Evidence map›Paper›PMID 42421789›Full record

ArticleJAMIA open2026

Computational phenotyping of sexually transmitted infections with the All of Us Research Program from 2010 to 2023.

Fanghui Shi, Huiyi Xia, Sharon Weissman, Xiaoming Li, Xueying Yang

Abstract read
In one paragraph

Article in JAMIA open, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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1 · What the graph read from it

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2 · The registry

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3 · Its place in the literature

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4 · The record

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5 · Who and what money

Authors and funding

5 authors.

Fanghui ShiSouth Carolina SmartState Center for Healthcare Quality, University of South Carolina Arnold School of Public Health, Columbia, SC 29208, United States.ORCID https://orcid.org/0000-0002-5603-5398
Huiyi XiaSouth Carolina SmartState Center for Healthcare Quality, University of South Carolina Arnold School of Public Health, Columbia, SC 29208, United States.
Sharon WeissmanSouth Carolina SmartState Center for Healthcare Quality, University of South Carolina Arnold School of Public Health, Columbia, SC 29208, United States.
Xiaoming LiSouth Carolina SmartState Center for Healthcare Quality, University of South Carolina Arnold School of Public Health, Columbia, SC 29208, United States.
Xueying YangSouth Carolina SmartState Center for Healthcare Quality, University of South Carolina Arnold School of Public Health, Columbia, SC 29208, United States.ORCID https://orcid.org/0000-0001-6788-0688

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Objectives: This study aims to develop comprehensive computable phenotyping algorithms that integrate multiple domains of electronic health record (EHR) data within the All of Us (AoU) Researcher Workbench to identify sexually transmitted infection (STI) cases and characterize STI patterns. Materials and Methods: We used AoU controlled tier data, version 8, which included participants enrolled from May 6, 2018, to October 1, 2023. Using data across multiple domains of EHRs in AoU, such as diagnostic codes, laboratory results, and medication records, we developed computational phenotyping algorithms to identify 3 leading STIs: chlamydia, gonorrhea, and syphilis. Results: Among 393 596 participants with EHR data in AoU, 2603 had confirmed chlamydia, 1520 had gonorrhea, and 2762 had syphilis. By utilizing information from diagnostic codes and medication records, we identified an additional 4843 individuals with possible/presumed chlamydia and 18 855 individuals with possible/presumed gonorrhea. Among 5784 participants with at least one confirmed STI, 115 (2.0%) had all 3 infections. A notable shift in STI trends occurred in 2019, with the occurrence increasing from 2010 to 2019 before declining through 2023 across different STIs. Discussion: The comprehensive computable phenotype algorithms developed in this study provide an enhanced approach to identifying STI cases that may be missed using traditional lab-based methods. Conclusion: The computable phenotypes developed in this study provide a practical framework for more nuanced STI classification and downstream analysis in All of Us. Our approach may also support clinical and public health efforts by improving case identification beyond laboratory-only methods and informing interventions addressing clinical and social needs.

Indexed as

All of Uscomputational phenotypingsexually transmitted infection

Identifiers

PMID42421789
PMCPMC13344838

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.