Who declines to respond to the reactions to race module?: findings from the South Carolina Behavioral Risk Factor Surveillance System, 2016–2017

Abstract

Background The inclusion of self-reported differential treatment by race/ethnicity in population-based public health surveillance and monitoring systems may provide an opportunity to address long-standing health inequalities. While there is a growing trend towards decreasing response rates and selective non-response in health surveys, research examining the magnitude of non-response related to self-reported discrimination warrants greater attention. This study examined the distribution of sociodemographic variables among respondents and non-respondents to the South Carolina Behavioral Risk Factor Surveillance System (SC-BRFSS) Reactions to Race module (6-question optional module capturing reports of race-based treatment). Methods Using data from SC-BRFSS (2016, 2017), we examined patterns of non-response to the Reactions to Race module and individual items in the module. Logistic regression models were employed to examine sociodemographic factors associated with non-response and weighted to account for complex sampling design. Results Among 21,847 respondents, 15.3% were non-responders. Significant differences in RTRM non-response were observed by key sociodemographic variables (e.g., age, race/ethnicity, labor market participation, and health insurance status). Individuals who were younger, Hispanic, homemakers/students, unreported income, and uninsured were over-represented among non-respondents. In adjusted analyses, Hispanics and individuals with unreported income were more likely to be non-responders in RTRM and across item, while retirees were less likely to be non-responders. Heterogeneity in levels of non-responses were observed across RTRM questions, with the highest level of non-response for questions assessing differential treatment in work (54.8%) and healthcare settings (26.9%). Conclusions Non-responders differed from responders according to some key sociodemographic variables, which could contribute to the underestimation of self-reported discrimination and race-related differential treatment and health outcomes. While we advocate for the use of population-based measures of self-reported racial discrimination to monitor and track state-level progress towards health equity, future efforts to estimate, assess, and address non-response variations by sociodemographic factors are warranted to improve understanding of lived experiences impacted by race-based differential treatment.

Genetic Ancestry and Somatic Mutations in Lung Adenocarcinoma: Insights from Real-World Clinico-Genomic Data

“…The extent of this missingness is complex and varies across sources, ranging from 30-70%, 23 but can largely be attributed to issues in data transmission and collection rather than simply patient abstention. 42,43…”

“…The extent of this missingness is complex and varies across sources, ranging from 30-70%, 23 but can largely be attributed to issues in data transmission and collection rather than simply patient abstention. 42,43 The present study aimed to analyze data from a real-world clinico-genomic database to provide insights into the associations between lung cancer molecular profiles and genetic ancestry or R/E categories. To address the challenges typically associated with RWD, we implemented several strategies.…”

Incorporating Real-World Clinico-Genomic Insights to Inform Diversity Enrollment Targets in Oncology Trials

“…12,51 Some of this missingness is not random; [12][13][14][15] underserved populations tend to provide self-identified R/E (SIRE) less frequently. 29 This gap significantly impacts the ability to define enrollment goals that align solely with disease epidemiology and biomarker prevalence.…”

“…However, RWD may include R/E data from third parties, healthcare providers, or others who either assign categories based on physical characteristics, 19 or omit this data, either unintentionally due to error, or intentionally to prevent discrimination. 28,29 Therefore, we refer to R/E data in RWD as "stated" R/E rather than SIRE. 13…”

Race and Ethnicity of Infants Enrolled in Neonatal Clinical Trials

“…If researchers fail to ask race and ethnicity questions in a way that supports meaningful answers from all parents, our data on these issues will remain incomplete and unclear. Research participants may be more reluctant to answer race and ethnicity questions if they are part of certain marginalized groups 155 or if the options presented do not reflect their lived experience. 156 The labels used to describe each race and ethnicity category were inconsistent.…”