Organizational Characteristics Associated with High Performance in Medicare’s Comprehensive End-Stage Renal Disease Care Initiative

Abstract

Background and objectives 

Medicare plans to extend financial structures tested through the Comprehensive End-Stage Renal Disease Care (CEC) Initiative—an alternative payment model for maintenance dialysis providers—to promote high-value care for beneficiaries with kidney failure. The End-Stage Renal Disease Seamless Care Organizations (ESCOs) that formed under the CEC Initiative varied greatly in their ability to generate cost savings and improve patient health outcomes. This study examined whether organizational or community characteristics were associated with ESCOs’ performance.

Design, setting, participants, & measurements 

We used a retrospective pooled cross-sectional analysis of all 37 ESCOs participating in the CEC Initiative during 2015–2018 (n=87 ESCO-years). Key exposures included ESCO characteristics: number of dialysis facilities, number and types of physicians, and years of CEC Initiative experience. Outcomes of interest included were above versus below median gross financial savings (2.4%) and standardized mortality ratio (0.93). We analyzed unadjusted differences between high- and low-performing ESCOs and then used multivariable logistic regression to construct average marginal effect estimates for parameters of interest.

Results 

Above-median gross savings were obtained by 23 (52%) ESCOs with no program experience, 14 (32%) organizations with 1 year of experience, and seven (16%) organizations with 2 years of experience. The adjusted likelihoods of achieving above-median gross savings were 23 (95% confidence interval, 8 to 37) and 48 (95% confidence interval, 24 to 68) percentage points higher for ESCOs with 1 or 2 years of program experience, respectively (versus none). The adjusted likelihood of achieving above-median gross savings was 1.7 (95% confidence interval, −3 to −1) percentage points lower with each additional affiliated dialysis facility. Adjusted mortality rates were lower for ESCOs located in areas with higher socioeconomic status.

Conclusions 

Smaller ESCOs, organizations with more experience in the CEC Initiative, and those located in more affluent areas performed better under the CEC Initiative.

Visual Abstract

Introduction

Individuals with kidney failure accounted for only 1% of Medicare beneficiaries but 7% of Medicare fee-for-service expenditures—$35.9 billion—in 2017 (¹). Patients with kidney failure experience high mortality rates and elevated rates of hospital and emergency department use (¹,²). To improve health outcomes and the value of care for patients with kidney failure, the 2019 Advancing American Kidney Health Executive Order expands the use of alternative payment models among US nephrologists and dialysis providers (³). Several of these new kidney disease–specific payment models—which will be implemented in January 2022—are on the basis of the financial structures of existing kidney failure–specific payment models tested by the Centers for Medicare & Medicaid Services (CMS).

Medicare’s first widely implemented kidney disease–specific payment model was the Comprehensive End-Stage Renal Disease Care (CEC) Initiative, a voluntary model offering financial incentives for enhanced care coordination among providers treating Medicare beneficiaries with kidney failure. In 2017, 14% of Medicare beneficiaries with kidney failure received treatment from providers participating in the CEC Initiative (⁴). The program’s incentives are structured as financial risk–sharing agreements between participating provider groups, called End-Stage Renal Disease Seamless Care Organizations (ESCOs), and CMS; ESCOs repay Medicare a share of “excess” expenditures if total inpatient and outpatient care costs for attributed beneficiaries are above a predetermined benchmark, and ESCOs share in Medicare’s savings if care costs fall below the benchmark, conditional on achieving a specified level of quality measure performance. ESCOs are led by at least one dialysis facility and one nephrologist, and they may also include many other providers (e.g., primary care physicians, non-nephrology specialists, and hospitals). ESCOs led by a dialysis facility associated with one of the nation’s two large dialysis organizations (LDOs) may receive financial penalties or bonuses under this model, whereas ESCOs led by independent or small dialysis organizations are eligible for bonuses only.

ESCOs have achieved small but significant reductions in both inpatient and outpatient spending, but performance results varied considerably across ESCOs (^456–7). The factors that have led some ESCOs to perform more effectively than others remain unclear. Evaluations of Medicare’s more established alternative payment models suggest that both organizational structures (e.g., size) and community characteristics (e.g., racial/ethnic composition) may influence performance (^{89101112131415–16}). It is unknown whether ESCOs exhibit similar patterns. If this is the case, expanding use of such payment models in kidney care may exacerbate extant disparities in kidney failure treatment and outcomes, as high-value services may be less available to less affluent communities (¹⁷). Moreover, if smaller ESCOs—or those located in lower–socioeconomic status communities—experience poorer financial performance, ESCO-based payment models may need refinement to avoid discouraging participation among these providers.

This study examines the relationship between ESCOs’ organizational and community characteristics and their performance under the CEC Initiative. Our findings can inform efforts to improve the quality and equity of nephrology care through alternative payment models, such as those initiated under the Advancing American Kidney Health Executive Order, and payment reforms for specialty care–reliant conditions.

Materials and Methods

Data

We used publicly available CMS reports to construct a novel dataset characterizing the organization-level performance, affiliated dialysis facilities, health care providers, and physician practices of all 37 ESCOs in the CEC Initiative between 2015 and 2019. These included 24 ESCOs that joined in the second year (2017–2018); all 37 continued through the third performance year (2018–2019). The CMS reports included ESCO-level performance measures and ESCO-submitted participating provider identifiers (¹⁸). We linked these ESCO data with additional provider- and practice-level information (degree type, specialty, hospital affiliation, and practice location) by National Provider Identification numbers from the National Plan and Provider Enumeration System registry maintained by CMS (¹⁸,¹⁹). Community sociodemographic characteristics were obtained at the county level from the Area Health Resources File.

Measures of End-Stage Renal Disease Seamless Care Organization Performance

We evaluated ESCO-level performance outcomes in terms of financial savings/losses and mortality using measures reported by CMS (¹⁸). Financial performance is an outcome of keen interest for providers and is tied directly to the CEC Initiative’s central incentives of shared savings and shared risk. We assessed financial performance using the gross savings/losses percentage: the ratio of ESCO’s total actual expenditures for attributed beneficiaries to total benchmark expenditures. Benchmark expenditures were calculated by CMS on the basis of a 3-year risk- and price-adjusted cross-sectional baseline of Medicare inpatient and outpatient claims. High (versus low) financial performance was distinguished as above- versus below-median gross savings/losses percentage.

The standardized mortality ratio (SMR) is a prominent marker of patient safety, which moderates the degree of financial savings ESCO is eligible to receive. SMR is calculated as the actual number of deaths among ESCO-attributed patients divided by the expected number of deaths (dialysis facility–level estimates, aggregated to ESCO) during the calendar year, adjusted for patient mix. Thus, when ESCO had SMR>1.0, its patients died at a rate higher than expected (^56–7). A high (versus low) SMR, relative to the median SMR across all ESCO-years, distinguished poorer versus better performance. CMS reported SMRs from the second performance year of the CEC Initiative onward.

Factors Associated with End-Stage Renal Disease Seamless Care Organization Performance

We constructed measures of ESCOs’ structure and composition as of the 2017–2018 model year, as well as community characteristics potentially associated with ESCOs’ overall performance and care delivery. For each ESCO, we quantified the number of dialysis facilities and the number and specialty mix of aligned providers, including total physicians; the ratio of attributed patient-years to physicians; and the presence (yes/no) of a primary care provider, surgical provider, or other non-nephrology specialist. Because an ESCO’s performance may be related to financial and institutional support (as from LDO leadership) and prior experience with the CEC Initiative (i.e., participation in ≥1 previous performance year), indicators of these contract-level characteristics were included in analyses.

On average, individuals travel >10 miles to obtain dialysis treatment (²⁰), potentially traversing multiple zip codes. Therefore, we chose to measure community characteristics using county-level data rather than at a finer level of analysis. ESCO-level community characteristics included in our models consisted of dialysis facility count-weighted averages of county-level measures of residents’ racial and ethnic composition (percentage non-Hispanic Black and percentage Hispanic residents) and socioeconomic status (percentage eligible for Medicaid and median household income).

Statistical Analyses

Data reflecting 87 ESCO-years were analyzed as a pooled cross-section for most measures (n=74 ESCO-years for which SMR was reported). Descriptive statistics were calculated to illustrate the diversity of practices, participating providers, and community characteristics of ESCOs. Two-sided t tests and chi-squared tests were used to test for unadjusted differences in these characteristics between low- and high-performing ESCOs with respect to financial performance and SMR.

Multivariable logistic regression was used to estimate these associations (risk differences calculated as average marginal effects, derived using marginal standardization techniques, and presented as percentage point changes) conditional on other observed ESCO characteristics. Regression models included performance year fixed effects to account for secular trends. SEMs were clustered at the ESCO level to address potential intraorganizational correlation of errors. To reduce the probability of type 1 error, we applied Bonferroni corrections to attenuate the range of P values we consider statistically significant for all comparative results, both adjusted and unadjusted (²¹,²²).

All analyses were conducted using Stata version 16.1 SE (²³).

Results

Characteristics of Comprehensive End-Stage Renal Disease Care Initiative End-Stage Renal Disease Seamless Care Organizations

The CEC Initiative’s 37 ESCOs varied greatly in their organizational composition and performance outcomes (Figure 1, Table 1, Supplemental Table 1). On average, ESCOs cared for 1375 attributed beneficiaries annually (mean=9.9 attributed months per year) and had total benchmark costs of $73,901,564 ($81,721 per beneficiary-year). The average gross savings/loss rate was 3% of benchmark expenditures saved (approximately $3 million gross savings). ESCOs achieved positive financial savings in 67 of 87 performance years (77%). The average SMR was 0.93 or a 7% reduction in adjusted mortality rates compared with expected. Greater than expected adjusted mortality rates (SMR>1) were reported in 35 of 74 performance years (47%), corresponding to 21 ESCOs.

Figure 1.: Distribution of End-Stage Renal Disease Seamless Care Organization (ESCO) performance on two outcome measures: gross savings/losses as a percentage of total benchmark and standardized mortality ratio (SMR). The source is the authors’ analysis of Centers for Medicare & Medicaid Services Comprehensive End-Stage Renal Disease Care Initiative performance data. n=87 ESCO-years included in the gross savings/losses histogram; n=74 ESCO-years included in the SMR histogram (SMR is not reported for the 13 ESCOs of performance year 1). Gross savings/losses are calculated as total benchmark minus aligned beneficiary expenditures as a percentage of the total benchmark; this quantity shows the total savings or losses as a percentage of the ESCO’s benchmark.

Table 1. - Characteristics and performance of End-Stage Renal Disease Seamless Care Organizations in aggregate

Characteristic	Mean (Interquartile Range)
Performance measures
Total beneficiary-years	1149 (528–1647)
Total benchmark expenditures, $ million	97 (44–138)
Total expenditures for aligned beneficiaries, $ million	96 (44–136)
Gross savings/losses, %	2.5 (0.4–4.8)
Savings/losses, $ million	3.1 (1.3–4.6)
Standardized mortality ratio	0.93 (0.86–1.0)
Organizational characteristics
Participating physicians	43 (13–50)
Nonphysician clinicians	16 (2–26)
Dialysis facilities	13 (5–16)
Participating practices	30 (10–22)
Beneficiary-yr per physician	44 (23–51)
Any primary care provider, n (%)	28 (76)
Any surgery provider, n (%)	7 (19)
Any non-nephrology specialty provider, n (%)	21 (57)
LDO led, n (%)	33 (89)
Community characteristics
Non-Hispanic Black, %	19 (8–24)
Hispanic, %	14 (4–21)
Medicaid eligible, %	8 (4–8)
Median household income, $ thousand	52.1 (44.8–58.1)

The source is the authors’ analysis of Centers for Medicare & Medicaid Services (CMS) Comprehensive End-Stage Renal Disease Care (CEC) Initiative performance data and novel physician-level End-Stage Renal Disease Seamless Care Organization (ESCO) data on all 37 ESCOs participating in the CEC Initiative’s first three performance years (2015–2019; n=87 ESCO-years). The total benchmark expenditures are defined by CMS as the expenditure target against which actual expenditures are compared to determine savings/losses achieved by ESCO. The gross savings/losses percentage is calculated as total benchmark minus aligned beneficiary expenditures as a percentage of the total benchmark; this quantity shows the total savings or losses as a percentage of ESCO’s benchmark. For ESCO’s shares of savings or losses, a positive value indicates the amount to be paid by CMS to ESCO, and a negative value indicates amounts paid by ESCO to CMS. This amount accounts for the nature of ESCO’s risk arrangement (shared savings percentage and cap). The standardized mortality rate (SMR) is the ratio of observed to expected mortality results. Scores less than one represent better than expected performance; scores greater than one represent worse than expected performance. ESCOs were categorized as including a primary care provider if they listed greater than or equal to one provider specializing in family or internal medicine or if CMS reports listed an included primary care practice. ESCOs included a surgery provider if they listed at least one provider with a surgical specialty or if CMS reports listed an included surgical practice. Non-nephrologist specialists were indicated if CMS reports listed any providers or practices that were classified as having a specialty other than internal medicine/nephrology and were not otherwise identified as primary care or surgery providers. The large dialysis organization (LDO)-led variable indicates if an ESCO is organized and led by one of the two LDOs in the United States. Community characteristics (percentage non-Hispanic Black, percentage Hispanic, percentage Medicaid eligible, and median household income) were evaluated at ESCO level using ESCO-affiliated dialysis facility weighted county averages of measures. The average US median household income was $51,091 in 2017.

On average, ESCOs had 13 dialysis facilities (range, 1–78) and 30 affiliated practices (range, 3–135). An average of 43 physicians (range, 2–248)—39 of whom specialized in nephrology—and 16 nonphysician clinicians were affiliated with each ESCO. Twenty-eight ESCOs had at least one affiliated primary care physician, seven had at least one affiliated surgeon, and 21 had one or more affiliated non-nephrology specialists. An average of 18% of the population identified as non-Hispanic Black and 13% of the population identified as Hispanic in ESCOs’ surrounding communities. In these communities, 7% of the population was Medicaid eligible; the average median household income was $52,153.

Unadjusted Characteristics of High-Performing End-Stage Renal Disease Seamless Care Organizations

The median gross savings/losses percentage was 2.4%; ESCOs reporting gross savings above 2.4% were considered high (versus low) financial performers. Financially high-performing ESCOs (n=44 ESCO-years) achieved an average of 5% gross savings, whereas low-performing ESCOs (n=43 ESCO-years) averaged losses of 0.03% (P<0.001). The median SMR was 0.93. ESCOs achieving low SMRs (<0.93) reported an average SMR of 0.85 (SD=0.06, n=37 ESCO-years). ESCOs with high SMRs (>0.93) reported an average SMR of 1.02 (SD=0.06, n=37 ESCO-years).

In unadjusted bivariate analyses (Table 2), mortality performance was associated with median household income in the community as well as ESCOs’ gross savings/losses. On average, ESCOs achieving low SMRs achieved 3% savings relative to benchmark expenditures, whereas ESCOs with high SMRs achieved 1% savings (P=0.003). Median household income was significantly higher where ESCOs achieved a low (versus high) SMR ($58,410 versus $45,890, respectively; P<0.001). Financial performance was not associated with any measure included in our analysis.

Table 2. - Bivariate analysis of End-Stage Renal Disease Seamless Care Organizations’ organizational and community characteristics by performance on financial and mortality outcome measures

Characteristic, mean (SD)	Financial Performance			Mortality Performance
	Low Performance	High Performance	P Value	High Ratio	Low Ratio	P Value
ESCO-years, n	43	44		37	37
Performance measure (outcome variable)
Gross savings/losses percentage	−0.026	5.03 (2.31)	<0.001	1.03 (2.74)	3.08 (2.91)	0.003
Standardized mortality ratio	0.95 (0.091)	0.91 (0.107)	0.10	0.85 (0.056)	1.02 (0.052)	<0.001
Contract characteristics, n (%)
Years ESCO experience			0.11			0.87
0	14 (33)	23 (52)		11 (30)	13 (35)
1	23 (54)	14 (32)		19 (51)	18 (49)
2	6 (14)	7 (16)		7 (19)	6 (16)
Large dialysis organization led	36 (84)	42 (96)	0.07	35 (95)	31 (84)	0.13
Organization characteristics
Physicians	34 (30)	56.7 (62)	0.03	39 (32)	47 (57)	0.44
Practices	22 (17)	41 (40)	0.004	28 (23)	31 (35)	0.76
Dialysis facilities	18 (20)	10 (7)	0.01	13 (11)	13 (17)	>0.99
Beneficiary-yr per physician	49 (50)	40 (35)	0.34	47 (53)	40 (25)	0.48
Any participating primary care provider, n (%)	34 (79)	30 (68)	0.25	26 (70)	30 (81)	0.28
Any participating surgery provider, n (%)	9 (21)	7 (16)	0.55	7 (19)	7 (19)	>0.99
Any participating non-nephrology specialty provider, n (%)	24 (55)	24 (55)	0.91	20 (54)	22 (60)	0.64
Community characteristics
Non-Hispanic Black, %	20 (12)	17 (13)	0.21	21 (14)	16 (10)	0.07
Hispanic, %	14 (16)	14 (10)	0.92	16 (17)	10 (8)	0.09
Medicaid eligible, %	7 (6)	8 (7)	0.76	6 (4)	8 (5)	0.82
Median household income, $ thousand	50 (10)	54 (10)	0.09	46 (5)	58 (10)	<0.001

The source is the authors’ analysis of Centers for Medicare & Medicaid Services (CMS) Comprehensive End-Stage Renal Disease Care Initiative performance data and novel End-Stage Renal Disease Seamless Care Organization (ESCO)-level data. n=87 ESCO-years for financial performance analyses; n=74 ESCO-years for mortality performance analyses (the standardized mortality ratio was not reported during the program’s first performance year [n=13 ESCO-years]). All descriptive results presented as percentages are rounded to the nearest whole number. Median gross/savings loss determining high versus low financial performance was +2.4% savings. Median standardized mortality ratio determining low versus high mortality ratio was 0.93. P values were derived using two-sided t tests for continuous variables and chi-squared tests for categorical variables. ESCOs were categorized as including a primary care provider if they listed greater than or equal to one provider specializing in family or internal medicine or if CMS reports listed an included primary care practice. ESCOs included a surgery provider if they listed at least one provider with a surgical specialty or if CMS reports listed an included surgical practice. Non-nephrologist specialists were indicated if CMS reports listed any providers or practices that were classified as having a specialty other than internal medicine/nephrology and were not otherwise identified as primary care or surgery providers. The large dialysis organization–led variable indicates if an ESCO is organized and led by one of the two large dialysis organizations in the United States. The Bonferroni correction was used to account for multiple comparisons. Thus, results with P=0.003 are considered statistically significant.

Adjusted Associations between Organizational and Community Characteristics and End-Stage Renal Disease Seamless Care Organization Performance

In adjusted logistic regression models controlling for other factors (Table 3), prior experience in the CEC Initiative was positively associated with high financial performance. ESCOs that had 1 or 2 years of experience in the CEC were 22.7 (95% confidence interval [95% CI], 8.4 to 37.1) and 48.3 percentage points (95% CI, 28.4 to 68.2) more likely to achieve high financial performance, respectively, compared with ESCOs in the first year of participation (P=0.002 and P<0.001, respectively). Each additional ESCO-affiliated dialysis facility (i.e., greater ESCO size) was also associated with a 1.7–percentage point (95% CI, −2.7 to −0.7) lower likelihood of ESCO achieving above-median financial performance (P=0.001). In addition, ESCOs with higher financial performance were located in communities with smaller racial/ethnic minority populations; where the non-Hispanic Black or Hispanic population was 1 percentage point higher, the likelihood of above-median gross savings was 0.9 (95% CI, −1.5 to −0.3) or 0.8 (95% CI, −1.6 to −0.1) percentage points lower, respectively (P<0.001 and P=0.03, respectively).

Table 3. - Organizational and community characteristics associated with high (versus low) performance under the Comprehensive End-Stage Renal Disease Care Initiative End-Stage Renal Disease Seamless Care Organizations 2015–2018

Characteristics	High Financial Performance		Low Mortality Ratio
	Risk Difference (95% Confidence Interval), %	P Value	Risk Difference (95% Confidence Interval), %	P Value
CMS contract
Years of CEC initiative experience (reference 0)	22.7 (8.4 to 37.1)	0.002	−8.8 (−3.0 to 12.4)	0.40
1	48.3 (28.4 to 68.2)	<0.001	−12.6 (−41.6 to 16.4)	0.40
2	−1.4 (−34.7 to 31.9)	0.90	−20.0 (−49.2 to 9.1)	0.20
LDO led
Organizational	0.02 (−0.3 to 0.3)	0.90	0.25 (0.00 to 0.5)	0.05
Physicians	−0.9 (−2.6 to 0.8)	0.30	0.86 (−0.07 to 1.8)	0.07
Beneficiary-yr per physician, per 10 beneficiary-yr per physician	0.16 (−0.2 to 0.5)	0.40	−0.2 (−0.6 to 0.1)	0.20
Practice	−1.7 (−2.7 to −0.7)	0.001	0.5 (0.06 to 1.0)	0.03
Dialysis facilities
Community	−0.89 (−1.5 to −0.3)	0.006	−0.31 (−1.7 to 1.1)	0.70
Non-Hispanic Black, %	−0.83 (−1.6 to −0.1)	0.03	−3.8 (−5.2 to −2.3)	<0.001
Hispanic, %	1.2 (−0.9 to 3.3)	0.20	0.33 (−1.9 to 2.5)	0.80
Medicaid eligible, %	0.15 (−0.6 to 0.9)	0.70	5.5 (3.9 to 7.2)	<0.001
Median household income, $ thousands	22.7 (8.4 to 37.1)	0.002	−8.8 (−3.0 to 12.4)	0.40

Data are presented as percentage point changes in outcomes associated with unit changes in model covariates. The source is the authors’ analysis of Centers for Medicare & Medicaid Services (CMS) Comprehensive End-Stage Renal Disease Care (CEC) Initiative performance data and novel End-Stage Renal Disease Seamless Care Organization (ESCO)-level data for all 37 ESCOs participating in the CEC Initiative’s first three performance years (2015–2019). Risk differences calculated as average marginal effects are derived using marginal standardization techniques and presented as percentage point changes. n=87 ESCO-years included in financial performance model; n=74 ESCO-years included in mortality performance models. Standardized mortality ratios (SMRs) were not reported for the 13 ESCOs participating in the first performance year (2015–2016) but were reported for these organizations during their second and third years of participation. Median gross savings/losses determining high versus low financial performance was +2.4% (savings). Median SMR determining low versus high mortality ratio was 0.93. Models included performance year fixed effect controls and used ESCO-level clustered SEMs. P values were calculated from F tests. The large dialysis organization (LDO)-led variable indicates if an ESCO is organized and led by one of the two LDOs in the United States. The SMR outcome of this model reflects (in the positive direction) a higher probability of achieving a low SMR: that is, lower mortality among the ESCO-attributed patient population. Thus, where the proportion of the population identifying as Hispanic was 1 percentage point higher, higher mortality is observed among ESCO-attributed patients on the order of a 3.8–percentage point reduction in an ESCO’s likelihood of achieving an SMR below 0.93. We used the Bonferroni correction to account for multiple comparisons. Thus, results were considered statistically significant if P=0.005.

Organizational characteristics were not associated with SMRs in our models. At the community level, where the proportion of the population identifying as Hispanic was 1 percentage point higher, the SMR was more likely to be high (i.e., 3.8–percentage point [95% CI, −5.2 to −2.3] lower likelihood of achieving a low SMR; P<0.001). Where the community’s median household income was $1,000 higher, the likelihood that an ESCO reported a low SMR was 5.5 (95% CI, 3.9 to 7.2) percentage points higher (P<0.001).

Discussion

This study uses novel ESCO-level data to provide foundational evidence on the relationships among organizational and community characteristics and the performance of ESCOs that formed under Medicare’s CEC Initiative, a voluntary alternative payment model to improve the value of care for patients with kidney failure. We found that ESCOs were diverse in terms of their organizational composition—that is, affiliated physicians, specialties, practices, and facilities—and the communities in which they formed. In regression analyses, prior experience in the CEC Initiative and the number of affiliated dialysis facilities was strongly associated with high financial performance, whereas SMRs were lower in communities where smaller percentages of the population identified as Hispanic and where median household income was higher.

Our analyses illuminate the heterogeneity in ESCO performance and the wide variation in organization and characteristics of the communities in which ESCOs operate. Our finding that ESCOs tended to form in communities with above-average financial well-being (i.e., high median household income and low percentage Medicaid eligible) and high representation of otherwise under-represented racial/ethnic minority populations (i.e., non-Hispanic Black and Hispanic) is consistent with previous analyses of ESCOs’ market characteristics (²⁴). These relationships may be driven, in part, by the CEC Initiative’s minimum patient volume requirement (≥350 Medicare beneficiaries receiving maintenance dialysis), which limits the geographic distribution of ESCOs to urban and suburban communities (⁶).

Studies examining the performance of Accountable Care Organizations (ACOs)—primary care–oriented analogs to ESCOs in place since 2013—indicate that key characteristics, such as ACO size, composition, leadership (^89–10,¹²,¹³,²⁵), provider mix (¹⁰,¹⁵), institutional risk-bearing experience (⁸,¹³,²⁵), and the geographic and demographic characteristics of the service area (¹⁰,¹¹), are associated with ACOs’ costs and quality of care. Our findings regarding ESCOs’ organization and performance are broadly consistent with several of these relationships. Our regression analyses indicate that, like ACOs, ESCOs achieve higher gross savings with additional prior exposure to risk-bearing contracts (⁸,²⁵). This result suggests that organizations with such experience acquire knowledge, relationships, and strategies crucial to successful operation in a risk-based environment and are able to optimize performance over time. Moreover, organizations may have implemented cost-saving interventions that required an initial investment and took time to operationalize before financial benefits would be observed in subsequent years. Notably, as shown in Table 2, our adjusted estimate related to risk-bearing experience takes the opposite sign of the corresponding unadjusted estimate; this may reflect that our models account for other factors associated with years in the CEC Initiative and program performance, such as CMS’s annual updates to the set of quality measures for which program participants are responsible.

Likewise, our findings that ESCO performance trended worse in communities with lower median income (higher mortality) or larger proportions of non-Hispanic Black (marginally worse financial performance) and Hispanic residents (higher mortality) align with studies of ACOs, other value-based payment programs outside of nephrology, and disparities in health and health care more broadly (^262728–29). Of note, providers in socioeconomically disadvantaged communities tend to have fewer care resources available to enhance their care processes (^303132–33), and in particular, deficits in access to infrastructure and financial, technologic, and human resources have been associated with poorer-quality performance in ACOs (^313233–34). These relationships may explain our finding that ESCOs in lower-income communities have poorer performance on mortality measures. In kidney disease care, such resources may be critical for ESCOs to undertake innovative reforms in care delivery, such as expanded home dialysis and transitional care programs and enhanced patient education about transplantation, which have been shown to generate financial savings, lead to high-quality measure performance, or both (^{35363738–39}). Future studies should examine how ESCO participation and performance may be associated with delivery system reforms and the availability of resources needed to implement them.

Considered together, our findings indicate that nephrologists and dialysis providers with smaller organization size, less risk-bearing experience, and limited access to resources in the community may experience greater barriers to participating in these models. This is consistent with developing evidence that the advent of the ACO program may have been associated with widening disparities in quality of care by race/ethnicity and socioeconomic status (¹⁴,⁴⁰,⁴¹). Indeed, these results may suggest that nephrologists and dialysis providers use community-level indicators of social determinants of health to inform their decisions about opting into voluntary alternative payment models. When designing and refining voluntary alternative payment models in kidney failure care, such as Medicare’s new Kidney Care Choices models (³), and in other areas of specialty care, policy makers should closely monitor for selective participation by providers by community socioeconomic status. Policy makers should also evaluate the effects of payment reforms on racial/ethnic and socioeconomic disparities in key outcomes. Of note, it may be appropriate to consider risk-adjusting payments for patient- or community-level socioeconomic status to avoid disproportionate financial penalties levied on providers serving socially disadvantaged populations. Such interventions may support policy makers’ goals of avoiding exacerbating disparities under alternative payment model contracts through these related mechanisms of selective provider participation and unbalanced access to high-value care and care improvement across communities.

This study has a few notable limitations. First, our analysis is cross-sectional; all observed relationships are associations and should not be interpreted as causal. Our measures of ESCO composition are derived from CMS reports for the 2017–2018 model year and, so, do not account for year to year changes in ESCO affiliations. Our analyses may be underpowered to detect statistical significance in certain relationships of interest due to the small number of ESCOs participating in the CEC Initiative (n=37) and the limited number of completed performance years. This small sample size inhibited estimation of stratified analysis and the inclusion of additional potentially relevant factors associated with ESCO performance at the organization (e.g., the specialties of affiliated physicians) and community (e.g., median educational attainment) levels in our models due to overfitting. The low level of non-LDO participation in the program’s first year (12 of 13 ESCOs in 2015–2016 were LDO led) makes it difficult to distinguish the effects of experience versus LDO leadership.

We used outcome measures utilized by CMS to assess clinical performance and determine financial bonuses or penalties. Although these outcomes are important, they are influenced by many different mechanisms, not solely organizational performance. Notably, an ESCO’s financial performance reflects not only the organization’s decreased costs of care but also its effectiveness in coordinating quality measure reporting efforts among affiliated providers and its contract type, among other factors (^56–7). At the population level, mortality among attributed patients on dialysis is also a function of many factors, including socioeconomic factors, social support, and other dynamics largely beyond kidney care providers’ control. Consequently, mortality should not be taken to represent in a singular way the quality or safety of care provided by ESCOs.

Future studies may examine measures of patient-centered care and outcomes, process of care measures, and other outcomes of interest. Other potentially important characteristics of ESCOs—such as patient panel characteristics and intraorganizational financial incentives (e.g., bonuses for individual physicians tied to ESCO shared savings [⁴²])—were not measurable using available data.

In this study, we provide timely information on the organizational- and community-level characteristics of participants in CMS’s CEC Initiative and their association with performance on key financial and patient mortality outcomes. We find that financial performance in the CEC Initiative is associated with organizational characteristics, especially prior experience in the CEC Initiative and the number of affiliated dialysis facilities, and that adjusted mortality rates are associated with markers of community socioeconomic status. These results suggest that dialysis providers and nephrologists with less experience in risk-bearing contracts, smaller organization size, and limited community or organizational resources may experience greater barriers to successful performance and sustained participation in these models.

Further, our findings illuminate factors that may bias evaluations of observed success among CEC Initiative participants; consequently, we infer that the effect of the CEC Initiative may not generalize to the general population of US nephrologists and their patients. Thus , policy makers may use our findings to inform the parameters of contemporary and developing payment models focused on populations with kidney failure (e.g., the Comprehensive Kidney Care Contracting model, the ESRD Treatment Choices model, and Kidney Care First), cancer (e.g., the Oncology Care Model), or other specialty care–reliant conditions.

Disclosures

All authors have nothing to disclose.

Funding

This work was supported by National Institute of Diabetes and Digestive and Kidney Diseases grants 1F31DK126501 and 1K01DK128384-01.