patients answer.

In this study, the research team compared different ways of asking patients with type 2 diabetes questions in a national survey. The questions asked patients about managing their diabetes and the medicines they prefer. The team wanted to see how accurately the different ways of asking questions measured patients' preferences. The study looked at whether patients thought the different ways of asking questions:

• Were easy to understand and answer
• Led to answers that matched what patients really wanted

This study evaluated the impact of atypical work hours on physiological indicators of health and chronic disease among law enforcement officers enrolled in the Buffalo Cardio-Metabolic Occupational Police Stress (BCOPS) study. Atypical work hours were defined as: work outside of a standard daytime work shift, the number of shift changes that occur over an extended period, the effect of cumulative overtime hours, and/or secondary employment. The data in this release include measures of global DNA methylation, which is an indicator of genomic instability and risk factor for several types of cancer; food logs documenting wake, sleep, and meal times during workdays and off-duty days; and survey data about psychosocial adaptive and maladaptive behaviors associated with atypical work hours.

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.

The Behavioral Risk Factor Surveillance System (BRFSS) is a system of health-related telephone surveys that collect state data about U.S. residents regarding their health-related risk behaviors, chronic health conditions, and use of preventive services. Established in 1984 with 15 states, BRFSS now collects data in all 50 states as well as the District of Columbia and three U.S. territories. BRFSS completes more than 400,000 adult interviews each year.

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.

Overview: The goal of the project was to develop a unique database linking chronic disease clinical data from an electronic medical record (EMR) of a large academic healthcare system to multi-payer claims data. The longitudinal relational database can be used to study clinical effectiveness of many diagnostic and treatment interventions. The population of patients used consisted of those patients who were attributed to the University of Michigan Health System (UMHS) as continuing care patients, who are also in adjudicated and validated chronic disease registries.

Data Access: These data are not available from ICPSR. The data are restricted to use by the principal investigator and cannot be shared.

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.

The purpose of this study was to expand the research capacity for comparative effectiveness evaluations of patients with multiple chronic conditions. Researchers administered a generic Quality of Life (QOL) instrument, physical symptom assessment, patient health questionnaire, and a tobacco screen through audio computer-assisted self-interviews (ACASI) and linked the responses to their electronic medical records (EMR) data. Researchers also calculated two co-morbidity indices (Chronic Disease Score and Charlson Co-morbidity Index).

This interventional pilot study was conducted in a primary care clinic to determine if patients would become more engaged in their own health and ask more questions of their physicians if they were provided data about patients similar to themselves. The study was conducted with 150 patients with a diagnosis of hypertension who had scheduled appointments with one of three participating physicians in the clinic. When they arrived at the clinic for their appointment, the patients were shown de-identified clinical data about similar patients with hypertension on a computer screen, given a printout of this information, and then proceeded to visit their physician. After the physician visit the patients completed a short survey. Their answers to the survey questions are recorded in the data file together with additional information about them, such as age, gender, race, smoking status and comorbidities.

The three participating physicians completed a short survey at the end of the study. The results of that survey are summarized in a table provided with the technical documentation.

Please note that as of June 2023, Sarah D. Gunnery, PhD is the current Principal Investigator of this data collection.

The Emergence and Evolution of Social Self-Management of Parkinson's Disease study (SocM-PD) is a mixed-method (quantitative-qualitative) prospective cohort study of how people with Parkinson's disease and their primary caregiver (as available) naturalistically manage chronic disease, wellness and social life in their home and community.

Researchers define social self-management as the practices and experiences that ensure personal social comfort while supporting mental and physical well-being. Articulating this model will guide research to identify social factors that are deleterious to or protective of quality of life when living with chronic disease. Parkinson's Disease offers a model for studying the effect of physical disease on the social self management of daily life when physical symptoms affect fundamental social capacities. The overall objective is to understand the emergence and evolution of the trajectories of the self-management of the social lives of people living with Parkinson's disease. The central hypothesis is that expressive capacity predicts systematic change in the pattern of social self-management and quality of life outcomes. Demographic variables include age, gender, ethnicity, income, marital status, education, and employment.

Purpose: Develop an easy-to-use data product to facilitate comparative effectiveness research involving complex patients.

Scope: Claims data can be difficult to use, requiring experience to most appropriately aggregate to the patient level and to create meaningful variables such as treatments, covariates, and endpoints. Easy to use data products will accelerate meaningful comparative effectiveness research (CER).

Methods: This project used data from the Medicare Chronic Condition Data Warehouse for patients hospitalized with acute myocardial infarction (AMI) or stroke in 2007 with two-year follow-up and one-year pre-admission baseline. The project joined over 100 raw data files per condition to create research-ready person- and service-level analytic files, code templates, and macros while at the same time adding uniformity in measures of comorbid conditions and other covariates. The data product was tested in a project on statin effectiveness in older patients with multiple comorbidities.

Results: A programmer/analyst with no administrative claims data experience was able to use the data product to create an analytic dataset with minimal support aside from the documentation provided. Analytic dataset creation used the conditions, procedures, and timeline macros provided. The data structure created for AMI adapted successfully for stroke. Complexity increased and statin treatment decreased with age. The two-year survival benefit of statins post-AMI increased with age.

Conclusion: Claims data can be made more user-friendly for CER research on complex conditions. The data product should be expanded by refreshing the cohort and increasing follow-up. Action is warranted to increase the rate of statin use among the oldest patients.

Data Access: These data are not available from ICPSR. The data cannot be made publicly available. Data are stored on University of Iowa College of Public Health secure servers, and may be used only for projects covered within the aims of the original research protocol and Centers for Medicare and Medicaid Services (CMS)-approved data use agreement. Data sharing is allowed only for research protocols approved under data re-use requests by the CMS privacy board. The CMS process for data re-use requests is described at Research Data Assistance Center (ResDac). Please note that as of May 2013, the DUA covering this work is set to expire February 1, 2014. Thereafter, per the terms of the DUA, datasets created for this project may not be available.

User guides are available from ICPSR for detailed descriptions of the data products, including a user guide for Acute Myocardial Infarction (AMI) Analytic Files and a user guide for Stroke and Transient Ischemic Attack (TIA) Analytic Files. Data dictionaries are available upon request. Please contact Nick Rudzianski ([email protected] or 319-335-9783) for more information.

Overview

Through electronic data collection and improving the efficiency of existing data processes to allow both more complete and specific identification of chronic illness, the study objectives included:

1. Greatly enhance the scope of existing algorithms to permit comprehensive identification of the 20 chronic conditions key to primary care.
2. Improve the specificity of the existing algorithms to permit more precise automated identification of chronic conditions, limiting the amount of human review required.
3. Revise the algorithms to permit identification of more than one condition in a text string.

The investigators developed advanced SAS text string search algorithms and developed a modified parsing table that included inclusion and exclusion patterns and resultant diagnoses. The automation searches through each input text string for the inclusion pattern that is not equivalent to the exclusion pattern and maps the string to the corresponding resultant diagnosis. This technique allows the search functions to be easily modified to include additional search criteria and scaled to encompass additional conditions.

Data Dictionary

A data dictionary for 24 chronic conditions was developed. The dictionary assigns ICD-9 diagnosis codes to problem list text in electronic health record data. The dictionary contains 78,458 records and exists in two forms, a Microsoft Access database and a SAS 9.2 dataset. The Microsoft Access database contains 24 tables, one for each condition. The SAS 9.2 dataset contains four fields. The 24 chronic conditions for which problem list text data were examined and assigned to ICD-9 codes. Conditions include Alcohol Use Disorder, Asthma and Allergic Rhinitis, Atherosclerosis, Atrial Fibrillation, Cerebrovascular Disease, Chronic Liver Disease, COPD, Chronic Renal Disease, Coronary Disease, Dementia, Depression, Diabetes Mellitus, Epilepsy, GERD, Heart Failure, Hyperlipidemia, Hypertension, Migraine Headache, Obesity, Osteoarthritis, Osteopenia/Osteoporosis, Parkinson's Disease, Peptic Ulcer Disease and Rheumatoid Arthritis.

Data Access

The data dictionary is not available from ICPSR. For use arrangements, please contact Ruth G. Jenkins, PhD ([email protected]) or Steven M. Ornstein, MD ([email protected]) at the Practice Partner Research Network (PPRNet), Medical University of South Carolina.

Researchers can use data from health registries or electronic health records to compare two or more treatments. Registries store data about patients with a specific health problem. These data include how well those patients respond to treatments and information about patient traits, such as age, weight, or blood pressure. But sometimes data about patient traits are missing.

Missing data about patient traits can lead to incorrect study results, especially when traits change over time. For example, weight can change over time, and the patient may not report their weight at some points along the way. Researchers use statistical methods to fill in these missing data.

In this study, the research team compared a new statistical method to fill in missing data with traditional methods. Traditional methods remove patients with missing data or fill in each missing number with a single estimate. The new method creates multiple possible estimates to fill in each missing number.

To access the methods, software, and R package, please visit the SimulateCER GitHub and SimTimeVar CRAN website.

Clinical practice guidelines are recommendations for doctors about when and how to treat health problems. Guidelines are often based on research that compares the benefits and harms of different tests or treatments for one health problem. But this research doesn't always consider that people may have other health problems or different preferences. Guideline developers need to know what is important to patients.

In this study, the research team developed a process to inform development of clinical guidelines. The team wanted to learn how the balance of benefits and harms of treatment options changes when it includes patient preferences. In this new process, the team

• Defined questions comparing treatment options based on input from patients with three or more long-term health problems
• Used data from prior research studies to answer these questions and assess the balance of benefits and harms of treatment options
• Used results from a patient survey, looking to see if the balance of benefits and harms could change when patients have different preferences.

INtegrated Services for Pain: Interventions to Reduce Pain Effectively (INSPIRE) was a pragmatic randomized trial conducted from 2019 to 2023 with adults receiving chronic opioid therapy (COT) of at least 20 morphine milligram equivalents (MME) daily for chronic noncancer pain (CNCP). Participants were recruited from primary care and specialty pain clinics at three academic health centers in North Carolina and Tennessee. The study compared the effectiveness of the two behavioral interventions, 1) shared decision making (SDM) versus 2) motivational interviewing plus cognitive behavioral therapy for chronic pain (MI+CBT), on change in opioid dose, physical function, and pain interference. INSPIRE combined data from electronic health records (EHR) on opioid dose from baseline to 18 months and comorbidities with participant survey data at baseline, 6, and 12 months on the following topics:

• physical function,
• pain interference,
• pain intensity,
• anxiety,
• depression,
• pain severity,
• discontinuation of opioids,
• intent to reduce opioids,
• opioid use relative to baseline,
• adverse events,
• demographics,
• health insurance coverage,
• health literacy,
• patient-centered communication, and
• types of pain treatment used.

The collection includes three analysis datasets:

1. Adverse Events Dataset - one record per subject per adverse event
2. Opioid Prescriptions Dataset (post-processed opioid prescriptions used to derive the study's primary outcome) - one record per subject per opioid prescription
3. Outcomes Dataset (contains all of the study's demographics, primary, secondary, exploratory, and subgroup analysis variables) - one record per subject per timepoint

The Los Angeles Metropolitan Area Studies [LAMAS] 6, 1973 collection reflects data gathered in 1973 as part of the Los Angeles Metropolitan Area Studies (LAMAS). The LAMAS, beginning in the spring of 1970, are a shared-time omnibus survey of Los Angeles County community members, usually repeated twice annually. The LAMAS were conducted ten times between 1970 and 1976 in an effort to develop a set of standard community profile measures appropriate for use in the planning and evaluation of public policy.

The LAMAS instruments, indexes, and scales used to track the development and course of social indicators (including social, psychological, health, and economic variables) and the impact of public policy on the community. Questions in this year of the LAMAS cover respondents' attitudes toward the following topics: air pollution, health care services in the community, local government politics, police relations, recreation and leisure time. In addition, participating researchers were given the option of submitting questions to be asked in addition to the core items. These additional question topics include: sleep habits, the true self, impact of computers, job seeking behavior, and mental health and psychological factors.

Demographic variables in this collection include sex, age, race, ethnicity, education, occupation, income, religion, marital status, birth place, and housing type.

Patients' beliefs and expectations may affect how they respond to treatment. But these feelings are hard to measure.

In this study, the research team created a set of surveys called Healing Encounters and Attitudes Lists, or HEAL. HEAL helps researchers understand patients' beliefs and expectations about treatment. HEAL measures patients'

• Connections with their doctors and nurses
• Feelings about their doctor's office and staff
• Expectations about treatment
• Outlook on life
• Strength of spiritual beliefs
• Comfort with complementary and alternative medicine, or CAM
• The team also used the Patient-Reported Outcomes Measurement Information System, or PROMIS, to measure patients' pain, health, and function. PROMIS is a set of surveys researchers and doctors use for many diseases and treatments.

The team wanted to learn if HEAL could predict how patients respond to treatment for chronic pain. Chronic pain is pain that lasts for months or years. The team used HEAL and PROMIS to look at why some groups of patients respond differently to treatment for chronic pain. Patients got either conventional treatment, such as physical therapy or medicine, or CAM, such as acupuncture, chiropractic treatment, or massage.

In 1995-1996, the MacArthur Midlife Research Network carried out a national survey of over 7,000 Americans aged 25 to 74 [ICPSR 2760]. The purpose of the study was to investigate the role of behavioral, psychological, and social factors in understanding age-related differences in physical and mental health. The study was innovative for its broad scientific scope, its diverse samples (which included siblings of the main sample respondents and a national sample of twin pairs), and its creative use of in-depth assessments in key areas (e.g. daily diary of stressful experiences [ICPSR 3725] and cognitive functioning [ICPSR 3596]) on a subset of participants. A detailed description of the study and findings generated by it are available at: http://www.midus.wisc.edu

With support from the National Institute on Aging, a follow-up of the original Midlife Development in the United States (MIDUS) sample was conducted in 2004 (MIDUS 2 [ICPSR 4652]). The daily stress and cognitive functioning projects were repeated and expanded at MIDUS 2; in addition the protocol was expanded to include biomarkers and neuroscience.

In 2013 a third wave (MIDUS 3) of survey data was collected on longitudinal participants. Data collection for this follow-up wave largely repeated baseline assessments (e.g., phone interview and extensive self-administered questionnaire), with additional questions in selected areas such as economic recession experiences. Cognitive functioning data were also collected at the same time, while data collection for the daily diary, biomarker, and neuroscience projects commenced in 2017.

MIDUS also maintains a Colectica portal, which allows users to interact with variables across waves and create customized subsets. Registration is required.