Modeling the Potential Impact of Missing Race and Ethnicity Data in Infectious Disease Surveillance Systems on Disparity Measures: Findings from Reported Chlamydia and Gonorrhea Diagnoses in the United States (Preprint)

Abstract

Monitoring progress towards population health equity goals requires developing robust disparity indicators. However, surveillance data gaps that result in undercounting racial/ethnic minority groups might influence observed disparity measures.

To assess the impact of missing race and ethnicity data in surveillance systems on disparity measures.

We explored variation in missing race/ethnicity information in reported annual chlamydia and gonorrhea diagnoses in the United States from 2007 through 2018 by state, year, sex at birth, and infection. For diagnoses with incomplete demographic information in 2018, we estimated disparity measures (relative rate ratio [RR] and risk difference [RD]) with five imputation scenarios, compared to the base case (no adjustments). The five scenarios used the racial/ethnic distribution of 1) chlamydia or gonorrhea diagnoses in the same state, 2) chlamydia or gonorrhea diagnoses in neighboring states, 3) chlamydia or gonorrhea diagnoses within the geographic region, 4) HIV diagnoses, and 5) syphilis diagnoses.

Nationally, in 2018, 31.9% of chlamydia and 22.1% of gonorrhea diagnoses had missing race/ethnicity information. Missingness differed by infection type but not by sex at birth. Missing race/ethnicity information varied widely across states and time (range across state-years: from 0.0% to 96.2%). The RR remained similar in the imputation scenarios, although the RD differed nationally and in some states.

We found that missing race/ethnicity information impacts measured disparities, which is important to consider when interpreting disparity metrics. Addressing missing information in surveillance systems requires systems-level solutions such as collecting more complete lab data, improved 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.