Multiple Imputation of Missing Race and Ethnicity in CDC COVID-19 Case-Level Surveillance Data
Bibliographic record
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.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.013 | 0.076 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".