Skip to main content
SearchLogin or Signup

Review 1: "Health Disparities and COVID-19: A Retrospective Study Examining Individual and Community Factors Causing Disproportionate COVID-19 Outcomes in Cook County, Illinois, March 16-May 31, 2020"

This study provides further empirical evidence about the racial differences in COVID-19 morbidity and mortality. The preprint is potentially informative and somewhat reliable, but there are unmeasured confounders and it lacks some clarity around some data points.

Published onSep 10, 2020
Review 1: "Health Disparities and COVID-19: A Retrospective Study Examining Individual and Community Factors Causing Disproportionate COVID-19 Outcomes in Cook County, Illinois, March 16-May 31, 2020"
1 of 2
key-enterThis Pub is a Review of
Health Disparities and COVID-19: A Retrospective Study Examining Individual and Community Factors Causing Disproportionate COVID-19 Outcomes in Cook County, Illinois, March 16-May 31, 2020
Description

Background: Early data from the COVID-19 pandemic suggests that the disease has had a disproportionate impact on communities of color causing higher infection and mortality rates within those communities. Methods: This study used demographic data from the 2018 US census estimates, mortality data from the Cook County Medical Examiners office, and testing results from the Illinois Department of Public Health to perform both bivariate and multivariate regression analyses to explore the role race plays in COVID-19 outcomes at the individual and community levels. Results: Principal findings show that: 1) while Black Americans make up 22% of Cook County population, they account for 36% of the county COVID-19 related deaths; 2) the average age of death from COVID-19 is seven years younger for minorities compared to Non-Hispanic White (White) decedents; 3) minorities were more likely than Whites to have seven of the top 10 co-morbidities at death; 4) residents of predominantly minority areas were twice as likely to test positive for COVID-19 (p = 0.0001, IRR 1.94, 95% CI 1.50, 2.50) than residents of predominantly White areas; and 5) residents of predominantly minority areas were 1.43 times more likely to die of COVID-19 than those in predominantly White areas (p = 0.03). Conclusions: There are notable differences in COVID-19 related outcomes between racial and ethnic groups at individual and community levels. We hope that this study will scientifically illustrate the health disparities experienced by communities of color and help to address the underlying systemic inequalities still prevalent within our country.

RR:C19 Evidence Scale rating by reviewer:

  • Potentially informative. The main claims made are not strongly justified by the methods and data, but may yield some insight. The results and conclusions of the study may resemble those from the hypothetical ideal study, but there is substantial room for doubt. Decision-makers should consider this evidence only with a thorough understanding of its weaknesses, alongside other evidence and theory. Decision-makers should not consider this actionable, unless the weaknesses are clearly understood and there is other theory and evidence to further support it.

***************************************

General comments:

The authors are to be congratulated to proving further empirical evidence about the racial differences in COVID-19 morbidity and mortality. However, I am not sure that the presented data analysis was as careful (line 61) as it could be? Given the nature of the variables (some being individual, and others being community level), I wondered why something like multi-level (mixed effects) models were not employed? I feel there is a danger that the findings may be subject to the ecological fallacy. I also urge the researchers to be informed by the REporting of studies Conducted using Observational Routinely-collected Data (RECORD) guidelines (see: https://www.record-statement.org/) in reporting their findings. I could not help but think of all the important unmeasured confounders that might be influential – and explain some of the differences noted. “Race” may simply be a marker for these unmeasured confounders. I got no sense of the quality of the variables/data in this study, how they were linked (and the various diagnostics around that), and the completeness of records. Many statistical tests were undertaken, yet no consideration of test multiplicity was evident. And new analyses/results appeared in the Discussion.

Specific comments:

Line 29. Replace bivariate and multivariate with bivariable and multivariable, respectively.

Line 35. Insert ‘nearly’ or ‘almost’ in front of ‘twice’.

Line 37. Please include 95% CI.

Line 49. Inset (US) after the United States.

Line 52. Spell out CDC.

Line 54. ‘Latinx’ is new to me, and not widely used by Hispanic or Latino populations (see: https://www.pewresearch.org/hispanic/2020/08/11/about-one-in-four-u-s-hispanics-have-heard-of-latinx-but-just-3-use-it/)

Lines 64-65. The authors wish to “explore[s] whether there is a causal relationship between race, neighborhood factors, and COVID-19 in Cook County, Illinois.” The study design and analysis limit any causal assertions.

Results section: inconsistent precision of reporting percentages and other summary statistics.

Line 122: Why is “Unknown” included in Table 1 for sex – when it is empty?

Line 124. Table 2 is not referenced in the text. It should be at line 124.

Line 126. ‘Univariable’ analysis is referred to – but it is bivariate – consistent with this described in the Abstract.

Line 132. The columns need labelling within the Table; ensure formatting is consistent (ie. “1,234”)

Line 135. I could not see where ** was used?

Line 146. A p-value of 0.0691 is non-significant. It is not ‘marginally significant’.

Line 147. The table title lacks description.

Line 148. I’m not sure I understand what ‘<0.00*’ means? Also, the format of this table is unclear. For Model 4, which IRR refers to poverty. I feel the table construction/layout deserves more attention.

Line 152 and elsewhere. I wonder why multi-level models were not employed? There are individual and community level variables. Individuals are nested within communities (as I understand these data). How will the potential effects of the ecological fallacy be avoided?

Line 165. Again the title is very short.

Line 165. Table 4. Why is it important to report the Estimate, SE, and Wald Chi-square – when the other details are included?

Line 176-178. What is the difference in age of death for minorities compare to Whites not dying of COVID-19? It may be that they generally die younger? This may be insightful – when speaking to unmeasured confounders and the accumulated life experience of disadvantage.

Line 194. As I understand it ‘poverty’ in a community level factor, yet ‘COVID-19 related death’ is an individual level factor. People’s SES will vary (potentially considerably) with those areas designated as being subject to poverty. So this might be an ecological fallacy affect.

Line 198. New analyses are presented within the Discussion. This is contrary to usual/accepted practice.

Line 267. I would be very cautious making the statement that “…that race, more than SES, is the only…”

Limitations: Variable coverage and quality of data? Validity of ‘Race’ variable. Data linkages? Emigration/immigration effects – how are they captured within the dataset.

Comments
0
comment

No comments here