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

Identification of Conserved and HLA Promiscuous DENV3 T-Cell Epitopes

Anti-dengue T-cell responses have been implicated in both protection and immunopathology. However, most of the T-cell studies for dengue include few epitopes, with limited knowledge of their inter-serotype variation and the breadth of their human leukocyte antigen (HLA) affinity. In order to expand our knowledge of HLA-restricted dengue epitopes, we screened T-cell responses against 477 overlapping peptides derived from structural and non-structural proteins of the dengue virus serotype 3 (DENV3) by use of HLA class I and II transgenic mice (TgM): A2, A24, B7, DR2, DR3 and DR4. TgM were inoculated with peptides pools and the T-cell immunogenic peptides were identified by ELISPOT. Nine HLA class I and 97 HLA class II novel DENV3 epitopes were identified based on immunogenicity in TgM and their HLA affinity was further confirmed by binding assays analysis. A subset of these epitopes activated memory T-cells from DENV3 immune volunteers and was also capable of priming naïve T-cells, ex vivo, from dengue IgG negative individuals. Analysis of inter- and intra-serotype variation of such an epitope (A02-restricted) allowed us to identify altered peptide ligands not only in DENV3 but also in other DENV serotypes. These studies also characterized the HLA promiscuity of 23 HLA class II epitopes bearing highly conserved sequences, six of which could bind to more than 10 different HLA molecules representing a large percentage of the global population. These epitope data are invaluable to investigate the role of T-cells in dengue immunity/pathogenesis and vaccine design. © 2013 Nascimento et al

An Event-Related Potential Examination of Contour Integration Deficits in Schizophrenia

Perceptual organization, which refers to the ability to integrate fragments of stimuli to form a representation of a whole edge, part, or object, is impaired in schizophrenia. A contour integration paradigm, involving detection of a set of Gabor patches forming an oval contour pointing to the right or left embedded in a field of randomly oriented Gabors, has been developed for use in clinical trials of schizophrenia. The purpose of the present study was to assess contributions of early and later stages of processing to deficits in contour integration, as well as to develop an event-related potential (ERP) analog of this task. Twenty-one patients with schizophrenia and 28 controls participated. The Gabor elements forming the contours were given a low or high degree of orientational jitter, making it either easy or difficult to identify the direction in which the contour was pointing. ERP results showed greater negative peaks at ~165 (N1 component) and ~270 ms for the low-jitter versus the high-jitter contours, with a much greater difference between jitter conditions at 270 ms. This later ERP component was previously termed Ncl for closure negativity. Source localization identified the Ncl in the lateral occipital object recognition area. Patients showed a significant decrease in the Ncl, but not N1, compared to controls, and this was associated with impaired behavioral ability to identify contours. In addition, an earlier negative peak was found at ~120 ms (termed N120) that differentiated jitter conditions, had a dorsal stream source, and differed between patients and controls. Patients also showed a deficit in the dorsal stream sensory P1 component. These results are in accord with impairments in distributed circuitry contributing to perceptual organization deficits and provide an ERP analog to the behavioral contour integration task

Transparent Reporting of Data Quality in Distributed Data Networks

Introduction: Poor data quality can be a serious threat to the validity and generalizability of clinical research findings. The growing availability of electronic administrative and clinical data is accompanied by a growing concern about the quality of these data for observational research and other analytic purposes. Currently, there are no widely accepted guidelines for reporting quality results that would enable investigators and consumers to independently determine if a data source is fit for use to support analytic inferences and reliable evidence generation. Model and Methods: We developed a conceptual model that captures the flow of data from data originator across successive data stewards and finally to the data consumer. This “data lifecycle” model illustrates how data quality issues can result in data being returned back to previous data custodians. We highlight the potential risks of poor data quality on clinical practice and research results. Because of the need to ensure transparent reporting of a data quality issues, we created a unifying data-quality reporting framework and a complementary set of 20 data-quality reporting recommendations for studies that use observational clinical and administrative data for secondary data analysis. We obtained stakeholder input on the perceived value of each recommendation by soliciting public comments via two face-to-face meetings of informatics and comparative-effectiveness investigators, through multiple public webinars targeted to the health services research community, and with an open access online wiki. Recommendations: Our recommendations propose reporting on both general and analysis-specific data quality features. The goals of these recommendations are to improve the reporting of data quality measures for studies that use observational clinical and administrative data, to ensure transparency and consistency in computing data quality measures, and to facilitate best practices and trust in the new clinical discoveries based on secondary use of observational data