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Objective: To determine whether health systems in the United States modify treatment or discharge decisions for otherwise similar patients based on health insurance coverage., Design: Regression discontinuity approach., Setting: American College of Surgeons' National Trauma Data Bank, 2007-17., Participants: Adults aged between 50 and 79 years with a total of 1 586 577 trauma encounters at level I and level II trauma centers in the US., Interventions: Eligibility for Medicare at age 65 years., Main Outcome Measures: The main outcome measure was change in health insurance coverage, complications, in-hospital mortality, processes of care in the trauma bay, treatment patterns during hospital admission, and discharge locations at age 65 years., Results: 1 586 577 trauma encounters were included. At age 65, a discontinuous increase of 9.6 percentage points (95% confidence interval 9.1 to 10.1) was observed in the share of patients with health insurance coverage through Medicare at age 65 years. Entry to Medicare at age 65 was also associated with a decrease in length of hospital stay for each encounter, of 0.33 days (95% confidence interval -0.42 to -0.24 days), or nearly 5%), which coincided with an increase in discharges to nursing homes (1.56 percentage points, 95% confidence interval 0.94 to 2.16 percentage points) and transfers to other inpatient facilities (0.57 percentage points, 0.33 to 0.80 percentage points), and a large decrease in discharges to home (1.99 percentage points, -2.73 to -1.27 percentage points). Relatively small (or no) changes were observed in treatment patterns during the patients' hospital admission, including no changes in potentially life saving treatments (eg, blood transfusions) or mortality., Conclusions: The findings suggest that differences in treatment for otherwise similar patients with trauma with different forms of insurance coverage arose during the discharge planning process, with little evidence that health systems modified treatment decisions based on patients' coverage., Competing Interests: Competing interests: All authors have completed the ICMJE uniform disclosure form at https://icmje.org/disclosure-of-interest/ and declare: no support from any organization for the submitted work; support from the Agency for Healthcare Research and Quality for JWS as principal investigator (grant K08-HS028672) and as a co-investigator (grant R01-HS027788). JWS also receives salary support from Blue Cross Blue Shield of Michigan through the collaborative quality initiative known as Michigan Social Health Interventions to Eliminate Disparities (MSHIELD), outside the submitted work; no financial relationships with any organizations that might have an interest in the submitted work in the previous three years; no other relationships or activities that could appear to have influenced the submitted work., (© Author(s) (or their employer(s)) 2019. Re-use permitted under CC BY-NC. No commercial re-use. See rights and permissions. Published by BMJ.)

Importance: Little is known about small-area variations in health care spending and utilization across the 3 major funders of health care in the US: Medicare, Medicaid, and private insurers., Objective: To measure regional health spending and utilization across Medicare, Medicaid, and the privately insured; to observe whether there are regions that are simultaneously low spending for all 3 payers; and to determine what factors are correlated with regional spending and utilization by payer., Design, Setting, and Participants: Observational cross-sectional analysis of the US health system in 2016 and 2017 for 241 of 306 hospital referral regions (HRRs) and 2 states. Participants include individuals with employer-sponsored coverage from Aetna, Humana, or UnitedHealth; individuals with Medicaid fee-for-service coverage in 2016 and 2017; and individuals with Medicare coverage. The analysis was carried out from January 2020 to May 2022., Main Outcomes and Measures: Spending per beneficiary and inpatient days per beneficiary by payer and overall., Results: The data include 25 381 167 individuals with employer-sponsored coverage, 69 891 299 with Medicaid coverage in 2016 and 2017, and 26 711 426 individuals with Medicare fee-for-service coverage. The percentage of enrollees who identified as female was 54.1% in the Medicaid program, 56.2% in the Medicare program, and 50.4% in private insurance. The mean (SD) age was 26.9 (21.8) years for Medicaid and 75.0 (7.9) years for Medicare enrollees; for private insurance enrollees, just age brackets were reported: 18 to 24 years (15.9%), 25 to 34 years (24.2%), 35 to 44 years (21.3%), 45 to 54 years (20.8%), and 55 to 64 years (17.8%). In 2017, the mean (SD) HRR-level spending per beneficiary was $4441 ($710) for private insurance, $10 281 ($1294) for Medicare, and $6127 ($1428) for Medicaid. Across HRRs, the correlation coefficients and 95% CIs were 0.020 (-0.106 to 0.146; P = .76) for private insurance and Medicare spending, 0.213 (0.090 to 0.330; P < .001) for private insurance and Medicaid, and 0.162 (0.037 to 0.282; P < .01) for Medicare and Medicaid. Just 3 HRRs (Boulder, Colorado; Bloomington, Illinois; and Olympia, Washington) were in the lowest spending quintile for all 3 insurance programs; 4 HRRs were in the highest (The Bronx, New York; Manhattan, New York; White Plains, New York; and Dallas, Texas). By contrast, the correlation coefficients and 95% CIs for utilization, measured in hospital days, were 0.465 (0.361 to 0.559; P < .001) for private insurance and Medicare, 0.527 (0.429 to 0.612; P < .001) for private insurance and Medicaid, and 0.278 (0.157 to 0.390; P < .001) for Medicare and Medicaid., Conclusions and Relevance: These findings suggest that payer-specific factors are correlated with health spending variation among Medicare beneficiaries, Medicaid beneficiaries, and the commercially insured and that payer-specific policies will be necessary to improve efficiency in the US health sector.

This cross-sectional regression discontinuity analysis compares deaths slightly younger and older than 65 years to examine the relationship between access to health insurance coverage and COVID-19 mortality., Competing Interests: Conflict of Interest Disclosures: None reported., (Copyright 2021 Wallace J et al. JAMA Health Forum.)

The Affordable Care Act (ACA) expanded Medicaid to millions of low-income near-elderly Americans, facilitating access to health care services, but did not change income eligibility for Medicaid for those 65 years and older. Therefore, following the ACA's coverage expansion, many newly-insured older enrollees will face a complex insurance transition on their 65th birthday: they will lose Medicaid coverage and transition from Medicaid to Medicare as their primary insurer. This transition in primary health insurance coverage includes changes to benefits, patient cost-sharing, and provider reimbursement, which could have profound consequences on the use of health services and associated health outcomes for low-income seniors. Using data from 2012, we estimate that 1.6 million current Medicaid beneficiaries and an additional 1.6 to 2.9 million low-income individuals who will gain Medicaid coverage under the ACA will be likely to make this transition in the next decade. Primary care physicians and policymakers can help mitigate the potential consequences of this insurance transition by preparing patients for Medicare's more restrictive insurance coverage, encouraging patients to sign up for available low-income subsidies, and understanding how the loss of Medicaid coverage impacts out-of-pocket costs.

Importance Several recent policy proposals have sought to expand the role of Medicaid in providing health insurance for low-income Americans, but there is little recent information on how physician participation in Medicaid compares with alternative forms of coverage for low-income Americans. Objective To compare the number of office-based physicians included in Medicaid managed care and health insurance exchange plans that operate in the same geographic markets. Design, Setting, and Participants This cross-sectional study used administrative data from physician network directories and survey data from office-based physicians for Kansas, Nebraska, New York, Tennessee, and Washington. The number of participants totaled 67 057 office-based physicians in the 5 sample states. Data were collected and analyzed from May 2018 to June 2019. Exposures Physician participation in a Medicaid managed care or health insurance exchange plan network. Main Outcomes and Measures The percentage of office-based physicians in a county who indicated during a phone survey that they participated in Medicaid; the percentage of office-based physicians in a county who participated in each Medicaid managed care and health insurance exchange plan network; and the percentage of office-based physicians in a county who participated in at least one Medicaid managed care plan or, separately, at least one health insurance exchange plan. Results Of the 67 057 office-based physicians in our sample, 49 983 reported in a telephone survey that they accepted Medicaid. This survey-based measure undercounted the percentage of physicians participating in Medicaid by 5.2% (95% CI, 2.3%-8.1%; P < .001) relative to a measure based on physician network directories. Medicaid managed care physician networks covered a mean (SD) of 63.4% (20.5%) of office-based physicians compared with health insurance exchange physician networks, which covered 51.0% (25.2%). In adjusted analyses, Medicaid managed care plans covered 6.2% (95% CI, 3.2%-9.3%, P < .001) more office-based physicians than health insurance exchange plans operating in the same counties. In the states where the same insurers participated in both markets (New York, Tennessee, Washington), the Medicaid managed care physician networks were 6.5% (95% CI, 3.2%-9.8%, P < .001) larger than the health insurance exchange networks offered by the same insurer. Conclusions and Relevance In this cross-sectional study of physician network data, Medicaid managed care physician networks included more office-based physicians than the physician networks of health insurance exchange plans operating in the same geographic markets. This suggests that Medicaid remains a viable option for expanding coverage in the United States. [ABSTRACT FROM AUTHOR]

Objective: Privately insured individuals frequently use out-of-network psychiatrists. Yet, whether treatment provided by psychiatrists who do not accept private insurance differs from treatment provided by those who do has not been studied. The investigators described provider characteristics, patient characteristics, and treatment patterns among psychiatrists who do not accept new patients with private insurance.Methods: Data for this study came from the National Ambulatory Medical Care Survey (2011-2014), a nationally representative annual cross-sectional survey of physicians providing ambulatory care. Responses of psychiatrists who report accepting any new patients (N=440) were examined, representing 7,634 visits.Results: Compared with psychiatrists accepting privately insured patients, those not accepting privately insured patients had fewer visits with patients with serious mental illness (42% versus 53%; p=0.016). These psychiatrists had a higher proportion of visits lasting longer than 30 minutes (48% versus 34%; p=0.026), and their patients were more likely to have had 10 or more visits in the past 12 months (41% versus 28%; p=0.013). There were no differences in the proportion of visits in which treatment included psychotherapy (48% versus 44%).Conclusions: Although psychiatrists not accepting patients with private insurance were less likely than other psychiatrists to treat patients with serious mental illness, their patients were more likely to have longer visits and a relatively high number of visits in the past year. The low rate of acceptance of insurance among psychiatrists may have the greatest effect among those most in need of services. [ABSTRACT FROM AUTHOR]

The Affordable Care Act (ACA) expanded Medicaid to millions of low-income near-elderly Americans, facilitating access to health care services, but did not change income eligibility for Medicaid for those 65 years and older. Therefore, following the ACA's coverage expansion, many newly-insured older enrollees will face a complex insurance transition on their 65th birthday: they will lose Medicaid coverage and transition from Medicaid to Medicare as their primary insurer. This transition in primary health insurance coverage includes changes to benefits, patient cost-sharing, and provider reimbursement, which could have profound consequences on the use of health services and associated health outcomes for low-income seniors. Using data from 2012, we estimate that 1.6 million current Medicaid beneficiaries and an additional 1.6 to 2.9 million low-income individuals who will gain Medicaid coverage under the ACA will be likely to make this transition in the next decade. Primary care physicians and policymakers can help mitigate the potential consequences of this insurance transition by preparing patients for Medicare's more restrictive insurance coverage, encouraging patients to sign up for available low-income subsidies, and understanding how the loss of Medicaid coverage impacts out-of-pocket costs. [ABSTRACT FROM AUTHOR]

The objective of risk adjustment is not to predict spending accurately but to support the social goals of a payment system, which include equity. Setting population-based payments at accurate predictions risks entrenching spending levels that are insufficient to mitigate the impact of social determinants on health care use and effectiveness. Instead, to advance equity, payments must be set above current levels of spending for historically disadvantaged groups. In analyses intended to guide such reallocations, we found that current risk adjustment for the community-dwelling Medicare population overpredicts annual spending for Black and Hispanic beneficiaries by $376-$1,264. The risk-adjusted spending for these populations is lower than spending for White beneficiaries despite the former populations' worse risk-adjusted health and functional status. Thus, continued movement from fee-for-service to population-based payment models that omit race and ethnicity from risk adjustment (as current models do) should result in sizable resource reallocations and incentives that support efforts to address racial and ethnic disparities in care. We found smaller overpredictions for less-educated beneficiaries and communities with higher proportions of residents who are Black, Hispanic, or less educated, suggesting that additional payment adjustments that depart from predictive accuracy are needed to support health equity. These findings also suggest that adding social risk factors as predictors to spending models used for risk adjustment may be counterproductive or accomplish little. [ABSTRACT FROM AUTHOR]