Evidence Review—Social Determinants of Health for Veterans

Duan-Porter, Wei; Martinson, Brian C.; Greer, Nancy; Taylor, Brent C.; Ullman, Kristen; McKenzie, Lauren; Rosebush, Christina; MacDonald, Roderick; Falde, Samuel; Wilt, Timothy J

doi:10.1007/s11606-018-4566-8

Cited by 23 publications

(13 citation statements)

References 146 publications

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“…Although this low degree of qualification for profiling was most dramatic when using one year of data (range: 38%‐72%), we noted that it did not dramatically improve when we combined three years of data (range: 52%‐77%). These findings act to highlight the population and systems differences between CMS and VA populations 16 and further support the idea that CMS‐derived models may not be optimized for use in the VA healthcare system.…”

Section: Discussionsupporting

confidence: 59%

Examining the Utility of 30‐day Readmission Rates and Hospital Profiling in the Veterans Health Administration

Wray

Vali

Walter

et al. 2019

Journal of Hospital Medicine

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U sing methodology created by the Centers for Medicare & Medicaid Services (CMS), the Department of Veterans Affairs (VA) calculates and reports hospital performance measures for several key conditions, including acute myocardial infarction (AMI), heart failure (HF), and pneumonia. 1 These measures are designed to benchmark individual hospitals against how average hospitals perform when caring for a similar case-mix index. Because readmissions to the hospital within 30-days of discharge are common and costly, this metric has garnered extensive attention in recent years. To summarize the 30-day readmission metric, the VA utilizes the Strategic Analytics for Improvement and Learning (SAIL) system to present internally its findings to VA practitioners and leadership. 2 The VA provides these data as a means to drive quality improvement and allow for comparison of individual hospitals' performance across measures throughout the VA healthcare system. Since 2010, the VA began using and publicly reporting the CMS-derived 30-day Risk-Stratified Readmission Rate (RSRR) on the Hospital Compare website. 3 Similar to CMS, the VA uses three years of combined data so that patients, providers, and other stakeholders can compare individual hospitals' performance across these measures. 1 In response to this, hospitals and healthcare organizations have implemented quality improvement and large-scale programmatic interventions in an attempt to improve quality around readmissions. 4-6 A recent assessment on how hospitals within the Medicare fee-for-service program have responded to such reporting found large degrees of variability, with more than half of the participating institutions facing penalties due to greater-than-expected readmission rates. 5 Although the VA utilizes the same CMS-derived model in its assessments and reporting, the variability and distri

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Section: Discussionsupporting

confidence: 59%

Examining the Utility of 30‐day Readmission Rates and Hospital Profiling in the Veterans Health Administration

Wray

Vali

Walter

et al. 2019

Journal of Hospital Medicine

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“…We drew on published frameworks and previous literature to develop SDoH search terms. 8–10 24–27 Terms were initially developed in MedLine (online appendix 1) and adapted for each database. After the initial search, we added the MeSH term ‘comorbidity’ into our MedLine search to examine if any studies had been missed through excluding the term ‘comorbidity’ and its linguistic variations.…”

Section: Methodsmentioning

confidence: 99%

Household and area-level social determinants of multimorbidity: a systematic review

Ingram

Ledden

Beardon

et al. 2020

J Epidemiol Community Health

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BackgroundNo clear synthesis of evidence examining household and area-level social determinants of multimorbidity exists. This study aimed to systematically review the existing literature on associations between household and area-level social determinants of health (SDoH) and multimorbidity prevalence or incidence in the general population.MethodsSix databases (MedLine, EMBASE, PsychINFO, Web of Science, CINAHL Plus and Scopus) were searched. The search was limited to peer-reviewed studies conducted in high-income countries and published in English between 2010 and 2019. A second reviewer screened all titles with abstracts and a subset of full texts. Study quality was assessed and protocol pre-registered (CRD42019135281).Results41 studies spanning North America, Europe and Australasia were included. Household income and area-level deprivation were the most explored with fairly consistent findings. The odds of multimorbidity were up to 4.4 times higher for participants with the lowest level of income compared with the highest level. Those living in the most deprived areas had the highest prevalence or incidence of multimorbidity (pooled OR 1.42, 95% CI 1.41 to 1.42). Associations between deprivation and multimorbidity differed by age and multimorbidity type. Findings from the few studies investigating household tenure, household composition and area-level rurality were mixed and contradictory; homeownership and rurality were associated with increased and decreased multimorbidity, while living alone was found to be associated with a higher risk of multimorbidity and not associated.ConclusionImproving our understanding of broader social determinants of multimorbidity—particularly at the household level—could help inform strategies to tackle multimorbidity.

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“…[5][6][7][8] Addressing the factors underlying health outcomes requires increasing the capacity for care delivered outside the traditional face-to-face encounter. 9,10 Access to care is influenced by numerous factors including patient and clinician preference and satisfaction, patient acuity and care requirements, clinical and support staffing, and available infrastructure. 11,12 Thus, new methods and models may be required to meet the VHA's ever-changing needs to ensure long-term access to care.…”

Section: Introductionmentioning

confidence: 99%

Accelerating Implementation of Virtual Care in an Integrated Health Care System: Future Research and Operations Priorities

et al. 2021

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BACKGROUND: Virtual care is critical to Veterans Health Administration (VHA) efforts to expand veterans' access to care. Health care policies such as the Veterans Access, Choice, and Accountability (CHOICE) Act and the Maintaining Internal Systems and Strengthening Integrated Outside Networks (MISSION) Act impact how the VHA provides care. Research on ways to refine virtual care delivery models to meet the needs of veterans, clinicians, and VHA stakeholders is needed. OBJECTIVE: Given the importance of virtual approaches for increasing access to high-quality VHA care, in December 2019, we convened a Think Tank, Accelerating Implementation of Virtual Care in VHA Practice, to consider challenges to virtual care research and practice across the VHA, discuss novel approaches to using and evaluating virtual care, assess perspectives on virtual care, and develop priorities to enhance virtual care in the VHA. METHODS: We used a participatory approach to develop potential priorities for virtual care research and activities at the VHA. We refined these priorities through forceranked prioritization and group discussion, and developed solutions for selected priorities. RESULTS: Think Tank attendees (n = 18) consisted of VHA stakeholders, including operations partners (e.g., Office of Rural Health, Office of Nursing Services, Health Services Research and Development), clinicians (e.g., physicians, nurses, psychologists, physician assistants), and health services researchers. We identified an initial list of fifteen potential priorities and narrowed these down to four. The four priorities were (1) scaling evidence-based practices, (2) centralizing virtual care, (3) creating highvalue care within the VHA with virtual care, and (4) identifying appropriate patients for virtual care. CONCLUSION: Our Think Tank took an important step in setting a partnered research agenda to optimize the use of virtual care within the VHA. We brought together research and operations stakeholders and identified possibilities, partnerships, and potential solutions for virtual care.

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Evidence Review—Social Determinants of Health for Veterans

Cited by 23 publications

References 146 publications

Examining the Utility of 30‐day Readmission Rates and Hospital Profiling in the Veterans Health Administration

Examining the Utility of 30‐day Readmission Rates and Hospital Profiling in the Veterans Health Administration

Household and area-level social determinants of multimorbidity: a systematic review

Accelerating Implementation of Virtual Care in an Integrated Health Care System: Future Research and Operations Priorities

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