In 2010, Patient-Centered Outcomes Research Institute (PCORI) was authorized by Congress to improve the quality and relevance of evidence available to help patients, caregivers, employers, insurers, and policy makers make better-informed health decisions. We conducted a qualitative analysis of behavioral health trials in the PCORI Addressing Disparities portfolio to examine cultural tailoring strategies across the following priority populations: racial and ethnic minorities, rural populations, people with low-income or low socioeconomic status, individuals with disabilities, people with low health literacy, and lesbian, gay, bisexual, and transgender (LGBT) communities. The Common Strategies for Enhancing Cultural Appropriateness model was used to examine cultural tailoring strategies within trials. We hypothesized increased intersectionality within a patient population at risk for disparities would correlate with the dosage and type of cultural tailoring strategies applied. Thirty-three behavioral health trials applied cultural tailoring strategies and a majority of trials (n = 30) used three or more strategies. Trends in cultural tailoring were associated with certain racial and ethnic groups; however, increased use of tailoring was not associated with the number of priority populations included in a trial. The PCORI Addressing Disparities portfolio demonstrates how a range of cultural tailoring strategies are used, within comparative clinical effectiveness research trials, to address the needs and intersectionality of patients to reduce health and healthcare disparities.

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.

Discrimination is a chronic psychosocial stressor that can accelerate aging. While strong empirical evidence demonstrating linkages between discrimination and adverse health outcomes, the role of discrimination in multimorbidity burden has received less attention. Prior research on discrimination and health largely uses a single, static, cross-sectional measure to predict health at a second time point, that may mask significant heterogeneity in the dynamic nature of repeated exposures to discrimination. Characterizing longitudinal patterns of discrimination may be a better predictor of health risk and provide insight on resilience processes that influence aging-related outcomes such as multimorbidity. However, this relationship is not well understood. We investigate the association between discrimination trajectories, resilience characteristics, and multimorbidity burden among a sample of middle-aged and older black adults. Specifying discrimination trajectories and resilience characteristics that differentially predict multimorbidity burden may inform the design of culturally-relevant interventions to delay the development and progression of multimorbidity.