Abstract
The COVID-19 pandemic has resulted in a disproportionate burden on racial and ethnic minority groups, but incompleteness in surveillance data limits understanding of disparities. CDC's case-based surveillance system contains case-level information on most COVID-19 cases in the United States. Data analyzed in this paper contain COVID-19 cases with case-level information through September 25, 2020, which represent 70.9% of all COVID-19 cases reported to CDC during the period. Case-level surveillance data are used to investigate COVID-19 disparities by race/ethnicity, sex, and age. However, demographic information on race and ethnicity is missing for a substantial percentage of COVID-19 cases (e.g., 35.8% and 47.2% of cases analyzed were missing race and ethnicity information, respectively). Our goal in this study was to impute missing race and ethnicity to derive more accurate incidence and incidence rate ratio (IRR) estimates for different racial and ethnic groups, and evaluate the results from imputation compared to complete case analysis, which involves removing cases with missing race/ethnicity information from the analysis. Two multiple imputation (MI) models were developed. Model 1 imputes race using six binary race variables, and Model 2 imputes race as a composite multinomial variable. Our evaluation found that compared with complete case analysis, MI reduced biases and improved coverage on incidence and IRR estimates for all race/ethnicity groups, except for the Non-Hispanic Multiple/other group. Our research highlights the importance of supplementing complete case analysis with additional methods of analysis to better describe racial and ethnic disparities. When race and ethnicity data are missing, multiple imputation may provide more accurate incidence and IRR estimates to monitor these disparities in tandem with efforts to improve the collection of race and ethnicity information for pandemic surveillance.
Highlights
The COVID-19 pandemic has disproportionately affected several racial and ethnic groups, with disparities reported in the number of cases, hospitalizations, and deaths [1,2,3,4]
Case-level surveillance data contained 49.39% missingness on race/ethnicity (35.82% and 47.24% missingness on race and ethnicity, respectively); as a result, the incidence estimates based on complete case analysis yielded incidence estimates by race/ethnicity approximately 50% lower than those based on the multiple imputation (MI) data
Based on the complete case analysis, all race/ethnicity groups except NH Asian had a higher risk of COVID-19 compared to NH White, with incidence rate ratio (IRR) estimates ranging from 2.13 (95% confidence interval (CI) = 1.88, 2.42) (NH Black) to 3.06 (NH Multiple/other)
Summary
The COVID-19 pandemic has disproportionately affected several racial and ethnic groups, with disparities reported in the number of cases, hospitalizations, and deaths [1,2,3,4]. Grundmeier et al, [13] developed a Multiple Imputation (MI) model which included the posterior probability of racial/ethnic membership derived from the BISG method as well as demographic and clinical characteristics related to an individual’s race/ethnicity. Our purpose was to develop MI models to impute missing race and ethnicity information in the CDC COVID-19 case-level surveillance data and evaluate the performance of these models regarding the incidence and incidence rate ratio estimates of COVID19 cases by race/ethnicity. Two MI models were constructed—one where race is imputed using six binary variables and one where race is imputed as a composite multinomial variable—and applied to the case-level surveillance data. 2. MULTIPLE IMPUTATION OF MISSING RACE AND ETHNICITY IN CDC COVID-19 CASE-LEVEL SURVEILLANCE DATA. Race and ethnicity were imputed as two separate variables, i.e., not combined into one variable because missing data patterns differed by race and ethnicity
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