Evidence map›Paper›PMID 39782917›Full record

ArticleInternational health2025

The sky's the limit: improving satellite imagery data literacy to address non-communicable diseases.

M Courtney Hughes, Elizabeth J Folkmann

Abstract read
In one paragraph

Article in International health, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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

2 authors.

M Courtney HughesDepartment of Public Health, Northern Illinois University, 209 Wirtz Hall, DeKalb, IL 60115, USA.ORCID 0000-0002-8699-5701
Elizabeth J FolkmannMax Nader Center for Rehabilitation Technologies and Outcome Research, Shirley Ryan Ability Lab, 355 East Erie Street, Chicago, IL 60611, USA.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Low-and middle-income countries experience 77% of the world's premature deaths caused by non-communicable diseases, and their underlying health determinant data are often scarce and inaccurate. Improving satellite imagery data literacy worldwide is an integral step toward using the vast amount of publicly available data collected via satellites, such as air pollution, green space and light at night-all determinants of non-communicable diseases. Existing machine learning-based algorithms enable automated analysis of satellite imagery data, but health officials and scientists must know where to find and how to apply these algorithms to measure risk and target interventions.

Indexed as

Noncommunicable DiseasesSatellite ImageryAlgorithmsDeveloping CountriesGlobal HealthHumansMachine Learningartificial intelligencedata literacydeveloping countriesmachine learningnon-communicable diseasessatellite imagery

Identifiers

PMID39782917
PMCPMC12406767

What Socratic 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 Socratic graph.