Article in Journal of the American Medical Informatics Association : JAMIA, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
2 · The registry
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
5 · Who and what money
Authors and funding
10 authors.
Allison E GatzDepartment of Medicine, Indiana University School of Medicine, Indianapolis, IN 46202, United States.ORCID 0009-0002-2636-4729
Chenxi XiongDepartment of Biostatistics and Health Data Science, Indiana University School of Medicine, Indianapolis, IN 46202, United States.
Yao ChenDepartment of Biostatistics and Health Data Science, Indiana University School of Medicine, Indianapolis, IN 46202, United States.
Shihui JiangDepartment of Biostatistics and Health Data Science, Indiana University School of Medicine, Indianapolis, IN 46202, United States.
Chi Mai NguyenDepartment of Biostatistics and Health Data Science, Indiana University School of Medicine, Indianapolis, IN 46202, United States.
Qianqian SongDepartment of Health Outcomes and Biomedical Informatics, College of Medicine, University of Florida, Gainesville, FL 32608, United States.ORCID 0000-0002-4455-5302
Xiaochun LiDepartment of Biostatistics and Health Data Science, Indiana University School of Medicine, Indianapolis, IN 46202, United States.ORCID 0000-0002-1547-3041
Pengyue ZhangDepartment of Biostatistics and Health Data Science, Indiana University School of Medicine, Indianapolis, IN 46202, United States.
Michael T EadonDepartment of Medicine, Indiana University School of Medicine, Indianapolis, IN 46202, United States.ORCID 0000-0003-3066-2876
Jing SuDepartment of Biostatistics and Health Data Science, Indiana University School of Medicine, Indianapolis, IN 46202, United States.ORCID 0000-0003-4917-6173
Funding
Technology to Empower Changes in Health (TECH) Network Participant Technologies CenterU24OD023176 · OD · SCRIPPS RESEARCH INSTITUTE, THE · PI TOPOL, ERIC JEFFREY · 2016 to 2022
$204.7M
Precision Medicine Initiative Cohort Program BiobankU24OD023121 · OD · MAYO CLINIC ROCHESTER · PI CEKANOVA, MARIA, CICEK, MINE · 2016 to 2024
$185.5M
Enhancing All of Us Data Resources for Nutrition Precision Health: the All of Us Data and Research CenterU2COD023196 · OD · VANDERBILT UNIVERSITY MEDICAL CENTER · PI GLAZER, DAVID, HARRIS, PAUL A. · 2016 to 2022
$143.7M
Adaptive Platform for Personalized EngagementU24OD023163 · OD · VIGNET, INC. · PI JAIN, PRADUMAN · 2017 to 2020
$102.6M
University of Arizona-Banner Health All of Us Research Program OT2OD026549 · OD · UNIVERSITY OF ARIZONA · PI MORENO, FRANCISCO A, REIMAN, ERIC MICHAEL · 2018 to 2023
$78.9M
California Precision Medicine Research Program ConsortiumOT2OD026552 · OD · UNIVERSITY OF CALIFORNIA, SAN DIEGO · PI ANTON-CULVER, HODA A, OHNO-MACHADO, LUCILA · 2018 to 2023
$73.4M
All of Us PennsylvaniaOT2OD026554 · OD · UNIVERSITY OF PITTSBURGH AT PITTSBURGH · PI REIS, STEVEN E, VISWESWARAN, SHYAM · 2018 to 2023
$72.1M
New York City Consortium for Precision MedicineOT2OD026556 · OD · COLUMBIA UNIVERSITY HEALTH SCIENCES · PI BIER, LOUISE E, GHARAVI, ALI G · 2018 to 2023
$67.3M
SouthEast Enrollment Center (SEEC) OT2OD026551 · OD · UNIVERSITY OF MIAMI SCHOOL OF MEDICINE · PI CARRASQUILLO, OLVEEN, COLON, VIVIAN · 2018 to 2023
$62.8M
Southern All of Us NetworkOT2OD026548 · OD · UNIVERSITY OF ALABAMA AT BIRMINGHAM · PI FOUAD, MONA N., KORF, BRUCE R · 2018 to 2023
$60.5M
Illinois Precision Medicine Consortium OT2OD026557 · OD · NORTHWESTERN UNIVERSITY AT CHICAGO · PI AHSAN, HABIBUL, ARGOS, MARIA · 2018 to 2023
$60.5M
Tumor Microenvironment and Metastasis ProgramP30CA082709 · NCI · INDIANA UNIV-PURDUE UNIV AT INDIANAPOLIS · PI David W Clapp · 1999 to 2026
objectiveTo assess the health disparities across social determinants of health (SDoH) domains for the risk of severe acidosis independent of demographical and clinical factors. MATERIALS AND
methodsA retrospective case-control study (n = 13 310, 1:4 matching) is performed using electronic health records (EHRs), SDoH surveys, and genomics data from the All of Us participants. The propensity score matching controls confounding effects due to EHR data availability. Conditional logistic regressions are used to estimate odds ratios describing associations between SDoHs and the risk of acidosis events, adjusted for demographic features, and clinical conditions.
resultsThose with employer-provided insurance and those with Medicaid plans show dramatically different risks [adjusted odds ratio (AOR): 0.761 vs 1.41]. Low-income groups demonstrate higher risk (household income less than $25k, AOR: 1.3-1.57) than high-income groups ($100-$200k, AOR: 0.597-0.867). Other high-risk factors include impaired mobility (AOR: 1.32), unemployment (AOR: 1.32), renters (AOR: 1.41), other non-house-owners (AOR: 1.7), and house instability (AOR: 1.25). Education was negatively associated with acidosis risk. DISCUSSION: Our work provides real-world evidence of the comprehensive health disparities due to socioeconomic and behavioral contributors in a cohort enriched in minority groups or underrepresented populations.
conclusionsSDoHs are strongly associated with systematic health disparities in the risk of severe metabolic acidosis. Types of health insurance, household income levels, housing status and stability, employment status, educational level, and mobility disability play significant roles after being adjusted for demographic features and clinical conditions. Comprehensive solutions are needed to improve equity in healthcare and reduce the risk of severe acidosis.
Indexed as
AcidosisHealth Status DisparitiesSocial Determinants of HealthAdolescentAdultAgedCase-Control StudiesElectronic Health RecordsFemaleHumansLogistic ModelsMaleMiddle AgedPropensity ScoreRetrospective StudiesRisk FactorsacidosisAll of Uselectronic health recordshealth inequitiessocial determinants of health
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.
Health disparities in the risk of severe acidosis: real-world evidence from the All of Us cohort. · full record | OpenQuestion