Accepted Manuscript Developing an Item Bank to Measure Economic Quality of Life for Individuals with Disabilities David S. Tulsky, Ph.D. Pamela A. Kisala, M.A. Jin-Shei Lai, Ph.D. Noelle Carlozzi, Ph.D. Joy Hammel, Ph.D. Allen W. Heinemann, Ph.D. PII:
S0003-9993(14)00265-2
DOI:
10.1016/j.apmr.2014.02.030
Reference:
YAPMR 55795
To appear in:
ARCHIVES OF PHYSICAL MEDICINE AND REHABILITATION
Received Date: 13 February 2014 Accepted Date: 22 February 2014
Please cite this article as: Tulsky DS, Kisala PA, Lai J-S, Carlozzi N, Hammel J, Heinemann AW, Developing an Item Bank to Measure Economic Quality of Life for Individuals with Disabilities, ARCHIVES OF PHYSICAL MEDICINE AND REHABILITATION (2014), doi: 10.1016/ j.apmr.2014.02.030. This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.
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RUNNING HEAD: Measuring Economic Quality of Life
Developing an Item Bank to Measure Economic Quality of Life for Individuals with
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Disabilities David S. Tulsky, Ph.D.1,2 Pamela A. Kisala, M.A.1
Noelle Carlozzi, Ph.D.4
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Joy Hammel, Ph.D.5
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Jin-Shei Lai, Ph.D.3
Allen W. Heinemann, Ph.D.3,6
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New York University Langone Medical Center, New York, NY 2
Feinberg School of Medicine, Northwestern University, Chicago, IL 4
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Kessler Foundation Research Center, West Orange, NJ
University of Michigan Medical School, Ann Arbor, MI 5
Rehabilitation Institute of Chicago, Chicago, IL
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University of Illinois at Chicago, Chicago, IL
Reprints will not be available from the corresponding author.
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Correspondence Address: David Tulsky, Ph.D., Dept. Rehabilitation Medicine, Rusk Institute for Rehabilitation, Ambulatory Care Center , New York University Langone Medical Center, 240 East 38th Street, 17th Floor, New York, NY 10016. [email protected] Funding was provided by National Institute on Disability and Rehabilitation Research (grant numbers H133B090024, H133A030807, and H133G070138) and the National Institutes of Health (5R01HD054659). We certify that no party having a direct interest in the results of the research supporting this article has or will confer a benefit on us or on any organization with which we are associated AND, if applicable, we certify that all financial and material support for this research and work are clearly identified in the title page of the manuscript.
ACCEPTED MANUSCRIPT Measuring Economic Quality of Life Abstract
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Objective: To develop and evaluate the psychometric properties of an item set measuring
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economic quality of life for use by individuals with disabilities.
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Design: Survey.
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Setting: Community settings. Inclusion criteria were a traumatic brain injury, spinal cord injury
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or stroke, age 18 years and older, and ability to read and speak English.
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Participants: 64 individuals with disabilities completed individual interviews, 172 participated
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in focus groups, and 15 completed cognitive interviews. We calibrated the items with 305 former
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rehabilitation inpatients.
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Intervention: None.
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Main Outcome Measure: Economic Quality of Life (ECQ).
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Results: Confirmatory factor showed acceptable fit indices (CFI=0.939, RMSEA=0.089) for the
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37 items. However, 3 items demonstrated local item dependency. Dropping 15 items improved
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fit and obviated local dependency. Rasch analysis of the remaining 28 items yielded a person
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reliability of .92, suggesting that these items discriminate about 4 ECQ levels.
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Conclusions: We developed a 28-item bank that measures economic aspects of QOL.
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Preliminary CFA and Rasch analysis results support the psychometric properties of this new
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measure. It fills a gap in HRQOL measurement by describing the economic barriers and
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facilitators of community participation. Future development will make the item bank available as
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a computer adaptive test.
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Keywords: Economic factors, quality of life, patient-reported outcomes, psychometrics,
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rehabilitation
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ACCEPTED MANUSCRIPT Measuring Economic Quality of Life Abbreviations
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CFA – Confirmatory Factor Analyses
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CFI – Comparative Fit Index
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ECQ – Economic Quality of Life
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HRQOL – Health-Related Quality of Life
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MnSq – Mean Square
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PROMIS – Patient Reported Outcomes Measurement Information System
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QOL – Quality of Life
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RMSEA – Root Mean Square Error of Approximation
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SCI – Spinal Cord Injury
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SCI-QOL – Spinal Cord Injury Quality of Life Measurement System
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TBI – Traumatic Brain Injury
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TBI-QOL – Traumatic Brain Injury Quality of Life Measurement System
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living, wealth, comfort, and material goods.1 Quantifiable units were earnings, type of
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automobile one drove, and the size of one’s house. During the 1928 presidential campaign, the
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Republican National Committee promised Americans that a vote for Herbert Hoover was a vote
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for “a car in every garage and a chicken in every pot” – implying that prosperity was the way to
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measure QOL.2 Dwight Eisenhower’s 1960 Commission on National Goals and Lyndon B.
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Johnson’s Great Society programs began to emphasize the social aspects of QOL2-4 and new
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definitions of QOL came to reflect subjective qualities that reflected lifestyle, happiness, and
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well-being.5,6 Subsequently, “Health-Related QOL (HRQOL) was defined as subjective
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evaluations of status or capacity that reflect a health condition. Adopting the World Health
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Organizations’ conceptual structure of health, measurement strategies to evaluate HRQOL
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focused on physical, emotional, and social well-being.7 Because of this shift, the influence of
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economic issues and subjective material resources on HRQOL has been minimized and little has
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been published about perceived economic resources and their influence on HRQOL.
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The economic burden of disability requires us to question why perceived economic factors are
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not conceptualized as part of HRQOL. Reduced household income is associated with lower
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levels of life satisfaction in older adults who have retired; loss of income after retirement is
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related to lower life satisfaction rather than to the loss of a role as worker.8 Perceived financial
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hardship is negatively related to HRQOL in a number of populations including patients with
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advanced cancer,9 breast cancer,10-12 human immunodeficiency virus,13 end-stage kidney
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disease,14 amyotrophic lateral sclerosis,15 Parkinson’s disease,16 and schizophrenia.17 While there
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is substantial support for a relationship between economic factors and HRQOL, there is a paucity
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of research focusing on patient reported economic factors.
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This dearth of research on economic quality of life is striking given the costs of disability. For
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example, first-year costs following traumatic spinal cord injury (SCI) range from $218,504 -
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$741,425 in the first year depending on the injury level and severity, with the highest estimates
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for high tetraplegia; for subsequent years, cost estimates range from $15,313 - $132,807
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inclusive of health care, medications, supplies, durable medical equipment, and personal
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assistance.18 For traumatic brain injury (TBI), medical costs alone are $4,906 higher than costs
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for individuals of the same age, sex, calendar year, and severity of non-head injuries; persons
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with TBI incur costs that are on average $22,838 more than persons without TBI.19 For stroke,
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first year cost estimates are $41,856 inclusive of ambulance, hospital stays, physician services,
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rehabilitation therapies, assistive devices, and home health care; annual follow-up costs are
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estimated at $64,800, including medication, informal care, and nursing home care.20 Unexpected
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health events are especially likely to translate into ongoing financial loss.21 Compounding the
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cost of disability is the inability to return to one’s previous occupation or employment status;
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11.9% of individuals with disabilities, compared to 6.3% of the non-disabled population, are
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unemployed.22 Chronic un- or under-employment and the associated income loss are significant
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barriers to HRQOL and participation.
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Economic barriers to health treatment, accessible housing, transportation, and assistive
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technology are critical to HRQOL. Economic barriers limit access to health care, resulting in
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increased morbidity and mortality. Individuals with disabilities often need home modifications
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that require structural renovations at substantial cost. The economic burden is compounded by
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HRQOL are important facilitators and barriers to community participation. Economic restrictions
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can have a profound impact on an individual’s economic QOL (ECQ). Subjective appraisals of
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the adequacy of resources to live life the way one wants include necessities like food and shelter,
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health and personal care resources, and discretionary expenses such as internet and social
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activities such as concerts or sporting events. Individuals who experience inadequate economic
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resources, despite objective indicators to the contrary, can experience economic hardship which,
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in turn, has a deleterious effect on QOL.
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Despite the paucity of literature, there is evidence that economic factors are perceived to be
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important contributors to HRQOL in persons with SCI.23-25 Individuals with higher net worth
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report smaller declines in well-being after the onset of disability than do those with lower net
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worth; however, these effects diminish with time—those with lower net worth recover some
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well-being.26 Disability is associated negatively with satisfaction with income, social life, and
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leisure time.27 While these findings highlight the importance of ECQ, the measures that have
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been used to assess this construct are limited.
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Commonly used measures of HRQOL usually omit economic-specific items, and when they are
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included, they tend to be limited and generic. For example, the General Social Survey28 and the
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QLQ-C3029 contain only one economic item and it is not designed for individuals with
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disabilities. Instruments with more complete coverage of economic concerns include the
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Wisconsin Quality of Life Index30 and the Family Quality of Life Survey-2006.31 These
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instruments were developed using classical test theory and have deficiencies in internal
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individual’s potential for economic self-sufficiency, objective information does not correlate
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highly with self-reported HRQOL.21 Large-scale, population-based data collection assessments
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of objective financial status (e.g., Health and Retirement Study)21 do not lend themselves to use
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in clinical or research settings. These measures do not reflect the financial needs of individuals
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with disability. For example, an individual with complete tetraplegia may have different financial
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needs than an individual with an uncomplicated mild TBI (e.g., a person with tetraplegia could
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require specialized attendant care, expensive adaptive equipment, and home modifications). An
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objective measure of economic characteristics would not reflect a person’s ability to afford
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assistive devices or rehabilitation services. Common expenditures are often more costly for
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individuals with disabilities. For example, a power wheelchair user’s needs are not met by the
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“cheapest car on the lot,” given their need for custom vehicle modifications. It is important to
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distinguish income and wealth; some people with high incomes spend much of their income and
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are in significant debt, whereas others live within their means despite lower incomes. Income is
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only modestly related to subjective well-being;32 if an individual perceives an economic barrier
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to participation in life activities, this perception defines a barrier to participation. Though items
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measuring the ability to “live life the way I want” do not provide the same information as
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objective indicators, they address the construct of ECQ. A new measure of ECQ containing this
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type of subjective items is necessary to improve assessment of HRQOL in individuals with
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disabilities.
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In summary, economic factors are neglected in HRQOL measures designed for disability
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populations. The available measures are limited in scope and do not consider financial barriers
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obtain a reliable measure of ECQ using few items. Therefore, the goal of this project was to
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develop a patient-reported measure of economic and financial quality of life that is relevant to
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individuals with disabilities. The new measure is designed to complement contemporary patient-
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reported measures including a family of measures focused on environmental factors33 (e.g.,
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contextual factors which may positively or negatively affect an individual’s ability to function)34
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and the SCI-QOL35 and TBI-QOL36 measurement systems.
METHODS
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Literature Review
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We completed a review of environmental factor instruments and identified 40 potential items
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related to ECQ. Then, we conducted a comprehensive literature review of patient-reported
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outcome measures using the search terms “Economic Satisfaction,” “Financial Satisfaction,”
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“Economic Well-Being,” and “Financial-Well Being.” We identified 21 items for inclusion in
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the ECQ item pool (Table 1).
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Individual Interviews
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We conducted semi-structured interviews with individuals with SCI (N=44) and TBI (N=20)
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focused on HRQOL. We contacted participants through hospital inpatient and outpatient
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referrals. Interviews typically lasted 2 hours; participants received a $20 honorarium. We based
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interview questions on the literature review; the interview consisted of open-ended questions
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about the general nature of HRQOL following injury Interviewers recorded responses verbatim
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and organized them by content area, from which we wrote 64 ECQ items. Participant-generated
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issues indicated important areas for further investigation in the focus groups described below.
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Focus Groups
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We conducted focus groups as part of the SCI-QOL35 and TBI-QOL36 projects during which
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economic concerns and their relation to HRQOL emerged. The SCI-QOL data included
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transcripts of 12 groups of 65 individuals and 4 clinician focus groups of 42 individuals.35,37 The
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TBI-QOL data included transcripts of 7 groups with former patients (N=33), TBI clinicians (2
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groups, N=15), and caregivers (4 groups, N=17).35,36 Qualitative analysis of the transcripts38
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allowed us to identify themes and domains, from which we wrote 31 additional items.
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Re-analyses of Focus Group Data for Economic QOL
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We pooled and integrated the qualitative data from the studies described above with a new set of
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20 focus groups that included more than 200 participants with diverse disabilities, age,
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race/ethnicity, and socioeconomic status.39 We transferred focus group transcripts to ATLAS.ti
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software and re-analyzed them for environmental constructs.40 We used an iterative, constant
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comparative method to analyze and interpret the transcripts.41-46 We generated 32 additional
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economic QOL items from focus group data. In total, we wrote 91he ECQ items.
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Item Development and Refinement
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Co-investigators completed a qualitative review of these items to improve clarity and
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conciseness, confirm the construct representativeness, and reduce redundancy. Five items were
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vague, 13 items were overly specific, 16 were inconsistent with the domain definition, and 28
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were redundant. We retained 37 items after removing the vague, overly specific, inconsistent,
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and 20 of the redundant items.
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Next, we sought to assure the content validity of the item pool using Willis’ methods.33 We
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conducted cognitive interviews with individuals with SCI, TBI, and stroke by asking them to
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answer each item, describe their thought processes in answering each item, and the meaning of
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each item. A minimum of 5 individuals reviewed each item. We revised the items based on this
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feedback. We assessed the reading level of the items using the Lexile FrameworkTM47 and
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rewrote items above a fifth-grade reading level. The resulting item pool contained 37 items
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ranging from a first through fifth grade reading level.
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Calibration Study Participants and Data Collection Procedures
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We recruited participants from a registry of former patients of a mid-western rehabilitation
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center. The calibration sample included 305 participants with TBI (n=100), stroke (n=100), and
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traumatic SCI (n=105). The institutional review board at the affiliated university reviewed and
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approved this study (approval #STU00018893). Inclusion criteria were age 18 years and older,
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ability to read and understand English at a fourth grade level, and traumatic SCI, TBI or stroke.
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Participants answered items during in-person (n=203) or telephone (n=102) interviews.
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Data Analysis
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We evaluated unidimensionality of the 37-item pool using confirmatory factor analyses (CFA),
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with the following criteria: 1) comparative fit index (CFI) >0.9,48 2) Root Mean Square Error of
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Approximation (RMSEA) < 0.08,49 and 3) R-square > 0.3.50 We examined residual correlations
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the polychoric correlation matrix and weighted least squares with adjustments for mean and
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variance estimation, the appropriate method to evaluate ordered categorical data. With an
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eventual goal of conducting 2-parameter IRT analyses following collection of a larger sample,
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for the current phase of the project we used 1-parameter (Rasch) analysis as implemented in
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WINSTEPS software.53 We chose this model rather than a 2-parameter model because it does
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not require as large sample size to obtain stable item parameters (e.g., 200 vs. 500).54 Rasch
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analysis provides a quantitative item review to assess the quality and appropriateness of the
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items. We used fit statistics for each item, reported as mean square (MnSq) values (criterion: 0.6-
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1.4), to evaluate fit to the Rasch model. At the scale level, Rasch analysis estimates the extent to
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which the items distinguish distinct levels of ECQ in the sample by reporting a separation index
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(criterion: separation >2). A separation index less than 1 indicates that the items are not able to
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distinguish more than 1 level of QOL, while a separation index of 2 indicates that 2 levels of
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QOL can be distinguished; a separation index above 2 indicates that at least 3 levels can be
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distinguished.55 We used the Rasch analysis-based information function at the item and scale
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levels to describe precision along the ECQ continuum.56-58 We converted the measure’s
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information function to reliability, in which reliability of 0.95 corresponds to an information
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function of 20.
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RESULTS
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Participant Demographic Characteristics
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Hammel et al report the demographic characteristics of the focus group sample.39 Demographic
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characteristics of the Cognitive Interview sample are shown in Table 2. Of the 15 participants, 6
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67% were male; 33% were Caucasian, 57% African-American, and 13% another race. Hispanic
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or Latino ethnicity was selected by 13%. 53% of participants reported the ability to walk, 47%
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reported using a walking device some or all of the time, and 47% reported using a wheelchair
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some or all of the time.
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Demographic characteristics of the Calibration sample are shown in Table 3. Mean age was 47.9
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years (SD 14.6); 64% were male; and 43% were Caucasian, 42% African-American, 2% more
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than one race, and 12% were another race. Hispanic or Latino ethnicity was selected by
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12%.76% of participants reported the ability to walk, 49% reported using a walking device some
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or all of the time, and 33% reported using a wheelchair some or all of the time. In the TBI
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sample, TBI severity59,60 ranged from mild (8%), moderate (6%), to severe (71%); 9% of cases
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had unknown severity. 39% sustained disability in motor vehicle crashes, 21% in falls, 20%
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through gunshot wounds or other acts of violence, and 19% other causes. In the SCI sample,
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51% were diagnosed with paraplegia and 46% with tetraplegia; 29% sustained completed
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injuries, 63% incomplete, and 7% unknown severity. In the stroke sample, 41% sustained
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hemorrhagic strokes, 34% ischemic, and 23% had unknown etiologies.
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CFA Findings
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Initial CFA results showed acceptable fit indices (CFI=0.939, RMSEA=0.089) for the 37 items.
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However, 3 items had R-square 0.95 between Rasch scaled-score -1.125 and 1.125; r > 0.90 between -1.87 and 1.87
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logit. The average sample ECQ measure was 0.89 logits (SD=1.33); 61% had measures with
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reliability > 0.95, 19% between 0.9 and 0.95, and 20% with reliability < 0.9. The items are more
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sensitive to respondents with ECQ above the mean than those with ECQ below the mean.
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DISCUSSION
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Costs of health care, accessible housing, reduced employment opportunities, and other economic
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factors are cited consistently as important issues related to quality of life by people with
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disabilities. Despite this need, economic factors that affect QOL following traumatic injury or
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stroke are an understudied area. This study used a comprehensive approach to develop a
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measurement tool to assess economic quality of life in individuals with disabilities.
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We developed item pools using a participatory action research62 approach and qualitative
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methods. This process began with a systematic literature review and continued with rigorous
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SCI, TBI, and stroke and clinicians who treat individuals with these disabilities. The item
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development procedures followed the guidelines developed by the Patient Reported Outcomes
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Measurement Information System (PROMIS).63 We developed the ECQ items based upon
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feedback and qualitative data from individuals with disabilities to ensure that the selected items
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were relevant. Our goal was to develop a scale with broad generalizability for health care and as
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such, items targeting specific conditions were not included.
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From these activities, we wrote an item pool of 37 items and administered them to a sample of
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individuals who sustained SCI, TBI, or stroke. Rasch analyses allowed us to describe item
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characteristics and identify items that do not fit the Rasch model and remove them from the item
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pool. We retained a final item pool of 28 items. The CFA fit statistics indicate that these items
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comprise a unidimensional construct which we interpret as reflecting economic QOL. Through
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an iterative process, we removed items that had high correlations with other items, thus reducing
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the degree of local dependence. The analyses allowed us to identify the items with the best
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information functions, items which best estimate individuals’ underlying ECQ.
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Study Limitations
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The data collected here are sufficient for test development. However, there are limitations to the
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data set. First, we recruited a convenience sample that is less representative of the population of
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people with disabilities than is desirable. Cases were recruited at only one center and were not
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stratified to represent the population. Second, a sample of 305 is not sufficient to complete a 2-
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parameter IRT analysis. When we recalibrate the items using a 2-parameter IRT analysis, we will
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Further, we were unable to evaluate the discriminability of each item, which is assumed to be
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equal by Rasch analysis but not by the 2-parameter model. A larger sample will allow us to
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estimate more stable calibration criteria and better estimates of ECQ. Finally, the current study
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included three disability subgroups. Given the universal applicability of ECQ, it is likely that
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these items would be appropriate for other disability subgroups and even a general health
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population.
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CONCLUSIONS
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Using participatory action research based qualitative methods in conjunction with a
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contemporary psychometric approach, we developed a 28-item bank that measures economic
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aspects of QOL. Preliminary CFA and Rasch analysis results support the psychometric
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properties of this measure. It fills a gap in HRQOL measurement by describing the economic
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barriers and facilitators that affect community participation. This preliminary test of the item
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bank requires confirmation with a larger, more representative sample. The analyses presented
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here serves as a first step to ensure that the item bank contains a hierarchy of items across
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different levels of ECQ and conforms to a 2-parameter IRT model. Future work will examine
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convergent and discriminant validity of the measure. This measure will meet the standards used
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in the PROMIS network and supplement existing banks, providing state-of-the-art measurement
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of an important content area. The final ECQ bank will complement emerging measurement
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systems (i.e., SCI-QOL64, TBI-QOL36) and other environmental factors scales33 for use in
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rehabilitation research and practice.
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64.
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Rehabilitation. Oct 2011;92(10):S44-S51.
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ACCEPTED MANUSCRIPT Measuring Economic Quality of Life Figure Captions: Figure 1. Person and Item Map of 28 Economic QOL Items Figure 2. Item Statistics in Measure Order
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Figure 3. Information Function of ECQ Items
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Figure 4. Distribution of ECQ scores distribution across the measurement continuum defined by Rasch analysis
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Table 1. ECQ Measures Identified by Literature Review Measure
# Potential item stems identified
Weaknesses
# Items in Final Item Bank
Measure of the 17 Quality of the Environment (MQE)52
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Measures/Items identified through Environmental Factors Literature Review •
Checklist format
•
Items either inconsistent with domain definition or overly specific
0
0
5
Craig Hospital Inventory of Environmental Factors (CHIEF)54
1
• •
Focus on barriers only Combines multiple aspects into single items30
0
Supports Intensity Scale55
2
• •
Checklist format Vague or overly specific items
0
Guernsey Community Participation and Leisure Assessment56
1
• •
Checklist format Inconsistent with domain definition
0
Assessment of Living Skills and Resources (ALSAR)57
1
•
Overly broad/general
0
•
Too specific
0
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Untitled survey of personal, social, and environmental influence58
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Child and Adolescent Scale of Environment (CASE)53
Interpersonal Support Evaluation List59
2
•
More an assessment of social support than economic QOL
0
Untitled list of emergent themes60
4
•
Too specific
0
Systematic Pedestrian and Cycling Environmental Scan (SPACES)61
1
•
Items not representative of ECQ construct
0
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Untitled list of environmental factors62
# Potential item stems identified
Weaknesses
•
4
# Items in Final Item Bank
Items are vague, irrelevant, or overly specific
0
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Measure
Untitled measure of environmental factors63
1
•
The Wisconsin Quality of Life Index (W-QLI)65
6
•
Family Quality of Life Survey-200666
13
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Developed for individuals with 0 Intellectual Disabilities only • Not considered reliable • Requires further development Measures/Items Identified through targeted Economic Quality of Life Literature Review • Solely addresses satisfaction 1 0 General Social 26 component Survey • Only 1 item addresses ECQ • Only 1 item addresses ECQ European 1 0 • Not targeted to those with Organization for physical or cognitive disabilities Research and Treatment of Cancer QLQ-C3064
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Not targeted to those with physical or cognitive disabilities
•
Does not take into account financial barriers related to disability
0
0
ACCEPTED MANUSCRIPT Table 2. Demographic Characteristics of Cognitive Interview Sample Variable
10 (67%) 5 (33%)
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2 (13%) 13 (87%) 5 (33%) 8 (57%) 2 (13%)
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3 (20%) 12 (80%)
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Uses a Wheelchair Some or All of the Time Yes No
2 (13%) 3 (20%) 4 (27%) 5 (33%) 1 (7%)
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Age Gender Male Female Ethnicity Hispanic Non-Hispanic Race Caucasian African-American Other Education Completed High School (Diploma or GED) Completed Some College or more Work Status Employed for wages Unemployed, looking for work Unemployed, not looking for work Retired due to disability Retired NOT due to disability Income Level Less than $20,000 Between $20.001 and $49,999 Between $50,000 and $99,999 Over $100,000 Declined to Respond Able to Walk some or all of the time Yes No Uses a Walking Device some or all of the time Yes No
Total Sample (N = 15) 45.5 years
10 (67%) 1 (7%) 2 (13%) 1 (7%) 1 (7%) 8 (53%) 7 (47%)
7 (47%) 8 (53%)
7 (47%) 8 (53%)
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134 (44%) 92 (30%) 61 (20%) 15 (5%) 3 (1%)
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196 (64%) 109 (36%)
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Age Marital Status Single Married Separated/Divorced Widowed Living with Partner Gender Male Female Ethnicity Hispanic Non-Hispanic Declined Race Caucasian African-American Other American Indian/Alaskan Native More than one race Declined to respond Language English Spanish Other Highest School Level Some HS Completed HS Some college Bachelor’s degree Some graduate school Advanced degree (MA, PhD, MD) High School Diploma HS diploma GED Vocational HS NA Declined Current Work Status Employed for wages Out of work LESS than 1 year, LOOKING Out of work LESS than 1 year, NOT looking Out of work MORE than 1 year, LOOKING
Total Sample (N = 305) 47.9 years ± 14.6
Out of work MORE than 1 year, NOT looking Homemaker
35 (12%) 269 (88%) 1 (
S0003-9993(14)00265-2
DOI:
10.1016/j.apmr.2014.02.030
Reference:
YAPMR 55795
To appear in:
ARCHIVES OF PHYSICAL MEDICINE AND REHABILITATION
Received Date: 13 February 2014 Accepted Date: 22 February 2014
Please cite this article as: Tulsky DS, Kisala PA, Lai J-S, Carlozzi N, Hammel J, Heinemann AW, Developing an Item Bank to Measure Economic Quality of Life for Individuals with Disabilities, ARCHIVES OF PHYSICAL MEDICINE AND REHABILITATION (2014), doi: 10.1016/ j.apmr.2014.02.030. This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.
ACCEPTED MANUSCRIPT
RUNNING HEAD: Measuring Economic Quality of Life
Developing an Item Bank to Measure Economic Quality of Life for Individuals with
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Disabilities David S. Tulsky, Ph.D.1,2 Pamela A. Kisala, M.A.1
Noelle Carlozzi, Ph.D.4
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Joy Hammel, Ph.D.5
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Jin-Shei Lai, Ph.D.3
Allen W. Heinemann, Ph.D.3,6
1
New York University Langone Medical Center, New York, NY 2
Feinberg School of Medicine, Northwestern University, Chicago, IL 4
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Kessler Foundation Research Center, West Orange, NJ
University of Michigan Medical School, Ann Arbor, MI 5
Rehabilitation Institute of Chicago, Chicago, IL
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University of Illinois at Chicago, Chicago, IL
Reprints will not be available from the corresponding author.
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Correspondence Address: David Tulsky, Ph.D., Dept. Rehabilitation Medicine, Rusk Institute for Rehabilitation, Ambulatory Care Center , New York University Langone Medical Center, 240 East 38th Street, 17th Floor, New York, NY 10016. [email protected] Funding was provided by National Institute on Disability and Rehabilitation Research (grant numbers H133B090024, H133A030807, and H133G070138) and the National Institutes of Health (5R01HD054659). We certify that no party having a direct interest in the results of the research supporting this article has or will confer a benefit on us or on any organization with which we are associated AND, if applicable, we certify that all financial and material support for this research and work are clearly identified in the title page of the manuscript.
ACCEPTED MANUSCRIPT Measuring Economic Quality of Life Abstract
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Objective: To develop and evaluate the psychometric properties of an item set measuring
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economic quality of life for use by individuals with disabilities.
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Design: Survey.
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Setting: Community settings. Inclusion criteria were a traumatic brain injury, spinal cord injury
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or stroke, age 18 years and older, and ability to read and speak English.
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Participants: 64 individuals with disabilities completed individual interviews, 172 participated
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in focus groups, and 15 completed cognitive interviews. We calibrated the items with 305 former
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rehabilitation inpatients.
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Intervention: None.
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Main Outcome Measure: Economic Quality of Life (ECQ).
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Results: Confirmatory factor showed acceptable fit indices (CFI=0.939, RMSEA=0.089) for the
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37 items. However, 3 items demonstrated local item dependency. Dropping 15 items improved
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fit and obviated local dependency. Rasch analysis of the remaining 28 items yielded a person
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reliability of .92, suggesting that these items discriminate about 4 ECQ levels.
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Conclusions: We developed a 28-item bank that measures economic aspects of QOL.
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Preliminary CFA and Rasch analysis results support the psychometric properties of this new
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measure. It fills a gap in HRQOL measurement by describing the economic barriers and
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facilitators of community participation. Future development will make the item bank available as
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a computer adaptive test.
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Keywords: Economic factors, quality of life, patient-reported outcomes, psychometrics,
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rehabilitation
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ACCEPTED MANUSCRIPT Measuring Economic Quality of Life Abbreviations
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CFA – Confirmatory Factor Analyses
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CFI – Comparative Fit Index
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ECQ – Economic Quality of Life
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HRQOL – Health-Related Quality of Life
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MnSq – Mean Square
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PROMIS – Patient Reported Outcomes Measurement Information System
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QOL – Quality of Life
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RMSEA – Root Mean Square Error of Approximation
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SCI – Spinal Cord Injury
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SCI-QOL – Spinal Cord Injury Quality of Life Measurement System
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TBI – Traumatic Brain Injury
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TBI-QOL – Traumatic Brain Injury Quality of Life Measurement System
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ACCEPTED MANUSCRIPT Measuring Economic Quality of Life Early definitions of Quality of Life (QOL) emphasized an individual’s or country’s standard of
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living, wealth, comfort, and material goods.1 Quantifiable units were earnings, type of
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automobile one drove, and the size of one’s house. During the 1928 presidential campaign, the
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Republican National Committee promised Americans that a vote for Herbert Hoover was a vote
40
for “a car in every garage and a chicken in every pot” – implying that prosperity was the way to
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measure QOL.2 Dwight Eisenhower’s 1960 Commission on National Goals and Lyndon B.
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Johnson’s Great Society programs began to emphasize the social aspects of QOL2-4 and new
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definitions of QOL came to reflect subjective qualities that reflected lifestyle, happiness, and
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well-being.5,6 Subsequently, “Health-Related QOL (HRQOL) was defined as subjective
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evaluations of status or capacity that reflect a health condition. Adopting the World Health
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Organizations’ conceptual structure of health, measurement strategies to evaluate HRQOL
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focused on physical, emotional, and social well-being.7 Because of this shift, the influence of
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economic issues and subjective material resources on HRQOL has been minimized and little has
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been published about perceived economic resources and their influence on HRQOL.
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The economic burden of disability requires us to question why perceived economic factors are
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not conceptualized as part of HRQOL. Reduced household income is associated with lower
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levels of life satisfaction in older adults who have retired; loss of income after retirement is
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related to lower life satisfaction rather than to the loss of a role as worker.8 Perceived financial
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hardship is negatively related to HRQOL in a number of populations including patients with
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advanced cancer,9 breast cancer,10-12 human immunodeficiency virus,13 end-stage kidney
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disease,14 amyotrophic lateral sclerosis,15 Parkinson’s disease,16 and schizophrenia.17 While there
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is substantial support for a relationship between economic factors and HRQOL, there is a paucity
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ACCEPTED MANUSCRIPT Measuring Economic Quality of Life 59
of research focusing on patient reported economic factors.
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This dearth of research on economic quality of life is striking given the costs of disability. For
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example, first-year costs following traumatic spinal cord injury (SCI) range from $218,504 -
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$741,425 in the first year depending on the injury level and severity, with the highest estimates
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for high tetraplegia; for subsequent years, cost estimates range from $15,313 - $132,807
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inclusive of health care, medications, supplies, durable medical equipment, and personal
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assistance.18 For traumatic brain injury (TBI), medical costs alone are $4,906 higher than costs
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for individuals of the same age, sex, calendar year, and severity of non-head injuries; persons
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with TBI incur costs that are on average $22,838 more than persons without TBI.19 For stroke,
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first year cost estimates are $41,856 inclusive of ambulance, hospital stays, physician services,
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rehabilitation therapies, assistive devices, and home health care; annual follow-up costs are
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estimated at $64,800, including medication, informal care, and nursing home care.20 Unexpected
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health events are especially likely to translate into ongoing financial loss.21 Compounding the
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cost of disability is the inability to return to one’s previous occupation or employment status;
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11.9% of individuals with disabilities, compared to 6.3% of the non-disabled population, are
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unemployed.22 Chronic un- or under-employment and the associated income loss are significant
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barriers to HRQOL and participation.
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Economic barriers to health treatment, accessible housing, transportation, and assistive
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technology are critical to HRQOL. Economic barriers limit access to health care, resulting in
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increased morbidity and mortality. Individuals with disabilities often need home modifications
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that require structural renovations at substantial cost. The economic burden is compounded by
4
ACCEPTED MANUSCRIPT Measuring Economic Quality of Life insufficient resources to afford transportation for work or leisure. The economic components of
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HRQOL are important facilitators and barriers to community participation. Economic restrictions
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can have a profound impact on an individual’s economic QOL (ECQ). Subjective appraisals of
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the adequacy of resources to live life the way one wants include necessities like food and shelter,
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health and personal care resources, and discretionary expenses such as internet and social
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activities such as concerts or sporting events. Individuals who experience inadequate economic
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resources, despite objective indicators to the contrary, can experience economic hardship which,
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in turn, has a deleterious effect on QOL.
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Despite the paucity of literature, there is evidence that economic factors are perceived to be
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important contributors to HRQOL in persons with SCI.23-25 Individuals with higher net worth
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report smaller declines in well-being after the onset of disability than do those with lower net
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worth; however, these effects diminish with time—those with lower net worth recover some
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well-being.26 Disability is associated negatively with satisfaction with income, social life, and
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leisure time.27 While these findings highlight the importance of ECQ, the measures that have
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been used to assess this construct are limited.
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Commonly used measures of HRQOL usually omit economic-specific items, and when they are
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included, they tend to be limited and generic. For example, the General Social Survey28 and the
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QLQ-C3029 contain only one economic item and it is not designed for individuals with
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disabilities. Instruments with more complete coverage of economic concerns include the
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Wisconsin Quality of Life Index30 and the Family Quality of Life Survey-2006.31 These
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instruments were developed using classical test theory and have deficiencies in internal
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individual’s potential for economic self-sufficiency, objective information does not correlate
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highly with self-reported HRQOL.21 Large-scale, population-based data collection assessments
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of objective financial status (e.g., Health and Retirement Study)21 do not lend themselves to use
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in clinical or research settings. These measures do not reflect the financial needs of individuals
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with disability. For example, an individual with complete tetraplegia may have different financial
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needs than an individual with an uncomplicated mild TBI (e.g., a person with tetraplegia could
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require specialized attendant care, expensive adaptive equipment, and home modifications). An
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objective measure of economic characteristics would not reflect a person’s ability to afford
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assistive devices or rehabilitation services. Common expenditures are often more costly for
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individuals with disabilities. For example, a power wheelchair user’s needs are not met by the
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“cheapest car on the lot,” given their need for custom vehicle modifications. It is important to
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distinguish income and wealth; some people with high incomes spend much of their income and
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are in significant debt, whereas others live within their means despite lower incomes. Income is
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only modestly related to subjective well-being;32 if an individual perceives an economic barrier
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to participation in life activities, this perception defines a barrier to participation. Though items
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measuring the ability to “live life the way I want” do not provide the same information as
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objective indicators, they address the construct of ECQ. A new measure of ECQ containing this
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type of subjective items is necessary to improve assessment of HRQOL in individuals with
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disabilities.
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In summary, economic factors are neglected in HRQOL measures designed for disability
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populations. The available measures are limited in scope and do not consider financial barriers
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ACCEPTED MANUSCRIPT Measuring Economic Quality of Life related to disability or the costs of health care and personal assistance. It would be difficult to
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obtain a reliable measure of ECQ using few items. Therefore, the goal of this project was to
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develop a patient-reported measure of economic and financial quality of life that is relevant to
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individuals with disabilities. The new measure is designed to complement contemporary patient-
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reported measures including a family of measures focused on environmental factors33 (e.g.,
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contextual factors which may positively or negatively affect an individual’s ability to function)34
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and the SCI-QOL35 and TBI-QOL36 measurement systems.
METHODS
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Literature Review
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We completed a review of environmental factor instruments and identified 40 potential items
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related to ECQ. Then, we conducted a comprehensive literature review of patient-reported
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outcome measures using the search terms “Economic Satisfaction,” “Financial Satisfaction,”
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“Economic Well-Being,” and “Financial-Well Being.” We identified 21 items for inclusion in
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the ECQ item pool (Table 1).
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Individual Interviews
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We conducted semi-structured interviews with individuals with SCI (N=44) and TBI (N=20)
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focused on HRQOL. We contacted participants through hospital inpatient and outpatient
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referrals. Interviews typically lasted 2 hours; participants received a $20 honorarium. We based
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interview questions on the literature review; the interview consisted of open-ended questions
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about the general nature of HRQOL following injury Interviewers recorded responses verbatim
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and organized them by content area, from which we wrote 64 ECQ items. Participant-generated
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issues indicated important areas for further investigation in the focus groups described below.
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Focus Groups
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We conducted focus groups as part of the SCI-QOL35 and TBI-QOL36 projects during which
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economic concerns and their relation to HRQOL emerged. The SCI-QOL data included
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transcripts of 12 groups of 65 individuals and 4 clinician focus groups of 42 individuals.35,37 The
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TBI-QOL data included transcripts of 7 groups with former patients (N=33), TBI clinicians (2
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groups, N=15), and caregivers (4 groups, N=17).35,36 Qualitative analysis of the transcripts38
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allowed us to identify themes and domains, from which we wrote 31 additional items.
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Re-analyses of Focus Group Data for Economic QOL
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We pooled and integrated the qualitative data from the studies described above with a new set of
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20 focus groups that included more than 200 participants with diverse disabilities, age,
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race/ethnicity, and socioeconomic status.39 We transferred focus group transcripts to ATLAS.ti
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software and re-analyzed them for environmental constructs.40 We used an iterative, constant
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comparative method to analyze and interpret the transcripts.41-46 We generated 32 additional
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economic QOL items from focus group data. In total, we wrote 91he ECQ items.
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Item Development and Refinement
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Co-investigators completed a qualitative review of these items to improve clarity and
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conciseness, confirm the construct representativeness, and reduce redundancy. Five items were
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vague, 13 items were overly specific, 16 were inconsistent with the domain definition, and 28
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were redundant. We retained 37 items after removing the vague, overly specific, inconsistent,
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and 20 of the redundant items.
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Next, we sought to assure the content validity of the item pool using Willis’ methods.33 We
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conducted cognitive interviews with individuals with SCI, TBI, and stroke by asking them to
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answer each item, describe their thought processes in answering each item, and the meaning of
179
each item. A minimum of 5 individuals reviewed each item. We revised the items based on this
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feedback. We assessed the reading level of the items using the Lexile FrameworkTM47 and
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rewrote items above a fifth-grade reading level. The resulting item pool contained 37 items
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ranging from a first through fifth grade reading level.
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Calibration Study Participants and Data Collection Procedures
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We recruited participants from a registry of former patients of a mid-western rehabilitation
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center. The calibration sample included 305 participants with TBI (n=100), stroke (n=100), and
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traumatic SCI (n=105). The institutional review board at the affiliated university reviewed and
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approved this study (approval #STU00018893). Inclusion criteria were age 18 years and older,
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ability to read and understand English at a fourth grade level, and traumatic SCI, TBI or stroke.
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Participants answered items during in-person (n=203) or telephone (n=102) interviews.
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Data Analysis
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We evaluated unidimensionality of the 37-item pool using confirmatory factor analyses (CFA),
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with the following criteria: 1) comparative fit index (CFI) >0.9,48 2) Root Mean Square Error of
195
Approximation (RMSEA) < 0.08,49 and 3) R-square > 0.3.50 We examined residual correlations
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197
the polychoric correlation matrix and weighted least squares with adjustments for mean and
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variance estimation, the appropriate method to evaluate ordered categorical data. With an
199
eventual goal of conducting 2-parameter IRT analyses following collection of a larger sample,
200
for the current phase of the project we used 1-parameter (Rasch) analysis as implemented in
201
WINSTEPS software.53 We chose this model rather than a 2-parameter model because it does
202
not require as large sample size to obtain stable item parameters (e.g., 200 vs. 500).54 Rasch
203
analysis provides a quantitative item review to assess the quality and appropriateness of the
204
items. We used fit statistics for each item, reported as mean square (MnSq) values (criterion: 0.6-
205
1.4), to evaluate fit to the Rasch model. At the scale level, Rasch analysis estimates the extent to
206
which the items distinguish distinct levels of ECQ in the sample by reporting a separation index
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(criterion: separation >2). A separation index less than 1 indicates that the items are not able to
208
distinguish more than 1 level of QOL, while a separation index of 2 indicates that 2 levels of
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QOL can be distinguished; a separation index above 2 indicates that at least 3 levels can be
210
distinguished.55 We used the Rasch analysis-based information function at the item and scale
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levels to describe precision along the ECQ continuum.56-58 We converted the measure’s
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information function to reliability, in which reliability of 0.95 corresponds to an information
213
function of 20.
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RESULTS
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Participant Demographic Characteristics
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Hammel et al report the demographic characteristics of the focus group sample.39 Demographic
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characteristics of the Cognitive Interview sample are shown in Table 2. Of the 15 participants, 6
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67% were male; 33% were Caucasian, 57% African-American, and 13% another race. Hispanic
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or Latino ethnicity was selected by 13%. 53% of participants reported the ability to walk, 47%
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reported using a walking device some or all of the time, and 47% reported using a wheelchair
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some or all of the time.
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Demographic characteristics of the Calibration sample are shown in Table 3. Mean age was 47.9
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years (SD 14.6); 64% were male; and 43% were Caucasian, 42% African-American, 2% more
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than one race, and 12% were another race. Hispanic or Latino ethnicity was selected by
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12%.76% of participants reported the ability to walk, 49% reported using a walking device some
229
or all of the time, and 33% reported using a wheelchair some or all of the time. In the TBI
230
sample, TBI severity59,60 ranged from mild (8%), moderate (6%), to severe (71%); 9% of cases
231
had unknown severity. 39% sustained disability in motor vehicle crashes, 21% in falls, 20%
232
through gunshot wounds or other acts of violence, and 19% other causes. In the SCI sample,
233
51% were diagnosed with paraplegia and 46% with tetraplegia; 29% sustained completed
234
injuries, 63% incomplete, and 7% unknown severity. In the stroke sample, 41% sustained
235
hemorrhagic strokes, 34% ischemic, and 23% had unknown etiologies.
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CFA Findings
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Initial CFA results showed acceptable fit indices (CFI=0.939, RMSEA=0.089) for the 37 items.
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However, 3 items had R-square 0.95 between Rasch scaled-score -1.125 and 1.125; r > 0.90 between -1.87 and 1.87
274
logit. The average sample ECQ measure was 0.89 logits (SD=1.33); 61% had measures with
275
reliability > 0.95, 19% between 0.9 and 0.95, and 20% with reliability < 0.9. The items are more
276
sensitive to respondents with ECQ above the mean than those with ECQ below the mean.
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DISCUSSION
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Costs of health care, accessible housing, reduced employment opportunities, and other economic
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factors are cited consistently as important issues related to quality of life by people with
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disabilities. Despite this need, economic factors that affect QOL following traumatic injury or
282
stroke are an understudied area. This study used a comprehensive approach to develop a
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measurement tool to assess economic quality of life in individuals with disabilities.
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We developed item pools using a participatory action research62 approach and qualitative
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methods. This process began with a systematic literature review and continued with rigorous
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SCI, TBI, and stroke and clinicians who treat individuals with these disabilities. The item
289
development procedures followed the guidelines developed by the Patient Reported Outcomes
290
Measurement Information System (PROMIS).63 We developed the ECQ items based upon
291
feedback and qualitative data from individuals with disabilities to ensure that the selected items
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were relevant. Our goal was to develop a scale with broad generalizability for health care and as
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such, items targeting specific conditions were not included.
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From these activities, we wrote an item pool of 37 items and administered them to a sample of
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individuals who sustained SCI, TBI, or stroke. Rasch analyses allowed us to describe item
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characteristics and identify items that do not fit the Rasch model and remove them from the item
298
pool. We retained a final item pool of 28 items. The CFA fit statistics indicate that these items
299
comprise a unidimensional construct which we interpret as reflecting economic QOL. Through
300
an iterative process, we removed items that had high correlations with other items, thus reducing
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the degree of local dependence. The analyses allowed us to identify the items with the best
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information functions, items which best estimate individuals’ underlying ECQ.
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Study Limitations
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The data collected here are sufficient for test development. However, there are limitations to the
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data set. First, we recruited a convenience sample that is less representative of the population of
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people with disabilities than is desirable. Cases were recruited at only one center and were not
308
stratified to represent the population. Second, a sample of 305 is not sufficient to complete a 2-
309
parameter IRT analysis. When we recalibrate the items using a 2-parameter IRT analysis, we will
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Further, we were unable to evaluate the discriminability of each item, which is assumed to be
312
equal by Rasch analysis but not by the 2-parameter model. A larger sample will allow us to
313
estimate more stable calibration criteria and better estimates of ECQ. Finally, the current study
314
included three disability subgroups. Given the universal applicability of ECQ, it is likely that
315
these items would be appropriate for other disability subgroups and even a general health
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population.
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CONCLUSIONS
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Using participatory action research based qualitative methods in conjunction with a
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contemporary psychometric approach, we developed a 28-item bank that measures economic
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aspects of QOL. Preliminary CFA and Rasch analysis results support the psychometric
322
properties of this measure. It fills a gap in HRQOL measurement by describing the economic
323
barriers and facilitators that affect community participation. This preliminary test of the item
324
bank requires confirmation with a larger, more representative sample. The analyses presented
325
here serves as a first step to ensure that the item bank contains a hierarchy of items across
326
different levels of ECQ and conforms to a 2-parameter IRT model. Future work will examine
327
convergent and discriminant validity of the measure. This measure will meet the standards used
328
in the PROMIS network and supplement existing banks, providing state-of-the-art measurement
329
of an important content area. The final ECQ bank will complement emerging measurement
330
systems (i.e., SCI-QOL64, TBI-QOL36) and other environmental factors scales33 for use in
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rehabilitation research and practice.
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Figure 4. Distribution of ECQ scores distribution across the measurement continuum defined by Rasch analysis
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Table 1. ECQ Measures Identified by Literature Review Measure
# Potential item stems identified
Weaknesses
# Items in Final Item Bank
Measure of the 17 Quality of the Environment (MQE)52
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Measures/Items identified through Environmental Factors Literature Review •
Checklist format
•
Items either inconsistent with domain definition or overly specific
0
0
5
Craig Hospital Inventory of Environmental Factors (CHIEF)54
1
• •
Focus on barriers only Combines multiple aspects into single items30
0
Supports Intensity Scale55
2
• •
Checklist format Vague or overly specific items
0
Guernsey Community Participation and Leisure Assessment56
1
• •
Checklist format Inconsistent with domain definition
0
Assessment of Living Skills and Resources (ALSAR)57
1
•
Overly broad/general
0
•
Too specific
0
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Untitled survey of personal, social, and environmental influence58
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Child and Adolescent Scale of Environment (CASE)53
Interpersonal Support Evaluation List59
2
•
More an assessment of social support than economic QOL
0
Untitled list of emergent themes60
4
•
Too specific
0
Systematic Pedestrian and Cycling Environmental Scan (SPACES)61
1
•
Items not representative of ECQ construct
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Untitled list of environmental factors62
# Potential item stems identified
Weaknesses
•
4
# Items in Final Item Bank
Items are vague, irrelevant, or overly specific
0
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Untitled measure of environmental factors63
1
•
The Wisconsin Quality of Life Index (W-QLI)65
6
•
Family Quality of Life Survey-200666
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Developed for individuals with 0 Intellectual Disabilities only • Not considered reliable • Requires further development Measures/Items Identified through targeted Economic Quality of Life Literature Review • Solely addresses satisfaction 1 0 General Social 26 component Survey • Only 1 item addresses ECQ • Only 1 item addresses ECQ European 1 0 • Not targeted to those with Organization for physical or cognitive disabilities Research and Treatment of Cancer QLQ-C3064
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Not targeted to those with physical or cognitive disabilities
•
Does not take into account financial barriers related to disability
0
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ACCEPTED MANUSCRIPT Table 2. Demographic Characteristics of Cognitive Interview Sample Variable
10 (67%) 5 (33%)
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2 (13%) 13 (87%) 5 (33%) 8 (57%) 2 (13%)
SC
3 (20%) 12 (80%)
EP
AC C
Uses a Wheelchair Some or All of the Time Yes No
2 (13%) 3 (20%) 4 (27%) 5 (33%) 1 (7%)
M AN U
TE D
Age Gender Male Female Ethnicity Hispanic Non-Hispanic Race Caucasian African-American Other Education Completed High School (Diploma or GED) Completed Some College or more Work Status Employed for wages Unemployed, looking for work Unemployed, not looking for work Retired due to disability Retired NOT due to disability Income Level Less than $20,000 Between $20.001 and $49,999 Between $50,000 and $99,999 Over $100,000 Declined to Respond Able to Walk some or all of the time Yes No Uses a Walking Device some or all of the time Yes No
Total Sample (N = 15) 45.5 years
10 (67%) 1 (7%) 2 (13%) 1 (7%) 1 (7%) 8 (53%) 7 (47%)
7 (47%) 8 (53%)
7 (47%) 8 (53%)
ACCEPTED MANUSCRIPT Table 3. Demographic Characteristics of Calibration Sample Variable
RI PT
134 (44%) 92 (30%) 61 (20%) 15 (5%) 3 (1%)
M AN U
SC
196 (64%) 109 (36%)
AC C
EP
TE D
Age Marital Status Single Married Separated/Divorced Widowed Living with Partner Gender Male Female Ethnicity Hispanic Non-Hispanic Declined Race Caucasian African-American Other American Indian/Alaskan Native More than one race Declined to respond Language English Spanish Other Highest School Level Some HS Completed HS Some college Bachelor’s degree Some graduate school Advanced degree (MA, PhD, MD) High School Diploma HS diploma GED Vocational HS NA Declined Current Work Status Employed for wages Out of work LESS than 1 year, LOOKING Out of work LESS than 1 year, NOT looking Out of work MORE than 1 year, LOOKING
Total Sample (N = 305) 47.9 years ± 14.6
Out of work MORE than 1 year, NOT looking Homemaker
35 (12%) 269 (88%) 1 (
