Evidence map›Paper›PMID 33531419›Full record

ArticleDiabetes care2021

Quantifying Variation in Treatment Utilization for Type 2 Diabetes Across Five Major University of California Health Systems.

Thomas A Peterson, Valy Fontil, Suneil K Koliwad, Ayan Patel, Atul J Butte

Open access · bronzeAbstract read
In one paragraph

Article in Diabetes care, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.

0numbers the graph read from it
0cells of the map it votes in
9citing papers in PubMed
1.3field-weighted citation impact, top 20% of its field
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

9 citing papers in PubMed, 11 citations in OpenAlex.

  1. Article
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  4. Predicting post-liver transplant outcomes in patients with acute-on-chronic liver failure using Expert-Augmented Machine Learning.American journal of transplantation : official journal of the American Society of Transplantation and the American Society of Transplant Surgeons · 2023
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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

5 authors at 3 institutions in 1 country.

Thomas A PetersonBakar Computational Health Sciences Institute, University of California, San Francisco, San Francisco, CA.
Valy FontilDivision of General Internal Medicine, University of California, San Francisco at Zuckerberg San Francisco General Hospital and Trauma Center, San Francisco, CA.ORCID 0000-0002-2562-6574
Suneil K KoliwadDivision of Endocrinology, University of California, San Francisco at Zuckerberg San Francisco General Hospital and Trauma Center, San Francisco, CA.
Ayan PatelCenter for Data-driven Insights and Innovation, University of California Health, University of California Office of the President, Oakland, CA.
Atul J ButteBakar Computational Health Sciences Institute, University of California, San Francisco, San Francisco, CA atul.butte@ucsf.edu.ORCID 0000-0002-7433-2740
San Francisco General Hospital · USUniversity of California, San Francisco · USUniversity of California System · US

Funding

Clinical and Translational Science InstituteUL1TR001872 · NCATS · UNIVERSITY OF CALIFORNIA, SAN FRANCISCO · PI COLLARD, HAROLD R, JACOBY, VANESSA · 2016 to 2025
$112.1M
ZIDOVUDINE &LAMIVUDINE &NEVIRAPINE COMBINATION THERAPY IN HIV 1 INFM01RR001271 · NCRR · UNIVERSITY OF CALIFORNIA, SAN FRANCISCO · PI KESSLER, DAVID A · 1985 to 2006
$22.7M
Research BaseP30DK063720 · NIDDK · UNIVERSITY OF CALIFORNIA, SAN FRANCISCO · PI KRUMMEL, MATTHEW F · 2003 to 2019
$21.8M
UCSF Nutrition Obesity Research CenterP30DK098722 · NIDDK · UNIVERSITY OF CALIFORNIA, SAN FRANCISCO · PI CHRISTIAN VAISSE · 2015 to 2026
$14.6M
Repositioning drugs for the treatment of small cell lung cancerR01GM079719 · NIGMS · UNIVERSITY OF CALIFORNIA, SAN FRANCISCO · PI BUTTE, ATUL J · 2006 to 2015
$4.4M
Immunologic and Fat-Associated Predictors of Insulin Resistance in Treated HIVR01DK112304 · NIDDK · UNIVERSITY OF CALIFORNIA, SAN FRANCISCO · PI HUNT, PETER W, KOLIWAD, SUNEIL KRISHNA · 2017 to 2021
$4.1M
Bring it Down San Francisco: Harnessing cloud-based technology to improve hypertension management in a low-income underserved populationK23HL136899 · NHLBI · UNIVERSITY OF CALIFORNIA, SAN FRANCISCO · PI FONTIL, VALY · 2018 to 2022
$887k
NCATS NIH HHS UL1 TR001872NCRR NIH HHS M01 RR001271NHLBI NIH HHS K23 HL136899NIDDK NIH HHS P30 DK063720NIDDK NIH HHS P30 DK098722NIDDK NIH HHS R01 DK112304NIGMS NIH HHS R01 GM079719
6 · The paper itself

Abstract

objectiveUsing the newly created University of California (UC) Health Data Warehouse, we present the first study to analyze antihyperglycemic treatment utilization across the five large UC academic health systems (Davis, Irvine, Los Angeles, San Diego, and San Francisco). RESEARCH DESIGN AND

methodsThis retrospective analysis used deidentified electronic health records (EHRs; 2014-2019) including 97,231 patients with type 2 diabetes from 1,003 UC-affiliated clinical settings. Significant differences between health systems and individual providers were identified using binomial probabilities with cohort matching.

resultsOur analysis reveals statistically different treatment utilization patterns not only between health systems but also among individual providers within health systems. We identified 21 differences among health systems and 29 differences among individual providers within these health systems, with respect to treatment intensifications within existing guidelines on top of either metformin monotherapy or dual therapy with metformin and a sulfonylurea. Next, we identified variation for medications within the same class (e.g., glipizide vs. glyburide among sulfonylureas), with 33 differences among health systems and 86 among individual providers. Finally, we identified 2 health systems and 55 individual providers who more frequently used medications with known cardioprotective benefits for patients with high cardiovascular disease risk, but also 1 health system and 8 providers who prescribed such medications less frequently for these patients.

conclusionsOur study used cohort-matching techniques to highlight real-world variation in care between health systems and individual providers. This demonstrates the power of EHRs to quantify differences in treatment utilization, a necessary step toward standardizing precision care for large populations.

Indexed as

Diabetes Mellitus, Type 2MetforminHumansHypoglycemic AgentsRetrospective StudiesSulfonylurea CompoundsHypoglycemic AgentsMetforminSulfonylurea Compounds

Identifiers

PMID33531419
PMCPMC7985428
OpenAlexW3128295290

What OpenQuestion holds

Texttitle and abstract
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 OpenQuestion graph.