ABSTRACT
Current maternal care recommendations in the United States focus on monitoring fetal development, management of pregnancy complications, and screening for behavioral health concerns. Often missing from these recommendations is support for patients experiencing socioeconomic or behavioral health challenges during pregnancy. A Pregnancy Medical Home (PMH) is a multidisciplinary maternal health care team with nurse navigators serving as patient advocates to improve the quality of care a patient receives and health outcomes for both mother and infant. Using bivariate comparisons between PMH patients and reference groups, as well as interviews with project team members and PMH graduates, this evaluation assessed the impact of a PMH at an academic medical university on patient care and birth outcomes. This PMH increased depression screenings during pregnancy and increased referrals to behavioral health care. This evaluation did not find improvements in maternal or infant birth outcomes. Interviews found notable successes and areas for program enhancement.
Subject(s)
Maternal Health Services , Patient-Centered Care , Quality Improvement , Humans , Pregnancy , Female , Patient-Centered Care/organization & administration , Quality Improvement/organization & administration , Maternal Health Services/standards , Maternal Health Services/organization & administration , Adult , Quality of Health Care/organization & administration , Pregnancy Outcome , United States , Patient Care Team/organization & administration , Pregnancy Complications/therapyABSTRACT
Publicly available genetic summary data have high utility in research and the clinic, including prioritizing putative causal variants, polygenic scoring, and leveraging common controls. However, summarizing individual-level data can mask population structure, resulting in confounding, reduced power, and incorrect prioritization of putative causal variants. This limits the utility of publicly available data, especially for understudied or admixed populations where additional research and resources are most needed. Although several methods exist to estimate ancestry in individual-level data, methods to estimate ancestry proportions in summary data are lacking. Here, we present Summix, a method to efficiently deconvolute ancestry and provide ancestry-adjusted allele frequencies (AFs) from summary data. Using continental reference ancestry, African (AFR), non-Finnish European (EUR), East Asian (EAS), Indigenous American (IAM), South Asian (SAS), we obtain accurate and precise estimates (within 0.1%) for all simulation scenarios. We apply Summix to gnomAD v.2.1 exome and genome groups and subgroups, finding heterogeneous continental ancestry for several groups, including African/African American (â¼84% AFR, â¼14% EUR) and American/Latinx (â¼4% AFR, â¼5% EAS, â¼43% EUR, â¼46% IAM). Compared to the unadjusted gnomAD AFs, Summix's ancestry-adjusted AFs more closely match respective African and Latinx reference samples. Even on modern, dense panels of summary statistics, Summix yields results in seconds, allowing for estimation of confidence intervals via block bootstrap. Given an accompanying R package, Summix increases the utility and equity of public genetic resources, empowering novel research opportunities.
