Who declines to respond to the reactions to race module?: findings from the South Carolina Behavioral Risk Factor Surveillance System, 2016–2017
| dc.contributor.author | Srivastav, Aditi | |
| dc.contributor.author | Robinson-Ector, Kaitlynn | |
| dc.contributor.author | Kipp, Colby | |
| dc.contributor.author | Strompolis, Melissa | |
| dc.contributor.author | White, Kellee | |
| dc.date.accessioned | 2021-12-13T15:47:36Z | |
| dc.date.available | 2021-12-13T15:47:36Z | |
| dc.date.issued | 2021-09-19 | |
| dc.description.abstract | 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). 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. 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%). 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. | en_US |
| dc.description.uri | https://doi.org/10.1186/s12889-021-11748-y | |
| dc.identifier | https://doi.org/10.13016/ngw8-zqwr | |
| dc.identifier.citation | Srivastav, A., Robinson-Ector, K., Kipp, C. et al. Who declines to respond to the reactions to race module?: findings from the South Carolina Behavioral Risk Factor Surveillance System, 2016–2017. BMC Public Health 21, 1703 (2021). | en_US |
| dc.identifier.uri | http://hdl.handle.net/1903/28237 | |
| dc.language.iso | en_US | en_US |
| dc.publisher | Springer Nature | en_US |
| dc.relation.isAvailableAt | Health Services Administration | |
| dc.relation.isAvailableAt | School of Public Health | |
| dc.relation.isAvailableAt | Digital Repository at the University of Maryland (DRUM) | |
| dc.relation.isAvailableAt | University of Maryland (College Park, MD) | |
| dc.subject | Racial discrimination | en_US |
| dc.subject | Reactions to race | en_US |
| dc.subject | Racism | en_US |
| dc.subject | Non-Response | en_US |
| dc.subject | Behavioral risk factor surveillance | en_US |
| dc.subject | Health surveys | en_US |
| dc.title | Who declines to respond to the reactions to race module?: findings from the South Carolina Behavioral Risk Factor Surveillance System, 2016–2017 | en_US |
| dc.type | Article | en_US |