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The American Journal of Managed Care May 2015
Comparison of Provider and Plan-Based Targeting Strategies for Disease Management
Ann M. Annis, MPH, RN; Jodi Summers Holtrop, PhD, MCHES; Min Tao, PhD; Hsiu-Ching Chang, PhD; and Zhehui Luo, PhD
Making Measurement Meaningful
Christine K. Cassel, MD, President and CEO, National Quality Forum
Care Fragmentation, Quality, and Costs Among Chronically Ill Patients
Brigham R. Frandsen, PhD; Karen E. Joynt, MD, MPH; James B. Rebitzer, PhD; and Ashish K. Jha, MD, MPH
Results From a National Survey on Chronic Care Management by Health Plans
Soeren Mattke, MD, DSc; Aparna Higgins, MA; and Robert Brook, MD, ScD
Association Between the Patient-Centered Medical Home and Healthcare Utilization
Rainu Kaushal, MD, MPH; Alison Edwards, MStat; and Lisa M. Kern, MD, MPH
Transforming Oncology Care: Payment and Delivery Reform for Person-Centered Care
Kavita Patel, MD, MS; Andrea Thoumi, MSc; Jeffrey Nadel, BA; John O'Shea, MD, MPA; and Mark McClellan, MD, PhD
True "Meaningful Use": Technology Meets Both Patient and Provider Needs
Heather Black, PhD; Rodalyn Gonzalez, BA; Chantel Priolo, MPH; Marilyn M. Schapira, MD, MPH; Seema S. Sonnad, PhD; C. William Hanson III, MD; Curtis P. Langlotz, MD, PhD; John T. Howell, MD; and Andrea J. Apter, MD, MSc
Innovative Care Models for High-Cost Medicare Beneficiaries: Delivery System and Payment Reform to Accelerate Adoption
Karen Davis, PhD, APN; Christine Buttorff, PhD; Bruce Leff, MD; Quincy M. Samus, PhD; Sarah Szanton, PhD, APN; Jennifer L. Wolff, PhD; and Farhan Bandeali, MSPH
Annual Diabetic Eye Examinations in a Managed Care Medicaid Population
Elham Hatef, MD, MPH; Bruce G. Vanderver, MD, MPH; Peter Fagan, PhD; Michael Albert, MD; and Miriam Alexander, MD, MPH
Systematic Review of Benefit Designs With Differential Cost Sharing for Prescription Drugs
Oluwatobi Awele Ogbechie, MD, MBA; and John Hsu, MD, MBA, MSCE
Changing Trends in Type 2 Diabetes Mellitus Treatment Intensification, 2002-2010
Rozalina G. McCoy, MD; Yuanhui Zhang, PhD; Jeph Herrin, PhD; Brian T. Denton, PhD; Jennifer E. Mason, PhD; Victor M. Montori, MD; Steven A. Smith, MD; Nilay D. Shah, PhD
Medicaid-Insured and Uninsured Were More Likely to Have Diabetes Emergency/Urgent Admissions
Monica A. Fisher, PhD, DDS, MPH, MS; and Zhen-qiang Ma, MD, MPH, MS
Currently Reading
Roles of Prices, Poverty, and Health in Medicare and Private Spending in Texas
Chapin White, PhD; Suthira Taychakhoonavudh, PhD; Rohan Parikh, MS; and Luisa Franzini, PhD

Roles of Prices, Poverty, and Health in Medicare and Private Spending in Texas

Chapin White, PhD; Suthira Taychakhoonavudh, PhD; Rohan Parikh, MS; and Luisa Franzini, PhD
Variation in private spending reflects the ability of the local population to pay for healthcare, whereas variation in Medicare is more driven by health status.
Objectives:  To investigate the roles of prices, poverty, and health in divergences between Medicare and private spending in Texas.
Study Design: Retrospective observational design using 2011 Blue Cross Blue Shield of Texas claims data and publicly available Medicare data.
Methods: We measured market-level spending per enrollee among the privately insured. Variation in Medicare and private spending per person are decomposed into prices and quantities, and their associations with poverty are measured. Markets are divided into 4 groups and are compared based on the ratio of Medicare to private spending: “high-private,” “proportional,” “high-Medicare,” and “extremely high-Medicare.”
Results: Among the privately insured, poverty appears to have large spillover effects; it is strongly associated with lower prices, quantities, and spending. Among Medicare beneficiaries, health status is a key driver of spending variation. The 2 markets with extremely high Medicare-to-private spending ratios (Harlingen and McAllen) are predominantly Hispanic communities with markedly higher rates of poverty and lack of insurance and also extremely low physician supply. The markets with relatively high private spending stand out for having good health-system performance and health outcomes, and higher than average hospital prices.
Conclusions: Variation in private spending appears to reflect the ability of the local population to pay for healthcare, whereas variation in Medicare is more heavily driven by health status, and presumably, by clinical need. These findings highlight the inadvisability of using Medicare spending as a proxy for systemwide spending, and the need for comprehensive market-level spending data that allow comparisons among populations with different sources of insurance coverage.
Am J Manag Care. 2015;21(5):e303-e311
Take-Away Points

In general, our approach begins with the premise that Medicare and private spending patterns diverge, and sets out to explore factors that might account for that divergence.
  • Markets with relatively high Medicare-to-private spending ratios (Harlingen and McAllen, Texas) have markedly higher rates of poverty and uninsurance and extremely low physician supply.
  • The markets with relatively high private spending stand out for having good health-system performance and health outcomes, and higher than average hospital prices.
  • In the privately insured population, there are inhibitory spillover effects of regional poverty on health spending.
  • Health status explains a larger portion of the Medicare spending variation.
Researchers have studied geographic variation in healthcare spending and practice patterns in the United States for at least 4 decades.1-3 The centerpiece of this work is the Dartmouth Atlas, a collection of studies based mainly on analyses of claims data from the US Medicare fee-for-service program.4 The Dartmouth researchers’ findings led to the claim that waste accounts for 30% of health spending in the United States.5 Furthermore, Fisher and colleagues examined the relationship between spending, quality, and outcomes, and reported that “residence in higher-spending regions does not cause improved quality, access to care, or survival (and may result in worse survival).”6,7

Medicare claims data lends well to geographic variation analyses, as the program covers individuals nationwide, and claims data are stored in a format that is uniform and relatively easy to work with. However, newer sources of claims data are beginning to be used that include other populations and allow for broader analyses of geographic variation. Most notably, the Institute of Medicine (IOM) sponsored a very ambitious collection of studies that examine geographic variation in healthcare prices, spending, and quality among both Medicare enrollees and the privately insured.8

From the existing studies of private claims data, it is clear that Medicare and private spending do not follow the same patterns. Chernew and colleagues, for example, reported that Hospital Referral Region (HRR)-level Medicare spending per beneficiary is negatively correlated with spending per enrollee among the privately insured.9 More recently, Newhouse and Garber, in a summary of the findings from the IOM Committee, noted that “There is almost no correlation between Medicare spending and commercial spending in an area.”10

The goals of our study are two-fold: the first goal is to describe the relationship between HRR-level Medicare spending per enrollee and private spending per enrollee. The second goal is to identify the differences between Medicare and private spending in the patterns of variation and, more specifically, the roles played by prices, quantities, poverty, and health status. In general, our approach begins with the premise that Medicare and private spending patterns will diverge, and sets out to explore factors that might account for that divergence.


Our general approach is: 1) to measure spending per enrollee for each market in Texas among Medicare beneficiaries and the privately insured; 2) to decompose Medicare and private spending variation into prices and quantities, and examine their associations with market-level poverty rates; and 3) to compare demographic and health-system characteristics of markets marked by relatively high Medicare spending with those marked by relatively high private spending. Individuals enrolled in employer-sponsored private coverage are rarely in poverty. Based on the Medical Expenditure Panel Survey, among the nonelderly privately insured population, only approximately 5% were in poverty in 2011 (8.8 million out of 179.5 million). Therefore, any associations between market-level poverty rates and private health spending presumably reflect community-level spillover effects. Such a spillover can occur if high concentrations of poverty or lack of insurance reduce what Pauly and Pagan (2012) refer to as “community aggregate demand” for healthcare.11 Because of such spillovers, privately insured individuals in high-poverty markets may end up using less care, or care of a lower quality, even though they themselves have the financial resources to access better care. The economic circumstances of Medicare beneficiaries vary much more widely than those of the privately insured. Therefore, any associations between market-level poverty and Medicare spending likely reflect a mix of direct effects (ie, the effects of poverty among Medicare beneficiaries) and community-level spillover effects, similar to those in the private sector.

This research was approved by a University of Texas Health Sciences Center, Houston, Institutional Review Board. This study used the enrollment and claims files from Blue Cross Blue Shield of Texas (BCBSTX) to measure spending per enrollee in 2011 for each of the 22 HRRs in Texas. HRRs are collections of zip codes that are defined by the Dartmouth Atlas and that represent markets for tertiary hospital care.12 We included all nonelderly enrollees in BCBSTX preferred provider organization (PPO) products, which account for 90% of BCBSTX enrollees. Our final population consisted of 3,484,693 BCBSTX members with valid zip codes, residing in Texas, aged 0 to 64 years, and enrolled in a PPO plan during 2011. For Medicare, we used publicly available 2011 HRR-level spending data produced by CMS13; these are for Medicare fee-for-service beneficiaries 65 years and older, and we included all Medicare spending except for part D drugs.

For each HRR, we calculated 2 spending indexes: one for BCBSTX and one for Medicare. Spending in the BCBSTX data includes all facility and professional claims, and represents the allowed amount, which includes any deductibles, co-pays, and coinsurance paid out of pocket by the patient, as well as payments made by BCBSTX to providers. Spending for Medicare represents only the amount paid by Medicare, and the spending indexes are separated into 3 components: an input prices index, an adjusted price index (adjusted for input prices), and a quantity index. Equal spending per person is divided by the Texas average ($3336 for BCBSTX and $10,165 for Medicare). The input prices index reflects the prices of labor, space, etc, relative to the Texas average. The adjusted price index reflects differences in the prices paid to medical providers above and beyond differences in input prices. The quantity index reflects differences in the number of services used per person. By definition, the overall spending index in each HRR equals the product of that HRR’s input prices index, its adjusted price index, and its quantity index.

The input prices index was calculated for each HRR using Medicare’s hospital wage index for facility claims, and Medicare’s geographic practice cost index for professional claims. To calculate the BCBSTX price index, we used indirect standardization, which involved using all the BCBSTX data to first calculate an average price for each specific type of service within each service category: hospital inpatient, hospital outpatient, and professional services. For this calculation of average prices, facility inpatient services were categorized based on diagnosis-related groups for each admission, while facility outpatient services were categorized based on Current Procedural Terminology (CPT) or Healthcare Common Procedure Coding System (HCPCS) codes and revenue codes. Professional claims were also categorized using the CPT or HCPCS codes. The price index for each service category was computed by comparing the actual allowed amount with the hypothetical allowed amount calculated using the Texas average price for each specific type of service. An overall price index was then calculated for each HRR by summing actual and hypothetical allowed amounts across all service categories and calculating the ratio of actual total spending divided by hypothetical total spending.14 Each HRR’s BCBSTX adjusted price index equals its price index divided by the input prices index. The BCBSTX quantity index is defined as the spending index divided by the price index.

To calculate Medicare price indices, we divided Medicare actual costs by Medicare standardized costs—both of which are reported in the publicly available Medicare data.14 CMS calculates standardized Medicare costs by applying uniform national prices for each type of service, similar to the approach we used to calculate hypothetical BCBSTX spending using Texas average prices. The Medicare quantity index is defined as the Medicare spending index divided by the Medicare price index.

We use health status scores to summarize the health status and expected healthcare utilization of the populations in different markets. The Medicare health status scores are based on mean hierarchical condition categories (HCCs) scores, as reported in the publicly available data. The BCBSTX risk scores are also HCCs, and are calculated from the BCBSTX enrollment and claims files. The Medicare and BCBSTX risk scores reflect age, sex, and diagnoses reported on claims. Health status-adjusted quantity indexes were calculated, for Medicare and BCBSTX, by dividing the quantity index by the health status index.

Variation in spending was allocated to 4 factors—input prices, adjusted prices, health status, and adjusted quantities—using a 3-step decomposition of variance approach. In the first step, the variation in total spending was allocated among service categories (ie, hospital inpatient, hospital outpatient, and professional services) using a population-weighted variance-covariance matrix. The second step involved decomposing variation within each service category into the 4 factors. In the third step, the variation attributable to each of the 4 factors was summed across service categories.

Poverty clearly has an important and well-documented impact on individual- and community-level health status, and it is also strongly related to input prices in a local market. Our analysis focused on whether poverty also has an association with the 2 “residual” factors: adjusted prices (adjusted for input prices) and adjusted quantities (adjusted for health status). In particular, we wanted to contrast the differing roles of poverty in Medicare versus BCBSTX. To do this, we performed 4 univariate regressions—poverty rates on Medicare prices, on Medicare quantities, on BCBSTX prices, and on BCBSTX quantities—and measured the r2 and the sign of the estimated coefficients. These univariate regressions allowed us to further decompose the variation in the residual factors into a “poverty effect” (which is in quotation marks because we are not attributing causality) and an “unexplained” factor.

We also performed a descriptive analysis in which we sorted the 22 Texas HRRs into 4 groups based on a ratio of the Medicare spending index to the private spending index. Harlingen and McAllen, both of which have a Medicare-private ratio exceeding 1.2, are placed in their own group (“extremely high-Medicare”). The remaining markets are defined as “high-Medicare” (the Medicare-private ratio exceeds 1.05), “proportional” (the Medicare-private ratio is between 0.95 and 1.05), and “high-private” (the Medicare-private ratio is less than 0.95). We focus our discussion on the markets at the 2 extremes—“extremely high-Medicare” and “high-private.”

We draw on several additional data sources to measure market-level demographics and health system characteristics. These sources include the 2012 Area Health Resources File,15 the 2012 County Health Rankings,16 and the “Prevention and Treatment” dimension score from The Commonwealth Fund Scorecard on Local Health System Performance.17 We created HRR-level measures from county-level data using a population-weighted crosswalk from zip code to county.18


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