Rohan Parikh, MS

  To investigate the roles of prices, poverty, and health in divergences between Medicare and private spending in Texas.

Retrospective observational design using 2011 Blue Cross Blue Shield of Texas claims data and publicly available Medicare data.

 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.”

 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.

: 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.

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.

 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.

 The Dartmouth researchers’ findings led to the claim that waste accounts for 30% of health spending in the United States.

 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).”

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.

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.

 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.”

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.

 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.

 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 CMS

; 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.

 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.

 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&mdash;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&mdash;and measured the 

 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,

 and the “Prevention and Treatment” dimension score from The Commonwealth Fund Scorecard on Local Health System Performance.

 We created HRR-level measures from county-level data using a population-weighted crosswalk from zip code to county.

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; 

 = .36), although that relationship is entirely due to 2 markets—Harlingen and McAllen&mdash;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 (

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.

 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; 

 = 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

 = 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; 

 = 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; 

 = 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.

McAllen has garnered an exceptional amount of attention in health policy circles.

 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”&mdash;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.

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 

). 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.”

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&mdash;and private spending that is relatively high&mdash;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 outcomes

; 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.

 That negative correlation is due to 2 outlier HRRs—McAllen and Harlingen&mdash;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.

 That nexus may, for reasons that are not fully understood, drive Medicare and private spending in opposite directions.

 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&mdash;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.

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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.

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

To explore the feasibility of using a distributed data model for ongoing reporting of local healthcare spending, specifically to investigate the contribution of utilization and pricing to geographic variation and trends in reimbursements for commercially insured beneficiaries younger than 65 years.

Commercial claims were obtained for beneficiaries in 5 states for the years 2008 to 2010 using a distributed data model. Claims were aggregated to the hospital service area (HSA) level and healthcare utilization was quantified using a novel, National Quality Forum—endorsed measure that is independent of price and allows for the calculation of resource use across all services in standardized units. We examined trends in utilization, prices, and reimbursements over time. To examine geographic variation, we mapped resource use by HSA in the 3 states from which we had data from multiple insurers. We calculated the correlation between commercial and Medicare reimbursements and utilization. Medicare claims were obtained from the Dartmouth Atlas.

We found that much of the recent growth in reimbursements for the commercially insured from 2008 to 2010 was due to increases in prices, particularly for outpatient services. As in the Medicare population, resource use by this population varied by HSA. While overall resource use patterns in the commercially insured did not mirror those among Medicare beneficiaries, we observed a strong correlation in inpatient hospital use.

This research demonstrates the feasibility and value of public reporting of standardized area-level utilization and price data using a distributed data model to understand variation and trends in reimbursements.

This study explored the use of a distributed data model and a novel method for calculating utilization to track local trends in spending for commercially insured beneficiaries.

Recent growth in spending for commercially insured beneficiaries was due principally to increases in prices, rather than increases in utilization.

Commercial utilization and spending varied across local areas and was not highly correlated with Medicare utilization and spending.

A distributed model may allow for nationwide reporting of spending and utilization to track the local effects of healthcare reform efforts.

Timely, local data are important to policy-makers, providers, patients, payers, and employers working to slow the growth of healthcare spending, which is a major focus of federal, state, and local healthcare reform initiatives. Community-based multistakeholder coalitions have formed across the country in an effort to influence their local healthcare markets and reduce costs. More than 40 percent of people in the United States live in a community with a multistakeholder coalition aimed at improving health and healthcare, including collaboratives focused on improving the exchange of health information, accelerating engagement by key local opinion leaders and stakeholders, or promoting quality improvement.

 All of these entities, however, lack the local data needed to determine if their efforts are making a difference.        

The factors contributing to rising healthcare spending differ across communities and depend on local context; understanding the drivers of local spending growth is complicated by the variety of inputs. Provider culture and supply, various market segments (outpatient, inpatient, long-term care), payer mix, regulation, and the competitiveness of hospital and physician markets all affect pricing, utilization, and ultimately, the total cost of care.

Research has shown that the relative contribution of these factors varies across markets and that drivers of commercial spending are not necessarily the same as drivers of Medicare spending. Chernew et al found that commercial spending was not correlated with Medicare spending across hospital referral regions.

 Examining the commercial and Medicare populations for El Paso and McAllen, Texas, Franzini et al found there was 86% greater per capita Medicare spending in McAllen than El Paso, but 7 percent less commercial spending.

 A recent Institute of Medicine report found that regional variation in spending in the commercial insurance market is due in large part to differences in price markups by providers. Differences in utilization, however, still explained over 30% of the regional variation in spending.

These diverse results indicate that the factors that contribute to rising healthcare spending are multifactorial and highlight the need for more comprehensive and detailed analysis of commercial data. The 2013 report issued by the Institute of Medicine, Variation in Health Care Spending: Target Decision Making, Not Geography, was unprecedented in that it combined both Medicare and commercial data from a variety of proprietary sources for analysis. The report underscored the importance of available data on both commercial and Medicare utilization and prices at a local level. These data could be useful to engage local stakeholders (such as employers), identify areas of potential waste, point regulators to localities where monopolistic pricing may be occurring, and evaluate the impact of local and national reform initiatives.

There is substantial uncertainty about the best approach to make local data publicly available. Feasible options might include a single federal database,

 combining state all-payer data (currently available in Colorado, Kansas, Maine, Maryland, Massachusetts, Minnesota, New Hampshire, Tennessee, Utah, and Vermont),6 using private data aggregators, or relying on a distributed data model.

 The Health Care Cost Institute has combined data from 4 large commercial insurers and has published broad reports on trends in employer-sponsored insurance, but has not produced any data at a local level.

 There are some community-level efforts under way to track healthcare spending and utilization locally, most notably in California, but they are relatively rare.

 In spite of the Institute of Medicine’s call for making more and better data available on both Medicare and commercial populations, it is not clear how this might be done.

In this paper we explore the feasibility and value of 2 potential approaches for making commercial spending and utilization data available at a local level. First, we work with an all-payer data set for New Hampshire, Maine, and Vermont, aggregated by Onpoint Health Data, a private, nonprofit organization. Second, we use a distributed data model to aggregate data from a single payer in 2 states. Our methods include a standardized approach to measuring utilization, and our partners submitted utilization and spending reports that were stripped of protected health information. These reports allowed us to aggregate data at a hospital service area (HSA) level and adjust for demographic characteristics. The findings demonstrate the feasibility of each of these approaches and the resulting data highlight the potential utility of tracking local healthcare spending.

  To test the distributed data model, we used data from 3 distinct sources. We obtained data on utilization and reimbursements for commercial beneficiaries in Maine, New Hampshire, and Vermont from an all-payer data set (2008-2010), along with data on beneficiaries of Blue Cross Blue Shield of Michigan (2009-2010) and Texas (2008- 2010) plans. The latter 2 states were chosen because of existing research relationships with relevant data providers. Each of the participating analytic teams applied standard software to measure utilization and submitted reports detailing summary information for beneficiaries in each age and gender category for each HSA. HSAs were defined in previous work by identifying the zip codes where the highest proportion of Medicare beneficiaries received their care from a single hospital.

 Data were then aggregated by the study authors and compared with corresponding HSA data from the Medicare program.

Each participating organization applied a standardized approach for defining the commercial population to be included in the analysis. Beneficiaries were required to be enrolled for a minimum of 9 months during the year, unless the member was born during the calendar year or was older than 65 years by the end of the year.

  Participating health plans and data providers used software developed by HealthPartners, a nonprofit healthcare organization and health plan, to measure a utilization amount associated with each insurance claim. The HealthPartners algorithm quantifies utilization using Total Care Relative Resource Values (TCRRVs), which are the basis for the National Quality Forum (NQF)-endorsed Total Resource Use measure.

 TCRRVs, which are expressed in dollars, measure the utilization and intensity of the services delivered to manage a patient’s healthcare needs. We chose to use the HealthPartners measure because it is independent of price and allows for the weighting and calculation of resource use across all medical services in standardized units. We did not want to obscure the effect of regional price differences on overall reimbursement amounts. In addition, we chose to use the measure because it makes resource use amounts equivalent for services offered across multiple settings. 

TCRRVs are unique in that they are relative within and across components of care (inpatient, outpatient, professional, and pharmacy), which allows for the isolation of resource use not only by component, but also on a total, per capita basis. The TCRRVs are made relative within the components of care using the CMS weighting system, including Medicare Severity Diagnosis Related Groups for inpatient care, Ambulatory Payment Classifications for outpatient care, and Relative Value Units for professional office care, while the TCRRVs for pharmaceutical expenditures are made relative by using the median average wholesale price per day for each National Drug Code. TCRRVs are calculated by multiplying the CMS weight by national average paid amount and the relevant service count. While the Health Care Cost Institute also uses CMS weights to measure the intensity of care, its methodology does not involve reporting (1) a resource use amount based on both utilization and intensity, or (2) a measure of total resource use across service lines.

The TCRRV algorithm was applied at the claim level by each data contributor and aggregated at the HSA level for transmission to study authors. (TCRRV weights are updated annually to correspond to updates in the CMS weight files. We used the 2011 TCRRV national weights.) Resource use was capped at $100,000 for each beneficiary in a given year and all components of care were reduced proportionally for both reimbursements and resource use. Very large resource use amounts greatly skew the mean in populations where the majority of beneficiaries have very low utilization and often represent extreme, unavoidable events. Capping reduced average resource use in 2008-2010 by an average of 11% across all HSAs (9.3% and 13.6% for HSAs in the lowest and highest quintile of resource use, respectively).

Both reimbursement and resource use (ie, utilization) amounts for each component of care (inpatient, outpatient, professional, and pharmacy) were transmitted to the study authors, as were denominator data (age in groups, gender, HSA of residence, number of people, and number of person-years). The HealthPartners software performs quality control checks at the claim level by comparing calculated resource use with the reimbursement amount. If the calculated resource use for a particular claim was outside of normal limits, we imputed resource use using the reimbursement amount multiplied by the ratio between resource use and reimbursement amount for all normal claims from that state, year, and component of care. The combination of numerator and denominator data allowed for adjustment and rate calculations. The transmitted data also included the prevalence of prescription drug and mental health carve-outs for each HSA. The Blue Cross Blue Shield of Michigan data did not include HSAspecific information on carve-out levels, but did include the overall state values by year. We used this information and exploratory models from the other states to impute prescription drug reimbursement and utilization values for Michigan HSAs. The imputation assumed a constant carve-out rate across the state each year.

Age, gender, and carve-out adjustment was performed using linear regression across the 5 states. Weighted averages for each HSA were calculated using the adjusted values for each year and component of care. State averages were created by weighting by the number of beneficiaries in each HSA. HSA average relative prices were defined as the ratio of resource use to the reimbursement amount. We normalized relative prices to 1 across all 5 states. Only HSAs with a sum of more than 1000 commercial beneficiaries over the study period were included. Analysis was also limited to those HSAs that were either in 1 of the 5 target states or overlapped the border of 1 of the 5 target states. These 2 exclusion factors reduced the total number of people per calendar year in the analyses by less than 0.1%. 

To examine geographic variation, we mapped resource use for each HSA in Maine, New Hampshire, and Vermont, reflecting procedures and services offered across all 4 components of care. We divided the HSAs into quintiles to display differences in resource use. Maps were not generated for Michigan or Texas, because data were from a single insurer in each state.

To further validate our methodology by attempting to replicate previous findings by examining the association between Medicare and commercial reimbursements and utilization. Age-, sex-, and race-adjusted reimburse- ment and utilization data at the HSA level for the feefor- service Medicare program were downloaded from the Dartmouth Atlas website.

 Utilization data were adjusted for prices across areas and settings.

 Using these data, we examined the association between calculated resource use and reimbursements for the commercially insured and Medicare (aged ≥65 years) populations by calculating a correlation coefficient and plotting the relationship, weighting the regression and the size of the data point by the sum of the number of commercial and Medicare beneficiaries in the HSA.

We identified 388 HSAs and a total of 16,819,237 beneficiary years for inclusion in our analysis (Table 1). The average number of commercial beneficiaries across 2008- 2010 included in the data set for each HSA ranged from 337 to 296,555. The majority of the beneficiaries in each of the 5 states were female. The age distribution was similar across the states, with about half of the population under 40 years and half aged 40 to 65 years. The only exception was Texas, where the population was slightly younger (a greater proportion in the 18-to-39-year age group). The age distributions for our study cohort were similar to those reported by the United States Census Bureau for privately insured individuals in each state.

Trends in Commercial Beneficiary Resource Use,

We examined trends in resource use and relative prices to investigate the extent to which changes in those 2 factors affected changes in reimbursements. The top portion of Figure 1 shows the year-to-year resource use trend per beneficiary by component of care and state. Profes- sional services constituted the highest proportion (42.9%) of resource use on average across the 5 states, followed by outpatient services (21.8%), inpatient services (20.0%), and pharmacy (15.1%). Annual resource use ranged from $4479 per commercial beneficiary in Vermont in 2008 to $5324 per beneficiary in Texas in 2009. The bottom portion of the figure shows calculated changes in resource use, prices, and reimbursements from year to year by component of care. Overall increases in reimbursements were greater in 2008- 2009 than 2009-2010. Reimbursements increased each year in each state, with the exception of Maine in 2010. Total resource use went down in each state between 2009 and 2010, mostly due to declines in inpatient (hospital) services. Increases in prices were larger than the decreases in resource use, however, so reimbursements still increased. The changes in reimbursements in each state can also be compared with changes in the consumer price index (CPI) overall and for medical care. The CPI decreased 0.4% between 2008 and 2009 and increased 1.6% between 2009 and 2010. The medical care CPI increased 3.2% between 2008 and 2009 and 3.4% between 2009 and 2010.15 Overall, growth in prices for commercial beneficiaries in our data set exceeded overall inflation in each state and year. In all but 2 cases, our measure of price increases also exceeded medical inflation.

Variation in Commercial Beneficiary Resource Use,

Resource use, relative prices, and reimbursement amounts varied among commercial beneficiaries across HSAs within our 5 states, and across states (Table 2). Resource use among commercial beneficiaries ranged from $3041 to $6280 across HSAs in the 5 states, which compares to a range of $6322 to $15,049 among Medicare beneficiaries. There was less variation in the commercial population (coefficient of variation = 0.10) than the Medicare population (coefficient of variation = 0.17). Within-state variation in resource use was most marked in Michigan. Relative prices ranged from 0.83 to 1.35 across HSAs in the 5 states, and reimbursements ranged from $1938 to $4626. Relative prices under 1 indicate that, for a given HSA, prices were below the average across all HSAs in the 5 states. The correlation between prices and resource use across HSAs was low (P = .03). 

Variation in resource use is further illustrated in Figure 2, which maps the ratio of resource use in each New England HSA to the weighted average of resource use across the region. Those HSAs with higher resource use relative to the regional average are darker in color. The HSAs with the highest utilization in this area were Rochester, New Hampshire; Ellsworth, Maine; and Augusta, Maine. The HSAs with the lowest utilization in this area were Rumford, Maine; Colebrook, New Hampshire; and St. Johnsbury, Vermont.

Overall resource use in the commercial population was not correlated with resource use in the Medicare population at the HSA level across these 5 states (P = .10, Figure 3), but the correlation across HSAs for inpatient resource use was higher (P = .42). A negative correlation was observed for overall commercial and Medicare reimbursements (P = —.36) and a low correlation for inpatient reimbursements (P = .10).

  This research combined data from 2 private payers and an all-payer data set to produce standardized estimates of utilization, pricing, and reimbursements at the local level across 5 states using a distributed data model. We were able to calculate HSA-level trends and variation for the commercially insured population aged <65 years in the select states using a standardized, NQF-endorsed measure of utilization. Our findings revealed marked variation in both relative prices and utilization, consistent with work by Dunn et al and Baker et al, and confirmed that commercial trends do not necessarily mirror trends in the Medicare population, as previously found by Chernew et al and the Institute of Medicine.

The goal of this research is to help communities understand local healthcare spending patterns and to begin to develop tailored solutions to slow growth in healthcare spending. This line of research differs from previous attempts to examine healthcare costs within the commercially insured population in several significant ways. First, it uses a validated, publicly available measure of utilization to calculate and standardize the magnitude of healthcare resource use across various commercially insured populations and service lines (outpatient, inpatient, professional, and pharmacy), taking into account both the frequency and intensity of care. This measure is independent of price and thus can be used to compare healthcare utilization for different health plans in different parts of the country. Second, we conducted our analysis at the level of the HSA, a relatively small unit that describes the geographic area where patients served by local hospitals reside. Finally, by using a distributed data model, this research demonstrates the feasibility of an approach that could be used either by local multistakeholder initiatives or by a national effort to report on local utilization and spending.

The main limitation of this study stems from it being pilot research: the number of payers included in our analn- ysis for each state varied significantly (61 in Maine, 37 in New Hampshire, 35 in Vermont, and 1 each in Michigan and Texas), along with the insurance products offered by those payers, which reduces comparability across states. The use of a distributed data model presents its own set of potential limitations, since no part of the team had access to claim-level data for all of the states. To address this issue, we created quality control reports to ensure that outputs were accurate and standardized across sites. 

Another limitation was our reliance on risk adjustment using solely age and gender indicators, rather than risk adjustment that included other demographic characteristics (income, education, etc) or information on medical conditions. Diagnosis-related risk adjustment has practical implications and may introduce additional biases

 we sought to demonstrate the viability of the distributed data model before compounding the complexity and cost of data aggregation by implementing a risk adjustment methodology. In future research, we plan to compare results using various risk adjustment methods. Lastly, a potential limitation to this study was our use of HSAs as the unit of analysis. HSAs were defined by Medicare utilization patterns in the 1990s and may not fully capture utilization patterns among commercial beneficiaries.

Key factors contributing to the success of our pilot included the generation of quality control reports for data providers and the data aggregator to ensure data accuracy, as well as the use of mock commercial claims to test aggregation algorithms. While it is possible to apply our methodology using other levels of analysis, we continue to believe that HSAs represent the best option as they, unlike counties, reflect actual patterns of healthcare use, and likely provide more actionable information for community- level interventions than hospital referral regions.

Routine reporting of timely data at a local level can provide actionable information to communities hoping to understand the sources of healthcare spending and growth. This information will be useful for many purposes: to engage employers in reducing healthcare costs, encourage accountability for prices and utilization among providers, encourage community-level shared savings programs (such as the Akron Accountable Care Collaborative

), and facilitate research and evaluation of regional and national reforms. In addition, as consolidation in physician and hospital markets increases, these data can support transparency among health systems and aid regulators in monitoring the effects of changes in payment incentives that might drive consolidation, such as accountable care organizations or bundled payment reforms.

This research provides a unique avenue to develop a nationwide, comprehensive commercial data set in which payers create measures of cost and utilization from their own claims using standard practices, with controls for data quality and consistency. In addition, it allows payers to avoid releasing protected health information (in which patients may be identified) or pricing data, which is commercially valuable and possibly subject to antitrust concerns. The goal is for both large and small payers to contribute to an all-inclusive national data set for public reporting. The barriers to this approach are not trivial: developing it requires the cooperation and effort of multiple competitive payers, as well as entrusting the protection of pricing information to a third party.

The authors are indebted to Sarah Kler of The Dartmouth Institute for Health Policy and Clinical Practice for assistance with mapping, as well as Greg Kotzbauer and Andrew Toler of The Dartmouth Institute, Robyn Rontal of Blue Cross Blue Shield of Michigan, and Marianne Udow-Phillips of the Center for Healthcare Research and Transformation for their insights and advice.

In addition, the authors would like to acknowledge the following entities as the original sources for the New Hampshire and Vermont data sets: the New Hampshire Department of Health and Human Services; the New Hampshire Insurance Department; the Vermont Department of Financial Regulation (formerly the Vermont Department of Banking, Insurance, Securities, and Health Care Administration); and the Green Mountain Care Board. The analyses and conclusions drawn from these data do not necessarily represent those of any agency of the state of New Hampshire or state of Vermont.

The Dartmouth Institute for Health Policy and Clinical Practice (CHC, WLS, DJG, ABM, ESF), the Department of Medicine (ESF), and the Department of Community and Family Medicine (ESF), Geisel School of Medicine at Dartmouth, Lebanon, NH; Blue Cross Blue Shield of Michigan, Detroit, (PGA); Center for Healthcare Research and Transformation, Ann Arbor, MI (NB); Onpoint Health Data, Portland, ME (KF, RS); Division of Management, Policy and Community Health, University of Texas School of Public Health, Houston (LF, RP); and HealthPartners, Inc, Bloomington, MN (GK, SK). Funding Source: This work was funded by grant 69088 from the Robert Wood Johnson Foundation.

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 (CHC, DJG, ABM, GK, SK, ESF); acquisition of data (CHC, DJG, ABM, PGA, NB, KF, LF, GK, SK, RS, ESF); analysis and interpretation of data (CHC, WLS, DJG, PGA, KF, LF, GK, SK, RP, RS); drafting of the manuscript (CHC, WLS, ABM, GK, SK, ESF); critical revision of the manuscript for important intellectual content (CHC, WLS, DJG, ABM, NB, LF, GK, SK, ESF); statistical analysis (CHC, WLS, GK, SK, RP); provision of study materials or patients (CHC); obtaining funding (CHC, ESF); administrative, technical, or logistic support (CHC, WLS, DJG, PGA, KF, GK, SK, RP, RS); and supervision (CHC, ESF).

Carrie H. Colla, PhD, 35 Centerra Pkwy, Lebanon, NH 03766. E-mail: carrie.h.colla@dartmouth.edu.

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The authors demonstrate the utility of distributed data models for reporting of local trends and variation in utilization, pricing, and spending for commercially insured beneficiaries.

Rohan Parikh, MS

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