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- Title
Methodological Considerations for Modeling Social Vulnerability and COVID-19 Risk—Response to Nayak et al.
- Authors
Khazanchi, Rohan; Beiter, Evan R.; Ganguli, Ishani
- Abstract
The growing body of literature on how COVID-19 differentially impacts marginalized populations highlights the need to be cautious when adjusting for clinical and sociodemographic variables.[2]-[4] In our study, we avoided adjusting for comorbidities because they fall on the causal pathway from social vulnerability to disease incidence and mortality.[2] Furthermore, overadjustment bias in risk prediction and resource allocation models may mislead policymakers to conclude that smaller differences after adjustment reflect smaller disparities, rather than recognizing that comorbidity rates themselves are a consequence of longstanding structural inequity.[5] With these issues in mind, we remain concerned that controlling for comorbidities may mask the starkness of COVID-19 inequities. To the Editor: We thank Nayak and colleagues for their interest in our study, which examined the correspondence between the Center for Disease Control's Social Vulnerability Index (SVI) and county-level COVID-19 cases and deaths.[1] In light of the mentioned methodological differences with their team's preprint, we welcome this opportunity to explore the implications of risk adjustment choices when modeling population-level health inequities. When Gross and colleagues[3] used indirect standardization to compare expected and observed COVID-19 mortality rates by race/ethnicity within age strata, they found that age-unadjusted mortality rates may underestimate disparities among populations that skew younger (e.g., Black and Latinx).
- Subjects
COVID-19; RACIAL inequality; DISEASE incidence; HEALTH equity; SARS-CoV-2
- Publication
JGIM: Journal of General Internal Medicine, 2021, Vol 36, Issue 4, p1115
- ISSN
0884-8734
- Publication type
Letter
- DOI
10.1007/s11606-021-06601-2