Evidence map›Paper›PMID 40997278›Full record

ArticleDiabetes care2025

Development and Internal Validation of the Multiethnic Type 2 Diabetes Outcomes Model for the U.S. (DOMUS).

Aaron N Winn, Zachary Newman, Amber Deckard, Melissa I Franco-Galicia, Erin M Staab, Monica E Peek, Anirban Basu, Philip Clarke, Wen Wan, Elbert S Huang and 10 more

Abstract readValidation Study
PubMed Full text at PMC Publisher
In one paragraph

Article in Diabetes care, 2025. 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.

3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

  1. Article
4 · The record

Corrections and comments

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

20 authors.

Aaron N WinnCollege of Pharmacy, University of Illinois Chicago, Chicago, IL.ORCID 0000-0003-2906-3913
Zachary NewmanDepartment of Surgery, School of Medicine, Emory University, Atlanta, GA.
Amber DeckardRush University Medical Center, Chicago, IL.
Melissa I Franco-GaliciaDepartment of Health Policy, School of Medicine, Stanford University, Palo Alto, CA.
Erin M StaabDivision of Medicine, Department of Internal Medicine, School of Medicine, University of Chicago, Chicago, IL.
Monica E PeekDivision of Medicine, Department of Internal Medicine, School of Medicine, University of Chicago, Chicago, IL.
Anirban BasuDepartment of Health Services, CHOICE Institute, University of Washington, Seattle, WA.
Philip ClarkeNuffield Department of Population Health, Health Economics Research Centre, Oxford University, Oxford, U.K.
Wen WanDivision of Medicine, Department of Internal Medicine, School of Medicine, University of Chicago, Chicago, IL.
Elbert S HuangDivision of Medicine, Department of Internal Medicine, School of Medicine, University of Chicago, Chicago, IL.
Andrew J KarterDivision of Research, Kaiser Permanente Northern California, Pleasanton, CA.
Donald MillerCenter for Assessment of Pharmaceutical Practices, School of Public Health, Boston University, Boston, MA.
M Reza SkandariCentre for Health Economics & Policy Innovation, Imperial College London, London, U.K.ORCID 0000-0002-9096-6102
Howard H MoffetDivision of Research, Kaiser Permanente Northern California, Pleasanton, CA.
Mengqi ZhuDivision of Medicine, Department of Internal Medicine, School of Medicine, University of Chicago, Chicago, IL.
Jennifer Y LiuDivision of Research, Kaiser Permanente Northern California, Pleasanton, CA.
Jyoti SarkerCollege of Pharmacy, University of Illinois Chicago, Chicago, IL.
Wael MohammedDark Peak Analytics, Sheffield, U.K.ORCID 0000-0003-0370-4903
Robert SmithDark Peak Analytics, Sheffield, U.K.ORCID 0000-0003-0245-3217
Neda LaiteerapongDivision of Medicine, Department of Internal Medicine, School of Medicine, University of Chicago, Chicago, IL.

Funding

Pilot Program CoreP30ES027792 · NIEHS · UNIVERSITY OF CHICAGO · PI Gokhan M. Mutlu, Gail S Prins · 2017 to 2026
$13.6M
Research Design, Data, and Analytics CoreP30DK092949 · NIDDK · UNIVERSITY OF CHICAGO · PI MILDA Renne SAUNDERS · 2011 to 2026
$10.0M
Translational Research Core - Health Engagement & Action Translational (HEAT)P30DK092924 · NIDDK · KAISER FOUNDATION RESEARCH INSTITUTE · PI Alyce Sophia Adams, HILARY Kessler SELIGMAN · 2011 to 2026
$9.2M
Predicting and Reducing Future Health Disparities for U.S. Adults with DiabetesR01MD013420 · NIMHD · UNIVERSITY OF CHICAGO · PI LAITEERAPONG, NEDA · 2018 to 2021
$1.6M
Chicago Center for Diabetes Translation Research 2P30DK092949NIDDK NIH HHS P30 DK092924NIDDK NIH HHS P30 DK092949NIEHS NIH HHS P30 ES027792NIMHD NIH HHS R01 MD013420
6 · The paper itself

Abstract

objectiveThe objective of this study was to develop and internally validate a mathematical model of the relationships between patient clinical and social risk factors and outcomes using data from a multiethnic population with type 2 diabetes. RESEARCH DESIGN AND

methodsWe constructed an incidence cohort of all adults (18 years or older) with newly diagnosed type 2 diabetes in the Kaiser Permanente Northern California (KPNC) health care system between 2005 and 2016 (n = 129,000), following patients for at least 1 year, but up to 12 years. Using this cohort, we modeled 17 distinct diabetes-related outcomes related to micro- and macrovascular disease, as well as atrial fibrillation, depression, dementia, relevant biomarkers, and mortality.

resultsData were randomly split into 50%, 25%, and 25% samples to perform model estimation, calibration, and validation, respectively. Empirical and simulated data were similar for the events and biomarkers, but some factors required calibration. After calibration, they closely aligned with empirical estimates.

conclusionsThe resulting Diabetes Outcome Model of the U.S. (DOMUS) is a major step forward in understanding diabetes progression and the role of social determinants of health. This model can be used by scientists, policymakers, and health system managers to better understand how choices can affect population health and health disparities, including the broad diversity of U.S. races and ethnicities. Moreover, this model can be used to realize longer-term comparative effectiveness in cost-effectiveness analyses for diabetes management in the future.

Indexed as

Diabetes Mellitus, Type 2AdultAgedCaliforniaEthnicityFemaleHumansMaleMiddle AgedModels, TheoreticalRisk FactorsUnited States

Identifiers

PMID40997278
PMCPMC12583405

What Socratic holds

Textmetadata
Read underepoch 390

Registered trials

None linked

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 Socratic graph.