Evidence map›Paper›PMID 28815121›Full record

ArticleAMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science2017

Enhancing Electronic Health Record Data with Geospatial Information.

Sherrie Xie, Rebecca Greenblatt, Michael Z Levy, Blanca E Himes

Abstract read
In one paragraph

Article in AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 26 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
26citing papers in PubMed, 1 pooled it
–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

26 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
  2. Article
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  6. Trends in publication and levels of social determinants of health reporting inJournal of clinical and translational science · 2024
    Article
  7. Article
  8. Geospatial divide in real-world EHR data: Analytical workflow to assess regional biases and potential impact on health equity.AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science · 2023
    Article
  9. Linking Ambient NO2 Pollution Measures with Electronic Health Record Data to Study Asthma Exacerbations.AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science · 2023
    Article
  10. Article
  11. Article
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  13. Application of Place-Based Methods to Lung Transplant Medicine.International journal of environmental research and public health · 2022
    Article
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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

4 authors.

Sherrie XieDepartment of Biostatistics and Epidemiology, University of Pennsylvania, Philadelphia, PA, USA.
Rebecca GreenblattDepartment of Biostatistics and Epidemiology, University of Pennsylvania, Philadelphia, PA, USA.
Michael Z LevyDepartment of Biostatistics and Epidemiology, University of Pennsylvania, Philadelphia, PA, USA.
Blanca E HimesDepartment of Biostatistics and Epidemiology, University of Pennsylvania, Philadelphia, PA, USA.

Funding

Translational Research Support CoreP30ES013508 · NIEHS · UNIVERSITY OF PENNSYLVANIA · PI A. Clementina Mesaros · 2006 to 2026
$35.3M
VMD-PhD Training in Infectious Disease-Related ResearchT32AI070077 · NIAID · UNIVERSITY OF PENNSYLVANIA · PI Michael Lee Atchison · 2008 to 2026
$3.5M
INTEGRATIVE GENOMICS APPROACHES TO MODEL THE GENETIC ARCHITECTURE OF ASTHMAR00HL105663 · NHLBI · UNIVERSITY OF PENNSYLVANIA · PI HIMES, BLANCA E · 2013 to 2015
$702k
NHLBI NIH HHS R00 HL105663NIAID NIH HHS T32 AI070077NIEHS NIH HHS P30 ES013508
6 · The paper itself

Abstract

Electronic Health Record (EHR)-derived data is a valuable resource for research, and efforts are underway to overcome some of its limitations by using data from external sources to gain a fuller picture of patient characteristics, symptoms, and exposures. Our goal was to assess the utility of augmenting EHR data with geocoded patient addresses to identify geospatial variation of disease that is not explained by EHR-derived demographic factors. Using 2011-2014 encounter data from 27,604 University of Pennsylvania Hospital System asthma patients, we identified factors associated with asthma exacerbations: risk was higher in female, black, middle aged to elderly, and obese patients, as well as those with positive smoking history and with Medicare or Medicaid vs. private insurance. Significant geospatial variability of asthma exacerbations was found using generalized additive models, even after adjusting for demographic factors. Our work shows that geospatial data can be used to cost-effectively enhance EHR data.

Identifiers

PMID28815121
PMCPMC5543367

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.