Publications

@article{nokey,

title = {Patient Voices Leading Change: A Call to Action for Careful, Kind, and Connected Patient-Partnered Research in PCORnet®},

author = {Greg Merritt and Ava Zebrick and Bill Stephens and Crispin Goytia and Melissa Bronson and Nadine Zemon and Neely Williams and Shirley Stowe},

doi = {10.1097/MLR.0000000000002264},

year  = {2026},

date = {2026-02-01},

journal = {Medical Care},

volume = {64},

issue = {2S Suppl 3},

pages = {S191-S195},

abstract = {As the 8 patient partners serving on the PCORnet® Steering Committee, we stand at the forefront of a transformative movement in clinical research. PCORnet® Network Partners have been pioneers in integrating patient voices into every aspect of the research process, and we applaud the progress in operationalizing the Patient-Centered Outcomes Research Institute's (PCORI) Framework for Patient Engagement and for leading the way as funders to change how to effectively involve patients and other interested parties in research. However, we believe that now is the time to amplify our efforts and call for a fundamental shift in how health research is conducted across the board. This commentary serves as both a reflection on our journey and a rallying cry for deeper, more authentic patient engagement and partnership in clinical research. The landscape of clinical research has undergone significant changes over the past decade, with patient engagement emerging as a cornerstone of patient-centered outcomes research. This shift is evidenced by major funding agencies now requiring patient engagement and a growing body of literature demonstrating improved study quality, recruitment, and relevance when patients are engaged as partners. As patient partners participating in PCORnet®, we have been at the forefront of this evolution, witnessing firsthand the progress made and the challenges and learnings that remain. Drawing on our experiences and evidence from the literature, we propose strategies to enhance patient involvement across all stages of research. We introduce and explore the concept that clinical research should be "careful, kind, and connected." Our reflections underscore that meaningful patient involvement is essential for advancing health outcomes and achieving a truly patient-partnered research ecosystem.},

keywords = {clinical research, healthcare innovation, patient engagement},

pubstate = {published},

tppubtype = {article}

}

@article{nokey,

title = {HIV Infection and Long COVID: A RECOVER Program, Electronic Health Record-Based Cohort Study},

author = {Kellie L. Hawkins and Dima Dandachi and Zoe Verzani and M. Daniel Brannock and Colby Lewis and Sajjad Abedian and Sohrab Jaferian and Shannon Wuller and Jennifer Truong and Margot Gage Witvliet and Gretchen Dymond and Hemalkumar B. Mehta and Payal B. Patel and Elaine Hill and Mark G. Weiner and Thomas W. Carton and Rainu Kaushal and Elen Feuerriegel and Huong G. Tran and Kristen Marks and Carlos R. Oliveira and Edward M. Gardner and Igho Ofotokun and Roy M. Gulick and Kristine M. Erlandson},

doi = {10.1093/cid/ciaf242},

year  = {2025},

date = {2025-10-06},

urldate = {2025-10-06},

journal = {Clinical Infectious Diseases},

volume = {81},

issue = {3},

pages = {427-438},

abstract = {Background: People with human immunodeficiency virus (HIV) may be at increased risk for long COVID after acute severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection. We investigated the association between HIV and long COVID in 2 large electronic health record databases.



Methods: Using data from the Patient-Centered Clinical Research Network (PCORnet) and the National Clinical Cohort Collaborative (N3C) from 1 January 2018 to 30 April 2024, our analytic sample included individuals aged ≥21 years with SARS-CoV-2. All individuals were classified as having HIV or not. We estimated the adjusted odds ratio (aOR) of long COVID by HIV status using logistic regression. Multivariable models controlled for potential associated factors and used 2 cohort definitions: a computed phenotype definition or ICD-10 code-based definition.



Results: We included 1 369 896 patients from PCORnet (11 964 with and 1 357 932 without HIV) and 3 312 355 patients from N3C (23 931 with and 3 288 424 without HIV). Using the computed phenotype definition of long COVID, we noted a small, but significant, increase in odds of developing long COVID among people with compared to those without HIV (PCORnet: aOR, 1.09 [95% confidence interval {CI}, 1.04-1.14]; N3C: aOR, 1.18 [95% CI, 1.13-1.23]). Using the ICD-10 definition of long COVID, there was no association between HIV and long COVID (PCORnet: aOR, 1.01 [95% CI, .88-1.16]; N3C: aOR, 1.07 [95% CI, .97-1.18]).



Conclusions: In this large multicenter study, people with HIV had a modestly increased risk of long COVID when defined by a computed phenotype, but not when using ICD-10 codes. These findings suggest that long COVID may be underrecognized in people with HIV and underscore challenges in diagnosing long COVID in populations with baseline chronic conditions.},

keywords = {chronic diseases, COVID-19, health disparities, HIV, long COVID},

pubstate = {published},

tppubtype = {article}

}

@article{nokey,

title = {Schizophrenia, Bipolar, or Major Depressive Disorder and Postacute Sequelae of COVID-19},

author = {Veer Vekaria and Rohith Kumar Thiruvalluru and Zoe Verzani and Sajjad Abedian and Mark Olfson and Braja Gopal Patra and Yunyu Xiao and Katherine S. Salamon and Karin Hoth and Frank Blancero and Maxwell M. Hornig-Rohan and Teresa Akintonwa and Mahfuza Sabiha and Mark G. Weiner and Thomas W. Carton and Rainu Kaushal and Jyotishman Pathak},

doi = {10.1001/jamanetworkopen.2025.40242},

year  = {2025},

date = {2025-10-01},

urldate = {2025-10-01},

journal = {JAMA Network Open},

volume = {8},

issue = {10},

pages = {e2540242},

abstract = {Importance: Given the increased vulnerability to COVID-19 among those with a serious mental illness (SMI), it remains unclear whether these individuals face a higher risk of developing postacute sequelae of SARS-CoV-2 (PASC). Understanding this association could inform secondary prevention efforts.



Objective: To identify the risk of developing PASC in patients with an SMI.



Design, setting, and participants: This longitudinal cohort study used data derived from large-scale electronic health records (EHRs) between March 2020 and April 2023, inclusive of 180-day follow-up. Patients included adults aged 21 years or older with a confirmed COVID-19 infection evidenced by a relevant laboratory result, diagnosis, or prescription order.



Exposures: Evidence of an SMI diagnosis (schizophrenia, bipolar disorder, or recurrent major depressive disorder) recorded before COVID-19 infection.



Main outcomes and measures: Evidence of PASC symptoms within 30 to 180 days' follow-up after COVID-19 infection reported as odds ratios (OR) mutually adjusted for age, sex, race and ethnicity, insurance type, Charlson Comorbidity Index (CCI) score, and COVID-19 severity.



Results: A total of 1 625 857 patients with a COVID-19 infection were included (mean [SD] age, 52 [17] years; 998 237 [61.4%] female, 204 237 [12.6%] non-Hispanic Black, 219 220 [13.5%] Hispanic, 833 411 [51.3%] non-Hispanic White, and 1 228 664 [75.6%] urban patients), of whom 258 523 (15.9%) had an SMI and 403 641 (24.8%) developed PASC. Individuals with an SMI had increased adjusted odds of developing PASC (OR, 1.10; 95% CI, 1.08-1.11; P < .001). Variables associated with greater odds of PASC among the study population included older age compared with age 22 to 34 years (35 to 44 years: OR, 1.04; 95% CI, 1.03-1.06; 45 to 64 years: OR, 1.11; 95% CI, 1.10-1.12; ≥65 years: OR, 1.18; 95% CI, 1.17-1.20), non-Hispanic Black and Hispanic compared with non-Hispanic White race and ethnicity (non-Hispanic Black: OR, 1.08; 95% CI, 1.07-1.10; Hispanic: OR, 1.12; 95% CI, 1.11-1.13), higher chronic disease burden vs no chronic disease (CCI 1 to 3: OR, 1.13; 95% CI, 1.12-1.14; CCI ≥4: OR, 1.23; 95% CI, 1.22-1.25), and hospitalization with initial COVID-19 infection vs no hospitalization (hospitalized: OR, 1.80; 95% CI, 1.77-1.82; hospitalized with ventilation: OR, 2.17; 95% CI, 2.12-2.22; P < .001). Compared with public insurance, commercial health insurance was associated with lower odds of PASC (OR, 0.85; 95% CI, 0.84-0.86).



Conclusions and relevance: In this cohort study of patients infected with COVID-19, patients with SMI compared with those without SMI were at increased risk of PASC, underscoring the need for coordinated mental health and COVID-19 care strategies.},

keywords = {COVID-19, long COVID, mental health},

pubstate = {published},

tppubtype = {article}

}

@article{nokey,

title = {COVID‑19 Pandemic‑Induced Healthcare Disruption and Chronic Kidney Disease Progression},

author = {Richard Liu and Rahul Abraham and Sarah Conderino and Rania Kanchi and Saul Blecker and John A. Dodson and Lorna E. Thorpe and David M. Charytan and Mara A. McAdams DeMarco and Wenbo Wu},

doi = {https://doi.org/10.1007/s11606-025-09832-9},

year  = {2025},

date = {2025-09-04},

urldate = {2025-09-04},

journal = {Journal of General Internal Medicine},

abstract = {Introduction

The coronavirus disease 2019 (COVID-19) pandemic caused unprecedented disruptions to healthcare systems worldwide, significantly affecting patients with chronic kidney disease (CKD). In this study, we evaluated the impact of the pandemic on healthcare-seeking behavior and CKD progression among patients in New York City.



Methods

Using electronic health records from PCORnet’s INSIGHT Clinical Research Network, we conducted a retrospective cohort study focused on 84,062 patients with CKD aged 50 years or older with multiple chronic conditions seen between 2017 and 2022. Patients were identified using pre-pandemic CKD diagnostic codes, and confirmed by estimated glomerular filtration rate (eGFR) measurements. Care disruption was defined as receiving fewer visits than recommended by Kidney Disease: Improving Global Outcomes (KDIGO) guidelines. We used linear mixed-effects models to estimate annual eGFR changes and analyze trends in care visits stratified by CKD stage and care disruption.



Results

The study cohort had a mean age of 75.8 years, 43.2% were male, and mean pre-pandemic eGFR was 51.1 mL/min/1.73 m2. Care visits declined sharply in 2020 across patients at all but the end stage, with incomplete recovery by 2022. Patients with adequate pre-pandemic care maintained their visits above KDIGO levels, while those with inadequate care increased visits during the pandemic. Pronounced eGFR decline occurred in 2020 (10.6%), with slower declines observed thereafter.



Conclusion

The COVID-19 pandemic disrupted CKD care, potentially leading to reduced healthcare-seeking behavior and accelerated kidney function decline in 2020. Slower decline post-2020 may reflect improved healthcare utilization, better medication adherence, and new therapies, and other factors.},

keywords = {chronic kidney disease progression, COVID-19},

pubstate = {published},

tppubtype = {article}

}

@article{nokey,

title = {Extracting post-acute sequelae of SARS-CoV-2 infection symptoms from clinical notes via hybrid natural language processing},

author = {Zilong Bai and Zihan Xu and Cong Sun and Chengxi Zang and H. Timothy Bunnell and Catherine Sinfield and Jacqueline Rutter and Aaron Thomas Martinez and L. Charles Bailey and Mark G. Weiner and Thomas T. Campion and Thomas W. Carton and Christopher B. Forrest and Rainu Kaushal and Fei Wang and Yifan Peng},

doi = {10.1038/s44401-025-00033-4},

year  = {2025},

date = {2025-08-21},

journal = {npj Health System},

volume = {21},

issue = {2},

abstract = {Accurately and efficiently diagnosing Post-Acute Sequelae of COVID-19 (PASC) remains challenging due to its myriad symptoms that evolve over long- and variable-time intervals. To address this issue, we developed a hybrid natural language processing pipeline that integrates rule-based named entity recognition with BERT-based assertion detection modules for PASC-symptom extraction and assertion detection from clinical notes. We developed a comprehensive PASC lexicon with clinical specialists. From 11 health systems of the RECOVER initiative network across the U.S., we curated 160 intake progress notes for model development and evaluation, and collected 47,654 progress notes for a population-level prevalence study. We achieved an average F1 score of 0.82 in one-site internal validation and 0.76 in 10-site external validation for assertion detection. Our pipeline processed each note at 2.448 ± 0.812 seconds on average. Spearman correlation tests showed ρ > 0.83 for positive mentions and ρ > 0.72 for negative ones, both with P < 0.0001. These demonstrate the effectiveness and efficiency of our models and its potential for improving PASC diagnosis.},

keywords = {COVID-19, long COVID, natural language processing},

pubstate = {published},

tppubtype = {article}

}

@article{nokey,

title = {Long-COVID incidence proportion in adults and children between 2020 and 2024},

author = {Hannah L. Mandel and Yun J. Yoo and Andrea J. Allen and Sajjad Abedian and Zoe Verzani and Elizabeth W. Karlson and Lawrence C. Kleinman and Praveen C. Mudumbi and Carlos R. Oliveira and Jennifer A. Muszynski and Rachel S. Gross and Thomas W. Carton and C. Kim and Emily Taylor and Heekyong Park and Jasmin Divers and J. Daniel Kelly and Jonathan Arnold and Carol Reynolds Geary and Chengxi Zang and Kelan G. Tantisira and Kyung E. Rhee and Michael Koropsak and Sindhu Mohandas and Andrew Vasey and Abu S. M. Mosa and Melissa Haendel and Christopher G. Chute and Shawn N. Murphy and Lisa O'Brien and Jacqueline Szmuszkovicz and Nicholas Guthe and Jorge L. Santana and Aliva De and Amanda L. Bogie and Katia C. Halabi and Lathika Mohanraj and Patricia A Kinser and Samuel E. Packard and Katherine R. Tuttle and Kathryn Hirabayashi and Rainu Kaushal and Emily Pfaff and Mark G. Weiner and Lorna E. Thorpe and Richard A. Moffitt},

doi = {10.1093/cid/ciaf046},

year  = {2025},

date = {2025-07-18},

urldate = {2025-07-18},

journal = {Clinical Infectious Diseases},

volume = {80},

issue = {6},

pages = {1247-1261},

abstract = {Background: Incidence estimates of post-acute sequelae of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection, also known as long COVID, have varied across studies and changed over time. We estimated long COVID incidence among adult and pediatric populations in 3 nationwide research networks of electronic health records (EHRs) participating in the RECOVER (Researching COVID to Enhance Recovery) Initiative using different classification algorithms (computable phenotypes).



Methods: This EHR-based retrospective cohort study included adult and pediatric patients with documented acute SARS-CoV-2 infection and 2 control groups: contemporary coronavirus disease 2019 (COVID-19)-negative and historical patients (2019). We examined the proportion of individuals identified as having symptoms or conditions consistent with probable long COVID within 30-180 days after COVID-19 infection (incidence proportion). Each network (the National COVID Cohort Collaborative [N3C], National Patient-Centered Clinical Research Network [PCORnet], and PEDSnet) implemented its own long COVID definition. We introduced a harmonized definition for adults in a supplementary analysis.



Results: Overall, 4% of children and 10%-26% of adults developed long COVID, depending on computable phenotype used. Excess incidence among SARS-CoV-2 patients was 1.5% in children and ranged from 5% to 6% among adults, representing a lower-bound incidence estimation based on our control groups. Temporal patterns were consistent across networks, with peaks associated with introduction of new viral variants.



Conclusions: Our findings indicate that preventing and mitigating long COVID remains a public health priority. Examining temporal patterns and risk factors for long COVID incidence informs our understanding of etiology and can improve prevention and management.},

keywords = {COVID-19, electronic health records, long COVID, public health surveillance},

pubstate = {published},

tppubtype = {article}

}

@article{nokey,

title = {Neural network models for predicting readmission among patients undergoing peripheral vascular intervention using electronic health record data and clinical registry data},

author = {Jialin Mao and Philip Goodney and Samprit Banerjee and Zoran Kostic and Kim Smolderen and Carlos Mena-Hurtado and Michael E. Matheny},

doi = {10.1136/bmjsit-2025-000387},

year  = {2025},

date = {2025-06-26},

journal = {BMJ Surgery, Interventions, & Health Technologies},

volume = {7},

issue = {1},

pages = {e000387},

abstract = {Objectives: To determine whether neural network models based on electronic health record (EHR) data can match and augment the performance of models based on clinical registry data in predicting readmission after peripheral vascular intervention (PVI).



Design: Observational cohort study.



Setting: Vascular Quality Initiative registry and INSIGHT Clinical Research Network EHR data from multiple academic institutions in New York City.



Participants: Patients undergoing PVI during January 1, 2013 to September 30, 2021.



Main outcome measures: Our outcome variable was 90-day readmission. We developed logistic regression (LR), multilevel perceptron (MLP), and recurrent neural network (RNN) models using registry alone, EHR data alone, and combined registry-EHR data. EHR data were evaluated using derived variables to match registry variables (EHR-derived data) and clinically meaningful code aggregation (EHR-direct data). Models were evaluated using area under the curve (AUC) for discrimination, Spiegelhalter z score for calibration, and Brier score for overall performance.



Results: The analytical cohort included 2348 patients undergoing PVI (mean age: 69.9±11.5 years). 832 (35%) patients were readmitted within 90 days. LR to predict 90-day readmission based on registry data alone had an AUC of 0.710, Spiegelhalter z score of 1.021, and Brier score of 0.211. MLP based on registry data alone had similar performance. MLP and RNN based on EHR-direct data (MLP: AUC=0.742, Spiegelhalter z=0.933, Brier=0.204; RNN: AUC=0.737, Spiegelhalter z=1.026, Brier=0.206) and registry+EHR-direct data (MLP: AUC=0.756, Spiegelhalter z=0.794, Brier=0.199; RNN: AUC=0.751, Spiegelhalter z=1.057, Brier=0.200) had improved performances. LR based on EHR-direct data and combined registry+EHR-direct data had worse performances.



Conclusions: EHR data, when used with neural network models, can be useful to establish readmission predictive models or augment clinical registry data. EHR-based models can be potentially embedded in the clinical workflow, but model performance may be constrained by the absence of certain information in clinical encounters, such as social determinants of health.},

keywords = {methodology, outcomes research, real-world data, vascular devices},

pubstate = {published},

tppubtype = {article}

}

@article{nokey,

title = {Severity of acute SARS-CoV-2 infection and risk of new-onset autoimmune disease: A RECOVER initiative study in nationwide U.S. cohorts },

author = {Shannon Wuller and Nora G. Singer and Colby Lewis and Elizabeth W. Karlson and Grant S. Schulert and Jason D. Goldman and Jennider Hadlock and Jonathan Arnold and Kathryn Hirabayashi and Lauren E. Stiles and Lawrence C. Kleinman and Lindsay G. Cowell and Mady Hornig and Margaret A. Hall and Mark G. Weiner and Michael Koropsak and Michelle F. Lamendola-Essel and Rachel Kenney and Richard A. Moffitt and Sajjad Abedian and Shari Esquenazi-Karonika and Steven G. Johnson and Stephenson Stroebel and Zachary S. Wallace and Karen H. Costenbader},

doi = {10.1371/journal.pone.0324513},

year  = {2025},

date = {2025-06-04},

urldate = {2025-06-04},

journal = {PLoS One},

volume = {20},

issue = {6},

pages = {e0324513},

abstract = {SARS-CoV-2 infection has been associated with increased autoimmune disease risk. Past studies have not aligned regarding the most prevalent autoimmune diseases after infection, however. Furthermore, the relationship between infection severity and new autoimmune disease risk has not been well examined. We used RECOVER's electronic health record (EHR) networks, N3C, PCORnet, and PEDSnet, to estimate types and frequency of autoimmune diseases arising after SARS-CoV-2 infection and assessed how infection severity related to autoimmune disease risk. We identified patients of any age with SARS-CoV-2 infection between April 1, 2020 and April 1, 2021, and assigned them to a World Health Organization COVID-19 severity category for adults or the PEDSnet acute COVID-19 illness severity classification system for children (<age 21). We collected baseline covariates from the EHR in the year pre-index infection date and followed patients for 2 years for new autoimmune disease, defined as ≥ 2 new ICD-9, ICD-10, or SNOMED codes in the same concept set, starting >30 days after SARS-CoV-2 infection index date and occurring ≥1 day apart. We calculated overall and infection severity-stratified incidence ratesper 1000 person-years for all autoimmune diseases. With least severe COVID-19 severity as reference, survival analyses examined incident autoimmune disease risk. The most common new-onset autoimmune diseases in all networks were thyroid disease, psoriasis/psoriatic arthritis, and inflammatory bowel disease. Among adults, inflammatory arthritis was the most common, and Sjögren's disease also had high incidence. Incident type 1 diabetes and hematological autoimmune diseases were specifically found in children. Across networks, after adjustment, patients with highest COVID-19 severity had highest risk for new autoimmune disease vs. those with least severe disease (N3C: adjusted Hazard Ratio, (aHR) 1.47 (95%CI 1.33-1.66); PCORnet aHR 1.14 (95%CI 1.02-1.26); PEDSnet: aHR 3.14 (95%CI 2.42-4.07)]. Overall, severe acute COVID-19 was most strongly associated with autoimmune disease risk in three EHR networks.},

keywords = {autoimmune disease, COVID-19},

pubstate = {published},

tppubtype = {article}

}

@article{nokey,

title = {Developing a Computable Phenotype for Identifying Children, Adolescents, and Young Adults With Diabetes Using Electronic Health Records in the DiCAYA Network},

author = {Hui Shao and Lorna E. Thorpe and Shahidul Islam and Jiang Bian and Yi Guo and Piaopiao Li and Sarah Bost and Dana Dabelea and Rebecca Conway and Tessa Crume and Brian S. Schwartz and Annemarie G. Hirsch and Katie S. Allen and Brian E. Dixon and Shaun J. Grannis and Eva Lustigova and Kristi Reynolds and Marc Rosenman and Victor W. Zhong and Anthony Wong and Pedro Rivera and Thuy Le and Meredith Akerman and Sarah Conderino and Anand Rajan and Angela D. Liese and Caroline Rudisill and Jihad S. Obeid and Joseph A. Ewing and Charles Bailey and Eneida A. Mendonca and Ibrahim Zaganjor and Deborah Rolka and Giuseppina Imperatore and Meda E. Pavkov and Jasmin Divers},

doi = {10.2337/dc24-1972},

year  = {2025},

date = {2025-06-01},

urldate = {2025-06-01},

journal = {Diabetes Care},

volume = {48},

issue = {6},

pages = {914-921},

abstract = {Objective: The Diabetes in Children, Adolescents, and Young Adults (DiCAYA) network seeks to create a nationwide electronic health record (EHR)-based diabetes surveillance system. This study aimed to develop a DiCAYA-wide EHR-based computable phenotype (CP) to identify prevalent cases of diabetes.



Research design and methods: We conducted network-wide chart reviews of 2,134 youth (aged <18 years) and 2,466 young adults (aged 18 to <45 years) among people with possible diabetes. Within this population, we compared the performance of three alternative CPs, using diabetes diagnoses determined by chart review as the gold standard. CPs were evaluated based on their accuracy in identifying diabetes and its subtype.



Results: The final DiCAYA CP requires at least one diabetes diagnosis code from clinical encounters. Subsequently, diabetes type classification was based on the ratio of type 1 diabetes (T1D) or type 2 diabetes (T2D) diagnosis codes in the EHR. For both youth and young adults, the sensitivity, specificity, and positive and negative predictive values (PPV and NPV, respectively) in finding diabetes cases were >90%, except for the specificity and NPV in young adults, which were slightly lower at 83.8% and 80.6%, respectively. The final DiCAYA CP achieved >90% sensitivity, specificity, PPV, and NPV in classifying T1D, and demonstrated lower but robust performance in identifying T2D, consistently maintaining >80% across metrics.



Conclusions: The DiCAYA CP effectively identifies overall diabetes and T1D in youth and young adults, though T2D misclassification in youth highlights areas for refinement. The simplicity of the DiCAYA CP enables broad deployment across diverse EHR systems for diabetes surveillance.},

keywords = {diabetes mellitus, electronic health records},

pubstate = {published},

tppubtype = {article}

}

@article{nokey,

title = {Evaluating Methods for Imputing Race and Ethnicity in Electronic Health Record Data},

author = {Sarah Conderino and Jasmin Divers and John A. Dodson and Lorna E. Thorpe and Mark G. Weiner and Samrachana Adhikari},

doi = {10.1111/1475-6773.14649},

year  = {2025},

date = {2025-05-27},

journal = {Health Services Research},

volume = {60},

issue = {5},

pages = {e14649},

abstract = {Objective: To compare anonymized and non-anonymized approaches for imputing race and ethnicity in descriptive studies of chronic disease burden using electronic health record (EHR)-based datasets.



Study setting and design: In this New York City-based study, we first conducted simulation analyses under different missing data mechanisms to assess the performance of Bayesian Improved Surname Geocoding (BISG), single imputation using neighborhood majority information, random forest imputation, and multiple imputation with chained equations (MICE). Imputation performance was measured using sensitivity, precision, and overall accuracy; agreement with self-reported race and ethnicity was measured with Cohen's kappa (κ). We then applied these methods to impute race and ethnicity in two EHR-based data sources and compared chronic disease burden (95% CIs) by race and ethnicity across imputation approaches.



Data sources and analytic sample: Our data sources included EHR data from NYU Langone Health and the INSIGHT Clinical Research Network from 3/6/2016 to 3/7/2020 extracted for a parent study on older adults in NYC with multiple chronic conditions.



Principal findings: Under simulation analyses, the non-anonymized BISG imputation provided the most accurate classification of race and ethnicity, ranging from 66% to 73% across missing data mechanisms. Anonymized imputation methods were more sensitive to the missing data mechanism, with agreement dropping when race and ethnicity was missing not at random (MNAR) (κ single = 0.25, κ MICE = 0.25, κ randomforest = 0.33). When these methods were applied to the NYU and INSIGHT cohorts, however, racial and ethnic distributions and chronic disease burden were consistent across all imputation methods. Slight improvements in the precision of estimates were observed under all imputation approaches compared to a complete case analysis.



Conclusions: BISG imputation may provide a more accurate racial and ethnic classification than single or multiple imputation using anonymized covariates, particularly if the missing data mechanism is MNAR. Descriptive studies of disease burden may not be sensitive to methods for imputing missing data.},

keywords = {electronic health records, methodology},

pubstate = {published},

tppubtype = {article}

}

@article{nokey,

title = {Long COVID after SARS-CoV-2 during pregnancy in the United States},

author = {Chengxi Zang and Daniel Guth and Ann M. Bruno and Zhenxing Xu and Haoyang Li and Nariman Ammar and Robert Chew and Nick Guthe and Emily Hadley and Rainu Kaushal and Tanzy Love and Brenda M. McGrath and Rena C. Patel and Elizabeth C. Seibert and Yalini Senathirajah and Sharad Kumar Singh and Fei Wang and Mark G. Weiner and Kenneth J. Wilkins and Yiye Zhang and Torri D. Metz and Elaine Hill and Thomas W. Carton},

doi = {10.1038/s41467-025-57849-9},

year  = {2025},

date = {2025-04-01},

urldate = {2025-04-01},

journal = {Nature Communications},

volume = {16},

issue = {1},

pages = {3005},

abstract = {Pregnancy alters immune responses and clinical manifestations of COVID-19, but its impact on Long COVID remains uncertain. This study investigated Long COVID risk in individuals with SARS-CoV-2 infection during pregnancy compared to reproductive-age females infected outside of pregnancy. A retrospective analysis of two U.S. databases, the National Patient-Centered Clinical Research Network (PCORnet) and the National COVID Cohort Collaborative (N3C), identified 29,975 pregnant individuals (aged 18-50) with SARS-CoV-2 infection in pregnancy from PCORnet and 42,176 from N3C between March 2020 and June 2023. At 180 days after infection, estimated Long COVID risks for those infected during pregnancy were 16.47 per 100 persons (95% CI, 16.00-16.95) in PCORnet using the PCORnet computational phenotype (CP) model and 4.37 per 100 persons (95% CI, 4.18-4.57) in N3C using the N3C CP model. Compared to matched non-pregnant individuals, the adjusted hazard ratios for Long COVID were 0.86 (95% CI, 0.83-0.90) in PCORnet and 0.70 (95% CI, 0.66-0.74) in N3C. The observed risk factors for Long COVID included Black race/ethnicity, advanced maternal age, first- and second-trimester infection, obesity, and comorbid conditions. While the findings suggest a high incidence of Long COVID among pregnant individuals, their risk was lower than that of matched non-pregnant females.},

keywords = {COVID-19, long COVID, maternal health},

pubstate = {published},

tppubtype = {article}

}

@article{nokey,

title = {Kidney Function Following COVID-19 in Children and Adolescents},

author = {Lu Li and Ting Zhou and Yiwen Lu and Jiajie Chen and Yuqing Lei and Qiong Wu and Jonathan Arnold and Michael J. Becich and Yuriy Bisyuk and Saul Blecker and Elizabeth A. Chrischilles and Dimitri A. Christakis and Carol Reynolds Geary and Ravi Jhaveri and Leslie Lenert and Mei Liu and Parsa Mirhaji and Hiroki Morizono and Abu S. M. Mosa and Ali Mirza Onder and Ruby Patel and William E. Smoyer and Bradley W. Taylor and David A. Williams and Bradley P. Dixon and Joseph T. Flynn and Caroline Gluck and Lyndsay A. Harshman and Mark M. Mitsnefes and Zubin J. Modi and Cynthia G. Pan and Hiren P. Patel and Priya S. Verghese and Christopher B. Forrest and Michelle R. Denburg and Yong Chen},

doi = {10.1001/jamanetworkopen.2025.4129},

year  = {2025},

date = {2025-04-01},

urldate = {2025-04-01},

journal = {JAMA Network Open},

volume = {8},

issue = {4},

pages = {e254129},

abstract = {Importance: It remains unclear whether children and adolescents with SARS-CoV-2 infection are at heightened risk for long-term kidney complications.



Objective: To investigate whether SARS-CoV-2 infection is associated with an increased risk of postacute kidney outcomes among pediatric patients, including those with preexisting kidney disease or acute kidney injury (AKI).



Design, setting, and participants: This retrospective cohort study used data from 19 health institutions in the National Institutes of Health Researching COVID to Enhance Recovery (RECOVER) initiative from March 1, 2020, to May 1, 2023 (follow-up ≤2 years completed December 1, 2024; index date cutoff, December 1, 2022). Participants included children and adolescents (aged <21 years) with at least 1 baseline visit (24 months to 7 days before the index date) and at least 1 follow-up visit (28 to 179 days after the index date).



Exposures: SARS-CoV-2 infection, determined by positive laboratory test results (polymerase chain reaction, antigen, or serologic) or relevant clinical diagnoses. A comparison group included children with documented negative test results and no history of SARS-CoV-2 infection.



Main outcomes and measures: Outcomes included new-onset chronic kidney disease (CKD) stage 2 or higher or CKD stage 3 or higher among those without preexisting CKD; composite kidney events (≥50% decline in estimated glomerular filtration rate [eGFR], eGFR ≤15 mL/min/1.73 m2, dialysis, transplant, or end-stage kidney disease diagnosis), and at least 30%, 40%, or 50% eGFR decline among those with preexisting CKD or acute-phase AKI. Hazard ratios (HRs) were estimated using Cox proportional hazards regression models with propensity score stratification.



Results: Among 1 900 146 pediatric patients (487 378 with and 1 412 768 without COVID-19), 969 937 (51.0%) were male, the mean (SD) age was 8.2 (6.2) years, and a range of comorbidities was represented. SARS-CoV-2 infection was associated with higher risk of new-onset CKD stage 2 or higher (HR, 1.17; 95% CI, 1.12-1.22) and CKD stage 3 or higher (HR, 1.35; 95% CI, 1.13-1.62). In those with preexisting CKD, COVID-19 was associated with an increased risk of composite kidney events (HR, 1.15; 95% CI, 1.04-1.27) at 28 to 179 days. Children with acute-phase AKI had elevated HRs (1.29; 95% CI, 1.21-1.38) at 90 to 179 days for composite outcomes.



Conclusions and relevance: In this large US cohort study of children and adolescents, SARS-CoV-2 infection was associated with a higher risk of adverse postacute kidney outcomes, particularly among those with preexisting CKD or AKI, suggesting the need for vigilant long-term monitoring.},

keywords = {COVID-19, kidney function, pediatrics},

pubstate = {published},

tppubtype = {article}

}

@article{nokey,

title = {Opportunities and Challenges in Using Electronic Health Record Systems to Study Postacute Sequelae of SARS-CoV-2 Infection: Insights From the NIH RECOVER Initiative},

author = {Hannah L. Mandel and Shruti N. Shah and L. Charles Bailey and Thomas W. Carton and Yu Chen and Shari Esquenazi-Karonika and Melissa Haendel and Mady Hornig and Rainu Kaushal and Carlos R. Oliveira and Alice A. Perlowski and Emily Pfaff and Suchitra Rao and Hanieh Razzaghi and Elle Seibert and Gelise L. Thomas and Mark G. Weiner and Lorna E. Thorpe and Jasmin Divers},

doi = {10.2196/59217},

year  = {2025},

date = {2025-03-05},

urldate = {2025-03-05},

journal = {Journal of Medical Internet Research},

volume = {27},

pages = {e59217},

abstract = {The benefits and challenges of electronic health records (EHRs) as data sources for clinical and epidemiologic research have been well described. However, several factors are important to consider when using EHR data to study novel, emerging, and multifaceted conditions such as postacute sequelae of SARS-CoV-2 infection or long COVID. In this article, we present opportunities and challenges of using EHR data to improve our understanding of long COVID, based on lessons learned from the National Institutes of Health (NIH)-funded RECOVER (REsearching COVID to Enhance Recovery) Initiative, and suggest steps to maximize the usefulness of EHR data when performing long COVID research.},

keywords = {COVID-19, electronic health records, long COVID},

pubstate = {published},

tppubtype = {article}

}

@article{nokey,

title = {COVID-related healthcare disruptions among older adults with multiple chronic conditions in New York City},

author = {Lorna E. Thorpe and Yuchen Meng and Sarah Conderino and Samrachana Adhikari and Stefanie Bendik and Mark G. Weiner and Cathy Rabin and Melissa Lee and Jenny Uguru and Jasmin Divers and Annie George and John A. Dodson},

doi = {10.1186/s12913-024-12114-5},

year  = {2025},

date = {2025-03-05},

journal = {BMC Health Services Research},

volume = {25},

issue = {1},

pages = {340},

abstract = {Background: Results from national surveys indicate that many older adults reported delayed medical care during the acute phase of the COVID-19 pandemic, yet few studies have used objective data to characterize healthcare utilization among vulnerable older adults in that period. In this study, we characterized healthcare utilization during the acute pandemic phase (March 7-October 6, 2020) and examined risk factors for total disruption of care among older adults with multiple chronic conditions (MCC) in New York City.



Methods: This retrospective cohort study used electronic health record data from NYC patients aged ≥ 50 years with a diagnosis of either hypertension or diabetes and at least one other chronic condition seen within six months prior to pandemic onset and after the acute pandemic period at one of several major academic medical centers contributing to the NYC INSIGHT clinical research network (n=276,383). We characterized patients by baseline (pre-pandemic) health status using cutoffs of systolic blood pressure (SBP) < 140mmHg and hemoglobin A1C (HbA1c) < 8.0% as: controlled (below both cutoffs), moderately uncontrolled (below one), or poorly controlled (above both, SBP > 160, HbA1C > 9.0%). Patients were then assessed for total disruption versus some care during shutdown using recommended care schedules per baseline health status. We identified independent predictors for total disruption using logistic regression, including age, sex, race/ethnicity, baseline health status, neighborhood poverty, COVID infection, number of chronic conditions, and quartile of prior healthcare visits.



Results: Among patients, 52.9% were categorized as controlled at baseline, 31.4% moderately uncontrolled, and 15.7% poorly controlled. Patients with poor baseline control were more likely to be older, female, non-white and from higher poverty neighborhoods than controlled patients (P < 0.001). Having fewer pre-pandemic healthcare visits was associated with total disruption during the acute pandemic period (adjusted odds ratio [aOR], 8.61, 95% Confidence Interval [CI], 8.30-8.93, comparing lowest to highest quartile). Other predictors of total disruption included self-reported Asian race, and older age.



Conclusions: This study identified patient groups at elevated risk for care disruption. Targeted outreach strategies during crises using prior healthcare utilization patterns and disease management measures from disease registries may improve care continuity.},

keywords = {care disruption, COVID-19, healthcare utilization, older adults},

pubstate = {published},

tppubtype = {article}

}

@article{nokey,

title = {Preventing "tipping points" in high comorbidity patients: A lifeline from health coaches - rationale, design and methods},

author = {Mary E. Charlson and Ilana Mittleman and Rosio Ramos and Andrea Cassells and T.J. Lin and Alice Eggleston and Marin T. Wells and James Hollenberg and Paul Pirraglia and Ginger Winston and Jonathan N. Tobin},

doi = {10.1016/j.cct.2025.107865},

year  = {2025},

date = {2025-02-28},

journal = {Contemporary Clinical Trials},

volume = {152},

pages = {107865},

abstract = {Background: This paper describes an innovative cluster randomized controlled trial design to evaluate the comparative effectiveness of two approaches to preventing significant destabilization, leading to unplanned hospitalization and increased disability for patients with high comorbidity, that is, multiple chronic diseases defined by an enhanced Charlson Comorbidity Index ≥4.



Methods: A total of 1974 patients were randomized in four waves at each of the sixteen Federally Qualified Health Centers (FQHCs) in four health systems -two in New York and two in Chicago. The two interventions compared 1) Patient-Centered Medical Home (PCMH) as implemented by the FQHCs (usual care control); or 2) PCMH plus a coaching intervention delivered by Health Coaches (experimental) helping patients identify life goals to encourage self-management enhanced by a positive affect/self-affirmation strategy. The two primary patient-centered clinical outcomes are 1) Unplanned hospitalizations; and 2) Within-patient changes in quality of life and disability, as measured by the World Health Organization Disability Assessment Scale 2 (WHODAS 2.0). The hypotheses are: 1) intervention patients will have a 5 % relative reduction in unplanned hospitalizations as compared to control patients; and 2) reduced disability measured by WHODAS2.0; 3) destabilization or 'tipping points' leading to hospitalization will be more often triggered by psychosocial issues than by medical Issues.



Conclusion: This cluster RCT has the potential to transform the care for patients with high comorbidity by helping motivate patients to engage in self-management and to successfully navigate the barriers, challenges, and stresses leading to destabilization, hospitalization, and increased disability.},

keywords = {chronic diseases, federally qualified health centers, practice-based research networks},

pubstate = {published},

tppubtype = {article}

}

@article{nokey,

title = {Pediatric Gastrointestinal Tract Outcomes During the Postacute Phase of COVID-19},

author = {Dazheng Zhang and Ronen Stein and Yiwen Lu and Ting Zhou and Yuqing Lei and Lu Li and Jiajie Chen and Jonathan Arnold and Michael J. Becich and Elizabeth A. Chrischilles and Cynthia H. Chuang and Dimitri A. Christakis and Daniel Fort and Carol Reynolds Geary and Mady Hornig and Rainu Kaushal and David M. Liebovitz and Abu S. M. Mosa and Hiroki Morizono and Parsa Mirhaji and Jennifer L. Dotson and Claudia Pulgarin and Marion R. Sills and Srinivasan Suresh and David A. Williams and Robert N. Baldassano and Christopher B. Forrest and Yong Chen},

doi = {10.1001/jamanetworkopen.2024.58366},

year  = {2025},

date = {2025-02-05},

urldate = {2025-02-05},

journal = {JAMA Network Open},

volume = {8},

issue = {2},

pages = {e2458366},

abstract = {Importance: The profile of gastrointestinal (GI) tract outcomes associated with the postacute and chronic phases of COVID-19 in children and adolescents remains unclear.



Objective: To investigate the risks of GI tract symptoms and disorders during the postacute (28-179 days after documented SARS-CoV-2 infection) and the chronic (180-729 days after documented SARS-CoV-2 infection) phases of COVID-19 in the pediatric population.



Design, setting, and participants: This retrospective cohort study was performed from March 1, 2020, to September 1, 2023, at 29 US health care institutions. Participants included pediatric patients 18 years or younger with at least 6 months of follow-up. Data analysis was conducted from November 1, 2023, to February 29, 2024.



Exposures: Presence or absence of documented SARS-CoV-2 infection. Documented SARS-CoV-2 infection included positive results of polymerase chain reaction analysis, serological tests, or antigen tests for SARS-CoV-2 or diagnosis codes for COVID-19 and postacute sequelae of SARS-CoV-2.



Main outcomes and measures: GI tract symptoms and disorders were identified by diagnostic codes in the postacute and chronic phases following documented SARS-CoV-2 infection. The adjusted risk ratios (ARRs) and 95% CI were determined using a stratified Poisson regression model, with strata computed based on the propensity score.



Results: The cohort consisted of 1 576 933 pediatric patients (mean [SD] age, 7.3 [5.7] years; 820 315 [52.0%] male). Of these, 413 455 patients had documented SARS-CoV-2 infection and 1 163 478 did not; 157 800 (13.6%) of those without documented SARS-CoV-2 infection had a complex chronic condition per the Pediatric Medical Complexity Algorithm. Patients with a documented SARS-CoV-2 infection had an increased risk of developing at least 1 GI tract symptom or disorder in both the postacute (8.64% vs 6.85%; ARR, 1.25; 95% CI, 1.24-1.27) and chronic (12.60% vs 9.47%; ARR, 1.28; 95% CI, 1.26-1.30) phases compared with patients without a documented infection. Specifically, the risk of abdominal pain was higher in COVID-19-positive patients during the postacute (2.54% vs 2.06%; ARR, 1.14; 95% CI, 1.11-1.17) and chronic (4.57% vs 3.40%; ARR, 1.24; 95% CI, 1.22-1.27) phases.



Conclusions and relevance: In this cohort study, the increased risk of GI tract symptoms and disorders was associated with the documented SARS-CoV-2 infection in children or adolescents during the postacute or chronic phase. Clinicians should note that lingering GI tract symptoms may be more common in children after documented SARS-CoV-2 infection than in those without documented infection.},

keywords = {COVID-19, gastroenterology, pediatrics},

pubstate = {published},

tppubtype = {article}

}

@article{nokey,

title = {Association of Mobile Stroke Unit Care and Spending, Utilization, and Death in New York City},

author = {Dhruv Khullar and Babak B. Navi and Saad Mir and Matthew E. Fink and Luisa Tinapay and Glenn Asaeda and Rainu Kaushal and Eshani Pareek and Amelia M. Bond},

doi = {10.1161/JAHA.124.036784},

year  = {2025},

date = {2025-01-16},

journal = {Journal of American Heart Association},

volume = {14},

issue = {2},

pages = {e036784},

abstract = {Background: Transport by mobile stroke units (MSUs), which provide access to computed tomography scanning and intravenous blood pressure medications and thrombolytics, reduces time to treatment and may improve short-term functional outcomes for patients with acute stroke. The longer-term clinical and financial impacts remain incompletely understood. The aim of the study was to determine whether MSU care is associated with better health, utilization, and spending outcomes for patients with suspected acute stroke.



Methods and results: This was a retrospective, observational study of Medicare patients transported by MSUs versus traditional ambulances in New York City, from October 2016 to December 2019. The study included 167 Medicare patients with suspected acute stroke transported by MSU and 2518 propensity score-matched controls. Primary outcomes included length of stay and discharge destination at the index hospitalization, as well as risk of repeat hospitalization, number of emergency department visits, total costs of care, and death at 1 year. Of 167 patients (mean age, 79.9 years; 56.3% women) transported by an MSU for suspected acute stroke, 61.1% had an ischemic stroke/transient ischemic attack, 7.8% had an intracerebral hemorrhage, and 31.1% had a stroke mimic or other diagnosis. Compared with propensity score-matched control patients, MSU patients were significantly more likely to receive tissue-type plasminogen activator (49.9% versus 9.4%; difference, 37.5 percentage points [95% CI, 30.2-45.6]; P<0.001) but experienced similar lengths of stay (5.9 versus 6.7 days, P=0.13) and were similarly likely to be discharged to a skilled nursing facility (15.6% versus 15.1%, P=0.86). At 1 year, MSU patients had a mortality rate of 21.6%, and control patients had a mortality rate of 28.4%, although the difference did not reach statistical significance (difference, 6.8 percentage points [95% CI, -13.3 to 0.3]; P=0.058). They had similar rates of any repeat hospitalization (24% versus 23.2%, P=0.82) and emergency department visits without hospitalization (14% versus 12%, P=0.86), and there were no significant differences in total spending or specific types of spending.



Conclusions: In this study of patients presenting with suspected acute stroke in New York City, transport by MSUs, compared with traditional ambulances, was associated with a trend toward a lower mortality rate at 1 year. Prospective trials and replication in other regions are warranted.},

keywords = {healthcare spending, mobile stroke unit, stroke outcomes},

pubstate = {published},

tppubtype = {article}

}

@article{nokey,

title = { Ethnic and racial differences in children and young people with respiratory and neurological post-acute sequelae of SARS-CoV-2: an electronic health record-based cohort study from the RECOVER Initiative},

author = {Suchitra Rao and Rodrigo Azuero-Dajud and Vital Lorman and Jeremy Landeo-Gutierrez and Kyung E. Rhee and Julie Ryu and C. Kim and Megan Carmilani and Rachel S. Gross and Sindhu Mohandas and Srinivasan Suresh and L. Charles Bailey and Victor Castro and Yalini Senathirajah and Shari Esquenazi-Karonika and Shawn N. Murphy and Steve Caddle and Lawrence C. Kleinman and Leah Castro-Baucom and Carlos R. Oliveira and Jonathan D. Klein and Alicia Chung and Lindsay G. Cowell and Charisse Madlock-Brown and Carol Reynolds Geary and Marion R. Sills and Lorna E. Thorpe and Jacqueline Szmuszkovicz and Kelan G. Tantisira},

doi = {10.1016/j.eclinm.2024.103042},

year  = {2025},

date = {2025-01-02},

urldate = {2025-01-02},

journal = {eClinical Medicine},

volume = {80},

pages = {103042},

abstract = {Background: Children from racial and ethnic minority groups are at greater risk for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection, but it is unclear whether they have increased risk for post-acute sequelae of SARS-CoV-2 (PASC). Our objectives were to assess whether the risk of respiratory and neurologic PASC differs by race/ethnicity and social drivers of health.



Methods: We conducted a retrospective cohort study of individuals <21 years seeking care at 24 health systems across the U.S, using electronic health record (EHR) data. Our cohort included those with a positive SARS-CoV-2 molecular, serology or antigen test, or with a COVID-19, multisystem inflammatory disease in children, or PASC diagnosis from February 29, 2020 to August 1, 2022. We identified children/youth with at least 2 codes associated with respiratory and neurologic PASC. We measured associations between sociodemographic and clinical characteristics and respiratory and neurologic PASC using odds ratios and 95% confidence intervals estimated from multivariable logistic regression models adjusted for other sociodemographic characteristics, social vulnerability index or area deprivation index, time period of cohort entry, presence and complexity of chronic respiratory (respectively, neurologic) condition and healthcare utilization.



Findings: Among 771,725 children in the cohort, 203,365 (26.3%) had SARS-CoV-2 infection. Among children with documented infection, 3217 children had respiratory PASC and 2009 children/youth had neurologic PASC. In logistic regression models, children <5 years (Odds Ratio [OR] 1.78, 95% CI 1.62-1.97), and of Hispanic White descent (OR 1.19, 95% CI 1.05-1.35) had higher odds of having respiratory PASC. Children/youth living in regions with higher area deprivation indices (OR 1.25, 95% CI 1.10-1.420 for 60-79th percentile) and with chronic complex respiratory conditions (OR 3.28, 95% CI 2.91-3.70) also had higher odds of respiratory PASC. In contrast, older (OR 1.57, 95% CI 1.40-1.77 for those aged 12-17 years), non-Hispanic White individuals and those with chronic pre-existing neurologic conditions (OR 2.04, 95% CI 1.78-2.35) were more likely to have a neurologic PASC diagnosis.



Interpretation: Racial and ethnic differences in healthcare utilization for neurologic and respiratory PASC may reflect social drivers of health and inequities in access to care.},

keywords = {COVID-19, long COVID, pediatrics, social determinants of health},

pubstate = {published},

tppubtype = {article}

}

@article{nokey,

title = {Estimation of prevalence of autoimmune diseases in the United States using electronic health record data},

author = {Aaron H. Abend and Ingrid He and Neil Bahroos and Stratos Christianakis and Ashley B. Crew and Leanna M. Wise and Gloria P. Lipori and Xing He and Shawn N. Murphy and Christopher D. Herrick and Jagannadha Avasarala and Mark G. Weiner and Jacob S. Zelko and Erica Matute-Arcos and Mark Abajian and Philip Ro Payne and Albert M. Lai and Heath A. Davis and Asher A. Hoberg and Chris E. Ortman and Amit D. Gode and Bradley W. Taylor and Kristen I. Osinski and Damian N. Di Florio and Noel R. Rose and Fredrick W. Miller and George C. Tsokos and DeLisa Fairweather},

doi = {10.1172/JCI178722},

year  = {2024},

date = {2024-12-12},

journal = {The Journal of Clinical Investigation},

volume = {135},

issue = {4},

pages = {e178722},

abstract = {BACKGROUND: Previous epidemiologic studies of autoimmune diseases in the US have included a limited number of diseases or used metaanalyses that rely on different data collection methods and analyses for each disease.



METHODS: To estimate the prevalence of autoimmune diseases in the US, we used electronic health record data from 6 large medical systems in the US. We developed a software program using common methodology to compute the estimated prevalence of autoimmune diseases alone and in aggregate that can be readily used by other investigators to replicate or modify the analysis over time.



RESULTS :Our findings indicate that over 15 million people, or 4.6% of the US population, have been diagnosed with at least 1 autoimmune disease from January 1, 2011, to June 1, 2022, and 34% of those are diagnosed with more than 1 autoimmune disease. As expected, females (63% of those with autoimmune disease) were almost twice as likely as males to be diagnosed with an autoimmune disease. We identified the top 20 autoimmune diseases based on prevalence and according to sex and age.CONCLUSIONHere, we provide, for what we believe to be the first time, a large-scale prevalence estimate of autoimmune disease in the US by sex and age.},

keywords = {autoimmune disease},

pubstate = {published},

tppubtype = {article}

}

@article{nokey,

title = {Towards a Multi-Stakeholder process for developing responsible AI governance in consumer health},

author = {Leon Rozenblit and Amy Price and Anthony Solomonides and Amanda L. Joseph and Gyana Srivastava and Steven Labkoff and Dave deBronkart and Reva Singh and Kiran Dattani and Monica Lopez-Gonzalez and Paul J. Barr and Eileen Koski and Baihan Lin and Erika Cheung and Mark G. Weiner and Tayler Williams and Tien Thi Thuy Bui and Yuri Quintana },

doi = {10.1016/j.ijmedinf.2024.105713},

year  = {2024},

date = {2024-11-22},

urldate = {2024-11-22},

journal = {International Journal of Medical Informatics},

volume = {195},

pages = {105713},

abstract = {Introduction: AI is big and moving fast into healthcare, creating opportunities and risks. However, current approaches to governance focus on high-level principles rather than tailored recommendations for specific domains like consumer health. This gap risks unintended consequences from generic guidelines misapplied across contexts and from providing answers before agreeing on the questions.



Objective: Our objective is to explore pragmatic multi-stakeholder approaches to govern consumer-facing health AI. The aims are to (1) establish an approach tailored for consumer health AI governance and (2) identify key constraints and desirable model characteristics.



Methods: This paper synthesizes insights informed by a 4-month multidisciplinary expert consensus process with nearly 200 participants. The deliberations provided guidance for the development of the proposed governance models in consumer health AI.



Results: (1) A Shared View of Consensus: A process for consumer health AI governance should limit the scope and incorporate multi-stakeholder perspectives centered on patient needs. Desirable model characteristics include adaptability, patient empowerment, and transparency. (2) Recommended Collaborative Process: A pathway for effective governance should begin by forming a Health AI Consumer Consortium (HAIC2) representing patients and aligning incentives across stakeholders.



Conclusions: While examples focus on the United States healthcare system, core themes around incorporating consumer voices, enabling transparency, and balancing innovation with thoughtful oversight while avoiding overambitious scope will have relevance globally. As consumer AI spreads worldwide, the multi-stakeholder alignment and patient empowerment principles proposed here may offer productive ways to ensure AI for consumers is safe, effective, equitable, and trustworthy (SEET).},

keywords = {artificial intelligence},

pubstate = {published},

tppubtype = {article}

}