Examination of the Moderating Effect of Race on the Relationship between Vitamin D Status and COVID-19 Test Positivity Using Propensity Score Methods

Abstract

Introduction:

With a well-established role in inflammation and immune function, vitamin D status has emerged as a potential factor for coronavirus disease-2019 (COVID-19).

Objective:

The purpose of this study was to evaluate the moderating effect of race on the relationship between vitamin D status and the risk of COVID-19 test positivity, and to compare propensity score (PS) model results to those obtained from classical bivariate and multivariable models, which have primarily comprised the literature to date.

Methods:

Electronic health record (EHR) data from TriNetX (unmatched n = 21,629; matched n = 16,602) were used to investigate the effect of vitamin D status, as measured by 25-hydroxyvitamin D [25(OH)D], on the odds of experiencing a positive COVID-19 test using multivariable logistic regression models with and without PS methodology.

Results:

Having normal (≥ 30 ng/mL) versus inadequate 25(OH)D (< 30 ng/mL) was not associated with COVID-19 positivity overall (OR = 0.913, p = 0.18), in White individuals (OR = 0.920, p = 0.31), or in Black individuals (OR = 1.006, p = 0.96). When 25(OH)D was analyzed on a continuum, a 10 ng/mL increase in 25(OH)D lowered the odds of having a positive COVID-19 test overall (OR = 0.949, p = 0.003) and among White (OR = 0.935, p = 0.003), but not Black individuals (OR = 0.994, p = 0.75).

Conclusions:

Models which use weighting and matching methods resulted in smaller estimated effect sizes than models which do not use weighting or matching. These findings suggest a minimal protective effect of vitamin D status on COVID-19 test positivity in White individuals and no protective effect in Black individuals.

Keywords:

• Coronavirus
• 25-hydroxyvitamin D
• matching
• weighting
• logistic regression

Acknowledgments

Research reported in this publication was supported in part by the National Center for Advancing Translational Sciences of the National Institutes of Health under Award Number UL1TR003015. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health. We thank Mattie Tenzer and David Bowers from Carilion Clinic Health Analytics Research and Daniel Graft from Carilion Clinic TSG for their support of the project with TriNetX and SPARC, Carilion Clinic’s secure research environment. No potential competing interests were reported by the authors.

Disclosure statement

No potential conflict of interest was reported by the author(s).