بررسی انگیزه انتخاب سطح خدمات در مبادلات بیمه سلامت خصوصی

Even with open enrollment and mandated purchase, incentives created by adverse selection may undermine the efficiency of service offerings by plans in the new health insurance Exchanges created by the Affordable Care Act. Using data on persons likely to participate in Exchanges drawn from five waves of the Medical Expenditure Panel Survey, we measure plan incentives in two ways. First, we construct predictive ratios, improving on current methods by taking into account the role of premiums in financing plans. Second, relying on an explicit model of plan profit maximization, we measure incentives based on the predictability and predictiveness of various medical diagnoses. Among the chronic diseases studied, plans have the greatest incentive to skimp on care for cancer, and mental health and substance abuse.

Several provisions of the Patient Protection and Affordable Care Act of 2010 (ACA) are designed to minimize adverse selection in Exchanges (also referred to as Marketplaces).1 Exchange plans may condition premiums only on age (with restricted rate bands), family size, smoking status, and geography, but not preexisting conditions or other factors. Coverage is regulated. The ACA also mandates that Exchanges engage in risk adjustment and implement temporary risk corridors and reinsurance programs.2 Risk adjustment is budget neutral: health plans drawing enrollees with lower than average health risk transfer funds to plans with higher than average health risks.3 These regulations may not fully address selection problems, however, because Exchange plans may engage in the difficult-to-regulate practice of distorting service offerings to attract “winners” and deter “losers.” For example, news stories already contain reports that plans are engaging in aggressive network management, possibly discouraging enrollees requiring more costly treatment.4 Aggressive network management will also generally lower premiums, making insurance purchase more attractive to good risks. Assessment of selection incentives is often undertaken by calculating “predictive ratios” for a group with a chronic illness (for example), with the ratio defined as the average risk adjusted payment divided by the average cost for the group (e.g., Pope et al., 2011). One of our contributions is to improve the methodology of predictive ratios. The idea of a predictive ratio is simple: show the revenue for a group in relation to the costs for the group. Profitable groups will be attractive to plans, unprofitable groups will be unattractive. While the idea is simple, its implementation in Medicare and in Exchanges has neglected that revenues (in both Medicare and the Exchanges) involve premiums as well as risk adjustment. Premiums themselves involve some “risk adjustment” in that premiums can be up to three times higher for an older than a younger person. In our construction of predictive ratios we anticipate equilibrium premiums to better characterize winning and losing groups. While predictive ratios are relatively easy to calculate, they are far from a complete description of incentives related to selection in managed care. Managed care plans are usually modeled as making discriminatory decisions about services (which is legal though regulated), not about individual persons or groups of people (which is not legal). Thus, a plan might set up a difficult-to-access network of specialists for a disease (e.g., cancer) if it wished to discourage people who would want to use this network in the plan. A plan can do that within limits, but it cannot discriminate on the basis of “pre-existing conditions.” In an alternative to predictive ratios, we use a theory-driven measure to characterize the services a plan would wish, in its own self-interest, to undersupply. Relying on an earlier literature referenced below, we characterize service-level incentives based on an explicit model of plan profit-maximization. A plan will want to stint on quality for services that are predictable by enrollees and predictive of net losses. This second measure, while more precise theoretically, involves more assumptions and empirical work to implement. We must estimate what individuals can predict for various sets of services, and measure the correlation of these predictions with total gains and losses for each person. We show how to implement both measures of incentives based on a “Exchange population” drawn from five panels of the Medical Expenditure Panel Survey (MEPS). Section 2 contains a brief review of the literature on adverse selection and health insurance markets, emphasizing studies relevant to the new Exchanges. Section 3 presents the economic rationale for our measures of incentives for plans to engage in service-level selection. Section 4 explains how we use the MEPS data to define and construct revenue and cost-related variables used to illustrate our methods. Characterizing plan revenue per person in an Exchange requires us to simulate risk-adjustment. After approximating the risk adjustment to be used in Exchanges, we find the zero-profit plan premiums consistent with the risk adjustment methodology. On the cost side, we assess plan incentives to select across seven disease areas – heart disease, injury, cancer, mental health and substance abuse, lower respiratory, diabetes, and joint and back disorders – a mix of chronic and acute conditions. The measure of predictability requires a statistical model estimating how well individuals can forecast use of various services. Our methods for estimating predictability are described in Section 5. Section 6 presents results for predictive ratios for groups of users, and the measure of incentives to over and underprovide services based on plan profit maximization. Among the disease areas studied, incentives for plans to underprovide services are strongest in the case of cancer, and mental health and substance abuse. A final Section 7 discusses the limitations of our approach, including those related to the uneven rollout of the Exchanges, and some possible next steps for research.

Architects of the new Exchanges have taken steps to mitigate the problem of adverse selection, including requiring open enrollment, regulating the benefit package, risk adjusting plan payments, implementing risk corridors, and requiring a temporary reinsurance feature. This paper develops a method for assessing incentives for adverse selection that may remain even after these fixes. We make two primary contributions. First, we emphasize the role of premiums in plan revenues and incentives. This is critical in Exchanges where revenues per person can vary by a factor of three or greater. Taking account of premiums requires addressing how premiums will be determined in equilibrium. We make the conventional assumption here by assuming a competitive (zero-profit) equilibrium.54 While natural, this assumption may not be correct. A limitation of our paper and a direction for future research would be to explore incentives in environments with imperfect competition among health plans (and possibly among providers). Second, we show how to operationalize the implications of profit maximization for incentives to engage in service-level selection. Specifically, drawing on earlier papers showing that services that are both predictable and predictive are subject to underprovision, we measure both predictability and predictiveness and the consequent incentives to underprovide by major service area. This is an empirical task that requires simulating the basics of the payment system in Exchanges and data from a population similar to those who will likely be participating in an Exchange. These requirements call attention to two limitations of our analysis: first, we capture the major, but not all of the financial features of Exchange payment, and second, our data are from a national survey, not from actual Exchange participants. Profit incentives to plans are mitigated in the short term by reinsurance features and indefinitely by risk corridors that limit gains and losses. These features are likely to reduce but not eliminate service-level selection incentives (Zhu et al., forthcoming). Risk adjustment is done at the plan level (i.e., Bronze, Silver, etc.) in Exchanges, and our analysis assumed one plan level. Importantly, as we discussed above, data from the public-use files in MEPS do not incorporate the fineness of the risk adjustment systems. Also in terms of data, our sample size is low for risk adjustment modeling. Another notable limitation is that we have assumed full compliance with the insurance mandate. Early enrollment in Exchanges did not go smoothly in late 2013, some states showed little enthusiasm for the policy, and nearly everywhere enrollment was slower than expected. Partial compliance may turn out to be uneven across population groups. If the mandate regulations are not very effective, groups with less to gain from participation, younger people and “better risks” generally may be less likely to participate. This will affect the overall size and vitality of the Exchanges as well as the mix of risks to be insured. A change in the composition of the risk pool due to noncompliance will make insurance more expensive on average, but it is unclear how it would affect incentives for selection for particular disease areas. Further, as noted in the introduction, most plans offered in the Exchanges will have narrower networks than current commercial plans. Although experience could well differ in such networks, it is hard to predict how, if at all, that would affect our conclusions. With these qualifications in mind, we find, nonetheless, strong incentives to underprovide care to persons with some chronic illnesses may remain in spite of risk adjustment and other payment system features designed to mitigate against underprovision. We measure these incentives using an improved version of a predictive ratio and by a selection index derived from plan profit maximization. These measures can be readily applied to data, including data as it emerges from Exchange experience. While it is not surprising that plans have incentives to avoid sick people, our methods allow us to go beyond this general statement and identify the disease areas that should be of special concern. Measured by predictive ratios, the strongest incentives are to discourage enrollment by people with mental health and substance abuse problems. Even though these disorders are themselves not very expensive, the people who use these services tend to use more of all other services, and in disease areas not tracked well by existing risk adjustment. By comparison, risk adjustment does relatively well in picking up the extra costs (across all diseases) for persons with cancer and diabetes. Using the selection index based on profit maximization, however, takes into account the predictability of various illnesses – with more predictable conditions creating stronger incentives for a plan to use as a selection device. With this approach, cancer rises to the top in terms of disincentives to supply, with mental health and substance abuse as number two. Incentives to a plan to over or under-provide services depend on the patterns of disease in the underlying population, but these illnesses were also found to be subject to incentives to undersupply in Medicare (Ellis and McGuire, 2007). Interestingly, cancer diagnoses were also among the least profitable diagnoses in the Medicare Advantage population studied by Newhouse et al. (2013). They examined 48 unique combinations of HCC's including single HCC's; all seven cancer diagnosis they examined were among the 12 lowest margin HCC's. The only mental health diagnosis they examined was major depressive, bipolar, or schizophrenia without another CMS-HCC category coded. Those individuals were around the median in profitability. The data suggested that both the ability to manage the disease medically and the market power of providers treating the disease mattered. These might differ for a Medicare Advantage population than for the populations in an Exchange. Importantly, Newhouse et al. found no evidence of selection despite substantial differences in margins across the categories. The distribution of the Medicare Advantage population across these HCC's was very close to that of the traditional Medicare population. Whether this is attributable to the effectiveness of Medicare regulations inhibiting selection or the costliness of selecting by disease or both is unknown. As data begin to come in from the Exchanges, incentives for undersupply by disease area can be assessed more accurately. State health insurance regulators can be alert to underservice in disease areas, perhaps paying attention to the level of payment and the depth of the networks plans create for these conditions. A more drastic approach to an area subject to underservice is to regulate health insurance contracts, such as by “carving out” the benefit and writing a separate contract (perhaps at the state level for all Exchange participants) for supply of care in the designated disease area. Modification of the terms of the payment system, altering rules for risk adjustment or premium setting, or changing reinsurance rules, for example, will have differential effects on incentives in different disease areas. Incentive effects of these possible changes or other policy options can be examined with the methods developed here.