Is Cost-Effectiveness Analysis Racist?

Performing and using cost-effectiveness analyses is a core activity for many of us working in HEOR. Many shortcomings of CEA are well known (it fails to account for budget impact, “real option value”, “the value of hope”, etc.), but CEA as currently performed has an even bigger problem than any of those: equity.

There is a racial divide in the US healthcare system. We state this as a fact because it is. Not convinced? Read the Lancet 2021 Commission on Public Policy and Health in the Trump Era report, or AHRQ’s most recent report on National Healthcare Quality and Disparities.

Black Americans are more likely to be uninsured, get worse care when they are insured, and die younger than their White counterparts. The idea that these profoundly divergent outcomes are predictable, much less intentional, might be hard to swallow, but the evidence is hard to ignore.

Adults ages 18-64 who were uninsured at the time of interview, by race/ethnicity, 2019

SOURCE: Centers for Disease Control and Prevention, National Center for Health Statistics, National Health Interview Survey Early Release Program, May 2020. https://www.cdc.gov/nchs/data/nhis/earlyrelease/insur202009-508.pdf.

Note: White, Black, and Asian are non-Hispanic. Hispanic includes all races. Data for Native Hawaiians/Pacific Islanders and American Indians and Alaska Natives were not available. People were defined as uninsured if they did not have any private health insurance, Medicare, Medicaid, Children’s Health Insurance Program (CHIP), state-sponsored or other government-sponsored health plan, or military plan; or if they had only Indian Health Service coverage or had only a private plan that paid for one type of service, such as accidents or dental care. 

Disparate racial outcomes were the predictable result of a “War on Drugs” that incarcerated 10 Black people for every White one, despite similar rates of drug use (the literal poster child for this “war” was the wildly over-hyped “crack baby,” nearly always illustrated as Black or brown); the predictable result of the decisions by nine Southern states with large Black populations not to expand Medicaid as part of the ACA; and the clearly intentional result of many policy decisions like redlining of Black neighborhoods by the Federal Housing Authority.

How does this relate to cost-effectiveness analysis? Even if we assume no current health professionals have inaccurate beliefs about Black people (clearly a false presumption: many physicians still believe Black patients have a higher pain tolerance, and Black patients are treated less aggressively for pain in the ED), racially motivated policies from the past have left their mark on current health outcomes.

By failing to address or even acknowledge racially divergent outcomes, CEA serves to perpetuate racial inequities.

Imagine a CEA comparing a screening test for peripheral artery disease, an often asymptomatic condition that can lead to amputation, to usual care. The risk of amputation is modeled as 8% over 10 years, and the test is found to have an incremental cost effectiveness ratio (ICER) of $65,000. But the risk of amputation in Black people (12%) is nearly double that in White people (7%), producing ICERs of $40,000 and $70,000, respectively. Considering the wide range of conditions for which US health outcomes differ by race, you can see how producing a single estimate of cost-effectiveness can hide dramatic variation in cost-effectiveness.

More subtle problems exist, too. Black Americans have overall worse health and, according to the most recent HHS statistics, dramatically shorter life expectancy overall. But simply using race-specific inputs for models can make things worse, not better. Prior to COVID, the racial gap in life expectancy had narrowed to 1-2 years at age 65, an average difference which itself hides huge variation by income and region (and which widened significantly in 2020). A pill that completely reversed a deadly condition with a high mortality rate that affected Black people and White people equally would add 10% fewer QALY for Black people, making the pill look less attractive in the population with a shorter life expectancy. People of Hispanic origin have longer life expectancy than non-Hispanic White people—3 years longer pre-COVID—so that same pill would look much more attractive in the Hispanic population. As modelers, we often concern ourselves with getting accurate inputs for things that matter far less and have a far smaller impact on the results of our models.

Life expectancy at birth, by Hispanic origin and race: United States, 2019 and 2020

SOURCE: National Center for Health Statistics, National Vital Statistics System, Mortality data.

“But,” one might argue, “It’s not the job of CEA to address societal inequities.” To which we would reply, CEA already “addresses” these inequities. It addresses them by assuming they are unimportant and that the existing structure of the society it serves is inherently equal. In this way, CEA inherently perpetuates existing inequities.

Society leans too hard toward people who look like the authors already. Let’s try as health economists to make CEA lean toward justice instead.

Contact Michael at mbroder@pharllc.com or visit PHAR at pharllc.com.

About the authors:

Dr. Michael Broder, a board-certified obstetrician and gynecologist, has 30 years’ experience in health economic and outcomes research. He received his research training in the Robert Wood Johnson Clinical Scholars Program at UCLA and RAND, attended medical school at Case Western Reserve University, and received his undergraduate degree from Harvard University.

In 2004, Dr. Broder founded PHAR, a clinically-focused health economics and outcomes research consultancy. PHAR is a team of dedicated, highly trained researchers —individuals who are singularly focused on delivering high-quality health economics and outcomes research insights to the life science industry. PHAR has successfully conducted hundreds of studies resulting in more than 800 publications on a wide variety of therapeutic areas, and maintains an expansive network of collaborators, including 8 of the top 10 academic institutions in the US, as measured by NIH funding. Download our bibliography here.

Unencumbered by corporate bureaucracy, PHAR can efficiently execute contracts and complete projects on time and on budget. PHAR prides itself on being reliable and responsive to clients’ changing needs, and welcoming the challenge of tackling problems others can’t.

Jesse Ortendahl has a background in mathematics and statistics and has 20 years’ experience developing disease simulation models for economic evaluations. At PHAR, Jesse conducts and oversees health economic projects using both quantitative and qualitative methods. In addition to traditional cost effectiveness and budget impact models, Jesse has led studies with more policy-relevant goals, such as identifying flaws in current health technology assessment approaches, forecasting the burden of disease and potential policy levers to reduce that burden, and estimating the ramifications of payer policies on cost and clinical outcomes. His work spans a wide range of clinical areas including oncology, cardiology, pulmonology, neurology, and psychiatry.

He was trained in health policy at the Harvard School of Public Health, with a focus on decision science. Jesse formerly served as a research analyst at the Center for Health Decision Science at the Harvard School of Public Health, conducting grant-funded research primarily on the cost effectiveness of different cancer prevention strategies in the US and internationally. He is an active member of the International Society for Pharmacoeconomics and Outcomes Research and the Society for Medical Decision Making, and he has designed and delivered seminars at clinical conferences around the country with the goal of training physicians to understand and interpret economic evaluations.

Michael Broder

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