The Normative Dimensions of Health Disparities

Understanding what conditions must be satisfied for a health inequality to be a health inequity (disparity) is crucial for health policy makers. The failure to understand what constitutes a health inequity, and confusing health inequalities with health inequities threatens the successful creation of health policies by diverting needed attention and resources away from addressing health inequalities that are health inequities. More generally, the failure threatens to undercut our ability to tell what research is relevant to the creation of health policies that aim to mitigate or eliminate health inequities. With this in mind, the principal aim of the present paper is to provide a framework within which to understand the relationships of concepts such as health difference, health inequality and health inequity to one another. Under the umbrella heading of “health disparities”, which is often used as a catch-all expression to refer to various, sometimes very different concepts of health, health outcomes and health determinants, the paper draws attention to two important axes in this framework; the axis of health inequalities (the empirical dimensions) and the axis of health inequities (the normative dimensions). Using the writings of John Dewey on valuation and value judgments, the paper explores how it is possible for a claim about the existence, prevalence or scope of health disparities to have both an empirical dimension and a normative dimension

Using Genome and Transcriptome Data From African-Ancestry Female Participants To Identify Putative Breast Cancer Susceptibility Genes

African-ancestry (AA) participants are underrepresented in genetics research. Here, we conducted a transcriptome-wide association study (TWAS) in AA female participants to identify putative breast cancer susceptibility genes. We built genetic models to predict levels of gene expression, exon junction, and 3\u27 UTR alternative polyadenylation using genomic and transcriptomic data generated in normal breast tissues from 150 AA participants and then used these models to perform association analyses using genomic data from 18,034 cases and 22,104 controls. At Bonferroni-corrected P \u3c 0.05, we identified six genes associated with breast cancer risk, including four genes not previously reported (CTD-3080P12.3, EN1, LINC01956 and NUP210L). Most of these genes showed a stronger association with risk of estrogen-receptor (ER) negative or triple-negative than ER-positive breast cancer. We also replicated the associations with 29 genes reported in previous TWAS at P \u3c 0.05 (one-sided), providing further support for an association of these genes with breast cancer risk. Our study sheds new light on the genetic basis of breast cancer and highlights the value of conducting research in AA populations