Thesis (M.A.)--University of Hawaii at Manoa, 2004. / Includes bibliographical references (leaves 88-97). / ix, 97 leaves, bound 29 cm

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This online COVID-19 pandemic era investigation with Black hypertensive adults (N=612) who were 93.6% U.S. born, 54.7% male, 44.3% female with a mean age of 37 years sought to identify predictors of self-ratings for being medication non-adherent and for racism and discrimination impacting engagement with medical providers. Findings showed over 70% were medication non-adherent on Morisky Medication Adherence Scale, and 49.3% self-classified as medication non-adherent. Regarding behaviors of following provider instructions for taking medication, maintaining appointments, and uninterrupted receipt of medication, these deteriorated during the pandemic, but improved by currently—as resilience; yet, maintaining appointments and uninterrupted receipt of medication were better currently than pre-pandemic. While social support was low and unchanged from before the pandemic to currently, social support with medications deteriorated during the pandemic, but improved currently. Participants rated providers as follows: having closest to moderate cultural competence; moderate level of discrimination; moderately high for discriminating against their personal demographics, identity, or appearance (e.g. 85.3% for being Black, 80.6% for skin color, 66% for hair); 64.5% exposed them to racism/ discrimination so it impacted engagement with providers for willingness to regularly attend appointments; and, low-moderate frequency of microaggressions related to being Black. Not surprisingly, moderate medical mistrust was found. Two backward stepwise logistic regression models highlighted recurrent predictors for medication adherence as being 1-less provider discrimination for demographics/ identity/ appearance, and 2-less frequent provider microaggressions for being Black; and, one highlighted higher provider cultural competence. In a third model, greater provider discrimination was a predictor of self-classifying for racism/discrimination impacting engagement with providers. Findings highlight less provider discrimination and less frequent microaggressions by providers as key experiences—such that lower levels of exposure to provider racism, discrimination and microaggressions emerge as powerful determinants of medication adherence. The study has important implications for the urgency of addressing providers’ racism, discrimination and racial microaggressions as factors playing a role in medication non-adherence and patients’ unwillingness to return for medical appointments. Training in cultural competence is vitally needed with specific attention in training to actually observing, addressing and changing providers’ behavior of enacting racism, discrimination, and microaggressions with Black hypertensive patients.

Background: Approximately 5 million older adults receive home healthcare (HHC) annually in the United Sates, and nearly 90% of HHC recipients are 65 years or older. HHC encompasses in-home interdisciplinary services such as skilled nursing, social work, and physical, speech, and occupational therapy. One in every five patients is hospitalized during their time in HHC. Researchers have explored machine learning models that use data in the electronic health record (EHR) to aid clinicians in identifying patients at high risk for hospitalization and emergency department (ED) visits. Failure to consider social risk factors can exacerbate health inequities. Some studies suggest that including social risk factors in machine learning models can help to mitigate bias in model performance among individuals from racial and ethnic minority groups. Prior literature has reported that a majority of social information is documented in clinical notes. In the HHC setting, there is a gap in understanding how social risk factors are documented in clinical notes and whether adding social risk factors in machine learning models can improve model performance. Thus, this dissertation aims to: 1) summarize the literature on machine learning conducted in the HHC setting, 2) extract social risk factors documented in HHC clinical notes, and 3) examine how social risk factors influence machine learning model performance. Methods: The data from this dissertation is from one HHC agency in New York, New York, including approximately 65,000 unique patients and 2.3 million clinical notes. The Biopsychosocial Model guided this study by providing a framework to report the features included in the machine learning models. To address the first aim, a scoping review was conducted to summarize the literature on machine learning applied to EHR data in the HHC setting. To address the second aim, a natural language processing system was developed to extract social risk factors from HHC clinical notes. Then, logistic regression was utilized to examine the association between the social risk factors documented in clinical notes and hospitalization and ED visits. Finally, to address the third aim, social risk factors were included in four machine learning models to predict hospitalization and ED visit risk in HHC. A sub-analysis was conducted to explore the utility of social risk factors in machine learning models across individuals from different racial and ethnic groups. Results: The results from all three aims suggest that there has been a rise in machine learning applied in HHC, but few studies have incorporated clinical notes. There are gaps in implementing machine learning models in practice and standardizing social risk factors in documentation. HHC clinicians are documenting the following social risk factors in 4% of their clinical notes: Social Environment, Physical Environment, Education and Literacy, Food Insecurity, and Access to Care. These social risk factors are significantly associated with hospitalization and ED visits; however, their contribution showed minimal differences in machine learning model performance. Conclusion: This dissertation study demonstrates the feasibility and utility of leveraging HHC clinicians’ clinical notes to understand social risk factors. Further exploration is needed to tease out the nuances in how HHC clinicians perceive, assess, and document social risk factors in the EHR. Stakeholders are encouraged to standardize social risk factors and develop informatics tools tailored to the HHC setting to improve the identification of patients at risk for hospitalization and ED visits.

The problem that this study addressed was the high rate of maternal mortality for Black women in the United States, which has been rising, including before the COVID-19 pandemic. The goal was to identify significant predictors of medical mistrust. The study recruited a convenience sample via an online social media campaign. The resultant sample was 100% Black and female (N=192) with a mean age of 33.23 (SD= 4.980, min=24, max=61), while 94.8% were born in the United States (n=182). Using background stepwise regression, the following were found to be significant predictors of a higher level of medical mistrust: older age (B = .033, p = .001); higher levels of education (B = 0.205, p = .000); lower annual household income (B = -.055, p = .026); higher level of perceived racism, discrimination, and inequity in treatment from medical staff (B = 0.137, p = .046); lower levels of cultural sensitivity/ competence/ humility ratings for medical staff (B = -.155, p = .002); higher past year mental distress (i.e., Depression, Anxiety, Insomnia and Trauma) (B = .369, p = .000); and lower levels of social support post-partum (B = -0.162, p = .004)—with 46.5% of the variance predicted by the model (R2 = 0.698, Adjusted R2 = 0.465). The study findings highlight a crisis of Black maternal mortality in the United States, as well as a crisis in healthcare service delivery to Black women, as uncovered via this study. The data betrays a dimension of the crisis in healthcare service delivery to Black women who report experiencing discrimination for being Black at 75.5%, for their appearance (skin tone, hair, etc.) at 62.0%, and for being overweight or obese at 28.6%. Implications of the findings are discussed, while recommendations for future research are offered. In terms of those implications, perhaps most importantly, this data effectively identifies the year after a high-risk birth hospitalization as an essential time for ensuring Black women enter counseling with licensed and certified mental health professionals.

The present study aimed to explore the objective and subjective experiences of “mistreatment” in maternity care in a diverse cohort of women who gave birth in New York City hospitals to identify the prevalence and risk factors of mistreatment and measure the relationship between mistreatment and mental health (Bohren et al., 2015). The study utilized a mixed-methods cross-sectional approach. To collect the quantitative data, 109 participants <1 year postpartum completed an anonymous online survey comprising a self-report measure of demographic, health and mental health information, several mental health questionnaires and two measures of mistreatment in maternity care. 8 of these participants were interviewed about their childbirth experience. The quantitative data was analyzed utilizing linear regression, moderation analysis and path analysis, and the qualitative data was thematically coded then analyzed using Reflexive Thematic (RT) analysis. These data were then triangulated using a mixed-methods model of mistreatment. In total, 10-15% of the sample experienced mistreatment in the form of Low to Very Low respect and/or autonomy in decision making in their maternity care. Forms of mistreatment included unwanted procedures, provider pressure to undergo procedures, dismissal of women’s concerns, racial discrimination, abandonment, and medical neglect. Approximately 25% of respondents received an unwanted intervention; this was the most significant predictor of mistreatment. This relationship was moderated by race, parity and birth plan. Black, Latinx and Hispanic women experienced the lowest levels of respect in maternity care. Mistreatment in maternity care was correlated with increased risk for postpartum mental illness: decreased respect and autonomy in childbirth was associated with increased postpartum depression and PTSD symptoms. Eight themes were identified in the qualitative analysis: Discrimination and Unfair Treatment, Confusion and Abandonment, Disregard for Patient Autonomy, Hospital-Level Drivers of Mistreatment, Women Treated as Passive, Normalization of Mistreatment, Self-Advocacy and Vulnerability and, Reclaiming Power through Knowledge. Together, the triangulated mixed- methods data were fit to render a comprehensive “model of mistreatment” to illustrate direct and indirect relationships between mistreatment, mental health, race, trauma history, and childbirth preparation. These findings demonstrate that mistreatment is a multi-determined phenomenon that is interdependent with mental health and requires systematic measurement in healthcare treatment, the integration of anti-racist and patient-centered care and improved childbirth education for patients.

This study addressed the long-standing crisis of Black severe maternal morbidity in the U.S., while the COVID-19 pandemic led to even worse outcomes. The purpose of the study was to identify significant predictors of the quality of patient-provider relationships during a birth hospitalization. The sample of Black women (N=182) gave moderate ratings for quality of patient provider relationships, and for level of trust, rapport, and communication with providers. Providers were rated as having a fair level of cultural sensitivity, competence, and humility—while 30.2% rated them as poor. For experiences of racism, discrimination and inequities in service delivery, combining categories of a “few times” and “many times,” 53.3% felt racially stereotyped or treated like a racial stereotype, 52.5% were treated with less respect than a White woman would have been, 39.7% were verbally abused or yelled at, 43.8% were scolded, ridiculed, mocked, and shamed, 47.2% felt belittled and put down, 42.7% felt threatened, coerced, lied to, and manipulated, and 46% felt their pain was not managed the same way as for a White woman. Women entered the hospital with risk factors of cardiovascular disease (20.3%), hypertension (23.6%), obesity (18.1%), and diabetes (13.7%). Further, 74.2% had COVID-19 in the past two years, 25.8% had long COVID-19, 34.1% had COVID-19 during their pregnancy, and 34.1% had COVID-19 at delivery. Medical events during their delivery hospitalization included hemorrhage (40.7%), blood clot (25.3%), and a hypertensive disorder of pregnancy (25.3%). Women had high rates (over 75%) of past year depression, anxiety, and trauma—with 68.1% receiving counseling; and higher rates (over 85%) the year post-partum—with 76.9% receiving counseling. Noteworthy significant predictors of a higher quality of patient-provider relationships were higher education, higher trust/ rapport/ communication with providers, and lower global racism/ discrimination/ inequities during service delivery—while entering the hospital with lower risk factors for pregnancy-related complications (69.8% of variance predicted). The study contributes to literature on the crisis of severe maternal morbidity for Black women in the U.S, as well as factors that need to be addressed to reduce it, while offering a cache of culturally appropriate measures for ongoing research.

Research objectives: This study examined research, media, and public opinion related to maternal health in order to understand some of the social and structural factors that influence the passage of comprehensive maternal health policies in the United States. This study also examined the messaging of race and racism in media and health communication. Research objectives were: 1.) To summarize the perinatal care experiences of Black birthing people through a scoping review of the literature, 2.) To explore media depictions of maternal mortality in terms of the groups, causes, and solutions discussed in coverage, and 3.) To test the effects of two different approaches to communicating maternal health on public beliefs about the causes of racial health disparities and public support for structural policies. Methods: The review of the literature followed a scoping review protocol and developed tailored search strings to retrieve relevant articles in three databases. The review protocol included developing selection criteria, screening articles retrieved from three databases, charting the data, and identifying themes across articles using an ecological health model as a conceptual guide. For the second paper – a content analysis of news media coverage of maternal mortality – relevant news articles were retrieved using NexisUni, an online database of newspaper articles. A codebook was developed deductively using previous research and grey literature on maternal health, and articles were subsequently coded for the presence or absence of codes that assessed how articles framed causes, solutions, and social groups in their coverage of maternal mortality in the United States. The third paper tested the effects of articles that communicated the maternal health issues faced by Black birthing people using a web-based survey experiment. Participants in this study were recruited using Qualtrics’ panel services, and were randomly assigned to read either a narrative or nonnarrative article communicating the relationship between race and adverse maternal health outcomes. Participants were then asked to respond to the questions that assessed their agreement with structural causes for racial health disparities and their support for policies to improve maternal health. Findings: The scoping review found that Black birthing people described factors at the interpersonal, organizational, community, and policy level in their accounts of their perinatal care experiences. This included their interactions with their providers, the dominant models of care in healthcare settings, institutional representation, and the limitations of care covered through existing Medicaid policies. The content analysis of media found that newspaper coverage of maternal health reflected the factors described in research. Media predominantly focused on structural causes and solutions for maternal health (e.g., access to services and care, social determinants of health, structural racism) and described racial disparities in maternal mortality. The final study built on the findings of the media analysis by testing the effects of news articles that described the role of social and structural factors on the maternal health outcomes of Black birthing people. Data from the experiment showed that participants who read a narrative article about the issue had greater support for structural policies than participants who read a nonnarrative article. The difference in agreement with structural causes for racial health disparities between participants in the narrative and nonnarrative groups was not statistically significant. Additionally, data showed significant differences in treatment effects and policy support across groups distinguished by race and gender.