Catálogo de publicaciones - revistas

<jats:title>Abstract</jats:title><jats:sec><jats:title>Objective</jats:title><jats:p>To evaluate the implementation and trust‐building strategies associated with successful partnership formation in scale‐up of the Veteran Sponsorship Initiative (VSI), an evidence‐based suicide prevention intervention enhancing connection to U.S. Department of Veterans Affairs (VA) and other resources during the military‐to‐civilian transition period.</jats:p></jats:sec><jats:sec><jats:title>Data Sources and Study Setting</jats:title><jats:p>Scaling VSI nationally required establishing partnerships across VA, the U.S. Department of Defense (DoD), and diverse public and private Veteran‐serving organizations. We assessed partnerships formalized with a signed memorandum during pre‐ and early implementation periods (October 2020–October 2022). To capture implementation activities, we conducted 39 periodic reflections with implementation team members over the same period.</jats:p></jats:sec><jats:sec><jats:title>Study Design</jats:title><jats:p>We conducted a qualitative case study evaluating the number of formalized VSI partnerships alongside directed qualitative content analysis of periodic reflections data using Atlas.ti 22.0.</jats:p></jats:sec><jats:sec><jats:title>Data Collection/Extraction Methods</jats:title><jats:p>We first independently coded reflections for implementation strategies, following the Expert Recommendations for Implementing Change (ERIC) taxonomy, and for trust‐building strategies, following the Theoretical Model for Trusting Relationships and Implementation; a second round of inductive coding explored emergent themes associated with partnership formation.</jats:p></jats:sec><jats:sec><jats:title>Principal Findings</jats:title><jats:p>During this period, VSI established 12 active partnerships with public and non‐profit agencies. The VSI team reported using 35 ERIC implementation strategies, including building a coalition and developing educational and procedural documents, and trust‐building strategies including demonstrating competence and credibility, frequent interactions, and responsiveness. Cultural competence in navigating DoD and VA and accepting and persisting through conflict also appeared to support scale‐up.</jats:p></jats:sec><jats:sec><jats:title>Conclusions</jats:title><jats:p>VSI's partnership‐formation efforts leveraged a variety of implementation strategies, particularly around strengthening stakeholder interrelationships and refining procedures for coordination and communication. VSI implementation activities were further characterized by an intentional focus on trust‐building over time. VSI's rapid scale‐up highlights the value of partnership formation for achieving coordinated interventions to address complex problems.</jats:p></jats:sec>

<jats:title>Abstract</jats:title><jats:sec><jats:title>Objective</jats:title><jats:p>To quantify the degree to which health care service area (HCSA) definitions captured hospitalizations and heterogeneity in social determinants of health (SDOH).</jats:p></jats:sec><jats:sec><jats:title>Data Sources and Study Setting</jats:title><jats:p>Geospatial data from the Centers for Medicare and Medicaid Services, the Census Bureau, and the Dartmouth Institute. Drive‐time isochrones from MapBox. Area Deprivation Index (ADI) data. 2017 inpatient discharge data from Arizona, Florida, Iowa, Maryland, Nebraska, New Jersey, New York, and Wisconsin, State Emergency Department Databases and State Inpatient Databases, Healthcare Cost and Utilization Project, Agency for Healthcare Research and Quality; and Fee‐For‐Service Medicare data in 48 states.</jats:p></jats:sec><jats:sec><jats:title>Study Design</jats:title><jats:p>Cross‐sectional, descriptive analysis.</jats:p></jats:sec><jats:sec><jats:title>Data Collection/Extraction Methods</jats:title><jats:p>The capture rate was the percentage of inpatient discharges occurring in the same HCSA as the hospital. We compared capture rates for each HCSA definition for different populations and by hospital type. We measured SDOH heterogeneity using the coefficient of variation of the ADI among ZIP codes within each HCSA.</jats:p></jats:sec><jats:sec><jats:title>Principal Findings</jats:title><jats:p>HCSA definitions captured a wide range of inpatient discharges, ranging from 20% to 50% for Public Use Microdata Areas (PUMAs) to 93%–97% for Metropolitan Statistical Areas (MSAs). Three‐quarters of inpatient discharges were from facilities within the same county as the patient's residential ZIP code, while nearly two‐thirds were within the same Hospital Service Area. From the hospital perspective, 74.7% of inpatient discharges originated from within a 30‐min drive and 90.1% within a 60‐min drive. Capture rates were the lowest for teaching hospitals. PUMAs and drive‐time‐based HCSAs encompassed more homogenous populations while MSAs, Commuting Zones, and Hospital Referral Regions captured the most variation.</jats:p></jats:sec><jats:sec><jats:title>Conclusions</jats:title><jats:p>The proportion of hospital discharges captured by each HCSA varied, with MSAs capturing the highest proportion of discharges and PUMAs capturing the lowest. Additionally, researchers face a trade‐off between capture rate and population homogeneity when deciding which HCSA to use.</jats:p></jats:sec>

<jats:title>Abstract</jats:title><jats:sec><jats:title>Objective</jats:title><jats:p>To examine differential changes in receipt of surgery at National Cancer Institute (NCI)‐designated comprehensive cancer centers (NCI‐CCC) and Commission on Cancer (CoC) accredited hospitals for patients with cancer more likely to be newly eligible for coverage under Affordable Care Act (ACA) insurance expansions, relative to those less likely to have been impacted by the ACA.</jats:p></jats:sec><jats:sec><jats:title>Data Sources and Study Setting</jats:title><jats:p>Pennsylvania Cancer Registry (PCR) for 2010–2019 linked with discharge records from the Pennsylvania Health Care Cost Containment Council (PHC4).</jats:p></jats:sec><jats:sec><jats:title>Study Design</jats:title><jats:p>Outcomes include whether cancer surgery was performed at an NCI‐CCC or a CoC‐accredited hospital. We conducted a difference‐in‐differences analysis, estimating linear probability models for each outcome that control for residence in a county with above median county‐level pre‐ACA uninsurance and the interaction between county‐level baseline uninsurance and cancer treatment post‐ACA to capture differential changes in access between those more and less likely to become newly eligible for insurance coverage (based on area‐level proxy). All models control for age, sex, race and ethnicity, cancer site and stage, census‐tract level urban/rural residence, Area Deprivation Index, and year‐ and county‐fixed effects.</jats:p></jats:sec><jats:sec><jats:title>Data Collection/Extraction Methods</jats:title><jats:p>We identified adults aged 26–64 in PCR with prostate, lung, or colorectal cancer who received cancer‐directed surgery and had a corresponding surgery discharge record in PHC4.</jats:p></jats:sec><jats:sec><jats:title>Principal Findings</jats:title><jats:p>We observe a differential increase in receiving care at an NCI‐CCC of 6.2 percentage points (95% CI: 2.6–9.8; baseline mean = 9.8%) among patients in high baseline uninsurance areas (<jats:italic>p</jats:italic> = 0.001). Our estimate of the differential change in care at the larger set of CoC hospitals is positive (3.9 percentage points [95% CI: −0.5‐8.2; baseline mean = 73.7%]) but not statistically significant (<jats:italic>p</jats:italic> = 0.079).</jats:p></jats:sec><jats:sec><jats:title>Conclusions</jats:title><jats:p>Our findings suggest that insurance expansions under the ACA were associated with increased access to NCI‐CCCs.</jats:p></jats:sec>

<jats:title>Abstract</jats:title><jats:sec><jats:title>Objective</jats:title><jats:p>To evaluate nationwide implementation of a Guidebook designed to standardize safety practices across VA‐delivered and VA‐purchased care (i.e., Community Care) and identify lessons learned and strategies to improve them.</jats:p></jats:sec><jats:sec><jats:title>Data Sources and Study Setting</jats:title><jats:p>Qualitative data collected from key informants at 18 geographically diverse VA facilities across 17 Veterans Integrated Services Networks (VISNs).</jats:p></jats:sec><jats:sec><jats:title>Study Design</jats:title><jats:p>We conducted semi‐structured interviews from 2019 to 2022 with VISN Patient Safety Officers (PSOs) and VA facility patient safety and quality managers (PSMs and QMs) and VA Facility Community Care (CC) staff to assess lessons learned by examining organizational contextual factors affecting Guidebook implementation based on the Consolidated Framework for Implementation Research (CFIR).</jats:p></jats:sec><jats:sec><jats:title>Data Collection/Extraction Methods</jats:title><jats:p>Interviews were conducted virtually with 45 facility staff and 10 VISN PSOs. Using directed content analysis, we identified CFIR factors affecting implementation. These factors were mapped to the Expert Recommendations for Implementing Change (ERIC) strategy compilation to identify lessons learned that could be useful to our operational partners in improving implementation processes. We met frequently with our partners to discuss findings and plan next steps.</jats:p></jats:sec><jats:sec><jats:title>Principal Findings</jats:title><jats:p>Six CFIR constructs were identified as both facilitators and barriers to Guidebook implementation: (1) planning for implementation; (2) engaging key knowledge holders; (3) available resources; (4) networks and communications; (5) culture; and (6) external policies. The two CFIR constructs that were only barriers included: (1) cosmopolitanism and (2) executing implementation.</jats:p></jats:sec><jats:sec><jats:title>Conclusions</jats:title><jats:p>Our findings suggest several important lessons: (1) engage all collaborators involved in implementation; (2) ensure end‐users have opportunities to provide feedback; (3) describe collaborators' purpose and roles/responsibilities clearly at the start; (4) communicate information widely and repeatedly; and (5) identify how multiple high priorities can be synergistic. This evaluation will help our partners and key VA leadership to determine next steps and future strategies for improving Guidebook implementation through collaboration with VA staff.</jats:p></jats:sec>

<jats:title>Abstract</jats:title><jats:sec><jats:title>Objective</jats:title><jats:p>To understand existing care practices and policies, and potential enhancements, to improve the effectiveness of the US Department of Veterans Affairs (VA) Supportive Services for Veteran Families (SSVF) Health Care Navigators (HCN) in linking Veterans experiencing housing instability in rural areas with health care services.</jats:p></jats:sec><jats:sec><jats:title>Data Sources and Study Setting</jats:title><jats:p>We used primary data collected during semistructured interviews with HCNs (n = 21) serving rural areas across the United States during Spring 2022.</jats:p></jats:sec><jats:sec><jats:title>Study Design</jats:title><jats:p>We applied the Consolidated Framework for Implementation Research (CFIR) 2009 and the Social Ecological Model (SEM) to the collection and analysis of qualitative data to understand how HCNs administer services within SSVF and the larger community.</jats:p></jats:sec><jats:sec><jats:title>Data Collection/Extraction Methods</jats:title><jats:p>We used rapid qualitative methods to summarize and analyze data. Templated matrix summaries identified facilitators and barriers to linking Veterans with health care services and policy and practice implications.</jats:p></jats:sec><jats:sec><jats:title>Principal Findings</jats:title><jats:p>Using CFIR 2009, we identified contextual factors affecting successful implementation of HCN services within SSVF; we offer a crosswalk between CFIR 2009 and the version updated in 2022. Framing facilitators and barriers within the SEM provided insight into whether implementation strategies should be addressed at a community, interpersonal, or intrapersonal level within the SEM. Facilitators included sufficient knowledge, training, and mentorship opportunities for HCNs and their capacity to collaborate within their organization and with other community‐based organizations. Barriers included lack of local technology and housing resources, inadequate understanding of Veterans' service eligibilities and pathways to access those services, and deficient collaboration with the VA.</jats:p></jats:sec><jats:sec><jats:title>Conclusions</jats:title><jats:p>Understanding facilitators and barriers experienced by HCN when linking unstably housed Veterans in rural areas with health care services can inform future strategies, including policy changes such as increased training to support HCNs' understanding of eligibility, benefits, and entitlements as well as improving communication and collaboration between VA and community partners.</jats:p></jats:sec>

<jats:title>Abstract</jats:title><jats:sec><jats:title>Objective</jats:title><jats:p>To describe <jats:italic>a</jats:italic> structured, iterative, data‐driven approach for modifying implementation strategies for a complex evidence‐based practice during a nationwide scale‐up initiative.</jats:p></jats:sec><jats:sec><jats:title>Data Sources and Study Setting</jats:title><jats:p>We scaled‐up implementation of Critical Time Intervention (CTI)—an evidence‐based case management model—across 32 diverse community‐based Veterans Affairs (VA) “Grant and Per Diem” case management (GPD‐CM) agencies that serve homeless‐experienced Veterans transitioning to independent living. Primary data were collected using qualitative methods.</jats:p></jats:sec><jats:sec><jats:title>Study Design</jats:title><jats:p>We embarked on a scale‐up initiative while conducting a pragmatic randomized evaluation using a roll‐out design, comparing two versions of a CTI implementation package tailored to VA's GPD‐CM program. We iteratively assessed contextual factors and implementation outcomes (e.g., acceptability); findings informed package modifications that were characterized using the Framework for Reporting Adaptations and Modifications to Evidence‐based Implementation Strategies.</jats:p></jats:sec><jats:sec><jats:title>Data Collection Methods</jats:title><jats:p>We conducted semi‐structured interviews with Veterans, GPD‐CM staff, and liaising VA clinicians; periodic reflections with liaising VA clinicians and implementation team members; and drew upon detailed meeting notes. We used rapid qualitative methods and content analysis to integrate data and characterize modifications.</jats:p></jats:sec><jats:sec><jats:title>Principal Findings</jats:title><jats:p>After each scale‐up wave—in response to variations in agency‐level characteristics— we made iterative modifications to the implementation package to increase CTI adoption and fidelity across the diverse contexts of our scale‐up sites. Modifications included adding, deleting, integrating, and altering the package; core package components were preserved.</jats:p></jats:sec><jats:sec><jats:title>Conclusions</jats:title><jats:p>Implementation packages for complex evidence‐based practices undergoing scale‐up in diverse contexts may benefit from iterative modifications to optimize practice adoption with fidelity. We offer a structured, pragmatic approach for iteratively identifying data‐driven, midstream implementation package adjustments, for use in both VA and non‐VA scale‐up initiatives. Our project demonstrates the importance of assessing for and making modifications in a scale‐up initiative, as well as the trade‐offs of projects having simultaneous formative and summative evaluation aims.</jats:p></jats:sec>

<jats:title>Abstract</jats:title><jats:sec><jats:title>Objective</jats:title><jats:p>To develop a claims‐based algorithm to determine the setting of a disease diagnosis.</jats:p></jats:sec><jats:sec><jats:title>Data Sources and Study Setting</jats:title><jats:p>Medicare enrollment and claims data from 2014 to 2019.</jats:p></jats:sec><jats:sec><jats:title>Study Design</jats:title><jats:p>We developed a claims‐based algorithm using facility indicators, revenue center codes, and place of service codes to identify settings where HCV diagnosis first appeared. When the first appearance was in a laboratory, we attempted to associate HCV diagnoses with subsequent clinical visits. Face validity was assessed by examining association of claims‐based diagnostic settings with treatment initiation.</jats:p></jats:sec><jats:sec><jats:title>Data Collection/Extraction Methods</jats:title><jats:p>Patients newly diagnosed with HCV and continuously enrolled in traditional Medicare Parts A, B, and D (12 months before and 6 months after index diagnosis) were included.</jats:p></jats:sec><jats:sec><jats:title>Principal Findings</jats:title><jats:p>Among 104,454 patients aged 18–64 and 66,726 aged ≥65, 70.1% and 69%, respectively, were diagnosed in outpatient settings, and 20.2% and 22.7%, respectively in laboratory or unknown settings. Logistic regression revealed significantly lower odds of treatment initiation after diagnosis in emergency departments/urgent cares, hospitals, laboratories, or unclassified settings, than in outpatient visits.</jats:p></jats:sec><jats:sec><jats:title>Conclusions</jats:title><jats:p>The algorithm identified the setting of HCV diagnosis in most cases, and found significant associations with treatment initiation, suggesting an approach that can be adapted for future claims‐based studies.</jats:p></jats:sec>

<jats:title>Abstract</jats:title><jats:sec><jats:title>Objective</jats:title><jats:p>To identify constructs that are critical in shaping Veterans' experiences with Veterans Health Administration (VA) women's healthcare, including any which have been underexplored or are not included in current VA surveys of patient experience.</jats:p></jats:sec><jats:sec><jats:title>Data Sources and Study Setting</jats:title><jats:p>From June 2022 to January 2023, we conducted 28 semi‐structured interviews with a diverse, national sample of Veterans who use VA women's healthcare.</jats:p></jats:sec><jats:sec><jats:title>Study Design</jats:title><jats:p>Using VA data, we divided Veteran VA‐users identified as female into four groups stratified by age (dichotomized at age 45) and race/ethnicity (non‐Hispanic White vs. all other). We enrolled Veterans continuously from each recruitment strata until thematic saturation was reached.</jats:p></jats:sec><jats:sec><jats:title>Data Collection/Extraction Methods</jats:title><jats:p>For this qualitative study, we asked Veterans about past VA healthcare experiences. Interview questions were guided by a priori domains identified from review of the literature, including trust, safety, respect, privacy, communication and discrimination. Analysis occurred concurrently with interviews, using inductive and deductive content analysis.</jats:p></jats:sec><jats:sec><jats:title>Principal Findings</jats:title><jats:p>We identified five themes influencing Veterans' experiences of VA women's healthcare: feeling valued and supported, bodily autonomy, discrimination, past military experiences and trauma, and accessible care. Each emergent theme was associated with multiple of the a priori domains we asked about in the interview guide.</jats:p></jats:sec><jats:sec><jats:title>Conclusions</jats:title><jats:p>Our findings underscore the need for a measure of patient experience tailored to VA women's healthcare. Existing patient experience measures used within VA fail to address several aspects of experience highlighted by our study, including bodily autonomy, the influence of past military experiences and trauma on healthcare, and discrimination. Understanding distinct factors that influence women and gender‐diverse Veterans' experiences with VA care is critical to advance efforts by VA to measure and improve the quality and equity of care for all Veterans.</jats:p></jats:sec>

<jats:title>Abstract</jats:title><jats:sec><jats:title>Objective</jats:title><jats:p>To investigate the impact of Medicaid expansion on state expenditures through the end of 2022.</jats:p></jats:sec><jats:sec><jats:title>Data Sources</jats:title><jats:p>We used data from the National Association of State Budget Officers (NASBO)'s State Expenditure Report, Kaiser Family Foundation (KFF)'s Medicaid expansion tracker, US Bureau of Labor Statistics data (BLS), US Bureau of Economic Analysis data (BEA), and Pandemic Response Accountability Committee Oversight (PRAC).</jats:p></jats:sec><jats:sec><jats:title>Study Design</jats:title><jats:p>We investigated spending per capita (by state population) across seven budget categories, including Medicaid spending, and four spending sources. We performed a difference‐in‐differences (DiD) analysis that compared within‐state changes in spending over time in expansion and nonexpansion states to estimate the effect of Medicaid expansion on state budgets. We adjusted for annual state unemployment rate, annual state per capita personal income, and state spending of Coronavirus Relief Funds (CRF) from 2020 to 2022 and included state and year fixed effects.</jats:p></jats:sec><jats:sec><jats:title>Data Collection/Extraction Methods</jats:title><jats:p>We linked annual state‐level data on state‐reported fiscal year expenditures from NASBO with state‐level characteristics from BLS and BEA data and with CRF state spending from PRAC.</jats:p></jats:sec><jats:sec><jats:title>Principal Findings</jats:title><jats:p>Medicaid expansion was associated with an average increase of 21% (95% confidence interval [CI]: 16%–25%) in per capita Medicaid spending after Medicaid expansion among states that expanded prior to 2020. After inclusion of an interaction term to separate between the coronavirus disease (COVID) era (2020–2022) and the prior period following expansion (2015–2019), we found that although Medicaid expansion led to an average increase of 33% (95% CI: 21%–45%) in federal funding of state expenditures in the post‐COVID years, it was not significantly associated with increased state spending.</jats:p></jats:sec><jats:sec><jats:title>Conclusions</jats:title><jats:p>There was no evidence of crowding out of other state expenditure categories or a substantial impact on total state spending, even in the COVID‐19 era. Increased federal expenditures may have shielded states from substantial budgetary impacts.</jats:p></jats:sec>

<jats:title>Abstract</jats:title><jats:sec><jats:title>Objective</jats:title><jats:p>Medicaid‐funded long‐term services and supports are increasingly provided through home‐ and community‐based services (HCBS) to promote continued community living. While an emerging body of evidence examines the direct benefits and costs of HCBS, there may also be unexplored synergies with Medicare‐funded post‐acute care (PAC). This study aimed to provide empirical evidence on how the use of Medicaid HCBS influences Medicare PAC utilization among the dually enrolled.</jats:p></jats:sec><jats:sec><jats:title>Data Sources</jats:title><jats:p>National Medicare claims, Medicaid claims, nursing home assessment data, and home health assessment data from 2016 to 2018.</jats:p></jats:sec><jats:sec><jats:title>Study Design</jats:title><jats:p>We estimated the relationship between prior Medicaid HCBS use and PAC (skilled nursing facilities [SNF] or home health) utilization in a national sample of duals with qualifying index hospitalizations. We used inverse probability weights to create balanced samples on observed characteristics and estimated multivariable regression with hospital fixed effects and extensive controls. We also conducted stratified analyses for key subgroups.</jats:p></jats:sec><jats:sec><jats:title>Data Extraction Methods</jats:title><jats:p>The primary sample included 887,598 hospital discharges from community‐dwelling duals who had an eligible index hospitalization between April 1, 2016, and September 30, 2018.</jats:p></jats:sec><jats:sec><jats:title>Principal Findings</jats:title><jats:p>We found HCBS use was associated with a 9 percentage‐point increase in the use of home health relative to SNF, conditional on using PAC, and a meaningful reduction in length of stay for those using SNF. In addition, in our primary sample, we found HCBS use to be associated with an overall increase in PAC use, given that the absolute increase in home health use was larger than the absolute decrease in SNF use. In other words, the use of Medicaid‐funded HCBS was associated with a shift in Medicare‐funded PAC use toward home‐based settings.</jats:p></jats:sec><jats:sec><jats:title>Conclusion</jats:title><jats:p>Our findings indicate potential synergies between Medicaid‐funded HCBS and increased use of home‐based PAC, suggesting policymakers should cautiously consider these dynamics in HCBS expansion efforts.</jats:p></jats:sec>