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
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.
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
Both Medicare and BCBSTX spending vary widely across Texas HRRs (). The Medicare spending index ranges from 0.84 in San Angelo to 1.27 in McAllen, while the BCBSTX spending index ranges from 0.73 in Harlingen to 1.13 in Beaumont. The correlation between HRR-level Medicare and BCBSTX spending is negative (correlation coefficient = —0.21; P = .36), although that relationship is entirely due to 2 markets—Harlingen and McAllen—that have extremely high Medicare spending and extremely low private spending. If those 2 markets are excluded, the correlation between Medicare and private spending becomes positive (r2 = 0.56; P = .01).
We find that price differences explain a relatively large share of the variation in BCBSTX spending (33%) compared with Medicare (17%) (). The IOM found a much larger discrepancy, and reported that prices accounted for 70% of the variation in private spending.10 The IOM study may have picked up much greater variation in prices due to its inclusion of multiple insurers, and markets spanning the country.
In Medicare, price differences are almost entirely explained by differences in input prices. Poverty rates are associated with slight increases in Medicare prices due to explicit adjustments in Medicare payment policy, such as wage index floors and “disproportionate share hospital” payments. (Regressing adjusted Medicare prices on poverty produces a point estimate of 0.00375 (standard error [SE] = 0.00083; P = .0002; r2 = 0.5081). This indicates that a 1 percentage-point increase in poverty is associated with a 0.4% increase in Medicare prices. In contrast, few of the differences in BCBSTX prices can be explained by differences in input prices, and poverty is associated with lower prices. Regressing adjusted BCBSTX prices on poverty produces a point estimate of —0.00375 (SE = 0.00239; P = .1316; r2 = 0.1100). This indicates that a 1 percentage-point increase in poverty is associated with a 0.4% reduction in private prices. In Medicare, differences in health status play a significant role, explaining 32% of total spending variation. In sharp contrast, differences in health status explain almost none of the spending variation in BCBSTX.
Market-level poverty rates are associated with reductions in the quantities of services received in Medicare and BCBSTX (Figure 2). Regressing adjusted Medicare quantities on poverty produces a point estimate of —0.00542 (SE = 0.00256; P = .0471; r2 = 0.1829). This indicates that a 1 percentage-point increase in poverty is associated with a 0.5% reduction in Medicare quantities. Regressing adjusted BCBSTX quantities on poverty produces a point estimate of —0.00851 (SE = 0.00207; P = .0005;
r2 = 0.4577), which indicates that a 1 percentage-point increase in poverty is associated with a 0.9% reduction in BCBSTX quantities. However, the inhibitory role of poverty on quantities is much larger among the privately insured, explaining 30% of overall spending variation. Both Medicare and BCBSTX exhibit large unexplained spending variation, although the unexplained variation in Medicare is essentially all due to quantities whereas in BCBSTX there is unexplained variation in both prices and quantities.
The McAllen-Harlingen Syndrome
McAllen has garnered an exceptional amount of attention in health policy circles.19,20 The best known article on McAllen, by Atul Gawande, cited Dartmouth’s Medicare data and attributed the high spending in McAllen to overuse—“patients in McAllen got more of pretty much everything”—and an ethic of profit maximization in the medical community. Comparing Medicare and private spending data provides a much richer, more nuanced picture of McAllen, and broader patterns in Texas. After Gawande’s article was published, Franzini and colleagues, using BCBSTX data, pointed out that: 1) spending among the privately insured in McAllen was actually a bit lower than in El Paso, McAllen’s allegedly more parsimonious cousin, and 2) McAllen’s demographics are distinctive, and do not simply mirror El Paso’s.21
McAllen and Harlingen, which lie side by side along Texas’ border with Mexico, are emblematic of the divergent forces driving Medicare and private spending patterns. Those markets stand out in our data on 3 counts: their Medicare spending is the highest in the state, their private spending is the lowest in the state, and their demographic and health system profiles are exceptional (see and ). The higher Medicare spending is due mainly to higher quantities of services, but Medicare prices are also about 6% higher than the Texas average. The higher Medicare prices in McAllen and Harlingen are due entirely to high inpatient hospital prices (not shown in Table 1). The hospital price boosts in Medicare reflect the fact that Medicare pays higher prices to hospitals that treat low-income patient populations. The exceptionally low private spending is due both to prices and quantities that are lower than the Texas average.
Beyond health spending, profound differences divide McAllen and Harlingen from the rest of Texas. The populations of McAllen and Harlingen are 90% Hispanic, versus less than 40% in the rest of the state. The poverty rate is also twice as high as the rest of the state, and more than half of Medicare beneficiaries are “dual eligibles.” The uninsured, as a share of the population of those aged under 65 years, is almost 40% in McAllen and Harlingen, compared with just over 25% in the rest of the state. McAllen and Harlingen also have an extremely low physician supply and the worst health system performance in the state. And while individuals self-report that they are in considerably worse than average health, mortality rates are low—this is an example of a broader phenomenon known as the “Hispanic paradox.”22
The Austin-Temple Syndrome
Excluding McAllen and Harlingen, the rest of the Texas markets are more uniform in their Medicare and private spending, and in their demographics. But there are still some differences worth noting. Ten markets, including Austin and neighboring Temple, have private spending that is relatively high compared with Medicare spending. Those markets tend to have Medicare spending that is lower than the rest of Texas—due to low quantities—and private spending that is relatively high—due to high prices.
The markets with relatively high private spending are socioeconomically similar to the state average, but differ from the rest of Texas on other dimensions. These markets have better than average rates of premature mortality, and adults 50 years and older are more likely to receive screening and recommended preventive care. These markets also tend to score well in The Commonwealth Fund’s “Prevention and Treatment” dimension, which combines measures of access, receipt of recommended care, and risk-adjusted outcomes.
Is Higher Spending Associated With Worse Outcomes?
Several researchers have pointed out that regions with higher Medicare spending tend to have both worse quality of care and outcomes6,7,23; however, that finding appears to apply only to Medicare spending, not private spending. In our analysis, areas with the highest private spending (the “high-private” markets) tended to have relatively low rates of hospital readmissions, higher rates of adults receiving recommended services, and better than average health outcomes. This undermines the notion that higher spending somehow leads to poor quality and outcomes, and raises the possibility that the negative relationship between Medicare spending and quality is an artifact of broader socioeconomic patterns.
We found that Medicare and private spending are negatively correlated across Texas HRRs, consistent with the finding of Chernew and colleagues.9 That negative correlation is due to 2 outlier HRRs—McAllen and Harlingen—that stand out both for their extremely high Medicare spending and their extremely low private spending. We examined those 2 markets in some detail, and found that they are unusual in having extremely high poverty rates and a very limited supply of physicians. Those HRRs could be dismissed as mere flukes, but they may also highlight a broader phenomenon: higher rates of poverty in a market are associated with lower unit prices and lower quantities of care among the privately insured.
Attributing McAllen’s unusually high Medicare spending to a “culture of money” almost certainly oversimplifies the situation. If McAllen and Harlingen were beset by profit-driven medical providers, private spending in those markets would likely be high as well; but in fact, private spending in those markets is remarkably low. Market-level poverty rates appear to play a key role, although further quantitative and qualitative analysis would be needed to unearth the tangled links between McAllen and Harlingen’s unique market features and its Medicare and private spending patterns.
Cooper has argued that health system performance and health outcomes are heavily impacted by what he terms the “affluence-poverty” nexus.24 That nexus may, for reasons that are not fully understood, drive Medicare and private spending in opposite directions.25 Cooper points out that private spending must be financed locally, so the level of spending may primarily reflect the local employed population’s affluence and ability to pay for care. These inhibitory spillover effects of regional poverty on health spending among the privately insured are consistent with our finding that poverty rates are associated with significantly lower private prices, quantities, and spending. Medicare spending, in contrast, is financed mainly by the federal and state governments, so the level of spending depends less on local affluence and more on health status and the incidence of disease. This notion is consistent with our findings that after adjusting for health risk, poverty had a lower impact on spending, prices, and quantities in the Medicare population compared with the privately insured population. Health status, on the other hand, explained a larger portion of the Medicare spending variation.
Wide variation in Medicare spending has been cited in policy debates as indicative of inefficiency in the healthcare system. Our analysis, consistent with the bulk of earlier research, finds significant variation in Medicare spending due to differences in the quantities of services provided. But our analysis also indicates that unexplained spending variation occurs among the privately insured, due to differences in both prices and quantities. This suggests that the policy-making discussion should be broadened to include inefficiencies in pricing by private health plans as well as inefficiencies in practice patterns. Our findings also suggest that the Medicare spending patterns in a market should not be treated as a “biopsy” of the entire healthcare system, and that poverty and other market factors play out differently in Medicare versus other market segments. Finally, one of the key takeaways from the Dartmouth Atlas—that higher spending is not associated with better quality or outcomes—may not apply to broader measures of healthcare spending.
Our study has the usual limitations of studies using claims data. Even though BCBSTX is the largest insurer in Texas, which is a large and under-studied state with wide diversity in its sociodemographics and composition of healthcare markets, our data represent the population insured by 1 carrier in 1 state. Additional limitations of this study are due to the limitations of BCBSTX and Medicare data, such as the inability to account for Medicaid contribution to healthcare spending on dual eligibles, the inability to control for Hispanic ethnicity in the BCBSTX data, and the inability to control for immigration status effects on enrollment rates in Medicare. Also, the different age ranges (younger than 65 years for BCBSTX and 65 years and older for Medicare) limit the comparability of the populations, but in a sub-analysis using the BCBSTX members aged 50 to 64 years (not shown), the findings were substantially unchanged. Despite these limitations, this study has the opportunity to provide important information on private-sector spending and its relation to Medicare spending.
The analysis of geographic variations in health spending has been significantly impeded by a lack of data on the privately insured and other non-Medicare populations. Fortunately, the IOM, The Commonwealth Fund, and others have undertaken major initiatives to fill those data gaps. But even the IOM’s effort remains piecemeal—they used private spending data from 2 proprietary databases that are not nationally representative and that reflect small subsets of the full population of privately insured Americans. Our study, which only includes 1 major private insurer, is similarly limited. The most promising data development is the creation of statewide all-payer claims databases in several states. Those databases, once they are made useable for research purposes, can be used to contrast spending patterns among different populations.
Healthcare spending per person varies widely from market to market in Texas, both among Medicare beneficiaries and the privately insured. But, based on our analysis, different factors are driving the variation in those two populations. Geographic variation in spending among the privately insured appears largely to reflect the ability of the local population to pay for healthcare—privately insured individuals in higher-income areas can afford to buy more healthcare services, and pay higher prices for those services. In Medicare, however, geographic variation in spending is driven heavily by differences in health status and, presumably, clinical need. These findings highlight the inadvisability of using geographic patterns of spending in Medicare as proxies for patterns in system-wide spending. These findings also highlight the need for comprehensive market-level spending data that allow comparisons among populations with different sources of insurance coverage.
The authors wish to thank The Commonwealth Fund, New York, NY, for financial support for this project. We thank Blue Cross Blue Shield of Texas for giving us access to their claims data. We also thank Cecilia Ganduglia, Ibrahim Abbas, and Tom Reynolds at the University of Texas School of Public Health (UTSPH), Houston, for research assistance; Osama Mikhail and Trudy Krause at UTSPH for helpful comments; and Mark Zezza and Stuart Guterman at the Commonwealth Fund for reviewing early versions of the manuscript.
Author Affiliations: RAND Corporation (CW), Arlington, VA; Chulalongkorn University (ST), Bangkok, Thailand; University of Texas School of Public Health (RP), Houston; University of Texas School of Public Health and University of Maryland School of Public Health (LF), Houston, TX and College Park, MD.
This project was funded by The Commonwealth Fund (Grant 20130066).
Source of Funding:
The authors report no relationship or financial interest with any entity that would pose a conflict of interest with the subject matter of this article.
Concept and design (LF, ST, CW); acquisition of data (LF); analysis and interpretation of data (LF, RP, ST, CW); drafting of the manuscript (LF, RP, ST, CW); critical revision of the manuscript for important intellectual content (LF, CW); statistical analysis (LF, RP, ST, CW); administrative, technical, or logistic support (RP).
Chapin White, PhD, RAND Corporation, 1200 South Hayes St, Arlington, VA 22202-5050. E-mail: firstname.lastname@example.org.
Address correspondence to:
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