Impact of the Affordable Health Care Act (ACA) on Racial/Ethnic Inequities in Access to and Utilization of Healthcare in New York City.

The Affordable Care Act (ACA) expanded health insurance coverage in the USA, but whether it increased healthcare utilization or reduced racial/ethnic inequities in access to and utilization of care is unclear. We evaluated the ACA impact on health insurance coverage, unmet medical need, and having a personal doctor and whether this impact was modified by racial/ethnic identity among New York City (NYC) residents. We used data from multiple years of the Community Health Survey (2009-2017) and used logistic regression to assess whether having health insurance, unmet medical need, or a personal doctor varied pre- (2009-2012) versus post-ACA (2013-2017), adjusting for age, sex, nativity status, and general health. We assessed effect measure modification by race/ethnicity and stratified if we found significant interaction. We found that health insurance coverage and having a personal doctor increased post-ACA (aOR = 1.44, p < 0.001 and aOR = 1.09, p = 0.024, respectively) while having unmet medical need decreased (aOR = 0.90, p = 0.004). There was little indication of interaction between ACA and race/ethnicity; in stratified models, the ACA had a stronger impact on health insurance coverage for those of other race than all other groups (aOR = 2.16, p = 0.002 versus aOR 1.22-1.54 for white, Black, and Hispanic adults) and a stronger impact on having a personal doctor for Hispanic adults (aOR 1.27, p < 0.001 versus weaker non-significant associations for other groups), with no effect modification for unmet medical need. Thus, it appears that ACA improved healthcare access and utilization but did not have a major impact on reducing racial/ethnic inequities in these outcomes in NYC.

BugSigDB captures patterns of differential abundance across a broad range of host-associated microbial signatures.

The literature of human and other host-associated microbiome studies is expanding rapidly, but systematic comparisons among published results of host-associated microbiome signatures of differential abundance remain difficult. We present BugSigDB, a community-editable database of manually curated microbial signatures from published differential abundance studies accompanied by information on study geography, health outcomes, host body site and experimental, epidemiological and statistical methods using controlled vocabulary. The initial release of the database contains >2,500 manually curated signatures from >600 published studies on three host species, enabling high-throughput analysis of signature similarity, taxon enrichment, co-occurrence and coexclusion and consensus signatures. These data allow assessment of microbiome differential abundance within and across experimental conditions, environments or body sites. Database-wide analysis reveals experimental conditions with the highest level of consistency in signatures reported by independent studies and identifies commonalities among disease-associated signatures, including frequent introgression of oral pathobionts into the gut.

Reporting guidelines for human microbiome research: the STORMS checklist.

The particularly interdisciplinary nature of human microbiome research makes the organization and reporting of results spanning epidemiology, biology, bioinformatics, translational medicine and statistics a challenge. Commonly used reporting guidelines for observational or genetic epidemiology studies lack key features specific to microbiome studies. Therefore, a multidisciplinary group of microbiome epidemiology researchers adapted guidelines for observational and genetic studies to culture-independent human microbiome studies, and also developed new reporting elements for laboratory, bioinformatics and statistical analyses tailored to microbiome studies. The resulting tool, called 'Strengthening The Organization and Reporting of Microbiome Studies' (STORMS), is composed of a 17-item checklist organized into six sections that correspond to the typical sections of a scientific publication, presented as an editable table for inclusion in supplementary materials. The STORMS checklist provides guidance for concise and complete reporting of microbiome studies that will facilitate manuscript preparation, peer review, and reader comprehension of publications and comparative analysis of published results.