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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 ( IF 3.5 ) Pub Date : 2021-09-19 , DOI: 10.1186/s12889-021-11748-y
Aditi Srivastav 1 , Kaitlynn Robinson-Ector 2 , Colby Kipp 3 , Melissa Strompolis 1 , Kellee White 2
Affiliation  

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.

中文翻译:

谁拒绝回应对种族模块的反应?:南卡罗来纳州行为风险因素监测系统的调查结果,2016-2017

将自我报告的种族/族裔差别待遇纳入基于人口的公共卫生监测和监测系统,可能为解决长期存在的健康不平等问题提供机会。虽然健康调查中回复率和选择性不回复的趋势越来越明显,但对与自我报告的歧视相关的不回复程度的研究值得更多关注。本研究调查了南卡罗来纳州行为风险因素监测系统 (SC-BRFSS) 种族反应模块(捕获基于种族的治疗报告的 6 个问题可选模块)的受访者和非受访者之间社会人口学变量的分布。使用来自 SC-BRFSS (2016, 2017) 的数据,我们检查了对 Reactions to Race 模块和模块中各个项目的无响应模式。逻辑回归模型被用来检查与无反应相关的社会人口学因素,并加权以解释复杂的抽样设计。在 21,847 名受访者中,15.3% 为无回应者。通过关键的社会人口学变量(例如,年龄、种族/民族、劳动力市场参与和健康保险状况)观察到 RTRM 无反应的显着差异。年轻的、西班牙裔的、家庭主妇/学生、未报告的收入和未投保的人在非受访者中的比例过高。在调整后的分析中,西班牙裔和未报告收入的个人更有可能在 RTRM 和跨项目中无反应,而退休人员不太可能无反应。在 RTRM 问题中观察到不回答水平的异质性,评估工作中的差别待遇 (54.8%) 和医疗保健环境 (26.9%) 的问题的不回答水平最高。根据一些关键的社会人口学变量,无反应者与反应者不同,这可能导致低估自我报告的歧视以及与种族相关的差别待遇和健康结果。虽然我们提倡使用基于人口的自我报告种族歧视措施来监测和跟踪州级在实现健康公平方面的进展,但未来有必要努力估计、评估和解决社会人口因素导致的无反应变化,以提高理解受基于种族的差别待遇影响的生活经历。对评估工作中的差别待遇 (54.8%) 和医疗保健环境 (26.9%) 的问题的不回答率最高。根据一些关键的社会人口学变量,无反应者与反应者不同,这可能导致低估自我报告的歧视以及与种族相关的差别待遇和健康结果。虽然我们提倡使用基于人口的自我报告种族歧视措施来监测和跟踪州级在实现健康公平方面的进展,但未来有必要努力估计、评估和解决社会人口因素导致的无反应变化,以提高理解受基于种族的差别待遇影响的生活经历。对评估工作中的差别待遇 (54.8%) 和医疗保健环境 (26.9%) 的问题的不回答率最高。根据一些关键的社会人口学变量,无反应者与反应者不同,这可能导致低估自我报告的歧视以及与种族相关的差别待遇和健康结果。虽然我们提倡使用基于人口的自我报告种族歧视措施来监测和跟踪州级在实现健康公平方面的进展,但未来有必要努力估计、评估和解决社会人口因素导致的无反应变化,以提高理解受基于种族的差别待遇影响的生活经历。9%)。根据一些关键的社会人口学变量,无反应者与反应者不同,这可能导致低估自我报告的歧视以及与种族相关的差别待遇和健康结果。虽然我们提倡使用基于人口的自我报告种族歧视措施来监测和跟踪州级在实现健康公平方面的进展,但未来有必要努力估计、评估和解决社会人口因素导致的无反应变化,以提高理解受基于种族的差别待遇影响的生活经历。9%)。根据一些关键的社会人口学变量,无反应者与反应者不同,这可能导致低估自我报告的歧视以及与种族相关的差别待遇和健康结果。虽然我们提倡使用基于人口的自我报告种族歧视措施来监测和跟踪州级在实现健康公平方面的进展,但未来有必要努力估计、评估和解决社会人口因素导致的无反应变化,以提高理解受基于种族的差别待遇影响的生活经历。
更新日期:2021-09-20
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