Evidence map›Paper›PMID 39220819›Full record

ArticleJournal of clinical and translational science2024

Interactive visualization tool to understand and monitor health disparities in diabetes care and outcomes.

Jashalynn C German, Andrew Stirling, Patti Gorgone, Amanda R Brucker, Angel Huang, Shwetha Dash, David J Halpern, Nrupen A Bhavsar, Eugenia R McPeek Hinz, Richard P Shannon and 2 more

Abstract read
In one paragraph

Article in Journal of clinical and translational science, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing 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

2 citing papers in PubMed.

  1. Article
  2. 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

12 authors.

Jashalynn C GermanDepartment of Medicine, Division of Endocrinology, Metabolism, and Nutrition, Duke University School of Medicine, Durham, NC, USA.ORCID https://orcid.org/0000-0002-8091-1941
Andrew StirlingDuke Health Technology Solutions, Duke University Health System, Durham, NC, USA.
Patti GorgoneDuke Health Technology Solutions, Duke University Health System, Durham, NC, USA.
Amanda R BruckerDepartment of Biostatistics and Bioinformatics, Duke University School of Medicine, Durham, NC, USA.
Angel HuangDepartment of Biostatistics and Bioinformatics, Duke University School of Medicine, Durham, NC, USA.ORCID https://orcid.org/0000-0003-0054-7148
Shwetha DashDuke Health Technology Solutions, Duke University Health System, Durham, NC, USA.
David J HalpernDepartment of Medicine, Division of General Internal Medicine, Duke University School of Medicine, Durham, NC, USA.
Nrupen A BhavsarDepartment of Biostatistics and Bioinformatics, Duke University School of Medicine, Durham, NC, USA.ORCID https://orcid.org/0000-0002-9937-5560
Eugenia R McPeek HinzDuke Health Technology Solutions, Duke University Health System, Durham, NC, USA.ORCID https://orcid.org/0000-0003-3759-9806
Richard P ShannonDuke University Health System, Durham, NC, USA.
Susan E SprattDepartment of Medicine, Division of Endocrinology, Metabolism, and Nutrition, Duke University School of Medicine, Durham, NC, USA.
Benjamin A GoldsteinDepartment of Biostatistics and Bioinformatics, Duke University School of Medicine, Durham, NC, USA.

Funding

Endocrinology and Metabolism Training ProgramT32DK007012 · NIDDK · DUKE UNIVERSITY · PI DAVID A. D'ALESSIO · 1986 to 2026
$6.8M
NIDDK NIH HHS T32 DK007012
6 · The paper itself

Abstract

Objective: Type 2 diabetes (T2DM) poses a significant public health challenge, with pronounced disparities in control and outcomes. Social determinants of health (SDoH) significantly contribute to these disparities, affecting healthcare access, neighborhood environments, and social context. We discuss the design, development, and use of an innovative web-based application integrating real-world data (electronic health record and geospatial files), to enhance comprehension of the impact of SDoH on T2 DM health disparities. Methods: We identified a patient cohort with diabetes from the institutional Diabetes Registry ( Results: The Tableau-powered digital health tool offers dynamic visualizations, identifying T2DM-related disparities. The dashboard allows for the exploration of contextual factors affecting diabetes management (e.g., food insecurity, built environment) and possesses capabilities to generate targeted patient lists for personalized diabetes care planning. Conclusion: As part of a broader health equity initiative, this application meets the needs of a diverse range of users. The interactive dashboard, incorporating clinical, sociodemographic, and environmental factors, enhances understanding at various levels and facilitates targeted interventions to address disparities in diabetes care and outcomes. Ultimately, this transformative approach aims to manage SDoH and improve patient care.

Indexed as

Health disparitiesreal-world datasocial determinants of healthtype 2 diabetesvisualization

Identifiers

PMID39220819
PMCPMC11362627

What OpenQuestion holds

Textmetadata
LicenceCC BY
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.