Evidence map›Paper›PMID 39047648›Full record

ArticleHealth & place2024

Opportunities and shortcomings of AI for spatial epidemiology and health disparities research on aging and the life course.

Hoda S Abdel Magid, Michael R Desjardins, Yingjie Hu

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Health & place, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing 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

3 citing papers in PubMed.

  1. Review
  2. Review
  3. Review
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

3 authors.

Hoda S Abdel MagidDepartment of Population and Public Health Sciences, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA; Dornsife Spatial Sciences Institute, University of Southern California, Los Angeles, CA, USA. Electronic address: hmagid@usc.edu.
Michael R DesjardinsDepartment of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA; Spatial Science for Public Health Center, Johns Hopkins Bloomberg School of Public Health, USA.
Yingjie HuGeoAI Lab, Department of Geography, University at Buffalo, Buffalo, NY, USA; Department of Computer Science and Engineering, University at Buffalo, Buffalo, NY, USA.

Funding

Leveraging Spatial Epidemiology to Reduce Hypertension DisparitiesR00HL161479 · NHLBI · UNIVERSITY OF SOUTHERN CALIFORNIA · PI ABDEL MAGID, HODA · 2023 to 2025
$863k
NHLBI NIH HHS R00 HL161479
6 · The paper itself

Abstract

Established spatial and life course methods have helped epidemiologists and health and medical geographers study the impact of individual and area-level determinants on health disparities. While these methods are effective, the emergence of Geospatial Artificial Intelligence (GeoAI) offers new opportunities to leverage complex and multi-scalar data in spatial aging and life course research. The objective of this perspective is three-fold: (1) to review established methods in aging, life course, and spatial epidemiology research; (2) to highlight some of the opportunities offered by GeoAI for enhancing research on health disparities across life course and aging research; (3) to discuss the shortcomings of using GeoAI methods in aging and life course studies.

Indexed as

AgingArtificial IntelligenceHealth Status DisparitiesHumansSpatial Analysis

Identifiers

PMID39047648
PMCPMC11402565

What OpenQuestion holds

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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.