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.

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.

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