BACKGROUND The Hospital Price Transparency Final Rule, effective January 1, 2021, requires hospitals to post online a machine-readable file that includes payer-specific negotiated commercial prices for all services. The regulation aims to improve the affordability of hospital care by promoting price competition. However, a low compliance level among hospitals would compromise the operational effectiveness of this regulation. Understanding hospitals’ compliance status to the regulation has important implications for its enforcement effort and effectiveness assessment. OBJECTIVE To analyze nationwide hospitals’ compliance status to the Hospital Price Transparency Rule. DESIGN Cross-sectional observational study. PARTICIPANTS A total of 3558 Medicare-certified general acute-care hospitals were examined. MAIN MEASURES A binary compliance rating was generated by using data collected by Turquoise Health. “Noncompliance” means that no machine-readable file was posted or the posted file contains no commercial negotiated prices. “Compliance” means that a machine-readable file was posted with commercial negotiated prices for at least one insurance plan. KEY RESULTS As of June 1, 2021, 55% of the 3558 Medicare-certified general acute-care hospitals we examined had not posted a machine-readable file containing commercial negotiated prices. Wide variations of compliance existed across states and hospital referral regions. A hospital’s compliance status is strongly associated with the average compliance status of peer hospitals in the same market. Hospitals with greater IT preparedness, for-profit hospitals, system-affiliated hospitals, large hospitals, and non-urban hospitals had greater compliance. More concentrated hospital markets had greater average compliance. CONCLUSIONS Hospitals take into consideration the behavior of their peers in the same market when making price disclosure decisions. Compliant hospitals are likely to have better IT preparedness, more financial resources and personnel expertise to mitigate the cost required for the implementation of the Price Transparency Rule. The compliance cost, therefore, might be a barrier for some hospitals. Supplementary Information The online version contains supplementary material available at 10.1007/s11606-021-07237-y.

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BackgroundAging and rural-urban disparities are two major social problems in today’s ever-developing China. Much of the existing literature has supported a negative association between adverse community setting with the cognitive functioning of seniors, but very few studies have empirically investigated the impact of rural-urban community settings on cognitive decline in the late life course of the population in developing countries.MethodsData of seniors aged 65 or above (n = 1709) within CHARLS (The China Health and Retirement Longitudinal Study, a sister study of HRS), a nationally representative longitudinal cohort (2011–2015) in China, were analyzed using a multilevel modeling (MLM) of time within individuals, and individual within communities. Cognitive impairment was assessed with an adapted Chinese version of Mini-Mental State Examination.ResultsUrban community setting showed a significant protective effect (β = − 1.978, p < .000) on cognitive impairment in simple linear regression, and the MLM results showed it also had a significant lower cognitive impairment baseline (β = − 2.278, p < .000). However, the curvature rate of cognitive decline was faster in urban community setting indicated by a positive interaction between the quadratic time term and urban community setting on cognitive impairment (β = 0.320, p < .05). A full model adjusting other individual SES factors was built after model fitness comparison, and the education factor accounted for most of the within and between community setting variance.ConclusionsThe findings suggest that urban community setting in one’s late-life course has a better initial cognitive status but a potentially faster decline rate in China, and this particular pattern of senior cognitive decline emphasize the importance of more specific preventive measures. Meanwhile, a more holistic perspective should be adopted while construct a risk factor model of community environment on cognitive function, and the influence at society level needs to be further explored in future research.

The tax-exempt status of nonprofit hospitals has received increased attention from policymakers interested in examining the value they provide instead of paying taxes. We use 2012 data from the Internal Revenue Service (IRS) Form 990, Centers for Medicare and Medicaid Services (CMS) Hospital Cost Reports, and American Hospital Association’s (AHA) Annual Survey to compare the value of community benefits with the tax exemption. We contrast nonprofit’s total community benefits to what for-profits provide and distinguish between charity and other community benefits. We find that the value of the tax exemption averages 5.9% of total expenses, while total community benefits average 7.6% of expenses, incremental nonprofit community benefits beyond those provided by for-profits average 5.7% of expenses, and incremental charity alone average 1.7% of expenses. The incremental community benefit exceeds the tax exemption for only 62% of nonprofits. Policymakers should be aware that the tax exemption is a rather blunt instrument, with many nonprofits benefiting greatly from it while providing relatively few community benefits.

IntroductionReducing inequalities in health care is one of the main challenges in all countries. In Iran as in other oil-exporting upper middle income countries, we expected to witness fewer inequalities especially in the health sector with the increase in governmental revenues.MethodsThis study presents an inequalities assessment of health care expenditures in Iran. We used data from the Household Income and Expenditure Survey (HIES) in Iran from 1984–2010. The analysis included 308,735 urban and 342,532 rural households.ResultsThe results suggest heightened inequality in health care expenditures in Iran over the past three decades, including an increase in the gap between urban and rural areas. Furthermore, inflation has affected the poor more than the rich. The Kakwani progressivity index in all years is positive, averaging 0.436 in rural and 0.470 in urban areas during the time period of analysis. Compared to inequality in income distribution over the last 30 years, health expenditures continuously show more inequality and progressivity over the same period of time.ConclusionsAccording to the result of our study, during this period Iran introduced four National Development Plans (NDPs); however, the NDPs failed to provide sustainable strategies for reducing inequalities in health care expenditures. Policies that protect vulnerable groups should be prioritized.

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Background: The Hospital Readmission Reduction Program (HRRP) disproportionately penalizes hospitals serving minority communities. The National Academy of Science, Engineering, and Medicine has recommended that the Centers for Medicare and Medicaid Services (CMS) consider adjusting for social risk factors in their risk adjustment methodology. This study examines the association between the racial and ethnic composition of a hospital market and the impact of other social risk factors on the probability of a hospital being penalized under the HRRP. Methods: This study analyzes data from CMS, the American Hospital Association (AHA) and the American Community Survey (ACS) for 3,168 hospitals from 2013 to 2017. We used logistic regression models to estimate the association between the penalty status under HRRP and the racial and ethnic composition of a hospital market, and explored whether this association was moderated by other social risk factors. Results: Our results indicate that the probability of being penalized increases with the percentage of Black and Asian residents in the hospital service area (HSA) and decreased with the percentage of Hispanic residents in the HSA. This association was reduced and became statistically insignificant when we controlled for other social risk factors. The strongest predictors of penalty status were the hospital’s share of Medicaid patients and the percent of persons without a high school diploma in the HSA. Conclusions: By incorporating relevant social risk factors in the reimbursement methodology, CMS could mitigate the negative effects of HRRP on hospitals serving minority communities.