Objective. To examine factors contributing to the loss of potential minority participants in a study of medication adherence among rheumatoid arthritis patients. Methods. Chi-square statistics were used to test for differences in refusal/ineligibility by race and site at 4 screening points (initial eligibility review, initial patient contact, adherence screening, and informed consent). Differences in criteria-specific risks for exclusion at initial eligibility review were examined across 4 sites by race. Odds ratios (95% confidence intervals) were estimated if differences were significant. Multivariate logistic regression was used to examine sociodemographic factors associated with the probability for ineligibility at the initial eligibility review. Stated reasons for refusal were qualitatively quantified. Results. A greater percentage of African Americans were lost at every screening point when compared with whites, but only the difference at the initial eligibility review was statistically significant. Conclusion. Factors associated with attrition included selection of area medical sites, research design issues, comorbid conditions, alcohol abuse, and being younger, unmarried, African American, and male.

Even after controlling for stage, comorbidity, age, and insurance status, black women with breast cancer (BC) in the USA have the lowest 5-year survival as compared with all other races for stage-matched disease. One potential cause of this survival difference is the disparity in cancer treatment, evident in many population clinical trials. Specifically, during BC chemotherapy, black women receive less relative dose intensity with more dose reductions and early chemotherapy cessation compared with white women. Symptom incidence, cancer-related distress, and ineffective communication, including the disparity in patientcenteredness of care surrounding patient symptom reporting and clinician assessment, are important factors contributing to racial disparity in dose reduction and early therapy termination. We present an evidence-based overview and an explanatory model for racial disparity in the symptom experience during BC chemotherapy that may lead to a reduction in dose intensity and a subsequent disparity in outcomes. This explanatory model, the Symptom Experience, Management, Outcomes and Adherence according to Race and Social determinants + Genomics Epigenomics and Metabolomics (SEMOARS + GEM), considers essential factors such as social determinants of health, clinician communication, symptoms and symptom management, genomics, epigenomics, and pharmacologic metabolism as contributory factors.

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For precision health care to be successful, an in-depth understanding of the biological mechanisms for symptom development and severity is essential. Omics-based research approaches facilitate identification of the biological underpinnings of symptoms. We reviewed literature for omics-based approaches and exemplar symptoms (sleep disruption, cognitive impairment, fatigue, gastrointestinal [GI] distress, and pain) to identify genes associated with the symptom or symptoms across disease processes. The review yielded 27 genes associated with more than one symptom. ABCB1 (MDR1), APOE, BDNF, CNR1, COMT, DAT1 (SLC6A3), DRD4, ESR1, HLA-DRB1, IL10, IL1B, IL6, LTA, PTGS2 (COX-2), SLC6A4, and TNF were associated with cognitive impairment and pain, which had the most genes in common. COMT and TNF were related to all symptoms except sleep disruption. IL1B was associated with all symptoms except cognitive impairment. IL10, IL1A, IL1B, IL1RN, IL6, and IL8 (CXCL8) were linked with all the exemplar symptoms in various combinations. ABCB1 (MDR1) and SLC6A4 were associated with cognitive impairment, GI distress, and pain. IL10 and IL6 were linked to cognitive impairment, fatigue, and pain. APOE and BDNF were associated with sleep disruption, cognitive impairment, and pain. The 27 genes were associated with canonical pathways including immune, inflammatory, and cell signaling. The pathway analysis generated a 15-gene model from the 27 as well as 3 networks, which incorporated new candidate genes. The findings support the hypothesis of overlapping biological underpinnings across the exemplar symptoms. Candidate genes may be targeted in future omics research to identify mechanisms of co-occurring symptoms for potential precision treatments.

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