Publications

@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 = {Examining Individual vs. Population Level Social Determinants of Health in a Cluster Randomized Trial of Health Coaches for Patients with Multiple Chronic Conditions},

author = {Mary E. Charlson and Martin T. Wells and James Hollenberg and Rosio Ramos and Guillerma Maritza Martinez and Martin J. Gerard and Andrea Cassells and T.J. Lin and Ilana Mittleman and Alice Eggleston and Jonathan N. Tobin},

doi = {10.1017/cts.2024.598},

year  = {2024},

date = {2024-11-06},

journal = {Journal of Clinical and Translational Science},

volume = {8},

issue = {1},

pages = {e191},

abstract = {Introduction: Social determinants of health (SDOH) are an important contributor to health status and health outcomes. In this analysis, we compare SDOH measured both at the individual and population levels in patients with high comorbidity who receive primary care at Federally Qualified Health Centers in New York and Chicago and enrolled in the Tipping Points trial.



Methods: We analyzed individual- and population-level measures of SDOH in 1,488 patients with high comorbidity (Charlson Comorbidity Index ≥ 4) enrolled in Tipping Points. At the individual level, we used a standardized patient-reported questionnaire. At the population level, we employed patient addresses to calculate the Social Deprivation Index (SDI) and Area Deprivation Index. Multivariable regressions were conducted in addition to qualitative feedback from stakeholders.



Results: Individual-level SDOH are distinct from population-level measures. Significant component predictors of population SDI are being unhoused, unable to pay for utilities, and difficulty accessing medical transportation. Qualitative findings mirrored these results. High comorbidity patients report significant SDOH challenges at the individual level. Fitting a binomial generalized linear model, the comorbidity score is significantly predicted by the composite individual SDOH index (p < 0.0001) controlling for age and race/ethnicity.



Conclusions: Individual- and population-level SDOH measures provide different risk assessments. The use of community-level SDI data is informative in the aggregate but should not be used to identify patients with individual unmet social needs. Health systems should implement a standardized individualized assessment of unmet SDOH needs and build strong, enduring partnerships with community-based organizations that can provide those services.},

keywords = {federally qualified health centers, social determinants of health},

pubstate = {published},

tppubtype = {article}

}

@article{nokey,

title = {Clinical subphenotypes in COVID-19: derivation, validation, prediction, temporal patterns, and interaction with social determinants of health},

author = {Chang Su and Yongkang Zhang and James H. Flory and Mark G. Weiner and Rainu Kaushal and Edward J. Schenck and Fei Wang},

doi = {10.1038/s41746-021-00481-w},

year  = {2021},

date = {2021-07-14},

journal = {npj Digital Medicine},

volume = {4},

issue = {1},

pages = {110},

abstract = {The coronavirus disease 2019 (COVID-19) is heterogeneous and our understanding of the biological mechanisms of host response to the viral infection remains limited. Identification of meaningful clinical subphenotypes may benefit pathophysiological study, clinical practice, and clinical trials. Here, our aim was to derive and validate COVID-19 subphenotypes using machine learning and routinely collected clinical data, assess temporal patterns of these subphenotypes during the pandemic course, and examine their interaction with social determinants of health (SDoH). We retrospectively analyzed 14418 COVID-19 patients in five major medical centers in New York City (NYC), between March 1 and June 12, 2020. Using clustering analysis, 4 biologically distinct subphenotypes were derived in the development cohort (N = 8199). Importantly, the identified subphenotypes were highly predictive of clinical outcomes (especially 60-day mortality). Sensitivity analyses in the development cohort, and rederivation and prediction in the internal (N = 3519) and external (N = 3519) validation cohorts confirmed the reproducibility and usability of the subphenotypes. Further analyses showed varying subphenotype prevalence across the peak of the outbreak in NYC. We also found that SDoH specifically influenced mortality outcome in Subphenotype IV, which is associated with older age, worse clinical manifestation, and high comorbidity burden. Our findings may lead to a better understanding of how COVID-19 causes disease in different populations and potentially benefit clinical trial development. The temporal patterns and SDoH implications of the subphenotypes may add insights to health policy to reduce social disparity in the pandemic.},

keywords = {clinical subphenotypes, COVID-19, social determinants of health},

pubstate = {published},

tppubtype = {article}

}

@article{nokey,

title = {Neighborhood-level Social Determinants of Health Improve Prediction of Preventable Hospitalization and Emergency Department Visits Beyond Claims History},

author = {Winnie Chi and Elena Andreyeva and Yongkang Zhang and Rainu Kaushal and Kevin Haynes},

doi = {10.1089/pop.2021.0047},

year  = {2021},

date = {2021-05-19},

journal = {Population Health Alliance},

volume = {6},

pages = {701-709},

abstract = {This study was conducted to assess if neighborhood-level social determinants of health improve model performance of predicting preventable hospitalization. Using medical and pharmacy claims and neighborhood-level social determinants and the split sample method (67% training with balanced sample and 33% validation), the authors developed prospective modeling for preventable hospital use, defined as hospitalization for ambulatory care sensitive conditions (Agency for Healthcare Research and Quality Prevention Quality Indicators 90 and 92) and preventable emergency department (ED) use (based on Billing's algorithm). Performance of age-gender only or age-gender with administrative claims models were compared to models with the addition of social determinants. Adding social determinants to age-gender only models and claim history models improves model performance as measured by Brier score, C statistics, and area under the precision-recall curve for preventable ED use measures while it leads to similar performance for predicting preventable hospital use compared to models without social determinants. Adding neighborhood-level social determinants improved prediction for preventable ED use in the absence of individual-level social determinants, regardless of the availability of full administrative claims history.},

keywords = {preventable hospitalization, social determinants of health},

pubstate = {published},

tppubtype = {article}

}