Varying Pharmacy Benefits With Clinical Status: The Case of Cholesterol-lowering Therapy
By Dana P. Goldman, PhD; Geoffrey F. Joyce, PhD; and Pinar Karaca-Mandic, PhD (From the RAND Corporation, Santa Monica, Calif.)
The American Journal of Managed Care
January 2006
Methods: Using claims data from 88 health plans, we studied 62 274 patients aged 20 years and older who initiated CL (cholesterol-lowering) therapy between 1997 and 2001. We examined the association between copayments and compliance in the year after initiation of therapy, and the association between compliance and subsequent hospital and emergency department (ED) use for up to 4 years after initiation.
Benefit Scenarios: We used estimates from both the compliance and service-use models to estimate the impact of 2 alternative BBC (benefit-based-copayment) designs relative to a base case of a $10 copayment for all patients (the modal copayment). We derived the predictions by first estimating compliance using our copayment and compliance model, and then predicting hospitalizations and ED visits with the compliance and service-use models. The first scenario was chosen to keep total pharmacy payments unchanged. Patients at high and medium risk had no copayments while copayments for low-risk individuals were increased from $10 to $22. In the second scenario, medium-and high-risk patients received the medication for free, while low-risk patients still paid $10. The estimates were computed assuming 6.3 million privately insured or Medicare-insured adults on CL therapy in the United States, as calculated using the 1999-2000 National Health and Nutrition Examination Survey.
Results: Table 3 (available at link below, though this text explains the findings in the table) shows the effects on the sample of 6.3 million CL users of 2 designs for BBCs. The base case is a $10 copayment for all patients. Under scenario 1, high- and medium-risk patients faced no copayment and low-risk patients had copayments of $22. Compared with the base case, full compliance increased 9 percentage points among the high-risk group (62% to 71%) and 10 percentage points among the medium-risk group (59% to 69%), and decreased from 52% to 44% among the low-risk group. There was no change in aggregate health plan payments for drugs because reduced use by the low-risk group was offset by increased use by the high-risk group. The high-and medium-risk groups had no out-of-pocket payments, but out-of-pocket payments by the low-risk group increased $280 million (from $272 million to $552 million). This scenario averted 79 837 hospitalizations overall, even after accounting for an additional 10 406 hospitalizations among the low-risk group. Similarly, ED use was reduced in aggregate by 31 411.
Scenario 2 eliminated copayments for high- and medium-risk patients with no change in copayments for low-risk patients. This benefit increased prescription drug spending by health plans ($486 million) and lowered spending by patients ($311 million). Scenario 2 resulted in 90 243 fewer hospitalizations and 36 493 fewer ED admissions compared with the base case.
Our scenarios with BBCs (Table 3) reduced the number of hospitalizations by approximately 80 000 to 90 000 annually and the number of ED visits by 30 000 to 35 000, resulting in net aggregate savings of more than $1 billion.
Conclusion: The challenge for the healthcare system is to make patients more sensitive to the cost of treatment without encouraging them to forego cost-effective care. Health plans increasingly recognize the need to differentiate coverage based on demonstrated value. For example, some health plans have eliminated copayments for some generic drugs, while others now assign drugs to tiers based on their cost effectiveness. The problem with these approaches is that clinical efficacy of any drug varies across patients.
We showed that strategically reducing copayments for patients who are most at risk can improve overall compliance and reduce use of other expensive services. In an era of consumer-directed healthcare and improved information technology, tailoring copayments to a patient’s expected therapeutic benefit can increase the clinical and economic efficacy of prescription medications.
http://www.ajmc.com/Article.cfm?Menu=1&ID=3072
Comment: By Don McCanne, M.D.
The RAND Health Insurance Experiment (RAND HIE), completed over twenty years ago, demonstrated that patient cost sharing (coinsurance) reduces the utilization of health services and thereby reduces total health care spending. It is the study most commonly cited by supporters of consumer-directed health care (CDHC), because it makes their case that health care spending will decline if patients are required to use some of their own funds whenever health care services are accessed.
Further analysis of the RAND data confirmed that patient cost sharing does decrease use of beneficial services. In conceding this point, the CDHC advocates have called for greater transparency so that patients can select beneficial services that provide value and reject services that are of little or no benefit. Although this trend may lead to posting of price lists and primitive measures of quality, the complexities of medical decisions over which are truly beneficial services are difficult enough for the physician and will never be within the purview of the patient. Deductibles, copayments, and coinsurance will always result in decreased use of beneficial services.
This new RAND study is interesting because it almost seems to be a flip of the prior RAND conclusions. In this study, eliminating copayments reduced total health care spending. However, this principle would apply only in instances wherein the intervention encouraged (e.g., cholesterol-lowering therapy) is less expensive than the consequences of not intervening (e.g., hospitalizations and ED visits).
The concept of adjusting cost sharing based on potential health care cost reductions does have some serious flaws. Just reading this study demonstrates that the administrative complexities are great even with the simple intervention of cholesterol-lowering drugs. It is unlikely that the data required for more complex clinical situations would be readily forthcoming, not to mention the complexity of pre-assigning patients in variable cost-sharing categories. This bird likely will never fly.
Another serious concern is that this study, by design, looked at the financial impact of benefit-based-copayments while seemingly dismissing, as a trade-off, the negative clinical impact that a drug-spending-neutral shift in copayments would produce. That is, in eliminating copayments for high- and medium-risk groups, the copayments for the low-risk group were increased from $10 to $22 in order to keep the total spending for this drug benefit budget-neutral. That $12 increase in copayments saves the health plan $280 million, but for this low-risk group, it results in 10,406 additional hospitalizations and 5,082 additional ED visits. That doesn’t even include additional morbidity and mortality.
As to the focus of the study, the authors state, “…more importantly, these benefits can be achieved without increasing a health plan’s pharmacy costs.”
They didn’t even look at a scenario that would have eliminated copayments for the low risk group. That would have increased plan costs even more, but how many hospitalizations would it have prevented?
The most fundamental flaw of consumer-directed health care is that it depends on demand-side spending control that reduces utilization of truly beneficial services. A single-payer national health insurance program would depend on more carefully targeted and patient-friendly supply-side mechanisms of containing health care costs.
For their next study, instead of defining demand-side mechanisms that would reduce health plan spending at a cost of reduced patient access, RAND should study supply-side mechanisms that would improve resource allocation for all of us, perhaps starting with the reduction of detrimental high-tech excesses that impair both quality and outcomes. Reducing costs while improving outcomes would certainly provide us with greater health care value.