35 research outputs found

    Variation in Payment Rates under Medicare’s Inpatient Prospective Payment System

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    Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/136420/1/hesr12490.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/136420/2/hesr12490-sup-0001-AuthorMatrix.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/136420/3/hesr12490_am.pd

    What are the Financial Implications of Public Quality Disclosure? Evidence from New York City’s Restaurant Food Safety Grading Policy

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    Grading schemes are an increasingly common method of quality disclosure for public services. Restaurant grading makes information about food safety practices more readily available and may reduce the prevalence of foodborne illnesses. However, it may also have meaningful financial repercussions. Using fine-grained administrative data that tracks food safety compliance and sales activity for the universe of graded restaurants in New York City and its bordering counties, we assess the aggregate financial effects from restaurant grading. Results indicate that the grading policy, after an initial period of adjustment, improves restaurants’ food safety compliance and reduces fines. While the average effect on revenues for graded restaurants across the municipality is null, the graded restaurants located geographically closer to an ungraded regime experience slower growth in revenues. There is also evidence of revenue convergence across graded and ungraded restaurants in the long-term

    Narrow Model: The Authors Respond

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    Improving The Management Of Care For High-Cost Medicaid Patients

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    Case finding for patients at risk of readmission to hospital: development of algorithm to identify high risk patients

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    Objective To develop a method of identifying patients at high risk of readmission to hospital in the next 12 months for practical use by primary care trusts and general practices in the NHS in England. Data sources Data from hospital episode statistics showing all admissions in NHS trusts in England over five years, 1999-2000 to 2003-4; data from the 2001 census for England. Population All residents in England admitted to hospital in the previous four years with a subset of “reference” conditions for which improved management may help to prevent future admissions. Design Multivariate statistical analysis of routinely collected data to develop an algorithm to predict patients at highest risk of readmission in the next 12 months. The algorithm was developed by using a 10% sample of hospital episode statistics data for all of England for the period indicated. The coefficients for 21 most powerful (and statistically significant) variables were then applied against a second 10% test sample to validate the findings of the algorithm from the first sample. Results The key factors predicting subsequent admission included age, sex, ethnicity, number of previous admissions, and clinical condition. The algorithm produces a risk score (from 0 to 100) for each patient admitted with a reference condition. At a risk score threshold of 50, the algorithm identified 54.3% of patients admitted with a reference condition who would have an admission in the next 12 months; 34.7% of patients were “flagged” incorrectly (they would not have a subsequent admission). At risk score threshold levels of 70 and 80, the rate of incorrectly “flagged” patients dropped to 22.6% and 15.7%, but the algorithm found a lower percentage of patients who would be readmitted. The algorithm is made freely available to primary care trusts via a website. Conclusions A method of predicting individual patients at highest risk of readmission to hospital in the next 12 months has been developed, which has a reasonable level of sensitivity and specificity. Using various assumptions a “business case” has been modelled to demonstrate to primary care trusts and practices the potential costs and impact of an intervention using the algorithm to reduce hospital admissions

    Temporal trends in motor vehicle fatalities in the United States, 1968 to 2010 - a joinpoint regression analysis.

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    BackgroundIn the past 40 years, a variety of factors might have impacted motor vehicle (MV) fatality trends in the US, including public health policies, engineering innovations, trauma care improvements, etc. These factors varied in their timing across states/localities, and many were targeted at particular population subgroups. In order to identify and quantify differential rates of change over time and differences in trend patterns between population subgroups, this study employed a novel analytic method to assess temporal trends in MV fatalities between 1968 and 2010, by age group and sex.MethodsCause-specific MV fatality data from traffic injuries between 1968 and 2010, based on death certificates filed in the 50 states, and DC were obtained from Centers for Disease Control and Prevention Wide-ranging Online Data for Epidemiologic Research (CDC WONDER). Long-term (1968 to 2010) and short-term (log-linear piecewise segments) trends in fatality rates were compared for males and females overall and in four separate age groups using joinpoint regression.ResultsMV fatalities declined on average by 2.4% per year in males and 2.2% per year in females between 1968 and 2010, with significant declines observed in all age groups and in both sexes. In males overall and those 25 to 64 years, sharp declines between 1968 and mid-to-late 1990s were followed by a stalling until the mid-2000s, but rates in females experienced a long-term steady decline of a lesser magnitude than males during this time. Trends in those aged <1 to 14 years and 15 to 24 years were mostly steady over time, but males had a larger decline than females in the latter age group between 1968 and the mid-2000s. In ages 65+, short-term trends were similar between sexes.ConclusionsDespite significant long-term declines in MV fatalities, the application of Joinpoint Regression found that progress in young adult and middle-aged adult males stalled in recent decades and rates in males declined relatively more than in females in certain age groups. Future research is needed to establish the causes of these observed trends, including the potential role of contemporaneous MV-related policies and their repeal. Such research is needed in order to better inform the design and evaluation of future population interventions addressing MV fatalities nationally
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