Racial Disparities in Health Care Use in Gentrifying Neighborhoods.

Objective: Racial disparities in health outcomes are a persistent threat in gentrifying neighborhoods. A contributor to health outcomes is health services utilization, the extent to which people receive care from a medical professional. There are documented racial disparities in health services utilization in the general population. We aim to determine whether racial disparities in health services utilization exist in gentrifying neighborhoods. Methods: We used data from the American Community Survey to identify gentrifying neighborhoods across the United States from 2006 to 2017. We collected data on three measures of healthcare services utilization (office-based physician visits, office-based nonphysician visits, and having a usual source of care) for 247 Black and 689 White non-Hispanic respondents of the 2014 Medical Expenditure Panel Survey living in gentrifying neighborhoods. We used modified Poisson models to determine whether there is a difference in the prevalence of health services utilization by race among residents of gentrifying neighborhoods. Results: After adjusting for age, gender, education, income, employment, insurance, marital status, region, and self-rated health, Black residents of gentrifying neighborhoods demonstrated a similar prevalence of having an office-based physician visit, a lower prevalence of having an office-based nonphysician visit (prevalence ratio: 0.74; 95% confidence interval, 0.60 to 0.91), and a lower prevalence of having a usual source of care (prevalence ratio: 0.87; 95% confidence interval, 0.77 to 0.98) than White residents. Conclusions: The existence of racial disparities in health services utilization in US gentrifying neighborhoods demonstrates a need for policy-relevant solutions to create a more equitable distribution of health resources.

The EstroGene Database Reveals Diverse Temporal, Context-Dependent, and Bidirectional Estrogen Receptor Regulomes in Breast Cancer.

As one of the most successful cancer therapeutic targets, estrogen receptor-α (ER/ESR1) has been extensively studied over the past few decades. Sequencing technological advances have enabled genome-wide analysis of ER action. However, comparison of individual studies is limited by different experimental designs, and few meta-analyses are available. Here, we established the EstroGene database through unified processing of data from 246 experiments including 136 transcriptomic, cistromic, and epigenetic datasets focusing on estradiol (E2)-triggered ER activation across 19 breast cancer cell lines. A user-friendly browser (https://estrogene.org/) was generated for multiomic data visualization involving gene inquiry under user-defined experimental conditions and statistical thresholds. Notably, annotation of metadata associated with public datasets revealed a considerable lack of experimental details. Comparison of independent RNA-seq or ER ChIP-seq data with the same design showed large variability and only strong effects could be consistently detected. Temporal estrogen response metasignatures were defined, and the association of E2 response rate with temporal transcriptional factors, chromatin accessibility, and heterogeneity of ER expression was evaluated. Unexpectedly, harmonizing 146 E2-induced transcriptomic datasets uncovered a subset of genes harboring bidirectional E2 regulation, which was linked to unique transcriptional factors and highly associated with immune surveillance in the clinical setting. Furthermore, the context dependent E2 response programs were characterized in MCF7 and T47D cell lines, the two most frequently used models in the EstroGene database. Collectively, the EstroGene database provides an informative and practical resource to the cancer research community to uniformly evaluate key reproducible features of ER regulomes and unravels modes of ER signaling. SIGNIFICANCE: A resource database integrating 246 publicly available ER profiling datasets facilitates meta-analyses and identifies estrogen response temporal signatures, a bidirectional program, and model-specific biases.

EstroGene database reveals diverse temporal, context-dependent and directional estrogen receptor regulomes in breast cancer.

As one of the most successful cancer therapeutic targets, estrogen receptor-α (ER/ESR1) has been extensively studied in decade-long. Sequencing technological advances have enabled genome-wide analysis of ER action. However, reproducibility is limited by different experimental design. Here, we established the EstroGene database through centralizing 246 experiments from 136 transcriptomic, cistromic and epigenetic datasets focusing on estradiol-treated ER activation across 19 breast cancer cell lines. We generated a user-friendly browser ( https://estrogene.org/ ) for data visualization and gene inquiry under user-defined experimental conditions and statistical thresholds. Notably, documentation-based meta-analysis revealed a considerable lack of experimental details. Comparison of independent RNA-seq or ER ChIP-seq data with the same design showed large variability and only strong effects could be consistently detected. We defined temporal estrogen response metasignatures and showed the association with specific transcriptional factors, chromatin accessibility and ER heterogeneity. Unexpectedly, harmonizing 146 transcriptomic analyses uncovered a subset of E2-bidirectionally regulated genes, which linked to immune surveillance in the clinical setting. Furthermore, we defined context dependent E2 response programs in MCF7 and T47D cell lines, the two most frequently used models in the field. Collectively, the EstroGene database provides an informative resource to the cancer research community and reveals a diverse mode of ER signaling.