Vertical data-driven mergers between health insurers and drug suppliers may facilitate health insurers’ efforts to discriminate against vulnerable populations, leaving them without meaningful access to care. Hence, they can widen the existing health inequalities in the United States and perpetuate inequities. However, US antitrust enforcers could address the significant harms that those mergers impose on high-risk consumers.
A new wave of data-driven health care mergers is upon us. Health insurers such as Aetna are merging with powerful drug suppliers like CVS, aiming to share access to our health data. They want to know where we go, what we buy, how much sleep we get; if we can resist sugar, junk food or nicotine; if we exercise and how often. In other words, they aim to shape a unique digital health ID for each of us. Why? On one hand, health insurers aim to reduce their risks, and therefore their costs, by improving our level of well-being. On the other, health insurers aim to reduce their risks, and therefore their costs, by discriminating against the most unhealthy of us.
Indeed, health insurers such as Aetna merge with drug suppliers such as CVS to increase their access to consumers’ prescription history, shopping habits, and health-related data. By analyzing this data, Aetna can identify the high-risk consumers that it is likely to attract and reduce their access to health insurance services. For instance, post-merger Aetna could use big data analytics to identify “the likely to get depressed” or “the likely to be diabetic” consumer groups and design its health plans in ways that will discourage those high-risk consumers from applying for health coverage. Aetna could achieve this goal by moving the drugs associated with their treatment to a higher cost-tier. Essentially, this means that those high-risk consumer groups may incur higher drug coverage costs following the merger.
An official complaint filed with the Department of Health and Human Services in May 2014 reveals that this risk is real. The complaint alleges that health insurers in Florida that are providing plans through the federal marketplace had designed their drug formularies to dissuade people with HIV from choosing their plans. Health insurers had classified all HIV drugs, including generics, in the highest cost-sharing tier.
However, this is not the only strategy health insurers often employ to exclude high-risk, high-cost consumers. Health insurers often attempt to prevent high-risk consumers from applying for health insurance coverage by failing to provide clear information about which drugs or types of treatment their plans actually cover. Also, they often avoid cooperating with specific health care providers that have a strong reputation for curing patients with HIV, hepatitis C, or other diseases that require access to continuous and costly care.
Arguably, such discriminatory practices can drive high-risk consumers out of the health insurance market, leaving them without any meaningful access to health care. In other words, such discriminatory practices may frustrate one of the core goals of the Affordable Care Act, (ACA) whose core mission is to prohibit health insurers from discriminating against citizens on the basis of their preexisting health conditions and social, racial, or economic background.
The same may happen if health insurers choose to increase their access to our health data by merging with tech giants such as Facebook. For example, Facebook has already explained how it collects health data from its users. Given also that numerous users rely on Facebook for comparing treatment options and sharing experiences with other users that face similar health challenges, there is plenty of health data that can easily be harnessed.
Importantly, data-driven mergers between health insurers and drug suppliers may not only leave high-risk consumers without any meaningful access to care, such mergers may even exacerbate the existing health disparities among different socio-economic groups. This is because clinical data demonstrate a strong link between social determinants of health and health disparities among certain populations. For instance, decades of research show that the most vulnerable populations are more likely to suffer from obesity and alcohol addiction and face higher structural barriers to adopting a healthier lifestyle. Racial and ethnic minorities in the United States are also at greater risk for certain diseases including hypertension, diabetes, and Covid-19. Thus, instead of increasing high-risk consumers’ access to health insurance, data-driven mergers between health insurers and drug suppliers may actually make access easier for those who need it the least: the low-risk consumers who live healthier lives.
Can regulators address the harm that these data-driven health care mergers create? Specifically, reduced access to health insurance services for high-risk consumers? Albeit crucial, this question is not easy to address. The reason is that antitrust law is primarily concerned with the overall welfare of society—it does not distinguish between different consumer groups. Under the traditional consumer welfare standard, both high-risk and low-risk consumers, healthy and unhealthy count equally. Thus, if a merger between a health insurer and a drug supplier leads to increased drug coverage costs for high-risk consumers but to reduced costs for lower-risk ones, the antitrust enforcers might accept the merger even though it may harm the most vulnerable populations that need access to health care.
This is a blind spot of antitrust law. By aggregating consumers into one group and without balancing the interests and specific circumstances of different consumer groups, antitrust law does not consider how a specific conduct or a transaction may affect different classes or types of consumers. Nonetheless, in the case at issue, if the antitrust enforcers failed to consider the merger’s impact on high-risk consumers, they may risk applying antitrust law to healthcare in a way that is not in line with the fundamental goals of the Affordable Care Act. In other words, they may risk allowing health insurers to discriminate against citizens on the basis of their pre-existing conditions. This begs the question: Do the antitrust enforcers have the analytical tools to assess a merger’s impact on specific segment of consumers?
In my recent article “Mergers that Harm Our Health,” I argue that the answer to this question should be a positive one. Specifically, I identify three ways in which the antitrust agencies can actually address the harm that data-driven mergers in health care may impose on high-risk “unprofitable” consumers. To illustrate:
1. The vulnerable, high-risk consumers constitute a separate relevant market
When enforcement agencies assess a merger, they seek to identify and ban any transaction that may produce market power or facilitate its exercise. A merger enhances market power if it is likely to encourage one or more firms to increase prices, diminish innovation, or otherwise harm consumers as a result of diminished competition. To assess a merger’s competitive effects, enforcers define the relevant market in which potential competitive effects are likely to be felt. To define the relevant market, antitrust agencies apply the SSNIP test. By applying the SSNIP test, they seek to identify the smallest set of products for which a hypothetical monopolist could profitably raise prices by 5 to 10 percent above the competitive level for a sustained period of time. According to the 2010 Horizontal Merger Guidelines, if a hypothetical monopolist can profitably target a specific segment of consumers for price increases, the antitrust agencies may “identify relevant markets defined around those targeted customers.”
As noted, a vertical merger between a health insurer and a drug supplier may lead to reduced drug coverage costs for a certain group of consumers—the less risky ones, but higher costs for the “high-risk” and more vulnerable ones. The merged entity may achieve this goal by increasing the costs of drug coverage for high-risk consumers. This specific segment of consumers would constitute a separate relevant market under the SSNIP test.
However, when one health insurer discriminates against a certain group of consumers, the rival health insurers may disproportionately bear the costs of those consumers. To avoid this burden, they may either leave the market or apply discriminatory strategies similar to the ones applied by the merged firm. Hence, following the merger, competition among health insurers for the high-risk consumer groups would be further reduced. This segment of consumers, unable to find affordable health plans would either incur the higher drug coverage costs or would remain uninsured. In other words, following the merger, the high-risk consumers would be considerably hurt. Because a merger between a health insurer and a drug supplier may lead to reduced competition among health insurers for the high-risk consumers and increased drug coverage costs for this specific group of consumers, the antitrust enforcers may take the view that the merger should be prohibited.
2. The merger would further increase the barriers to entry into the health insurance services market
Yet, antitrust enforcers may ban the proposed merger for another reason. Post-merger, successful entry into the health insurance services market may also require entry into the retail pharmacy market. Indeed, unless a potential entrant gained access to consumers’ prescription history and health-related data, it may be unable to compete in the health insurance services market. Without ensuring access to consumers’ health data, prescription, and shopping history, a health insurer may be less able to target the “healthier” low-risk consumers and avoid “the high risk, unprofitable” ones. This may further deter entry into the health insurance services market. Ultimately, competition in the health insurance services market would be reduced. Absent any competitive constraint in the health insurance services market, health insurers would increase the health insurers premiums. In the long term, both high-risk and low risk consumers would suffer.
3. The merger would facilitate the health insurer’s effort to evade the ACA
Another way in which antitrust enforcers may address the harm that these data driven mergers impose on high-risk consumers is by taking the stance that the envisaged merger may facilitate the merged entity’s efforts to evade the ACA, which aims to prohibit pre-existing condition exclusions and discriminatory premium rates. Importantly, so far, the agencies have not assessed a merger’s impact on the policy goals of the ACA. However, prominent scholars in the United States have argued that a vertical merger that facilitates the evasion of a price regulation or any regulation that prohibits price discrimination can raise anticompetitive concerns and could therefore be prohibited. Albeit in a limited number of cases, the agencies have embraced this approach. Hence, there is a possibility that the agencies could allege that a merger between a health insurer and a drug supplier should be banned on the basis that it facilitates the merged entity’s efforts to evade the ACA.
While the antitrust enforcers may try to prohibit the merger between a health insurer and a drug supplier on the basis of the above concerns, the merging entities could contend that their proposed deal does not necessarily hurt competition and consumers in light of the significant efficiencies it is likely to create. They could argue that the harm the envisaged merger may cause to high-risk consumers would be outweighed by the benefits it may bring to the lower-risk ones. Such benefits may be lower out-of-pocket costs for drug utilization and increased access to health insurance services. Another benefit of such a merger would be access to lower-cost care, according to CVS, which has 1,100 Minute Clinics in its pharmacies that treat minor health conditions. Post-merger, CVS-Aetna would route customers needing urgent, non-complex care to these Minute Clinics.
A merger between Aetna and CVS would also allow Aetna to improve its access to patients’ purchasing history and health-related data. Hence, post-merger Aetna-CVS would be able to identify the patients that are not being properly treated and ensure their access to health care services. For instance, the merged firm could identify the high- risk asthma patients who have not been prescribed medicines and manage their care before they end up in emergency rooms with life threatening asthma episodes. Nonetheless, the antitrust enforcers may not necessarily accept these efficiency claims for several reasons including that such efficiencies would occur in the market for primary healthcare services and not the health insurance services market.
I believe that the question of whether and, if so, how antitrust enforcers can address the harm data-driven mergers impose on a specific segment of consumers could not be more topical. The more tech giants such as Facebook or Google are entering the digital health market, the more mergers we may see between health insurers and digital platforms. And the more mergers between tech giants and health insurers emerge, the more opportunities there will be for discrimination against the most unhealthy among us. If regulators fail to consider the harm data-driven mergers in the healthcare field may impose on the vulnerable populations, they may risk applying antitrust law in a way that disregards the policy goals of the ACA and contributes to the existing health inequities. The social costs of health disparities are high. They include significant health care costs, premature deaths, and illness-related lost productivity. Additionally, diseases that are prevalent in poor neighborhoods ultimately spread into wealthy communities. Hence, health inequalities affect not only the most vulnerable populations but the well-being of society as a whole.