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| The Way We Ration Ventilators Is Biased | The Way We Ration Ventilators Is Biased |
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| Rationing ventilators might be a necessary response in the new Covid-19 world. Many hospitals decide who gets them by selecting patients most likely to benefit. Experts agree this is the best way of saving most lives. The approach, the model guidance, may sound fair. But baked into it are biases that disadvantage groups who, even without a Covid-19 infection, experience worse health because of historical and structural reasons, especially black people. | |
| Consider this case. Three patients are waiting to be admitted to an intensive care unit that has one remaining bed. All have equally severe Covid-19 symptoms. John is an otherwise healthy, white 40-year-old man. Rosa is a 45-year-old African-American woman with underlying health issues. Linda is a white 56-year-old woman with Down syndrome. Who should be admitted? | Consider this case. Three patients are waiting to be admitted to an intensive care unit that has one remaining bed. All have equally severe Covid-19 symptoms. John is an otherwise healthy, white 40-year-old man. Rosa is a 45-year-old African-American woman with underlying health issues. Linda is a white 56-year-old woman with Down syndrome. Who should be admitted? |
| Linda has high blood pressure and complications from an earlier heart surgery. She would need care for longer than John. Clinicians might also consider her age; life expectancy for people with Down is around 60 years. Linda has fewer years left than John or Rosa. Implicitly or explicitly, clinicians might not admit her on this ground. | Linda has high blood pressure and complications from an earlier heart surgery. She would need care for longer than John. Clinicians might also consider her age; life expectancy for people with Down is around 60 years. Linda has fewer years left than John or Rosa. Implicitly or explicitly, clinicians might not admit her on this ground. |
| Rosa grew up in a “redlined” part of town. Accessing health care has been as challenging as accessing loans or healthy food. Her community has significant distrust toward the health care system. The Tuskegee Syphilis Study still casts a long shadow. Day-to-day racism experienced in the hospitals adds to this. Some years she had insurance, some years she did not, which did not help with managing her asthma and diabetes. Taken together, Rosa’s health is such that her statistical odds are similar to Linda’s. | Rosa grew up in a “redlined” part of town. Accessing health care has been as challenging as accessing loans or healthy food. Her community has significant distrust toward the health care system. The Tuskegee Syphilis Study still casts a long shadow. Day-to-day racism experienced in the hospitals adds to this. Some years she had insurance, some years she did not, which did not help with managing her asthma and diabetes. Taken together, Rosa’s health is such that her statistical odds are similar to Linda’s. |
| The “saving most lives” model would admit John. He is likely to spend the least time in the intensive care unit, allowing more patients to be put on the ventilator. And since his life expectancy is the greatest, the time he spends on the ventilator enables him to live longer than Linda or Rosa. | The “saving most lives” model would admit John. He is likely to spend the least time in the intensive care unit, allowing more patients to be put on the ventilator. And since his life expectancy is the greatest, the time he spends on the ventilator enables him to live longer than Linda or Rosa. |
| But this “colorblind" approach ignores the structural reasons underlying Rosa’s health issues. And it has also come under legal scrutiny for unfairly disadvantaging disabled people. In response, guidance for ventilator rationing emphasizes that “race, ethnicity, gender, insurance status, perceptions of social worth, immigration status, among others” should be irrelevant — but even this approach can risk leaving several of the identified groups at a major disadvantage. | But this “colorblind" approach ignores the structural reasons underlying Rosa’s health issues. And it has also come under legal scrutiny for unfairly disadvantaging disabled people. In response, guidance for ventilator rationing emphasizes that “race, ethnicity, gender, insurance status, perceptions of social worth, immigration status, among others” should be irrelevant — but even this approach can risk leaving several of the identified groups at a major disadvantage. |
| It would be one thing if only personal choice accounted for differences in health and life expectancy. Or, if across racial or ethnic lines, income or insurance statuses, each group had the same share of people with major health conditions like diabetes or asthma, which can affect baseline health. | It would be one thing if only personal choice accounted for differences in health and life expectancy. Or, if across racial or ethnic lines, income or insurance statuses, each group had the same share of people with major health conditions like diabetes or asthma, which can affect baseline health. |
| But that is not the case. Baseline health is far worse among lower-income, uninsured, disabled and particular racial and ethnic groups. Life expectancy likewise differs across groups. The reasons are overwhelmingly structural and historical. | But that is not the case. Baseline health is far worse among lower-income, uninsured, disabled and particular racial and ethnic groups. Life expectancy likewise differs across groups. The reasons are overwhelmingly structural and historical. |
| For example, health is highly connected to place. Black people are 75 percent more likely to live near oil, gas and petrochemical facilities than the average American because these industries are so concentrated in black communities. Health is also affected by social status, with worse off groups at a disadvantage. Without affordable universal health coverage, poorer and uninsured people have a harder time managing health problems. And the collective experiences of communities like Rosa’s mean that even with access to health care, a lack of trust can have the same consequence as a lack of means. | For example, health is highly connected to place. Black people are 75 percent more likely to live near oil, gas and petrochemical facilities than the average American because these industries are so concentrated in black communities. Health is also affected by social status, with worse off groups at a disadvantage. Without affordable universal health coverage, poorer and uninsured people have a harder time managing health problems. And the collective experiences of communities like Rosa’s mean that even with access to health care, a lack of trust can have the same consequence as a lack of means. |
| These background conditions should matter in how we assess who should be put on a ventilator. But they don’t. Instead, the ‘save the most lives’ guidance assigns patients points from one to eight, taking into account a person’s physiology and life expectancy. A lower score means a patient is more likely to benefit from treatment. If a person has fewer than five years to live, two points will be added. This approach unduly incorporates factors beyond peoples’ control. | These background conditions should matter in how we assess who should be put on a ventilator. But they don’t. Instead, the ‘save the most lives’ guidance assigns patients points from one to eight, taking into account a person’s physiology and life expectancy. A lower score means a patient is more likely to benefit from treatment. If a person has fewer than five years to live, two points will be added. This approach unduly incorporates factors beyond peoples’ control. |
| For example, creatinine levels, which reflect kidney function, vary across income and racial groups. African-Americans, who have higher creatinine levels on average, would be assigned a higher risk. And while the model guidance may claim to be unbiased in considering life expectancy, it incorporates additional conditions like diabetes, high blood pressure or asthma, which are directly affected by structural or historical disadvantage. | For example, creatinine levels, which reflect kidney function, vary across income and racial groups. African-Americans, who have higher creatinine levels on average, would be assigned a higher risk. And while the model guidance may claim to be unbiased in considering life expectancy, it incorporates additional conditions like diabetes, high blood pressure or asthma, which are directly affected by structural or historical disadvantage. |
| Life expectancy across geographic, income and racial groups can vary by up to 30 years. For example, inner-city residents of Chicago, who are more likely to be black, can expect to live to 60 years. Those in suburban areas, who are typically white, live to 90. In the model guidance, the 56-year-old inner-city black patient could receive two penalty points — whereas a 60-year-old white suburban patient would receive none. | Life expectancy across geographic, income and racial groups can vary by up to 30 years. For example, inner-city residents of Chicago, who are more likely to be black, can expect to live to 60 years. Those in suburban areas, who are typically white, live to 90. In the model guidance, the 56-year-old inner-city black patient could receive two penalty points — whereas a 60-year-old white suburban patient would receive none. |
| The simplest way of increasing fairness would be to adjust scores by using so-called weights. In this way, disadvantaged populations would not be penalized for factors beyond their control, and would have a fairer chance at receiving a ventilator. We already gather the information needed to assign these weights (race, ethnic group, income, insurance status) when patients’ data are first recorded. | The simplest way of increasing fairness would be to adjust scores by using so-called weights. In this way, disadvantaged populations would not be penalized for factors beyond their control, and would have a fairer chance at receiving a ventilator. We already gather the information needed to assign these weights (race, ethnic group, income, insurance status) when patients’ data are first recorded. |
| An alternative approach might assign weights based on something like the Area Deprivation Index that requires only ZIP code data, typically tracks race, and avoids many of the legal complications that can be raised. | An alternative approach might assign weights based on something like the Area Deprivation Index that requires only ZIP code data, typically tracks race, and avoids many of the legal complications that can be raised. |
| In triaging patients, hospitals using the standard approach should state how they consider social, structural and historical determinants of health, or whether they disregard them. This information is especially important in regions where disadvantaged groups make up a minority on the population but account for a majority of coronavirus cases. | In triaging patients, hospitals using the standard approach should state how they consider social, structural and historical determinants of health, or whether they disregard them. This information is especially important in regions where disadvantaged groups make up a minority on the population but account for a majority of coronavirus cases. |
| When we look back on our social response to the crisis and we tally up who was selected for ventilator access, it seems certain that groups that were already disadvantaged were disadvantaged once more. While data is still emerging, in New York City, age-adjusted death rates are twice as high for Latino residents and African-Americans, compared to whites. | When we look back on our social response to the crisis and we tally up who was selected for ventilator access, it seems certain that groups that were already disadvantaged were disadvantaged once more. While data is still emerging, in New York City, age-adjusted death rates are twice as high for Latino residents and African-Americans, compared to whites. |
| Unsurprisingly, a large national survey found that black and Hispanic respondents were more concerned with getting infected and requiring hospitalization than whites, and more frequently knew someone who was infected or had died. A majority of black respondents feel that urgency of needs should determine ventilator access, rather than selecting those most likely to benefit. | Unsurprisingly, a large national survey found that black and Hispanic respondents were more concerned with getting infected and requiring hospitalization than whites, and more frequently knew someone who was infected or had died. A majority of black respondents feel that urgency of needs should determine ventilator access, rather than selecting those most likely to benefit. |
| Adding fairness weights can avoid exacerbating these divisions now and in the future. Imagine yourself in the position of a clinician (or hospital press officer), needing to explain why counting up the points meant that Rosa has to be moved off a ventilator, so that John can be admitted. | Adding fairness weights can avoid exacerbating these divisions now and in the future. Imagine yourself in the position of a clinician (or hospital press officer), needing to explain why counting up the points meant that Rosa has to be moved off a ventilator, so that John can be admitted. |
| The standard method for rationing ventilators recognizes that racial and other discrimination is not acceptable. Yet, it needs to be modified. We should not have to wait for another black person to refuse to vacate a place they have every reason to be in. | The standard method for rationing ventilators recognizes that racial and other discrimination is not acceptable. Yet, it needs to be modified. We should not have to wait for another black person to refuse to vacate a place they have every reason to be in. |
| Harald Schmidt is an assistant professor of medical ethics and health policy at the University of Pennsylvania. He is co-editor of “Rationing and Resource Allocation in Healthcare.” | Harald Schmidt is an assistant professor of medical ethics and health policy at the University of Pennsylvania. He is co-editor of “Rationing and Resource Allocation in Healthcare.” |
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