Race and wellness – Part II

Going beyond the data

“The COVID-19 pandemic has certainly laid bare the health inequities that we have in our society. There has never been a time that the social determinants of health have been exposed so clearly, so rawly. People in vulnerable communities are dying from a disease because of their race and because they’re poor. That should not be acceptable in the richest country on earth.”¹

–  Robert Harrington, MD, Chief of Medicine, Stanford Medicine

In Race and wellness – Part I: Examining health disparities, we probed the meaning and analyzed the prevalence of racial and socioeconomic health disparities in our nation, and we began the discussion of the inherently racist elements in our society that underpin these disparities. To fully grasp this issue, however, one first must come to understand that the very data we think will help us understand the inequities is, in effect, fueling them. Following this analysis, readers are encouraged to see Race and wellness – Part III: Mending the inequities.

While we have culled information and data from many sources, both at Stanford and from other organizations and individuals, BeWell is honored to have had the chance to speak with N. Kenji Taylor, MD, MSc, AAHIVS — an instructor of medicine at Stanford Medicine, a physician with Stanford Health Care and Roots Community Health Center (Oakland, CA) and a member of the Stanford-Intermountain Delivery Science Fellowship — about his work and perspective on this important topic of race and wellness.

Understanding the pitfalls of disparity “data”

At the conclusion of Race and wellness – Part I: Examining health disparities, Dr. Taylor explained that racism (in its many forms) underpins all of the determinants that produce our nation’s racial health inequities, citing the findings that Blacks are 2.7 times more likely to be hospitalized with COVID-19 even after adjusting for age, sex, comorbidities and income.²  

In other words, biology and socioeconomic factors do not fully explain racial health disparities. Readers may ask, how can a focus on genetic or biologic determinants of health outcomes be an inherently racist exercise? After all, aren’t genes and biology “factual” and “scientific”? 

Turns out, that depends heavily on what context those facts are placed into, and what underlies or causes those facts. Here’s the tricky part, as ably explained by two Stanford psychology department members, Rebecca Hetey and Jennifer Eberhardt, in a 2018 research paper:

“The statistics about disparities may instead provide an opportunity to justify and rationalize the disparities found within that system.”³

While Hetey and Eberhardt were studying the criminal justice system, their findings truly do apply to the health care system, as do their conclusions about what to do about “these ironic effects” brought about by the use of statistics on disparities:

“With the goals of spurring future research and mitigating this paradoxical and unintended effect, we propose three potential strategies for more effectively presenting information about racial disparities: (a) offer context, (b) challenge associations, and (c) highlight institutions.”

Data can perpetuate myths, disparities, and racism

A perspective paper recently published in New England Journal of Medicine  provides one of the best explanations for why “disparity data” can actually be harmful:

“Disparity figures without explanatory context can perpetuate harmful myths and misunderstandings that actually undermine the goal of eliminating health inequities. Such clarifying perspective is required not just for COVID-19 but also for future epidemics.”⁴

Authors Chowkwanyun and Reed go on to delineate “several key dangers of insufficient contextualization” — and after each numbered statement , Dr. Taylor has added his explanation (shown in emphasized text) of the faulty fall-out thinking or action which results:

1. Data in a vacuum can give rise to biologic explanations for racial health disparities. Such explanations posit that congenital qualities unique to specific racial minorities predispose them to higher rates of a particular disease.

Faulty fall-out thinking: “Blacks are just more genetically susceptible to COVID-19 and we can’t do anything to change that.”

(Such an argument completely ignores that race is a social construct.)

2. Lone disparity figures can give rise to explanations grounded in racial stereotypes about behavioral patterns.

Faulty fall-out thinking: “We should tell them not to eat like that.”

3. Geographic disaggregation of COVID-19 data is welcome but requires caution. … In the case of COVID-19, place-based stigma might be further amplified by association with sickness and could in turn lead to blaming of local residents’ allegedly deviant behavior, repressive forms of surveillance, calls for demolition, or simply neglect by a society that wishes to distance itself from such areas.

Faulty fall-out action: “Businesses leave Black communities because they are seen as hotbeds for COVID-19 and less desirable.”

The authors explain that the above dangers may feed into a fourth one:

4. In the recent past, the perception (however erroneous) that certain social problems are primarily “racial” — and therefore of concern only to supposed minority interest groups — has been used to rationalize neglect and funding cuts.

Faulty fall-out thinking/action: “This is a ‘Black problem’ and we need to focus our limited resources on the general population.”

COVID-19 and racial disparities: What’s the real “Why?”

Faulty U.S. data collection

The task of accurately portraying the fundamental drivers of higher COVID-19 morbidity and mortality in various sub-groups is made all the more difficult by the deficient health care data collection and reporting systems in the U.S. Biostatisticians lament that the U.S. is embarrassingly behind in this realm.⁵

Dr. Taylor, sourcing The COVID Tracking Project, cites examples of this glaring problem. The state of Texas only reports race/ethnicity in 25% of cases/deaths. West Virginia does not even track a Hispanics category; they lump cases into white, black, other. Some states lump Hispanics into their white category. Even California only reports ethnicity data for 71% of cases (although it does so for 96% of deaths).⁶

Local-area data: looking behind the numbers

Because of our unreliable national data, we must rely heavily on local data and context right now in an attempt to probe what’s really driving the racial and socioeconomic health disparities plaguing our country.

Studying the data from one of the most ethnically diverse areas of the Bay Area Peninsula — East San Jose — the high incidence of COVID-19 appears to relate in large part to the racial/socioeconomic factors of (1) employment status, (2) health care access, (3) residential segregation and (4) economic inequality, according to Alex Studemeister, MD, Medical Director of Infection Control at Regional Medical Center (RMC) of San Jose. In a presentation citing data from the hospitalized cases of COVID-19 (from 2.28.20 to 5.31.20) at RMC, of those individuals who had been employed when taken ill, more than 50% worked in either construction or in food services jobs.⁷

Elsa Villarino, MD, PhD, assistant health officer for County of Santa Clara’s Public Health Dept., and a co-presenter, notes that it’s unclear if these essential workers had to work in conditions with limited PPE (personal protective equipment). But she speculates that workplace disadvantages are compounded by family transmissions. Contact tracing shows that after someone is infected at work, they then return home and infect other family members. Workers who know they may be in contact with COVID-19 are supposed to “self-isolate” when they get home — which means having their own bedroom and their own bathroom. “But this is difficult for many families, even for families who think they are sheltering in place.”⁷

In addition, Dr. Studemeister observes that a significant percentage of patients are “coming in late” to the hospital, often staying home when sick for 5-7 days and thus arriving at the hospital at or past “the peak viral load,” resulting in a more severe set of medical issues when hospitalized. Whether this delay in seeking care has to do with workers afraid they’ll lose their job if they go to the hospital, or other factors, remains to be fully determined.

In a recent presentation at Stanford Department of Medicine’s Grand Rounds, Sarah Rudman, MD, MPH, the assistant public health officer of the Santa Clara County Public Health Department, echoed the observation that “fears” are making it difficult for infected and exposed family members, in certain disproportionately affected neighborhoods such as East San Jose, to properly self-isolate when recommended to do so — not only because of employment fears, but fears relating to childcare issues, putting food on the table and taking care of their families.⁸

Diagnostic testing also seems underutilized in poorer communities like East San Jose. As related in a recent presentation by Fernando Mendoza, MD, MPH, a Stanford professor of pediatrics and the associate dean of minority advising and programs, health care access points are clearly uneven across the region. East San Jose has only one hospital, whereas “all the other hospitals are on the west side of the Bay Area Peninsula.”⁹

Dr. Taylor’s findings from the East Oakland area paint a similar picture. Of those individuals who tested positive for COVID-19 (as of June 1), 34% were essential workers and 31% had a shared living situation. “Patients are afraid to seek medical care despite having the symptoms of COVID-19.” Of course, getting tested at the right time is not easy for many of us in this pandemic, because of the 3-4 days when you might be “asymptomatic” even though you are COVID-19 positive for the virus. Taylor notes that 45% of those tested in East Oakland were asymptomatic at the time of the testing.⁹

Causes behind the causes

Determining what’s truly behind the rise in COVID-19 cases and deaths in any one or in multiple areas is difficult under the best of circumstances, even before consideration of segmentation by racial or socioeconomic groups. Contact tracing efforts are still in early stages of development, and their level of sophistication varies from region to region. In addition, following up on social determinants is inherently fraught with data “confounders.” Plus, data on social determinants alone is hardly enough. Such data can mislead if it is taken out of context, misrepresented, or not even challenged. It is what’s behind the social determinants, the “why’s” (e.g., why  do certain individuals have substandard access to care, or why  do certain employment stressors or family constraints pertain to the data), that analysts will need to probe before even getting close to a thorough evaluation of racial health disparities, their extent, and possible remedies for them.  

To continue the discussion, see Race and wellness – Part III: Mending the inequities.

Lane McKenna
August 2020

Sources:

1. Harrington, R. Stanford Department of Medicine Grand Rounds: June 3, 2020.

2. Azar K et. al. Disparities In Outcomes Among COVID-19 Patients In A Large Health Care System In California. Health Affairs. May 21, 2020.

3. Hetey, R and Eberhardt J. The Numbers Don’t Speak for Themselves: Racial Disparities and the Persistence of Inequality in the Criminal Justice System. Association for Psychological Science. Current Directions in Psychological Science. Sage Publications. 2018, Vol. 27(3) 183–187.

4. Chowkwanyun M and Reed A. Perspective: Racial Health Disparities and COVID-19 — Caution and Context. New England Journal of Medicine. July 16, 2020.

5. Kendi, I. Why Don’t We Know Who the Coronavirus Victims Are? The Atlantic. April 1, 2020.

6. The COVID Tracking Project. The Atlantic Monthly Group. 2020.

7. Studemeister, A. and Villarino, E. Stanford Department of Medicine Grand Rounds: May 27, 2020

8. Rudman, S. Stanford Department of Medicine Grand Rounds: July 29, 2020

9. Mendoza, F. and Taylor, K. Stanford Department of Medicine Grand Rounds: June 10, 2020