Differences in adverse outcomes across race and ethnicity among Veterans with similar predicted risks of an overdose or suicide-related event.

OBJECTIVE: To evaluate the degree to which differences in incidence of mortality and serious adverse events exist across patient race and ethnicity among Veterans Health Administration (VHA) patients receiving outpatient opioid prescriptions and who have similar predicted risks of adverse outcomes. Patients were assigned scores via the VHA Stratification Tool for Opioid Risk Mitigation (STORM), a model used to predict the risk of experiencing overdose- or suicide-related health care events or death. Individuals with the highest STORM risk scores are targeted for case review. DESIGN: Retrospective cohort study of high-risk veterans who received an outpatient prescription opioid between 4/2018-3/2019. SETTING: All VHA medical centers. PARTICIPANTS: In total, 84 473 patients whose estimated risk scores were between 0.0420 and 0.0609, the risk scores associated with the top 5%-10% of risk in the STORM development sample. METHODS: We examined the expected probability of mortality and serious adverse events (SAEs; overdose or suicide-related events) given a patient's risk score and race. RESULTS: Given a similar risk score, Black patients were less likely than White patients to have a recorded SAE within 6 months of risk score calculation. Black, Hispanic, and Asian patients were less likely than White patients with similar risk scores to die within 6 months of risk score calculation. Some of the mortality differences were driven by age differences in the composition of racial and ethnic groups in our sample. CONCLUSIONS: Our results suggest that relying on the STORM model to identify patients who may benefit from an interdisciplinary case review may identify patients with clinically meaningful differences in outcome risk across race and ethnicity.

Patterns of Healthcare Utilization and Spending Among Homebound Older Adults in the USA: an Observational Study.

BACKGROUND: Homebound older adults have complex social, medical, and financial needs, but little is known about their healthcare utilization and spending. OBJECTIVE: To characterize healthcare utilization and spending among homebound older adults. DESIGN: Cohort study using National Health and Aging Trends Study data linked to Medicare Fee-for-Service (FFS) claims data. PARTICIPANTS: Adults aged 70 years and older with Medicare FFS coverage (n = 6468). MAIN MEASURES: In a person-year analysis, survey-weighted rates and adjusted marginal differences in inpatient, outpatient, and emergency department utilization and spending 12 months post-interview were calculated by homebound status, defined as reporting never or rarely (no more than 1 day/week) leaving home in the last month. KEY RESULTS: Compared to the non-homebound, homebound observations had lower annual unadjusted rates of accessing primary care (60.9% vs 71.9%, p < 0.001) and specialist care (61.0% vs 74.9%, p < 0.001) and higher annual rates of emergency department use (54.0% vs 32.6%, p < 0.001) and hospitalization (39.8% vs 19.8%, p < 0.001). Total annual Medicare spending was $11,346 higher among the homebound compared to the non-homebound (p < 0.001). In a single year analysis (2015), homebound older adults accounted for 11.0% of Medicare spending among those over 70 despite making up only 5.7% of this population. 13.6% of the homebound were in the 95th percentile or above of Medicare spending in 2015. In models adjusting for demographic, clinical, and geographic characteristics, homebound status was associated with a decreased likelihood of having an annual primary care or specialist visit and $2226 additional total annual Medicare spending. CONCLUSIONS: Homebound older adults use more hospital-based care and less outpatient care than the non-homebound, contributing to higher levels of overall Medicare spending.

Effect of a Predictive Analytics-Targeted Program in Patients on Opioids: a Stepped-Wedge Cluster Randomized Controlled Trial.

BACKGROUND: Risk of overdose, suicide, and other adverse outcomes are elevated among sub-populations prescribed opioid analgesics. To address this, the Veterans Health Administration (VHA) developed the Stratification Tool for Opioid Risk Mitigation (STORM)-a provider-facing dashboard that utilizes predictive analytics to stratify patients prescribed opioids based on risk for overdose/suicide. OBJECTIVE: To evaluate the impact of the case review mandate on serious adverse events (SAEs) and all-cause mortality among high-risk Veterans. DESIGN: A 23-month stepped-wedge cluster randomized controlled trial in all 140 VHA medical centers between 2018 and 2020. PARTICIPANTS: A total of 44,042 patients actively prescribed opioid analgesics with high STORM risk scores (i.e., percentiles 1% to 5%) for an overdose or suicide-related event. INTERVENTION: A mandate requiring providers to perform case reviews on opioid analgesic-prescribed patients at high risk of overdose/suicide. MAIN MEASURES: Nine serious adverse events (SAEs), case review completion, number of risk mitigation strategies, and all-cause mortality. KEY RESULTS: Mandated review inclusion was associated with a significant decrease in all-cause mortality within 4 months of inclusion (OR: 0.78; 95% CI: 0.65-0.94). There was no detectable effect on SAEs. Stepped-wedge analyses found that mandated review patients were five times more likely to receive a case review than non-mandated patients with similar risk (OR: 5.1; 95% CI: 3.64-7.23) and received more risk mitigation strategies than non-mandated patients (0.498; CI: 0.39-0.61). CONCLUSIONS: Among VHA patients prescribed opioid analgesics, identifying high risk patients and mandating they receive an interdisciplinary case review was associated with a decrease in all-cause mortality. Results suggest that providers can leverage predictive analytic-targeted population health approaches and interdisciplinary collaboration to improve patient outcomes. TRIAL REGISTRATION: ISRCTN16012111.

Grading Evidence to Support Legislative and Budget Proposals-Veterans Health Administration's Key to Implementing the Evidence Act.

OBJECTIVES: The Foundations for Evidence-Based Policymaking Act of 2018 requires cabinet-level agencies to use evidence to justify and support budget and policy making. As investigators from the Quality Enhancement Research Initiative (QUERI) program, we were tasked with assisting Veterans Health Administration (VHA) leadership with the implementation of the Evidence-Based Policymaking Act of 2018. Through meetings with stakeholders, we identified a gap in the review process for legislative and budget proposals; no systematic process existed to evaluate the supporting evidence base for proposals. METHODS: Here, we describe the development, refinement, and use of a checklist to assess the strength of evidence included in VHA legislative and budget proposals for changes to care delivery; clinical, research, and administrative operations; and staffing and workforce issues. RESULTS: The evidence assessment checklist is now part of the regular review process for VHA legislative and budget proposals. It is also being adapted for use elsewhere within the Department of Veterans Affairs. The checklist has provided a framework for briefings and training on best practices for using evidence to guide policy and budget decisions. CONCLUSION: Including evidence reviews in the legislative and budget proposal prioritization process may be an effective institutional arrangement to promote the use of evidence to inform high-level health policy decisions and to build a "culture of evidence" within the government.

Impact of Mandated Case Review Policy on Opioid Discontinuation and Mortality Among High-Risk Long-Term Opioid Therapy Patients: The STORM Stepped-Wedge Cluster Randomized Controlled Trial.

BACKGROUND: Although long-term opioid therapy (LTOT) has its own risks, opioid discontinuation could pose harm for high-risk Veterans Health Administration (VHA) patients receiving LTOT. There is limited information on the impact of a mandate requiring providers to perform case reviews on high-risk patients with an active opioid prescription (ie, mandated case review policy) on opioid discontinuation and mortality. METHODS: Our study is a secondary data analysis of a 23-month stepped-wedge cluster randomized controlled trial between April 2018 and March 2020. The study included 10 685 LTOT patients with a predicted risk of a serious adverse event between the top 1% to 5% nationally who entered the risk range between 4/18/2018 and 11/9/2019. We examined whether the mandated case review policy had an impact on opioid discontinuation and mortality for the patients. RESULTS: Among 10 685 LTOT patients (88.2% male; mean [SD] age, 61.1 [11.7] years), 29.1% experienced discontinuation and the mortality rate was 9.5%. Patients under mandated case review had a decreased risk of opioid discontinuation (average marginal effect [AME], -11.16 [95% CI, -15.30 to -7.01] percentage points) and all-cause mortality (AME, -3.31 [95% CI, -5.63 to -1.00] percentage points), relative to patients who were not under the mandate. CONCLUSIONS: The VHA mandated case review policy was associated with lower probability of discontinuation and all-cause mortality for high-risk patients receiving LTOT. Interventions that maintain care engagement while optimizing pain management for high-risk patients may be beneficial for minimizing mortality and other risks associated with discontinuation.

Randomized Policy Evaluation of the Veterans Health Administration Stratification Tool for Opioid Risk Mitigation (STORM).

BACKGROUND: The Veterans Health Administration (VHA) developed a dashboard Stratification Tool for Opioid Risk Mitigation (STROM) to guide clinical practice interventions. VHA released a policy mandating that high-risk patients of an adverse event based on the STORM dashboard are to be reviewed by an interdisciplinary team of clinicians. AIM: Randomized program evaluation to evaluate if patients in the oversight arm had a lower risk of opioid-related serious adverse events (SAEs) or death compared to those in the non-oversight arm. SETTING AND PARTICIPANTS: One-hundred and forty VHA facilities (aka medical centers) were randomly assigned to two groups: oversight and non-oversight arms. VHA patients who were prescribed opioids between April 18, 2018, and November 8, 2019, were included in the cohort. PROGRAM DESCRIPTION: We hypothesized that patients cared for by VHA facilities that received the policy with the oversight accountability language would achieve lower opioid-related SAEs or death. PROGRAM EVALUATION: We did not observe a relationship between the oversight arm and opioid-related SAEs or death. Patients in the non-oversight arm had a significantly higher chance of receiving a case review compared to those in the oversight arm. DISCUSSION: Even though our findings were unexpected, the STORM policy overall was likely successful in focusing the provider's attention on very high-risk patients.

Patient and Caregiver Prioritization of Palliative and End-of-Life Cancer Care Quality Measures.

BACKGROUND: Development and prioritization of quality measures typically relies on experts in clinical medicine, but patients and their caregivers may have different perspectives on quality measurement priorities. OBJECTIVE: To inform priorities for health system implementation of palliative cancer and end-of-life care quality measures by eliciting perspectives of patients and caregivers. DESIGN: Using modified RAND-UCLA Appropriateness Panel methods and materials tailored for knowledgeable lay participants, we convened a panel to rate cancer palliative care process quality measure concepts before and after a 1-day, in-person meeting. PARTICIPANTS: Nine patients and caregivers with experience living with or caring for patients with cancer. MAIN MEASURES: Panelists rated each concept on importance for providing patient- and family-centered care on a nine-point scale and each panelist nominated five highest priority measure concepts ("top 5"). KEY RESULTS: Cancer patient and caregiver panelists rated all measure concepts presented as highly important to patient- and family- centered care (median rating ≥ 7) in pre-panel (mean rating range, 6.9-8.8) and post-panel ratings (mean rating range, 7.2-8.9). Forced choice nominations of the "top 5" helped distinguish similarly rated measure concepts. Measure concepts nominated into the "top 5" by three or more panelists included two measure concepts of communication (goals of care discussions and discussion of prognosis), one measure concept on providing comprehensive assessments of patients, and three on symptoms including pain management plans, improvement in pain, and depression management plans. Patients and caregivers nominated one additional measure concept (pain screening) back into consideration, bringing the total number of measure concepts under consideration to 21. CONCLUSIONS: Input from cancer patients and caregivers helped identify quality measurement priorities for health system implementation. Forced choice nominations were useful to discriminate concepts with the highest perceived importance. Our approach serves as a model for incorporating patient and caregiver priorities in quality measure development and implementation.

Vector-based kernel weighting: A simple estimator for improving precision and bias of average treatment effects in multiple treatment settings.

Treatment effect estimation must account for observed confounding, in which factors affect treatment assignment and outcomes simultaneously. Ignoring observed confounding risks concluding that a helpful treatment is not beneficial or that a treatment is safe when actually harmful. Propensity score matching or weighting adjusts for observed confounding, but the best way to use propensity scores for multiple treatments is unknown. It is unclear when choice of a different weighting or matching strategy leads to divergent inferences. We used Monte Carlo simulations (1000 replications) to examine sensitivity of multivalued treatment inferences to propensity score weighting or matching strategies. We consider five variants of propensity score adjustment: inverse probability of treatment weights, generalized propensity score matching, kernel weights (KW), vector matching, and a new hybrid that is easily implemented-vector-based kernel weighting (VBKW). VBKW matches observations with similar propensity score vectors, assigning greater KW to observations with similar probabilities within a given bandwidth. We varied degree of propensity score model misspecification, sample size, treatment effect heterogeneity, initial covariate imbalance, and sample distribution across treatment groups. We evaluated sensitivity of results to propensity score estimation technique (multinomial logit or multinomial probit). Across simulations, VBKW performed equally or better than the other methods in terms of bias, efficiency, and covariate balance measured via prognostic scores. Our simulations suggest that VBKW is amenable to full automation and is less sensitive to PS model misspecification than other methods used to account for observed confounding in multivalued treatment analyses.

Opioid Discontinuation Among Patients Receiving High-Dose Long-Term Opioid Therapy in the Veterans Health Administration.

BACKGROUND: Prior opioid discontinuation studies have focused on one of two characteristics of opioid prescribing, its duration (long term vs not) or dosage (high vs low). Questions remain about the experience of patients with high-dose, long-term opioid therapy (HLOT) prescriptions who are likely to be at the highest risk for adverse events. OBJECTIVE: We address the following questions among the Veterans Health Administration (VHA) patients receiving HLOT: 1), How has the prevalence of discontinuation of opioids changed over time? 2), How do patient characteristics vary between those who do and do not discontinue? And 3), how does the prevalence of discontinuation vary geographically? DESIGN: A retrospective observational study of VHA patients with HLOT between fiscal year (FY) 2014 and FY2018. PARTICIPANTS: We identified 1,281,330 patients from VHA outpatient opioid prescription data with at least a 1-day opioid supply between FY2014 and FY2018. We identified and excluded those receiving palliative care or diagnosed with metastatic cancer. MAIN MEASURES: For a given patient and month, a patient having a 3-month moving average of ≥ 90 daily morphine milligram equivalent (MME) was defined as having HLOT. Similarly, we used a three-month average MME of zero as discontinuation. KEY RESULTS: The prevalence of discontinuation among patients with HLOT increased from 6.3% in FY2014 to 7.8% in FY2018. Across the years, patients who discontinued were younger, less likely to be married, and more likely to have comorbidities related to substance use disorders compared with patients who continued to receive HLOT. Incidence of discontinuation among those with HLOT increased in more than half (64%) of the 129 VHA medical centers. CONCLUSION: Prevalence of patients receiving HLOT in the VHA decreased as the incidence of discontinuation increased. Further research is needed to understand the process by which patients are discontinued and to assess the relationship between discontinuation and health outcomes.

Research Lifecycle to Increase the Substantial Real-world Impact of Research: Accelerating Innovations to Application.

BACKGROUND: US health care systems face a growing demand to incorporate innovations that improve patient outcomes at a lower cost. Funding agencies increasingly must demonstrate the impact of research investments on public health. The Learning Health System promotes continuous institutional innovation, yet specific processes to develop innovations for further research and implementation into real-world health care settings to maximize health impacts have not been specified. OBJECTIVE: We describe the Research Lifecycle and how it leverages institutional priorities to support the translation of research discoveries to clinical application, serving as a broader operational approach to enhance the Learning Health System. METHODS: Developed by the US Department of Veterans Affairs Office of Research and Development Research-to-Real-World Workgroup, the Research Lifecycle incorporates frameworks from product development, translational science, and implementation science methods. The Lifecycle is based on Workgroup recommendations to overcome barriers to more direct translation of innovations to clinical application and support practice implementation and sustainability. RESULTS: The Research Lifecycle posits 5 phases which support a seamless pathway from discovery to implementation: prioritization (leadership priority alignment), discovery (innovation development), validation (clinical, operational feasibility), scale-up and spread (implementation strategies, performance monitoring), and sustainability (business case, workforce training). An example of how the Research Lifecycle has been applied within a health system is provided. CONCLUSIONS: The Research Lifecycle aligns research and health system investments to maximize real-world practice impact via a feasible pathway, where priority-driven innovations are adapted for effective clinical use and supported through implementation strategies, leading to continuous improvement in real-world health care.

Randomized program evaluation of the Veterans Health Administration Stratification Tool for Opioid Risk Mitigation (STORM): A research and clinical operations partnership to examine effectiveness.

The United States is facing an opioid crisis in which overdose is the leading cause of injury death-misuse of opioids constitutes the vast majority of those deaths. In 2016 alone, over 42,000 people died from opioid overdose, an increase of 27% from the prior year. Deployment of the Stratification Tool for Opioid Risk Mitigation (STORM), a clinical decision support tool to improve opioid safety, is one response by the Veterans Health Administration (VHA) to the opioid crisis. STORM identifies VHA patients at very high risk of opioid-related adverse events and lists potential risk mitigation strategies. Deployment of STORM also helps VHA meet certain requirements of the Comprehensive Addiction and Recovery Act of 2016. In alignment with the VHA's learning health care system initiative, a multidisciplinary team designed a randomized evaluation of a policy approach to mandating case reviews of very-high-risk patients identified by STORM and the impacts of patient inclusion versus exclusion in mandated STORM case reviews using a stepped-wedge design. The STORM evaluation involves drafting the policy notice, shepherding it through the VHA approval process, and implementing the cluster randomized design. This mixed-methods evaluation includes (1) a qualitative assessment of medical center implementation strategies with the aim of understanding of how STORM is incorporated into practice, and (2) quantitative analyses of the relations between policy mandates and STORM inclusion on opioid-related adverse events. The findings from this synergistic research design will yield critical insights for VHA leadership to refine opioid prescribing-related policy and practice.

Cost analysis of a prospective multi-site cohort study of palliative care consultation teams for adults with advanced cancer: Where do cost-savings come from?

BACKGROUND: Studies report cost-savings from hospital-based palliative care consultation teams compared to usual care only, but drivers of observed differences are unclear. AIM: To analyse cost-differences associated with palliative care consultation teams using two research questions: (Q1) What is the association between early palliative care consultation team intervention, and intensity of services and length of stay, compared to usual care only? (Q2) What is the association between early palliative care consultation team intervention and day-to-day hospital costs, compared to a later intervention? DESIGN: Prospective multi-site cohort study (2007-2011). Patients who received a consultation were placed in the intervention group, those who did not in the comparison group. Intervention group was stratified by timing, and groups were matched using propensity scores. SETTING/PARTICIPANTS: Adults admitted to three US hospitals with advanced cancer. Principle analytic sample contains 863 patients ( nUC = 637; nPC EARLY = 177; nPC LATE = 49) discharged alive. RESULTS: Cost-savings from early palliative care accrue due to both reduced length of stay and reduced intensity of treatment, with an estimated 63% of savings associated with shorter length of stay. A reduction in day-to-day costs is observable in the days immediately following initial consult but does not persist indefinitely. A comparison of early and late palliative care consultation team cost-effects shows negligible difference once the intervention is administered. CONCLUSION: Reduced length of stay is the biggest driver of cost-saving from early consultation for patients with advanced cancer. Patient- and family-centred discussions on goals of care and transition planning initiated by palliative care consultation teams may be at least as important in driving cost-savings as the reduction of unnecessary tests and pharmaceuticals identified by previous studies.

The end-of-life experience: modifiable predictors of caregivers' bereavement adjustment.

BACKGROUND: The objective of the current study was to determine the best set of predictors of psychological disorders, regrets, health-related quality of life, and mental health function among bereaved caregivers of patients with cancer, thereby identifying promising targets for interventions to improve bereavement adjustment. METHODS: Coping with Cancer is a longitudinal study of patients with advanced cancer and their informal caregivers who were enrolled from 2002 to 2008. The main outcome measure was bereavement adjustment of 245 caregivers (eg, depression, anxiety, and regrets) 6 months after the loss of the patient. The Structured Clinical Interview of the Diagnostic and Statistical Manual of Mental Disorders determined whether caregivers met the criteria for major depressive disorder or an anxiety disorder. Changes in health-related quality of life and mental health function from baseline to after the patient's death were assessed with the Medical Outcomes Study Short Form (SF-36). RESULTS: Greater than 50% of the caregivers reported regret about the cancer patient's end-of-life care; better patient quality of death (adjusted odds ratio, 0.77; 95% confidence interval, 0.67-0.88) reduced the risk of bereavement regret. The incidence of major depressive disorder or anxiety among the bereaved caregivers was 12.6% and was less likely for caregivers with better mental health before the loss of the patient (adjusted odds ratio, 0.03; 95% confidence interval, 0.004-0.25). Better patient quality of death also predicted improved caregiver health-related quality of life (adjusted standardized beta, .28; P < .001). The completion of a do-not-resuscitate order was found to be predictive of improved mental health from before the death of the patient to after the death (adjusted standardized beta, .29; P < .001). CONCLUSIONS: Reducing caregiver distress, encouraging advance care planning by patients, and improving patients' quality of death appear to be promising targets of interventions to improve caregiver bereavement adjustment.

End-of-life treatment preferences: a key to reducing ethnic/racial disparities in advance care planning?

BACKGROUND: The objective of this study was to identify targets for interventions to reduce end-of-life care disparities among patients with advanced cancer. To do this, the authors evaluated the degree to which end-of-life care values and preferences are associated with advance care planning within racial/ethnic minority groups. METHODS: The Coping with Cancer study recruited patients with advanced cancer from outpatient clinics in 5 states from 2002 to 2008. Then, the rates of 1 type of advance care planning-do-not-resuscitate (DNR) orders-reported at baseline interviews by 606 patients were investigated. Bivariate tests determined associations among DNR order completion, religious values, and treatment preferences within racial/ethnic groups. RESULTS: Non-Latino white patients were significantly more likely to have a DNR order (45%) than black (25%) and Latino (20%) patients (P<.001). A preference against specific life-prolonging treatment (eg, chemotherapy, ventilation) was the only factor significantly associated with higher DNR order likelihood in each group, with non-Latino white patients more likely than Latino or black patients to express preferences against life-prolonging care (eg, 26% of non-Latino white patients, 46% of black patients, and 41% of Latino patients wanted a feeding tube if it would extend life for 1 more day; P<.001). CONCLUSIONS: Preferences against life-prolonging care differ dramatically by race/ethnicity, but they have a uniform significant association with DNR order completion rates across racial/ethnic groups of patients with advanced cancer. Advance care planning interventions that target preferences associated with DNR orders across racial/ethnic groups may reach a broad patient population and reduce end-of-life care disparities.

Unmet need for mental health care among veterans receiving palliative care: assessment is not enough.

OBJECTIVE: To determine whether inpatient palliative care teams' assessments of psychological distress affect receipt of in-hospital mental health care (psychotherapy, psychological support, and health and behavior interventions) for seriously ill veterans. METHODS: Retrospective review of medical records from 287 seriously ill veterans who received inpatient palliative care consults between 2008 and 2010 in the NY/NJ Veterans Healthcare Network. RESULTS: Of the veterans who were cognitively or physically able to answer questions on the Condensed Memorial Symptom Assessment Scale, 44% reported psychological distress. Of those with distress, 38% accessed mental health care. In logistic regression models adjusted for sociodemographic and health characteristics, there was no evidence that psychological distress reported during the palliative care consult was associated with subsequent mental health care receipt from any type of provider. CONCLUSIONS: Efforts to increase mental health care to psychologically distressed palliative care patients need to convert assessments into receipt of needed care.

Measuring restrictiveness of Medicare Advantage networks: A claims-based approach.

OBJECTIVE: To develop and validate a measure of provider network restrictiveness in the Medicare Advantage (MA) population. DATA SOURCES: Prescription drug event data and beneficiary information for Part D enrollees from the Center for Medicare and Medicaid Services, along with prescriber identifiers; geographic variables from the Area Health Resources Files. STUDY DESIGN: A prediction model was used to predict the unique number of primary care providers that would have been seen by MA beneficiaries absent network restrictions. The model was trained and validated on Traditional Medicare (TM) beneficiaries. A pseudo-Poisson and a random forest model were evaluated. An observed-to-expected (O/E) ratio was calculated as the number of unique providers seen by MA beneficiaries divided by the number expected based the TM prediction model. Multivariable linear models were used to assess the relationship between network restrictiveness and plan and market factors. DATA COLLECTION/EXTRACTION METHODS: Prescription drug event data were obtained for a 20% random sample of beneficiaries enrolled in prescription drug coverage from 2011 to 2017. PRINCIPAL FINDINGS: Health Maintenance Organization plans were more restrictive (O/E = 55.5%; 95% CI 55.3%-55.7%) than Health Maintenance Organization-Point of Service plans (67.2%; 95% CI 66.7%-67.8%) or Preferred Provider Organization plans (74.7%; 95% CI 74.3%-75.1%), and rural areas had more restrictive networks (31.6%; 95% CI 29.0%-34.2%) than metropolitan areas (61.5%; 95% CI 61.3%-61.7%). Multivariable results confirmed these findings, and also indicated that increased provider supply was associated with less restrictive networks. CONCLUSIONS: We developed a means of estimating provider network restrictiveness in MA from claims data. Our results validate the approach, providing confidence for wider application (e.g., for other markets and specialties) and use for regulation.

Access to insurance navigation support through the State Health Insurance Assistance Program (SHIP).

Medicare enrollment is complex, particularly for low-income individuals who are dually eligible for Medicare and Medicaid, and the wrong plan choice can adversely impact beneficiaries' out-of-pocket costs and access to providers and medications. The State Health Insurance Assistance Program (SHIP) is a federal program that provides counseling on Medicare coverage, but the degree to which SHIP services are accessible to low-income beneficiaries is unknown. We interviewed SHIP counselors and coordinators to characterize factors affecting access to and quality of SHIP services for low-income beneficiaries. Availability of volunteers was cited as the primary barrier to SHIP services. Topics related to dual eligibility for Medicare and Medicaid were frequently covered in counseling sessions, and staff expressed a desire for more training related to Medicaid and integrated-care programs. Our results suggest that additional counselors and increased training on topics relevant to dually eligible individuals may improve SHIP's ability to provide health insurance-related information to low-income Medicare beneficiaries.

Potential inequities in access to in-person SHIP counseling services.

OBJECTIVES: Counseling and education on Medicare coverage options are available through the federal State Health Insurance Assistance Program (SHIP), but little is known about the population that SHIP reaches. STUDY DESIGN: Cross-sectional study. METHODS: Using a novel data source on SHIP counseling site locations, we characterized the availability of in-person SHIP counseling by zip code tabulation area (ZCTA) and used linear regression and t tests to evaluate whether SHIP counseling sites are disproportionately located in higher-income communities. RESULTS: Our sample included 1511 SHIP counseling sites. More than half (63%) of the localities in our sample have a SHIP site within the ZCTA or county. Twenty-four percent do not have a SHIP site within the county but have one in an adjacent county. The remaining 13% do not have a nearby SHIP site. There is a disproportionate number of individuals eligible for Medicare in localities without a SHIP site. Moreover, the population living in areas without in-person SHIP sites is more likely to have low income and fewer years of education than the population living in areas with a SHIP site. CONCLUSIONS: These results suggest that there are areas where in-person SHIP service expansion or other additional navigation support may be warranted.

The effects of the Veterans Health Administration's Referral Coordination Initiative on referral patterns and waiting times for specialty care.

OBJECTIVE: To investigate whether the Veterans Health Administration's (VA) 2019 Referral Coordination Initiative (RCI) was associated with changes in the proportion of VA specialty referrals completed by community-based care (CC) providers and mean appointment waiting times for VA and CC providers. DATA SOURCES/STUDY SETTINGS: Monthly facility level VA data for 3,097,366 specialty care referrals for eight high-volume specialties (cardiology, dermatology, gastroenterology, neurology, ophthalmology, orthopedics, physical therapy, and podiatry) from October 1, 2019 to May 30, 2022. STUDY DESIGN: We employed a staggered difference-in-differences approach to evaluate RCI's effects on referral patterns and wait times. Our unit of analysis was facility-month. We dichotomized facilities into high and low RCI use based on the proportion of total referrals for a specialty. We stratified our analysis by specialty and the staffing model that high RCI users adopted: centralized, decentralized, and hybrid. DATA COLLECTION/EXTRACTION METHODS: Administrative data on referrals and waiting times were extracted from the VA's corporate data warehouse. Data on staffing models were provided by the VA's Office of Integrated Veteran Care. PRINCIPAL FINDINGS: We did not reject the null hypotheses that high RCI use do not change CC referral rates or waiting times in any of the care settings for most specialties. For example, high RCI use for physical therapy-the highest volume specialty studied-was associated with -0.054 (95% confidence interval [CI]: -0.114 to 0.006) and 2.0 days (95% CI: -4.8 to 8.8) change in CC referral rate and waiting time at CC providers, respectively, among centralized staffing model adopters. CONCLUSIONS: In the initial years of the RCI program, RCI does not have a measurable effect on waiting times or CC referral rates. Our findings do not support concerns that RCI might be impeding Veterans' access to CC providers. Future evaluations should examine whether RCI facilitates Veterans' ability to receive care in their preferred setting.