Medicare and private spending trends from 2008 to 2012 diverge in Texas.

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research-article2014

MCRXXX10.1177/1077558714563174Medical Care Research and ReviewFranzini et al.

Empirical Research

Medicare and Private Spending Trends From 2008 to 2012 Diverge in Texas

Medical Care Research and Review 2015, Vol. 72(1) 96–112 © The Author(s) 2014 Reprints and permissions: sagepub.com/journalsPermissions.nav DOI: 10.1177/1077558714563174 mcr.sagepub.com

Luisa Franzini1, Suthira Taychakhoonavudh2, Rohan Parikh1, and Chapin White3

Abstract The recent relatively slow growth in health care spending masks significant differences among payers, clinical settings, and geographic areas. To better understand the spending slowdown, we focus on 2008-2012 trends in Texas among Medicare feefor-service beneficiaries and enrollees in Blue Cross Blue Shield of Texas (BCBSTX). Spending per person for Medicare grew only 1.5% per year on average, compared with 5.2% for BCBSTX. In Medicare, utilization rates were relatively flat, while prices grew more slowly than input prices. In BCBSTX, spending growth was driven by increases in negotiated prices, in particular hospital prices. We find that geographic variation declined sharply in Medicare, due to drops in spending on post–acute care in two notoriously high-spending regions but rose slightly in BCBSTX. The aggregate spending trends mask two divergent stories: spending growth in Medicare is very slow, but price increases continue to drive unsustainable spending growth among the privately insured. Keywords medical costs, Medicare, private insurance, trends

This article, submitted to Medical Care Research and Review on July 30, 2014, was revised and accepted for publication on November 3, 2014. 1University

of Texas School of Public Health, Houston, TX, USA University, Bangkok, Thailand 3RAND Corporation, Arlington, VA, USA 2Chulalongkorn

Corresponding Author: Luisa Franzini, University of Texas School of Public Health, 1200 Pressler Street, Houston, TX 77030, USA. Email: [email protected]

Downloaded from mcr.sagepub.com at NORTH DAKOTA STATE UNIV LIB on May 30, 2015

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Introduction Health care spending growth has been on the decline since the mid-2000s, and the share of the economy devoted to health care actually declined from 2012 to 2013, falling to 17.2% (Martin, Hartman, Whittle, Catlin, & National Health Expenditure Accounts Team, 2014). A lively discussion has ensued in the literature on the causes of the slowdown in spending growth, and whether it is sustainable (Chandra, Holmes, & Skinner, 2013; Cuckler et al., 2013; Cutler & Sahni, 2013; Fuchs, 2013; Hamel, Blumenthal, Stremikis, & Cutler, 2013; Hartman, Martin, Benson, & Catlin, 2013; Holahan & McMorrow, 2013; Ryu, Gibson, McKellar, & Chernew, 2013; White & Ginsburg, 2012). However, the dynamics of the slowdown varies by payer, as private and public payers face different incentives and operating environments. It is important to understand the factors that contribute to spending growth in each payer in order to develop policies that foster continued moderate growth across the health care system. In this article, we investigate the role of prices and utilization in 2008-2012 spending trends for the major private insurer in Texas, Blue Cross Blue Shield of Texas (BCBSTX), and for Medicare. We compared spending trends in BCBSTX excluding and including pharmacy spending and in Medicare excluding and including post– acute care (PAC) spending. We found that spending per enrollee grew faster in BCBSTX than in Medicare. While utilization trends were similar for the two payers, the increases in private spending were mainly because of rising negotiated prices. Next, we examine variation in spending across hospital referral regions (HRRs) in Texas from 2008 to 2012 in the private sector and Medicare. While variation in BCBSTX spending increased slightly, it declined in Medicare. We further examine the decline in PAC spending variation, which drives the overall decline in Medicare variation. Remarkably, much of the decline in overall and PAC Medicare spending variation could be attributed to spending in McAllen and Harlingen regressing toward the mean after those areas were publicized as being high-spending outliers (Franzini et al., 2011; Franzini, Mikhail, & Skinner, 2010; Gawande, 2009).

Background The causes of the recent spending slowdown and their implications for future spending growth have been debated, and researchers disagree if the slowdown represents a temporary side effect of the Great Recession or a permanent shift (Chandra et al., 2013; Cuckler et al., 2013; Cutler & Sahni, 2013; Fuchs, 2013; Hamel et al., 2013; Hartman et al., 2013; Holahan & McMorrow, 2013; Ryu et al., 2013; White & Ginsburg, 2012). While the recession certainly was a major contributor, structural changes in the health care market in response to a protracted economic slowdown that put pressure on providers to reduce their costs and led to cost-containing policies likely played a role (Holahan & McMorrow, 2013; Ryu et al., 2013). Demand-side changes, such as increased cost sharing, also played a role, partly due to higher enrollment in high deductible plans (Kaiser Family Foundation, 2013).


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Findings on the effect of the Affordable Care Act (ACA) on current and projected savings seem mixed (Chandra et al., 2013; Cuckler et al., 2013; Hartman et al., 2013; Holahan & McMorrow, 2013; White & Ginsburg, 2012). To date, not enough evidence exists to predict if health care spending will rebound, as the economy recovers as it has after past downturns (Chandra et al., 2013; Cuckler et al., 2013; Cutler & Sahni, 2013; Fuchs, 2013). Some recent reports showed a sharp rebound in health spending in early 2014 due to the coverage expansions in the ACA (Altarum Institute, 2014). But that rebound turned out to be a forecasting error that was subsequently revised away, which underscores the level of uncertainty surrounding trends going forward (Bartash, 2014). All payers experienced the recent slowdown in spending growth, but Medicare and the private sector exhibited different dynamics. The slowing growth in Medicare per enrollee spending could be partly explained by the beginning of an influx of healthier baby boomers into Medicare as well as government policies that have reduced provider payment updates (Holahan & McMorrow, 2013; White & Ginsburg, 2012). The ACA also reduces provider payment updates, as well as encouraging payment and delivery system reforms (Davis et al., 2010). Health care spending trends in the privately insured population have paralleled the slow growth in Medicare (Hartman et al., 2013; Health Care Cost Institute, 2013). Changes in demand, due to changes in benefit design that have higher cost sharing, have been estimated to account for about one fifth of the decrease in private spending growth (Ryu et al., 2013). But continued growth in negotiated prices, particularly for hospitals, have pushed private per capita spending up in 2012 (Health Care Cost Institute, 2013). The current slow growth in spending contrasts with the early 2000s, which was a period of high-spending growth. From 2001 to 2006, outpatient services and pharmaceuticals explained, respectively, two thirds and one third of the growth, with utilization accounting for the entire growth in outpatient spending and about three quarters of growth in drug spending among the privately insured (Bundorf, Royalty, & Baker, 2009). From 2011 to 2012, spending increases were attributed to increased spending on hospitals, physicians, and clinical services and a decline in drug spending (Martin, Lassman, Washington, & Catlin, 2012). Negotiated prices in the private sector were considered the cause of continued growth (Health Care Cost Institute, 2012), despite the difficulty in isolating prices’ contribution to spending growth (Chandra et al., 2013; Hamel et al., 2013).

New Contributions In this article, we use newly available data from the largest private insurer in Texas to provide an in-depth analysis of health care spending in Medicare (excluding Medicare Advantage) and the private sector from 2008 to 2012. We decompose statewide spending trends into service prices, input prices, and utilization, overall and by service categories, to identify the drivers of growth for each payer. Then, we report how geographic variation in spending has changed over time for each payer. Our study adds to the current literature by presenting Medicare and private trends side by side, drilling


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down to the HRR level, and using data from a Blue Cross Blue Shield plan—these “Blues” plans are dominant insurers in most states but are not represented in the Health Care Cost Institute’s data. Our study also adds to the geographic variation literature that investigates efficiency in health care delivery (Fisher, 2005; Fisher, Bynum, & Skinner, 2009) by comparing the spending trajectories of high- and low-spending regions by payer and by identifying service categories, for example, PAC in Medicare, and regions, for example, McAllen, that mainly contribute to variation. This evidence can be used to tailor policies by payers, service categories, and regions. While the study is limited to one state, Texas is a large and understudied state with wide diversity in its sociodemographics and composition of health care markets. Thus, this study has the opportunity to be an important new contribution on private sector spending growth and its relation to Medicare spending growth.

Conceptual Model The conceptual framework for understanding spending trends in the private and public sectors is based on the notion that spending trends can be decomposed into trends in prices and utilization, each with its own determinants. The theoretical foundations for price determination in the private sector are grounded on models that explain how prices are negotiated between payers and providers. While the negotiation process is sophisticated and complex, final negotiated prices are heavily dependent on the relative market power of providers and payers (Ginsburg, 2003). Medicare, on the other hand, uses a nationally administered price-setting system that is exhaustively and publicly documented (Centers for Medicare and Medicaid Services [CMS], 2013b). Several theories attempt to understand the determinants of utilization in health care. Health care utilization is determined by patients’ health status and by providers’ factors. Because of the asymmetric information available to patients and providers regarding the appropriateness of medical treatments, providers heavily influence utilization. Economic theories, including moral hazard and induced demand, are useful in explaining demand and supply for health care services (Auster & Oaxaca, 1981; Pauly, 1968).

Data and Method We measured spending per person among BCBSTX enrollees and among Medicare fee-for-service beneficiaries for each year from 2008 to 2012 for Texas overall, and for each HRR in Texas. The 22 HRRs in Texas represent regional health care markets for tertiary medical care (The Dartmouth Institute for Health Policy and Clinical Practice, 2013). Spending per person is decomposed into prices and quantities and by service category. Prices reflect the allowed amounts, that is, the negotiated prices that are actually received by providers, for BCBSTX and the amount paid by Medicare for Medicare. Price indices are created, which adjust for differences in case mix, and price indices are further decomposed into input prices (which measure geographic variation in the


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cost of doing business, including differences in wages and other inputs) and adjusted prices (adjusted for case mix and input prices). Adjusted prices represent negotiated prices after controlling for variations in case mix and cost of doing business. Quantities are calculated as spending per enrollee divided by the price index, and so reflect both the volume and the intensity of services. The BCBSTX service categories include hospital inpatient, facility outpatient, professional, and pharmacy. For Medicare, we did not have data on pharmacy but included PAC. Texas trends from 2008 to 2012 for spending indices, price indices, and utilization indices are reported for the two payers. Coefficients of variation (CVs) for each year measure trends in spending variation across HRR. Further decomposition of CV by service categories allows us to measure the contribution of each service category to trends in spending variation for both BCBSTX and Medicare.

Data BCBSTX Data. We used claims data on 1,669,119 (2008), 1,913,154 (2009), 2,175,169 (2010), 2,262,354 (2011), and 2,161,007 (2012) BCBSTX members residing in Texas, aged 0 to 64 years, enrolled in preferred provider organization plans, and with BCBSTX pharmacy benefits. Approximately 95% of all BCBSTX subscribers are in preferred provider organization plans. The BCBSTX plans cover all medical, mental health, and diagnostic services. Only 63% of enrollees had prescription drug spending data; the remainder either did not have a pharmacy benefit or received it through a non-BCBSTX carveout. Our analyses for BCBSTX include only members with pharmacy benefits. Medicare Data. The Medicare data used for comparing trends in the private and public sectors were obtained from the CMS (CMS 2013d). These are the publicly available HRR-level data on program spending (excluding beneficiary cost sharing) for Medicare fee-for-service beneficiaries, including all Medicare spending except for Part D prescription drugs. We used the Medicare Trustees report (CMS, 2013a) on national trends in Medicare prices to compute the Medicare price index. Additional Data. In addition, we obtained the prospective payment system hospital market basket data and the quarterly Medicare Economic Index from the CMS website to compute input prices for facility claims and professional claims, respectively (CMS, 2012, 2013c). The input prices index for pharmacy claims was computed using GDP (gross domestic product) deflator (Hartman et al., 2013). Data Limitations. The limitations of our data are the usual limitations of using claims data, which include a lack of detailed clinical information and the difficulty of adjusting for health status. Also, the BCBSTX data are not representative of the privately insured U.S. population, as they represent only one payer in one state. However, the strengths of the data come from the fact that BCBSTX represents about a third of all


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commercial sector enrollment in Texas, an important large state with a diverse and growing population. Measures. Our main measure is “medical” spending per enrollee, which we measure for BCBSTX and Medicare, and which includes inpatient hospital care, outpatient facility services, and professional services. We also create two measures of total spending per enrollee that are not strictly comparable—total spending in BCBSTX includes medical spending plus prescription drugs, while total spending in Medicare includes medical spending plus PAC. Spending in the BCBSTX data includes any deductible, co-pay, and coinsurance paid out-of-pocket by the patient, as well as payments made by BCBSTX to providers. Spending was computed as spending per member per month multiplied by 12 to obtain spending per member per year estimates. We computed two types of indices: by year to investigate Texas-wide trends over time and by HRR to investigate geographic variation. Spending indices across years were obtained by dividing spending per enrollee in each year by spending per enrollee in 2008. We decomposed the spending index into a price index and a quantity index, which was obtained by dividing the spending index by the price index (Franzini et al., 2014; White, 2012). The quantity index reflects both the number of units of service provided (e.g., number of inpatient hospital stays per enrollee) and the intensity of those services (e.g., the average case mix weight of inpatient hospital stays). BCBSTX Price Index. The steps in calculating the price indexes in BCBSTX were as follows: (1) calculate the all-Texas average price for each specific type of service using claims from all 5 years, (2) assign a hypothetical spending amount to each HRR using the all-Texas average price, and (3) calculate a price index by dividing the actual spending by the hypothetical spending. The price index was further decomposed into the input price index and the adjusted price index (service prices adjusted for input prices). Indices are computed first for each service category and then overall. These are Paasche-type indices, meaning that the mix of services as well as their weights are allowed to vary from year to year. Yearly indices were calibrated to be 1.00 in 2008. Using the same methodology, we computed the spending indices across HRRs for each year using Texas average prices in that year to calculate hypothetical spending (Franzini et al., 2014; White, 2012). Medicare Price Index. CMS, when calculating Medicare spending by HRR, included only amounts paid by the program, not including deductibles, co-payments, and so on. We would have preferred to analyze total spending on Medicare-covered services, but those data were not made publicly available. We argue, for two reasons, that patterns of growth and variation in program payments are extremely similar to patterns in total spending on Medicare-covered services: First, beneficiary cost-sharing provisions were very stable over this period and, second, program payments averaged at the HRR level are very highly correlated with total payments averaged at the HRR level (more so than for individual beneficiaries).


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The HRR-level Medicare data published by CMS allow us to compare Medicare prices across HRRs within a given year, but they do not allow us to measure trends over time in Medicare prices. To measure Texas-wide trends in Medicare facility and PAC prices, we used the “Components of Historical and Projected Increases in HI Inpatient Hospital Payments”—Table IV.A1 in the 2013 Medicare Trustees report. That table reports historical year-over-year percent increases for three factors that together determine Medicare inpatient hospital price trends: (1) the “input price index,” which reflects increases in the prices that hospitals pay for labor and other inputs; (2) the “unit input intensity allowance,” which is an amount specified in legislation that is added to or subtracted from the input price index to yield the prospective payment update factor; and (3) an “other sources” factor—which is a residual category reported by CMS that reflects, for example, adjustments for case mix “creep” and cuts in disproportionate share hospital payments. We used the year-over-year percent increases in the input prices to create an input price index (2008 = 1.00), and we combined the year-over-year percentage increases in all three factors to create a Medicare price index for facility and PAC (2008 = 1.00). To create the Medicare-adjusted price index for facility and PAC, we divided the price index by the input price index—the adjusted price index reflects year-over-year percentage increases in the input intensity analysis and “other sources.” The Medicare price index for professional claims was computed from the Modified Medicare Economic Index and was decomposed into an input price index and an adjusted price index. All of these were obtained from the Medicare Trustees report (CMS, 2013a). We assumed that Texas-wide trends in input prices were the same in BCBSTX and Medicare and so used the Medicare input prices in the Trustees report for both.

Statistical Methods Spending Trends. To investigate trends in spending, prices, and utilization, we plotted the yearly indices for spending, prices, input prices, adjusted prices, and quantities overall and by service category for BCBSTX and Medicare and compared growth rates across payers. Variation Trends. Next, we investigated trends in spending variation across HRR in two steps. First, we computed CVs (CV = standard deviation divided by the mean) to measure variation by HRR for yearly spending per member for BCBSTX and Medicare (Chernew, Sabik, Chandra, Gibson, & Newhouse, 2010; Franzini et al., 2014; Philipson, Seabury, Lockwood, Goldman, & Lakdawalla, 2010). We report CVs in each year for BCBCTX medical spending and total spending (including drugs) and for Medicare medical spending and total spending (including PAC). Next, we allocated the variation in spending among service categories using the weighted variance–covariance matrix across service categories. For example, we computed the shares of medical spending variation that were attributable to inpatient, outpatient, and professional services. We then used these shares to estimate the share of CV due to each service category by multiplying the share by the CV. All analyses are weighted by HRR population.


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Confidence intervals are not computed as the analyses are based on complete populations, not samples.

Results Trends in Spending Levels Overall, Medicare did a better job than the private sector at limiting spending growth between 2008 and 2012. The average annual growth in medical spending per person was 1.5% in Medicare and 4.9% in BCBSTX. Medicare spending growth fell below national growth in GDP per capita over that period, 2.5% (U.S. Department of Commerce, Bureau of Economic Analysis, 2014). This corresponds to increases in medical spending per enrollee of 23% in BCBSTX and of 6% in Medicare between 2008 and 2012 (see Figure 1 and Table 1). Patterns of change in utilization were similar for both payers, though a bit slower in Medicare compared with BCBSTX. Outpatient utilization increased significantly for both populations (19% for Medicare vs. 18% for BCBSTX), while inpatient utilization declined slightly (−6% vs. −3%) and professional services registered a small increase (2% vs. 8%). There was a large divergence in price trends between Medicare and BCBSTX. Medicare prices fell in real terms for medical services and were 4% lower overall in 2012 than in 2008. In BCBSTX, in contrast, real prices were 4% higher in 2012 than in 2008. While both Medicare and BCBSTX experienced a rise in input prices (which were common to both payers), BCBSTX also faced higher negotiated prices after adjusting for input prices, especially for hospital-based services (14% for inpatient prices and 6% for outpatient prices). Prices for professional services fell slightly in real terms in both Medicare and BCBSTX.

Trends in Geographic Variation We see two opposite trends in geographic variations in Medicare and BCBSTX spending per person, with variation increasing somewhat for BCBSTX but decreasing significantly for Medicare. We start by comparing medical spending per enrollee, which is similarly defined across the two payers. In 2008, the CV across HRRs was higher for Medicare (0.088) compared to BCBSTX (0.073), but the Medicare CV (0.067) was lower than the BCBSTX CV (0.084) in 2012 (Figure 2). The shares of variation in BCBSTX spending due to the service categories did not change significantly over this time period, though there was a small increase in the outpatient share. Including drugs in BCBSTX per member spending did not appreciably affect the findings. Because the Institute of Medicine and others (Mechanic, 2014) identified PAC spending as a driver of Medicare spending variation, we investigated variation in overall Medicare spending per enrollee including PAC (including hospice). The CV including PAC (0.128) was much higher than that excluding PAC (0.088) in 2008. In 2012,


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Figure 1. Trends in 2008-2012 Medicare and BCBSTX spending, prices, and utilization by service categories. Note. BCBSTX = Blue Cross Blue Shield of Texas.

although the CV for Medicare spending per enrollee including PAC (0.085) was still higher than that excluding PAC (0.067), the downward trend in variation in CV including PAC between 2008 and 2012 is even more noticeable (Figure 2). PAC accounted for a large share (about half) of the variation in Medicare spending per enrollee each year, but declined over time. Focusing on PAC spending alone, the CVs are considerably higher than for medical spending but have been declining rapidly, from 0.227 in 2008 to 0.143 in 2012.


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Table 1. Percentage Change From 2008-2012 in Medicare and BCBSTX Spending, Prices, Adjusted Prices, Input Prices, and Utilization by Service Categories.

BCBSTX Medical Overallb Inpatient Outpatient Professional services Drugs Medicare Medical Overallb Inpatient Outpatient Professional services Post–acute care

Spending in Spending in Percentage Change From 2008-2012 2008 per 2012 per Adjusted Input member member Spending Price pricea Quantity prices ($) ($) 2,575.91 3,217.99 654.10 756.68 1,165.13

3,177.96 3,879.97 809.27 1,052.74 1,315.96

23 21 24 39 13

14 16 27 18 5

4 6 14 6 −2

8 4 −3 18 8

9 9 12 12 7

642.08

702.01

9

21

13

−10

7

6,329.20 9,195.07 2,852.75 991.57 2,484.88

6,732.36 9,748.10 2,835.96 1,252.63 2,643.78

6 6 −1 26 6

5 5 6 6 4

−4 −5 −5 −5 −3

1 1 −6 19 2

10 11 12 12 7

2,865.87

3,015.73

5

6

−5

−1

12

Note. BCBSTX = Blue Cross Blue Shield of Texas. a. Price adjusted for input prices. b. The overall category includes medical and drugs for BCBSTX and includes medical and post–acute care for Medicare.

Variation in Medicare PAC spending per enrollee is separated into its components (skilled nursing facilities, inpatient rehabilitation facilities, long-term care hospitals, home health agencies, and hospice) in Figure 3. Home health agencies accounted for the largest share of PAC spending, 39% in 2008 declining to 35% in 2012, and also contributed the lion’s share of PAC spending variation, though the contribution declines from 76% in 2008 to 52% in 2012. A look at Medicare PAC spending per enrollee across Texas HRRs in 2008 and growth rates in 2008-2012 (Figure 4) identifies a pattern of low spenders increasing spending and high spenders reducing spending. McAllen and Harlingen were highspender outliers with 2008 PAC spending per enrollee 97% and 59%, respectively, higher than the Texas average. Remarkably, these ratios fell to 35% and 28% higher than the state average in 2012, with Medicare PAC spending per enrollee dropping by 27% in McAllen and 15% in Harlingen (see the appendix). The corresponding drop in home health spending was 41% (from $2,834 to $1,900) and 33% (from $3,673 to $2,165) in McAllen and Harlingen, respectively. The share of Medicare enrollees receiving home health also fell significantly, by 20% in Harlingen (from 30% to 24%)


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CV BCBSTX medical only

CV BCBSTX including RX

0.14

0.14

0.12

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BCBS_prof

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BCBS_pharm BCBS_prof BCBS_outp

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0 2008

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2011

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CV Medicare medical only

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CV Medicare with post-acute care

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0.12 0.1

0.1 0.08

mdcr_prof

0.08

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mdcr_outp

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mdcr_pac mdcr_prof mdcr_outp

0.04

mdcr_inp

0.02

0.02 0

0 2008

2009

2010

2011

2008

2012

2009

2010

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2012

Figure 2. Contribution of service categories to Medicare and BCBSTX spending CVs across Texas hospital referral regions. Note. BCBSTX = Blue Cross Blue Shield of Texas; CV = coefficient of variation; RX = prescription drug; mdcr = Medicare; prof = professional; inp = inpatient; outp = outpatient; pharm = pharmacy; pac = post–acute care.

CV Medicare post-acute care 0.3 0.25 0.2

mdcr_snf mdcr_hp

0.15

mdcr_irf 0.1

mdcr_ltch mdcr_hh

0.05 0 2008

2009

2010

2011

2012

-0.05

Figure 3. Contribution of service categories to Medicare post–acute care spending CVs across Texas hospital referral regions.

Note. CV = coefficient of variation; mdcr = Medicare; snf = skilled nursing facilities; hp = hospice; irf = inpatient rehabilitation facilities; ltch = long-term care hospitals; hh = home health agencies.


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Figure 4. Scatter plot of post–acute Medicare spending per enrollee in 2008 and growth rates 2008-2012 by hospital referral region. Note. Dotted lines represent Texas averages.

and 27% (from 34% to 25%) in McAllen. Despite these drops, the two HRRs still had the highest spending per enrollee in 2012 for both PAC and home health as well as the highest percentage of beneficiaries receiving home health.

Discussion In general, private health plans pay higher prices than Medicare or Medicaid for hospital services (Robinson, 2011; White, 2013; White, Bond, Reschovsky, 2013). Our analysis shows that the Medicare–private price gap has widened in Texas, as input price adjusted Medicare prices declined while BCBSTX prices increased. The price trends in Medicare are slower than the growth in input prices, which could be sustainable, provided hospitals can increase their productivity over time (Stensland, Gaumer, & Miller, 2010; White & Wu, 2014). The private price increases would not require any productivity improvements by hospitals but would lead to unsustainable growth in premiums for the privately insured.

Medicare Spending Trends While the lack of data on Medicare Advantage plans prevents us from generalizing to the whole Medicare population, the fee-for-service Medicare spending per beneficiary


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fell slightly in nominal terms from 2010 to 2012, due to very slow price growth and falling quantities of PAC and hospital inpatient services. The only precedent for that type of trend is following the Balanced Budget Act of 1997, which made significant changes to Medicare payment policy (White, 2008). Slow growth in Medicare spending may continue, due to the productivity adjustments in the ACA, which started in 2011 and are scheduled to continue indefinitely, and the other payment and system reforms that are being developed and implemented.

Trends in Medicare PAC Spending The Institute of Medicine and others have shone a spotlight on PAC as a key driver of variation in Medicare. Our analysis confirms the key role that PAC plays as a driver of variation, but it also shows that something changed dramatically over the past 5 years, at least in Texas. McAllen has been called out as the “poster child” for out-of-control Medicare spending, but Medicare spending in McAllen has moved toward the Texas mean from 2008 to 2012, mainly due to decreases in PAC spending, and specifically in home health spending. These findings raise two questions: first, why was PAC so high in McAllen and Harlingen, and second, what happened between 2008 and 2012 for those two HRRs to move from major outliers to closer to the normal range? The answer to these questions is complex, but the sociodemographic characteristics of the Medicare population in the region helped answer the first question by contributing to high Medicare spending and specifically PAC spending. McAllen and Harlingen have poverty rates that are double the rest of Texas (36% vs. 18%) and uninsurance rates that are significantly higher (38% vs. 27%), characteristics that are known to increase Medicare costs (Glied, 2014; McWilliams, Meara, Zaslavsky, & Ayanian, 2009). The population in the region suffers from a high rate of undertreated chronic conditions and is difficult and expensive to treat once it obtains Medicare coverage, perhaps contributing to the high use of PAC. In answering the second question, we notice that the period between 2008 and 2012 coincides with concerted efforts to increase pressure to reduce inappropriate use, nationally and in McAllen. Gawande (2009), in his now-famous New Yorker piece, spotlighted overtreatment in McAllen, and an environment where potentially dubious practices and fraud can flourish. The spotlight this put on the area may have contributed to a reduction in the area’s high treatment rates. At the local level, around 2010, the Office of Inspector General, Texas Health and Human Services Commission, increased activities to reduce Medicare fraud in the Rio Grande Valley. At the national level, a policy that contributed to curbing home health use was the imposition of the “face-to-face” examination requirement for receiving home health care under Medicare introduced by the ACA to prevent home health fraud (Patient Protection and Affordable Care Act, 2010). The certification of eligibility for Medicare home health services became effective March 23, 2010, and required the certifying physician to document a face-to-face encounter between the physician, or a nonphysician practitioner working with the physician, and the patient within the 90 days prior to the start of care, or within the 30 days after the start of care (American College of Physicians, 2014). The


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timing of the federal antifraud efforts and the implementation of the “face-to-face” mandate coincides with the sharp drop in home health spending and utilization we observe in McAllen and Harlingen in 2010, with continuing decreases in 2011 and 2012 as these measures become more established. Overall, the slow spending growth in fee-for-service Medicare and the sharp spending decrease in traditionally very high spending regions indicate that Medicare has been successful in controlling spending and curbing excesses. On the other hand, price increases continue to drive excess spending growth among the privately insured, and policies and reforms are needed to address these increasing spending trends.

Appendix Contribution of McAllen and Harlingen to the Reduction in Medicare Spending Variation The contributions of McAllen and Harlingen to the reduction in Medicare spending variation in Texas—particularly in PAC spending—is further supported by recomputing the CV and the share of variation attributable to each service category, excluding the McAllen and Harlingen HRRs. It indicates that much of the variation in overall Medicare spending was due to those two HRRs. The CV excluding McAllen and Harlingen varies between 0.086 in 2008 and 0.075 in 2012, with PAC accounting for about 40% of the variation in each year. The relative reductions in PAC spending in McAllen and Harlingen were part of a general trend toward reduction in the variation in PAC spending. Contribution of service categories to Medicare spending coefficients of variation across Texas hospital referral region excluding McAllen and Harlingen. 0.14 0.12 0.10 Post acute care

0.08

professional

0.06

outpaent inpaent

0.04 0.02 0.00 2008

2009

2010

2011

2012


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Acknowledgments The authors wish to thank the Commonwealth Fund for financial support for this project and Blue Cross Blue Shield of Texas for providing the data. We also thank Cecilia Ganduglia, Ibrahim Abbas, and Tom Reynolds at UTSPH 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.

Declaration of Conflicting Interests The authors declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding The authors disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: Funding for this study was provided by the Commonwealth Fund.

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