In this dissertation, I aim to examine how policy packages focusing on improving reproductive conditions impact adverse maternal outcomes at the local level. I use the National Institute of Reproductive Health’s Local Reproductive Freedom Index (NIRH LRFI) to advance scholarship and policy advocacy on how policies included in that index may lessen the overall maternal mortality rate while also perhaps diminishing the disparity in maternal mortality between women of color and white women in 50 major U.S. cities. I argue that policy may be an effective—though under-studied—avenue for reducing the risk of maternal mortality because policies can be used to target specific social factors that research has demonstrated place individuals at higher risk of a negative outcome. However, these policy avenues usefully can be considered as interconnected policy packages rather than merely as single-policy interventions. Additionally, current research in this area suggests that there is a clear need for more research that combines SDH with policies aimed at improving health outcomes for reproducing individuals, particularly for communities of color (Creanga et al. 2015b; Kozhimannil, Vogelsang, and Hardeman 2015; Osypuk et al. 2015). As such, I situate maternal mortality, and specifically racial disparities in maternal mortality, among social determinants of health. Overall, I argue that to successfully decrease aggregate maternal mortality rates and lessen racial disparities in reproductive health, it is essential to center the experiences and needs of women of color and use a critical lens when evaluating measures assessing reproductive freedom.

Breast cancer (BCa) is a leading cause of cancer-related female deaths worldwide and is a complex disease consisting of many different subtypes with varying clinical course and outcomes. Triple negative breast cancer (TNBC), an aggressive and highly metastatic subtype, is most prevalent in women of African ancestry (WAA) but the causes of this disparity are not fully understood. The goal of this study was to investigate the epidemiological and genetic profiles in ancestrally-related WAA in Barbados and Nigeria to advance knowledge and lay the foundation for development of improved or novel BCa therapeutics. To gain insight about TNBC across the African continent, a systematic review and meta-analysis was conducted. TNBC frequencies on average across Africa were estimated at 26.8% but were highest in West African countries (46.0%). We also sought to identify the epidemiological profile of BCa in Barbados—a Caribbean island with significant West African ancestry. We reviewed pathological reports for BCa from the sole public hospital in Barbados and compared those data with USA population-based data. We found a high prevalence of high prevalence of TNBC amongst women diagnosed with breast cancer in Barbados (25%), compared to 21% in non-Hispanic Black and 10% in non-Hispanic White women in the USA for the 2010-2016 period. We also investigated the somatic mutational profile of WAA with TNBC in Barbados and Nigeria using whole exome sequencing (WES) of formalin-fixed paraffinembedded TNBC tissues. This investigation revealed novel and pathogenic variants in well-known cancer-associated genes such as TP53, BRCA1 and MDC1. The somatic mutation signature in Nigerian tissues correlated with aflatoxin signature, implying a role for environmental factors influencing the genomics profile in this cohort. Copy number variants were revealed at high frequencies for PIK3CA, FGFR2 and HIF1AN genes. Collectively, these findings uncovered novel epidemiological and genetic trends in WAA with high prevalence of the aggressive TNBC subtype / Thesis / Doctor of Philosophy (PhD) / Breast cancer (BCa) is a leading cause of cancer-related death in women worldwide. Although Caucasian women are diagnosed with BCa more than women of African ancestry (WAA), more WAA unfortunately die from BCa. The reasons for this disparity are currently unknown, however, a higher proportion of WAA are diagnosed with an aggressive type of BCa called triple negative breast cancer (TNBC). This might partially explain the high cancer death rate in WAA. To understand this disparity in BCa incidence and outcomes, we investigated TNBC disease trends across the African continent and in Barbados (a Caribbean island with predominantly African ancestry) and found a high proportion of TNBC diagnoses in Barbados and West African countries. We also discovered a novel genetic profile within these groups that may be useful to develop new cancer therapies that would decrease TNBC aggressiveness and death in these populations.

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Functional analyses have gained more interest as we have easier access to massive data sets. However, such data sets often contain large heterogeneities, noise, and dimensionalities. When generalizing the analyses from vectors to functions, classical methods might not work directly. This dissertation considers noisy information reduction in functional analyses from two perspectives: functional variable selection to reduce the dimensionality and functional clustering to group similar observations and thus reduce the sample size. The complicated data structures and relations can be easily modeled by a Bayesian hierarchical model, or developed from a more generic one by changing the prior distributions. Hence, this dissertation focuses on the development of Bayesian approaches for functional analyses due to their flexibilities. A nonparametric Bayesian approach, such as the Dirichlet process mixture (DPM) model, has a nonparametric distribution as the prior. This approach provides flexibility and reduces assumptions, especially for functional clustering, because the DPM model has an automatic clustering property, so the number of clusters does not need to be specified in advance. Furthermore, a weighted Dirichlet process mixture (WDPM) model allows for more heterogeneities from the data by assuming more than one unknown prior distribution. It also gathers more information from the data by introducing a weight function that assigns different candidate priors, such that the less similar observations are more separated. Thus, the WDPM model will improve the clustering and model estimation results. In this dissertation, we used an advanced nonparametric Bayesian approach to study functional variable selection and functional clustering methods. We proposed 1) a stochastic search functional selection method with application to 1-M matched case-crossover studies for aseptic meningitis, to examine the time-varying unknown relationship and find out important covariates affecting disease contractions; 2) a functional clustering method via the WDPM model, with application to three pathways related to genetic diabetes data, to identify essential genes distinguishing between normal and disease groups; and 3) a combined functional clustering, with the WDPM model, and variable selection approach with application to high-frequency spectral data, to select wavelengths associated with breast cancer racial disparities. / Doctor of Philosophy / As we have easier access to massive data sets, functional analyses have gained more interest to analyze data providing information about curves, surfaces, or others varying over a continuum. However, such data sets often contain large heterogeneities and noise. When generalizing the analyses from vectors to functions, classical methods might not work directly. This dissertation considers noisy information reduction in functional analyses from two perspectives: functional variable selection to reduce the dimensionality and functional clustering to group similar observations and thus reduce the sample size. The complicated data structures and relations can be easily modeled by a Bayesian hierarchical model due to its flexibility. Hence, this dissertation focuses on the development of nonparametric Bayesian approaches for functional analyses. Our proposed methods can be applied in various applications: the epidemiological studies on aseptic meningitis with clustered binary data, the genetic diabetes data, and breast cancer racial disparities.

The disproportionate burden of covid-19 pandemic on racialized groups in developed countries has made socio-political and socio-economic inequalities even more apparent. This thesis utilizes critical race thoery (CRT), framing theory and the ”What’s the ’problem’ represented to be?”-approach to conduct a critical analysis of how the representation of the underlying causes of racial disparities in covid-19 mortality framed by the Swedish Public Health Agency. The published report on migrants and covid-19 ”Migrants and COVID-19 – Confirmed cases, ICU-cases and mortality from 13 March 2020 to 15 February 2021 among foreign-born in Sweden” is analyzed through qualitative content analysis. In the report, the Swedish Public Health Agency analyzes underlying causes to differences of covid-19 outcome based on country of birth, which suggests that the population born in other countries is affected by the covid-19 more than the population born in Sweden. The content analysis of the official document on foreign-borns and covid-19 mortality, released by the Swedish Public Health agency, suggests that the agency has represented the underlying causes of racial disparities in covid-19 mortality in Sweden with a socio-economic inequality frame, and from a CRT perspective, the representation is guided by colorblind ideology that does not problematize the role of racism in the society. The knowledge produced in this thesis aims to contribute to the field of CRT studies in Sweden with empirical knowledge about problematization of the covid-19 pandemic outcomes in Sweden

Indiana University-Purdue University Indianapolis (IUPUI) / With over 40 years of research on the well-documented issue of racial disparities in school discipline, scholars have begun to explore a plethora of plausible causalities for this phenomenon. Recent literature on the causal agents have centered on cultural differences and/or racial prejudices held by educators. Building from this emerging logic, this dissertation specifically focused on the disposition (e.g. enduring traits, character type, mentality, and temperament) of educators and its influence, if any, on discipline-related outcomes. Additionally, this exploratory study sought to build a conceptual map for future research to explore how educators' dispositions may act as conduits between systemic racism and the historic racial disparities in discipline-related outcomes. Through an intensive, multiyear embedded case study of four middle schools with both high and low rates of racial disproportionality in school discipline and with the creation and use of the Four Domains, this dissertation explored whether discipline-related outcomes are the result of systemic racism mediated by educators' dispositions. Findings from the analysis suggested the existence of shared characteristics among the dispositions of those categorized as high and low referring. Specific to those findings, trends within low referring teachers suggested that low referring teachers maintain high and consistent expectations of student behavior, but allowed for flexibility in how their discipline response was mediated out among their students. Despite a deferred approach within discipline response, low referring teachers were consistent and did not show favoritism. On the contrary, high referring teachers were inconsistent with their responses and demonstrated biases in actions and beliefs. Accordingly, it was found that high referring teachers held racially deficit beliefs about Black students and their families. Additionally, high referring teachers were more represented by the Four Domains in comparison to lower referring teachers. As a result, findings from the Four Domains support the existence of a causal link among systemic racism, higher referring teachers, and racial disparities in school discipline. In particular, it was found that classroom teachers engage in and hold racially deficit views of Blacks and these same teachers disproportionately refer Black students for out-of-school suspension.