Effect of PACE on Costs, Nursing Home Admissions, and Mortality: 2006-2011. IV. DISCUSSION

03/01/2015

Our main findings on Medicare and Medicaid expenditures across all eight states are in line with results from prior studies meeting standards for a credible evaluation that also found little or no effect of PACE on Medicare costs but significantly higher Medicaid costs under PACE.19 However, prior studies found that the gap in Medicaid spending decreased over time, while in our case the Medicaid spending gap was fairly stable in the pooled analysis across all eight states, using the preferred comparison group strategy of keeping both NH entrants and HCBS waiver enrollees in the matched comparison group. We do detect a reduction in the Medicaid spending gap over time using the second matched comparison group of HCBS waiver enrollees alone, in line with findings from prior studies meeting standards for a credible evaluation (Mancuso, Yamashiro, and Felver 2005; Foster, Schmitz, and Kemper 2007).

More importantly, we uncover several differences in the state-specific findings using the matched comparison group comprised of both HCBS and NH entrants. New York stands out with significantly lower Medicaid expenditures under PACE, but with significantly higher Medicare expenditures in several intervals, with the net effect being of lower or similar total expenditures for PACE enrollees compared to predicted expenditures.20 The findings for New York differ markedly from those in earlier studies meeting standards for a rigorous evaluation, and are likely driven by both lower (and slower-growing) PACE capitation payments compared to that in other states and possibly more generous FFS coverage of and payments for HCBS waiver services and NH care in New York. Among other state-specific findings, Medicaid spending under PACE in California was significantly higher-than-predicted expenditures, and the gap increased with the length of enrollment--as Medicaid capitation payments increased and predicted FFS costs declined during the first two years after enrollment in PACE. In Massachusetts, the Medicaid spending gap was positive in spite of lower capitation payments than other states, and due to even lower predicted expenditures, but the Medicaid spending gap decreased to statistically insignificant levels with the length of time since enrollment.21

Our finding of lower mortality under PACE is supported by similar findings in several earlier studies (Chatterji et al. 1998; Mancuso, Yamashiro, and Felver 2005; Wieland et al. 2010). While our estimated treatment-comparison differences are larger than other studies found when using our composite comparison group, the more conservative estimates of 5-6 percentage points difference in mortality rates based on the comparison to the matched sample drawn only from new enrollees in HCBS programs are similar in size to those found by others. However, the mortality findings are likely to be susceptible to unobserved differences in health and functional status between the groups and need to be interpreted with caution. In particular, it is unclear whether the favorable findings for mortality can strictly be interpreted as an effect of PACE or not, if, for instance, terminally ill patients or those with higher disease severity are less likely to enroll in PACE leading to lower mortality for PACE enrollees. Also, the inclusion of NH entrants--who are likely to be sicker--in the first matched comparison group, together with imperfect risk adjustment due to absence of baseline data on health and functional status immediately preceding enrollment, could further bias the mortality findings in favor of PACE.

The findings for NH utilization with the matched comparison sample of HCBS waiver enrollees alone are important and nuanced, in that PACE enrollees were found to have a significantly higher likelihood of being in a NH during the followup period, but their proportion of days in the NH was similar to that for the matched HCBS waiver enrollees for most of the followup period, as was their likelihood of being in a NH for at least 30 days. Furthermore, PACE enrollees were significantly less likely to be in a NH for at least 90 days in any interval compared to their matched HCBS waiver counterparts, although the cumulative risk of being in a NH for at least 90 days was similar across PACE and matched HCBS enrollees across all intervals. The pattern in these results was similar but more accentuated once New York was dropped from the sample. Taken together, these apparently divergent findings can only be reconciled if: (1) there was little overlap among PACE enrollees who experienced long-term NH stays in each successive interval, while there was greater overlap across intervals among members of the HCBS comparison group who experience long-term NH stays, and have longer stays in each interval than PACE enrollees; and (2) PACE enrollees were more likely than the comparison group to use the NH for short-stay, recuperative purposes, but less likely to have to move into a NH or be institutionalized for long time periods. This could easily be the case if PACE plans substitute short-term NH stays for hospital admissions, since they are not bound by the Medicare requirement imposed on FFS Medicare patients of a three-day hospital admission prior to being eligible for a skilled NH admission. Indeed, at least two prior studies either hinted at or reached similar conclusions. For instance, Beauchamp et al. (2008) attributed their finding of greater NH use among PACE enrollees compared to their matched HCBS counterparts to the possibly greater use of NHs for short-stay purposes under PACE, although they did not provide any direct evidence for the same. A second study by Nadash (2004), with a somewhat weak design, obtained similar findings of higher NH utilization for PACE enrollees, with the median length of stay being shorter under PACE. In an older study, Chatterji et al. (1998), however, found NH utilization, in general, to be lower under PACE.

Our study, therefore, is among the first to offer strong evidence for two opposing effects of PACE on NH utilization--higher rates of utilization with shorter lengths of stay--with the net effect being a similar proportion of time spent in a long-term care institution across PACE and matched HCBS enrollees during each six-month interval and also similar cumulative rates of having had a long-term NH stay across all intervals. Combined with findings in the earlier literature that shows lower hospital utilization under PACE (for example, see Chatterji et al. 1998; Kane et al. 2006; Beauchamp et al. 2008; Meret-Hanke 2011) these findings suggest that PACE enrollees possibly spend a greater amount of time in the community (that is, in neither a hospital nor a NH) as opposed to their FFS counterparts.

Compared to the matched comparison group comprised of both NH entrants and HCBS waiver enrollees, PACE enrollees had significantly lower utilization of NHs with large, negative differences on all NH utilization outcomes. These findings are along expected lines and are perhaps less credibly interpreted as impacts, since baseline period NH entrants in the mixed comparison group are likely to have higher NH utilization in the followup period, skewing the results strongly in favor of PACE. One can argue that some PACE enrollees would likely have had to enter a NH had PACE not been an option, and therefore, this comparison group provides a valid counterfactual. However, it seems unlikely that over half of PACE entrants would have gone into a NH for a long-term or permanent stay at a comparable point-in-time as they entered PACE, had PACE not been an option for them. Thus, the estimates relying on this mixed HCBS/NH comparison group are likely an overestimate of PACE effects on long-term NH stays.

Our study has several limitations. First, even after matching and establishing baseline equivalence between the treatment and matched comparison groups, we cannot rule out the possibility that PACE and matched HCBS waiver enrollees or NH entrants differ along unobserved or unmeasured characteristics. There is likely to be a complex pattern of unobserved factors underlying the decision to enroll in PACE that could also affect outcomes, with the effect of PACE on outcomes--mediated by such unobservables--working in opposite directions in some cases. If, as claimed by PACE advocates, PACE enrollees are sicker than comparable entrants in HCBS waiver programs, and therefore, have higher costs, then we should also expect to see higher mortality under PACE. Our findings, however, point towards significantly lower mortality under PACE, with similar or higher costs than FFS beneficiaries in HCBS waiver programs or in NHs. This suggests that either PACE significantly lowers mortality risk in spite of the greater sickness of PACE enrollees, or the complex pattern of unobserved factors leading to PACE enrollment has positive correlation with costs and negative correlation with mortality. For instance, it is possible that PACE is a more attractive proposition for beneficiaries with functional and cognitive impairments and a high need for daily management of their conditions, but less so for beneficiaries who have severe medical conditions or who are terminally ill. This latter group, which would include those who are truly homebound, could find it more valuable to continue existing relationships with their primary care physician or enroll in hospice care or enter a NH while still being in FFS Medicare and Medicaid. This, in turn, could lead to lower mortality rate among PACE enrollees, but they still may be just as expensive, or more expensive, to care for as comparison group individuals with comparable illnesses who do not enroll in PACE. In other words, the mortality findings could be biased in favor of PACE due to such unobserved differences, especially when using the matched comparison sample comprised of both NH entrants and HCBS waiver enrollees, since NH entrants are more likely to die in the near term than HCBS recipients. Limited by claims and enrollment data alone in our analysis, we were not able to explore the underlying differences--for example, differences in health and functional status--right before or at sample entry across PACE, HCBS, and NH entrants, which would have been possible with primary data collection.

To address such concerns about unobserved differences across the treatment and matched comparison groups, which are typical of most observational studies with a nonexperimental research design, we carried out three sensitivity tests. First, we dropped about 16 percent of PACE enrollees, who were either enrolled in HCBS waiver services in the six months prior to enrolling in PACE or were in a NH in the 90 days prior to enrolling in PACE, and also dropped around 4 percent of the HCBS waiver enrollees who were in a NH in the 90 days prior to enrolling in the waiver program. These exclusions were applied prior to matching PACE enrollees to the combined pool of waiver enrollees and NH entrants. After rerunning the matching algorithm and the outcomes analysis, our main findings for expenditures and mortality effects remained unaltered. Second, we selected our matched comparison group in two ways, first including in the pool of potential matches both those newly enrolled in an HCBS waiver program and those entering a NH, and then restricting it to only those enrolled in an HCBS waiver. While estimated effects on expenditures and mortality were different using alternative comparison group strategies, the direction and statistical significance of the findings were similar, leading to the same conclusions. For instance, the magnitude of the estimated difference in mortality rates (with lower mortality under PACE) was smaller using the matched comparison group of HCBS waiver enrollees alone, while the estimated gap in expenditures (higher actual payments under PACE) was also smaller, especially for Medicaid payments. This is consistent with the expected lower costs in the comparison group of HCBS waiver enrollees alone. Finally, as a third sensitivity test (results not shown) we controlled for a mortality indicator in the expenditures regression for the first two intervals (months 1-6, and months 7-12) to account for unobserved differences in health and functional status that possibly accounts for the high mortality gap in favor of PACE in these first two intervals. We found that our findings for Medicare and Medicaid expenditures were broadly similar in the first two intervals even after controlling for mortality during these periods. Predicted means were lower in the first two intervals with consequent reductions in the estimated difference between actual and predicted expenditures for PACE enrollees, but the direction of the difference and the statistical significance of the estimates were unchanged.

A second limitation was that although we utilize several data sources for Medicare and Medicaid enrollment and claims information, we were only able to obtain administrative data. More specifically, we lack information on certain beneficiary characteristics, especially their physical and cognitive functional status before enrollment--likely to be a crucial determinant for enrolling in PACE or HCBS waiver services as well as for NH entry, long-term care utilization, and predicted costs--that could have allowed us to use a richer set of variables for matching the treatment and comparison group members at baseline. We partially mitigate this inadequacy by using information on several chronic conditions, including one for cognitive impairment (whether an enrollee had Alzheimer's or dementia), defined using CCW's claims-based algorithm, as well as information on utilization of acute care (inpatient and ER) and post-acute care (SNF and home health) services  (likely to be a marker for functional impairment) in matching. However, the Medicare expenditures and service utilization variables used in matching were based on claims information in the MBSF for the calendar year prior to the year of sample entry. As such, for at least some beneficiaries (for example, those enrolling in PACE or HCBS or entering a NH later in the year), we do not observe expenditures and service utilization in the months immediately preceding sample entry. Therefore, our analysis might fail to capture or control for possibly high service utilization and expenditures or the onset of new chronic conditions or ADL limitations that may have precipitated the enrollment of beneficiaries in PACE or HCBS or their admission to a NH. While this missing data exists for all three groups, it may be particularly acute for NH entrants immediately prior to entering a NH, leading to incomplete risk adjustment in the models predicting expenditures and mortality. If true, use of the blended HCBS/NH comparison group would lead to overestimates of the costs and mortality rate that PACE enrollees would have experienced in the absence of PACE, biasing results for both outcomes in favor of the PACE program when this comparison sample is used. Due to the fact that our study included individuals who enrolled in PACE or waiver programs (or entered nursing facilities) 5-7 years ago, and the fact that observations were drawn from eight different states, it was not possible to obtain retrospective data on NH assessments or care plans at the time of enrollment.

A third limitation is that, given the lag in the preparation and availability of certain secondary datasets, such as the MAX and the Timeline file, we were constrained in our use of a maximum of a 42-month followup for several outcomes. This is especially a concern for the findings on Medicaid expenditures where the gap between actual and predicted Medicaid expenditures for PACE enrollees was found to gradually diminish over time for at least one state--Massachusetts. With a longer followup--preferably five years or more--it would have been possible to test whether the Medicaid expenditure gap falls to a statistically insignificant level. NH use could also diverge for the PACE and comparison samples with a longer followup. However, as shown by the mortality findings, at least a third of the sample died by month 42 after enrollment. Therefore, 42 months of followup is still a reasonably long period of time to observe cost outcomes for this population, given the tradeoff between a longer followup and the decrease in sample size over time.

Fourth, PACE enrollees in our sample are lost to followup once they disenroll from PACE (we only examine capitation payments for PACE enrollees), although we follow all FFS costs for the matched HCBS enrollees and NH entrants, as long as they are alive and not in managed care.22 So, costs for the comparison group enrollees could potentially be overstated compared to that for enrollees in the treatment group. Note, however, that this limitation actually makes our findings of capitated Medicaid costs being higher-than-predicted FFS costs a relatively conservative estimate. Thus, our findings of higher Medicaid (and overall) expenditures on PACE is not driven by this difference between treatment and comparison samples due to disenrollment, but rather reinforced by it.

Our findings should be of interest to policymakers and researchers interested in the role of PACE in improving outcomes among the frail and elderly duals, and controlling Medicare and Medicaid expenditures for such beneficiaries. Our expenditure findings suggest that although Medicaid capitation payments for PACE enrollees under the existing PACE program structure exceed what we expect their Medicaid FFS expenditures would have been for the eight states combined, the results for New York are very different, showing that PACE capitation rates there were set below what we estimate these enrollees would have cost Medicaid had they not enrolled in PACE. The favorable mortality and NH findings also suggest that PACE is likely to be good option for many frail dual eligibles, and that the Medicare capitation rate is comparable to what FFS costs are likely to have been. However, as always, caution should be exercised in drawing inferences from this study about the likely effects of expanding PACE to other areas or changing its structure to include in home visits ("PACE at home"), since we cannot predict how such a change would affect the types of individuals who choose to enroll in PACE or the effects of PACE on them. If future investigations into PACE's effects are conducted, they would benefit greatly from data on the functioning, family support systems, and other characteristics of PACE entrants at the time of enrollment compared to those of beneficiaries in the comparison group. Finding a way to measure hospitalizations from the same source for PACE enrollees and the comparison group would also be worthwhile, given the importance of this measure to both enrollees' well-being and costs to the capitated PACE plans. Such studies should also incorporate a longer followup for Medicaid expenditures and NH outcomes.

The data limitations of this study make it difficult to assess the implications of our findings for setting PACE payment rates. Taken at face value, the study suggests that Medicaid incurs substantially higher costs for PACE enrollees than it would have had these enrollees not entered PACE, but instead relied on Medicaid long-term care services provided either through HCBS programs or NHs. However, the study was unable to compare PACE and HCBS enrollees in terms of ADL limitations or ongoing conditions such as incontinence or dementia at the time of enrollment, so it is possible that the PACE enrollees would have cost more than our projections suggest, had they not been able to enroll in PACE. The similarity of our findings to previous findings suggests that the estimated overpayment is probably real,however, especially given the finding that even when new entrants to NHs are included in the comparison sample, the capitation payment exceeds the projected cost that PACE enrollees would have incurred.

On the other hand, policymakers must consider the findings that mortality rates and long-term NH stays are lower for PACE enrollees than for their nonPACE counterparts. Furthermore, the study could not measure the trajectory of personal outcomes in overall health, quality of care, and satisfaction with the quality of daily life. Nor was the study able to assess the effects of PACE on family caregivers, which are likely to be highly favorable. All of these quality and length of life considerations could lead policymakers to conclude that even if costs are higher under PACE, the benefits may be worth some additional cost. States and the Federal Government may therefore wish to investigate the care needs and outcomes of Medicaid enrollees receiving care under PACE and HCBS to ascertain whether the two groups are comparable at enrollment and whether quality of life outcomes are similar or different for the two programs in making their decisions.

One important consideration for policymakers is the substantial variation across states in Medicaid capitation rates paid to PACE plans, and the very different divergence from their FFS outlays for individuals receiving HCBS care. States may find it beneficial to compare their actuarial processes to ensure the most accurate methods and calculation of Medicaid PACE capitation rates. This is not to say, of course, that rates should be the same in all states and localities. But a greater understanding and agreement regarding the nature of Medicaid risk adjustment for the PACE population, and the factors considered in setting base rates, would be of significant value in arriving at a well-founded approach to rate-setting.

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