Background: Racial discrimination experiences are associated with the activation of stress biology pathways and signs of accelerated biological aging, including alterations in DNA methylation (DNAm). Coping strategies may mitigate stress from racial discrimination and protect against long-term adverse health outcomes. Methods: We conducted a secondary analysis of data from the Intergenerational Impact of Genetic and Psychological Factors on Blood Pressure cohort, an all-African-American sample, to test the hypothesis that social support can protect against accelerated biological aging associated with experiences of racial discrimination. We measured biological aging from saliva DNAm using six epigenetic clocks. Clock values were residualized on participant age and the estimated proportion of epithelial cells contributing to the DNA sample and standardized to M = 0, SD = 1 within the analysis sample. The primary analysis was focused on the second-generation PhenoAge and GrimAge clocks and the third-generation DunedinPACE “speedometer,” which previous studies have linked with racial discrimination. Results: In our sample (n = 234; mean age = 31.9 years; SD = 5.80), we found evidence consistent with our hypothesis in the case of the PhenoAge clock, but not the other clocks. Among mothers who did not seek social support, experiences of racial discrimination were associated with an older PhenoAge (b = 0.26, 95% CI = 0.02–0.50, p = 0.03). However, social-support seeking mitigated this risk; at the highest levels of social support, no adverse consequences of discrimination were observed (interaction b = −0.01, 95% CI = −0.02–−0.00, p = 0.03). Conclusions: The replication of results is needed. Future research should also investigate additional adaptive and maladaptive coping strategies utilized by African American women and mothers to identify protective measures that influence health outcomes.

Background: Stigmatizing language is common in clinical notes and can adversely affect the quality of patient care. Natural language processing (NLP) is a promising technology for identifying such language across large volumes of clinical notes in electronic health records. Objective: This study proposes an NLP-driven reduce stigmatizing language (ENGAGE) system to automatically identify and replace stigmatizing language. Methods: Using a mixed methods study, we will extract electronic health record data for patients admitted to 2 large, diverse home health care (HHC) organizations between January 2019 and December 2021. We propose the following 4 aims: aim 1 is to refine the ontology of stigmatizing language in HHC by (1) interviewing a diverse sample of HHC nurses and patients to identify terms to avoid and (2) analyzing clinical notes from various regions in the United States to categorize stigmatizing language. Aim 2 is to determine the best NLP approach for accurately identifying stigmatizing language by training algorithms and comparing their performance to human annotations. Aim 3 is to analyze the prevalence of stigmatizing language based on patients’ race and ethnicity using adjusted statistical analyses of a sample of approximately half a million HHC patients (34% racial and ethnic minority groups). Aim 4 is to develop the NLP-driven ENGAGE system by (1) testing NLP methods (rule based; “delete, retrieve, and generate”; and transformers) for suggesting alternative wording and (2) designing and refining the user interface for clinical trial preparation. Results: We received funding from the National Institute on Minority Health and Health Disparities in September 2023. Recruitment began in May 2024, and as of March 2025, interviews have been completed for 9 enrolled participants. We anticipate completing all study aims by April 2027. Conclusions: This study will leverage extensive data sources to examine stigmatizing language in HHC settings and contribute to the development of systems aimed at effectively reducing the use of such language among HHC nurses. International Registered Report Identifier (IRRID): DERR1-10.2196/69753

BACKGROUND Stigmatizing language is common in clinical notes and can adversely affect the quality of patient care. Natural language processing (NLP) is a promising technology for identifying such language across large volumes of clinical notes in electronic health records. OBJECTIVE This study proposes an NLP-driven reduce stigmatizing language (ENGAGE) system to automatically identify and replace stigmatizing language. METHODS Using a mixed methods study, we will extract electronic health record data for patients admitted to 2 large, diverse home health care (HHC) organizations between January 2019 and December 2021. We propose the following 4 aims: aim 1 is to refine the ontology of stigmatizing language in HHC by (1) interviewing a diverse sample of HHC nurses and patients to identify terms to avoid and (2) analyzing clinical notes from various regions in the United States to categorize stigmatizing language. Aim 2 is to determine the best NLP approach for accurately identifying stigmatizing language by training algorithms and comparing their performance to human annotations. Aim 3 is to analyze the prevalence of stigmatizing language based on patients’ race and ethnicity using adjusted statistical analyses of a sample of approximately half a million HHC patients (34% racial and ethnic minority groups). Aim 4 is to develop the NLP-driven ENGAGE system by (1) testing NLP methods (rule based; “delete, retrieve, and generate”; and transformers) for suggesting alternative wording and (2) designing and refining the user interface for clinical trial preparation. RESULTS We received funding from the National Institute on Minority Health and Health Disparities in September 2023. Recruitment began in May 2024, and as of March 2025, interviews have been completed for 9 enrolled participants. We anticipate completing all study aims by April 2027. CONCLUSIONS This study will leverage extensive data sources to examine stigmatizing language in HHC settings and contribute to the development of systems aimed at effectively reducing the use of such language among HHC nurses. CLINICALTRIAL INTERNATIONAL REGISTERED REPORT DERR1-10.2196/69753