Towards Equity in Health: Researchers Take Stock

Utilizing Implementation Science to Bridge Cerebrovascular Health Disparities: a Local to Global Perspective

PURPOSE OF REVIEW

Stroke is a prime example of a medical disorder whose incidence, prevalence, and outcomes are strongly characterized by health disparities across the globe. This scoping literature review seeks to depict how implementation science could be utilized to advance health equity in the prevention, acute treatment, and post-acute management of stroke in the underserved regions of high-income countries as well as in all low-income countries.

RECENT FINDINGS

A major reason for the persisting and widening cerebrovascular disease disparities is that evidence-based stroke prevention and treatment interventions have been differentially translated (if at all) to various populations and settings. The field of implementation science is endowed with frameworks, theories, methodological approaches, and outcome measures, including equity indices, which could be harnessed to facilitate the translation of evidence-based interventions into clinical practice for underserved and vulnerable communities. Encouragingly, there are several novel frameworks, which eminently merge implementation science constructs with health equity determinants, thereby opening up key opportunities to bridge burgeoning worldwide gaps in cerebrovascular health equity.

Closing the health equity gap: A role for implementation science?

Beryne Odeny discusses strategies to improve equity in health care and health research.

The Impact of Grantsmanship Self-Efficacy on Early Stage Investigators of The National Research Mentoring Network Steps Toward Academic Research (NRMN STAR)

The NRMN STAR program was created to address the persistent underrepresentation in grant submissions and receipt of National Institutes of Health (NIH) awards by racial/ethnic minority groups. In our current study, we assessed program impact on trainees' self-efficacy related to grant writing. The program was conducted with two cohorts: one in June 2014 and one in June 2015. We used a 19-item grant writing self-efficacy scale drawn from the 88-item Clinical Research Assessment Inventory of three domains (conceptualizing, designing, and funding a study) to predict whether self-efficacy influences researchers' grant submissions. Trainees were assessed prior to and following program completion with subsequent assessments at 6 and 12 months beyond participation. The majority of trainees were Black (62%), female (62%), and had obtained a PhD (90%). More than half (52%) were assistant professors and 57% had none or <1 year of research experience beyond postdoctoral training. However, 24% of trainees reported no postdoctoral research training. NRMN STAR trainees' self-efficacy significantly improved on all three domains exhibiting a 2.0-point mean change score on two domains (conceptualizing and design) and 3.7 point mean change score on the domain, funding a study. Findings suggest that NRMN's STAR provides impactful, confidence-building training for diverse, early stage investigators with little-to-no skills, experiences, or low self-efficacy in writing research grants.

The Use of Racial Categories in Precision Medicine Research

Scholars have shown that promoting diversity and inclusion in precision medicine research is important for ethical and scientific reasons. The processes for classifying the populations that enroll in biomedical research, however, are often unclear, inconsistent, and poorly justified. Precision medicine research promises increasingly meticulous approaches to defining research cohorts and assessing the multivariate factors at the root of racial health disparities. Insofar as precision medicine is promoted to members of historically underrepresented populations as a tool for illuminating these factors, the use of race-based classifications is fraught with risks for society and medicine. This article examines the drivers and limitations of the ongoing use of race by investigators juxtaposed with recent efforts to enroll underrepresented populations in precision medicine research.

PopCluster: an algorithm to identify genetic variants with ethnicity-dependent effects

MOTIVATION

Over the last decade, more diverse populations have been included in genome-wide association studies. If a genetic variant has a varying effect on a phenotype in different populations, genome-wide association studies applied to a dataset as a whole may not pinpoint such differences. It is especially important to be able to identify population-specific effects of genetic variants in studies that would eventually lead to development of diagnostic tests or drug discovery.

RESULTS

In this paper, we propose PopCluster: an algorithm to automatically discover subsets of individuals in which the genetic effects of a variant are statistically different. PopCluster provides a simple framework to directly analyze genotype data without prior knowledge of subjects' ethnicities. PopCluster combines logistic regression modeling, principal component analysis, hierarchical clustering and a recursive bottom-up tree parsing procedure. The evaluation of PopCluster suggests that the algorithm has a stable low false positive rate (∼4%) and high true positive rate (>80%) in simulations with large differences in allele frequencies between cases and controls. Application of PopCluster to data from genetic studies of longevity discovers ethnicity-dependent heterogeneity in the association of rs3764814 (USP42) with the phenotype.

AVAILABILITY AND IMPLEMENTATION

PopCluster was implemented using the R programming language, PLINK and Eigensoft software, and can be found at the following GitHub repository: https://github.com/gurinovich/PopCluster with instructions on its installation and usage.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

A genome-wide association and admixture mapping study of bronchodilator drug response in African Americans with asthma

Short-acting β₂-adrenergic receptor agonists (SABAs) are the most commonly prescribed asthma medications worldwide. Response to SABAs is measured as bronchodilator drug response (BDR), which varies among racial/ethnic groups in the U.S^{1, 2}. However, the genetic variation that contributes to BDR is largely undefined in African Americans with asthma³. To identify genetic variants that may contribute to differences in BDR in African Americans with asthma, we performed a genome-wide association study (GWAS) of BDR in 949 African American children with asthma, genotyped with the Axiom World Array 4 (Affymetrix, Santa Clara, CA) followed by imputation using 1000 Genomes phase III genotypes. We used linear regression models adjusting for age, sex, body mass index (BMI) and genetic ancestry to test for an association between BDR and genotype at single nucleotide polymorphisms (SNPs). To increase power and distinguish between shared vs. population-specific associations with BDR in children with asthma, we performed a meta-analysis across 949 African Americans and 1,830 Latinos (Total=2,779). Lastly, we performed genome-wide admixture mapping to identify regions whereby local African or European ancestry is associated with BDR in African Americans. We identified a population-specific association with an intergenic SNP on chromosome 9q21 that was significantly associated with BDR (rs73650726, p=7.69×10⁻⁹). A trans-ethnic meta-analysis across African Americans and Latinos identified three additional SNPs within the intron of PRKG1 that were significantly associated with BDR (rs7903366, rs7070958, and rs7081864, p≤5×10⁻⁸). Our results failed to replicate in three additional populations of 416 Latinos and 1,615 African Americans. Our findings indicate that both population specific and shared genetic variation contributes to differences in BDR in minority children with asthma, and that the genetic underpinnings of BDR may differ between racial/ethnic groups.

Diversity and inclusion in genomic research: why the uneven progress?

Conducting genomic research in diverse populations has led to numerous advances in our understanding of human history, biology, and health disparities, in addition to discoveries of vital clinical significance. Conducting genomic research in diverse populations is also important in ensuring that the genomic revolution does not exacerbate health disparities by facilitating discoveries that will disproportionately benefit well-represented populations. Despite the general agreement on the need for genomic research in diverse populations in terms of equity and scientific progress, genomic research remains largely focused on populations of European descent. In this article, we describe the rationale for conducting genomic research in diverse populations by reviewing examples of advances facilitated by their inclusion. We also explore some of the factors that perpetuate the disproportionate attention on well-represented populations. Finally, we discuss ongoing efforts to ameliorate this continuing bias. Collaborative and intensive efforts at all levels of research, from the funding of studies to the publication of their findings, will be necessary to ensure that genomic research does not conserve historical inequalities or curtail the contribution that genomics could make to the health of all humanity.