The Latest on Geographic Variation in Medicare Spending: A Demographic Divide Persists But Variation Has Narrowed

Data and Methods

The primary data source for this analysis is the February 2015 update of the Medicare Geographic Variation Public Use File (GV PUF) from the Centers for Medicare & Medicaid Services (CMS).1 We analyzed data at the county level (the most-granular level available), focusing on 2007 and 2013, the earliest and latest years in the 2015 GV PUF. The GV PUF reports spending and beneficiary characteristics only among Medicare beneficiaries enrolled in both Parts A and B and in traditional Medicare (i.e., excluding beneficiaries enrolled in a private Medicare Advantage plan). To avoid mistakenly identifying counties as high- or low-spending based on a few individual outliers, our analysis included only the 736 counties with an average of 10,000 or more traditional Medicare beneficiaries from 2007 through 2013. The national averages discussed in our analysis include all counties, regardless of the number of beneficiaries in any given year.

We ranked the 736 counties in our analysis using three different spending measures. The first measure is unadjusted Medicare per capita spending in 2013, which enables us to show how counties rank on their actual Medicare per capita spending levels, including the effects of differences in prices and health risk. (In this context, ‘price’ refers to Medicare payment rates for services, which are set based on formulas that take into account differences in local wages and certain provider characteristics such as add-on payments for teaching hospitals.) The second measure is price and health-risk adjusted Medicare per capita spending in 2013, which enables us to show how counties rank when differences in prices and health risk are factored out. The third measure is the average annual rate of growth from 2007 to 2013 in unadjusted Medicare per capita spending.

For each of these three spending measures, we identified and compared the counties at the top and bottom of the rankings. For ease of display, we focus on comparisons of the 20 top and bottom counties. We tested whether our results were sensitive to the selection of 20 counties in each group by replicating our analysis for groups of 50 counties; the results were not appreciably different. Our analysis compares beneficiary-weighted averages across these groups of 20 counties for Medicare beneficiary characteristics, health care provider supply measures, and service spending and use, as well as the county-level poverty rate:

  • Beneficiary characteristics include percent black, percent Hispanic, percent eligible for both Medicare and Medicaid, and health risk scores (“hierarchical condition categories,” or HCCs) from the GV PUF; the share of traditional Medicare beneficiaries with five or more chronic conditions, based on our analysis of a five percent sample of Medicare claims from the 2013 CMS Chronic Conditions Data Warehouse (CCW); and the percent of county residents ages 65 and over living in poverty, based on our analysis of American Community Survey (ACS) 2008-2012 five-year pooled data from the 2013-2014 Area Health Resources File (AHRF); and the percent of beneficiaries living in metropolitan areas, based on our analysis of the AHRF.
  • Measures of health care provider supply are reported per 10,000 county residents and include the number of physicians, primary care physicians as a percent of all physicians, hospital beds, skilled nursing facility beds, home health agencies, ambulatory surgical centers, and hospices. Supply measures are from our analysis of the AHRF for various years.
  • Measures of spending and utilization from the GV PUF include the share of beneficiaries using specific Medicare-covered services, spending per capita and per user, and event counts (days, visits, procedures) per 1,000 beneficiaries.
  • County-level poverty from the AHRF is the poverty rate among county residents of all ages.

To examine whether geographic variation in county-level Medicare per capita spending increased or decreased between 2007 and 2013, we calculated the “coefficient of variation” (COV) in county-level spending. This measure is useful for measuring trends in spending variation, while adjusting for inflation.

Our analyses use the entire population of U.S. counties and the entire population of Medicare fee-for-service beneficiaries within those counties, rather than a random sample. Although conventional tests of statistical significance are not necessary in this situation, we present results from statistical significance testing for the comparisons of county-level averages in the appendix tables. For more details on the data and methods used in this analysis, see Appendix 1: Data and Methods.

Introduction Findings

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