Developing Evidence to Support Policy: Protocol for the StrAtegic PoLicy EvIdence-Based Evaluation CeNTer (SALIENT).

BACKGROUND: All federal agencies are required to support appropriation requests with evidence and evaluation (US Public Law 115-435; the Evidence Act). The StrAtegic PoLicy EvIdence-Based Evaluation CeNTer (SALIENT) is 1 of 6 centers that help the Department of Veterans Affairs (VA) meet this requirement. OBJECTIVE: Working with the existing VA evaluation structure, SALIENT evaluations will contribute to (1) optimize policies and programs for veteran populations; (2) improve outcomes regarding health, equity, cost, and provider well-being; (3) advance the science of dissemination and knowledge translation; and (4) expand the implementation and dissemination science workforce. METHODS: We leverage the Lean Sprint methodology (iterative, incremental, rule-governed approach to clearly defined, and time-boxed work) and 3 cores to develop our evaluation plans collaboratively with operational partners and key stakeholders including veterans, policy experts, and clinicians. The Operations Core will work with evaluation teams to develop timelines, facilitate work, monitor progress, and guide quality improvement within SALIENT. The Methods Core will work with evaluation teams to identify the most appropriate qualitative, quantitative, and mixed methods approaches to address each evaluation, ensure that the analyses are conducted appropriately, and troubleshoot when problems with data acquisition and analysis arise. The Knowledge Translation (KT) Core will target key partners and decision makers using a needs-based market segmentation approach to ensure that needs are incorporated in the dissemination of knowledge. The KT Core will create communications briefs, playbooks, and other materials targeted at these market segments to facilitate implementation of evidence-based practices and maximize the impact of evaluation results. RESULTS: The SALIENT team has developed a center infrastructure to support high-priority evaluations, often to be responsive to shifting operational needs and priorities. Our team has engaged in our core missions and operations to rapidly evaluate a high-priority areas, develop a comprehensive Lean Sprint systems redesign approach to training, and accelerate rapid knowledge translation. CONCLUSIONS: With an array of interdisciplinary expertise, operational partnerships, and integrated resources, SALIENT has an established and evolving infrastructure to rapidly develop and implement high-impact evaluations. Projects are developed with sustained efficiency approaches that can pivot to new priorities as needed and effectively translate knowledge for key stakeholders and policy makers, while creating a learning health system infrastructure to foster the next generation of evaluation and implementation scientists. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): PRR1-10.2196/59830.

Trends in Bundled Outpatient Behavioral Health Services in VA-Direct Versus VA-Purchased Care.

The Veterans Health Administration (VA) increasingly purchases community-based care (CC) to improve healthcare access, including behavioral health. In 2018, VA introduced standardized episodes of care (SEOCs) to guide authorization and purchase of CC services for specific indications in a defined timeframe without bundling payment. In this retrospective cross-sectional study, we describe trends in VA and CC behavioral healthcare utilization using the VA Outpatient Psychiatry SEOC definition. Counts of Outpatient Psychiatry SEOC-allowable service and procedure codes during fiscal years 2016-2019 were organized according to four SEOC-defined service types (evaluation and management, laboratory services, psychiatry services, transitional care) and measured as percentages of all included codes. Trends comparing behavioral healthcare utilization between Veterans using any CC versus VA only were analyzed using a linear mixed effects model. We identified nearly 3 million Veterans who registered 60 million qualifying service and procedure codes, with overall utilization increasing 77.8% in CC versus 5.2% in VA. Veterans receiving any CC comprised 3.9% of the cohort and 4.7% of all utilization. When examining service type as a percent of all Outpatient Psychiatry SEOC-allowable care among Veterans using CC, psychiatry services increased 12.2%, while transitional care decreased 8.8%. In regression analysis, shifts in service type utilization reflected descriptive results but with attenuated effect sizes. In sum, Outpatient Psychiatry SEOC-allowable service utilization grew, and service type composition changed, significantly more in CC than in VA. The role of SEOCs and their incentives may be important when evaluating future behavioral healthcare quality and value in bundled services.

Acute hospitalizations and outcomes in Veterans Affairs Hospitals 2011 to 2017.

Hospitals within the Veterans Affairs (VA) health care system exhibited growing use of observation care. It is unknown how this affected VA hospital performance since observation care is not included in acute inpatient measures. To examine changes in VA hospitalization outcomes and whether it was affected by shifting acute inpatient care to observation care. Longitudinal analysis of 986,355 acute hospitalizations and observation stays in 11 states 2011 to 2017. We estimated temporal changes in 30-day mortality, 30-day readmissions, costs, and length of stay (LOS) for all hospitalizations and 6 conditions in adjusted models. Changes in mortality and readmissions were compared including and excluding observation care. A 9% drop in acute hospitalizations was offset by a 157% increase in observation stays 2011 to 2017. A 30-day mortality decreased but readmissions did not when observation stays were included (all P < .05). Mean costs increased modestly; mean LOS was unchanged. There were differences by condition. VA hospital mortality decreased; there was no change in readmissions.

Information and resources VA health system leaders need to manage enrollment and retention for Post-9/11 veterans.

OBJECTIVE: To understand Veterans Health Administration (VA) leaders' information and resource needs for managing post-9/11 Veterans' VA enrollment and retention. DATA SOURCES AND STUDY SETTING: Interviews conducted from March-May 2022 of VA Medical Center (VAMC) leaders (N = 27) across 15 sites, using stratified sampling based on VAMC characteristics: enrollment rates, number of recently separated Veterans in catchment area, and state Medicaid expansion status. STUDY DESIGN: Interview questions were developed using Petersen et al.'s Factors Influencing Choice of Healthcare System framework as a guide. Interviews were transcribed verbatim, and two coders analyzed the interviews using Atlas.ti, a qualitative software program. Coders followed the qualitative coding philosophy developed by Crabtree and Miller, a process of developing codes for salient concepts as they are identified during the analysis process. DATA COLLECTION/EXTRACTION METHODS: Two coders analyzed 22% (N = 6) of the interviews and discussed and adjudicated any discrepancies. One coder independently coded the remainder of the interviews. PRINCIPAL FINDINGS: Several key themes were identified regarding facilitators and barriers for VA enrollment including reputation for high-quality VA care, convenience of VA services, awareness of VA services and benefits, and VA mental health services. Nearly every VA leader actively used tools and data to understand enrollment and retention rates and sought to enroll and retain more Veterans. To improve the management of enrollment and retention, VA leaders would like data shared in an easily understandable format and the capability to share data between the VA and community healthcare systems. CONCLUSIONS: Enrollment and retention information is important for healthcare leaders to guide their health system decisions. Various tools are currently being used to try to understand the data. However, a multifunctional tool is needed to better aggregate the data to provide VA leadership with key information on Veterans' enrollment and retention.

Lower comorbidity scores and severity levels in Veterans Health Administration hospitals: a cross-sectional study.

BACKGROUND: Previous studies found that documentation of comorbidities differed when Veterans received care within versus outside Veterans Health Administration (VHA). Changes to medical center funding, increased attention to performance reporting, and expansion of Clinical Documentation Improvement programs, however, may have caused coding in VHA to change. METHODS: Using repeated cross-sectional data, we compared Elixhauser-van Walraven scores and Medicare Severity Diagnosis Related Group (DRG) severity levels for Veterans' admissions across settings and payers over time, utilizing a linkage of VHA and all-payer discharge data for 2012-2017 in seven US states. To minimize selection bias, we analyzed records for Veterans admitted to both VHA and non-VHA hospitals in the same year. Using generalized linear models, we adjusted for patient and hospital characteristics. RESULTS: Following adjustment, VHA admissions consistently had the lowest predicted mean comorbidity scores (4.44 (95% CI 4.34-4.55)) and lowest probability of using the most severe DRG (22.1% (95% CI 21.4%-22.8%)). In contrast, Medicare-covered admissions had the highest predicted mean comorbidity score (5.71 (95% CI 5.56-5.85)) and highest probability of using the top DRG (35.3% (95% CI 34.2%-36.4%)). CONCLUSIONS: More effective strategies may be needed to improve VHA documentation, and current risk-adjusted comparisons should account for differences in coding intensity.

Racial and Ethnic Differences in Health Care Experiences for Veterans Receiving VA Community Care from 2016 to 2021.

BACKGROUND: Prior research documented racial and ethnic disparities in health care experiences within the Veterans Health Administration (VA). Little is known about such differences in VA-funded community care programs, through which a growing number of Veterans receive health care. Community care is available to Veterans when care is not available through the VA, nearby, or in a timely manner. OBJECTIVE: To examine differences in Veterans' experiences with VA-funded community care by race and ethnicity and assess changes in these experiences from 2016 to 2021. DESIGN: Observational analyses of Veterans' ratings of community care experiences by self-reported race and ethnicity. We used linear and logistic regressions to estimate racial and ethnic differences in community care experiences, sequentially adjusting for demographic, health, insurance, and socioeconomic factors. PARTICIPANTS: Respondents to the 2016-2021 VA Survey of Healthcare Experiences of Patients-Community Care Survey. MEASURES: Care ratings in nine domains. KEY RESULTS: The sample of 231,869 respondents included 24,306 Black Veterans (mean [SD] age 56.5 [12.9] years, 77.5% male) and 16,490 Hispanic Veterans (mean [SD] age 54.6 [15.9] years, 85.3% male). In adjusted analyses pooled across study years, Black and Hispanic Veterans reported significantly lower ratings than their White and non-Hispanic counterparts in five of nine domains (overall rating of community providers, scheduling a recent appointment, provider communication, non-appointment access, and billing), with adjusted differences ranging from - 0.04 to - 0.13 standard deviations (SDs) of domain scores. Black and Hispanic Veterans reported higher ratings with eligibility determination and scheduling initial appointments than their White and non-Hispanic counterparts, and Black Veterans reported higher ratings of care coordination, with adjusted differences of 0.05 to 0.21 SDs. Care ratings improved from 2016 to 2021, but differences between racial and ethnic groups persisted. CONCLUSIONS: This study identified small but persistent racial and ethnic differences in Veterans' experiences with VA-funded community care, with Black and Hispanic Veterans reporting lower ratings in five domains and, respectively, higher ratings in three and two domains. Interventions to improve Black and Hispanic Veterans' patient experience could advance equity in VA community care.

Hospital and Patient Factors Affecting Veterans' Hospital Choice.

Veterans enrolled in the Veterans Affairs (VA) health care system gained greater access to non-VA care beginning in 2014. We examined hospital and Veteran characteristics associated with hospital choice. We conducted a longitudinal study of elective hospitalizations 2011 to 2017 in 11 states and modeled patients' choice of VA hospital, large non-VA hospital, or small non-VA hospital in conditional logit models. Patients had higher odds of choosing a hospital with an academic affiliation, better patient experience rating, location closer to them, and a more common hospital type. Patients who were male, racial/ethnic minorities, had higher VA enrollment priority, and had a mental health comorbidity were more likely than other patients to choose a VA hospital than a non-VA hospital. Our findings suggest that patients respond to certain hospital attributes. VA hospitals may need to maintain or achieve high levels of quality and patient experience to attract or retain patients in the future.

Outcomes of Veterans Treated in Veterans Affairs Hospitals vs Non-Veterans Affairs Hospitals.

Importance: Many veterans enrolled in the Veterans Affairs (VA) health care system have access to non-VA care through insurance and VA-purchased community care. Prior comparisons of VA and non-VA hospital outcomes have been limited to subpopulations. Objective: To compare outcomes for 6 acute conditions in VA and non-VA hospitals for younger and older veterans using VA and all-payer discharge data. Design, Setting, and Participants: This cohort study used a repeated cross-sectional analysis of hospitalization records for acute myocardial infarction (AMI), coronary artery bypass graft (CABG), gastrointestinal (GI) hemorrhage, heart failure (HF), pneumonia, and stroke. Participants included VA enrollees from 11 states at VA and non-VA hospitals from 2012 to 2017. Analysis was conducted from July 1, 2022, to October 18, 2023. Exposures: Treatment in VA or non-VA hospital. Main Outcome and Measures: Thirty-day mortality, 30-day readmission, length of stay (LOS), and costs. Average treatment outcomes of VA hospitals were estimated using inverse probability weighted regression adjustment to account for selection into hospitals. Models were stratified by veterans' age (aged less than 65 years and aged 65 years and older). Results: There was a total of 593 578 hospitalizations and 414 861 patients with mean (SD) age 75 (12) years, 405 602 males (98%), 442 297 hospitalizations of non-Hispanic White individuals (75%) and 73 155 hospitalizations of non-Hispanic Black individuals (12%) overall. VA hospitalizations had a lower probability of 30-day mortality for HF (age ≥65 years, -0.02 [95% CI, -0.03 to -0.01]) and stroke (age <65 years, -0.03 [95% CI, -0.05 to -0.02]; age ≥65 years, -0.05 [95% CI, -0.07 to -0.03]). VA hospitalizations had a lower probability of 30-day readmission for CABG (age <65 years, -0.04 [95% CI, -0.06 to -0.01]; age ≥65 years, -0.05 [95% CI, -0.07 to -0.02]), GI hemorrhage (age <65 years, -0.04 [95% CI, -0.06 to -0.03]), HF (age <65 years, -0.05 [95% CI, -0.07 to -0.03]), pneumonia (age <65 years, -0.04 [95% CI, -0.06 to -0.03]; age ≥65 years, -0.03 [95% CI, -0.04 to -0.02]), and stroke (age <65 years, -0.11 [95% CI, -0.13 to -0.09]; age ≥65 years, -0.13 [95% CI, -0.16 to -0.10]) but higher probability of readmission for AMI (age <65 years, 0.04 [95% CI, 0.01 to 0.06]). VA hospitalizations had a longer mean LOS and higher costs for all conditions, except AMI and stroke in younger patients. Conclusions and Relevance: In this cohort study of veterans, VA hospitalizations had lower mortality for HF and stroke and lower readmissions, longer LOS, and higher costs for most conditions compared with non-VA hospitalizations with differences by condition and age group. There were tradeoffs between better outcomes and higher resource use in VA hospitals for some conditions.

Differences in use of Veterans Health Administration and non-Veterans Health Administration hospitals by rural and urban Veterans after access expansions.

PURPOSE: To examine changes in rural and urban Veterans' utilization of acute inpatient care in Veterans Health Administration (VHA) and non-VHA hospitals following access expansion from the Veterans Choice Act, which expanded eligibility for VHA-paid community hospitalization. METHODS: Using repeated cross-sectional data of VHA enrollees' hospitalizations in 9 states (AZ, CA, CT, FL, LA, MA, NY, PA, and SC) between 2012 and 2017, we compared rural and urban Veterans' probability of admission in VHA and non-VHA hospitals by payer over time for elective and nonelective hospitalizations using multinomial logistic regression to adjust for patient-level sociodemographic features. We also used generalized linear models to compare rural and urban Veterans' travel distances to hospitals. FINDINGS: Over time, the probability of VHA-paid community hospitalization increased more for rural Veterans than urban Veterans. For elective inpatient care, rural Veterans' probability of VHA-paid admission increased from 2.9% (95% CI 2.6%-3.2%) in 2012 to 6.5% (95% CI 5.8%-7.1%) in 2017. These changes were associated with a temporal trend that preceded and continued after the implementation of the Veterans Choice Act. Overall travel distances to hospitalizations were similar over time; however, the mean distance traveled decreased from 39.2 miles (95% CI 35.1-43.3) in 2012 to 32.3 miles (95% CI 30.2-34.4) in 2017 for rural Veterans receiving elective inpatient care in VHA-paid hospitals. CONCLUSIONS: Despite limited access to rural hospitals, these data demonstrate an increase in rural Veterans' use of non-VHA hospitals for acute inpatient care and a small reduction in distance traveled to elective inpatient services.

Choosing Veterans Affairs: Determinants of post-9/11 Veterans' enrollment in Veterans Affairs health care.

Following recent policy changes, younger Veterans have particularly increased options for where to receive their health care. Although existing research provides some understanding of non-modifiable individual (e.g., age) and external community (e.g., non-VA provider supply) factors that influence VA enrollment, this study focused on modifiable facility access and quality factors that could influence Veterans' decisions to enroll in VA. In this cohort study, we examined enrollment in and use of VA services in the year following military separation as the binary outcome using mixed-effects logistic regressions, stratified by Active and Reserve Components. This study included 260,777 Active and 101,572 Reserve Component post-9/11 Veterans separated from the military in fiscal years 2016 to 2017. Independent variables included 4 access measures for timeliness of VA care and 3 VA quality measures, which are included in VA Medical Centers' performance plans. Eligible Veterans were more likely to enroll in VA when the closest VA had higher quality scores. After accounting for timeliness of VA care and non-modifiable characteristics, rating of primary care (PC) providers was associated with higher VA enrollment for Active Component (odds ratio [OR] = 1.014, 95% confidence interval [CI]: 1.007-1.020). Higher mental health (MH) continuity (OR = 1.039, 95% CI: 1.000-1.078) and rating of PC providers (OR = 1.009, 95% CI: 1.000-1.017) were associated with higher VA enrollment for Reserve Component. Improving facility-specific quality of care may be a way to increase VA enrollment. In a changing policy environment, study results will help VA leadership target changes they can make to manage enrollment of Veterans in VA and deliver needed foundational services.

Differences Between VHA-Delivered and VHA-Purchased Behavioral Health Care in Service and Patient Characteristics.

OBJECTIVE: Federal legislation has expanded Veterans Health Administration (VHA) enrollees' access to VHA-purchased "community care." This study examined differences in the amount and type of behavioral health care delivered in VHA and purchased in the community, along with patient characteristics and area supply and demand factors. METHODS: This retrospective cross-sectional study examined data for 204,094 VHA enrollees with 448,648 inpatient behavioral health stays and 3,467,010 enrollees with 55,043,607 outpatient behavioral health visits from fiscal years 2016 to 2019. Standardized mean differences (SMDs) were calculated for patient and provider characteristics at the outpatient-visit level for VHA and community care. Linear probability models assessed the association between severity of behavioral health condition and site of care. RESULTS: Twenty percent of inpatient stays were purchased through community care, with severe behavioral health conditions more likely to be treated in VHA inpatient care. In the outpatient setting, community care accounted for 3% of behavioral health care visits, with increasing use over time. For outpatient care, veterans receiving community care were more likely than those receiving VHA care to see clinicians with fewer years of training (SMD=1.06). CONCLUSIONS: With a large portion of inpatient behavioral health care occurring in the community and increased use of outpatient behavioral health care with less highly trained community providers, coordination between VHA and the community is essential to provide appropriate inpatient follow-up care and address outpatient needs. This is especially critical given VHA's expertise in providing behavioral health care to veterans and its legislative responsibility to ensure integrated care.

Do Community-level Disadvantages Account for Racial Disparities in the Safety of Spine Surgery? A Large Database Study Based on Medicare Claims.

BACKGROUND: Racial health disparities across orthopaedic surgery subspecialties, including spine surgery, are well established. However, the underlying causes of these disparities, particularly relating to social determinants of health, are not fully understood. QUESTIONS/PURPOSES: (1) Is there a racial difference in 90-day mortality, readmission, and complication rates ("safety outcomes") among Medicare beneficiaries after spine surgery? (2) To what degree does the Centers for Disease Control and Prevention Social Vulnerability Index (SVI), a community-level marker of social determinants of health, account for racial disparities in safety outcomes? METHODS: To examine racial differences in 90-day mortality, readmission, and complications after spine surgery, we retrospectively identified all 419,533 Medicare beneficiaries aged 65 or older who underwent inpatient spine surgery from 2015 to 2019; we excluded 181,588 patients with endstage renal disease or Social Security disability insurance entitlements, who were on Medicare HMO, or who had missing SVI data. Because of the nearly universal coverage of those age 65 or older, Medicare data offer a large cohort that is broadly generalizable, provides improved precision for relatively rare safety outcomes, and is free of confounding from differential insurance access across races. The Master Beneficiary Summary File includes enrollees' self-reported race based on a restrictive list of mutually exclusive options. Even though this does not fully capture the entirety of racial diversity, it is self-reported by patients. Identification of spine surgery was based on five Diagnosis Related Groups labeled "cervical fusion," "fusion, except cervical," "anterior-posterior combined fusion," "complex fusion," and "back or neck, except fusion." Although heterogeneous, these cohorts do not reflect inherently different biology that would lead us to expect differences in safety outcomes by race. We report specific types of complications that did and did not involve readmission. Although complications vary in severity, we report them as composite measures while being cognizant of the inherent limitations of making inferences based on aggregate measures. The SVI was chosen as the mediating variable because it aggregates important social determinants of health and has been shown to be a marker of high risk of poor public health response to external stressors. Patients were categorized into three groups based on a ranking of the four SVI themes: socioeconomic status, household composition, minority status and language, and housing and transportation. We report the "average race effects" among Black patients compared with White patients using nearest-neighbor Mahalanobis matching by age, gender, comorbidities, and spine surgery type. Mahalanobis matching provided the best balance among propensity-type matching methods. Before matching, Black patients in Medicare undergoing spine surgery were disproportionately younger with more comorbidities and were less likely to undergo cervical fusion. To estimate the contribution of the SVI on racial disparities in safety outcomes, we report the average race effect between models with and without the addition of the four SVI themes. RESULTS: After matching on age, gender, comorbidities, and spine surgery type, Black patients were on average more likely than White patients to be readmitted (difference of 1.5% [95% CI 0.9% to 2.1%]; p < 0.001) and have complications with (difference of 1.2% [95% CI 0.5% to 1.9%]; p = 0.002) or without readmission (difference of 3.6% [95% CI 2.9% to 4.3%]; p < 0.001). Adding the SVI to the model attenuated these differences, explaining 17% to 49% of the racial differences in safety, depending on the outcome. An observed higher rate of 90-day mortality among Black patients was explained entirely by matching using non-SVI patient demographics (difference of 0.00% [95% CI -0.3% to 0.3%]; p = 0.99). However, even after adjusting for the SVI, Black patients had more readmissions and complications. CONCLUSION: Social disadvantage explains up to nearly 50% of the disparities in safety outcomes between Black and White Medicare patients after spine surgery. This argument highlights an important contribution of socioeconomic circumstances and societal barriers to achieving equal outcomes. But even after accounting for the SVI, there remained persistently unequal safety outcomes among Black patients compared with White patients, suggesting that other unmeasured factors contribute to the disparities. This is consistent with evidence documenting Black patients' disadvantages within a system of seemingly equal access and resources. Research on racial health disparities in orthopaedics should account for the SVI to avoid suggesting that race causes any observed differences in complications among patients when other factors related to social deprivation are more likely to be determinative. Focused social policies aiming to rectify structural disadvantages faced by disadvantaged communities may lead to a meaningful reduction in racial health disparities. LEVEL OF EVIDENCE: Level III, therapeutic study.

Criminal legal involvement among recently separated veterans: Findings from the LIMBIC study.

OBJECTIVE: This study investigated individual-level and neighborhood-level predictors of criminal legal involvement of veterans during the critical transition period from military to civilian life. HYPOTHESES: We hypothesized that substance use, mental health, and personality disorders will increase the incidence of criminal legal involvement, which will be highest among veterans living in socioeconomically disadvantaged neighborhoods after military discharge. METHOD: We analyzed data from a longitudinal cohort study of 418,624 veterans who entered Department of Veterans Affairs (VA) health care after leaving the military. Department of Defense (DoD) data on clinical diagnoses, demographics, and military history were linked to VA data on neighborhood of residence and criminal legal involvement. RESULTS: Criminal legal involvement in the 2 years following military discharge was most strongly predicted by younger age, substance use disorder, and being male. Other predictors included the military branch in which veterans served, deployment history, traumatic brain injury, serious mental illness, personality disorder, having fewer physical health conditions, and living in socioeconomically disadvantaged neighborhoods. These factors combined in multivariable analysis yielded a very large effect size for predicting criminal legal involvement after military separation (area under the curve = .82). The incidence of criminal legal involvement was 10 times higher among veterans with co-occurring substance use disorder, serious mental illness, and personality disorder than among veterans with none of these diagnoses, and these rates were highest among veterans residing in more socioeconomically disadvantaged neighborhoods. CONCLUSIONS: To our knowledge, this is the largest longitudinal study of risk factors for criminal legal involvement in veterans following military discharge. The findings supported the hypothesis that veterans with co-occurring mental disorders living in socioeconomically disadvantaged neighborhoods were at higher risk of criminal legal involvement, underscoring the complex interplay of individual-level and neighborhood-level risk factors for criminal legal involvement after veterans leave the military. These results can inform policy and programs, such as the DoD Transition Assistance Program (TAP) and the VA Military to Civilian Readiness Pathway program (M2C Ready), to enhance community reintegration and prevent criminal legal involvement among veterans transitioning from military to civilian life. (PsycInfo Database Record (c) 2022 APA, all rights reserved).

Association of Quality of Care With Where Veterans Choose to Get Knee Replacement Surgery.

Importance: Recent legislation expanded veterans' access to Veterans Health Administration (VA)-purchased care. Quality should be considered when choosing where to get total knee arthroplasty (TKA), but currently available quality metrics provide little guidance. Objective: To determine whether an association exists between the proportion of TKAs performed (vs purchased) at each VA facility and the quality of care provided (as measured by short-term complication rates). Design, Setting, and Participants: This 3-year cohort study used VA and community care data (fiscal year 2017 to fiscal year 2019) from the VA's Corporate Data Warehouse. Complications were defined following the Centers for Medicare and Medicaid Services' methodology. The setting included 140 VA health care facilities performing or purchasing TKAs. Participants included veterans who had 43 371 primary TKA procedures that were either VA-performed or VA-purchased during the study period. Exposures: Of the 43 371 primary TKA procedures, 18 964 (43.7%) were VA-purchased. Main Outcomes and Measures: The primary outcome was risk-standardized short-term complication rates of VA-performed or VA-purchased TKAs. The association between the proportion of TKAs performed at each VA facility and quality of VA-performed and VA-purchased care was examined using a regression model. Subgroups were also identified for facilities that had complication rates above or below the overall mean complication rate and for facilities that performed more or less than half of the facility's TKAs. Results: Among the study sample's 41 775 veterans who underwent 43 371 TKAs, 38 725 (89.3%) were male, 6406 (14.8%) were Black, 33 211 (76.6%) were White, and 1367 (3.2%) had other race or ethnicity (including American Indian or Alaska Native, Asian, and Native Hawaiian or other Pacific Islander); mean (SD) age was 66.9 (8.5) years. VA-performed and VA-purchased TKAs had a mean (SD) raw overall short-term complication rate of 2.97% (0.08%). There was no association between the proportion of TKAs performed in VA facilities and risk-standardized complication rates for VA-performed TKAs, and no association for VA-purchased TKAs. Conclusions and Relevance: In this cohort study, surgical quality did not have an association with where veterans had TKA, possibly because meaningful comparative data are lacking. Reporting local and community risk-standardized complication rates may inform veterans' decisions and improve care. Combining these data with the proportion of TKAs performed at each site could facilitate administrative decisions on where resources should be allocated to improve care.

Health Care Access Expansions and Use of Veterans Affairs and Other Hospitals by Veterans.

This cohort study examines changes in the use of Veterans Affairs (VA) and non-VA hospitals by VA enrollees and mortality associated with these policies.

Partnered implementation of the veteran sponsorship initiative: protocol for a randomized hybrid type 2 effectiveness-implementation trial.

BACKGROUND: The USA is undergoing a suicide epidemic for its youngest Veterans (18-to-34-years-old) as their suicide rate has almost doubled since 2001. Veterans are at the highest risk during their first-year post-discharge, thus creating a "deadly gap." In response, the nation has developed strategies that emphasize a preventive, universal, and public health approach and embrace the value of community interventions. The three-step theory of suicide suggests that community interventions that reduce reintegration difficulties and promote connectedness for Veterans as they transition to civilian life have the greatest likelihood of reducing suicide. Recent research shows that the effectiveness of community interventions can be enhanced when augmented by volunteer and certified sponsors (1-on-1) who actively engage with Veterans, as part of the Veteran Sponsorship Initiative (VSI). METHOD/DESIGN: The purpose of this randomized hybrid type 2 effectiveness-implementation trial is to evaluate the implementation of the VSI in six cities in Texas in collaboration with the US Departments of Defense, Labor and Veterans Affairs, Texas government, and local stakeholders. Texas is an optimal location for this large-scale implementation as it has the second largest population of these young Veterans and is home to the largest US military installation, Fort Hood. The first aim is to determine the effectiveness of the VSI, as evidenced by measures of reintegration difficulties, health/psychological distress, VA healthcare utilization, connectedness, and suicidal risk. The second aim is to determine the feasibility and potential utility of a stakeholder-engaged plan for implementing the VSI in Texas with the intent of future expansion in more states. The evaluators will use a stepped wedge design with a sequential roll-out to participating cities over time. Participants (n=630) will be enrolled on military installations six months prior to discharge. Implementation efforts will draw upon a bundled implementation strategy that includes strategies such as ongoing training, implementation facilitation, and audit and feedback. Formative and summative evaluations will be guided by the Reach, Effectiveness, Adoption, Implementation, and Maintenance (RE-AIM) framework and will include interviews with participants and periodic reflections with key stakeholders to longitudinally identify barriers and facilitators to implementation. DISCUSSION: This evaluation will have important implications for the national implementation of community interventions that address the epidemic of Veteran suicide. Aligned with the Evidence Act, it is the first large-scale implementation of an evidence-based practice that conducts a thorough assessment of TSMVs during the "deadly gap." TRIAL REGISTRATION: ClinicalTrials.gov ID number: NCT05224440 . Registered on 04 February 2022.

Patient-centered quality measurement for opioid use disorder: Development of a taxonomy to address gaps in research and practice.

Background: Evidence-based treatment is provided infrequently and inconsistently to patients with opioid use disorder (OUD). Treatment guidelines call for high-quality, patient-centered care that meets individual preferences and needs, but it is unclear whether current quality measures address individualized aspects of care and whether measures of patient-centered OUD care are supported by evidence. Methods: We conducted an environmental scan of OUD care quality to (1) evaluate patient-centeredness in current OUD quality measures endorsed by national agencies and in national OUD treatment guidelines; and (2) review literature evidence for patient-centered care in OUD diagnosis and management, including gaps in current guidelines, performance data, and quality measures. We then synthesized these findings to develop a new quality measurement taxonomy that incorporates patient-centered aspects of care and identifies priority areas for future research and quality measure development. Results: Across 31 endorsed OUD quality measures, only two measures of patient experience incorporated patient preferences and needs, while national guidelines emphasized providing patient-centered care. Among 689 articles reviewed, evidence varied for practices of patient-centered care. Many practices were supported by guidelines and substantial evidence, while others lacked evidence despite guideline support. Our synthesis of findings resulted in EQuIITable Care, a taxonomy comprised of six classifications: (1) patient Experience and engagement, (2) Quality of life; (3) Identification of patient risks; (4) Interventions to mitigate patient risks; (5) Treatment; and (6) Care coordination and navigation. Conclusions: Current quality measurement for OUD lacks patient-centeredness. EQuIITable Care for OUD provides a roadmap to develop measures of patient-centered care for OUD.

Association Between Mental Health Conditions and Outpatient Care Fragmentation: a National Study of Older High-Risk Veterans.

BACKGROUND: Healthcare fragmentation may lead to adverse consequences and may be amplified among older, sicker patients with mental health (MH) conditions. OBJECTIVE: To determine whether older Veterans with MH conditions have more fragmented outpatient non-MH care, compared with older Veterans with no MH conditions. DESIGN: Retrospective cohort study using FY2014 Veterans Health Administration (VHA) administrative data linked to Medicare data. PARTICIPANTS: 125,481 VHA patients ≥ 65 years old who were continuously enrolled in Medicare Fee-for-Service Parts A and B and were at high risk for hospitalization. MAIN OUTCOME AND MEASURES: The main outcome was non-MH care fragmentation as measured by (1) non-MH provider count and (2) Usual Provider of Care (UPC), the proportion of care with the most frequently seen non-MH provider. We tested the association between no vs. any MH conditions and outcomes using Poisson regression and fractional regression with logit link, respectively. We also compared Veterans with no MH condition with each MH condition and combinations of MH conditions, adjusting for sociodemographics, comorbidities, and drive-time to VHA specialty care. KEY RESULTS: In total, 47.3% had at least one MH condition. Compared to those without MH conditions, Veterans with MH conditions had less fragmented care, with fewer non-MH providers (IRR = 0.96; 95% CI: 0.96-0.96) and more concentrated care with their usual provider (OR = 1.08 for a higher UPC; 95% CI: 1.07, 1.09) in adjusted models. Secondary analyses showed that those with individual MH conditions (e.g., depression) had fewer non-MH providers (IRR range: 0.86-0.98) and more concentrated care (OR range: 1.04-1.20). A similar pattern was observed when examining combinations of MH conditions (IRR range: 0.80-0.90; OR range: 1.16-1.30). CONCLUSIONS: Contrary to expectations, having a MH condition was associated with less fragmented non-MH care among older, high-risk Veterans. Further research will determine if this is due to different needs, underuse, or appropriate use of healthcare.

Anticipating VA/non-VA care coordination demand for Veterans at high risk for hospitalization.

ABSTRACT: U.S. Veterans Affairs (VA) patients' multi-system use can create challenges for VA clinicians who are responsible for coordinating Veterans' use of non-VA care, including VA-purchased care ("Community Care") and Medicare.To examine the relationship between drive distance and time-key eligibility criteria for Community Care-and VA reliance (proportion of care received in VA versus Medicare and Community Care) among Veterans at high risk for hospitalization. We used prepolicy data to anticipate the impact of the 2014 Choice Act and 2018 Maintaining Internal Systems and Strengthening Integrated Outside Networks Act (MISSION Act), which expanded access to Community Care.Cross-sectional analysis using fractional logistic regressions to examine the relationship between a Veteran's reliance on VA for outpatient primary, mental health, and other specialty care and their drive distance/time to a VA facility.Thirteen thousand seven hundred three Veterans over the age of 65 years enrolled in VA and fee-for-service Medicare in federal fiscal year 2014 who were in the top 10th percentile for hospitalization risk.Key explanatory variables were patients' drive distance to VA > 40 miles (Choice Act criteria) and drive time to VA ≥ 30 minutes for primary and mental health care and ≥60 minutes for specialty care (MISSION Act criteria).Veterans at high risk for hospitalization with drive distance eligibility had increased odds of an outpatient specialty care visit taking place in VA when compared to Veterans who did not meet Choice Act eligibility criteria (odds ratio = 1.10, 95% confidence interval 1.05-1.15). However, drive time eligibility (MISSION Act criteria) was associated with significantly lower odds of an outpatient specialty care visit taking place in VA (odds ratio = 0.69, 95% confidence interval 0.67, 0.71). Neither drive distance nor drive time were associated with reliance for outpatient primary care or mental health care.VA patients who are at high risk for hospitalization may continue to rely on VA for outpatient primary care and mental health care despite access to outside services, but may increase use of outpatient specialty care in the community in the MISSION era, increasing demand for multi-system care coordination.

Outpatient care fragmentation in Veterans Affairs patients at high-risk for hospitalization.

OBJECTIVE: To examine outpatient care fragmentation and its association with future hospitalization among patients at high risk for hospitalization. DATA SOURCES: Veterans Affairs (VA) and Medicare data. STUDY DESIGN: We conducted a longitudinal study, using logistic regression to examine how outpatient care fragmentation in FY14 (as measured by number of unique providers, Breslau's Usual Provider of Care (UPC), Bice-Boxerman's Continuity of Care Index (COCI), and Modified Modified Continuity Index (MMCI)) was associated with all-cause hospitalizations and hospitalizations related to ambulatory care sensitive conditions (ACSC) in FY15. We also examined how fragmentation varied by patient's age, gender, race, ethnicity, marital status, rural status, history of homelessness, number of chronic conditions, Medicare utilization, and mental health care utilization. DATA EXTRACTION METHODS: We extracted data for 130,704 VA patients ≥65 years old with a hospitalization risk ≥90th percentile and ≥ four outpatient visits in the baseline year. PRINCIPAL FINDINGS: The mean (SD) of FY14 outpatient visits was 13.2 (8.6). Fragmented care (more providers, less care with a usual provider, more dispersed care based on COCI) was more common among patients with more chronic conditions and those receiving mental health care. In adjusted models, most fragmentation measures were not associated with all-cause hospitalization, and patients with low levels of fragmentation (more concentrated care based on UPC, COCI, and MMCI) had a higher likelihood of an ACSC-related hospitalization (AOR, 95% CI = 1.21 (1.09-1.35), 1.27 (1.14-1.42), and 1.28 (1.18-1.40), respectively). CONCLUSIONS: Contrary to expectations, outpatient care fragmentation was not associated with elevated all-cause hospitalization rates among VA patients in the top 10th percentile for risk of admission; in fact, fragmented care was linked to lower rates of hospitalization for ACSCs. In integrated settings such as the VA, multiple providers, and dispersed care might offer access to timely or specialized care that offsets risks of fragmentation, particularly for conditions that are sensitive to ambulatory care.