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Federal Register / Vol. 86, No. 213 / Monday, November 8, 2021 / Rules and Regulations
estimates reported in Table 18 of the CY
2022 ESRD PPS proposed rule 86 FR
36425 that used the same years of data, but without the changes from the CY
2022 ESRD PPS proposed rule to this final rule. To clarify, the CY 2022 ESRD
PPS proposed rule estimates are not the estimates reported in Table 19 of the Specialty Care Models final rule 85 FR
61354; the final rule used data from CYs 2016 and 2017 and this final rule used the most recent data available, from CYs 2018 and 2019. There was no impact reported in years 2021 and 2022
since the payment adjustments were not effective until MY3. In addition, the changes did not apply to the HDPA or the Kidney Disease Patient Education Services Costs and HD Training Costs.
As expected, Table 19 shows that the changes had a small effect on Medicare savings; a reduction of $10 million in savings for the net impact to Medicare spending over the 4.5-year period can be attributed to the changes in this final rule from the CY 2022 ESRD PPS
proposed rule.
As was the case in the Specialty Care Models final rule 85 FR 61353, the projections do not include the Part B
premium revenue offset because the payment adjustments under the ETC
Model will not affect beneficiary costsharing. Any potential effects on Medicare Advantage capitation payments were also excluded from the projections. This approach is consistent with how CMS has previously conveyed the primary FFS effects anticipated for an uncertain model without also assessing the potential impact on Medicare Advantage rates.
Returning to Table 18, as anticipated, the expected Medicare program savings were driven by the net effect of the Facility PPA; a reduction in Medicare spending of $65 million over the period from July 1, 2022 through June 30, 2027.
In comparison, the net effect of the Clinician PPA was only $8 million in Medicare savings. This estimate was based on an empirical study of historical home dialysis utilization and transplant waitlist rates for Medicare FFS beneficiaries that CMS virtually attributed to ESRD facilities and to Managing Clinicians based on the plurality of associated spending at the beneficiary level. We analyzed the base variation in those facility/practice level measures and simulated the effect of the payment policy assuming providers and suppliers respond by marginally increasing their share of patients utilizing home dialysis. Random variables were used to vary the effectiveness that individual providers and suppliers might show in such progression over time and to simulate
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the level of year-to-year variation already noted in the base multi-year data that was analyzed. The uncertainty in the projection was illustrated in sections VII.C.2.b.3a and VII.C.2.b.3b of the Specialty Care Models final rule 85 FR 61354, respectively, through alternate scenarios assuming that the benchmarks against which ETC Participants are measured were to not be updated. In those sensitivity analyses, we analyzed a modified version of the model that included a fixed benchmark for the home dialysis and transplant waitlist rates as well as a separate sensitivity analysis that assumed a rolling benchmark for the home dialysis rate and a fixed benchmark for the transplant waitlist rate.
For this final rule, we are continuing with the approach applied in the CY
2022 ESRD PPS proposed rule by modeling a preset benchmark growth rate in this rule but continue to incorporate sensitivity to a range of potential behavioral changes for the home dialysis rate and transplant waitlist rate for ETC facilities and Managing Clinicians assumed to participate in the model. Kidney disease patient education services on treatment modalities and home dialysis HD
training for incident dialysis beneficiaries are relatively small outlays and were projected to represent only relatively modest increases in Medicare spending each year.
The key assumptions underlying the impact estimate are that each aggregation groups share of total maintenance dialysis provided in the home setting was assumed to grow by up to an assumed maximum growth averaging 3-percentage points per year.
Factors underlying this assumption about the home dialysis growth rate include: known limitations that may prevent patients from being able to dialyze at home, such as certain common disease types that make peritoneal dialysis impractical for example, obesity; current equipment and staffing constraints; and the likelihood that a patient new to maintenance dialysis starts dialysis at home compared to the likelihood that a current dialysis patient who dialyzes in center switches to dialysis at home. In any given trial of the simulation, the maximum growth rate was chosen from a uniform distribution of 0 to 5percentage points per year. Preliminary data from CMS show that the growth rate for home dialysis was 3.9 percent in CY 2020 for beneficiaries meeting the eligibility criteria for the ETC Model.
This growth rate is within range to what was observed prior to the establishment
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of the Advancing American Kidney Health initiative in 2019 and it also shows that the COVID19 PHE did not cause the home dialysis growth assumption to become invalid. The 3percentage point per year average max growth rate will, in effect, move the average market peritoneal dialysis rate about 10 percent to the highest market baseline peritoneal dialysis rate for example, Bend, Oregon HRR at about 25
percent, which we believe is a reasonable upper bound on growth over the duration of the ETC Model for the purposes of this actuarial model.
Aggregation groups were assumed to achieve anywhere from zero to 100
percent of such maximum growth in any given year. Thus, the average projected growth for the share of maintenance dialysis provided in the home was 1.5percentage points per year expressed as the percentage of total dialysis. In contrast, we do not include an official assumption that the overall number of kidney transplants will increase and provide justification for this assumption in sections VI.C.2.b.4 and VI.C.2.b.5
of the Specialty Care Models final rule 85 FR 61355. However, as part of the sensitivity analysis for the savings calculations for the model, we laid out a different savings scenario if the ETC
Learning Collaborative described in VI.C.2.b.6 of the Specialty Care Models final rule 85 FR 61355 were to be successful in decreasing the discard rate of deceased donor kidneys and increasing the utilization rate of deceased donor kidneys that have been retrieved.
4. Sensitivity Analysis: Medicare Savings EstimateResults for the 10th and 90th Percentiles Using the primary specification for the Medicare estimate with preset benchmark updates for home dialysis and transplant waitlist rates, we compared the results for the top 10th and 90th percentiles of the 400
individual simulations to the average of all simulation results reported in Table 18. Since the impact on Medicare spending for the ETC Model using the present benchmark updates is estimated to be in savings rather than losses, the top 10th and 90th percentiles represent the most optimistic and conservative projections, respectively. The overall net PPA and HDPA for the top 10th and 90th percentiles using the present benchmark updates method are $102
million in savings and $9 million in losses encompassing the mean estimate of $43 million in savings in Table 18.
The overall uncertainty of the impact of the model is further illustrated in Table 19, the change from the CY 2022 ESRD

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