Modeling the Potential Impact of Missing Race and Ethnicity Data in Infectious Disease Surveillance Systems on Disparity Measures: Scenario Analysis of Different Imputation Strategies

• Type of Material: Article
• Authors: Bahareh Ansari, University at Albany, Albany; Rachel Hart-Malloy, University at Albany, Albany; Eli Rosenberg, Emory University; Monica Trigg, Emory University; Erika G Martin, University at Albany, Albany
• Language: English
• Date: 2022-11-01
• Publisher: JMIR PUBLICATIONS, INC
• Publication Version: Final Published Version
• Copyright Statement: ©Bahareh Ansari, Rachel Hart-Malloy, Eli S Rosenberg, Monica Trigg, Erika G Martin. Originally published in JMIR Public Health and Surveillance (https://publichealth.jmir.org), 09.11.2022.
• License: Creative Commons Attribution 4.0 International
• Final Published Version (URL): http://dx.doi.org/10.2196/38037
• Title of Journal or Parent Work: JMIR PUBLIC HEALTH AND SURVEILLANCE
• Volume: 8
• Issue: 11
• Start Page: e38037
• End Page: e38037
• Grant/Funding Information: This work was funded by the US Centers for Disease Control and Prevention, National Center for HIV, Viral Hepatitis, STD, and TB Prevention Epidemiologic and Economic Modeling Agreement (NEEMA #NU38PS004650). The findings and conclusions in this report are those of the authors and do not necessarily represent the official position of the funding agency or the New York State Department of Health.
• Keywords: RACE/ETHNICITY DATA; Life Sciences & Biomedicine; imputation; Science & Technology; health equity; UNITED-STATES; CANCER; HEALTH; surveillance; sexually transmitted diseases; Public, Environmental & Occupational Health; missing data; SOCIAL DETERMINANTS
• Research Categories: Biology, Biostatistics; Health Sciences, Epidemiology

Abstract

Background: Monitoring progress toward population health equity goals requires developing robust disparity indicators. However, surveillance data gaps that result in undercounting racial and ethnic minority groups might influence the observed disparity measures. Objective: This study aimed to assess the impact of missing race and ethnicity data in surveillance systems on disparity measures. Methods: We explored variations in missing race and ethnicity information in reported annual chlamydia and gonorrhea diagnoses in the United States from 2007 to 2018 by state, year, reported sex, and infection. For diagnoses with incomplete demographic information in 2018, we estimated disparity measures (relative rate ratio and rate difference) with 5 imputation scenarios compared with the base case (no adjustments). The 5 scenarios used the racial and ethnic distribution of chlamydia or gonorrhea diagnoses in the same state, chlamydia or gonorrhea diagnoses in neighboring states, chlamydia or gonorrhea diagnoses within the geographic region, HIV diagnoses, and syphilis diagnoses. Results: In 2018, a total of 31.93% (560,551/1,755,510) of chlamydia and 22.11% (128,790/582,475) of gonorrhea diagnoses had missing race and ethnicity information. Missingness differed by infection type but not by reported sex. Missing race and ethnicity information varied widely across states and times (range across state-years: from 0.0% to 96.2%). The rate ratio remained similar in the imputation scenarios, although the rate difference differed nationally and in some states. Conclusions: We found that missing race and ethnicity information affects measured disparities, which is important to consider when interpreting disparity metrics. Addressing missing information in surveillance systems requires system-level solutions, such as collecting more complete laboratory data, improving the linkage of data systems, and designing more efficient data collection procedures. As a short-term solution, local public health agencies can adapt these imputation scenarios to their aggregate data to adjust surveillance data for use in population indicators of health equity.