One explanation for the differential impact of MC across facility ownership types is that MC results in standardization across facilities of different profit status. That is, in the absence of MC, we observe that FPs offer the narrowest range of services, publics the widest, and NFPs fall in the middle. Managed care could be a standardizing force making the service offerings more similar across ownership types. Thus, FPs add services while publics reduce them.
There are other possible explanations as well. For example, it may be that public and private SAT facilities have contracts with different types of MC organizations, which in turn may have different goals. For example, MC associated with state funding (e.g., block grants) may want to reduce the range of services offered in order to treat as many people as possible with limited funds. In contrast, private health insurance may have quality of care as a more important goal, and may therefore require additional types of treatment to increase effectiveness and offset other medical expenses.
The move from inpatient to outpatient settings associated with MC may also differentially impact facilities. If, for example, FPs treat more clients who, in the absence of MC, would be in inpatient care, then their client base would need a broader range of services in the presence of MC. Note that FPs may be more likely to treat those who otherwise would have been in inpatient care because their client base tends to have more generous coverage (which, in turn, may be more likely to cover inpatient care but for MC controls).
Empirically identifying which of the above reasons best explains the observed differential impacts, however, is beyond the capability of our data and is also beyond the scope of our paper.
The finding that MC significantly decreases wraparound service offerings (i.e., transitional services and other ancillary services) in public SAT facilities is consistent with Kapur and Weisbrod (2000)
and Hodgkin et al. (2004)
, both of which found that public health care organizations respond to funding cuts by reducing quality of care, as opposed to turning away clients (i.e., publics view themselves as the “supplier of last resort”).6
It also supports Alexander, Lemak, and Campbell's (2003)
speculation that as MC expands into the public SAT sector, cost containment may reduce the range and type of services available to vulnerable populations. The finding that MC appears to increase assessment services across all ownership types is consistent with the view that MC relies heavily on administrative controls such as utilization review.
This study is subject to a number of limitations. First, NSSATS records MC and specific service offerings as binary variables. Thus, we do not know the intensity, strength, or types of MC mechanisms at each facility; nor do we know whether a specific service has been received, who receives it, nor the intensity nor quality of the service. It is important to recognize, however, that using a binary indicator of MC and counts of binary indicators of specific services is a conservative approach. That is, the relatively blunt nature of binary variables could bias against finding significant results. Second, NSSATS does not collect data on client characteristics, so we cannot control directly for client mix. However, we mitigate potential omitted variable bias by using several proxies for client mix, including types of payment accepted and whether payment assistance is offered. Third, there may be some bias in the results for publics due to weak instruments. However, we expect this bias to be small given that the corresponding F
-statistic (in ) is both significant and much larger than 1.0 (Bound, Jaeger, and Baker 1995
). Fourth, a general limitation of the IV approach is that it is not possible to prove the validity of the instruments used. While our instruments pass the overidentification test, it is still necessary to assume the validity of one of them to confirm the validity of the other. Although we believe that both instruments are intuitively plausible (as discussed in detail in “Methods”), others may disagree. Finally, approximately 20 percent of the facilities in NSSATS are missing one or more covariates used in the study and are therefore excluded from our final study sample. Although we have no reason to believe that data are missing in such a way as to bias the results (and we control for many important covariates including setting, modality, focus, and size), we cannot rule out this possibility.
We recognize that the aforementioned NSSATS data limitations constrain our analyses. Offsetting these limitations, however, is the fact that we have a large dataset that comprises a majority of SAT facilities in the U.S. and that contains data on a wide range of specific service offerings. Thus, our statistical power is high and our results are likely to be widely generalizable. Further, although we have only a crude measure of MC, we are able to move the literature forward by discovering interesting differential impacts of MC by provider profit status, thereby adding to both the SAT and ownership type literatures.