Research

My work sits at the intersection of health economics and health services research: how policy shapes opioid use disorder and its treatment, how large administrative datasets can be used credibly, and how health shocks affect mothers and children.

Publications

Peer-reviewed and forthcoming.

2026 J. Human Resources accepted

Effects of Election-Related Stress on Birth Outcomes

Ezra Golberstein, Daniel Guth & David Slusky

Accepted, Journal of Human Resources (special issue)

maternal health

2026 PLOS ONE

Criterion validity and divergent risk profiles of long-term opioid therapy across Medicare and Medicaid

Robert W. Hurley*, Daniel Guth*, Elaine L. Hill & Meredith C. B. Adams (*co-first authors)

PLOS ONE, 21(4), e0347943 (2026) · open access

opioidsclaims methods

bibtex
@article{hurley2026criterion,
  author  = {Hurley, Robert W. and Guth, Daniel and Hill, Elaine L. and Adams, Meredith C. B.},
  title   = {Criterion validity and divergent risk profiles of long-term opioid therapy across {Medicare} and {Medicaid}},
  journal = {PLOS ONE},
  volume  = {21},
  number  = {4},
  pages   = {e0347943},
  year    = {2026},
  doi     = {10.1371/journal.pone.0347943}
}
2026 Pain Medicine

Evidence-based framework for identifying opioid use disorder in administrative data: a systematic review and methodological development study

Robert W. Hurley, Khadijah T. Bland, Mira D. Chaskes, Daniel Guth, Elaine L. Hill & Meredith C. B. Adams

Pain Medicine, 27(2), 145–159 (2026)

opioidsclaims methods

bibtex
@article{hurley2026oudframework,
  author  = {Hurley, Robert W. and Bland, Khadijah T. and Chaskes, Mira D. and Guth, Daniel and Hill, Elaine L. and Adams, Meredith C. B.},
  title   = {Evidence-based framework for identifying opioid use disorder in administrative data: a systematic review and methodological development study},
  journal = {Pain Medicine},
  volume  = {27},
  number  = {2},
  pages   = {145--159},
  year    = {2026},
  doi     = {10.1093/pm/pnaf116}
}
2025 Nature Communications

Long COVID after SARS-CoV-2 during pregnancy in the United States

Chengxi Zang*, Daniel Guth*, Ann M. Bruno*, …, Torri D. Metz, Elaine L. Hill & Thomas W. Carton, on behalf of the RECOVER PCORnet and N3C EHR Consortia and the RECOVER Pregnancy Cohort (*co-first authors)

Nature Communications, 16, 3005 (2025)

maternal healthcovid-19ehr

pressWeill Cornell Newsroom · Neuroscience News · Contemporary OB/GYN · RECOVER R3 Seminar

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.

bibtex
@article{zang2025longcovid,
  author  = {Zang, Chengxi and Guth, Daniel and Bruno, Ann M. and others},
  title   = {Long {COVID} after {SARS-CoV-2} during pregnancy in the {United States}},
  journal = {Nature Communications},
  volume  = {16},
  pages   = {3005},
  year    = {2025},
  doi     = {10.1038/s41467-025-57849-9}
}
2025 Journal of Glaucoma

Longitudinal trends in resource utilization associated with newly diagnosed primary angle closure glaucoma in the United States

Daniel Guth, Galo Apolo, Seth A. Seabury, Khristina Lung, Brian Toy & Benjamin Y. Xu

Journal of Glaucoma (2025)

utilization & costsophthalmology

bibtex
@article{guth2025glaucomacosts,
  author  = {Guth, Daniel and Apolo, Galo and Seabury, Seth A. and Lung, Khristina and Toy, Brian and Xu, Benjamin Y.},
  title   = {Longitudinal trends in resource utilization associated with newly diagnosed primary angle closure glaucoma in the {United States}},
  journal = {Journal of Glaucoma},
  year    = {2025},
  doi     = {10.1097/IJG.0000000000002612}
}
2021 Am. J. Ophthalmology

Glaucoma expert-level detection of angle closure in goniophotographs with convolutional neural networks: the Chinese American Eye Study

Michael Chiang, Daniel Guth, Anmol Pardeshi, Jasmeen Randhawa, Alice Shen, Meghan Shan, Justin Dredge, Annie Nguyen, Kimberly Gokoffski, Brandon Wong, Brian Song, Shan Lin, Rohit Varma & Benjamin Y. Xu

American Journal of Ophthalmology, 226, 100–107 (2021)

machine learningophthalmology

bibtex
@article{chiang2021angleclosure,
  author  = {Chiang, Michael and Guth, Daniel and Pardeshi, Anmol and Randhawa, Jasmeen and Shen, Alice and Shan, Meghan and Dredge, Justin and Nguyen, Annie and Gokoffski, Kimberly and Wong, Brandon and Song, Brian and Lin, Shan and Varma, Rohit and Xu, Benjamin Y.},
  title   = {Glaucoma expert-level detection of angle closure in goniophotographs with convolutional neural networks: the {Chinese American Eye Study}},
  journal = {American Journal of Ophthalmology},
  volume  = {226},
  pages   = {100--107},
  year    = {2021},
  doi     = {10.1016/j.ajo.2021.02.004}
}
2018 Math. in Computer Sci.

Persistent topology of syntax

Alexander Port, Iulia Gheorghita, Daniel Guth, John M. Clark, Crystal Liang, Shival Dasu & Matilde Marcolli

Mathematics in Computer Science, 12(1), 33–50 (2018)

machine learningmathematics

bibtex
@article{port2018topology,
  author  = {Port, Alexander and Gheorghita, Iulia and Guth, Daniel and Clark, John M. and Liang, Crystal and Dasu, Shival and Marcolli, Matilde},
  title   = {Persistent topology of syntax},
  journal = {Mathematics in Computer Science},
  volume  = {12},
  number  = {1},
  pages   = {33--50},
  year    = {2018},
  doi     = {10.1007/s11786-017-0329-x}
}