There has been a persistent media narrative that COVID has disproportionately affected ethnic minorities, such as this recent Houston Chronicle op-ed that ran under the headline of “We must learn from the historic decimation of Hispanic communities.”  There are many ways in which to compare COVID outcomes between various demographics, but the ultimate measure is by the number of fatalities suffered by each group.

So, I thought it would be interesting with the epidemic winding down to see how the actual fatality numbers by ethnicity are coming out.  To my surprise, the actual raw data paint a very different, more nuanced, and potentially more revealing picture about the nature of COVID than the agenda-driven spin currently prevalent in the media and among many academics.

I looked at datasets available from the CDC and the Texas Department of State Health  Services (DSHS).  As of June 30, the CDC and the DSHS had categorized the ethnicity of approximately 593,000 and 51,000 COVID related deaths, respectively.

Below are tables I have compiled from the data on those sites.  In these tables the “Over/Under Prorata Share” is the difference between that ethnicity’s percent of the total population and the percentage of total COVID fatalities.  The “# of Fatalities if @ Population %” is the number of fatalities each group would have suffered had all COVID fatalities been distributed among each ethnic group based on their relative share of the total population.  The “Over/Under Actual Fatalities” is the difference between the number of fatalities each group would have suffered if its fatality rate had been the average for the total population instead of its actual fatality rate.

For example, in first row of the US table, Asians made up 3.8% of all COVID fatalities, but represent 5.2% of the US population.  If Asians had suffered fatalities at the same percentage as their share of the US population, there would have been 34,400 fatalities in that group instead of the actual number of fatalities at 22,730, a difference of minus 11,670.  In other words, had Asians died at a rate equal to their population percentage, another 11,670 Asians would have died.

As you can see, the most remarkable thing about the data is how little variation there is based on ethnicity, especially in Texas.  Certainly, the disparities are nothing to the extent of consensus media narrative.  That narrative is largely based on a statistical restatement of the raw data which the CDC and some academicians have done, which they refer to to as “weighting.”  According to its website, the CDC weighted the data to account for age and “how the race and Hispanic origin population is distributed in relation to the geographic areas impacted by COVID-19.”

Attempting to adjust for age makes some sense since we know that COVID fatalities overwhelmingly occurred in seniors.  However, the CDC adjustment inexplicably is based on the 2000 Census data¹, which, of course, could introduce a huge error, especially with respect to the share of the population which is Latino.  This is particularly befuddling since we have census estimates of the ethnic population breakdown as late as 2019.

CDC’s explanation of the second factor² is nearly indecipherable, but appears to more heavily weight fatalities in areas with the most severe outbreaks. Of course, the more severe outbreaks have mostly urban areas where ethnic minorities are more heavily concentrated. As a result, this methodology would necessarily increase the fatality prevalence in minority groups.

After the CDC “weighting”, the relative fatality rate for Latinos and Asians almost doubles and the rate for whites is cut in half.  Interestingly, there is a very small change for African-Americans.  The CDC contends that the weighting more accurately represents the fatality rates in each ethnic group.  To me it appears to be a gross distortion of the raw data.

To me, the most striking takeaway from the comparison is the degree to which Asians have been underrepresented in COVID fatalities.  Of course, that fact has rarely been reported because it undermines the “disproportionate-impact-of-COVID-on-communities-of-color” narrative.

The low prevalence in the Asian community is interesting because it may be further evidence supporting some research which has suggested that Asians’ immune systems may have a genetic advantage in fighting off coronavirus infections.³   The discovery of the mechanism of such a genetic advantage, if it does in fact exist, could have a profound impact on our understanding of how our immune system reacts to viruses and on the future development of vaccines and therapeutics.

But that was not the media’s or the CDC’s take-away.  I did a Google search for “disproportionate impact of COVID on communities of color.”  The search returned 1.3 million results.  I scanned through the first 20 results.  I did not find a single article which reported the comparative fatality raw data.  Instead, every article I scanned maintained that there was a large disparity, using words like “devastating” and “catastrophic” to describe the extent of the disparity.  None described how the raw data had been altered by the “weighting” methodologies.

It is little wonder that a majority of Americans say they distrust the media or that trust in the CDC has plummeted during the pandemic.  Hopefully, someday we will get back to  more fact-based reporting and research instead of agenda-driven journalism and “science”; but I would not hold my breath.

Notes:

1. “Age-standardized distributions show what disparities would look like, assuming that all of the groups had the same age distribution as the 2000 standard population.”  Technical Notes, Provisional Death Counts for Coronavirus Disease

2. “County-level population counts by race and Hispanic origin were multiplied by the corresponding total count of COVID-19 deaths by county (of residence). These weighted counts were then summed to the state (or national) level.  The percentage of the population within each race and Hispanic origin group by state (or for the US) was then estimated using these weighted counts.  Counties with no COVID-19 deaths received a weight of zero, and thus do not contribute to the weighted population totals.  Population counts for counties with large numbers of COVID-19 deaths are upweighted proportionately to their numbers of COVID-19 deaths.  These weighted population distributions ensure that the population estimates and percentages of COVID-19 deaths represent comparable geographic areas, in order to provide information about whether certain racial and ethnic subgroups are experiencing a disproportionate burden of COVID-19 mortality.”  Technical Notes, Provisional Death Counts for Coronavirus Disease

3. Yamamoto N, Bauer G. Apparent difference in fatalities between Central Europe and East Asia due to SARS-COV-2 and COVID-19: Four hypotheses for possible explanation. Med Hypotheses. 2020;144:110160. doi:10.1016/j.mehy.2020.110160; An ancient viral epidemic involving host coronavirus interacting genes more than 20,000 years ago in East Asia; Yassine Souilmi, M. Elise Lauterbur, Ray Tobler, Christian D. Huber, Angad S. Johar, David Enard