1. Descriptive Statistics
Table 8-1 presents descriptive statistics for the 1995 MH/SA expenditure outcome and selected regressors (for brevity, not all risk-adjusters are shown in the table), based on the 70 percent sample used for estimation. As one might predict, given the past literature showing selection of healthier patients into HMOs, the HMO population has lower MH/SA expenditures and lower rates of almost every type of health condition than is seen in the fee-for-service population enrolled in the behavioral health care carve-out. (The exceptions were that the HMO enrollees had higher rates of preventive/administrative care, pregnancy, and dental care.) Eleven percent of the HMO enrollees had any MH/SA expenditures in 1995, compared with 13 percent of the carve-out enrollees, and the average levels of 1995 MH/SA expenditures among users were $669 vs. $1,072 respectively. Average 1995 MH/SA expenditures among the entire sample (users and non-users) were $75 for HMO enrollees and $141 for carve-out enrollees. A relatively low proportion of the sample met the claims-based criteria for psychiatric disability we developed, probably because almost two-thirds of the population was employed (rather than dependents) and hence by definition unlikely to be severely disabled. Respectively, 2.5 percent of the HMO enrollees and 3.1 percent of the carve-out enrollees had conditions meeting the study criteria for severe psychiatric disability.
|TABLE 8-1. Descriptive Statistics by Plan Type, for the 70% Sample, 1995|
|Has any MH/SA expenditures||11%||13%|
|MH/SA expenditures (everybody)||$75 (SD=$741)||$141 (SD=1,060)|
|MH/SA expenditures (users only)||$669 (SD=$2,123)||$1,072 (SD=$2,750)|
|HCFA wage index||1.17 (SD=0.03)||1.15 (SD=0.06)|
|Age||40 (SD=11)||40 (SD=19)|
|Ambulatory Diagnostic Groups:|
|Time Limited: Minor||16.9%||22.3%|
|Time Limited: Minor-Primary Infections||22.6%||25.2%|
|Time Limited: Major-Primary Infections||4.9%||6.1%|
|Likely to Recur: Discrete||14.2%||17.8%|
|Likely to Recur: Discrete-Infections||16.5%||18.2%|
|Likely to Recur: Progressive||1.4%||2.4%|
|Chronic Medical: Stable||28.5%||39.4%|
|Chronic Medical: Unstable||10.7%||18.3%|
|Chronic Specialty: Stable-Orthopedic||2.7%||3.8%|
|Chronic Specialty: Stable-Ear, Nose, Throat||0.8%||1.2%|
|Chronic Specialty: Stable-Eye||1.5%||4.2%|
|Chronic Specialty: Unstable-Orthopedic||1.5%||2.4%|
|Chronic Specialty: Unstable-Ear, Nose, Throat||0.1%||0.2%|
|Chronic Specialty: Unstable-Eye||1.5%||5.1%|
|Injuries/Adverse Effects: Minor||11.2%||11.9%|
|Injuries/Adverse Effects: Major||8.3%||9.0%|
|Psychosocial: Time Limited, Minor||4.5%||4.5%|
|Psychosocial: Recurrent/Persistent, Stable||7.4%||8.5%|
|Psychosocial: Recurrent/Persistent, Unstable||1.8%||2.6%|
|See and Reassure||3.6%||5.5%|
2. Comparison of Predictive Ability of Risk-Adjustment Models
Table 8-2 and Table 8-3 respectively present the synthetic R2 and mean absolute prediction errors for each risk-adjustment model, based on the entire sample. The simple demographic model performs the worst among both the HMO and carve-out populations. ACGs alone and the model controlling only for whether the enrollee has any psychiatric disability also perform relatively poorly. The model controlling for HCCs in conjunction with the type of psychiatric disability performs the best, although the performance of ADGs in conjunction with psychiatric disability is virtually the same. HCCs and ADGs alone perform substantially worse. Thus in both cases, adding separate controls for the type of psychiatric disability improves performance.
To a certain extent, these comparisons depend on the performance measures used. The "value-added" of controlling separately for the type of psychiatric disability appears to be greater when using R2 values as the criterion for measuring performance rather than prediction errors. The same holds true in comparing the predictive abilities of the ADGs and HCCs; the difference between these methodologies appears large when comparing the proportion of variance explained but negligible when comparing mean absolute prediction errors.
|TABLE 8-2. Comparison of R2 Values across Risk-Adjustment Models, for the 30% Sample, 1995|
|Risk Adjustment Model||HMO
|Demographics, any psychiatric disability||0.040||0.51|
|Demographics, type of psychiatric disability||0.153||0.082|
|Demographics, Ambulatory Care Groups (ACGs)||0.010||0.013|
|Demographics, Ambulatory Diagnostic Groups (ADGs)||0.069||0.080|
|Demographics, Hierarchical Coexisting Conditions (HCCs)||0.097||0.119|
|Demographics, ADGs, type of psychiatric disability||0.167||0.108|
|Demographics, HCCs, type of psychiatric disability||0.193||0.141|
|TABLE 8-3. Comparison of Absolute Prediction Errors across Risk-Adjustment Models, for the 30% Sample, 1995|
|Risk Adjustment Model||HMO
|Demographics||$133 ($629)||$256 ($1,253)|
|Demographics, any psychiatric disability||$125 ($618)||$232 ($1,225)|
|Demographics, type of psychiatric disability||$116 ($581)||$223 ($1,205)|
|Demographics, Ambulatory Care Groups (ACGs)||$129 ($627)||$243 ($1,248)|
|Demographics, Ambulatory Diagnostic Groups (ADGs)||$116 ($610)||$199 ($1,211)|
|Demographics, Hierarchical Coexisting Conditions (HCCs)||$115 ($610)||$199 ($1,184)|
|Demographics, ADGs, type of psychiatric disability||$108 ($577)||$192 ($1,193)|
|Demographics, HCCs, type of psychiatric disability||$108 ($568)||$190 ($1,169)|
|NOTE: Means and (in parentheses) standard deviations shown.|
3. Predictive Ratios for People with and without Psychiatric Disabilities
For each risk-adjustment model, Table 8-4 gives the ratios of payments to actual expenditures for the subsamples of enrollees with and without psychiatric disability. These figures illustrate the point that incentives for competing carve-outs to avoid the psychiatrically disabled remain even after risk-adjustment, although they are attenuated. The predictive ratios below one for the enrollees with psychiatric disability imply that health plans will be paid less than the actual expenditures of this population as a group. The predictive ratios above one for the enrollees without psychiatric disability imply that on average, health plans will be overpaid for the non-psychiatrically disabled.
The relative performance of the different risk-adjustment systems in tailoring MH/SA payments to the expected costs of the disabled vs. non-disabled populations is similar to the rankings derived from Table 8-2 and Table 8-3. (Although the model controlling for the presence of psychiatric disability now appears to do better than the one controlling for the type of psychiatric disability, this phenomenon is probably an artifact arising from the use of the psychiatric disability indicator to stratify the sample.) The simple demographic model performs very poorly, while the model controlling for both HCCs and the type of psychiatric disability performs quite well. However, even using the latter methodology, carve-outs would be paid only 83-85 percent of what it would actually cost them to care for people with psychiatric disability.
|TABLE 8-4. Comparison of Predictive Ratios for People with and without Psychiatric Disability, for the 30% Sample, 1995|
|Demographics, any psychiatric disability||0.80||1.07||0.76||1.13|
|Demographics, type of psychiatric disability||0.73||1.10||0.75||1.14|
|Demographics, Ambulatory Care Groups (ACGs)||0.16||1.30||0.17||1.47|
|Demographics, Ambulatory Diagnostic Groups (ADGs)||0.74||1.09||0.72||1.16|
|Demographics, Hierarchical Coexisting Conditions (HCCs)||0.78||1.08||0.75||1.14|
|Demographics, ADGs, type of psychiatric disability||0.85||1.05||0.82||1.10|
|Demographics, HCCs, type of psychiatric disability||0.85||1.05||0.83||1.09|
|NOTES: Predictive ratio = total payments/total actual expenditure. Calculations are based on 30% sample.|
4. Comparison of Payment Methodologies
Table 8-5 summarizes the profits and losses on MH/SA services that would be incurred by the seven HMOs in the study under each payment methodology. The table shows the per-enrollee profits of the health plans with the greatest and least financial gains under each system. These figures provide an example of what the financial implications of each type of contractual arrangement might be for competing behavioral health care carve-outs experiencing similar natural selection.
Because the full capitation system is constrained to be budget neutral, the emergence of plans that are financial "winners" is necessarily accompanied by plans that are "losers." Full capitation leads to the largest variation in financial performance among the plans. Similarly, mixed payment results in substantial profits and losses, although by design, these are only half as large as under full capitation. Soft capitation contracts implemented using plan averages also result in some plans earning profits and other plans suffering losses, although the magnitudes of the financial gains and losses are not as large as with systems relying entirely or partially on pure capitation.
|TABLE 8-5. Range of HMO Per Enrollee Profits under Full Capitation, Soft Capitation and Mixed Payment, for the 30% Sample, 1995|
|Risk Adjustment Model||Full
|Demographics|| max: +$26.86
| max: +$13.43
|Demographics, any psychiatric disability||max: +$30.99
|Demographics, type of psychiatric disability||max: +$35.18
|Demographics, ACGs||max: +$26.38
|Demographics, ADGs||max: +$34.84
|Demographics, HCCs||max: +$30.79
|Demographics, ADGs, type of psychiatric disability||max: +$35.89
|Demographics, HCCs, type of psychiatric disability||max: +$37.82
|NOTE: Enrollees of seven HMOs are included in the population. 50-50 capitation has limits set at +10 percent of the target amount, while 60-40 capitation has limits set at +25 percent of the target amount.|
The phenomenon that all of the plans would make a small per enrollee profit under individual soft capitation supports previous findings and makes sense in light of the risk-sharing arrangements. Individuals with psychiatric disability tend to have target amounts far below actual expenditures, so the purchaser will end up financing most of the care for this small but costly population. Individuals without psychiatric disability tend to have target amounts above actual expenditures; however, because this population is much larger than the population with psychiatric disability and the capitation payments are budget neutral, target amounts are not that much above actual expenditures. Thus a relatively high proportion of the cost savings of the non-disabled population falls within the risk corridors and is retained in part by the health plans; a relatively low proportion of the excess costs of the disabled population falls within the risk corridors and is shared by the plan. In other words, it is likely that individuals exceed the upper bound more frequently than they fall below the lower bound. Thus, the purchasers absorb much of the risk of insuring potentially high-cost enrollees.
As a numerical example, suppose that a plan enrolls 20 patients with psychiatric disability, each of whom costs $140, and 80 patients without disability, each of whom costs $90. The risk-adjustment is imperfect, so the target amounts are only $120 for each patient with disability, but $95 for each patient without disability. With risk corridors defined as plus or minus 10 percent of the target amount, the plan retains half of all savings on the 80 patients without disability, since their actual expenditures are $90, which is still above the lower risk corridor of $85.50. However, the plan only incurs part of the losses on the 20 patients with disability, since their actual expenditures are $140, above their upper risk corridor of $132. Thus the profits (80 x $2.50 = $200) are greater than the losses (20 x $6 = $120).
Although it cannot be seen from the results presented in the table, the health plan experiencing the greatest profits or losses was not always the same. Certain plans clearly appeared to attract disproportionate shares of enrollees with heavy MH/SA needs, while others appeared to enroll large numbers of healthy patients. Yet even the relative financial performance of each plan depended to some extent on both the reimbursement and risk-adjustment methodologies chosen. These choices are therefore a powerful policy instrument, with serious implications for the viability of competing carve-out vendors.