Issues With Variability in Electronic Health Record Data About Race and Ethnicity: Descriptive Analysis of the National COVID Cohort Collaborative Data Enclave

Background:The adverse impact of COVID-19 on marginalized and under-resourced communities of color has highlighted the need for accurate, comprehensive race and ethnicity data. However, a significant technical challenge related to integrating race and ethnicity data in large, consolidated databases is the lack of consistency in how data about race and ethnicity are collected and structured by health care organizations. Objective:This study aims to evaluate and describe variations in how health care systems collect and report information about the race and ethnicity of their patients and to assess how well these data are integrated when aggregated into a large clinical database. Methods:At the time of our analysis, the National COVID Cohort Collaborative (N3C) Data Enclave contained records from 6.5 million patients contributed by 56 health care institutions. We quantified the variability in the harmonized race and ethnicity data in the N3C Data Enclave by analyzing the conformance to health care standards for such data. We conducted a descriptive analysis by comparing the harmonized data available for research purposes in the database to the original source data contributed by health care institutions. To make the comparison, we tabulated the original source codes, enumerating how many patients had been reported with each encoded value and how many distinct ways each category was reported. The nonconforming data were also cross tabulated by 3 factors: patient ethnicity, the number of data partners using each code, and which data models utilized those particular encodings. For the nonconforming data, we used an inductive approach to sort the source encodings into categories. For example, values such as “Declined” were grouped with “Refused,” and “Multiple Race” was grouped with “Two or more races” and “Multiracial.” Results:“No matching concept” was the second largest harmonized concept used by the N3C to describe the race of patients in their database. In addition, 20.7% of the race data did not conform to the standard; the largest category was data that were missing. Hispanic or Latino patients were overrepresented in the nonconforming racial data, and data from American Indian or Alaska Native patients were obscured. Although only a small proportion of the source data had not been mapped to the correct concepts (0.6%), Black or African American and Hispanic/Latino patients were overrepresented in this category. Conclusions:Differences in how race and ethnicity data are conceptualized and encoded by health care institutions can affect the quality of the data in aggregated clinical databases. The impact of data quality issues in the N3C Data Enclave was not equal across all races and ethnicities, which has the potential to introduce bias in analyses and conclusions drawn from these data. Transparency about how data have been transformed can help users make accurate analyses and inferences and eventually better guide clinical care and public policy

Issues with variability in electronic health record data about race and ethnicity: Descriptive analysis of the National COVID Cohort Collaborative Data Enclave

BACKGROUND: The adverse impact of COVID-19 on marginalized and under-resourced communities of color has highlighted the need for accurate, comprehensive race and ethnicity data. However, a significant technical challenge related to integrating race and ethnicity data in large, consolidated databases is the lack of consistency in how data about race and ethnicity are collected and structured by health care organizations. OBJECTIVE: This study aims to evaluate and describe variations in how health care systems collect and report information about the race and ethnicity of their patients and to assess how well these data are integrated when aggregated into a large clinical database. METHODS: At the time of our analysis, the National COVID Cohort Collaborative (N3C) Data Enclave contained records from 6.5 million patients contributed by 56 health care institutions. We quantified the variability in the harmonized race and ethnicity data in the N3C Data Enclave by analyzing the conformance to health care standards for such data. We conducted a descriptive analysis by comparing the harmonized data available for research purposes in the database to the original source data contributed by health care institutions. To make the comparison, we tabulated the original source codes, enumerating how many patients had been reported with each encoded value and how many distinct ways each category was reported. The nonconforming data were also cross tabulated by 3 factors: patient ethnicity, the number of data partners using each code, and which data models utilized those particular encodings. For the nonconforming data, we used an inductive approach to sort the source encodings into categories. For example, values such as Declined were grouped with Refused, and Multiple Race was grouped with Two or more races and Multiracial. RESULTS: No matching concept was the second largest harmonized concept used by the N3C to describe the race of patients in their database. In addition, 20.7% of the race data did not conform to the standard; the largest category was data that were missing. Hispanic or Latino patients were overrepresented in the nonconforming racial data, and data from American Indian or Alaska Native patients were obscured. Although only a small proportion of the source data had not been mapped to the correct concepts (0.6%), Black or African American and Hispanic/Latino patients were overrepresented in this category. CONCLUSIONS: Differences in how race and ethnicity data are conceptualized and encoded by health care institutions can affect the quality of the data in aggregated clinical databases. The impact of data quality issues in the N3C Data Enclave was not equal across all races and ethnicities, which has the potential to introduce bias in analyses and conclusions drawn from these data. Transparency about how data have been transformed can help users make accurate analyses and inferences and eventually better guide clinical care and public policy

Collaboration With Deaf Communities to Conduct Accessible Health Surveillance

Introduction Populations of deaf sign language users experience health disparities unmeasured by current public health surveillance. Population-specific health data are necessary to collaboratively identify health priorities and evaluate interventions. Standardized, reproducible, and language-concordant data collection in sign language is impossible via written or telephone surveys. Methods Deaf and hearing researchers, community members, and other stakeholders developed a broad computer-based health survey based on the telephone-administered Behavioral Risk Factor Surveillance System. They translated survey items from English to sign language, evaluated the translations, and filmed the survey items for inclusion in their custom software. They initiated the second Rochester Deaf Health Survey in 2013 (n=211). Analyses (conducted in 2015) compared Rochester Deaf Health Survey 2013 findings with those of the Behavioral Risk Factor Surveillance System with the general adult population in the same community (2012, n=1,816). Results The Rochester Deaf Health Survey 2013 participants’ mean age was 44.7 (range, 18—87) years. Most were deaf since birth or early childhood (87.1%) and highly educated (53.6% with ≥4 years of college). The median household income was \u3c $35,000. The prevalence of current smokers was low (8.1%). Nearly all (93.8%) reported having health insurance, yet barriers to appropriate health care were evident, with high emergency department use (16.2% with two or more past-year visits) and 22.7% forgoing needed health care in the past year because of cost. Conclusions Community-engaged research with deaf populations identifies strengths and priorities, providing essential information otherwise missing from existing public health surveillance, and forming a foundation for collaborative dissemination to facilitate broader inclusion of deaf communities

FE65 as a link between VLDLR and APP to regulate their trafficking and processing

<p>Abstract</p> <p>Background</p> <p>Several studies found that FE65, a cytoplasmic adaptor protein, interacts with APP and LRP1, altering the trafficking and processing of APP. We have previously shown that FE65 interacts with the ApoE receptor, ApoER2, altering its trafficking and processing. Interestingly, it has been shown that FE65 can act as a linker between APP and LRP1 or ApoER2. In the present study, we tested whether FE65 can interact with another ApoE receptor, VLDLR, thereby altering its trafficking and processing, and whether FE65 can serve as a linker between APP and VLDLR.</p> <p>Results</p> <p>We found that FE65 interacted with VLDLR using GST pull-down and co-immunoprecipitation assays in COS7 cells and in brain lysates. This interaction occurs via the PTB1 domain of FE65. Co-transfection with FE65 and full length VLDLR increased secreted VLDLR (sVLDLR); however, the levels of VLDLR C-terminal fragment (CTF) were undetectable as a result of proteasomal degradation. Additionally, FE65 increased cell surface levels of VLDLR. Moreover, we identified a novel complex between VLDLR and APP, which altered trafficking and processing of both proteins. Furthermore, immunoprecipitation results demonstrated that the presence of FE65 increased the interaction between APP and VLDLR <it>in vitro </it>and <it>in vivo</it>.</p> <p>Conclusions</p> <p>These data suggest that FE65 can regulate VLDLR trafficking and processing. Additionally, the interaction between VLDLR and APP altered both protein's trafficking and processing. Finally, our data suggest that FE65 serves as a link between VLDLR and APP. This novel interaction adds to a growing body of literature indicating trimeric complexes with various ApoE Receptors and APP.</p

Combining sequence-based prediction methods and circular dichroism and infrared spectroscopic data to improve protein secondary structure determinations

<p>Abstract</p> <p>Background</p> <p>A number of sequence-based methods exist for protein secondary structure prediction. Protein secondary structures can also be determined experimentally from circular dichroism, and infrared spectroscopic data using empirical analysis methods. It has been proposed that comparable accuracy can be obtained from sequence-based predictions as from these biophysical measurements. Here we have examined the secondary structure determination accuracies of sequence prediction methods with the empirically determined values from the spectroscopic data on datasets of proteins for which both crystal structures and spectroscopic data are available.</p> <p>Results</p> <p>In this study we show that the sequence prediction methods have accuracies nearly comparable to those of spectroscopic methods. However, we also demonstrate that combining the spectroscopic and sequences techniques produces significant overall improvements in secondary structure determinations. In addition, combining the extra information content available from synchrotron radiation circular dichroism data with sequence methods also shows improvements.</p> <p>Conclusion</p> <p>Combining sequence prediction with experimentally determined spectroscopic methods for protein secondary structure content significantly enhances the accuracy of the overall results obtained.</p

Sympatric woodland Myotis bats form tight-knit social groups with exclusive roost home ranges

Background: The structuring of wild animal populations can influence population dynamics, disease spread, and information transfer. Social network analysis potentially offers insights into these processes but is rarely, if ever, used to investigate more than one species in a community. We therefore compared the social, temporal and spatial networks of sympatric Myotis bats (M. nattereri (Natterer's bats) and M. daubentonii (Daubenton's bats)), and asked: (1) are there long-lasting social associations within species? (2) do the ranges occupied by roosting social groups overlap within or between species? (3) are M. daubentonii bachelor colonies excluded from roosting in areas used by maternity groups? Results: Using data on 490 ringed M. nattereri and 978 M. daubentonii from 379 colonies, we found that both species formed stable social groups encompassing multiple colonies. M. nattereri formed 11 mixed-sex social groups with few (4.3%) inter-group associations. Approximately half of all M. nattereri were associated with the same individuals when recaptured, with many associations being long-term (>100 days). In contrast, M. daubentonii were sexually segregated; only a quarter of pairs were associated at recapture after a few days, and inter-sex associations were not long-lasting. Social groups of M. nattereri and female M. daubentonii had small roost home ranges (mean 0.2 km2 in each case). Intra-specific overlap was low, but inter-specific overlap was high, suggesting territoriality within but not between species. M. daubentonii bachelor colonies did not appear to be excluded from roosting areas used by females. Conclusions: Our data suggest marked species- and sex-specific patterns of disease and information transmission are likely between bats of the same genus despite sharing a common habitat. The clear partitioning of the woodland amongst social groups, and their apparent reliance on small patches of habitat for roosting, means that localised woodland management may be more important to bat conservation than previously recognised

Impacts of Hurricanes Katrina and Rita on the microbial landscape of the New Orleans area

Author Posting. © The Author(s), 2007. This is the author's version of the work. It is posted here by permission of National Academy of Sciences of the USA for personal use, not for redistribution. The definitive version was published in Proceedings of the National Academy of Sciences 104 (2007): 9029-9034, doi:10.1073/pnas.0610552104.Floodwaters in New Orleans from Hurricanes Katrina and Rita were observed to contain high levels of fecal indicator bacteria and microbial pathogens, generating concern about long-term impacts of these floodwaters on the sediment and water quality of the New Orleans area and Lake Pontchartrain. We show here that fecal indicator microbe concentrations in offshore waters from Lake Pontchartrain returned to prehurricane concentrations within 2 months of the flooding induced by these hurricanes. Vibrio and Legionella species within the lake were more abundant in samples collected shortly after the floodwaters had receded compared with samples taken within the subsequent 3 months; no evidence of a long-term hurricane-induced algal bloom was observed. Giardia and Cryptosporidium were detected in canal waters. Elevated levels of fecal indicator bacteria observed in sediment could not be solely attributed to impacts from floodwaters, as both flooded and nonflooded areas exhibited elevated levels of fecal indicator bacteria. Evidence from measurements of Bifidobacterium and bacterial diversity analysis suggest that the fecal indicator bacteria observed in the sediment were from human fecal sources. Epidemiologic studies are highly recommended to evaluate the human health effects of the sediments deposited by the floodwaters.This work was funded by NSF-NIEHS Oceans and Human Health Program (NSF OCE0432368, OCE0432479, OCE0430724 and NIEHS P50 ES12736, ES012740, ES012742), the NSF-SGER Program (OCE 0554402, OCE 0554674, OCE 0554850, OCE0600130), the NSF-REU Program, and by the Georgia Sea Grant College Program (NA04OAR170033)

Horizontal transfer of an adaptive chimeric photoreceptor from bryophytes to ferns

Ferns are well known for their shade-dwelling habits. Their ability to thrive under low-light conditions has been linked to the evolution of a novel chimeric photoreceptor-neochrome-that fuses red-sensing phytochrome and blue-sensing phototropin modules into a single gene, thereby optimizing phototropic responses. Despite being implicated in facilitating the diversification of modern ferns, the origin of neochrome has remained a mystery. We present evidence for neochrome in hornworts (a bryophyte lineage) and demonstrate that ferns acquired neochrome from hornworts via horizontal gene transfer (HGT). Fern neochromes are nested within hornwort neochromes in our large-scale phylogenetic reconstructions of phototropin and phytochrome gene families. Divergence date estimates further support the HGT hypothesis, with fern and hornwort neochromes diverging 179 Mya, long after the split between the two plant lineages (at least 400 Mya). By analyzing the draft genome of the hornwort Anthoceros punctatus, we also discovered a previously unidentified phototropin gene that likely represents the ancestral lineage of the neochrome phototropin module. Thus, a neochrome originating in hornworts was transferred horizontally to ferns, where it may have played a significant role in the diversification of modern ferns