Computer Assisted Quality of Life and Symptom Assessment of Complex Patients from April 2011-August 2012: Chicago, Illinois (ICPSR 34543)
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).
Epidemiologic Catchment Area Program Sites 1-4, 1979-1983 with National Death Index Data through 2007 (ICPSR 36621)
The Epidemiologic Catchment Area (ECA) program of research was initiated in response to the 1977 report of the President's Commission on Mental Health. The purpose was to collect data on the prevalence and incidence of mental disorders and on the use of and need for services by the mentally ill. Independent research teams at five universities (Yale University, Johns Hopkins University, Washington University, Duke University, and University of California at Los Angeles), in collaboration with the National Institute for Mental Health, conducted the studies with a core of common questions and sample characteristics. The sites were areas that had previously been designated as Community Mental Health Center catchment areas: New Haven, Connecticut, Baltimore, Maryland, St. Louis, Missouri, Durham, North Carolina, and Los Angeles, California. Each site sampled over 3,000 community residents and 500 residents of institutions, yielding 20,861 respondents overall. The longitudinal ECA design incorporated two waves of personal interviews administered one year apart and a brief telephone interview in between (for the household sample). The diagnostic interview used in the ECA was the NIMH Diagnostic Interview Schedule (DIS), Version III (with the exception of the Yale Wave I survey, which used Version II). Diagnoses were categorized according to the DIAGNOSTIC AND STATISTICAL MANUAL OF MENTAL DISORDERS, 3rd Edition (DSM-III). Diagnoses derived from the DIS include manic episode, dysthymia, bipolar disorder, single episode major depression, recurrent major depression, atypical bipolar disorder, alcohol abuse or dependence, drug abuse or dependence, schizophrenia, schizophreniform, obsessive compulsive disorder, phobia, somatization, panic, antisocial personality, and anorexia nervosa. The DIS uses the Mini-Mental State Examination (MMSE), which measures cognitive functioning, as an indirect measure of the DSM-III Organic Mental Disorders. In the ECA survey, this diagnosis is called cognitive impairment.
This collection features data from 17,327 participants across 2,005 variables. Data from the Los Angeles, California, Catchment (UCLA) are not included. Baseline data (Wave 1) and Wave 2 data were linked to the National Death Index through 2007, which includes primary and contributing causes of death, International Classification of Disease (ICD) codes, and nature of injury variables.
Midlife in the United States (MIDUS Refresher 1): Biomarker Project, 2012-2016 (ICPSR 36901)
The MIDUS Refresher study Survey (2011-2014 ICPSR 36532) recruited a national probability sample of 3,577 adults, aged 25 to 74, designed to replenish the original MIDUS 1 baseline cohort and paralleling the five decadal age groups of the MIDUS 1 baseline survey (ICPSR 2760). The MIDUS Refresher survey employed the same comprehensive assessments as those assembled on the core longitudinal MIDUS sample, but with additional questions about impacts of the economic recession of 2008-09. The MIDUS Refresher Biomarker study (2012-2016) obtained data from 863 respondents (n=746 Main sample, n=117 African Americans from Milwaukee) who completed the MIDUS Refresher Survey.
The purpose of the Refresher Biomarker Project (Project 4) parallels that of the MIDUS 2 Biomarker project (ICPSR 29282), which collected comprehensive biological assessments on a subsample of MIDUS respondents, thus facilitating analyses that integrate behavioral and psychosocial factors with biological regulation/dysregulation, broadly defined. The aim was to use such data to explicate biopsychosocial pathways that contributed to diverse health outcomes. A further theme was to examine period effects on health (mental and physical) related to the economic recession by comparing the pre-recession MIDUS sample with the post-recession MIDUS Refresher sample. A further objective of the MIDUS Refresher sample was to strengthen cross-project analyses by increasing the sample sizes available for testing hypotheses regarding the interplay of key factors (e.g., socioeconomic status, gender, psychosocial factors, biological factors) in mid- and later-life health.
Biomarker data collection was carried out at hypothalamic-pituitary-adrenal axis, the autonomic nervous system, the immune system, cardiovascular system, musculoskeletal system, antioxidants, and three General Clinical Research Centers (at UCLA, University of Wisconsin, and Georgetown University). The biomarkers reflect functioning of the metabolic processes. Our specimens (fasting blood draw, 12-hour urine, saliva) allowed for assessment of multiple indicators within these major systems. The protocol also included assessments by clinicians or trained staff, including vital signs, morphology, functional capacities including 3 dimensional gait analysis, bone densitometry, body composition, ankle brachial index, medication usage, and a physical exam. Project staff obtained indicators of heart-rate variability, beat to beat blood pressure, respiration, and salivary cortisol assessments during an experimental protocol that included both a cognitive and orthostatic challenge. Finally, to augment the self-reported data collected in Survey (Project 1), participants completed a medical history, self-administered questionnaire, and self-reported sleep assessments. For respondents at one site (UW-Madison), objective sleep assessments were also obtained with an Actiwatch(R) activity monitor.