Racial Disparities in Diabetes Care and Outcomes for Patients with Visual Impairment: A Descriptive Analysis of the TriNetX Research Network.

Background: This research delves into the confluence of racial disparities and health inequities among individuals with disabilities, with a focus on those contending with both diabetes and visual impairment. Methods: Utilizing data from the TriNetX Research Network, which includes electronic medical records of roughly 115 million patients from 83 anonymous healthcare organizations, this study employs a directed acyclic graph (DAG) to pinpoint confounders and augment interpretation. We identified patients with visual impairments using ICD-10 codes, deliberately excluding diabetes-related ophthalmology complications. Our approach involved multiple race-stratified analyses, comparing co-morbidities like chronic pulmonary disease in visually impaired patients against their counterparts. We assessed healthcare access disparities by examining the frequency of annual visits, instances of two or more A1c measurements, and glomerular filtration rate (GFR) measurements. Additionally, we evaluated diabetes outcomes by comparing the risk ratio of uncontrolled diabetes (A1c > 9.0) and chronic kidney disease in patients with and without visual impairments. Results: The incidence of diabetes was substantially higher (nearly double) in individuals with visual impairments across White, Asian, and African American populations. Higher rates of chronic kidney disease were observed in visually impaired individuals, with a risk ratio of 1.79 for African American, 2.27 for White, and non-significant for the Asian group. A statistically significant difference in the risk ratio for uncontrolled diabetes was found only in the White cohort (0.843). White individuals without visual impairments were more likely to receive two A1c tests, a trend not significant in other racial groups. African Americans with visual impairments had a higher rate of glomerular filtration rate testing. However, White individuals with visual impairments were less likely to undergo GFR testing, indicating a disparity in kidney health monitoring. This pattern of disparity was not observed in the Asian cohort. Conclusions: This study uncovers pronounced disparities in diabetes incidence and management among individuals with visual impairments, particularly among White, Asian, and African American groups. Our DAG analysis illuminates the intricate interplay between SDoH, healthcare access, and frequency of crucial diabetes monitoring practices, highlighting visual impairment as both a medical and social issue.

Racial differences in healthcare expenditures for prevalent multimorbidity combinations in the USA: a cross-sectional study.

BACKGROUND: We aimed to model total charges for the most prevalent multimorbidity combinations in the USA and assess model accuracy across Asian/Pacific Islander, African American, Biracial, Caucasian, Hispanic, and Native American populations. METHODS: We used Cerner HealthFacts data from 2016 to 2017 to model the cost of previously identified prevalent multimorbidity combinations among 38 major diagnostic categories for cohorts stratified by age (45-64 and 65 +). Examples of prevalent multimorbidity combinations include lipedema with hypertension or hypertension with diabetes. We applied generalized linear models (GLM) with gamma distribution and log link function to total charges for all cohorts and assessed model accuracy using residual analysis. In addition to 38 major diagnostic categories, our adjusted model incorporated demographic, BMI, hospital, and census division information. RESULTS: The mean ages were 55 (45-64 cohort, N = 333,094) and 75 (65 + cohort, N = 327,260), respectively. We found actual total charges to be highest for African Americans (means $78,544 [45-64], $176,274 [65 +]) and lowest for Hispanics (means $29,597 [45-64], $66,911 [65 +]). African American race was strongly predictive of higher costs (p < 0.05 [45-64]; p < 0.05 [65 +]). Each total charge model had a good fit. With African American as the index race, only Asian/Pacific Islander and Biracial were non-significant in the 45-64 cohort and Biracial in the 65 + cohort. Mean residuals were lowest for Hispanics in both cohorts, highest in African Americans for the 45-64 cohort, and highest in Caucasians for the 65 + cohort. Model accuracy varied substantially by race when multimorbidity grouping was considered. For example, costs were markedly overestimated for 65 + Caucasians with multimorbidity combinations that included heart disease (e.g., hypertension + heart disease and lipidemia + hypertension + heart disease). Additionally, model residuals varied by age/obesity status. For instance, model estimates for Hispanic patients were highly underestimated for most multimorbidity combinations in the 65 + with obesity cohort compared with other age/obesity status groupings. CONCLUSIONS: Our finding demonstrates the need for more robust models to ensure the healthcare system can better serve all populations. Future cost modeling efforts will likely benefit from factoring in multimorbidity type stratified by race/ethnicity and age/obesity status.

Risk factors associated with post-acute sequelae of SARS-CoV-2: an N3C and NIH RECOVER study.

BACKGROUND: More than one-third of individuals experience post-acute sequelae of SARS-CoV-2 infection (PASC, which includes long-COVID). The objective is to identify risk factors associated with PASC/long-COVID diagnosis. METHODS: This was a retrospective case-control study including 31 health systems in the United States from the National COVID Cohort Collaborative (N3C). 8,325 individuals with PASC (defined by the presence of the International Classification of Diseases, version 10 code U09.9 or a long-COVID clinic visit) matched to 41,625 controls within the same health system and COVID index date within ± 45 days of the corresponding case's earliest COVID index date. Measurements of risk factors included demographics, comorbidities, treatment and acute characteristics related to COVID-19. Multivariable logistic regression, random forest, and XGBoost were used to determine the associations between risk factors and PASC. RESULTS: Among 8,325 individuals with PASC, the majority were > 50 years of age (56.6%), female (62.8%), and non-Hispanic White (68.6%). In logistic regression, middle-age categories (40 to 69 years; OR ranging from 2.32 to 2.58), female sex (OR 1.4, 95% CI 1.33-1.48), hospitalization associated with COVID-19 (OR 3.8, 95% CI 3.05-4.73), long (8-30 days, OR 1.69, 95% CI 1.31-2.17) or extended hospital stay (30 + days, OR 3.38, 95% CI 2.45-4.67), receipt of mechanical ventilation (OR 1.44, 95% CI 1.18-1.74), and several comorbidities including depression (OR 1.50, 95% CI 1.40-1.60), chronic lung disease (OR 1.63, 95% CI 1.53-1.74), and obesity (OR 1.23, 95% CI 1.16-1.3) were associated with increased likelihood of PASC diagnosis or care at a long-COVID clinic. Characteristics associated with a lower likelihood of PASC diagnosis or care at a long-COVID clinic included younger age (18 to 29 years), male sex, non-Hispanic Black race, and comorbidities such as substance abuse, cardiomyopathy, psychosis, and dementia. More doctors per capita in the county of residence was associated with an increased likelihood of PASC diagnosis or care at a long-COVID clinic. Our findings were consistent in sensitivity analyses using a variety of analytic techniques and approaches to select controls. CONCLUSIONS: This national study identified important risk factors for PASC diagnosis such as middle age, severe COVID-19 disease, and specific comorbidities. Further clinical and epidemiological research is needed to better understand underlying mechanisms and the potential role of vaccines and therapeutics in altering PASC course.

Effect of Nirmatrelvir/Ritonavir (Paxlovid) on Hospitalization among Adults with COVID-19: an EHR-based Target Trial Emulation from N3C.

This study leverages electronic health record data in the National COVID Cohort Collaborative's (N3C) repository to investigate disparities in Paxlovid treatment and to emulate a target trial assessing its effectiveness in reducing COVID-19 hospitalization rates. From an eligible population of 632,822 COVID-19 patients seen at 33 clinical sites across the United States between December 23, 2021 and December 31, 2022, patients were matched across observed treatment groups, yielding an analytical sample of 410,642 patients. We estimate a 65% reduced odds of hospitalization among Paxlovid-treated patients within a 28-day follow-up period, and this effect did not vary by patient vaccination status. Notably, we observe disparities in Paxlovid treatment, with lower rates among Black and Hispanic or Latino patients, and within socially vulnerable communities. Ours is the largest study of Paxlovid's real-world effectiveness to date, and our primary findings are consistent with previous randomized control trials and real-world studies.

Coding long COVID: characterizing a new disease through an ICD-10 lens.

BACKGROUND: Naming a newly discovered disease is a difficult process; in the context of the COVID-19 pandemic and the existence of post-acute sequelae of SARS-CoV-2 infection (PASC), which includes long COVID, it has proven especially challenging. Disease definitions and assignment of a diagnosis code are often asynchronous and iterative. The clinical definition and our understanding of the underlying mechanisms of long COVID are still in flux, and the deployment of an ICD-10-CM code for long COVID in the USA took nearly 2 years after patients had begun to describe their condition. Here, we leverage the largest publicly available HIPAA-limited dataset about patients with COVID-19 in the US to examine the heterogeneity of adoption and use of U09.9, the ICD-10-CM code for "Post COVID-19 condition, unspecified." METHODS: We undertook a number of analyses to characterize the N3C population with a U09.9 diagnosis code (n = 33,782), including assessing person-level demographics and a number of area-level social determinants of health; diagnoses commonly co-occurring with U09.9, clustered using the Louvain algorithm; and quantifying medications and procedures recorded within 60 days of U09.9 diagnosis. We stratified all analyses by age group in order to discern differing patterns of care across the lifespan. RESULTS: We established the diagnoses most commonly co-occurring with U09.9 and algorithmically clustered them into four major categories: cardiopulmonary, neurological, gastrointestinal, and comorbid conditions. Importantly, we discovered that the population of patients diagnosed with U09.9 is demographically skewed toward female, White, non-Hispanic individuals, as well as individuals living in areas with low poverty and low unemployment. Our results also include a characterization of common procedures and medications associated with U09.9-coded patients. CONCLUSIONS: This work offers insight into potential subtypes and current practice patterns around long COVID and speaks to the existence of disparities in the diagnosis of patients with long COVID. This latter finding in particular requires further research and urgent remediation.

Generalisable long COVID subtypes: findings from the NIH N3C and RECOVER programmes.

BACKGROUND: Stratification of patients with post-acute sequelae of SARS-CoV-2 infection (PASC, or long COVID) would allow precision clinical management strategies. However, long COVID is incompletely understood and characterised by a wide range of manifestations that are difficult to analyse computationally. Additionally, the generalisability of machine learning classification of COVID-19 clinical outcomes has rarely been tested. METHODS: We present a method for computationally modelling PASC phenotype data based on electronic healthcare records (EHRs) and for assessing pairwise phenotypic similarity between patients using semantic similarity. Our approach defines a nonlinear similarity function that maps from a feature space of phenotypic abnormalities to a matrix of pairwise patient similarity that can be clustered using unsupervised machine learning. FINDINGS: We found six clusters of PASC patients, each with distinct profiles of phenotypic abnormalities, including clusters with distinct pulmonary, neuropsychiatric, and cardiovascular abnormalities, and a cluster associated with broad, severe manifestations and increased mortality. There was significant association of cluster membership with a range of pre-existing conditions and measures of severity during acute COVID-19. We assigned new patients from other healthcare centres to clusters by maximum semantic similarity to the original patients, and showed that the clusters were generalisable across different hospital systems. The increased mortality rate originally identified in one cluster was consistently observed in patients assigned to that cluster in other hospital systems. INTERPRETATION: Semantic phenotypic clustering provides a foundation for assigning patients to stratified subgroups for natural history or therapy studies on PASC. FUNDING: NIH (TR002306/OT2HL161847-01/OD011883/HG010860), U.S.D.O.E. (DE-AC02-05CH11231), Donald A. Roux Family Fund at Jackson Laboratory, Marsico Family at CU Anschutz.

Coding Long COVID: Characterizing a new disease through an ICD-10 lens.

Background: Naming a newly discovered disease is a difficult process; in the context of the COVID-19 pandemic and the existence of post-acute sequelae of SARS-CoV-2 infection (PASC), which includes Long COVID, it has proven especially challenging. Disease definitions and assignment of a diagnosis code are often asynchronous and iterative. The clinical definition and our understanding of the underlying mechanisms of Long COVID are still in flux, and the deployment of an ICD-10-CM code for Long COVID in the US took nearly two years after patients had begun to describe their condition. Here we leverage the largest publicly available HIPAA-limited dataset about patients with COVID-19 in the US to examine the heterogeneity of adoption and use of U09.9, the ICD-10-CM code for "Post COVID-19 condition, unspecified." Methods: We undertook a number of analyses to characterize the N3C population with a U09.9 diagnosis code ( n = 21,072), including assessing person-level demographics and a number of area-level social determinants of health; diagnoses commonly co-occurring with U09.9, clustered using the Louvain algorithm; and quantifying medications and procedures recorded within 60 days of U09.9 diagnosis. We stratified all analyses by age group in order to discern differing patterns of care across the lifespan. Results: We established the diagnoses most commonly co-occurring with U09.9, and algorithmically clustered them into four major categories: cardiopulmonary, neurological, gastrointestinal, and comorbid conditions. Importantly, we discovered that the population of patients diagnosed with U09.9 is demographically skewed toward female, White, non-Hispanic individuals, as well as individuals living in areas with low poverty, high education, and high access to medical care. Our results also include a characterization of common procedures and medications associated with U09.9-coded patients. Conclusions: This work offers insight into potential subtypes and current practice patterns around Long COVID, and speaks to the existence of disparities in the diagnosis of patients with Long COVID. This latter finding in particular requires further research and urgent remediation.

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.

Risk Factors Associated with Post-Acute Sequelae of SARS-CoV-2 in an EHR Cohort: A National COVID Cohort Collaborative (N3C) Analysis as part of the NIH RECOVER program.

Background: More than one-third of individuals experience post-acute sequelae of SARS-CoV-2 infection (PASC, which includes long-COVID). Objective: To identify risk factors associated with PASC/long-COVID. Design: Retrospective case-control study. Setting: 31 health systems in the United States from the National COVID Cohort Collaborative (N3C). Patients: 8,325 individuals with PASC (defined by the presence of the International Classification of Diseases, version 10 code U09.9 or a long-COVID clinic visit) matched to 41,625 controls within the same health system. Measurements: Risk factors included demographics, comorbidities, and treatment and acute characteristics related to COVID-19. Multivariable logistic regression, random forest, and XGBoost were used to determine the associations between risk factors and PASC. Results: Among 8,325 individuals with PASC, the majority were >50 years of age (56.6%), female (62.8%), and non-Hispanic White (68.6%). In logistic regression, middle-age categories (40 to 69 years; OR ranging from 2.32 to 2.58), female sex (OR 1.4, 95% CI 1.33-1.48), hospitalization associated with COVID-19 (OR 3.8, 95% CI 3.05-4.73), long (8-30 days, OR 1.69, 95% CI 1.31-2.17) or extended hospital stay (30+ days, OR 3.38, 95% CI 2.45-4.67), receipt of mechanical ventilation (OR 1.44, 95% CI 1.18-1.74), and several comorbidities including depression (OR 1.50, 95% CI 1.40-1.60), chronic lung disease (OR 1.63, 95% CI 1.53-1.74), and obesity (OR 1.23, 95% CI 1.16-1.3) were associated with increased likelihood of PASC diagnosis or care at a long-COVID clinic. Characteristics associated with a lower likelihood of PASC diagnosis or care at a long-COVID clinic included younger age (18 to 29 years), male sex, non-Hispanic Black race, and comorbidities such as substance abuse, cardiomyopathy, psychosis, and dementia. More doctors per capita in the county of residence was associated with an increased likelihood of PASC diagnosis or care at a long-COVID clinic. Our findings were consistent in sensitivity analyses using a variety of analytic techniques and approaches to select controls. Conclusions: This national study identified important risk factors for PASC such as middle age, severe COVID-19 disease, and specific comorbidities. Further clinical and epidemiological research is needed to better understand underlying mechanisms and the potential role of vaccines and therapeutics in altering PASC course.

Advancing Interoperability of Patient-level Social Determinants of Health Data to Support COVID-19 Research.

Including social determinants of health (SDoH) data in health outcomes research is essential for studying the sources of healthcare disparities and developing strategies to mitigate stressors. In this report, we describe a pragmatic design and approach to explore the encoding needs for transmitting SDoH screening tool responses from a large safety-net hospital into the National Covid Cohort Collaborative (N3C) OMOP dataset. We provide a stepwise account of designing data mapping and ingestion for patient-level SDoH and summarize the results of screening. Our approach demonstrates that sharing of these important data - typically stored as non-standard, EHR vendor specific codes - is feasible. As SDoH screening gains broader use nationally, the approach described in this paper could be used for other screening instruments and improve the interoperability of these important data.

Generalizable Long COVID Subtypes: Findings from the NIH N3C and RECOVER Programs.

Accurate stratification of patients with post-acute sequelae of SARS-CoV-2 infection (PASC, or long COVID) would allow precision clinical management strategies. However, the natural history of long COVID is incompletely understood and characterized by an extremely wide range of manifestations that are difficult to analyze computationally. In addition, the generalizability of machine learning classification of COVID-19 clinical outcomes has rarely been tested. We present a method for computationally modeling PASC phenotype data based on electronic healthcare records (EHRs) and for assessing pairwise phenotypic similarity between patients using semantic similarity. Our approach defines a nonlinear similarity function that maps from a feature space of phenotypic abnormalities to a matrix of pairwise patient similarity that can be clustered using unsupervised machine learning procedures. Using k-means clustering of this similarity matrix, we found six distinct clusters of PASC patients, each with distinct profiles of phenotypic abnormalities. There was a significant association of cluster membership with a range of pre-existing conditions and with measures of severity during acute COVID-19. Two of the clusters were associated with severe manifestations and displayed increased mortality. We assigned new patients from other healthcare centers to one of the six clusters on the basis of maximum semantic similarity to the original patients. We show that the identified clusters were generalizable across different hospital systems and that the increased mortality rate was consistently observed in two of the clusters. Semantic phenotypic clustering can provide a foundation for assigning patients to stratified subgroups for natural history or therapy studies on PASC.

Multimorbidity patterns across race/ethnicity as stratified by age and obesity.

The objective of our study is to assess differences in prevalence of multimorbidity by race/ethnicity. We applied the FP-growth algorithm on middle-aged and elderly cohorts stratified by race/ethnicity, age, and obesity level. We used 2016-2017 data from the Cerner HealthFacts electronic health record data warehouse. We identified disease combinations that are shared by all races/ethnicities, those shared by some, and those that are unique to one group for each age/obesity level. Our findings demonstrate that even after stratifying by age and obesity, there are differences in multimorbidity prevalence across races/ethnicities. There are multimorbidity combinations distinct to some racial groups-many of which are understudied. Some multimorbidities are shared by some but not all races/ethnicities. African Americans presented with the most distinct multimorbidities at an earlier age. The identification of prevalent multimorbidity combinations amongst subpopulations provides information specific to their unique clinical needs.

The Launch of the iCoDE Standard Project.

INTRODUCTION: The first meeting of the Integration of Continuous Glucose Monitor Data into the Electronic Health Record (iCoDE) project, organized by Diabetes Technology Society, took place virtually on January 27, 2022. METHODS: Clinicians, government officials, data aggregators, attorneys, and standards experts spoke in panels and breakout groups. Three themes were covered: 1) why digital health data integration into the electronic health record (EHR) is needed, 2) what integrated continuously monitored glucose data will look like, and 3) how this process can be achieved in a way that will satisfy clinicians, healthcare organizations, and regulatory experts. RESULTS: The meeting themes were addressed within eight sessions: 1) What Do Inpatient Clinicians Want to See With Integration of CGM Data into the EHR?, 2) What Do Outpatient Clinicians Want to See With Integration of CGM Data into the EHR?, 3) Why Are Data Standards and Guidances Useful?, 4) What Value Can Data Integration Services Add?, 5) What Are Examples of Successful Integration?, 6) Which Privacy, Security, and Regulatory Issues Must Be Addressed to Integrate CGM Data into the EHR?, 7) Breakout Group Discussions, and 8) Presentation of Breakout Group Ideas. CONCLUSIONS: Creation of data standards and workflow guidance are necessary components of the Integration of Continuous Glucose Monitor Data into the Electronic Health Record (iCoDE) standard project. This meeting, which launched iCoDE, will be followed by a set of working group meetings intended to create the needed standard.

Social Determinants of Health Factors for Gene-Environment COVID-19 Research: Challenges and Opportunities.

The characteristics of a person's health status are often guided by how they live, grow, learn, their genetics, as well as their access to health care. Yet, all too often, studies examining the relationship between social determinants of health (behavioral, sociocultural, and physical environmental factors), the role of demographics, and health outcomes poorly represent these relationships, leading to misinterpretations, limited study reproducibility, and datasets with limited representativeness and secondary research use capacity. This is a profound hurdle in what questions can or cannot be rigorously studied about COVID-19. In practice, gene-environment interactions studies have paved the way for including these factors into research. Similarly, our understanding of social determinants of health continues to expand with diverse data collection modalities as health systems, patients, and community health engagement aim to fill the knowledge gaps toward promoting health and wellness. Here, a conceptual framework is proposed, adapted from the population health framework, socioecological model, and causal modeling in gene-environment interaction studies to integrate the core constructs from each domain with practical considerations needed for multidisciplinary science.

Clinical, social, and policy factors in COVID-19 cases and deaths: methodological considerations for feature selection and modeling in county-level analyses.

BACKGROUND: There is a need to evaluate how the choice of time interval contributes to the lack of consistency of SDoH variables that appear as important to COVID-19 disease burden within an analysis for both case counts and death counts. METHODS: This study identified SDoH variables associated with U.S county-level COVID-19 cumulative case and death incidence for six different periods: the first 30, 60, 90, 120, 150, and 180 days since each county had COVID-19 one case per 10,000 residents. The set of SDoH variables were in the following domains: resource deprivation, access to care/health resources, population characteristics, traveling behavior, vulnerable populations, and health status. A generalized variance inflation factor (GVIF) analysis was used to identify variables with high multicollinearity. For each dependent variable, a separate model was built for each of the time periods. We used a mixed-effect generalized linear modeling of counts normalized per 100,000 population using negative binomial regression. We performed a Kolmogorov-Smirnov goodness of fit test, an outlier test, and a dispersion test for each model. Sensitivity analysis included altering the county start date to the day each county reached 10 COVID-19 cases per 10,000. RESULTS: Ninety-seven percent (3059/3140) of the counties were represented in the final analysis. Six features proved important for both the main and sensitivity analysis: adults-with-college-degree, days-sheltering-in-place-at-start, prior-seven-day-median-time-home, percent-black, percent-foreign-born, over-65-years-of-age, black-white-segregation, and days-since-pandemic-start. These variables belonged to the following categories: COVID-19 related, vulnerable populations, and population characteristics. Our diagnostic results show that across our outcomes, the models of the shorter time periods (30 days, 60 days, and 900 days) have a better fit. CONCLUSION: Our findings demonstrate that the set of SDoH features that are significant for COVID-19 outcomes varies based on the time from the start date of the pandemic and when COVID-19 was present in a county. These results could assist researchers with variable selection and inform decision makers when creating public health policy.

Characterizing Long COVID: Deep Phenotype of a Complex Condition.

BACKGROUND: Numerous publications describe the clinical manifestations of post-acute sequelae of SARS-CoV-2 (PASC or "long COVID"), but they are difficult to integrate because of heterogeneous methods and the lack of a standard for denoting the many phenotypic manifestations. Patient-led studies are of particular importance for understanding the natural history of COVID-19, but integration is hampered because they often use different terms to describe the same symptom or condition. This significant disparity in patient versus clinical characterization motivated the proposed ontological approach to specifying manifestations, which will improve capture and integration of future long COVID studies. METHODS: The Human Phenotype Ontology (HPO) is a widely used standard for exchange and analysis of phenotypic abnormalities in human disease but has not yet been applied to the analysis of COVID-19. FUNDING: We identified 303 articles published before April 29, 2021, curated 59 relevant manuscripts that described clinical manifestations in 81 cohorts three weeks or more following acute COVID-19, and mapped 287 unique clinical findings to HPO terms. We present layperson synonyms and definitions that can be used to link patient self-report questionnaires to standard medical terminology. Long COVID clinical manifestations are not assessed consistently across studies, and most manifestations have been reported with a wide range of synonyms by different authors. Across at least 10 cohorts, authors reported 31 unique clinical features corresponding to HPO terms; the most commonly reported feature was Fatigue (median 45.1%) and the least commonly reported was Nausea (median 3.9%), but the reported percentages varied widely between studies. INTERPRETATION: Translating long COVID manifestations into computable HPO terms will improve analysis, data capture, and classification of long COVID patients. If researchers, clinicians, and patients share a common language, then studies can be compared/pooled more effectively. Furthermore, mapping lay terminology to HPO will help patients assist clinicians and researchers in creating phenotypic characterizations that are computationally accessible, thereby improving the stratification, diagnosis, and treatment of long COVID. FUNDING: U24TR002306; UL1TR001439; P30AG024832; GBMF4552; R01HG010067; UL1TR002535; K23HL128909; UL1TR002389; K99GM145411.

Assessing the Prevalence of Ahima-Identified Health Informatics and Information Management Careers and Related Skills: A Cross-Sectional Study.

This study's objective was to identify the prevalence of the American Health Information Management Association (AHIMA) career map jobs and determine which job categories, degrees, and skills are associated with higher pay. We extracted data from SimplyHired, a major employment website, from December 2018 to December 2019. We retrieved 12,688 career posts. We found differences in average salary by career category (p-value 0.00). Most jobs were in coding and revenue cycle (CRC) and information governance (IG) categories. The highest average salaries were in data analytics (DA) and informatics (IN). Each career category had a unique set of skills associated with the highest paying jobs. Eighty-two percent of CRC, 67 percent of IG, 65 percent of IN, and 83 percent of DA jobs listed in the AHIMA career map were present in the extracted dataset. These results can help employees, academics, and industry leaders understand the health informatics and information management (HIM) workforce landscape.

Extracting Patient-level Social Determinants of Health into the OMOP Common Data Model.

Deficiencies in data sharing capabilities limit Social Determinants of Health (SDoH) analysis as part of COVID-19 research. The National COVID Cohort Collaborative (N3C) is an example of an Electronic Health Record (EHR) database of patients tested for COVID-19 that could benefit from a SDoH elements framework that captures various screening instruments in EHR data warehouse systems. This paper uses the University of Washington Enterprise Data Warehouse (a data contributor to N3C) to demonstrate how SDoH can be represented and managed to be made available within an OMOP common data model. We found that these data varied by type of social determinants data and where it was collected, in the time period that it was collected, and in how it was represented.

Time to Development of Overt Diabetes and Macrovascular and Microvascular Complications Among Patients With Prediabetes: A Retrospective Cohort Study.

Objective In this study, we aimed to determine the effect of age, gender, race, and obesity on the development of overt diabetes and macro/microvascular events among patients with prediabetes. Methods This was a retrospective cohort study of patient records available through a national electronic health record (EHR) database from 2012 to 2017. Patients with prediabetes in the baseline year of 2012 were identified. Macro/microvascular events were defined as the diagnosis of myocardial infarction (MI), stroke, or chronic kidney disease (CKD). The effects of age, gender, race, and obesity on the incidence of diabetes and macro/microvascular events between 2013-2017 were assessed using the multivariate Cox proportional-hazards model. Results Among the total 5,230 patients with prediabetes in 2012, 16.7% developed overt diabetes, and 19.7% developed a macro/microvascular event. Elderly patients (HR: 2.96, 95% CI: 2.12-4.13), males (HR: 1.38, 95% CI: 1.20-1.59), and African-Americans (HR: 1.47, 95% CI: 1.26-1.73) were at a higher risk of experiencing a macro/microvascular event. Additionally, male gender (HR: 1.27, 95% CI: 1.11-1.46) and obesity (HR: 1.24, 95% CI: 1.08-1.43) were significant factors associated with the development of overt diabetes. Furthermore, when diabetes status was added as an interaction term to the Cox proportional-hazards model, no statistical difference was found with respect to any of the other independent variables. It can therefore be inferred that those with prediabetes and overt diabetes had a similar risk of developing macro/microvascular events. Conclusions Based on our findings, factors including advanced age, obesity, male gender, and African race significantly impact the progression to diabetes and associated macro/microvascular events.