Statistical Methods to Examine Racial and Ethnic Disparities in the Surgical Literature: A Review and Recommendations for Improvement.

OBJECTIVE: We characterized the quality of statistical methods for studies of racial and ethnic disparities in the surgical-relevant literature during 2021-2022. BACKGROUND: Hundreds of scientific papers are published each year describing racial and ethnic disparities in surgical access, quality, and outcomes. The content and design quality of this literature has never been systematically reviewed. METHODS: We searched for 2021-2022 studies focused on describing racial and/or ethnic disparities in surgical or perioperative access, process quality, or outcomes. Identified studies were characterized in terms of three methodological criteria: 1) adjustment for variables related to both race/ethnicity and outcomes, including social determinants of health (SDOH); 2) accounting for clustering of patients within hospitals or other subunits ("providers") and; 3) distinguishing within- and between-provider effects. RESULTS: We identified 224 papers describing racial and/or ethnic differences. Of the 38 single institution studies, 24 (63.2%) adjusted for at least one SDOH variable. Of the 186 multisite studies, 113 (60.8%) adjusted for at least one SDOH variable, and 43 (23.1%) accounted for clustering of patients within providers using appropriate statistical methods. Only 10 (5.4%) of multi-institution studies made efforts to examine how much of overall disparities were driven by within versus between provider effects. CONCLUSIONS: Most recently published papers on racial and ethnic disparities in the surgical literature do not meet these important statistical design criteria and therefore may risk inaccuracy in the estimation of group differences in surgical access, quality, and outcomes. The most potent leverage points for these improvements are changes to journal publication guidelines and policies.

Racial and Ethnic and Rural Variations in Access to Primary Care for Veterans Following the MISSION Act.

Importance: The 2018 Veterans Affairs Maintaining Internal Systems and Strengthening Integrated Outside Networks (VA MISSION) Act was implemented to increase timely access to care by expanding veterans' opportunities to receive Veterans Affairs (VA)-purchased care in the community (community care [CC]). Because health equity is a major VA priority, it is important to know whether Black and Hispanic veterans compared with White veterans experienced equitable access to primary care within the VA MISSION Act. Objective: To examine whether utilization of and wait times for primary care differed between Black and Hispanic veterans compared with White veterans in rural and urban areas after the implementation of the VA MISSION Act. Design, Setting, and Participants: This cross-sectional study used VA and CC outpatient and consult data from the VA's Corporate Data Warehouse for fiscal years 2021 to 2022 (October 1, 2020, to September 30, 2022). Separate fixed-effects multivariable models were run to predict CC utilization and wait times. Each model was run twice, once comparing Black and White veterans and then comparing Hispanic and White veterans. Adjusted risk ratios (ARRs) were calculated for Black and Hispanic veterans compared with White veterans within rurality status for both outcomes. Main Outcomes and Measures: VA and CC primary care utilization as measured by primary care visits (utilization cohort); VA and CC primary care access as measured by mean wait times (access cohort). Results: A total of 5 046 087 veterans (994 517 [19.7%] Black, 390 870 [7.7%] Hispanic, and 3 660 700 [72.6%] White individuals) used primary care from fiscal years 2021 to 2022. Utilization increased for all 3 racial and ethnicity groups, more so in CC than VA primary care. ARRs were significantly less than 1 regardless of rurality status, indicating Black and Hispanic veterans compared with White veterans were less likely to utilize CC for primary care. There were 468 246 primary care consultations during the study period. The overall mean (SD) wait time was 33.3 (32.4) days. Despite decreases in wait times over time, primary care wait times remained longer in CC than in VA. Black veterans compared with White veterans had significantly longer wait times in CC (ARRs >1) but significantly shorter wait times in VA (ARRS <1) regardless of rurality status in VA and CC. CC wait times for Hispanic veterans compared with White veterans were longer in rural areas only and in VA rural and urban areas (ARRs >1). Conclusion and Relevance: The results of this cross-sectional study suggest that additional research should explore the determinants and implications of utilization differences among Black and Hispanic veterans compared with White veterans. Efforts to promote equitable primary care access for all veterans are needed so that policy changes can be more effective in ensuring timely access to care for all veterans.

Participation of Veterans Affairs Medical Centers in veteran-centric community-based service navigation networks: A mixed methods study.

OBJECTIVE: To understand the determinants and benefits of cross-sector partnerships between Veterans Affairs Medical Centers (VAMCs) and geographically affiliated AmericaServes Network coordination centers that address Veteran health-related social needs. DATA SOURCES AND SETTING: Semi-structured interviews were conducted with AmericaServes and VAMC staff across seven regional networks. We matched administrative data to calculate the percentage of AmericaServes referrals that were successfully resolved (i.e., requested support was provided) in each network overall and stratified by whether clients were also VAMC patients. STUDY DESIGN: Convergent parallel mixed-methods study guided by Himmelman's Developmental Continuum of Change Strategies (DCCS) for interorganizational collaboration. DATA COLLECTION: Fourteen AmericaServes staff and 17 VAMC staff across seven networks were recruited using snowball sampling and interviewed between October 2021 and April 2022. Rapid qualitative analysis methods were used to characterize the extent and determinants of VAMC participation in networks. PRINCIPAL FINDINGS: On the DCCS continuum of participation, three networks were classified as networking, two as coordinating, one as cooperating, and one as collaborating. Barriers to moving from networking to collaborating included bureaucratic resistance to change, VAMC leadership buy-in, and not having VAMCs staff use the shared technology platform. Facilitators included ongoing communication, a shared mission of serving Veterans, and having designated points-of-contact between organizations. The percentage of referrals that were successfully resolved was lowest in networks engaged in networking (65.3%) and highest in cooperating (85.6%) and collaborating (83.1%) networks. For coordinating, cooperating, and collaborating networks, successfully resolved referrals were more likely among Veterans who were also VAMC patients than among Veterans served only by AmericaServes. CONCLUSIONS: VAMCs participate in AmericaServes Networks at varying levels. When partnerships are more advanced, successful resolution of referrals is more likely, especially among Veterans who are dually served by both organizations. Although challenges to establishing partnerships exist, this study highlights effective strategies to overcome them.

Rural Veterans' Experiences with Social Risk Factors: Impacts, Challenges, and Care System Recommendations.

BACKGROUND: Social risk factors, such as food insecurity and financial needs, are associated with increased risk of cardiovascular diseases, health conditions that are highly prevalent in rural populations. A better understanding of rural Veterans' experiences with social risk factors can inform expansion of Veterans Health Administration (VHA) efforts to address social needs. OBJECTIVE: To examine social risk and need from rural Veterans' lived experiences and develop recommendations for VHA to address social needs. DESIGN: We conducted semi-structured interviews with participants purposively sampled for racial diversity. The interview guide was informed by Andersen's Behavioral Model of Health Services Use and the Outcomes from Addressing Social Determinants of Health in Systems framework. PARTICIPANTS: Rural Veterans with or at risk of cardiovascular disease who participated in a parent survey and agreed to be recontacted. APPROACH: Interviews were recorded and transcribed. We analyzed transcripts using directed qualitative content analysis to identify themes. KEY RESULTS: Interviews (n = 29) took place from March to June 2022. We identified four themes: (1) Social needs can impact access to healthcare, (2) Structural factors can make it difficult to get help for social needs, (3) Some Veterans are reluctant to seek help, and (4) Veterans recommended enhancing resource dissemination and navigation support. CONCLUSIONS: VHA interventions should include active dissemination of information on social needs resources and navigation support to help Veterans access resources. Community-based organizations (e.g., Veteran Service Organizations) could be key partners in the design and implementation of future social need interventions.

Developing a brief assessment of social risks for the Veterans Health Administration Survey of Healthcare Experiences of Patients.

OBJECTIVE: To determine whether a 6- or 12-month look-back period affected rates of reported social risks in a social risk survey for use in the Veterans Health Administration and to assess associations of social risks with overall health and mental health. STUDY DESIGN: Cross-sectional survey of respondents randomized to 6- or 12-month look-back period. DATA SOURCES AND STUDY SETTING: Online survey with a convenience sample of Veterans in June and July 2021. DATA COLLECTION/EXTRACTION METHODS: Veteran volunteers were recruited by email to complete a survey assessing social risks, including financial strain, adult caregiving, childcare, food insecurity, housing, transportation, internet access, loneliness/isolation, stress, discrimination, and legal issues. Outcomes included self-reported overall health and mental health. Chi-squared tests compared the prevalence of reported social risks between 6- and 12-month look-back periods. Spearman correlations assessed associations among social risks. Bivariate and multivariable logistic regression models estimated associations between social risks and fair/poor overall and mental health. PRINCIPAL FINDINGS: Of 3418 Veterans contacted, 1063 (31.10%) responded (87.11% male; 85.61% non-Hispanic White; median age = 70, interquartile range [IQR] = 61-74). Prevalence of most reported social risks did not significantly differ by look-back period. Most social risks were weakly intercorrelated (median |r| = 0.24, IQR = 0.16-0.31). Except for legal issues, all social risks were associated with higher odds of fair/poor overall health and mental health in bivariate models. In models containing all significant social risks from bivariate models, adult caregiving and stress remained significant predictors of overall health; food insecurity, housing, loneliness/isolation, and stress remained significant for mental health. CONCLUSIONS: Six- and 12-month look-back periods yielded similar rates of reported social risks. Although most individual social risks are associated with fair/poor overall and mental health, when examined together, only adult caregiving, stress, loneliness/isolation, food, and housing remain significant.

Prevalence and Determinants of Unmet Social Needs Among Rural and Urban Veterans.

PURPOSE: To examine the prevalence and determinants of nine unmet social needs among rural compared with urban Veterans. METHODS: Retrospective study using survey data collected in 2020 merged with Veterans Health Administration (VA) administrative data. For each unmet need, separate logistic regression modes were run predicting the odds of rural compared with urban Veterans endorsing the need adjusting for sociodemographic characteristics and comorbidities. FINDINGS: 2,801 Veterans responded to the survey (53.7% response rate). Veterans experienced high rates of need (e.g., 22% reported food insecurity). Unmet need prevalence varied minimally between rural and urban Veterans and where they did, rural Veterans were less likely to endorse the need (e.g., loneliness). For many unmet needs, Black compared with White Veterans were at higher risk. Regional unmet need disparities were also observed. CONCLUSIONS: As VA considers expanding unmet need interventions, tailoring interventions to the sub-populations most at risk may be warranted.

Relationship Between Unmet Social Needs and Care Access in a Veteran Cohort.

BACKGROUND: The association between unmet social needs (e.g., food insecurity) and adverse health outcomes is well-established, especially for patients with and at risk for cardiovascular disease (CVD). This has motivated healthcare systems to focus on unmet social needs. Yet, little is known about the mechanisms by which unmet social needs impact health, which limits healthcare-based intervention design and evaluation. One conceptual framework posits that unmet social needs may impact health by limiting care access, but this remains understudied. OBJECTIVE: Examine the relationship between unmet social needs and care access. DESIGN: Cross-sectional study design using survey data on unmet needs merged with administrative data from the Veterans Health Administration (VA) Corporate Data Warehouse (September 2019-March 2021) and multivariable models to predict care access outcomes. Pooled and separate rural and urban logistic regression models were utilized with adjustments from sociodemographics, region, and comorbidity. SUBJECTS: A national stratified random sample of VA-enrolled Veterans with and at risk for CVD who responded to the survey. MAIN MEASURES: No-show appointments were defined dichotomously as patients with one or more missed outpatient visits. Medication non-adherence was measured as proportion of days covered and defined dichotomously as adherence less than 80%. KEY RESULTS: Greater burden of unmet social needs was associated with significantly higher odds of no-show appointments (OR = 3.27, 95% CI = 2.43, 4.39) and medication non-adherence (OR = 1.59, 95% CI = 1.19, 2.13), with similar associations observed for rural and urban Veterans. Social disconnection and legal needs were especially strong predictors of care access measures. CONCLUSIONS: Findings suggest that unmet social needs may adversely impact care access. Findings also point to specific unmet social needs that may be especially impactful and thus might be prioritized for interventions, in particular social disconnection and legal needs.

Using the Kitagawa Decomposition to Measure Overall-and Individual Facility Contributions to-Within-facility and Between-facility Differences: Analyzing Racial and Ethnic Wait Time Disparities in the Veterans Health Administration.

BACKGROUND: Identifying whether differences in health care disparities are due to within-facility or between-facility differences is key to disparity reductions. The Kitagawa decomposition divides the difference between 2 means into within-facility differences and between-facility differences that are measured on the same scale as the original disparity. It also enables the identification of facilities that contribute most to within-facility differences (based on facility-level disparities and the proportion of patient population served) and between-facility differences. OBJECTIVES: Illustrate the value of a 2-stage Kitagawa decomposition to partition a disparity into within-facility and between-facility differences and to measure the contribution of individual facilities to each type of difference. SUBJECTS: Veterans receiving a new outpatient consult for cardiology or orthopedic services during fiscal years 2019-2021. MEASURES: Wait time for a new-patient consult. METHODS: In stage 1, we predicted wait time for each Veteran from a multivariable model; in stage 2, we aggregated individual predictions to determine mean adjusted wait times for Hispanic, Black, and White Veterans and then decomposed differences in wait times between White Veterans and each of the other groups. RESULTS: Noticeably longer wait times were experienced by Hispanic Veterans for cardiology (2.32 d, 6.8% longer) and Black Veterans for orthopedics (3.49 d, 10.3% longer) in both cases due entirely to within-facility differences. The results for Hispanic Veterans using orthopedics illustrate how positive within-facility differences (0.57 d) can be offset by negative between-facility differences (-0.34 d), resulting in a smaller overall disparity (0.23 d). Selecting 10 facilities for interventions in orthopedics based on the largest contributions to within-in facility differences instead of the largest disparities resulted in a higher percentage of Veterans impacted (31% and 12% of Black and White Veterans, respectively, versus 9% and 10% of Black and White Veterans, respectively) and explained 21% of the overall within-facility difference versus 11%. CONCLUSIONS: The Kitagawa approach allows the identification of disparities that might otherwise be undetected. It also allows the targeting of interventions at those facilities where improvements will have the largest impact on the overall disparity.

Disparities in Wait Times for Care Among US Veterans by Race and Ethnicity.

Importance: Prior studies indicate that Black and Hispanic vs White veterans wait longer for care. However, these studies do not capture the COVID-19 pandemic, which caused care access disruptions, nor implementation of the US Department of Veterans Affairs (VA) Maintaining Internal Systems and Strengthening Integrated Outside Networks Act (MISSION), which is intended to improve care access by increasing veterans' options to use community clinicians. Objective: To determine whether wait times increased differentially for Black and Hispanic compared with White veterans from the pre-COVID-19 to COVID-19 periods given concurrent MISSION implementation. Design, Setting, and Participants: This cross-sectional study used data from the VA's Corporate Data Warehouse for fiscal years 2019 to 2021 (October 1, 2018, to September 30, 2021). Participants included Black, Hispanic, and White veterans with a new consultation for outpatient cardiology and/or orthopedic services during the study period. Multivariable mixed-effects models were used to estimate individual-level adjusted wait times and a likelihood ratio test of the significance of wait time disparity change over time. Main Outcomes and Measures: Overall mean wait times and facility-level adjusted relative mean wait time ratios. Results: The study included 1 162 148 veterans (mean [SD] age, 63.4 [14.4] years; 80.8% men). Significant wait time disparities were evident for orthopedic services (eg, Black veterans had wait times 2.09 [95% CI, 1.57-2.61] days longer than those for White veterans) in the pre-COVID-19 period, but not for cardiology services. Mean wait times increased from the pre-COVID-19 to COVID-19 periods for both services for all 3 racial and ethnic groups (eg, Hispanic wait times for cardiology services increased 5.09 [95% CI, 3.62-6.55] days). Wait time disparities for Black veterans (4.10 [95% CI, 2.44-5.19] days) and Hispanic veterans (4.40 [95% CI, 2.76-6.05] days) vs White veterans (3.75 [95% CI, 2.30-5.19] days) increased significantly from the pre-COVID-19 to COVID-19 periods (P < .001). During the COVID-19 period, significant disparities were evident for orthopedic services (eg, mean wait times for Hispanic vs White veterans were 1.98 [95% CI, 1.32-2.64] days longer) but not for cardiology services. Although there was variation in wait time ratios across the 140 facilities, only 6 facility wait time ratios were significant during the pre-COVID-19 period and 26 during the COVID-19 period. Conclusions and Relevance: These findings suggest that wait time disparities increased from the pre-COVID-19 to COVID-19 periods, especially for orthopedic services for both Black and Hispanic veterans, despite MISSION's goal to improve access. Facility-level analyses identified potential sites that could be targeted to reduce disparities.

Interaction between Legal and Social Needs in Their Association with Self-rated Health in a National Sample of Veterans.

Many health-related social needs, such as financial insecurity, are interconnected with legal needs. However, little is known about which social needs are more likely to be associated with legal needs, or whether legal and other needs interact to affect health. Using data from a 2020 national mailed survey assessing social needs among Veterans who had or were at risk for cardiovascular disease (N=2,801) and linked administrative data, linear regression models tested interactions between legal and other social needs, and their associations with self-rated health. In a model examining the interaction of financial and legal needs, experiencing financial but not legal needs was as strongly associated with worse health (b=-0.58, 95% CI -0.69, -0.46) as experiencing both financial and legal needs (b= -0.55, 95% CI -0.70, -0.40). Financial needs are important to Veterans' health and further research is needed to determine how financial and legal needs should be triaged by providers.

Randomised controlled trial evaluating the effects of screening and referral for social determinants of health on Veterans' outcomes: protocol.

INTRODUCTION: Health policy leaders recommend screening and referral (S&R) for unmet social needs (eg, food) in clinical settings, and the American Heart Association recently concluded that the most significant opportunities for reducing cardiovascular disease (CVD) death and disability lie with addressing the social determinants of CVD outcomes. A limited but promising evidence base supports these recommendations, but more rigorous research is needed to guide health care-based S&R efforts. Funded by the Veteran Health Administration (VA), the study described in this paper will assess the efficacy of S&R on Veterans' connections to new resources to address social needs, reduction of unmet needs and health-related outcomes (adherence, utilisation and clinical outcomes). METHODS AND ANALYSIS: We will conduct a 1-year mixed-methods randomised controlled trial at three VA sites, enrolling Veterans with CVD and CVD-risk. 880 Veterans experiencing one or more social needs will be randomised within each site (n=293 per site) to one of three study arms representing referral mechanisms of varying intensity (screening only, screening and provision of resource sheet(s), screening and provision of resource sheet(s) plus social work assistance). For each Veteran, we will examine associations of unmet social needs with health-related outcomes at baseline, and longitudinally compare the impact of each approach on connection to new resources (primary outcome) and follow-up outcomes over a 12-month period. We will additionally conduct qualitative interviews with key stakeholders, including Veterans to identify potential explanatory factors related to the relative success of the interventions. ETHICS AND DISSEMINATION: Ethics approval was obtained from the VA Central Internal Review Board on 13 July 2021 (reference #: 20-07-Amendment No. 02). Findings will be disseminated through reports, lay summaries, policy briefs, academic publications, and conference presentations. TRIAL REGISTRATION NUMBER: NCT04977583.

Medicaid Expansion and Change in Federally Qualified Health Center Accessibility From 2008 to 2016.

BACKGROUND: The Affordable Care Act expanded health coverage for low-income residents through Medicaid expansion and increased funding for Health Center Program New Access Points from 2009 to 2015, improving federally qualified health center (FQHC) accessibility. The extent to which these provisions progressed synergistically as intended when states could opt out of Medicaid expansion is unknown. OBJECTIVE: To compare change in FQHC accessibility among census tracts in Medicaid expansion and nonexpansion states. RESEARCH DESIGN: Tract-level FQHC accessibility scores for 2008 and 2016 were estimated applying the 2-step floating catchment area method to American Community Survey and Health Resources and Services Administration data. Multivariable linear regression compared changes in FQHC accessibility between tracts in Medicaid expansion and nonexpansion states, adjusting for sociodemographic and health system factors and accounting for state-level clustering. SUBJECTS: In total, 7058 census tracts across 10 states. RESULTS: FQHC accessibility increased comparably among tracts in Medicaid expansion and nonexpansion states (coef: 0.3; 95% CI: -0.3, 0.8; P -value: 0.36). FQHC accessibility increased more in tracts with higher poverty and uninsured rates, and those with lower proportions of non-English speakers and Black or African American residents. CONCLUSION: Similar gains in FQHC accessibility across Medicaid expansion and nonexpansion states indicate improvements progressed independently from Medicaid expansion, rather than synergistically as expected. Accessibility increases appeared consistent with HRSA's goal to improve access for individuals experiencing economic barriers to health care but not for those experiencing cultural or language barriers to health care.

Uptake of hepatitis C virus treatment in a multi-state Medicaid population, 2013-2017.

OBJECTIVE: To examine trends in the direct acting antiviral (DAA) uptake in a multi-state Medicaid population with hepatitis C virus (HCV) prior to and after ledipasvir/sofosbuvir (LDV/SOF) approval and changes in prior authorization (PA) requirements. DATA SOURCES: Analyses utilized enrollment, medical, and pharmacy claims in four states, December 2013-December 2017. STUDY DESIGN: An interrupted time series examined trends in uptake (1+ claim for a DAA) before and after two events: LDV/SOV approval (October 2014) and lifting of PA requirements for 40% of members (July 2016). Analyses were also performed in subgroups defined by the number and dates of change in PA requirements in members' Medicaid plans. DATA COLLECTION/EXTRACTION METHODS: Members aged 18-64 years with an ICD code for HCV were included in the sample from diagnosis date until treatment initiation or Medicaid disenrollment. PRINCIPAL FINDINGS: The annual sample size ranged from 38,302 to 45,005 with approximately 30% ages 18-34 years and 40% female. In December 2013, 0.08% was treated, rising to 0.74% in December 2017 (p < 0.001). Uptake increased from 0.34%/month in October 2014 to 0.70%/month after LDV/SOF approval, (p < 0.001), and increased relative to the pre-LDV/SOV trend through June 2016 (p = 0.04). Uptake increased to 1.18%/month after PA change, (p < 0.001) and remained flat through 2017 (p = 0.64). Cumulatively, 20.1% were treated by December 2017. In plans with few/no requirements through 2017, uptake increased to 1.19%/month after LDV/SOF approval (p < 0.001) and remained flat through 2017 (p = 0.11), with 22.2% cumulatively treated. Among plans that lifted PA requirements from three to zero in mid-2016, uptake did not increase after LDV/SOF approval (p = 0.36) but did increase to 1.41%/month (p < 0.001) after PA change, with 18.1% cumulatively treated. CONCLUSIONS: HCV Treatment increased through 2017. LDV/SOF approval and lifting PA requirements led to an increase in uptake followed by flat monthly utilization. Cumulative uptake was higher in plans with few/no PA requirements relative to those with three requirements through mid-2016.

A Multi-site Case Study of Care Coordination Between Primary Care and Specialty Care.

BACKGROUND: Care coordination is critical for patients with multiple chronic conditions, but fragmentation of care persists. Providers' perspectives of facilitators and barriers to coordination are needed to improve care. OBJECTIVES: We sought to understand providers' perspectives on care coordination for patients having multiple chronic diseases served by multiple providers. RESEARCH DESIGN: Based upon our earlier survey of patients with multiple chronic conditions, we selected 8 medical centers having high and low coordination. We interviewed providers to identify facilitators and barriers to coordination and compare them between patient-rated high sites and low sites and between primary care (PC)-mental health (MH) and PC-medical/surgical specialty care. SUBJECTS: Physicians, nurses and other clinicians in PC, cardiology, and MH (N=102) in 8 Veterans Affairs medical centers. RESULTS: We identified warm handoffs, professional relationships, and physical proximity as facilitators, and service agreements, reporting relationships and staffing as barriers. PC-MH coordination was reported as better than PC-medical/surgical specialty coordination. Facilitators were more prevalent and barriers less prevalent in sites rated high by patients than sites rated low, and between PC-MH than between PC-specialty care. DISCUSSION: We noted that professional relationships were highly related to coordination and both affected other facilitators and barriers and were affected by them. We suggested actions to improve relationships directly, and to address other facilitators and barriers that affect relationships and coordination. Among these is the use of the Primary Care Mental Health Integration model.

Interorganizational Care Coordination of Rural Veterans by Veterans Affairs and Community Care Programs: A Systematic Review.

BACKGROUND: In the unique context of rural Veterans' health care needs, expansion of US Department of Veterans Affairs and Community Care programs under the MISSION Act, and the uncertainties of coronavirus disease 2019 (COVID-19), it is critical to understand what may support effective interorganizational care coordination for increased access to high-quality care. OBJECTIVES: We conducted a systematic review to examine the interorganizational care coordination initiatives that Veterans Affairs (VA) and community partners have pursued in caring for rural Veterans, including challenges and opportunities, organizational domains shaping care coordination, and among these, initiatives that improve or impede health care outcomes. RESEARCH DESIGN: We followed Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines to search 2 electronic databases (PubMed and Embase) for peer-reviewed articles published between January 2009 and May 2020. Building on prior research, we conducted a systematic review. RESULTS: Sixteen articles met our criteria. Each captured a unique health care focus while examining common challenges. Four organizational domains emerged: policy and administration, culture, mechanisms, and relational practices. Exemplars highlight how initiatives improve or impede rural health care delivery. CONCLUSIONS: This is the first systematic review, to our knowledge, examining interorganizational care coordination of rural Veterans by VA and Community Care programs. Results provide exemplars of interorganizational care coordination domains and program effectiveness. It suggests that partners' efforts to align their coordination domains can improve health care, with rurality serving as a critical contextual factor. Findings are important for policies, practices, and research of VA and Community Care partners committed to improving access and health care for rural Veterans.

Did Access to Care Improve Since Passage of the Veterans Choice Act?: Differences Between Rural and Urban Veterans.

BACKGROUND: The 2014 Veterans Choice Program aimed to improve care access for Veterans through expanded availability of community care (CC). Increased access to CC could particularly benefit rural Veterans, who often face obstacles in obtaining medical care at the Veterans Health Administration (VHA). However, whether Veterans Choice Program improved timely access to care for this vulnerable population is understudied. OBJECTIVES: To examine wait times among rural and urban Veterans for 5 outpatient specialty care services representing the top requests for CC services among rural Veterans. RESEARCH DESIGN: Retrospective study using VHA and CC outpatient consult data from VHA's Corporate Data Warehouse in Fiscal Year (FY) 2015 (October 1, 2014 to September 30, 2015) and FY2018 (October 1, 2017 to September 30, 2018). SUBJECTS: All Veterans who received a new patient consult for physical therapy, cardiology, optometry, orthopedics, and/or dental services in VHA and/or CC. MEASURES: Wait time, care setting (VHA/CC), rural/urban status, sociodemographics, and comorbidities. RESULTS: Our sample included 1,112,876 Veterans. Between FY2015 and FY2018, mean wait times decreased for all services for both rural and urban Veterans; declines were greatest in VHA (eg, mean optometry wait times for rural Veterans in VHA vs. CC declined 8.3 vs. 6.4 d, respectively, P<0.0001). By FY2018, for both rural and urban Veterans, CC mean wait times for most services were longer than VHA wait times. CONCLUSIONS: Timely care access for all Veterans improved between FY15 and FY18, particularly in VHA. As expansion of CC continues under the MISSION Act, more research is needed to evaluate quality of care across VHA and CC and what role, if any, wait times play.

Rural Veterans' Experiences With Outpatient Care in the Veterans Health Administration Versus Community Care.

BACKGROUND: The 2014 Veterans Access, Choice and Accountability Act was intended to improve Veterans' access to timely health care by expanding their options to receive community care (CC) paid for by the Veterans Health Administration (VA). Although CC could particularly benefit rural Veterans, we know little about rural Veterans' experiences with CC. OBJECTIVE: The objective of this study was to compare rural Veterans' experiences with CC and VA outpatient health care services to those of urban Veterans and examine changes over time. RESEARCH DESIGN: Retrospective, cross-sectional study using data from the Survey of Healthcare Experiences of Patients (SHEP) and VA Corporate Data Warehouse. Subjects: All Veterans who responded to the SHEP survey in Fiscal Year (FY) 16 or FY19. MEASURES: Outcomes were 4 measures of care experience (Access, Communication, Coordination, and Provider Rating). Independent variables included care setting (CC/VA), rural/urban status, and demographic and clinical characteristics. RESULTS: Compared with urban Veterans, rural Veterans rated CC the same (for specialty care) or better (for primary care). Rural Veterans reported worse experiences in CC versus VA, except for specialty care Access. Rural Veterans' care experiences improved between FY16 and FY19 in both CC and VA, with greater improvements in CC. CONCLUSIONS: Rural Veterans' reported comparable or better experiences in CC compared with urban Veterans, but rural Veterans' CC experiences still lagged behind their experiences in VA for primary care. As growing numbers of Veterans use CC, VA should ensure that rural and urban Veterans' experiences with CC are at least comparable to their experiences with VA care.

Barriers and Facilitators to Implementation of Value-Based Care Models in New Medicaid Accountable Care Organizations in Massachusetts: A Study Protocol.

Introduction: Massachusetts established 17 new Medicaid accountable care organizations (ACOs) and 24 affiliated Community Partners (CPs) in 2018 as part of a large-scale healthcare reform effort to improve care value. The new ACOs will receive $1.8 billion dollars in state and federal funding over 5 years through the Delivery System Reform Incentive Program (DSRIP). The multi-faceted study described in this protocol aims to address gaps in knowledge about Medicaid ACOs' impact on healthcare value by identifying barriers and facilitators to implementation and sustainment of the DSRIP-funded programs. Methods and analysis: The study's four components are: (1) Document Review to characterize the ACOs and CPs; (2) Semi-structured Key Informant Interviews (KII) with ACO and CP leadership, state-level Medicaid administrators, and patients; (3) Site visits with selected ACOs and CPs; and (4) Surveys of ACO clinical teams and CP staff. The Consolidated Framework for Implementation Research's (CFIR) serves as the study's conceptual framework; its versatile menu of constructs, arranged across five domains (Intervention Characteristics, Inner Setting, Outer Setting, Characteristics of Individuals, and Processes) guides identification of barriers and facilitators across multiple organizational contexts. For example, KII interview guides focus on understanding how Inner and Outer Setting factors may impact implementation. Document Review analysis includes extraction and synthesis of ACO-specific DSRIP-funded programs (i.e., Intervention Characteristics); KIIs and site visit data will be qualitatively analyzed using thematic analytic techniques; surveys will be analyzed using descriptive statistics (e.g., counts, frequencies, means, and standard deviations). Discussion: Understanding barriers and facilitators to implementing and sustaining Medicaid ACOs with varied organizational structures will provide critical context for understanding the overall impact of the Medicaid ACO experiment in Massachusetts. It will also provide important insights for other states considering the ACO model for their Medicaid programs. Ethics and dissemination: IRB determinations were that the overall study did not constitute human subjects research and that each phase of primary data collection should be submitted for IRB review and approval. Study results will be disseminated through traditional channels such as peer reviewed journals, through publicly available reports on the mass.gov website; and directly to key stakeholders in ACO and CP leadership.

Veterans Affairs and Rural Community Providers' Perspectives on Interorganizational Care Coordination: A Qualitative Analysis.

OBJECTIVE: To investigate challenges in care coordination between US Department of Veterans Affairs (VA) clinics and community providers serving rural veterans. METHODS: We completed qualitative interviews in 2017-2018 with a geographically diverse sample of 57 VA and community staff. Interviews were audio-recorded and transcribed verbatim. We used Rapid Qualitative Inquiry (RQI) to guide analyses. RESULTS: Results suggested 5 pivotal domains related to interorganizational care coordination at these sites: organizational mechanisms; organizational culture; relational coordination; contextual factors; and the role of the third party administrators charged with management of scheduling and reimbursement of community services through recent legislation. Across these domains, strategies to bridge gaps between organizations (eg, contracts with third party administrators, development of VA-based community care offices, provision of boundary-spanning staff) at times exacerbated coordination challenges. CONCLUSIONS: Steps taken to improve interorganizational care coordination between VA and community clinics may inadvertently complicate an already complex process. Our findings emphasize the importance of attending to key contextual barriers in coordinating care for rural veterans, and they illustrate the value of fundamental structural and relational approaches to enhancing such care coordination.

A Mixed Methods Study of the Association of Non-Veterans Affairs Care With Veterans' and Clinicians' Experiences of Care Coordination.

BACKGROUND: Poor coordination between the Department of Veterans Affairs (VA) and non-VA care may negatively impact health care quality. Recent legislation is intended to increase Veterans' access to care, in part through increased use of non-VA care. However, a possible consequence may be diminished patient experiences of coordination. OBJECTIVE: The objective of this study was to determine VA patients' and clinicians' experiences of coordination across VA and non-VA settings. DESIGN: Observational mixed methods using patient surveys and clinician interviews. Sampled patients were diagnosed with type 2 diabetes mellitus and either cardiovascular or mental health comorbidities. PARTICIPANTS AND MEASURES: Patient perspectives on coordination were elicited between April and September 2016 through a national survey supplemented with VA administrative records (N=5372). Coordination was measured with the 8-dimension Patient Perceptions of Integrated Care survey. Receipt of non-VA care was measured through patient self-report. Clinician perspectives were elicited through individual interviews (N=100) between May and October 2017. RESULTS: Veterans who received both VA and non-VA care reported significantly worse care coordination experiences than Veterans who only receive care in VA. Clinicians report limited information exchange capabilities, which, combined with bureaucratic and opaque procedures, adversely impact clinical decision-making. CONCLUSIONS: VA is working through a shift in how Veterans receive health care by increasing access to care from non-VA providers. Study findings suggest that VA should prioritize coordination of care in addition to access. This could include requiring monitoring of patient-experienced care coordination, surveys of referring and consulting clinicians, and pilot testing and evaluation of interventions to improve coordination.