Introduction: Although telemedicine emerged during the COVID-19 pandemic as a critical mode of health care delivery, there may be differences in the perceived ease of patient-clinician communication and quality of care for telemedicine versus in-person visits, as well as variation in perceptions across patient subgroups. We examined patients’ experiences with and preferences for telemedicine relative to in-person care, based on their most recent visit.

Methods: We conducted a survey of 2,668 adults in a large academic health care system in November 2021. The survey captured patients’ reasons for their most recent visit, perceptions on patient-clinician communication and quality of care, and attitudes toward telemedicine versus in-person care.

Results: Among respondents, 552 (21%) had a telemedicine visit. Patients with telemedicine and in-person visits had similar agreement on ease of patient-clinician communication and perceived quality of the visit on average. However, for individuals 65 years of age or older, men, and those not needing urgent care, telemedicine was associated with worse perceptions of patient-clinician communication (65 years of age or older: adjusted odds ratio [aOR], 0.51; 95% confidence interval [CI], 0.31-0.85; men: aOR, 0.50; 95% CI, 0.31-0.81; urgent care: aOR 0.67; 95% CI, 0.49-0.91) and lower perceived quality (65 years of age or older, aOR 0.51; 95% CI, 0.30-0.86; men: 0.51; 95% CI, 0.32-0.83; urgent care: aOR 0.68; 95% CI, 0.49-0.93).

Conclusion: Patient-perceived quality of care and patient-clinician communication were similar for telemedicine and in-person visits overall. However, among men, older adults, and those not seeking urgent care, patients using telemedicine had lower perceptions of patient-clinician communication and quality.

In this cross-sectional study, we describe the prevalence of procedural denials of coverage in Medicaid and the Children’s Health Insurance Program (CHIP) across states in 2019, before the pause on redeterminations during the COVID-19 pandemic. We also evaluate the association between rates of procedural denials and states’ renewal processes to determine whether states’ upstream operational investments may protect against high denial rates.

Strained hospital capacity is associated with adverse patient outcomes. Anecdotal evidence suggests that during the COVID-19 pandemic in the US, some hospitals experienced capacity constraints while others in the same market had surplus capacity, a phenomenon known as “load imbalance.” Our study evaluated the prevalence of intensive care unit load imbalance and the characteristics of hospitals most likely to be over capacity while other nearby hospitals were under capacity. Of the 290 hospital referral regions (HRRs) analyzed, 154 (53.1 percent) experienced load imbalance during the study period. HRRs experiencing the most imbalance had higher proportions of Black residents. Hospitals with the highest Medicaid patient shares and Black Medicare patient shares were significantly more likely to be over capacity, while other hospitals in their market were under capacity. Our findings highlight that hospital load imbalance was common during the COVID-19 pandemic. Policies to coordinate transfers may decrease strain during periods of high demand and ease the burden on hospitals that serve a higher proportion of patients from racial minority groups.

Importance: The Medicare Merit-based Incentive Payment System (MIPS) influences reimbursement for hundreds of thousands of US physicians, but little is known about whether program performance accurately captures the quality of care they provide.

Objective: To examine whether primary care physicians’ MIPS scores are associated with performance on process and outcome measures.

Design, Setting, and Participants: Cross-sectional study of 80 246 US primary care physicians participating in the MIPS program in 2019.

Exposures: MIPS score.

Main Outcomes and Measures: The association between physician MIPS scores and performance on 5 unadjusted process measures, 6 adjusted outcome measures, and a composite outcome measure.

Results: The study population included 3.4 million patients attributed to 80 246 primary care physicians, including 4773 physicians with low MIPS scores (≤30), 6151 physicians with medium MIPS scores (>30-75), and 69 322 physicians with high MIPS scores (>75). Compared with physicians with high MIPS scores, physicians with low MIPS scores had significantly worse mean performance on 3 of 5 process measures: diabetic eye examinations (56.1% vs 63.2%; difference, −7.1 percentage points [95% CI, −8.0 to −6.2]; P < .001), diabetic HbA1c screening (84.6% vs 89.4%; difference, −4.8 percentage points [95% CI, −5.4 to −4.2]; P < .001), and mammography screening (58.2% vs 70.4%; difference, −12.2 percentage points [95% CI, −13.1 to −11.4]; P < .001) but significantly better mean performance on rates of influenza vaccination (78.0% vs 76.8%; difference, 1.2 percentage points [95% CI, 0.0 to 2.5]; P = .045] and tobacco screening (95.0% vs 94.1%; difference, 0.9 percentage points [95% CI, 0.3 to 1.5]; P = .001). MIPS scores were inconsistently associated with risk-adjusted patient outcomes: compared with physicians with high MIPS scores, physicians with low MIPS scores had significantly better mean performance on 1 outcome (307.6 vs 316.4 emergency department visits per 1000 patients; difference, −8.9 [95% CI, −13.7 to −4.1]; P < .001), worse performance on 1 outcome (255.4 vs 225.2 all-cause hospitalizations per 1000 patients; difference, 30.2 [95% CI, 24.8 to 35.7]; P < .001), and did not have significantly different performance on 4 ambulatory care–sensitive admission outcomes. Nineteen percent of physicians with low MIPS scores had composite outcomes performance in the top quintile, while 21% of physicians with high MIPS scores had outcomes in the bottom quintile. Physicians with low MIPS scores but superior outcomes cared for more medically complex and socially vulnerable patients, compared with physicians with low MIPS scores and poor outcomes.

Conclusions and Relevance: Among US primary care physicians in 2019, MIPS scores were inconsistently associated with performance on process and outcome measures. These findings suggest that the MIPS program may be ineffective at measuring and incentivizing quality improvement among US physicians.

Little is known about how Medicaid disproportionate share hospital payments, which are intended to support hospitals that serve low-income patients, are allocated or whether allocation patterns have changed over time. We employed alternative definitions of targeting, or the degree to which allocations were made in a manner consistent with the statutory goals and intent of the program, to examine disproportionate share hospital payment allocations in forty-nine participating states. The most recent data indicate that 57.2 percent of acute care hospitals received disproportionate share hospital payments, totaling more than $14.5 billion, in 2015. The majority of payments went to hospitals with Medicaid shares above the state-specific median (89.1 percent), hospitals with uncompensated care shares above the state-specific median (60.6 percent), or hospitals deemed as disproportionate share per statutory definitions (64.6 percent). However, among all hospitals receiving these payments, up to 31.6 percent of payments were allocated to hospitals that did not meet a given definition, and 3.2 percent went to hospitals that met none of them. These findings suggest that although the majority of the payments were targeted to hospitals serving low-income patients, opportunities exist to better align allocation with statutory goals and intent or to revise applicable statute.

This cross-sectional study examines the allocation of Medicare and Medicaid Disproportionate Share Hospital payments by race.

This cross-sectional study uses publicly available, nonpartisan data to evaluate trends in lobbying expenditures across health care industries.

This cross-sectional study examines trends in referrals for and timely delivery of primary and specialty health care among individuals incarcerated in California state prisons during the COVID-19 pandemic.

States have increasingly outsourced the provision of Medicaid services to private managed care plans. To ensure that plans maintain access to care, many states set network adequacy standards that require plans to contract with a minimum number of physicians. In this study we used data from the period 2015–17 for four states to assess the level of Medicaid participation among physicians listed in the provider network directories of each managed care plan. We found that about one-third of outpatient primary care and specialist physicians contracted with Medicaid managed care plans in our sample saw fewer than ten Medicaid beneficiaries in a year. Care was highly concentrated: 25 percent of primary care physicians provided 86 percent of the care, and 25 percent of specialists, on average, provided 75 percent of the care. Our findings suggest that current network adequacy standards might not reflect actual access; new methods are needed that account for beneficiaries’ preferences and physicians’ willingness to serve Medicaid patients.

Importance: The US Merit-based Incentive Payment System (MIPS) is a major Medicare value-based payment program aimed at improving quality and reducing costs. Little is known about how physicians’ performance varies by social risk of their patients.

Objective: To determine the relationship between patient social risk and physicians’ scores in the first year of MIPS.

Design, setting, and participants: Cross-sectional study of physicians participating in MIPS in 2017.

Exposures: Physicians in the highest quintile of proportion of dually eligible patients served; physicians in the 3 middle quintiles; and physicians in the lowest quintile.

Main outcomes and measures: The primary outcome was the 2017 composite MIPS score (range, 0-100; higher scores indicate better performance). Payment rates were adjusted -4% to 4% based on scores.

Results: The final sample included 284 544 physicians (76.1% men, 60.1% with ≥20 years in practice, 11.9% in rural location, 26.8% hospital-based, and 24.6% in primary care). The mean composite MIPS score was 73.3. Physicians in the highest risk quintile cared for 52.0% of dually eligible patients; those in the 3 middle risk quintiles, 21.8%; and those in the lowest risk quintile, 6.6%. After adjusting for medical complexity, the mean MIPS score for physicians in the highest risk quintile (64.7) was lower relative to scores for physicians in the middle 3 (75.4) and lowest (75.9) risk quintiles (difference for highest vs middle 3, -10.7 [95% CI, -11.0 to -10.4]; highest vs lowest, -11.2 [95% CI, -11.6 to -10.8]; P < .001). This relationship was found across specialties except psychiatry. Compared with physicians in the lowest risk quintile, physicians in the highest risk quintile were more likely to work in rural areas (12.7% vs 6.4%; difference, 6.3 percentage points [95% CI, 6.0 to 6.7]; P < .001) but less likely to care for more than 1000 Medicare beneficiaries (9.4% vs 17.8%; difference, -8.3 percentage points [95% CI, -8.7 to -8.0]; P < .001) or to have more than 20 years in practice (56.7% vs 70.6%; difference, -13.9 percentage points [95% CI, -14.4 to -13.3]; P < .001). For physicians in the highest risk quintile, several characteristics were associated with higher MIPS scores, including practicing in a larger group (mean score, 82.4 for more than 50 physicians vs 46.1 for 1-5 physicians; difference, 36.2 [95% CI, 35.3 to 37.2]; P < .001) and reporting through an alternative payment model (mean score, 79.5 for alternative payment model vs 59.9 for reporting as individual; difference, 19.7 [95% CI, 18.9 to 20.4]; P < .001).

Conclusions and relevance: In this cross-sectional analysis of physicians who participated in the first year of the Medicare MIPS program, physicians with the highest proportion of patients dually eligible for Medicare and Medicaid had significantly lower MIPS scores compared with other physicians. Further research is needed to understand the reasons underlying the differences in physician MIPS scores by levels of patient social risk.

OBJECTIVE: To assess the effect of the 2014 Medicaid expansion on Medicaid managed care plan quality.

DATA SOURCES: Three composite measures of plan-level quality constructed from the Health Care Effectiveness Data and Information Set.

STUDY SETTING: One hundred and sixty-three plans in 27 Medicaid expansion states and 100 plans in 14 nonexpansion states.

STUDY DESIGN: Quasi-experimental difference-in-differences (DID) analysis, comparing quality before (2011-13) and after (2014-15) Medicaid expansion in states that elected to expand Medicaid eligibility and those that did not.

PRINCIPAL FINDINGS: Mean plan enrollment increased from 130,533 to 274,259 in expansion states and from 105,449 to 148,194 in nonexpansion states. The proportion of enrollees receiving recommended preventive care increased from 62.6 to 65.2 percent in expansion states and from 59.3 to 62.5 percent in nonexpansion states (adjusted DID: -0.7 percentage points [95% CI -2.2, 0.7]). The proportion of enrollees receiving recommended chronic disease care management increased from 65.4 to 66.0 percent in expansion states and from 62.5 to 63.1 percent in nonexpansion states (adjusted DID: 1.1 percentage points [95% CI -0.5, 2.6]). We observed similar patterns for the receipt of recommended maternity care.

CONCLUSIONS: Medicaid expansion increased enrollment in managed care plans, but it did not result in erosion of quality.

Recent research has shown that concern about the apprehension and deportation of undocumented immigrants can affect how members of their households who are eligible for public benefits choose to participate in public programs. The extent to which this “chilling effect” broadly affects adults’ Medicaid enrollment nationally remains unclear, in part because of the difficulty of isolating undocumented immigrants in survey data. In this study we identified households that likely included undocumented immigrants and then examined whether gains in health care coverage due to the expansion of Medicaid eligibility under the Affordable Care Act (ACA) were dampened for eligible people living in households with mixed immigration status. We found no significant differences in coverage gains for people in mixed- relative to non-mixed-status households in expansion states. Coverage gains were significantly lower, however, for people in mixed-status households relative to those in non-mixed-status households in nonexpansion states. These findings suggest that household immigration status may have dampened the “woodwork effect,” whereby the ACA enhanced knowledge about program availability, in turn increasing Medicaid enrollment in nonexpansion states among people previously eligible for the program but not enrolled in it.

IMPORTANCE: State Medicaid programs have increasingly contracted with insurers to provide medical care services for enrollees (Medicaid managed care plans). Insurers that provide these plans can exit Medicaid programs each year, with unclear effects on quality of care and health care experiences.

OBJECTIVE: To determine the frequency and interstate variation of health plan exit from Medicaid managed care and evaluate the relationship between health plan exit and market-level quality.

DESIGN, SETTING, AND PARTICIPANTS: Retrospective cohort of all comprehensive Medicaid managed care plans (N = 390) during the interval 2006-2014.

EXPOSURES: Plan exit, defined as the withdrawal of a managed care plan from a state’s Medicaid program.

MAIN OUTCOMES AND MEASURES: Eight measures from the Healthcare Effectiveness Data and Information Set were used to construct 3 composite indicators of quality (preventive care, chronic disease care management, and maternity care). Four measures from the Consumer Assessment of Healthcare Providers and Systems were combined into a composite indicator of patient experience, reflecting the proportion of beneficiaries rating experiences as 8 or above on a 0-to-10-point scale. Outcome data were available for 248 plans (68% of plans operating prior to 2014, representing 78% of beneficiaries).

RESULTS: Of the 366 comprehensive Medicaid managed care plans operating prior to 2014, 106 exited Medicaid. These exiting plans enrolled 4 848 310 Medicaid beneficiaries, with a mean of 606 039 beneficiaries affected by plan exits annually. Six states had a mean of greater than 10% of Medicaid managed care recipients enrolled in plans that exited, whereas 10 states experienced no plan exits. Plans that exited from a state’s Medicaid market performed significantly worse prior to exiting than those that remained in terms of preventive care (57.5% vs 60.4%; difference, 2.9% [95% CI, 0.3% to 5.5%]), maternity care (69.7% vs 73.6%; difference, 3.8% [95% CI, 1.7% to 6.0%]), and patient experience (73.5% vs 74.8%; difference, 1.3% [95% CI, 0.6% to 1.9%]). There was no significant difference between exiting and nonexiting plans for the quality of chronic disease care management (76.2% vs 77.1%; difference, 1.0% [95% CI, -2.1% to 4.0%]). There was also no significant change in overall market performance before and after the exit of a plan: 0.7-percentage point improvement in preventive care quality (95% CI, -4.9 to 6.3); 0.2-percentage point improvement in chronic disease care management quality (95% CI, -5.8 to 6.2); 0.7-percentage point decrease in maternity care quality (95% CI, -6.4 to 5.0]); and a 0.6-percentage point improvement in patient experience ratings (95% CI, -3.9 to 5.1). Medicaid beneficiaries enrolled in exiting plans had access to coverage for a higher-quality plan, with 78% of plans in the same county having higher quality for preventive care, 71.1% for chronic disease management, 65.5% for maternity care, and 80.8% for patient experience.

CONCLUSIONS AND RELEVANCE: Between 2006 and 2014, health plan exit from the US Medicaid program was frequent. Plans that exited generally had lower quality ratings than those that remained, and the exits were not associated with significant overall changes in quality or patient experience in the plans in the Medicaid market.

US minority populations receive fewer effective health services than whites. Using Medicare administrative data for 2006-11, we found no consistent, corresponding protection against the receipt of ineffective health services. Compared with whites, blacks and Hispanics were often more likely to receive the low-value services studied.

To account for tobacco users’ excess health care costs and encourage cessation, the Affordable Care Act (ACA) allowed insurers to impose a surcharge on tobacco users’ premiums for plans offered on the health insurance exchanges, or Marketplaces. Low-income tax credits for Marketplace coverage were based on premiums for non-tobacco users, which means that these credits did not offset any surcharge costs. Thus, this policy greatly increased out-of-pocket premiums for many tobacco users. Using data for 2011-14 from the Behavioral Risk Factor Surveillance System, we examined the effect of tobacco surcharges on insurance status and smoking cessation in the first year of the exchanges’ implementation, among adults most likely to purchase insurance from them. Relative to smokers who faced no surcharges, smokers facing medium or high surcharges had significantly reduced coverage (reductions of 4.3 percentage points and 11.6 percentage points, respectively), but no significant differences in smoking cessation. In contrast, those facing low surcharges showed significantly less smoking cessation. Taken together, these findings suggest that tobacco surcharges conflicted with a major goal of the ACA-increased financial protection-without increasing smoking cessation. States should consider these potential effects when deciding whether to limit surcharges to less than the federal maximum.

BACKGROUND: Specialty societies in the United States identified low-value tests and procedures that contribute to waste and poor health care quality via implementation of the American Board of Internal Medicine Foundation’s Choosing Wisely initiative.

OBJECTIVE: To develop claims-based algorithms, to use them to estimate the prevalence of select Choosing Wisely services and to examine the demographic, health and health care system correlates of low-value care at a regional level.

DESIGN: Using Medicare data from 2006 to 2011, we created claims-based algorithms to measure the prevalence of 11 Choosing Wisely-identified low-value services and examined geographic variation across hospital referral regions (HRRs). We created a composite low-value care score for each HRR and used linear regression to identify regional characteristics associated with more intense use of low-value services.

PATIENTS: Fee-for-service Medicare beneficiaries over age 65.

MAIN MEASURES: Prevalence of selected Choosing Wisely low-value services.

KEY RESULTS: The national average annual prevalence of the selected Choosing Wisely low-value services ranged from 1.2% (upper urinary tract imaging in men with benign prostatic hyperplasia) to 46.5% (preoperative cardiac testing for low-risk, non-cardiac procedures). Prevalence across HRRs varied significantly. Regional characteristics associated with higher use of low-value services included greater overall per capita spending, a higher specialist to primary care ratio and higher proportion of minority beneficiaries.

CONCLUSIONS: Identifying and measuring low-value health services is a prerequisite for improving quality and eliminating waste. Our findings suggest that the delivery of wasteful and potentially harmful services may be a fruitful area for further research and policy intervention for HRRs with higher per-capita spending. These findings should inform action by physicians, health systems, policymakers, payers and consumer educators to improve the value of health care by targeting services and areas with greater use of potentially inappropriate care.

Using Medicare administrative data from 2006 to 2011, we estimated the proportion of low-risk Medicare beneficiaries receiving non-invasive cardiac screening tests without a clear, pertinent symptomatic indication, as well as the regional variation in and spending associated with these tests. For comparison and as a validation of our patient risk assignment, we measured cardiac testing in beneficiaries with or at high risk for cardiac disease.

OBJECTIVES: The accountable care organization (ACO) model is currently being pursued by private insurers, as well as federal and state governments. Little is known, however, about the prevalence of private payer ACO contracts and the characteristics of contract structures or how these compare with public ACO contracts.

STUDY DESIGN AND METHODS: Cross-sectional analysis of the National Survey of Accountable Care Organizations (n=173) on ACO contracts with public and private payers and private payer contract characteristics.

RESULTS: Most ACOs had only 1 ACO contract (57%). About half of ACOs had a contract with a private payer. The single most common private payer ACO contract was an upside-only shared savings model (41%), although the majority of private contracts included some form of downside risk (56%). A large majority of contracts made shared savings contingent upon quality performance (79%), and some included bonus payments for quality performance (39%). Most private payer contracts included upfront payments, such as care management payments (56%) or capital investment (17%). Organizations with private ACO contracts were larger and more advanced than ACOs with only public payer contracts.

CONCLUSIONS: While there are fewer ACOs with commercial contracts than public contracts, commercial contracts are more likely to include both downside risk and upfront payments.

OBJECTIVES: To explore the feasibility of using a distributed data model for ongoing reporting of local healthcare spending, specifically to investigate the contribution of utilization and pricing to geographic variation and trends in reimbursements for commercially insured beneficiaries younger than 65 years.

STUDY DESIGN: Retrospective descriptive analysis.

METHODS: Commercial claims were obtained for beneficiaries in 5 states for the years 2008 to 2010 using a distributed data model. Claims were aggregated to the hospital service area (HSA) level and healthcare utilization was quantified using a novel, National Quality Forum-endorsed measure that is independent of price and allows for the calculation of resource use across all services in standardized units. We examined trends in utilization, prices, and reimbursements over time. To examine geographic variation, we mapped resource use by HSA in the 3 states from which we had data from multiple insurers. We calculated the correlation between commercial and Medicare reimbursements and utilization. Medicare claims were obtained from the Dartmouth Atlas.

RESULTS: We found that much of the recent growth in reimbursements for the commercially insured from 2008 to 2010 was due to increases in prices, particularly for outpatient services. As in the Medicare population, resource use by this population varied by HSA. While overall resource use patterns in the commercially insured did not mirror those among Medicare beneficiaries, we observed a strong correlation in inpatient hospital use.

CONCLUSIONS: This research demonstrates the feasibility and value of public reporting of standardized area-level utilization and price data using a distributed data model to understand variation and trends in reimbursements.

INTRODUCTION: The American College of Rheumatology, through the Choosing Wisely initiative, identified measuring bone density more often than every 2 years as care “physicians and patients should question.” We measured the prevalence and described the geographic variation of short-interval (repeated in under 2 years) DXAs among Medicare beneficiaries and estimated the cost of this testing and its responsiveness to payment change.

METHODS: Using 100 % Medicare claims data, 2006-2011, we identified DXAs and short-interval DXAs for female Medicare beneficiaries over age 66. We determined the population rate of DXAs and short-interval DXAs, as well as Medicare spending on short-interval DXAs, nationally and by hospital referral region (HRR).

RESULTS: DXA use was stable 2008-2011 (12.4 to 11.5 DXAs per 100 women). DXA use varied across HRRs: in 2011, overall DXA use ranged from 6.3 to 23.0 per 100 women (coefficient of variation = 0.18), and short-interval DXAs ranged from 0.3 to 8.0 per 100 women (coefficient of variation = 0.64). Short-interval DXA use fluctuated substantially with payment changes; other DXAs did not. Short-interval DXAs, which represented 10.1 % of all DXAs, cost Medicare approximately US$16 million in 2011.

CONCLUSIONS: One out of ten DXAs was administered in a time frame shorter than recommended and at a substantial cost to Medicare. DXA use varied across regions. Short-interval DXA use was responsive to reimbursement changes, suggesting carefully designed policy and payment reform may reduce this care identified by rheumatologists as low value.

BACKGROUND: Many oncologists reduce chemotherapy doses in obese patients due to fear of excess toxic effect from very large weight-based calculations. While recent guidelines advise against this practice, quantitative summarization of the supporting evidence is not available.

MATERIALS AND METHODS: We systematically identified studies that compared toxic effect or survival outcomes between obese and normal-weight adults receiving chemotherapy dosed by actual body weight (ABW). We pooled odds ratios (OR) and 95% confidence intervals (CI) using random-effects models.

RESULTS: Of 5490 records screened, 12 studies representing 9314 relevant patients met inclusion criteria. The large majority of reported toxic effect and survival outcomes did not statistically differ between obese and normal-weight subjects. Exceptions included five studies in which one or more toxic effect or survival outcomes statistically favored obese patients, and one study that statistically favored normal-weight patients. Pooling usable data, rates of toxic effects were similar or lower in obese patients (grade 3/4 hematologic toxic effect: OR 0.73, CI 0.55-0.98, 4 studies; grade 3/4 nonhematologic toxic effect: OR 0.98, CI 0.76-1.26, 3 subgroups; any grade 3/4 toxic effect: OR 0.75, CI 0.65-0.87, three studies).

CONCLUSIONS: Obese patients receiving chemotherapy based on ABW experience similar or lower rates of toxic effects compared with normal-weight patients, and survival outcomes do not differ.

Mast cells have emerged as critical intermediaries in the regulation of peripheral tolerance. Their presence in many precancerous lesions and tumors is associated with a poor prognosis, suggesting mast cells may promote an immunosuppressive tumor microenvironment and impede the development of protective anti-tumor immunity. The studies presented herein investigate how mast cells influence tumor-specific T cell responses. Male MB49 tumor cells, expressing HY antigens, induce anti-tumor IFN-γ(+) T cell responses in female mice. However, normal female mice cannot control progressive MB49 tumor growth. In contrast, mast cell-deficient c-Kit(Wsh) (W(sh)) female mice controlled tumor growth and exhibited enhanced survival. The role of mast cells in curtailing the development of protective immunity was shown by increased mortality in mast cell-reconstituted W(sh) mice with tumors. Confirmation of enhanced immunity in female W(sh) mice was provided by (1) higher frequency of tumor-specific IFN-γ(+) CD8(+) T cells in tumor-draining lymph nodes compared with WT females and (2) significantly increased ratios of intratumoral CD4(+) and CD8(+) T effector cells relative to tumor cells in W(sh) mice compared to WT. These studies are the first to reveal that mast cells impair both regional adaptive immune responses and responses within the tumor microenvironment to diminish protective anti-tumor immunity.

The hypothesis that bystander inflammatory signals promote memory B cell (B(MEM)) self-renewal and differentiation in an antigen-independent manner is critically evaluated herein. To comprehensively address this hypothesis, a detailed analysis is presented examining the response profiles of B-2 lineage B220(+)IgG(+) B(MEM) toward cognate protein antigen in comparison to bystander inflammatory signals. After in vivo antigen encounter, quiescent B(MEM) clonally expand. Surprisingly, proliferating B(MEM) do not acquire germinal center (GC) B cell markers before generating daughter B(MEM) and differentiating into plasma cells or form structurally identifiable GCs. In striking contrast to cognate antigen, inflammatory stimuli, including Toll-like receptor agonists or bystander T cell activation, fail to induce even low levels of B(MEM) proliferation or differentiation in vivo. Under the extreme conditions of adjuvanted protein vaccination or acute viral infection, no detectable bystander proliferation or differentiation of B(MEM) occurred. The absence of a B(MEM) response to nonspecific inflammatory signals clearly shows that B(MEM) proliferation and differentiation is a process tightly controlled by the availability of cognate antigen.

The self-encoded ligands MICA (human) and Rae-1 (mouse) for the cytotoxic lymphocyte activating receptor NKG2D are highly expressed in carcinomas and inflammatory lesions and have been linked to immunosurveillance and graft rejection. However, whether NKG2D ligands have an intrinsic ability to acutely regulate tissue-associated immune compartments is not known. Here we show that epidermis-specific upregulation of Rae-1 induced rapid, coincident and reversible changes in the organization of tissue-resident V(gamma)5V(delta)1 TCRgammadelta+ intraepithelial T cells and Langerhans cells, swiftly followed by epithelial infiltration by unconventional alphabeta T cells. Whereas local V(gamma)5V(delta)1+ T cells limited carcinogenesis, Langerhans cells unexpectedly promoted it. These results provide unique insight into the early phases of tissue immunosurveillance and indicate that acute changes in NKG2D ligands may alone initiate a rapid, multifaceted immunosurveillance response in vivo.

State and federal policymakers have articulated concerns about the budgetary implications of expanding Medicaid eligibility, arguing that investments in Medicaid could induce “benefit lock,” whereby recipients of Medicaid become dependent on the program and other forms of public assistance. In this study, we examine the long-run effect of receiving Medicaid as a child on the receipt of means-tested public assistance in adulthood. We conducted a regression discontinuity analysis, leveraging quasi-random exposure to increased Medicaid eligibility in childhood by birthdate. We found that the increase in Medicaid eligibility in childhood was not associated with significant changes in receipt of medical, cash, or non-cash public assistance in later life. At most, subgroups exposed to the greatest increases in Medicaid eligibility exhibited later life decreases in non-cash assistance of 2 to 3 percentage points.

This Viewpoint discusses how revisiting the design and implementation of the disproportionate share hospital program represents a key policy lever for improving health equity in the US.

In-depth study of Medicaid, the program that insures 93 million people with low income, has been compromised by a lack of data. Only comprehensive, high-quality claims data can provide the granular information on Medicaid enrollees’ characteristics, use of health care, and spending that are needed to inform sound policy decisions.

In 2019, the Centers for Medicare and Medicaid Services (CMS) released the Transformed Medicaid Statistical Information System Analytic Files (TAF) in a bid to make Medicaid claims data from all states and territories available in a national repository. TAF provides researchers with higher-quality, more usable information about enrollees relative to previous datasets.

In 2022, we partnered with AcademyHealth to form the Medicaid Data Learning Network to provide a mechanism for TAF researchers to share best practices. That experience has identified three important opportunities for state and federal policymakers to improve TAF going forward.

The Medicaid program remains relatively understudied despite providing health care coverage to over 92 million people in the United States as of April 2023. This is due, in large part, to its federalist structure and the lack of a cohesive, national administrative claims data infrastructure. The Centers for Medicare and Medicaid Services (CMS) has made significant efforts to enhance the Medicaid Statistical Information System (MSIS), which compiles data from state Medicaid agencies to inform overall program improvements. In 2019, CMS released the latest generation of federal Medicaid claims data, the T-MSIS (Transformed-MSIS) Analytic Files (TAF), to replace the Medicaid Analytic eXtract (MAX).

Though the TAF data represent a significant improvement in quality and usability over MAX, they remain highly complex, with varying data quality, eligibility categories, and data elements across states. As researchers begin to work with these data, there is an important opportunity to share learnings and approaches to avoid duplicative efforts and to distill key methodological standards. This effort will help ensure Medicaid research using the TAF is high quality, relevant, and impactful. With support from the Commonwealth Fund and the Robert Wood Johnson Foundation, AcademyHealth’s Evidence-Informed State Health Policy Institute (ESHPI) established the Medicaid Data Learning Network (MDLN) to provide the opportunity to foster peer-shared learning among TAF users. Through a learning series curriculum, the MDLN provides a forum for TAF researchers to share what they have learned using the dataset and to develop consensus on best practices. These insights can then be disseminated to CMS, state Medicaid agencies, and the broader health services research community. The MDLN’s ultimate goal is to improve the quality of the TAF data over time, expand opportunities for health services researchers to use Medicaid claims data, and increase the number of researchers engaged in Medicaid-focused research.

Health equity is a priority for the Centers for Medicare and Medicaid and many state Medicaid programs. To advance this, CMS must invest in the data infrastructure needed to continuously evaluate health equity in Medicaid, including through improved race and ethnicity data and a national survey of Medicaid beneficiaries.

The Medicaid program currently enrolls over 86 million people, making it the single largest payer for health care services in the United States. Medicaid spending represents 16% of national health expenditures and, on average, 20% of state budgets. Medicaid covers 40% of births and 38% of children. It disproportionately enrolls individuals from racial and ethnic minority populations and other subgroups that have historically faced structural inequity in the United States, making it an important policy lever for addressing disparities in health care access, utilization, and health outcomes. Yet, health economics research on basic policy questions in Medicaid has largely lagged research in Medicare and commercial insurance markets.

Enrollment in the Medicaid program has swelled to nearly 83 million people since the onset of the COVID-19 pandemic. This growth - an increase of approximately 17% - has been attributed to new enrollment of those experiencing pandemic-induced job losses and the continuous coverage provisions of the federally declared Public Health Emergency (PHE), which prohibits state Medicaid agencies from terminating coverage for most beneficiaries. With the provisions set to expire, it is projected that millions of beneficiaries may lose their coverage as states resume regular redeterminations of eligibility. In the absence of proactive measures from states and the federal government, access to care may be compromised for a broad swath of the US population, potentially compounding inequities laid bare by the COVID-19 pandemic.

Coverage of the “routine costs” associated with clinical trial participation will soon be guaranteed for Medicaid beneficiaries for the first time, which could help reduce inequities that compromise both the scientific process and access to new therapeutics.

This Viewpoint reviews the revenue sources and financial liquidity of nonfederal US general hospitals to estimate the economic effects pandemic-related reductions in elective procedures and outpatient revenues might have, and argues for targeted government financial support for smaller, independent, and more rural institutions.

The Dartmouth Atlas of Health Care has long documented widespread variations in use of effective care. In its 2003 landmark study on the quality of health care in the United States, the RAND Corporation found that just over half of Americans received recommended preventive care, acute care, and care for chronic conditions.

Needed attention is now being paid to use of ineffective—or low-value—care, a form of overtreatment. While definitions vary, “low-value care” most often refers to medical services, including tests and procedures, that should not be performed given their potential for harm or the existence of comparably effective and often less expensive alternatives. Spending on overtreatment amounted to between $158 and $226 billion in 2011 and is estimated to be one of the biggest contributors to waste in the US health care system, second only to administrative complexity. Reducing the use of low-value care is particularly appealing given the opportunity it represents to both lower costs and increase quality. Before low-value care can be reduced, however, it must be identified and described.

More than 55 percent of the U.S. population now lives in a local area with an accountable care organization (ACO), in which a group of providers is held accountable by a payer for the total cost and quality of care for a defined set of patients. The spread of ACOs, however, by no means ensures their success.

Significant questions remain about whether the goals of the model—better care at lower costs—will be achieved.