Jason Shafrin, PhD, serves as the vice president of Health Economics at PRECISIONheor, and Meena Venkatachalam, MSc, is the senior director of Health Economics at PRECISIONheor.
The reality of inequality in the United States is yet again center stage, with more emphasis seen than ever before. The combination of repeated examples of police brutality, the health crisis generated from COVID-19, and the impending financial crisis have highlighted the extent of systemic racisms and inequality across the country. While inequality can manifest itself across multiple dimensions, of most relevance to your author’s line of work is the clear inequality in health outcomes that exists in the United States.
Consider just 3 dimensions of inequality in health care: (i) differential health insurance coverage rates, (ii) the differential impact of barriers to accessing care, and (iii) differential risk of disease exposure.Regarding the first dimension, African-Americans spend 5 more years of their life without insurance compared to non-Hispanic white Americans.1 COVID-19 has exacerbated the impact of these differences in health insurance coverage as COVID patients without insurance may be more reluctant to seek care if they do not have sufficient means to pay for it.
Secondly, individuals with lower socioeconomic status often face additional barriers to get care even if they are insured: lack of transportation, fewer child care options, inability to work flexible schedules, and communication and language barriers, among others. All of these barriers lead to lower access and utilization of health care services. Third, Blacks and Hispanics are disproportionally represented in essential jobs, increasing their risk of exposure to COVID-19. While 27% of Whites are employed in essential industries, 38% of African-Americans are employed in these occupations with more exposure to infections and close proximity to others.2
For those of us working in health care—and particular health care innovation—the events and statistics described above should be a clarion call for members of our industry to look in the mirror and examine how we can improve health outcomes for the most disadvantaged in our society. That includes not only insuring adequate representation of minority groups in clinical trials, but also structuring value assessment to appropriately incentivize the development of innovative treatments for disadvantaged populations.
Ensuring minorities are represented in clinical trials
One clear step to ensure innovation works for disadvantaged populations is to ensure their inclusion in clinical trials. Achieving this goal requires action along 2 dimensions. First, for a given disease, life sciences companies need to ensure that the trial population is representative of the disease of interest, including ensuring that a sufficient number of minorities are enrolled. The FDA has an annual snapshot where they compare enrollment in clinical trials against the racial and ethnic composition of the country as a whole.3 Data from 2019 appears to demonstrate that the racial composition of all approved drugs in 2019 largely reflected the racial composition of the US as a whole,4 but these trends need to continue to be monitored.
The second dimension is ensuring that the drugs that are tested in clinical trials include treatments for diseases that impact minorities. The key to incentivizing life science companies to conduct these clinical trials is for decision makers to use value frameworks that reward companies that create treatments for diseases affecting disadvantaged populations. Decision makers need to start thinking more broadly about what value for money is in their specific population and how to quantify the value of innovations for disadvantaged populations.
Improving value assessment to incentivize innovation for disadvantaged groups
Traditional value frameworks only measure the value of health gains relative to cost for an anonymous person and do not consider whether a treatment would reduce inequality of health outcomes. For instance, when discussing the valuation of health gains—often quantified into quality-adjusted life years (QALYs)—the National Institute for Health Care Excellence (NICE) in the United Kingdom states that “an additional QALY is of equal value regardless of other characteristics of the individuals, such as their socio-demographic characteristics, their age, or their level of health.”5 In the United States, the Institute for Clinical and Economic Review (ICER) has used a similar approach in their 2020-2023 value assessment framework.6
The benefit of these approaches is that they treat all health gains equally. The disadvantage, however, is that a new treatment which expands life by hypothetically 1 year is valued the same even if that 1 year is provided to a patient population currently experiencing lower than average health outcomes (e.g. life expectancy of 40 years) in comparison to a patient population experiencing higher than average health outcomes (e.g. life expectancy of 100 years). The current approaches do not incentivize the development of treatments for our members of society who currently have the worst health outcomes.
While decision-makers traditionally have ignored the issue of inequality, academic researchers have already developed tools to quantify a treatment’s value from reductions in inequality. Two common methods for doing so are distributional cost effectiveness analysis (DCEA) and multiple-criteria decision analysis (MCDA). DCEA values treatments not only based on health gains and cost but also whether these health gains accrue to disadvantaged groups.7
The approach is similar to traditional cost effectiveness analysis but health gains—typically measured in QALYs—are estimated by patient subgroup. Subgroups are defined by patient groups where, in theory, differences in health outcomes should not be seen but exist due to inequality–e.g. gender, education, socioeconomic status, race, etc. For each of these subgroups, QALY gains are weighted based on whether they accrue to patients with worse than average outcomes at baseline (up-weighted), or better than average outcomes at baseline (down-weighted). While DCEA is a bit more complicated than traditional CEA, it does provide a single, simple measure of value often expressed as an incremental cost per QALY gained.
For decision-makers who prefer to make more transparent the tradeoff between equity and efficiency, another common approach is MCDA.8 MCDA allows decisionmakers to explicitly prioritize different treatment attributes—such as a treatment’s efficacy, safety, cost, or impact on disadvantaged groups—when conducting value assessments. Treatment attributes are selected and decisionmakers explicitly make tradeoffs across the presented dimensions among available treatment options. MCDA provides a framework to help make these decisions more accessible for different uses in real-world situations.
Where we go from here
Although inequality in health outcomes is a multi-faceted problem, as health care professionals we have a social responsibility to help reduce health inequality in whichever way we can. Improving the representativeness of clinical trials and updating our value assessment frameworks are just 2 small ways we can play our role in the fight against health inequality. If collectively as managed care organizations, life science companies, decision-makers and researchers we can push for these efforts, it will no doubt in the future impact research and development, value frameworks, reimbursement, and health outcomes for disadvantaged populations.