Nancy D. Beaulieu, PhD

To compare supply of and access to physicians and hospitals for low- and high-income communities in the United States following the Affordable Care Act.

Cross-sectional study using 2015 data on physicians and hospitals from CMS, SK&A, commercial claims, and the American Hospital Association.

We computed densities of primary care and specialty physicians in low- and high-income zip code tabulation areas (ZCTAs), further stratified by urban, suburban, and rural categories. We also estimated geodetic distances to the nearest general acute care, teaching, children’s, and psychiatric hospitals for each ZCTA.

Urban low-income ZCTAs were 0.5% more likely to have very low primary care physician density (<5 physicians per 10,000 population) relative to high-income ZCTAs. The disparity was great in rural areas (where the analogous figure was 1.7%) and greatest in suburban areas, where low-income ZCTAs were 7.4% more likely to have very low primary care density relative to high-income ZCTAs. The percentage of urban and suburban low-income ZCTAs with very low specialty physician density was 1.3 to 1.4 times larger than that of high-income ZCTAs. Although the average distance to a general acute care hospital was similar for low- and high-income ZCTAs (median difference range, 0.4-2.1 miles), residents of rural low-income areas had to travel substantially farther to teaching, children’s, and psychiatric hospitals (10.2-35.6 miles).

Our findings document geographic variation in provider supply related to income and suggest that improving access to care may require greater attention to local delivery systems.

The American Journal of Accountable Care. 2019;7(4):11-18

One of the chief goals of the Affordable Care Act (ACA) was to expand access to healthcare to all Americans, particularly through insurance coverage.

 However, while health insurance increases access to care, it does not guarantee such access.

 Specifically, the structural aspects of the local delivery system—supply, location, and organization of healthcare resources&mdash;all vary widely across markets, but they have received much less attention than coverage as a driver of disparate access. Although considerable literature relates availability of primary care to beneficial outcomes and lower costs,

 it does not focus on disparities in access related to income. Although many studies have shown that Medicaid expansions have reduced the number of uninsured Americans,

 few studies have looked at primary care supply after many states’ Medicaid expansions took effect in 2014. The effects of improved financial access for disadvantaged populations depend on the nature of their local delivery systems.

Barriers to access posed by lack of healthcare facilities and trained physicians has been well documented.

 Policies to address scarcity of healthcare resources date back to at least the 1946 Hill-Burton Act. Today, the Health Resources and Services Administration identifies Health Professional Shortage Areas (HPSAs) or Medically Underserved Areas/Populations based on shortage designation criteria that it develops in collaboration with state partners.

However, the HPSA definition of a shortage area is based on context and specific applications, not a broad assessment of community needs. While adequacy of the supply of providers for low-income individuals is largely unknown, dependence on local safety net providers is substantial and potentially precarious

: Today, 1 in 13 Americans obtain care from the 10,000 community health center sites funded by the ACA.

Much of the existing literature relating provider supply to outcomes focuses on rural areas. Prior research suggests that rural populations experience greater difficulties in accessing needed care compared with urban populations.

 Rural areas often encounter challenges in attracting and retaining providers because providers feel “overburdened and underpaid,” among other reasons, compared with those in urban areas.

Yet rural is not synonymous with low income, and quantitative analyses of delivery systems in low-income areas are lacking despite the focus on access expansion policies for low-income populations. Existing research on this topic is largely qualitative and in the form of case studies of high-functioning safety net organizations.

 The large literature on the safety net generally takes the provider as the unit of observation and hence cannot speak to differences among communities in access to care associated with the local availability of providers. There has been little research to characterize healthcare delivery systems in low-income communities nationally.

We use a unique relational database to examine potential barriers to access and to quantify variation in the supply of primary care and specialty physicians, as well as general acute care and specialty hospitals, across the United States. In this paper, we present a large-scale, national description of the local healthcare delivery systems found in low- versus high-income communities following the ACA.

We selected zip code tabulation areas (ZCTAs) as our primary unit of observation, excluding 667 ZCTAs with fewer than 10 residents and 517 ZCTAs with missing income data. Our final database included 32,322 ZCTAs with 316,196,416 residents (99.9% of the US population in 2015).

ZCTAs capture variation in income better than larger units such as primary care service areas (PCSAs). There is often substantial income heterogeneity among the constituent zip codes within a single PCSA, for example. Because individuals may seek care outside of their ZCTAs, we assign to each ZTCA measures of provider supply that encompass a wider area (described below).

 and using income data from the American Community Survey (ACS), we classified a ZCTA as low-income if the percentage of individuals with income below 200% of the federal poverty level (FPL) fell in the top decile of the national distribution (57.4%-100% population <200% FPL; distributions were computed using all ZCTAs with poverty data and after the population size restriction for metropolitan areas). Analogously, we classified a ZCTA as high-income if the percentage of individuals with income above 500% of the FPL fell in the top decile of the national distribution (43.0%-100% of population >500% FPL). Our results were robust to other thresholds, such as percentage of individuals with income below 100% or below 300% of the FPL.

In a sensitivity analysis, we classified low- and high-income ZCTAs based on their state income distributions and refer to these as relative low- and high-income ZCTAs. Specifically, we classified ZCTAs in each state as being a low- (or high-) income community if the percentage of individuals in the ZCTA with low (high) income fell in the top decile of the state distribution. Income distributions vary from state to state, and relative low-income status in a state may be as important for disparities as national low-income status. Implications for state- and federal-level policy may also differ.

 Our data on practicing physicians and their practice locations were derived from 2015 data from the CMS Provider Enrollment, Chain, and Ownership file; SK&A physician file; and CMS MAX Provider Characteristics (verified in the National Plan and Provider Enumeration System) file. The total number of physicians included in this study (n = 818,951) compared favorably with numbers in other sources. The American Medical Association Masterfile contains 829,962 physicians in 2013 and the 2014 SK&A data file contains 565,000 unique physician National Provider Identifiers. Nurse practitioners and physician assistants also deliver care to patients, in some states do not require physician supervision, and sometimes serve as patients’ primary care clinicians. However, they were excluded from this study because there was no comprehensive data source on these nonphysician clinicians.

Because some patients cross ZCTA lines for care, we assigned to each ZCTA the physician supply of its PCSA. The ZCTA unit of analysis captures low income more accurately than broader geographic areas, whereas PCSA-level physician supply considers border crossing and more accurately represents the service area in which an individual obtains care. Specifically, we computed primary care and specialist physician density (ie, number of physicians per 10,000 population) for each PCSA and assigned these measures to their corresponding ZCTAs. We counted the 236,575 physicians who practiced at more than 1 site by proportions; for example, a physician who practiced at 2 sites was assigned 0.5 effort at each.

We classified physicians as primary care or specialist. Physicians trained in family medicine, pediatrics, and internal medicine (with no subspecialty training) were classified as primary care physicians. We included only non—hospital-based specialists in our tabulations of specialist supply (ie, not emergency medicine, radiology, anesthesiology, or pathology). Of the 818,951 National Provider Identifiers included in this study, 274,449 (33.5%) were classified as primary care physicians and 396,588 (48.4%) as specialists.

 Our hospital data were derived from CMS’ Provider of Services file, the American Hospital Association survey, and the SK&A hospital file from 2015. We classified 6337 short-term acute care hospitals into general acute care (n = 5033), children’s (n = 187), and psychiatric (n = 2402) hospitals. We identified 1302 (22.0%) teaching hospitals based on the hospital’s receipt of graduate medical education payments from CMS in 2015.

 A hospital could be classified as more than 1 type.

We computed 2 measures of the availability of hospital services. First, we computed geodetic distances (shortest curve between 2 points along the surface of the earth) from each ZCTA centroid to the closest general acute care, psychiatric, children’s, and teaching hospital. To reduce the effects of outliers, we winsorized the distances at 1% and 99% for each respective type of hospital and geographic category. Second, analogous to how we assigned PCSA data to ZTCAs for physician supply, we assigned to each ZCTA the number of beds (by hospital type) in the Hospital Service Area (HSA) in which it was located. We converted zip codes to ZCTAs and then assigned each ZCTA to an HSA using a zip code to HSA crosswalk from Dartmouth Atlas. A total of 170 ZCTAs could not be assigned to an HSA.

Building on methods from a previous study and corresponding to geographical distinctions in the Behavioral Risk Factor Surveillance Survey,

 we classified ZCTAs as urban, suburban, or rural (see 

]). The proportion of individuals in urban areas was high across both low- and high-income areas (

). Although it was 10-fold smaller in terms of individuals, rural areas had nearly twice as many ZCTAs as urban areas. The number of high-income individuals in suburban areas was comparable with that in urban areas, but there were twice as many suburban as urban ZCTAs. Using data from the 2010 US Census

 and rolling 5-year ACS estimates for 2011 to 2015,

 we tabulated socioeconomic characteristics for each ZCTA in our sample.

We first compared the distributions of provider supply in low- and high-income urban, suburban, and rural communities. Next, we used weighted linear regression analysis to examine the relationship between the percentage of low-income ZCTA residents and the supply of physicians (in the ZCTA’s PCSA) and hospital services (in the ZCTA’s HSA).

To assess whether the relationship between physician availability and income varied by urban/suburban/rural designation, we included interaction terms between our measure of low-income (ie, percentage of ZCTA residents with incomes <200% FPL) and urban/suburban/rural indicators. Because physicians affiliated with hospitals may have a wider service area than the PCSA, we controlled for the presence of a general acute care hospital in the PCSA. If we drop this control, the disparities that we measure are attenuated because low-income areas with general acute care hospitals have higher physician supply (see eAppendix). Standard errors were clustered at the PCSA level.

In a sensitivity analysis, we estimated regressions with state fixed effects to explore whether income-related differences in provider supply operate on a national and/or state level.

Our final data set included 32,322 ZCTAs with 316,196,416 residents: 27.6 million (8.7%) in low-income ZCTAs and 46.5 million (14.7%) in high-income ZCTAs. ZCTAs identified as low-income for this study represent socioeconomically disadvantaged populations with high concentrations of poverty and racial and ethnic minorities and substantially lower rates of health insurance (

Low-income ZCTAs were concentrated in the southern region of the United States and high-income ZCTAs in the northeast and mid-Atlantic regions (

). Low-income ZCTAs were disproportionately located in rural areas (57% compared with 21% of high-income ZCTAs), whereas the majority of high-income ZCTAs were located in suburban areas (52% compared with 16% of low-income ZCTAs). Seventy-six percent of people from low-income ZCTAs and 44% of people from high-income ZCTAs lived in urban areas (eAppendix Table 1).

Urban low-income ZCTAs were 0.5% more likely than high-income ZCTAs to have very low primary care physician density (we define this to be fewer than 5 physicians per 10,000 population) (

). The disparity was great in rural areas (where the analogous figure was 1.7%) and greatest in suburban areas, where low-income ZCTAs were 7.4% more likely to have very low primary care physician density. The percentage of low-income ZCTAs in urban and suburban areas with very low specialty physician density was 1.3 to 1.4 times larger than that for high-income ZCTAs (63.6% vs 50.1% in urban areas; 92.1% vs 64.4% in suburban areas).

There was considerable variation within states in both physician supply and rates of low-income ZCTAs (

); 82% of total variation in low-income percentage was within states (97% for primary care and 93% for specialty supply). Relative income-related disparities in physician supply might be present if providers chose to locate in higher-income areas within states. To investigate within-state income-related disparities, we added state fixed effects to our regression models. The within-state estimates of disparities were smaller but still significant for primary care physicians (median density was 27% greater for high-income ZCTAs compared with 50% greater) and specialists in suburban areas (70% greater compared with 147% greater) (

Regressions suggested a significant linear relationship between the percentage of a ZCTA’s population with income below 200% of the FPL and the supply of physicians in the ZCTA’s PCSA. We plotted the association between ZCTA percent low-income and physician density separately by urban/suburban/rural category (

 for the full set of regression coefficients). In suburban areas, these regression results implied that moving from the 75th percentile of percent low-income (45.8%) to the 25th percentile (23.4%) was associated with an increase in suburban areas of 0.6 primary care and 1.6 specialist physicians per 10,000 PCSA population. The mean percentage of ZCTA residents living below 200% of the FPL was 35.5% (median, 34.2%; SD, 17.1%; range, 0%-100%).

On average, residents in low-income urban areas lived closer to general acute care, teaching, and psychiatric hospitals than residents in high-income urban areas (

). However, differential median distances were large for teaching, children’s, and psychiatric hospitals in suburban and rural areas (ranging from 15.6 to 30.7 miles).

We modeled the relationship between the percent of ZCTA residents who were low-income and the distance between the ZCTA’s centroid and the closest hospital (

). On average, residents of low-income ZCTAs needed to travel 0.5 and 0.2 miles longer to get to the closest general acute care hospital in suburban and rural areas, respectively. Differential distances were similar for teaching, children’s, and psychiatric hospitals.

In this national study, we documented that physician supply is lower in low- compared with high-income communities and that residents in low-income communities need to travel greater distances to specialty but not necessarily general acute care hospitals. Disparities in physician supply for low- versus high-income communities are greater in suburban areas and for specialty physicians. Disparities were lessened but not eliminated when we examined within-state variation in income and provider supply. Our findings likely underestimate the extent of income disparities in access to healthcare because some physicians do not deliver services to uninsured or Medicaid patients.

Prior work has shown geographic variation in the delivery of healthcare and the importance of the relationship between provider availability and rates of utilization of services such as physician visits.

 Provider availability is also related to quality of care.

 However, much of this work is outdated, and more recent debates on disparities have limited evidence-based discussion of delivery system infrastructure. This study quantifies the current local delivery systems on a national scale by describing the supply of primary care and specialty physicians, as well as access to different types of hospitals. We show that disparities exist between low- and high-income communities, particularly in physician supply in suburban areas and in distance to specialty hospitals for residents in rural areas.

Our study focuses on one piece of the pathway from access to outcomes (ie, delivery system). Although there is support for this relationship in the literature,

 our lack of claims data prevents us from making connections to outcomes in this paper. We cannot be certain that areas with fewer providers are underserved or that places with more abundant providers are overserved. In addition, it is possible that some of the uninsured, particularly those in low-income, urban areas, utilize emergency department or safety net clinics as a default primary care access point. We did not have these data. Nevertheless, given concerns about primary care shortages generally, we think it is unlikely that residents in low-income communities with access to fewer primary care physicians are optimally served and believe that policies to address health disparities would benefit from greater attention to the local delivery system. Similarly, given the increasing interest among policy makers in transportation issues for low-income patients, understanding travel distances is important.

There are also measurement issues worth noting. First, defining markets is challenging because patients can cross boundaries. The measurement of disparities in provider supply related to income may be sensitive to how markets are defined and academic medical centers are treated. Large urban medical centers have many academic physicians who see patients on a part-time basis. These centers are mostly located in urban, often low-income, communities and may artificially inflate provider supply estimates. Second, without recent national Medicaid claims data, we could not examine the extent to which being restricted to providers who accept Medicaid contributed to disparities in access. Third, we did not have cost and quality data for comparing within and across urban, suburban, and rural areas. Fourth, because of data limitations, we could not identify all of the sites in which nonphysician providers such as nurse practitioners delivered care, and therefore did not include them in our analyses. Finally, our results are based on averages across all markets, masking much heterogeneity at the local level. Although the heterogeneity within and across local areas may make it difficult to generalize across the United States, this high-level view is important for informing both federal and local policy.

This study complements other related work. Specifically, recent studies of access focus attention on health insurance expansions (ie, patients’ ability to pay for care).

 The 2016 National Healthcare Quality and Disparities report documented income- and race-related disparities in healthcare utilization and quality.

 A more recent study by Chetty and colleagues went a step further in documenting substantial local area variation in income-related disparities in health outcomes that were uncorrelated with health insurance coverage.

This study on local delivery systems broadens the policy discussion to include nonfinancial barriers to accessing care such as the resources and services available in local communities.

The stratification of our analyses by urban, suburban, and rural areas generated findings consistent with and complementary to those of Schnake-Mahl and Sommers.

 Based on data from a nationally representative survey defining access as having health insurance, a usual source of care, unmet need due to cost, and receipt of a routine checkup, they find that suburban low-income residents face higher barriers to accessing care. Combining their findings with ours suggests that suburban areas may be high-potential places for delivery system interventions to close the divide between the low- and high-income residents. Improving access to care for low-income populations may require greater attention to provider availability and access to specialty hospitals in local delivery systems.

Massachusetts Institute of Technology, Sloan School of Management (CAN), Cambridge, MA; Department of Health Care Policy, Harvard Medical School (CAN, MEC, NDB), Boston, MA; University of California, San Francisco, School of Medicine (IO), San Francisco, CA.

 Research reported in this study was supported by The Commonwealth Fund under award number 20160683. The content is solely the responsibility of the authors and does not necessarily represent the official views of The Commonwealth Fund.

 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 (CAN, MEC, IO, NDB); acquisition of data (CAN, IO, NDB); analysis and interpretation of data (CAN, MEC, IO, NDB); drafting of the manuscript (CAN, NDB); critical revision of the manuscript for important intellectual content (CAN, MEC, NDB); statistical analysis (CAN, IO, NDB); obtaining funding (MEC); administrative, technical, or logistic support (CAN, IO); and supervision (CAN, NDB).

Articles

This large-scale, national study shows geographic variation in provider supply and hospital access for low- and high-income communities following the Affordable Care Act.

Comparison of Healthcare Delivery Systems in Low- and High-Income Communities

 Although we know that healthcare costs are concentrated among a small number of patients, we know much less about the concentration of these costs among providers or markets. This is important because it could help us to understand why some patients are higher-cost compared with others and enable us to develop interventions to reduce costs for these patients.

We used a 20% sample of Medicare fee-for-service claims data from 2011 and 2012, and defined high-cost patients as those in the top 10% of standardized costs. We then characterized high-concentration hospitals as those with the highest proportion of high-cost patient claims, and high-concentration markets as the Hospital Referral Regions (HRRs) with the highest proportion of high-cost patients. We compared the characteristics and outcomes of each.

 High-concentration hospitals had 69% of their inpatient Medicare claims from high-cost Medicare beneficiaries compared with 51% for the remaining 90% of hospitals. These hospitals were more likely to be for-profit and major teaching hospitals, located in urban settings, and have higher readmission rates. High-concentration HRRs had 13% high-cost patients compared with 9.5% for the remaining 90% of HRRs. These HRRs had a smaller supply of total physicians, a greater supply of cardiologists, higher rates of emergency department visits, and significantly higher expenditures on care in the last 6 months of life.

High-cost beneficiaries are only modestly concentrated in specific hospitals and healthcare markets.

		High-cost patients were only modestly concentrated in hospitals; those that disproportionately cared for high-cost beneficiaries were generally larger, academic teaching institutions with better outcomes on mortality but higher readmission rates. 

		High-cost patients were only modestly concentrated in markets. We saw differences between the markets that were more concentrated versus not; concentrated markets had much higher proportions of racial and ethnic minorities and individuals in poverty. 

		Efforts to lower spending among high-cost patients should remain broad. Policy efforts need to target the special needs of organizations and communities disproportionately serving high-cost beneficiaries.

High and rising healthcare costs are the single biggest challenge facing federal and state governments, many businesses, and families. These healthcare costs are highly concentrated among a small number of patients; for example, just 10% of Medicare patients account for more than half of all Medicare spending.

 There is broad consensus that we will need to improve care for this population of highly expensive patients in order to manage spending and improve outcomes.

Although we know that healthcare costs are concentrated among a small number of patients, we know much less about the concentration of healthcare costs among providers or markets. This is important information as it could help us to understand why some patients are higher-cost compared with others and help us begin to develop interventions to reduce costs for these patients. For example, if differences in practice patterns between providers are a major driver of costs, we would expect to see high-cost patients clustered within a subset of providers. Such clustering would suggest that interventions might be more effective if they targeted physicians with a high proportion of high-cost patients. Similarly, it is possible that geographic variation in practice patterns is the dominant feature; if this were true, we would expect to see clustering of high-cost patients within communities and we would need to design interventions that address the practice patterns or underlying medical needs of these communities. However, we know very little about whether care for high-cost patients is concentrated and, if so, whether the characteristics of the providers and communities where these patients are disproportionately situated differ from those providers or communities with a lower concentration of high-cost beneficiaries.

Hospitals are the setting where most expenditures for high-cost patients are incurred. Therefore, in this study, we set out to answer 2 sets of questions: 1) Are high-cost patients concentrated among certain hospitals? If so, how do hospitals that disproportionately care for high-cost patients differ from other hospitals? 2) Are high-cost patients concentrated within certain communities? If so, how do communities with a high proportion of high-cost patients differ from those communities with lower proportions of high-cost patients?

We used a 20% sample of Medicare fee-for-service (FFS) claims data from 2011 and 2012. Patients enrolled in Medicare Advantage and those not continuously enrolled in Parts A and B during the study period were excluded from our analysis because we do not have complete data on annual healthcare costs in these subpopulations. We excluded those younger than 65 years because they are a highly heterogeneous population, achieving eligibility for Medicare based on clinical conditions (eg, end-stage renal disease) that are highly correlated with spending.

Patient race was categorized in the Medicare data based on self-report. We assigned comorbidities using CMS Hierarchical Condition Categories and based on diagnoses in inpatient, outpatient, and carrier claims. Cost information was aggregated from all Medicare claims files, including inpatient, outpatient, carrier, skilled nursing facility, home health, hospice, and durable medical equipment. 

International Classification of Diseases, Ninth Revision, Clinical Modification 

) codes were used to identify comorbidities.

We focused on general acute care nonfederal hospitals and only included those with a minimum of 17 beneficiaries in 2012 (from our 20% sample), thus eliminating the very smallest of hospitals. Failure to exclude these hospitals would have led to the identification of very small hospitals as disproportionately caring for the highest-cost patients; for example, in a hospital with only 3 Medicare claims, 1 from a high-cost patient would be reported as having 33% of its patients being high-cost. We used the American Hospital Association Annual Survey to obtain data on hospital structural characteristics and the Hospital Compare database to obtain data on hospital quality and patient experience.

 We calculated both mortality and readmissions using standard methods with adjustment via the Elixhauser comorbidity adjustment scheme, which is commonly used with administrative data,

 and which we have used in prior work in this area.

 We used Hospital Referral Regions (HRRs), as defined by the Dartmouth Atlas for Health Care,

 to analyze healthcare markets. We obtained data on HRR Medicare spending from the Dartmouth Atlas. Regional demographic characteristics and data on physician and hospital bed supply were obtained from the Area Resource File. We used the Commonwealth Scorecard Health System Data Center to obtain data on population health measures.

n order to identify high-cost beneficiaries, we first calculated total standardized costs of care for each Medicare beneficiary in our sample using CMS methodology.

 By standardizing costs of care, we can identify patients who use a comparable amount of medical care across differing regions in which actual costs of care may vary. For example, although Medicare may pay more for a chest radiograph at a teaching hospital in New York City than at a community hospital in Phoenix, based on differences in wage index and other factors, standardized cost using the Medicare Wage Index assigns the same value to the service in both places. We then defined high-cost patients as those whose spending was in the top decile nationally in 2012.

To define high-concentration hospitals, we first calculated the percentage of the hospital’s claims attributable to high-cost patients. We then calculated the distribution of this percentage among our sample of hospitals and categorized hospitals as high-concentration if their percentage of claims from high-cost patients was in the top decile nationally.

We next calculated the concentration of high-cost patients within markets and then compared high-concentration and non—high-concentration HRRs. We used HRRs as our geographical unit of observation for healthcare markets. For each market, we computed the percentage of beneficiaries in our sample residing in each HRR who were designated as high-cost patients. We then computed the distribution of this percentage and labeled HRRs as high-concentration markets if their percentage of beneficiaries who were high-cost was in the top decile nationally. We computed statistics on the distribution of annual patient costs, the percentage of beneficiaries in a region who were high-cost patients, and the percentage of a hospital’s claims attributable to high-cost patients. Because costs of end-of-life care constitute a substantial portion of overall Medicare costs, decedents were overrepresented among the group of highest-cost patients; thus, we conducted our analyses with and without those patients who died during the period January 1, 2012, through June 30, 2013. The results were qualitatively similar and we present our primary results based on including decedents.

After categorizing high-cost and non—high-cost patients, we first compared characteristics between these 2 groups of patients. We examined the following beneficiary-level predictors of high-cost status: age, race, gender, Medicaid eligibility, and comorbidities. We also investigated the relationship between a number of hospital characteristics and high-cost concentration status. We examined the relationship between high-cost concentration and the following market-level variables: percentages of blacks, Hispanics/Latinos, and the population in poverty; total physician supply; cardiologist supply; number of short- and long-term general hospital beds; and the rate of spending in the last 6 months of life. We also examined the relationship between high-concentration HRRs and health system performance on quality metrics, including potentially avoidable emergency department visits among Medicare beneficiaries, potentially preventable mortality, and percentage of adults 50 years or older receiving recommended screening and preventive care. For all bivariate analyses we used 

 tests to assess the statistical significance of differences based on high-cost and

For our multivariate analyses, we estimated logistic regression models of the likelihood of being a high-cost concentration hospital or high-cost concentration HRR as a function of hospital and market characteristics. In the hospital-level regression, we clustered our standard errors within market.

All analyses were performed using SAS version 9.4 (SAS Institute, Cary, North Carolina).

Characteristics of High-Cost and Non—High-Cost Beneficiaries

There were 4,937,361 Medicare beneficiaries in our 20% sample file, 493,736 of which were categorized as high-cost. As expected, a large percentage of Medicare costs (55%) were concentrated in a small percentage of beneficiaries (10%). The average cost for the high-cost cohort was more than 10-fold higher than that of the non—high-cost cohort. Compared with non–high-cost patients, high-cost patients were more likely to be black (9.84% vs 7.2%; 

<.0001) and nearly twice as likely to be eligible for Medicaid (22.5% vs 12.7%; 

). As expected, high-cost patients also had higher rates of chronic disease, including mental health conditions, congestive heart failure, renal failure, chronic obstructive pulmonary disease, and vascular disease (Table 1).

Concentration of High-Cost Beneficiaries by Hospitals

High-cost beneficiaries accounted for a disproportionate share of the Medicare claims at every hospital. The highest decile of &shy;hospitals had 69% of their inpatient Medicare claims from high-cost Medicare beneficiaries compared with 51% for the remaining 90% of hospitals. The hospitals in the lowest decile of concentration, by contrast, still had 32.6% of their inpatient claims come from high-cost beneficiaries.

If high-cost beneficiary hospital claims and inpatient costs were evenly distributed nationally, we would expect high-cost hospitals to account for 10% of all inpatient claims and costs. We found that high-cost patients and their inpatient expenditures were only slightly more concentrated: just 12.54% of hospital claims attributable to high-cost beneficiaries and 14% of inpatient costs were for care delivered at hospitals in the highest decile of concentration of high-cost patients (

). The median cost per claim in high-concentration hospitals was 15% greater than the median cost per claim in other hospitals (

Concentration of High-Cost Beneficiaries in the Markets

Similar to the hospital analysis, we found only a modest concentration of high-cost patients in the markets. The percentage of Medicare beneficiaries in each HRR considered high-cost varied modestly across communities (

). In the 10% of markets with the highest proportion of high-cost patients, on average, 13% of beneficiaries were high-cost compared with 7% of beneficiaries in the lowest decile of concentration. The 30 HRRs with the highest concentration of high-cost patients—representing 10% of all HRRs in the United States&mdash;accounted for 9.7% of Medicare costs. The median annual per-beneficiary total standardized cost in these HRRs was $14,193 per year in high-concentration markets compared with $11,077 in all other markets (

Comparing High-Concentration Hospitals Versus Other Hospitals

We found substantial differences in the characteristics of hospitals that disproportionately care for high-cost patients (ie, the high-concentration hospitals) versus other institutions in bivariate (eAppendix Table 1) and multivariate (

) analyses. The hospitals with the greatest proportion of claims from high-cost patients were more likely to be for-profit hospitals and teaching hospitals. Possibly reflecting resource constraints, high-concentration hospitals were more likely to receive disproportionate share payments and to have lower nurse-to-patient staffing ratios (although the latter was found only in bivariate analyses). High-concentration and lower-concentration hospitals scored similarly on many quality metrics; however, high-concentration hospitals were more likely to have higher 30-day readmission rates and slightly lower 30-day mortality rates (Table 4 and 

Differences Between Markets With More Versus Fewer High-Cost Patients

We found significant differences between the 30 HRRs with the highest concentration of high-cost patients and the 276 other HRRS with lower concentrations of high-cost patients. In bivariate analyses, high-concentration HRRs were more likely to have a higher percentage of beneficiaries who were black (18.5% vs 10.1%; 

= .0009), living in poverty (19.1% vs 15.5%), and living in urban areas (82.2% vs 71.3%) (

). High-concentration HRRs also had a greater supply of specialists—but an equal supply of total physicians&mdash;and a lower supply of long-term hospital beds despite a higher population density.

High-concentration HRRs have both high costs of care and high rates of care utilization. In high-concentration HRRs, the Dartmouth Hospital Care Intensity Index (1.39 for high-concentration hospitals and 0.94 for non—high-concentration hospitals) and the Medicare end-of-life spending measure ($79,446 for

concentrated HRRs and $65,538 for nonconcentrated HRRs) were both significantly higher in high-concentration HRRs than in lower-concentration HRRs. Although the general pattern of these findings held in multivariate analyses (

), supply-side factors and end-of-life spending of the market were more influential in the likelihood of being high-concentration than sociodemographic characteristics of the market.

We examined the concentration of high-cost patients in hospitals and markets and found only a modest degree of concentration. However, the hospitals and markets that disproportionately care for high-cost beneficiaries were markedly different than those that cared for fewer such patients: in general, these hospitals were either academic teaching or for-profit institutions operating in urban settings and serving a greater proportion of low-income patients. Similarly, we saw differences between the markets that were more concentrated versus not, with concentrated markets having a greater supply of specialists and a lower supply of long-term care beds. Spending in the last 6 months of life was also significantly higher in high-cost concentration HRRs. Taken together, these findings suggest that although high-cost patients are widely distributed, particular provider and delivery system characteristics are associated with a higher degree of concentration.

These findings have important implications for our approach to caring for high-cost beneficiaries. Although there must be a broad approach to identifying and caring for these patients, our findings suggest that we must pay special attention to the providers where these patients receive their care. The high-concentration hospitals were more likely to be teaching hospitals, which may reflect the more specialized and costly types of care provided in these institutions. However, high-concentration hospitals were also more likely to be for-profit; whether this reflects differences in practice patterns or patient mix is unclear and warrants further examination. Additionally, given that the high-concentration hospitals tended to care for a poorer population, it is possible that the higher costs were reflective of a greater degree of medical and social need. Initiatives like the Disproportionate Share Hospital (DSH) program were designed for this reason and provide additional payments to help safety net providers care for the poor. Whether DSH payment cuts under the Affordable Care Act, in conjunction with insurance expansion, will ultimately hamper or help these facilities that care for high-cost beneficiaries is unclear.

There are important limitations to our study. First, our analyses are based on data for the traditional FFS Medicare population; the patterns of high-cost concentration may differ in other populations. Second, our measure of patient costs did not include prescription drug expenditures. However, given that drug costs constitute approximately 10% of total healthcare spending, small differences for this kind of expenditure are unlikely to explain our findings. Third, we used the most recent Medicare data available at the time of our analysis (calendar year 2012), but patterns may have changed in more recent years. Fourth, we used the 10th percentile as our cutoff for being considered high-cost and high-concentration. This cutoff has been used by our group and others in previous research

 and was chosen to allow a population large enough to be nationally representative, but small enough to represent potential targets for intervention to reduce costs; nevertheless, cost is a continuous variable. The use of alternative cutoffs (eg, the top 1% or 5% of spenders, as has also been examined previously)

 might yield different patterns in cost concentration. Finally, our multivariate analyses reflect cross-sectional associations and the relationships identified cannot be interpreted as causal. Furthermore, we could not address the endogeneity of hospitals’ location and strategy decisions.

High-cost beneficiaries are only modestly concentrated in specific hospitals and healthcare markets; as such, efforts to efficiently manage costs and care among the high-cost cohort should remain broad. However, the providers and communities where these patients disproportionately receive their care are meaningfully different, suggesting that additional research on the mechanisms underlying these differences might benefit policy efforts to improve care for these high-cost beneficiaries. 

 Department of Health Care Policy, Harvard Medical School (NDB, RCW), Boston, MA; Department of Health Policy and Management, Harvard T.H. Chan School of Public Health (KEJ, AKJ), Boston, MA.

 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 (NDB, AKJ, KEJ); acquisition of data (NDB); analysis and interpretation of data (NDB, KEJ, RCW); drafting of the manuscript (NDB, AKJ, KEJ, RCW); critical revision of the manuscript for important intellectual content (NDB, AKJ, KEJ); statistical analysis (NDB, RCW); obtaining funding (AKJ); administrative, technical, or logistic support (NDB); and supervision (NDB, AKJ).

Ashish K. Jha, MD, MPH, Harvard T.H. Chan School of Public Health, 42 Church St, Cambridge, MA 02138. E-mail: ajha@hsph.harvard.edu.

High-cost patients are only modestly concentrated in specific hospitals and healthcare markets.

Nancy D. Beaulieu, PhD

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