The unique aspects of this study are reflected by the fact that multiple dimensions of costs were reduced under capitation. These include access or probability of service use, intensity of use as measured by the average cost of care, differential changes in the types of care provided, and differences attributable to the characteristics of local service systems under capitation. Because these results are based on following a fixed set of consumers with serious mental illness known to the service system prior to capitation, interpretation of the findings generally and in relation to other studies of capitation must be made clearly and carefully.
The overarching measurement of the effect of capitation in relation to the study subjects is the average cost per person (). Reductions in the estimated cost per person found for the capitated areas follow from identified changes in probability of service use and average costs of treatment given any service use. These reductions in cost per person range from moderate (20 percent by the second year for the DC areas) to very large (more than 66 percent for the MBHO areas) given ranges of estimated savings found under public and private mental health capitation of 25 percent to 40 percent (Frank, Koyangi, and McGuire 1997
). It is important to note, however, that comparison of these study results to other general studies of mental health capitation has several caveats.
First, studies of capitation's effects on cost typically focus on the cost per service user, thus excluding effects of changes in access on population or total member average costs. Second, most study results reflect aggregate results for multiple service sites whereas studies incorporating site specific observations under uniform changes in financing policy have found large variation in effects (Scheffler, Wallace, Hu, et al. 2000
). Third, many study results are for one, as opposed to two or more years. Last, reflected are the effects of capitation on a particular consumer sub-population known to the system prior to capitation. Thus, these results cannot be directly extrapolated to overall levels of system cost or access, which will reflect changes for other consumer groups or for consumers newly accessing services.
The differences in effect size for the two types of capitated organizations conforms with the general expectation that the not-for-profit DC areas would respond relatively less to the incentives of capitation than the MBHO areas, where a for-profit firm is involved. These results indicate, as previously suggested, that the 5 percent profit cap on the MBHO areas does not meaningfully constrain their incentive to save money. In addition, in key informant interviews the 5 percent cap was not indicated as a significant issue (if mentioned at all) in terms of MBHO activities. Indeed, if MHASAs are competing as expected against the state's desire to see savings produced for redistribution within the system, then the MBHO areas would need to obtain at least 5 percent more savings than the DC areas to be viewed as equally successful. This 5 percent difference is not sufficient to explain the full variation in DC and MBHO cost changes. Differences in initial conditions between these areas, and differences in services management that may or may not be related to the for-profit involvement in the MBHO MHASAs, also appear to provide likely explanations. Similarly, one cannot assume that the large cost reductions in the MBHO areas were related to lowered quality or effectiveness of care (Cuffel, Bloom, Wallace, etal. 2002
With regard to cost per service user only, the DC areas show no change due to capitation, while the MBHO areas reduce cost per user by 58 percent by the end of the second year. The reductions in service intensity for MBHO areas distinguish service process change for MBHO areas from DC areas. However, the estimated initial cost per user based on equivalent sample characteristics is much higher for the MBHO than the DC or FFS areas. The higher MBHO initial cost per user reflects much higher initial probability of using expensive state hospital services among MBHO subjects. This raises the total service cost per user for MBHO areas even without identified differences in average costs for those who use state hospital care. For the MBHO areas simply to reduce average user costs to equal the DC areas, or the next highest user cost level, would require a 23 percent reduction. A reasonable interpretation of these facts is that a large portion of the MBHO areas' change in user costs reflects “low hanging fruit” in regard to higher relative use of state hospital care and unrelated to MBHO organizational structure. Managed behavioral health organization MHASAs were much more aggressive in reducing census at the state hospital serving their areas than the DC MHASAs were for their respective state hospital.
Reductions in the probability of local inpatient use for the capitated service areas were found, consistent with results in other capitation experiments (Reed, Hennessy, and Babigian 1992
; Reed et al. 1994
; Dickey, Normand, Azeni, et al. 1996
). The extension of this decrease to the control group makes it difficult to interpret these findings as an effect of capitation. This may, however, signal a “spillover effect” from the capitation pilot implementation because statewide implementation has been anticipated, barring failure of the pilot. First year decreases in inpatient user costs identified are also consistent with other states' experience with capitation where most of the savings are accrued during the first year (Reed, Hennessy, and Babigian 1992
, Reed et al. 1994
). Chief executive officers of the community mental health centers in the capitated sites have claimed that they have been able to negotiate better hospital rates when they use local hospitals. However, evidence of cost reductions by the second year is much weaker and spans all areas. Again, there is a possible spillover effect evidenced by price reductions for the FFS area by the second year after capitation.
The changes in local inpatient cost and use, however, do not apparently have much impact on total service use or costs, because the prevalence of inpatient use is low and many inpatient users are concurrently using outpatient services within the periods measured. They also do not differentiate the DC and MBHO areas. Changes in probability of use and cost of outpatient services are the dominant factors in capitated area changes and in differentiating MBHO and DC effects. The low relative prevalence of inpatient use among this representative sample of consumers with serious mental illness may well reflect a historical trend in managing inpatient use and substituting outpatient services. From this perspective, expectations for gaining future cost savings would shift towards “fine-tuning” access to outpatient care.
The analysis of continuous versus intermittent users suggests that consumers for whom intermittent use is related to capitation share characteristics of both continuous users and intermittent users not related to capitation. This suggests that the capitated areas have incurred cost savings for persons with severe mental illness through marginal expansion of the criteria for persons to receive intermittent versus continuous care. Such a change is generally consistent with re-evaluations of the level of cost-effective care. There is some indication of longer waiting times from appointment to service for both areas that could be construed as increased general barriers to access. There is no strong indication of consumers reporting refusal, reduction, or discontinuation of services with the few significant effects for DC consumers. Further analysis indicated that consumers reporting these conditions are much more likely to be receiving services.
Different investment patterns may explain the greater reduction in probability of use and cost for outpatient services in the MBHO area. The DC areas invested in new service development prior to capitation, while MBHO areas waited until savings were evident after the first year of operation. This is reflected in consumers' reports of new service provision only in the MBHO area and after one year of capitation (Wave 3 or later). Delays in new service development may have led to greater decreases in outpatient use for MBHO areas within the context of other treatment protocol changes brought on by capitation.
Consumers reported reductions in pre-scheduled appointments and decreased waiting times at outpatient clinics in the MBHO areas. This may signal a more fundamental, and potentially more efficient or effective, change in access to treatment. A greater focus on immediate, walk-in access, and less reliance on scheduled “maintenance” outpatient visits would be consistent with these findings.
The foregoing discussion highlights some general limitations of a natural experimental design as discussed in the methods section. Though we selected service regions that were comparable, and randomly selected participants within each region, we were unable to randomly allocate the regions to capitation or FFS. Therefore, we were unable to avoid initial differences in treatment patterns for like groups of subjects. These initial differences can be plausibly related in some cases to the extent of the intervention effects found. This is a limitation to the extent that one presupposed that the capitation program effects found in this study could be exactly replicated elsewhere regardless of local context. Alternatively, this can be seen as a strength of natural experimental designs, as opposed to true experimental designs that organize out initial differences, because most policies or interventions do interact with their local environment. Creating two distinct treatment groups allows exploration of the policy relevant question: How do local conditions influence policy outcomes?