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

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.

Validation of Multi-State EHR-Based Network for Disease Surveillance (MENDS) Data and Implications for Improving Data Quality and Representativeness

“…Continuing to support health systems in making these improvements helps ensure accurate information for public health programs and interventions that address health equity. Awareness that the quality of race and ethnicity data can vary widely across systems ( 20 ) can also help researchers interpret MENDS surveillance estimates and facilitate use of these data — along with traditional sources of chronic disease surveillance data ( 21 , 22 ).…”

Incorporating a Health Equity Lens Into Surveillance Information Systems: Opportunities and Challenges

“…There are numerous places in the national STD case-based surveillance data life cycle where biases, errors, and missingness can be introduced in the collection and processing of the SOGI and REAL data needed to address health equity. Nationally, nearly one-third of chlamydial cases and more than two-fifths of gonorrhea cases have missing race and ethnicity data, with a trend of increasing missingness among chlamydial cases 12. If these data are missing from provider STD case reports and laboratory data, these data can be collected by disease intervention specialists; however, it is infeasible to interview all newly diagnosed individuals, given limited resources and disease burden, and most jurisdictions prioritize at least one STD or high-risk population, with syphilis cases commonly prioritized 13.…”

“…Nationally, nearly one-third of chlamydial cases and more than two-fifths of gonorrhea cases have missing race and ethnicity data, with a trend of increasing missingness among chlamydial cases. 12 If these data are missing from provider STD case reports and laboratory data, these data can be collected by disease intervention specialists; however, it is infeasible to interview all newly diagnosed individuals, given limited resources and disease burden, and most jurisdictions prioritize at least one STD or high-risk population, with syphilis cases commonly prioritized. 13 Furthermore, approximately 80% of HIV and syphilis case patients receive interviews by disease intervention specialists 14,15 ; individuals with newly diagnosed HIV and syphilis are typically prioritized for intervention and reasons for not receiving an interview include not being located or declining engagement with disease intervention specialists.…”