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      Convergence in health care spending across counties in New York from 2007 through 2016

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      PLoS ONE
      Public Library of Science

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          Abstract

          Background

          One approach considered for reducing health care spending is to narrow the gap in spending between high- and low-spending areas. The goal would be to reduce spending in the high areas to similar levels achieved in areas that use health care more efficiently. This paper examined the degree to which high-spending areas remain high-spending and which types of service lead to convergence or divergence in spending in New York State.

          Methods

          This analysis utilized publicly available data on county-level spending trends for the Medicare fee-for-service population from 2007 to 2016. The study applied methods previously used to evaluate changes in the regional variation of health care spending nationally to county-level data within New York.

          Results

          The spread of health care spending converged slightly over the ten-year period analyzed. There was also evidence for regression to the mean-effects and changes in the relative rankings of spending across counties during this time. While there was strong evidence for convergence, many high-spending counties in 2007 remained high-spending in 2016. There were also differences in which services drove spending variation at the national level compared to within New York.

          Conclusions

          These findings point to counties with consistently high spending as a potential focus for health care cost-control efforts. Moreover, efforts to reduce unwarranted variation in spending may need to be tailored to the circumstances of particular regions as there are geographic differences in which services drive spending variation. Regression to the mean effects also have important implications for the specifications of alternative provider payment models, such as accountable care organizations, which promote convergence in spending by utilizing spending targets.

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          Most cited references16

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          Regional cohesion: evidence and theories of regional growth and convergence

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            Regional variations in diagnostic practices.

            Current methods of risk adjustment rely on diagnoses recorded in clinical and administrative records. Differences among providers in diagnostic practices could lead to bias. We used Medicare claims data from 1999 through 2006 to measure trends in diagnostic practices for Medicare beneficiaries. Regions were grouped into five quintiles according to the intensity of hospital and physician services that beneficiaries in the region received. We compared trends with respect to diagnoses, laboratory testing, imaging, and the assignment of Hierarchical Condition Categories (HCCs) among beneficiaries who moved to regions with a higher or lower intensity of practice. Beneficiaries within each quintile who moved during the study period to regions with a higher or lower intensity of practice had similar numbers of diagnoses and similar HCC risk scores (as derived from HCC coding algorithms) before their move. The number of diagnoses and the HCC measures increased as the cohort aged, but they increased to a greater extent among beneficiaries who moved to regions with a higher intensity of practice than among those who moved to regions with the same or lower intensity of practice. For example, among beneficiaries who lived initially in regions in the lowest quintile, there was a greater increase in the average number of diagnoses among those who moved to regions in a higher quintile than among those who moved to regions within the lowest quintile (increase of 100.8%; 95% confidence interval [CI], 89.6 to 112.1; vs. increase of 61.7%; 95% CI, 55.8 to 67.4). Moving to each higher quintile of intensity was associated with an additional 5.9% increase (95% CI, 5.2 to 6.7) in HCC scores, and results were similar with respect to laboratory testing and imaging. Substantial differences in diagnostic practices that are unlikely to be related to patient characteristics are observed across U.S. regions. The use of clinical or claims-based diagnoses in risk adjustment may introduce important biases in comparative-effectiveness studies, public reporting, and payment reforms. 2010 Massachusetts Medical Society
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              Prices don't drive regional Medicare spending variations.

              Per capita Medicare spending is more than twice as high in New York City and Miami than in places like Salem, Oregon. How much of these differences can be explained by Medicare's paying more to compensate for the higher cost of goods and services in such areas? To answer this question, we analyzed Medicare spending after adjusting for local price differences in 306 Hospital Referral Regions. The price-adjustment analysis resulted in less variation in what Medicare pays regionally, but not much. The findings suggest that utilization-not local price differences-drives Medicare regional payment variations, along with special payments for medical education and care for the poor.
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                Author and article information

                Contributors
                Role: ConceptualizationRole: Data curationRole: Formal analysisRole: Funding acquisitionRole: InvestigationRole: MethodologyRole: Project administrationRole: ResourcesRole: VisualizationRole: Writing – original draftRole: Writing – review & editing
                Role: Editor
                Journal
                PLoS One
                PLoS ONE
                plos
                plosone
                PLoS ONE
                Public Library of Science (San Francisco, CA USA )
                1932-6203
                24 April 2019
                2019
                : 14
                : 4
                : e0215850
                Affiliations
                [001]New York State Health Foundation, New York City, New York, United States of America
                Yokohama City University, JAPAN
                Author notes

                Competing Interests: The author has declared that no competing interests exist.

                Author information
                http://orcid.org/0000-0003-1905-4343
                Article
                PONE-D-18-34380
                10.1371/journal.pone.0215850
                6481841
                31017951
                5191182d-4ce6-4434-ad4e-b03489220a35
                © 2019 Mark A. Zezza

                This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

                History
                : 30 November 2018
                : 9 April 2019
                Page count
                Figures: 1, Tables: 7, Pages: 16
                Funding
                The author received no specific funding for this work.
                Categories
                Research Article
                Custom metadata
                All data are available on the Medicare Geographic Variation website from the “State/County Table – All Beneficiaries” File: https://www.cms.gov/Research-Statistics-Data-and-Systems/Statistics-Trends-and-Reports/Medicare-Geographic-Variation/GV_PUF.html.

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