• Home
  • Health & Wellness
  • Disclaimer
    • Terms of Use
    • Privacy Policy
    • DMCA Notice
  • Home
  • Health & Wellness
  • Disclaimer
    • Terms of Use
    • Privacy Policy
    • DMCA Notice
24/7 Health News
No Result
View All Result
Home Article

Including race in clinical algorithms can both reduce and increase health inequities – it depends on what doctors use them for

May 27, 2023
in Article
Including race in clinical algorithms can both reduce and increase health inequities – it depends on what doctors use them for
An increasing number of health care decisions rely on information from algorithms. Tom Werner/Digital Vision via Getty Images

Health practitioners are increasingly concerned that because race is a social construct, and the biological mechanisms of how race affects clinical outcomes are often unknown, including race in predictive algorithms for clinical decision-making may worsen inequities.

For example, to calculate an estimate of kidney function called the estimated glomerular filtration rate, or eGFR, health care providers use an algorithm based on age, biological sex, race (Black or non-Black) and serum creatinine, a waste product the kidneys release into the blood. A higher eGFR value means better kidney health. These eGFR predictions are used to allocate kidney transplants in the U.S.

Based on this algorithm, which was trained on actual GFR values from patients, a Black patient would be assigned a higher eGFR than a non-Black patient of the same age, sex and serum creatinine level. This implies that some Black patients would be considered to have healthier kidneys than otherwise similar non-Black patients and less likely to be assigned a kidney transplant.

Biased clinical algorithms can lead to inaccurate diagnoses and delayed treatment.

In 2021, however, researchers found that excluding race in the original eGFR equations could lead to larger discrepancies between estimated and actual GFR values for both Black and non-Black patients. They also found adding an additional biomarker called cystatin C can improve predictions. However, even with this biomarker, excluding race from the algorithm still led to elevated discrepanies across races.

I am a health economist and statistician who studies how unobserved factors in data can result in biases that lead to inefficiencies, inequities and disparities in health care. My recently published research suggests that excluding race from certain diagnostic algorithms could worsen health inequities.

Table of Contents

  • Different approaches to fairness
  • Equality of opportunity
  • Evaluating clinical algorithms for fairness
  • Unanswered questions and future work

Different approaches to fairness

Researchers use different economic frameworks to understand how society allocates resources. Two key frameworks are utilitarianism and equality of opportunity.

A purely utilitarian outlook seeks to identify what features would get the most out of a positive outcome or reduce the harm from a negative one, ignoring who possesses those features. This approach allocates resources to those with the most opportunities to generate positive outcomes or mitigate negative ones.

A utilitarian approach would always include race and ethnicity to improve the prediction power and accuracy of algorithms, regardless of whether it’s fair. For example, utilitarian policies would aim to maximize overall survival among people seeking organ transplants. They would allocate organs to those who would survive the longest from transplantation, even if those who may not survive the longest due to circumstances outside their control and need the organs most would die sooner without the transplant.

Although utilitarian approaches do not take fairness into account, an approach that does would ask two questions: How do we define fairness? Are there conditions when maximizing an algorithm’s prediction power and accuracy would not conflict with fairness?

To answer these questions, I apply the equality of opportunity framework, which aims to allocate resources in a way that allows everyone the same chance of obtaining similar outcomes, without being disadvantaged by circumstances outside of their control. Researchers have used this framework in many contexts, such as political science, economics and law. The U.S. Supreme Court has also applied equality of opportunity in several landmark rulings in education.

Health care worker looking at tablet in an exam room
Including different variables in clinical algorithms can lead to very different results.
SDI Productions/E+ via Getty Images

Equality of opportunity

There are two fundamental principles in equality of opportunity.

First, inequality of outcomes is unethical if it results from differences in circumstances that are outside of an individual’s own control, such as the income of a child’s parents, exposure to systemic racism or living in violent and unsafe environments. This can be remedied by compensating individuals with disadvantaged circumstances in a way that allows them the same opportunity to obtain certain health outcomes as those who are not disadvantaged by their circumstances.

Second, inequality of outcomes for people in similar circumstances that result from differences in individual effort, such as practicing health-promoting behaviors like diet and exercise, is not unethical, and policymakers can reward those achieving better outcomes through such behaviors. However, differences in individual effort that occur because of circumstances, such as living in an area with limited access to healthy food, are not addressed under equality of opportunity. Keeping all circumstances the same, any differences in effort between individuals should be due to preferences, free will and perceived benefits and costs. This is called accountable effort. So, two individuals with the same circumstances should be rewarded according to their accountable efforts, and society should accept the resulting differences in outcomes.

Equality of opportunity implies that if algorithms were to be used for clinical decision-making, then it is necessary to understand what causes variation in the predictions they make.

If variation in predictions results from differences in circumstances or biological conditions but not from individual accountable effort, then it is appropriate to use the algorithm for compensation, such as allocating kidneys so everyone has an equal opportunity to live the same length of life, but not for reward, such as allocating kidneys to those who would live the longest with the kidneys.

In contrast, if variation in predictions results from differences in individual accountable effort but not from their circumstances, then it is appropriate to use the algorithm for reward but not compensation.

Evaluating clinical algorithms for fairness

To hold machine learning and other artificial intelligence algorithms accountable to a standard of equity, I applied the principles of equality of opportunity to
evaluate whether race should be included in clinical algorithms. I ran simulations under both ideal data conditions, where all data on a person’s circumstances is available, and real data conditions, where some data on a person’s circumstances is missing.

In these simulations, I unequivocally assume that race is a social and not biological construct. Variables such as race and ethnicity are often proxies for various circumstances individuals face that are out of their control, such as systemic racism that contributes to health disparities.

As a social construct, race is often a proxy for nonbiological circumstances.

I evaluated two categories of algorithms.

The first, diagnostic algorithms, makes predictions based on outcomes that have already occurred at the time of decision-making. For example, diagnostic algorithms are used to predict the presence of gallstones in patients with abdominal pain or urinary tract infections, or to detect breast cancer using radiologic imaging.

The second, prognostic algorithms, predicts future outcomes that have not yet occurred at the time of decision-making. For example, prognostic algorithms are used to predict whether a patient will live if they do or do not obtain a kidney transplant.

I found that, under an equality of opportunity approach, diagnostic models that do not take race into account would increase systemic inequities and discrimination. I found similar results for prognostic models intended to compensate for individual circumstances. For example, excluding race from algorithms that predict the future survival of patients with kidney failure would fail to identify those with underlying circumstances that make them more vulnerable.

Including race in prognostic models intended to reward individual efforts can also increase disparities. For example, including race in algorithms that predict how much longer a person would live after a kidney transplant may fail to account for individual circumstances that could limit how much longer they live.

Unanswered questions and future work

Better biomarkers may one day be able to better predict health outcomes than race and ethnicity. Until then, including race in certain clinical algorithms could help reduce disparities.

Although my study uses an equality of opportunity framework to measure how race and ethnicity affect the results of prediction algorithms, researchers don’t know whether other ways to approach fairness would lead to different recommendations. How to choose between different approaches to fairness also remains to be seen. Moreover, there are questions about how multiracial groups should be coded in health databases and algorithms.

My colleagues and I are exploring many of these unanswered questions to reduce algorithmic discrimination. We believe our work will readily extend to other areas outside of health, including education, crime and labor markets.

The Conversation

Anirban Basu received funding support from a consortium of ten biomedical companies to the University of Washington through an unrestricted gift.

ShareTweetSharePin
Next Post
Employers need to prioritize employee mental health if they want to attract new talent

Employers need to prioritize employee mental health if they want to attract new talent

Most Read

What causes stuttering? A speech pathology researcher explains the science and the misconceptions around this speech disorder

What causes stuttering? A speech pathology researcher explains the science and the misconceptions around this speech disorder

December 15, 2022
morning back pain

Morning Again Ache Trigger Is Not the Mattress

October 11, 2021

Why Circadian Rhythms Matter for Your Health

July 30, 2024
lower back pain relief exercises

5 decrease again ache aid workouts

October 11, 2021

4 steps to building a healthier relationship with your phone

January 28, 2025
3 years after legalization, we have shockingly little information about how it changed cannabis use and health harms

3 years after legalization, we have shockingly little information about how it changed cannabis use and health harms

October 15, 2021
Nasal vaccines promise to stop the COVID-19 virus before it gets to the lungs – an immunologist explains how they work

Nasal vaccines promise to stop the COVID-19 virus before it gets to the lungs – an immunologist explains how they work

December 14, 2022
bleeding in gum

When The Bleeding in gum Is Severe ?

October 11, 2021
Good Night Sleep

6 Causes of Good Evening Sleep

October 11, 2021
Kick up your heels – ballroom dancing offers benefits to the aging brain and could help stave off dementia

Kick up your heels – ballroom dancing offers benefits to the aging brain and could help stave off dementia

January 3, 2023
Biden is getting prostate cancer treatment, but that’s not the best choice for all men − a cancer researcher describes how she helped her father decide

Biden is getting prostate cancer treatment, but that’s not the best choice for all men − a cancer researcher describes how she helped her father decide

May 20, 2025
Ten small changes you can make today to prevent weight gain

Ten small changes you can make today to prevent weight gain

October 12, 2021

COVID vaccines: how one can pace up rollout in poorer international locations

October 5, 2021

This Simple Hygiene Habit Could Cut Your Risk of Stroke, New Research Reveals

February 1, 2025

Multiple sclerosis: the link with earlier infection just got stronger – new study

October 12, 2021
Support and collaboration with health-care providers can help people make health decisions

Support and collaboration with health-care providers can help people make health decisions

December 16, 2021
Greece to make COVID vaccines mandatory for over-60s, but do vaccine mandates work?

Greece to make COVID vaccines mandatory for over-60s, but do vaccine mandates work?

December 1, 2021
Five ways to avoid pain and injury when starting a new exercise regime

Five ways to avoid pain and injury when starting a new exercise regime

December 30, 2022
woman covered with white blanket

Exploring the Impact of Sleep Patterns on Mental Health

August 4, 2024

Maximize Your Performance – Sync with Your Circadian Rhythms

August 9, 2024
Why are some people faster than others? 2 exercise scientists explain the secrets of running speed

Why are some people faster than others? 2 exercise scientists explain the secrets of running speed

April 29, 2024
Backlash to transgender health care isn’t new − but the faulty science used to justify it has changed to meet the times

Backlash to transgender health care isn’t new − but the faulty science used to justify it has changed to meet the times

January 30, 2024
News of war can impact your mental health — here’s how to cope

Binge-eating disorder is more common than many realise, yet it’s rarely discussed – here’s what you need to know

December 2, 2022
As viral infections skyrocket, masks are still a tried-and-true way to help keep yourself and others safe

As viral infections skyrocket, masks are still a tried-and-true way to help keep yourself and others safe

December 14, 2022
GPs don’t give useful weight-loss advice – new study

GPs don’t give useful weight-loss advice – new study

December 16, 2022
Nutrition advice is rife with misinformation − a medical education specialist explains how to tell valid health information from pseudoscience

Nutrition advice is rife with misinformation − a medical education specialist explains how to tell valid health information from pseudoscience

January 28, 2025
FDA limits access to COVID-19 vaccine to older adults and other high-risk groups – a public health expert explains the new rules

FDA limits access to COVID-19 vaccine to older adults and other high-risk groups – a public health expert explains the new rules

May 21, 2025
Four ways to avoid gaining weight over the festive period – but also why you shouldn’t fret about it too much

Four ways to avoid gaining weight over the festive period – but also why you shouldn’t fret about it too much

December 22, 2022
Nurses’ attitudes toward COVID-19 vaccination for their children are highly influenced by partisanship, a new study finds

Nurses’ attitudes toward COVID-19 vaccination for their children are highly influenced by partisanship, a new study finds

December 2, 2022
How hot is too hot for the human body? Our lab found heat + humidity gets dangerous faster than many people realize

How hot is too hot for the human body? Our lab found heat + humidity gets dangerous faster than many people realize

July 6, 2022
  • Home
  • Health & Wellness
  • Disclaimer

© 2020 DAILY HEALTH NEWS

  • Home
  • Health & Wellness
  • Disclaimer
    • Terms of Use
    • Privacy Policy
    • DMCA Notice

© 2020 DAILY HEALTH NEWS