Evaluating Support for the Current Classification of Eukaryotic Diversity

Perspectives on the classification of eukaryotic diversity have changed rapidly in recent years, as the four eukaryotic groups within the five-kingdom classification—plants, animals, fungi, and protists—have been transformed through numerous permutations into the current system of six “supergroups.” The intent of the supergroup classification system is to unite microbial and macroscopic eukaryotes based on phylogenetic inference. This supergroup approach is increasing in popularity in the literature and is appearing in introductory biology textbooks. We evaluate the stability and support for the current six-supergroup classification of eukaryotes based on molecular genealogies. We assess three aspects of each supergroup: (1) the stability of its taxonomy, (2) the support for monophyly (single evolutionary origin) in molecular analyses targeting a supergroup, and (3) the support for monophyly when a supergroup is included as an out-group in phylogenetic studies targeting other taxa. Our analysis demonstrates that supergroup taxonomies are unstable and that support for groups varies tremendously, indicating that the current classification scheme of eukaryotes is likely premature. We highlight several trends contributing to the instability and discuss the requirements for establishing robust clades within the eukaryotic tree of life

Assessing social and behavioral data in electronic health records: Availability, Accuracy, and Applicability

Problem statement: There is an increased appreciation of the importance of social and behavioral determinants of health (SBDH) on health outcomes, but no standards for collecting this information in various clinical data sources. The increased use of electronic health records (EHRs) provides a unique opportunity to understand SBDH and its impact on health. This dissertation aims to understand perspectives of, and assess trends in SBDH data collection, and compare rates of SBDH ICD10 code documentation in an EHR and insurance claims. Method: A qualitative study was undertaken to understand the facilitators and barriers to accessing SBDH information in an EHR. Using data from 2017, a cross-sectional retrospective data analysis was performed in an EHR’s social history table. Logistic regressions were used to calculate odds ratios to identify factors associated with completion rates. The documentation of behavior related ICD10 codes within a linked EHR, and insurance claims was compared with information in the social history section of the EHR. Results: Providers and researchers felt that SBDH data captured in the EHR was inconsistent and unreliable. Health systems should prioritize capturing some SBDH in a consistent manner, but it is unclear which variables to select. Individuals who are black, female, and between the ages of 30-65 are more likely to have their behavior documented in the social history section. With the move to ICD10, a wider range of SBDH information can be coded in a patient’s EHR and claims record. At this study site, the overlap of codes across these two data systems is limited and thus a fuller picture of the patient’s situation can be obtained by merging both sources. Conclusion: It appears that SBDH data collection is not consistent at this site. To improve this, clearer guidelines on how to capture SBDH risk factors are needed. Since there is no widely accepted “gold standard”, information in the EHR and insurance claims vary, which makes it more challenging to effectively understand SBDH factors in order to assess and enhance health outcomes. Improving data collection and data reliability will allow providers and researchers alike to utilize digital data for both patient care and population health

Development of a Social Risk Score in the Electronic Health Record to Identify Social Needs Among Underserved Populations: Retrospective Study

BackgroundPatients with unmet social needs and social determinants of health (SDOH) challenges continue to face a disproportionate risk of increased prevalence of disease, health care use, higher health care costs, and worse outcomes. Some existing predictive models have used the available data on social needs and SDOH challenges to predict health-related social needs or the need for various social service referrals. Despite these one-off efforts, the work to date suggests that many technical and organizational challenges must be surmounted before SDOH-integrated solutions can be implemented on an ongoing, wide-scale basis within most US-based health care organizations. ObjectiveWe aimed to retrieve available information in the electronic health record (EHR) relevant to the identification of persons with social needs and to develop a social risk score for use within clinical practice to better identify patients at risk of having future social needs. MethodsWe conducted a retrospective study using EHR data (2016-2021) and data from the US Census American Community Survey. We developed a prospective model using current year-1 risk factors to predict future year-2 outcomes within four 2-year cohorts. Predictors of interest included demographics, previous health care use, comorbidity, previously identified social needs, and neighborhood characteristics as reflected by the area deprivation index. The outcome variable was a binary indicator reflecting the likelihood of the presence of a patient with social needs. We applied a generalized estimating equation approach, adjusting for patient-level risk factors, the possible effect of geographically clustered data, and the effect of multiple visits for each patient. ResultsThe study population of 1,852,228 patients included middle-aged (mean age range 53.76-55.95 years), White (range 324,279/510,770, 63.49% to 290,688/488,666, 64.79%), and female (range 314,741/510,770, 61.62% to 278,488/448,666, 62.07%) patients from neighborhoods with high socioeconomic status (mean area deprivation index percentile range 28.76-30.31). Between 8.28% (37,137/448,666) and 11.55% (52,037/450,426) of patients across the study cohorts had at least 1 social need documented in their EHR, with safety issues and economic challenges (ie, financial resource strain, employment, and food insecurity) being the most common documented social needs (87,152/1,852,228, 4.71% and 58,242/1,852,228, 3.14% of overall patients, respectively). The model had an area under the curve of 0.702 (95% CI 0.699-0.705) in predicting prospective social needs in the overall study population. Previous social needs (odds ratio 3.285, 95% CI 3.237-3.335) and emergency department visits (odds ratio 1.659, 95% CI 1.634-1.684) were the strongest predictors of future social needs. ConclusionsOur model provides an opportunity to make use of available EHR data to help identify patients with high social needs. Our proposed social risk score could help identify the subset of patients who would most benefit from further social needs screening and data collection to avoid potentially more burdensome primary data collection on all patients in a target population of interest

A State-wide Health IT Infrastructure for Population Health: Building a Community-wide Electronic Platform for Maryland’s All-Payer Global Budget

Maryland Department of Health (MDH) has been preparing for alignment of its population health initiatives with Maryland’s unique All-Payer hospital global budget program. In order to operationalize population health initiatives, it is required to identify a starter set of measures addressing community level health interventions and to collect interoperable data for those measures. The broad adoption of electronic health records (EHRs) with ongoing data collection on almost all patients in the state, combined with hospital participation in health information exchange (HIE) initiatives, provides an unprecedented opportunity for near real-time assessment of the health of the communities. MDH’s EHR-based monitoring complements, and perhaps replaces, ad-hoc assessments based on limited surveys, billing, and other administrative data. This article explores the potential expansion of health IT capacity as a method to improve population health across Maryland.First, we propose a progression plan for four selected community-wide population health measures: body mass index, blood pressure, smoking status, and falls-related injuries. We then present an assessment of the current and near real-time availability of digital data in Maryland including the geographic granularity on which each measure can be assessed statewide. Finally, we provide general recommendations to improve interoperable data collection for selected measures over time via the Maryland HIE. This paper is intended to serve as a high- level guiding framework for communities across the US that are undergoing healthcare transformation toward integrated models of care using universal interoperable EHRs

A State-wide Health IT Infrastructure for Population Health: Building a Community-wide Electronic Platform for Maryland’s All-Payer Global Budget

Maryland Department of Health (MDH) has been preparing for alignment of its population health initiatives with Maryland’s unique All-Payer hospital global budget program. In order to operationalize population health initiatives, it is required to identify a starter set of measures addressing community level health interventions and to collect interoperable data for those measures. The broad adoption of electronic health records (EHRs) with ongoing data collection on almost all patients in the state, combined with hospital participation in health information exchange (HIE) initiatives, provides an unprecedented opportunity for near real-time assessment of the health of the communities. MDH’s EHR-based monitoring complements, and perhaps replaces, ad-hoc assessments based on limited surveys, billing, and other administrative data. This article explores the potential expansion of health IT capacity as a method to improve population health across Maryland.First, we propose a progression plan for four selected community-wide population health measures: body mass index, blood pressure, smoking status, and falls-related injuries. We then present an assessment of the current and near real-time availability of digital data in Maryland including the geographic granularity on which each measure can be assessed statewide. Finally, we provide general recommendations to improve interoperable data collection for selected measures over time via the Maryland HIE. This paper is intended to serve as a high- level guiding framework for communities across the US that are undergoing healthcare transformation toward integrated models of care using universal interoperable EHRs

Trends in Supergroup Taxonomy

<p>A comparison of three formal classifications illustrates trends within (A) “Amoebozoa” [<a href="http://www.plosgenetics.org/article/info:doi/10.1371/journal.pgen.0020220#pgen-0020220-b007" target="_blank">7</a>,<a href="http://www.plosgenetics.org/article/info:doi/10.1371/journal.pgen.0020220#pgen-0020220-b045" target="_blank">45</a>,<a href="http://www.plosgenetics.org/article/info:doi/10.1371/journal.pgen.0020220#pgen-0020220-b047" target="_blank">47</a>]; (B) “Excavata” [<a href="http://www.plosgenetics.org/article/info:doi/10.1371/journal.pgen.0020220#pgen-0020220-b007" target="_blank">7</a>,<a href="http://www.plosgenetics.org/article/info:doi/10.1371/journal.pgen.0020220#pgen-0020220-b033" target="_blank">33</a>,<a href="http://www.plosgenetics.org/article/info:doi/10.1371/journal.pgen.0020220#pgen-0020220-b060" target="_blank">60</a>]; (C) “Plantae” [<a href="http://www.plosgenetics.org/article/info:doi/10.1371/journal.pgen.0020220#pgen-0020220-b002" target="_blank">2</a>,<a href="http://www.plosgenetics.org/article/info:doi/10.1371/journal.pgen.0020220#pgen-0020220-b006" target="_blank">6</a>,<a href="http://www.plosgenetics.org/article/info:doi/10.1371/journal.pgen.0020220#pgen-0020220-b007" target="_blank">7</a>]; and (D) “Rhizaria” [<a href="http://www.plosgenetics.org/article/info:doi/10.1371/journal.pgen.0020220#pgen-0020220-b006" target="_blank">6</a>,<a href="http://www.plosgenetics.org/article/info:doi/10.1371/journal.pgen.0020220#pgen-0020220-b007" target="_blank">7</a>,<a href="http://www.plosgenetics.org/article/info:doi/10.1371/journal.pgen.0020220#pgen-0020220-b033" target="_blank">33</a>]. A majority of solid, horizontal lines would indicate temporal stability of supergroup classification. For visual simplicity we do not indicate groups newly included in the supergroups or taxonomic restructuring within subgroups. Asterisk indicates a newly introduced term. “Chromalveolata” and “Opisthokonta” are not included because only one formal taxonomy exists for both groups. See <a href="http://www.plosgenetics.org/article/info:doi/10.1371/journal.pgen.0020220#pgen-0020220-g001" target="_blank">Figure 1</a> for further notes.</p

Support for Membership and Supergroup Monophyly from “Excavata”-Targeted Molecular Genealogies

<p>Member taxa: Di, Diplomonadida; Rt, Retortamonadida; Cp, <i>Carpediemonas</i>; Tr, <i>Trimastix</i>; Ox, Oxymonadida; Ht, Heterolobosea; Eu, Euglenozoa; Ml, <i>Malawimonas;</i> Jk, Jakobida; Pa, Parabasalia; Dy, <i>Diphylleia.</i> Hypothesized subgroups: Fornicata clade (Di + Rt + Cp) monophyletic, Preaxostyla clade (Ox + Tr) monophyletic, ♦ Discicristata clade (Ht + Eu) monophyletic. The position of <i>Diphylleia,</i> Dy, was not considered when scoring the monophyly of “Excavata” as the inclusion of this organism within “Excavata” is controversial and has been removed from recent classifications (see text). See <a href="http://www.plosgenetics.org/article/info:doi/10.1371/journal.pgen.0020220#pgen-0020220-g004" target="_blank">Figure 4</a> for further notes. References cited in this figure are [<a href="http://www.plosgenetics.org/article/info:doi/10.1371/journal.pgen.0020220#pgen-0020220-b033" target="_blank">33</a>,<a href="http://www.plosgenetics.org/article/info:doi/10.1371/journal.pgen.0020220#pgen-0020220-b040" target="_blank">40</a>,<a href="http://www.plosgenetics.org/article/info:doi/10.1371/journal.pgen.0020220#pgen-0020220-b049" target="_blank">49</a>,<a href="http://www.plosgenetics.org/article/info:doi/10.1371/journal.pgen.0020220#pgen-0020220-b060" target="_blank">60</a>,<a href="http://www.plosgenetics.org/article/info:doi/10.1371/journal.pgen.0020220#pgen-0020220-b115" target="_blank">115</a>,<a href="http://www.plosgenetics.org/article/info:doi/10.1371/journal.pgen.0020220#pgen-0020220-b123" target="_blank">123</a>–<a href="http://www.plosgenetics.org/article/info:doi/10.1371/journal.pgen.0020220#pgen-0020220-b128" target="_blank">128</a>].</p

Support for Membership and Supergroup Monophyly from “Plantae”-Targeted Molecular Genealogies

<p>Member taxa: Gr, Chloroplastida = Viridiplantae (Green algae, including land plants); Rd, Rhodophyceae (Red algae); Gl, Glaucophyta. See <a href="http://www.plosgenetics.org/article/info:doi/10.1371/journal.pgen.0020220#pgen-0020220-g004" target="_blank">Figure 4</a> for general notes and <a href="http://www.plosgenetics.org/article/info:doi/10.1371/journal.pgen.0020220#pgen-0020220-g005" target="_blank">Figure 5</a> for plastid-specific notes. References cited in this figure are [<a href="http://www.plosgenetics.org/article/info:doi/10.1371/journal.pgen.0020220#pgen-0020220-b033" target="_blank">33</a>,<a href="http://www.plosgenetics.org/article/info:doi/10.1371/journal.pgen.0020220#pgen-0020220-b049" target="_blank">49</a>,<a href="http://www.plosgenetics.org/article/info:doi/10.1371/journal.pgen.0020220#pgen-0020220-b053" target="_blank">53</a>,<a href="http://www.plosgenetics.org/article/info:doi/10.1371/journal.pgen.0020220#pgen-0020220-b054" target="_blank">54</a>,<a href="http://www.plosgenetics.org/article/info:doi/10.1371/journal.pgen.0020220#pgen-0020220-b115" target="_blank">115</a>,<a href="http://www.plosgenetics.org/article/info:doi/10.1371/journal.pgen.0020220#pgen-0020220-b133" target="_blank">133</a>–<a href="http://www.plosgenetics.org/article/info:doi/10.1371/journal.pgen.0020220#pgen-0020220-b139" target="_blank">139</a>].</p

Support for Membership and Supergroup Monophyly from “Chromalveolata”-Targeted Molecular Genealogies

<div><p>Member taxa: Al, Alveolata; St, Stramenopiles (Heterokonts); Ha, Haptophyta; Cr, Cryptophyceae. Monophyletic “Plantae” from plastid genealogies includes secondarily derived plastids. See <a href="http://www.plosgenetics.org/article/info:doi/10.1371/journal.pgen.0020220#pgen-0020220-g004" target="_blank">Figure 4</a> for further notes. References cited in this figure are [<a href="http://www.plosgenetics.org/article/info:doi/10.1371/journal.pgen.0020220#pgen-0020220-b033" target="_blank">33</a>,<a href="http://www.plosgenetics.org/article/info:doi/10.1371/journal.pgen.0020220#pgen-0020220-b049" target="_blank">49</a>,<a href="http://www.plosgenetics.org/article/info:doi/10.1371/journal.pgen.0020220#pgen-0020220-b055" target="_blank">55</a>,<a href="http://www.plosgenetics.org/article/info:doi/10.1371/journal.pgen.0020220#pgen-0020220-b056" target="_blank">56</a>,<a href="http://www.plosgenetics.org/article/info:doi/10.1371/journal.pgen.0020220#pgen-0020220-b107" target="_blank">107</a>,<a href="http://www.plosgenetics.org/article/info:doi/10.1371/journal.pgen.0020220#pgen-0020220-b108" target="_blank">108</a>,<a href="http://www.plosgenetics.org/article/info:doi/10.1371/journal.pgen.0020220#pgen-0020220-b115" target="_blank">115</a>–<a href="http://www.plosgenetics.org/article/info:doi/10.1371/journal.pgen.0020220#pgen-0020220-b122" target="_blank">122</a>].</p><p>Loc, location (genome) from which the gene of interest originated; Pla, plastid genome; Nuc, nuclear genome; Mit, mitochondrial genome.</p></div