Publications
1.
Mandel, Hannah L.; Yoo, Yun J.; Allen, Andrea J.; Abedian, Sajjad; Verzani, Zoe; Karlson, Elizabeth W.; Kleinman, Lawrence C.; Mudumbi, Praveen C.; Oliveira, Carlos R.; Muszynski, Jennifer A.; Gross, Rachel S.; Carton, Thomas W.; Kim, C.; Taylor, Emily; Park, Heekyong; Divers, Jasmin; Kelly, J. Daniel; Arnold, Jonathan; Geary, Carol Reynolds; Zang, Chengxi; Tantisira, Kelan G.; Rhee, Kyung E.; Koropsak, Michael; Mohandas, Sindhu; Vasey, Andrew; Mosa, Abu S. M.; Haendel, Melissa; Chute, Christopher G.; Murphy, Shawn N.; O'Brien, Lisa; Szmuszkovicz, Jacqueline; Guthe, Nicholas; Santana, Jorge L.; De, Aliva; Bogie, Amanda L.; Halabi, Katia C.; Mohanraj, Lathika; Kinser, Patricia A; Packard, Samuel E.; Tuttle, Katherine R.; Hirabayashi, Kathryn; Kaushal, Rainu; Pfaff, Emily; Weiner, Mark G.; Thorpe, Lorna E.; Moffitt, Richard A.
Long-COVID incidence proportion in adults and children between 2020 and 2024 Journal Article
In: Clinical Infectious Diseases, vol. 80, iss. 6, pp. 1247-1261, 2025.
Abstract | Links | BibTeX | Tags: COVID-19, electronic health records, long COVID, public health surveillance
@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}
}
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.
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.
2.
Conderino, Sarah; Bendik, Stefanie; Richards, Thomas B.; Pulgarin, Claudia; Chan, Pui Ying; Townsend, Julie; Lim, Sungwoo; Roberts, Timothy R.; Thorpe, Lorna E.
In: BMC Medical Informatics and Decision Making, vol. 22, iss. 1, pp. 91, 2022.
Abstract | Links | BibTeX | Tags: common data model, early detection of cancer, electronic health records, public health informatics, public health surveillance
@article{nokey,
title = {The use of electronic health records to inform cancer surveillance efforts: a scoping review and test of indicators for public health surveillance of cancer prevention and control},
author = {Sarah Conderino and Stefanie Bendik and Thomas B. Richards and Claudia Pulgarin and Pui Ying Chan and Julie Townsend and Sungwoo Lim and Timothy R. Roberts and Lorna E. Thorpe},
doi = {10.1186/s12911-022-01831-8},
year = {2022},
date = {2022-04-06},
urldate = {2022-04-06},
journal = {BMC Medical Informatics and Decision Making},
volume = {22},
issue = {1},
pages = {91},
abstract = {Introduction: State cancer prevention and control programs rely on public health surveillance data to set objectives to improve cancer prevention and control, plan interventions, and evaluate state-level progress towards achieving those objectives. The goal of this project was to evaluate the validity of using electronic health records (EHRs) based on common data model variables to generate indicators for surveillance of cancer prevention and control for these public health programs.
Methods: Following the methodological guidance from the PRISMA Extension for Scoping Reviews, we conducted a literature scoping review to assess how EHRs are used to inform cancer surveillance. We then developed 26 indicators along the continuum of the cascade of care, including cancer risk factors, immunizations to prevent cancer, cancer screenings, quality of initial care after abnormal screening results, and cancer burden. Indicators were calculated within a sample of patients from the New York City (NYC) INSIGHT Clinical Research Network using common data model EHR data and were weighted to the NYC population using post-stratification. We used prevalence ratios to compare these estimates to estimates from the raw EHR of NYU Langone Health to assess quality of information within INSIGHT, and we compared estimates to results from existing surveillance sources to assess validity.
Results: Of the 401 identified articles, 15% had a study purpose related to surveillance. Our indicator comparisons found that INSIGHT EHR-based measures for risk factor indicators were similar to estimates from external sources. In contrast, cancer screening and vaccination indicators were substantially underestimated as compared to estimates from external sources. Cancer screenings and vaccinations were often recorded in sections of the EHR that were not captured by the common data model. INSIGHT estimates for many quality-of-care indicators were higher than those calculated using a raw EHR.
Conclusion: Common data model EHR data can provide rich information for certain indicators related to the cascade of care but may have substantial biases for others that limit their use in informing surveillance efforts for cancer prevention and control programs.},
keywords = {common data model, early detection of cancer, electronic health records, public health informatics, public health surveillance},
pubstate = {published},
tppubtype = {article}
}
Introduction: State cancer prevention and control programs rely on public health surveillance data to set objectives to improve cancer prevention and control, plan interventions, and evaluate state-level progress towards achieving those objectives. The goal of this project was to evaluate the validity of using electronic health records (EHRs) based on common data model variables to generate indicators for surveillance of cancer prevention and control for these public health programs.
Methods: Following the methodological guidance from the PRISMA Extension for Scoping Reviews, we conducted a literature scoping review to assess how EHRs are used to inform cancer surveillance. We then developed 26 indicators along the continuum of the cascade of care, including cancer risk factors, immunizations to prevent cancer, cancer screenings, quality of initial care after abnormal screening results, and cancer burden. Indicators were calculated within a sample of patients from the New York City (NYC) INSIGHT Clinical Research Network using common data model EHR data and were weighted to the NYC population using post-stratification. We used prevalence ratios to compare these estimates to estimates from the raw EHR of NYU Langone Health to assess quality of information within INSIGHT, and we compared estimates to results from existing surveillance sources to assess validity.
Results: Of the 401 identified articles, 15% had a study purpose related to surveillance. Our indicator comparisons found that INSIGHT EHR-based measures for risk factor indicators were similar to estimates from external sources. In contrast, cancer screening and vaccination indicators were substantially underestimated as compared to estimates from external sources. Cancer screenings and vaccinations were often recorded in sections of the EHR that were not captured by the common data model. INSIGHT estimates for many quality-of-care indicators were higher than those calculated using a raw EHR.
Conclusion: Common data model EHR data can provide rich information for certain indicators related to the cascade of care but may have substantial biases for others that limit their use in informing surveillance efforts for cancer prevention and control programs.
Methods: Following the methodological guidance from the PRISMA Extension for Scoping Reviews, we conducted a literature scoping review to assess how EHRs are used to inform cancer surveillance. We then developed 26 indicators along the continuum of the cascade of care, including cancer risk factors, immunizations to prevent cancer, cancer screenings, quality of initial care after abnormal screening results, and cancer burden. Indicators were calculated within a sample of patients from the New York City (NYC) INSIGHT Clinical Research Network using common data model EHR data and were weighted to the NYC population using post-stratification. We used prevalence ratios to compare these estimates to estimates from the raw EHR of NYU Langone Health to assess quality of information within INSIGHT, and we compared estimates to results from existing surveillance sources to assess validity.
Results: Of the 401 identified articles, 15% had a study purpose related to surveillance. Our indicator comparisons found that INSIGHT EHR-based measures for risk factor indicators were similar to estimates from external sources. In contrast, cancer screening and vaccination indicators were substantially underestimated as compared to estimates from external sources. Cancer screenings and vaccinations were often recorded in sections of the EHR that were not captured by the common data model. INSIGHT estimates for many quality-of-care indicators were higher than those calculated using a raw EHR.
Conclusion: Common data model EHR data can provide rich information for certain indicators related to the cascade of care but may have substantial biases for others that limit their use in informing surveillance efforts for cancer prevention and control programs.