Invisible partners in care: Snapshot of well-being among caregivers receiving comprehensive support from Veterans Affairs.
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2019-03
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Abstract
Background and aims
Since May 2011, over 23 000 caregivers of Veterans seriously injured on or after September 11, 2001 have enrolled in the Program of Comprehensive Assistance for Family Caregivers (PCAFC). PCAFC provides caregivers training, a stipend, and access to health care. The aim of this study is to describe the characteristics of caregivers in PCAFC and examine associations between caregiver characteristics and caregiver well-being outcomes.Methods
We sent a web survey invitation to 10 000 PCAFC caregivers enrolled as of September 2015. Using linear and logistic regressions, we examine associations between PCAFC caregiver characteristics and caregiver outcomes: perceived financial strain, depressive symptoms (Center for Epidemiologic Studies Depression Scale [CESD-10]), perceived quality of Veteran's Veterans Health Administration (VHA) care, and self-reported caregiver health.Results
We had complete survey data for 899 respondents. Since becoming a caregiver, approximately 50% of respondents reported reducing or stopping work. Mean time spent providing care was 3.8 years (median 3, IQR 1-5) with an average of 4.9 weekdays (median 5, IQR 5-5) and 1.9 weekend days (median 2, IQR 2-2). The mean CESD-10 score was 8.2 (median 7, 4-12), at the cutoff for screening positive for depressive symptoms. A longer duration of caregiving was associated with having 0.08 increase in rating of financial strain (95% CI, 0.02-0.14). Caregiver rating of the Veteran's health status as "fair" or better was a strong predictor of better caregiver outcomes, ie, self-reported caregiver health. However, higher levels of education were associated with worse caregiver outcomes, ie, lower global satisfaction with VHA care, higher CESD-10 score, and higher rating of financial strain.Conclusions
Higher depressive symptoms among longer duration caregivers, coupled with high rates of reductions in hours worked, suggest interventions are needed to address the long-term emotional and financial needs of these caregivers of post-9/11 Veterans and identify subpopulations at risk for worse outcomes.Type
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Publication Info
Miller, Katherine EM, Jennifer H Lindquist, Maren K Olsen, Valerie Smith, Corrine I Voils, Eugene Z Oddone, Nina R Sperber, Megan Shepherd-Banigan, et al. (2019). Invisible partners in care: Snapshot of well-being among caregivers receiving comprehensive support from Veterans Affairs. Health science reports, 2(3). p. e112. 10.1002/hsr2.112 Retrieved from https://hdl.handle.net/10161/26150.
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Scholars@Duke

Maren Karine Olsen
Health services research, longitudinal data methods, missing data methods

Valerie A. Smith
Valerie A. Smith, DrPH, is a Professor in the Duke University Department of Population Health Sciences. Her methodological research interests include methods for semicontinuous and zero-inflated data, cost and utilization modeling, causal inference methods, observational study design, and longitudinal data analysis.
Dr. Smith works largely in collaboration with a multidisciplinary team of researchers, with a focus on health policy interventions, health care utilization and expenditure patterns, program and policy evaluation, bariatric surgery and obesity treatment evaluation, aging, and caregiving.
Areas of expertise: Biostatistics, Health Services Research, Health Economics, and Health Policy

Eugene Zaverio Oddone
I am a health services researcher whose primary research interests are: 1) evaluating the effectiveness of primary care with an emphasis on chronic disease, 2) assessing the reasons and testing interventions to reduce racial variation in access the health care and utilization of health services, 3) determining appropriate interventions to improve blood pressure control for hypertensive patients treated in primary care. I have research expertise in racial variation, blood pressure control, disease management, and tele-medicine. I also have methodologic expertise in designing and testing health services interventions in multi-site clinical trials.
Key words: primary care, racial variation, quality of care, hypertension

Nina Sperber
My research career has centered on understanding how to improve delivery of new evidence-based practices in health care systems. I create study designs that integrate qualitative and quantitative methods (mixed-methods) and apply Implementation Science and System Science approaches. I currently have a developing body of academic work that uses participatory system dynamics modeling as a strategy to identify system level factors that affect development and implementation of equitable AI tools. For the VA health care system, I direct a cross-functional team that conducts rapid turnaround projects for high priority needs by VHA national, regional, and facility leaders.

Megan E Shepherd-Banigan
Dr. Megan Shepherd-Banigan designs research studies to improve the health, emotional well-being, and social functioning of adults with mental and physical disabilities. Her methods combine empirical approaches that address methodologically challenging research questions in health systems and policy research. Dr. Shepherd-Banigan uses large survey and administrative datasets to evaluate the impact of policies that support family members to care for adults with disabilities.
Dr. Shepherd-Banigan won a VA Career Development Award from 2019-2024 and is studying ways to strengthen family support for veterans under-going traumatic stress treatment. She also leads a project that surveys family caregivers of Vietnam-era veterans who might be eligible for expanded support services under the VA Mission Act to evaluate program impacts. As co-investigator on an NIA-funded CARE IDEAS study (Terri Wetle, PI) , she is investigating end-of-life-care planning and well-being among dementia care dyads. Finally, Dr. Shepherd-Banigan is leading a project in partnership with the Rosalynn Carter Institute for Caregivers to identify creative empirically-based approaches to support family caregivers.
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