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Review 1: "The relationship between neighborhood poverty and COVID-19 mortality within racial/ethnic groups (Cook County, Illinois)"

This study adds to the literature on disparities in COVID-19 morbidity and mortality, though the data used may preclude some important, finer-grained analyses of different sources of outcome disparities.

Published onNov 16, 2020
Review 1: "The relationship between neighborhood poverty and COVID-19 mortality within racial/ethnic groups (Cook County, Illinois)"
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The relationship between neighborhood poverty and COVID-19 mortality within racial/ethnic groups (Cook County, Illinois)
Description

Abstract Background Prior research has identified higher rates of COVID-19 mortality among people of color (relative to non-Hispanic whites) and populations in high-poverty neighborhoods (relative to wealthier neighborhoods). It is unclear, however, whether non-Hispanic whites in high-poverty neighborhoods experience elevated mortality, or whether people of color living in wealthy areas are relatively protected. Exploring socioeconomic position in combination with race/ethnicity can lead to a more detailed understanding of the specific processes that result in COVID-19 inequities.Methods and Findings We used census and individual-level mortality data for the non-Hispanic white, non-Hispanic Black, and Hispanic/Latinx populations of Cook County, Illinois, USA. We excluded deaths related to nursing homes and other institutions. We calculated age and gender-adjusted mortality rates by race/ethnicity, census tract poverty quartile, and age group (0-64 and ≥65 years).Within all racial/ethnic groups, COVID-19 mortality rates were greatest in the highest-poverty quartile and lowest in the lowest-poverty quartile. The mortality rate for younger non-Hispanic whites in the highest-poverty quartile was 13.5 times that of younger non-Hispanic whites in the lowest-poverty quartile (95% CI: 8.5, 21.4). For young people in the highest-poverty quartile, the non-Hispanic white and Black mortality rates were similar. Among younger people in the lowest-poverty quartile, non-Hispanic Black and Hispanic/Latinx people had mortality rates nearly three times that of non-Hispanic whites. For the older population, the mortality rate among non-Hispanic whites in the highest-poverty quartile was less than that of lowest-poverty non-Hispanic Black and Hispanic/Latinx populations.Conclusions Our findings suggest racial/ethnic inequalities in COVID-19 mortality are partly, but not entirely, attributable to the higher average socioeconomic position of non-Hispanic whites relative to the non-Hispanic Black and Hispanic/Latinx populations. Future research on health equity in COVID-19 outcomes should collect and analyze individual-level data on the potential mechanisms driving population distributions of exposure, severe illness, and death.

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.

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Review:

Claims are potentially informative by the data and methods used. Decision-makers should consider the claims in this study not actionable (except to prompt further research), unless the weaknesses are clearly understood and there is other theory and evidence to further support them based on the methods and data.

The findings from the study add to the body of literature that there are racial/ethnic and socioeconomic inequalities in COVI-19 mortality rates, and we gain some additional insight into how the two factors interact to affect COVID-19 mortality. To strengthen findings, the authors may consider testing the trends in COVID-19 mortality by poverty quartile and/or testing the interaction term. It would be of interest to know what demographic information is available and can be presented to better understand the sample. While the findings are valuable, the authors fail to orient their findings in the discussion within the current body of literature and they do not speak to the implications of their findings. 

While the authors speak to the ecological fallacy presented in the study from attributing area-based poverty measures to individual-level socioeconomic position (i.e. a high-income individual may live in a high-poverty area), the authors fail to distinguish and address the difference between incident location and residential location. For instance, a case may be identified in a different census tract from where they live, and as such, the cases within a census tract are not necessarily representative of the individuals living within the census tract. If the authors are assuming that the cases are being identified within the census tract where they live, then that should be stated, and the implications discussed.

The area-based poverty measure may be a better indication of the quality of care and institutional differences in healthcare delivery across the different poverty quartiles than individual socioeconomic differences—especially given the ecological fallacy and questionable representativeness of the cases to the target population. The title implies differences in neighborhood poverty, but the authors do not actually speak to the differences that may be occurring due to the neighborhood. Considering that the authors conclude COVID-19 mortality are partly, but not entirely, attributable to higher average socioeconomic position, it may be worth discussing contextual differences across the hospitals/facilities that serve these areas/people. The authors note that “Discussions of racial/ethnic groups in relation to COVID-19 often lack context about the modifiable social and economic processes, rooted in structural racism, that lead to inequality;” however, they do not speak to how these results provide that context and/or could be actionable, nor do they refer to structural racism within healthcare systems that may be experienced regardless of location or socioeconomic status.   

Additional information should be provided regarding proportion missing for geocoded addresses, where individuals were dropped due to insufficient data, and how decisions were made in terms of categorizing the SES exposure (why by quartiles? Was continuous or a threshold explored?).

Limitations of a cross-sectional analysis should also be further noted. Factors related to differential mortality across geographies should also be further delineated, such as access to care, type of care, insurance status, and lags between diagnoses and mortality reports. 

Recommendation: Minor Revise: while it may not require significant methodological revision, their discussion requires marked improvement prior to publication.

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