Publications

There are significant racial and ethnic inequities in neonatal care. These inequities are the result of the multiple levels of racism: structural, institutional, interpersonal, and internalized racism. Recently, there has been significantly increased attention to measuring and addressing inequities for minoritized infants and their families. One way to support these strategies is to clarify the bioethical justification for allocating clinical, research and educational resources to address inequity. In this narrative review, we describe two bioethical frameworks-the four pillars and the ethics of care-as a foundation to justify equity-focused research, investigation, and interventions. We focus on these two frameworks with specific attention to their application to micro- and macro-level racism. Finally, we conclude with a case example to illustrate how to employ bioethical frameworks in neonatal health equity efforts. Through this narrative review, we demonstrate that pursing neonatal health equity is, in fact, a bioethical imperative.

BACKGROUND: Preterm birth (PTB) is associated with adverse short- and long-term health. There are known racial, ethnic, and socioeconomic inequities in PTB. Because of historical de jure and modern-day de facto segregation and neighborhood divestment, neighborhoods are a source of structural racism and disenfranchisement and a potential target for policies and interventions to reduce PTB inequity. However, the role of neighborhoods on PTB, specifically among high-risk birthing people, is largely unexplored.

METHODS: The Boston Birth Cohort is a longitudinal birth cohort of birthing people-infant dyads at a safety-net hospital in Boston, MA between 2000 and 2018. The primary outcome was preterm birth at 35 weeks or prior. The primary predictor was neighborhood defined by census tract. We used generalized linear mixed effects models to test our hypothesis that neighborhood accounts for a signficiant proportion of PTB risk among socially at-risk birthing people.

RESULTS: In multilevel models, neighborhoods were a significant predictor of preterm birth, yet accounted for only 3% of the variability in outcome. In models stratified by race, individual-level factors such as prior preterm birth, nativity status, and advanced birthing person age were significant predictors of PTB.

CONCLUSIONS: Neighborhood is a significant, though small, predictor of preterm birth in a high-risk birthing population. These findings suggest that individual-level interventions, rather than neighborhood-level policies, may be more effective in reducing preterm birth among high-risk birthing populations.

The promise of genomic medicine lies in the opportunity to improve health outcomes via a personalized approach to management, grounded in genetic and genomic variation unique to an individual. However, disparities and inequities mar this remarkable landscape of genomic innovation. Prior efforts to understand these inequities have focused on populations for which genetic testing is relatively protocolized or where test utility varies greatly by ancestry groups, where equitable outcomes are more clearly defined. We therefore consider the current landscape of rare disease genomics, in which diagnostic approaches vary widely and utility remains to be fully understood, and suggest a path forward: how ecosocial theory may be used to guide novel equity-focused initiatives that incorporate illness narratives to improve population health. We present examples of narrative medicine in rare disease and reimagine the role this discipline may play in genomic sequencing studies, toward incorporation of the unique illness narrative into clinical genetics and genomics practice. Approaches that broaden the definitions of disease and of outcomes of interest will force the field to grapple with its racist history and begin to advance health equity and promote justice so that genomic medicine may truly deliver on its promise.

CONTEXT: Infant race and ethnicity are used ubiquitously in research and reporting, though inconsistent approaches to data collection and definitions yield variable results. The consistency of these data has an impact on reported findings and outcomes.

OBJECTIVE: To systematically review and examine concordance among differing race and ethnicity data collection techniques presented in perinatal health care literature.

DATA SOURCES: PubMed, CINAHL, and Ovid were searched on June 17, 2021.

STUDY SELECTION: English language articles published between 1980 and 2021 were included if they reported on the United States' infant population and compared 2 or more methods of capturing race and/or ethnicity.

DATA EXTRACTION: Two authors independently evaluated articles for inclusion and quality, with disagreements resolved by a third reviewer.

RESULTS: Our initial search identified 4329 unique citations. Forty articles passed title/abstract review and were reviewed in full text. Nineteen were considered relevant and assessed for quality and bias, from which 12 studies were ultimately included. Discordance in infant race and ethnicity data were common among multiple data collection methods, including those frequently used in perinatal health outcomes research. Infants of color and those born to racially and/or ethnically discordant parents were the most likely to be misclassified across data sources.

LIMITATIONS: Studies were heterogeneous in methodology and populations of study and data could not be compiled for analysis.

CONCLUSIONS: Racial and ethnic misclassification of infants leads to inaccurate measurement and reporting of infant morbidity and mortality, often underestimating burden in minoritized populations while overestimating it in the non-Hispanic/Latinx white population.

BACKGROUND AND OBJECTIVES: High-risk infant follow-up programs (HRIFs) are a recommended standard of care for all extremely low birth weight (ELBW) infants to help mitigate known risks to long-term health and development. However, participation is variable, with known racial and ethnic inequities, though hospital-level drivers of inequity remain unknown. We conducted a study using a large, multicenter cohort of ELBW infants to explore within- and between-hospital inequities in HRIF participation.

METHODS: Vermont Oxford Network collected data on 19 503 ELBW infants born between 2006 and 2017 at 58 US hospitals participating in the ELBW Follow-up Project. Primary outcome was evaluation in HRIF at 18 to 24 months' corrected age. The primary predictor was infant race and ethnicity, defined as maternal race (non-Hispanic white, non-Hispanic Black, Hispanic, Asian American, Native American, other). We used generalized linear mixed models to test within- and between-hospital variation and inequities in HRIF participation.

RESULTS: Among the 19 503 infants, 44.7% (interquartile range 31.1-63.3) were seen in HRIF. Twenty six percent of the total variation in HRIF participation rates was due to between-hospital variation. In adjusted models, Black infants had significantly lower odds of HRIF participation compared with white infants (adjusted odds ratio, 0.73; 95% confidence interval, 0.64-0.83). The within-hospital effect of race varied significantly between hospitals.

CONCLUSIONS: There are significant racial inequities in HRIF participation, with notable variation within and between hospitals. Further study is needed to identify potential hospital-level targets for interventions to reduce this inequity.

BACKGROUND: Identifying a precise genetic diagnosis can improve outcomes for individuals with rare disease, though the resources required to do so may impede access and exacerbate healthcare disparities leading to inequitable care. Our objective was therefore to determine the effect of multiple sociodemographic factors on the yield of the diagnostic evaluation for genetics outpatients.

METHODS: This is a retrospective cohort study from 2017 to 2019 of outpatient genetics referrals at a pediatric academic tertiary care center. Exposures included: primary language, insurance type, and neighborhood resources (via the Childhood Opportunity Index, COI). The primary outcome was identification of a genetic diagnosis within 2 years of the initial clinic visit.

RESULTS: COI quintile was not significantly associated with the odds of diagnosis but was significantly associated with clinic attendance, with lower neighborhood resources leading to incomplete referrals. Limited English proficiency was associated with a higher odds of diagnosis, though at an older age. Public insurance was associated with increased access to genetic testing.

CONCLUSIONS: Lower neighborhood resources are negatively associated with clinic attendance. Our findings further suggest delays in care and a referral bias for more severe phenotypes among families with limited English proficiency. Improved access to clinical genetics is needed to improve diagnostic equity.

IMPACT: The resources required to identify a genetic diagnosis may impede access and exacerbate healthcare disparities leading to inequitable care. In an analysis of pediatric outpatient genetics referrals, we observed a significant association between neighborhood resources and clinic attendance but not diagnostic yield for those attending, and a higher diagnostic yield for families with limited English proficiency, suggesting referral bias for more severe phenotypes. Thus, the primary barrier to finding a genetic diagnosis was initiation of care, not the ensuing diagnostic odyssey. Further research efforts should be directed at increasing access to clinical genetics evaluations for children with rare disease.

BACKGROUND: Inequity in neonatology may be potentiated within neonatal intensive care units (NICUs) by the effects of bias. Addressing bias can lead to improved, more equitable care. Understanding perceptions of bias can inform targeted interventions to reduce the impact of bias. We conducted a mixed methods study to characterize the perceptions of bias among NICU staff.

METHODS: Surveys were distributed to all staff (N = 245) in a single academic Level IV NICU. Respondents rated the impact of bias on their own and others' behaviors on 5-point Likert scales and answered one open-ended question. Kruskal-Wallis test (KWT) and Levene's test were used for quantitative analysis and thematic analysis was used for qualitative analysis.

RESULTS: We received 178 responses. More respondents agreed that bias had a greater impact on others' vs. their own behaviors (KWT p < 0.05). Respondents agreed that behaviors were influenced more by implicit than explicit biases (KWT p < 0.05). Qualitative analysis resulted in nine unique themes.

CONCLUSIONS: Staff perceive a high impact of bias across different domains with increased perceived impact of implicit vs. explicit bias. Staff perceive a greater impact of others' biases vs. their own. Mixed methods studies can help identify unique, unit-responsive approaches to reduce bias.

IMPACT: Healthcare staff have awareness of bias and its impact on their behaviors with patients, families, and staff. Healthcare staff believe that implicit bias impacts their behaviors more than explicit bias, and that they have less bias than others. Healthcare staff have ideas for strategies and approaches to mitigate the impact of bias. Mixed method studies are effective ways of understanding environment-specific perceptions of bias, and contextual assets and barriers when creating interventions to reduce bias and improve equity. Generating interventions to reduce the impact of bias in healthcare requires a context-specific understanding of perceptions of bias among staff.

PURPOSE: Advances in genomic research have led to the diagnosis of rare, early-onset diseases for thousands of individuals. Unfortunately, the benefits of advanced genetic diagnostic technology are not distributed equitably among the population, as has been seen in many other healthcare contexts. Even quantifying and describing inequities in genetic diagnostic yield is challenging due to variation in referrals to clinical genetics practices and other barriers to clinical genetic testing.

METHODS: The Rare Genomes Project (RGP) at the Broad Institute of MIT and Harvard offers research genome sequencing to individuals with rare disease who remain genetically undiagnosed through direct interaction with the individual or family. This presents an opportunity for diagnosis beyond the clinical context, thus eliminating many barriers to access.

FINDINGS: An initial goal of RGP was to equalize access to genomic sequencing by decoupling testing access from proximity to a major medical center and physician referral. However, our study participants are overwhelmingly non-disadvantaged, as evidenced by their access to specialist care and genetic testing prior to RGP enrollment, and are also predominantly white.

IMPLICATIONS: We therefore describe our novel initiative to diversify RGP enrollment in order to advance equity in rare disease genetic diagnosis and research. In addition to the moral imperative of medical equity, this is also critical in order to fully understand the genomic underpinnings of rare disease. We utilize a mixed methods approach to understand the priorities and values of underrepresented communities, existing disparities, and the obstacles to addressing them: all of which is necessary to promote equity in future genomic medicine initiatives.

Preterm birth is associated with an increased risk of neurodevelopmental and neurobehavioral impairments including attention-deficit/hyperactivity disorder (ADHD), the most common neurobehavioral disorder of childhood. In this narrative review, we examine the known associations between prematurity and ADHD and highlight the impact of both prematurity and ADHD on multiple domains across the pediatric life-course. We develop a framework for understanding the health services journey of individuals with ADHD to access appropriate services and treatments for ADHD, the "ADHD Care Cascade". We then discuss the many racial and ethnic inequities that affect the risk of preterm birth as well as the steps along the "ADHD Care Cascade". By using a life-course approach, we highlight the ways in which inequities are layered over time to magnify the neurodevelopmental impact of preterm birth on the most vulnerable children across the life-course.

PURPOSE: Advances in genomic research have facilitated rare disease diagnosis for thousands of individuals. Unfortunately, the benefits of advanced genetic diagnostic technology are not distributed equitably among the population, as has been seen in many other health care contexts. Quantifying and describing inequities in genetic diagnostic yield is inherently challenging due to barriers to both clinical and research genetic testing. We therefore present an implementation protocol developed to expand access to our rare disease genomic research study and to further understand existing inequities.

METHODS AND FINDINGS: The Rare Genomes Project (RGP) at the Broad Institute of MIT and Harvard offers research genome sequencing to individuals with rare disease who remain genetically undiagnosed through direct interaction with the individual or family. This presents an opportunity for diagnosis beyond the clinical context, thus eliminating many barriers to access. An initial goal of RGP was to equalize access to genomic sequencing by decoupling testing access from proximity to a major medical center and physician referral. However, study participants over the initial 3 years of this project were predominantly white and well resourced. To further understand and address the lack of diversity within RGP, we developed a novel protocol embedded within the larger RGP study, in an approach informed by an implementation science framework. The aims of this protocol were: (1) to diversify recruitment and enrollment within RGP; (2) understand the process and context of implementing genomic medicine for rare disease diagnosis; and (3) investigate the value of a diagnosis for underserved populations.

IMPLICATIONS: Improved understanding of existing inequities and potential strategies to address them are needed to advance equity in rare disease genetic diagnosis and research. In addition to the moral imperative of equity in genomic medicine, this approach is critical in order to fully understand the genomic underpinnings of rare disease.