Pregnancy and Emergency Department Utilization in North Carolina, 2016-2021: A Population-Based Surveillance Study.

Introduction: Pregnancy-associated complaints are a common reason for emergency department visits for women of reproductive age. Emergency department utilization during pregnancy is associated with worse birth outcomes for both mothers and infants. We used statewide North Carolina emergency department surveillance data between 2016 and 2021 to describe the sociodemographic factors associated with the use of emergency department for pregnancy-associated problems and subsequent hospital admission. Methods: North Carolina Disease Event Tracking and Epidemiologic Collection Tool is a syndromic surveillance system that includes all emergency department encounters at civilian acute-care facilities in North Carolina. We analyzed all emergency department visits between January 1, 2016 and December 31, 2021 for female patients aged 15-44 years residing in North Carolina with at least 1 ICD-10-CM code (analysis occurred in July 2021-October 2022). Each emergency department visit was categorized as pregnancy-associated if assigned ICD-10-CM code(s) indicated pregnancy. We stratified visits by age, race, ethnicity, county of residence, and insurance and compared them with estimated pregnant population proportions using 1-sample t-tests. We used multivariable logistic regression to determine whether pregnancy-associated visits were more likely to be associated with hospital admission and then to determine sociodemographic predictors of admission among pregnancy-associated emergency department visits. Results: More than 6.4 million emergency department visits were included (N=6,471,197); 10.1% (n=655,476) were pregnancy-associated, significantly higher than the proportion of women estimated to be pregnant at any given time in North Carolina (4.6%, p<0.0001) and increased over time (8.6% in 2016 vs 11.1% in 2021, p<0.0001). Pregnancy-associated visits were lower than expected for ages 25-44 years and higher than expected for those aged 15-24 years, for those of Black race, and for patients residing in rural or suburban areas. The proportion admitted was higher for pregnancy-associated emergency department visits than for nonpregnancy associated (15.6% vs 7.0%, AOR=3.06 [95% CI=3.03, 3.09]). Pregnancy-associated emergency department visits for patients of Black race had 0.58 times (95% CI=0.57, 0.59) the odds of admission compared with White patients. Conclusions: Emergency department utilization during pregnancy is common. The proportion of pregnancy-associated emergency department visits among reproductive-age women is increasing, as are inpatient admissions from the emergency department for pregnancy-associated diagnoses. Use of public health surveillance databases such as the North Carolina Disease Event Tracking and Epidemiologic Collection Tool may help identify opportunities for improving disparities in maternal health care, especially related to access to care.

SCDC: bulk gene expression deconvolution by multiple single-cell RNA sequencing references.

Recent advances in single-cell RNA sequencing (scRNA-seq) enable characterization of transcriptomic profiles with single-cell resolution and circumvent averaging artifacts associated with traditional bulk RNA sequencing (RNA-seq) data. Here, we propose SCDC, a deconvolution method for bulk RNA-seq that leverages cell-type specific gene expression profiles from multiple scRNA-seq reference datasets. SCDC adopts an ENSEMBLE method to integrate deconvolution results from different scRNA-seq datasets that are produced in different laboratories and at different times, implicitly addressing the problem of batch-effect confounding. SCDC is benchmarked against existing methods using both in silico generated pseudo-bulk samples and experimentally mixed cell lines, whose known cell-type compositions serve as ground truths. We show that SCDC outperforms existing methods with improved accuracy of cell-type decomposition under both settings. To illustrate how the ENSEMBLE framework performs in complex tissues under different scenarios, we further apply our method to a human pancreatic islet dataset and a mouse mammary gland dataset. SCDC returns results that are more consistent with experimental designs and that reproduce more significant associations between cell-type proportions and measured phenotypes.

Destin: toolkit for single-cell analysis of chromatin accessibility.

SUMMARY: Single-cell assay of transposase-accessible chromatin followed by sequencing (scATAC-seq) is an emerging new technology for the study of gene regulation with single-cell resolution. The data from scATAC-seq are unique-sparse, binary and highly variable even within the same cell type. As such, neither methods developed for bulk ATAC-seq nor single-cell RNA-seq data are appropriate. Here, we present Destin, a bioinformatic and statistical framework for comprehensive scATAC-seq data analysis. Destin performs cell-type clustering via weighted principle component analysis, weighting accessible chromatin regions by existing genomic annotations and publicly available regulomic datasets. The weights and additional tuning parameters are determined via model-based likelihood. We evaluated the performance of Destin using downsampled bulk ATAC-seq data of purified samples and scATAC-seq data from seven diverse experiments. Compared to existing methods, Destin was shown to outperform across all datasets and platforms. For demonstration, we further applied Destin to 2088 adult mouse forebrain cells and identified cell-type-specific association of previously reported schizophrenia GWAS loci. AVAILABILITY AND IMPLEMENTATION: Destin toolkit is freely available as an R package at https://github.com/urrutiag/destin. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

CODEX2: full-spectrum copy number variation detection by high-throughput DNA sequencing.

High-throughput DNA sequencing enables detection of copy number variations (CNVs) on the genome-wide scale with finer resolution compared to array-based methods but suffers from biases and artifacts that lead to false discoveries and low sensitivity. We describe CODEX2, as a statistical framework for full-spectrum CNV profiling that is sensitive for variants with both common and rare population frequencies and that is applicable to study designs with and without negative control samples. We demonstrate and evaluate CODEX2 on whole-exome and targeted sequencing data, where biases are the most prominent. CODEX2 outperforms existing methods and, in particular, significantly improves sensitivity for common CNVs.

Integrative pipeline for profiling DNA copy number and inferring tumor phylogeny.

Summary: Copy number variation is an important and abundant source of variation in the human genome, which has been associated with a number of diseases, especially cancer. Massively parallel next-generation sequencing allows copy number profiling with fine resolution. Such efforts, however, have met with mixed successes, with setbacks arising partly from the lack of reliable analytical methods to meet the diverse and unique challenges arising from the myriad experimental designs and study goals in genetic studies. In cancer genomics, detection of somatic copy number changes and profiling of allele-specific copy number (ASCN) are complicated by experimental biases and artifacts as well as normal cell contamination and cancer subclone admixture. Furthermore, careful statistical modeling is warranted to reconstruct tumor phylogeny by both somatic ASCN changes and single nucleotide variants. Here we describe a flexible computational pipeline, MARATHON, which integrates multiple related statistical software for copy number profiling and downstream analyses in disease genetic studies. Availability and implementation: MARATHON is publicly available at https://github.com/yuchaojiang/MARATHON. Supplementary information: Supplementary data are available at Bioinformatics online.

Rare variant testing across methods and thresholds using the multi-kernel sequence kernel association test (MK-SKAT).

Analysis of rare genetic variants has focused on region-based analysis wherein a subset of the variants within a genomic region is tested for association with a complex trait. Two important practical challenges have emerged. First, it is difficult to choose which test to use. Second, it is unclear which group of variants within a region should be tested. Both depend on the unknown true state of nature. Therefore, we develop the Multi-Kernel SKAT (MK-SKAT) which tests across a range of rare variant tests and groupings. Specifically, we demonstrate that several popular rare variant tests are special cases of the sequence kernel association test which compares pair-wise similarity in trait value to similarity in the rare variant genotypes between subjects as measured through a kernel function. Choosing a particular test is equivalent to choosing a kernel. Similarly, choosing which group of variants to test also reduces to choosing a kernel. Thus, MK-SKAT uses perturbation to test across a range of kernels. Simulations and real data analyses show that our framework controls type I error while maintaining high power across settings: MK-SKAT loses power when compared to the kernel for a particular scenario but has much greater power than poor choices.

Salivary proteins associated with hyperglycemia in diabetes: a proteomic analysis.

Effective monitoring of glucose levels is necessary for patients to achieve greater control over their diabetes. However, only about a quarter of subjects with diabetes who requires close serum glucose monitoring, regularly check their serum glucose daily. One of the potential barriers to patient compliance is the blood sampling requirement. Saliva and its protein contents can be altered in subjects with diabetes, possibly due to changes in glycemic control. We propose here that salivary proteomes of subjects with diabetes may be different based on their glycemic control as reflected in A1C levels. A total of 153 subjects with type 1 or 2 diabetes were recruited. Subjects in each type of diabetes were divided into 5 groups based on their A1C levels; <7, 7-8, 8-9, 9-10, >10. To examine the global proteomic changes associated with A1C, the proteomic profiling of pooled saliva samples from each group was created using label-free quantitative proteomics. Similar proteomic analysis for individual subjects (N=4, for each group) were then applied to examine proteins that may be less abundant in pooled samples. Principle component analysis (PCA) and cluster analysis (p<0.01 and p<0.001) were used to define the proteomic differences. We, therefore, defined the salivary proteomic changes associated with A1C changes. This study demonstrates that differences exist between salivary proteomic profiles in subjects with diabetes based on the A1C levels.

Unmet health needs identified by Haitian women as priorities for attention: a qualitative study.

This 2009 qualitative study investigated Haitian women's most pressing health needs, barriers to meeting those needs and proposed solutions, and how they thought the community and outside organizations should be involved in addressing their needs. The impetus for the study was to get community input into the development of a Family Health Centre in Leogane, Haiti. Individual interviews and focus group discussions were conducted with 52 adult women in six communities surrounding Leogane. The most pressing health needs named by the women were accessible, available and affordable health care, potable water, enough food to eat, improved economy, employment, sanitation and education, including health education. Institutional corruption, lack of infrastructure and social organization, the cost of health care, distance from services and lack of transport as barriers to care were also important themes. The involvement of foreign organizations and local community groups, including grassroots women's groups who would work in the best interests of other women, were identified as the most effective solutions. Organizations seeking to improve women's health care in Haiti should develop services and interventions that prioritize community partnership and leadership, foster partnerships with government, and focus on public health needs.